High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

LCM-MVSNet		95.70 1	96.40 1	93.61 2	98.67 1	85.39 46	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 15	85.07 73	99.27 1	99.54 1

WR-MVS_H		89.91 53	91.31 35	85.71 144	96.32 9	62.39 346	89.54 84	93.31 88	90.21 11	95.57 10	95.66 36	81.42 144	95.90 16	80.94 129	98.80 2	98.84 5

ACMP	79.16 10	90.54 37	90.60 52	90.35 44	94.36 50	80.98 92	89.16 92	94.05 42	79.03 119	92.87 55	93.74 120	90.60 12	95.21 64	82.87 104	98.76 3	94.87 80
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	77.89 11	90.73 33	91.50 26	88.44 82	93.00 89	76.26 152	89.65 80	95.55 8	87.72 26	93.89 31	94.94 56	91.62 3	93.44 144	78.35 163	98.76 3	95.61 56

PS-CasMVS		90.06 46	91.92 16	84.47 181	96.56 6	58.83 422	89.04 94	92.74 119	91.40 5	96.12 4	96.06 28	87.23 52	95.57 42	79.42 150	98.74 5	99.00 2

LPG-MVS_test		91.47 21	91.68 21	90.82 36	94.75 40	81.69 83	90.00 67	94.27 25	82.35 77	93.67 39	94.82 62	91.18 5	95.52 46	85.36 68	98.73 6	95.23 67

LGP-MVS_train				90.82 36	94.75 40	81.69 83		94.27 25	82.35 77	93.67 39	94.82 62	91.18 5	95.52 46	85.36 68	98.73 6	95.23 67

PEN-MVS		90.03 48	91.88 19	84.48 180	96.57 5	58.88 419	88.95 95	93.19 94	91.62 4	96.01 6	96.16 26	87.02 55	95.60 41	78.69 159	98.72 8	98.97 3

CP-MVSNet		89.27 65	90.91 46	84.37 182	96.34 8	58.61 425	88.66 103	92.06 142	90.78 6	95.67 7	95.17 51	81.80 139	95.54 45	79.00 156	98.69 9	98.95 4

TranMVSNet+NR-MVSNet		87.86 87	88.76 81	85.18 157	94.02 62	64.13 315	84.38 199	91.29 169	84.88 49	92.06 75	93.84 114	86.45 64	93.73 125	73.22 268	98.66 10	97.69 9

NR-MVSNet		86.00 120	86.22 123	85.34 154	93.24 84	64.56 308	82.21 280	90.46 199	80.99 91	88.42 166	91.97 193	77.56 188	93.85 120	72.46 278	98.65 11	97.61 10

UA-Net		91.49 19	91.53 25	91.39 26	94.98 34	82.95 73	93.52 7	92.79 117	88.22 22	88.53 162	97.64 6	83.45 102	94.55 90	86.02 59	98.60 12	96.67 30

FC-MVSNet-test		85.93 124	87.05 104	82.58 251	92.25 111	56.44 446	85.75 162	93.09 101	77.33 143	91.94 79	94.65 67	74.78 234	93.41 146	75.11 226	98.58 13	97.88 7

DTE-MVSNet		89.98 50	91.91 18	84.21 192	96.51 7	57.84 433	88.93 96	92.84 115	91.92 3	96.16 3	96.23 23	86.95 56	95.99 11	79.05 155	98.57 14	98.80 6

UniMVSNet (Re)		86.87 99	86.98 108	86.55 119	93.11 87	68.48 263	83.80 218	92.87 113	80.37 97	89.61 136	91.81 203	77.72 185	94.18 105	75.00 227	98.53 15	96.99 24

Baseline_NR-MVSNet		84.00 187	85.90 132	78.29 363	91.47 146	53.44 476	82.29 276	87.00 296	79.06 118	89.55 138	95.72 35	77.20 197	86.14 374	72.30 279	98.51 16	95.28 64

TDRefinement		93.52 2	93.39 4	93.88 1	95.94 14	90.26 3	95.70 4	96.46 2	90.58 8	92.86 56	96.29 21	88.16 37	94.17 107	86.07 55	98.48 17	97.22 18

ACMM	79.39 9	90.65 34	90.99 43	89.63 57	95.03 33	83.53 65	89.62 81	93.35 84	79.20 116	93.83 33	93.60 126	90.81 8	92.96 160	85.02 76	98.45 18	92.41 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
NormalMVS		86.47 110	85.32 150	89.94 50	94.43 43	80.42 98	88.63 104	93.59 73	74.56 183	85.12 272	90.34 269	66.19 326	94.20 102	76.57 198	98.44 19	95.19 69

lecture		92.43 8	93.50 2	89.21 65	94.43 43	79.31 111	92.69 19	95.72 7	88.48 21	94.43 19	95.73 33	91.34 4	94.68 82	90.26 3	98.44 19	93.63 156

MP-MVS-pluss		90.81 31	91.08 39	89.99 49	95.97 13	79.88 103	88.13 110	94.51 19	75.79 163	92.94 53	94.96 55	88.36 32	95.01 72	90.70 2	98.40 21	95.09 74
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		91.67 16	91.58 24	91.96 13	95.29 30	87.62 16	93.38 9	93.36 81	83.16 69	91.06 96	94.00 104	88.26 34	95.71 38	87.28 35	98.39 22	92.55 219

UniMVSNet_NR-MVSNet		86.84 101	87.06 103	86.17 131	92.86 94	67.02 281	82.55 265	91.56 159	83.08 71	90.92 98	91.82 202	78.25 177	93.99 113	74.16 240	98.35 23	97.49 13

DU-MVS		86.80 102	86.99 107	86.21 129	93.24 84	67.02 281	83.16 246	92.21 136	81.73 83	90.92 98	91.97 193	77.20 197	93.99 113	74.16 240	98.35 23	97.61 10

MTAPA		91.52 18	91.60 23	91.29 29	96.59 4	86.29 28	92.02 38	91.81 153	84.07 57	92.00 77	94.40 82	86.63 60	95.28 61	88.59 10	98.31 25	92.30 239

ACMH	76.49 14	89.34 62	91.14 37	83.96 200	92.50 103	70.36 235	89.55 82	93.84 55	81.89 82	94.70 16	95.44 44	90.69 9	88.31 320	83.33 96	98.30 26	93.20 179
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
reproduce_model		92.89 4	93.18 7	92.01 12	94.20 53	88.23 12	92.87 13	94.32 22	90.25 10	95.65 8	95.74 32	87.75 45	95.72 37	89.60 4	98.27 27	92.08 252

COLMAP_ROB	83.01 3	91.97 13	91.95 15	92.04 10	93.68 69	86.15 31	93.37 10	95.10 13	90.28 9	92.11 73	95.03 54	89.75 21	94.93 74	79.95 140	98.27 27	95.04 76
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
reproduce-ours		92.86 5	93.22 5	91.76 22	94.39 45	87.71 14	92.40 28	94.38 20	89.82 12	95.51 11	95.49 42	89.64 22	95.82 27	89.13 6	98.26 29	91.76 263

our_new_method		92.86 5	93.22 5	91.76 22	94.39 45	87.71 14	92.40 28	94.38 20	89.82 12	95.51 11	95.49 42	89.64 22	95.82 27	89.13 6	98.26 29	91.76 263

ACMMP		91.91 14	91.87 20	92.03 11	95.53 26	85.91 36	93.35 11	94.16 33	82.52 76	92.39 68	94.14 95	89.15 26	95.62 40	87.35 32	98.24 31	94.56 97
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
HPM-MVS		92.13 11	92.20 13	91.91 16	95.58 25	84.67 55	93.51 8	94.85 15	82.88 73	91.77 83	93.94 111	90.55 13	95.73 36	88.50 11	98.23 32	95.33 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_0728_THIRD									85.33 41	93.75 36	94.65 67	87.44 50	95.78 33	87.41 30	98.21 33	92.98 195

MP-MVS		91.14 28	90.91 46	91.83 19	96.18 10	86.88 22	92.20 31	93.03 106	82.59 75	88.52 163	94.37 84	86.74 58	95.41 55	86.32 49	98.21 33	93.19 180
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
SteuartSystems-ACMMP		91.16 27	91.36 30	90.55 40	93.91 64	80.97 93	91.49 45	93.48 78	82.82 74	92.60 63	93.97 105	88.19 35	96.29 5	87.61 25	98.20 35	94.39 112
Skip Steuart: Steuart Systems R&D Blog.
MSC_two_6792asdad				88.81 72	91.55 141	77.99 126		91.01 181					96.05 8	87.45 28	98.17 36	92.40 230

No_MVS				88.81 72	91.55 141	77.99 126		91.01 181					96.05 8	87.45 28	98.17 36	92.40 230

HPM-MVS_fast		92.50 7	92.54 9	92.37 5	95.93 15	85.81 41	92.99 12	94.23 28	85.21 45	92.51 65	95.13 52	90.65 10	95.34 58	88.06 15	98.15 38	95.95 45

mPP-MVS		91.69 15	91.47 28	92.37 5	96.04 12	88.48 11	92.72 18	92.60 126	83.09 70	91.54 85	94.25 89	87.67 48	95.51 48	87.21 36	98.11 39	93.12 185

WR-MVS		83.56 202	84.40 181	81.06 296	93.43 77	54.88 464	78.67 364	85.02 330	81.24 88	90.74 107	91.56 212	72.85 272	91.08 223	68.00 331	98.04 40	97.23 17

XVG-ACMP-BASELINE		89.98 50	89.84 57	90.41 42	94.91 36	84.50 57	89.49 86	93.98 44	79.68 108	92.09 74	93.89 113	83.80 97	93.10 156	82.67 108	98.04 40	93.64 155

DeepC-MVS	82.31 4	89.15 67	89.08 69	89.37 62	93.64 70	79.07 114	88.54 106	94.20 31	73.53 204	89.71 129	94.82 62	85.09 83	95.77 35	84.17 89	98.03 42	93.26 176
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		85.35 139	86.27 122	82.60 250	91.86 126	57.31 438	85.10 179	93.05 103	75.83 162	91.02 97	93.97 105	73.57 257	92.91 164	73.97 247	98.02 43	97.58 12

tt0320-xc		86.67 105	88.41 84	81.44 287	93.45 74	60.44 387	83.96 210	88.50 253	87.26 28	90.90 103	97.90 3	85.61 78	86.40 367	70.14 304	98.01 44	97.47 14

Anonymous20231211		88.40 76	89.62 62	84.73 171	90.46 174	65.27 301	88.86 97	93.02 107	87.15 29	93.05 50	97.10 10	82.28 125	92.02 186	76.70 195	97.99 45	96.88 26

PGM-MVS		91.20 26	90.95 45	91.93 14	95.67 22	85.85 39	90.00 67	93.90 49	80.32 99	91.74 84	94.41 81	88.17 36	95.98 12	86.37 48	97.99 45	93.96 133

APDe-MVS		91.22 25	91.92 16	89.14 67	92.97 90	78.04 125	92.84 16	94.14 37	83.33 67	93.90 29	95.73 33	88.77 28	96.41 2	87.60 26	97.98 47	92.98 195
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS		90.06 46	91.32 34	86.29 124	94.16 57	72.56 197	90.54 57	91.01 181	83.61 64	93.75 36	94.65 67	89.76 19	95.78 33	86.42 46	97.97 48	90.55 306
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				86.79 114	94.25 52	72.45 201	90.54 57	94.10 40					95.88 17	86.42 46	97.97 48	92.02 255

ZNCC-MVS		91.26 24	91.34 33	91.01 33	95.73 20	83.05 71	92.18 32	94.22 30	80.14 102	91.29 91	93.97 105	87.93 43	95.87 19	88.65 9	97.96 50	94.12 126

SED-MVS		90.46 39	91.64 22	86.93 111	94.18 54	72.65 191	90.47 60	93.69 64	83.77 60	94.11 27	94.27 85	90.28 15	95.84 25	86.03 56	97.92 51	92.29 241

IU-MVS					94.18 54	72.64 193		90.82 188	56.98 461	89.67 131				85.78 64	97.92 51	93.28 173

CLD-MVS		83.18 215	82.64 229	84.79 168	89.05 212	67.82 272	77.93 376	92.52 127	68.33 304	85.07 276	81.54 462	82.06 131	92.96 160	69.35 312	97.91 53	93.57 162
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
IS-MVSNet		86.66 106	86.82 112	86.17 131	92.05 119	66.87 285	91.21 48	88.64 250	86.30 36	89.60 137	92.59 168	69.22 307	94.91 75	73.89 248	97.89 54	96.72 29

ACMMP_NAP		90.65 34	91.07 41	89.42 61	95.93 15	79.54 109	89.95 71	93.68 68	77.65 138	91.97 78	94.89 57	88.38 31	95.45 53	89.27 5	97.87 55	93.27 174

test_241102_TWO								93.71 60	83.77 60	93.49 41	94.27 85	89.27 24	95.84 25	86.03 56	97.82 56	92.04 254

DPE-MVS		90.53 38	91.08 39	88.88 70	93.38 78	78.65 118	89.15 93	94.05 42	84.68 51	93.90 29	94.11 97	88.13 38	96.30 4	84.51 86	97.81 57	91.70 267
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test-260524					93.36 80	75.43 166		93.68 68		91.87 80		86.66 59	95.37 56	85.83 63	97.78 58

MED-MVS		90.78 32	91.50 26	88.60 78	94.38 47	76.12 156	92.12 33	93.85 53	85.28 43	93.24 44	94.84 59	87.06 54	95.85 23	84.99 77	97.78 58	93.84 139

OurMVSNet-221017-0		90.01 49	89.74 59	90.83 35	93.16 86	80.37 100	91.91 41	93.11 99	81.10 90	95.32 13	97.24 9	72.94 270	94.85 76	85.07 73	97.78 58	97.26 16

SMA-MVS		90.31 40	90.48 53	89.83 54	95.31 29	79.52 110	90.98 51	93.24 92	75.37 173	92.84 57	95.28 48	85.58 79	96.09 7	87.92 17	97.76 61	93.88 137
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
ACMMPR		91.49 19	91.35 32	91.92 15	95.74 19	85.88 38	92.58 23	93.25 91	81.99 79	91.40 87	94.17 94	87.51 49	95.87 19	87.74 21	97.76 61	93.99 130

HFP-MVS		91.30 23	91.39 29	91.02 32	95.43 28	84.66 56	92.58 23	93.29 90	81.99 79	91.47 86	93.96 108	88.35 33	95.56 43	87.74 21	97.74 63	92.85 201

region2R		91.44 22	91.30 36	91.87 18	95.75 18	85.90 37	92.63 22	93.30 89	81.91 81	90.88 104	94.21 90	87.75 45	95.87 19	87.60 26	97.71 64	93.83 142

tt0320		86.63 107	88.36 85	81.41 288	93.57 71	60.73 384	84.37 200	88.61 252	87.00 30	90.75 106	97.98 2	85.54 80	86.45 364	69.75 309	97.70 65	97.06 22

aaatest				88.50 80	94.38 47	76.12 156	92.12 33	93.85 53	77.53 142	93.24 44	93.18 141		95.85 23	84.99 77	97.69 66	93.54 166

aaEdge-Enhanced		90.09 43	90.66 50	88.38 84	92.82 97	76.12 156	89.40 90	93.70 61	83.72 62	92.39 68	93.18 141	88.02 41	95.47 51	84.99 77	97.69 66	93.54 166

GST-MVS		90.96 30	91.01 42	90.82 36	95.45 27	82.73 74	91.75 43	93.74 58	80.98 92	91.38 88	93.80 115	87.20 53	95.80 29	87.10 39	97.69 66	93.93 134

UniMVSNet_ETH3D		89.12 68	90.72 49	84.31 189	97.00 2	64.33 314	89.67 79	88.38 258	88.84 16	94.29 22	97.57 7	90.48 14	91.26 212	72.57 277	97.65 69	97.34 15

sc_t1		87.70 91	88.94 73	83.99 198	93.47 73	67.15 276	85.05 180	88.21 266	86.81 31	91.87 80	97.65 5	85.51 81	87.91 328	74.22 236	97.63 70	96.92 25

v7n		90.13 42	90.96 44	87.65 99	91.95 122	71.06 225	89.99 69	93.05 103	86.53 34	94.29 22	96.27 22	82.69 112	94.08 110	86.25 52	97.63 70	97.82 8

XVS		91.54 17	91.36 30	92.08 8	95.64 23	86.25 29	92.64 20	93.33 85	85.07 46	89.99 119	94.03 102	86.57 61	95.80 29	87.35 32	97.62 72	94.20 118

X-MVStestdata		85.04 149	82.70 226	92.08 8	95.64 23	86.25 29	92.64 20	93.33 85	85.07 46	89.99 119	16.05 554	86.57 61	95.80 29	87.35 32	97.62 72	94.20 118

SR-MVS-dyc-post		92.41 9	92.41 10	92.39 4	94.13 59	88.95 7	92.87 13	94.16 33	88.75 17	93.79 34	94.43 78	88.83 27	95.51 48	87.16 37	97.60 74	92.73 204

