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.

FOURS1					99.91 1	99.93 1	99.87 5	99.56 57	99.10 8	99.81 24

SED-MVS		99.61 2	99.52 6	99.88 6	99.84 33	99.90 2	99.60 77	99.48 145	99.08 12	99.91 1	99.81 62	99.20 7	99.96 19	98.91 71	99.85 58	99.79 57

test_241102_ONE					99.84 33	99.90 2		99.48 145	99.07 14	99.91 1	99.74 119	99.20 7	99.76 178

DVP-MVS		99.57 8	99.47 10	99.88 6	99.85 26	99.89 4	99.57 95	99.37 231	99.10 8	99.81 24	99.80 78	98.94 34	99.96 19	98.93 68	99.86 51	99.81 43
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				99.91 2	99.84 33	99.89 4	99.57 95	99.51 104					99.96 19	98.93 68	99.86 51	99.88 7

test0726					99.85 26	99.89 4	99.62 70	99.50 124	99.10 8	99.86 12	99.82 49	98.94 34

APDe-MVS		99.66 1	99.57 1	99.92 1	99.77 51	99.89 4	99.75 31	99.56 57	99.02 16	99.88 5	99.85 29	99.18 10	99.96 19	99.22 39	99.92 11	99.90 1

DVP-MVS++.		99.59 3	99.50 8	99.88 6	99.51 154	99.88 8	99.87 5	99.51 104	98.99 26	99.88 5	99.81 62	99.27 5	99.96 19	98.85 85	99.80 87	99.81 43

test_one_0601					99.81 41	99.88 8		99.49 132	98.97 32	99.65 73	99.81 62	99.09 14

IU-MVS					99.84 33	99.88 8		99.32 258	98.30 92	99.84 14				98.86 83	99.85 58	99.89 2

DPE-MVS		99.46 25	99.32 32	99.91 2	99.78 46	99.88 8	99.36 198	99.51 104	98.73 56	99.88 5	99.84 38	98.72 63	99.96 19	98.16 172	99.87 40	99.88 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		99.37 50	99.20 63	99.88 6	99.90 4	99.87 12	99.30 213	99.52 91	97.18 215	99.60 88	99.79 90	98.79 50	99.95 46	98.83 91	99.91 16	99.83 31
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
ACMMP_NAP		99.47 23	99.34 28	99.88 6	99.87 16	99.86 13	99.47 151	99.48 145	98.05 128	99.76 40	99.86 23	98.82 47	99.93 72	98.82 95	99.91 16	99.84 20

zzz-MVS		99.49 16	99.36 23	99.89 4	99.90 4	99.86 13	99.36 198	99.47 163	98.79 52	99.68 57	99.81 62	98.43 84	99.97 11	98.88 74	99.90 23	99.83 31

MTAPA		99.52 14	99.39 18	99.89 4	99.90 4	99.86 13	99.66 52	99.47 163	98.79 52	99.68 57	99.81 62	98.43 84	99.97 11	98.88 74	99.90 23	99.83 31

HPM-MVS++		99.39 48	99.23 61	99.87 12	99.75 64	99.84 16	99.43 165	99.51 104	98.68 60	99.27 163	99.53 214	98.64 71	99.96 19	98.44 148	99.80 87	99.79 57

SR-MVS		99.43 35	99.29 47	99.86 21	99.75 64	99.83 17	99.59 83	99.62 33	98.21 103	99.73 46	99.79 90	98.68 66	99.96 19	98.44 148	99.77 97	99.79 57

SMA-MVS		99.44 31	99.30 43	99.85 28	99.73 77	99.83 17	99.56 102	99.47 163	97.45 189	99.78 34	99.82 49	99.18 10	99.91 94	98.79 97	99.89 33	99.81 43
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
test_part2					99.81 41	99.83 17				99.77 36

XVS		99.53 12	99.42 14	99.87 12	99.85 26	99.83 17	99.69 40	99.68 19	98.98 29	99.37 141	99.74 119	98.81 48	99.94 57	98.79 97	99.86 51	99.84 20

X-MVStestdata		96.55 293	95.45 308	99.87 12	99.85 26	99.83 17	99.69 40	99.68 19	98.98 29	99.37 141	64.01 371	98.81 48	99.94 57	98.79 97	99.86 51	99.84 20

APD-MVS_3200maxsize		99.48 20	99.35 26	99.85 28	99.76 54	99.83 17	99.63 64	99.54 74	98.36 85	99.79 29	99.82 49	98.86 43	99.95 46	98.62 120	99.81 83	99.78 65

test1172		99.43 35	99.29 47	99.85 28	99.75 64	99.82 23	99.60 77	99.56 57	98.28 93	99.74 44	99.79 90	98.53 75	99.95 46	98.55 137	99.78 94	99.79 57

SR-MVS-dyc-post		99.45 27	99.31 39	99.85 28	99.76 54	99.82 23	99.63 64	99.52 91	98.38 81	99.76 40	99.82 49	98.53 75	99.95 46	98.61 123	99.81 83	99.77 67

RE-MVS-def			99.34 28		99.76 54	99.82 23	99.63 64	99.52 91	98.38 81	99.76 40	99.82 49	98.75 59		98.61 123	99.81 83	99.77 67

MP-MVS		99.33 55	99.15 67	99.87 12	99.88 12	99.82 23	99.66 52	99.46 173	98.09 118	99.48 113	99.74 119	98.29 96	99.96 19	97.93 190	99.87 40	99.82 38
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		99.47 23	99.33 30	99.87 12	99.87 16	99.81 27	99.64 62	99.67 22	98.08 122	99.55 100	99.64 170	98.91 39	99.96 19	98.72 106	99.90 23	99.82 38

SteuartSystems-ACMMP		99.54 10	99.42 14	99.87 12	99.82 38	99.81 27	99.59 83	99.51 104	98.62 62	99.79 29	99.83 42	99.28 4	99.97 11	98.48 142	99.90 23	99.84 20
Skip Steuart: Steuart Systems R&D Blog.
MSP-MVS		99.42 40	99.27 53	99.88 6	99.89 9	99.80 29	99.67 48	99.50 124	98.70 58	99.77 36	99.49 227	98.21 99	99.95 46	98.46 146	99.77 97	99.88 7
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
HFP-MVS		99.49 16	99.37 21	99.86 21	99.87 16	99.80 29	99.66 52	99.67 22	98.15 109	99.68 57	99.69 143	99.06 16	99.96 19	98.69 111	99.87 40	99.84 20

region2R		99.48 20	99.35 26	99.87 12	99.88 12	99.80 29	99.65 59	99.66 27	98.13 111	99.66 68	99.68 150	98.96 28	99.96 19	98.62 120	99.87 40	99.84 20

#test#		99.43 35	99.29 47	99.86 21	99.87 16	99.80 29	99.55 111	99.67 22	97.83 146	99.68 57	99.69 143	99.06 16	99.96 19	98.39 150	99.87 40	99.84 20

ZD-MVS					99.71 88	99.79 33		99.61 35	96.84 245	99.56 96	99.54 210	98.58 73	99.96 19	96.93 270	99.75 102

testtj		99.12 88	98.87 107	99.86 21	99.72 82	99.79 33	99.44 159	99.51 104	97.29 205	99.59 91	99.74 119	98.15 105	99.96 19	96.74 278	99.69 115	99.81 43

GST-MVS		99.40 47	99.24 59	99.85 28	99.86 22	99.79 33	99.60 77	99.67 22	97.97 134	99.63 77	99.68 150	98.52 77	99.95 46	98.38 152	99.86 51	99.81 43

ACMMPR		99.49 16	99.36 23	99.86 21	99.87 16	99.79 33	99.66 52	99.67 22	98.15 109	99.67 63	99.69 143	98.95 31	99.96 19	98.69 111	99.87 40	99.84 20

mPP-MVS		99.44 31	99.30 43	99.86 21	99.88 12	99.79 33	99.69 40	99.48 145	98.12 113	99.50 109	99.75 113	98.78 51	99.97 11	98.57 131	99.89 33	99.83 31

HPM-MVS_fast		99.51 15	99.40 17	99.85 28	99.91 1	99.79 33	99.76 30	99.56 57	97.72 160	99.76 40	99.75 113	99.13 12	99.92 83	99.07 55	99.92 11	99.85 16

APD-MVS		99.27 64	99.08 75	99.84 35	99.75 64	99.79 33	99.50 130	99.50 124	97.16 217	99.77 36	99.82 49	98.78 51	99.94 57	97.56 226	99.86 51	99.80 53
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		99.45 27	99.31 39	99.86 21	99.87 16	99.78 40	99.58 90	99.65 32	97.84 145	99.71 50	99.80 78	99.12 13	99.97 11	98.33 158	99.87 40	99.83 31

MSC_two_6792asdad				99.87 12	99.51 154	99.76 41		99.33 248					99.96 19	98.87 78	99.84 65	99.89 2

No_MVS				99.87 12	99.51 154	99.76 41		99.33 248					99.96 19	98.87 78	99.84 65	99.89 2

abl_6		99.44 31	99.31 39	99.83 36	99.85 26	99.75 43	99.66 52	99.59 43	98.13 111	99.82 22	99.81 62	98.60 72	99.96 19	98.46 146	99.88 36	99.79 57

CP-MVS		99.45 27	99.32 32	99.85 28	99.83 37	99.75 43	99.69 40	99.52 91	98.07 123	99.53 103	99.63 176	98.93 38	99.97 11	98.74 102	99.91 16	99.83 31

LS3D		99.27 64	99.12 70	99.74 59	99.18 245	99.75 43	99.56 102	99.57 51	98.45 74	99.49 112	99.85 29	97.77 115	99.94 57	98.33 158	99.84 65	99.52 153

MCST-MVS		99.43 35	99.30 43	99.82 38	99.79 44	99.74 46	99.29 217	99.40 212	98.79 52	99.52 106	99.62 182	98.91 39	99.90 109	98.64 118	99.75 102	99.82 38

OPU-MVS				99.64 80	99.56 146	99.72 47	99.60 77				99.70 135	99.27 5	99.42 251	98.24 164	99.80 87	99.79 57

HPM-MVS		99.42 40	99.28 51	99.83 36	99.90 4	99.72 47	99.81 15	99.54 74	97.59 172	99.68 57	99.63 176	98.91 39	99.94 57	98.58 129	99.91 16	99.84 20
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CDPH-MVS		99.13 82	98.91 102	99.80 43	99.75 64	99.71 49	99.15 253	99.41 206	96.60 263	99.60 88	99.55 205	98.83 46	99.90 109	97.48 233	99.83 74	99.78 65

CNVR-MVS		99.42 40	99.30 43	99.78 48	99.62 128	99.71 49	99.26 233	99.52 91	98.82 47	99.39 136	99.71 131	98.96 28	99.85 134	98.59 128	99.80 87	99.77 67

DP-MVS Recon		99.12 88	98.95 98	99.65 75	99.74 72	99.70 51	99.27 224	99.57 51	96.40 280	99.42 125	99.68 150	98.75 59	99.80 164	97.98 186	99.72 109	99.44 174

ETH3D-3000-0.1		99.21 70	99.02 85	99.77 50	99.73 77	99.69 52	99.38 191	99.51 104	97.45 189	99.61 84	99.75 113	98.51 78	99.91 94	97.45 238	99.83 74	99.71 98

nrg030		98.64 150	98.42 155	99.28 149	99.05 273	99.69 52	99.81 15	99.46 173	98.04 129	99.01 216	99.82 49	96.69 148	99.38 255	99.34 28	94.59 314	98.78 231

