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.

CHOSEN 280x420		96.80 35	96.85 27	96.66 91	97.85 113	94.42 49	94.76 300	98.36 23	92.50 60	95.62 95	97.52 140	97.92 1	97.38 214	98.31 33	98.80 94	98.20 172

GG-mvs-BLEND				96.98 68	96.53 155	94.81 38	87.20 338	97.74 68		93.91 122	96.40 181	96.56 2	96.94 228	95.08 93	98.95 88	99.20 106

gg-mvs-nofinetune		90.00 190	87.71 211	96.89 79	96.15 171	94.69 43	85.15 344	97.74 68	68.32 345	92.97 135	60.16 355	96.10 3	96.84 230	93.89 113	98.87 89	99.14 109

MSP-MVS		97.77 8	98.18 2	96.53 97	99.54 36	90.14 138	99.41 54	97.70 79	95.46 17	98.60 21	99.19 32	95.71 4	99.49 104	98.15 36	99.85 11	99.95 11
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
baseline2		94.04 105	93.80 106	94.74 154	93.07 263	90.25 135	98.12 196	98.16 33	89.86 121	86.53 206	96.95 164	95.56 5	98.05 173	91.44 141	94.53 154	95.93 215

OPU-MVS				99.49 2	99.64 20	98.51 2	99.77 8				99.19 32	95.12 6	99.97 20	99.90 1	99.92 3	99.99 1

tttt0517		93.30 128	93.01 120	94.17 173	95.57 186	86.47 218	98.51 157	97.60 98	85.99 222	90.55 164	97.19 153	94.80 7	98.31 158	85.06 210	91.86 185	97.74 180

thisisatest0530		94.00 106	93.52 109	95.43 134	95.76 181	90.02 147	98.99 102	97.60 98	86.58 214	91.74 144	97.36 146	94.78 8	98.34 157	86.37 197	92.48 175	97.94 178

thisisatest0515		94.75 88	94.19 90	96.43 101	96.13 175	92.64 88	99.47 40	97.60 98	87.55 197	93.17 130	97.59 138	94.71 9	98.42 156	88.28 177	93.20 164	98.24 169

test_0728_THIRD									93.01 49	99.07 8	99.46 11	94.66 10	99.97 20	99.25 11	99.82 15	99.95 11

ET-MVSNet_ETH3D		92.56 144	91.45 152	95.88 120	96.39 159	94.13 55	99.46 45	96.97 179	92.18 71	66.94 342	98.29 115	94.65 11	94.28 320	94.34 109	83.82 238	99.24 102

MVSTER		92.71 138	92.32 131	93.86 184	97.29 130	92.95 82	99.01 100	96.59 191	90.09 117	85.51 211	94.00 217	94.61 12	96.56 242	90.77 151	83.03 244	92.08 245

DWT-MVSNet_test		94.36 100	93.95 101	95.62 127	96.99 143	89.47 159	96.62 264	97.38 141	90.96 95	93.07 133	97.27 147	93.73 13	98.09 168	85.86 205	93.65 162	99.29 97

DPM-MVS		97.86 7	97.25 17	99.68 1	98.25 102	99.10 1	99.76 11	97.78 63	96.61 4	98.15 31	99.53 7	93.62 14	100.00 1	91.79 139	99.80 23	99.94 14

SED-MVS		98.18 2	98.10 4	98.41 14	99.63 21	95.24 21	99.77 8	97.72 74	94.17 24	99.30 4	99.54 3	93.32 15	99.98 10	99.70 2	99.81 19	99.99 1

test_241102_ONE					99.63 21	95.24 21		97.72 74	94.16 26	99.30 4	99.49 10	93.32 15	99.98 10

DPE-MVS		98.11 5	98.00 5	98.44 13	99.50 43	95.39 18	99.29 67	97.72 74	94.50 20	98.64 20	99.54 3	93.32 15	99.97 20	99.58 7	99.90 5	99.95 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		98.07 6	98.00 5	98.29 15	99.66 15	95.20 26	99.72 13	97.47 128	93.95 29	99.07 8	99.46 11	93.18 18	99.97 20	99.64 5	99.82 15	99.69 60
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
test0726					99.66 15	95.20 26	99.77 8	97.70 79	93.95 29	99.35 3	99.54 3	93.18 18

test_241102_TWO								97.72 74	94.17 24	99.23 6	99.54 3	93.14 20	99.98 10	99.70 2	99.82 15	99.99 1

CNVR-MVS		98.46 1	98.38 1	98.72 6	99.80 4	96.19 12	99.80 7	97.99 43	97.05 3	99.41 2	99.59 2	92.89 21	100.00 1	98.99 13	99.90 5	99.96 8

MCST-MVS		98.18 2	97.95 7	98.86 3	99.85 3	96.60 7	99.70 16	97.98 44	97.18 2	95.96 84	99.33 21	92.62 22	100.00 1	98.99 13	99.93 1	99.98 6

NCCC		98.12 4	98.11 3	98.13 20	99.76 6	94.46 46	99.81 5	97.88 49	96.54 5	98.84 14	99.46 11	92.55 23	99.98 10	98.25 34	99.93 1	99.94 14

SteuartSystems-ACMMP		97.25 17	97.34 15	97.01 62	97.38 127	91.46 104	99.75 12	97.66 83	94.14 28	98.13 32	99.26 24	92.16 24	99.66 80	97.91 40	99.64 43	99.90 20
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		97.44 15	97.46 11	97.39 49	99.12 72	93.49 67	98.52 154	97.50 123	94.46 21	98.99 10	98.64 95	91.58 25	99.08 139	98.49 24	99.83 13	99.60 73
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
TSAR-MVS + GP.		96.95 29	96.91 25	97.07 59	98.88 86	91.62 100	99.58 29	96.54 198	95.09 18	96.84 66	98.63 97	91.16 26	99.77 67	99.04 12	96.42 131	99.81 31

EPP-MVSNet		93.75 114	93.67 107	94.01 180	95.86 178	85.70 242	98.67 136	97.66 83	84.46 246	91.36 153	97.18 154	91.16 26	97.79 187	92.93 130	93.75 160	98.53 153

HPM-MVS++		97.72 9	97.59 9	98.14 19	99.53 41	94.76 39	99.19 71	97.75 66	95.66 13	98.21 30	99.29 22	91.10 28	99.99 5	97.68 42	99.87 7	99.68 61

旧先验1					98.97 80	92.90 83		97.74 68			99.15 41	91.05 29			99.33 70	99.60 73

train_agg		97.20 22	97.08 19	97.57 42	99.57 33	93.17 72	99.38 57	97.66 83	90.18 113	98.39 27	99.18 35	90.94 30	99.66 80	98.58 22	99.85 11	99.88 24

test_8					99.55 35	93.07 76	99.37 60	97.64 89	90.18 113	98.36 29	99.19 32	90.94 30	99.64 86

TEST9					99.57 33	93.17 72	99.38 57	97.66 83	89.57 133	98.39 27	99.18 35	90.88 32	99.66 80

SD-MVS		97.51 12	97.40 14	97.81 32	99.01 79	93.79 61	99.33 65	97.38 141	93.73 40	98.83 15	99.02 57	90.87 33	99.88 44	98.69 17	99.74 28	99.77 44
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
agg_prior1		97.12 24	97.03 21	97.38 50	99.54 36	92.66 84	99.35 62	97.64 89	90.38 107	97.98 40	99.17 37	90.84 34	99.61 89	98.57 23	99.78 27	99.87 27

APDe-MVS		97.53 11	97.47 10	97.70 36	99.58 29	93.63 62	99.56 32	97.52 117	93.59 43	98.01 39	99.12 46	90.80 35	99.55 94	99.26 10	99.79 25	99.93 17

RRT_test8_iter05		91.04 170	90.40 172	92.95 201	96.20 169	89.75 153	98.97 104	96.38 206	88.52 161	82.00 253	93.51 232	90.69 36	96.73 236	90.43 153	76.91 277	92.38 233

IB-MVS	89.43 6	92.12 151	90.83 164	95.98 118	95.40 194	90.78 124	99.81 5	98.06 38	91.23 91	85.63 210	93.66 227	90.63 37	98.78 145	91.22 143	71.85 318	98.36 164
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
ETH3 D test6400		97.67 10	97.33 16	98.69 7	99.69 9	96.43 9	99.63 24	97.73 72	91.05 92	98.66 19	99.53 7	90.59 38	99.71 73	99.32 8	99.80 23	99.91 18

segment_acmp												90.56 39

test_prior3		97.07 26	97.09 18	97.01 62	99.58 29	91.77 95	99.57 30	97.57 107	91.43 85	98.12 34	98.97 63	90.43 40	99.49 104	98.33 30	99.81 19	99.79 34

test_prior2							99.57 30		91.43 85	98.12 34	98.97 63	90.43 40		98.33 30	99.81 19

xxxxxxxxxxxxxcwj		97.51 12	97.42 13	97.78 34	99.34 53	93.85 59	99.65 22	95.45 272	95.69 11	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

SF-MVS		97.22 21	96.92 24	98.12 22	99.11 73	94.88 32	99.44 48	97.45 130	89.60 131	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

ETH3D-3000-0.1		97.29 16	97.01 22	98.12 22	99.18 69	94.97 30	99.47 40	97.52 117	89.85 122	98.79 16	99.46 11	90.41 44	99.69 75	98.78 15	99.67 38	99.70 57

DeepPCF-MVS	93.56 1	96.55 41	97.84 8	92.68 209	98.71 92	78.11 321	99.70 16	97.71 78	98.18 1	97.36 53	99.76 1	90.37 45	99.94 33	99.27 9	99.54 57	99.99 1

SMA-MVS		97.24 18	96.99 23	98.00 27	99.30 60	94.20 53	99.16 76	97.65 88	89.55 135	99.22 7	99.52 9	90.34 46	99.99 5	98.32 32	99.83 13	99.82 30
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
ZD-MVS					99.67 13	93.28 70		97.61 96	87.78 188	97.41 51	99.16 39	90.15 47	99.56 93	98.35 29	99.70 35

CostFormer		92.89 136	92.48 130	94.12 175	94.99 214	85.89 237	92.89 317	97.00 178	86.98 205	95.00 105	90.78 278	90.05 48	97.51 207	92.92 131	91.73 189	98.96 121

testtj		97.23 20	97.05 20	97.75 35	99.75 7	93.34 69	99.16 76	97.74 68	91.28 89	98.40 26	99.29 22	89.95 49	99.98 10	98.20 35	99.70 35	99.94 14

MSLP-MVS++		97.50 14	97.45 12	97.63 38	99.65 19	93.21 71	99.70 16	98.13 36	94.61 19	97.78 45	99.46 11	89.85 50	99.81 62	97.97 38	99.91 4	99.88 24

9.14			96.87 26		99.34 53		99.50 38	97.49 125	89.41 138	98.59 22	99.43 16	89.78 51	99.69 75	98.69 17	99.62 47

Regformer-1		96.97 28	96.80 30	97.47 44	99.46 47	93.11 74	98.89 112	97.94 45	92.89 54	96.90 59	99.02 57	89.78 51	99.53 97	97.06 49	99.26 76	99.75 48