RE-MVS-def			92.61 8		94.13 59	88.95 7	92.87 13	94.16 33	88.75 17	93.79 34	94.43 78	90.64 11		87.16 37	97.60 74	92.73 204

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 24	93.02 88	85.17 48	92.47 27	95.05 14	87.65 27	93.21 47	94.39 83	90.09 18	95.08 70	86.67 44	97.60 74	94.18 121

TestfortrainingZip a		91.12 29	92.04 14	88.36 86	94.38 47	76.05 159	92.12 33	93.73 59	85.28 43	93.85 32	94.84 59	88.66 29	95.18 66	87.89 18	97.59 77	93.84 139

Anonymous20240521		80.18 293	81.25 263	76.95 393	83.15 401	60.84 382	82.46 268	85.99 310	68.76 296	86.78 220	93.73 121	59.13 377	77.44 456	73.71 252	97.55 78	92.56 218

9.14			89.29 65		91.84 129		88.80 99	95.32 12	75.14 175	91.07 95	92.89 157	87.27 51	93.78 124	83.69 95	97.55 78

OPM-MVS		89.80 54	89.97 55	89.27 63	94.76 39	79.86 104	86.76 138	92.78 118	78.78 122	92.51 65	93.64 125	88.13 38	93.84 122	84.83 82	97.55 78	94.10 127
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LTVRE_ROB	86.10 1	93.04 3	93.44 3	91.82 21	93.73 68	85.72 42	96.79 1	95.51 9	88.86 15	95.63 9	96.99 12	84.81 87	93.16 153	91.10 1	97.53 81	96.58 33
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
SF-MVS		90.27 41	90.80 48	88.68 77	92.86 94	77.09 141	91.19 49	95.74 5	81.38 87	92.28 70	93.80 115	86.89 57	94.64 85	85.52 67	97.51 82	94.30 117

MIMVSNet1		83.63 199	84.59 172	80.74 302	94.06 61	62.77 333	82.72 259	84.53 342	77.57 140	90.34 112	95.92 30	76.88 209	85.83 384	61.88 394	97.42 83	93.62 157

ACMMP++															97.35 84

SR-MVS		92.23 10	92.34 11	91.91 16	94.89 37	87.85 13	92.51 25	93.87 52	88.20 23	93.24 44	94.02 103	90.15 17	95.67 39	86.82 42	97.34 85	92.19 247

nrg030		87.85 88	88.49 82	85.91 137	90.07 185	69.73 243	87.86 116	94.20 31	74.04 192	92.70 62	94.66 66	85.88 73	91.50 200	79.72 143	97.32 86	96.50 34

pmmvs6		86.52 109	88.06 88	81.90 271	92.22 113	62.28 349	84.66 190	89.15 242	83.54 66	89.85 125	97.32 8	88.08 40	86.80 356	70.43 301	97.30 87	96.62 31

SD-MVS		88.96 70	89.88 56	86.22 128	91.63 135	77.07 142	89.82 74	93.77 57	78.90 120	92.88 54	92.29 184	86.11 70	90.22 257	86.24 53	97.24 88	91.36 278
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
CPTT-MVS		89.39 61	88.98 72	90.63 39	95.09 32	86.95 20	92.09 37	92.30 135	79.74 107	87.50 202	92.38 177	81.42 144	93.28 149	83.07 100	97.24 88	91.67 269

APD-MVS		89.54 59	89.63 61	89.26 64	92.57 100	81.34 90	90.19 66	93.08 102	80.87 94	91.13 94	93.19 140	86.22 68	95.97 13	82.23 114	97.18 90	90.45 308
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
wuyk23d		75.13 373	79.30 304	62.63 511	75.56 507	75.18 168	80.89 316	73.10 458	75.06 176	94.76 15	95.32 45	87.73 47	52.85 547	34.16 545	97.11 91	59.85 543

PMVS	80.48 6	90.08 44	90.66 50	88.34 87	96.71 3	92.97 1	90.31 64	89.57 233	88.51 20	90.11 115	95.12 53	90.98 7	88.92 295	77.55 182	97.07 92	83.13 455
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
OMC-MVS		88.19 79	87.52 94	90.19 47	91.94 124	81.68 85	87.49 122	93.17 95	76.02 155	88.64 159	91.22 228	84.24 93	93.37 147	77.97 177	97.03 93	95.52 57

test_prior2							83.37 236		75.43 171	84.58 292	91.57 211	81.92 136		79.54 148	96.97 94

DKM		82.99 220	82.10 237	85.66 145	90.69 170	88.83 9	82.94 253	78.86 407	66.54 335	92.02 76	88.74 320	67.79 315	78.28 452	74.39 232	96.96 95	89.85 326

ALIKED-MNN		76.42 356	75.39 363	79.52 335	84.57 361	84.06 60	84.33 201	82.48 370	49.85 512	80.53 394	88.35 328	54.52 424	77.10 459	56.89 434	96.96 95	77.39 514

RoMa-HiRes		85.97 122	85.47 144	87.48 100	91.66 134	89.37 4	87.18 126	83.89 349	71.47 252	94.29 22	91.35 221	75.59 220	81.39 426	76.88 194	96.92 97	91.68 268

EPP-MVSNet		85.47 132	85.04 156	86.77 115	91.52 144	69.37 248	91.63 44	87.98 270	81.51 86	87.05 216	91.83 201	66.18 328	95.29 59	70.75 295	96.89 98	95.64 54

VDDNet		84.35 170	85.39 148	81.25 290	95.13 31	59.32 407	85.42 171	81.11 389	86.41 35	87.41 204	96.21 24	73.61 256	90.61 246	66.33 346	96.85 99	93.81 146

VPNet		80.25 290	81.68 247	75.94 412	92.46 104	47.98 507	76.70 401	81.67 382	73.45 206	84.87 284	92.82 161	74.66 238	86.51 362	61.66 397	96.85 99	93.33 170

SixPastTwentyTwo		87.20 96	87.45 96	86.45 121	92.52 102	69.19 253	87.84 117	88.05 267	81.66 84	94.64 17	96.53 19	65.94 329	94.75 80	83.02 102	96.83 101	95.41 59

VPA-MVSNet		83.47 208	84.73 163	79.69 329	90.29 177	57.52 436	81.30 303	88.69 249	76.29 151	87.58 201	94.44 77	80.60 155	87.20 345	66.60 344	96.82 102	94.34 114

usedtu_dtu_shiyan2		78.92 308	78.15 323	81.25 290	91.33 148	73.10 186	80.75 321	79.00 406	74.19 191	79.17 415	92.04 191	67.17 319	81.33 427	42.86 528	96.81 103	89.31 339

Gipuma		84.44 167	86.33 121	78.78 350	84.20 370	73.57 178	89.55 82	90.44 200	84.24 56	84.38 298	94.89 57	76.35 216	80.40 437	76.14 209	96.80 104	82.36 465
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ZD-MVS					92.22 113	80.48 97		91.85 149	71.22 257	90.38 111	92.98 151	86.06 71	96.11 6	81.99 118	96.75 105

CDPH-MVS		86.17 118	85.54 142	88.05 94	92.25 111	75.45 165	83.85 215	92.01 143	65.91 343	86.19 239	91.75 207	83.77 98	94.98 73	77.43 186	96.71 106	93.73 149

DKM-HiRes		83.22 214	82.10 237	86.59 117	91.79 132	88.73 10	82.92 254	77.76 415	69.00 292	91.15 93	89.69 294	63.65 349	81.20 430	76.19 206	96.70 107	89.86 325

KD-MVS_self_test		81.93 251	83.14 214	78.30 362	84.75 358	52.75 480	80.37 328	89.42 237	70.24 273	90.26 114	93.39 131	74.55 241	86.77 357	68.61 326	96.64 108	95.38 60

DP-MVS		88.60 75	89.01 70	87.36 103	91.30 149	77.50 134	87.55 119	92.97 111	87.95 25	89.62 134	92.87 158	84.56 88	93.89 119	77.65 180	96.62 109	90.70 298

TransMVSNet (Re)		84.02 186	85.74 139	78.85 348	91.00 161	55.20 462	82.29 276	87.26 282	79.65 109	88.38 168	95.52 40	83.00 107	86.88 352	67.97 332	96.60 110	94.45 106

ambc				82.98 233	90.55 173	64.86 305	88.20 108	89.15 242		89.40 142	93.96 108	71.67 291	91.38 208	78.83 157	96.55 111	92.71 207

train_agg		85.98 121	85.28 151	88.07 93	92.34 108	79.70 106	83.94 211	90.32 206	65.79 345	84.49 295	90.97 239	81.93 134	93.63 130	81.21 123	96.54 112	90.88 292

VDD-MVS		84.23 176	84.58 173	83.20 226	91.17 157	65.16 304	83.25 240	84.97 333	79.79 106	87.18 208	94.27 85	74.77 235	90.89 232	69.24 313	96.54 112	93.55 165

HPM-MVS++		88.93 71	88.45 83	90.38 43	94.92 35	85.85 39	89.70 76	91.27 173	78.20 130	86.69 226	92.28 185	80.36 158	95.06 71	86.17 54	96.49 114	90.22 313

test_djsdf		89.62 57	89.01 70	91.45 25	92.36 107	82.98 72	91.98 39	90.08 217	71.54 249	94.28 25	96.54 18	81.57 142	94.27 97	86.26 50	96.49 114	97.09 20

SPE-MVS-test		87.00 98	86.43 116	88.71 75	89.46 197	77.46 135	89.42 89	95.73 6	77.87 136	81.64 373	87.25 358	82.43 117	94.53 92	77.65 180	96.46 116	94.14 125

RoMa-SfM		83.52 205	82.69 227	86.00 135	90.77 166	89.30 5	85.98 155	81.47 386	65.77 348	92.99 51	89.25 306	69.55 304	78.65 450	72.01 281	96.45 117	90.04 321

test1111		78.53 320	78.85 311	77.56 377	92.22 113	47.49 510	82.61 261	69.24 488	72.43 231	85.28 268	94.20 91	51.91 440	90.07 269	65.36 358	96.45 117	95.11 73

test9_res														80.83 131	96.45 117	90.57 304

Anonymous20240529		86.20 115	87.13 101	83.42 220	90.19 180	64.55 309	84.55 193	90.71 190	85.85 39	89.94 122	95.24 50	82.13 128	90.40 252	69.19 316	96.40 120	95.31 63

anonymousdsp		89.73 56	88.88 76	92.27 7	89.82 190	86.67 24	90.51 59	90.20 214	69.87 276	95.06 14	96.14 27	84.28 92	93.07 157	87.68 23	96.34 121	97.09 20

PHI-MVS		86.38 111	85.81 135	88.08 92	88.44 235	77.34 138	89.35 91	93.05 103	73.15 217	84.76 289	87.70 347	78.87 170	94.18 105	80.67 134	96.29 122	92.73 204

PS-MVSNAJss		88.31 78	87.90 90	89.56 59	93.31 81	77.96 128	87.94 115	91.97 145	70.73 264	94.19 26	96.67 16	76.94 203	94.57 88	83.07 100	96.28 123	96.15 37

v10		86.54 108	87.10 102	84.84 165	88.16 246	63.28 325	86.64 141	92.20 137	75.42 172	92.81 59	94.50 74	74.05 249	94.06 111	83.88 91	96.28 123	97.17 19

CNVR-MVS		87.81 89	87.68 92	88.21 89	92.87 92	77.30 140	85.25 175	91.23 174	77.31 144	87.07 215	91.47 217	82.94 108	94.71 81	84.67 84	96.27 125	92.62 212

EC-MVSNet		88.01 84	88.32 86	87.09 105	89.28 201	72.03 208	90.31 64	96.31 3	80.88 93	85.12 272	89.67 295	84.47 90	95.46 52	82.56 109	96.26 126	93.77 148

mmtdpeth		85.13 146	85.78 137	83.17 229	84.65 359	74.71 170	85.87 158	90.35 205	77.94 133	83.82 316	96.96 14	77.75 183	80.03 440	78.44 160	96.21 127	94.79 92

MM		87.64 92	87.15 100	89.09 68	89.51 195	76.39 151	88.68 102	86.76 297	84.54 52	83.58 323	93.78 117	73.36 265	96.48 1	87.98 16	96.21 127	94.41 111

114514_t		83.10 218	82.54 232	84.77 169	92.90 91	69.10 255	86.65 140	90.62 194	54.66 478	81.46 377	90.81 250	76.98 202	94.38 95	72.62 276	96.18 129	90.82 294

agg_prior2														79.68 144	96.16 130	90.22 313

AllTest		87.97 86	87.40 98	89.68 55	91.59 136	83.40 66	89.50 85	95.44 10	79.47 110	88.00 180	93.03 149	82.66 113	91.47 202	70.81 292	96.14 131	94.16 123

TestCases				89.68 55	91.59 136	83.40 66		95.44 10	79.47 110	88.00 180	93.03 149	82.66 113	91.47 202	70.81 292	96.14 131	94.16 123

EPNet		80.37 286	78.41 320	86.23 126	76.75 494	73.28 182	87.18 126	77.45 418	76.24 152	68.14 511	88.93 315	65.41 333	93.85 120	69.47 311	96.12 133	91.55 273
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testf1		89.30 63	89.12 67	89.84 52	88.67 225	85.64 43	90.61 55	93.17 95	86.02 37	93.12 48	95.30 46	84.94 84	89.44 286	74.12 242	96.10 134	94.45 106

APD_test2		89.30 63	89.12 67	89.84 52	88.67 225	85.64 43	90.61 55	93.17 95	86.02 37	93.12 48	95.30 46	84.94 84	89.44 286	74.12 242	96.10 134	94.45 106

pm-mvs1		83.69 196	84.95 159	79.91 324	90.04 187	59.66 401	82.43 271	87.44 278	75.52 169	87.85 187	95.26 49	81.25 146	85.65 388	68.74 324	96.04 136	94.42 110

test2506		74.12 390	73.39 390	76.28 408	91.85 127	44.20 525	84.06 207	48.20 552	72.30 237	81.90 363	94.20 91	27.22 548	89.77 278	64.81 364	96.02 137	94.87 80

ECVR-MVS		78.44 324	78.63 315	77.88 371	91.85 127	48.95 503	83.68 222	69.91 483	72.30 237	84.26 308	94.20 91	51.89 441	89.82 275	63.58 375	96.02 137	94.87 80

DenseAffine		81.00 272	79.38 301	85.84 140	90.25 179	87.48 17	81.47 295	78.40 411	65.68 351	89.63 133	86.45 370	58.79 380	82.05 421	67.78 334	95.99 139	87.99 377

mvs_tets		89.78 55	89.27 66	91.30 28	93.51 72	84.79 53	89.89 73	90.63 193	70.00 275	94.55 18	96.67 16	87.94 42	93.59 135	84.27 88	95.97 140	95.52 57

EGC-MVSNET		74.79 383	69.99 442	89.19 66	94.89 37	87.00 19	91.89 42	86.28 302	1.09 556	2.23 560	95.98 29	81.87 137	89.48 282	79.76 142	95.96 141	91.10 283

MGCNet		85.37 138	84.58 173	87.75 96	85.28 344	73.36 179	86.54 144	85.71 315	77.56 141	81.78 371	92.47 175	70.29 300	96.02 10	85.59 66	95.96 141	93.87 138

DeepPCF-MVS	81.24 5	87.28 95	86.21 124	90.49 41	91.48 145	84.90 51	83.41 235	92.38 131	70.25 272	89.35 143	90.68 256	82.85 111	94.57 88	79.55 147	95.95 143	92.00 256

DVP-MVS++		90.07 45	91.09 38	87.00 108	91.55 141	72.64 193	96.19 2	94.10 40	85.33 41	93.49 41	94.64 70	81.12 147	95.88 17	87.41 30	95.94 144	92.48 222

PC_three_1452									58.96 443	90.06 116	91.33 222	80.66 154	93.03 159	75.78 213	95.94 144	92.48 222

Elysia		88.71 72	88.89 74	88.19 90	91.26 152	72.96 187	88.10 111	93.59 73	84.31 53	90.42 109	94.10 98	74.07 246	94.82 77	88.19 13	95.92 146	96.80 27

StellarMVS		88.71 72	88.89 74	88.19 90	91.26 152	72.96 187	88.10 111	93.59 73	84.31 53	90.42 109	94.10 98	74.07 246	94.82 77	88.19 13	95.92 146	96.80 27

jajsoiax		89.41 60	88.81 80	91.19 31	93.38 78	84.72 54	89.70 76	90.29 211	69.27 284	94.39 20	96.38 20	86.02 72	93.52 140	83.96 90	95.92 146	95.34 61

ANet_high		83.17 216	85.68 140	75.65 418	81.24 425	45.26 522	79.94 333	92.91 112	83.83 59	91.33 89	96.88 15	80.25 159	85.92 377	68.89 320	95.89 149	95.76 48

tt0805		88.09 83	89.79 58	82.98 233	93.26 83	63.94 318	91.10 50	89.64 230	85.07 46	90.91 101	91.09 234	89.16 25	91.87 191	82.03 116	95.87 150	93.13 182