SF-MVS		99.38 49	99.24 59	99.79 46	99.79 44	99.68 54	99.57 95	99.54 74	97.82 151	99.71 50	99.80 78	98.95 31	99.93 72	98.19 167	99.84 65	99.74 78

SD-MVS		99.41 44	99.52 6	99.05 170	99.74 72	99.68 54	99.46 154	99.52 91	99.11 7	99.88 5	99.91 5	99.43 1	97.70 353	98.72 106	99.93 10	99.77 67
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
3Dnovator+	97.12 13	99.18 74	98.97 94	99.82 38	99.17 251	99.68 54	99.81 15	99.51 104	99.20 4	98.72 258	99.89 10	95.68 182	99.97 11	98.86 83	99.86 51	99.81 43

QAPM		98.67 147	98.30 164	99.80 43	99.20 240	99.67 57	99.77 27	99.72 11	94.74 325	98.73 257	99.90 7	95.78 178	99.98 6	96.96 267	99.88 36	99.76 72

ACMMP		99.45 27	99.32 32	99.82 38	99.89 9	99.67 57	99.62 70	99.69 18	98.12 113	99.63 77	99.84 38	98.73 62	99.96 19	98.55 137	99.83 74	99.81 43
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
TSAR-MVS + MP.		99.58 5	99.50 8	99.81 41	99.91 1	99.66 59	99.63 64	99.39 216	98.91 41	99.78 34	99.85 29	99.36 2	99.94 57	98.84 88	99.88 36	99.82 38
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MAR-MVS		98.86 122	98.63 136	99.54 96	99.37 198	99.66 59	99.45 155	99.54 74	96.61 261	99.01 216	99.40 256	97.09 133	99.86 128	97.68 216	99.53 136	99.10 197
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
3Dnovator	97.25 9	99.24 69	99.05 77	99.81 41	99.12 258	99.66 59	99.84 9	99.74 10	99.09 11	98.92 232	99.90 7	95.94 171	99.98 6	98.95 65	99.92 11	99.79 57

TEST9					99.67 103	99.65 62	99.05 272	99.41 206	96.22 291	98.95 227	99.49 227	98.77 54	99.91 94

train_agg		99.02 107	98.77 121	99.77 50	99.67 103	99.65 62	99.05 272	99.41 206	96.28 284	98.95 227	99.49 227	98.76 56	99.91 94	97.63 217	99.72 109	99.75 73

NCCC		99.34 53	99.19 64	99.79 46	99.61 132	99.65 62	99.30 213	99.48 145	98.86 43	99.21 179	99.63 176	98.72 63	99.90 109	98.25 163	99.63 128	99.80 53

test_part1		97.75 243	97.24 276	99.29 146	99.59 138	99.63 65	99.65 59	99.49 132	96.17 295	98.44 290	99.69 143	89.80 323	99.47 238	98.68 113	93.66 327	98.78 231

agg_prior1		99.01 110	98.76 123	99.76 53	99.67 103	99.62 66	98.99 288	99.40 212	96.26 287	98.87 240	99.49 227	98.77 54	99.91 94	97.69 214	99.72 109	99.75 73

agg_prior					99.67 103	99.62 66		99.40 212		98.87 240			99.91 94

test_8					99.67 103	99.61 68	99.03 278	99.41 206	96.28 284	98.93 231	99.48 233	98.76 56	99.91 94

test12				99.75 54	99.64 119	99.61 68		99.29 270		99.21 179		98.38 89	99.89 117		99.74 105	99.74 78

ETH3D cwj APD-0.16		99.06 101	98.84 113	99.72 64	99.51 154	99.60 70	99.23 238	99.44 194	97.04 230	99.39 136	99.67 156	98.30 95	99.92 83	97.27 245	99.69 115	99.64 124

ETH3 D test6400		98.70 143	98.35 159	99.73 61	99.69 98	99.60 70	99.16 249	99.45 185	95.42 313	99.27 163	99.60 189	97.39 122	99.91 94	95.36 311	99.83 74	99.70 100

1121		99.09 97	98.87 107	99.75 54	99.74 72	99.60 70	99.27 224	99.48 145	96.82 248	99.25 170	99.65 163	98.38 89	99.93 72	97.53 229	99.67 122	99.73 85

xxxxxxxxxxxxxcwj		99.43 35	99.32 32	99.75 54	99.76 54	99.59 73	99.14 255	99.53 85	99.00 23	99.71 50	99.80 78	98.95 31	99.93 72	98.19 167	99.84 65	99.74 78

save fliter					99.76 54	99.59 73	99.14 255	99.40 212	99.00 23

新几何1				99.75 54	99.75 64	99.59 73		99.54 74	96.76 249	99.29 158	99.64 170	98.43 84	99.94 57	96.92 272	99.66 123	99.72 91

旧先验1					99.74 72	99.59 73		99.54 74			99.69 143	98.47 81			99.68 120	99.73 85

DeepC-MVS_fast	98.69 1	99.49 16	99.39 18	99.77 50	99.63 122	99.59 73	99.36 198	99.46 173	99.07 14	99.79 29	99.82 49	98.85 44	99.92 83	98.68 113	99.87 40	99.82 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior4						99.56 78	98.99 288

VNet		99.11 93	98.90 103	99.73 61	99.52 152	99.56 78	99.41 174	99.39 216	99.01 19	99.74 44	99.78 97	95.56 185	99.92 83	99.52 7	98.18 214	99.72 91

DPM-MVS		98.95 115	98.71 127	99.66 71	99.63 122	99.55 80	98.64 330	99.10 294	97.93 137	99.42 125	99.55 205	98.67 69	99.80 164	95.80 300	99.68 120	99.61 132

UA-Net		99.42 40	99.29 47	99.80 43	99.62 128	99.55 80	99.50 130	99.70 15	98.79 52	99.77 36	99.96 1	97.45 121	99.96 19	98.92 70	99.90 23	99.89 2

FIs		98.78 138	98.63 136	99.23 156	99.18 245	99.54 82	99.83 12	99.59 43	98.28 93	98.79 252	99.81 62	96.75 146	99.37 258	99.08 54	96.38 274	98.78 231

VPA-MVSNet		98.29 171	97.95 193	99.30 143	99.16 253	99.54 82	99.50 130	99.58 49	98.27 96	99.35 147	99.37 264	92.53 280	99.65 218	99.35 24	94.46 315	98.72 245

AdaColmap		99.01 110	98.80 118	99.66 71	99.56 146	99.54 82	99.18 247	99.70 15	98.18 107	99.35 147	99.63 176	96.32 159	99.90 109	97.48 233	99.77 97	99.55 145

114514_t		98.93 116	98.67 131	99.72 64	99.85 26	99.53 85	99.62 70	99.59 43	92.65 344	99.71 50	99.78 97	98.06 108	99.90 109	98.84 88	99.91 16	99.74 78

DP-MVS		99.16 78	98.95 98	99.78 48	99.77 51	99.53 85	99.41 174	99.50 124	97.03 232	99.04 213	99.88 15	97.39 122	99.92 83	98.66 116	99.90 23	99.87 12

OpenMVS	96.50 16	98.47 155	98.12 173	99.52 108	99.04 274	99.53 85	99.82 13	99.72 11	94.56 328	98.08 308	99.88 15	94.73 219	99.98 6	97.47 235	99.76 100	99.06 208

Regformer-2		99.54 10	99.47 10	99.75 54	99.71 88	99.52 88	99.49 140	99.49 132	98.94 36	99.83 19	99.76 108	99.01 19	99.94 57	99.15 48	99.87 40	99.80 53

PHI-MVS		99.30 58	99.17 66	99.70 67	99.56 146	99.52 88	99.58 90	99.80 8	97.12 221	99.62 81	99.73 126	98.58 73	99.90 109	98.61 123	99.91 16	99.68 107

MVS_111021_LR		99.41 44	99.33 30	99.65 75	99.77 51	99.51 90	98.94 302	99.85 6	98.82 47	99.65 73	99.74 119	98.51 78	99.80 164	98.83 91	99.89 33	99.64 124

test222					99.75 64	99.49 91	98.91 305	99.49 132	96.42 278	99.34 150	99.65 163	98.28 97			99.69 115	99.72 91

DROMVSNet		99.44 31	99.39 18	99.58 90	99.56 146	99.49 91	99.88 1	99.58 49	98.38 81	99.73 46	99.69 143	98.20 100	99.70 205	99.64 1	99.82 80	99.54 147

test_prior3		99.21 70	99.05 77	99.68 68	99.67 103	99.48 93	98.96 296	99.56 57	98.34 87	99.01 216	99.52 217	98.68 66	99.83 148	97.96 187	99.74 105	99.74 78

test_prior				99.68 68	99.67 103	99.48 93		99.56 57					99.83 148			99.74 78

CS-MVS		99.34 53	99.31 39	99.43 126	99.44 182	99.47 95	99.68 45	99.56 57	98.41 78	99.62 81	99.41 252	98.35 92	99.76 178	99.52 7	99.76 100	99.05 209

MVS_111021_HR		99.41 44	99.32 32	99.66 71	99.72 82	99.47 95	98.95 300	99.85 6	98.82 47	99.54 101	99.73 126	98.51 78	99.74 182	98.91 71	99.88 36	99.77 67

CPTT-MVS		99.11 93	98.90 103	99.74 59	99.80 43	99.46 97	99.59 83	99.49 132	97.03 232	99.63 77	99.69 143	97.27 129	99.96 19	97.82 199	99.84 65	99.81 43

FC-MVSNet-test		98.75 141	98.62 141	99.15 163	99.08 267	99.45 98	99.86 8	99.60 40	98.23 100	98.70 265	99.82 49	96.80 142	99.22 286	99.07 55	96.38 274	98.79 230

Regformer-1		99.53 12	99.47 10	99.72 64	99.71 88	99.44 99	99.49 140	99.46 173	98.95 35	99.83 19	99.76 108	99.01 19	99.93 72	99.17 45	99.87 40	99.80 53

PAPM_NR		99.04 104	98.84 113	99.66 71	99.74 72	99.44 99	99.39 186	99.38 222	97.70 162	99.28 160	99.28 287	98.34 93	99.85 134	96.96 267	99.45 138	99.69 103

alignmvs		98.81 134	98.56 149	99.58 90	99.43 183	99.42 101	99.51 124	98.96 309	98.61 63	99.35 147	98.92 325	94.78 213	99.77 174	99.35 24	98.11 220	99.54 147

Regformer-4		99.59 3	99.54 4	99.73 61	99.76 54	99.41 102	99.58 90	99.49 132	99.02 16	99.88 5	99.80 78	99.00 25	99.94 57	99.45 19	99.92 11	99.84 20

CNLPA		99.14 80	98.99 90	99.59 87	99.58 140	99.41 102	99.16 249	99.44 194	98.45 74	99.19 185	99.49 227	98.08 107	99.89 117	97.73 208	99.75 102	99.48 164

