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 3	98.67 1	85.39 37	95.54 5	97.36 1	96.97 1	99.04 1	99.05 1	96.61 1	95.92 16	85.07 64	99.27 1	99.54 1

mamv4		95.37 2	94.51 2	97.96 1	96.31 10	98.41 1	91.05 46	97.23 2	95.32 2	99.01 2	97.26 6	80.16 135	98.99 1	95.15 1	99.14 2	96.47 30

WR-MVS_H		89.91 50	91.31 33	85.71 128	96.32 9	62.39 271	89.54 79	93.31 70	90.21 12	95.57 11	95.66 33	81.42 121	95.90 17	80.94 110	98.80 3	98.84 5

ACMP	79.16 10	90.54 35	90.60 49	90.35 45	94.36 46	80.98 69	89.16 86	94.05 41	79.03 108	92.87 49	93.74 111	90.60 11	95.21 61	82.87 89	98.76 4	94.87 71
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMH+	77.89 11	90.73 31	91.50 25	88.44 78	93.00 81	76.26 119	89.65 75	95.55 8	87.72 26	93.89 30	94.94 52	91.62 3	93.44 128	78.35 140	98.76 4	95.61 50

PS-CasMVS		90.06 43	91.92 15	84.47 155	96.56 6	58.83 318	89.04 88	92.74 97	91.40 6	96.12 5	96.06 26	87.23 48	95.57 41	79.42 130	98.74 6	99.00 2

LPG-MVS_test		91.47 21	91.68 20	90.82 37	94.75 41	81.69 63	90.00 62	94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 60	98.73 7	95.23 61

LGP-MVS_train				90.82 37	94.75 41	81.69 63		94.27 24	82.35 68	93.67 37	94.82 56	91.18 4	95.52 45	85.36 60	98.73 7	95.23 61

PEN-MVS		90.03 45	91.88 18	84.48 154	96.57 5	58.88 315	88.95 89	93.19 75	91.62 5	96.01 7	96.16 24	87.02 50	95.60 40	78.69 136	98.72 9	98.97 3

CP-MVSNet		89.27 62	90.91 44	84.37 156	96.34 8	58.61 321	88.66 97	92.06 116	90.78 7	95.67 8	95.17 47	81.80 117	95.54 44	79.00 134	98.69 10	98.95 4

TranMVSNet+NR-MVSNet		87.86 81	88.76 74	85.18 137	94.02 58	64.13 247	84.38 175	91.29 140	84.88 44	92.06 65	93.84 105	86.45 58	93.73 111	73.22 209	98.66 11	97.69 9

NR-MVSNet		86.00 108	86.22 108	85.34 135	93.24 76	64.56 243	82.21 239	90.46 164	80.99 82	88.42 138	91.97 166	77.56 158	93.85 107	72.46 219	98.65 12	97.61 10

UA-Net		91.49 19	91.53 24	91.39 27	94.98 35	82.95 58	93.52 7	92.79 95	88.22 22	88.53 134	97.64 3	83.45 86	94.55 83	86.02 54	98.60 13	96.67 25

FC-MVSNet-test		85.93 110	87.05 95	82.58 213	92.25 101	56.44 337	85.75 146	93.09 81	77.33 131	91.94 68	94.65 61	74.78 193	93.41 130	75.11 185	98.58 14	97.88 7

DTE-MVSNet		89.98 47	91.91 17	84.21 164	96.51 7	57.84 326	88.93 90	92.84 94	91.92 4	96.16 4	96.23 21	86.95 51	95.99 12	79.05 133	98.57 15	98.80 6

UniMVSNet (Re)		86.87 91	86.98 97	86.55 106	93.11 79	68.48 206	83.80 190	92.87 92	80.37 87	89.61 113	91.81 174	77.72 156	94.18 95	75.00 186	98.53 16	96.99 22

Baseline_NR-MVSNet		84.00 158	85.90 115	78.29 282	91.47 134	53.44 360	82.29 235	87.00 240	79.06 107	89.55 115	95.72 32	77.20 163	86.14 304	72.30 220	98.51 17	95.28 58

TDRefinement		93.52 3	93.39 4	93.88 2	95.94 15	90.26 4	95.70 4	96.46 3	90.58 9	92.86 50	96.29 19	88.16 35	94.17 97	86.07 50	98.48 18	97.22 17

ACMM	79.39 9	90.65 32	90.99 41	89.63 57	95.03 34	83.53 51	89.62 76	93.35 66	79.20 105	93.83 31	93.60 116	90.81 7	92.96 144	85.02 66	98.45 19	92.41 182
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MP-MVS-pluss		90.81 30	91.08 37	89.99 50	95.97 14	79.88 75	88.13 102	94.51 18	75.79 148	92.94 47	94.96 51	88.36 30	95.01 68	90.70 3	98.40 20	95.09 66
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CP-MVS		91.67 16	91.58 23	91.96 14	95.29 31	87.62 13	93.38 9	93.36 65	83.16 60	91.06 82	94.00 95	88.26 32	95.71 37	87.28 32	98.39 21	92.55 175

UniMVSNet_NR-MVSNet		86.84 93	87.06 94	86.17 118	92.86 86	67.02 220	82.55 227	91.56 130	83.08 62	90.92 84	91.82 173	78.25 149	93.99 102	74.16 191	98.35 22	97.49 13

DU-MVS		86.80 94	86.99 96	86.21 116	93.24 76	67.02 220	83.16 210	92.21 111	81.73 74	90.92 84	91.97 166	77.20 163	93.99 102	74.16 191	98.35 22	97.61 10

MTAPA		91.52 18	91.60 22	91.29 30	96.59 4	86.29 21	92.02 33	91.81 127	84.07 49	92.00 66	94.40 76	86.63 54	95.28 58	88.59 10	98.31 24	92.30 189

ACMH	76.49 14	89.34 59	91.14 35	83.96 170	92.50 94	70.36 184	89.55 77	93.84 52	81.89 73	94.70 17	95.44 40	90.69 8	88.31 268	83.33 81	98.30 25	93.20 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
reproduce_model		92.89 5	93.18 7	92.01 13	94.20 49	88.23 9	92.87 13	94.32 21	90.25 11	95.65 9	95.74 30	87.75 41	95.72 36	89.60 4	98.27 26	92.08 201

COLMAP_ROB	83.01 3	91.97 13	91.95 14	92.04 11	93.68 65	86.15 24	93.37 10	95.10 13	90.28 10	92.11 63	95.03 50	89.75 20	94.93 70	79.95 121	98.27 26	95.04 67
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 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 212

our_new_method		92.86 6	93.22 5	91.76 23	94.39 44	87.71 11	92.40 27	94.38 19	89.82 13	95.51 12	95.49 38	89.64 21	95.82 26	89.13 6	98.26 28	91.76 212

ACMMP		91.91 14	91.87 19	92.03 12	95.53 27	85.91 28	93.35 11	94.16 32	82.52 67	92.39 61	94.14 89	89.15 25	95.62 39	87.35 29	98.24 30	94.56 81
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 17	95.58 26	84.67 46	93.51 8	94.85 15	82.88 64	91.77 70	93.94 102	90.55 12	95.73 35	88.50 11	98.23 31	95.33 56
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_0728_THIRD									85.33 38	93.75 34	94.65 61	87.44 46	95.78 32	87.41 27	98.21 32	92.98 157

MP-MVS		91.14 28	90.91 44	91.83 20	96.18 11	86.88 17	92.20 30	93.03 86	82.59 66	88.52 135	94.37 78	86.74 53	95.41 53	86.32 44	98.21 32	93.19 147
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 28	90.55 41	93.91 60	80.97 70	91.49 40	93.48 63	82.82 65	92.60 57	93.97 96	88.19 33	96.29 6	87.61 22	98.20 34	94.39 92
Skip Steuart: Steuart Systems R&D Blog.
MSC_two_6792asdad				88.81 71	91.55 129	77.99 94		91.01 149					96.05 9	87.45 25	98.17 35	92.40 184

No_MVS				88.81 71	91.55 129	77.99 94		91.01 149					96.05 9	87.45 25	98.17 35	92.40 184

HPM-MVS_fast		92.50 8	92.54 9	92.37 6	95.93 16	85.81 33	92.99 12	94.23 27	85.21 40	92.51 58	95.13 48	90.65 9	95.34 55	88.06 13	98.15 37	95.95 41

mPP-MVS		91.69 15	91.47 26	92.37 6	96.04 13	88.48 8	92.72 18	92.60 102	83.09 61	91.54 72	94.25 83	87.67 44	95.51 47	87.21 33	98.11 38	93.12 151

WR-MVS		83.56 170	84.40 152	81.06 241	93.43 70	54.88 350	78.67 290	85.02 270	81.24 79	90.74 90	91.56 182	72.85 220	91.08 195	68.00 262	98.04 39	97.23 16

XVG-ACMP-BASELINE		89.98 47	89.84 54	90.41 43	94.91 37	84.50 48	89.49 81	93.98 43	79.68 97	92.09 64	93.89 104	83.80 81	93.10 140	82.67 93	98.04 39	93.64 128

DeepC-MVS	82.31 4	89.15 64	89.08 66	89.37 62	93.64 66	79.07 83	88.54 98	94.20 30	73.53 176	89.71 107	94.82 56	85.09 68	95.77 34	84.17 75	98.03 41	93.26 144
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FIs		85.35 119	86.27 107	82.60 212	91.86 116	57.31 330	85.10 160	93.05 83	75.83 147	91.02 83	93.97 96	73.57 207	92.91 148	73.97 197	98.02 42	97.58 12

Anonymous20231211		88.40 71	89.62 59	84.73 147	90.46 157	65.27 236	88.86 91	93.02 87	87.15 28	93.05 46	97.10 8	82.28 106	92.02 170	76.70 164	97.99 43	96.88 23

PGM-MVS		91.20 26	90.95 43	91.93 15	95.67 23	85.85 31	90.00 62	93.90 48	80.32 89	91.74 71	94.41 75	88.17 34	95.98 13	86.37 43	97.99 43	93.96 109

APDe-MVS		91.22 25	91.92 15	89.14 66	92.97 82	78.04 93	92.84 16	94.14 36	83.33 58	93.90 28	95.73 31	88.77 27	96.41 3	87.60 23	97.98 45	92.98 157
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS		90.06 43	91.32 32	86.29 111	94.16 53	72.56 152	90.54 52	91.01 149	83.61 55	93.75 34	94.65 61	89.76 18	95.78 32	86.42 41	97.97 46	90.55 250
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				86.79 102	94.25 48	72.45 156	90.54 52	94.10 39					95.88 18	86.42 41	97.97 46	92.02 204

ZNCC-MVS		91.26 24	91.34 31	91.01 34	95.73 21	83.05 56	92.18 31	94.22 29	80.14 92	91.29 78	93.97 96	87.93 40	95.87 20	88.65 9	97.96 48	94.12 104

SED-MVS		90.46 37	91.64 21	86.93 99	94.18 50	72.65 146	90.47 55	93.69 56	83.77 52	94.11 26	94.27 79	90.28 14	95.84 24	86.03 51	97.92 49	92.29 191

IU-MVS					94.18 50	72.64 148		90.82 154	56.98 358	89.67 109				85.78 57	97.92 49	93.28 142

CLD-MVS		83.18 177	82.64 185	84.79 144	89.05 184	67.82 214	77.93 298	92.52 103	68.33 248	85.07 213	81.54 361	82.06 110	92.96 144	69.35 244	97.91 51	93.57 133
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 97	86.82 101	86.17 118	92.05 109	66.87 223	91.21 43	88.64 206	86.30 33	89.60 114	92.59 146	69.22 250	94.91 71	73.89 198	97.89 52	96.72 24

ACMMP_NAP		90.65 32	91.07 39	89.42 61	95.93 16	79.54 80	89.95 66	93.68 58	77.65 127	91.97 67	94.89 53	88.38 29	95.45 51	89.27 5	97.87 53	93.27 143

test_241102_TWO								93.71 55	83.77 52	93.49 39	94.27 79	89.27 23	95.84 24	86.03 51	97.82 54	92.04 203

DPE-MVS		90.53 36	91.08 37	88.88 69	93.38 71	78.65 87	89.15 87	94.05 41	84.68 45	93.90 28	94.11 91	88.13 36	96.30 5	84.51 72	97.81 55	91.70 216
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
OurMVSNet-221017-0		90.01 46	89.74 56	90.83 36	93.16 78	80.37 72	91.91 36	93.11 79	81.10 81	95.32 14	97.24 7	72.94 219	94.85 72	85.07 64	97.78 56	97.26 15

SMA-MVS		90.31 38	90.48 50	89.83 54	95.31 30	79.52 81	90.98 47	93.24 74	75.37 157	92.84 51	95.28 44	85.58 67	96.09 8	87.92 15	97.76 57	93.88 113
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 30	91.92 16	95.74 20	85.88 30	92.58 22	93.25 73	81.99 70	91.40 74	94.17 88	87.51 45	95.87 20	87.74 18	97.76 57	93.99 107

