High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

FOURS1					99.82 1	98.66 26	99.69 1	98.95 34	97.46 22	99.39 15

zzz-MVS		98.55 31	98.25 39	99.46 15	99.76 2	98.64 27	98.55 158	98.74 107	97.27 38	98.02 99	99.39 16	94.81 80	99.96 1	97.91 46	99.79 21	99.77 22

MTAPA		98.58 24	98.29 36	99.46 15	99.76 2	98.64 27	98.90 84	98.74 107	97.27 38	98.02 99	99.39 16	94.81 80	99.96 1	97.91 46	99.79 21	99.77 22

MSP-MVS		98.74 9	98.55 11	99.29 34	99.75 4	98.23 54	99.26 23	98.88 50	97.52 16	99.41 13	98.78 117	96.00 37	99.79 95	97.79 56	99.59 75	99.85 4
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
MP-MVS		98.33 51	98.01 54	99.28 38	99.75 4	98.18 58	99.22 30	98.79 95	96.13 86	97.92 112	99.23 47	94.54 87	99.94 3	96.74 117	99.78 25	99.73 40
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		98.51 36	98.26 38	99.25 42	99.75 4	98.04 65	99.28 21	98.81 79	96.24 80	98.35 85	99.23 47	95.46 54	99.94 3	97.42 82	99.81 10	99.77 22

HPM-MVS_fast		98.38 44	98.13 47	99.12 60	99.75 4	97.86 73	99.44 7	98.82 73	94.46 166	98.94 43	99.20 54	95.16 72	99.74 110	97.58 72	99.85 3	99.77 22

region2R		98.61 18	98.38 21	99.29 34	99.74 8	98.16 60	99.23 26	98.93 38	96.15 84	98.94 43	99.17 58	95.91 42	99.94 3	97.55 76	99.79 21	99.78 15

ACMMPR		98.59 21	98.36 23	99.29 34	99.74 8	98.15 61	99.23 26	98.95 34	96.10 89	98.93 47	99.19 57	95.70 47	99.94 3	97.62 69	99.79 21	99.78 15

HPM-MVS		98.36 46	98.10 50	99.13 57	99.74 8	97.82 77	99.53 4	98.80 90	94.63 159	98.61 70	98.97 90	95.13 73	99.77 104	97.65 67	99.83 9	99.79 12
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		98.23 55	97.95 57	99.09 62	99.74 8	97.62 84	99.03 60	99.41 6	95.98 91	97.60 132	99.36 28	94.45 92	99.93 18	97.14 90	98.85 128	99.70 52
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
ZNCC-MVS		98.49 37	98.20 45	99.35 25	99.73 12	98.39 39	99.19 36	98.86 63	95.77 98	98.31 88	99.10 72	95.46 54	99.93 18	97.57 75	99.81 10	99.74 35

DVP-MVS		99.03 3	98.83 4	99.63 4	99.72 13	99.25 2	98.97 73	98.58 152	97.62 11	99.45 11	99.46 11	97.42 9	99.94 3	98.47 19	99.81 10	99.69 55
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_SECOND				99.71 1	99.72 13	99.35 1	98.97 73	98.88 50					99.94 3	98.47 19	99.81 10	99.84 6

test0726					99.72 13	99.25 2	99.06 55	98.88 50	97.62 11	99.56 6	99.50 4	97.42 9

GST-MVS		98.43 41	98.12 48	99.34 26	99.72 13	98.38 40	99.09 51	98.82 73	95.71 101	98.73 60	99.06 81	95.27 67	99.93 18	97.07 93	99.63 68	99.72 44

MP-MVS-pluss		98.31 53	97.92 59	99.49 12	99.72 13	98.88 18	98.43 174	98.78 98	94.10 174	97.69 124	99.42 14	95.25 69	99.92 24	98.09 37	99.80 17	99.67 65
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		98.63 17	98.40 19	99.32 31	99.72 13	98.29 51	99.23 26	98.96 32	96.10 89	98.94 43	99.17 58	96.06 33	99.92 24	97.62 69	99.78 25	99.75 30

#test#		98.54 33	98.27 37	99.32 31	99.72 13	98.29 51	98.98 72	98.96 32	95.65 105	98.94 43	99.17 58	96.06 33	99.92 24	97.21 89	99.78 25	99.75 30

PGM-MVS		98.49 37	98.23 43	99.27 41	99.72 13	98.08 64	98.99 69	99.49 5	95.43 115	99.03 37	99.32 35	95.56 50	99.94 3	96.80 113	99.77 28	99.78 15

SED-MVS		99.09 1	98.91 1	99.63 4	99.71 21	99.24 5	99.02 63	98.87 57	97.65 9	99.73 1	99.48 6	97.53 7	99.94 3	98.43 23	99.81 10	99.70 52

IU-MVS					99.71 21	99.23 7		98.64 140	95.28 125	99.63 4				98.35 29	99.81 10	99.83 7

test_241102_ONE					99.71 21	99.24 5		98.87 57	97.62 11	99.73 1	99.39 16	97.53 7	99.74 110

XVS		98.70 10	98.49 17	99.34 26	99.70 24	98.35 48	99.29 19	98.88 50	97.40 25	98.46 76	99.20 54	95.90 43	99.89 38	97.85 52	99.74 43	99.78 15

X-MVStestdata		94.06 268	92.30 288	99.34 26	99.70 24	98.35 48	99.29 19	98.88 50	97.40 25	98.46 76	43.50 367	95.90 43	99.89 38	97.85 52	99.74 43	99.78 15

TSAR-MVS + MP.		98.78 7	98.62 8	99.24 43	99.69 26	98.28 53	99.14 41	98.66 135	96.84 56	99.56 6	99.31 37	96.34 22	99.70 118	98.32 30	99.73 45	99.73 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CSCG		97.85 70	97.74 66	98.20 118	99.67 27	95.16 189	99.22 30	99.32 7	93.04 228	97.02 149	98.92 102	95.36 61	99.91 33	97.43 81	99.64 66	99.52 89

test_one_0601					99.66 28	99.25 2		98.86 63	97.55 15	99.20 25	99.47 8	97.57 6

CP-MVS		98.57 27	98.36 23	99.19 46	99.66 28	97.86 73	99.34 15	98.87 57	95.96 92	98.60 71	99.13 67	96.05 35	99.94 3	97.77 57	99.86 1	99.77 22

CPTT-MVS		97.72 75	97.32 87	98.92 72	99.64 30	97.10 105	99.12 46	98.81 79	92.34 253	98.09 93	99.08 79	93.01 111	99.92 24	96.06 138	99.77 28	99.75 30

test_part2					99.63 31	99.18 10				99.27 20

ACMMP_NAP		98.61 18	98.30 35	99.55 9	99.62 32	98.95 17	98.82 103	98.81 79	95.80 97	99.16 30	99.47 8	95.37 60	99.92 24	97.89 49	99.75 40	99.79 12

MCST-MVS		98.65 14	98.37 22	99.48 13	99.60 33	98.87 19	98.41 177	98.68 124	97.04 51	98.52 74	98.80 115	96.78 15	99.83 59	97.93 45	99.61 71	99.74 35

DPE-MVS		98.92 5	98.67 7	99.65 2	99.58 34	99.20 9	98.42 176	98.91 44	97.58 14	99.54 8	99.46 11	97.10 12	99.94 3	97.64 68	99.84 8	99.83 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		99.02 4	98.84 3	99.55 9	99.57 35	98.96 16	99.39 8	98.93 38	97.38 28	99.41 13	99.54 1	96.66 16	99.84 56	98.86 2	99.85 3	99.87 1

abl_6		98.30 54	98.03 53	99.13 57	99.56 36	97.76 80	99.13 44	98.82 73	96.14 85	99.26 21	99.37 24	93.33 107	99.93 18	96.96 98	99.67 58	99.69 55