DELS-MVS		99.48 20	99.42 14	99.65 75	99.72 82	99.40 104	99.05 272	99.66 27	99.14 6	99.57 95	99.80 78	98.46 82	99.94 57	99.57 4	99.84 65	99.60 134
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
HyFIR lowres test		99.11 93	98.92 100	99.65 75	99.90 4	99.37 105	99.02 281	99.91 3	97.67 167	99.59 91	99.75 113	95.90 174	99.73 189	99.53 6	99.02 172	99.86 13

UniMVSNet (Re)		98.29 171	98.00 186	99.13 164	99.00 279	99.36 106	99.49 140	99.51 104	97.95 135	98.97 225	99.13 305	96.30 160	99.38 255	98.36 156	93.34 330	98.66 275

原ACMM1				99.65 75	99.73 77	99.33 107		99.47 163	97.46 186	99.12 195	99.66 162	98.67 69	99.91 94	97.70 213	99.69 115	99.71 98

canonicalmvs		99.02 107	98.86 111	99.51 110	99.42 184	99.32 108	99.80 19	99.48 145	98.63 61	99.31 153	98.81 328	97.09 133	99.75 181	99.27 36	97.90 224	99.47 169

XXY-MVS		98.38 164	98.09 177	99.24 154	99.26 226	99.32 108	99.56 102	99.55 67	97.45 189	98.71 259	99.83 42	93.23 258	99.63 226	98.88 74	96.32 276	98.76 237

IS-MVSNet		99.05 103	98.87 107	99.57 92	99.73 77	99.32 108	99.75 31	99.20 283	98.02 132	99.56 96	99.86 23	96.54 152	99.67 211	98.09 176	99.13 160	99.73 85

API-MVS		99.04 104	99.03 82	99.06 168	99.40 192	99.31 111	99.55 111	99.56 57	98.54 66	99.33 151	99.39 260	98.76 56	99.78 172	96.98 265	99.78 94	98.07 336

ETV-MVS		99.26 66	99.21 62	99.40 128	99.46 175	99.30 112	99.56 102	99.52 91	98.52 68	99.44 121	99.27 290	98.41 88	99.86 128	99.10 52	99.59 132	99.04 210

Regformer-3		99.57 8	99.53 5	99.68 68	99.76 54	99.29 113	99.58 90	99.44 194	99.01 19	99.87 11	99.80 78	98.97 27	99.91 94	99.44 21	99.92 11	99.83 31

Fast-Effi-MVS+		98.70 143	98.43 154	99.51 110	99.51 154	99.28 114	99.52 120	99.47 163	96.11 302	99.01 216	99.34 273	96.20 163	99.84 139	97.88 193	98.82 185	99.39 180

PatchMatch-RL		98.84 133	98.62 141	99.52 108	99.71 88	99.28 114	99.06 270	99.77 9	97.74 159	99.50 109	99.53 214	95.41 189	99.84 139	97.17 256	99.64 126	99.44 174

F-COLMAP		99.19 72	99.04 80	99.64 80	99.78 46	99.27 116	99.42 172	99.54 74	97.29 205	99.41 129	99.59 192	98.42 87	99.93 72	98.19 167	99.69 115	99.73 85

NR-MVSNet		97.97 211	97.61 229	99.02 174	98.87 296	99.26 117	99.47 151	99.42 204	97.63 170	97.08 334	99.50 224	95.07 202	99.13 300	97.86 195	93.59 328	98.68 260

WR-MVS		98.06 193	97.73 218	99.06 168	98.86 299	99.25 118	99.19 246	99.35 237	97.30 204	98.66 268	99.43 245	93.94 247	99.21 291	98.58 129	94.28 319	98.71 247

CP-MVSNet		98.09 190	97.78 210	99.01 175	98.97 285	99.24 119	99.67 48	99.46 173	97.25 209	98.48 288	99.64 170	93.79 251	99.06 309	98.63 119	94.10 322	98.74 243

CS-MVS-test		99.30 58	99.25 57	99.45 120	99.46 175	99.23 120	99.80 19	99.57 51	98.28 93	99.53 103	99.44 242	98.16 104	99.79 167	99.38 22	99.61 131	99.34 184

DeepC-MVS	98.35 2	99.30 58	99.19 64	99.64 80	99.82 38	99.23 120	99.62 70	99.55 67	98.94 36	99.63 77	99.95 2	95.82 177	99.94 57	99.37 23	99.97 3	99.73 85
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
tfpnnormal		97.84 227	97.47 243	98.98 179	99.20 240	99.22 122	99.64 62	99.61 35	96.32 282	98.27 302	99.70 135	93.35 257	99.44 246	95.69 302	95.40 299	98.27 328

ab-mvs		98.86 122	98.63 136	99.54 96	99.64 119	99.19 123	99.44 159	99.54 74	97.77 154	99.30 155	99.81 62	94.20 238	99.93 72	99.17 45	98.82 185	99.49 163

MSDG		98.98 112	98.80 118	99.53 102	99.76 54	99.19 123	98.75 320	99.55 67	97.25 209	99.47 114	99.77 104	97.82 113	99.87 125	96.93 270	99.90 23	99.54 147

EIA-MVS		99.18 74	99.09 74	99.45 120	99.49 166	99.18 125	99.67 48	99.53 85	97.66 168	99.40 134	99.44 242	98.10 106	99.81 159	98.94 66	99.62 129	99.35 182

test_yl		98.86 122	98.63 136	99.54 96	99.49 166	99.18 125	99.50 130	99.07 299	98.22 101	99.61 84	99.51 221	95.37 191	99.84 139	98.60 126	98.33 204	99.59 138

DCV-MVSNet		98.86 122	98.63 136	99.54 96	99.49 166	99.18 125	99.50 130	99.07 299	98.22 101	99.61 84	99.51 221	95.37 191	99.84 139	98.60 126	98.33 204	99.59 138

CANet		99.25 68	99.14 68	99.59 87	99.41 187	99.16 128	99.35 204	99.57 51	98.82 47	99.51 108	99.61 186	96.46 154	99.95 46	99.59 2	99.98 2	99.65 117

MSLP-MVS++		99.46 25	99.47 10	99.44 125	99.60 136	99.16 128	99.41 174	99.71 13	98.98 29	99.45 117	99.78 97	99.19 9	99.54 235	99.28 34	99.84 65	99.63 128

casdiffmvs		99.13 82	98.98 93	99.56 94	99.65 117	99.16 128	99.56 102	99.50 124	98.33 90	99.41 129	99.86 23	95.92 172	99.83 148	99.45 19	99.16 156	99.70 100

WTY-MVS		99.06 101	98.88 106	99.61 85	99.62 128	99.16 128	99.37 194	99.56 57	98.04 129	99.53 103	99.62 182	96.84 141	99.94 57	98.85 85	98.49 201	99.72 91

EI-MVSNet-Vis-set		99.58 5	99.56 3	99.64 80	99.78 46	99.15 132	99.61 76	99.45 185	99.01 19	99.89 4	99.82 49	99.01 19	99.92 83	99.56 5	99.95 6	99.85 16

RRT_MVS		98.60 152	98.44 153	99.05 170	98.88 292	99.14 133	99.49 140	99.38 222	97.76 155	99.29 158	99.86 23	95.38 190	99.36 262	98.81 96	97.16 260	98.64 279

EI-MVSNet-UG-set		99.58 5	99.57 1	99.64 80	99.78 46	99.14 133	99.60 77	99.45 185	99.01 19	99.90 3	99.83 42	98.98 26	99.93 72	99.59 2	99.95 6	99.86 13

MVS_Test		99.10 96	98.97 94	99.48 114	99.49 166	99.14 133	99.67 48	99.34 241	97.31 203	99.58 93	99.76 108	97.65 118	99.82 155	98.87 78	99.07 167	99.46 171

baseline		99.15 79	99.02 85	99.53 102	99.66 112	99.14 133	99.72 35	99.48 145	98.35 86	99.42 125	99.84 38	96.07 165	99.79 167	99.51 9	99.14 159	99.67 110

Effi-MVS+		98.81 134	98.59 147	99.48 114	99.46 175	99.12 137	98.08 353	99.50 124	97.50 185	99.38 139	99.41 252	96.37 158	99.81 159	99.11 51	98.54 198	99.51 159

Vis-MVSNet		99.12 88	98.97 94	99.56 94	99.78 46	99.10 138	99.68 45	99.66 27	98.49 70	99.86 12	99.87 20	94.77 216	99.84 139	99.19 42	99.41 141	99.74 78
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PCF-MVS	97.08 14	97.66 261	97.06 282	99.47 117	99.61 132	99.09 139	98.04 354	99.25 275	91.24 349	98.51 285	99.70 135	94.55 228	99.91 94	92.76 342	99.85 58	99.42 176
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
GeoE		98.85 130	98.62 141	99.53 102	99.61 132	99.08 140	99.80 19	99.51 104	97.10 225	99.31 153	99.78 97	95.23 199	99.77 174	98.21 165	99.03 170	99.75 73

HY-MVS	97.30 7	98.85 130	98.64 135	99.47 117	99.42 184	99.08 140	99.62 70	99.36 232	97.39 198	99.28 160	99.68 150	96.44 156	99.92 83	98.37 154	98.22 210	99.40 179

PVSNet_Blended_VisFu		99.36 51	99.28 51	99.61 85	99.86 22	99.07 142	99.47 151	99.93 2	97.66 168	99.71 50	99.86 23	97.73 116	99.96 19	99.47 17	99.82 80	99.79 57

PS-CasMVS		97.93 213	97.59 232	98.95 184	98.99 280	99.06 143	99.68 45	99.52 91	97.13 219	98.31 299	99.68 150	92.44 286	99.05 310	98.51 140	94.08 323	98.75 239

EPP-MVSNet		99.13 82	98.99 90	99.53 102	99.65 117	99.06 143	99.81 15	99.33 248	97.43 193	99.60 88	99.88 15	97.14 131	99.84 139	99.13 49	98.94 176	99.69 103

PAPR		98.63 151	98.34 160	99.51 110	99.40 192	99.03 145	98.80 315	99.36 232	96.33 281	99.00 221	99.12 308	98.46 82	99.84 139	95.23 313	99.37 146	99.66 113

MVSTER		98.49 154	98.32 162	99.00 177	99.35 201	99.02 146	99.54 114	99.38 222	97.41 196	99.20 182	99.73 126	93.86 250	99.36 262	98.87 78	97.56 236	98.62 289

1112_ss		98.98 112	98.77 121	99.59 87	99.68 102	99.02 146	99.25 235	99.48 145	97.23 212	99.13 193	99.58 195	96.93 140	99.90 109	98.87 78	98.78 188	99.84 20

LFMVS		97.90 218	97.35 263	99.54 96	99.52 152	99.01 148	99.39 186	98.24 346	97.10 225	99.65 73	99.79 90	84.79 354	99.91 94	99.28 34	98.38 203	99.69 103

PLC	97.94 4	99.02 107	98.85 112	99.53 102	99.66 112	99.01 148	99.24 237	99.52 91	96.85 244	99.27 163	99.48 233	98.25 98	99.91 94	97.76 204	99.62 129	99.65 117
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UniMVSNet_NR-MVSNet		98.22 174	97.97 189	98.96 182	98.92 289	98.98 150	99.48 146	99.53 85	97.76 155	98.71 259	99.46 240	96.43 157	99.22 286	98.57 131	92.87 337	98.69 255

DU-MVS		98.08 192	97.79 207	98.96 182	98.87 296	98.98 150	99.41 174	99.45 185	97.87 140	98.71 259	99.50 224	94.82 210	99.22 286	98.57 131	92.87 337	98.68 260