Regformer-2		96.94 31	96.78 31	97.42 46	99.46 47	92.97 81	98.89 112	97.93 46	92.86 56	96.88 60	99.02 57	89.74 53	99.53 97	97.03 50	99.26 76	99.75 48

PAPM		96.35 47	95.94 55	97.58 40	94.10 234	95.25 20	98.93 107	98.17 31	94.26 23	93.94 121	98.72 89	89.68 54	97.88 181	96.36 67	99.29 74	99.62 71

ETH3D cwj APD-0.16		96.94 31	96.58 36	98.01 26	98.62 95	94.73 41	99.13 88	97.38 141	88.44 168	98.53 24	99.39 19	89.66 55	99.69 75	98.43 27	99.61 51	99.61 72

CSCG		94.87 85	94.71 80	95.36 137	99.54 36	86.49 217	99.34 64	98.15 34	82.71 275	90.15 172	99.25 25	89.48 56	99.86 52	94.97 97	98.82 93	99.72 54

PHI-MVS		96.65 38	96.46 38	97.21 56	99.34 53	91.77 95	99.70 16	98.05 39	86.48 217	98.05 36	99.20 31	89.33 57	99.96 27	98.38 28	99.62 47	99.90 20

TESTMET0.1,1		93.82 112	93.26 114	95.49 131	95.21 198	90.25 135	99.15 82	97.54 113	89.18 142	91.79 143	94.87 205	89.13 58	97.63 200	86.21 198	96.29 136	98.60 151

APD-MVS		96.95 29	96.72 32	97.63 38	99.51 42	93.58 63	99.16 76	97.44 134	90.08 118	98.59 22	99.07 51	89.06 59	99.42 115	97.92 39	99.66 39	99.88 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CDS-MVSNet		93.47 121	93.04 119	94.76 152	94.75 223	89.45 160	98.82 118	97.03 175	87.91 185	90.97 158	96.48 179	89.06 59	96.36 256	89.50 161	92.81 170	98.49 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Patchmatch-test		86.25 251	84.06 266	92.82 203	94.42 228	82.88 282	82.88 353	94.23 310	71.58 334	79.39 285	90.62 287	89.00 61	96.42 253	63.03 339	91.37 195	99.16 108

CDPH-MVS		96.56 40	96.18 45	97.70 36	99.59 28	93.92 57	99.13 88	97.44 134	89.02 147	97.90 43	99.22 29	88.90 62	99.49 104	94.63 104	99.79 25	99.68 61

MG-MVS		97.24 18	96.83 29	98.47 12	99.79 5	95.71 15	99.07 92	99.06 9	94.45 22	96.42 77	98.70 92	88.81 63	99.74 70	95.35 88	99.86 10	99.97 7

patchmatchnet-post											84.86 336	88.73 64	96.81 232

Regformer-3		96.50 42	96.36 41	96.91 73	99.34 53	91.72 98	98.71 127	97.90 48	92.48 61	96.00 81	98.95 70	88.60 65	99.52 100	96.44 65	98.83 91	99.49 83

test12				97.83 31	99.33 59	94.45 47		97.55 110		97.56 46		88.60 65	99.50 103		99.71 34	99.55 77

1121		95.19 80	94.45 85	97.42 46	98.88 86	92.58 89	96.22 277	97.75 66	85.50 229	96.86 63	99.01 61	88.59 67	99.90 40	87.64 185	99.60 52	99.79 34

MVS_111021_HR		96.69 36	96.69 33	96.72 87	98.58 97	91.00 120	99.14 85	99.45 1	93.86 35	95.15 102	98.73 87	88.48 68	99.76 68	97.23 48	99.56 55	99.40 89

Regformer-4		96.45 45	96.33 43	96.81 80	99.34 53	91.44 105	98.71 127	97.88 49	92.43 62	95.97 83	98.95 70	88.42 69	99.51 101	96.40 66	98.83 91	99.49 83

sam_mvs1												88.39 70				98.84 132

PatchmatchNet		92.05 153	91.04 157	95.06 145	96.17 170	89.04 164	91.26 330	97.26 147	89.56 134	90.64 163	90.56 291	88.35 71	97.11 220	79.53 261	96.07 141	99.03 116
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		92.78 137	92.16 136	94.65 157	96.27 163	87.45 197	91.83 325	97.10 169	89.10 145	94.68 109	90.69 282	88.22 72	97.73 196	89.78 159	91.80 187	98.77 142

DELS-MVS		97.12 24	96.60 35	98.68 8	98.03 110	96.57 8	99.84 3	97.84 53	96.36 8	95.20 101	98.24 116	88.17 73	99.83 57	96.11 72	99.60 52	99.64 67
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				95.26 141	98.20 104	87.28 202		97.60 98	85.21 232	98.48 25	99.15 41	88.15 74	98.72 151	90.29 154	99.45 62	99.78 38

原ACMM1				96.18 109	99.03 78	90.08 141		97.63 93	88.98 148	97.00 58	98.97 63	88.14 75	99.71 73	88.23 178	99.62 47	98.76 143

新几何1				97.40 48	98.92 84	92.51 91		97.77 65	85.52 227	96.69 72	99.06 53	88.08 76	99.89 43	84.88 213	99.62 47	99.79 34

test-mter		93.27 130	92.89 122	94.40 165	94.94 217	87.27 203	99.15 82	97.25 148	88.95 150	91.57 147	94.04 213	88.03 77	97.58 203	85.94 202	96.13 137	98.36 164

JIA-IIPM		85.97 254	84.85 254	89.33 282	93.23 260	73.68 334	85.05 345	97.13 164	69.62 341	91.56 149	68.03 353	88.03 77	96.96 226	77.89 274	93.12 165	97.34 190

test_yl		95.27 78	94.60 82	97.28 53	98.53 98	92.98 79	99.05 95	98.70 16	86.76 211	94.65 110	97.74 130	87.78 79	99.44 112	95.57 84	92.61 172	99.44 87

DCV-MVSNet		95.27 78	94.60 82	97.28 53	98.53 98	92.98 79	99.05 95	98.70 16	86.76 211	94.65 110	97.74 130	87.78 79	99.44 112	95.57 84	92.61 172	99.44 87

PAPM_NR		95.43 72	95.05 77	96.57 95	99.42 49	90.14 138	98.58 151	97.51 120	90.65 100	92.44 139	98.90 76	87.77 81	99.90 40	90.88 148	99.32 71	99.68 61

RRT_MVS		91.95 154	91.09 155	94.53 161	96.71 152	95.12 28	98.64 140	96.23 217	89.04 146	85.24 213	95.06 202	87.71 82	96.43 252	89.10 172	82.06 251	92.05 247

HFP-MVS		96.42 46	96.26 44	96.90 74	99.69 9	90.96 121	99.47 40	97.81 58	90.54 103	96.88 60	99.05 54	87.57 83	99.96 27	95.65 79	99.72 30	99.78 38

#test#		96.48 43	96.34 42	96.90 74	99.69 9	90.96 121	99.53 36	97.81 58	90.94 96	96.88 60	99.05 54	87.57 83	99.96 27	95.87 76	99.72 30	99.78 38

tpm2		91.77 156	91.09 155	93.82 186	94.83 221	85.56 245	92.51 322	97.16 161	84.00 252	93.83 124	90.66 284	87.54 85	97.17 218	87.73 184	91.55 192	98.72 144

EPNet		96.82 34	96.68 34	97.25 55	98.65 93	93.10 75	99.48 39	98.76 12	96.54 5	97.84 44	98.22 117	87.49 86	99.66 80	95.35 88	97.78 113	99.00 117
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		96.22 52	96.15 50	96.42 102	99.67 13	89.62 157	99.70 16	97.61 96	90.07 119	96.00 81	99.16 39	87.43 87	99.92 36	96.03 74	99.72 30	99.70 57

miper_enhance_ethall		90.33 181	89.70 176	92.22 214	97.12 137	88.93 168	98.35 178	95.96 232	88.60 159	83.14 233	92.33 251	87.38 88	96.18 270	86.49 196	77.89 269	91.55 263

test_post											46.00 362	87.37 89	97.11 220

XVS		96.47 44	96.37 40	96.77 81	99.62 25	90.66 129	99.43 51	97.58 104	92.41 66	96.86 63	98.96 68	87.37 89	99.87 47	95.65 79	99.43 64	99.78 38

X-MVStestdata		90.69 177	88.66 197	96.77 81	99.62 25	90.66 129	99.43 51	97.58 104	92.41 66	96.86 63	29.59 366	87.37 89	99.87 47	95.65 79	99.43 64	99.78 38

DP-MVS Recon		95.85 64	95.15 75	97.95 28	99.87 2	94.38 50	99.60 27	97.48 126	86.58 214	94.42 112	99.13 45	87.36 92	99.98 10	93.64 119	98.33 106	99.48 85

DeepC-MVS_fast	93.52 2	97.16 23	96.84 28	98.13 20	99.61 27	94.45 47	98.85 115	97.64 89	96.51 7	95.88 87	99.39 19	87.35 93	99.99 5	96.61 60	99.69 37	99.96 8
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPR		96.35 47	95.82 59	97.94 29	99.63 21	94.19 54	99.42 53	97.55 110	92.43 62	93.82 125	99.12 46	87.30 94	99.91 38	94.02 111	99.06 81	99.74 51

Patchmatch-RL test		81.90 294	80.13 297	87.23 304	80.71 351	70.12 346	84.07 350	88.19 356	83.16 267	70.57 330	82.18 342	87.18 95	92.59 334	82.28 244	62.78 338	98.98 119

sam_mvs												87.08 96

EI-MVSNet-Vis-set		95.76 69	95.63 69	96.17 111	99.14 71	90.33 133	98.49 161	97.82 55	91.92 74	94.75 107	98.88 78	87.06 97	99.48 109	95.40 87	97.17 124	98.70 146

1112_ss		92.71 138	91.55 150	96.20 108	95.56 187	91.12 113	98.48 162	94.69 299	88.29 174	86.89 202	98.50 104	87.02 98	98.66 153	84.75 214	89.77 206	98.81 137

Test_1112_low_res		92.27 149	90.97 158	96.18 109	95.53 190	91.10 115	98.47 164	94.66 300	88.28 175	86.83 204	93.50 233	87.00 99	98.65 154	84.69 215	89.74 207	98.80 138

MDTV_nov1_ep13			90.47 171		96.14 172	88.55 177	91.34 329	97.51 120	89.58 132	92.24 141	90.50 295	86.99 100	97.61 202	77.64 275	92.34 177

region2R		96.30 50	96.17 47	96.70 88	99.70 8	90.31 134	99.46 45	97.66 83	90.55 102	97.07 57	99.07 51	86.85 101	99.97 20	95.43 86	99.74 28	99.81 31

baseline1		92.61 142	91.28 153	96.58 94	97.05 141	94.63 44	97.72 221	96.20 219	89.82 123	88.56 186	96.85 169	86.85 101	97.82 185	88.42 175	80.10 259	97.30 191