3Dnovator+	83.92 2	89.97 52	89.66 60	90.92 34	91.27 151	81.66 87	91.25 47	94.13 38	88.89 14	88.83 153	94.26 88	77.55 189	95.86 22	84.88 80	95.87 150	95.24 66

HQP_MVS		87.75 90	87.43 97	88.70 76	93.45 74	76.42 149	89.45 87	93.61 70	79.44 112	86.55 228	92.95 155	74.84 232	95.22 62	80.78 132	95.83 152	94.46 104

plane_prior5								93.61 70					95.22 62	80.78 132	95.83 152	94.46 104

cl____		80.42 284	80.23 283	81.02 297	79.99 454	59.25 409	77.07 394	87.02 293	67.37 324	86.18 241	89.21 309	63.08 353	90.16 261	76.31 204	95.80 154	93.65 154

DIV-MVS_self_test		80.43 283	80.23 283	81.02 297	79.99 454	59.25 409	77.07 394	87.02 293	67.38 323	86.19 239	89.22 308	63.09 352	90.16 261	76.32 203	95.80 154	93.66 151

DeepC-MVS_fast	80.27 8	86.23 113	85.65 141	87.96 95	91.30 149	76.92 143	87.19 125	91.99 144	70.56 265	84.96 280	90.69 254	80.01 161	95.14 68	78.37 162	95.78 156	91.82 261
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Casviewmamba		88.12 82	88.82 79	86.03 134	89.14 206	68.35 264	86.40 146	94.70 17	79.80 105	90.92 98	93.72 122	87.83 44	93.81 123	81.09 125	95.75 157	95.92 47

LFMVS		80.15 294	80.56 277	78.89 345	89.19 205	55.93 449	85.22 176	73.78 451	82.96 72	84.28 306	92.72 166	57.38 395	90.07 269	63.80 374	95.75 157	90.68 299

ACMMP++_ref															95.74 159

原ACMM1				84.60 176	92.81 98	74.01 175		91.50 161	62.59 393	82.73 347	90.67 259	76.53 212	94.25 99	69.24 313	95.69 160	85.55 415

SymmetryMVS		84.79 157	83.54 198	88.55 79	92.44 105	80.42 98	88.63 104	82.37 373	74.56 183	85.12 272	90.34 269	66.19 326	94.20 102	76.57 198	95.68 161	91.03 286

tfpnnormal		81.79 255	82.95 220	78.31 361	88.93 217	55.40 458	80.83 318	82.85 365	76.81 147	85.90 251	94.14 95	74.58 239	86.51 362	66.82 342	95.68 161	93.01 192

FE-MVSNET2		82.80 224	83.51 199	80.67 308	89.08 210	58.46 426	82.40 273	89.26 238	71.25 256	88.24 172	94.07 100	75.75 218	89.56 281	65.91 352	95.67 163	93.98 131

SIFT-PointCN		72.17 419	71.14 427	75.23 422	77.93 479	79.30 112	72.22 469	64.71 514	62.60 392	84.13 310	81.00 466	46.91 468	67.69 513	55.17 454	95.64 164	78.70 505

mvs5depth		83.82 193	84.54 175	81.68 279	82.23 408	68.65 261	86.89 132	89.90 222	80.02 104	87.74 192	97.86 4	64.19 341	82.02 422	76.37 202	95.63 165	94.35 113

ALIKED-LG		78.19 326	77.07 337	81.54 282	84.95 351	86.95 20	86.16 152	83.96 348	56.64 465	87.21 206	90.05 285	51.36 445	78.05 454	57.73 428	95.60 166	79.63 495

TAPA-MVS	77.73 12	85.71 127	84.83 161	88.37 85	88.78 224	79.72 105	87.15 128	93.50 77	69.17 285	85.80 252	89.56 296	80.76 152	92.13 182	73.21 273	95.51 167	93.25 177
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		90.60 36	90.34 54	91.38 27	89.03 213	84.23 58	93.58 6	94.68 18	90.65 7	90.33 113	93.95 110	84.50 89	95.37 56	80.87 130	95.50 168	94.53 101

v8		86.22 114	86.83 111	84.36 184	87.82 254	62.35 348	86.42 145	91.33 168	76.78 148	92.73 61	94.48 76	73.41 262	93.72 126	83.10 99	95.41 169	97.01 23

Vis-MVSNet (Re-imp)		77.82 330	77.79 329	77.92 370	88.82 221	51.29 493	83.28 238	71.97 471	74.04 192	82.23 354	89.78 291	57.38 395	89.41 288	57.22 431	95.41 169	93.05 188

OPU-MVS				88.27 88	91.89 125	77.83 129	90.47 60				91.22 228	81.12 147	94.68 82	74.48 231	95.35 171	92.29 241

FMVSNet1		84.55 165	85.45 146	81.85 273	90.27 178	61.05 374	86.83 135	88.27 263	78.57 126	89.66 132	95.64 37	75.43 222	90.68 241	69.09 317	95.33 172	93.82 143

test12				86.57 118	90.74 167	72.63 195		90.69 191		82.76 345		79.20 166	94.80 79		95.32 173	92.27 243

NCCC		87.36 94	86.87 110	88.83 71	92.32 110	78.84 117	86.58 142	91.09 179	78.77 123	84.85 285	90.89 245	80.85 150	95.29 59	81.14 124	95.32 173	92.34 236

Patchmtry		76.56 352	77.46 331	73.83 436	79.37 465	46.60 515	82.41 272	76.90 426	73.81 195	85.56 261	92.38 177	48.07 464	83.98 408	63.36 379	95.31 175	90.92 290

XVG-OURS		89.18 66	88.83 78	90.23 46	94.28 51	86.11 33	85.91 156	93.60 72	80.16 101	89.13 149	93.44 128	83.82 96	90.98 226	83.86 92	95.30 176	93.60 159

ArgMatch-SfM		79.08 304	77.37 334	84.22 191	87.80 255	86.73 23	79.32 348	78.45 409	56.81 463	89.54 140	84.95 403	55.35 419	79.21 444	68.89 320	95.21 177	86.73 402

TSAR-MVS + GP.		83.95 189	82.69 227	87.72 97	89.27 202	81.45 89	83.72 220	81.58 384	74.73 180	85.66 256	86.06 380	72.56 277	92.69 168	75.44 220	95.21 177	89.01 355

test_0402		88.65 74	89.58 63	85.88 139	92.55 101	72.22 205	84.01 208	89.44 236	88.63 19	94.38 21	95.77 31	86.38 67	93.59 135	79.84 141	95.21 177	91.82 261

TinyColmap		81.25 265	82.34 235	77.99 369	85.33 343	60.68 385	82.32 275	88.33 260	71.26 255	86.97 217	92.22 188	77.10 200	86.98 350	62.37 385	95.17 180	86.31 406

SIFT-PCN-Cal		71.86 421	71.21 425	73.82 437	77.43 486	78.37 120	71.75 473	65.73 507	62.15 406	84.04 312	81.59 461	50.59 453	64.96 531	52.46 481	95.15 181	78.14 510

PMatch-SfM		81.28 264	79.37 302	87.00 108	89.23 203	85.40 45	81.27 305	81.28 388	65.97 339	92.13 71	90.30 275	44.94 495	85.43 389	74.06 245	95.14 182	90.18 318

Anonymous202405211		80.51 281	81.19 267	78.49 356	88.48 233	57.26 439	76.63 403	82.49 369	81.21 89	84.30 305	92.24 187	67.99 313	86.24 369	62.22 386	95.13 183	91.98 258

tttt0517		81.07 270	79.58 298	85.52 149	88.99 215	66.45 290	87.03 130	75.51 438	73.76 196	88.32 170	90.20 278	37.96 516	94.16 109	79.36 152	95.13 183	95.93 46

DP-MVS Recon		84.05 183	83.22 209	86.52 120	91.73 133	75.27 167	83.23 243	92.40 129	72.04 241	82.04 360	88.33 329	77.91 182	93.95 117	66.17 347	95.12 185	90.34 312

PCF-MVS	74.62 15	82.15 242	80.92 271	85.84 140	89.43 198	72.30 203	80.53 325	91.82 151	57.36 457	87.81 188	89.92 289	77.67 186	93.63 130	58.69 418	95.08 186	91.58 272
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CSCG		86.26 112	86.47 115	85.60 147	90.87 164	74.26 174	87.98 114	91.85 149	80.35 98	89.54 140	88.01 334	79.09 168	92.13 182	75.51 218	95.06 187	90.41 309

SDMVSNet		81.90 254	83.17 213	78.10 366	88.81 222	62.45 345	76.08 415	86.05 308	73.67 197	83.41 327	93.04 147	82.35 119	80.65 434	70.06 306	95.03 188	91.21 280

sd_testset		79.95 299	81.39 260	75.64 419	88.81 222	58.07 430	76.16 414	82.81 366	73.67 197	83.41 327	93.04 147	80.96 149	77.65 455	58.62 419	95.03 188	91.21 280

plane_prior						76.42 149	87.15 128		75.94 159						95.03 188

new-patchmatchnet		70.10 444	73.37 391	60.29 521	81.23 426	16.95 560	59.54 531	74.62 442	62.93 388	80.97 382	87.93 338	62.83 356	71.90 479	55.24 453	95.01 191	92.00 256

v1192		84.57 162	84.69 168	84.21 192	87.75 258	62.88 329	83.02 249	91.43 163	69.08 289	89.98 121	90.89 245	72.70 275	93.62 133	82.41 111	94.97 192	96.13 38

v1921920		84.23 176	84.37 182	83.79 205	87.64 264	61.71 361	82.91 255	91.20 175	67.94 312	90.06 116	90.34 269	72.04 285	93.59 135	82.32 112	94.91 193	96.07 40

CL-MVSNet_self_test		76.81 346	77.38 333	75.12 424	86.90 296	51.34 491	73.20 459	80.63 395	68.30 305	81.80 368	88.40 326	66.92 322	80.90 431	55.35 452	94.90 194	93.12 185

PMatch-Up-SfM		81.93 251	80.09 291	87.42 102	89.08 210	86.10 34	81.31 300	83.35 358	67.64 320	92.96 52	90.69 254	45.71 483	85.82 385	75.20 224	94.89 195	90.35 311

hybridcas		86.07 119	87.02 105	83.19 228	87.76 257	62.85 331	84.53 197	93.42 79	75.52 169	89.88 124	93.31 133	86.15 69	91.68 196	77.76 179	94.89 195	95.05 75

CS-MVS		88.14 80	87.67 93	89.54 60	89.56 194	79.18 113	90.47 60	94.77 16	79.37 114	84.32 302	89.33 303	83.87 95	94.53 92	82.45 110	94.89 195	94.90 78

v144192		84.24 175	84.41 180	83.71 209	87.59 266	61.57 362	82.95 252	91.03 180	67.82 316	89.80 126	90.49 266	73.28 266	93.51 141	81.88 121	94.89 195	96.04 42

LCM-MVSNet-Re		83.48 207	85.06 155	78.75 351	85.94 328	55.75 453	80.05 331	94.27 25	76.47 149	96.09 5	94.54 73	83.31 104	89.75 280	59.95 409	94.89 195	90.75 295

casdiffmvs_mvg		86.72 103	87.51 95	84.36 184	87.09 287	65.22 302	84.16 204	94.23 28	77.89 134	91.28 92	93.66 124	84.35 91	92.71 166	80.07 137	94.87 200	95.16 72
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ArgMatch-Sym		78.58 319	76.86 343	83.71 209	87.61 265	86.40 27	78.19 370	77.45 418	55.72 468	88.82 154	82.01 455	59.68 373	78.75 449	67.43 337	94.86 201	85.98 408

SIFT-NCMNet		71.70 425	70.97 428	73.90 434	77.55 485	81.03 91	71.58 476	63.31 521	63.91 381	87.12 209	81.00 466	50.00 457	64.64 533	49.37 501	94.86 201	76.04 517

ELoFTR		73.12 406	73.47 389	72.08 457	81.84 414	77.60 133	80.51 326	66.79 504	49.99 511	89.23 146	88.83 316	47.19 466	65.24 530	61.99 391	94.85 203	73.39 523

casdiffseed414692147		85.64 128	86.08 128	84.32 187	87.49 270	65.55 300	85.81 161	93.00 110	75.85 161	87.50 202	93.40 130	83.10 105	91.71 195	73.70 256	94.84 204	95.69 51

APD_test1		88.40 76	87.91 89	89.88 51	89.50 196	86.65 26	89.98 70	91.91 148	84.26 55	90.87 105	93.92 112	82.18 127	89.29 290	73.75 251	94.81 205	93.70 150

v1240		84.30 172	84.51 177	83.65 211	87.65 263	61.26 370	82.85 257	91.54 160	67.94 312	90.68 108	90.65 260	71.71 290	93.64 129	82.84 105	94.78 206	96.07 40

MSLP-MVS++		85.00 152	86.03 129	81.90 271	91.84 129	71.56 218	86.75 139	93.02 107	75.95 158	87.12 209	89.39 300	77.98 180	89.40 289	77.46 184	94.78 206	84.75 424

IterMVS-LS		84.73 159	84.98 157	83.96 200	87.35 275	63.66 319	83.25 240	89.88 223	76.06 153	89.62 134	92.37 180	73.40 264	92.52 171	78.16 168	94.77 208	95.69 51
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		83.66 197	83.69 197	83.57 216	90.05 186	72.26 204	86.29 148	90.00 219	78.19 131	81.65 372	87.16 360	83.40 103	94.24 100	61.69 396	94.76 209	84.21 435

BP-MVS1		82.81 223	81.67 248	86.23 126	87.88 253	68.53 262	86.06 154	84.36 343	75.65 165	85.14 271	90.19 279	45.84 481	94.42 94	85.18 71	94.72 210	95.75 49

LoFTR		76.52 353	76.53 348	76.49 403	83.36 392	80.97 93	80.82 319	68.96 490	62.47 398	92.13 71	89.95 286	51.45 444	74.61 471	64.97 363	94.67 211	73.87 522

ITE_SJBPF				90.11 48	90.72 168	84.97 50		90.30 209	81.56 85	90.02 118	91.20 230	82.40 118	90.81 236	73.58 260	94.66 212	94.56 97

v1144		84.54 166	84.72 165	84.00 197	87.67 262	62.55 338	82.97 251	90.93 185	70.32 270	89.80 126	90.99 238	73.50 258	93.48 142	81.69 122	94.65 213	95.97 43

test20.03		73.75 397	74.59 376	71.22 462	81.11 427	51.12 495	70.15 489	72.10 470	70.42 267	80.28 399	91.50 213	64.21 340	74.72 470	46.96 517	94.58 214	87.82 385

ALIKED-NN		74.80 382	73.22 394	79.55 333	82.93 404	83.79 62	81.84 287	82.56 367	47.43 517	74.33 475	88.03 333	53.21 430	76.31 461	54.08 463	94.57 215	78.54 506

TSAR-MVS + MP.		88.14 80	87.82 91	89.09 68	95.72 21	76.74 145	92.49 26	91.19 176	67.85 315	86.63 227	94.84 59	79.58 165	95.96 14	87.62 24	94.50 216	94.56 97
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SSC-MVS3.2		73.90 394	75.67 359	68.61 485	84.11 372	41.28 534	64.17 521	72.83 461	72.09 240	79.08 417	87.94 336	70.31 299	73.89 473	55.99 442	94.49 217	90.67 301

HQP3-MVS								92.68 120							94.47 218

HQP-MVS		84.61 161	84.06 189	86.27 125	91.19 154	70.66 228	84.77 183	92.68 120	73.30 212	80.55 391	90.17 282	72.10 282	94.61 86	77.30 188	94.47 218	93.56 163

SIFT-CM-Cal		73.20 405	71.85 414	77.25 387	79.80 459	82.49 77	73.51 454	64.83 513	62.27 404	83.49 326	82.81 446	51.79 442	69.71 490	53.70 467	94.43 220	79.53 496

test_fmvsmconf0.01_n		86.68 104	86.52 114	87.18 104	85.94 328	78.30 121	86.93 131	92.20 137	65.94 341	89.16 147	93.16 144	83.10 105	89.89 274	87.81 20	94.43 220	93.35 169

c3_l		81.64 257	81.59 252	81.79 278	80.86 433	59.15 413	78.61 365	90.18 215	68.36 303	87.20 207	87.11 362	69.39 305	91.62 197	78.16 168	94.43 220	94.60 96

SIFT-UM-Cal		73.50 400	72.76 402	75.71 417	79.21 467	81.68 85	72.85 464	68.91 491	62.93 388	85.31 267	83.39 434	52.88 433	67.56 514	54.97 457	94.42 223	77.89 511

fmvsm_s_conf0.5_n_9		87.04 97	87.02 105	87.08 106	89.67 192	75.87 161	84.60 191	89.74 225	74.40 188	89.92 123	93.41 129	80.45 156	90.63 244	86.66 45	94.37 224	94.73 94

