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.

DeepPCF-MVS	93.56 1	96.55 41	97.84 8	92.68 211	98.71 92	78.11 323	99.70 18	97.71 78	98.18 1	97.36 55	99.76 1	90.37 45	99.94 33	99.27 9	99.54 57	99.99 1

CNVR-MVS		98.46 1	98.38 1	98.72 6	99.80 4	96.19 12	99.80 8	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

SED-MVS		98.18 2	98.10 4	98.41 14	99.63 21	95.24 21	99.77 10	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_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

test0726					99.66 15	95.20 26	99.77 10	97.70 79	93.95 29	99.35 3	99.54 3	93.18 18

DPE-MVS		98.11 5	98.00 5	98.44 13	99.50 43	95.39 18	99.29 69	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
ETH3 D test6400		97.67 10	97.33 16	98.69 7	99.69 9	96.43 9	99.63 26	97.73 72	91.05 94	98.66 19	99.53 7	90.59 38	99.71 73	99.32 8	99.80 23	99.91 18

DPM-MVS		97.86 7	97.25 17	99.68 1	98.25 102	99.10 1	99.76 13	97.78 63	96.61 4	98.15 32	99.53 7	93.62 14	100.00 1	91.79 141	99.80 23	99.94 14

SMA-MVS		97.24 18	96.99 23	98.00 27	99.30 60	94.20 53	99.16 78	97.65 88	89.55 137	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
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

ETH3D-3000-0.1		97.29 16	97.01 22	98.12 22	99.18 69	94.97 30	99.47 42	97.52 117	89.85 124	98.79 16	99.46 11	90.41 44	99.69 75	98.78 15	99.67 38	99.70 57

DVP-MVS		98.07 6	98.00 5	98.29 15	99.66 15	95.20 26	99.72 15	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
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

MSLP-MVS++		97.50 14	97.45 12	97.63 38	99.65 19	93.21 71	99.70 18	98.13 36	94.61 19	97.78 47	99.46 11	89.85 50	99.81 62	97.97 38	99.91 4	99.88 24

NCCC		98.12 4	98.11 3	98.13 20	99.76 6	94.46 46	99.81 6	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

9.14			96.87 26		99.34 53		99.50 40	97.49 125	89.41 140	98.59 22	99.43 16	89.78 51	99.69 75	98.69 17	99.62 47

xxxxxxxxxxxxxcwj		97.51 12	97.42 13	97.78 34	99.34 53	93.85 59	99.65 24	95.45 274	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 50	97.45 130	89.60 133	98.70 17	99.42 17	90.42 42	99.72 71	98.47 25	99.65 40	99.77 44

ETH3D cwj APD-0.16		96.94 31	96.58 36	98.01 26	98.62 95	94.73 41	99.13 90	97.38 141	88.44 170	98.53 24	99.39 19	89.66 55	99.69 75	98.43 27	99.61 51	99.61 72

DeepC-MVS_fast	93.52 2	97.16 23	96.84 28	98.13 20	99.61 27	94.45 47	98.85 117	97.64 89	96.51 7	95.88 89	99.39 19	87.35 93	99.99 5	96.61 62	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
MCST-MVS		98.18 2	97.95 7	98.86 3	99.85 3	96.60 7	99.70 18	97.98 44	97.18 2	95.96 86	99.33 21	92.62 22	100.00 1	98.99 13	99.93 1	99.98 6

testtj		97.23 20	97.05 20	97.75 35	99.75 7	93.34 69	99.16 78	97.74 68	91.28 91	98.40 26	99.29 22	89.95 49	99.98 10	98.20 35	99.70 35	99.94 14

HPM-MVS++		97.72 9	97.59 9	98.14 19	99.53 41	94.76 39	99.19 73	97.75 66	95.66 13	98.21 30	99.29 22	91.10 28	99.99 5	97.68 43	99.87 7	99.68 61

SteuartSystems-ACMMP		97.25 17	97.34 15	97.01 62	97.38 127	91.46 104	99.75 14	97.66 83	94.14 28	98.13 33	99.26 24	92.16 24	99.66 80	97.91 41	99.64 43	99.90 20
Skip Steuart: Steuart Systems R&D Blog.
MP-MVS-pluss		95.80 68	95.30 72	97.29 52	98.95 83	92.66 84	98.59 151	97.14 162	88.95 152	93.12 133	99.25 25	85.62 126	99.94 33	96.56 64	99.48 59	99.28 99
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CSCG		94.87 87	94.71 82	95.36 138	99.54 36	86.49 219	99.34 66	98.15 34	82.71 277	90.15 174	99.25 25	89.48 56	99.86 52	94.97 99	98.82 94	99.72 54

zzz-MVS		96.21 53	95.96 55	96.96 70	99.29 61	91.19 110	98.69 134	97.45 130	92.58 57	94.39 115	99.24 27	86.43 115	99.99 5	96.22 70	99.40 68	99.71 55

MTAPA		96.09 55	95.80 64	96.96 70	99.29 61	91.19 110	97.23 242	97.45 130	92.58 57	94.39 115	99.24 27	86.43 115	99.99 5	96.22 70	99.40 68	99.71 55

CDPH-MVS		96.56 40	96.18 45	97.70 36	99.59 28	93.92 57	99.13 90	97.44 134	89.02 149	97.90 45	99.22 29	88.90 62	99.49 104	94.63 106	99.79 25	99.68 61

API-MVS		94.78 89	94.18 94	96.59 93	99.21 68	90.06 147	98.80 122	97.78 63	83.59 262	93.85 125	99.21 30	83.79 148	99.97 20	92.37 137	99.00 84	99.74 51

PHI-MVS		96.65 38	96.46 38	97.21 56	99.34 53	91.77 95	99.70 18	98.05 39	86.48 219	98.05 37	99.20 31	89.33 57	99.96 27	98.38 28	99.62 47	99.90 20

OPU-MVS				99.49 2	99.64 20	98.51 2	99.77 10				99.19 32	95.12 6	99.97 20	99.90 1	99.92 3	99.99 1

MSP-MVS		97.77 8	98.18 2	96.53 97	99.54 36	90.14 140	99.41 56	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
test_8					99.55 35	93.07 76	99.37 62	97.64 89	90.18 115	98.36 29	99.19 32	90.94 30	99.64 86

TEST9					99.57 33	93.17 72	99.38 59	97.66 83	89.57 135	98.39 27	99.18 35	90.88 32	99.66 80

train_agg		97.20 22	97.08 19	97.57 42	99.57 33	93.17 72	99.38 59	97.66 83	90.18 115	98.39 27	99.18 35	90.94 30	99.66 80	98.58 22	99.85 11	99.88 24

agg_prior1		97.12 24	97.03 21	97.38 50	99.54 36	92.66 84	99.35 64	97.64 89	90.38 109	97.98 41	99.17 37	90.84 34	99.61 89	98.57 23	99.78 27	99.87 27

MAR-MVS		94.43 101	94.09 96	95.45 135	99.10 75	87.47 198	98.39 177	97.79 62	88.37 173	94.02 122	99.17 37	78.64 209	99.91 38	92.48 136	98.85 91	98.96 123
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
ZD-MVS					99.67 13	93.28 70		97.61 96	87.78 190	97.41 53	99.16 39	90.15 47	99.56 93	98.35 29	99.70 35

CP-MVS		96.22 52	96.15 50	96.42 102	99.67 13	89.62 158	99.70 18	97.61 96	90.07 121	96.00 83	99.16 39	87.43 87	99.92 36	96.03 76	99.72 30	99.70 57

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

testdata				95.26 142	98.20 104	87.28 204		97.60 98	85.21 234	98.48 25	99.15 41	88.15 74	98.72 151	90.29 156	99.45 62	99.78 38

ACMMP_NAP		96.59 39	96.18 45	97.81 32	98.82 89	93.55 64	98.88 116	97.59 102	90.66 100	97.98 41	99.14 43	86.59 109	100.00 1	96.47 66	99.46 60	99.89 23

PS-MVSNAJ		96.87 33	96.40 39	98.29 15	97.35 128	97.29 3	99.03 99	97.11 166	95.83 10	98.97 11	99.14 43	82.48 174	99.60 91	98.60 19	99.08 80	98.00 178

DP-MVS Recon		95.85 66	95.15 77	97.95 28	99.87 2	94.38 50	99.60 28	97.48 126	86.58 216	94.42 114	99.13 45	87.36 92	99.98 10	93.64 121	98.33 108	99.48 85

test1172		95.92 63	96.07 52	95.46 134	99.42 49	87.24 209	98.51 159	97.24 150	90.29 112	96.56 78	99.12 46	86.73 105	99.36 121	97.33 48	99.42 67	99.78 38

SR-MVS		96.13 54	96.16 49	96.07 114	99.42 49	89.04 165	98.59 151	97.33 146	90.44 107	96.84 68	99.12 46	86.75 103	99.41 117	97.47 45	99.44 63	99.76 47

APDe-MVS		97.53 11	97.47 10	97.70 36	99.58 29	93.63 62	99.56 33	97.52 117	93.59 43	98.01 40	99.12 46	90.80 35	99.55 94	99.26 10	99.79 25	99.93 17

PAPR		96.35 47	95.82 60	97.94 29	99.63 21	94.19 54	99.42 55	97.55 110	92.43 63	93.82 127	99.12 46	87.30 94	99.91 38	94.02 113	99.06 81	99.74 51

xiu_mvs_v2_base		96.66 37	96.17 47	98.11 24	97.11 138	96.96 4	99.01 102	97.04 173	95.51 16	98.86 13	99.11 50	82.19 180	99.36 121	98.59 21	98.14 109	98.00 178

region2R		96.30 50	96.17 47	96.70 88	99.70 8	90.31 136	99.46 47	97.66 83	90.55 104	97.07 59	99.07 51	86.85 101	99.97 20	95.43 88	99.74 28	99.81 31

APD-MVS		96.95 29	96.72 32	97.63 38	99.51 42	93.58 63	99.16 78	97.44 134	90.08 120	98.59 22	99.07 51	89.06 59	99.42 115	97.92 40	99.66 39	99.88 24
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
新几何1				97.40 48	98.92 84	92.51 91		97.77 65	85.52 229	96.69 74	99.06 53	88.08 76	99.89 43	84.88 215	99.62 47	99.79 34

HFP-MVS		96.42 46	96.26 44	96.90 74	99.69 9	90.96 122	99.47 42	97.81 58	90.54 105	96.88 62	99.05 54	87.57 83	99.96 27	95.65 81	99.72 30	99.78 38

#test#		96.48 43	96.34 42	96.90 74	99.69 9	90.96 122	99.53 38	97.81 58	90.94 98	96.88 62	99.05 54	87.57 83	99.96 27	95.87 78	99.72 30	99.78 38

ACMMPR		96.28 51	96.14 51	96.73 85	99.68 12	90.47 134	99.47 42	97.80 60	90.54 105	96.83 70	99.03 56	86.51 113	99.95 30	95.65 81	99.72 30	99.75 48

