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 61	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	62.39 268	89.54 79	93.31 70	90.21 12	95.57 11	95.66 33	81.42 121	95.90 17	80.94 107	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 86	98.76 4	94.87 70
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	78.35 137	98.76 4	95.61 50

PS-CasMVS		90.06 43	91.92 15	84.47 154	96.56 6	58.83 315	89.04 88	92.74 97	91.40 6	96.12 5	96.06 26	87.23 48	95.57 41	79.42 127	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 58	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 58	98.73 7	95.23 61

PEN-MVS		90.03 45	91.88 18	84.48 153	96.57 5	58.88 312	88.95 89	93.19 75	91.62 5	96.01 7	96.16 24	87.02 50	95.60 40	78.69 133	98.72 9	98.97 3

CP-MVSNet		89.27 62	90.91 44	84.37 155	96.34 8	58.61 318	88.66 97	92.06 116	90.78 7	95.67 8	95.17 47	81.80 117	95.54 44	79.00 131	98.69 10	98.95 4

TranMVSNet+NR-MVSNet		87.86 81	88.76 74	85.18 137	94.02 58	64.13 244	84.38 174	91.29 140	84.88 44	92.06 65	93.84 105	86.45 58	93.73 111	73.22 206	98.66 11	97.69 9

NR-MVSNet		86.00 108	86.22 108	85.34 135	93.24 76	64.56 240	82.21 236	90.46 163	80.99 82	88.42 137	91.97 166	77.56 157	93.85 107	72.46 216	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	88.53 133	97.64 3	83.45 86	94.55 83	86.02 53	98.60 13	96.67 25

FC-MVSNet-test		85.93 110	87.05 95	82.58 210	92.25 101	56.44 334	85.75 146	93.09 81	77.33 131	91.94 68	94.65 61	74.78 191	93.41 130	75.11 182	98.58 14	97.88 7

DTE-MVSNet		89.98 47	91.91 17	84.21 163	96.51 7	57.84 323	88.93 90	92.84 94	91.92 4	96.16 4	96.23 21	86.95 51	95.99 12	79.05 130	98.57 15	98.80 6

UniMVSNet (Re)		86.87 91	86.98 97	86.55 106	93.11 79	68.48 203	83.80 189	92.87 92	80.37 87	89.61 113	91.81 174	77.72 155	94.18 95	75.00 183	98.53 16	96.99 22

Baseline_NR-MVSNet		84.00 155	85.90 115	78.29 279	91.47 134	53.44 357	82.29 232	87.00 237	79.06 107	89.55 115	95.72 32	77.20 162	86.14 301	72.30 217	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 49	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 63	98.45 19	92.41 181
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 31	98.39 21	92.55 174

UniMVSNet_NR-MVSNet		86.84 93	87.06 94	86.17 118	92.86 86	67.02 217	82.55 224	91.56 130	83.08 62	90.92 84	91.82 173	78.25 149	93.99 102	74.16 188	98.35 22	97.49 13

DU-MVS		86.80 94	86.99 96	86.21 116	93.24 76	67.02 217	83.16 207	92.21 111	81.73 74	90.92 84	91.97 166	77.20 162	93.99 102	74.16 188	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	92.30 188

ACMH	76.49 14	89.34 59	91.14 35	83.96 168	92.50 94	70.36 181	89.55 77	93.84 52	81.89 73	94.70 17	95.44 40	90.69 8	88.31 266	83.33 78	98.30 25	93.20 145
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	92.08 199

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	79.95 118	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	91.76 210

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	91.76 210

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 28	98.24 30	94.56 80
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 26	98.21 32	92.98 156

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	88.52 134	94.37 78	86.74 53	95.41 53	86.32 43	98.21 32	93.19 146
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 21	98.20 34	94.39 91
Skip Steuart: Steuart Systems R&D Blog.
MSC_two_6792asdad				88.81 71	91.55 129	77.99 94		91.01 148					96.05 9	87.45 24	98.17 35	92.40 183

No_MVS				88.81 71	91.55 129	77.99 94		91.01 148					96.05 9	87.45 24	98.17 35	92.40 183

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 32	98.11 38	93.12 150

WR-MVS		83.56 167	84.40 151	81.06 238	93.43 70	54.88 347	78.67 287	85.02 267	81.24 79	90.74 90	91.56 181	72.85 217	91.08 195	68.00 259	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 90	98.04 39	93.64 127

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 72	98.03 41	93.26 143
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	82.60 209	91.86 116	57.31 327	85.10 160	93.05 83	75.83 147	91.02 83	93.97 96	73.57 205	92.91 148	73.97 194	98.02 42	97.58 12

Anonymous20231211		88.40 71	89.62 59	84.73 146	90.46 157	65.27 233	88.86 91	93.02 87	87.15 28	93.05 46	97.10 8	82.28 106	92.02 170	76.70 161	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 42	97.99 43	93.96 108

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 22	97.98 45	92.98 156
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	91.01 148	83.61 55	93.75 34	94.65 61	89.76 18	95.78 32	86.42 40	97.97 46	90.55 247
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 40	97.97 46	92.02 202

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	94.12 103

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 50	97.92 49	92.29 189

IU-MVS					94.18 50	72.64 148		90.82 153	56.98 355	89.67 109				85.78 55	97.92 49	93.28 141

CLD-MVS		83.18 174	82.64 182	84.79 144	89.05 184	67.82 211	77.93 295	92.52 103	68.33 247	85.07 211	81.54 358	82.06 110	92.96 144	69.35 241	97.91 51	93.57 132
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	66.87 220	91.21 43	88.64 205	86.30 33	89.60 114	92.59 146	69.22 247	94.91 71	73.89 195	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	93.27 142

test_241102_TWO								93.71 55	83.77 52	93.49 39	94.27 79	89.27 23	95.84 24	86.03 50	97.82 54	92.04 201

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 69	97.81 55	91.70 214
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	72.94 216	94.85 72	85.07 61	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	93.88 112
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 17	97.76 57	93.99 106

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 17	97.74 59	92.85 160

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 22	97.71 60	93.83 115

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 35	97.69 61	93.93 109

UniMVSNet_ETH3D		89.12 65	90.72 47	84.31 161	97.00 2	64.33 243	89.67 74	88.38 208	88.84 17	94.29 22	97.57 4	90.48 13	91.26 189	72.57 215	97.65 62	97.34 14

v7n		90.13 40	90.96 42	87.65 91	91.95 112	71.06 174	89.99 64	93.05 83	86.53 31	94.29 22	96.27 20	82.69 93	94.08 100	86.25 46	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 28	97.62 64	94.20 96

X-MVStestdata		85.04 127	82.70 180	92.08 9	95.64 24	86.25 22	92.64 19	93.33 67	85.07 41	89.99 100	16.05 429	86.57 55	95.80 28	87.35 28	97.62 64	94.20 96

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 33	97.60 66	92.73 163

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 33	97.60 66	92.73 163

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 39	97.60 66	94.18 99

Anonymous20240521		80.18 231	81.25 208	76.95 298	83.15 323	60.84 291	82.46 227	85.99 250	68.76 242	86.78 173	93.73 112	59.13 304	77.44 366	73.71 199	97.55 69	92.56 173

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 77	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 66	97.55 69	94.10 104
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 57	97.51 73	94.30 95

MIMVSNet1		83.63 164	84.59 143	80.74 242	94.06 57	62.77 261	82.72 218	84.53 276	77.57 129	90.34 93	95.92 28	76.88 174	85.83 309	61.88 310	97.42 74	93.62 128

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 37	97.34 76	92.19 195

nrg030		87.85 82	88.49 75	85.91 122	90.07 166	69.73 187	87.86 106	94.20 30	74.04 168	92.70 56	94.66 60	85.88 66	91.50 181	79.72 121	97.32 77	96.50 29

pmmvs6		86.52 99	88.06 79	81.90 220	92.22 103	62.28 271	84.66 167	89.15 199	83.54 57	89.85 104	97.32 5	88.08 38	86.80 287	70.43 232	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 47	97.24 79	91.36 222
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	87.50 160	92.38 153	81.42 121	93.28 133	83.07 82	97.24 79	91.67 215

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 96	97.18 81	90.45 249
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
wuyk23d		75.13 284	79.30 237	62.63 392	75.56 392	75.18 127	80.89 254	73.10 360	75.06 160	94.76 16	95.32 41	87.73 43	52.85 424	34.16 422	97.11 82	59.85 420

PMVS	80.48 6	90.08 41	90.66 48	88.34 81	96.71 3	92.97 2	90.31 59	89.57 194	88.51 21	90.11 96	95.12 49	90.98 6	88.92 254	77.55 151	97.07 83	83.13 360
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	88.64 130	91.22 189	84.24 78	93.37 131	77.97 147	97.03 84	95.52 51

test_prior2							83.37 199		75.43 155	84.58 222	91.57 180	81.92 115		79.54 125	96.97 85

EPP-MVSNet		85.47 116	85.04 133	86.77 103	91.52 132	69.37 191	91.63 39	87.98 217	81.51 77	87.05 170	91.83 172	66.18 262	95.29 56	70.75 227	96.89 86	95.64 48

VDDNet		84.35 142	85.39 128	81.25 233	95.13 32	59.32 305	85.42 153	81.11 304	86.41 32	87.41 161	96.21 22	73.61 204	90.61 214	66.33 270	96.85 87	93.81 119

VPNet		80.25 228	81.68 195	75.94 312	92.46 95	47.98 387	76.70 315	81.67 300	73.45 178	84.87 218	92.82 139	74.66 194	86.51 292	61.66 313	96.85 87	93.33 138

SixPastTwentyTwo		87.20 89	87.45 87	86.45 108	92.52 93	69.19 196	87.84 107	88.05 215	81.66 75	94.64 18	96.53 17	65.94 263	94.75 74	83.02 84	96.83 89	95.41 53

VPA-MVSNet		83.47 170	84.73 138	79.69 258	90.29 160	57.52 326	81.30 248	88.69 204	76.29 138	87.58 159	94.44 71	80.60 131	87.20 279	66.60 268	96.82 90	94.34 93

Gipuma		84.44 140	86.33 106	78.78 268	84.20 299	73.57 136	89.55 77	90.44 164	84.24 48	84.38 227	94.89 53	76.35 179	80.40 352	76.14 170	96.80 91	82.36 370
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 99	96.75 92

CDPH-MVS		86.17 107	85.54 124	88.05 86	92.25 101	75.45 125	83.85 186	92.01 117	65.91 274	86.19 189	91.75 177	83.77 82	94.98 69	77.43 154	96.71 93	93.73 122

KD-MVS_self_test		81.93 200	83.14 173	78.30 278	84.75 288	52.75 361	80.37 260	89.42 197	70.24 229	90.26 95	93.39 119	74.55 196	86.77 288	68.61 254	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	77.65 149	96.62 95	90.70 240

TransMVSNet (Re)		84.02 154	85.74 121	78.85 267	91.00 146	55.20 346	82.29 232	87.26 224	79.65 98	88.38 139	95.52 37	83.00 90	86.88 285	67.97 260	96.60 96	94.45 86

ambc				82.98 198	90.55 156	64.86 237	88.20 100	89.15 199		89.40 118	93.96 99	71.67 234	91.38 188	78.83 132	96.55 97	92.71 166

train_agg		85.98 109	85.28 130	88.07 85	92.34 98	79.70 78	83.94 182	90.32 170	65.79 276	84.49 224	90.97 198	81.93 113	93.63 115	81.21 104	96.54 98	90.88 234