MCST-MVS		84.36 169	83.93 193	85.63 146	91.59 136	71.58 216	83.52 231	92.13 139	61.82 408	83.96 314	89.75 292	79.93 163	93.46 143	78.33 164	94.34 225	91.87 260

test_fmvsmconf0.1_n		86.18 117	85.88 133	87.08 106	85.26 345	78.25 122	85.82 160	91.82 151	65.33 358	88.55 161	92.35 183	82.62 115	89.80 276	86.87 41	94.32 226	93.18 181

MatchFormer		68.98 458	69.54 448	67.33 491	76.37 501	74.77 169	79.54 338	57.73 544	46.87 518	89.77 128	86.43 371	41.98 507	65.54 526	52.83 479	94.31 227	61.67 541

thisisatest0530		79.07 305	77.33 335	84.26 190	87.13 282	64.58 307	83.66 223	75.95 433	68.86 294	85.22 269	87.36 356	38.10 513	93.57 138	75.47 219	94.28 228	94.62 95

SIFT-ConvMatch		74.17 389	72.94 399	77.87 372	80.47 441	83.15 69	74.56 437	63.87 518	63.44 383	85.61 258	83.95 420	53.15 431	69.97 488	57.21 432	94.21 229	80.48 488

baseline		85.20 142	85.93 131	83.02 231	86.30 315	62.37 347	84.55 193	93.96 45	74.48 185	87.12 209	92.03 192	82.30 122	91.94 187	78.39 161	94.21 229	94.74 93

test_fmvsmconf_n		85.88 125	85.51 143	86.99 110	84.77 357	78.21 123	85.40 172	91.39 166	65.32 359	87.72 193	91.81 203	82.33 120	89.78 277	86.68 43	94.20 231	92.99 193

h-mvs33		84.25 174	82.76 225	88.72 74	91.82 131	82.60 75	84.00 209	84.98 332	71.27 253	86.70 224	90.55 265	63.04 354	93.92 118	78.26 166	94.20 231	89.63 331

MVSMamba_PlusPlus		87.53 93	88.86 77	83.54 218	92.03 120	62.26 350	91.49 45	92.62 123	88.07 24	88.07 177	96.17 25	72.24 281	95.79 32	84.85 81	94.16 233	92.58 216

LuminaMVS		83.94 190	83.51 199	85.23 155	89.78 191	71.74 211	84.76 186	87.27 281	72.60 230	89.31 144	90.60 264	64.04 342	90.95 227	79.08 154	94.11 234	92.99 193

BridgeMVS		84.80 155	85.40 147	83.00 232	88.95 216	61.44 364	90.42 63	92.37 133	71.48 251	88.72 158	93.13 145	70.16 302	95.15 67	79.26 153	94.11 234	92.41 228

alignmvs		83.94 190	83.98 191	83.80 204	87.80 255	67.88 271	84.54 195	91.42 165	73.27 215	88.41 167	87.96 335	72.33 279	90.83 235	76.02 211	94.11 234	92.69 208

USDC		76.63 349	76.73 346	76.34 407	83.46 387	57.20 440	80.02 332	88.04 268	52.14 497	83.65 321	91.25 227	63.24 350	86.65 359	54.66 460	94.11 234	85.17 419

MVS_111021_HR		84.63 160	84.34 184	85.49 152	90.18 181	75.86 162	79.23 354	87.13 287	73.35 209	85.56 261	89.34 302	83.60 101	90.50 248	76.64 197	94.05 238	90.09 320

VNet		79.31 303	80.27 282	76.44 405	87.92 251	53.95 472	75.58 423	84.35 344	74.39 189	82.23 354	90.72 252	72.84 273	84.39 402	60.38 407	93.98 239	90.97 288

gbinet_0.2-2-1-0.02		76.14 359	74.88 371	79.92 323	80.33 449	60.02 395	75.80 418	82.44 371	66.36 338	79.24 413	75.07 522	56.11 408	90.17 260	64.60 369	93.95 240	89.58 332

FMVSNet2		81.31 263	81.61 251	80.41 314	86.38 310	58.75 423	83.93 213	86.58 300	72.43 231	87.65 194	92.98 151	63.78 346	90.22 257	66.86 339	93.92 241	92.27 243

MGCFI-Net		85.04 149	85.95 130	82.31 261	87.52 268	63.59 321	86.23 150	93.96 45	73.46 205	88.07 177	87.83 345	86.46 63	90.87 234	76.17 208	93.89 242	92.47 224

GDP-MVS		82.17 240	80.85 274	86.15 133	88.65 227	68.95 259	85.65 165	93.02 107	68.42 302	83.73 318	89.54 297	45.07 493	94.31 96	79.66 145	93.87 243	95.19 69

LF4IMVS		82.75 226	81.93 244	85.19 156	82.08 409	80.15 102	85.53 167	88.76 247	68.01 309	85.58 260	87.75 346	71.80 288	86.85 354	74.02 246	93.87 243	88.58 362

viewdifsd2359ckpt09		83.64 198	83.18 212	85.03 161	87.26 277	66.99 283	85.32 173	93.83 56	65.57 353	84.99 279	89.40 299	77.30 193	93.57 138	71.16 291	93.80 245	94.54 100

SIFT-UMatch		73.61 398	72.65 406	76.46 404	80.19 452	82.31 78	74.23 441	64.86 512	64.03 378	84.69 290	84.19 417	50.89 449	67.79 511	57.03 433	93.79 246	79.28 499

sasdasda		85.50 129	86.14 125	83.58 214	87.97 248	67.13 277	87.55 119	94.32 22	73.44 207	88.47 164	87.54 350	86.45 64	91.06 224	75.76 214	93.76 247	92.54 220

canonicalmvs		85.50 129	86.14 125	83.58 214	87.97 248	67.13 277	87.55 119	94.32 22	73.44 207	88.47 164	87.54 350	86.45 64	91.06 224	75.76 214	93.76 247	92.54 220

v2v482		84.09 179	84.24 186	83.62 212	87.13 282	61.40 365	82.71 260	89.71 228	72.19 239	89.55 138	91.41 218	70.70 297	93.20 151	81.02 128	93.76 247	96.25 36

casdiffmvs		85.21 141	85.85 134	83.31 223	86.17 320	62.77 333	83.03 248	93.93 47	74.69 181	88.21 173	92.68 167	82.29 124	91.89 190	77.87 178	93.75 250	95.27 65
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
testing3-2		70.72 439	70.97 428	69.95 470	88.93 217	34.80 547	69.85 491	66.59 505	78.42 128	77.58 439	85.55 387	31.83 532	82.08 420	46.28 519	93.73 251	92.98 195

fmvsm_s_conf0.5_n_6		84.05 183	84.14 187	83.81 203	87.75 258	71.17 223	83.42 234	91.10 178	67.90 314	84.53 293	90.70 253	73.01 269	88.73 303	85.09 72	93.72 252	91.53 275

UGNet		82.78 225	81.64 249	86.21 129	86.20 319	76.24 153	86.86 133	85.68 316	77.07 146	73.76 478	92.82 161	69.64 303	91.82 193	69.04 319	93.69 253	90.56 305
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
旧先验1					91.97 121	71.77 210		81.78 379			91.84 200	73.92 251			93.65 254	83.61 443

AUN-MVS		81.18 268	78.78 312	88.39 83	90.93 162	82.14 80	82.51 267	83.67 354	64.69 369	80.29 397	85.91 384	51.07 448	92.38 175	76.29 205	93.63 255	90.65 302

KinetiMVS		85.95 123	86.10 127	85.50 151	87.56 267	69.78 241	83.70 221	89.83 224	80.42 96	87.76 191	93.24 139	73.76 255	91.54 199	85.03 75	93.62 256	95.19 69

hse-mvs2		83.47 208	81.81 246	88.47 81	91.03 160	82.27 79	82.61 261	83.69 353	71.27 253	86.70 224	86.05 381	63.04 354	92.41 174	78.26 166	93.62 256	90.71 297

mamba_0408		83.44 211	82.88 222	85.11 158	89.13 207	68.97 256	72.73 465	91.28 170	72.90 222	85.68 253	90.61 262	76.78 210	93.97 115	73.37 264	93.47 258	92.38 233

SSM_04072		81.44 261	82.88 222	77.10 389	89.13 207	68.97 256	72.73 465	91.28 170	72.90 222	85.68 253	90.61 262	76.78 210	69.94 489	73.37 264	93.47 258	92.38 233

SSM_0407		84.89 154	84.85 160	85.01 163	89.13 207	68.97 256	85.60 166	91.58 157	74.41 186	85.68 253	91.49 214	78.54 171	93.69 127	73.71 252	93.47 258	92.38 233

E5new		85.44 134	86.37 117	82.66 245	88.22 241	61.86 356	83.59 225	93.70 61	73.64 199	87.62 195	93.30 134	85.85 74	91.26 212	78.02 171	93.40 261	94.86 84

E6new		85.44 134	86.37 117	82.66 245	88.23 239	61.86 356	83.59 225	93.69 64	73.64 199	87.61 197	93.30 134	85.85 74	91.26 212	78.02 171	93.40 261	94.86 84

E6		85.44 134	86.37 117	82.66 245	88.23 239	61.86 356	83.59 225	93.69 64	73.64 199	87.61 197	93.30 134	85.85 74	91.26 212	78.02 171	93.40 261	94.86 84

E5		85.44 134	86.37 117	82.66 245	88.22 241	61.86 356	83.59 225	93.70 61	73.64 199	87.62 195	93.30 134	85.85 74	91.26 212	78.02 171	93.40 261	94.86 84

MVS_111021_LR		84.28 173	83.76 196	85.83 142	89.23 203	83.07 70	80.99 313	83.56 355	72.71 228	86.07 242	89.07 313	81.75 141	86.19 372	77.11 190	93.36 265	88.24 369

GBi-Net		82.02 247	82.07 239	81.85 273	86.38 310	61.05 374	86.83 135	88.27 263	72.43 231	86.00 247	95.64 37	63.78 346	90.68 241	65.95 349	93.34 266	93.82 143

test1		82.02 247	82.07 239	81.85 273	86.38 310	61.05 374	86.83 135	88.27 263	72.43 231	86.00 247	95.64 37	63.78 346	90.68 241	65.95 349	93.34 266	93.82 143

FMVSNet3		78.80 313	78.55 316	79.57 332	82.89 405	56.89 443	81.76 289	85.77 314	69.04 290	86.00 247	90.44 267	51.75 443	90.09 267	65.95 349	93.34 266	91.72 265

test_fmvsmvis_n_1920		85.22 140	85.36 149	84.81 167	85.80 332	76.13 155	85.15 178	92.32 134	61.40 415	91.33 89	90.85 248	83.76 99	86.16 373	84.31 87	93.28 269	92.15 250

fmvsm_s_conf0.5_n_10		85.20 142	85.25 152	85.02 162	86.01 326	71.31 220	84.96 181	91.76 155	69.10 287	88.90 150	92.56 171	73.84 253	90.63 244	86.88 40	93.26 270	93.13 182

viewmacassd2359aftdt		84.04 185	84.78 162	81.81 276	86.43 307	60.32 389	81.95 285	92.82 116	71.56 248	86.06 243	92.98 151	81.79 140	90.28 253	76.18 207	93.24 271	94.82 90

K. test v3		85.14 145	84.73 163	86.37 122	91.13 158	69.63 245	85.45 170	76.68 429	84.06 58	92.44 67	96.99 12	62.03 357	94.65 84	80.58 135	93.24 271	94.83 89

Anonymous20231206		71.38 431	71.88 413	69.88 471	86.31 314	54.37 467	70.39 487	74.62 442	52.57 492	76.73 444	88.76 318	59.94 369	72.06 478	44.35 526	93.23 273	83.23 453

SSM_0404		85.16 144	85.09 154	85.36 153	90.14 182	69.52 246	86.17 151	91.58 157	74.41 186	86.55 228	91.49 214	78.54 171	93.97 115	73.71 252	93.21 274	92.59 215

fmvsm_l_mol_unc0.5_1		82.09 243	83.08 216	79.12 342	85.01 350	56.67 445	77.48 388	81.51 385	68.49 301	92.60 63	95.50 41	72.88 271	88.10 322	81.09 125	93.20 275	92.58 216

D2MVS		76.84 345	75.67 359	80.34 315	80.48 440	62.16 353	73.50 455	84.80 339	57.61 454	82.24 353	87.54 350	51.31 446	87.65 334	70.40 302	93.19 276	91.23 279

miper_lstm_enhance		76.45 355	76.10 354	77.51 381	76.72 495	60.97 381	64.69 517	85.04 329	63.98 380	83.20 333	88.22 330	56.67 400	78.79 448	73.22 268	93.12 277	92.78 203

新几何1				82.95 235	93.96 63	78.56 119		80.24 396	55.45 472	83.93 315	91.08 235	71.19 294	88.33 319	65.84 353	93.07 278	81.95 470

lessismore_v0				85.95 136	91.10 159	70.99 226		70.91 479		91.79 82	94.42 80	61.76 358	92.93 162	79.52 149	93.03 279	93.93 134

TAMVS		78.08 328	76.36 351	83.23 225	90.62 171	72.87 189	79.08 356	80.01 399	61.72 411	81.35 379	86.92 365	63.96 345	88.78 301	50.61 492	93.01 280	88.04 375

ETV-MVS		84.31 171	83.91 195	85.52 149	88.58 231	70.40 233	84.50 198	93.37 80	78.76 124	84.07 311	78.72 490	80.39 157	95.13 69	73.82 250	92.98 281	91.04 285

EPNet_dtu		72.87 409	71.33 421	77.49 382	77.72 481	60.55 386	82.35 274	75.79 434	66.49 337	58.39 545	81.06 465	53.68 427	85.98 375	53.55 469	92.97 282	85.95 410
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SIFT-NN-PointCN		72.35 416	71.17 426	75.90 413	77.68 482	80.93 96	73.48 456	63.14 523	60.88 427	80.94 384	82.91 443	52.54 437	67.74 512	55.98 443	92.95 283	79.05 503

Effi-MVS+-dtu		85.82 126	83.38 206	93.14 3	87.13 282	91.15 2	87.70 118	88.42 257	74.57 182	83.56 324	85.65 386	78.49 175	94.21 101	72.04 280	92.88 284	94.05 129

viewdifsd2359ckpt11		82.46 232	82.98 219	80.88 299	83.53 383	61.00 377	79.46 345	85.97 311	69.48 282	87.89 185	91.31 224	82.10 129	88.61 310	74.28 234	92.86 285	93.02 189

viewmsd2359difaftdt		82.46 232	82.99 218	80.88 299	83.52 384	61.00 377	79.46 345	85.97 311	69.48 282	87.89 185	91.31 224	82.10 129	88.61 310	74.28 234	92.86 285	93.02 189

CANet		83.79 195	82.85 224	86.63 116	86.17 320	72.21 206	83.76 219	91.43 163	77.24 145	74.39 473	87.45 354	75.36 223	95.42 54	77.03 191	92.83 287	92.25 245

fmvsm_s_conf0.5_n_3		86.19 116	87.27 99	82.95 235	86.91 295	70.38 234	85.31 174	92.61 125	75.59 167	88.32 170	92.87 158	82.22 126	88.63 309	88.80 8	92.82 288	89.83 327

API-MVS		82.28 235	82.61 230	81.30 289	86.29 316	69.79 240	88.71 101	87.67 276	78.42 128	82.15 356	84.15 419	77.98 180	91.59 198	65.39 357	92.75 289	82.51 464

fmvsm_s_conf0.5_n_8		85.48 131	85.75 138	84.68 174	87.10 285	69.98 239	84.28 202	92.68 120	74.77 179	87.90 184	92.36 182	73.94 250	90.41 251	85.95 61	92.74 290	93.66 151

test_yl		78.71 317	78.51 317	79.32 339	84.32 367	58.84 420	78.38 366	85.33 322	75.99 156	82.49 348	86.57 368	58.01 389	90.02 271	62.74 382	92.73 291	89.10 349

DCV-MVSNet		78.71 317	78.51 317	79.32 339	84.32 367	58.84 420	78.38 366	85.33 322	75.99 156	82.49 348	86.57 368	58.01 389	90.02 271	62.74 382	92.73 291	89.10 349

VortexMVS		80.51 281	80.63 275	80.15 320	83.36 392	61.82 360	80.63 323	88.00 269	67.11 329	87.23 205	89.10 312	63.98 343	88.00 325	73.63 258	92.63 293	90.64 303

SIFT-NCM-Cal		73.77 396	72.70 404	76.99 391	82.03 410	83.73 63	75.59 422	63.01 524	63.50 382	84.80 287	83.94 421	55.86 412	67.80 510	52.94 476	92.62 294	79.44 497

wanda-best-256-512		74.97 377	73.85 382	78.35 359	80.36 444	58.13 427	73.10 461	83.53 356	64.04 377	77.62 435	75.71 516	56.22 405	88.60 312	61.42 399	92.61 295	88.32 366