HFP-MVS		91.30 23	91.39 27	91.02 33	95.43 29	84.66 47	92.58 22	93.29 72	81.99 70	91.47 73	93.96 99	88.35 31	95.56 42	87.74 18	97.74 59	92.85 161

region2R		91.44 22	91.30 34	91.87 19	95.75 19	85.90 29	92.63 21	93.30 71	81.91 72	90.88 88	94.21 84	87.75 41	95.87 20	87.60 23	97.71 60	93.83 116

GST-MVS		90.96 29	91.01 40	90.82 37	95.45 28	82.73 59	91.75 38	93.74 54	80.98 83	91.38 75	93.80 106	87.20 49	95.80 28	87.10 36	97.69 61	93.93 110

UniMVSNet_ETH3D		89.12 65	90.72 47	84.31 162	97.00 2	64.33 246	89.67 74	88.38 211	88.84 17	94.29 22	97.57 4	90.48 13	91.26 189	72.57 218	97.65 62	97.34 14

v7n		90.13 40	90.96 42	87.65 91	91.95 112	71.06 176	89.99 64	93.05 83	86.53 31	94.29 22	96.27 20	82.69 93	94.08 100	86.25 47	97.63 63	97.82 8

XVS		91.54 17	91.36 28	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	94.03 93	86.57 55	95.80 28	87.35 29	97.62 64	94.20 97

X-MVStestdata		85.04 127	82.70 183	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	16.05 432	86.57 55	95.80 28	87.35 29	97.62 64	94.20 97

SR-MVS-dyc-post		92.41 9	92.41 10	92.39 5	94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	88.83 26	95.51 47	87.16 34	97.60 66	92.73 164

RE-MVS-def			92.61 8		94.13 55	88.95 6	92.87 13	94.16 32	88.75 18	93.79 32	94.43 72	90.64 10		87.16 34	97.60 66	92.73 164

APD-MVS_3200maxsize		92.05 12	92.24 12	91.48 25	93.02 80	85.17 39	92.47 26	95.05 14	87.65 27	93.21 43	94.39 77	90.09 17	95.08 66	86.67 40	97.60 66	94.18 100

Anonymous20240521		80.18 234	81.25 211	76.95 301	83.15 326	60.84 294	82.46 230	85.99 253	68.76 242	86.78 174	93.73 112	59.13 307	77.44 369	73.71 202	97.55 69	92.56 174

9.14			89.29 62		91.84 119		88.80 93	95.32 12	75.14 159	91.07 81	92.89 136	87.27 47	93.78 110	83.69 80	97.55 69

OPM-MVS		89.80 51	89.97 52	89.27 63	94.76 40	79.86 76	86.76 127	92.78 96	78.78 111	92.51 58	93.64 115	88.13 36	93.84 109	84.83 69	97.55 69	94.10 105
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
LTVRE_ROB	86.10 1	93.04 4	93.44 3	91.82 22	93.73 64	85.72 34	96.79 1	95.51 9	88.86 16	95.63 10	96.99 10	84.81 72	93.16 137	91.10 2	97.53 72	96.58 28
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 39	90.80 46	88.68 76	92.86 86	77.09 108	91.19 44	95.74 6	81.38 78	92.28 62	93.80 106	86.89 52	94.64 78	85.52 59	97.51 73	94.30 96

MIMVSNet1		83.63 167	84.59 144	80.74 245	94.06 57	62.77 264	82.72 221	84.53 279	77.57 129	90.34 93	95.92 28	76.88 175	85.83 312	61.88 313	97.42 74	93.62 129

ACMMP++															97.35 75

SR-MVS		92.23 10	92.34 11	91.91 17	94.89 38	87.85 10	92.51 24	93.87 51	88.20 23	93.24 42	94.02 94	90.15 16	95.67 38	86.82 38	97.34 76	92.19 197

nrg030		87.85 82	88.49 75	85.91 122	90.07 166	69.73 190	87.86 106	94.20 30	74.04 168	92.70 56	94.66 60	85.88 66	91.50 181	79.72 124	97.32 77	96.50 29

pmmvs6		86.52 99	88.06 79	81.90 223	92.22 103	62.28 274	84.66 168	89.15 200	83.54 57	89.85 104	97.32 5	88.08 38	86.80 290	70.43 235	97.30 78	96.62 26

SD-MVS		88.96 67	89.88 53	86.22 115	91.63 123	77.07 109	89.82 69	93.77 53	78.90 109	92.88 48	92.29 159	86.11 63	90.22 222	86.24 48	97.24 79	91.36 225
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 58	88.98 69	90.63 40	95.09 33	86.95 16	92.09 32	92.30 110	79.74 96	87.50 161	92.38 153	81.42 121	93.28 133	83.07 85	97.24 79	91.67 217

APD-MVS		89.54 56	89.63 58	89.26 64	92.57 91	81.34 68	90.19 61	93.08 82	80.87 85	91.13 80	93.19 122	86.22 62	95.97 14	82.23 99	97.18 81	90.45 252
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
wuyk23d		75.13 287	79.30 240	62.63 395	75.56 395	75.18 127	80.89 257	73.10 363	75.06 160	94.76 16	95.32 41	87.73 43	52.85 427	34.16 425	97.11 82	59.85 423

PMVS	80.48 6	90.08 41	90.66 48	88.34 81	96.71 3	92.97 2	90.31 59	89.57 195	88.51 21	90.11 96	95.12 49	90.98 6	88.92 254	77.55 154	97.07 83	83.13 363
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
OMC-MVS		88.19 74	87.52 85	90.19 48	91.94 114	81.68 65	87.49 112	93.17 76	76.02 142	88.64 131	91.22 191	84.24 78	93.37 131	77.97 150	97.03 84	95.52 51

test_prior2							83.37 202		75.43 155	84.58 224	91.57 181	81.92 115		79.54 128	96.97 85

EPP-MVSNet		85.47 116	85.04 133	86.77 103	91.52 132	69.37 194	91.63 39	87.98 220	81.51 77	87.05 171	91.83 172	66.18 265	95.29 56	70.75 230	96.89 86	95.64 48

VDDNet		84.35 144	85.39 128	81.25 236	95.13 32	59.32 308	85.42 153	81.11 307	86.41 32	87.41 162	96.21 22	73.61 206	90.61 214	66.33 273	96.85 87	93.81 120

VPNet		80.25 231	81.68 198	75.94 315	92.46 95	47.98 390	76.70 318	81.67 303	73.45 178	84.87 220	92.82 139	74.66 196	86.51 295	61.66 316	96.85 87	93.33 139

SixPastTwentyTwo		87.20 89	87.45 87	86.45 108	92.52 93	69.19 199	87.84 107	88.05 218	81.66 75	94.64 18	96.53 17	65.94 266	94.75 74	83.02 87	96.83 89	95.41 53

VPA-MVSNet		83.47 173	84.73 138	79.69 261	90.29 160	57.52 329	81.30 251	88.69 205	76.29 138	87.58 160	94.44 71	80.60 131	87.20 282	66.60 271	96.82 90	94.34 94

Gipuma		84.44 141	86.33 106	78.78 271	84.20 302	73.57 136	89.55 77	90.44 165	84.24 48	84.38 230	94.89 53	76.35 180	80.40 355	76.14 173	96.80 91	82.36 373
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ZD-MVS					92.22 103	80.48 71		91.85 123	71.22 217	90.38 92	92.98 131	86.06 64	96.11 7	81.99 102	96.75 92

CDPH-MVS		86.17 107	85.54 124	88.05 86	92.25 101	75.45 125	83.85 187	92.01 117	65.91 276	86.19 191	91.75 178	83.77 82	94.98 69	77.43 157	96.71 93	93.73 123

KD-MVS_self_test		81.93 203	83.14 176	78.30 281	84.75 291	52.75 364	80.37 263	89.42 198	70.24 229	90.26 95	93.39 119	74.55 198	86.77 291	68.61 257	96.64 94	95.38 54

DP-MVS		88.60 70	89.01 67	87.36 93	91.30 136	77.50 101	87.55 109	92.97 90	87.95 25	89.62 111	92.87 137	84.56 73	93.89 106	77.65 152	96.62 95	90.70 243

TransMVSNet (Re)		84.02 157	85.74 121	78.85 270	91.00 146	55.20 349	82.29 235	87.26 227	79.65 98	88.38 140	95.52 37	83.00 90	86.88 288	67.97 263	96.60 96	94.45 87

ambc				82.98 201	90.55 156	64.86 240	88.20 100	89.15 200		89.40 118	93.96 99	71.67 237	91.38 188	78.83 135	96.55 97	92.71 167

train_agg		85.98 109	85.28 130	88.07 85	92.34 98	79.70 78	83.94 183	90.32 171	65.79 278	84.49 227	90.97 200	81.93 113	93.63 115	81.21 107	96.54 98	90.88 237

VDD-MVS		84.23 150	84.58 145	83.20 195	91.17 142	65.16 239	83.25 206	84.97 273	79.79 95	87.18 164	94.27 79	74.77 194	90.89 203	69.24 245	96.54 98	93.55 136

HPM-MVS++		88.93 68	88.45 76	90.38 44	94.92 36	85.85 31	89.70 71	91.27 141	78.20 119	86.69 179	92.28 160	80.36 133	95.06 67	86.17 49	96.49 100	90.22 256

test_djsdf		89.62 54	89.01 67	91.45 26	92.36 97	82.98 57	91.98 34	90.08 182	71.54 211	94.28 24	96.54 16	81.57 119	94.27 89	86.26 45	96.49 100	97.09 19

SPE-MVS-test		87.00 90	86.43 105	88.71 74	89.46 176	77.46 102	89.42 84	95.73 7	77.87 125	81.64 287	87.25 283	82.43 98	94.53 84	77.65 152	96.46 102	94.14 103

test1111		78.53 251	78.85 245	77.56 294	92.22 103	47.49 392	82.61 223	69.24 388	72.43 198	85.28 209	94.20 85	51.91 345	90.07 231	65.36 284	96.45 103	95.11 65

test9_res														80.83 112	96.45 103	90.57 248

Anonymous20240529		86.20 104	87.13 92	83.42 189	90.19 162	64.55 244	84.55 170	90.71 156	85.85 36	89.94 103	95.24 46	82.13 109	90.40 218	69.19 248	96.40 105	95.31 57

anonymousdsp		89.73 53	88.88 70	92.27 8	89.82 171	86.67 18	90.51 54	90.20 179	69.87 232	95.06 15	96.14 25	84.28 77	93.07 141	87.68 20	96.34 106	97.09 19

PHI-MVS		86.38 100	85.81 118	88.08 84	88.44 205	77.34 105	89.35 85	93.05 83	73.15 189	84.76 222	87.70 273	78.87 144	94.18 95	80.67 115	96.29 107	92.73 164

PS-MVSNAJss		88.31 73	87.90 81	89.56 59	93.31 73	77.96 96	87.94 105	91.97 119	70.73 222	94.19 25	96.67 14	76.94 169	94.57 81	83.07 85	96.28 108	96.15 33

v10		86.54 98	87.10 93	84.84 141	88.16 211	63.28 257	86.64 130	92.20 112	75.42 156	92.81 53	94.50 68	74.05 202	94.06 101	83.88 77	96.28 108	97.17 18

CNVR-MVS		87.81 83	87.68 83	88.21 83	92.87 84	77.30 107	85.25 156	91.23 142	77.31 132	87.07 170	91.47 184	82.94 91	94.71 75	84.67 70	96.27 110	92.62 171

EC-MVSNet		88.01 78	88.32 77	87.09 95	89.28 180	72.03 163	90.31 59	96.31 4	80.88 84	85.12 212	89.67 240	84.47 75	95.46 50	82.56 94	96.26 111	93.77 122

mmtdpeth		85.13 124	85.78 120	83.17 197	84.65 292	74.71 128	85.87 143	90.35 170	77.94 122	83.82 245	96.96 12	77.75 154	80.03 358	78.44 137	96.21 112	94.79 77

MM		87.64 85	87.15 91	89.09 67	89.51 174	76.39 118	88.68 96	86.76 241	84.54 46	83.58 251	93.78 108	73.36 214	96.48 2	87.98 14	96.21 112	94.41 91

114514_t		83.10 180	82.54 188	84.77 145	92.90 83	69.10 201	86.65 129	90.62 160	54.66 370	81.46 289	90.81 210	76.98 168	94.38 87	72.62 217	96.18 114	90.82 239

agg_prior2														79.68 125	96.16 115	90.22 256

AllTest		87.97 80	87.40 89	89.68 55	91.59 124	83.40 52	89.50 80	95.44 10	79.47 99	88.00 150	93.03 129	82.66 94	91.47 182	70.81 227	96.14 116	94.16 101

TestCases				89.68 55	91.59 124	83.40 52		95.44 10	79.47 99	88.00 150	93.03 129	82.66 94	91.47 182	70.81 227	96.14 116	94.16 101

EPNet		80.37 227	78.41 253	86.23 113	76.75 384	73.28 140	87.18 116	77.45 327	76.24 139	68.14 395	88.93 252	65.41 269	93.85 107	69.47 243	96.12 118	91.55 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
testf1		89.30 60	89.12 64	89.84 52	88.67 196	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 246	74.12 193	96.10 119	94.45 87