SR-MVS		96.13 54	96.16 49	96.07 114	99.42 49	89.04 164	98.59 149	97.33 146	90.44 105	96.84 66	99.12 46	86.75 103	99.41 117	97.47 43	99.44 63	99.76 47

test222					98.32 101	91.21 108	98.08 201	97.58 104	83.74 256	95.87 88	99.02 57	86.74 104			99.64 43	99.81 31

test1172		95.92 62	96.07 52	95.46 132	99.42 49	87.24 207	98.51 157	97.24 150	90.29 110	96.56 76	99.12 46	86.73 105	99.36 121	97.33 47	99.42 67	99.78 38

SR-MVS-dyc-post		95.75 70	95.86 58	95.41 135	99.22 66	87.26 205	98.40 172	97.21 154	89.63 129	96.67 73	98.97 63	86.73 105	99.36 121	96.62 58	99.31 72	99.60 73

MDTV_nov1_ep13_2view						91.17 112	91.38 328		87.45 199	93.08 132		86.67 107		87.02 189		98.95 125

ETV-MVS		96.00 57	96.00 53	96.00 116	96.56 154	91.05 118	99.63 24	96.61 189	93.26 48	97.39 52	98.30 114	86.62 108	98.13 165	98.07 37	97.57 115	98.82 136

ZNCC-MVS		96.09 55	95.81 61	96.95 72	99.42 49	91.19 109	99.55 33	97.53 114	89.72 126	95.86 89	98.94 75	86.59 109	99.97 20	95.13 92	99.56 55	99.68 61

ACMMP_NAP		96.59 39	96.18 45	97.81 32	98.82 89	93.55 64	98.88 114	97.59 102	90.66 98	97.98 40	99.14 43	86.59 109	100.00 1	96.47 64	99.46 60	99.89 23

WTY-MVS		95.97 59	95.11 76	98.54 10	97.62 119	96.65 6	99.44 48	98.74 13	92.25 69	95.21 100	98.46 110	86.56 111	99.46 111	95.00 96	92.69 171	99.50 82

HY-MVS	88.56 7	95.29 77	94.23 89	98.48 11	97.72 115	96.41 10	94.03 308	98.74 13	92.42 65	95.65 94	94.76 207	86.52 112	99.49 104	95.29 90	92.97 167	99.53 78

ACMMPR		96.28 51	96.14 51	96.73 85	99.68 12	90.47 132	99.47 40	97.80 60	90.54 103	96.83 68	99.03 56	86.51 113	99.95 30	95.65 79	99.72 30	99.75 48

EPMVS		92.59 143	91.59 149	95.59 130	97.22 132	90.03 146	91.78 326	98.04 40	90.42 106	91.66 146	90.65 285	86.49 114	97.46 209	81.78 249	96.31 134	99.28 99

zzz-MVS		96.21 53	95.96 54	96.96 70	99.29 61	91.19 109	98.69 132	97.45 130	92.58 57	94.39 113	99.24 27	86.43 115	99.99 5	96.22 68	99.40 68	99.71 55

MTAPA		96.09 55	95.80 62	96.96 70	99.29 61	91.19 109	97.23 240	97.45 130	92.58 57	94.39 113	99.24 27	86.43 115	99.99 5	96.22 68	99.40 68	99.71 55

GST-MVS		95.97 59	95.66 65	96.90 74	99.49 45	91.22 107	99.45 47	97.48 126	89.69 127	95.89 86	98.72 89	86.37 117	99.95 30	94.62 105	99.22 79	99.52 79

alignmvs		95.77 68	95.00 78	98.06 25	97.35 128	95.68 16	99.71 15	97.50 123	91.50 83	96.16 80	98.61 98	86.28 118	99.00 141	96.19 70	91.74 188	99.51 81

EI-MVSNet-UG-set		95.43 72	95.29 72	95.86 121	99.07 77	89.87 149	98.43 166	97.80 60	91.78 77	94.11 118	98.77 83	86.25 119	99.48 109	94.95 98	96.45 130	98.22 170

mPP-MVS		95.90 63	95.75 63	96.38 104	99.58 29	89.41 161	99.26 68	97.41 138	90.66 98	94.82 106	98.95 70	86.15 120	99.98 10	95.24 91	99.64 43	99.74 51

EIA-MVS		95.11 81	95.27 73	94.64 158	96.34 161	86.51 216	99.59 28	96.62 188	92.51 59	94.08 119	98.64 95	86.05 121	98.24 162	95.07 94	98.50 103	99.18 107

PLC	91.07 3	94.23 103	94.01 96	94.87 149	99.17 70	87.49 195	99.25 69	96.55 196	88.43 169	91.26 154	98.21 119	85.92 122	99.86 52	89.77 160	97.57 115	97.24 193
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PGM-MVS		95.85 64	95.65 67	96.45 100	99.50 43	89.77 152	98.22 187	98.90 11	89.19 141	96.74 70	98.95 70	85.91 123	99.92 36	93.94 112	99.46 60	99.66 65

MP-MVS-pluss		95.80 66	95.30 71	97.29 52	98.95 83	92.66 84	98.59 149	97.14 162	88.95 150	93.12 131	99.25 25	85.62 124	99.94 33	96.56 62	99.48 59	99.28 99
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MVSFormer		94.71 92	94.08 95	96.61 92	95.05 212	94.87 33	97.77 217	96.17 222	86.84 208	98.04 37	98.52 102	85.52 125	95.99 277	89.83 157	98.97 85	98.96 121

lupinMVS		96.32 49	95.94 55	97.44 45	95.05 212	94.87 33	99.86 2	96.50 200	93.82 38	98.04 37	98.77 83	85.52 125	98.09 168	96.98 54	98.97 85	99.37 90

MP-MVS		96.00 57	95.82 59	96.54 96	99.47 46	90.13 140	99.36 61	97.41 138	90.64 101	95.49 96	98.95 70	85.51 127	99.98 10	96.00 75	99.59 54	99.52 79
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS_3200maxsize		95.64 71	95.65 67	95.62 127	99.24 65	87.80 188	98.42 167	97.22 153	88.93 152	96.64 75	98.98 62	85.49 128	99.36 121	96.68 57	99.27 75	99.70 57

HyFIR lowres test		93.68 117	93.29 113	94.87 149	97.57 123	88.04 185	98.18 191	98.47 21	87.57 196	91.24 155	95.05 203	85.49 128	97.46 209	93.22 126	92.82 168	99.10 111

EPNet_dtu		92.28 148	92.15 137	92.70 208	97.29 130	84.84 257	98.64 140	97.82 55	92.91 53	93.02 134	97.02 161	85.48 130	95.70 292	72.25 312	94.89 152	97.55 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet (Re-imp)		93.26 131	93.00 121	94.06 177	96.14 172	86.71 215	98.68 134	96.70 186	88.30 173	89.71 179	97.64 136	85.43 131	96.39 254	88.06 181	96.32 133	99.08 113

test_post1							90.74 335				41.37 365	85.38 132	96.36 256	83.16 234

RE-MVS-def			95.70 64		99.22 66	87.26 205	98.40 172	97.21 154	89.63 129	96.67 73	98.97 63	85.24 133		96.62 58	99.31 72	99.60 73

tpm		89.67 194	88.95 190	91.82 224	92.54 268	81.43 294	92.95 316	95.92 238	87.81 187	90.50 166	89.44 310	84.99 134	95.65 293	83.67 231	82.71 247	98.38 161

HPM-MVS		95.41 74	95.22 74	95.99 117	99.29 61	89.14 162	99.17 75	97.09 170	87.28 201	95.40 97	98.48 107	84.93 135	99.38 119	95.64 83	99.65 40	99.47 86
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test-LLR		93.11 134	92.68 125	94.40 165	94.94 217	87.27 203	99.15 82	97.25 148	90.21 111	91.57 147	94.04 213	84.89 136	97.58 203	85.94 202	96.13 137	98.36 164

test0.0.03 1		88.96 203	88.61 198	90.03 265	91.09 288	84.43 262	98.97 104	97.02 176	90.21 111	80.29 273	96.31 185	84.89 136	91.93 343	72.98 309	85.70 225	93.73 222

PatchT		85.44 264	83.19 271	92.22 214	93.13 262	83.00 277	83.80 352	96.37 207	70.62 336	90.55 164	79.63 348	84.81 138	94.87 310	58.18 349	91.59 191	98.79 139

TAMVS		92.62 141	92.09 139	94.20 172	94.10 234	87.68 190	98.41 169	96.97 179	87.53 198	89.74 177	96.04 190	84.77 139	96.49 248	88.97 173	92.31 178	98.42 157

CR-MVSNet		88.83 209	87.38 216	93.16 197	93.47 253	86.24 225	84.97 346	94.20 311	88.92 153	90.76 161	86.88 329	84.43 140	94.82 312	70.64 316	92.17 182	98.41 158

Patchmtry		83.61 286	81.64 286	89.50 278	93.36 257	82.84 283	84.10 349	94.20 311	69.47 342	79.57 283	86.88 329	84.43 140	94.78 313	68.48 324	74.30 294	90.88 285

dp		90.16 187	88.83 193	94.14 174	96.38 160	86.42 219	91.57 327	97.06 172	84.76 243	88.81 184	90.19 303	84.29 142	97.43 213	75.05 293	91.35 196	98.56 152

miper_ehance_all_eth		88.94 204	88.12 207	91.40 231	95.32 195	86.93 211	97.85 213	95.55 266	84.19 249	81.97 254	91.50 265	84.16 143	95.91 284	84.69 215	77.89 269	91.36 271

MVS_111021_LR		95.78 67	95.94 55	95.28 140	98.19 106	87.69 189	98.80 120	99.26 7	93.39 45	95.04 104	98.69 93	84.09 144	99.76 68	96.96 55	99.06 81	98.38 161

tpmvs		89.16 199	87.76 209	93.35 193	97.19 133	84.75 259	90.58 336	97.36 144	81.99 286	84.56 217	89.31 313	83.98 145	98.17 163	74.85 296	90.00 205	97.12 195

API-MVS		94.78 87	94.18 92	96.59 93	99.21 68	90.06 145	98.80 120	97.78 63	83.59 260	93.85 123	99.21 30	83.79 146	99.97 20	92.37 135	99.00 84	99.74 51

cl-mvsnet2		89.57 196	88.79 194	91.91 221	97.94 112	87.62 192	97.98 206	96.51 199	85.03 237	82.37 244	91.79 259	83.65 147	96.50 246	85.96 201	77.89 269	91.61 260

CS-MVS		95.39 76	95.39 70	95.40 136	95.54 188	89.66 155	99.62 26	95.98 228	91.72 79	97.48 50	98.41 111	83.64 148	97.46 209	97.46 44	98.64 100	99.06 115

Test By Simon												83.62 149

PVSNet_BlendedMVS		93.36 126	93.20 115	93.84 185	98.77 90	91.61 101	99.47 40	98.04 40	91.44 84	94.21 116	92.63 249	83.50 150	99.87 47	97.41 45	83.37 242	90.05 308

PVSNet_Blended		95.94 61	95.66 65	96.75 83	98.77 90	91.61 101	99.88 1	98.04 40	93.64 42	94.21 116	97.76 128	83.50 150	99.87 47	97.41 45	97.75 114	98.79 139