FE-blended-shiyan7		74.97 377	73.85 382	78.35 359	80.36 444	58.13 427	73.10 461	83.53 356	64.03 378	77.62 435	75.71 516	56.22 405	88.60 312	61.42 399	92.61 295	88.32 366

blended_shiyan6		76.05 362	75.11 366	78.87 346	81.74 416	59.15 413	75.08 430	83.79 351	64.69 369	79.37 408	78.37 492	58.30 385	88.69 305	61.99 391	92.61 295	88.77 358

usedtu_blend_shiyan5		77.07 342	76.43 350	78.99 344	80.36 444	59.77 399	83.25 240	88.32 261	74.91 177	77.62 435	75.71 516	56.22 405	88.89 296	58.91 416	92.61 295	88.32 366

fmvsm_l_conf0.5_n_9		83.98 188	84.46 178	82.53 254	86.11 323	70.65 230	82.45 270	89.17 241	67.72 319	86.74 223	91.49 214	79.20 166	85.86 383	84.71 83	92.60 299	91.07 284

blended_shiyan8		76.05 362	75.11 366	78.86 347	81.76 415	59.18 412	75.09 429	83.81 350	64.70 368	79.37 408	78.35 493	58.30 385	88.68 306	62.03 390	92.56 300	88.73 360

testgi		72.36 415	74.61 374	65.59 501	80.56 439	42.82 531	68.29 498	73.35 455	66.87 332	81.84 365	89.93 288	72.08 284	66.92 518	46.05 522	92.54 301	87.01 396

SIFT-NN-CMatch		72.68 411	71.28 422	76.88 397	78.79 472	82.59 76	73.68 450	61.02 534	60.35 434	81.79 370	83.09 437	52.94 432	68.88 499	57.28 430	92.53 302	79.16 501

guyue		81.57 258	81.37 261	82.15 265	86.39 308	66.13 293	81.54 294	83.21 360	69.79 277	87.77 190	89.95 286	65.36 334	87.64 335	75.88 212	92.49 303	92.67 209

FMVSNet5		72.10 420	71.69 415	73.32 441	81.57 421	53.02 479	76.77 400	78.37 412	63.31 384	76.37 447	91.85 199	36.68 519	78.98 445	47.87 512	92.45 304	87.95 379

balanced_ft_v1		83.49 206	83.93 193	82.19 263	86.46 305	59.61 403	90.81 52	90.92 186	71.78 246	88.08 176	92.56 171	66.97 320	94.54 91	75.34 222	92.42 305	92.42 226

AstraMVS		81.67 256	81.40 259	82.48 256	87.06 290	66.47 289	81.41 297	81.68 381	68.78 295	88.00 180	90.95 243	65.70 331	87.86 332	76.66 196	92.38 306	93.12 185

CDS-MVSNet		77.32 337	75.40 361	83.06 230	89.00 214	72.48 200	77.90 377	82.17 375	60.81 428	78.94 418	83.49 429	59.30 375	88.76 302	54.64 461	92.37 307	87.93 381
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
FE-MVSNET		78.46 321	79.36 303	75.75 415	86.53 301	54.53 466	78.03 371	85.35 321	69.01 291	85.41 264	90.68 256	64.27 338	85.73 386	62.59 384	92.35 308	87.00 397

patch_mono-2		78.89 310	79.39 300	77.41 383	84.78 356	68.11 268	75.60 420	83.11 362	60.96 426	79.36 410	89.89 290	75.18 225	72.97 475	73.32 266	92.30 309	91.15 282

dcpmvs_2		84.23 176	85.14 153	81.50 284	88.61 229	61.98 354	82.90 256	93.11 99	68.66 298	92.77 60	92.39 176	78.50 174	87.63 336	76.99 192	92.30 309	94.90 78

CNLPA		83.55 203	83.10 215	84.90 164	89.34 200	83.87 61	84.54 195	88.77 245	79.09 117	83.54 325	88.66 324	74.87 230	81.73 424	66.84 341	92.29 311	89.11 348

F-COLMAP		84.97 153	83.42 204	89.63 57	92.39 106	83.40 66	88.83 98	91.92 147	73.19 216	80.18 402	89.15 311	77.04 201	93.28 149	65.82 354	92.28 312	92.21 246

thres600view7		75.97 364	75.35 364	77.85 374	87.01 291	51.84 489	80.45 327	73.26 456	75.20 174	83.10 335	86.31 376	45.54 484	89.05 292	55.03 456	92.24 313	92.66 210

PVSNet_BlendedMVS		78.80 313	77.84 328	81.65 280	84.43 363	63.41 322	79.49 342	90.44 200	61.70 412	75.43 462	87.07 363	69.11 308	91.44 204	60.68 405	92.24 313	90.11 319

DELS-MVS		81.44 261	81.25 263	82.03 268	84.27 369	62.87 330	76.47 408	92.49 128	70.97 261	81.64 373	83.83 422	75.03 226	92.70 167	74.29 233	92.22 315	90.51 307
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
E4		84.75 158	85.46 145	82.61 249	88.17 244	61.55 363	81.39 298	93.55 76	73.13 219	86.83 219	92.83 160	84.17 94	91.48 201	76.92 193	92.19 316	94.80 91

fmvsm_s_conf0.5_n_5		84.56 163	84.71 166	84.11 196	87.92 251	72.09 207	84.80 182	88.64 250	64.43 372	88.77 155	91.78 205	78.07 179	87.95 327	85.85 62	92.18 317	92.30 239

testdata				79.54 334	92.87 92	72.34 202		80.14 398	59.91 439	85.47 263	91.75 207	67.96 314	85.24 391	68.57 328	92.18 317	81.06 483

viewmanbaseed2359cas		82.95 222	83.43 203	81.52 283	85.18 347	60.03 394	81.36 299	92.38 131	69.55 280	84.84 286	91.38 219	79.85 164	90.09 267	74.22 236	92.09 319	94.43 109

SIFT-MNN		74.38 388	73.27 392	77.72 375	82.37 407	83.68 64	76.29 410	67.76 495	64.16 375	84.33 301	84.30 412	50.36 456	68.84 500	57.79 427	92.07 320	80.66 487

SSC-MVS		77.55 334	81.64 249	65.29 504	90.46 174	20.33 558	73.56 453	68.28 492	85.44 40	88.18 175	94.64 70	70.93 295	81.33 427	71.25 288	92.03 321	94.20 118

cl22		78.97 307	78.21 322	81.24 293	77.74 480	59.01 416	77.46 389	87.13 287	65.79 345	84.32 302	85.10 398	58.96 379	90.88 233	75.36 221	92.03 321	93.84 139

miper_ehance_all_eth		80.34 287	80.04 292	81.24 293	79.82 458	58.95 417	77.66 380	89.66 229	65.75 349	85.99 250	85.11 397	68.29 312	91.42 206	76.03 210	92.03 321	93.33 170

miper_enhance_ethall		77.83 329	76.93 341	80.51 311	76.15 502	58.01 432	75.47 425	88.82 244	58.05 450	83.59 322	80.69 469	64.41 337	91.20 218	73.16 274	92.03 321	92.33 238

GeoE		85.45 133	85.81 135	84.37 182	90.08 183	67.07 280	85.86 159	91.39 166	72.33 236	87.59 199	90.25 276	84.85 86	92.37 176	78.00 175	91.94 325	93.66 151

fmvsm_s_conf0.5_n_11		84.56 163	84.69 168	84.15 195	86.53 301	71.29 221	85.53 167	92.62 123	70.54 266	82.75 346	91.20 230	77.33 192	88.55 314	83.80 94	91.93 326	92.61 214

testing915		74.11 391	74.71 373	72.32 456	85.86 330	47.86 508	81.27 305	75.02 440	67.81 317	76.45 446	86.10 379	55.88 411	84.39 402	52.09 483	91.92 327	84.70 425

SIFT-NN-UMatch		72.46 413	71.25 423	76.08 411	78.57 474	81.88 82	74.36 438	61.59 532	61.99 407	80.24 401	83.46 430	51.20 447	68.08 509	57.95 426	91.91 328	78.28 508

fmvsm_s_conf0.1_n_2		83.82 193	83.49 201	84.84 165	85.99 327	70.19 237	80.93 315	87.58 277	67.26 327	87.94 183	92.37 180	71.40 293	88.01 324	86.03 56	91.87 329	96.31 35

DPM-MVS		80.10 296	79.18 305	82.88 241	90.71 169	69.74 242	78.87 360	90.84 187	60.29 436	75.64 461	85.92 383	67.28 317	93.11 155	71.24 289	91.79 330	85.77 413

v148		82.31 234	82.48 233	81.81 276	85.59 338	59.66 401	81.47 295	86.02 309	72.85 224	88.05 179	90.65 260	70.73 296	90.91 231	75.15 225	91.79 330	94.87 80

fmvsm_s_conf0.5_n_2		83.62 200	83.29 208	84.62 175	85.43 342	70.18 238	80.61 324	87.24 283	67.14 328	87.79 189	91.87 195	71.79 289	87.98 326	86.00 60	91.77 332	95.71 50

fmvsm_s_conf0.5_n_7		82.04 246	82.05 241	82.01 269	86.98 293	71.07 224	78.70 362	89.45 235	68.07 308	78.14 426	91.61 210	74.19 244	85.92 377	79.61 146	91.73 333	89.05 352

SP-MNN		77.71 333	77.85 327	77.29 385	78.48 475	75.90 160	79.14 355	79.46 401	69.61 279	81.56 376	84.60 409	54.98 423	69.02 498	81.08 127	91.72 334	86.95 398

test222					93.31 81	76.54 146	79.38 347	77.79 414	52.59 491	82.36 352	90.84 249	66.83 323			91.69 335	81.25 478

testing3		71.53 429	70.79 430	73.77 439	88.89 219	41.86 533	76.60 406	59.12 539	72.83 225	80.97 382	82.08 453	19.80 555	87.33 343	65.12 360	91.68 336	92.13 251

eth_miper_zixun_eth		80.84 275	80.22 285	82.71 243	81.41 423	60.98 380	77.81 378	90.14 216	67.31 326	86.95 218	87.24 359	64.26 339	92.31 178	75.23 223	91.61 337	94.85 88

pmmvs-eth3d		78.42 325	77.04 339	82.57 253	87.44 274	74.41 173	80.86 317	79.67 400	55.68 469	84.69 290	90.31 274	60.91 362	85.42 390	62.20 387	91.59 338	87.88 382

Vis-MVSNet		86.86 100	86.58 113	87.72 97	92.09 117	77.43 137	87.35 123	92.09 141	78.87 121	84.27 307	94.05 101	78.35 176	93.65 128	80.54 136	91.58 339	92.08 252
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FE-MVS		79.98 298	78.86 309	83.36 221	86.47 304	66.45 290	89.73 75	84.74 340	72.80 226	84.22 309	91.38 219	44.95 494	93.60 134	63.93 372	91.50 340	90.04 321

thisisatest0515		73.00 408	70.52 434	80.46 312	81.45 422	59.90 397	73.16 460	74.31 446	57.86 451	76.08 454	77.78 497	37.60 517	92.12 184	65.00 361	91.45 341	89.35 338

ppachtmachnet_test		74.73 384	74.00 381	76.90 395	80.71 436	56.89 443	71.53 478	78.42 410	58.24 447	79.32 412	82.92 442	57.91 392	84.26 405	65.60 356	91.36 342	89.56 333

E2		84.06 181	84.61 170	82.40 259	87.49 270	61.31 367	81.03 311	93.36 81	71.83 244	86.02 244	91.87 195	82.91 109	91.37 209	75.66 216	91.33 343	94.53 101

E3		84.06 181	84.61 170	82.40 259	87.49 270	61.30 368	81.03 311	93.36 81	71.83 244	86.01 246	91.87 195	82.91 109	91.36 210	75.66 216	91.33 343	94.53 101

FA-MVS(test-final)		83.13 217	83.02 217	83.43 219	86.16 322	66.08 294	88.00 113	88.36 259	75.55 168	85.02 277	92.75 165	65.12 335	92.50 172	74.94 228	91.30 345	91.72 265

OpenMVS	76.72 13	81.98 249	82.00 242	81.93 270	84.42 365	68.22 266	88.50 107	89.48 234	66.92 331	81.80 368	91.86 198	72.59 276	90.16 261	71.19 290	91.25 346	87.40 391

XFeat-MNN		64.44 487	63.82 487	66.28 497	61.83 553	67.23 274	61.52 527	63.95 517	44.72 528	85.19 270	74.40 525	36.05 521	66.04 524	55.58 447	91.14 347	65.57 536

viewdifsd2359ckpt07		83.41 212	84.35 183	80.56 310	85.84 331	58.93 418	79.47 343	91.28 170	73.01 221	87.59 199	92.07 189	85.24 82	88.68 306	73.59 259	91.11 348	94.09 128

fmvsm_l_conf0.5_n_3		85.11 148	84.96 158	85.56 148	87.49 270	75.69 163	84.71 188	90.61 195	67.64 320	84.88 283	92.05 190	82.30 122	88.36 318	83.84 93	91.10 349	92.62 212

EG-PatchMatch MVS		84.08 180	84.11 188	83.98 199	92.22 113	72.61 196	82.20 282	87.02 293	72.63 229	88.86 151	91.02 237	78.52 173	91.11 222	73.41 262	91.09 350	88.21 370

3Dnovator	80.37 7	84.80 155	84.71 166	85.06 160	86.36 313	74.71 170	88.77 100	90.00 219	75.65 165	84.96 280	93.17 143	74.06 248	91.19 219	78.28 165	91.09 350	89.29 342

thres100view900		75.45 370	75.05 370	76.66 400	87.27 276	51.88 488	81.07 310	73.26 456	75.68 164	83.25 332	86.37 373	45.54 484	88.80 298	51.98 486	90.99 352	89.31 339

tfpn200view9		74.86 380	74.23 379	76.74 399	86.24 317	52.12 485	79.24 352	73.87 449	73.34 210	81.82 366	84.60 409	46.02 475	88.80 298	51.98 486	90.99 352	89.31 339

thres400		75.14 372	74.23 379	77.86 373	86.24 317	52.12 485	79.24 352	73.87 449	73.34 210	81.82 366	84.60 409	46.02 475	88.80 298	51.98 486	90.99 352	92.66 210

cascas		76.29 358	74.81 372	80.72 304	84.47 362	62.94 328	73.89 448	87.34 279	55.94 466	75.16 467	76.53 511	63.97 344	91.16 220	65.00 361	90.97 355	88.06 374

MSP-MVS		89.08 69	88.16 87	91.83 19	95.76 17	86.14 32	92.75 17	93.90 49	78.43 127	89.16 147	92.25 186	72.03 286	96.36 3	88.21 12	90.93 356	92.98 195
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
WBMVS		68.76 460	68.43 459	69.75 473	83.29 395	40.30 537	67.36 505	72.21 468	57.09 460	77.05 443	85.53 389	33.68 526	80.51 435	48.79 505	90.90 357	88.45 365

ab-mvs		79.67 301	80.56 277	76.99 391	88.48 233	56.93 441	84.70 189	86.06 307	68.95 293	80.78 388	93.08 146	75.30 224	84.62 397	56.78 435	90.90 357	89.43 337

test_fmvsm_n_1920		83.60 201	82.89 221	85.74 143	85.22 346	77.74 131	84.12 206	90.48 197	59.87 440	86.45 237	91.12 233	75.65 219	85.89 381	82.28 113	90.87 359	93.58 161

MAR-MVS		80.24 291	78.74 314	84.73 171	86.87 298	78.18 124	85.75 162	87.81 275	65.67 352	77.84 430	78.50 491	73.79 254	90.53 247	61.59 398	90.87 359	85.49 417
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
EI-MVSNet-Vis-set		85.12 147	84.53 176	86.88 112	84.01 375	72.76 190	83.91 214	85.18 325	80.44 95	88.75 156	85.49 390	80.08 160	91.92 188	82.02 117	90.85 361	95.97 43

usedtu_dtu_shiyan1		75.70 368	75.08 368	77.56 377	84.10 373	55.50 456	73.58 451	84.89 334	62.48 395	78.16 424	84.24 414	58.14 387	87.47 338	59.35 413	90.82 362	89.72 328

FE-MVSNET3		75.70 368	75.08 368	77.56 377	84.10 373	55.50 456	73.58 451	84.89 334	62.48 395	78.16 424	84.24 414	58.14 387	87.47 338	59.34 414	90.82 362	89.72 328

diffmvs_AUTHOR		81.24 266	81.55 255	80.30 316	80.61 438	60.22 390	77.98 375	90.48 197	67.77 318	83.34 330	89.50 298	74.69 237	87.42 340	78.78 158	90.81 364	93.27 174

EI-MVSNet-UG-set		85.04 149	84.44 179	86.85 113	83.87 379	72.52 199	83.82 216	85.15 326	80.27 100	88.75 156	85.45 392	79.95 162	91.90 189	81.92 120	90.80 365	96.13 38