SF-MVS		98.59 21	98.32 34	99.41 19	99.54 37	98.71 22	99.04 57	98.81 79	95.12 134	99.32 18	99.39 16	96.22 23	99.84 56	97.72 60	99.73 45	99.67 65

test1172		98.56 29	98.35 25	99.16 53	99.53 38	97.94 71	99.09 51	98.83 71	96.52 70	99.05 36	99.34 33	95.34 62	99.82 67	97.86 51	99.64 66	99.73 40

SR-MVS		98.57 27	98.35 25	99.24 43	99.53 38	98.18 58	99.09 51	98.82 73	96.58 67	99.10 33	99.32 35	95.39 58	99.82 67	97.70 65	99.63 68	99.72 44

DP-MVS Recon		97.86 69	97.46 80	99.06 64	99.53 38	98.35 48	98.33 186	98.89 47	92.62 242	98.05 95	98.94 99	95.34 62	99.65 127	96.04 139	99.42 102	99.19 137

SMA-MVS		98.58 24	98.25 39	99.56 8	99.51 41	99.04 15	98.95 77	98.80 90	93.67 204	99.37 16	99.52 3	96.52 20	99.89 38	98.06 39	99.81 10	99.76 28
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
APD-MVS		98.35 47	98.00 55	99.42 18	99.51 41	98.72 21	98.80 110	98.82 73	94.52 163	99.23 23	99.25 45	95.54 52	99.80 83	96.52 123	99.77 28	99.74 35
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HPM-MVS++		98.58 24	98.25 39	99.55 9	99.50 43	99.08 11	98.72 127	98.66 135	97.51 17	98.15 90	98.83 112	95.70 47	99.92 24	97.53 78	99.67 58	99.66 69

APD-MVS_3200maxsize		98.53 35	98.33 33	99.15 56	99.50 43	97.92 72	99.15 40	98.81 79	96.24 80	99.20 25	99.37 24	95.30 65	99.80 83	97.73 59	99.67 58	99.72 44

114514_t		96.93 118	96.27 131	98.92 72	99.50 43	97.63 83	98.85 96	98.90 45	84.80 348	97.77 117	99.11 70	92.84 112	99.66 126	94.85 175	99.77 28	99.47 102

PAPM_NR		97.46 90	97.11 94	98.50 96	99.50 43	96.41 136	98.63 144	98.60 145	95.18 130	97.06 147	98.06 189	94.26 97	99.57 138	93.80 212	98.87 127	99.52 89

SR-MVS-dyc-post		98.54 33	98.35 25	99.13 57	99.49 47	97.86 73	99.11 47	98.80 90	96.49 71	99.17 28	99.35 30	95.34 62	99.82 67	97.72 60	99.65 62	99.71 48

RE-MVS-def			98.34 29		99.49 47	97.86 73	99.11 47	98.80 90	96.49 71	99.17 28	99.35 30	95.29 66		97.72 60	99.65 62	99.71 48

testtj		98.33 51	97.95 57	99.47 14	99.49 47	98.70 23	98.83 100	98.86 63	95.48 112	98.91 49	99.17 58	95.48 53	99.93 18	95.80 148	99.53 89	99.76 28

9.14			98.06 51		99.47 50		98.71 128	98.82 73	94.36 168	99.16 30	99.29 41	96.05 35	99.81 74	97.00 94	99.71 54

ETH3D-3000-0.1		98.35 47	98.00 55	99.38 20	99.47 50	98.68 25	98.67 138	98.84 68	94.66 158	99.11 32	99.25 45	95.46 54	99.81 74	96.80 113	99.73 45	99.63 77

CDPH-MVS		97.94 64	97.49 78	99.28 38	99.47 50	98.44 36	97.91 239	98.67 132	92.57 245	98.77 56	98.85 108	95.93 41	99.72 112	95.56 158	99.69 56	99.68 61

ZD-MVS					99.46 53	98.70 23		98.79 95	93.21 221	98.67 63	98.97 90	95.70 47	99.83 59	96.07 135	99.58 78

xxxxxxxxxxxxxcwj		98.70 10	98.50 15	99.30 33	99.46 53	98.38 40	98.21 203	98.52 163	97.95 3	99.32 18	99.39 16	96.22 23	99.84 56	97.72 60	99.73 45	99.67 65

save fliter					99.46 53	98.38 40	98.21 203	98.71 117	97.95 3

EI-MVSNet-Vis-set		98.47 39	98.39 20	98.69 81	99.46 53	96.49 131	98.30 194	98.69 121	97.21 41	98.84 51	99.36 28	95.41 57	99.78 99	98.62 6	99.65 62	99.80 11

EI-MVSNet-UG-set		98.41 42	98.34 29	98.61 86	99.45 57	96.32 140	98.28 197	98.68 124	97.17 44	98.74 58	99.37 24	95.25 69	99.79 95	98.57 9	99.54 88	99.73 40

F-COLMAP		97.09 114	96.80 107	97.97 135	99.45 57	94.95 203	98.55 158	98.62 144	93.02 229	96.17 185	98.58 138	94.01 100	99.81 74	93.95 207	98.90 123	99.14 146

Regformer-3		98.59 21	98.50 15	98.86 76	99.43 59	97.05 106	98.40 178	98.68 124	97.43 24	99.06 35	99.31 37	95.80 46	99.77 104	98.62 6	99.76 34	99.78 15

Regformer-4		98.64 15	98.53 12	98.99 66	99.43 59	97.37 92	98.40 178	98.79 95	97.46 22	99.09 34	99.31 37	95.86 45	99.80 83	98.64 4	99.76 34	99.79 12

ETH3 D test6400		97.59 84	97.01 99	99.34 26	99.40 61	98.56 30	98.20 206	98.81 79	91.63 276	98.44 80	98.85 108	93.98 102	99.82 67	94.11 203	99.69 56	99.64 74

Regformer-1		98.66 13	98.51 14	99.12 60	99.35 62	97.81 79	98.37 180	98.76 102	97.49 18	99.20 25	99.21 50	96.08 32	99.79 95	98.42 25	99.73 45	99.75 30

Regformer-2		98.69 12	98.52 13	99.19 46	99.35 62	98.01 67	98.37 180	98.81 79	97.48 19	99.21 24	99.21 50	96.13 30	99.80 83	98.40 27	99.73 45	99.75 30

新几何1				99.16 53	99.34 64	98.01 67		98.69 121	90.06 313	98.13 91	98.95 98	94.60 85	99.89 38	91.97 265	99.47 95	99.59 84

1121		97.37 100	96.77 114	99.16 53	99.34 64	97.99 70	98.19 210	98.68 124	90.14 312	98.01 103	98.97 90	94.80 82	99.87 47	93.36 224	99.46 98	99.61 79

DP-MVS		96.59 130	95.93 142	98.57 88	99.34 64	96.19 146	98.70 132	98.39 191	89.45 323	94.52 215	99.35 30	91.85 133	99.85 53	92.89 241	98.88 125	99.68 61

SD-MVS		98.64 15	98.68 6	98.53 94	99.33 67	98.36 47	98.90 84	98.85 67	97.28 34	99.72 3	99.39 16	96.63 18	97.60 327	98.17 33	99.85 3	99.64 74
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
HyFIR lowres test		96.90 120	96.49 125	98.14 121	99.33 67	95.56 174	97.38 275	99.65 2	92.34 253	97.61 131	98.20 180	89.29 184	99.10 188	96.97 96	97.60 177	99.77 22

OMC-MVS		97.55 88	97.34 86	98.20 118	99.33 67	95.92 162	98.28 197	98.59 147	95.52 111	97.97 106	99.10 72	93.28 109	99.49 149	95.09 171	98.88 125	99.19 137

原ACMM1				98.65 84	99.32 70	96.62 122		98.67 132	93.27 220	97.81 116	98.97 90	95.18 71	99.83 59	93.84 210	99.46 98	99.50 95