HPM-MVS_fast		94.89 84	94.62 81	95.70 126	99.11 73	88.44 180	99.14 85	97.11 166	85.82 224	95.69 93	98.47 108	83.46 152	99.32 127	93.16 127	99.63 46	99.35 91

thres200		93.69 115	92.59 128	96.97 69	97.76 114	94.74 40	99.35 62	99.36 2	89.23 140	91.21 156	96.97 163	83.42 153	98.77 146	85.08 209	90.96 197	97.39 189

tfpn200view9		93.43 123	92.27 133	96.90 74	97.68 117	94.84 35	99.18 73	99.36 2	88.45 165	90.79 159	96.90 166	83.31 154	98.75 148	84.11 224	90.69 199	97.12 195

thres400		93.39 125	92.27 133	96.73 85	97.68 117	94.84 35	99.18 73	99.36 2	88.45 165	90.79 159	96.90 166	83.31 154	98.75 148	84.11 224	90.69 199	96.61 204

thres100view900		93.34 127	92.15 137	96.90 74	97.62 119	94.84 35	99.06 94	99.36 2	87.96 183	90.47 167	96.78 171	83.29 156	98.75 148	84.11 224	90.69 199	97.12 195

thres600view7		93.18 132	92.00 140	96.75 83	97.62 119	94.92 31	99.07 92	99.36 2	87.96 183	90.47 167	96.78 171	83.29 156	98.71 152	82.93 238	90.47 203	96.61 204

PVSNet_Blended_VisFu		94.67 93	94.11 93	96.34 106	97.14 135	91.10 115	99.32 66	97.43 136	92.10 73	91.53 150	96.38 184	83.29 156	99.68 78	93.42 124	96.37 132	98.25 168

hse-mvs3		92.47 146	91.95 142	94.05 178	97.13 136	85.01 255	98.36 177	98.08 37	93.85 36	96.27 78	96.73 173	83.19 159	99.43 114	95.81 77	68.09 328	97.70 181

hse-mvs2		91.67 158	91.51 151	92.15 218	96.22 165	82.61 287	97.74 220	97.53 114	93.85 36	96.27 78	96.15 186	83.19 159	97.44 212	95.81 77	66.86 333	96.40 211

AUN-MVS		90.17 186	89.50 179	92.19 216	96.21 166	82.67 285	97.76 219	97.53 114	88.05 180	91.67 145	96.15 186	83.10 161	97.47 208	88.11 180	66.91 332	96.43 210

IS-MVSNet		93.00 135	92.51 129	94.49 162	96.14 172	87.36 200	98.31 182	95.70 256	88.58 160	90.17 171	97.50 141	83.02 162	97.22 217	87.06 188	96.07 141	98.90 128

tpm cat1		88.89 205	87.27 218	93.76 187	95.79 179	85.32 249	90.76 334	97.09 170	76.14 323	85.72 209	88.59 316	82.92 163	98.04 174	76.96 279	91.43 193	97.90 179

UniMVSNet_NR-MVSNet		89.60 195	88.55 201	92.75 207	92.17 273	90.07 142	98.74 126	98.15 34	88.37 171	83.21 229	93.98 218	82.86 164	95.93 281	86.95 191	72.47 312	92.25 237

cl_fuxian		88.19 222	87.23 219	91.06 237	94.97 215	86.17 229	97.72 221	95.38 278	83.43 262	81.68 261	91.37 267	82.81 165	95.72 291	84.04 227	73.70 300	91.29 275

TAPA-MVS	87.50 9	90.35 180	89.05 188	94.25 171	98.48 100	85.17 252	98.42 167	96.58 194	82.44 281	87.24 197	98.53 101	82.77 166	98.84 144	59.09 347	97.88 109	98.72 144
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
KD-MVS_2432*1600		82.98 287	80.52 295	90.38 255	94.32 230	88.98 166	92.87 318	95.87 247	80.46 302	73.79 318	87.49 322	82.76 167	93.29 326	70.56 317	46.53 355	88.87 324

miper_refine_blended		82.98 287	80.52 295	90.38 255	94.32 230	88.98 166	92.87 318	95.87 247	80.46 302	73.79 318	87.49 322	82.76 167	93.29 326	70.56 317	46.53 355	88.87 324

CANet		97.00 27	96.49 37	98.55 9	98.86 88	96.10 13	99.83 4	97.52 117	95.90 9	97.21 54	98.90 76	82.66 169	99.93 35	98.71 16	98.80 94	99.63 69

CPTT-MVS		94.60 96	94.43 86	95.09 143	99.66 15	86.85 212	99.44 48	97.47 128	83.22 265	94.34 115	98.96 68	82.50 170	99.55 94	94.81 99	99.50 58	98.88 129

mvs_anonymous		92.50 145	91.65 148	95.06 145	96.60 153	89.64 156	97.06 246	96.44 204	86.64 213	84.14 221	93.93 219	82.49 171	96.17 271	91.47 140	96.08 140	99.35 91

pcd_1.5k_mvsjas		6.87 338	9.16 341	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	82.48 172	0.00 368	0.00 366	0.00 366	0.00 364

PS-MVSNAJss		89.54 197	89.05 188	91.00 239	88.77 315	84.36 263	97.39 230	95.97 230	88.47 162	81.88 256	93.80 223	82.48 172	96.50 246	89.34 166	83.34 243	92.15 242

PS-MVSNAJ		96.87 33	96.40 39	98.29 15	97.35 128	97.29 3	99.03 97	97.11 166	95.83 10	98.97 11	99.14 43	82.48 172	99.60 91	98.60 19	99.08 80	98.00 176

UA-Net		93.30 128	92.62 127	95.34 138	96.27 163	88.53 179	95.88 287	96.97 179	90.90 97	95.37 98	97.07 159	82.38 175	99.10 138	83.91 228	94.86 153	98.38 161

FIs		90.70 176	89.87 175	93.18 196	92.29 270	91.12 113	98.17 193	98.25 26	89.11 144	83.44 227	94.82 206	82.26 176	96.17 271	87.76 183	82.76 246	92.25 237

ACMMP		94.67 93	94.30 87	95.79 123	99.25 64	88.13 183	98.41 169	98.67 19	90.38 107	91.43 151	98.72 89	82.22 177	99.95 30	93.83 116	95.76 145	99.29 97
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
xiu_mvs_v2_base		96.66 37	96.17 47	98.11 24	97.11 138	96.96 4	99.01 100	97.04 173	95.51 16	98.86 13	99.11 50	82.19 178	99.36 121	98.59 21	98.14 107	98.00 176

cl-mvsnet1		87.82 224	86.81 225	90.87 244	94.87 220	85.39 248	97.81 214	95.22 290	82.92 273	80.76 267	91.31 269	81.99 179	95.81 289	81.36 250	75.04 285	91.42 269

miper_lstm_enhance		86.90 238	86.20 234	89.00 288	94.53 227	81.19 300	96.74 260	95.24 286	82.33 282	80.15 275	90.51 294	81.99 179	94.68 316	80.71 256	73.58 302	91.12 279

cl-mvsnet____		87.82 224	86.79 226	90.89 243	94.88 219	85.43 246	97.81 214	95.24 286	82.91 274	80.71 268	91.22 270	81.97 181	95.84 287	81.34 251	75.06 284	91.40 270

FC-MVSNet-test		90.22 184	89.40 182	92.67 210	91.78 280	89.86 150	97.89 209	98.22 28	88.81 155	82.96 234	94.66 208	81.90 182	95.96 279	85.89 204	82.52 249	92.20 241

UniMVSNet (Re)		89.50 198	88.32 204	93.03 198	92.21 272	90.96 121	98.90 111	98.39 22	89.13 143	83.22 228	92.03 253	81.69 183	96.34 262	86.79 194	72.53 311	91.81 252

MVS_Test		93.67 118	92.67 126	96.69 89	96.72 150	92.66 84	97.22 241	96.03 227	87.69 194	95.12 103	94.03 215	81.55 184	98.28 161	89.17 170	96.46 129	99.14 109

sss		94.85 86	93.94 102	97.58 40	96.43 158	94.09 56	98.93 107	99.16 8	89.50 136	95.27 99	97.85 123	81.50 185	99.65 84	92.79 133	94.02 159	98.99 118

eth_miper_zixun_eth		87.76 226	87.00 223	90.06 262	94.67 225	82.65 286	97.02 249	95.37 279	84.19 249	81.86 259	91.58 264	81.47 186	95.90 285	83.24 232	73.61 301	91.61 260

jason		95.40 75	94.86 79	97.03 61	92.91 265	94.23 52	99.70 16	96.30 211	93.56 44	96.73 71	98.52 102	81.46 187	97.91 178	96.08 73	98.47 104	98.96 121
jason: jason.
IterMVS-LS		88.34 218	87.44 214	91.04 238	94.10 234	85.85 239	98.10 199	95.48 270	85.12 233	82.03 252	91.21 271	81.35 188	95.63 294	83.86 229	75.73 281	91.63 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		89.87 192	89.38 183	91.36 233	94.32 230	85.87 238	97.61 226	96.59 191	85.10 234	85.51 211	97.10 157	81.30 189	96.56 242	83.85 230	83.03 244	91.64 255

RPMNet		85.07 267	81.88 284	94.64 158	93.47 253	86.24 225	84.97 346	97.21 154	64.85 351	90.76 161	78.80 349	80.95 190	99.27 129	53.76 352	92.17 182	98.41 158

114514_t		94.06 104	93.05 118	97.06 60	99.08 76	92.26 93	98.97 104	97.01 177	82.58 277	92.57 137	98.22 117	80.68 191	99.30 128	89.34 166	99.02 83	99.63 69

CNLPA		93.64 119	92.74 124	96.36 105	98.96 82	90.01 148	99.19 71	95.89 245	86.22 220	89.40 180	98.85 79	80.66 192	99.84 55	88.57 174	96.92 125	99.24 102

diffmvs		94.59 97	94.19 90	95.81 122	95.54 188	90.69 127	98.70 131	95.68 258	91.61 80	95.96 84	97.81 125	80.11 193	98.06 172	96.52 63	95.76 145	98.67 147

abl_6		94.63 95	94.48 84	95.09 143	98.61 96	86.96 210	98.06 203	96.97 179	89.31 139	95.86 89	98.56 100	79.82 194	99.64 86	94.53 107	98.65 99	98.66 150

casdiffmvs		93.98 107	93.43 110	95.61 129	95.07 211	89.86 150	98.80 120	95.84 250	90.98 94	92.74 136	97.66 135	79.71 195	98.10 167	94.72 102	95.37 149	98.87 131

Effi-MVS+		93.87 111	93.15 116	96.02 115	95.79 179	90.76 125	96.70 262	95.78 251	86.98 205	95.71 92	97.17 155	79.58 196	98.01 176	94.57 106	96.09 139	99.31 95

baseline		93.91 109	93.30 112	95.72 125	95.10 209	90.07 142	97.48 229	95.91 242	91.03 93	93.54 127	97.68 133	79.58 196	98.02 175	94.27 110	95.14 150	99.08 113

canonicalmvs		95.02 83	93.96 100	98.20 17	97.53 125	95.92 14	98.71 127	96.19 221	91.78 77	95.86 89	98.49 106	79.53 198	99.03 140	96.12 71	91.42 194	99.66 65