APD_test2		89.30 60	89.12 64	89.84 52	88.67 196	85.64 35	90.61 50	93.17 76	86.02 34	93.12 44	95.30 42	84.94 69	89.44 246	74.12 193	96.10 119	94.45 87

pm-mvs1		83.69 165	84.95 136	79.91 257	90.04 168	59.66 305	82.43 231	87.44 224	75.52 154	87.85 153	95.26 45	81.25 123	85.65 314	68.74 255	96.04 121	94.42 90

test2506		74.12 299	73.39 300	76.28 312	91.85 117	44.20 406	84.06 180	48.20 431	72.30 204	81.90 278	94.20 85	27.22 431	89.77 239	64.81 289	96.02 122	94.87 71

ECVR-MVS		78.44 252	78.63 249	77.88 290	91.85 117	48.95 386	83.68 193	69.91 384	72.30 204	84.26 239	94.20 85	51.89 346	89.82 236	63.58 299	96.02 122	94.87 71

mvs_tets		89.78 52	89.27 63	91.30 29	93.51 67	84.79 44	89.89 68	90.63 159	70.00 231	94.55 19	96.67 14	87.94 39	93.59 120	84.27 74	95.97 124	95.52 51

EGC-MVSNET		74.79 294	69.99 338	89.19 65	94.89 38	87.00 15	91.89 37	86.28 245	1.09 433	2.23 435	95.98 27	81.87 116	89.48 242	79.76 123	95.96 125	91.10 230

MVS_0304		85.37 118	84.58 145	87.75 88	85.28 281	73.36 137	86.54 133	85.71 256	77.56 130	81.78 285	92.47 151	70.29 244	96.02 11	85.59 58	95.96 125	93.87 114

DeepPCF-MVS	81.24 5	87.28 88	86.21 109	90.49 42	91.48 133	84.90 42	83.41 201	92.38 107	70.25 228	89.35 119	90.68 215	82.85 92	94.57 81	79.55 127	95.95 127	92.00 205

DVP-MVS++		90.07 42	91.09 36	87.00 97	91.55 129	72.64 148	96.19 2	94.10 39	85.33 38	93.49 39	94.64 64	81.12 124	95.88 18	87.41 27	95.94 128	92.48 178

PC_three_1452									58.96 341	90.06 97	91.33 187	80.66 130	93.03 143	75.78 176	95.94 128	92.48 178

jajsoiax		89.41 57	88.81 73	91.19 32	93.38 71	84.72 45	89.70 71	90.29 176	69.27 235	94.39 20	96.38 18	86.02 65	93.52 124	83.96 76	95.92 130	95.34 55

ANet_high		83.17 178	85.68 122	75.65 317	81.24 343	45.26 403	79.94 268	92.91 91	83.83 51	91.33 76	96.88 13	80.25 134	85.92 307	68.89 252	95.89 131	95.76 43

tt0805		88.09 77	89.79 55	82.98 201	93.26 75	63.94 250	91.10 45	89.64 192	85.07 41	90.91 86	91.09 196	89.16 24	91.87 175	82.03 100	95.87 132	93.13 149

3Dnovator+	83.92 2	89.97 49	89.66 57	90.92 35	91.27 138	81.66 66	91.25 42	94.13 37	88.89 15	88.83 126	94.26 82	77.55 159	95.86 23	84.88 67	95.87 132	95.24 60

HQP_MVS		87.75 84	87.43 88	88.70 75	93.45 68	76.42 116	89.45 82	93.61 59	79.44 101	86.55 181	92.95 134	74.84 191	95.22 59	80.78 113	95.83 134	94.46 85

plane_prior5								93.61 59					95.22 59	80.78 113	95.83 134	94.46 85

cl____		80.42 225	80.23 227	81.02 242	79.99 357	59.25 309	77.07 313	87.02 237	67.37 263	86.18 193	89.21 247	63.08 284	90.16 224	76.31 170	95.80 136	93.65 127

DIV-MVS_self_test		80.43 224	80.23 227	81.02 242	79.99 357	59.25 309	77.07 313	87.02 237	67.38 262	86.19 191	89.22 246	63.09 283	90.16 224	76.32 169	95.80 136	93.66 125

DeepC-MVS_fast	80.27 8	86.23 102	85.65 123	87.96 87	91.30 136	76.92 110	87.19 115	91.99 118	70.56 223	84.96 216	90.69 214	80.01 137	95.14 64	78.37 139	95.78 138	91.82 210
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LFMVS		80.15 235	80.56 221	78.89 269	89.19 183	55.93 339	85.22 157	73.78 356	82.96 63	84.28 237	92.72 144	57.38 319	90.07 231	63.80 298	95.75 139	90.68 244

ACMMP++_ref															95.74 140

原ACMM1				84.60 151	92.81 89	74.01 133		91.50 132	62.59 303	82.73 267	90.67 217	76.53 176	94.25 91	69.24 245	95.69 141	85.55 326

tfpnnormal		81.79 206	82.95 179	78.31 280	88.93 189	55.40 345	80.83 259	82.85 293	76.81 135	85.90 199	94.14 89	74.58 197	86.51 295	66.82 269	95.68 142	93.01 155

mvs5depth		83.82 162	84.54 147	81.68 230	82.23 331	68.65 204	86.89 121	89.90 186	80.02 94	87.74 156	97.86 2	64.19 275	82.02 343	76.37 168	95.63 143	94.35 93

TAPA-MVS	77.73 12	85.71 113	84.83 137	88.37 80	88.78 195	79.72 77	87.15 117	93.50 62	69.17 236	85.80 200	89.56 241	80.76 128	92.13 166	73.21 214	95.51 144	93.25 145
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LS3D		90.60 34	90.34 51	91.38 28	89.03 185	84.23 49	93.58 6	94.68 17	90.65 8	90.33 94	93.95 101	84.50 74	95.37 54	80.87 111	95.50 145	94.53 84

v8		86.22 103	86.83 100	84.36 158	87.82 219	62.35 273	86.42 134	91.33 139	76.78 136	92.73 55	94.48 70	73.41 211	93.72 112	83.10 84	95.41 146	97.01 21

Vis-MVSNet (Re-imp)		77.82 257	77.79 258	77.92 289	88.82 192	51.29 377	83.28 204	71.97 372	74.04 168	82.23 273	89.78 238	57.38 319	89.41 248	57.22 340	95.41 146	93.05 153

OPU-MVS				88.27 82	91.89 115	77.83 97	90.47 55				91.22 191	81.12 124	94.68 76	74.48 188	95.35 148	92.29 191

FMVSNet1		84.55 139	85.45 126	81.85 225	90.27 161	61.05 289	86.83 124	88.27 215	78.57 115	89.66 110	95.64 34	75.43 183	90.68 211	69.09 249	95.33 149	93.82 117

test12				86.57 105	90.74 151	72.63 150		90.69 157		82.76 266		79.20 141	94.80 73		95.32 150	92.27 193

NCCC		87.36 87	86.87 99	88.83 70	92.32 100	78.84 86	86.58 131	91.09 147	78.77 112	84.85 221	90.89 205	80.85 127	95.29 56	81.14 108	95.32 150	92.34 187

Patchmtry		76.56 274	77.46 259	73.83 329	79.37 366	46.60 396	82.41 232	76.90 333	73.81 171	85.56 205	92.38 153	48.07 361	83.98 331	63.36 302	95.31 152	90.92 235

XVG-OURS		89.18 63	88.83 72	90.23 47	94.28 47	86.11 26	85.91 141	93.60 61	80.16 91	89.13 123	93.44 118	83.82 80	90.98 198	83.86 78	95.30 153	93.60 131

TSAR-MVS + GP.		83.95 159	82.69 184	87.72 89	89.27 181	81.45 67	83.72 192	81.58 305	74.73 162	85.66 201	86.06 302	72.56 225	92.69 152	75.44 181	95.21 154	89.01 284

test_0402		88.65 69	89.58 60	85.88 124	92.55 92	72.22 160	84.01 181	89.44 197	88.63 20	94.38 21	95.77 29	86.38 61	93.59 120	79.84 122	95.21 154	91.82 210

TinyColmap		81.25 212	82.34 191	77.99 288	85.33 280	60.68 296	82.32 234	88.33 213	71.26 216	86.97 172	92.22 163	77.10 166	86.98 286	62.37 307	95.17 156	86.31 318

Anonymous202405211		80.51 223	81.19 214	78.49 277	88.48 203	57.26 331	76.63 320	82.49 296	81.21 80	84.30 236	92.24 162	67.99 256	86.24 299	62.22 308	95.13 157	91.98 207

tttt0517		81.07 214	79.58 237	85.52 132	88.99 187	66.45 227	87.03 119	75.51 344	73.76 172	88.32 142	90.20 228	37.96 406	94.16 99	79.36 131	95.13 157	95.93 42

DP-MVS Recon		84.05 155	83.22 172	86.52 107	91.73 122	75.27 126	83.23 208	92.40 105	72.04 208	82.04 276	88.33 260	77.91 153	93.95 104	66.17 274	95.12 159	90.34 255

PCF-MVS	74.62 15	82.15 197	80.92 217	85.84 125	89.43 177	72.30 158	80.53 261	91.82 125	57.36 354	87.81 154	89.92 236	77.67 157	93.63 115	58.69 331	95.08 160	91.58 220
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CSCG		86.26 101	86.47 104	85.60 130	90.87 149	74.26 132	87.98 104	91.85 123	80.35 88	89.54 117	88.01 264	79.09 142	92.13 166	75.51 179	95.06 161	90.41 253

SDMVSNet		81.90 205	83.17 175	78.10 285	88.81 193	62.45 270	76.08 331	86.05 251	73.67 173	83.41 254	93.04 127	82.35 100	80.65 352	70.06 239	95.03 162	91.21 227

sd_testset		79.95 239	81.39 209	75.64 318	88.81 193	58.07 323	76.16 330	82.81 294	73.67 173	83.41 254	93.04 127	80.96 126	77.65 368	58.62 332	95.03 162	91.21 227

plane_prior						76.42 116	87.15 117		75.94 146						95.03 162

new-patchmatchnet		70.10 337	73.37 301	60.29 403	81.23 344	16.95 438	59.54 414	74.62 347	62.93 301	80.97 293	87.93 268	62.83 287	71.90 386	55.24 355	95.01 165	92.00 205

v1192		84.57 137	84.69 143	84.21 164	87.75 221	62.88 261	83.02 213	91.43 134	69.08 238	89.98 102	90.89 205	72.70 223	93.62 118	82.41 96	94.97 166	96.13 34

v1921920		84.23 150	84.37 153	83.79 175	87.64 227	61.71 280	82.91 217	91.20 143	67.94 255	90.06 97	90.34 224	72.04 232	93.59 120	82.32 97	94.91 167	96.07 36

CL-MVSNet_self_test		76.81 269	77.38 261	75.12 321	86.90 248	51.34 375	73.20 359	80.63 312	68.30 249	81.80 283	88.40 259	66.92 261	80.90 349	55.35 354	94.90 168	93.12 151

CS-MVS		88.14 75	87.67 84	89.54 60	89.56 173	79.18 82	90.47 55	94.77 16	79.37 103	84.32 233	89.33 245	83.87 79	94.53 84	82.45 95	94.89 169	94.90 69

v144192		84.24 149	84.41 151	83.71 179	87.59 228	61.57 281	82.95 216	91.03 148	67.82 259	89.80 105	90.49 221	73.28 215	93.51 125	81.88 105	94.89 169	96.04 38

LCM-MVSNet-Re		83.48 172	85.06 132	78.75 272	85.94 272	55.75 343	80.05 266	94.27 24	76.47 137	96.09 6	94.54 67	83.31 88	89.75 241	59.95 326	94.89 169	90.75 240

casdiffmvs_mvg		86.72 95	87.51 86	84.36 158	87.09 243	65.22 237	84.16 177	94.23 27	77.89 123	91.28 79	93.66 114	84.35 76	92.71 150	80.07 118	94.87 172	95.16 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD_test1		88.40 71	87.91 80	89.88 51	89.50 175	86.65 20	89.98 65	91.91 122	84.26 47	90.87 89	93.92 103	82.18 108	89.29 250	73.75 201	94.81 173	93.70 124

v1240		84.30 146	84.51 149	83.65 180	87.65 226	61.26 286	82.85 219	91.54 131	67.94 255	90.68 91	90.65 218	71.71 236	93.64 114	82.84 90	94.78 174	96.07 36

MSLP-MVS++		85.00 130	86.03 112	81.90 223	91.84 119	71.56 172	86.75 128	93.02 87	75.95 145	87.12 165	89.39 243	77.98 151	89.40 249	77.46 155	94.78 174	84.75 335

IterMVS-LS		84.73 134	84.98 134	83.96 170	87.35 233	63.66 251	83.25 206	89.88 187	76.06 140	89.62 111	92.37 156	73.40 213	92.52 155	78.16 145	94.77 176	95.69 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		83.66 166	83.69 165	83.57 185	90.05 167	72.26 159	86.29 136	90.00 184	78.19 120	81.65 286	87.16 285	83.40 87	94.24 92	61.69 315	94.76 177	84.21 345