VDD-MVS		84.23 148	84.58 144	83.20 192	91.17 142	65.16 236	83.25 203	84.97 270	79.79 95	87.18 163	94.27 79	74.77 192	90.89 203	69.24 242	96.54 98	93.55 135

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	86.69 178	92.28 160	80.36 133	95.06 67	86.17 48	96.49 100	90.22 253

test_djsdf		89.62 54	89.01 67	91.45 26	92.36 97	82.98 57	91.98 34	90.08 181	71.54 211	94.28 24	96.54 16	81.57 119	94.27 89	86.26 44	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	81.64 284	87.25 280	82.43 98	94.53 84	77.65 149	96.46 102	94.14 102

test1111		78.53 248	78.85 242	77.56 291	92.22 103	47.49 389	82.61 220	69.24 385	72.43 198	85.28 207	94.20 85	51.91 342	90.07 231	65.36 281	96.45 103	95.11 65

test9_res														80.83 109	96.45 103	90.57 245

Anonymous20240529		86.20 104	87.13 92	83.42 186	90.19 162	64.55 241	84.55 169	90.71 155	85.85 36	89.94 103	95.24 46	82.13 109	90.40 218	69.19 245	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	90.20 178	69.87 232	95.06 15	96.14 25	84.28 77	93.07 141	87.68 19	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	84.76 220	87.70 270	78.87 144	94.18 95	80.67 112	96.29 107	92.73 163

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	76.94 168	94.57 81	83.07 82	96.28 108	96.15 33

v10		86.54 98	87.10 93	84.84 141	88.16 211	63.28 254	86.64 130	92.20 112	75.42 156	92.81 53	94.50 68	74.05 200	94.06 101	83.88 74	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	87.07 169	91.47 183	82.94 91	94.71 75	84.67 67	96.27 110	92.62 170

EC-MVSNet		88.01 78	88.32 77	87.09 95	89.28 180	72.03 162	90.31 59	96.31 4	80.88 84	85.12 210	89.67 237	84.47 75	95.46 50	82.56 91	96.26 111	93.77 121

mmtdpeth		85.13 124	85.78 120	83.17 194	84.65 289	74.71 128	85.87 143	90.35 169	77.94 122	83.82 242	96.96 12	77.75 153	80.03 355	78.44 134	96.21 112	94.79 76

MM		87.64 85	87.15 91	89.09 67	89.51 174	76.39 118	88.68 96	86.76 238	84.54 46	83.58 248	93.78 108	73.36 212	96.48 2	87.98 14	96.21 112	94.41 90

114514_t		83.10 177	82.54 185	84.77 145	92.90 83	69.10 198	86.65 129	90.62 159	54.66 367	81.46 286	90.81 208	76.98 167	94.38 87	72.62 214	96.18 114	90.82 236

agg_prior2														79.68 122	96.16 115	90.22 253

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	88.00 149	93.03 129	82.66 94	91.47 182	70.81 224	96.14 116	94.16 100

TestCases				89.68 55	91.59 124	83.40 52		95.44 10	79.47 99	88.00 149	93.03 129	82.66 94	91.47 182	70.81 224	96.14 116	94.16 100

EPNet		80.37 224	78.41 250	86.23 113	76.75 381	73.28 140	87.18 116	77.45 324	76.24 139	68.14 392	88.93 249	65.41 266	93.85 107	69.47 240	96.12 118	91.55 219
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	74.12 190	96.10 119	94.45 86

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	74.12 190	96.10 119	94.45 86

pm-mvs1		83.69 162	84.95 136	79.91 254	90.04 168	59.66 302	82.43 228	87.44 221	75.52 154	87.85 152	95.26 45	81.25 123	85.65 311	68.74 252	96.04 121	94.42 89

test2506		74.12 296	73.39 297	76.28 309	91.85 117	44.20 403	84.06 179	48.20 428	72.30 204	81.90 275	94.20 85	27.22 428	89.77 239	64.81 286	96.02 122	94.87 70

ECVR-MVS		78.44 249	78.63 246	77.88 287	91.85 117	48.95 383	83.68 192	69.91 381	72.30 204	84.26 236	94.20 85	51.89 343	89.82 236	63.58 296	96.02 122	94.87 70

mvs_tets		89.78 52	89.27 63	91.30 29	93.51 67	84.79 44	89.89 68	90.63 158	70.00 231	94.55 19	96.67 14	87.94 39	93.59 120	84.27 71	95.97 124	95.52 51

EGC-MVSNET		74.79 291	69.99 335	89.19 65	94.89 38	87.00 15	91.89 37	86.28 242	1.09 430	2.23 432	95.98 27	81.87 116	89.48 242	79.76 120	95.96 125	91.10 227

MVS_0304		85.37 118	84.58 144	87.75 88	85.28 278	73.36 137	86.54 133	85.71 253	77.56 130	81.78 282	92.47 151	70.29 241	96.02 11	85.59 56	95.96 125	93.87 113

DeepPCF-MVS	81.24 5	87.28 88	86.21 109	90.49 42	91.48 133	84.90 42	83.41 198	92.38 107	70.25 228	89.35 119	90.68 212	82.85 92	94.57 81	79.55 124	95.95 127	92.00 203

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 26	95.94 128	92.48 177

PC_three_1452									58.96 338	90.06 97	91.33 186	80.66 130	93.03 143	75.78 173	95.94 128	92.48 177

jajsoiax		89.41 57	88.81 73	91.19 32	93.38 71	84.72 45	89.70 71	90.29 175	69.27 235	94.39 20	96.38 18	86.02 65	93.52 124	83.96 73	95.92 130	95.34 55

ANet_high		83.17 175	85.68 122	75.65 314	81.24 340	45.26 400	79.94 265	92.91 91	83.83 51	91.33 76	96.88 13	80.25 134	85.92 304	68.89 249	95.89 131	95.76 43

tt0805		88.09 77	89.79 55	82.98 198	93.26 75	63.94 247	91.10 45	89.64 191	85.07 41	90.91 86	91.09 194	89.16 24	91.87 175	82.03 97	95.87 132	93.13 148

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	77.55 158	95.86 23	84.88 64	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	86.55 180	92.95 134	74.84 189	95.22 59	80.78 110	95.83 134	94.46 84

plane_prior5								93.61 59					95.22 59	80.78 110	95.83 134	94.46 84

cl____		80.42 222	80.23 224	81.02 239	79.99 354	59.25 306	77.07 310	87.02 234	67.37 261	86.18 191	89.21 244	63.08 281	90.16 224	76.31 167	95.80 136	93.65 126

DIV-MVS_self_test		80.43 221	80.23 224	81.02 239	79.99 354	59.25 306	77.07 310	87.02 234	67.38 260	86.19 189	89.22 243	63.09 280	90.16 224	76.32 166	95.80 136	93.66 124

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	84.96 214	90.69 211	80.01 137	95.14 64	78.37 136	95.78 138	91.82 208
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LFMVS		80.15 232	80.56 218	78.89 266	89.19 183	55.93 336	85.22 157	73.78 353	82.96 63	84.28 234	92.72 144	57.38 316	90.07 231	63.80 295	95.75 139	90.68 241

ACMMP++_ref															95.74 140

原ACMM1				84.60 150	92.81 89	74.01 133		91.50 132	62.59 300	82.73 264	90.67 214	76.53 175	94.25 91	69.24 242	95.69 141	85.55 323

tfpnnormal		81.79 203	82.95 176	78.31 277	88.93 189	55.40 342	80.83 256	82.85 290	76.81 135	85.90 197	94.14 89	74.58 195	86.51 292	66.82 266	95.68 142	93.01 154

mvs5depth		83.82 159	84.54 146	81.68 227	82.23 328	68.65 201	86.89 121	89.90 185	80.02 94	87.74 155	97.86 2	64.19 272	82.02 340	76.37 165	95.63 143	94.35 92

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	85.80 198	89.56 238	80.76 128	92.13 166	73.21 211	95.51 144	93.25 144
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	80.87 108	95.50 145	94.53 83

v8		86.22 103	86.83 100	84.36 157	87.82 218	62.35 270	86.42 134	91.33 139	76.78 136	92.73 55	94.48 70	73.41 209	93.72 112	83.10 81	95.41 146	97.01 21

Vis-MVSNet (Re-imp)		77.82 254	77.79 255	77.92 286	88.82 192	51.29 374	83.28 201	71.97 369	74.04 168	82.23 270	89.78 235	57.38 316	89.41 248	57.22 337	95.41 146	93.05 152

OPU-MVS				88.27 82	91.89 115	77.83 97	90.47 55				91.22 189	81.12 124	94.68 76	74.48 185	95.35 148	92.29 189

FMVSNet1		84.55 138	85.45 126	81.85 222	90.27 161	61.05 286	86.83 124	88.27 212	78.57 115	89.66 110	95.64 34	75.43 182	90.68 211	69.09 246	95.33 149	93.82 116

test12				86.57 105	90.74 151	72.63 150		90.69 156		82.76 263		79.20 141	94.80 73		95.32 150	92.27 191

NCCC		87.36 87	86.87 99	88.83 70	92.32 100	78.84 86	86.58 131	91.09 146	78.77 112	84.85 219	90.89 203	80.85 127	95.29 56	81.14 105	95.32 150	92.34 186

Patchmtry		76.56 271	77.46 256	73.83 326	79.37 363	46.60 393	82.41 229	76.90 330	73.81 171	85.56 203	92.38 153	48.07 358	83.98 328	63.36 299	95.31 152	90.92 232

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 75	95.30 153	93.60 130

TSAR-MVS + GP.		83.95 156	82.69 181	87.72 89	89.27 181	81.45 67	83.72 191	81.58 302	74.73 162	85.66 199	86.06 299	72.56 222	92.69 152	75.44 178	95.21 154	89.01 281

test_0402		88.65 69	89.58 60	85.88 124	92.55 92	72.22 160	84.01 180	89.44 196	88.63 20	94.38 21	95.77 29	86.38 61	93.59 120	79.84 119	95.21 154	91.82 208

TinyColmap		81.25 209	82.34 188	77.99 285	85.33 277	60.68 293	82.32 231	88.33 210	71.26 216	86.97 171	92.22 163	77.10 165	86.98 283	62.37 304	95.17 156	86.31 315

Anonymous202405211		80.51 220	81.19 211	78.49 274	88.48 203	57.26 328	76.63 317	82.49 293	81.21 80	84.30 233	92.24 162	67.99 253	86.24 296	62.22 305	95.13 157	91.98 205

tttt0517		81.07 211	79.58 234	85.52 132	88.99 187	66.45 224	87.03 119	75.51 341	73.76 172	88.32 141	90.20 225	37.96 403	94.16 99	79.36 128	95.13 157	95.93 42

DP-MVS Recon		84.05 153	83.22 169	86.52 107	91.73 122	75.27 126	83.23 205	92.40 105	72.04 208	82.04 273	88.33 257	77.91 152	93.95 104	66.17 271	95.12 159	90.34 252