OMC-MVS		93.90 110	93.62 108	94.73 155	98.63 94	87.00 209	98.04 204	96.56 195	92.19 70	92.46 138	98.73 87	79.49 199	99.14 136	92.16 137	94.34 157	98.03 175

MVS		93.92 108	92.28 132	98.83 4	95.69 183	96.82 5	96.22 277	98.17 31	84.89 241	84.34 220	98.61 98	79.32 200	99.83 57	93.88 114	99.43 64	99.86 28

VNet		95.08 82	94.26 88	97.55 43	98.07 109	93.88 58	98.68 134	98.73 15	90.33 109	97.16 56	97.43 144	79.19 201	99.53 97	96.91 56	91.85 186	99.24 102

CHOSEN 1792x2688		94.35 101	93.82 105	95.95 119	97.40 126	88.74 174	98.41 169	98.27 25	92.18 71	91.43 151	96.40 181	78.88 202	99.81 62	93.59 120	97.81 110	99.30 96

ADS-MVSNet2		87.62 231	86.88 224	89.86 267	96.21 166	79.14 312	87.15 339	92.99 325	83.01 268	89.91 175	87.27 325	78.87 203	92.80 332	74.20 300	92.27 179	97.64 182

ADS-MVSNet		88.99 202	87.30 217	94.07 176	96.21 166	87.56 194	87.15 339	96.78 185	83.01 268	89.91 175	87.27 325	78.87 203	97.01 225	74.20 300	92.27 179	97.64 182

nrg030		90.23 183	88.87 191	94.32 168	91.53 283	93.54 65	98.79 124	95.89 245	88.12 179	84.55 218	94.61 209	78.80 205	96.88 229	92.35 136	75.21 283	92.53 231

F-COLMAP		92.07 152	91.75 147	93.02 199	98.16 107	82.89 281	98.79 124	95.97 230	86.54 216	87.92 190	97.80 126	78.69 206	99.65 84	85.97 200	95.93 143	96.53 209

MAR-MVS		94.43 99	94.09 94	95.45 133	99.10 75	87.47 196	98.39 175	97.79 62	88.37 171	94.02 120	99.17 37	78.64 207	99.91 38	92.48 134	98.85 90	98.96 121
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
PCF-MVS	89.78 5	91.26 164	89.63 177	96.16 112	95.44 192	91.58 103	95.29 296	96.10 225	85.07 236	82.75 235	97.45 143	78.28 208	99.78 66	80.60 257	95.65 148	97.12 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS	91.02 4	94.56 98	93.92 103	96.46 99	97.16 134	90.76 125	98.39 175	97.11 166	93.92 31	88.66 185	98.33 112	78.14 209	99.85 54	95.02 95	98.57 101	98.78 141
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS_H		86.53 247	85.49 245	89.66 275	91.04 289	83.31 275	97.53 228	98.20 30	84.95 240	79.64 281	90.90 276	78.01 210	95.33 301	76.29 286	72.81 307	90.35 300

Fast-Effi-MVS+		91.72 157	90.79 165	94.49 162	95.89 177	87.40 199	99.54 35	95.70 256	85.01 239	89.28 182	95.68 194	77.75 211	97.57 206	83.22 233	95.06 151	98.51 154

1314		93.44 122	91.98 141	97.84 30	95.24 196	94.38 50	96.22 277	97.92 47	90.18 113	82.28 245	97.71 132	77.63 212	99.80 64	91.94 138	98.67 98	99.34 93

NR-MVSNet		87.74 229	86.00 237	92.96 200	91.46 284	90.68 128	96.65 263	97.42 137	88.02 182	73.42 320	93.68 225	77.31 213	95.83 288	84.26 220	71.82 319	92.36 234

BH-w/o		92.32 147	91.79 145	93.91 183	96.85 145	86.18 228	99.11 90	95.74 254	88.13 178	84.81 215	97.00 162	77.26 214	97.91 178	89.16 171	98.03 108	97.64 182

PMMVS		93.62 120	93.90 104	92.79 204	96.79 148	81.40 295	98.85 115	96.81 183	91.25 90	96.82 69	98.15 121	77.02 215	98.13 165	93.15 128	96.30 135	98.83 135

CVMVSNet		90.30 182	90.91 160	88.46 295	94.32 230	73.58 335	97.61 226	97.59 102	90.16 116	88.43 188	97.10 157	76.83 216	92.86 329	82.64 240	93.54 163	98.93 126

LCM-MVSNet-Re		88.59 215	88.61 198	88.51 294	95.53 190	72.68 339	96.85 254	88.43 355	88.45 165	73.14 322	90.63 286	75.82 217	94.38 319	92.95 129	95.71 147	98.48 156

LS3D		90.19 185	88.72 195	94.59 160	98.97 80	86.33 224	96.90 252	96.60 190	74.96 326	84.06 223	98.74 86	75.78 218	99.83 57	74.93 294	97.57 115	97.62 185

pmmvs4		87.58 232	86.17 235	91.80 225	89.58 305	88.92 169	97.25 238	95.28 282	82.54 278	80.49 270	93.17 239	75.62 219	96.05 276	82.75 239	78.90 263	90.42 299

BH-untuned		91.46 162	90.84 162	93.33 194	96.51 157	84.83 258	98.84 117	95.50 269	86.44 219	83.50 226	96.70 174	75.49 220	97.77 189	86.78 195	97.81 110	97.40 188

AdaColmap		93.82 112	93.06 117	96.10 113	99.88 1	89.07 163	98.33 179	97.55 110	86.81 210	90.39 169	98.65 94	75.09 221	99.98 10	93.32 125	97.53 118	99.26 101

bset_n11_16_dypcd		89.07 201	87.85 208	92.76 206	86.16 337	90.66 129	97.30 234	95.62 261	89.78 125	83.94 224	93.15 241	74.85 222	95.89 286	91.34 142	78.48 265	91.74 253

DU-MVS		88.83 209	87.51 213	92.79 204	91.46 284	90.07 142	98.71 127	97.62 95	88.87 154	83.21 229	93.68 225	74.63 223	95.93 281	86.95 191	72.47 312	92.36 234

Baseline_NR-MVSNet		85.83 257	84.82 255	88.87 291	88.73 316	83.34 274	98.63 142	91.66 341	80.41 304	82.44 241	91.35 268	74.63 223	95.42 299	84.13 223	71.39 321	87.84 329

v148		86.38 249	85.06 249	90.37 257	89.47 309	84.10 266	98.52 154	95.48 270	83.80 255	80.93 266	90.22 301	74.60 225	96.31 264	80.92 254	71.55 320	90.69 294

3Dnovator+	87.72 8	93.43 123	91.84 144	98.17 18	95.73 182	95.08 29	98.92 109	97.04 173	91.42 87	81.48 263	97.60 137	74.60 225	99.79 65	90.84 149	98.97 85	99.64 67

test_part1		88.43 217	86.68 227	93.67 190	97.56 124	92.40 92	98.12 196	96.55 196	82.26 283	80.31 272	93.16 240	74.59 227	96.62 239	85.00 212	72.61 310	91.99 249

v8		86.11 252	84.45 261	91.10 236	89.99 298	86.85 212	97.24 239	95.36 280	81.99 286	79.89 279	89.86 306	74.53 228	96.39 254	78.83 269	72.32 314	90.05 308

DP-MVS		88.75 213	86.56 229	95.34 138	98.92 84	87.45 197	97.64 225	93.52 322	70.55 337	81.49 262	97.25 148	74.43 229	99.88 44	71.14 315	94.09 158	98.67 147

GeoE		90.60 179	89.56 178	93.72 189	95.10 209	85.43 246	99.41 54	94.94 293	83.96 254	87.21 198	96.83 170	74.37 230	97.05 224	80.50 259	93.73 161	98.67 147

cdsmvs_eth3d_5k		22.52 333	30.03 336	0.00 350	0.00 371	0.00 372	0.00 362	97.17 160	0.00 367	0.00 368	98.77 83	74.35 231	0.00 368	0.00 366	0.00 366	0.00 364

Effi-MVS+-dtu		89.97 191	90.68 167	87.81 299	95.15 203	71.98 341	97.87 212	95.40 276	91.92 74	87.57 192	91.44 266	74.27 232	96.84 230	89.45 162	93.10 166	94.60 220

mvs-test1		91.57 159	92.20 135	89.70 272	95.15 203	74.34 331	99.51 37	95.40 276	91.92 74	91.02 157	97.25 148	74.27 232	98.08 171	89.45 162	95.83 144	96.67 203

WR-MVS		88.54 216	87.22 220	92.52 211	91.93 278	89.50 158	98.56 152	97.84 53	86.99 203	81.87 257	93.81 222	74.25 234	95.92 283	85.29 207	74.43 292	92.12 243

FMVSNet3		88.81 211	87.08 221	93.99 181	96.52 156	94.59 45	98.08 201	96.20 219	85.85 223	82.12 248	91.60 263	74.05 235	95.40 300	79.04 265	80.24 256	91.99 249

V42		87.00 237	85.68 242	90.98 240	89.91 299	86.08 232	98.32 181	95.61 263	83.67 259	82.72 236	90.67 283	74.00 236	96.53 244	81.94 248	74.28 295	90.32 301

D2MVS		87.96 223	87.39 215	89.70 272	91.84 279	83.40 273	98.31 182	98.49 20	88.04 181	78.23 298	90.26 297	73.57 237	96.79 234	84.21 221	83.53 240	88.90 323

v1144		86.83 240	85.31 247	91.40 231	89.75 302	87.21 208	98.31 182	95.45 272	83.22 265	82.70 237	90.78 278	73.36 238	96.36 256	79.49 262	74.69 289	90.63 296

HQP2-MVS												73.34 239

HQP-MVS		91.50 160	91.23 154	92.29 213	93.95 238	86.39 221	99.16 76	96.37 207	93.92 31	87.57 192	96.67 175	73.34 239	97.77 189	93.82 117	86.29 217	92.72 227

v10		85.73 261	84.01 267	90.87 244	90.03 297	86.73 214	97.20 242	95.22 290	81.25 294	79.85 280	89.75 307	73.30 241	96.28 268	76.87 280	72.64 309	89.61 315

v2v482		87.27 235	85.76 240	91.78 229	89.59 304	87.58 193	98.56 152	95.54 267	84.53 245	82.51 240	91.78 260	73.11 242	96.47 249	82.07 245	74.14 298	91.30 274

HQP_MVS		91.26 164	90.95 159	92.16 217	93.84 245	86.07 233	99.02 98	96.30 211	93.38 46	86.99 199	96.52 177	72.92 243	97.75 194	93.46 122	86.17 220	92.67 229

plane_prior6					93.92 242	86.02 235						72.92 243

QAPM		91.41 163	89.49 180	97.17 58	95.66 185	93.42 68	98.60 147	97.51 120	80.92 299	81.39 264	97.41 145	72.89 245	99.87 47	82.33 243	98.68 97	98.21 171

v144192		86.40 248	84.89 253	90.91 241	89.48 308	85.59 243	98.21 189	95.43 275	82.45 280	82.62 238	90.58 290	72.79 246	96.36 256	78.45 271	74.04 299	90.79 288