Regformer-1		96.97 28	96.80 30	97.47 44	99.46 47	93.11 74	98.89 114	97.94 45	92.89 54	96.90 61	99.02 57	89.78 51	99.53 97	97.06 51	99.26 76	99.75 48

Regformer-2		96.94 31	96.78 31	97.42 46	99.46 47	92.97 81	98.89 114	97.93 46	92.86 56	96.88 62	99.02 57	89.74 53	99.53 97	97.03 52	99.26 76	99.75 48

test222					98.32 101	91.21 109	98.08 203	97.58 104	83.74 258	95.87 90	99.02 57	86.74 104			99.64 43	99.81 31

SD-MVS		97.51 12	97.40 14	97.81 32	99.01 79	93.79 61	99.33 67	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
1121		95.19 82	94.45 87	97.42 46	98.88 86	92.58 89	96.22 279	97.75 66	85.50 231	96.86 65	99.01 61	88.59 67	99.90 40	87.64 187	99.60 52	99.79 34

APD-MVS_3200maxsize		95.64 73	95.65 69	95.62 129	99.24 65	87.80 190	98.42 169	97.22 153	88.93 154	96.64 77	98.98 62	85.49 130	99.36 121	96.68 59	99.27 75	99.70 57

SR-MVS-dyc-post		95.75 72	95.86 59	95.41 137	99.22 66	87.26 207	98.40 174	97.21 154	89.63 131	96.67 75	98.97 63	86.73 105	99.36 121	96.62 60	99.31 72	99.60 73

RE-MVS-def			95.70 66		99.22 66	87.26 207	98.40 174	97.21 154	89.63 131	96.67 75	98.97 63	85.24 135		96.62 60	99.31 72	99.60 73

test_prior3		97.07 26	97.09 18	97.01 62	99.58 29	91.77 95	99.57 31	97.57 107	91.43 87	98.12 35	98.97 63	90.43 40	99.49 104	98.33 30	99.81 19	99.79 34

test_prior2							99.57 31		91.43 87	98.12 35	98.97 63	90.43 40		98.33 30	99.81 19

原ACMM1				96.18 109	99.03 78	90.08 143		97.63 93	88.98 150	97.00 60	98.97 63	88.14 75	99.71 73	88.23 180	99.62 47	98.76 145

XVS		96.47 44	96.37 40	96.77 81	99.62 25	90.66 131	99.43 53	97.58 104	92.41 67	96.86 65	98.96 68	87.37 89	99.87 47	95.65 81	99.43 64	99.78 38

CPTT-MVS		94.60 98	94.43 88	95.09 145	99.66 15	86.85 214	99.44 50	97.47 128	83.22 267	94.34 117	98.96 68	82.50 172	99.55 94	94.81 101	99.50 58	98.88 131

Regformer-3		96.50 42	96.36 41	96.91 73	99.34 53	91.72 98	98.71 129	97.90 48	92.48 61	96.00 83	98.95 70	88.60 65	99.52 100	96.44 67	98.83 92	99.49 83

Regformer-4		96.45 45	96.33 43	96.81 80	99.34 53	91.44 105	98.71 129	97.88 49	92.43 63	95.97 85	98.95 70	88.42 69	99.51 101	96.40 68	98.83 92	99.49 83

MP-MVS		96.00 57	95.82 60	96.54 96	99.47 46	90.13 142	99.36 63	97.41 138	90.64 103	95.49 98	98.95 70	85.51 129	99.98 10	96.00 77	99.59 54	99.52 79
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PGM-MVS		95.85 66	95.65 69	96.45 100	99.50 43	89.77 154	98.22 189	98.90 11	89.19 143	96.74 72	98.95 70	85.91 124	99.92 36	93.94 114	99.46 60	99.66 65

mPP-MVS		95.90 64	95.75 65	96.38 104	99.58 29	89.41 162	99.26 70	97.41 138	90.66 100	94.82 108	98.95 70	86.15 121	99.98 10	95.24 93	99.64 43	99.74 51

ZNCC-MVS		96.09 55	95.81 62	96.95 72	99.42 49	91.19 110	99.55 34	97.53 114	89.72 128	95.86 91	98.94 75	86.59 109	99.97 20	95.13 94	99.56 55	99.68 61

CANet		97.00 27	96.49 37	98.55 9	98.86 88	96.10 13	99.83 5	97.52 117	95.90 9	97.21 56	98.90 76	82.66 171	99.93 35	98.71 16	98.80 95	99.63 69

PAPM_NR		95.43 74	95.05 79	96.57 95	99.42 49	90.14 140	98.58 153	97.51 120	90.65 102	92.44 141	98.90 76	87.77 81	99.90 40	90.88 150	99.32 71	99.68 61

EI-MVSNet-Vis-set		95.76 71	95.63 71	96.17 111	99.14 71	90.33 135	98.49 163	97.82 55	91.92 76	94.75 109	98.88 78	87.06 97	99.48 109	95.40 89	97.17 126	98.70 148

CNLPA		93.64 121	92.74 126	96.36 105	98.96 82	90.01 150	99.19 73	95.89 247	86.22 222	89.40 182	98.85 79	80.66 194	99.84 55	88.57 176	96.92 127	99.24 103

xiu_mvs_v1_base_debu		94.73 91	93.98 99	96.99 65	95.19 201	95.24 21	98.62 145	96.50 200	92.99 50	97.52 49	98.83 80	72.37 250	99.15 133	97.03 52	96.74 128	96.58 208

xiu_mvs_v1_base		94.73 91	93.98 99	96.99 65	95.19 201	95.24 21	98.62 145	96.50 200	92.99 50	97.52 49	98.83 80	72.37 250	99.15 133	97.03 52	96.74 128	96.58 208

xiu_mvs_v1_base_debi		94.73 91	93.98 99	96.99 65	95.19 201	95.24 21	98.62 145	96.50 200	92.99 50	97.52 49	98.83 80	72.37 250	99.15 133	97.03 52	96.74 128	96.58 208

cdsmvs_eth3d_5k		22.52 335	30.03 338	0.00 352	0.00 373	0.00 374	0.00 364	97.17 160	0.00 369	0.00 370	98.77 83	74.35 233	0.00 370	0.00 368	0.00 368	0.00 366

EI-MVSNet-UG-set		95.43 74	95.29 73	95.86 123	99.07 77	89.87 151	98.43 168	97.80 60	91.78 79	94.11 120	98.77 83	86.25 120	99.48 109	94.95 100	96.45 132	98.22 172

lupinMVS		96.32 49	95.94 56	97.44 45	95.05 214	94.87 33	99.86 2	96.50 200	93.82 38	98.04 38	98.77 83	85.52 127	98.09 168	96.98 56	98.97 85	99.37 90

LS3D		90.19 187	88.72 197	94.59 162	98.97 80	86.33 226	96.90 254	96.60 190	74.96 328	84.06 225	98.74 86	75.78 220	99.83 57	74.93 296	97.57 117	97.62 187

MVS_111021_HR		96.69 36	96.69 33	96.72 87	98.58 97	91.00 121	99.14 87	99.45 1	93.86 35	95.15 104	98.73 87	88.48 68	99.76 68	97.23 50	99.56 55	99.40 89

OMC-MVS		93.90 112	93.62 110	94.73 157	98.63 94	87.00 211	98.04 206	96.56 195	92.19 71	92.46 140	98.73 87	79.49 201	99.14 136	92.16 139	94.34 159	98.03 177

GST-MVS		95.97 60	95.66 67	96.90 74	99.49 45	91.22 108	99.45 49	97.48 126	89.69 129	95.89 88	98.72 89	86.37 118	99.95 30	94.62 107	99.22 79	99.52 79

PAPM		96.35 47	95.94 56	97.58 40	94.10 236	95.25 20	98.93 109	98.17 31	94.26 23	93.94 123	98.72 89	89.68 54	97.88 181	96.36 69	99.29 74	99.62 71

ACMMP		94.67 95	94.30 89	95.79 125	99.25 64	88.13 185	98.41 171	98.67 19	90.38 109	91.43 153	98.72 89	82.22 179	99.95 30	93.83 118	95.76 147	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
MG-MVS		97.24 18	96.83 29	98.47 12	99.79 5	95.71 15	99.07 94	99.06 9	94.45 22	96.42 79	98.70 92	88.81 63	99.74 70	95.35 90	99.86 10	99.97 7

MVS_111021_LR		95.78 69	95.94 56	95.28 141	98.19 106	87.69 191	98.80 122	99.26 7	93.39 45	95.04 106	98.69 93	84.09 146	99.76 68	96.96 57	99.06 81	98.38 163

AdaColmap		93.82 114	93.06 119	96.10 113	99.88 1	89.07 164	98.33 181	97.55 110	86.81 212	90.39 171	98.65 94	75.09 223	99.98 10	93.32 127	97.53 120	99.26 101

EIA-MVS		95.11 83	95.27 75	94.64 160	96.34 161	86.51 218	99.59 29	96.62 188	92.51 59	94.08 121	98.64 95	86.05 122	98.24 162	95.07 96	98.50 105	99.18 108

TSAR-MVS + MP.		97.44 15	97.46 11	97.39 49	99.12 72	93.49 67	98.52 156	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 30	96.54 198	95.09 18	96.84 68	98.63 97	91.16 26	99.77 67	99.04 12	96.42 133	99.81 31

alignmvs		95.77 70	95.00 80	98.06 25	97.35 128	95.68 16	99.71 17	97.50 123	91.50 85	96.16 82	98.61 98	86.28 119	99.00 141	96.19 72	91.74 190	99.51 81

MVS		93.92 110	92.28 134	98.83 4	95.69 184	96.82 5	96.22 279	98.17 31	84.89 243	84.34 222	98.61 98	79.32 202	99.83 57	93.88 116	99.43 64	99.86 28

abl_6		94.63 97	94.48 86	95.09 145	98.61 96	86.96 212	98.06 205	96.97 179	89.31 141	95.86 91	98.56 100	79.82 196	99.64 86	94.53 109	98.65 101	98.66 152

CS-MVS-test		95.99 59	96.01 53	95.93 121	95.70 183	90.90 125	99.86 2	96.13 225	92.45 62	98.17 31	98.53 101	86.43 115	97.62 202	97.94 39	98.88 89	99.26 101