XVG-OURS-SEG-HR		89.59 58	89.37 64	90.28 45	94.47 42	85.95 35	86.84 134	93.91 48	80.07 103	86.75 222	93.26 138	93.64 2	90.93 229	84.60 85	90.75 366	93.97 132

icg_test_0407_2		78.46 321	79.68 296	74.78 428	85.76 333	62.46 340	68.51 497	87.91 271	65.23 360	82.12 357	87.92 339	77.27 195	72.67 476	71.67 283	90.74 367	89.20 343

IMVS_0407		81.08 269	81.23 265	80.62 309	85.76 333	62.46 340	82.46 268	87.91 271	65.23 360	82.12 357	87.92 339	77.27 195	90.18 259	71.67 283	90.74 367	89.20 343

IMVS_0404		77.24 338	77.75 330	75.73 416	85.76 333	62.46 340	70.84 483	87.91 271	65.23 360	72.21 487	87.92 339	67.48 316	75.53 466	71.67 283	90.74 367	89.20 343

IMVS_0403		80.93 274	81.00 268	80.72 304	85.76 333	62.46 340	81.82 288	87.91 271	65.23 360	82.07 359	87.92 339	75.91 217	90.50 248	71.67 283	90.74 367	89.20 343

TestfortrainingZip				84.49 179	88.84 220	70.49 231	92.12 33	91.01 181	84.70 50	82.82 344	89.25 306	74.30 242	94.06 111		90.73 371	88.92 356

ET-MVSNet_ETH3D		75.28 371	72.77 401	82.81 242	83.03 403	68.11 268	77.09 393	76.51 430	60.67 431	77.60 438	80.52 473	38.04 514	91.15 221	70.78 294	90.68 372	89.17 347

EI-MVSNet		82.61 227	82.42 234	83.20 226	83.25 397	63.66 319	83.50 232	85.07 327	76.06 153	86.55 228	85.10 398	73.41 262	90.25 254	78.15 170	90.67 373	95.68 53

MVSTER		77.09 341	75.70 358	81.25 290	75.27 511	61.08 373	77.49 387	85.07 327	60.78 429	86.55 228	88.68 321	43.14 504	90.25 254	73.69 257	90.67 373	92.42 226

reproduce_monomvs		74.09 392	73.23 393	76.65 402	76.52 496	54.54 465	77.50 386	81.40 387	65.85 344	82.86 343	86.67 367	27.38 546	84.53 399	70.24 303	90.66 375	90.89 291

Patchmatch-RL test		74.48 385	73.68 385	76.89 396	84.83 355	66.54 287	72.29 468	69.16 489	57.70 452	86.76 221	86.33 374	45.79 482	82.59 415	69.63 310	90.65 376	81.54 474

CMPMVS	59.41 20	75.12 374	73.57 386	79.77 326	75.84 505	67.22 275	81.21 308	82.18 374	50.78 506	76.50 445	87.66 348	55.20 420	82.99 414	62.17 389	90.64 377	89.09 351
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
viewcassd2359sk11		83.53 204	83.96 192	82.25 262	86.97 294	61.13 372	80.80 320	93.22 93	70.97 261	85.36 265	91.08 235	81.84 138	91.29 211	74.79 229	90.58 378	94.33 115

SP-SuperGlue		80.13 295	80.14 287	80.11 321	79.95 456	80.97 93	80.94 314	80.77 393	76.46 150	82.92 339	85.73 385	58.75 381	70.83 485	85.20 70	90.50 379	88.53 363

SIFT-NN-NCMNet		72.70 410	71.25 423	77.06 390	81.65 419	84.07 59	75.19 427	63.15 522	61.29 419	78.74 420	83.21 435	53.60 428	69.25 495	53.99 464	90.47 380	77.86 512

SP-LightGlue		79.92 300	79.74 295	80.46 312	80.22 451	81.52 88	81.28 304	81.81 378	75.89 160	81.60 375	84.90 404	55.82 413	71.10 484	85.62 65	90.47 380	88.76 359

WB-MVS		76.06 361	80.01 293	64.19 508	89.96 189	20.58 557	72.18 470	68.19 493	83.21 68	86.46 236	93.49 127	70.19 301	78.97 446	65.96 348	90.46 382	93.02 189

onestephybrid01		81.22 267	80.90 272	82.18 264	80.05 453	64.49 310	79.47 343	89.23 239	69.10 287	81.96 361	89.27 304	75.02 227	89.12 291	73.71 252	90.24 383	92.92 199

fmvsm_l_conf0.5_n		82.06 245	81.54 256	83.60 213	83.94 376	73.90 176	83.35 237	86.10 305	58.97 442	83.80 317	90.36 268	74.23 243	86.94 351	82.90 103	90.22 384	89.94 323

V42		83.47 208	83.37 207	83.75 207	83.16 400	63.33 324	81.31 300	90.23 213	69.51 281	90.91 101	90.81 250	74.16 245	92.29 180	80.06 138	90.22 384	95.62 55

fmvsm_s_conf0.5_n_4		84.38 168	84.27 185	84.74 170	87.25 278	70.84 227	83.55 230	88.45 256	68.64 299	86.29 238	91.31 224	74.97 229	88.42 316	87.87 19	90.07 386	94.95 77

PM-MVS		80.20 292	79.00 306	83.78 206	88.17 244	86.66 25	81.31 300	66.81 503	69.64 278	88.33 169	90.19 279	64.58 336	83.63 411	71.99 282	90.03 387	81.06 483

PLC	73.85 16	82.09 243	80.31 281	87.45 101	90.86 165	80.29 101	85.88 157	90.65 192	68.17 307	76.32 449	86.33 374	73.12 268	92.61 170	61.40 401	90.02 388	89.44 335
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n_a		81.46 260	80.87 273	83.25 224	83.73 382	73.21 185	83.00 250	85.59 318	58.22 448	82.96 337	90.09 284	72.30 280	86.65 359	81.97 119	89.95 389	89.88 324

ttmdpeth		71.72 424	70.67 431	74.86 426	73.08 526	55.88 450	77.41 390	69.27 487	55.86 467	78.66 421	93.77 119	38.01 515	75.39 467	60.12 408	89.87 390	93.31 172

E3new		83.08 219	83.39 205	82.14 266	86.49 303	61.00 377	80.64 322	93.12 98	70.30 271	84.78 288	90.34 269	80.85 150	91.24 217	74.20 239	89.83 391	94.17 122

UWE-MVS		66.43 475	65.56 480	69.05 478	84.15 371	40.98 535	73.06 463	64.71 514	54.84 476	76.18 452	79.62 482	29.21 541	80.50 436	38.54 539	89.75 392	85.66 414

CANet_DTU		77.81 331	77.05 338	80.09 322	81.37 424	59.90 397	83.26 239	88.29 262	69.16 286	67.83 515	83.72 424	60.93 361	89.47 283	69.22 315	89.70 393	90.88 292

diffmvs		80.40 285	80.48 280	80.17 319	79.02 470	60.04 392	77.54 384	90.28 212	66.65 334	82.40 350	87.33 357	73.50 258	87.35 342	77.98 176	89.62 394	93.13 182
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVStest1		70.05 446	69.26 449	72.41 454	58.62 554	55.59 455	76.61 405	65.58 508	53.44 485	89.28 145	93.32 132	22.91 553	71.44 483	74.08 244	89.52 395	90.21 317

PDCNetPlus		57.49 509	56.93 512	59.15 524	56.36 555	47.35 513	52.32 544	77.34 421	39.50 543	63.50 533	73.19 527	13.19 559	56.86 544	47.51 513	89.48 396	73.22 524

viewdifsd2359ckpt13		82.22 237	81.98 243	82.95 235	85.48 341	64.44 311	83.17 245	92.11 140	65.97 339	83.72 319	89.73 293	77.60 187	90.80 237	70.61 299	89.42 397	93.59 160

PMMVS2		55.64 512	59.27 506	44.74 530	64.30 549	12.32 562	40.60 546	49.79 550	53.19 487	65.06 529	84.81 405	53.60 428	49.76 550	32.68 547	89.41 398	72.15 526

Fast-Effi-MVS+-dtu		82.54 230	81.41 258	85.90 138	85.60 337	76.53 148	83.07 247	89.62 232	73.02 220	79.11 416	83.51 428	80.74 153	90.24 256	68.76 323	89.29 399	90.94 289

thres200		72.34 417	71.55 419	74.70 430	83.48 386	51.60 490	75.02 431	73.71 452	70.14 274	78.56 423	80.57 472	46.20 473	88.20 321	46.99 516	89.29 399	84.32 431

jason		77.42 336	75.75 357	82.43 258	87.10 285	69.27 249	77.99 374	81.94 377	51.47 501	77.84 430	85.07 401	60.32 366	89.00 293	70.74 296	89.27 401	89.03 353
jason: jason.
hybridnocas07		79.65 302	79.65 297	79.63 331	78.06 476	59.34 406	77.00 398	88.72 248	66.51 336	81.08 381	89.36 301	72.35 278	87.12 346	74.56 230	89.20 402	92.44 225

MG-MVS		80.32 288	80.94 270	78.47 357	88.18 243	52.62 483	82.29 276	85.01 331	72.01 242	79.24 413	92.54 173	69.36 306	93.36 148	70.65 297	89.19 403	89.45 334

viewmamba		81.97 250	82.13 236	81.47 286	80.43 442	62.46 340	79.31 349	89.99 221	71.08 259	83.39 329	90.21 277	78.08 178	88.73 303	77.55 182	89.16 404	93.23 178

SP-DiffGlue		78.90 309	78.86 309	79.02 343	80.36 444	79.68 108	81.86 286	80.17 397	71.69 247	86.02 244	83.77 423	57.33 397	69.38 491	79.38 151	89.12 405	88.02 376

SIFT-NN		71.05 434	69.58 446	75.45 421	80.35 448	81.93 81	74.31 439	63.57 520	61.17 425	75.98 455	81.67 460	46.63 471	65.25 529	53.44 471	89.09 406	79.18 500

myMVS_eth3d28		65.83 480	65.85 475	65.78 500	83.42 389	35.71 545	67.29 506	68.01 494	67.58 322	69.80 504	77.72 499	32.29 529	74.30 472	37.49 541	89.06 407	87.32 392

BH-untuned		80.96 273	80.99 269	80.84 301	88.55 232	68.23 265	80.33 329	88.46 255	72.79 227	86.55 228	86.76 366	74.72 236	91.77 194	61.79 395	88.99 408	82.52 463

EIA-MVS		82.19 239	81.23 265	85.10 159	87.95 250	69.17 254	83.22 244	93.33 85	70.42 267	78.58 422	79.77 481	77.29 194	94.20 102	71.51 287	88.96 409	91.93 259

PVSNet_Blended_VisFu		81.55 259	80.49 279	84.70 173	91.58 139	73.24 184	84.21 203	91.67 156	62.86 390	80.94 384	87.16 360	67.27 318	92.87 165	69.82 308	88.94 410	87.99 377

MVSFormer		82.23 236	81.57 254	84.19 194	85.54 339	69.26 250	91.98 39	90.08 217	71.54 249	76.23 450	85.07 401	58.69 382	94.27 97	86.26 50	88.77 411	89.03 353

lupinMVS		76.37 357	74.46 377	82.09 267	85.54 339	69.26 250	76.79 399	80.77 393	50.68 508	76.23 450	82.82 444	58.69 382	88.94 294	69.85 307	88.77 411	88.07 372

SP-NN		76.57 350	76.54 347	76.66 400	77.40 487	75.50 164	78.02 372	78.77 408	68.60 300	75.98 455	83.71 425	55.56 416	66.71 519	82.06 115	88.74 413	87.76 386

RPSCF		88.00 85	86.93 109	91.22 30	90.08 183	89.30 5	89.68 78	91.11 177	79.26 115	89.68 130	94.81 65	82.44 116	87.74 333	76.54 200	88.74 413	96.61 32

test_fmvs3		75.72 367	75.20 365	77.27 386	75.01 514	69.47 247	78.93 357	84.88 336	46.67 520	87.08 214	87.84 344	50.44 455	71.62 481	77.42 187	88.53 415	90.72 296

hybrid		79.06 306	78.94 307	79.40 338	77.99 478	59.05 415	77.07 394	88.49 254	64.42 373	80.52 395	88.78 317	71.45 292	86.82 355	73.23 267	88.52 416	92.34 236

RRT-MVS		82.97 221	83.44 202	81.57 281	85.06 349	58.04 431	87.20 124	90.37 203	77.88 135	88.59 160	93.70 123	63.17 351	93.05 158	76.49 201	88.47 417	93.62 157

PAPM_NR		83.23 213	83.19 211	83.33 222	90.90 163	65.98 295	88.19 109	90.78 189	78.13 132	80.87 387	87.92 339	73.49 260	92.42 173	70.07 305	88.40 418	91.60 271

testing222		66.93 467	65.30 481	71.81 459	83.38 390	45.83 519	72.06 471	67.50 496	64.12 376	69.68 505	76.37 512	27.34 547	83.00 413	38.88 536	88.38 419	86.62 403

xiu_mvs_v1_base_debu		80.84 275	80.14 287	82.93 238	88.31 236	71.73 212	79.53 339	87.17 284	65.43 354	79.59 404	82.73 447	76.94 203	90.14 264	73.22 268	88.33 420	86.90 399

xiu_mvs_v1_base		80.84 275	80.14 287	82.93 238	88.31 236	71.73 212	79.53 339	87.17 284	65.43 354	79.59 404	82.73 447	76.94 203	90.14 264	73.22 268	88.33 420	86.90 399

xiu_mvs_v1_base_debi		80.84 275	80.14 287	82.93 238	88.31 236	71.73 212	79.53 339	87.17 284	65.43 354	79.59 404	82.73 447	76.94 203	90.14 264	73.22 268	88.33 420	86.90 399

XXY-MVS		74.44 387	76.19 353	69.21 477	84.61 360	52.43 484	71.70 474	77.18 424	60.73 430	80.60 389	90.96 241	75.44 221	69.35 494	56.13 441	88.33 420	85.86 412

Fast-Effi-MVS+		81.04 271	80.57 276	82.46 257	87.50 269	63.22 326	78.37 368	89.63 231	68.01 309	81.87 364	82.08 453	82.31 121	92.65 169	67.10 338	88.30 424	91.51 276

MDA-MVSNet-bldmvs		77.47 335	76.90 342	79.16 341	79.03 469	64.59 306	66.58 510	75.67 436	73.15 217	88.86 151	88.99 314	66.94 321	81.23 429	64.71 365	88.22 425	91.64 270

PAPR		78.84 312	78.10 326	81.07 295	85.17 348	60.22 390	82.21 280	90.57 196	62.51 394	75.32 465	84.61 408	74.99 228	92.30 179	59.48 412	88.04 426	90.68 299

mvsmamba		80.30 289	78.87 308	84.58 177	88.12 247	67.55 273	92.35 30	84.88 336	63.15 387	85.33 266	90.91 244	50.71 451	95.20 65	66.36 345	87.98 427	90.99 287

BH-RMVSNet		80.53 280	80.22 285	81.49 285	87.19 281	66.21 292	77.79 379	86.23 303	74.21 190	83.69 320	88.50 325	73.25 267	90.75 238	63.18 381	87.90 428	87.52 389

Effi-MVS+		83.90 192	84.01 190	83.57 216	87.22 280	65.61 299	86.55 143	92.40 129	78.64 125	81.34 380	84.18 418	83.65 100	92.93 162	74.22 236	87.87 429	92.17 249

SD_0403		76.08 360	76.77 344	73.98 433	87.08 289	49.45 502	83.62 224	84.68 341	63.31 384	75.13 468	87.47 353	71.85 287	84.56 398	49.97 495	87.86 430	87.94 380

MVS_Test		82.47 231	83.22 209	80.22 318	82.62 406	57.75 435	82.54 266	91.96 146	71.16 258	82.89 341	92.52 174	77.41 190	90.50 248	80.04 139	87.84 431	92.40 230

viewmambaseed2359dif		78.80 313	78.47 319	79.78 325	80.26 450	59.28 408	77.31 391	87.13 287	60.42 433	82.37 351	88.67 323	74.58 239	87.87 331	67.78 334	87.73 432	92.19 247

QAPM		82.59 228	82.59 231	82.58 251	86.44 306	66.69 286	89.94 72	90.36 204	67.97 311	84.94 282	92.58 170	72.71 274	92.18 181	70.63 298	87.73 432	88.85 357

PVSNet_Blended		76.49 354	75.40 361	79.76 327	84.43 363	63.41 322	75.14 428	90.44 200	57.36 457	75.43 462	78.30 494	69.11 308	91.44 204	60.68 405	87.70 434	84.42 430

dtuonlycased		77.13 340	76.99 340	77.55 380	88.60 230	57.48 437	74.18 442	81.70 380	55.62 470	85.10 275	88.40 326	74.87 230	82.26 419	56.73 436	87.66 435	92.90 200

pmmvs5		70.73 438	70.07 439	72.72 448	77.03 492	52.73 481	74.14 443	75.65 437	50.36 510	72.17 488	85.37 395	55.42 418	80.67 433	52.86 477	87.59 436	84.77 423

dtuplus		78.46 321	78.13 324	79.45 337	80.90 432	59.52 404	77.65 381	86.72 298	61.21 422	82.91 340	89.26 305	73.46 261	87.27 344	63.53 377	87.49 437	91.55 273