CNVR-MVS		98.78 7	98.56 10	99.45 17	99.32 70	98.87 19	98.47 168	98.81 79	97.72 6	98.76 57	99.16 63	97.05 13	99.78 99	98.06 39	99.66 61	99.69 55

TEST9					99.31 72	98.50 34	97.92 237	98.73 111	92.63 241	97.74 120	98.68 127	96.20 26	99.80 83

train_agg		97.97 59	97.52 75	99.33 30	99.31 72	98.50 34	97.92 237	98.73 111	92.98 230	97.74 120	98.68 127	96.20 26	99.80 83	96.59 119	99.57 79	99.68 61

test_prior3		98.22 56	97.90 60	99.19 46	99.31 72	98.22 55	97.80 251	98.84 68	96.12 87	97.89 114	98.69 125	95.96 39	99.70 118	96.89 103	99.60 72	99.65 71

test_prior				99.19 46	99.31 72	98.22 55		98.84 68					99.70 118			99.65 71

PatchMatch-RL		96.59 130	96.03 140	98.27 112	99.31 72	96.51 130	97.91 239	99.06 22	93.72 196	96.92 154	98.06 189	88.50 208	99.65 127	91.77 269	99.00 120	98.66 185

agg_prior1		97.95 63	97.51 77	99.28 38	99.30 77	98.38 40	97.81 250	98.72 113	93.16 224	97.57 133	98.66 130	96.14 29	99.81 74	96.63 118	99.56 84	99.66 69

agg_prior					99.30 77	98.38 40		98.72 113		97.57 133			99.81 74

CHOSEN 1792x2688		97.12 112	96.80 107	98.08 128	99.30 77	94.56 222	98.05 226	99.71 1	93.57 208	97.09 143	98.91 103	88.17 214	99.89 38	96.87 109	99.56 84	99.81 10

test_8					99.29 80	98.44 36	97.89 243	98.72 113	92.98 230	97.70 123	98.66 130	96.20 26	99.80 83

旧先验1					99.29 80	97.48 88		98.70 120			99.09 77	95.56 50			99.47 95	99.61 79

PLC	95.07 4	97.20 108	96.78 110	98.44 102	99.29 80	96.31 142	98.14 217	98.76 102	92.41 251	96.39 180	98.31 168	94.92 79	99.78 99	94.06 205	98.77 132	99.23 132
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
COLMAP_ROB	93.27 12	95.33 188	94.87 189	96.71 211	99.29 80	93.24 268	98.58 150	98.11 241	89.92 315	93.57 259	99.10 72	86.37 252	99.79 95	90.78 283	98.10 159	97.09 233
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
NCCC		98.61 18	98.35 25	99.38 20	99.28 84	98.61 29	98.45 169	98.76 102	97.82 5	98.45 79	98.93 100	96.65 17	99.83 59	97.38 84	99.41 103	99.71 48

PVSNet_Blended_VisFu		97.70 76	97.46 80	98.44 102	99.27 85	95.91 163	98.63 144	99.16 17	94.48 165	97.67 125	98.88 105	92.80 113	99.91 33	97.11 91	99.12 117	99.50 95

MVS_111021_LR		98.34 49	98.23 43	98.67 83	99.27 85	96.90 112	97.95 235	99.58 3	97.14 46	98.44 80	99.01 87	95.03 76	99.62 134	97.91 46	99.75 40	99.50 95

MSLP-MVS++		98.56 29	98.57 9	98.55 90	99.26 87	96.80 116	98.71 128	99.05 24	97.28 34	98.84 51	99.28 42	96.47 21	99.40 158	98.52 17	99.70 55	99.47 102

AllTest		95.24 192	94.65 197	96.99 192	99.25 88	93.21 269	98.59 148	98.18 226	91.36 283	93.52 261	98.77 119	84.67 279	99.72 112	89.70 301	97.87 166	98.02 210

TestCases				96.99 192	99.25 88	93.21 269		98.18 226	91.36 283	93.52 261	98.77 119	84.67 279	99.72 112	89.70 301	97.87 166	98.02 210

PVSNet_BlendedMVS		96.73 125	96.60 120	97.12 186	99.25 88	95.35 184	98.26 200	99.26 8	94.28 169	97.94 109	97.46 242	92.74 114	99.81 74	96.88 106	93.32 253	96.20 319

PVSNet_Blended		97.38 99	97.12 93	98.14 121	99.25 88	95.35 184	97.28 286	99.26 8	93.13 225	97.94 109	98.21 179	92.74 114	99.81 74	96.88 106	99.40 105	99.27 129

DeepC-MVS	95.98 3	97.88 68	97.58 70	98.77 78	99.25 88	96.93 110	98.83 100	98.75 105	96.96 54	96.89 156	99.50 4	90.46 164	99.87 47	97.84 54	99.76 34	99.52 89
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	96.70 1	98.55 31	98.34 29	99.18 50	99.25 88	98.04 65	98.50 165	98.78 98	97.72 6	98.92 48	99.28 42	95.27 67	99.82 67	97.55 76	99.77 28	99.69 55
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OPU-MVS				99.37 23	99.24 94	99.05 14	99.02 63				99.16 63	97.81 3	99.37 160	97.24 87	99.73 45	99.70 52

test222					99.23 95	97.17 104	97.40 273	98.66 135	88.68 329	98.05 95	98.96 96	94.14 98			99.53 89	99.61 79

TSAR-MVS + GP.		98.38 44	98.24 42	98.81 77	99.22 96	97.25 101	98.11 222	98.29 212	97.19 43	98.99 42	99.02 83	96.22 23	99.67 125	98.52 17	98.56 141	99.51 93

SteuartSystems-ACMMP		98.90 6	98.75 5	99.36 24	99.22 96	98.43 38	99.10 50	98.87 57	97.38 28	99.35 17	99.40 15	97.78 5	99.87 47	97.77 57	99.85 3	99.78 15
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		98.47 39	98.34 29	98.88 75	99.22 96	97.32 93	97.91 239	99.58 3	97.20 42	98.33 86	99.00 88	95.99 38	99.64 129	98.05 41	99.76 34	99.69 55

testdata				98.26 114	99.20 99	95.36 182		98.68 124	91.89 268	98.60 71	99.10 72	94.44 93	99.82 67	94.27 197	99.44 100	99.58 86

DVP-MVS++.		99.08 2	98.89 2	99.64 3	99.17 100	99.23 7	99.69 1	98.88 50	97.32 31	99.53 9	99.47 8	97.81 3	99.94 3	98.47 19	99.72 52	99.74 35

MSC_two_6792asdad				99.62 6	99.17 100	99.08 11		98.63 142					99.94 3	98.53 11	99.80 17	99.86 2

No_MVS				99.62 6	99.17 100	99.08 11		98.63 142					99.94 3	98.53 11	99.80 17	99.86 2

PVSNet	91.96 18	96.35 139	96.15 135	96.96 196	99.17 100	92.05 283	96.08 331	98.68 124	93.69 200	97.75 119	97.80 217	88.86 199	99.69 123	94.26 198	99.01 119	99.15 144

test12				99.18 50	99.16 104	98.19 57		98.53 161		98.07 94		95.13 73	99.72 112		99.56 84	99.63 77

AdaColmap		97.15 111	96.70 115	98.48 99	99.16 104	96.69 121	98.01 230	98.89 47	94.44 167	96.83 157	98.68 127	90.69 161	99.76 106	94.36 192	99.29 111	98.98 162

PHI-MVS		98.34 49	98.06 51	99.18 50	99.15 106	98.12 63	99.04 57	99.09 20	93.32 217	98.83 53	99.10 72	96.54 19	99.83 59	97.70 65	99.76 34	99.59 84

TAPA-MVS	93.98 7	95.35 186	94.56 201	97.74 151	99.13 107	94.83 208	98.33 186	98.64 140	86.62 337	96.29 182	98.61 133	94.00 101	99.29 165	80.00 352	99.41 103	99.09 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MG-MVS		97.81 71	97.60 69	98.44 102	99.12 108	95.97 155	97.75 255	98.78 98	96.89 55	98.46 76	99.22 49	93.90 103	99.68 124	94.81 178	99.52 91	99.67 65