TranMVSNet+NR-MVSNet		87.75 227	86.31 232	92.07 220	90.81 291	88.56 176	98.33 179	97.18 159	87.76 189	81.87 257	93.90 220	72.45 247	95.43 298	83.13 236	71.30 322	92.23 239

xiu_mvs_v1_base_debu		94.73 89	93.98 97	96.99 65	95.19 199	95.24 21	98.62 143	96.50 200	92.99 50	97.52 47	98.83 80	72.37 248	99.15 133	97.03 50	96.74 126	96.58 206

xiu_mvs_v1_base		94.73 89	93.98 97	96.99 65	95.19 199	95.24 21	98.62 143	96.50 200	92.99 50	97.52 47	98.83 80	72.37 248	99.15 133	97.03 50	96.74 126	96.58 206

xiu_mvs_v1_base_debi		94.73 89	93.98 97	96.99 65	95.19 199	95.24 21	98.62 143	96.50 200	92.99 50	97.52 47	98.83 80	72.37 248	99.15 133	97.03 50	96.74 126	96.58 206

test_djsdf		88.26 221	87.73 210	89.84 268	88.05 324	82.21 289	97.77 217	96.17 222	86.84 208	82.41 243	91.95 258	72.07 251	95.99 277	89.83 157	84.50 231	91.32 273

3Dnovator	87.35 11	93.17 133	91.77 146	97.37 51	95.41 193	93.07 76	98.82 118	97.85 52	91.53 82	82.56 239	97.58 139	71.97 252	99.82 60	91.01 146	99.23 78	99.22 105

CANet_DTU		94.31 102	93.35 111	97.20 57	97.03 142	94.71 42	98.62 143	95.54 267	95.61 14	97.21 54	98.47 108	71.88 253	99.84 55	88.38 176	97.46 120	97.04 200

CP-MVSNet		86.54 246	85.45 246	89.79 270	91.02 290	82.78 284	97.38 232	97.56 109	85.37 230	79.53 284	93.03 243	71.86 254	95.25 303	79.92 260	73.43 305	91.34 272

PatchMatch-RL		91.47 161	90.54 169	94.26 170	98.20 104	86.36 223	96.94 250	97.14 162	87.75 190	88.98 183	95.75 193	71.80 255	99.40 118	80.92 254	97.39 121	97.02 201

our_test_3		84.47 276	82.80 275	89.50 278	89.01 312	83.90 269	97.03 247	94.56 302	81.33 293	75.36 312	90.52 293	71.69 256	94.54 318	68.81 322	76.84 278	90.07 306

XVG-OURS		90.83 173	90.49 170	91.86 222	95.23 197	81.25 299	95.79 292	95.92 238	88.96 149	90.02 174	98.03 122	71.60 257	99.35 125	91.06 145	87.78 212	94.98 218

v1192		86.32 250	84.71 257	91.17 235	89.53 307	86.40 220	98.13 194	95.44 274	82.52 279	82.42 242	90.62 287	71.58 258	96.33 263	77.23 276	74.88 286	90.79 288

Vis-MVSNet		92.64 140	91.85 143	95.03 147	95.12 205	88.23 181	98.48 162	96.81 183	91.61 80	92.16 142	97.22 151	71.58 258	98.00 177	85.85 206	97.81 110	98.88 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PVSNet	87.13 12	93.69 115	92.83 123	96.28 107	97.99 111	90.22 137	99.38 57	98.93 10	91.42 87	93.66 126	97.68 133	71.29 260	99.64 86	87.94 182	97.20 123	98.98 119

v1921920		86.02 253	84.44 262	90.77 246	89.32 310	85.20 250	98.10 199	95.35 281	82.19 284	82.25 246	90.71 280	70.73 261	96.30 267	76.85 281	74.49 291	90.80 287

EU-MVSNet		84.19 279	84.42 263	83.52 324	88.64 318	67.37 350	96.04 283	95.76 253	85.29 231	78.44 295	93.18 238	70.67 262	91.48 345	75.79 290	75.98 279	91.70 254

XVG-OURS-SEG-HR		90.95 171	90.66 168	91.83 223	95.18 202	81.14 302	95.92 284	95.92 238	88.40 170	90.33 170	97.85 123	70.66 263	99.38 119	92.83 132	88.83 208	94.98 218

v7n		84.42 277	82.75 278	89.43 281	88.15 322	81.86 291	96.75 259	95.67 259	80.53 300	78.38 296	89.43 311	69.89 264	96.35 261	73.83 304	72.13 316	90.07 306

ppachtmachnet_test		83.63 285	81.57 288	89.80 269	89.01 312	85.09 254	97.13 244	94.50 303	78.84 309	76.14 304	91.00 274	69.78 265	94.61 317	63.40 337	74.36 293	89.71 314

MSDG		88.29 220	86.37 231	94.04 179	96.90 144	86.15 230	96.52 266	94.36 308	77.89 317	79.22 287	96.95 164	69.72 266	99.59 92	73.20 308	92.58 174	96.37 212

CLD-MVS		91.06 168	90.71 166	92.10 219	94.05 237	86.10 231	99.55 33	96.29 214	94.16 26	84.70 216	97.17 155	69.62 267	97.82 185	94.74 101	86.08 222	92.39 232
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1240		85.77 260	84.11 265	90.73 247	89.26 311	85.15 253	97.88 211	95.23 289	81.89 289	82.16 247	90.55 292	69.60 268	96.31 264	75.59 291	74.87 287	90.72 293

Fast-Effi-MVS+-dtu		88.84 207	88.59 200	89.58 276	93.44 256	78.18 319	98.65 138	94.62 301	88.46 164	84.12 222	95.37 200	68.91 269	96.52 245	82.06 246	91.70 190	94.06 221

anonymousdsp		86.69 242	85.75 241	89.53 277	86.46 335	82.94 278	96.39 268	95.71 255	83.97 253	79.63 282	90.70 281	68.85 270	95.94 280	86.01 199	84.02 235	89.72 313

VPA-MVSNet		89.10 200	87.66 212	93.45 192	92.56 267	91.02 119	97.97 207	98.32 24	86.92 207	86.03 208	92.01 255	68.84 271	97.10 222	90.92 147	75.34 282	92.23 239

ab-mvs		91.05 169	89.17 186	96.69 89	95.96 176	91.72 98	92.62 321	97.23 152	85.61 226	89.74 177	93.89 221	68.55 272	99.42 115	91.09 144	87.84 211	98.92 127

CL-MVSNet_2432*1600		79.89 302	78.34 302	84.54 320	81.56 349	75.01 328	96.88 253	95.62 261	81.10 295	75.86 308	85.81 335	68.49 273	90.26 347	63.21 338	56.51 348	88.35 326

PEN-MVS		85.21 266	83.93 268	89.07 287	89.89 301	81.31 298	97.09 245	97.24 150	84.45 247	78.66 291	92.68 248	68.44 274	94.87 310	75.98 288	70.92 323	91.04 281

BH-RMVSNet		91.25 166	89.99 174	95.03 147	96.75 149	88.55 177	98.65 138	94.95 292	87.74 191	87.74 191	97.80 126	68.27 275	98.14 164	80.53 258	97.49 119	98.41 158

GA-MVS		90.10 188	88.69 196	94.33 167	92.44 269	87.97 187	99.08 91	96.26 215	89.65 128	86.92 201	93.11 242	68.09 276	96.96 226	82.54 242	90.15 204	98.05 174

MDA-MVSNet_test_wron		79.65 303	77.05 307	87.45 302	87.79 328	80.13 308	96.25 275	94.44 304	73.87 330	51.80 353	87.47 324	68.04 277	92.12 341	66.02 331	67.79 330	90.09 304

OpenMVS	85.28 14	90.75 175	88.84 192	96.48 98	93.58 251	93.51 66	98.80 120	97.41 138	82.59 276	78.62 292	97.49 142	68.00 278	99.82 60	84.52 218	98.55 102	96.11 214

YYNet1		79.64 304	77.04 308	87.43 303	87.80 327	79.98 309	96.23 276	94.44 304	73.83 331	51.83 352	87.53 321	67.96 279	92.07 342	66.00 332	67.75 331	90.23 303

DTE-MVSNet		84.14 280	82.80 275	88.14 296	88.95 314	79.87 310	96.81 255	96.24 216	83.50 261	77.60 300	92.52 250	67.89 280	94.24 321	72.64 311	69.05 326	90.32 301

MVP-Stereo		86.61 245	85.83 239	88.93 290	88.70 317	83.85 270	96.07 282	94.41 307	82.15 285	75.64 310	91.96 257	67.65 281	96.45 251	77.20 278	98.72 96	86.51 340
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XXY-MVS		87.75 227	86.02 236	92.95 201	90.46 295	89.70 154	97.71 223	95.90 243	84.02 251	80.95 265	94.05 212	67.51 282	97.10 222	85.16 208	78.41 266	92.04 248

PS-CasMVS		85.81 258	84.58 260	89.49 280	90.77 292	82.11 290	97.20 242	97.36 144	84.83 242	79.12 289	92.84 246	67.42 283	95.16 305	78.39 272	73.25 306	91.21 277

ACMM	86.95 13	88.77 212	88.22 206	90.43 253	93.61 250	81.34 297	98.50 159	95.92 238	87.88 186	83.85 225	95.20 201	67.20 284	97.89 180	86.90 193	84.90 228	92.06 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TransMVSNet (Re)		81.97 292	79.61 300	89.08 286	89.70 303	84.01 267	97.26 237	91.85 340	78.84 309	73.07 325	91.62 262	67.17 285	95.21 304	67.50 326	59.46 345	88.02 328

OPM-MVS		89.76 193	89.15 187	91.57 230	90.53 294	85.58 244	98.11 198	95.93 237	92.88 55	86.05 207	96.47 180	67.06 286	97.87 182	89.29 169	86.08 222	91.26 276

TR-MVS		90.77 174	89.44 181	94.76 152	96.31 162	88.02 186	97.92 208	95.96 232	85.52 227	88.22 189	97.23 150	66.80 287	98.09 168	84.58 217	92.38 176	98.17 173

IterMVS-SCA-FT		85.73 261	84.64 259	89.00 288	93.46 255	82.90 280	96.27 272	94.70 298	85.02 238	78.62 292	90.35 296	66.61 288	93.33 325	79.38 264	77.36 276	90.76 290

SCA		90.64 178	89.25 185	94.83 151	94.95 216	88.83 170	96.26 274	97.21 154	90.06 120	90.03 173	90.62 287	66.61 288	96.81 232	83.16 234	94.36 156	98.84 132

IterMVS		85.81 258	84.67 258	89.22 283	93.51 252	83.67 271	96.32 271	94.80 295	85.09 235	78.69 290	90.17 304	66.57 290	93.17 328	79.48 263	77.42 275	90.81 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
LPG-MVS_test		88.86 206	88.47 203	90.06 262	93.35 258	80.95 304	98.22 187	95.94 235	87.73 192	83.17 231	96.11 188	66.28 291	97.77 189	90.19 155	85.19 226	91.46 266