FMVSNet3		98.03 199	97.76 215	98.84 212	99.39 195	98.98 150	99.40 182	99.38 222	96.67 255	99.07 207	99.28 287	92.93 263	98.98 320	97.10 258	96.65 265	98.56 305

xiu_mvs_v1_base_debu		99.29 61	99.27 53	99.34 133	99.63 122	98.97 153	99.12 257	99.51 104	98.86 43	99.84 14	99.47 236	98.18 101	99.99 1	99.50 10	99.31 147	99.08 202

xiu_mvs_v1_base		99.29 61	99.27 53	99.34 133	99.63 122	98.97 153	99.12 257	99.51 104	98.86 43	99.84 14	99.47 236	98.18 101	99.99 1	99.50 10	99.31 147	99.08 202

xiu_mvs_v1_base_debi		99.29 61	99.27 53	99.34 133	99.63 122	98.97 153	99.12 257	99.51 104	98.86 43	99.84 14	99.47 236	98.18 101	99.99 1	99.50 10	99.31 147	99.08 202

sss		99.17 76	99.05 77	99.53 102	99.62 128	98.97 153	99.36 198	99.62 33	97.83 146	99.67 63	99.65 163	97.37 126	99.95 46	99.19 42	99.19 155	99.68 107

anonymousdsp		98.44 157	98.28 165	98.94 185	98.50 333	98.96 157	99.77 27	99.50 124	97.07 227	98.87 240	99.77 104	94.76 217	99.28 276	98.66 116	97.60 232	98.57 304

diffmvs		99.14 80	99.02 85	99.51 110	99.61 132	98.96 157	99.28 219	99.49 132	98.46 73	99.72 49	99.71 131	96.50 153	99.88 122	99.31 31	99.11 161	99.67 110

testdata				99.54 96	99.75 64	98.95 159		99.51 104	97.07 227	99.43 122	99.70 135	98.87 42	99.94 57	97.76 204	99.64 126	99.72 91

MVS		97.28 281	96.55 289	99.48 114	98.78 307	98.95 159	99.27 224	99.39 216	83.53 359	98.08 308	99.54 210	96.97 138	99.87 125	94.23 325	99.16 156	99.63 128

Test_1112_low_res		98.89 118	98.66 134	99.57 92	99.69 98	98.95 159	99.03 278	99.47 163	96.98 234	99.15 191	99.23 294	96.77 145	99.89 117	98.83 91	98.78 188	99.86 13

PS-MVSNAJ		99.32 56	99.32 32	99.30 143	99.57 142	98.94 162	98.97 295	99.46 173	98.92 40	99.71 50	99.24 293	99.01 19	99.98 6	99.35 24	99.66 123	98.97 218

VPNet		97.84 227	97.44 251	99.01 175	99.21 238	98.94 162	99.48 146	99.57 51	98.38 81	99.28 160	99.73 126	88.89 332	99.39 253	99.19 42	93.27 332	98.71 247

MVSFormer		99.17 76	99.12 70	99.29 146	99.51 154	98.94 162	99.88 1	99.46 173	97.55 177	99.80 27	99.65 163	97.39 122	99.28 276	99.03 57	99.85 58	99.65 117

lupinMVS		99.13 82	99.01 89	99.46 119	99.51 154	98.94 162	99.05 272	99.16 288	97.86 141	99.80 27	99.56 202	97.39 122	99.86 128	98.94 66	99.85 58	99.58 142

xiu_mvs_v2_base		99.26 66	99.25 57	99.29 146	99.53 150	98.91 166	99.02 281	99.45 185	98.80 51	99.71 50	99.26 291	98.94 34	99.98 6	99.34 28	99.23 152	98.98 217

test_djsdf		98.67 147	98.57 148	98.98 179	98.70 318	98.91 166	99.88 1	99.46 173	97.55 177	99.22 176	99.88 15	95.73 180	99.28 276	99.03 57	97.62 231	98.75 239

Vis-MVSNet (Re-imp)		98.87 119	98.72 125	99.31 139	99.71 88	98.88 168	99.80 19	99.44 194	97.91 139	99.36 144	99.78 97	95.49 188	99.43 250	97.91 191	99.11 161	99.62 130

pmmvs4		98.13 186	97.90 198	98.81 216	98.61 327	98.87 169	98.99 288	99.21 282	96.44 276	99.06 211	99.58 195	95.90 174	99.11 305	97.18 255	96.11 280	98.46 316

jason		99.13 82	99.03 82	99.45 120	99.46 175	98.87 169	99.12 257	99.26 273	98.03 131	99.79 29	99.65 163	97.02 136	99.85 134	99.02 59	99.90 23	99.65 117
jason: jason.
Patchmtry		97.75 243	97.40 257	98.81 216	99.10 263	98.87 169	99.11 263	99.33 248	94.83 323	98.81 248	99.38 261	94.33 234	99.02 315	96.10 293	95.57 295	98.53 306

TransMVSNet (Re)		97.15 284	96.58 288	98.86 208	99.12 258	98.85 172	99.49 140	98.91 316	95.48 312	97.16 332	99.80 78	93.38 256	99.11 305	94.16 327	91.73 342	98.62 289

V42		98.06 193	97.79 207	98.86 208	98.98 283	98.84 173	99.69 40	99.34 241	96.53 267	99.30 155	99.37 264	94.67 222	99.32 271	97.57 225	94.66 312	98.42 319

WR-MVS_H		98.13 186	97.87 203	98.90 195	99.02 277	98.84 173	99.70 38	99.59 43	97.27 207	98.40 293	99.19 299	95.53 186	99.23 283	98.34 157	93.78 326	98.61 298

FMVSNet2		97.72 249	97.36 261	98.80 218	99.51 154	98.84 173	99.45 155	99.42 204	96.49 269	98.86 245	99.29 285	90.26 316	98.98 320	96.44 288	96.56 268	98.58 303

BH-RMVSNet		98.41 161	98.08 178	99.40 128	99.41 187	98.83 176	99.30 213	98.77 326	97.70 162	98.94 229	99.65 163	92.91 266	99.74 182	96.52 286	99.55 135	99.64 124

ET-MVSNet_ETH3D		96.49 295	95.64 306	99.05 170	99.53 150	98.82 177	98.84 311	97.51 358	97.63 170	84.77 360	99.21 298	92.09 290	98.91 331	98.98 62	92.21 341	99.41 178

v2v482		98.06 193	97.77 212	98.92 189	98.90 290	98.82 177	99.57 95	99.36 232	96.65 257	99.19 185	99.35 270	94.20 238	99.25 281	97.72 210	94.97 308	98.69 255

v8		97.95 212	97.63 228	98.93 187	98.95 287	98.81 179	99.80 19	99.41 206	96.03 307	99.10 200	99.42 248	94.92 206	99.30 274	96.94 269	94.08 323	98.66 275

PVSNet_BlendedMVS		98.86 122	98.80 118	99.03 173	99.76 54	98.79 180	99.28 219	99.91 3	97.42 195	99.67 63	99.37 264	97.53 119	99.88 122	98.98 62	97.29 255	98.42 319

PVSNet_Blended		99.08 99	98.97 94	99.42 127	99.76 54	98.79 180	98.78 317	99.91 3	96.74 250	99.67 63	99.49 227	97.53 119	99.88 122	98.98 62	99.85 58	99.60 134

baseline1		98.31 168	97.95 193	99.38 131	99.50 164	98.74 182	99.59 83	98.93 311	98.41 78	99.14 192	99.60 189	94.59 225	99.79 167	98.48 142	93.29 331	99.61 132

CDS-MVSNet		99.09 97	99.03 82	99.25 152	99.42 184	98.73 183	99.45 155	99.46 173	98.11 115	99.46 116	99.77 104	98.01 109	99.37 258	98.70 108	98.92 179	99.66 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
UGNet		98.87 119	98.69 129	99.40 128	99.22 236	98.72 184	99.44 159	99.68 19	99.24 3	99.18 188	99.42 248	92.74 270	99.96 19	99.34 28	99.94 9	99.53 152
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
PMMVS		98.80 137	98.62 141	99.34 133	99.27 224	98.70 185	98.76 319	99.31 261	97.34 200	99.21 179	99.07 310	97.20 130	99.82 155	98.56 134	98.87 182	99.52 153

v1192		97.81 234	97.44 251	98.91 193	98.88 292	98.68 186	99.51 124	99.34 241	96.18 294	99.20 182	99.34 273	94.03 245	99.36 262	95.32 312	95.18 303	98.69 255

v10		97.85 224	97.52 237	98.86 208	98.99 280	98.67 187	99.75 31	99.41 206	95.70 310	98.98 223	99.41 252	94.75 218	99.23 283	96.01 296	94.63 313	98.67 267

v1144		97.98 208	97.69 221	98.85 211	98.87 296	98.66 188	99.54 114	99.35 237	96.27 286	99.23 175	99.35 270	94.67 222	99.23 283	96.73 279	95.16 304	98.68 260

v144192		97.92 216	97.60 230	98.87 205	98.83 302	98.65 189	99.55 111	99.34 241	96.20 292	99.32 152	99.40 256	94.36 233	99.26 280	96.37 291	95.03 307	98.70 251

1314		98.68 146	98.54 150	99.11 165	98.89 291	98.65 189	99.27 224	99.49 132	96.89 242	97.99 313	99.56 202	97.72 117	99.83 148	97.74 207	99.27 150	98.84 227

MG-MVS		99.13 82	99.02 85	99.45 120	99.57 142	98.63 191	99.07 267	99.34 241	98.99 26	99.61 84	99.82 49	97.98 110	99.87 125	97.00 263	99.80 87	99.85 16

pm-mvs1		97.68 257	97.28 272	98.88 201	99.06 270	98.62 192	99.50 130	99.45 185	96.32 282	97.87 316	99.79 90	92.47 282	99.35 266	97.54 228	93.54 329	98.67 267

TranMVSNet+NR-MVSNet		97.93 213	97.66 224	98.76 222	98.78 307	98.62 192	99.65 59	99.49 132	97.76 155	98.49 287	99.60 189	94.23 237	98.97 327	98.00 185	92.90 335	98.70 251

TSAR-MVS + GP.		99.36 51	99.36 23	99.36 132	99.67 103	98.61 194	99.07 267	99.33 248	99.00 23	99.82 22	99.81 62	99.06 16	99.84 139	99.09 53	99.42 140	99.65 117

v7n		97.87 221	97.52 237	98.92 189	98.76 311	98.58 195	99.84 9	99.46 173	96.20 292	98.91 233	99.70 135	94.89 208	99.44 246	96.03 295	93.89 325	98.75 239

thisisatest0530		98.35 166	98.03 183	99.31 139	99.63 122	98.56 196	99.54 114	96.75 363	97.53 182	99.73 46	99.65 163	91.25 309	99.89 117	98.62 120	99.56 133	99.48 164

TAMVS		99.12 88	99.08 75	99.24 154	99.46 175	98.55 197	99.51 124	99.46 173	98.09 118	99.45 117	99.82 49	98.34 93	99.51 236	98.70 108	98.93 177	99.67 110

PEN-MVS		97.76 239	97.44 251	98.72 225	98.77 310	98.54 198	99.78 25	99.51 104	97.06 229	98.29 301	99.64 170	92.63 277	98.89 333	98.09 176	93.16 333	98.72 245