TAPA-MVS	87.50 9	90.35 182	89.05 190	94.25 173	98.48 100	85.17 254	98.42 169	96.58 194	82.44 283	87.24 199	98.53 101	82.77 168	98.84 144	59.09 349	97.88 111	98.72 146
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVSFormer		94.71 94	94.08 97	96.61 92	95.05 214	94.87 33	97.77 219	96.17 222	86.84 210	98.04 38	98.52 103	85.52 127	95.99 279	89.83 159	98.97 85	98.96 123

jason		95.40 77	94.86 81	97.03 61	92.91 267	94.23 52	99.70 18	96.30 211	93.56 44	96.73 73	98.52 103	81.46 189	97.91 178	96.08 75	98.47 106	98.96 123
jason: jason.
1112_ss		92.71 140	91.55 152	96.20 108	95.56 189	91.12 114	98.48 164	94.69 301	88.29 176	86.89 204	98.50 105	87.02 98	98.66 153	84.75 216	89.77 208	98.81 139

ab-mvs-re		8.21 339	10.94 342	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	98.50 105	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

canonicalmvs		95.02 85	93.96 102	98.20 17	97.53 125	95.92 14	98.71 129	96.19 221	91.78 79	95.86 91	98.49 107	79.53 200	99.03 140	96.12 73	91.42 196	99.66 65

HPM-MVS		95.41 76	95.22 76	95.99 117	99.29 61	89.14 163	99.17 77	97.09 170	87.28 203	95.40 99	98.48 108	84.93 137	99.38 119	95.64 85	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
CANet_DTU		94.31 104	93.35 113	97.20 57	97.03 142	94.71 42	98.62 145	95.54 269	95.61 14	97.21 56	98.47 109	71.88 255	99.84 55	88.38 178	97.46 122	97.04 202

HPM-MVS_fast		94.89 86	94.62 83	95.70 128	99.11 73	88.44 182	99.14 87	97.11 166	85.82 226	95.69 95	98.47 109	83.46 153	99.32 127	93.16 129	99.63 46	99.35 91

WTY-MVS		95.97 60	95.11 78	98.54 10	97.62 119	96.65 6	99.44 50	98.74 13	92.25 70	95.21 102	98.46 111	86.56 111	99.46 111	95.00 98	92.69 173	99.50 82

CS-MVS		95.86 65	95.81 62	95.98 118	95.62 187	91.26 107	99.80 8	96.12 226	92.15 74	97.93 44	98.45 112	85.88 125	97.55 208	97.56 44	98.80 95	99.14 110

DROMVSNet		95.24 81	95.28 74	95.12 143	95.48 193	88.95 169	99.55 34	95.95 235	91.59 83	97.46 52	98.38 113	83.18 162	97.42 215	97.32 49	98.58 102	98.97 122

DeepC-MVS	91.02 4	94.56 100	93.92 105	96.46 99	97.16 134	90.76 127	98.39 177	97.11 166	93.92 31	88.66 187	98.33 114	78.14 211	99.85 54	95.02 97	98.57 103	98.78 143
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
LFMVS		92.23 152	90.84 164	96.42 102	98.24 103	91.08 118	98.24 188	96.22 218	83.39 265	94.74 110	98.31 115	61.12 313	98.85 143	94.45 110	92.82 170	99.32 94

ETV-MVS		96.00 57	96.00 54	96.00 116	96.56 154	91.05 119	99.63 26	96.61 189	93.26 48	97.39 54	98.30 116	86.62 108	98.13 165	98.07 37	97.57 117	98.82 138

ET-MVSNet_ETH3D		92.56 146	91.45 154	95.88 122	96.39 159	94.13 55	99.46 47	96.97 179	92.18 72	66.94 344	98.29 117	94.65 11	94.28 322	94.34 111	83.82 240	99.24 103

DELS-MVS		97.12 24	96.60 35	98.68 8	98.03 110	96.57 8	99.84 4	97.84 53	96.36 8	95.20 103	98.24 118	88.17 73	99.83 57	96.11 74	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
EPNet		96.82 34	96.68 34	97.25 55	98.65 93	93.10 75	99.48 41	98.76 12	96.54 5	97.84 46	98.22 119	87.49 86	99.66 80	95.35 90	97.78 115	99.00 118
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
114514_t		94.06 106	93.05 120	97.06 60	99.08 76	92.26 93	98.97 106	97.01 177	82.58 279	92.57 139	98.22 119	80.68 193	99.30 128	89.34 168	99.02 83	99.63 69

PLC	91.07 3	94.23 105	94.01 98	94.87 151	99.17 70	87.49 197	99.25 71	96.55 196	88.43 171	91.26 156	98.21 121	85.92 123	99.86 52	89.77 162	97.57 117	97.24 195
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
VDD-MVS		91.24 169	90.18 175	94.45 166	97.08 139	85.84 242	98.40 174	96.10 227	86.99 205	93.36 130	98.16 122	54.27 334	99.20 130	96.59 63	90.63 204	98.31 169

PMMVS		93.62 122	93.90 106	92.79 206	96.79 148	81.40 297	98.85 117	96.81 183	91.25 92	96.82 71	98.15 123	77.02 217	98.13 165	93.15 130	96.30 137	98.83 137

XVG-OURS		90.83 175	90.49 172	91.86 224	95.23 199	81.25 301	95.79 294	95.92 240	88.96 151	90.02 176	98.03 124	71.60 259	99.35 125	91.06 147	87.78 214	94.98 220

XVG-OURS-SEG-HR		90.95 173	90.66 170	91.83 225	95.18 204	81.14 304	95.92 286	95.92 240	88.40 172	90.33 172	97.85 125	70.66 265	99.38 119	92.83 134	88.83 210	94.98 220

sss		94.85 88	93.94 104	97.58 40	96.43 158	94.09 56	98.93 109	99.16 8	89.50 138	95.27 101	97.85 125	81.50 187	99.65 84	92.79 135	94.02 161	98.99 119

diffmvs		94.59 99	94.19 92	95.81 124	95.54 190	90.69 129	98.70 133	95.68 260	91.61 81	95.96 86	97.81 127	80.11 195	98.06 172	96.52 65	95.76 147	98.67 149

BH-RMVSNet		91.25 168	89.99 176	95.03 149	96.75 149	88.55 179	98.65 140	94.95 294	87.74 193	87.74 193	97.80 128	68.27 277	98.14 164	80.53 260	97.49 121	98.41 160

F-COLMAP		92.07 154	91.75 149	93.02 201	98.16 107	82.89 283	98.79 126	95.97 231	86.54 218	87.92 192	97.80 128	78.69 208	99.65 84	85.97 202	95.93 145	96.53 211

PVSNet_Blended		95.94 62	95.66 67	96.75 83	98.77 90	91.61 101	99.88 1	98.04 40	93.64 42	94.21 118	97.76 130	83.50 151	99.87 47	97.41 46	97.75 116	98.79 141

VDDNet		90.08 191	88.54 204	94.69 158	94.41 231	87.68 192	98.21 191	96.40 205	76.21 324	93.33 131	97.75 131	54.93 332	98.77 146	94.71 105	90.96 199	97.61 188

test_yl		95.27 79	94.60 84	97.28 53	98.53 98	92.98 79	99.05 97	98.70 16	86.76 213	94.65 112	97.74 132	87.78 79	99.44 112	95.57 86	92.61 174	99.44 87

DCV-MVSNet		95.27 79	94.60 84	97.28 53	98.53 98	92.98 79	99.05 97	98.70 16	86.76 213	94.65 112	97.74 132	87.78 79	99.44 112	95.57 86	92.61 174	99.44 87

1314		93.44 124	91.98 143	97.84 30	95.24 198	94.38 50	96.22 279	97.92 47	90.18 115	82.28 247	97.71 134	77.63 214	99.80 64	91.94 140	98.67 100	99.34 93

baseline		93.91 111	93.30 114	95.72 127	95.10 211	90.07 144	97.48 231	95.91 244	91.03 95	93.54 129	97.68 135	79.58 198	98.02 175	94.27 112	95.14 152	99.08 115

PVSNet	87.13 12	93.69 117	92.83 125	96.28 107	97.99 111	90.22 139	99.38 59	98.93 10	91.42 89	93.66 128	97.68 135	71.29 262	99.64 86	87.94 184	97.20 125	98.98 120

casdiffmvs		93.98 109	93.43 112	95.61 131	95.07 213	89.86 152	98.80 122	95.84 252	90.98 96	92.74 138	97.66 137	79.71 197	98.10 167	94.72 104	95.37 151	98.87 133

Vis-MVSNet (Re-imp)		93.26 133	93.00 123	94.06 179	96.14 172	86.71 217	98.68 136	96.70 186	88.30 175	89.71 181	97.64 138	85.43 133	96.39 256	88.06 183	96.32 135	99.08 115

3Dnovator+	87.72 8	93.43 125	91.84 146	98.17 18	95.73 182	95.08 29	98.92 111	97.04 173	91.42 89	81.48 265	97.60 139	74.60 227	99.79 65	90.84 151	98.97 85	99.64 67

thisisatest0515		94.75 90	94.19 92	96.43 101	96.13 175	92.64 88	99.47 42	97.60 98	87.55 199	93.17 132	97.59 140	94.71 9	98.42 156	88.28 179	93.20 166	98.24 171

3Dnovator	87.35 11	93.17 135	91.77 148	97.37 51	95.41 195	93.07 76	98.82 120	97.85 52	91.53 84	82.56 241	97.58 141	71.97 254	99.82 60	91.01 148	99.23 78	99.22 106

CHOSEN 280x420		96.80 35	96.85 27	96.66 91	97.85 113	94.42 49	94.76 302	98.36 23	92.50 60	95.62 97	97.52 142	97.92 1	97.38 216	98.31 33	98.80 95	98.20 174

IS-MVSNet		93.00 137	92.51 131	94.49 164	96.14 172	87.36 202	98.31 184	95.70 258	88.58 162	90.17 173	97.50 143	83.02 164	97.22 219	87.06 190	96.07 143	98.90 130

OpenMVS	85.28 14	90.75 177	88.84 194	96.48 98	93.58 253	93.51 66	98.80 122	97.41 138	82.59 278	78.62 294	97.49 144	68.00 280	99.82 60	84.52 220	98.55 104	96.11 216

PCF-MVS	89.78 5	91.26 166	89.63 179	96.16 112	95.44 194	91.58 103	95.29 298	96.10 227	85.07 238	82.75 237	97.45 145	78.28 210	99.78 66	80.60 259	95.65 150	97.12 197
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
VNet		95.08 84	94.26 90	97.55 43	98.07 109	93.88 58	98.68 136	98.73 15	90.33 111	97.16 58	97.43 146	79.19 203	99.53 97	96.91 58	91.85 188	99.24 103

QAPM		91.41 165	89.49 182	97.17 58	95.66 186	93.42 68	98.60 149	97.51 120	80.92 301	81.39 266	97.41 147	72.89 247	99.87 47	82.33 245	98.68 99	98.21 173

thisisatest0530		94.00 108	93.52 111	95.43 136	95.76 181	90.02 149	98.99 104	97.60 98	86.58 216	91.74 146	97.36 148	94.78 8	98.34 157	86.37 199	92.48 177	97.94 180

DWT-MVSNet_test		94.36 102	93.95 103	95.62 129	96.99 143	89.47 160	96.62 266	97.38 141	90.96 97	93.07 135	97.27 149	93.73 13	98.09 168	85.86 207	93.65 164	99.29 97

mvs-test1		91.57 161	92.20 137	89.70 274	95.15 205	74.34 333	99.51 39	95.40 278	91.92 76	91.02 159	97.25 150	74.27 234	98.08 171	89.45 164	95.83 146	96.67 205