Anonymous20231211		94.10 264	93.26 273	96.61 221	99.11 109	94.28 231	99.01 65	98.88 50	86.43 339	92.81 284	97.57 235	81.66 308	98.68 240	94.83 176	89.02 308	96.88 253

ETH3D cwj APD-0.16		97.96 60	97.52 75	99.29 34	99.05 110	98.52 32	98.33 186	98.68 124	93.18 222	98.68 62	99.13 67	94.62 84	99.83 59	96.45 125	99.55 87	99.52 89

CNLPA		97.45 93	97.03 98	98.73 79	99.05 110	97.44 91	98.07 224	98.53 161	95.32 123	96.80 161	98.53 142	93.32 108	99.72 112	94.31 196	99.31 110	99.02 158

DPM-MVS		97.55 88	96.99 101	99.23 45	99.04 112	98.55 31	97.17 294	98.35 198	94.85 149	97.93 111	98.58 138	95.07 75	99.71 117	92.60 245	99.34 108	99.43 110

hse-mvs3		96.17 146	95.62 155	97.81 145	99.03 113	94.45 224	98.64 143	98.75 105	97.48 19	98.67 63	98.72 124	89.76 174	99.86 52	97.95 43	81.59 346	99.11 149

Anonymous20240529		95.10 200	94.22 219	97.75 150	99.01 114	94.26 233	98.87 93	98.83 71	85.79 345	96.64 165	98.97 90	78.73 326	99.85 53	96.27 130	94.89 223	99.12 148

Anonymous202405211		95.28 190	94.49 204	97.67 158	99.00 115	93.75 247	98.70 132	97.04 316	90.66 300	96.49 176	98.80 115	78.13 331	99.83 59	96.21 133	95.36 222	99.44 109

DELS-MVS		98.40 43	98.20 45	98.99 66	99.00 115	97.66 81	97.75 255	98.89 47	97.71 8	98.33 86	98.97 90	94.97 77	99.88 46	98.42 25	99.76 34	99.42 112
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
DeepPCF-MVS	96.37 2	97.93 66	98.48 18	96.30 250	99.00 115	89.54 322	97.43 272	98.87 57	98.16 2	99.26 21	99.38 23	96.12 31	99.64 129	98.30 31	99.77 28	99.72 44

thres100view900		95.38 182	94.70 195	97.41 172	98.98 118	94.92 204	98.87 93	96.90 325	95.38 118	96.61 167	96.88 291	84.29 284	99.56 140	88.11 315	96.29 206	97.76 215

thres600view7		95.49 174	94.77 191	97.67 158	98.98 118	95.02 196	98.85 96	96.90 325	95.38 118	96.63 166	96.90 290	84.29 284	99.59 136	88.65 314	96.33 204	98.40 196

tfpn200view9		95.32 189	94.62 198	97.43 171	98.94 120	94.98 200	98.68 135	96.93 323	95.33 121	96.55 171	96.53 307	84.23 287	99.56 140	88.11 315	96.29 206	97.76 215

thres400		95.38 182	94.62 198	97.65 161	98.94 120	94.98 200	98.68 135	96.93 323	95.33 121	96.55 171	96.53 307	84.23 287	99.56 140	88.11 315	96.29 206	98.40 196

MSDG		95.93 155	95.30 170	97.83 142	98.90 122	95.36 182	96.83 318	98.37 195	91.32 287	94.43 222	98.73 123	90.27 168	99.60 135	90.05 294	98.82 130	98.52 192

RPSCF		94.87 215	95.40 159	93.26 327	98.89 123	82.06 360	98.33 186	98.06 257	90.30 309	96.56 169	99.26 44	87.09 238	99.49 149	93.82 211	96.32 205	98.24 203

VNet		97.79 72	97.40 84	98.96 70	98.88 124	97.55 86	98.63 144	98.93 38	96.74 61	99.02 38	98.84 110	90.33 167	99.83 59	98.53 11	96.66 193	99.50 95

LFMVS		95.86 158	94.98 184	98.47 100	98.87 125	96.32 140	98.84 99	96.02 339	93.40 214	98.62 69	99.20 54	74.99 347	99.63 132	97.72 60	97.20 183	99.46 106

UA-Net		97.96 60	97.62 68	98.98 68	98.86 126	97.47 89	98.89 88	99.08 21	96.67 64	98.72 61	99.54 1	93.15 110	99.81 74	94.87 174	98.83 129	99.65 71

WTY-MVS		97.37 100	96.92 104	98.72 80	98.86 126	96.89 114	98.31 192	98.71 117	95.26 126	97.67 125	98.56 141	92.21 124	99.78 99	95.89 143	96.85 188	99.48 100

IS-MVSNet		97.22 105	96.88 105	98.25 115	98.85 128	96.36 138	99.19 36	97.97 262	95.39 117	97.23 139	98.99 89	91.11 152	98.93 214	94.60 184	98.59 139	99.47 102

test_part1		94.82 216	93.82 246	97.82 144	98.84 129	97.82 77	99.03 60	98.81 79	92.31 257	92.51 296	97.89 205	81.96 305	98.67 241	94.80 179	88.24 315	96.98 239

VDD-MVS		95.82 161	95.23 172	97.61 163	98.84 129	93.98 239	98.68 135	97.40 302	95.02 141	97.95 107	99.34 33	74.37 351	99.78 99	98.64 4	96.80 189	99.08 154

CHOSEN 280x420		97.18 109	97.18 92	97.20 180	98.81 131	93.27 266	95.78 338	99.15 18	95.25 127	96.79 162	98.11 186	92.29 120	99.07 191	98.56 10	99.85 3	99.25 131

thres200		95.25 191	94.57 200	97.28 177	98.81 131	94.92 204	98.20 206	97.11 312	95.24 129	96.54 173	96.22 319	84.58 281	99.53 146	87.93 319	96.50 200	97.39 226

XVG-OURS-SEG-HR		96.51 134	96.34 128	97.02 191	98.77 133	93.76 245	97.79 253	98.50 171	95.45 114	96.94 151	99.09 77	87.87 224	99.55 145	96.76 116	95.83 219	97.74 217

XVG-OURS		96.55 133	96.41 126	96.99 192	98.75 134	93.76 245	97.50 269	98.52 163	95.67 103	96.83 157	99.30 40	88.95 198	99.53 146	95.88 144	96.26 210	97.69 220

test_yl		97.22 105	96.78 110	98.54 92	98.73 135	96.60 125	98.45 169	98.31 204	94.70 152	98.02 99	98.42 153	90.80 158	99.70 118	96.81 111	96.79 190	99.34 116

DCV-MVSNet		97.22 105	96.78 110	98.54 92	98.73 135	96.60 125	98.45 169	98.31 204	94.70 152	98.02 99	98.42 153	90.80 158	99.70 118	96.81 111	96.79 190	99.34 116

CANet		98.05 58	97.76 65	98.90 74	98.73 135	97.27 96	98.35 183	98.78 98	97.37 30	97.72 122	98.96 96	91.53 143	99.92 24	98.79 3	99.65 62	99.51 93

Vis-MVSNet (Re-imp)		96.87 121	96.55 122	97.83 142	98.73 135	95.46 179	99.20 34	98.30 210	94.96 144	96.60 168	98.87 106	90.05 170	98.59 249	93.67 216	98.60 138	99.46 106

PAPR		96.84 122	96.24 133	98.65 84	98.72 139	96.92 111	97.36 279	98.57 153	93.33 216	96.67 164	97.57 235	94.30 96	99.56 140	91.05 280	98.59 139	99.47 102

canonicalmvs		97.67 77	97.23 90	98.98 68	98.70 140	98.38 40	99.34 15	98.39 191	96.76 60	97.67 125	97.40 248	92.26 121	99.49 149	98.28 32	96.28 209	99.08 154