PatchmatchNet1														46.85 518	87.28 438	83.48 446
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
FBQ-MVS		71.59 428	69.67 445	77.34 384	84.84 354	56.41 447	81.26 307	76.51 430	62.70 391	73.28 480	75.95 513	36.93 518	88.04 323	48.28 509	87.27 439	87.56 388

IB-MVS	62.13 19	71.64 426	68.97 455	79.66 330	80.80 435	62.26 350	73.94 447	76.90 426	63.27 386	68.63 510	76.79 508	33.83 525	91.84 192	59.28 415	87.26 440	84.88 422
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
N_pmnet		70.20 442	68.80 457	74.38 431	80.91 430	84.81 52	59.12 533	76.45 432	55.06 474	75.31 466	82.36 450	55.74 414	54.82 545	47.02 515	87.24 441	83.52 445

fmvsm_s_conf0.1_n		82.17 240	81.59 252	83.94 202	86.87 298	71.57 217	85.19 177	77.42 420	62.27 404	84.47 297	91.33 222	76.43 213	85.91 379	83.14 97	87.14 442	94.33 115

fmvsm_s_conf0.5_n		81.91 253	81.30 262	83.75 207	86.02 325	71.56 218	84.73 187	77.11 425	62.44 401	84.00 313	90.68 256	76.42 214	85.89 381	83.14 97	87.11 443	93.81 146

fmvsm_s_conf0.1_n_a		82.58 229	81.93 244	84.50 178	87.68 261	73.35 180	86.14 153	77.70 416	61.64 413	85.02 277	91.62 209	77.75 183	86.24 369	82.79 106	87.07 444	93.91 136

pmmvs4		74.92 379	72.98 398	80.73 303	84.95 351	71.71 215	76.23 412	77.59 417	52.83 490	77.73 434	86.38 372	56.35 403	84.97 394	57.72 429	87.05 445	85.51 416

test_fmvs2		73.57 399	72.80 400	75.90 413	72.74 529	68.84 260	77.07 394	84.32 345	45.14 526	82.89 341	84.22 416	48.37 462	70.36 487	73.40 263	87.03 446	88.52 364

MIMVSNet		71.09 433	71.59 416	69.57 475	87.23 279	50.07 500	78.91 358	71.83 472	60.20 438	71.26 491	91.76 206	55.08 422	76.09 462	41.06 532	87.02 447	82.54 462

testing91		69.94 449	68.99 454	72.80 447	83.81 381	45.89 518	71.57 477	73.64 454	68.24 306	70.77 498	77.82 496	34.37 524	84.44 401	53.64 468	87.00 448	88.07 372

fmvsm_s_conf0.5_n_a		82.21 238	81.51 257	84.32 187	86.56 300	73.35 180	85.46 169	77.30 422	61.81 409	84.51 294	90.88 247	77.36 191	86.21 371	82.72 107	86.97 449	93.38 168

PRO-TEST		78.77 316	78.12 325	80.70 306	83.83 380	62.76 335	82.20 282	88.77 245	64.67 371	75.01 469	83.52 427	70.67 298	89.92 273	67.67 336	86.75 450	89.44 335

HyFIR lowres test		75.12 374	72.66 405	82.50 255	91.44 147	65.19 303	72.47 467	87.31 280	46.79 519	80.29 397	84.30 412	52.70 436	92.10 185	51.88 490	86.73 451	90.22 313

test_vis3_rt		71.42 430	70.67 431	73.64 440	69.66 538	70.46 232	66.97 509	89.73 226	42.68 537	88.20 174	83.04 438	43.77 499	60.07 538	65.35 359	86.66 452	90.39 310

MSDG		80.06 297	79.99 294	80.25 317	83.91 378	68.04 270	77.51 385	89.19 240	77.65 138	81.94 362	83.45 431	76.37 215	86.31 368	63.31 380	86.59 453	86.41 404

Patchmatch-test		65.91 478	67.38 465	61.48 518	75.51 508	43.21 530	68.84 495	63.79 519	62.48 395	72.80 484	83.42 432	44.89 496	59.52 540	48.27 510	86.45 454	81.70 471

mvs_anonymous		78.13 327	78.76 313	76.23 410	79.24 466	50.31 499	78.69 363	84.82 338	61.60 414	83.09 336	92.82 161	73.89 252	87.01 347	68.33 330	86.41 455	91.37 277

IterMVS-SCA-FT		80.64 279	79.41 299	84.34 186	83.93 377	69.66 244	76.28 411	81.09 390	72.43 231	86.47 235	90.19 279	60.46 364	93.15 154	77.45 185	86.39 456	90.22 313

testing99		69.27 455	68.15 462	72.63 449	83.29 395	45.45 520	71.15 479	71.08 477	67.34 325	70.43 500	77.77 498	32.24 530	84.35 404	53.72 466	86.33 457	88.10 371

E-PMN		61.59 498	61.62 498	61.49 517	66.81 543	55.40 458	53.77 542	60.34 536	66.80 333	58.90 543	65.50 539	40.48 510	66.12 523	55.72 445	86.25 458	62.95 540

EMVS		61.10 501	60.81 500	61.99 514	65.96 546	55.86 451	53.10 543	58.97 541	67.06 330	56.89 549	63.33 540	40.98 508	67.03 517	54.79 459	86.18 459	63.08 539

dtuonly		66.56 474	67.23 467	64.55 506	69.44 539	43.53 528	66.34 511	72.11 469	48.23 515	68.04 512	83.21 435	55.95 409	66.59 521	55.55 449	86.17 460	83.53 444

ETVMVS		64.67 484	63.34 492	68.64 482	83.44 388	41.89 532	69.56 494	61.70 531	61.33 418	68.74 508	75.76 515	28.76 542	79.35 441	34.65 544	86.16 461	84.67 426

our_test_3		71.85 422	71.59 416	72.62 450	80.71 436	53.78 473	69.72 492	71.71 475	58.80 444	78.03 427	80.51 474	56.61 401	78.84 447	62.20 387	86.04 462	85.23 418

MASt3R-SfM		63.18 491	63.70 489	61.64 516	63.57 550	67.13 277	64.25 520	57.31 545	37.50 548	82.96 337	80.95 468	45.96 478	49.82 549	54.93 458	85.89 463	67.95 533

EU-MVSNet		75.12 374	74.43 378	77.18 388	83.11 402	59.48 405	85.71 164	82.43 372	39.76 542	85.64 257	88.76 318	44.71 497	87.88 330	73.86 249	85.88 464	84.16 436

GA-MVS		75.83 365	74.61 374	79.48 336	81.87 412	59.25 409	73.42 457	82.88 364	68.68 297	79.75 403	81.80 457	50.62 452	89.46 284	66.85 340	85.64 465	89.72 328

MVS		73.21 404	72.59 407	75.06 425	80.97 429	60.81 383	81.64 292	85.92 313	46.03 524	71.68 490	77.54 500	68.47 311	89.77 278	55.70 446	85.39 466	74.60 521

PatchT		70.52 440	72.76 402	63.79 510	79.38 464	33.53 548	77.63 382	65.37 510	73.61 203	71.77 489	92.79 164	44.38 498	75.65 465	64.53 370	85.37 467	82.18 467

TR-MVS		76.77 347	75.79 356	79.72 328	86.10 324	65.79 297	77.14 392	83.02 363	65.20 364	81.40 378	82.10 451	66.30 324	90.73 240	55.57 448	85.27 468	82.65 458

BH-w/o		76.57 350	76.07 355	78.10 366	86.88 297	65.92 296	77.63 382	86.33 301	65.69 350	80.89 386	79.95 478	68.97 310	90.74 239	53.01 475	85.25 469	77.62 513

Syy-MVS		69.40 454	70.03 441	67.49 490	81.72 417	38.94 539	71.00 480	61.99 526	61.38 416	70.81 495	72.36 530	61.37 360	79.30 442	64.50 371	85.18 470	84.22 433

myMVS_eth3d		64.66 485	63.89 486	66.97 494	81.72 417	37.39 542	71.00 480	61.99 526	61.38 416	70.81 495	72.36 530	20.96 554	79.30 442	49.59 499	85.18 470	84.22 433

IterMVS		76.91 344	76.34 352	78.64 353	80.91 430	64.03 316	76.30 409	79.03 404	64.88 367	83.11 334	89.16 310	59.90 370	84.46 400	68.61 326	85.15 472	87.42 390
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WB-MVSnew		68.72 461	69.01 453	67.85 487	83.22 399	43.98 526	74.93 432	65.98 506	55.09 473	73.83 477	79.11 484	65.63 332	71.89 480	38.21 540	85.04 473	87.69 387

OpenMVS_ROB	70.19 17	77.77 332	77.46 331	78.71 352	84.39 366	61.15 371	81.18 309	82.52 368	62.45 400	83.34 330	87.37 355	66.20 325	88.66 308	64.69 366	85.02 474	86.32 405

KD-MVS_2432*1600		66.87 469	65.81 477	70.04 468	67.50 541	47.49 510	62.56 524	79.16 402	61.21 422	77.98 428	80.61 470	25.29 551	82.48 416	53.02 473	84.92 475	80.16 490

miper_refine_blended		66.87 469	65.81 477	70.04 468	67.50 541	47.49 510	62.56 524	79.16 402	61.21 422	77.98 428	80.61 470	25.29 551	82.48 416	53.02 473	84.92 475	80.16 490

test_fmvs1_n		70.94 435	70.41 437	72.53 452	73.92 517	66.93 284	75.99 416	84.21 347	43.31 534	79.40 407	79.39 483	43.47 500	68.55 503	69.05 318	84.91 477	82.10 468

test-LLR		67.21 466	66.74 471	68.63 483	76.45 499	55.21 460	67.89 499	67.14 500	62.43 402	65.08 527	72.39 528	43.41 501	69.37 492	61.00 402	84.89 478	81.31 476

test-mter		65.00 483	63.79 488	68.63 483	76.45 499	55.21 460	67.89 499	67.14 500	50.98 505	65.08 527	72.39 528	28.27 544	69.37 492	61.00 402	84.89 478	81.31 476

PS-MVSNAJ		77.04 343	76.53 348	78.56 354	87.09 287	61.40 365	75.26 426	87.13 287	61.25 420	74.38 474	77.22 506	76.94 203	90.94 228	64.63 367	84.83 480	83.35 450

xiu_mvs_v2_base		77.19 339	76.75 345	78.52 355	87.01 291	61.30 368	75.55 424	87.12 291	61.24 421	74.45 472	78.79 489	77.20 197	90.93 229	64.62 368	84.80 481	83.32 451

pmmvs3		62.47 493	60.02 505	69.80 472	71.58 533	64.00 317	70.52 486	58.44 542	39.77 541	66.05 520	75.84 514	27.10 549	72.28 477	46.15 521	84.77 482	73.11 525

MDTV_nov1_ep13			68.29 461		78.03 477	43.87 527	74.12 444	72.22 467	52.17 495	67.02 518	85.54 388	45.36 488	80.85 432	55.73 444	84.42 483

test_fmvs1		69.57 452	69.05 452	71.14 464	69.15 540	65.77 298	73.98 446	83.32 359	42.83 536	77.77 433	78.27 495	43.39 503	68.50 504	68.39 329	84.38 484	79.15 502

1112_ss		74.82 381	73.74 384	78.04 368	89.57 193	60.04 392	76.49 407	87.09 292	54.31 479	73.66 479	79.80 479	60.25 367	86.76 358	58.37 420	84.15 485	87.32 392

XFeat-NN		59.92 505	59.04 507	62.58 512	63.37 551	64.42 312	55.18 540	60.26 537	41.73 538	77.26 442	69.20 534	31.98 531	58.40 543	48.23 511	84.12 486	64.93 538

testing11		67.38 465	65.93 474	71.73 460	83.37 391	46.60 515	70.95 482	69.40 485	62.47 398	66.14 519	76.66 509	31.22 533	84.10 406	49.10 503	84.10 487	84.49 427

nomal-1		66.61 473	65.11 483	71.13 465	75.60 506	61.96 355	65.47 514	69.28 486	57.45 456	70.78 497	77.26 504	35.65 522	73.16 474	50.42 493	84.07 488	78.25 509

PatchMatch-RL		74.48 385	73.22 394	78.27 364	87.70 260	85.26 47	75.92 417	70.09 481	64.34 374	76.09 453	81.25 464	65.87 330	78.07 453	53.86 465	83.82 489	71.48 527

UBG		64.34 488	63.35 491	67.30 492	83.50 385	40.53 536	67.46 504	65.02 511	54.77 477	67.54 517	74.47 524	32.99 528	78.50 451	40.82 533	83.58 490	82.88 457

MDA-MVSNet_test_wron		70.05 446	70.44 435	68.88 480	73.84 518	53.47 475	58.93 535	67.28 498	58.43 445	87.09 213	85.40 393	59.80 372	67.25 516	59.66 411	83.54 491	85.92 411

YYNet1		70.06 445	70.44 435	68.90 479	73.76 519	53.42 477	58.99 534	67.20 499	58.42 446	87.10 212	85.39 394	59.82 371	67.32 515	59.79 410	83.50 492	85.96 409

Test_1112_low_res		73.90 394	73.08 396	76.35 406	90.35 176	55.95 448	73.40 458	86.17 304	50.70 507	73.14 481	85.94 382	58.31 384	85.90 380	56.51 438	83.22 493	87.20 394

PVSNet	58.17 21	66.41 476	65.63 479	68.75 481	81.96 411	49.88 501	62.19 526	72.51 465	51.03 504	68.04 512	75.34 521	50.84 450	74.77 468	45.82 523	82.96 494	81.60 473

gg-mvs-nofinetune		68.96 459	69.11 451	68.52 486	76.12 503	45.32 521	83.59 225	55.88 546	86.68 32	64.62 531	97.01 11	30.36 536	83.97 409	44.78 525	82.94 495	76.26 516

CR-MVSNet		74.00 393	73.04 397	76.85 398	79.58 460	62.64 336	82.58 263	76.90 426	50.50 509	75.72 459	92.38 177	48.07 464	84.07 407	68.72 325	82.91 496	83.85 440

RPMNet		78.88 311	78.28 321	80.68 307	79.58 460	62.64 336	82.58 263	94.16 33	74.80 178	75.72 459	92.59 168	48.69 461	95.56 43	73.48 261	82.91 496	83.85 440

test_vis1_n		70.29 441	69.99 442	71.20 463	75.97 504	66.50 288	76.69 402	80.81 392	44.22 530	75.43 462	77.23 505	50.00 457	68.59 502	66.71 343	82.85 498	78.52 507

test0.0.03 1		64.66 485	64.36 484	65.57 502	75.03 513	46.89 514	64.69 517	61.58 533	62.43 402	71.18 493	77.54 500	43.41 501	68.47 505	40.75 534	82.65 499	81.35 475

HY-MVS	64.64 18	73.03 407	72.47 410	74.71 429	83.36 392	54.19 470	82.14 284	81.96 376	56.76 464	69.57 506	86.21 378	60.03 368	84.83 396	49.58 500	82.65 499	85.11 420

SCA		73.32 401	72.57 408	75.58 420	81.62 420	55.86 451	78.89 359	71.37 476	61.73 410	74.93 470	83.42 432	60.46 364	87.01 347	58.11 424	82.63 501	83.88 437

test_f		64.31 489	65.85 475	59.67 522	66.54 544	62.24 352	57.76 537	70.96 478	40.13 540	84.36 299	82.09 452	46.93 467	51.67 548	61.99 391	81.89 502	65.12 537

CHOSEN 1792x2688		72.45 414	70.56 433	78.13 365	90.02 188	63.08 327	68.72 496	83.16 361	42.99 535	75.92 457	85.46 391	57.22 398	85.18 393	49.87 498	81.67 503	86.14 407

WTY-MVS		67.91 464	68.35 460	66.58 496	80.82 434	48.12 506	65.96 512	72.60 463	53.67 484	71.20 492	81.68 459	58.97 378	69.06 497	48.57 506	81.67 503	82.55 461

TESTMET0.1,1		61.29 499	60.32 503	64.19 508	72.06 530	51.30 492	67.89 499	62.09 525	45.27 525	60.65 538	69.01 535	27.93 545	64.74 532	56.31 439	81.65 505	76.53 515

dmvs_re		66.81 471	66.98 468	66.28 497	76.87 493	58.68 424	71.66 475	72.24 466	60.29 436	69.52 507	73.53 526	52.38 438	64.40 534	44.90 524	81.44 506	75.76 518

PAPM		71.77 423	70.06 440	76.92 394	86.39 308	53.97 471	76.62 404	86.62 299	53.44 485	63.97 532	84.73 407	57.79 394	92.34 177	39.65 535	81.33 507	84.45 429