LGP-MVS_train				90.06 262	93.35 258	80.95 304		95.94 235	87.73 192	83.17 231	96.11 188	66.28 291	97.77 189	90.19 155	85.19 226	91.46 266

ACMP	87.39 10	88.71 214	88.24 205	90.12 261	93.91 243	81.06 303	98.50 159	95.67 259	89.43 137	80.37 271	95.55 195	65.67 293	97.83 184	90.55 152	84.51 230	91.47 265
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LTVRE_ROB	81.71 19	84.59 273	82.72 279	90.18 259	92.89 266	83.18 276	93.15 315	94.74 296	78.99 308	75.14 313	92.69 247	65.64 294	97.63 200	69.46 320	81.82 253	89.74 312
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
pm-mvs1		84.68 271	82.78 277	90.40 254	89.58 305	85.18 251	97.31 233	94.73 297	81.93 288	76.05 305	92.01 255	65.48 295	96.11 274	78.75 270	69.14 325	89.91 311

cascas		90.93 172	89.33 184	95.76 124	95.69 183	93.03 78	98.99 102	96.59 191	80.49 301	86.79 205	94.45 210	65.23 296	98.60 155	93.52 121	92.18 181	95.66 217

tfpnnormal		83.65 284	81.35 290	90.56 250	91.37 286	88.06 184	97.29 235	97.87 51	78.51 312	76.20 303	90.91 275	64.78 297	96.47 249	61.71 342	73.50 303	87.13 337

pmmvs5		85.87 255	84.40 264	90.30 258	88.53 319	84.23 264	98.60 147	93.71 318	81.53 291	80.29 273	92.02 254	64.51 298	95.52 296	82.04 247	78.34 267	91.15 278

RPSCF		85.33 265	85.55 244	84.67 319	94.63 226	62.28 352	93.73 310	93.76 316	74.38 329	85.23 214	97.06 160	64.09 299	98.31 158	80.98 252	86.08 222	93.41 226

N_pmnet		70.19 321	69.87 323	71.12 337	88.24 321	30.63 368	95.85 290	28.70 368	70.18 339	68.73 334	86.55 331	64.04 300	93.81 322	53.12 353	73.46 304	88.94 322

DSMNet-mixed		81.60 295	81.43 289	82.10 328	84.36 341	60.79 353	93.63 312	86.74 357	79.00 307	79.32 286	87.15 327	63.87 301	89.78 348	66.89 329	91.92 184	95.73 216

FMVSNet5		82.29 290	80.54 294	87.52 301	93.79 248	84.01 267	93.73 310	92.47 331	76.92 320	74.27 315	86.15 334	63.69 302	89.24 349	69.07 321	74.79 288	89.29 319

GBi-Net		86.67 243	84.96 250	91.80 225	95.11 206	88.81 171	96.77 256	95.25 283	82.94 270	82.12 248	90.25 298	62.89 303	94.97 307	79.04 265	80.24 256	91.62 257

test1		86.67 243	84.96 250	91.80 225	95.11 206	88.81 171	96.77 256	95.25 283	82.94 270	82.12 248	90.25 298	62.89 303	94.97 307	79.04 265	80.24 256	91.62 257

FMVSNet2		86.90 238	84.79 256	93.24 195	95.11 206	92.54 90	97.67 224	95.86 249	82.94 270	80.55 269	91.17 272	62.89 303	95.29 302	77.23 276	79.71 262	91.90 251

VPNet		88.30 219	86.57 228	93.49 191	91.95 276	91.35 106	98.18 191	97.20 158	88.61 158	84.52 219	94.89 204	62.21 306	96.76 235	89.34 166	72.26 315	92.36 234

PVSNet_0	83.28 16	87.31 234	85.16 248	93.74 188	94.78 222	84.59 260	98.91 110	98.69 18	89.81 124	78.59 294	93.23 237	61.95 307	99.34 126	94.75 100	55.72 350	97.30 191

jajsoiax		87.35 233	86.51 230	89.87 266	87.75 329	81.74 292	97.03 247	95.98 228	88.47 162	80.15 275	93.80 223	61.47 308	96.36 256	89.44 164	84.47 232	91.50 264

OurMVSNet-221017-0		84.13 281	83.59 270	85.77 313	87.81 326	70.24 344	94.89 299	93.65 320	86.08 221	76.53 302	93.28 236	61.41 309	96.14 273	80.95 253	77.69 274	90.93 283

Anonymous20231206		80.76 297	79.42 301	84.79 318	84.78 340	72.98 336	96.53 265	92.97 326	79.56 305	74.33 314	88.83 314	61.27 310	92.15 340	60.59 344	75.92 280	89.24 320

LFMVS		92.23 150	90.84 162	96.42 102	98.24 103	91.08 117	98.24 186	96.22 218	83.39 263	94.74 108	98.31 113	61.12 311	98.85 143	94.45 108	92.82 168	99.32 94

UGNet		91.91 155	90.85 161	95.10 142	97.06 140	88.69 175	98.01 205	98.24 27	92.41 66	92.39 140	93.61 228	60.52 312	99.68 78	88.14 179	97.25 122	96.92 202
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
SixPastTwentyTwo		82.63 289	81.58 287	85.79 312	88.12 323	71.01 343	95.17 297	92.54 330	84.33 248	72.93 326	92.08 252	60.41 313	95.61 295	74.47 298	74.15 297	90.75 291

mvs_tets		87.09 236	86.22 233	89.71 271	87.87 325	81.39 296	96.73 261	95.90 243	88.19 177	79.99 277	93.61 228	59.96 314	96.31 264	89.40 165	84.34 233	91.43 268

COLMAP_ROB	82.69 18	84.54 274	82.82 274	89.70 272	96.72 150	78.85 313	95.89 285	92.83 328	71.55 335	77.54 301	95.89 192	59.40 315	99.14 136	67.26 327	88.26 209	91.11 280
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20231211		84.72 270	82.65 281	90.91 241	97.71 116	84.55 261	97.28 236	96.67 187	66.88 348	79.18 288	90.87 277	58.47 316	96.60 240	82.61 241	74.20 296	91.59 262

MS-PatchMatch		86.75 241	85.92 238	89.22 283	91.97 275	82.47 288	96.91 251	96.14 224	83.74 256	77.73 299	93.53 231	58.19 317	97.37 216	76.75 282	98.35 105	87.84 329

test20.03		78.51 310	77.48 305	81.62 330	83.07 345	71.03 342	96.11 281	92.83 328	81.66 290	69.31 333	89.68 308	57.53 318	87.29 353	58.65 348	68.47 327	86.53 339

MVS-HIRNet		79.01 305	75.13 315	90.66 248	93.82 247	81.69 293	85.16 343	93.75 317	54.54 353	74.17 316	59.15 357	57.46 319	96.58 241	63.74 336	94.38 155	93.72 223

MDA-MVSNet-bldmvs		77.82 313	74.75 317	87.03 305	88.33 320	78.52 317	96.34 270	92.85 327	75.57 324	48.87 355	87.89 318	57.32 320	92.49 337	60.79 343	64.80 337	90.08 305

ACMH	83.09 17	84.60 272	82.61 282	90.57 249	93.18 261	82.94 278	96.27 272	94.92 294	81.01 297	72.61 328	93.61 228	56.54 321	97.79 187	74.31 299	81.07 255	90.99 282
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ITE_SJBPF				87.93 297	92.26 271	76.44 325		93.47 323	87.67 195	79.95 278	95.49 198	56.50 322	97.38 214	75.24 292	82.33 250	89.98 310

pmmvs-eth3d		78.71 308	76.16 312	86.38 308	80.25 352	81.19 300	94.17 306	92.13 336	77.97 314	66.90 343	82.31 341	55.76 323	92.56 335	73.63 306	62.31 341	85.38 344

K. test v3		81.04 296	79.77 299	84.83 317	87.41 330	70.23 345	95.60 294	93.93 315	83.70 258	67.51 340	89.35 312	55.76 323	93.58 324	76.67 283	68.03 329	90.67 295

AllTest		84.97 268	83.12 272	90.52 251	96.82 146	78.84 314	95.89 285	92.17 334	77.96 315	75.94 306	95.50 196	55.48 325	99.18 131	71.15 313	87.14 213	93.55 224

TestCases				90.52 251	96.82 146	78.84 314		92.17 334	77.96 315	75.94 306	95.50 196	55.48 325	99.18 131	71.15 313	87.14 213	93.55 224

DIV-MVS_2432*1600		77.47 314	75.88 313	82.24 326	81.59 348	68.93 348	92.83 320	94.02 314	77.03 319	73.14 322	83.39 339	55.44 327	90.42 346	67.95 325	57.53 347	87.38 332

CMPMVS	58.40 21	80.48 298	80.11 298	81.59 331	85.10 339	59.56 354	94.14 307	95.95 234	68.54 344	60.71 350	93.31 234	55.35 328	97.87 182	83.06 237	84.85 229	87.33 334
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Anonymous20240529		87.66 230	85.58 243	93.92 182	97.59 122	85.01 255	98.13 194	97.13 164	66.69 349	88.47 187	96.01 191	55.09 329	99.51 101	87.00 190	84.12 234	97.23 194

VDDNet		90.08 189	88.54 202	94.69 156	94.41 229	87.68 190	98.21 189	96.40 205	76.21 322	93.33 129	97.75 129	54.93 330	98.77 146	94.71 103	90.96 197	97.61 186

ACMH+	83.78 15	84.21 278	82.56 283	89.15 285	93.73 249	79.16 311	96.43 267	94.28 309	81.09 296	74.00 317	94.03 215	54.58 331	97.67 197	76.10 287	78.81 264	90.63 296

VDD-MVS		91.24 167	90.18 173	94.45 164	97.08 139	85.84 240	98.40 172	96.10 225	86.99 203	93.36 128	98.16 120	54.27 332	99.20 130	96.59 61	90.63 202	98.31 167

lessismore_v0				85.08 315	85.59 338	69.28 347		90.56 348		67.68 339	90.21 302	54.21 333	95.46 297	73.88 302	62.64 339	90.50 298

USDC		84.74 269	82.93 273	90.16 260	91.73 281	83.54 272	95.00 298	93.30 324	88.77 156	73.19 321	93.30 235	53.62 334	97.65 199	75.88 289	81.54 254	89.30 318

Anonymous202405211		88.84 207	87.03 222	94.27 169	98.14 108	84.18 265	98.44 165	95.58 265	76.79 321	89.34 181	96.88 168	53.42 335	99.54 96	87.53 187	87.12 215	99.09 112

XVG-ACMP-BASELINE		85.86 256	84.95 252	88.57 292	89.90 300	77.12 324	94.30 304	95.60 264	87.40 200	82.12 248	92.99 245	53.42 335	97.66 198	85.02 211	83.83 236	90.92 284

test_0402		78.81 307	76.33 311	86.26 309	91.18 287	78.44 318	95.88 287	91.34 345	68.55 343	70.51 331	89.91 305	52.65 337	94.99 306	47.14 355	79.78 261	85.34 346