DP-MVS		88.75 215	86.56 231	95.34 139	98.92 84	87.45 199	97.64 227	93.52 324	70.55 339	81.49 264	97.25 150	74.43 231	99.88 44	71.14 317	94.09 160	98.67 149

TR-MVS		90.77 176	89.44 183	94.76 154	96.31 162	88.02 188	97.92 210	95.96 233	85.52 229	88.22 191	97.23 152	66.80 289	98.09 168	84.58 219	92.38 178	98.17 175

Vis-MVSNet		92.64 142	91.85 145	95.03 149	95.12 207	88.23 183	98.48 164	96.81 183	91.61 81	92.16 144	97.22 153	71.58 260	98.00 177	85.85 208	97.81 112	98.88 131
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
gm-plane-assit					94.69 226	88.14 184			88.22 178		97.20 154		98.29 160	90.79 152

tttt0517		93.30 130	93.01 122	94.17 175	95.57 188	86.47 220	98.51 159	97.60 98	85.99 224	90.55 166	97.19 155	94.80 7	98.31 158	85.06 212	91.86 187	97.74 182

EPP-MVSNet		93.75 116	93.67 109	94.01 182	95.86 178	85.70 244	98.67 138	97.66 83	84.46 248	91.36 155	97.18 156	91.16 26	97.79 187	92.93 132	93.75 162	98.53 155

Effi-MVS+		93.87 113	93.15 118	96.02 115	95.79 179	90.76 127	96.70 264	95.78 253	86.98 207	95.71 94	97.17 157	79.58 198	98.01 176	94.57 108	96.09 141	99.31 95

CLD-MVS		91.06 170	90.71 168	92.10 221	94.05 239	86.10 233	99.55 34	96.29 214	94.16 26	84.70 218	97.17 157	69.62 269	97.82 185	94.74 103	86.08 224	92.39 234
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EI-MVSNet		89.87 194	89.38 185	91.36 235	94.32 232	85.87 240	97.61 228	96.59 191	85.10 236	85.51 213	97.10 159	81.30 191	96.56 244	83.85 232	83.03 246	91.64 257

CVMVSNet		90.30 184	90.91 162	88.46 297	94.32 232	73.58 337	97.61 228	97.59 102	90.16 118	88.43 190	97.10 159	76.83 218	92.86 331	82.64 242	93.54 165	98.93 128

UA-Net		93.30 130	92.62 129	95.34 139	96.27 163	88.53 181	95.88 289	96.97 179	90.90 99	95.37 100	97.07 161	82.38 177	99.10 138	83.91 230	94.86 155	98.38 163

RPSCF		85.33 267	85.55 246	84.67 321	94.63 228	62.28 354	93.73 312	93.76 318	74.38 331	85.23 216	97.06 162	64.09 301	98.31 158	80.98 254	86.08 224	93.41 228

EPNet_dtu		92.28 150	92.15 139	92.70 210	97.29 130	84.84 259	98.64 142	97.82 55	92.91 53	93.02 136	97.02 163	85.48 132	95.70 294	72.25 314	94.89 154	97.55 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
BH-w/o		92.32 149	91.79 147	93.91 185	96.85 145	86.18 230	99.11 92	95.74 256	88.13 180	84.81 217	97.00 164	77.26 216	97.91 178	89.16 173	98.03 110	97.64 184

thres200		93.69 117	92.59 130	96.97 69	97.76 114	94.74 40	99.35 64	99.36 2	89.23 142	91.21 158	96.97 165	83.42 154	98.77 146	85.08 211	90.96 199	97.39 191

baseline2		94.04 107	93.80 108	94.74 156	93.07 265	90.25 137	98.12 198	98.16 33	89.86 123	86.53 208	96.95 166	95.56 5	98.05 173	91.44 143	94.53 156	95.93 217

MSDG		88.29 222	86.37 233	94.04 181	96.90 144	86.15 232	96.52 268	94.36 310	77.89 319	79.22 289	96.95 166	69.72 268	99.59 92	73.20 310	92.58 176	96.37 214

tfpn200view9		93.43 125	92.27 135	96.90 74	97.68 117	94.84 35	99.18 75	99.36 2	88.45 167	90.79 161	96.90 168	83.31 155	98.75 148	84.11 226	90.69 201	97.12 197

thres400		93.39 127	92.27 135	96.73 85	97.68 117	94.84 35	99.18 75	99.36 2	88.45 167	90.79 161	96.90 168	83.31 155	98.75 148	84.11 226	90.69 201	96.61 206

Anonymous202405211		88.84 209	87.03 224	94.27 171	98.14 108	84.18 267	98.44 167	95.58 267	76.79 323	89.34 183	96.88 170	53.42 337	99.54 96	87.53 189	87.12 217	99.09 114

baseline1		92.61 144	91.28 155	96.58 94	97.05 141	94.63 44	97.72 223	96.20 219	89.82 125	88.56 188	96.85 171	86.85 101	97.82 185	88.42 177	80.10 261	97.30 193

GeoE		90.60 181	89.56 180	93.72 191	95.10 211	85.43 248	99.41 56	94.94 295	83.96 256	87.21 200	96.83 172	74.37 232	97.05 226	80.50 261	93.73 163	98.67 149

thres100view900		93.34 129	92.15 139	96.90 74	97.62 119	94.84 35	99.06 96	99.36 2	87.96 185	90.47 169	96.78 173	83.29 157	98.75 148	84.11 226	90.69 201	97.12 197

thres600view7		93.18 134	92.00 142	96.75 83	97.62 119	94.92 31	99.07 94	99.36 2	87.96 185	90.47 169	96.78 173	83.29 157	98.71 152	82.93 240	90.47 205	96.61 206

hse-mvs3		92.47 148	91.95 144	94.05 180	97.13 136	85.01 257	98.36 179	98.08 37	93.85 36	96.27 80	96.73 175	83.19 160	99.43 114	95.81 79	68.09 330	97.70 183

BH-untuned		91.46 164	90.84 164	93.33 196	96.51 157	84.83 260	98.84 119	95.50 271	86.44 221	83.50 228	96.70 176	75.49 222	97.77 189	86.78 197	97.81 112	97.40 190

NP-MVS					93.94 243	86.22 229					96.67 177

HQP-MVS		91.50 162	91.23 156	92.29 215	93.95 240	86.39 223	99.16 78	96.37 207	93.92 31	87.57 194	96.67 177	73.34 241	97.77 189	93.82 119	86.29 219	92.72 229

HQP_MVS		91.26 166	90.95 161	92.16 219	93.84 247	86.07 235	99.02 100	96.30 211	93.38 46	86.99 201	96.52 179	72.92 245	97.75 194	93.46 124	86.17 222	92.67 231

plane_prior4											96.52 179

CDS-MVSNet		93.47 123	93.04 121	94.76 154	94.75 225	89.45 161	98.82 120	97.03 175	87.91 187	90.97 160	96.48 181	89.06 59	96.36 258	89.50 163	92.81 172	98.49 157
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OPM-MVS		89.76 195	89.15 189	91.57 232	90.53 296	85.58 246	98.11 200	95.93 239	92.88 55	86.05 209	96.47 182	67.06 288	97.87 182	89.29 171	86.08 224	91.26 278

GG-mvs-BLEND				96.98 68	96.53 155	94.81 38	87.20 340	97.74 68		93.91 124	96.40 183	96.56 2	96.94 230	95.08 95	98.95 88	99.20 107

CHOSEN 1792x2688		94.35 103	93.82 107	95.95 120	97.40 126	88.74 176	98.41 171	98.27 25	92.18 72	91.43 153	96.40 183	78.88 204	99.81 62	93.59 122	97.81 112	99.30 96

tmp_tt		53.66 329	52.86 331	56.05 344	32.75 370	41.97 366	73.42 359	76.12 366	21.91 365	39.68 361	96.39 185	42.59 353	65.10 363	78.00 275	14.92 364	61.08 357

PVSNet_Blended_VisFu		94.67 95	94.11 95	96.34 106	97.14 135	91.10 116	99.32 68	97.43 136	92.10 75	91.53 152	96.38 186	83.29 157	99.68 78	93.42 126	96.37 134	98.25 170

test0.0.03 1		88.96 205	88.61 200	90.03 267	91.09 290	84.43 264	98.97 106	97.02 176	90.21 113	80.29 275	96.31 187	84.89 138	91.93 345	72.98 311	85.70 227	93.73 224

hse-mvs2		91.67 160	91.51 153	92.15 220	96.22 165	82.61 289	97.74 222	97.53 114	93.85 36	96.27 80	96.15 188	83.19 160	97.44 213	95.81 79	66.86 335	96.40 213

AUN-MVS		90.17 188	89.50 181	92.19 218	96.21 166	82.67 287	97.76 221	97.53 114	88.05 182	91.67 147	96.15 188	83.10 163	97.47 210	88.11 182	66.91 334	96.43 212

LPG-MVS_test		88.86 208	88.47 205	90.06 264	93.35 260	80.95 306	98.22 189	95.94 237	87.73 194	83.17 233	96.11 190	66.28 293	97.77 189	90.19 157	85.19 228	91.46 268

LGP-MVS_train				90.06 264	93.35 260	80.95 306		95.94 237	87.73 194	83.17 233	96.11 190	66.28 293	97.77 189	90.19 157	85.19 228	91.46 268

TAMVS		92.62 143	92.09 141	94.20 174	94.10 236	87.68 192	98.41 171	96.97 179	87.53 200	89.74 179	96.04 192	84.77 141	96.49 250	88.97 175	92.31 180	98.42 159

Anonymous20240529		87.66 232	85.58 245	93.92 184	97.59 122	85.01 257	98.13 196	97.13 164	66.69 351	88.47 189	96.01 193	55.09 331	99.51 101	87.00 192	84.12 236	97.23 196

COLMAP_ROB	82.69 18	84.54 276	82.82 276	89.70 274	96.72 150	78.85 315	95.89 287	92.83 330	71.55 337	77.54 303	95.89 194	59.40 317	99.14 136	67.26 329	88.26 211	91.11 282
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchMatch-RL		91.47 163	90.54 171	94.26 172	98.20 104	86.36 225	96.94 252	97.14 162	87.75 192	88.98 185	95.75 195	71.80 257	99.40 118	80.92 256	97.39 123	97.02 203

Fast-Effi-MVS+		91.72 159	90.79 167	94.49 164	95.89 177	87.40 201	99.54 37	95.70 258	85.01 241	89.28 184	95.68 196	77.75 213	97.57 207	83.22 235	95.06 153	98.51 156