BP-MVS1		82.81 182	81.67 199	86.23 113	87.88 218	68.53 205	86.06 140	84.36 280	75.65 150	85.14 211	90.19 229	45.84 374	94.42 86	85.18 62	94.72 178	95.75 44

ITE_SJBPF				90.11 49	90.72 152	84.97 41		90.30 174	81.56 76	90.02 99	91.20 193	82.40 99	90.81 207	73.58 204	94.66 179	94.56 81

v1144		84.54 140	84.72 140	84.00 168	87.67 225	62.55 268	82.97 215	90.93 152	70.32 227	89.80 105	90.99 199	73.50 208	93.48 126	81.69 106	94.65 180	95.97 39

test20.03		73.75 304	74.59 289	71.22 350	81.11 345	51.12 379	70.15 380	72.10 371	70.42 224	80.28 307	91.50 183	64.21 274	74.72 380	46.96 399	94.58 181	87.82 302

TSAR-MVS + MP.		88.14 75	87.82 82	89.09 67	95.72 22	76.74 112	92.49 25	91.19 144	67.85 258	86.63 180	94.84 55	79.58 140	95.96 15	87.62 21	94.50 182	94.56 81
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		73.90 302	75.67 279	68.61 372	84.11 304	41.28 414	64.17 405	72.83 364	72.09 207	79.08 320	87.94 266	70.31 243	73.89 382	55.99 347	94.49 183	90.67 246

HQP3-MVS								92.68 98							94.47 184

HQP-MVS		84.61 136	84.06 159	86.27 112	91.19 139	70.66 179	84.77 162	92.68 98	73.30 184	80.55 301	90.17 232	72.10 229	94.61 79	77.30 159	94.47 184	93.56 134

test_fmvsmconf0.01_n		86.68 96	86.52 103	87.18 94	85.94 272	78.30 89	86.93 120	92.20 112	65.94 274	89.16 121	93.16 124	83.10 89	89.89 235	87.81 17	94.43 186	93.35 138

c3_l		81.64 207	81.59 203	81.79 229	80.86 349	59.15 312	78.61 291	90.18 180	68.36 247	87.20 163	87.11 287	69.39 248	91.62 179	78.16 145	94.43 186	94.60 80

MCST-MVS		84.36 143	83.93 162	85.63 129	91.59 124	71.58 170	83.52 197	92.13 114	61.82 312	83.96 243	89.75 239	79.93 139	93.46 127	78.33 141	94.34 188	91.87 209

test_fmvsmconf0.1_n		86.18 106	85.88 116	87.08 96	85.26 282	78.25 90	85.82 145	91.82 125	65.33 288	88.55 133	92.35 158	82.62 96	89.80 237	86.87 37	94.32 189	93.18 148

thisisatest0530		79.07 242	77.33 262	84.26 163	87.13 239	64.58 242	83.66 194	75.95 339	68.86 241	85.22 210	87.36 281	38.10 403	93.57 123	75.47 180	94.28 190	94.62 79

baseline		85.20 122	85.93 114	83.02 199	86.30 261	62.37 272	84.55 170	93.96 44	74.48 165	87.12 165	92.03 165	82.30 103	91.94 171	78.39 138	94.21 191	94.74 78

test_fmvsmconf_n		85.88 111	85.51 125	86.99 98	84.77 290	78.21 91	85.40 154	91.39 137	65.32 289	87.72 157	91.81 174	82.33 101	89.78 238	86.68 39	94.20 192	92.99 156

h-mvs33		84.25 148	82.76 182	88.72 73	91.82 121	82.60 60	84.00 182	84.98 272	71.27 214	86.70 177	90.55 220	63.04 285	93.92 105	78.26 143	94.20 192	89.63 268

MVSMamba_PlusPlus		87.53 86	88.86 71	83.54 187	92.03 110	62.26 275	91.49 40	92.62 100	88.07 24	88.07 147	96.17 23	72.24 228	95.79 31	84.85 68	94.16 194	92.58 173

balanced_conf03		84.80 132	85.40 127	83.00 200	88.95 188	61.44 282	90.42 58	92.37 108	71.48 213	88.72 130	93.13 125	70.16 246	95.15 63	79.26 132	94.11 195	92.41 182

alignmvs		83.94 160	83.98 161	83.80 174	87.80 220	67.88 213	84.54 172	91.42 136	73.27 187	88.41 139	87.96 265	72.33 226	90.83 206	76.02 175	94.11 195	92.69 168

USDC		76.63 272	76.73 269	76.34 311	83.46 314	57.20 332	80.02 267	88.04 219	52.14 386	83.65 249	91.25 190	63.24 281	86.65 293	54.66 359	94.11 195	85.17 330

MVS_111021_HR		84.63 135	84.34 154	85.49 134	90.18 163	75.86 123	79.23 282	87.13 232	73.35 181	85.56 205	89.34 244	83.60 85	90.50 216	76.64 165	94.05 198	90.09 262

VNet		79.31 241	80.27 226	76.44 309	87.92 216	53.95 356	75.58 337	84.35 281	74.39 166	82.23 273	90.72 212	72.84 221	84.39 326	60.38 324	93.98 199	90.97 233

FMVSNet2		81.31 211	81.61 202	80.41 251	86.38 256	58.75 319	83.93 185	86.58 243	72.43 198	87.65 158	92.98 131	63.78 278	90.22 222	66.86 266	93.92 200	92.27 193

MGCFI-Net		85.04 127	85.95 113	82.31 219	87.52 229	63.59 253	86.23 138	93.96 44	73.46 177	88.07 147	87.83 271	86.46 57	90.87 205	76.17 172	93.89 201	92.47 180

GDP-MVS		82.17 195	80.85 219	86.15 120	88.65 198	68.95 202	85.65 149	93.02 87	68.42 246	83.73 247	89.54 242	45.07 385	94.31 88	79.66 126	93.87 202	95.19 63

LF4IMVS		82.75 184	81.93 195	85.19 136	82.08 332	80.15 74	85.53 150	88.76 204	68.01 252	85.58 204	87.75 272	71.80 234	86.85 289	74.02 196	93.87 202	88.58 287

sasdasda		85.50 114	86.14 110	83.58 183	87.97 213	67.13 217	87.55 109	94.32 21	73.44 179	88.47 136	87.54 276	86.45 58	91.06 196	75.76 177	93.76 204	92.54 176

canonicalmvs		85.50 114	86.14 110	83.58 183	87.97 213	67.13 217	87.55 109	94.32 21	73.44 179	88.47 136	87.54 276	86.45 58	91.06 196	75.76 177	93.76 204	92.54 176

v2v482		84.09 153	84.24 156	83.62 181	87.13 239	61.40 283	82.71 222	89.71 190	72.19 206	89.55 115	91.41 185	70.70 242	93.20 135	81.02 109	93.76 204	96.25 32

casdiffmvs		85.21 121	85.85 117	83.31 192	86.17 266	62.77 264	83.03 212	93.93 46	74.69 163	88.21 144	92.68 145	82.29 105	91.89 174	77.87 151	93.75 207	95.27 59
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		70.72 332	70.97 325	69.95 357	88.93 189	34.80 427	69.85 382	66.59 401	78.42 117	77.58 335	85.55 308	31.83 418	82.08 342	46.28 400	93.73 208	92.98 157

fmvsm_s_conf0.5_n_6		84.05 155	84.14 157	83.81 173	87.75 221	71.17 175	83.42 200	91.10 146	67.90 257	84.53 225	90.70 213	73.01 218	88.73 260	85.09 63	93.72 209	91.53 222

UGNet		82.78 183	81.64 200	86.21 116	86.20 265	76.24 120	86.86 122	85.68 257	77.07 134	73.76 366	92.82 139	69.64 247	91.82 177	69.04 251	93.69 210	90.56 249
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					91.97 111	71.77 165		81.78 302			91.84 171	73.92 203			93.65 211	83.61 353

AUN-MVS		81.18 213	78.78 246	88.39 79	90.93 147	82.14 62	82.51 229	83.67 286	64.69 293	80.29 305	85.91 306	51.07 349	92.38 159	76.29 171	93.63 212	90.65 247

hse-mvs2		83.47 173	81.81 197	88.47 77	91.03 145	82.27 61	82.61 223	83.69 285	71.27 214	86.70 177	86.05 303	63.04 285	92.41 158	78.26 143	93.62 213	90.71 242

MVS_111021_LR		84.28 147	83.76 164	85.83 126	89.23 182	83.07 55	80.99 255	83.56 287	72.71 196	86.07 194	89.07 250	81.75 118	86.19 302	77.11 161	93.36 214	88.24 290

GBi-Net		82.02 200	82.07 192	81.85 225	86.38 256	61.05 289	86.83 124	88.27 215	72.43 198	86.00 195	95.64 34	63.78 278	90.68 211	65.95 276	93.34 215	93.82 117

test1		82.02 200	82.07 192	81.85 225	86.38 256	61.05 289	86.83 124	88.27 215	72.43 198	86.00 195	95.64 34	63.78 278	90.68 211	65.95 276	93.34 215	93.82 117

FMVSNet3		78.80 247	78.55 250	79.57 263	82.89 329	56.89 335	81.76 243	85.77 255	69.04 239	86.00 195	90.44 222	51.75 347	90.09 230	65.95 276	93.34 215	91.72 214

test_fmvsmvis_n_1920		85.22 120	85.36 129	84.81 143	85.80 274	76.13 122	85.15 159	92.32 109	61.40 319	91.33 76	90.85 208	83.76 83	86.16 303	84.31 73	93.28 218	92.15 199

K. test v3		85.14 123	84.73 138	86.37 109	91.13 143	69.63 192	85.45 152	76.68 336	84.06 50	92.44 60	96.99 10	62.03 288	94.65 77	80.58 116	93.24 219	94.83 76

Anonymous20231206		71.38 326	71.88 317	69.88 358	86.31 260	54.37 352	70.39 378	74.62 347	52.57 382	76.73 338	88.76 253	59.94 300	72.06 385	44.35 407	93.23 220	83.23 361

D2MVS		76.84 268	75.67 279	80.34 252	80.48 355	62.16 278	73.50 356	84.80 277	57.61 352	82.24 272	87.54 276	51.31 348	87.65 276	70.40 236	93.19 221	91.23 226

miper_lstm_enhance		76.45 276	76.10 274	77.51 295	76.72 385	60.97 293	64.69 403	85.04 269	63.98 297	83.20 258	88.22 261	56.67 323	78.79 365	73.22 209	93.12 222	92.78 163

新几何1				82.95 203	93.96 59	78.56 88		80.24 313	55.45 364	83.93 244	91.08 197	71.19 239	88.33 267	65.84 279	93.07 223	81.95 378

lessismore_v0				85.95 121	91.10 144	70.99 177		70.91 380		91.79 69	94.42 74	61.76 289	92.93 146	79.52 129	93.03 224	93.93 110

TAMVS		78.08 255	76.36 271	83.23 194	90.62 154	72.87 144	79.08 283	80.01 315	61.72 315	81.35 291	86.92 290	63.96 277	88.78 258	50.61 380	93.01 225	88.04 296

ETV-MVS		84.31 145	83.91 163	85.52 132	88.58 201	70.40 182	84.50 174	93.37 64	78.76 113	84.07 241	78.72 386	80.39 132	95.13 65	73.82 200	92.98 226	91.04 231

EPNet_dtu		72.87 312	71.33 324	77.49 296	77.72 375	60.55 297	82.35 233	75.79 340	66.49 273	58.39 426	81.06 364	53.68 338	85.98 305	53.55 365	92.97 227	85.95 321
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		85.82 112	83.38 169	93.14 4	87.13 239	91.15 3	87.70 108	88.42 210	74.57 164	83.56 252	85.65 307	78.49 147	94.21 93	72.04 221	92.88 228	94.05 106

CANet		83.79 164	82.85 181	86.63 104	86.17 266	72.21 161	83.76 191	91.43 134	77.24 133	74.39 362	87.45 279	75.36 184	95.42 52	77.03 162	92.83 229	92.25 195

fmvsm_s_conf0.5_n_3		86.19 105	87.27 90	82.95 203	86.91 247	70.38 183	85.31 155	92.61 101	75.59 152	88.32 142	92.87 137	82.22 107	88.63 262	88.80 8	92.82 230	89.83 266

API-MVS		82.28 191	82.61 186	81.30 235	86.29 262	69.79 188	88.71 95	87.67 222	78.42 117	82.15 275	84.15 333	77.98 151	91.59 180	65.39 283	92.75 231	82.51 372

test_yl		78.71 249	78.51 251	79.32 266	84.32 299	58.84 316	78.38 292	85.33 262	75.99 143	82.49 268	86.57 293	58.01 313	90.02 233	62.74 305	92.73 232	89.10 279

DCV-MVSNet		78.71 249	78.51 251	79.32 266	84.32 299	58.84 316	78.38 292	85.33 262	75.99 143	82.49 268	86.57 293	58.01 313	90.02 233	62.74 305	92.73 232	89.10 279

testgi		72.36 315	74.61 287	65.59 386	80.56 354	42.82 411	68.29 388	73.35 360	66.87 270	81.84 280	89.93 235	72.08 231	66.92 409	46.05 403	92.54 234	87.01 311