PCF-MVS	74.62 15	82.15 194	80.92 214	85.84 125	89.43 177	72.30 158	80.53 258	91.82 125	57.36 351	87.81 153	89.92 233	77.67 156	93.63 115	58.69 328	95.08 160	91.58 218
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	88.01 261	79.09 142	92.13 166	75.51 176	95.06 161	90.41 250

SDMVSNet		81.90 202	83.17 172	78.10 282	88.81 193	62.45 267	76.08 328	86.05 248	73.67 173	83.41 251	93.04 127	82.35 100	80.65 349	70.06 236	95.03 162	91.21 224

sd_testset		79.95 236	81.39 206	75.64 315	88.81 193	58.07 320	76.16 327	82.81 291	73.67 173	83.41 251	93.04 127	80.96 126	77.65 365	58.62 329	95.03 162	91.21 224

plane_prior						76.42 116	87.15 117		75.94 146						95.03 162

new-patchmatchnet		70.10 334	73.37 298	60.29 400	81.23 341	16.95 435	59.54 411	74.62 344	62.93 298	80.97 290	87.93 265	62.83 284	71.90 383	55.24 352	95.01 165	92.00 203

v1192		84.57 137	84.69 142	84.21 163	87.75 220	62.88 258	83.02 210	91.43 134	69.08 238	89.98 102	90.89 203	72.70 220	93.62 118	82.41 93	94.97 166	96.13 34

v1921920		84.23 148	84.37 152	83.79 172	87.64 225	61.71 277	82.91 214	91.20 143	67.94 254	90.06 97	90.34 221	72.04 229	93.59 120	82.32 94	94.91 167	96.07 36

CL-MVSNet_self_test		76.81 266	77.38 258	75.12 318	86.90 245	51.34 372	73.20 356	80.63 309	68.30 248	81.80 280	88.40 256	66.92 258	80.90 346	55.35 351	94.90 168	93.12 150

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	84.32 230	89.33 242	83.87 79	94.53 84	82.45 92	94.89 169	94.90 68

v144192		84.24 147	84.41 150	83.71 176	87.59 226	61.57 278	82.95 213	91.03 147	67.82 257	89.80 105	90.49 218	73.28 213	93.51 125	81.88 102	94.89 169	96.04 38

LCM-MVSNet-Re		83.48 169	85.06 132	78.75 269	85.94 269	55.75 340	80.05 263	94.27 24	76.47 137	96.09 6	94.54 67	83.31 88	89.75 241	59.95 323	94.89 169	90.75 237

casdiffmvs_mvg		86.72 95	87.51 86	84.36 157	87.09 240	65.22 234	84.16 176	94.23 27	77.89 123	91.28 79	93.66 114	84.35 76	92.71 150	80.07 115	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	73.75 198	94.81 173	93.70 123

v1240		84.30 144	84.51 148	83.65 177	87.65 224	61.26 283	82.85 216	91.54 131	67.94 254	90.68 91	90.65 215	71.71 233	93.64 114	82.84 87	94.78 174	96.07 36

MSLP-MVS++		85.00 130	86.03 112	81.90 220	91.84 119	71.56 171	86.75 128	93.02 87	75.95 145	87.12 164	89.39 240	77.98 150	89.40 249	77.46 152	94.78 174	84.75 332

IterMVS-LS		84.73 134	84.98 134	83.96 168	87.35 231	63.66 248	83.25 203	89.88 186	76.06 140	89.62 111	92.37 156	73.40 211	92.52 155	78.16 142	94.77 176	95.69 46
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AdaColmap		83.66 163	83.69 162	83.57 182	90.05 167	72.26 159	86.29 136	90.00 183	78.19 120	81.65 283	87.16 282	83.40 87	94.24 92	61.69 312	94.76 177	84.21 342

BP-MVS1		82.81 179	81.67 196	86.23 113	87.88 217	68.53 202	86.06 140	84.36 277	75.65 150	85.14 209	90.19 226	45.84 371	94.42 86	85.18 60	94.72 178	95.75 44

ITE_SJBPF				90.11 49	90.72 152	84.97 41		90.30 173	81.56 76	90.02 99	91.20 191	82.40 99	90.81 207	73.58 201	94.66 179	94.56 80

v1144		84.54 139	84.72 140	84.00 166	87.67 223	62.55 265	82.97 212	90.93 151	70.32 227	89.80 105	90.99 197	73.50 206	93.48 126	81.69 103	94.65 180	95.97 39

test20.03		73.75 301	74.59 286	71.22 347	81.11 342	51.12 376	70.15 377	72.10 368	70.42 224	80.28 304	91.50 182	64.21 271	74.72 377	46.96 396	94.58 181	87.82 299

TSAR-MVS + MP.		88.14 75	87.82 82	89.09 67	95.72 22	76.74 112	92.49 25	91.19 144	67.85 256	86.63 179	94.84 55	79.58 140	95.96 15	87.62 20	94.50 182	94.56 80
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		73.90 299	75.67 276	68.61 369	84.11 301	41.28 411	64.17 402	72.83 361	72.09 207	79.08 317	87.94 263	70.31 240	73.89 379	55.99 344	94.49 183	90.67 243

HQP3-MVS								92.68 98							94.47 184

HQP-MVS		84.61 136	84.06 156	86.27 112	91.19 139	70.66 176	84.77 161	92.68 98	73.30 184	80.55 298	90.17 229	72.10 226	94.61 79	77.30 156	94.47 184	93.56 133

test_fmvsmconf0.01_n		86.68 96	86.52 103	87.18 94	85.94 269	78.30 89	86.93 120	92.20 112	65.94 272	89.16 121	93.16 124	83.10 89	89.89 235	87.81 16	94.43 186	93.35 137

c3_l		81.64 204	81.59 200	81.79 226	80.86 346	59.15 309	78.61 288	90.18 179	68.36 246	87.20 162	87.11 284	69.39 245	91.62 179	78.16 142	94.43 186	94.60 79

MCST-MVS		84.36 141	83.93 159	85.63 129	91.59 124	71.58 169	83.52 195	92.13 114	61.82 309	83.96 240	89.75 236	79.93 139	93.46 127	78.33 138	94.34 188	91.87 207

test_fmvsmconf0.1_n		86.18 106	85.88 116	87.08 96	85.26 279	78.25 90	85.82 145	91.82 125	65.33 286	88.55 132	92.35 158	82.62 96	89.80 237	86.87 36	94.32 189	93.18 147

thisisatest0530		79.07 239	77.33 259	84.26 162	87.13 236	64.58 239	83.66 193	75.95 336	68.86 241	85.22 208	87.36 278	38.10 400	93.57 123	75.47 177	94.28 190	94.62 78

baseline		85.20 122	85.93 114	83.02 196	86.30 258	62.37 269	84.55 169	93.96 44	74.48 165	87.12 164	92.03 165	82.30 103	91.94 171	78.39 135	94.21 191	94.74 77

test_fmvsmconf_n		85.88 111	85.51 125	86.99 98	84.77 287	78.21 91	85.40 154	91.39 137	65.32 287	87.72 156	91.81 174	82.33 101	89.78 238	86.68 38	94.20 192	92.99 155

h-mvs33		84.25 146	82.76 179	88.72 73	91.82 121	82.60 60	84.00 181	84.98 269	71.27 214	86.70 176	90.55 217	63.04 282	93.92 105	78.26 140	94.20 192	89.63 265

MVSMamba_PlusPlus		87.53 86	88.86 71	83.54 184	92.03 110	62.26 272	91.49 40	92.62 100	88.07 24	88.07 146	96.17 23	72.24 225	95.79 31	84.85 65	94.16 194	92.58 172

balanced_conf03		84.80 132	85.40 127	83.00 197	88.95 188	61.44 279	90.42 58	92.37 108	71.48 213	88.72 129	93.13 125	70.16 243	95.15 63	79.26 129	94.11 195	92.41 181

alignmvs		83.94 157	83.98 158	83.80 171	87.80 219	67.88 210	84.54 171	91.42 136	73.27 187	88.41 138	87.96 262	72.33 223	90.83 206	76.02 172	94.11 195	92.69 167

USDC		76.63 269	76.73 266	76.34 308	83.46 311	57.20 329	80.02 264	88.04 216	52.14 383	83.65 246	91.25 188	63.24 278	86.65 290	54.66 356	94.11 195	85.17 327

MVS_111021_HR		84.63 135	84.34 153	85.49 134	90.18 163	75.86 123	79.23 279	87.13 229	73.35 181	85.56 203	89.34 241	83.60 85	90.50 216	76.64 162	94.05 198	90.09 259

VNet		79.31 238	80.27 223	76.44 306	87.92 216	53.95 353	75.58 334	84.35 278	74.39 166	82.23 270	90.72 210	72.84 218	84.39 323	60.38 321	93.98 199	90.97 230

FMVSNet2		81.31 208	81.61 199	80.41 248	86.38 253	58.75 316	83.93 184	86.58 240	72.43 198	87.65 157	92.98 131	63.78 275	90.22 222	66.86 263	93.92 200	92.27 191

MGCFI-Net		85.04 127	85.95 113	82.31 216	87.52 227	63.59 250	86.23 138	93.96 44	73.46 177	88.07 146	87.83 268	86.46 57	90.87 205	76.17 169	93.89 201	92.47 179

GDP-MVS		82.17 192	80.85 216	86.15 120	88.65 198	68.95 199	85.65 149	93.02 87	68.42 245	83.73 244	89.54 239	45.07 382	94.31 88	79.66 123	93.87 202	95.19 63

LF4IMVS		82.75 181	81.93 192	85.19 136	82.08 329	80.15 74	85.53 150	88.76 203	68.01 251	85.58 202	87.75 269	71.80 231	86.85 286	74.02 193	93.87 202	88.58 284

sasdasda		85.50 114	86.14 110	83.58 180	87.97 213	67.13 214	87.55 109	94.32 21	73.44 179	88.47 135	87.54 273	86.45 58	91.06 196	75.76 174	93.76 204	92.54 175

canonicalmvs		85.50 114	86.14 110	83.58 180	87.97 213	67.13 214	87.55 109	94.32 21	73.44 179	88.47 135	87.54 273	86.45 58	91.06 196	75.76 174	93.76 204	92.54 175

v2v482		84.09 151	84.24 154	83.62 178	87.13 236	61.40 280	82.71 219	89.71 189	72.19 206	89.55 115	91.41 184	70.70 239	93.20 135	81.02 106	93.76 204	96.25 32

casdiffmvs		85.21 121	85.85 117	83.31 189	86.17 263	62.77 261	83.03 209	93.93 46	74.69 163	88.21 143	92.68 145	82.29 105	91.89 174	77.87 148	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		70.72 329	70.97 322	69.95 354	88.93 189	34.80 424	69.85 379	66.59 398	78.42 117	77.58 332	85.55 305	31.83 415	82.08 339	46.28 397	93.73 208	92.98 156

UGNet		82.78 180	81.64 197	86.21 116	86.20 262	76.24 120	86.86 122	85.68 254	77.07 134	73.76 363	92.82 139	69.64 244	91.82 177	69.04 248	93.69 209	90.56 246
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	71.77 164		81.78 299			91.84 171	73.92 201			93.65 210	83.61 350