Anonymous20231211		97.88 219	97.54 236	98.90 195	99.71 88	98.53 199	99.48 146	99.57 51	94.16 331	98.81 248	99.68 150	93.23 258	99.42 251	98.84 88	94.42 317	98.76 237

v1921920		97.80 236	97.45 246	98.84 212	98.80 303	98.53 199	99.52 120	99.34 241	96.15 299	99.24 171	99.47 236	93.98 246	99.29 275	95.40 309	95.13 305	98.69 255

PS-MVSNAJss		98.92 117	98.92 100	98.90 195	98.78 307	98.53 199	99.78 25	99.54 74	98.07 123	99.00 221	99.76 108	99.01 19	99.37 258	99.13 49	97.23 256	98.81 228

COLMAP_ROB	97.56 6	98.86 122	98.75 124	99.17 160	99.88 12	98.53 199	99.34 207	99.59 43	97.55 177	98.70 265	99.89 10	95.83 176	99.90 109	98.10 175	99.90 23	99.08 202
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
mvs_anonymous		99.03 106	98.99 90	99.16 161	99.38 196	98.52 203	99.51 124	99.38 222	97.79 152	99.38 139	99.81 62	97.30 127	99.45 241	99.35 24	98.99 174	99.51 159

CHOSEN 1792x2688		99.19 72	99.10 72	99.45 120	99.89 9	98.52 203	99.39 186	99.94 1	98.73 56	99.11 197	99.89 10	95.50 187	99.94 57	99.50 10	99.97 3	99.89 2

mvs_tets		98.40 163	98.23 167	98.91 193	98.67 321	98.51 205	99.66 52	99.53 85	98.19 104	98.65 274	99.81 62	92.75 268	99.44 246	99.31 31	97.48 246	98.77 235

thisisatest0515		98.14 185	97.79 207	99.19 158	99.50 164	98.50 206	98.61 331	96.82 362	96.95 238	99.54 101	99.43 245	91.66 302	99.86 128	98.08 180	99.51 137	99.22 191

CR-MVSNet		98.17 181	97.93 196	98.87 205	99.18 245	98.49 207	99.22 243	99.33 248	96.96 236	99.56 96	99.38 261	94.33 234	99.00 318	94.83 319	98.58 194	99.14 194

RPMNet		96.72 291	95.90 301	99.19 158	99.18 245	98.49 207	99.22 243	99.52 91	88.72 355	99.56 96	97.38 351	94.08 244	99.95 46	86.87 361	98.58 194	99.14 194

AllTest		98.87 119	98.72 125	99.31 139	99.86 22	98.48 209	99.56 102	99.61 35	97.85 143	99.36 144	99.85 29	95.95 169	99.85 134	96.66 284	99.83 74	99.59 138

TestCases				99.31 139	99.86 22	98.48 209		99.61 35	97.85 143	99.36 144	99.85 29	95.95 169	99.85 134	96.66 284	99.83 74	99.59 138

Anonymous20240529		98.09 190	97.68 222	99.34 133	99.66 112	98.44 211	99.40 182	99.43 202	93.67 335	99.22 176	99.89 10	90.23 319	99.93 72	99.26 37	98.33 204	99.66 113

jajsoiax		98.43 158	98.28 165	98.88 201	98.60 328	98.43 212	99.82 13	99.53 85	98.19 104	98.63 276	99.80 78	93.22 260	99.44 246	99.22 39	97.50 242	98.77 235

v1240		97.69 255	97.32 269	98.79 219	98.85 300	98.43 212	99.48 146	99.36 232	96.11 302	99.27 163	99.36 267	93.76 253	99.24 282	94.46 322	95.23 302	98.70 251

CANet_DTU		98.97 114	98.87 107	99.25 152	99.33 206	98.42 214	99.08 266	99.30 265	99.16 5	99.43 122	99.75 113	95.27 195	99.97 11	98.56 134	99.95 6	99.36 181

tttt0517		98.42 159	98.14 171	99.28 149	99.66 112	98.38 215	99.74 34	96.85 361	97.68 164	99.79 29	99.74 119	91.39 306	99.89 117	98.83 91	99.56 133	99.57 143

PatchT		97.03 287	96.44 291	98.79 219	98.99 280	98.34 216	99.16 249	99.07 299	92.13 345	99.52 106	97.31 354	94.54 229	98.98 320	88.54 355	98.73 190	99.03 211

Baseline_NR-MVSNet		97.76 239	97.45 246	98.68 228	99.09 265	98.29 217	99.41 174	98.85 322	95.65 311	98.63 276	99.67 156	94.82 210	99.10 307	98.07 183	92.89 336	98.64 279

CSCG		99.32 56	99.32 32	99.32 138	99.85 26	98.29 217	99.71 37	99.66 27	98.11 115	99.41 129	99.80 78	98.37 91	99.96 19	98.99 61	99.96 5	99.72 91

bset_n11_16_dypcd		98.16 182	97.97 189	98.73 223	98.26 338	98.28 219	97.99 355	98.01 351	97.68 164	99.10 200	99.63 176	95.68 182	99.15 296	98.78 100	96.55 269	98.75 239

PAPM		97.59 265	97.09 281	99.07 167	99.06 270	98.26 220	98.30 348	99.10 294	94.88 322	98.08 308	99.34 273	96.27 161	99.64 221	89.87 351	98.92 179	99.31 187

OMC-MVS		99.08 99	99.04 80	99.20 157	99.67 103	98.22 221	99.28 219	99.52 91	98.07 123	99.66 68	99.81 62	97.79 114	99.78 172	97.79 201	99.81 83	99.60 134

EPNet		98.86 122	98.71 127	99.30 143	97.20 353	98.18 222	99.62 70	98.91 316	99.28 2	98.63 276	99.81 62	95.96 168	99.99 1	99.24 38	99.72 109	99.73 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous202405211		98.30 170	97.98 188	99.26 151	99.57 142	98.16 223	99.41 174	98.55 342	96.03 307	99.19 185	99.74 119	91.87 293	99.92 83	99.16 47	98.29 209	99.70 100

GG-mvs-BLEND				98.45 252	98.55 331	98.16 223	99.43 165	93.68 371		97.23 329	98.46 339	89.30 329	99.22 286	95.43 308	98.22 210	97.98 344

gg-mvs-nofinetune		96.17 302	95.32 310	98.73 223	98.79 304	98.14 225	99.38 191	94.09 370	91.07 351	98.07 311	91.04 364	89.62 327	99.35 266	96.75 277	99.09 165	98.68 260

DTE-MVSNet		97.51 271	97.19 278	98.46 251	98.63 324	98.13 226	99.84 9	99.48 145	96.68 254	97.97 314	99.67 156	92.92 264	98.56 337	96.88 274	92.60 340	98.70 251

VDDNet		97.55 266	97.02 283	99.16 161	99.49 166	98.12 227	99.38 191	99.30 265	95.35 314	99.68 57	99.90 7	82.62 358	99.93 72	99.31 31	98.13 219	99.42 176

thres200		97.61 264	97.28 272	98.62 230	99.64 119	98.03 228	99.26 233	98.74 330	97.68 164	99.09 205	98.32 344	91.66 302	99.81 159	92.88 339	98.22 210	98.03 339

baseline2		97.87 221	97.55 233	98.82 214	99.18 245	98.02 229	99.41 174	96.58 365	96.97 235	96.51 339	99.17 300	93.43 255	99.57 231	97.71 211	99.03 170	98.86 225

IterMVS-LS		98.46 156	98.42 155	98.58 235	99.59 138	98.00 230	99.37 194	99.43 202	96.94 240	99.07 207	99.59 192	97.87 111	99.03 313	98.32 160	95.62 294	98.71 247
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
GA-MVS		97.85 224	97.47 243	99.00 177	99.38 196	97.99 231	98.57 334	99.15 289	97.04 230	98.90 235	99.30 283	89.83 322	99.38 255	96.70 281	98.33 204	99.62 130

cl-mvsnet____		98.01 204	97.84 205	98.55 240	99.25 230	97.97 232	98.71 324	99.34 241	96.47 275	98.59 282	99.54 210	95.65 184	99.21 291	97.21 249	95.77 289	98.46 316

EI-MVSNet		98.67 147	98.67 131	98.68 228	99.35 201	97.97 232	99.50 130	99.38 222	96.93 241	99.20 182	99.83 42	97.87 111	99.36 262	98.38 152	97.56 236	98.71 247

tfpn200view9		97.72 249	97.38 259	98.72 225	99.69 98	97.96 234	99.50 130	98.73 335	97.83 146	99.17 189	98.45 340	91.67 300	99.83 148	93.22 335	98.18 214	98.37 325

thres400		97.77 238	97.38 259	98.92 189	99.69 98	97.96 234	99.50 130	98.73 335	97.83 146	99.17 189	98.45 340	91.67 300	99.83 148	93.22 335	98.18 214	98.96 220

cl-mvsnet1		98.01 204	97.85 204	98.48 246	99.24 231	97.95 236	98.71 324	99.35 237	96.50 268	98.60 281	99.54 210	95.72 181	99.03 313	97.21 249	95.77 289	98.46 316

thres600view7		97.86 223	97.51 239	98.92 189	99.72 82	97.95 236	99.59 83	98.74 330	97.94 136	99.27 163	98.62 335	91.75 296	99.86 128	93.73 330	98.19 213	98.96 220

CHOSEN 280x420		99.12 88	99.13 69	99.08 166	99.66 112	97.89 238	98.43 341	99.71 13	98.88 42	99.62 81	99.76 108	96.63 149	99.70 205	99.46 18	99.99 1	99.66 113

cl-mvsnet2		97.85 224	97.64 227	98.48 246	99.09 265	97.87 239	98.60 333	99.33 248	97.11 224	98.87 240	99.22 295	92.38 287	99.17 295	98.21 165	95.99 283	98.42 319

TR-MVS		97.76 239	97.41 256	98.82 214	99.06 270	97.87 239	98.87 309	98.56 341	96.63 260	98.68 267	99.22 295	92.49 281	99.65 218	95.40 309	97.79 226	98.95 223

thres100view900		97.76 239	97.45 246	98.69 227	99.72 82	97.86 241	99.59 83	98.74 330	97.93 137	99.26 168	98.62 335	91.75 296	99.83 148	93.22 335	98.18 214	98.37 325

test0.0.03 1		97.71 253	97.42 255	98.56 238	98.41 336	97.82 242	98.78 317	98.63 339	97.34 200	98.05 312	98.98 322	94.45 231	98.98 320	95.04 316	97.15 261	98.89 224

JIA-IIPM		97.50 272	97.02 283	98.93 187	98.73 313	97.80 243	99.30 213	98.97 307	91.73 347	98.91 233	94.86 359	95.10 201	99.71 199	97.58 221	97.98 222	99.28 189

mvs-test1		98.86 122	98.84 113	98.89 198	99.33 206	97.77 244	99.44 159	99.30 265	98.47 71	99.10 200	99.43 245	96.78 143	99.95 46	98.73 104	99.02 172	98.96 220

XVG-OURS-SEG-HR		98.69 145	98.62 141	98.89 198	99.71 88	97.74 245	99.12 257	99.54 74	98.44 77	99.42 125	99.71 131	94.20 238	99.92 83	98.54 139	98.90 181	99.00 214