FMVSNet5		72.10 318	71.69 318	73.32 332	81.57 339	53.02 363	76.77 317	78.37 322	63.31 298	76.37 340	91.85 170	36.68 408	78.98 362	47.87 395	92.45 235	87.95 298

CDS-MVSNet		77.32 263	75.40 281	83.06 198	89.00 186	72.48 155	77.90 299	82.17 299	60.81 328	78.94 321	83.49 338	59.30 305	88.76 259	54.64 360	92.37 236	87.93 299
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
patch_mono-2		78.89 244	79.39 239	77.41 297	84.78 289	68.11 210	75.60 335	83.11 290	60.96 327	79.36 315	89.89 237	75.18 186	72.97 383	73.32 208	92.30 237	91.15 229

dcpmvs_2		84.23 150	85.14 131	81.50 233	88.61 200	61.98 279	82.90 218	93.11 79	68.66 244	92.77 54	92.39 152	78.50 146	87.63 277	76.99 163	92.30 237	94.90 69

CNLPA		83.55 171	83.10 177	84.90 140	89.34 179	83.87 50	84.54 172	88.77 203	79.09 106	83.54 253	88.66 257	74.87 190	81.73 345	66.84 268	92.29 239	89.11 278

F-COLMAP		84.97 131	83.42 168	89.63 57	92.39 96	83.40 52	88.83 92	91.92 121	73.19 188	80.18 309	89.15 249	77.04 167	93.28 133	65.82 280	92.28 240	92.21 196

thres600view7		75.97 280	75.35 283	77.85 292	87.01 245	51.84 373	80.45 262	73.26 361	75.20 158	83.10 260	86.31 299	45.54 376	89.05 251	55.03 357	92.24 241	92.66 169

PVSNet_BlendedMVS		78.80 247	77.84 257	81.65 231	84.43 295	63.41 254	79.49 276	90.44 165	61.70 316	75.43 353	87.07 288	69.11 251	91.44 184	60.68 322	92.24 241	90.11 261

DELS-MVS		81.44 210	81.25 211	82.03 221	84.27 301	62.87 262	76.47 325	92.49 104	70.97 220	81.64 287	83.83 334	75.03 187	92.70 151	74.29 189	92.22 243	90.51 251
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
fmvsm_s_conf0.5_n_5		84.56 138	84.71 141	84.11 167	87.92 216	72.09 162	84.80 161	88.64 206	64.43 294	88.77 127	91.78 176	78.07 150	87.95 272	85.85 56	92.18 244	92.30 189

testdata				79.54 264	92.87 84	72.34 157		80.14 314	59.91 337	85.47 207	91.75 178	67.96 257	85.24 316	68.57 259	92.18 244	81.06 391

SSC-MVS		77.55 260	81.64 200	65.29 389	90.46 157	20.33 436	73.56 355	68.28 390	85.44 37	88.18 146	94.64 64	70.93 240	81.33 347	71.25 224	92.03 246	94.20 97

cl22		78.97 243	78.21 255	81.24 238	77.74 374	59.01 313	77.46 309	87.13 232	65.79 278	84.32 233	85.10 319	58.96 309	90.88 204	75.36 182	92.03 246	93.84 115

miper_ehance_all_eth		80.34 228	80.04 234	81.24 238	79.82 360	58.95 314	77.66 302	89.66 191	65.75 281	85.99 198	85.11 318	68.29 255	91.42 186	76.03 174	92.03 246	93.33 139

miper_enhance_ethall		77.83 256	76.93 266	80.51 249	76.15 391	58.01 325	75.47 339	88.82 202	58.05 348	83.59 250	80.69 365	64.41 272	91.20 190	73.16 215	92.03 246	92.33 188

GeoE		85.45 117	85.81 118	84.37 156	90.08 164	67.07 219	85.86 144	91.39 137	72.33 203	87.59 159	90.25 227	84.85 71	92.37 160	78.00 148	91.94 250	93.66 125

fmvsm_s_conf0.1_n_2		83.82 162	83.49 166	84.84 141	85.99 271	70.19 186	80.93 256	87.58 223	67.26 266	87.94 152	92.37 156	71.40 238	88.01 270	86.03 51	91.87 251	96.31 31

DPM-MVS		80.10 236	79.18 241	82.88 208	90.71 153	69.74 189	78.87 287	90.84 153	60.29 334	75.64 352	85.92 305	67.28 258	93.11 139	71.24 225	91.79 252	85.77 324

v148		82.31 190	82.48 189	81.81 228	85.59 276	59.66 305	81.47 248	86.02 252	72.85 192	88.05 149	90.65 218	70.73 241	90.91 202	75.15 184	91.79 252	94.87 71

fmvsm_s_conf0.5_n_2		83.62 168	83.29 171	84.62 150	85.43 279	70.18 187	80.61 260	87.24 228	67.14 267	87.79 155	91.87 168	71.79 235	87.98 271	86.00 55	91.77 254	95.71 45

test222					93.31 73	76.54 113	79.38 277	77.79 324	52.59 381	82.36 271	90.84 209	66.83 262			91.69 255	81.25 386

testing3		71.53 324	70.79 326	73.77 330	88.89 191	41.86 413	76.60 323	59.12 420	72.83 193	80.97 293	82.08 355	19.80 437	87.33 281	65.12 286	91.68 256	92.13 200

eth_miper_zixun_eth		80.84 217	80.22 229	82.71 210	81.41 341	60.98 292	77.81 300	90.14 181	67.31 265	86.95 173	87.24 284	64.26 273	92.31 162	75.23 183	91.61 257	94.85 75

pmmvs-eth3d		78.42 253	77.04 265	82.57 215	87.44 232	74.41 131	80.86 258	79.67 316	55.68 363	84.69 223	90.31 226	60.91 293	85.42 315	62.20 309	91.59 258	87.88 300

Vis-MVSNet		86.86 92	86.58 102	87.72 89	92.09 107	77.43 104	87.35 113	92.09 115	78.87 110	84.27 238	94.05 92	78.35 148	93.65 113	80.54 117	91.58 259	92.08 201
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FE-MVS		79.98 238	78.86 244	83.36 190	86.47 253	66.45 227	89.73 70	84.74 278	72.80 194	84.22 240	91.38 186	44.95 386	93.60 119	63.93 296	91.50 260	90.04 263

thisisatest0515		73.00 311	70.52 330	80.46 250	81.45 340	59.90 303	73.16 360	74.31 351	57.86 349	76.08 347	77.78 391	37.60 407	92.12 168	65.00 287	91.45 261	89.35 273

ppachtmachnet_test		74.73 295	74.00 294	76.90 303	80.71 352	56.89 335	71.53 370	78.42 321	58.24 345	79.32 317	82.92 346	57.91 316	84.26 328	65.60 282	91.36 262	89.56 269

FA-MVS(test-final)		83.13 179	83.02 178	83.43 188	86.16 268	66.08 230	88.00 103	88.36 212	75.55 153	85.02 214	92.75 143	65.12 270	92.50 156	74.94 187	91.30 263	91.72 214

OpenMVS	76.72 13	81.98 202	82.00 194	81.93 222	84.42 297	68.22 208	88.50 99	89.48 196	66.92 269	81.80 283	91.86 169	72.59 224	90.16 224	71.19 226	91.25 264	87.40 306

fmvsm_l_conf0.5_n_3		85.11 126	84.96 135	85.56 131	87.49 231	75.69 124	84.71 166	90.61 161	67.64 260	84.88 219	92.05 164	82.30 103	88.36 266	83.84 79	91.10 265	92.62 171

EG-PatchMatch MVS		84.08 154	84.11 158	83.98 169	92.22 103	72.61 151	82.20 241	87.02 237	72.63 197	88.86 124	91.02 198	78.52 145	91.11 194	73.41 206	91.09 266	88.21 291

3Dnovator	80.37 7	84.80 132	84.71 141	85.06 139	86.36 259	74.71 128	88.77 94	90.00 184	75.65 150	84.96 216	93.17 123	74.06 201	91.19 191	78.28 142	91.09 266	89.29 276

thres100view900		75.45 284	75.05 285	76.66 307	87.27 234	51.88 372	81.07 254	73.26 361	75.68 149	83.25 257	86.37 296	45.54 376	88.80 255	51.98 375	90.99 268	89.31 274

tfpn200view9		74.86 292	74.23 292	76.74 306	86.24 263	52.12 369	79.24 280	73.87 354	73.34 182	81.82 281	84.60 328	46.02 369	88.80 255	51.98 375	90.99 268	89.31 274

thres400		75.14 286	74.23 292	77.86 291	86.24 263	52.12 369	79.24 280	73.87 354	73.34 182	81.82 281	84.60 328	46.02 369	88.80 255	51.98 375	90.99 268	92.66 169

cascas		76.29 278	74.81 286	80.72 247	84.47 294	62.94 260	73.89 353	87.34 225	55.94 361	75.16 358	76.53 404	63.97 276	91.16 192	65.00 287	90.97 271	88.06 295

MSP-MVS		89.08 66	88.16 78	91.83 20	95.76 18	86.14 25	92.75 17	93.90 48	78.43 116	89.16 121	92.25 161	72.03 233	96.36 4	88.21 12	90.93 272	92.98 157
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		68.76 352	68.43 352	69.75 360	83.29 320	40.30 417	67.36 394	72.21 370	57.09 357	77.05 337	85.53 310	33.68 413	80.51 353	48.79 390	90.90 273	88.45 289

ab-mvs		79.67 240	80.56 221	76.99 300	88.48 203	56.93 333	84.70 167	86.06 250	68.95 240	80.78 298	93.08 126	75.30 185	84.62 322	56.78 341	90.90 273	89.43 272

test_fmvsm_n_1920		83.60 169	82.89 180	85.74 127	85.22 283	77.74 99	84.12 179	90.48 163	59.87 338	86.45 189	91.12 195	75.65 181	85.89 310	82.28 98	90.87 275	93.58 132

MAR-MVS		80.24 232	78.74 248	84.73 147	86.87 250	78.18 92	85.75 146	87.81 221	65.67 283	77.84 329	78.50 387	73.79 205	90.53 215	61.59 317	90.87 275	85.49 328
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		85.12 125	84.53 148	86.88 100	84.01 305	72.76 145	83.91 186	85.18 265	80.44 86	88.75 128	85.49 311	80.08 136	91.92 172	82.02 101	90.85 277	95.97 39

EI-MVSNet-UG-set		85.04 127	84.44 150	86.85 101	83.87 309	72.52 154	83.82 188	85.15 266	80.27 90	88.75 128	85.45 313	79.95 138	91.90 173	81.92 104	90.80 278	96.13 34

XVG-OURS-SEG-HR		89.59 55	89.37 61	90.28 46	94.47 43	85.95 27	86.84 123	93.91 47	80.07 93	86.75 176	93.26 121	93.64 2	90.93 200	84.60 71	90.75 279	93.97 108

ET-MVSNet_ETH3D		75.28 285	72.77 308	82.81 209	83.03 328	68.11 210	77.09 312	76.51 337	60.67 331	77.60 334	80.52 369	38.04 404	91.15 193	70.78 229	90.68 280	89.17 277

EI-MVSNet		82.61 185	82.42 190	83.20 195	83.25 322	63.66 251	83.50 198	85.07 267	76.06 140	86.55 181	85.10 319	73.41 211	90.25 219	78.15 147	90.67 281	95.68 47

MVSTER		77.09 265	75.70 278	81.25 236	75.27 399	61.08 288	77.49 308	85.07 267	60.78 329	86.55 181	88.68 255	43.14 395	90.25 219	73.69 203	90.67 281	92.42 181

reproduce_monomvs		74.09 300	73.23 302	76.65 308	76.52 386	54.54 351	77.50 307	81.40 306	65.85 277	82.86 265	86.67 292	27.38 429	84.53 323	70.24 237	90.66 283	90.89 236

Patchmatch-RL test		74.48 296	73.68 296	76.89 304	84.83 288	66.54 225	72.29 363	69.16 389	57.70 350	86.76 175	86.33 297	45.79 375	82.59 338	69.63 242	90.65 284	81.54 382

CMPMVS	59.41 20	75.12 288	73.57 297	79.77 258	75.84 394	67.22 216	81.21 252	82.18 298	50.78 395	76.50 339	87.66 274	55.20 333	82.99 337	62.17 311	90.64 285	89.09 281
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WB-MVS		76.06 279	80.01 235	64.19 392	89.96 170	20.58 435	72.18 364	68.19 391	83.21 59	86.46 188	93.49 117	70.19 245	78.97 363	65.96 275	90.46 286	93.02 154

fmvsm_l_conf0.5_n		82.06 199	81.54 206	83.60 182	83.94 306	73.90 134	83.35 203	86.10 248	58.97 340	83.80 246	90.36 223	74.23 199	86.94 287	82.90 88	90.22 287	89.94 264