DSMNet-mixed		60.98 502	61.61 499	59.09 525	72.88 527	45.05 523	74.70 434	46.61 553	26.20 550	65.34 525	90.32 273	55.46 417	63.12 536	41.72 531	81.30 508	69.09 531

sss		66.92 468	67.26 466	65.90 499	77.23 489	51.10 496	64.79 516	71.72 474	52.12 498	70.13 502	80.18 476	57.96 391	65.36 528	50.21 494	81.01 509	81.25 478

UWE-MVS-28		58.44 508	57.71 510	60.65 520	73.58 521	31.23 550	69.68 493	48.80 551	53.12 489	61.79 535	78.83 488	30.98 534	68.40 506	21.58 551	80.99 510	82.33 466

tpm		67.95 463	68.08 464	67.55 489	78.74 473	43.53 528	75.60 420	67.10 502	54.92 475	72.23 486	88.10 332	42.87 505	75.97 463	52.21 482	80.95 511	83.15 454

MonoMVSNet		76.66 348	77.26 336	74.86 426	79.86 457	54.34 468	86.26 149	86.08 306	71.08 259	85.59 259	88.68 321	53.95 426	85.93 376	63.86 373	80.02 512	84.32 431

tpm2		68.45 462	66.83 470	73.30 443	78.93 471	48.50 504	79.76 335	71.76 473	47.50 516	69.92 503	83.60 426	42.07 506	88.40 317	48.44 508	79.51 513	83.01 456

FPMVS		72.29 418	72.00 412	73.14 444	88.63 228	85.00 49	74.65 435	67.39 497	71.94 243	77.80 432	87.66 348	50.48 454	75.83 464	49.95 496	79.51 513	58.58 545

UnsupCasMVSNet_bld		69.21 456	69.68 444	67.82 488	79.42 463	51.15 494	67.82 502	75.79 434	54.15 481	77.47 440	85.36 396	59.26 376	70.64 486	48.46 507	79.35 515	81.66 472

CostFormer		69.98 448	68.68 458	73.87 435	77.14 490	50.72 497	79.26 351	74.51 444	51.94 499	70.97 494	84.75 406	45.16 492	87.49 337	55.16 455	79.23 516	83.40 449

1314		73.22 403	72.56 409	75.20 423	80.41 443	57.84 433	81.64 292	85.36 320	51.68 500	73.10 482	76.65 510	61.45 359	85.19 392	63.54 376	79.21 517	82.59 459

test_vis1_n_1920		71.30 432	71.58 418	70.47 466	77.58 484	59.99 396	74.25 440	84.22 346	51.06 503	74.85 471	79.10 485	55.10 421	68.83 501	68.86 322	79.20 518	82.58 460

baseline1		73.26 402	73.54 387	72.43 453	84.92 353	47.79 509	79.89 334	74.00 447	65.93 342	78.81 419	86.28 377	56.36 402	81.63 425	56.63 437	79.04 519	87.87 383

PMMVS		61.65 497	60.38 502	65.47 503	65.40 548	69.26 250	63.97 522	61.73 530	36.80 549	60.11 540	68.43 536	59.42 374	66.35 522	48.97 504	78.57 520	60.81 542

baseline2		69.77 450	66.89 469	78.41 358	79.51 462	58.09 429	76.23 412	69.57 484	57.50 455	64.82 530	77.45 502	46.02 475	88.44 315	53.08 472	77.83 521	88.70 361

test_vis1_rt		65.64 481	64.09 485	70.31 467	66.09 545	70.20 236	61.16 528	81.60 383	38.65 544	72.87 483	69.66 533	52.84 434	60.04 539	56.16 440	77.77 522	80.68 485

MS-PatchMatch		70.93 436	70.22 438	73.06 445	81.85 413	62.50 339	73.82 449	77.90 413	52.44 493	75.92 457	81.27 463	55.67 415	81.75 423	55.37 451	77.70 523	74.94 520

UnsupCasMVSNet_eth		71.63 427	72.30 411	69.62 474	76.47 498	52.70 482	70.03 490	80.97 391	59.18 441	79.36 410	88.21 331	60.50 363	69.12 496	58.33 422	77.62 524	87.04 395

CVMVSNet		72.62 412	71.41 420	76.28 408	83.25 397	60.34 388	83.50 232	79.02 405	37.77 547	76.33 448	85.10 398	49.60 460	87.41 341	70.54 300	77.54 525	81.08 481

test_cas_vis1_n_1920		69.20 457	69.12 450	69.43 476	73.68 520	62.82 332	70.38 488	77.21 423	46.18 523	80.46 396	78.95 487	52.03 439	65.53 527	65.77 355	77.45 526	79.95 492

GG-mvs-BLEND				67.16 493	73.36 522	46.54 517	84.15 205	55.04 547		58.64 544	61.95 542	29.93 537	83.87 410	38.71 538	76.92 527	71.07 528

CHOSEN 280x420		59.08 506	56.52 513	66.76 495	76.51 497	64.39 313	49.62 545	59.00 540	43.86 531	55.66 550	68.41 537	35.55 523	68.21 508	43.25 527	76.78 528	67.69 534

tpmvs		70.16 443	69.56 447	71.96 458	74.71 515	48.13 505	79.63 336	75.45 439	65.02 365	70.26 501	81.88 456	45.34 489	85.68 387	58.34 421	75.39 529	82.08 469

MVP-Stereo		75.81 366	73.51 388	82.71 243	89.35 199	73.62 177	80.06 330	85.20 324	60.30 435	73.96 476	87.94 336	57.89 393	89.45 285	52.02 485	74.87 530	85.06 421
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
0.4-1-1-0.1		64.02 490	60.59 501	74.31 432	73.99 516	55.62 454	67.66 503	72.78 462	55.53 471	60.35 539	58.45 543	29.26 540	86.88 352	52.84 478	74.42 531	80.42 489

0.4-1-1-0.2		62.43 495	58.81 509	73.31 442	70.85 535	54.20 469	64.36 519	72.99 459	53.70 483	57.51 547	54.59 545	29.52 538	86.44 365	51.70 491	74.02 532	79.30 498

0.3-1-1-0.015		62.57 492	58.82 508	73.82 437	71.85 532	54.96 463	65.63 513	72.97 460	54.16 480	56.95 548	55.43 544	26.76 550	86.59 361	52.05 484	73.55 533	79.92 493

new_pmnet		55.69 511	57.66 511	49.76 529	75.47 509	30.59 551	59.56 530	51.45 549	43.62 533	62.49 534	75.48 519	40.96 509	49.15 551	37.39 542	72.52 534	69.55 530

mvsany_test3		65.48 482	62.97 493	73.03 446	69.99 537	76.17 154	64.83 515	43.71 554	43.68 532	80.25 400	87.05 364	52.83 435	63.09 537	51.92 489	72.44 535	79.84 494

PatchmatchNet		69.71 451	68.83 456	72.33 455	77.66 483	53.60 474	79.29 350	69.99 482	57.66 453	72.53 485	82.93 441	46.45 472	80.08 439	60.91 404	72.09 536	83.31 452
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		61.16 500	62.92 494	55.87 526	79.09 468	35.34 546	71.83 472	57.98 543	46.56 521	59.05 542	91.14 232	49.95 459	76.43 460	38.74 537	71.92 537	55.84 546

tpmrst		66.28 477	66.69 472	65.05 505	72.82 528	39.33 538	78.20 369	70.69 480	53.16 488	67.88 514	80.36 475	48.18 463	74.75 469	58.13 423	70.79 538	81.08 481

tpm cat1		66.76 472	65.21 482	71.42 461	77.09 491	50.62 498	78.01 373	73.68 453	44.89 527	68.64 509	79.00 486	45.51 486	82.42 418	49.91 497	70.15 539	81.23 480

ADS-MVSNet2		65.87 479	63.64 490	72.55 451	73.16 524	56.92 442	67.10 507	74.81 441	49.74 513	66.04 521	82.97 439	46.71 469	77.26 457	42.29 529	69.96 540	83.46 447

ADS-MVSNet		61.90 496	62.19 497	61.03 519	73.16 524	36.42 544	67.10 507	61.75 529	49.74 513	66.04 521	82.97 439	46.71 469	63.21 535	42.29 529	69.96 540	83.46 447

JIA-IIPM		69.41 453	66.64 473	77.70 376	73.19 523	71.24 222	75.67 419	65.56 509	70.42 267	65.18 526	92.97 154	33.64 527	83.06 412	53.52 470	69.61 542	78.79 504

dmvs_testset		60.59 504	62.54 496	54.72 528	77.26 488	27.74 553	74.05 445	61.00 535	60.48 432	65.62 524	67.03 538	55.93 410	68.23 507	32.07 548	69.46 543	68.17 532

EPMVS		62.47 493	62.63 495	62.01 513	70.63 536	38.74 540	74.76 433	52.86 548	53.91 482	67.71 516	80.01 477	39.40 511	66.60 520	55.54 450	68.81 544	80.68 485

blend_shiyan4		70.82 437	68.15 462	78.83 349	81.06 428	59.77 399	74.58 436	83.79 351	64.94 366	77.34 441	75.47 520	29.39 539	88.89 296	58.91 416	67.86 545	87.84 384

MVE	40.22 23	51.82 513	50.47 516	55.87 526	62.66 552	51.91 487	31.61 549	39.28 556	40.65 539	50.76 551	74.98 523	56.24 404	44.67 552	33.94 546	64.11 546	71.04 529
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		60.70 503	60.29 504	61.92 515	72.04 531	38.67 541	70.83 484	64.08 516	51.28 502	60.75 537	77.28 503	36.59 520	71.58 482	47.41 514	62.34 547	75.52 519

GLUNet-SfM		36.71 515	36.32 518	37.87 532	23.81 558	32.04 549	38.61 547	29.05 558	18.10 551	70.60 499	50.66 547	18.79 556	40.81 554	17.68 554	59.57 548	40.74 548

mvsany_test1		58.48 507	56.47 514	64.50 507	65.90 547	68.21 267	56.95 538	42.11 555	38.30 545	65.69 523	77.19 507	56.96 399	59.35 541	46.16 520	58.96 549	65.93 535

PVSNet_0	51.08 22	56.10 510	54.97 515	59.48 523	75.12 512	53.28 478	55.16 541	61.89 528	44.30 529	59.16 541	62.48 541	54.22 425	65.91 525	35.40 543	47.01 550	59.25 544

tmp_tt		20.25 519	24.50 522	7.49 538	4.47 562	8.70 564	34.17 548	25.16 559	1.00 557	32.43 554	18.49 553	39.37 512	9.21 558	21.64 550	43.75 551	4.57 554

test_method		30.46 517	29.60 520	33.06 533	17.99 560	3.84 565	13.62 550	73.92 448	2.79 555	18.29 557	53.41 546	28.53 543	43.25 553	22.56 549	35.27 552	52.11 547

DeepMVS_CX				24.13 535	32.95 557	29.49 552		21.63 560	12.07 552	37.95 553	45.07 549	30.84 535	19.21 556	17.94 553	33.06 553	23.69 551

dongtai		41.90 514	42.65 517	39.67 531	70.86 534	21.11 555	61.01 529	21.42 561	57.36 457	57.97 546	50.06 548	16.40 557	58.73 542	21.03 552	27.69 554	39.17 549

MVS_clip		14.31 520	16.37 523	8.11 537	18.08 559	12.42 561	12.95 551	3.12 564	3.73 554	28.79 555	35.98 551	8.84 560	4.85 559	12.31 555	23.54 555	7.07 552

VLMVS_CLIP		13.55 521	14.55 524	10.53 536	11.59 561	10.03 563	11.68 552	18.47 563	4.20 553	20.50 556	24.42 552	8.69 561	16.48 557	8.18 556	23.25 556	5.10 553

kuosan		30.83 516	32.17 519	26.83 534	53.36 556	19.02 559	57.90 536	20.44 562	38.29 546	38.01 552	37.82 550	15.18 558	33.45 555	7.74 557	20.76 557	28.03 550

MVS_baseline		4.35 526	5.47 529	0.99 540	3.75 563	0.34 569	2.10 553	0.79 567	0.13 561	12.26 558	14.40 555	2.36 563	0.00 563	1.87 558	11.56 558	2.62 556

VLMVS		3.03 527	3.34 530	2.13 539	3.00 564	1.87 566	1.95 554	1.16 565	0.16 560	5.10 559	6.49 556	5.23 562	1.51 560	1.34 559	5.59 559	3.02 555

testmvs		5.91 525	7.65 528	0.72 542	1.20 565	0.37 568	59.14 532	0.67 568	0.49 559	1.11 561	2.76 559	0.94 565	0.24 562	1.02 561	1.47 560	1.55 558

test123		6.27 524	8.08 527	0.84 541	1.11 566	0.57 567	62.90 523	0.82 566	0.54 558	1.07 562	2.75 560	1.26 564	0.30 561	1.04 560	1.26 561	1.66 557

mmdepth		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

monomultidepth		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

test_blank		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uanet_test		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

DCPMVS		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

cdsmvs_eth3d_5k		20.81 518	27.75 521	0.00 543	0.00 567	0.00 570	0.00 555	85.44 319	0.00 562	0.00 563	82.82 444	81.46 143	0.00 563	0.00 562	0.00 562	0.00 559

pcd_1.5k_mvsjas		6.41 523	8.55 526	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	76.94 203	0.00 563	0.00 562	0.00 562	0.00 559

sosnet-low-res		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

sosnet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uncertanet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

Regformer		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

ab-mvs-re		6.65 522	8.87 525	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	79.80 479	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

uanet		0.00 528	0.00 531	0.00 543	0.00 567	0.00 570	0.00 555	0.00 569	0.00 562	0.00 563	0.00 561	0.00 566	0.00 563	0.00 562	0.00 562	0.00 559

Meshroom													0.00 563
: In preparation.
AliceVision / Meshro													0.00 563
: In preparation.
AliceVision_Meshroom													0.00 563
: In preparation.
PatchmatchNet2					0.00 567	20.88 556	55.62 539	59.13 538	52.38 494
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													54.72 546
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						37.39 542								52.61 480

FOURS1					96.08 11	87.41 18	96.19 2	95.83 4	92.95 2	96.57 2

test_one_0601					93.85 66	73.27 183		94.11 39	86.57 33	93.47 43	94.64 70	88.42 30

eth-test2					0.00 567

eth-test					0.00 567

test_241102_ONE					94.18 54	72.65 191		93.69 64	83.62 63	94.11 27	93.78 117	90.28 15	95.50 50

save fliter					93.75 67	77.44 136	86.31 147	89.72 227	70.80 263

test0726					94.16 57	72.56 197	90.63 54	93.90 49	83.61 64	93.75 36	94.49 75	89.76 19

GSMVS																83.88 437

test_part2					93.86 65	77.77 130				92.84 57

sam_mvs1												46.11 474				83.88 437

sam_mvs												45.92 480

MTGPA								91.81 153

test_post1							78.85 361				3.13 557	45.19 491	80.13 438	58.11 424

test_post											3.10 558	45.43 487	77.22 458

patchmatchnet-post											81.71 458	45.93 479	87.01 347

MTMP							90.66 53	33.14 557

gm-plane-assit					75.42 510	44.97 524			52.17 495		72.36 530		87.90 329	54.10 462

TEST9					92.34 108	79.70 106	83.94 211	90.32 206	65.41 357	84.49 295	90.97 239	82.03 132	93.63 130

test_8					92.09 117	78.87 116	83.82 216	90.31 208	65.79 345	84.36 299	90.96 241	81.93 134	93.44 144

agg_prior					91.58 139	77.69 132		90.30 209		84.32 302			93.18 152

test_prior4						78.97 115	84.59 192

test_prior				86.32 123	90.59 172	71.99 209		92.85 114					94.17 107			92.80 202

旧先验2							81.73 290		56.88 462	86.54 234			84.90 395	72.81 275

新几何2							81.72 291

无先验							82.81 258	85.62 317	58.09 449				91.41 207	67.95 333		84.48 428

原ACMM2							82.26 279

testdata2													86.43 366	63.52 378

segment_acmp												81.94 133

testdata1							79.62 337		73.95 194

plane_prior7					93.45 74	77.31 139

plane_prior6					92.61 99	76.54 146						74.84 232

plane_prior4											92.95 155

plane_prior3						76.85 144			77.79 137	86.55 228

plane_prior2							89.45 87		79.44 112

plane_prior1					92.83 96

n2								0.00 569

nn								0.00 569

door-mid								74.45 445

test11								91.46 162

door								72.57 464

HQP5-MVS						70.66 228

HQP-NCC					91.19 154		84.77 183		73.30 212	80.55 391

ACMP_Plane					91.19 154		84.77 183		73.30 212	80.55 391

BP-MVS														77.30 188

HQP4-MVS										80.56 390			94.61 86			93.56 163

HQP2-MVS												72.10 282

NP-MVS					91.95 122	74.55 172					90.17 282

MDTV_nov1_ep13_2view						27.60 554	70.76 485		46.47 522	61.27 536		45.20 490		49.18 502		83.75 442

Test By Simon												79.09 168

High-resolution multi-view benchmark