XVG-OURS		98.73 142	98.68 130	98.88 201	99.70 95	97.73 246	98.92 303	99.55 67	98.52 68	99.45 117	99.84 38	95.27 195	99.91 94	98.08 180	98.84 184	99.00 214

miper_ehance_all_eth		98.18 180	98.10 174	98.41 257	99.23 232	97.72 247	98.72 323	99.31 261	96.60 263	98.88 238	99.29 285	97.29 128	99.13 300	97.60 219	95.99 283	98.38 324

miper_enhance_ethall		98.16 182	98.08 178	98.41 257	98.96 286	97.72 247	98.45 340	99.32 258	96.95 238	98.97 225	99.17 300	97.06 135	99.22 286	97.86 195	95.99 283	98.29 327

v148		97.79 237	97.55 233	98.50 243	98.74 312	97.72 247	99.54 114	99.33 248	96.26 287	98.90 235	99.51 221	94.68 221	99.14 297	97.83 198	93.15 334	98.63 287

cl_fuxian		98.12 188	98.04 182	98.38 261	99.30 215	97.69 250	98.81 314	99.33 248	96.67 255	98.83 246	99.34 273	97.11 132	98.99 319	97.58 221	95.34 300	98.48 310

TAPA-MVS	97.07 15	97.74 246	97.34 266	98.94 185	99.70 95	97.53 251	99.25 235	99.51 104	91.90 346	99.30 155	99.63 176	98.78 51	99.64 221	88.09 357	99.87 40	99.65 117
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MIMVSNet		97.73 247	97.45 246	98.57 236	99.45 181	97.50 252	99.02 281	98.98 306	96.11 302	99.41 129	99.14 304	90.28 315	98.74 335	95.74 301	98.93 177	99.47 169

UniMVSNet_ETH3D		97.32 280	96.81 286	98.87 205	99.40 192	97.46 253	99.51 124	99.53 85	95.86 309	98.54 284	99.77 104	82.44 359	99.66 214	98.68 113	97.52 239	99.50 162

miper_lstm_enhance		98.00 206	97.91 197	98.28 272	99.34 205	97.43 254	98.88 307	99.36 232	96.48 273	98.80 250	99.55 205	95.98 167	98.91 331	97.27 245	95.50 298	98.51 308

eth_miper_zixun_eth		98.05 198	97.96 191	98.33 264	99.26 226	97.38 255	98.56 336	99.31 261	96.65 257	98.88 238	99.52 217	96.58 150	99.12 304	97.39 242	95.53 297	98.47 312

cascas		97.69 255	97.43 254	98.48 246	98.60 328	97.30 256	98.18 352	99.39 216	92.96 343	98.41 292	98.78 331	93.77 252	99.27 279	98.16 172	98.61 191	98.86 225

PVSNet	96.02 17	98.85 130	98.84 113	98.89 198	99.73 77	97.28 257	98.32 347	99.60 40	97.86 141	99.50 109	99.57 199	96.75 146	99.86 128	98.56 134	99.70 114	99.54 147

hse-mvs3		97.70 254	97.28 272	98.97 181	99.70 95	97.27 258	99.36 198	99.45 185	98.94 36	99.66 68	99.64 170	94.93 204	99.99 1	99.48 15	84.36 354	99.65 117

MDA-MVSNet-bldmvs		94.96 314	93.98 320	97.92 293	98.24 339	97.27 258	99.15 253	99.33 248	93.80 334	80.09 366	99.03 315	88.31 339	97.86 350	93.49 333	94.36 318	98.62 289

GBi-Net		97.68 257	97.48 241	98.29 269	99.51 154	97.26 260	99.43 165	99.48 145	96.49 269	99.07 207	99.32 280	90.26 316	98.98 320	97.10 258	96.65 265	98.62 289

test1		97.68 257	97.48 241	98.29 269	99.51 154	97.26 260	99.43 165	99.48 145	96.49 269	99.07 207	99.32 280	90.26 316	98.98 320	97.10 258	96.65 265	98.62 289

FMVSNet1		96.84 289	96.36 292	98.29 269	99.32 213	97.26 260	99.43 165	99.48 145	95.11 317	98.55 283	99.32 280	83.95 355	98.98 320	95.81 299	96.26 277	98.62 289

MDA-MVSNet_test_wron		95.45 309	94.60 315	98.01 287	98.16 340	97.21 263	99.11 263	99.24 277	93.49 338	80.73 365	98.98 322	93.02 261	98.18 341	94.22 326	94.45 316	98.64 279

VDD-MVS		97.73 247	97.35 263	98.88 201	99.47 174	97.12 264	99.34 207	98.85 322	98.19 104	99.67 63	99.85 29	82.98 356	99.92 83	99.49 14	98.32 208	99.60 134

test-LLR		98.06 193	97.90 198	98.55 240	98.79 304	97.10 265	98.67 326	97.75 354	97.34 200	98.61 279	98.85 326	94.45 231	99.45 241	97.25 247	99.38 142	99.10 197

test-mter		97.49 275	97.13 280	98.55 240	98.79 304	97.10 265	98.67 326	97.75 354	96.65 257	98.61 279	98.85 326	88.23 340	99.45 241	97.25 247	99.38 142	99.10 197

YYNet1		95.36 311	94.51 317	97.92 293	97.89 342	97.10 265	99.10 265	99.23 278	93.26 341	80.77 364	99.04 314	92.81 267	98.02 345	94.30 323	94.18 321	98.64 279

ACMM	97.58 5	98.37 165	98.34 160	98.48 246	99.41 187	97.10 265	99.56 102	99.45 185	98.53 67	99.04 213	99.85 29	93.00 262	99.71 199	98.74 102	97.45 247	98.64 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		98.19 178	98.10 174	98.45 252	98.88 292	97.07 269	99.28 219	99.38 222	98.57 65	99.22 176	99.81 62	92.12 289	99.66 214	98.08 180	97.54 238	98.61 298
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Patchmatch-test		97.93 213	97.65 225	98.77 221	99.18 245	97.07 269	99.03 278	99.14 291	96.16 297	98.74 256	99.57 199	94.56 227	99.72 193	93.36 334	99.11 161	99.52 153

hse-mvs2		97.50 272	97.14 279	98.59 232	99.49 166	97.05 271	99.28 219	99.22 279	98.94 36	99.66 68	99.42 248	94.93 204	99.65 218	99.48 15	83.80 356	99.08 202

LPG-MVS_test		98.22 174	98.13 172	98.49 244	99.33 206	97.05 271	99.58 90	99.55 67	97.46 186	99.24 171	99.83 42	92.58 278	99.72 193	98.09 176	97.51 240	98.68 260

LGP-MVS_train				98.49 244	99.33 206	97.05 271		99.55 67	97.46 186	99.24 171	99.83 42	92.58 278	99.72 193	98.09 176	97.51 240	98.68 260

AUN-MVS		96.88 288	96.31 293	98.59 232	99.48 173	97.04 274	99.27 224	99.22 279	97.44 192	98.51 285	99.41 252	91.97 291	99.66 214	97.71 211	83.83 355	99.07 207

plane_prior7					99.29 219	97.03 275

ACMP	97.20 11	98.06 193	97.94 195	98.45 252	99.37 198	97.01 276	99.44 159	99.49 132	97.54 180	98.45 289	99.79 90	91.95 292	99.72 193	97.91 191	97.49 245	98.62 289
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
plane_prior3						97.00 277			98.69 59	99.11 197

Fast-Effi-MVS+-dtu		98.77 140	98.83 117	98.60 231	99.41 187	96.99 278	99.52 120	99.49 132	98.11 115	99.24 171	99.34 273	96.96 139	99.79 167	97.95 189	99.45 138	99.02 213

plane_prior6					99.27 224	96.98 279						92.71 273

HQP_MVS		98.27 173	98.22 168	98.44 255	99.29 219	96.97 280	99.39 186	99.47 163	98.97 32	99.11 197	99.61 186	92.71 273	99.69 209	97.78 202	97.63 229	98.67 267

plane_prior						96.97 280	99.21 245		98.45 74						97.60 232

ACMH	97.28 8	98.10 189	97.99 187	98.44 255	99.41 187	96.96 282	99.60 77	99.56 57	98.09 118	98.15 306	99.91 5	90.87 313	99.70 205	98.88 74	97.45 247	98.67 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
NP-MVS					99.23 232	96.92 283					99.40 256

Effi-MVS+-dtu		98.78 138	98.89 105	98.47 250	99.33 206	96.91 284	99.57 95	99.30 265	98.47 71	99.41 129	98.99 319	96.78 143	99.74 182	98.73 104	99.38 142	98.74 243

HQP5-MVS						96.83 285

HQP-MVS		98.02 201	97.90 198	98.37 262	99.19 242	96.83 285	98.98 292	99.39 216	98.24 97	98.66 268	99.40 256	92.47 282	99.64 221	97.19 253	97.58 234	98.64 279

CLD-MVS		98.16 182	98.10 174	98.33 264	99.29 219	96.82 287	98.75 320	99.44 194	97.83 146	99.13 193	99.55 205	92.92 264	99.67 211	98.32 160	97.69 228	98.48 310
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LTVRE_ROB	97.16 12	98.02 201	97.90 198	98.40 259	99.23 232	96.80 288	99.70 38	99.60 40	97.12 221	98.18 305	99.70 135	91.73 298	99.72 193	98.39 150	97.45 247	98.68 260
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
pmmvs5		97.52 269	97.30 271	98.16 278	98.57 330	96.73 289	99.27 224	98.90 318	96.14 300	98.37 295	99.53 214	91.54 305	99.14 297	97.51 231	95.87 287	98.63 287

BH-untuned		98.42 159	98.36 157	98.59 232	99.49 166	96.70 290	99.27 224	99.13 292	97.24 211	98.80 250	99.38 261	95.75 179	99.74 182	97.07 261	99.16 156	99.33 186

IB-MVS	95.67 18	96.22 299	95.44 309	98.57 236	99.21 238	96.70 290	98.65 329	97.74 356	96.71 252	97.27 328	98.54 338	86.03 350	99.92 83	98.47 145	86.30 352	99.10 197
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
ACMH+	97.24 10	97.92 216	97.78 210	98.32 266	99.46 175	96.68 292	99.56 102	99.54 74	98.41 78	97.79 320	99.87 20	90.18 320	99.66 214	98.05 184	97.18 259	98.62 289

EU-MVSNet		97.98 208	98.03 183	97.81 301	98.72 315	96.65 293	99.66 52	99.66 27	98.09 118	98.35 297	99.82 49	95.25 198	98.01 346	97.41 241	95.30 301	98.78 231

D2MVS		98.41 161	98.50 151	98.15 279	99.26 226	96.62 294	99.40 182	99.61 35	97.71 161	98.98 223	99.36 267	96.04 166	99.67 211	98.70 108	97.41 251	98.15 334

MVP-Stereo		97.81 234	97.75 216	97.99 289	97.53 346	96.60 295	98.96 296	98.85 322	97.22 213	97.23 329	99.36 267	95.28 194	99.46 240	95.51 306	99.78 94	97.92 348
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TESTMET0.1,1		97.55 266	97.27 275	98.40 259	98.93 288	96.53 296	98.67 326	97.61 357	96.96 236	98.64 275	99.28 287	88.63 336	99.45 241	97.30 244	99.38 142	99.21 192