API-MVS		97.41 97	97.25 89	97.91 138	98.70 140	96.80 116	98.82 103	98.69 121	94.53 161	98.11 92	98.28 172	94.50 91	99.57 138	94.12 202	99.49 93	97.37 228

MAR-MVS		96.91 119	96.40 127	98.45 101	98.69 142	96.90 112	98.66 141	98.68 124	92.40 252	97.07 146	97.96 198	91.54 142	99.75 108	93.68 214	98.92 122	98.69 181
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
PS-MVSNAJ		97.73 74	97.77 64	97.62 162	98.68 143	95.58 173	97.34 281	98.51 166	97.29 33	98.66 67	97.88 206	94.51 88	99.90 36	97.87 50	99.17 116	97.39 226

alignmvs		97.56 87	97.07 97	99.01 65	98.66 144	98.37 46	98.83 100	98.06 257	96.74 61	98.00 105	97.65 227	90.80 158	99.48 153	98.37 28	96.56 197	99.19 137

Vis-MVSNet		97.42 96	97.11 94	98.34 109	98.66 144	96.23 143	99.22 30	99.00 27	96.63 66	98.04 97	99.21 50	88.05 219	99.35 161	96.01 141	99.21 112	99.45 108
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		97.46 90	97.28 88	97.99 134	98.64 146	95.38 181	99.33 18	98.31 204	93.61 207	97.19 140	99.07 80	94.05 99	99.23 170	96.89 103	98.43 149	99.37 115

ab-mvs		96.42 137	95.71 150	98.55 90	98.63 147	96.75 119	97.88 244	98.74 107	93.84 188	96.54 173	98.18 182	85.34 269	99.75 108	95.93 142	96.35 203	99.15 144

PCF-MVS	93.45 11	94.68 224	93.43 268	98.42 105	98.62 148	96.77 118	95.48 343	98.20 222	84.63 349	93.34 269	98.32 167	88.55 206	99.81 74	84.80 339	98.96 121	98.68 182
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
xiu_mvs_v2_base		97.66 78	97.70 67	97.56 166	98.61 149	95.46 179	97.44 270	98.46 176	97.15 45	98.65 68	98.15 183	94.33 95	99.80 83	97.84 54	98.66 137	97.41 224

sss		97.39 98	96.98 102	98.61 86	98.60 150	96.61 124	98.22 202	98.93 38	93.97 182	98.01 103	98.48 147	91.98 131	99.85 53	96.45 125	98.15 157	99.39 113

Test_1112_low_res		96.34 140	95.66 154	98.36 108	98.56 151	95.94 158	97.71 257	98.07 252	92.10 263	94.79 209	97.29 253	91.75 135	99.56 140	94.17 200	96.50 200	99.58 86

1112_ss		96.63 127	96.00 141	98.50 96	98.56 151	96.37 137	98.18 214	98.10 243	92.92 233	94.84 205	98.43 151	92.14 126	99.58 137	94.35 193	96.51 199	99.56 88

BH-untuned		95.95 154	95.72 147	96.65 216	98.55 153	92.26 279	98.23 201	97.79 272	93.73 195	94.62 212	98.01 193	88.97 197	99.00 204	93.04 234	98.51 143	98.68 182

LS3D		97.16 110	96.66 119	98.68 82	98.53 154	97.19 103	98.93 81	98.90 45	92.83 238	95.99 190	99.37 24	92.12 127	99.87 47	93.67 216	99.57 79	98.97 163

hse-mvs2		95.71 165	95.30 170	96.93 198	98.50 155	93.53 256	98.36 182	98.10 243	97.48 19	98.67 63	97.99 195	89.76 174	99.02 200	97.95 43	80.91 350	98.22 204

AUN-MVS		94.53 237	93.73 255	96.92 201	98.50 155	93.52 257	98.34 184	98.10 243	93.83 190	95.94 192	97.98 197	85.59 264	99.03 197	94.35 193	80.94 349	98.22 204

baseline1		95.84 159	95.12 177	98.01 133	98.49 157	95.98 150	98.73 123	97.03 317	95.37 120	96.22 183	98.19 181	89.96 172	99.16 176	94.60 184	87.48 323	98.90 169

HY-MVS	93.96 8	96.82 123	96.23 134	98.57 88	98.46 158	97.00 107	98.14 217	98.21 220	93.95 183	96.72 163	97.99 195	91.58 138	99.76 106	94.51 189	96.54 198	98.95 166

ETV-MVS		97.96 60	97.81 63	98.40 106	98.42 159	97.27 96	98.73 123	98.55 157	96.84 56	98.38 83	97.44 245	95.39 58	99.35 161	97.62 69	98.89 124	98.58 191

tttt0517		96.07 148	95.51 158	97.78 147	98.41 160	94.84 206	99.28 21	94.33 358	94.26 171	97.64 129	98.64 132	84.05 291	99.47 154	95.34 162	97.60 177	99.03 157

EIA-MVS		97.75 73	97.58 70	98.27 112	98.38 161	96.44 134	99.01 65	98.60 145	95.88 94	97.26 138	97.53 238	94.97 77	99.33 163	97.38 84	99.20 113	99.05 156

thisisatest0530		96.01 151	95.36 164	97.97 135	98.38 161	95.52 177	98.88 91	94.19 360	94.04 176	97.64 129	98.31 168	83.82 298	99.46 155	95.29 166	97.70 174	98.93 167

GeoE		96.58 132	96.07 137	98.10 127	98.35 163	95.89 165	99.34 15	98.12 238	93.12 226	96.09 186	98.87 106	89.71 176	98.97 205	92.95 237	98.08 160	99.43 110

xiu_mvs_v1_base_debu		97.60 81	97.56 72	97.72 152	98.35 163	95.98 150	97.86 246	98.51 166	97.13 47	99.01 39	98.40 155	91.56 139	99.80 83	98.53 11	98.68 133	97.37 228

xiu_mvs_v1_base		97.60 81	97.56 72	97.72 152	98.35 163	95.98 150	97.86 246	98.51 166	97.13 47	99.01 39	98.40 155	91.56 139	99.80 83	98.53 11	98.68 133	97.37 228

xiu_mvs_v1_base_debi		97.60 81	97.56 72	97.72 152	98.35 163	95.98 150	97.86 246	98.51 166	97.13 47	99.01 39	98.40 155	91.56 139	99.80 83	98.53 11	98.68 133	97.37 228

baseline		97.64 79	97.44 82	98.25 115	98.35 163	96.20 144	99.00 67	98.32 202	96.33 78	98.03 98	99.17 58	91.35 146	99.16 176	98.10 36	98.29 155	99.39 113

BH-w/o		95.38 182	95.08 179	96.26 252	98.34 168	91.79 287	97.70 258	97.43 300	92.87 236	94.24 232	97.22 258	88.66 202	98.84 226	91.55 273	97.70 174	98.16 207

DROMVSNet		98.21 57	98.11 49	98.49 98	98.34 168	97.26 100	99.61 3	98.43 183	96.78 58	98.87 50	98.84 110	93.72 104	99.01 203	98.91 1	99.50 92	99.19 137

MVS_Test		97.28 103	97.00 100	98.13 123	98.33 170	95.97 155	98.74 119	98.07 252	94.27 170	98.44 80	98.07 188	92.48 116	99.26 166	96.43 127	98.19 156	99.16 143

casdiffmvs		97.63 80	97.41 83	98.28 111	98.33 170	96.14 147	98.82 103	98.32 202	96.38 76	97.95 107	99.21 50	91.23 150	99.23 170	98.12 35	98.37 150	99.48 100

diffmvs		97.58 85	97.40 84	98.13 123	98.32 172	95.81 168	98.06 225	98.37 195	96.20 82	98.74 58	98.89 104	91.31 148	99.25 167	98.16 34	98.52 142	99.34 116