ACMP	87.39 10	88.71 216	88.24 207	90.12 263	93.91 245	81.06 305	98.50 161	95.67 261	89.43 139	80.37 273	95.55 197	65.67 295	97.83 184	90.55 154	84.51 232	91.47 267
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
AllTest		84.97 270	83.12 274	90.52 253	96.82 146	78.84 316	95.89 287	92.17 336	77.96 317	75.94 308	95.50 198	55.48 327	99.18 131	71.15 315	87.14 215	93.55 226

TestCases				90.52 253	96.82 146	78.84 316		92.17 336	77.96 317	75.94 308	95.50 198	55.48 327	99.18 131	71.15 315	87.14 215	93.55 226

ITE_SJBPF				87.93 299	92.26 273	76.44 327		93.47 325	87.67 197	79.95 280	95.49 200	56.50 324	97.38 216	75.24 294	82.33 252	89.98 312

testgi		82.29 292	81.00 295	86.17 312	87.24 333	74.84 332	97.39 232	91.62 344	88.63 159	75.85 311	95.42 201	46.07 351	91.55 346	66.87 332	79.94 262	92.12 245

Fast-Effi-MVS+-dtu		88.84 209	88.59 202	89.58 278	93.44 258	78.18 321	98.65 140	94.62 303	88.46 166	84.12 224	95.37 202	68.91 271	96.52 247	82.06 248	91.70 192	94.06 223

ACMM	86.95 13	88.77 214	88.22 208	90.43 255	93.61 252	81.34 299	98.50 161	95.92 240	87.88 188	83.85 227	95.20 203	67.20 286	97.89 180	86.90 195	84.90 230	92.06 248
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RRT_MVS		91.95 156	91.09 157	94.53 163	96.71 152	95.12 28	98.64 142	96.23 217	89.04 148	85.24 215	95.06 204	87.71 82	96.43 254	89.10 174	82.06 253	92.05 249

HyFIR lowres test		93.68 119	93.29 115	94.87 151	97.57 123	88.04 187	98.18 193	98.47 21	87.57 198	91.24 157	95.05 205	85.49 130	97.46 211	93.22 128	92.82 170	99.10 113

VPNet		88.30 221	86.57 230	93.49 193	91.95 278	91.35 106	98.18 193	97.20 158	88.61 160	84.52 221	94.89 206	62.21 308	96.76 237	89.34 168	72.26 317	92.36 236

TESTMET0.1,1		93.82 114	93.26 116	95.49 133	95.21 200	90.25 137	99.15 84	97.54 113	89.18 144	91.79 145	94.87 207	89.13 58	97.63 200	86.21 200	96.29 138	98.60 153

FIs		90.70 178	89.87 177	93.18 198	92.29 272	91.12 114	98.17 195	98.25 26	89.11 146	83.44 229	94.82 208	82.26 178	96.17 273	87.76 185	82.76 248	92.25 239

HY-MVS	88.56 7	95.29 78	94.23 91	98.48 11	97.72 115	96.41 10	94.03 310	98.74 13	92.42 66	95.65 96	94.76 209	86.52 112	99.49 104	95.29 92	92.97 169	99.53 78

FC-MVSNet-test		90.22 186	89.40 184	92.67 212	91.78 282	89.86 152	97.89 211	98.22 28	88.81 157	82.96 236	94.66 210	81.90 184	95.96 281	85.89 206	82.52 251	92.20 243

nrg030		90.23 185	88.87 193	94.32 170	91.53 285	93.54 65	98.79 126	95.89 247	88.12 181	84.55 220	94.61 211	78.80 207	96.88 231	92.35 138	75.21 285	92.53 233

cascas		90.93 174	89.33 186	95.76 126	95.69 184	93.03 78	98.99 104	96.59 191	80.49 303	86.79 207	94.45 212	65.23 298	98.60 155	93.52 123	92.18 183	95.66 219

UniMVSNet_ETH3D		85.65 265	83.79 271	91.21 236	90.41 298	80.75 308	95.36 297	95.78 253	78.76 313	81.83 262	94.33 213	49.86 345	96.66 239	84.30 221	83.52 243	96.22 215

XXY-MVS		87.75 229	86.02 238	92.95 203	90.46 297	89.70 156	97.71 225	95.90 245	84.02 253	80.95 267	94.05 214	67.51 284	97.10 224	85.16 210	78.41 268	92.04 250

test-LLR		93.11 136	92.68 127	94.40 167	94.94 219	87.27 205	99.15 84	97.25 148	90.21 113	91.57 149	94.04 215	84.89 138	97.58 204	85.94 204	96.13 139	98.36 166

test-mter		93.27 132	92.89 124	94.40 167	94.94 219	87.27 205	99.15 84	97.25 148	88.95 152	91.57 149	94.04 215	88.03 77	97.58 204	85.94 204	96.13 139	98.36 166

MVS_Test		93.67 120	92.67 128	96.69 89	96.72 150	92.66 84	97.22 243	96.03 229	87.69 196	95.12 105	94.03 217	81.55 186	98.28 161	89.17 172	96.46 131	99.14 110

ACMH+	83.78 15	84.21 280	82.56 285	89.15 287	93.73 251	79.16 313	96.43 269	94.28 311	81.09 298	74.00 319	94.03 217	54.58 333	97.67 197	76.10 289	78.81 266	90.63 298

MVSTER		92.71 140	92.32 133	93.86 186	97.29 130	92.95 82	99.01 102	96.59 191	90.09 119	85.51 213	94.00 219	94.61 12	96.56 244	90.77 153	83.03 246	92.08 247

UniMVSNet_NR-MVSNet		89.60 197	88.55 203	92.75 209	92.17 275	90.07 144	98.74 128	98.15 34	88.37 173	83.21 231	93.98 220	82.86 166	95.93 283	86.95 193	72.47 314	92.25 239

mvs_anonymous		92.50 147	91.65 150	95.06 147	96.60 153	89.64 157	97.06 248	96.44 204	86.64 215	84.14 223	93.93 221	82.49 173	96.17 273	91.47 142	96.08 142	99.35 91

TranMVSNet+NR-MVSNet		87.75 229	86.31 234	92.07 222	90.81 293	88.56 178	98.33 181	97.18 159	87.76 191	81.87 259	93.90 222	72.45 249	95.43 300	83.13 238	71.30 324	92.23 241

ab-mvs		91.05 171	89.17 188	96.69 89	95.96 176	91.72 98	92.62 323	97.23 152	85.61 228	89.74 179	93.89 223	68.55 274	99.42 115	91.09 146	87.84 213	98.92 129

WR-MVS		88.54 218	87.22 222	92.52 213	91.93 280	89.50 159	98.56 154	97.84 53	86.99 205	81.87 259	93.81 224	74.25 236	95.92 285	85.29 209	74.43 294	92.12 245

PS-MVSNAJss		89.54 199	89.05 190	91.00 241	88.77 317	84.36 265	97.39 232	95.97 231	88.47 164	81.88 258	93.80 225	82.48 174	96.50 248	89.34 168	83.34 245	92.15 244

jajsoiax		87.35 235	86.51 232	89.87 268	87.75 331	81.74 294	97.03 249	95.98 230	88.47 164	80.15 277	93.80 225	61.47 310	96.36 258	89.44 166	84.47 234	91.50 266

DU-MVS		88.83 211	87.51 215	92.79 206	91.46 286	90.07 144	98.71 129	97.62 95	88.87 156	83.21 231	93.68 227	74.63 225	95.93 283	86.95 193	72.47 314	92.36 236

NR-MVSNet		87.74 231	86.00 239	92.96 202	91.46 286	90.68 130	96.65 265	97.42 137	88.02 184	73.42 322	93.68 227	77.31 215	95.83 290	84.26 222	71.82 321	92.36 236

IB-MVS	89.43 6	92.12 153	90.83 166	95.98 118	95.40 196	90.78 126	99.81 6	98.06 38	91.23 93	85.63 212	93.66 229	90.63 37	98.78 145	91.22 145	71.85 320	98.36 166
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
mvs_tets		87.09 238	86.22 235	89.71 273	87.87 327	81.39 298	96.73 263	95.90 245	88.19 179	79.99 279	93.61 230	59.96 316	96.31 266	89.40 167	84.34 235	91.43 270

UGNet		91.91 157	90.85 163	95.10 144	97.06 140	88.69 177	98.01 207	98.24 27	92.41 67	92.39 142	93.61 230	60.52 314	99.68 78	88.14 181	97.25 124	96.92 204
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
ACMH	83.09 17	84.60 274	82.61 284	90.57 251	93.18 263	82.94 280	96.27 274	94.92 296	81.01 299	72.61 330	93.61 230	56.54 323	97.79 187	74.31 301	81.07 257	90.99 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		86.75 243	85.92 240	89.22 285	91.97 277	82.47 290	96.91 253	96.14 224	83.74 258	77.73 301	93.53 233	58.19 319	97.37 218	76.75 284	98.35 107	87.84 331

RRT_test8_iter05		91.04 172	90.40 174	92.95 203	96.20 169	89.75 155	98.97 106	96.38 206	88.52 163	82.00 255	93.51 234	90.69 36	96.73 238	90.43 155	76.91 279	92.38 235

Test_1112_low_res		92.27 151	90.97 160	96.18 109	95.53 191	91.10 116	98.47 166	94.66 302	88.28 177	86.83 206	93.50 235	87.00 99	98.65 154	84.69 217	89.74 209	98.80 140

CMPMVS	58.40 21	80.48 300	80.11 300	81.59 333	85.10 341	59.56 356	94.14 309	95.95 235	68.54 346	60.71 352	93.31 236	55.35 330	97.87 182	83.06 239	84.85 231	87.33 336
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		84.74 271	82.93 275	90.16 262	91.73 283	83.54 274	95.00 300	93.30 326	88.77 158	73.19 323	93.30 237	53.62 336	97.65 199	75.88 291	81.54 256	89.30 320

OurMVSNet-221017-0		84.13 283	83.59 272	85.77 315	87.81 328	70.24 346	94.89 301	93.65 322	86.08 223	76.53 304	93.28 238	61.41 311	96.14 275	80.95 255	77.69 276	90.93 285

PVSNet_0	83.28 16	87.31 236	85.16 250	93.74 190	94.78 224	84.59 262	98.91 112	98.69 18	89.81 126	78.59 296	93.23 239	61.95 309	99.34 126	94.75 102	55.72 352	97.30 193

EU-MVSNet		84.19 281	84.42 265	83.52 326	88.64 320	67.37 352	96.04 285	95.76 255	85.29 233	78.44 297	93.18 240	70.67 264	91.48 347	75.79 292	75.98 281	91.70 256

pmmvs4		87.58 234	86.17 237	91.80 227	89.58 307	88.92 171	97.25 240	95.28 284	82.54 280	80.49 272	93.17 241	75.62 221	96.05 278	82.75 241	78.90 265	90.42 301

test_part1		88.43 219	86.68 229	93.67 192	97.56 124	92.40 92	98.12 198	96.55 196	82.26 285	80.31 274	93.16 242	74.59 229	96.62 241	85.00 214	72.61 312	91.99 251

bset_n11_16_dypcd		89.07 203	87.85 210	92.76 208	86.16 339	90.66 131	97.30 236	95.62 263	89.78 127	83.94 226	93.15 243	74.85 224	95.89 288	91.34 144	78.48 267	91.74 255