AUN-MVS		81.18 210	78.78 243	88.39 79	90.93 147	82.14 62	82.51 226	83.67 283	64.69 291	80.29 302	85.91 303	51.07 346	92.38 159	76.29 168	93.63 211	90.65 244

hse-mvs2		83.47 170	81.81 194	88.47 77	91.03 145	82.27 61	82.61 220	83.69 282	71.27 214	86.70 176	86.05 300	63.04 282	92.41 158	78.26 140	93.62 212	90.71 239

MVS_111021_LR		84.28 145	83.76 161	85.83 126	89.23 182	83.07 55	80.99 252	83.56 284	72.71 196	86.07 192	89.07 247	81.75 118	86.19 299	77.11 158	93.36 213	88.24 287

GBi-Net		82.02 197	82.07 189	81.85 222	86.38 253	61.05 286	86.83 124	88.27 212	72.43 198	86.00 193	95.64 34	63.78 275	90.68 211	65.95 273	93.34 214	93.82 116

test1		82.02 197	82.07 189	81.85 222	86.38 253	61.05 286	86.83 124	88.27 212	72.43 198	86.00 193	95.64 34	63.78 275	90.68 211	65.95 273	93.34 214	93.82 116

FMVSNet3		78.80 244	78.55 247	79.57 260	82.89 326	56.89 332	81.76 240	85.77 252	69.04 239	86.00 193	90.44 219	51.75 344	90.09 230	65.95 273	93.34 214	91.72 212

test_fmvsmvis_n_1920		85.22 120	85.36 129	84.81 143	85.80 271	76.13 122	85.15 159	92.32 109	61.40 316	91.33 76	90.85 206	83.76 83	86.16 300	84.31 70	93.28 217	92.15 197

K. test v3		85.14 123	84.73 138	86.37 109	91.13 143	69.63 189	85.45 152	76.68 333	84.06 50	92.44 60	96.99 10	62.03 285	94.65 77	80.58 113	93.24 218	94.83 75

Anonymous20231206		71.38 323	71.88 314	69.88 355	86.31 257	54.37 349	70.39 375	74.62 344	52.57 379	76.73 335	88.76 250	59.94 297	72.06 382	44.35 404	93.23 219	83.23 358

D2MVS		76.84 265	75.67 276	80.34 249	80.48 352	62.16 275	73.50 353	84.80 274	57.61 349	82.24 269	87.54 273	51.31 345	87.65 273	70.40 233	93.19 220	91.23 223

miper_lstm_enhance		76.45 273	76.10 271	77.51 292	76.72 382	60.97 290	64.69 400	85.04 266	63.98 294	83.20 255	88.22 258	56.67 320	78.79 362	73.22 206	93.12 221	92.78 162

新几何1				82.95 200	93.96 59	78.56 88		80.24 310	55.45 361	83.93 241	91.08 195	71.19 236	88.33 265	65.84 276	93.07 222	81.95 375

lessismore_v0				85.95 121	91.10 144	70.99 175		70.91 377		91.79 69	94.42 74	61.76 286	92.93 146	79.52 126	93.03 223	93.93 109

TAMVS		78.08 252	76.36 268	83.23 191	90.62 154	72.87 144	79.08 280	80.01 312	61.72 312	81.35 288	86.92 287	63.96 274	88.78 258	50.61 377	93.01 224	88.04 293

ETV-MVS		84.31 143	83.91 160	85.52 132	88.58 201	70.40 179	84.50 173	93.37 64	78.76 113	84.07 238	78.72 383	80.39 132	95.13 65	73.82 197	92.98 225	91.04 228

EPNet_dtu		72.87 309	71.33 321	77.49 293	77.72 372	60.55 294	82.35 230	75.79 337	66.49 271	58.39 423	81.06 361	53.68 335	85.98 302	53.55 362	92.97 226	85.95 318
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+-dtu		85.82 112	83.38 166	93.14 4	87.13 236	91.15 3	87.70 108	88.42 207	74.57 164	83.56 249	85.65 304	78.49 147	94.21 93	72.04 218	92.88 227	94.05 105

CANet		83.79 161	82.85 178	86.63 104	86.17 263	72.21 161	83.76 190	91.43 134	77.24 133	74.39 359	87.45 276	75.36 183	95.42 52	77.03 159	92.83 228	92.25 193

fmvsm_s_conf0.5_n_3		86.19 105	87.27 90	82.95 200	86.91 244	70.38 180	85.31 155	92.61 101	75.59 152	88.32 141	92.87 137	82.22 107	88.63 261	88.80 8	92.82 229	89.83 263

API-MVS		82.28 188	82.61 183	81.30 232	86.29 259	69.79 185	88.71 95	87.67 219	78.42 117	82.15 272	84.15 330	77.98 150	91.59 180	65.39 280	92.75 230	82.51 369

test_yl		78.71 246	78.51 248	79.32 263	84.32 296	58.84 313	78.38 289	85.33 259	75.99 143	82.49 265	86.57 290	58.01 310	90.02 233	62.74 302	92.73 231	89.10 276

DCV-MVSNet		78.71 246	78.51 248	79.32 263	84.32 296	58.84 313	78.38 289	85.33 259	75.99 143	82.49 265	86.57 290	58.01 310	90.02 233	62.74 302	92.73 231	89.10 276

testgi		72.36 312	74.61 284	65.59 383	80.56 351	42.82 408	68.29 385	73.35 357	66.87 268	81.84 277	89.93 232	72.08 228	66.92 406	46.05 400	92.54 233	87.01 308

FMVSNet5		72.10 315	71.69 315	73.32 329	81.57 336	53.02 360	76.77 314	78.37 319	63.31 295	76.37 337	91.85 170	36.68 405	78.98 359	47.87 392	92.45 234	87.95 295

CDS-MVSNet		77.32 260	75.40 278	83.06 195	89.00 186	72.48 155	77.90 296	82.17 296	60.81 325	78.94 318	83.49 335	59.30 302	88.76 259	54.64 357	92.37 235	87.93 296
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
patch_mono-2		78.89 241	79.39 236	77.41 294	84.78 286	68.11 207	75.60 332	83.11 287	60.96 324	79.36 312	89.89 234	75.18 185	72.97 380	73.32 205	92.30 236	91.15 226

dcpmvs_2		84.23 148	85.14 131	81.50 230	88.61 200	61.98 276	82.90 215	93.11 79	68.66 244	92.77 54	92.39 152	78.50 146	87.63 274	76.99 160	92.30 236	94.90 68

CNLPA		83.55 168	83.10 174	84.90 140	89.34 179	83.87 50	84.54 171	88.77 202	79.09 106	83.54 250	88.66 254	74.87 188	81.73 342	66.84 265	92.29 238	89.11 275

F-COLMAP		84.97 131	83.42 165	89.63 57	92.39 96	83.40 52	88.83 92	91.92 121	73.19 188	80.18 306	89.15 246	77.04 166	93.28 133	65.82 277	92.28 239	92.21 194

thres600view7		75.97 277	75.35 280	77.85 289	87.01 242	51.84 370	80.45 259	73.26 358	75.20 158	83.10 257	86.31 296	45.54 373	89.05 251	55.03 354	92.24 240	92.66 168

PVSNet_BlendedMVS		78.80 244	77.84 254	81.65 228	84.43 292	63.41 251	79.49 273	90.44 164	61.70 313	75.43 350	87.07 285	69.11 248	91.44 184	60.68 319	92.24 240	90.11 258

DELS-MVS		81.44 207	81.25 208	82.03 218	84.27 298	62.87 259	76.47 322	92.49 104	70.97 220	81.64 284	83.83 331	75.03 186	92.70 151	74.29 186	92.22 242	90.51 248
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
testdata				79.54 261	92.87 84	72.34 157		80.14 311	59.91 334	85.47 205	91.75 177	67.96 254	85.24 313	68.57 256	92.18 243	81.06 388

SSC-MVS		77.55 257	81.64 197	65.29 386	90.46 157	20.33 433	73.56 352	68.28 387	85.44 37	88.18 145	94.64 64	70.93 237	81.33 344	71.25 221	92.03 244	94.20 96

cl22		78.97 240	78.21 252	81.24 235	77.74 371	59.01 310	77.46 306	87.13 229	65.79 276	84.32 230	85.10 316	58.96 306	90.88 204	75.36 179	92.03 244	93.84 114

miper_ehance_all_eth		80.34 225	80.04 231	81.24 235	79.82 357	58.95 311	77.66 299	89.66 190	65.75 279	85.99 196	85.11 315	68.29 252	91.42 186	76.03 171	92.03 244	93.33 138

miper_enhance_ethall		77.83 253	76.93 263	80.51 246	76.15 388	58.01 322	75.47 336	88.82 201	58.05 345	83.59 247	80.69 362	64.41 269	91.20 190	73.16 212	92.03 244	92.33 187

GeoE		85.45 117	85.81 118	84.37 155	90.08 164	67.07 216	85.86 144	91.39 137	72.33 203	87.59 158	90.25 224	84.85 71	92.37 160	78.00 145	91.94 248	93.66 124

fmvsm_s_conf0.1_n_2		83.82 159	83.49 163	84.84 141	85.99 268	70.19 183	80.93 253	87.58 220	67.26 264	87.94 151	92.37 156	71.40 235	88.01 268	86.03 50	91.87 249	96.31 31

DPM-MVS		80.10 233	79.18 238	82.88 205	90.71 153	69.74 186	78.87 284	90.84 152	60.29 331	75.64 349	85.92 302	67.28 255	93.11 139	71.24 222	91.79 250	85.77 321

v148		82.31 187	82.48 186	81.81 225	85.59 273	59.66 302	81.47 245	86.02 249	72.85 192	88.05 148	90.65 215	70.73 238	90.91 202	75.15 181	91.79 250	94.87 70

fmvsm_s_conf0.5_n_2		83.62 165	83.29 168	84.62 149	85.43 276	70.18 184	80.61 257	87.24 225	67.14 265	87.79 154	91.87 168	71.79 232	87.98 269	86.00 54	91.77 252	95.71 45

test222					93.31 73	76.54 113	79.38 274	77.79 321	52.59 378	82.36 268	90.84 207	66.83 259			91.69 253	81.25 383

testing3		71.53 321	70.79 323	73.77 327	88.89 191	41.86 410	76.60 320	59.12 417	72.83 193	80.97 290	82.08 352	19.80 434	87.33 278	65.12 283	91.68 254	92.13 198

eth_miper_zixun_eth		80.84 214	80.22 226	82.71 207	81.41 338	60.98 289	77.81 297	90.14 180	67.31 263	86.95 172	87.24 281	64.26 270	92.31 162	75.23 180	91.61 255	94.85 74

pmmvs-eth3d		78.42 250	77.04 262	82.57 212	87.44 230	74.41 131	80.86 255	79.67 313	55.68 360	84.69 221	90.31 223	60.91 290	85.42 312	62.20 306	91.59 256	87.88 297

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	84.27 235	94.05 92	78.35 148	93.65 113	80.54 114	91.58 257	92.08 199
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
FE-MVS		79.98 235	78.86 241	83.36 187	86.47 250	66.45 224	89.73 70	84.74 275	72.80 194	84.22 237	91.38 185	44.95 383	93.60 119	63.93 293	91.50 258	90.04 260

thisisatest0515		73.00 308	70.52 327	80.46 247	81.45 337	59.90 300	73.16 357	74.31 348	57.86 346	76.08 344	77.78 388	37.60 404	92.12 168	65.00 284	91.45 259	89.35 270

ppachtmachnet_test		74.73 292	74.00 291	76.90 300	80.71 349	56.89 332	71.53 367	78.42 318	58.24 342	79.32 314	82.92 343	57.91 313	84.26 325	65.60 279	91.36 260	89.56 266