OurMVSNet-221017-0		97.88 219	97.77 212	98.19 276	98.71 317	96.53 296	99.88 1	99.00 304	97.79 152	98.78 253	99.94 3	91.68 299	99.35 266	97.21 249	96.99 263	98.69 255

ADS-MVSNet		98.20 177	98.08 178	98.56 238	99.33 206	96.48 298	99.23 238	99.15 289	96.24 289	99.10 200	99.67 156	94.11 242	99.71 199	96.81 275	99.05 168	99.48 164

testgi		97.65 262	97.50 240	98.13 280	99.36 200	96.45 299	99.42 172	99.48 145	97.76 155	97.87 316	99.45 241	91.09 310	98.81 334	94.53 321	98.52 199	99.13 196

test_0402		96.64 292	96.24 294	97.85 297	98.85 300	96.43 300	99.44 159	99.26 273	93.52 337	96.98 336	99.52 217	88.52 337	99.20 293	92.58 344	97.50 242	97.93 347

ITE_SJBPF				98.08 281	99.29 219	96.37 301		98.92 313	98.34 87	98.83 246	99.75 113	91.09 310	99.62 227	95.82 298	97.40 252	98.25 330

IterMVS-SCA-FT		97.82 232	97.75 216	98.06 283	99.57 142	96.36 302	99.02 281	99.49 132	97.18 215	98.71 259	99.72 130	92.72 271	99.14 297	97.44 239	95.86 288	98.67 267

K. test v3		97.10 286	96.79 287	98.01 287	98.72 315	96.33 303	99.87 5	97.05 360	97.59 172	96.16 343	99.80 78	88.71 333	99.04 311	96.69 282	96.55 269	98.65 277

XVG-ACMP-BASELINE		97.83 229	97.71 220	98.20 275	99.11 260	96.33 303	99.41 174	99.52 91	98.06 127	99.05 212	99.50 224	89.64 326	99.73 189	97.73 208	97.38 253	98.53 306

IterMVS		97.83 229	97.77 212	98.02 286	99.58 140	96.27 305	99.02 281	99.48 145	97.22 213	98.71 259	99.70 135	92.75 268	99.13 300	97.46 236	96.00 282	98.67 267
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
SixPastTwentyTwo		97.50 272	97.33 268	98.03 284	98.65 322	96.23 306	99.77 27	98.68 338	97.14 218	97.90 315	99.93 4	90.45 314	99.18 294	97.00 263	96.43 273	98.67 267

BH-w/o		98.00 206	97.89 202	98.32 266	99.35 201	96.20 307	99.01 286	98.90 318	96.42 278	98.38 294	99.00 318	95.26 197	99.72 193	96.06 294	98.61 191	99.03 211

TDRefinement		95.42 310	94.57 316	97.97 290	89.83 368	96.11 308	99.48 146	98.75 327	96.74 250	96.68 338	99.88 15	88.65 335	99.71 199	98.37 154	82.74 357	98.09 335

RRT_test8_iter05		97.72 249	97.60 230	98.08 281	99.23 232	96.08 309	99.63 64	99.49 132	97.54 180	98.94 229	99.81 62	87.99 343	99.35 266	99.21 41	96.51 271	98.81 228

EPMVS		97.82 232	97.65 225	98.35 263	98.88 292	95.98 310	99.49 140	94.71 369	97.57 175	99.26 168	99.48 233	92.46 285	99.71 199	97.87 194	99.08 166	99.35 182

pmmvs-eth3d		95.34 312	94.73 314	97.15 317	95.53 361	95.94 311	99.35 204	99.10 294	95.13 315	93.55 353	97.54 349	88.15 342	97.91 348	94.58 320	89.69 348	97.61 351

FMVSNet5		96.43 297	96.19 295	97.15 317	99.11 260	95.89 312	99.32 209	99.52 91	94.47 330	98.34 298	99.07 310	87.54 347	97.07 357	92.61 343	95.72 292	98.47 312

KD-MVS_2432*1600		94.62 316	93.72 322	97.31 315	97.19 354	95.82 313	98.34 344	99.20 283	95.00 320	97.57 322	98.35 342	87.95 344	98.10 343	92.87 340	77.00 362	98.01 340

miper_refine_blended		94.62 316	93.72 322	97.31 315	97.19 354	95.82 313	98.34 344	99.20 283	95.00 320	97.57 322	98.35 342	87.95 344	98.10 343	92.87 340	77.00 362	98.01 340

UnsupCasMVSNet_eth		96.44 296	96.12 296	97.40 314	98.65 322	95.65 315	99.36 198	99.51 104	97.13 219	96.04 345	98.99 319	88.40 338	98.17 342	96.71 280	90.27 345	98.40 322

MIMVSNet1		95.51 308	95.04 312	96.92 325	97.38 348	95.60 316	99.52 120	99.50 124	93.65 336	96.97 337	99.17 300	85.28 353	96.56 361	88.36 356	95.55 296	98.60 301

CVMVSNet		98.57 153	98.67 131	98.30 268	99.35 201	95.59 317	99.50 130	99.55 67	98.60 64	99.39 136	99.83 42	94.48 230	99.45 241	98.75 101	98.56 197	99.85 16

SCA		98.19 178	98.16 169	98.27 273	99.30 215	95.55 318	99.07 267	98.97 307	97.57 175	99.43 122	99.57 199	92.72 271	99.74 182	97.58 221	99.20 154	99.52 153

LF4IMVS		97.52 269	97.46 245	97.70 306	98.98 283	95.55 318	99.29 217	98.82 325	98.07 123	98.66 268	99.64 170	89.97 321	99.61 228	97.01 262	96.68 264	97.94 346

EPNet_dtu		98.03 199	97.96 191	98.23 274	98.27 337	95.54 320	99.23 238	98.75 327	99.02 16	97.82 318	99.71 131	96.11 164	99.48 237	93.04 338	99.65 125	99.69 103
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TinyColmap		97.12 285	96.89 285	97.83 299	99.07 268	95.52 321	98.57 334	98.74 330	97.58 174	97.81 319	99.79 90	88.16 341	99.56 232	95.10 314	97.21 257	98.39 323

pmmvs6		96.53 294	96.09 297	97.82 300	98.69 319	95.47 322	99.37 194	99.47 163	93.46 339	97.41 325	99.78 97	87.06 348	99.33 270	96.92 272	92.70 339	98.65 277

test20.03		96.12 303	95.96 300	96.63 329	97.44 347	95.45 323	99.51 124	99.38 222	96.55 266	96.16 343	99.25 292	93.76 253	96.17 362	87.35 359	94.22 320	98.27 328

lessismore_v0				97.79 302	98.69 319	95.44 324		94.75 368		95.71 347	99.87 20	88.69 334	99.32 271	95.89 297	94.93 310	98.62 289

DIV-MVS_2432*1600		95.00 313	94.34 318	96.96 323	97.07 356	95.39 325	99.56 102	99.44 194	95.11 317	97.13 333	97.32 353	91.86 294	97.27 356	90.35 350	81.23 359	98.23 332

PatchmatchNet		98.31 168	98.36 157	98.19 276	99.16 253	95.32 326	99.27 224	98.92 313	97.37 199	99.37 141	99.58 195	94.90 207	99.70 205	97.43 240	99.21 153	99.54 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ppachtmachnet_test		97.49 275	97.45 246	97.61 307	98.62 325	95.24 327	98.80 315	99.46 173	96.11 302	98.22 303	99.62 182	96.45 155	98.97 327	93.77 329	95.97 286	98.61 298

USDC		97.34 279	97.20 277	97.75 303	99.07 268	95.20 328	98.51 338	99.04 302	97.99 133	98.31 299	99.86 23	89.02 330	99.55 234	95.67 304	97.36 254	98.49 309

ADS-MVSNet2		98.02 201	98.07 181	97.87 296	99.33 206	95.19 329	99.23 238	99.08 297	96.24 289	99.10 200	99.67 156	94.11 242	98.93 330	96.81 275	99.05 168	99.48 164

MDTV_nov1_ep13_2view						95.18 330	99.35 204		96.84 245	99.58 93		95.19 200		97.82 199		99.46 171

new_pmnet		96.38 298	96.03 298	97.41 313	98.13 341	95.16 331	99.05 272	99.20 283	93.94 332	97.39 326	98.79 329	91.61 304	99.04 311	90.43 349	95.77 289	98.05 338

tpm		97.67 260	97.55 233	98.03 284	99.02 277	95.01 332	99.43 165	98.54 343	96.44 276	99.12 195	99.34 273	91.83 295	99.60 229	97.75 206	96.46 272	99.48 164

our_test_3		97.65 262	97.68 222	97.55 310	98.62 325	94.97 333	98.84 311	99.30 265	96.83 247	98.19 304	99.34 273	97.01 137	99.02 315	95.00 317	96.01 281	98.64 279

Anonymous20240521		96.20 301	95.89 302	97.13 319	97.72 345	94.96 334	99.79 24	99.29 270	93.01 342	97.20 331	99.03 315	89.69 325	98.36 340	91.16 347	96.13 279	98.07 336

MVS_0304		96.79 290	96.52 290	97.59 308	99.22 236	94.92 335	99.04 277	99.59 43	96.49 269	98.43 291	98.99 319	80.48 362	99.39 253	97.15 257	99.27 150	98.47 312

DWT-MVSNet_test		97.53 268	97.40 257	97.93 292	99.03 276	94.86 336	99.57 95	98.63 339	96.59 265	98.36 296	98.79 329	89.32 328	99.74 182	98.14 174	98.16 218	99.20 193

tpmrst		98.33 167	98.48 152	97.90 295	99.16 253	94.78 337	99.31 211	99.11 293	97.27 207	99.45 117	99.59 192	95.33 193	99.84 139	98.48 142	98.61 191	99.09 201

tpmvs		97.98 208	98.02 185	97.84 298	99.04 274	94.73 338	99.31 211	99.20 283	96.10 306	98.76 255	99.42 248	94.94 203	99.81 159	96.97 266	98.45 202	98.97 218

pmmvs3		94.09 322	93.25 325	96.60 330	94.76 363	94.49 339	98.92 303	98.18 349	89.66 352	96.48 340	98.06 347	86.28 349	97.33 355	89.68 352	87.20 351	97.97 345

MDTV_nov1_ep13			98.32 162		99.11 260	94.44 340	99.27 224	98.74 330	97.51 184	99.40 134	99.62 182	94.78 213	99.76 178	97.59 220	98.81 187

tpm2		97.44 277	97.34 266	97.74 304	99.15 256	94.36 341	99.45 155	98.94 310	93.45 340	98.90 235	99.44 242	91.35 307	99.59 230	97.31 243	98.07 221	99.29 188

PVSNet_0	94.43 19	96.09 304	95.47 307	97.94 291	99.31 214	94.34 342	97.81 356	99.70 15	97.12 221	97.46 324	98.75 332	89.71 324	99.79 167	97.69 214	81.69 358	99.68 107

Anonymous20231206		96.22 299	96.03 298	96.79 328	97.31 351	94.14 343	99.63 64	99.08 297	96.17 295	97.04 335	99.06 312	93.94 247	97.76 352	86.96 360	95.06 306	98.47 312