GA-MVS		90.10 190	88.69 198	94.33 169	92.44 271	87.97 189	99.08 93	96.26 215	89.65 130	86.92 203	93.11 244	68.09 278	96.96 228	82.54 244	90.15 206	98.05 176

CP-MVSNet		86.54 248	85.45 248	89.79 272	91.02 292	82.78 286	97.38 234	97.56 109	85.37 232	79.53 286	93.03 245	71.86 256	95.25 305	79.92 262	73.43 307	91.34 274

LF4IMVS		81.94 295	81.17 294	84.25 323	87.23 334	68.87 351	93.35 316	91.93 341	83.35 266	75.40 313	93.00 246	49.25 348	96.65 240	78.88 270	78.11 270	87.22 338

XVG-ACMP-BASELINE		85.86 258	84.95 254	88.57 294	89.90 302	77.12 326	94.30 306	95.60 266	87.40 202	82.12 250	92.99 247	53.42 337	97.66 198	85.02 213	83.83 238	90.92 286

PS-CasMVS		85.81 260	84.58 262	89.49 282	90.77 294	82.11 292	97.20 244	97.36 144	84.83 244	79.12 291	92.84 248	67.42 285	95.16 307	78.39 274	73.25 308	91.21 279

LTVRE_ROB	81.71 19	84.59 275	82.72 281	90.18 261	92.89 268	83.18 278	93.15 317	94.74 298	78.99 310	75.14 315	92.69 249	65.64 296	97.63 200	69.46 322	81.82 255	89.74 314
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
PEN-MVS		85.21 268	83.93 270	89.07 289	89.89 303	81.31 300	97.09 247	97.24 150	84.45 249	78.66 293	92.68 250	68.44 276	94.87 312	75.98 290	70.92 325	91.04 283

PVSNet_BlendedMVS		93.36 128	93.20 117	93.84 187	98.77 90	91.61 101	99.47 42	98.04 40	91.44 86	94.21 118	92.63 251	83.50 151	99.87 47	97.41 46	83.37 244	90.05 310

DTE-MVSNet		84.14 282	82.80 277	88.14 298	88.95 316	79.87 312	96.81 257	96.24 216	83.50 263	77.60 302	92.52 252	67.89 282	94.24 323	72.64 313	69.05 328	90.32 303

miper_enhance_ethall		90.33 183	89.70 178	92.22 216	97.12 137	88.93 170	98.35 180	95.96 233	88.60 161	83.14 235	92.33 253	87.38 88	96.18 272	86.49 198	77.89 271	91.55 265

SixPastTwentyTwo		82.63 291	81.58 289	85.79 314	88.12 325	71.01 345	95.17 299	92.54 332	84.33 250	72.93 328	92.08 254	60.41 315	95.61 297	74.47 300	74.15 299	90.75 293

UniMVSNet (Re)		89.50 200	88.32 206	93.03 200	92.21 274	90.96 122	98.90 113	98.39 22	89.13 145	83.22 230	92.03 255	81.69 185	96.34 264	86.79 196	72.53 313	91.81 254

pmmvs5		85.87 257	84.40 266	90.30 260	88.53 321	84.23 266	98.60 149	93.71 320	81.53 293	80.29 275	92.02 256	64.51 300	95.52 298	82.04 249	78.34 269	91.15 280

pm-mvs1		84.68 273	82.78 279	90.40 256	89.58 307	85.18 253	97.31 235	94.73 299	81.93 290	76.05 307	92.01 257	65.48 297	96.11 276	78.75 272	69.14 327	89.91 313

VPA-MVSNet		89.10 202	87.66 214	93.45 194	92.56 269	91.02 120	97.97 209	98.32 24	86.92 209	86.03 210	92.01 257	68.84 273	97.10 224	90.92 149	75.34 284	92.23 241

MVP-Stereo		86.61 247	85.83 241	88.93 292	88.70 319	83.85 272	96.07 284	94.41 309	82.15 287	75.64 312	91.96 259	67.65 283	96.45 253	77.20 280	98.72 98	86.51 342
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test_djsdf		88.26 223	87.73 212	89.84 270	88.05 326	82.21 291	97.77 219	96.17 222	86.84 210	82.41 245	91.95 260	72.07 253	95.99 279	89.83 159	84.50 233	91.32 275

cl-mvsnet2		89.57 198	88.79 196	91.91 223	97.94 112	87.62 194	97.98 208	96.51 199	85.03 239	82.37 246	91.79 261	83.65 149	96.50 248	85.96 203	77.89 271	91.61 262

v2v482		87.27 237	85.76 242	91.78 231	89.59 306	87.58 195	98.56 154	95.54 269	84.53 247	82.51 242	91.78 262	73.11 244	96.47 251	82.07 247	74.14 300	91.30 276

TinyColmap		80.42 301	77.94 305	87.85 300	92.09 276	78.58 318	93.74 311	89.94 352	74.99 327	69.77 334	91.78 262	46.09 350	97.58 204	65.17 337	77.89 271	87.38 334

TransMVSNet (Re)		81.97 294	79.61 302	89.08 288	89.70 305	84.01 269	97.26 239	91.85 342	78.84 311	73.07 327	91.62 264	67.17 287	95.21 306	67.50 328	59.46 347	88.02 330

FMVSNet3		88.81 213	87.08 223	93.99 183	96.52 156	94.59 45	98.08 203	96.20 219	85.85 225	82.12 250	91.60 265	74.05 237	95.40 302	79.04 267	80.24 258	91.99 251

eth_miper_zixun_eth		87.76 228	87.00 225	90.06 264	94.67 227	82.65 288	97.02 251	95.37 281	84.19 251	81.86 261	91.58 266	81.47 188	95.90 287	83.24 234	73.61 303	91.61 262

miper_ehance_all_eth		88.94 206	88.12 209	91.40 233	95.32 197	86.93 213	97.85 215	95.55 268	84.19 251	81.97 256	91.50 267	84.16 145	95.91 286	84.69 217	77.89 271	91.36 273

Effi-MVS+-dtu		89.97 193	90.68 169	87.81 301	95.15 205	71.98 343	97.87 214	95.40 278	91.92 76	87.57 194	91.44 268	74.27 234	96.84 232	89.45 164	93.10 168	94.60 222

cl_fuxian		88.19 224	87.23 221	91.06 239	94.97 217	86.17 231	97.72 223	95.38 280	83.43 264	81.68 263	91.37 269	82.81 167	95.72 293	84.04 229	73.70 302	91.29 277

Baseline_NR-MVSNet		85.83 259	84.82 257	88.87 293	88.73 318	83.34 276	98.63 144	91.66 343	80.41 306	82.44 243	91.35 270	74.63 225	95.42 301	84.13 225	71.39 323	87.84 331

cl-mvsnet1		87.82 226	86.81 227	90.87 246	94.87 222	85.39 250	97.81 216	95.22 292	82.92 275	80.76 269	91.31 271	81.99 181	95.81 291	81.36 252	75.04 287	91.42 271

cl-mvsnet____		87.82 226	86.79 228	90.89 245	94.88 221	85.43 248	97.81 216	95.24 288	82.91 276	80.71 270	91.22 272	81.97 183	95.84 289	81.34 253	75.06 286	91.40 272

IterMVS-LS		88.34 220	87.44 216	91.04 240	94.10 236	85.85 241	98.10 201	95.48 272	85.12 235	82.03 254	91.21 273	81.35 190	95.63 296	83.86 231	75.73 283	91.63 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet2		86.90 240	84.79 258	93.24 197	95.11 208	92.54 90	97.67 226	95.86 251	82.94 272	80.55 271	91.17 274	62.89 305	95.29 304	77.23 278	79.71 264	91.90 253

TDRefinement		78.01 313	75.31 316	86.10 313	70.06 360	73.84 335	93.59 315	91.58 345	74.51 330	73.08 326	91.04 275	49.63 347	97.12 221	74.88 297	59.47 346	87.33 336

ppachtmachnet_test		83.63 287	81.57 290	89.80 271	89.01 314	85.09 256	97.13 246	94.50 305	78.84 311	76.14 306	91.00 276	69.78 267	94.61 319	63.40 339	74.36 295	89.71 316

tfpnnormal		83.65 286	81.35 292	90.56 252	91.37 288	88.06 186	97.29 237	97.87 51	78.51 314	76.20 305	90.91 277	64.78 299	96.47 251	61.71 344	73.50 305	87.13 339

WR-MVS_H		86.53 249	85.49 247	89.66 277	91.04 291	83.31 277	97.53 230	98.20 30	84.95 242	79.64 283	90.90 278	78.01 212	95.33 303	76.29 288	72.81 309	90.35 302

Anonymous20231211		84.72 272	82.65 283	90.91 243	97.71 116	84.55 263	97.28 238	96.67 187	66.88 350	79.18 290	90.87 279	58.47 318	96.60 242	82.61 243	74.20 298	91.59 264

v1144		86.83 242	85.31 249	91.40 233	89.75 304	87.21 210	98.31 184	95.45 274	83.22 267	82.70 239	90.78 280	73.36 240	96.36 258	79.49 264	74.69 291	90.63 298

CostFormer		92.89 138	92.48 132	94.12 177	94.99 216	85.89 239	92.89 319	97.00 178	86.98 207	95.00 107	90.78 280	90.05 48	97.51 209	92.92 133	91.73 191	98.96 123

v1921920		86.02 255	84.44 264	90.77 248	89.32 312	85.20 252	98.10 201	95.35 283	82.19 286	82.25 248	90.71 282	70.73 263	96.30 269	76.85 283	74.49 293	90.80 289

anonymousdsp		86.69 244	85.75 243	89.53 279	86.46 337	82.94 280	96.39 270	95.71 257	83.97 255	79.63 284	90.70 283	68.85 272	95.94 282	86.01 201	84.02 237	89.72 315

tpmrst		92.78 139	92.16 138	94.65 159	96.27 163	87.45 199	91.83 327	97.10 169	89.10 147	94.68 111	90.69 284	88.22 72	97.73 196	89.78 161	91.80 189	98.77 144

V42		87.00 239	85.68 244	90.98 242	89.91 301	86.08 234	98.32 183	95.61 265	83.67 261	82.72 238	90.67 285	74.00 238	96.53 246	81.94 250	74.28 297	90.32 303

tpm2		91.77 158	91.09 157	93.82 188	94.83 223	85.56 247	92.51 324	97.16 161	84.00 254	93.83 126	90.66 286	87.54 85	97.17 220	87.73 186	91.55 194	98.72 146

EPMVS		92.59 145	91.59 151	95.59 132	97.22 132	90.03 148	91.78 328	98.04 40	90.42 108	91.66 148	90.65 287	86.49 114	97.46 211	81.78 251	96.31 136	99.28 99