FA-MVS(test-final)		83.13 176	83.02 175	83.43 185	86.16 265	66.08 227	88.00 103	88.36 209	75.55 153	85.02 212	92.75 143	65.12 267	92.50 156	74.94 184	91.30 261	91.72 212

OpenMVS	76.72 13	81.98 199	82.00 191	81.93 219	84.42 294	68.22 205	88.50 99	89.48 195	66.92 267	81.80 280	91.86 169	72.59 221	90.16 224	71.19 223	91.25 262	87.40 303

fmvsm_l_conf0.5_n_3		85.11 126	84.96 135	85.56 131	87.49 229	75.69 124	84.71 165	90.61 160	67.64 258	84.88 217	92.05 164	82.30 103	88.36 264	83.84 76	91.10 263	92.62 170

EG-PatchMatch MVS		84.08 152	84.11 155	83.98 167	92.22 103	72.61 151	82.20 238	87.02 234	72.63 197	88.86 124	91.02 196	78.52 145	91.11 194	73.41 203	91.09 264	88.21 288

3Dnovator	80.37 7	84.80 132	84.71 141	85.06 139	86.36 256	74.71 128	88.77 94	90.00 183	75.65 150	84.96 214	93.17 123	74.06 199	91.19 191	78.28 139	91.09 264	89.29 273

thres100view900		75.45 281	75.05 282	76.66 304	87.27 232	51.88 369	81.07 251	73.26 358	75.68 149	83.25 254	86.37 293	45.54 373	88.80 255	51.98 372	90.99 266	89.31 271

tfpn200view9		74.86 289	74.23 289	76.74 303	86.24 260	52.12 366	79.24 277	73.87 351	73.34 182	81.82 278	84.60 325	46.02 366	88.80 255	51.98 372	90.99 266	89.31 271

thres400		75.14 283	74.23 289	77.86 288	86.24 260	52.12 366	79.24 277	73.87 351	73.34 182	81.82 278	84.60 325	46.02 366	88.80 255	51.98 372	90.99 266	92.66 168

cascas		76.29 275	74.81 283	80.72 244	84.47 291	62.94 257	73.89 350	87.34 222	55.94 358	75.16 355	76.53 401	63.97 273	91.16 192	65.00 284	90.97 269	88.06 292

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	72.03 230	96.36 4	88.21 12	90.93 270	92.98 156
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		68.76 349	68.43 349	69.75 357	83.29 317	40.30 414	67.36 391	72.21 367	57.09 354	77.05 334	85.53 307	33.68 410	80.51 350	48.79 387	90.90 271	88.45 286

ab-mvs		79.67 237	80.56 218	76.99 297	88.48 203	56.93 330	84.70 166	86.06 247	68.95 240	80.78 295	93.08 126	75.30 184	84.62 319	56.78 338	90.90 271	89.43 269

test_fmvsm_n_1920		83.60 166	82.89 177	85.74 127	85.22 280	77.74 99	84.12 178	90.48 162	59.87 335	86.45 188	91.12 193	75.65 180	85.89 307	82.28 95	90.87 273	93.58 131

MAR-MVS		80.24 229	78.74 245	84.73 146	86.87 247	78.18 92	85.75 146	87.81 218	65.67 281	77.84 326	78.50 384	73.79 203	90.53 215	61.59 314	90.87 273	85.49 325
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	84.53 147	86.88 100	84.01 302	72.76 145	83.91 185	85.18 262	80.44 86	88.75 127	85.49 308	80.08 136	91.92 172	82.02 98	90.85 275	95.97 39

EI-MVSNet-UG-set		85.04 127	84.44 149	86.85 101	83.87 306	72.52 154	83.82 187	85.15 263	80.27 90	88.75 127	85.45 310	79.95 138	91.90 173	81.92 101	90.80 276	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	86.75 175	93.26 121	93.64 2	90.93 200	84.60 68	90.75 277	93.97 107

ET-MVSNet_ETH3D		75.28 282	72.77 305	82.81 206	83.03 325	68.11 207	77.09 309	76.51 334	60.67 328	77.60 331	80.52 366	38.04 401	91.15 193	70.78 226	90.68 278	89.17 274

EI-MVSNet		82.61 182	82.42 187	83.20 192	83.25 319	63.66 248	83.50 196	85.07 264	76.06 140	86.55 180	85.10 316	73.41 209	90.25 219	78.15 144	90.67 279	95.68 47

MVSTER		77.09 262	75.70 275	81.25 233	75.27 396	61.08 285	77.49 305	85.07 264	60.78 326	86.55 180	88.68 252	43.14 392	90.25 219	73.69 200	90.67 279	92.42 180

reproduce_monomvs		74.09 297	73.23 299	76.65 305	76.52 383	54.54 348	77.50 304	81.40 303	65.85 275	82.86 262	86.67 289	27.38 426	84.53 320	70.24 234	90.66 281	90.89 233

Patchmatch-RL test		74.48 293	73.68 293	76.89 301	84.83 285	66.54 222	72.29 360	69.16 386	57.70 347	86.76 174	86.33 294	45.79 372	82.59 335	69.63 239	90.65 282	81.54 379

CMPMVS	59.41 20	75.12 285	73.57 294	79.77 255	75.84 391	67.22 213	81.21 249	82.18 295	50.78 392	76.50 336	87.66 271	55.20 330	82.99 334	62.17 308	90.64 283	89.09 278
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
WB-MVS		76.06 276	80.01 232	64.19 389	89.96 170	20.58 432	72.18 361	68.19 388	83.21 59	86.46 187	93.49 117	70.19 242	78.97 360	65.96 272	90.46 284	93.02 153

fmvsm_l_conf0.5_n		82.06 196	81.54 203	83.60 179	83.94 303	73.90 134	83.35 200	86.10 245	58.97 337	83.80 243	90.36 220	74.23 197	86.94 284	82.90 85	90.22 285	89.94 261

V42		83.47 170	83.37 167	83.75 174	83.16 322	63.33 253	81.31 246	90.23 177	69.51 234	90.91 86	90.81 208	74.16 198	92.29 164	80.06 116	90.22 285	95.62 49

PM-MVS		80.20 230	79.00 239	83.78 173	88.17 210	86.66 19	81.31 246	66.81 397	69.64 233	88.33 140	90.19 226	64.58 268	83.63 331	71.99 219	90.03 287	81.06 388

PLC	73.85 16	82.09 195	80.31 222	87.45 92	90.86 150	80.29 73	85.88 142	90.65 157	68.17 250	76.32 339	86.33 294	73.12 215	92.61 154	61.40 315	90.02 288	89.44 268
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
fmvsm_l_conf0.5_n_a		81.46 206	80.87 215	83.25 190	83.73 308	73.21 143	83.00 211	85.59 256	58.22 343	82.96 259	90.09 231	72.30 224	86.65 290	81.97 100	89.95 289	89.88 262

ttmdpeth		71.72 318	70.67 324	74.86 320	73.08 410	55.88 337	77.41 307	69.27 384	55.86 359	78.66 320	93.77 110	38.01 402	75.39 374	60.12 322	89.87 290	93.31 140

UWE-MVS		66.43 362	65.56 368	69.05 362	84.15 300	40.98 412	73.06 358	64.71 404	54.84 365	76.18 342	79.62 375	29.21 421	80.50 351	38.54 416	89.75 291	85.66 322

CANet_DTU		77.81 255	77.05 261	80.09 253	81.37 339	59.90 300	83.26 202	88.29 211	69.16 237	67.83 395	83.72 332	60.93 289	89.47 243	69.22 244	89.70 292	90.88 234

diffmvs		80.40 223	80.48 221	80.17 252	79.02 367	60.04 297	77.54 302	90.28 176	66.65 270	82.40 267	87.33 279	73.50 206	87.35 277	77.98 146	89.62 293	93.13 148
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		70.05 336	69.26 339	72.41 341	58.62 432	55.59 341	76.61 319	65.58 400	53.44 372	89.28 120	93.32 120	22.91 432	71.44 387	74.08 192	89.52 294	90.21 257

PMMVS2		55.64 392	59.27 390	44.74 408	64.30 430	12.32 436	40.60 423	49.79 426	53.19 374	65.06 409	84.81 321	53.60 336	49.76 426	32.68 424	89.41 295	72.15 407

Fast-Effi-MVS+-dtu		82.54 185	81.41 205	85.90 123	85.60 272	76.53 115	83.07 208	89.62 193	73.02 191	79.11 316	83.51 334	80.74 129	90.24 221	68.76 251	89.29 296	90.94 231

thres200		72.34 313	71.55 319	74.70 323	83.48 310	51.60 371	75.02 339	73.71 354	70.14 230	78.56 322	80.57 365	46.20 364	88.20 267	46.99 395	89.29 296	84.32 338

jason		77.42 259	75.75 274	82.43 215	87.10 239	69.27 192	77.99 294	81.94 298	51.47 387	77.84 326	85.07 319	60.32 294	89.00 252	70.74 228	89.27 298	89.03 279
jason: jason.
MG-MVS		80.32 226	80.94 213	78.47 275	88.18 209	52.62 364	82.29 232	85.01 268	72.01 209	79.24 315	92.54 149	69.36 246	93.36 132	70.65 229	89.19 299	89.45 267

myMVS_eth3d28		65.83 367	65.85 363	65.78 382	83.42 313	35.71 422	67.29 392	68.01 389	67.58 259	69.80 385	77.72 390	32.29 413	74.30 378	37.49 418	89.06 300	87.32 304

BH-untuned		80.96 213	80.99 212	80.84 241	88.55 202	68.23 204	80.33 261	88.46 206	72.79 195	86.55 180	86.76 288	74.72 193	91.77 178	61.79 311	88.99 301	82.52 368

EIA-MVS		82.19 191	81.23 210	85.10 138	87.95 215	69.17 197	83.22 206	93.33 67	70.42 224	78.58 321	79.77 374	77.29 161	94.20 94	71.51 220	88.96 302	91.93 206

PVSNet_Blended_VisFu		81.55 205	80.49 220	84.70 148	91.58 127	73.24 142	84.21 175	91.67 129	62.86 299	80.94 292	87.16 282	67.27 256	92.87 149	69.82 238	88.94 303	87.99 294

MVSFormer		82.23 189	81.57 202	84.19 165	85.54 274	69.26 193	91.98 34	90.08 181	71.54 211	76.23 340	85.07 319	58.69 307	94.27 89	86.26 44	88.77 304	89.03 279

lupinMVS		76.37 274	74.46 287	82.09 217	85.54 274	69.26 193	76.79 313	80.77 308	50.68 394	76.23 340	82.82 344	58.69 307	88.94 253	69.85 237	88.77 304	88.07 290

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	87.74 272	76.54 163	88.74 306	96.61 27

test_fmvs3		75.72 280	75.20 281	77.27 295	75.01 399	69.47 190	78.93 281	84.88 271	46.67 401	87.08 168	87.84 267	50.44 351	71.62 385	77.42 155	88.53 307	90.72 238