BH-RMVSNet		95.92 156	95.32 168	97.69 156	98.32 172	94.64 214	98.19 210	97.45 298	94.56 160	96.03 188	98.61 133	85.02 272	99.12 182	90.68 285	99.06 118	99.30 125

Fast-Effi-MVS+		96.28 143	95.70 151	98.03 132	98.29 174	95.97 155	98.58 150	98.25 218	91.74 271	95.29 198	97.23 257	91.03 155	99.15 179	92.90 239	97.96 163	98.97 163

UGNet		96.78 124	96.30 130	98.19 120	98.24 175	95.89 165	98.88 91	98.93 38	97.39 27	96.81 160	97.84 211	82.60 302	99.90 36	96.53 122	99.49 93	98.79 175
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
MVSTER		96.06 149	95.72 147	97.08 189	98.23 176	95.93 161	98.73 123	98.27 213	94.86 148	95.07 199	98.09 187	88.21 212	98.54 253	96.59 119	93.46 248	96.79 262

ET-MVSNet_ETH3D		94.13 261	92.98 276	97.58 164	98.22 177	96.20 144	97.31 284	95.37 347	94.53 161	79.56 357	97.63 231	86.51 247	97.53 330	96.91 100	90.74 283	99.02 158

GBi-Net		94.49 240	93.80 248	96.56 228	98.21 178	95.00 197	98.82 103	98.18 226	92.46 246	94.09 239	97.07 270	81.16 309	97.95 313	92.08 259	92.14 264	96.72 271

test1		94.49 240	93.80 248	96.56 228	98.21 178	95.00 197	98.82 103	98.18 226	92.46 246	94.09 239	97.07 270	81.16 309	97.95 313	92.08 259	92.14 264	96.72 271

FMVSNet2		94.47 242	93.61 261	97.04 190	98.21 178	96.43 135	98.79 114	98.27 213	92.46 246	93.50 264	97.09 267	81.16 309	98.00 311	91.09 276	91.93 267	96.70 275

Effi-MVS+		97.12 112	96.69 116	98.39 107	98.19 181	96.72 120	97.37 277	98.43 183	93.71 197	97.65 128	98.02 191	92.20 125	99.25 167	96.87 109	97.79 169	99.19 137

mvs_anonymous		96.70 126	96.53 124	97.18 182	98.19 181	93.78 244	98.31 192	98.19 223	94.01 179	94.47 217	98.27 175	92.08 129	98.46 261	97.39 83	97.91 164	99.31 122

LCM-MVSNet-Re		95.22 193	95.32 168	94.91 296	98.18 183	87.85 346	98.75 116	95.66 345	95.11 135	88.96 333	96.85 294	90.26 169	97.65 325	95.65 156	98.44 147	99.22 133

FMVSNet3		94.97 209	94.26 218	97.11 187	98.18 183	96.62 122	98.56 156	98.26 217	93.67 204	94.09 239	97.10 263	84.25 286	98.01 309	92.08 259	92.14 264	96.70 275

CANet_DTU		96.96 117	96.55 122	98.21 117	98.17 185	96.07 149	97.98 233	98.21 220	97.24 40	97.13 142	98.93 100	86.88 243	99.91 33	95.00 173	99.37 107	98.66 185

thisisatest0515		95.61 173	94.89 188	97.76 149	98.15 186	95.15 191	96.77 319	94.41 356	92.95 232	97.18 141	97.43 246	84.78 277	99.45 156	94.63 181	97.73 173	98.68 182

CS-MVS-test		97.90 67	97.83 62	98.11 126	98.14 187	96.49 131	99.35 13	98.40 188	96.31 79	98.27 89	98.31 168	94.42 94	99.05 192	98.07 38	99.20 113	98.80 174

IterMVS-LS		95.46 175	95.21 173	96.22 253	98.12 188	93.72 250	98.32 191	98.13 237	93.71 197	94.26 230	97.31 252	92.24 122	98.10 301	94.63 181	90.12 289	96.84 258
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cl-mvsnet2		94.68 224	94.19 221	96.13 257	98.11 189	93.60 252	96.94 305	98.31 204	92.43 250	93.32 270	96.87 293	86.51 247	98.28 291	94.10 204	91.16 278	96.51 303

VDDNet		95.36 185	94.53 202	97.86 140	98.10 190	95.13 193	98.85 96	97.75 274	90.46 304	98.36 84	99.39 16	73.27 353	99.64 129	97.98 42	96.58 196	98.81 173

MVSFormer		97.57 86	97.49 78	97.84 141	98.07 191	95.76 169	99.47 5	98.40 188	94.98 142	98.79 54	98.83 112	92.34 118	98.41 273	96.91 100	99.59 75	99.34 116

lupinMVS		97.44 94	97.22 91	98.12 125	98.07 191	95.76 169	97.68 259	97.76 273	94.50 164	98.79 54	98.61 133	92.34 118	99.30 164	97.58 72	99.59 75	99.31 122

TAMVS		97.02 115	96.79 109	97.70 155	98.06 193	95.31 186	98.52 160	98.31 204	93.95 183	97.05 148	98.61 133	93.49 106	98.52 255	95.33 163	97.81 168	99.29 127

CDS-MVSNet		96.99 116	96.69 116	97.90 139	98.05 194	95.98 150	98.20 206	98.33 201	93.67 204	96.95 150	98.49 146	93.54 105	98.42 266	95.24 169	97.74 172	99.31 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CS-MVS		97.94 64	97.90 60	98.06 130	98.04 195	96.85 115	99.04 57	98.39 191	96.17 83	98.50 75	98.29 171	94.60 85	99.02 200	98.61 8	99.43 101	98.30 202

ADS-MVSNet2		94.58 233	94.40 213	95.11 291	98.00 196	88.74 334	96.04 332	97.30 305	90.15 310	96.47 177	96.64 304	87.89 222	97.56 329	90.08 292	97.06 184	99.02 158

ADS-MVSNet		95.00 205	94.45 209	96.63 219	98.00 196	91.91 285	96.04 332	97.74 275	90.15 310	96.47 177	96.64 304	87.89 222	98.96 209	90.08 292	97.06 184	99.02 158

IterMVS		94.09 265	93.85 245	94.80 302	97.99 198	90.35 314	97.18 292	98.12 238	93.68 202	92.46 299	97.34 249	84.05 291	97.41 332	92.51 252	91.33 274	96.62 284
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PVSNet_0	88.72 19	91.28 301	90.03 307	95.00 294	97.99 198	87.29 349	94.84 348	98.50 171	92.06 264	89.86 326	95.19 335	79.81 320	99.39 159	92.27 256	69.79 360	98.33 200

IterMVS-SCA-FT		94.11 263	93.87 243	94.85 299	97.98 200	90.56 312	97.18 292	98.11 241	93.75 192	92.58 292	97.48 241	83.97 293	97.41 332	92.48 254	91.30 275	96.58 288

EI-MVSNet		95.96 153	95.83 145	96.36 246	97.93 201	93.70 251	98.12 220	98.27 213	93.70 199	95.07 199	99.02 83	92.23 123	98.54 253	94.68 180	93.46 248	96.84 258

CVMVSNet		95.43 178	96.04 139	93.57 321	97.93 201	83.62 355	98.12 220	98.59 147	95.68 102	96.56 169	99.02 83	87.51 230	97.51 331	93.56 220	97.44 179	99.60 82

PMMVS		96.60 128	96.33 129	97.41 172	97.90 203	93.93 240	97.35 280	98.41 186	92.84 237	97.76 118	97.45 244	91.10 153	99.20 173	96.26 131	97.91 164	99.11 149

Effi-MVS+-dtu		96.29 141	96.56 121	95.51 278	97.89 204	90.22 315	98.80 110	98.10 243	96.57 68	96.45 179	96.66 301	90.81 156	98.91 216	95.72 151	97.99 162	97.40 225

mvs-test1		96.60 128	96.68 118	96.37 245	97.89 204	91.81 286	98.56 156	98.10 243	96.57 68	96.52 175	97.94 200	90.81 156	99.45 156	95.72 151	98.01 161	97.86 214