V42		83.47 173	83.37 170	83.75 177	83.16 325	63.33 256	81.31 249	90.23 178	69.51 234	90.91 86	90.81 210	74.16 200	92.29 164	80.06 119	90.22 287	95.62 49

fmvsm_s_conf0.5_n_4		84.38 142	84.27 155	84.74 146	87.25 235	70.84 178	83.55 196	88.45 209	68.64 245	86.29 190	91.31 189	74.97 189	88.42 264	87.87 16	90.07 289	94.95 68

PM-MVS		80.20 233	79.00 242	83.78 176	88.17 210	86.66 19	81.31 249	66.81 400	69.64 233	88.33 141	90.19 229	64.58 271	83.63 334	71.99 222	90.03 290	81.06 391

PLC	73.85 16	82.09 198	80.31 225	87.45 92	90.86 150	80.29 73	85.88 142	90.65 158	68.17 251	76.32 342	86.33 297	73.12 217	92.61 154	61.40 318	90.02 291	89.44 271
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n_a		81.46 209	80.87 218	83.25 193	83.73 311	73.21 143	83.00 214	85.59 259	58.22 346	82.96 262	90.09 234	72.30 227	86.65 293	81.97 103	89.95 292	89.88 265

ttmdpeth		71.72 321	70.67 327	74.86 323	73.08 413	55.88 340	77.41 310	69.27 387	55.86 362	78.66 323	93.77 110	38.01 405	75.39 377	60.12 325	89.87 293	93.31 141

UWE-MVS		66.43 365	65.56 371	69.05 365	84.15 303	40.98 415	73.06 361	64.71 407	54.84 368	76.18 345	79.62 378	29.21 424	80.50 354	38.54 419	89.75 294	85.66 325

CANet_DTU		77.81 258	77.05 264	80.09 256	81.37 342	59.90 303	83.26 205	88.29 214	69.16 237	67.83 398	83.72 335	60.93 292	89.47 243	69.22 247	89.70 295	90.88 237

diffmvs		80.40 226	80.48 224	80.17 255	79.02 370	60.04 300	77.54 305	90.28 177	66.65 272	82.40 270	87.33 282	73.50 208	87.35 280	77.98 149	89.62 296	93.13 149
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		70.05 339	69.26 342	72.41 344	58.62 435	55.59 344	76.61 322	65.58 403	53.44 375	89.28 120	93.32 120	22.91 435	71.44 390	74.08 195	89.52 297	90.21 260

PMMVS2		55.64 395	59.27 393	44.74 411	64.30 433	12.32 439	40.60 426	49.79 429	53.19 377	65.06 412	84.81 324	53.60 339	49.76 429	32.68 427	89.41 298	72.15 410

Fast-Effi-MVS+-dtu		82.54 188	81.41 208	85.90 123	85.60 275	76.53 115	83.07 211	89.62 194	73.02 191	79.11 319	83.51 337	80.74 129	90.24 221	68.76 254	89.29 299	90.94 234

thres200		72.34 316	71.55 322	74.70 326	83.48 313	51.60 374	75.02 342	73.71 357	70.14 230	78.56 325	80.57 368	46.20 367	88.20 269	46.99 398	89.29 299	84.32 341

jason		77.42 262	75.75 277	82.43 218	87.10 242	69.27 195	77.99 297	81.94 301	51.47 390	77.84 329	85.07 322	60.32 297	89.00 252	70.74 231	89.27 301	89.03 282
jason: jason.
MG-MVS		80.32 229	80.94 216	78.47 278	88.18 209	52.62 367	82.29 235	85.01 271	72.01 209	79.24 318	92.54 149	69.36 249	93.36 132	70.65 232	89.19 302	89.45 270

myMVS_eth3d28		65.83 370	65.85 366	65.78 385	83.42 316	35.71 425	67.29 395	68.01 392	67.58 261	69.80 388	77.72 393	32.29 416	74.30 381	37.49 421	89.06 303	87.32 307

BH-untuned		80.96 216	80.99 215	80.84 244	88.55 202	68.23 207	80.33 264	88.46 208	72.79 195	86.55 181	86.76 291	74.72 195	91.77 178	61.79 314	88.99 304	82.52 371

EIA-MVS		82.19 194	81.23 213	85.10 138	87.95 215	69.17 200	83.22 209	93.33 67	70.42 224	78.58 324	79.77 377	77.29 162	94.20 94	71.51 223	88.96 305	91.93 208

PVSNet_Blended_VisFu		81.55 208	80.49 223	84.70 149	91.58 127	73.24 142	84.21 176	91.67 129	62.86 302	80.94 295	87.16 285	67.27 259	92.87 149	69.82 241	88.94 306	87.99 297

MVSFormer		82.23 192	81.57 205	84.19 166	85.54 277	69.26 196	91.98 34	90.08 182	71.54 211	76.23 343	85.07 322	58.69 310	94.27 89	86.26 45	88.77 307	89.03 282

lupinMVS		76.37 277	74.46 290	82.09 220	85.54 277	69.26 196	76.79 316	80.77 311	50.68 397	76.23 343	82.82 347	58.69 310	88.94 253	69.85 240	88.77 307	88.07 293

RPSCF		88.00 79	86.93 98	91.22 31	90.08 164	89.30 5	89.68 73	91.11 145	79.26 104	89.68 108	94.81 59	82.44 97	87.74 275	76.54 166	88.74 309	96.61 27

test_fmvs3		75.72 283	75.20 284	77.27 298	75.01 402	69.47 193	78.93 284	84.88 274	46.67 404	87.08 169	87.84 270	50.44 354	71.62 388	77.42 158	88.53 310	90.72 241

RRT-MVS		82.97 181	83.44 167	81.57 232	85.06 285	58.04 324	87.20 114	90.37 168	77.88 124	88.59 132	93.70 113	63.17 282	93.05 142	76.49 167	88.47 311	93.62 129

PAPM_NR		83.23 176	83.19 174	83.33 191	90.90 148	65.98 231	88.19 101	90.78 155	78.13 121	80.87 297	87.92 269	73.49 210	92.42 157	70.07 238	88.40 312	91.60 219

testing222		66.93 359	65.30 372	71.81 347	83.38 317	45.83 400	72.06 365	67.50 393	64.12 296	69.68 389	76.37 405	27.34 430	83.00 336	38.88 416	88.38 313	86.62 315

xiu_mvs_v1_base_debu		80.84 217	80.14 231	82.93 205	88.31 206	71.73 166	79.53 273	87.17 229	65.43 284	79.59 311	82.73 349	76.94 169	90.14 227	73.22 209	88.33 314	86.90 312

xiu_mvs_v1_base		80.84 217	80.14 231	82.93 205	88.31 206	71.73 166	79.53 273	87.17 229	65.43 284	79.59 311	82.73 349	76.94 169	90.14 227	73.22 209	88.33 314	86.90 312

xiu_mvs_v1_base_debi		80.84 217	80.14 231	82.93 205	88.31 206	71.73 166	79.53 273	87.17 229	65.43 284	79.59 311	82.73 349	76.94 169	90.14 227	73.22 209	88.33 314	86.90 312

XXY-MVS		74.44 298	76.19 273	69.21 364	84.61 293	52.43 368	71.70 367	77.18 331	60.73 330	80.60 299	90.96 202	75.44 182	69.35 395	56.13 346	88.33 314	85.86 323

Fast-Effi-MVS+		81.04 215	80.57 220	82.46 217	87.50 230	63.22 258	78.37 294	89.63 193	68.01 252	81.87 279	82.08 355	82.31 102	92.65 153	67.10 265	88.30 318	91.51 223

MDA-MVSNet-bldmvs		77.47 261	76.90 267	79.16 268	79.03 369	64.59 241	66.58 399	75.67 342	73.15 189	88.86 124	88.99 251	66.94 260	81.23 348	64.71 290	88.22 319	91.64 218

PAPR		78.84 246	78.10 256	81.07 240	85.17 284	60.22 299	82.21 239	90.57 162	62.51 304	75.32 356	84.61 327	74.99 188	92.30 163	59.48 329	88.04 320	90.68 244

mvsmamba		80.30 230	78.87 243	84.58 152	88.12 212	67.55 215	92.35 29	84.88 274	63.15 300	85.33 208	90.91 204	50.71 351	95.20 62	66.36 272	87.98 321	90.99 232

BH-RMVSNet		80.53 222	80.22 229	81.49 234	87.19 238	66.21 229	77.79 301	86.23 246	74.21 167	83.69 248	88.50 258	73.25 216	90.75 208	63.18 304	87.90 322	87.52 304

Effi-MVS+		83.90 161	84.01 160	83.57 185	87.22 237	65.61 235	86.55 132	92.40 105	78.64 114	81.34 292	84.18 332	83.65 84	92.93 146	74.22 190	87.87 323	92.17 198

MVS_Test		82.47 189	83.22 172	80.22 254	82.62 330	57.75 328	82.54 228	91.96 120	71.16 218	82.89 263	92.52 150	77.41 160	90.50 216	80.04 120	87.84 324	92.40 184

QAPM		82.59 186	82.59 187	82.58 213	86.44 254	66.69 224	89.94 67	90.36 169	67.97 254	84.94 218	92.58 148	72.71 222	92.18 165	70.63 233	87.73 325	88.85 285

PVSNet_Blended		76.49 275	75.40 281	79.76 259	84.43 295	63.41 254	75.14 341	90.44 165	57.36 354	75.43 353	78.30 388	69.11 251	91.44 184	60.68 322	87.70 326	84.42 340

pmmvs5		70.73 331	70.07 335	72.72 338	77.03 382	52.73 365	74.14 348	75.65 343	50.36 399	72.17 374	85.37 316	55.42 332	80.67 351	52.86 371	87.59 327	84.77 334

IB-MVS	62.13 19	71.64 322	68.97 348	79.66 262	80.80 351	62.26 275	73.94 352	76.90 333	63.27 299	68.63 394	76.79 401	33.83 412	91.84 176	59.28 330	87.26 328	84.88 333
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		70.20 335	68.80 350	74.38 327	80.91 347	84.81 43	59.12 416	76.45 338	55.06 366	75.31 357	82.36 352	55.74 329	54.82 426	47.02 397	87.24 329	83.52 354

fmvsm_s_conf0.1_n		82.17 195	81.59 203	83.94 172	86.87 250	71.57 171	85.19 158	77.42 328	62.27 311	84.47 229	91.33 187	76.43 177	85.91 308	83.14 82	87.14 330	94.33 95

fmvsm_s_conf0.5_n		81.91 204	81.30 210	83.75 177	86.02 270	71.56 172	84.73 165	77.11 332	62.44 308	84.00 242	90.68 215	76.42 178	85.89 310	83.14 82	87.11 331	93.81 120

fmvsm_s_conf0.1_n_a		82.58 187	81.93 195	84.50 153	87.68 224	73.35 138	86.14 139	77.70 325	61.64 317	85.02 214	91.62 180	77.75 154	86.24 299	82.79 91	87.07 332	93.91 112

pmmvs4		74.92 291	72.98 306	80.73 246	84.95 286	71.71 169	76.23 328	77.59 326	52.83 380	77.73 333	86.38 295	56.35 326	84.97 319	57.72 339	87.05 333	85.51 327

test_fmvs2		73.57 305	72.80 307	75.90 316	72.74 416	68.84 203	77.07 313	84.32 282	45.14 410	82.89 263	84.22 331	48.37 359	70.36 392	73.40 207	87.03 334	88.52 288

MIMVSNet		71.09 328	71.59 319	69.57 362	87.23 236	50.07 384	78.91 285	71.83 373	60.20 336	71.26 377	91.76 177	55.08 335	76.09 373	41.06 412	87.02 335	82.54 370

testing91		69.94 342	68.99 347	72.80 337	83.81 310	45.89 399	71.57 369	73.64 359	68.24 250	70.77 383	77.82 390	34.37 411	84.44 325	53.64 364	87.00 336	88.07 293

fmvsm_s_conf0.5_n_a		82.21 193	81.51 207	84.32 161	86.56 252	73.35 138	85.46 151	77.30 329	61.81 313	84.51 226	90.88 207	77.36 161	86.21 301	82.72 92	86.97 337	93.38 137

HyFIR lowres test		75.12 288	72.66 310	82.50 216	91.44 135	65.19 238	72.47 362	87.31 226	46.79 403	80.29 305	84.30 330	52.70 342	92.10 169	51.88 379	86.73 338	90.22 256

test_vis3_rt		71.42 325	70.67 327	73.64 331	69.66 423	70.46 181	66.97 398	89.73 188	42.68 420	88.20 145	83.04 342	43.77 390	60.07 421	65.35 285	86.66 339	90.39 254

MSDG		80.06 237	79.99 236	80.25 253	83.91 308	68.04 212	77.51 306	89.19 199	77.65 127	81.94 277	83.45 339	76.37 179	86.31 298	63.31 303	86.59 340	86.41 316

Patchmatch-test		65.91 368	67.38 357	61.48 400	75.51 396	43.21 410	68.84 386	63.79 409	62.48 305	72.80 371	83.42 340	44.89 387	59.52 423	48.27 394	86.45 341	81.70 379

mvs_anonymous		78.13 254	78.76 247	76.23 314	79.24 367	50.31 383	78.69 289	84.82 276	61.60 318	83.09 261	92.82 139	73.89 204	87.01 283	68.33 261	86.41 342	91.37 224