RRT-MVS		82.97 178	83.44 164	81.57 229	85.06 282	58.04 321	87.20 114	90.37 167	77.88 124	88.59 131	93.70 113	63.17 279	93.05 142	76.49 164	88.47 308	93.62 128

PAPM_NR		83.23 173	83.19 171	83.33 188	90.90 148	65.98 228	88.19 101	90.78 154	78.13 121	80.87 294	87.92 266	73.49 208	92.42 157	70.07 235	88.40 309	91.60 217

testing222		66.93 356	65.30 369	71.81 344	83.38 314	45.83 397	72.06 362	67.50 390	64.12 293	69.68 386	76.37 402	27.34 427	83.00 333	38.88 413	88.38 310	86.62 312

xiu_mvs_v1_base_debu		80.84 214	80.14 228	82.93 202	88.31 206	71.73 165	79.53 270	87.17 226	65.43 282	79.59 308	82.73 346	76.94 168	90.14 227	73.22 206	88.33 311	86.90 309

xiu_mvs_v1_base		80.84 214	80.14 228	82.93 202	88.31 206	71.73 165	79.53 270	87.17 226	65.43 282	79.59 308	82.73 346	76.94 168	90.14 227	73.22 206	88.33 311	86.90 309

xiu_mvs_v1_base_debi		80.84 214	80.14 228	82.93 202	88.31 206	71.73 165	79.53 270	87.17 226	65.43 282	79.59 308	82.73 346	76.94 168	90.14 227	73.22 206	88.33 311	86.90 309

XXY-MVS		74.44 295	76.19 270	69.21 361	84.61 290	52.43 365	71.70 364	77.18 328	60.73 327	80.60 296	90.96 200	75.44 181	69.35 392	56.13 343	88.33 311	85.86 320

Fast-Effi-MVS+		81.04 212	80.57 217	82.46 214	87.50 228	63.22 255	78.37 291	89.63 192	68.01 251	81.87 276	82.08 352	82.31 102	92.65 153	67.10 262	88.30 315	91.51 220

MDA-MVSNet-bldmvs		77.47 258	76.90 264	79.16 265	79.03 366	64.59 238	66.58 396	75.67 339	73.15 189	88.86 124	88.99 248	66.94 257	81.23 345	64.71 287	88.22 316	91.64 216

PAPR		78.84 243	78.10 253	81.07 237	85.17 281	60.22 296	82.21 236	90.57 161	62.51 301	75.32 353	84.61 324	74.99 187	92.30 163	59.48 326	88.04 317	90.68 241

mvsmamba		80.30 227	78.87 240	84.58 151	88.12 212	67.55 212	92.35 29	84.88 271	63.15 297	85.33 206	90.91 202	50.71 348	95.20 62	66.36 269	87.98 318	90.99 229

BH-RMVSNet		80.53 219	80.22 226	81.49 231	87.19 235	66.21 226	77.79 298	86.23 243	74.21 167	83.69 245	88.50 255	73.25 214	90.75 208	63.18 301	87.90 319	87.52 301

Effi-MVS+		83.90 158	84.01 157	83.57 182	87.22 234	65.61 232	86.55 132	92.40 105	78.64 114	81.34 289	84.18 329	83.65 84	92.93 146	74.22 187	87.87 320	92.17 196

MVS_Test		82.47 186	83.22 169	80.22 251	82.62 327	57.75 325	82.54 225	91.96 120	71.16 218	82.89 260	92.52 150	77.41 159	90.50 216	80.04 117	87.84 321	92.40 183

QAPM		82.59 183	82.59 184	82.58 210	86.44 251	66.69 221	89.94 67	90.36 168	67.97 253	84.94 216	92.58 148	72.71 219	92.18 165	70.63 230	87.73 322	88.85 282

PVSNet_Blended		76.49 272	75.40 278	79.76 256	84.43 292	63.41 251	75.14 338	90.44 164	57.36 351	75.43 350	78.30 385	69.11 248	91.44 184	60.68 319	87.70 323	84.42 337

pmmvs5		70.73 328	70.07 332	72.72 335	77.03 379	52.73 362	74.14 345	75.65 340	50.36 396	72.17 371	85.37 313	55.42 329	80.67 348	52.86 368	87.59 324	84.77 331

IB-MVS	62.13 19	71.64 319	68.97 345	79.66 259	80.80 348	62.26 272	73.94 349	76.90 330	63.27 296	68.63 391	76.79 398	33.83 409	91.84 176	59.28 327	87.26 325	84.88 330
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		70.20 332	68.80 347	74.38 324	80.91 344	84.81 43	59.12 413	76.45 335	55.06 363	75.31 354	82.36 349	55.74 326	54.82 423	47.02 394	87.24 326	83.52 351

fmvsm_s_conf0.1_n		82.17 192	81.59 200	83.94 170	86.87 247	71.57 170	85.19 158	77.42 325	62.27 308	84.47 226	91.33 186	76.43 176	85.91 305	83.14 79	87.14 327	94.33 94

fmvsm_s_conf0.5_n		81.91 201	81.30 207	83.75 174	86.02 267	71.56 171	84.73 164	77.11 329	62.44 305	84.00 239	90.68 212	76.42 177	85.89 307	83.14 79	87.11 328	93.81 119

fmvsm_s_conf0.1_n_a		82.58 184	81.93 192	84.50 152	87.68 222	73.35 138	86.14 139	77.70 322	61.64 314	85.02 212	91.62 179	77.75 153	86.24 296	82.79 88	87.07 329	93.91 111

pmmvs4		74.92 288	72.98 303	80.73 243	84.95 283	71.71 168	76.23 325	77.59 323	52.83 377	77.73 330	86.38 292	56.35 323	84.97 316	57.72 336	87.05 330	85.51 324

test_fmvs2		73.57 302	72.80 304	75.90 313	72.74 413	68.84 200	77.07 310	84.32 279	45.14 407	82.89 260	84.22 328	48.37 356	70.36 389	73.40 204	87.03 331	88.52 285

MIMVSNet		71.09 325	71.59 316	69.57 359	87.23 233	50.07 381	78.91 282	71.83 370	60.20 333	71.26 374	91.76 176	55.08 332	76.09 370	41.06 409	87.02 332	82.54 367

testing91		69.94 339	68.99 344	72.80 334	83.81 307	45.89 396	71.57 366	73.64 356	68.24 249	70.77 380	77.82 387	34.37 408	84.44 322	53.64 361	87.00 333	88.07 290

fmvsm_s_conf0.5_n_a		82.21 190	81.51 204	84.32 160	86.56 249	73.35 138	85.46 151	77.30 326	61.81 310	84.51 223	90.88 205	77.36 160	86.21 298	82.72 89	86.97 334	93.38 136

HyFIR lowres test		75.12 285	72.66 307	82.50 213	91.44 135	65.19 235	72.47 359	87.31 223	46.79 400	80.29 302	84.30 327	52.70 339	92.10 169	51.88 376	86.73 335	90.22 253

test_vis3_rt		71.42 322	70.67 324	73.64 328	69.66 420	70.46 178	66.97 395	89.73 187	42.68 417	88.20 144	83.04 339	43.77 387	60.07 418	65.35 282	86.66 336	90.39 251

MSDG		80.06 234	79.99 233	80.25 250	83.91 305	68.04 209	77.51 303	89.19 198	77.65 127	81.94 274	83.45 336	76.37 178	86.31 295	63.31 300	86.59 337	86.41 313

Patchmatch-test		65.91 365	67.38 354	61.48 397	75.51 393	43.21 407	68.84 383	63.79 406	62.48 302	72.80 368	83.42 337	44.89 384	59.52 420	48.27 391	86.45 338	81.70 376

mvs_anonymous		78.13 251	78.76 244	76.23 311	79.24 364	50.31 380	78.69 286	84.82 273	61.60 315	83.09 258	92.82 139	73.89 202	87.01 280	68.33 258	86.41 339	91.37 221

IterMVS-SCA-FT		80.64 218	79.41 235	84.34 159	83.93 304	69.66 188	76.28 324	81.09 305	72.43 198	86.47 186	90.19 226	60.46 292	93.15 138	77.45 153	86.39 340	90.22 253

testing99		69.27 345	68.15 352	72.63 336	83.29 317	45.45 398	71.15 368	71.08 375	67.34 262	70.43 381	77.77 389	32.24 414	84.35 324	53.72 360	86.33 341	88.10 289

E-PMN		61.59 380	61.62 383	61.49 396	66.81 424	55.40 342	53.77 420	60.34 416	66.80 269	58.90 421	65.50 420	40.48 397	66.12 409	55.72 346	86.25 342	62.95 418

EMVS		61.10 383	60.81 385	61.99 394	65.96 427	55.86 338	53.10 421	58.97 419	67.06 266	56.89 425	63.33 421	40.98 395	67.03 405	54.79 355	86.18 343	63.08 417

ETVMVS		64.67 371	63.34 377	68.64 366	83.44 312	41.89 409	69.56 382	61.70 413	61.33 319	68.74 389	75.76 404	28.76 422	79.35 356	34.65 421	86.16 344	84.67 333

our_test_3		71.85 316	71.59 316	72.62 337	80.71 349	53.78 354	69.72 380	71.71 373	58.80 339	78.03 323	80.51 367	56.61 321	78.84 361	62.20 306	86.04 345	85.23 326

EU-MVSNet		75.12 285	74.43 288	77.18 296	83.11 324	59.48 304	85.71 148	82.43 294	39.76 421	85.64 200	88.76 250	44.71 385	87.88 271	73.86 196	85.88 346	84.16 343

GA-MVS		75.83 278	74.61 284	79.48 262	81.87 331	59.25 306	73.42 354	82.88 289	68.68 243	79.75 307	81.80 355	50.62 349	89.46 244	66.85 264	85.64 347	89.72 264

MVS		73.21 306	72.59 308	75.06 319	80.97 343	60.81 292	81.64 243	85.92 251	46.03 405	71.68 373	77.54 391	68.47 251	89.77 239	55.70 347	85.39 348	74.60 405

PatchT		70.52 330	72.76 306	63.79 391	79.38 362	33.53 425	77.63 300	65.37 402	73.61 175	71.77 372	92.79 142	44.38 386	75.65 373	64.53 291	85.37 349	82.18 372

TR-MVS		76.77 267	75.79 273	79.72 257	86.10 266	65.79 230	77.14 308	83.02 288	65.20 288	81.40 287	82.10 350	66.30 260	90.73 210	55.57 348	85.27 350	82.65 363

BH-w/o		76.57 270	76.07 272	78.10 282	86.88 246	65.92 229	77.63 300	86.33 241	65.69 280	80.89 293	79.95 371	68.97 250	90.74 209	53.01 367	85.25 351	77.62 399

Syy-MVS		69.40 344	70.03 334	67.49 374	81.72 333	38.94 416	71.00 369	61.99 408	61.38 317	70.81 378	72.36 412	61.37 288	79.30 357	64.50 292	85.18 352	84.22 340

myMVS_eth3d		64.66 372	63.89 373	66.97 377	81.72 333	37.39 419	71.00 369	61.99 408	61.38 317	70.81 378	72.36 412	20.96 433	79.30 357	49.59 382	85.18 352	84.22 340