QAPM		96.29 141	95.40 159	98.96 70	97.85 206	97.60 85	99.23 26	98.93 38	89.76 318	93.11 278	99.02 83	89.11 190	99.93 18	91.99 264	99.62 70	99.34 116

3Dnovator+	94.38 6	97.43 95	96.78 110	99.38 20	97.83 207	98.52 32	99.37 10	98.71 117	97.09 50	92.99 281	99.13 67	89.36 182	99.89 38	96.97 96	99.57 79	99.71 48

ACMH+	92.99 14	94.30 250	93.77 251	95.88 268	97.81 208	92.04 284	98.71 128	98.37 195	93.99 181	90.60 321	98.47 148	80.86 314	99.05 192	92.75 243	92.40 263	96.55 294

3Dnovator	94.51 5	97.46 90	96.93 103	99.07 63	97.78 209	97.64 82	99.35 13	99.06 22	97.02 52	93.75 255	99.16 63	89.25 185	99.92 24	97.22 88	99.75 40	99.64 74

miper_lstm_enhance		94.33 248	94.07 229	95.11 291	97.75 210	90.97 304	97.22 289	98.03 259	91.67 275	92.76 286	96.97 283	90.03 171	97.78 323	92.51 252	89.64 295	96.56 292

cl_fuxian		94.79 219	94.43 211	95.89 267	97.75 210	93.12 272	97.16 295	98.03 259	92.23 259	93.46 266	97.05 275	91.39 144	98.01 309	93.58 219	89.21 304	96.53 297

TR-MVS		94.94 212	94.20 220	97.17 183	97.75 210	94.14 236	97.59 265	97.02 319	92.28 258	95.75 193	97.64 229	83.88 295	98.96 209	89.77 298	96.15 214	98.40 196

Fast-Effi-MVS+-dtu		95.87 157	95.85 144	95.91 265	97.74 213	91.74 290	98.69 134	98.15 234	95.56 108	94.92 203	97.68 226	88.98 196	98.79 232	93.19 229	97.78 170	97.20 232

MIMVSNet		93.26 282	92.21 289	96.41 243	97.73 214	93.13 271	95.65 340	97.03 317	91.27 291	94.04 242	96.06 322	75.33 345	97.19 335	86.56 325	96.23 212	98.92 168

miper_ehance_all_eth		95.01 204	94.69 196	95.97 262	97.70 215	93.31 265	97.02 301	98.07 252	92.23 259	93.51 263	96.96 285	91.85 133	98.15 297	93.68 214	91.16 278	96.44 310

SCA		95.46 175	95.13 176	96.46 240	97.67 216	91.29 300	97.33 282	97.60 282	94.68 155	96.92 154	97.10 263	83.97 293	98.89 220	92.59 247	98.32 154	99.20 134

ACMP	93.49 10	95.34 187	94.98 184	96.43 242	97.67 216	93.48 258	98.73 123	98.44 180	94.94 147	92.53 294	98.53 142	84.50 283	99.14 180	95.48 161	94.00 237	96.66 281
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
eth_miper_zixun_eth		94.68 224	94.41 212	95.47 280	97.64 218	91.71 291	96.73 322	98.07 252	92.71 240	93.64 256	97.21 259	90.54 163	98.17 296	93.38 222	89.76 293	96.54 295

ACMH	92.88 16	94.55 235	93.95 238	96.34 248	97.63 219	93.26 267	98.81 109	98.49 175	93.43 213	89.74 327	98.53 142	81.91 306	99.08 190	93.69 213	93.30 254	96.70 275
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMM	93.85 9	95.69 168	95.38 163	96.61 221	97.61 220	93.84 243	98.91 83	98.44 180	95.25 127	94.28 229	98.47 148	86.04 259	99.12 182	95.50 160	93.95 239	96.87 255
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Patchmatch-test		94.42 244	93.68 259	96.63 219	97.60 221	91.76 288	94.83 349	97.49 295	89.45 323	94.14 237	97.10 263	88.99 193	98.83 228	85.37 335	98.13 158	99.29 127

RRT_test8_iter05		94.56 234	94.19 221	95.67 275	97.60 221	91.34 296	98.93 81	98.42 185	94.75 151	93.39 267	97.87 207	79.00 325	98.61 245	96.78 115	90.99 281	97.07 234

cl-mvsnet____		94.51 239	94.01 233	96.02 259	97.58 223	93.40 262	97.05 299	97.96 264	91.73 273	92.76 286	97.08 269	89.06 192	98.13 299	92.61 244	90.29 288	96.52 300

tpm cat1		93.36 277	92.80 279	95.07 293	97.58 223	87.97 344	96.76 320	97.86 270	82.17 353	93.53 260	96.04 323	86.13 255	99.13 181	89.24 309	95.87 218	98.10 208

MVS-HIRNet		89.46 318	88.40 317	92.64 330	97.58 223	82.15 359	94.16 354	93.05 365	75.73 359	90.90 317	82.52 360	79.42 322	98.33 281	83.53 344	98.68 133	97.43 223

PatchmatchNet		95.71 165	95.52 157	96.29 251	97.58 223	90.72 309	96.84 317	97.52 291	94.06 175	97.08 144	96.96 285	89.24 186	98.90 219	92.03 263	98.37 150	99.26 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cl-mvsnet1		94.52 238	94.03 230	95.99 260	97.57 227	93.38 263	97.05 299	97.94 265	91.74 271	92.81 284	97.10 263	89.12 189	98.07 305	92.60 245	90.30 287	96.53 297

tpmrst		95.63 170	95.69 152	95.44 282	97.54 228	88.54 337	96.97 303	97.56 284	93.50 210	97.52 135	96.93 289	89.49 178	99.16 176	95.25 168	96.42 202	98.64 187

FMVSNet1		93.19 285	92.07 290	96.56 228	97.54 228	95.00 197	98.82 103	98.18 226	90.38 307	92.27 302	97.07 270	73.68 352	97.95 313	89.36 308	91.30 275	96.72 271

miper_enhance_ethall		95.10 200	94.75 193	96.12 258	97.53 230	93.73 249	96.61 325	98.08 250	92.20 262	93.89 247	96.65 303	92.44 117	98.30 286	94.21 199	91.16 278	96.34 313

CLD-MVS		95.62 171	95.34 165	96.46 240	97.52 231	93.75 247	97.27 287	98.46 176	95.53 109	94.42 223	98.00 194	86.21 254	98.97 205	96.25 132	94.37 224	96.66 281
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MDTV_nov1_ep13			95.40 159		97.48 232	88.34 340	96.85 316	97.29 306	93.74 194	97.48 136	97.26 254	89.18 187	99.05 192	91.92 266	97.43 180

IB-MVS	91.98 17	93.27 281	91.97 292	97.19 181	97.47 233	93.41 261	97.09 298	95.99 340	93.32 217	92.47 298	95.73 327	78.06 332	99.53 146	94.59 186	82.98 341	98.62 188
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_0304		92.81 289	92.01 291	95.23 286	97.46 234	91.33 298	98.17 215	98.81 79	91.13 296	93.80 253	95.68 332	66.08 360	98.06 306	90.79 282	96.13 215	96.32 316

tpmvs		94.60 230	94.36 214	95.33 285	97.46 234	88.60 336	96.88 314	97.68 276	91.29 289	93.80 253	96.42 312	88.58 203	99.24 169	91.06 278	96.04 217	98.17 206

LPG-MVS_test		95.62 171	95.34 165	96.47 237	97.46 234	93.54 254	98.99 69	98.54 159	94.67 156	94.36 225	98.77 119	85.39 266	99.11 185	95.71 153	94.15 232	96.76 266