IterMVS-SCA-FT		80.64 221	79.41 238	84.34 160	83.93 307	69.66 191	76.28 327	81.09 308	72.43 198	86.47 187	90.19 229	60.46 295	93.15 138	77.45 156	86.39 343	90.22 256

testing99		69.27 348	68.15 355	72.63 339	83.29 320	45.45 401	71.15 371	71.08 378	67.34 264	70.43 384	77.77 392	32.24 417	84.35 327	53.72 363	86.33 344	88.10 292

E-PMN		61.59 383	61.62 386	61.49 399	66.81 427	55.40 345	53.77 423	60.34 419	66.80 271	58.90 424	65.50 423	40.48 400	66.12 412	55.72 349	86.25 345	62.95 421

EMVS		61.10 386	60.81 388	61.99 397	65.96 430	55.86 341	53.10 424	58.97 422	67.06 268	56.89 428	63.33 424	40.98 398	67.03 408	54.79 358	86.18 346	63.08 420

ETVMVS		64.67 374	63.34 380	68.64 369	83.44 315	41.89 412	69.56 385	61.70 416	61.33 322	68.74 392	75.76 407	28.76 425	79.35 359	34.65 424	86.16 347	84.67 336

our_test_3		71.85 319	71.59 319	72.62 340	80.71 352	53.78 357	69.72 383	71.71 376	58.80 342	78.03 326	80.51 370	56.61 324	78.84 364	62.20 309	86.04 348	85.23 329

EU-MVSNet		75.12 288	74.43 291	77.18 299	83.11 327	59.48 307	85.71 148	82.43 297	39.76 424	85.64 202	88.76 253	44.71 388	87.88 274	73.86 199	85.88 349	84.16 346

GA-MVS		75.83 281	74.61 287	79.48 265	81.87 334	59.25 309	73.42 357	82.88 292	68.68 243	79.75 310	81.80 358	50.62 352	89.46 244	66.85 267	85.64 350	89.72 267

MVS		73.21 309	72.59 311	75.06 322	80.97 346	60.81 295	81.64 246	85.92 254	46.03 408	71.68 376	77.54 394	68.47 254	89.77 239	55.70 350	85.39 351	74.60 408

PatchT		70.52 333	72.76 309	63.79 394	79.38 365	33.53 428	77.63 303	65.37 405	73.61 175	71.77 375	92.79 142	44.38 389	75.65 376	64.53 294	85.37 352	82.18 375

TR-MVS		76.77 270	75.79 276	79.72 260	86.10 269	65.79 233	77.14 311	83.02 291	65.20 290	81.40 290	82.10 353	66.30 263	90.73 210	55.57 351	85.27 353	82.65 366

BH-w/o		76.57 273	76.07 275	78.10 285	86.88 249	65.92 232	77.63 303	86.33 244	65.69 282	80.89 296	79.95 374	68.97 253	90.74 209	53.01 370	85.25 354	77.62 402

Syy-MVS		69.40 347	70.03 337	67.49 377	81.72 336	38.94 419	71.00 372	61.99 411	61.38 320	70.81 381	72.36 415	61.37 291	79.30 360	64.50 295	85.18 355	84.22 343

myMVS_eth3d		64.66 375	63.89 376	66.97 380	81.72 336	37.39 422	71.00 372	61.99 411	61.38 320	70.81 381	72.36 415	20.96 436	79.30 360	49.59 385	85.18 355	84.22 343

IterMVS		76.91 267	76.34 272	78.64 274	80.91 347	64.03 248	76.30 326	79.03 319	64.88 292	83.11 259	89.16 248	59.90 301	84.46 324	68.61 257	85.15 357	87.42 305
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WB-MVSnew		68.72 353	69.01 346	67.85 374	83.22 324	43.98 407	74.93 343	65.98 402	55.09 365	73.83 365	79.11 380	65.63 268	71.89 387	38.21 420	85.04 358	87.69 303

OpenMVS_ROB	70.19 17	77.77 259	77.46 259	78.71 273	84.39 298	61.15 287	81.18 253	82.52 295	62.45 307	83.34 256	87.37 280	66.20 264	88.66 261	64.69 291	85.02 359	86.32 317

KD-MVS_2432*1600		66.87 361	65.81 368	70.04 355	67.50 425	47.49 392	62.56 408	79.16 317	61.21 325	77.98 327	80.61 366	25.29 433	82.48 339	53.02 368	84.92 360	80.16 395

miper_refine_blended		66.87 361	65.81 368	70.04 355	67.50 425	47.49 392	62.56 408	79.16 317	61.21 325	77.98 327	80.61 366	25.29 433	82.48 339	53.02 368	84.92 360	80.16 395

test_fmvs1_n		70.94 329	70.41 333	72.53 342	73.92 404	66.93 222	75.99 332	84.21 284	43.31 417	79.40 314	79.39 379	43.47 391	68.55 400	69.05 250	84.91 362	82.10 376

test-LLR		67.21 358	66.74 362	68.63 370	76.45 389	55.21 347	67.89 389	67.14 397	62.43 309	65.08 410	72.39 413	43.41 392	69.37 393	61.00 319	84.89 363	81.31 384

test-mter		65.00 373	63.79 377	68.63 370	76.45 389	55.21 347	67.89 389	67.14 397	50.98 394	65.08 410	72.39 413	28.27 427	69.37 393	61.00 319	84.89 363	81.31 384

PS-MVSNAJ		77.04 266	76.53 270	78.56 275	87.09 243	61.40 283	75.26 340	87.13 232	61.25 323	74.38 363	77.22 399	76.94 169	90.94 199	64.63 292	84.83 365	83.35 358

xiu_mvs_v2_base		77.19 264	76.75 268	78.52 276	87.01 245	61.30 285	75.55 338	87.12 235	61.24 324	74.45 361	78.79 385	77.20 163	90.93 200	64.62 293	84.80 366	83.32 359

pmmvs3		62.47 379	60.02 392	69.80 359	71.58 419	64.00 249	70.52 377	58.44 423	39.77 423	66.05 403	75.84 406	27.10 432	72.28 384	46.15 402	84.77 367	73.11 409

MDTV_nov1_ep13			68.29 354		78.03 373	43.87 408	74.12 349	72.22 369	52.17 384	67.02 401	85.54 309	45.36 380	80.85 350	55.73 348	84.42 368

test_fmvs1		69.57 345	69.05 345	71.14 352	69.15 424	65.77 234	73.98 351	83.32 288	42.83 419	77.77 332	78.27 389	43.39 394	68.50 401	68.39 260	84.38 369	79.15 399

1112_ss		74.82 293	73.74 295	78.04 287	89.57 172	60.04 300	76.49 324	87.09 236	54.31 371	73.66 367	79.80 375	60.25 298	86.76 292	58.37 333	84.15 370	87.32 307

testing11		67.38 357	65.93 365	71.73 348	83.37 318	46.60 396	70.95 374	69.40 386	62.47 306	66.14 402	76.66 402	31.22 419	84.10 329	49.10 388	84.10 371	84.49 337

PatchMatch-RL		74.48 296	73.22 303	78.27 283	87.70 223	85.26 38	75.92 333	70.09 382	64.34 295	76.09 346	81.25 363	65.87 267	78.07 367	53.86 362	83.82 372	71.48 411

UBG		64.34 377	63.35 379	67.30 378	83.50 312	40.53 416	67.46 393	65.02 406	54.77 369	67.54 400	74.47 411	32.99 415	78.50 366	40.82 413	83.58 373	82.88 365

MDA-MVSNet_test_wron		70.05 339	70.44 331	68.88 367	73.84 405	53.47 359	58.93 418	67.28 395	58.43 343	87.09 168	85.40 314	59.80 303	67.25 407	59.66 328	83.54 374	85.92 322

YYNet1		70.06 338	70.44 331	68.90 366	73.76 406	53.42 361	58.99 417	67.20 396	58.42 344	87.10 167	85.39 315	59.82 302	67.32 406	59.79 327	83.50 375	85.96 320

Test_1112_low_res		73.90 302	73.08 304	76.35 310	90.35 159	55.95 338	73.40 358	86.17 247	50.70 396	73.14 368	85.94 304	58.31 312	85.90 309	56.51 343	83.22 376	87.20 309

PVSNet	58.17 21	66.41 366	65.63 370	68.75 368	81.96 333	49.88 385	62.19 410	72.51 367	51.03 393	68.04 396	75.34 409	50.84 350	74.77 378	45.82 404	82.96 377	81.60 381

gg-mvs-nofinetune		68.96 351	69.11 344	68.52 373	76.12 392	45.32 402	83.59 195	55.88 425	86.68 29	64.62 414	97.01 9	30.36 422	83.97 332	44.78 406	82.94 378	76.26 404

CR-MVSNet		74.00 301	73.04 305	76.85 305	79.58 361	62.64 266	82.58 225	76.90 333	50.50 398	75.72 350	92.38 153	48.07 361	84.07 330	68.72 256	82.91 379	83.85 350

RPMNet		78.88 245	78.28 254	80.68 248	79.58 361	62.64 266	82.58 225	94.16 32	74.80 161	75.72 350	92.59 146	48.69 358	95.56 42	73.48 205	82.91 379	83.85 350

test_vis1_n		70.29 334	69.99 338	71.20 351	75.97 393	66.50 226	76.69 319	80.81 310	44.22 413	75.43 353	77.23 398	50.00 355	68.59 399	66.71 270	82.85 381	78.52 401

test0.0.03 1		64.66 375	64.36 374	65.57 387	75.03 401	46.89 395	64.69 403	61.58 417	62.43 309	71.18 379	77.54 394	43.41 392	68.47 402	40.75 414	82.65 382	81.35 383

HY-MVS	64.64 18	73.03 310	72.47 314	74.71 325	83.36 319	54.19 354	82.14 242	81.96 300	56.76 360	69.57 390	86.21 301	60.03 299	84.83 321	49.58 386	82.65 382	85.11 331

SCA		73.32 306	72.57 312	75.58 319	81.62 338	55.86 341	78.89 286	71.37 377	61.73 314	74.93 359	83.42 340	60.46 295	87.01 283	58.11 337	82.63 384	83.88 347

test_f		64.31 378	65.85 366	59.67 404	66.54 428	62.24 277	57.76 420	70.96 379	40.13 422	84.36 231	82.09 354	46.93 363	51.67 428	61.99 312	81.89 385	65.12 419

CHOSEN 1792x2688		72.45 314	70.56 329	78.13 284	90.02 169	63.08 259	68.72 387	83.16 289	42.99 418	75.92 348	85.46 312	57.22 321	85.18 318	49.87 384	81.67 386	86.14 319

WTY-MVS		67.91 356	68.35 353	66.58 382	80.82 350	48.12 389	65.96 400	72.60 365	53.67 374	71.20 378	81.68 360	58.97 308	69.06 397	48.57 391	81.67 386	82.55 369

TESTMET0.1,1		61.29 384	60.32 390	64.19 392	72.06 417	51.30 376	67.89 389	62.09 410	45.27 409	60.65 420	69.01 419	27.93 428	64.74 416	56.31 344	81.65 388	76.53 403

dmvs_re		66.81 363	66.98 359	66.28 383	76.87 383	58.68 320	71.66 368	72.24 368	60.29 334	69.52 391	73.53 412	52.38 343	64.40 417	44.90 405	81.44 389	75.76 405

PAPM		71.77 320	70.06 336	76.92 302	86.39 255	53.97 355	76.62 321	86.62 242	53.44 375	63.97 415	84.73 326	57.79 318	92.34 161	39.65 415	81.33 390	84.45 339

DSMNet-mixed		60.98 387	61.61 387	59.09 406	72.88 414	45.05 404	74.70 345	46.61 432	26.20 430	65.34 408	90.32 225	55.46 331	63.12 419	41.72 411	81.30 391	69.09 415

sss		66.92 360	67.26 358	65.90 384	77.23 379	51.10 380	64.79 402	71.72 375	52.12 387	70.13 386	80.18 372	57.96 315	65.36 415	50.21 381	81.01 392	81.25 386

UWE-MVS-28		58.44 392	57.71 394	60.65 402	73.58 408	31.23 429	69.68 384	48.80 430	53.12 379	61.79 417	78.83 384	30.98 420	68.40 403	21.58 431	80.99 393	82.33 374

tpm		67.95 355	68.08 356	67.55 376	78.74 372	43.53 409	75.60 335	67.10 399	54.92 367	72.23 373	88.10 263	42.87 396	75.97 374	52.21 373	80.95 394	83.15 362

MonoMVSNet		76.66 271	77.26 263	74.86 323	79.86 359	54.34 353	86.26 137	86.08 249	71.08 219	85.59 203	88.68 255	53.95 337	85.93 306	63.86 297	80.02 395	84.32 341

tpm2		68.45 354	66.83 361	73.30 333	78.93 371	48.50 387	79.76 270	71.76 374	47.50 402	69.92 387	83.60 336	42.07 397	88.40 265	48.44 393	79.51 396	83.01 364

FPMVS		72.29 317	72.00 316	73.14 334	88.63 199	85.00 40	74.65 346	67.39 394	71.94 210	77.80 331	87.66 274	50.48 353	75.83 375	49.95 382	79.51 396	58.58 425