IterMVS		76.91 264	76.34 269	78.64 271	80.91 344	64.03 245	76.30 323	79.03 316	64.88 290	83.11 256	89.16 245	59.90 298	84.46 321	68.61 254	85.15 354	87.42 302
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
WB-MVSnew		68.72 350	69.01 343	67.85 371	83.22 321	43.98 404	74.93 340	65.98 399	55.09 362	73.83 362	79.11 377	65.63 265	71.89 384	38.21 417	85.04 355	87.69 300

OpenMVS_ROB	70.19 17	77.77 256	77.46 256	78.71 270	84.39 295	61.15 284	81.18 250	82.52 292	62.45 304	83.34 253	87.37 277	66.20 261	88.66 260	64.69 288	85.02 356	86.32 314

KD-MVS_2432*1600		66.87 358	65.81 365	70.04 352	67.50 422	47.49 389	62.56 405	79.16 314	61.21 322	77.98 324	80.61 363	25.29 430	82.48 336	53.02 365	84.92 357	80.16 392

miper_refine_blended		66.87 358	65.81 365	70.04 352	67.50 422	47.49 389	62.56 405	79.16 314	61.21 322	77.98 324	80.61 363	25.29 430	82.48 336	53.02 365	84.92 357	80.16 392

test_fmvs1_n		70.94 326	70.41 330	72.53 339	73.92 401	66.93 219	75.99 329	84.21 281	43.31 414	79.40 311	79.39 376	43.47 388	68.55 397	69.05 247	84.91 359	82.10 373

test-LLR		67.21 355	66.74 359	68.63 367	76.45 386	55.21 344	67.89 386	67.14 394	62.43 306	65.08 407	72.39 410	43.41 389	69.37 390	61.00 316	84.89 360	81.31 381

test-mter		65.00 370	63.79 374	68.63 367	76.45 386	55.21 344	67.89 386	67.14 394	50.98 391	65.08 407	72.39 410	28.27 424	69.37 390	61.00 316	84.89 360	81.31 381

PS-MVSNAJ		77.04 263	76.53 267	78.56 272	87.09 240	61.40 280	75.26 337	87.13 229	61.25 320	74.38 360	77.22 396	76.94 168	90.94 199	64.63 289	84.83 362	83.35 355

xiu_mvs_v2_base		77.19 261	76.75 265	78.52 273	87.01 242	61.30 282	75.55 335	87.12 232	61.24 321	74.45 358	78.79 382	77.20 162	90.93 200	64.62 290	84.80 363	83.32 356

pmmvs3		62.47 376	60.02 389	69.80 356	71.58 416	64.00 246	70.52 374	58.44 420	39.77 420	66.05 400	75.84 403	27.10 429	72.28 381	46.15 399	84.77 364	73.11 406

MDTV_nov1_ep13			68.29 351		78.03 370	43.87 405	74.12 346	72.22 366	52.17 381	67.02 398	85.54 306	45.36 377	80.85 347	55.73 345	84.42 365

test_fmvs1		69.57 342	69.05 342	71.14 349	69.15 421	65.77 231	73.98 348	83.32 285	42.83 416	77.77 329	78.27 386	43.39 391	68.50 398	68.39 257	84.38 366	79.15 396

1112_ss		74.82 290	73.74 292	78.04 284	89.57 172	60.04 297	76.49 321	87.09 233	54.31 368	73.66 364	79.80 372	60.25 295	86.76 289	58.37 330	84.15 367	87.32 304

testing11		67.38 354	65.93 362	71.73 345	83.37 315	46.60 393	70.95 371	69.40 383	62.47 303	66.14 399	76.66 399	31.22 416	84.10 326	49.10 385	84.10 368	84.49 334

PatchMatch-RL		74.48 293	73.22 300	78.27 280	87.70 221	85.26 38	75.92 330	70.09 379	64.34 292	76.09 343	81.25 360	65.87 264	78.07 364	53.86 359	83.82 369	71.48 408

UBG		64.34 374	63.35 376	67.30 375	83.50 309	40.53 413	67.46 390	65.02 403	54.77 366	67.54 397	74.47 408	32.99 412	78.50 363	40.82 410	83.58 370	82.88 362

MDA-MVSNet_test_wron		70.05 336	70.44 328	68.88 364	73.84 402	53.47 356	58.93 415	67.28 392	58.43 340	87.09 167	85.40 311	59.80 300	67.25 404	59.66 325	83.54 371	85.92 319

YYNet1		70.06 335	70.44 328	68.90 363	73.76 403	53.42 358	58.99 414	67.20 393	58.42 341	87.10 166	85.39 312	59.82 299	67.32 403	59.79 324	83.50 372	85.96 317

Test_1112_low_res		73.90 299	73.08 301	76.35 307	90.35 159	55.95 335	73.40 355	86.17 244	50.70 393	73.14 365	85.94 301	58.31 309	85.90 306	56.51 340	83.22 373	87.20 306

PVSNet	58.17 21	66.41 363	65.63 367	68.75 365	81.96 330	49.88 382	62.19 407	72.51 364	51.03 390	68.04 393	75.34 406	50.84 347	74.77 375	45.82 401	82.96 374	81.60 378

gg-mvs-nofinetune		68.96 348	69.11 341	68.52 370	76.12 389	45.32 399	83.59 194	55.88 422	86.68 29	64.62 411	97.01 9	30.36 419	83.97 329	44.78 403	82.94 375	76.26 401

CR-MVSNet		74.00 298	73.04 302	76.85 302	79.58 358	62.64 263	82.58 222	76.90 330	50.50 395	75.72 347	92.38 153	48.07 358	84.07 327	68.72 253	82.91 376	83.85 347

RPMNet		78.88 242	78.28 251	80.68 245	79.58 358	62.64 263	82.58 222	94.16 32	74.80 161	75.72 347	92.59 146	48.69 355	95.56 42	73.48 202	82.91 376	83.85 347

test_vis1_n		70.29 331	69.99 335	71.20 348	75.97 390	66.50 223	76.69 316	80.81 307	44.22 410	75.43 350	77.23 395	50.00 352	68.59 396	66.71 267	82.85 378	78.52 398

test0.0.03 1		64.66 372	64.36 371	65.57 384	75.03 398	46.89 392	64.69 400	61.58 414	62.43 306	71.18 376	77.54 391	43.41 389	68.47 399	40.75 411	82.65 379	81.35 380

HY-MVS	64.64 18	73.03 307	72.47 311	74.71 322	83.36 316	54.19 351	82.14 239	81.96 297	56.76 357	69.57 387	86.21 298	60.03 296	84.83 318	49.58 383	82.65 379	85.11 328

SCA		73.32 303	72.57 309	75.58 316	81.62 335	55.86 338	78.89 283	71.37 374	61.73 311	74.93 356	83.42 337	60.46 292	87.01 280	58.11 334	82.63 381	83.88 344

test_f		64.31 375	65.85 363	59.67 401	66.54 425	62.24 274	57.76 417	70.96 376	40.13 419	84.36 228	82.09 351	46.93 360	51.67 425	61.99 309	81.89 382	65.12 416

CHOSEN 1792x2688		72.45 311	70.56 326	78.13 281	90.02 169	63.08 256	68.72 384	83.16 286	42.99 415	75.92 345	85.46 309	57.22 318	85.18 315	49.87 381	81.67 383	86.14 316

WTY-MVS		67.91 353	68.35 350	66.58 379	80.82 347	48.12 386	65.96 397	72.60 362	53.67 371	71.20 375	81.68 357	58.97 305	69.06 394	48.57 388	81.67 383	82.55 366

TESTMET0.1,1		61.29 381	60.32 387	64.19 389	72.06 414	51.30 373	67.89 386	62.09 407	45.27 406	60.65 417	69.01 416	27.93 425	64.74 413	56.31 341	81.65 385	76.53 400

dmvs_re		66.81 360	66.98 356	66.28 380	76.87 380	58.68 317	71.66 365	72.24 365	60.29 331	69.52 388	73.53 409	52.38 340	64.40 414	44.90 402	81.44 386	75.76 402

PAPM		71.77 317	70.06 333	76.92 299	86.39 252	53.97 352	76.62 318	86.62 239	53.44 372	63.97 412	84.73 323	57.79 315	92.34 161	39.65 412	81.33 387	84.45 336

DSMNet-mixed		60.98 384	61.61 384	59.09 403	72.88 411	45.05 401	74.70 342	46.61 429	26.20 427	65.34 405	90.32 222	55.46 328	63.12 416	41.72 408	81.30 388	69.09 412

sss		66.92 357	67.26 355	65.90 381	77.23 376	51.10 377	64.79 399	71.72 372	52.12 384	70.13 383	80.18 369	57.96 312	65.36 412	50.21 378	81.01 389	81.25 383

UWE-MVS-28		58.44 389	57.71 391	60.65 399	73.58 405	31.23 426	69.68 381	48.80 427	53.12 376	61.79 414	78.83 381	30.98 417	68.40 400	21.58 428	80.99 390	82.33 371

tpm		67.95 352	68.08 353	67.55 373	78.74 369	43.53 406	75.60 332	67.10 396	54.92 364	72.23 370	88.10 260	42.87 393	75.97 371	52.21 370	80.95 391	83.15 359

MonoMVSNet		76.66 268	77.26 260	74.86 320	79.86 356	54.34 350	86.26 137	86.08 246	71.08 219	85.59 201	88.68 252	53.95 334	85.93 303	63.86 294	80.02 392	84.32 338

tpm2		68.45 351	66.83 358	73.30 330	78.93 368	48.50 384	79.76 267	71.76 371	47.50 399	69.92 384	83.60 333	42.07 394	88.40 263	48.44 390	79.51 393	83.01 361

FPMVS		72.29 314	72.00 313	73.14 331	88.63 199	85.00 40	74.65 343	67.39 391	71.94 210	77.80 328	87.66 271	50.48 350	75.83 372	49.95 379	79.51 393	58.58 422

UnsupCasMVSNet_bld		69.21 346	69.68 337	67.82 372	79.42 361	51.15 375	67.82 389	75.79 337	54.15 369	77.47 333	85.36 314	59.26 303	70.64 388	48.46 389	79.35 395	81.66 377

CostFormer		69.98 338	68.68 348	73.87 325	77.14 377	50.72 378	79.26 276	74.51 346	51.94 385	70.97 377	84.75 322	45.16 381	87.49 275	55.16 353	79.23 396	83.40 354

1314		73.22 305	72.56 310	75.20 317	80.41 353	57.84 323	81.64 243	85.36 258	51.68 386	73.10 366	76.65 400	61.45 287	85.19 314	63.54 297	79.21 397	82.59 364

test_vis1_n_1920		71.30 324	71.58 318	70.47 350	77.58 374	59.99 299	74.25 344	84.22 280	51.06 389	74.85 357	79.10 378	55.10 331	68.83 395	68.86 250	79.20 398	82.58 365

baseline1		73.26 304	73.54 295	72.43 340	84.92 284	47.79 388	79.89 266	74.00 349	65.93 273	78.81 319	86.28 297	56.36 322	81.63 343	56.63 339	79.04 399	87.87 298