MIMVSNet		84.48 275	81.83 285	92.42 212	91.73 281	87.36 200	85.52 342	94.42 306	81.40 292	81.91 255	87.58 320	51.92 338	92.81 331	73.84 303	88.15 210	97.08 199

UnsupCasMVSNet_eth		78.90 306	76.67 310	85.58 314	82.81 347	74.94 329	91.98 324	96.31 210	84.64 244	65.84 346	87.71 319	51.33 339	92.23 339	72.89 310	56.50 349	89.56 316

new-patchmatchnet		74.80 318	72.40 321	81.99 329	78.36 355	72.20 340	94.44 302	92.36 332	77.06 318	63.47 347	79.98 347	51.04 340	88.85 350	60.53 345	54.35 351	84.92 347

pmmvs6		79.90 301	77.31 306	87.67 300	84.17 342	78.13 320	95.86 289	93.68 319	67.94 346	72.67 327	89.62 309	50.98 341	95.75 290	74.80 297	66.04 334	89.14 321

FMVSNet1		83.94 283	81.32 291	91.80 225	91.94 277	88.81 171	96.77 256	95.25 283	77.98 313	78.25 297	90.25 298	50.37 342	94.97 307	73.27 307	77.81 273	91.62 257

UniMVSNet_ETH3D		85.65 263	83.79 269	91.21 234	90.41 296	80.75 306	95.36 295	95.78 251	78.76 311	81.83 260	94.33 211	49.86 343	96.66 237	84.30 219	83.52 241	96.22 213

Anonymous20240521		78.63 309	76.90 309	83.82 322	82.82 346	72.86 337	95.72 293	93.57 321	73.55 332	72.17 329	84.79 337	49.69 344	92.51 336	65.29 334	74.50 290	86.09 342

TDRefinement		78.01 311	75.31 314	86.10 311	70.06 358	73.84 333	93.59 313	91.58 343	74.51 328	73.08 324	91.04 273	49.63 345	97.12 219	74.88 295	59.47 344	87.33 334

LF4IMVS		81.94 293	81.17 292	84.25 321	87.23 332	68.87 349	93.35 314	91.93 339	83.35 264	75.40 311	93.00 244	49.25 346	96.65 238	78.88 268	78.11 268	87.22 336

new_pmnet		76.02 315	73.71 318	82.95 325	83.88 343	72.85 338	91.26 330	92.26 333	70.44 338	62.60 348	81.37 343	47.64 347	92.32 338	61.85 341	72.10 317	83.68 349

TinyColmap		80.42 299	77.94 303	87.85 298	92.09 274	78.58 316	93.74 309	89.94 350	74.99 325	69.77 332	91.78 260	46.09 348	97.58 203	65.17 335	77.89 269	87.38 332

testgi		82.29 290	81.00 293	86.17 310	87.24 331	74.84 330	97.39 230	91.62 342	88.63 157	75.85 309	95.42 199	46.07 349	91.55 344	66.87 330	79.94 260	92.12 243

OpenMVS_ROB	73.86 20	77.99 312	75.06 316	86.77 307	83.81 344	77.94 322	96.38 269	91.53 344	67.54 347	68.38 335	87.13 328	43.94 350	96.08 275	55.03 351	81.83 252	86.29 341

tmp_tt		53.66 327	52.86 329	56.05 342	32.75 368	41.97 364	73.42 357	76.12 364	21.91 363	39.68 359	96.39 183	42.59 351	65.10 361	78.00 273	14.92 362	61.08 355

pmmvs3		72.86 320	69.76 324	82.17 327	73.86 356	74.19 332	94.20 305	89.01 354	64.23 352	67.72 338	80.91 345	41.48 352	88.65 351	62.40 340	54.02 352	83.68 349

UnsupCasMVSNet_bld		73.85 319	70.14 322	84.99 316	79.44 353	75.73 326	88.53 337	95.24 286	70.12 340	61.94 349	74.81 350	41.41 353	93.62 323	68.65 323	51.13 354	85.62 343

MVS_0304		84.13 281	82.66 280	88.52 293	93.07 263	80.15 307	95.81 291	98.21 29	79.27 306	86.85 203	86.40 332	41.33 354	94.69 315	76.36 285	86.69 216	90.73 292

MIMVSNet1		75.92 316	73.30 319	83.81 323	81.29 350	75.57 327	92.26 323	92.05 337	73.09 333	67.48 341	86.18 333	40.87 355	87.64 352	55.78 350	70.68 324	88.21 327

EG-PatchMatch MVS		79.92 300	77.59 304	86.90 306	87.06 333	77.90 323	96.20 280	94.06 313	74.61 327	66.53 344	88.76 315	40.40 356	96.20 269	67.02 328	83.66 239	86.61 338

DeepMVS_CX				76.08 334	90.74 293	51.65 360		90.84 347	86.47 218	57.89 351	87.98 317	35.88 357	92.60 333	65.77 333	65.06 336	83.97 348

test_method		70.10 322	68.66 325	74.41 335	86.30 336	55.84 357	94.47 301	89.82 351	35.18 358	66.15 345	84.75 338	30.54 358	77.96 358	70.40 319	60.33 343	89.44 317

PM-MVS		74.88 317	72.85 320	80.98 332	78.98 354	64.75 351	90.81 333	85.77 358	80.95 298	68.23 337	82.81 340	29.08 359	92.84 330	76.54 284	62.46 340	85.36 345

ambc				79.60 333	72.76 357	56.61 356	76.20 355	92.01 338		68.25 336	80.23 346	23.34 360	94.73 314	73.78 305	60.81 342	87.48 331

FPMVS		61.57 323	60.32 326	65.34 339	60.14 362	42.44 363	91.02 332	89.72 352	44.15 355	42.63 357	80.93 344	19.02 361	80.59 357	42.50 356	72.76 308	73.00 353

EMVS		39.96 332	39.88 334	40.18 346	59.57 363	32.12 367	84.79 348	64.57 367	26.27 361	26.14 362	44.18 364	18.73 362	59.29 364	17.03 362	17.67 361	29.12 360

Gipuma		54.77 326	52.22 330	62.40 341	86.50 334	59.37 355	50.20 360	90.35 349	36.52 357	41.20 358	49.49 359	18.33 363	81.29 355	32.10 358	65.34 335	46.54 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		41.02 331	40.93 333	41.29 345	61.97 360	33.83 365	84.00 351	65.17 366	27.17 360	27.56 360	46.72 361	17.63 364	60.41 363	19.32 361	18.82 359	29.61 359

PMMVS2		58.97 325	55.07 328	70.69 338	62.72 359	55.37 358	85.97 341	80.52 361	49.48 354	45.94 356	68.31 352	15.73 365	80.78 356	49.79 354	37.12 357	75.91 352

ANet_high		50.71 328	46.17 331	64.33 340	44.27 366	52.30 359	76.13 356	78.73 362	64.95 350	27.37 361	55.23 358	14.61 366	67.74 360	36.01 357	18.23 360	72.95 354

LCM-MVSNet		60.07 324	56.37 327	71.18 336	54.81 364	48.67 361	82.17 354	89.48 353	37.95 356	49.13 354	69.12 351	13.75 367	81.76 354	59.28 346	51.63 353	83.10 351

PMVS	41.42 23	45.67 329	42.50 332	55.17 343	34.28 367	32.37 366	66.24 358	78.71 363	30.72 359	22.04 364	59.59 356	4.59 368	77.85 359	27.49 359	58.84 346	55.29 356
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		16.71 335	16.73 339	16.65 347	60.15 361	25.22 369	41.24 361	5.17 369	6.56 364	5.48 367	3.61 367	3.64 369	22.72 365	15.20 363	9.52 363	1.99 363

MVE	44.00 22	41.70 330	37.64 335	53.90 344	49.46 365	43.37 362	65.09 359	66.66 365	26.19 362	25.77 363	48.53 360	3.58 370	63.35 362	26.15 360	27.28 358	54.97 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test123		16.58 336	19.47 338	7.91 348	3.59 370	5.37 370	94.32 303	1.39 371	2.49 366	13.98 366	44.60 363	2.91 371	2.65 366	11.35 365	0.57 365	15.70 361

testmvs		18.81 334	23.05 337	6.10 349	4.48 369	2.29 371	97.78 216	3.00 370	3.27 365	18.60 365	62.71 354	1.53 372	2.49 367	14.26 364	1.80 364	13.50 362

uanet_test		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet-low-res		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

sosnet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uncertanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

Regformer		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

ab-mvs-re		8.21 337	10.94 340	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	98.50 104	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

uanet		0.00 339	0.00 342	0.00 350	0.00 371	0.00 372	0.00 362	0.00 372	0.00 367	0.00 368	0.00 368	0.00 373	0.00 368	0.00 366	0.00 366	0.00 364

IU-MVS					99.63 21	95.38 19		97.73 72	95.54 15	99.54 1				99.69 4	99.81 19	99.99 1

save fliter					99.34 53	93.85 59	99.65 22	97.63 93	95.69 11

test_0728_SECOND				98.77 5	99.66 15	96.37 11	99.72 13	97.68 81					99.98 10	99.64 5	99.82 15	99.96 8

GSMVS																98.84 132

test_part2					99.54 36	95.42 17				98.13 32

MTGPA								97.45 130

MTMP							99.21 70	91.09 346

gm-plane-assit					94.69 224	88.14 182			88.22 176		97.20 152		98.29 160	90.79 150

test9_res														98.60 19	99.87 7	99.90 20

agg_prior2														97.84 41	99.87 7	99.91 18

agg_prior					99.54 36	92.66 84		97.64 89		97.98 40			99.61 89

test_prior4						92.00 94	99.41 54

test_prior				97.01 62	99.58 29	91.77 95		97.57 107					99.49 104			99.79 34

旧先验2							98.67 136		85.75 225	98.96 12			98.97 142	93.84 115

新几何2							98.26 185

无先验							98.52 154	97.82 55	87.20 202				99.90 40	87.64 185		99.85 29

原ACMM2							98.69 132

testdata2													99.88 44	84.16 222

testdata1							97.89 209		92.43 62

plane_prior7					93.84 245	85.73 241

plane_prior5								96.30 211					97.75 194	93.46 122	86.17 220	92.67 229

plane_prior4											96.52 177

plane_prior3						85.91 236			93.65 41	86.99 199

plane_prior2							99.02 98		93.38 46

plane_prior1					93.90 244

plane_prior						86.07 233	99.14 85		93.81 39						86.26 219

n2								0.00 372

nn								0.00 372

door-mid								84.90 360

test11								97.68 81

door								85.30 359

HQP5-MVS						86.39 221

HQP-NCC					93.95 238		99.16 76		93.92 31	87.57 192

ACMP_Plane					93.95 238		99.16 76		93.92 31	87.57 192

BP-MVS														93.82 117

HQP4-MVS										87.57 192			97.77 189			92.72 227

HQP3-MVS								96.37 207							86.29 217

NP-MVS					93.94 241	86.22 227					96.67 175

ACMMP++_ref															82.64 248

ACMMP++															83.83 236

High-resolution multi-view benchmark