UnsupCasMVSNet_bld		69.21 349	69.68 340	67.82 375	79.42 364	51.15 378	67.82 392	75.79 340	54.15 372	77.47 336	85.36 317	59.26 306	70.64 391	48.46 392	79.35 398	81.66 380

CostFormer		69.98 341	68.68 351	73.87 328	77.14 380	50.72 381	79.26 279	74.51 349	51.94 388	70.97 380	84.75 325	45.16 384	87.49 278	55.16 356	79.23 399	83.40 357

1314		73.22 308	72.56 313	75.20 320	80.41 356	57.84 326	81.64 246	85.36 261	51.68 389	73.10 369	76.65 403	61.45 290	85.19 317	63.54 300	79.21 400	82.59 367

test_vis1_n_1920		71.30 327	71.58 321	70.47 353	77.58 377	59.99 302	74.25 347	84.22 283	51.06 392	74.85 360	79.10 381	55.10 334	68.83 398	68.86 253	79.20 401	82.58 368

baseline1		73.26 307	73.54 298	72.43 343	84.92 287	47.79 391	79.89 269	74.00 352	65.93 275	78.81 322	86.28 300	56.36 325	81.63 346	56.63 342	79.04 402	87.87 301

PMMVS		61.65 382	60.38 389	65.47 388	65.40 432	69.26 196	63.97 406	61.73 415	36.80 429	60.11 421	68.43 420	59.42 304	66.35 411	48.97 389	78.57 403	60.81 422

baseline2		69.77 343	66.89 360	78.41 279	79.51 363	58.09 322	76.23 328	69.57 385	57.50 353	64.82 413	77.45 396	46.02 369	88.44 263	53.08 367	77.83 404	88.70 286

test_vis1_rt		65.64 371	64.09 375	70.31 354	66.09 429	70.20 185	61.16 411	81.60 304	38.65 425	72.87 370	69.66 418	52.84 340	60.04 422	56.16 345	77.77 405	80.68 393

MS-PatchMatch		70.93 330	70.22 334	73.06 335	81.85 335	62.50 269	73.82 354	77.90 323	52.44 383	75.92 348	81.27 362	55.67 330	81.75 344	55.37 353	77.70 406	74.94 407

UnsupCasMVSNet_eth		71.63 323	72.30 315	69.62 361	76.47 388	52.70 366	70.03 381	80.97 309	59.18 339	79.36 315	88.21 262	60.50 294	69.12 396	58.33 335	77.62 407	87.04 310

CVMVSNet		72.62 313	71.41 323	76.28 312	83.25 322	60.34 298	83.50 198	79.02 320	37.77 428	76.33 341	85.10 319	49.60 357	87.41 279	70.54 234	77.54 408	81.08 389

test_cas_vis1_n_1920		69.20 350	69.12 343	69.43 363	73.68 407	62.82 263	70.38 379	77.21 330	46.18 407	80.46 304	78.95 383	52.03 344	65.53 414	65.77 281	77.45 409	79.95 397

GG-mvs-BLEND				67.16 379	73.36 409	46.54 398	84.15 178	55.04 426		58.64 425	61.95 426	29.93 423	83.87 333	38.71 418	76.92 410	71.07 412

CHOSEN 280x420		59.08 390	56.52 396	66.76 381	76.51 387	64.39 245	49.62 425	59.00 421	43.86 414	55.66 429	68.41 421	35.55 410	68.21 405	43.25 408	76.78 411	67.69 417

tpmvs		70.16 336	69.56 341	71.96 346	74.71 403	48.13 388	79.63 271	75.45 345	65.02 291	70.26 385	81.88 357	45.34 381	85.68 313	58.34 334	75.39 412	82.08 377

MVP-Stereo		75.81 282	73.51 299	82.71 210	89.35 178	73.62 135	80.06 265	85.20 264	60.30 333	73.96 364	87.94 266	57.89 317	89.45 245	52.02 374	74.87 413	85.06 332
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
new_pmnet		55.69 394	57.66 395	49.76 410	75.47 397	30.59 430	59.56 413	51.45 428	43.62 416	62.49 416	75.48 408	40.96 399	49.15 430	37.39 422	72.52 414	69.55 414

mvsany_test3		65.48 372	62.97 381	73.03 336	69.99 422	76.17 121	64.83 401	43.71 433	43.68 415	80.25 308	87.05 289	52.83 341	63.09 420	51.92 378	72.44 415	79.84 398

PatchmatchNet		69.71 344	68.83 349	72.33 345	77.66 376	53.60 358	79.29 278	69.99 383	57.66 351	72.53 372	82.93 345	46.45 366	80.08 357	60.91 321	72.09 416	83.31 360
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		61.16 385	62.92 382	55.87 407	79.09 368	35.34 426	71.83 366	57.98 424	46.56 405	59.05 423	91.14 194	49.95 356	76.43 372	38.74 417	71.92 417	55.84 426

tpmrst		66.28 367	66.69 363	65.05 390	72.82 415	39.33 418	78.20 295	70.69 381	53.16 378	67.88 397	80.36 371	48.18 360	74.75 379	58.13 336	70.79 418	81.08 389

tpm cat1		66.76 364	65.21 373	71.42 349	77.09 381	50.62 382	78.01 296	73.68 358	44.89 411	68.64 393	79.00 382	45.51 378	82.42 341	49.91 383	70.15 419	81.23 388

ADS-MVSNet2		65.87 369	63.64 378	72.55 341	73.16 411	56.92 334	67.10 396	74.81 346	49.74 400	66.04 404	82.97 343	46.71 364	77.26 370	42.29 409	69.96 420	83.46 355

ADS-MVSNet		61.90 381	62.19 385	61.03 401	73.16 411	36.42 424	67.10 396	61.75 414	49.74 400	66.04 404	82.97 343	46.71 364	63.21 418	42.29 409	69.96 420	83.46 355

JIA-IIPM		69.41 346	66.64 364	77.70 293	73.19 410	71.24 174	75.67 334	65.56 404	70.42 224	65.18 409	92.97 133	33.64 414	83.06 335	53.52 366	69.61 422	78.79 400

dmvs_testset		60.59 389	62.54 384	54.72 409	77.26 378	27.74 432	74.05 350	61.00 418	60.48 332	65.62 407	67.03 422	55.93 328	68.23 404	32.07 428	69.46 423	68.17 416

EPMVS		62.47 379	62.63 383	62.01 396	70.63 421	38.74 420	74.76 344	52.86 427	53.91 373	67.71 399	80.01 373	39.40 401	66.60 410	55.54 352	68.81 424	80.68 393

MVE	40.22 23	51.82 396	50.47 399	55.87 407	62.66 434	51.91 371	31.61 428	39.28 435	40.65 421	50.76 430	74.98 410	56.24 327	44.67 431	33.94 426	64.11 425	71.04 413
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		60.70 388	60.29 391	61.92 398	72.04 418	38.67 421	70.83 375	64.08 408	51.28 391	60.75 419	77.28 397	36.59 409	71.58 389	47.41 396	62.34 426	75.52 406

mvsany_test1		58.48 391	56.47 397	64.50 391	65.90 431	68.21 209	56.95 421	42.11 434	38.30 426	65.69 406	77.19 400	56.96 322	59.35 424	46.16 401	58.96 427	65.93 418

PVSNet_0	51.08 22	56.10 393	54.97 398	59.48 405	75.12 400	53.28 362	55.16 422	61.89 413	44.30 412	59.16 422	62.48 425	54.22 336	65.91 413	35.40 423	47.01 428	59.25 424

tmp_tt		20.25 401	24.50 404	7.49 416	4.47 439	8.70 440	34.17 427	25.16 437	1.00 434	32.43 433	18.49 431	39.37 402	9.21 435	21.64 430	43.75 429	4.57 431

test_method		30.46 399	29.60 402	33.06 413	17.99 438	3.84 441	13.62 429	73.92 353	2.79 432	18.29 434	53.41 427	28.53 426	43.25 432	22.56 429	35.27 430	52.11 427

DeepMVS_CX				24.13 415	32.95 437	29.49 431		21.63 438	12.07 431	37.95 432	45.07 429	30.84 421	19.21 434	17.94 433	33.06 431	23.69 430

dongtai		41.90 397	42.65 400	39.67 412	70.86 420	21.11 434	61.01 412	21.42 439	57.36 354	57.97 427	50.06 428	16.40 438	58.73 425	21.03 432	27.69 432	39.17 428

kuosan		30.83 398	32.17 401	26.83 414	53.36 436	19.02 437	57.90 419	20.44 440	38.29 427	38.01 431	37.82 430	15.18 439	33.45 433	7.74 434	20.76 433	28.03 429

testmvs		5.91 405	7.65 408	0.72 418	1.20 440	0.37 443	59.14 415	0.67 442	0.49 436	1.11 436	2.76 435	0.94 441	0.24 437	1.02 436	1.47 434	1.55 433

test123		6.27 404	8.08 407	0.84 417	1.11 441	0.57 442	62.90 407	0.82 441	0.54 435	1.07 437	2.75 436	1.26 440	0.30 436	1.04 435	1.26 435	1.66 432

mmdepth		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

monomultidepth		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

test_blank		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

uanet_test		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

DCPMVS		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

cdsmvs_eth3d_5k		20.81 400	27.75 403	0.00 419	0.00 442	0.00 444	0.00 430	85.44 260	0.00 437	0.00 438	82.82 347	81.46 120	0.00 438	0.00 437	0.00 436	0.00 434

pcd_1.5k_mvsjas		6.41 403	8.55 406	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	76.94 169	0.00 438	0.00 437	0.00 436	0.00 434

sosnet-low-res		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

sosnet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

uncertanet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

Regformer		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

ab-mvs-re		6.65 402	8.87 405	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	79.80 375	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

uanet		0.00 406	0.00 409	0.00 419	0.00 442	0.00 444	0.00 430	0.00 443	0.00 437	0.00 438	0.00 437	0.00 442	0.00 438	0.00 437	0.00 436	0.00 434

WAC-MVS						37.39 422								52.61 372

FOURS1					96.08 12	87.41 14	96.19 2	95.83 5	92.95 3	96.57 3

test_one_0601					93.85 62	73.27 141		94.11 38	86.57 30	93.47 41	94.64 64	88.42 28

eth-test2					0.00 442

eth-test					0.00 442

test_241102_ONE					94.18 50	72.65 146		93.69 56	83.62 54	94.11 26	93.78 108	90.28 14	95.50 49

save fliter					93.75 63	77.44 103	86.31 135	89.72 189	70.80 221

test0726					94.16 53	72.56 152	90.63 49	93.90 48	83.61 55	93.75 34	94.49 69	89.76 18

GSMVS																83.88 347

test_part2					93.86 61	77.77 98				92.84 51

sam_mvs1												46.11 368				83.88 347

sam_mvs												45.92 373

MTGPA								91.81 127

test_post1							78.85 288				3.13 433	45.19 383	80.13 356	58.11 337

test_post											3.10 434	45.43 379	77.22 371

patchmatchnet-post											81.71 359	45.93 372	87.01 283

MTMP							90.66 48	33.14 436

gm-plane-assit					75.42 398	44.97 405			52.17 384		72.36 415		87.90 273	54.10 361

TEST9					92.34 98	79.70 78	83.94 183	90.32 171	65.41 287	84.49 227	90.97 200	82.03 111	93.63 115

test_8					92.09 107	78.87 85	83.82 188	90.31 173	65.79 278	84.36 231	90.96 202	81.93 113	93.44 128

agg_prior					91.58 127	77.69 100		90.30 174		84.32 233			93.18 136

test_prior4						78.97 84	84.59 169

test_prior				86.32 110	90.59 155	71.99 164		92.85 93					94.17 97			92.80 162

旧先验2							81.73 244		56.88 359	86.54 186			84.90 320	72.81 216

新几何2							81.72 245

无先验							82.81 220	85.62 258	58.09 347				91.41 187	67.95 264		84.48 338

原ACMM2							82.26 238

testdata2													86.43 297	63.52 301

segment_acmp												81.94 112

testdata1							79.62 272		73.95 170

plane_prior7					93.45 68	77.31 106

plane_prior6					92.61 90	76.54 113						74.84 191

plane_prior4											92.95 134

plane_prior3						76.85 111			77.79 126	86.55 181

plane_prior2							89.45 82		79.44 101

plane_prior1					92.83 88

n2								0.00 443

nn								0.00 443

door-mid								74.45 350

test11								91.46 133

door								72.57 366

HQP5-MVS						70.66 179

HQP-NCC					91.19 139		84.77 162		73.30 184	80.55 301

ACMP_Plane					91.19 139		84.77 162		73.30 184	80.55 301

BP-MVS														77.30 159

HQP4-MVS										80.56 300			94.61 79			93.56 134

HQP2-MVS												72.10 229

NP-MVS					91.95 112	74.55 130					90.17 232

MDTV_nov1_ep13_2view						27.60 433	70.76 376		46.47 406	61.27 418		45.20 382		49.18 387		83.75 352

Test By Simon												79.09 142

High-resolution multi-view benchmark