PMMVS		61.65 379	60.38 386	65.47 385	65.40 429	69.26 193	63.97 403	61.73 412	36.80 426	60.11 418	68.43 417	59.42 301	66.35 408	48.97 386	78.57 400	60.81 419

baseline2		69.77 340	66.89 357	78.41 276	79.51 360	58.09 319	76.23 325	69.57 382	57.50 350	64.82 410	77.45 393	46.02 366	88.44 262	53.08 364	77.83 401	88.70 283

test_vis1_rt		65.64 368	64.09 372	70.31 351	66.09 426	70.20 182	61.16 408	81.60 301	38.65 422	72.87 367	69.66 415	52.84 337	60.04 419	56.16 342	77.77 402	80.68 390

MS-PatchMatch		70.93 327	70.22 331	73.06 332	81.85 332	62.50 266	73.82 351	77.90 320	52.44 380	75.92 345	81.27 359	55.67 327	81.75 341	55.37 350	77.70 403	74.94 404

UnsupCasMVSNet_eth		71.63 320	72.30 312	69.62 358	76.47 385	52.70 363	70.03 378	80.97 306	59.18 336	79.36 312	88.21 259	60.50 291	69.12 393	58.33 332	77.62 404	87.04 307

CVMVSNet		72.62 310	71.41 320	76.28 309	83.25 319	60.34 295	83.50 196	79.02 317	37.77 425	76.33 338	85.10 316	49.60 354	87.41 276	70.54 231	77.54 405	81.08 386

test_cas_vis1_n_1920		69.20 347	69.12 340	69.43 360	73.68 404	62.82 260	70.38 376	77.21 327	46.18 404	80.46 301	78.95 380	52.03 341	65.53 411	65.77 278	77.45 406	79.95 394

GG-mvs-BLEND				67.16 376	73.36 406	46.54 395	84.15 177	55.04 423		58.64 422	61.95 423	29.93 420	83.87 330	38.71 415	76.92 407	71.07 409

CHOSEN 280x420		59.08 387	56.52 393	66.76 378	76.51 384	64.39 242	49.62 422	59.00 418	43.86 411	55.66 426	68.41 418	35.55 407	68.21 402	43.25 405	76.78 408	67.69 414

tpmvs		70.16 333	69.56 338	71.96 343	74.71 400	48.13 385	79.63 268	75.45 342	65.02 289	70.26 382	81.88 354	45.34 378	85.68 310	58.34 331	75.39 409	82.08 374

MVP-Stereo		75.81 279	73.51 296	82.71 207	89.35 178	73.62 135	80.06 262	85.20 261	60.30 330	73.96 361	87.94 263	57.89 314	89.45 245	52.02 371	74.87 410	85.06 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
new_pmnet		55.69 391	57.66 392	49.76 407	75.47 394	30.59 427	59.56 410	51.45 425	43.62 413	62.49 413	75.48 405	40.96 396	49.15 427	37.39 419	72.52 411	69.55 411

mvsany_test3		65.48 369	62.97 378	73.03 333	69.99 419	76.17 121	64.83 398	43.71 430	43.68 412	80.25 305	87.05 286	52.83 338	63.09 417	51.92 375	72.44 412	79.84 395

PatchmatchNet		69.71 341	68.83 346	72.33 342	77.66 373	53.60 355	79.29 275	69.99 380	57.66 348	72.53 369	82.93 342	46.45 363	80.08 354	60.91 318	72.09 413	83.31 357
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MVS-HIRNet		61.16 382	62.92 379	55.87 404	79.09 365	35.34 423	71.83 363	57.98 421	46.56 402	59.05 420	91.14 192	49.95 353	76.43 369	38.74 414	71.92 414	55.84 423

tpmrst		66.28 364	66.69 360	65.05 387	72.82 412	39.33 415	78.20 292	70.69 378	53.16 375	67.88 394	80.36 368	48.18 357	74.75 376	58.13 333	70.79 415	81.08 386

tpm cat1		66.76 361	65.21 370	71.42 346	77.09 378	50.62 379	78.01 293	73.68 355	44.89 408	68.64 390	79.00 379	45.51 375	82.42 338	49.91 380	70.15 416	81.23 385

ADS-MVSNet2		65.87 366	63.64 375	72.55 338	73.16 408	56.92 331	67.10 393	74.81 343	49.74 397	66.04 401	82.97 340	46.71 361	77.26 367	42.29 406	69.96 417	83.46 352

ADS-MVSNet		61.90 378	62.19 382	61.03 398	73.16 408	36.42 421	67.10 393	61.75 411	49.74 397	66.04 401	82.97 340	46.71 361	63.21 415	42.29 406	69.96 417	83.46 352

JIA-IIPM		69.41 343	66.64 361	77.70 290	73.19 407	71.24 173	75.67 331	65.56 401	70.42 224	65.18 406	92.97 133	33.64 411	83.06 332	53.52 363	69.61 419	78.79 397

dmvs_testset		60.59 386	62.54 381	54.72 406	77.26 375	27.74 429	74.05 347	61.00 415	60.48 329	65.62 404	67.03 419	55.93 325	68.23 401	32.07 425	69.46 420	68.17 413

EPMVS		62.47 376	62.63 380	62.01 393	70.63 418	38.74 417	74.76 341	52.86 424	53.91 370	67.71 396	80.01 370	39.40 398	66.60 407	55.54 349	68.81 421	80.68 390

MVE	40.22 23	51.82 393	50.47 396	55.87 404	62.66 431	51.91 368	31.61 425	39.28 432	40.65 418	50.76 427	74.98 407	56.24 324	44.67 428	33.94 423	64.11 422	71.04 410
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
dp		60.70 385	60.29 388	61.92 395	72.04 415	38.67 418	70.83 372	64.08 405	51.28 388	60.75 416	77.28 394	36.59 406	71.58 386	47.41 393	62.34 423	75.52 403

mvsany_test1		58.48 388	56.47 394	64.50 388	65.90 428	68.21 206	56.95 418	42.11 431	38.30 423	65.69 403	77.19 397	56.96 319	59.35 421	46.16 398	58.96 424	65.93 415

PVSNet_0	51.08 22	56.10 390	54.97 395	59.48 402	75.12 397	53.28 359	55.16 419	61.89 410	44.30 409	59.16 419	62.48 422	54.22 333	65.91 410	35.40 420	47.01 425	59.25 421

tmp_tt		20.25 398	24.50 401	7.49 413	4.47 436	8.70 437	34.17 424	25.16 434	1.00 431	32.43 430	18.49 428	39.37 399	9.21 432	21.64 427	43.75 426	4.57 428

test_method		30.46 396	29.60 399	33.06 410	17.99 435	3.84 438	13.62 426	73.92 350	2.79 429	18.29 431	53.41 424	28.53 423	43.25 429	22.56 426	35.27 427	52.11 424

DeepMVS_CX				24.13 412	32.95 434	29.49 428		21.63 435	12.07 428	37.95 429	45.07 426	30.84 418	19.21 431	17.94 430	33.06 428	23.69 427

dongtai		41.90 394	42.65 397	39.67 409	70.86 417	21.11 431	61.01 409	21.42 436	57.36 351	57.97 424	50.06 425	16.40 435	58.73 422	21.03 429	27.69 429	39.17 425

kuosan		30.83 395	32.17 398	26.83 411	53.36 433	19.02 434	57.90 416	20.44 437	38.29 424	38.01 428	37.82 427	15.18 436	33.45 430	7.74 431	20.76 430	28.03 426

testmvs		5.91 402	7.65 405	0.72 415	1.20 437	0.37 440	59.14 412	0.67 439	0.49 433	1.11 433	2.76 432	0.94 438	0.24 434	1.02 433	1.47 431	1.55 430

test123		6.27 401	8.08 404	0.84 414	1.11 438	0.57 439	62.90 404	0.82 438	0.54 432	1.07 434	2.75 433	1.26 437	0.30 433	1.04 432	1.26 432	1.66 429

mmdepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

monomultidepth		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

test_blank		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet_test		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

DCPMVS		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

cdsmvs_eth3d_5k		20.81 397	27.75 400	0.00 416	0.00 439	0.00 441	0.00 427	85.44 257	0.00 434	0.00 435	82.82 344	81.46 120	0.00 435	0.00 434	0.00 433	0.00 431

pcd_1.5k_mvsjas		6.41 400	8.55 403	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	76.94 168	0.00 435	0.00 434	0.00 433	0.00 431

sosnet-low-res		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

sosnet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uncertanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

Regformer		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

ab-mvs-re		6.65 399	8.87 402	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	79.80 372	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

uanet		0.00 403	0.00 406	0.00 416	0.00 439	0.00 441	0.00 427	0.00 440	0.00 434	0.00 435	0.00 434	0.00 439	0.00 435	0.00 434	0.00 433	0.00 431

WAC-MVS						37.39 419								52.61 369

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					0.00 439

eth-test					0.00 439

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	89.72 188	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																83.88 344

test_part2					93.86 61	77.77 98				92.84 51

sam_mvs1												46.11 365				83.88 344

sam_mvs												45.92 370

MTGPA								91.81 127

test_post1							78.85 285				3.13 430	45.19 380	80.13 353	58.11 334

test_post											3.10 431	45.43 376	77.22 368

patchmatchnet-post											81.71 356	45.93 369	87.01 280

MTMP							90.66 48	33.14 433

gm-plane-assit					75.42 395	44.97 402			52.17 381		72.36 412		87.90 270	54.10 358

TEST9					92.34 98	79.70 78	83.94 182	90.32 170	65.41 285	84.49 224	90.97 198	82.03 111	93.63 115

test_8					92.09 107	78.87 85	83.82 187	90.31 172	65.79 276	84.36 228	90.96 200	81.93 113	93.44 128

agg_prior					91.58 127	77.69 100		90.30 173		84.32 230			93.18 136

test_prior4						78.97 84	84.59 168

test_prior				86.32 110	90.59 155	71.99 163		92.85 93					94.17 97			92.80 161

旧先验2							81.73 241		56.88 356	86.54 185			84.90 317	72.81 213

新几何2							81.72 242

无先验							82.81 217	85.62 255	58.09 344				91.41 187	67.95 261		84.48 335

原ACMM2							82.26 235

testdata2													86.43 294	63.52 298

segment_acmp												81.94 112

testdata1							79.62 269		73.95 170

plane_prior7					93.45 68	77.31 106

plane_prior6					92.61 90	76.54 113						74.84 189

plane_prior4											92.95 134

plane_prior3						76.85 111			77.79 126	86.55 180

plane_prior2							89.45 82		79.44 101

plane_prior1					92.83 88

n2								0.00 440

nn								0.00 440

door-mid								74.45 347

test11								91.46 133

door								72.57 363

HQP5-MVS						70.66 176

HQP-NCC					91.19 139		84.77 161		73.30 184	80.55 298

ACMP_Plane					91.19 139		84.77 161		73.30 184	80.55 298

BP-MVS														77.30 156

HQP4-MVS										80.56 297			94.61 79			93.56 133

HQP2-MVS												72.10 226

NP-MVS					91.95 112	74.55 130					90.17 229

MDTV_nov1_ep13_2view						27.60 430	70.76 373		46.47 403	61.27 415		45.20 379		49.18 384		83.75 349

Test By Simon												79.09 142

High-resolution multi-view benchmark