LCM-MVSNet-Re		88.59 217	88.61 200	88.51 296	95.53 191	72.68 341	96.85 256	88.43 357	88.45 167	73.14 324	90.63 288	75.82 219	94.38 321	92.95 131	95.71 149	98.48 158

SCA		90.64 180	89.25 187	94.83 153	94.95 218	88.83 172	96.26 276	97.21 154	90.06 122	90.03 175	90.62 289	66.61 290	96.81 234	83.16 236	94.36 158	98.84 134

Patchmatch-test		86.25 253	84.06 268	92.82 205	94.42 230	82.88 284	82.88 355	94.23 312	71.58 336	79.39 287	90.62 289	89.00 61	96.42 255	63.03 341	91.37 197	99.16 109

v1192		86.32 252	84.71 259	91.17 237	89.53 309	86.40 222	98.13 196	95.44 276	82.52 281	82.42 244	90.62 289	71.58 260	96.33 265	77.23 278	74.88 288	90.79 290

v144192		86.40 250	84.89 255	90.91 243	89.48 310	85.59 245	98.21 191	95.43 277	82.45 282	82.62 240	90.58 292	72.79 248	96.36 258	78.45 273	74.04 301	90.79 290

PatchmatchNet		92.05 155	91.04 159	95.06 147	96.17 170	89.04 165	91.26 332	97.26 147	89.56 136	90.64 165	90.56 293	88.35 71	97.11 222	79.53 263	96.07 143	99.03 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		85.77 262	84.11 267	90.73 249	89.26 313	85.15 255	97.88 213	95.23 291	81.89 291	82.16 249	90.55 294	69.60 270	96.31 266	75.59 293	74.87 289	90.72 295

our_test_3		84.47 278	82.80 277	89.50 280	89.01 314	83.90 271	97.03 249	94.56 304	81.33 295	75.36 314	90.52 295	71.69 258	94.54 320	68.81 324	76.84 280	90.07 308

miper_lstm_enhance		86.90 240	86.20 236	89.00 290	94.53 229	81.19 302	96.74 262	95.24 288	82.33 284	80.15 277	90.51 296	81.99 181	94.68 318	80.71 258	73.58 304	91.12 281

MDTV_nov1_ep13			90.47 173		96.14 172	88.55 179	91.34 331	97.51 120	89.58 134	92.24 143	90.50 297	86.99 100	97.61 203	77.64 277	92.34 179

IterMVS-SCA-FT		85.73 263	84.64 261	89.00 290	93.46 257	82.90 282	96.27 274	94.70 300	85.02 240	78.62 294	90.35 298	66.61 290	93.33 327	79.38 266	77.36 278	90.76 292

D2MVS		87.96 225	87.39 217	89.70 274	91.84 281	83.40 275	98.31 184	98.49 20	88.04 183	78.23 300	90.26 299	73.57 239	96.79 236	84.21 223	83.53 242	88.90 325

GBi-Net		86.67 245	84.96 252	91.80 227	95.11 208	88.81 173	96.77 258	95.25 285	82.94 272	82.12 250	90.25 300	62.89 305	94.97 309	79.04 267	80.24 258	91.62 259

test1		86.67 245	84.96 252	91.80 227	95.11 208	88.81 173	96.77 258	95.25 285	82.94 272	82.12 250	90.25 300	62.89 305	94.97 309	79.04 267	80.24 258	91.62 259

FMVSNet1		83.94 285	81.32 293	91.80 227	91.94 279	88.81 173	96.77 258	95.25 285	77.98 315	78.25 299	90.25 300	50.37 344	94.97 309	73.27 309	77.81 275	91.62 259

v148		86.38 251	85.06 251	90.37 259	89.47 311	84.10 268	98.52 156	95.48 272	83.80 257	80.93 268	90.22 303	74.60 227	96.31 266	80.92 256	71.55 322	90.69 296

lessismore_v0				85.08 317	85.59 340	69.28 349		90.56 350		67.68 341	90.21 304	54.21 335	95.46 299	73.88 304	62.64 341	90.50 300

dp		90.16 189	88.83 195	94.14 176	96.38 160	86.42 221	91.57 329	97.06 172	84.76 245	88.81 186	90.19 305	84.29 144	97.43 214	75.05 295	91.35 198	98.56 154

IterMVS		85.81 260	84.67 260	89.22 285	93.51 254	83.67 273	96.32 273	94.80 297	85.09 237	78.69 292	90.17 306	66.57 292	93.17 330	79.48 265	77.42 277	90.81 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
test_0402		78.81 309	76.33 313	86.26 311	91.18 289	78.44 320	95.88 289	91.34 347	68.55 345	70.51 333	89.91 307	52.65 339	94.99 308	47.14 357	79.78 263	85.34 348

v8		86.11 254	84.45 263	91.10 238	89.99 300	86.85 214	97.24 241	95.36 282	81.99 288	79.89 281	89.86 308	74.53 230	96.39 256	78.83 271	72.32 316	90.05 310

v10		85.73 263	84.01 269	90.87 246	90.03 299	86.73 216	97.20 244	95.22 292	81.25 296	79.85 282	89.75 309	73.30 243	96.28 270	76.87 282	72.64 311	89.61 317

test20.03		78.51 312	77.48 307	81.62 332	83.07 347	71.03 344	96.11 283	92.83 330	81.66 292	69.31 335	89.68 310	57.53 320	87.29 355	58.65 350	68.47 329	86.53 341

pmmvs6		79.90 303	77.31 308	87.67 302	84.17 344	78.13 322	95.86 291	93.68 321	67.94 348	72.67 329	89.62 311	50.98 343	95.75 292	74.80 299	66.04 336	89.14 323

tpm		89.67 196	88.95 192	91.82 226	92.54 270	81.43 296	92.95 318	95.92 240	87.81 189	90.50 168	89.44 312	84.99 136	95.65 295	83.67 233	82.71 249	98.38 163

v7n		84.42 279	82.75 280	89.43 283	88.15 324	81.86 293	96.75 261	95.67 261	80.53 302	78.38 298	89.43 313	69.89 266	96.35 263	73.83 306	72.13 318	90.07 308

K. test v3		81.04 298	79.77 301	84.83 319	87.41 332	70.23 347	95.60 296	93.93 317	83.70 260	67.51 342	89.35 314	55.76 325	93.58 326	76.67 285	68.03 331	90.67 297

tpmvs		89.16 201	87.76 211	93.35 195	97.19 133	84.75 261	90.58 338	97.36 144	81.99 288	84.56 219	89.31 315	83.98 147	98.17 163	74.85 298	90.00 207	97.12 197

Anonymous20231206		80.76 299	79.42 303	84.79 320	84.78 342	72.98 338	96.53 267	92.97 328	79.56 307	74.33 316	88.83 316	61.27 312	92.15 342	60.59 346	75.92 282	89.24 322

EG-PatchMatch MVS		79.92 302	77.59 306	86.90 308	87.06 335	77.90 325	96.20 282	94.06 315	74.61 329	66.53 346	88.76 317	40.40 358	96.20 271	67.02 330	83.66 241	86.61 340

tpm cat1		88.89 207	87.27 220	93.76 189	95.79 179	85.32 251	90.76 336	97.09 170	76.14 325	85.72 211	88.59 318	82.92 165	98.04 174	76.96 281	91.43 195	97.90 181

DeepMVS_CX				76.08 336	90.74 295	51.65 362		90.84 349	86.47 220	57.89 353	87.98 319	35.88 359	92.60 335	65.77 335	65.06 338	83.97 350

MDA-MVSNet-bldmvs		77.82 315	74.75 319	87.03 307	88.33 322	78.52 319	96.34 272	92.85 329	75.57 326	48.87 357	87.89 320	57.32 322	92.49 339	60.79 345	64.80 339	90.08 307

UnsupCasMVSNet_eth		78.90 308	76.67 312	85.58 316	82.81 349	74.94 331	91.98 326	96.31 210	84.64 246	65.84 348	87.71 321	51.33 341	92.23 341	72.89 312	56.50 351	89.56 318

MIMVSNet		84.48 277	81.83 287	92.42 214	91.73 283	87.36 202	85.52 344	94.42 308	81.40 294	81.91 257	87.58 322	51.92 340	92.81 333	73.84 305	88.15 212	97.08 201

YYNet1		79.64 306	77.04 310	87.43 305	87.80 329	79.98 311	96.23 278	94.44 306	73.83 333	51.83 354	87.53 323	67.96 281	92.07 344	66.00 334	67.75 333	90.23 305

KD-MVS_2432*1600		82.98 289	80.52 297	90.38 257	94.32 232	88.98 167	92.87 320	95.87 249	80.46 304	73.79 320	87.49 324	82.76 169	93.29 328	70.56 319	46.53 357	88.87 326

miper_refine_blended		82.98 289	80.52 297	90.38 257	94.32 232	88.98 167	92.87 320	95.87 249	80.46 304	73.79 320	87.49 324	82.76 169	93.29 328	70.56 319	46.53 357	88.87 326

MDA-MVSNet_test_wron		79.65 305	77.05 309	87.45 304	87.79 330	80.13 310	96.25 277	94.44 306	73.87 332	51.80 355	87.47 326	68.04 279	92.12 343	66.02 333	67.79 332	90.09 306

ADS-MVSNet2		87.62 233	86.88 226	89.86 269	96.21 166	79.14 314	87.15 341	92.99 327	83.01 270	89.91 177	87.27 327	78.87 205	92.80 334	74.20 302	92.27 181	97.64 184

ADS-MVSNet		88.99 204	87.30 219	94.07 178	96.21 166	87.56 196	87.15 341	96.78 185	83.01 270	89.91 177	87.27 327	78.87 205	97.01 227	74.20 302	92.27 181	97.64 184

DSMNet-mixed		81.60 297	81.43 291	82.10 330	84.36 343	60.79 355	93.63 314	86.74 359	79.00 309	79.32 288	87.15 329	63.87 303	89.78 350	66.89 331	91.92 186	95.73 218

OpenMVS_ROB	73.86 20	77.99 314	75.06 318	86.77 309	83.81 346	77.94 324	96.38 271	91.53 346	67.54 349	68.38 337	87.13 330	43.94 352	96.08 277	55.03 353	81.83 254	86.29 343

CR-MVSNet		88.83 211	87.38 218	93.16 199	93.47 255	86.24 227	84.97 348	94.20 313	88.92 155	90.76 163	86.88 331	84.43 142	94.82 314	70.64 318	92.17 184	98.41 160

Patchmtry		83.61 288	81.64 288	89.50 280	93.36 259	82.84 285	84.10 351	94.20 313	69.47 344	79.57 285	86.88 331	84.43 142	94.78 315	68.48 326	74.30 296	90.88 287

N_pmnet		70.19 323	69.87 325	71.12 339	88.24 323	30.63 370	95.85 292	28.70 370	70.18 341	68.73 336	86.55 333	64.04 302	93.81 324	53.12 355	73.46 306	88.94 324