CostFormer		97.72 249	97.73 218	97.71 305	99.15 256	94.02 344	99.54 114	99.02 303	94.67 326	99.04 213	99.35 270	92.35 288	99.77 174	98.50 141	97.94 223	99.34 184

UnsupCasMVSNet_bld		93.53 323	92.51 326	96.58 331	97.38 348	93.82 345	98.24 349	99.48 145	91.10 350	93.10 355	96.66 355	74.89 363	98.37 339	94.03 328	87.71 350	97.56 353

tpm cat1		97.39 278	97.36 261	97.50 312	99.17 251	93.73 346	99.43 165	99.31 261	91.27 348	98.71 259	99.08 309	94.31 236	99.77 174	96.41 290	98.50 200	99.00 214

dp		97.75 243	97.80 206	97.59 308	99.10 263	93.71 347	99.32 209	98.88 320	96.48 273	99.08 206	99.55 205	92.67 276	99.82 155	96.52 286	98.58 194	99.24 190

MVS-HIRNet		95.75 307	95.16 311	97.51 311	99.30 215	93.69 348	98.88 307	95.78 366	85.09 358	98.78 253	92.65 361	91.29 308	99.37 258	94.85 318	99.85 58	99.46 171

CL-MVSNet_2432*1600		94.49 318	93.97 321	96.08 333	96.16 357	93.67 349	98.33 346	99.38 222	95.13 315	97.33 327	98.15 346	92.69 275	96.57 360	88.67 354	79.87 360	97.99 343

DSMNet-mixed		97.25 282	97.35 263	96.95 324	97.84 343	93.61 350	99.57 95	96.63 364	96.13 301	98.87 240	98.61 337	94.59 225	97.70 353	95.08 315	98.86 183	99.55 145

MS-PatchMatch		97.24 283	97.32 269	96.99 321	98.45 335	93.51 351	98.82 313	99.32 258	97.41 196	98.13 307	99.30 283	88.99 331	99.56 232	95.68 303	99.80 87	97.90 349

OpenMVS_ROB	92.34 20	94.38 320	93.70 324	96.41 332	97.38 348	93.17 352	99.06 270	98.75 327	86.58 356	94.84 351	98.26 345	81.53 360	99.32 271	89.01 353	97.87 225	96.76 355

gm-plane-assit					98.54 332	92.96 353			94.65 327		99.15 303		99.64 221	97.56 226

EG-PatchMatch MVS		95.97 305	95.69 305	96.81 327	97.78 344	92.79 354	99.16 249	98.93 311	96.16 297	94.08 352	99.22 295	82.72 357	99.47 238	95.67 304	97.50 242	98.17 333

new-patchmatchnet		94.48 319	94.08 319	95.67 335	95.08 362	92.41 355	99.18 247	99.28 272	94.55 329	93.49 354	97.37 352	87.86 346	97.01 358	91.57 345	88.36 349	97.61 351

LCM-MVSNet-Re		97.83 229	98.15 170	96.87 326	99.30 215	92.25 356	99.59 83	98.26 345	97.43 193	96.20 342	99.13 305	96.27 161	98.73 336	98.17 171	98.99 174	99.64 124

DeepPCF-MVS	98.18 3	98.81 134	99.37 21	97.12 320	99.60 136	91.75 357	98.61 331	99.44 194	99.35 1	99.83 19	99.85 29	98.70 65	99.81 159	99.02 59	99.91 16	99.81 43

RPSCF		98.22 174	98.62 141	96.99 321	99.82 38	91.58 358	99.72 35	99.44 194	96.61 261	99.66 68	99.89 10	95.92 172	99.82 155	97.46 236	99.10 164	99.57 143

Patchmatch-RL test		95.84 306	95.81 304	95.95 334	95.61 359	90.57 359	98.24 349	98.39 344	95.10 319	95.20 348	98.67 334	94.78 213	97.77 351	96.28 292	90.02 346	99.51 159

Gipuma		90.99 326	90.15 329	93.51 337	98.73 313	90.12 360	93.98 362	99.45 185	79.32 361	92.28 356	94.91 358	69.61 364	97.98 347	87.42 358	95.67 293	92.45 361
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PM-MVS		92.96 324	92.23 327	95.14 336	95.61 359	89.98 361	99.37 194	98.21 347	94.80 324	95.04 350	97.69 348	65.06 365	97.90 349	94.30 323	89.98 347	97.54 354

PMMVS2		86.87 327	85.37 331	91.35 343	90.21 367	83.80 362	98.89 306	97.45 359	83.13 360	91.67 358	95.03 357	48.49 370	94.70 364	85.86 362	77.62 361	95.54 358

ambc				93.06 339	92.68 364	82.36 363	98.47 339	98.73 335		95.09 349	97.41 350	55.55 368	99.10 307	96.42 289	91.32 343	97.71 350

DeepMVS_CX				93.34 338	99.29 219	82.27 364		99.22 279	85.15 357	96.33 341	99.05 313	90.97 312	99.73 189	93.57 332	97.77 227	98.01 340

LCM-MVSNet		86.80 328	85.22 332	91.53 342	87.81 369	80.96 365	98.23 351	98.99 305	71.05 363	90.13 359	96.51 356	48.45 371	96.88 359	90.51 348	85.30 353	96.76 355

CMPMVS	69.68 23	94.13 321	94.90 313	91.84 341	97.24 352	80.01 366	98.52 337	99.48 145	89.01 353	91.99 357	99.67 156	85.67 352	99.13 300	95.44 307	97.03 262	96.39 357
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
N_pmnet		94.95 315	95.83 303	92.31 340	98.47 334	79.33 367	99.12 257	92.81 374	93.87 333	97.68 321	99.13 305	93.87 249	99.01 317	91.38 346	96.19 278	98.59 302

ANet_high		77.30 333	74.86 337	84.62 347	75.88 373	77.61 368	97.63 358	93.15 373	88.81 354	64.27 369	89.29 365	36.51 372	83.93 370	75.89 365	52.31 367	92.33 362

EMVS		80.02 332	79.22 335	82.43 350	91.19 365	76.40 369	97.55 359	92.49 375	66.36 368	83.01 363	91.27 363	64.63 366	85.79 369	65.82 368	60.65 366	85.08 365

E-PMN		80.61 331	79.88 334	82.81 348	90.75 366	76.38 370	97.69 357	95.76 367	66.44 367	83.52 361	92.25 362	62.54 367	87.16 368	68.53 367	61.40 365	84.89 366

MVE	76.82 21	76.91 334	74.31 338	84.70 346	85.38 372	76.05 371	96.88 360	93.17 372	67.39 366	71.28 368	89.01 366	21.66 377	87.69 367	71.74 366	72.29 364	90.35 363
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		91.10 325	91.36 328	90.31 344	95.85 358	73.72 372	94.89 361	99.25 275	68.39 365	95.82 346	99.02 317	80.50 361	98.95 329	93.64 331	94.89 311	98.25 330

tmp_tt		82.80 330	81.52 333	86.66 345	66.61 375	68.44 373	92.79 364	97.92 352	68.96 364	80.04 367	99.85 29	85.77 351	96.15 363	97.86 195	43.89 368	95.39 359

FPMVS		84.93 329	85.65 330	82.75 349	86.77 370	63.39 374	98.35 343	98.92 313	74.11 362	83.39 362	98.98 322	50.85 369	92.40 366	84.54 363	94.97 308	92.46 360

PMVS	70.75 22	75.98 335	74.97 336	79.01 351	70.98 374	55.18 375	93.37 363	98.21 347	65.08 369	61.78 370	93.83 360	21.74 376	92.53 365	78.59 364	91.12 344	89.34 364
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		40.18 336	41.29 341	36.84 352	86.18 371	49.12 376	79.73 365	22.81 377	27.64 370	25.46 373	28.45 372	21.98 375	48.89 371	55.80 369	23.56 371	12.51 369

test123		39.01 338	42.50 340	28.53 353	39.17 376	20.91 377	98.75 320	19.17 378	19.83 372	38.57 371	66.67 368	33.16 373	15.42 372	37.50 371	29.66 370	49.26 367

testmvs		39.17 337	43.78 339	25.37 354	36.04 377	16.84 378	98.36 342	26.56 376	20.06 371	38.51 372	67.32 367	29.64 374	15.30 373	37.59 370	39.90 369	43.98 368

uanet_test		0.02 342	0.03 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.27 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

cdsmvs_eth3d_5k		24.64 339	32.85 342	0.00 355	0.00 378	0.00 379	0.00 366	99.51 104	0.00 373	0.00 374	99.56 202	96.58 150	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		8.27 341	11.03 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.27 373	99.01 19	0.00 374	0.00 372	0.00 372	0.00 370

sosnet-low-res		0.02 342	0.03 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.27 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

sosnet		0.02 342	0.03 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.27 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uncertanet		0.02 342	0.03 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.27 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

Regformer		0.02 342	0.03 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.27 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

ab-mvs-re		8.30 340	11.06 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	99.58 195	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

uanet		0.02 342	0.03 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.27 373	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

PC_three_1452									98.18 107	99.84 14	99.70 135	99.31 3	98.52 338	98.30 162	99.80 87	99.81 43

eth-test2					0.00 378

eth-test					0.00 378

test_241102_TWO								99.48 145	99.08 12	99.88 5	99.81 62	98.94 34	99.96 19	98.91 71	99.84 65	99.88 7

9.14			99.10 72		99.72 82		99.40 182	99.51 104	97.53 182	99.64 76	99.78 97	98.84 45	99.91 94	97.63 217	99.82 80

test_0728_THIRD									98.99 26	99.81 24	99.80 78	99.09 14	99.96 19	98.85 85	99.90 23	99.88 7

GSMVS																99.52 153

sam_mvs1												94.86 209				99.52 153

sam_mvs												94.72 220

MTGPA								99.47 163

test_post1							99.23 238				65.14 370	94.18 241	99.71 199	97.58 221

test_post											65.99 369	94.65 224	99.73 189

patchmatchnet-post											98.70 333	94.79 212	99.74 182

MTMP							99.54 114	98.88 320

test9_res														97.49 232	99.72 109	99.75 73

agg_prior2														97.21 249	99.73 108	99.75 73

test_prior2							98.96 296		98.34 87	99.01 216	99.52 217	98.68 66		97.96 187	99.74 105

旧先验2							98.96 296		96.70 253	99.47 114			99.94 57	98.19 167

新几何2							99.01 286

无先验							98.99 288	99.51 104	96.89 242				99.93 72	97.53 229		99.72 91

原ACMM2							98.95 300

testdata2													99.95 46	96.67 283

segment_acmp												98.96 28

testdata1							98.85 310		98.32 91

plane_prior5								99.47 163					99.69 209	97.78 202	97.63 229	98.67 267

plane_prior4											99.61 186

plane_prior2							99.39 186		98.97 32

plane_prior1					99.26 226

n2								0.00 379

nn								0.00 379

door-mid								98.05 350

test11								99.35 237

door								97.92 352

HQP-NCC					99.19 242		98.98 292		98.24 97	98.66 268

ACMP_Plane					99.19 242		98.98 292		98.24 97	98.66 268

BP-MVS														97.19 253

HQP4-MVS										98.66 268			99.64 221			98.64 279

HQP3-MVS								99.39 216							97.58 234

HQP2-MVS												92.47 282

ACMMP++_ref															97.19 258

ACMMP++															97.43 250

Test By Simon												98.75 59

High-resolution multi-view benchmark