MVS_0304		84.13 283	82.66 282	88.52 295	93.07 265	80.15 309	95.81 293	98.21 29	79.27 308	86.85 205	86.40 334	41.33 356	94.69 317	76.36 287	86.69 218	90.73 294

MIMVSNet1		75.92 318	73.30 321	83.81 325	81.29 352	75.57 329	92.26 325	92.05 339	73.09 335	67.48 343	86.18 335	40.87 357	87.64 354	55.78 352	70.68 326	88.21 329

FMVSNet5		82.29 292	80.54 296	87.52 303	93.79 250	84.01 269	93.73 312	92.47 333	76.92 322	74.27 317	86.15 336	63.69 304	89.24 351	69.07 323	74.79 290	89.29 321

CL-MVSNet_2432*1600		79.89 304	78.34 304	84.54 322	81.56 351	75.01 330	96.88 255	95.62 263	81.10 297	75.86 310	85.81 337	68.49 275	90.26 349	63.21 340	56.51 350	88.35 328

patchmatchnet-post											84.86 338	88.73 64	96.81 234

Anonymous20240521		78.63 311	76.90 311	83.82 324	82.82 348	72.86 339	95.72 295	93.57 323	73.55 334	72.17 331	84.79 339	49.69 346	92.51 338	65.29 336	74.50 292	86.09 344

test_method		70.10 324	68.66 327	74.41 337	86.30 338	55.84 359	94.47 303	89.82 353	35.18 360	66.15 347	84.75 340	30.54 360	77.96 360	70.40 321	60.33 345	89.44 319

DIV-MVS_2432*1600		77.47 316	75.88 315	82.24 328	81.59 350	68.93 350	92.83 322	94.02 316	77.03 321	73.14 324	83.39 341	55.44 329	90.42 348	67.95 327	57.53 349	87.38 334

PM-MVS		74.88 319	72.85 322	80.98 334	78.98 356	64.75 353	90.81 335	85.77 360	80.95 300	68.23 339	82.81 342	29.08 361	92.84 332	76.54 286	62.46 342	85.36 347

pmmvs-eth3d		78.71 310	76.16 314	86.38 310	80.25 354	81.19 302	94.17 308	92.13 338	77.97 316	66.90 345	82.31 343	55.76 325	92.56 337	73.63 308	62.31 343	85.38 346

Patchmatch-RL test		81.90 296	80.13 299	87.23 306	80.71 353	70.12 348	84.07 352	88.19 358	83.16 269	70.57 332	82.18 344	87.18 95	92.59 336	82.28 246	62.78 340	98.98 120

new_pmnet		76.02 317	73.71 320	82.95 327	83.88 345	72.85 340	91.26 332	92.26 335	70.44 340	62.60 350	81.37 345	47.64 349	92.32 340	61.85 343	72.10 319	83.68 351

FPMVS		61.57 325	60.32 328	65.34 341	60.14 364	42.44 365	91.02 334	89.72 354	44.15 357	42.63 359	80.93 346	19.02 363	80.59 359	42.50 358	72.76 310	73.00 355

pmmvs3		72.86 322	69.76 326	82.17 329	73.86 358	74.19 334	94.20 307	89.01 356	64.23 354	67.72 340	80.91 347	41.48 354	88.65 353	62.40 342	54.02 354	83.68 351

ambc				79.60 335	72.76 359	56.61 358	76.20 357	92.01 340		68.25 338	80.23 348	23.34 362	94.73 316	73.78 307	60.81 344	87.48 333

new-patchmatchnet		74.80 320	72.40 323	81.99 331	78.36 357	72.20 342	94.44 304	92.36 334	77.06 320	63.47 349	79.98 349	51.04 342	88.85 352	60.53 347	54.35 353	84.92 349

PatchT		85.44 266	83.19 273	92.22 216	93.13 264	83.00 279	83.80 354	96.37 207	70.62 338	90.55 166	79.63 350	84.81 140	94.87 312	58.18 351	91.59 193	98.79 141

RPMNet		85.07 269	81.88 286	94.64 160	93.47 255	86.24 227	84.97 348	97.21 154	64.85 353	90.76 163	78.80 351	80.95 192	99.27 129	53.76 354	92.17 184	98.41 160

UnsupCasMVSNet_bld		73.85 321	70.14 324	84.99 318	79.44 355	75.73 328	88.53 339	95.24 288	70.12 342	61.94 351	74.81 352	41.41 355	93.62 325	68.65 325	51.13 356	85.62 345

LCM-MVSNet		60.07 326	56.37 329	71.18 338	54.81 366	48.67 363	82.17 356	89.48 355	37.95 358	49.13 356	69.12 353	13.75 369	81.76 356	59.28 348	51.63 355	83.10 353

PMMVS2		58.97 327	55.07 330	70.69 340	62.72 361	55.37 360	85.97 343	80.52 363	49.48 356	45.94 358	68.31 354	15.73 367	80.78 358	49.79 356	37.12 359	75.91 354

JIA-IIPM		85.97 256	84.85 256	89.33 284	93.23 262	73.68 336	85.05 347	97.13 164	69.62 343	91.56 151	68.03 355	88.03 77	96.96 228	77.89 276	93.12 167	97.34 192

testmvs		18.81 336	23.05 339	6.10 351	4.48 371	2.29 373	97.78 218	3.00 372	3.27 367	18.60 367	62.71 356	1.53 374	2.49 369	14.26 366	1.80 366	13.50 364

gg-mvs-nofinetune		90.00 192	87.71 213	96.89 79	96.15 171	94.69 43	85.15 346	97.74 68	68.32 347	92.97 137	60.16 357	96.10 3	96.84 232	93.89 115	98.87 90	99.14 110

PMVS	41.42 23	45.67 331	42.50 334	55.17 345	34.28 369	32.37 368	66.24 360	78.71 365	30.72 361	22.04 366	59.59 358	4.59 370	77.85 361	27.49 361	58.84 348	55.29 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVS-HIRNet		79.01 307	75.13 317	90.66 250	93.82 249	81.69 295	85.16 345	93.75 319	54.54 355	74.17 318	59.15 359	57.46 321	96.58 243	63.74 338	94.38 157	93.72 225

ANet_high		50.71 330	46.17 333	64.33 342	44.27 368	52.30 361	76.13 358	78.73 364	64.95 352	27.37 363	55.23 360	14.61 368	67.74 362	36.01 359	18.23 362	72.95 356

Gipuma		54.77 328	52.22 332	62.40 343	86.50 336	59.37 357	50.20 362	90.35 351	36.52 359	41.20 360	49.49 361	18.33 365	81.29 357	32.10 360	65.34 337	46.54 360
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	44.00 22	41.70 332	37.64 337	53.90 346	49.46 367	43.37 364	65.09 361	66.66 367	26.19 364	25.77 365	48.53 362	3.58 372	63.35 364	26.15 362	27.28 360	54.97 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		41.02 333	40.93 335	41.29 347	61.97 362	33.83 367	84.00 353	65.17 368	27.17 362	27.56 362	46.72 363	17.63 366	60.41 365	19.32 363	18.82 361	29.61 361

test_post											46.00 364	87.37 89	97.11 222

test123		16.58 338	19.47 340	7.91 350	3.59 372	5.37 372	94.32 305	1.39 373	2.49 368	13.98 368	44.60 365	2.91 373	2.65 368	11.35 367	0.57 367	15.70 363

EMVS		39.96 334	39.88 336	40.18 348	59.57 365	32.12 369	84.79 350	64.57 369	26.27 363	26.14 364	44.18 366	18.73 364	59.29 366	17.03 364	17.67 363	29.12 362

test_post1							90.74 337				41.37 367	85.38 134	96.36 258	83.16 236

X-MVStestdata		90.69 179	88.66 199	96.77 81	99.62 25	90.66 131	99.43 53	97.58 104	92.41 67	96.86 65	29.59 368	87.37 89	99.87 47	95.65 81	99.43 64	99.78 38

wuyk23d		16.71 337	16.73 341	16.65 349	60.15 363	25.22 371	41.24 363	5.17 371	6.56 366	5.48 369	3.61 369	3.64 371	22.72 367	15.20 365	9.52 365	1.99 365

uanet_test		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

pcd_1.5k_mvsjas		6.87 340	9.16 343	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	82.48 174	0.00 370	0.00 368	0.00 368	0.00 366

sosnet-low-res		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

sosnet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uncertanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

Regformer		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

uanet		0.00 341	0.00 344	0.00 352	0.00 373	0.00 374	0.00 364	0.00 374	0.00 369	0.00 370	0.00 370	0.00 375	0.00 370	0.00 368	0.00 368	0.00 366

eth-test2					0.00 373

eth-test					0.00 373

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 24	97.63 93	95.69 11

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

GSMVS																98.84 134

test_part2					99.54 36	95.42 17				98.13 33

sam_mvs1												88.39 70				98.84 134

sam_mvs												87.08 96

MTGPA								97.45 130

MTMP							99.21 72	91.09 348

test9_res														98.60 19	99.87 7	99.90 20

agg_prior2														97.84 42	99.87 7	99.91 18

agg_prior					99.54 36	92.66 84		97.64 89		97.98 41			99.61 89

test_prior4						92.00 94	99.41 56

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

旧先验2							98.67 138		85.75 227	98.96 12			98.97 142	93.84 117

新几何2							98.26 187

无先验							98.52 156	97.82 55	87.20 204				99.90 40	87.64 187		99.85 29

原ACMM2							98.69 134

testdata2													99.88 44	84.16 224

segment_acmp												90.56 39

testdata1							97.89 211		92.43 63

test12				97.83 31	99.33 59	94.45 47		97.55 110		97.56 48		88.60 65	99.50 103		99.71 34	99.55 77

plane_prior7					93.84 247	85.73 243

plane_prior6					93.92 244	86.02 237						72.92 245

plane_prior5								96.30 211					97.75 194	93.46 124	86.17 222	92.67 231

plane_prior3						85.91 238			93.65 41	86.99 201

plane_prior2							99.02 100		93.38 46

plane_prior1					93.90 246

plane_prior						86.07 235	99.14 87		93.81 39						86.26 221

n2								0.00 374

nn								0.00 374

door-mid								84.90 362

test11								97.68 81

door								85.30 361

HQP5-MVS						86.39 223

HQP-NCC					93.95 240		99.16 78		93.92 31	87.57 194

ACMP_Plane					93.95 240		99.16 78		93.92 31	87.57 194

BP-MVS														93.82 119

HQP4-MVS										87.57 194			97.77 189			92.72 229

HQP3-MVS								96.37 207							86.29 219

HQP2-MVS												73.34 241

MDTV_nov1_ep13_2view						91.17 113	91.38 330		87.45 201	93.08 134		86.67 107		87.02 191		98.95 127

ACMMP++_ref															82.64 250

ACMMP++															83.83 238

Test By Simon												83.62 150

High-resolution multi-view benchmark