LGP-MVS_train				96.47 237	97.46 234	93.54 254		98.54 159	94.67 156	94.36 225	98.77 119	85.39 266	99.11 185	95.71 153	94.15 232	96.76 266

jason		97.32 102	97.08 96	98.06 130	97.45 238	95.59 172	97.87 245	97.91 268	94.79 150	98.55 73	98.83 112	91.12 151	99.23 170	97.58 72	99.60 72	99.34 116
jason: jason.
HQP_MVS		96.14 147	95.90 143	96.85 204	97.42 239	94.60 220	98.80 110	98.56 155	97.28 34	95.34 195	98.28 172	87.09 238	99.03 197	96.07 135	94.27 226	96.92 244

plane_prior7					97.42 239	94.63 215

ITE_SJBPF				95.44 282	97.42 239	91.32 299		97.50 293	95.09 138	93.59 257	98.35 161	81.70 307	98.88 222	89.71 300	93.39 252	96.12 321

LTVRE_ROB	92.95 15	94.60 230	93.90 241	96.68 215	97.41 242	94.42 226	98.52 160	98.59 147	91.69 274	91.21 314	98.35 161	84.87 275	99.04 196	91.06 278	93.44 251	96.60 286
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
plane_prior1					97.37 243

plane_prior6					97.35 244	94.61 218						87.09 238

DWT-MVSNet_test		94.82 216	94.36 214	96.20 254	97.35 244	90.79 307	98.34 184	96.57 338	92.91 234	95.33 197	96.44 311	82.00 304	99.12 182	94.52 188	95.78 220	98.70 180

dp		94.15 260	93.90 241	94.90 297	97.31 246	86.82 351	96.97 303	97.19 311	91.22 293	96.02 189	96.61 306	85.51 265	99.02 200	90.00 296	94.30 225	98.85 170

NP-MVS					97.28 247	94.51 223					97.73 220

CostFormer		94.95 210	94.73 194	95.60 277	97.28 247	89.06 329	97.53 268	96.89 327	89.66 320	96.82 159	96.72 299	86.05 257	98.95 213	95.53 159	96.13 215	98.79 175

VPA-MVSNet		95.75 163	95.11 178	97.69 156	97.24 249	97.27 96	98.94 79	99.23 12	95.13 133	95.51 194	97.32 251	85.73 261	98.91 216	97.33 86	89.55 298	96.89 252

tpm2		94.19 257	93.76 253	95.46 281	97.23 250	89.04 330	97.31 284	96.85 331	87.08 336	96.21 184	96.79 297	83.75 299	98.74 235	92.43 255	96.23 212	98.59 189

EPMVS		94.99 206	94.48 205	96.52 233	97.22 251	91.75 289	97.23 288	91.66 366	94.11 173	97.28 137	96.81 296	85.70 262	98.84 226	93.04 234	97.28 182	98.97 163

FMVSNet5		91.81 296	90.92 299	94.49 310	97.21 252	92.09 281	98.00 232	97.55 289	89.31 325	90.86 318	95.61 333	74.48 349	95.32 355	85.57 332	89.70 294	96.07 323

HQP-NCC					97.20 253		98.05 226		96.43 73	94.45 218

ACMP_Plane					97.20 253		98.05 226		96.43 73	94.45 218

HQP-MVS		95.72 164	95.40 159	96.69 214	97.20 253	94.25 234	98.05 226	98.46 176	96.43 73	94.45 218	97.73 220	86.75 244	98.96 209	95.30 164	94.18 230	96.86 257

UniMVSNet_ETH3D		94.24 254	93.33 270	96.97 195	97.19 256	93.38 263	98.74 119	98.57 153	91.21 294	93.81 252	98.58 138	72.85 354	98.77 234	95.05 172	93.93 240	98.77 177

OpenMVS	93.04 13	95.83 160	95.00 182	98.32 110	97.18 257	97.32 93	99.21 33	98.97 30	89.96 314	91.14 315	99.05 82	86.64 246	99.92 24	93.38 222	99.47 95	97.73 218

VPNet		94.99 206	94.19 221	97.40 174	97.16 258	96.57 127	98.71 128	98.97 30	95.67 103	94.84 205	98.24 178	80.36 317	98.67 241	96.46 124	87.32 326	96.96 241

GA-MVS		94.81 218	94.03 230	97.14 184	97.15 259	93.86 242	96.76 320	97.58 283	94.00 180	94.76 210	97.04 276	80.91 312	98.48 257	91.79 268	96.25 211	99.09 151

FIs		96.51 134	96.12 136	97.67 158	97.13 260	97.54 87	99.36 11	99.22 14	95.89 93	94.03 243	98.35 161	91.98 131	98.44 264	96.40 128	92.76 260	97.01 237

1314		96.25 145	95.73 146	97.79 146	97.13 260	95.55 176	98.19 210	98.59 147	93.47 211	92.03 307	97.82 215	91.33 147	99.49 149	94.62 183	98.44 147	98.32 201

D2MVS		95.18 196	95.08 179	95.48 279	97.10 262	92.07 282	98.30 194	99.13 19	94.02 178	92.90 282	96.73 298	89.48 179	98.73 236	94.48 190	93.60 247	95.65 332

DeepMVS_CX				86.78 339	97.09 263	72.30 365		95.17 351	75.92 358	84.34 352	95.19 335	70.58 355	95.35 353	79.98 353	89.04 307	92.68 357

RRT_MVS		96.04 150	95.53 156	97.56 166	97.07 264	97.32 93	98.57 155	98.09 248	95.15 132	95.02 201	98.44 150	88.20 213	98.58 251	96.17 134	93.09 257	96.79 262

PAPM		94.95 210	94.00 234	97.78 147	97.04 265	95.65 171	96.03 334	98.25 218	91.23 292	94.19 235	97.80 217	91.27 149	98.86 225	82.61 346	97.61 176	98.84 172

CR-MVSNet		94.76 221	94.15 225	96.59 224	97.00 266	93.43 259	94.96 345	97.56 284	92.46 246	96.93 152	96.24 315	88.15 215	97.88 321	87.38 321	96.65 194	98.46 194

RPMNet		92.81 289	91.34 297	97.24 178	97.00 266	93.43 259	94.96 345	98.80 90	82.27 352	96.93 152	92.12 354	86.98 241	99.82 67	76.32 360	96.65 194	98.46 194

UniMVSNet (Re)		95.78 162	95.19 174	97.58 164	96.99 268	97.47 89	98.79 114	99.18 16	95.60 106	93.92 246	97.04 276	91.68 136	98.48 257	95.80 148	87.66 322	96.79 262

FC-MVSNet-test		96.42 137	96.05 138	97.53 168	96.95 269	97.27 96	99.36 11	99.23 12	95.83 96	93.93 245	98.37 159	92.00 130	98.32 282	96.02 140	92.72 261	97.00 238

tfpnnormal		93.66 273	92.70 282	96.55 231	96.94 270	95.94 158	98.97 73	99.19 15	91.04 297	91.38 313	97.34 249	84.94 274	98.61 245	85.45 334	89.02 308	95.11 340

TESTMET0.1,1		94.18 259	93.69 258	95.63 276	96.92 271	89.12 328	96.91 308	94.78 353	93.17 223	94.88 204	96.45 310	78.52 327	98.92 215	93.09 231	98.50 144	98.85 170

TinyColmap		92.31 294	91.53 295	94.65 306	96.92 271	89.75 318	96.92 306	96.68 335	90.45 305	89.62 328	97.85 210	76.06 343	98.81 230	86.74 324	92.51 262	95.41 334

cascas		94.63 229	93.86 244	96.93 198	96.91 273	94.27 232	96.00 335	98.51 166	85.55 346	94.54 214	96.23 317	84.20 289	98.87 223	95.80 148	96.98 187	97.66 221

nrg030		96.28 143	95.72 147	97.96 137	96.90 274	98.15 61	99.39 8	98.31 204	95.47 113	94.42 223	98.35 161	92.09 128	98.69 237	97.50 80	89.05 306	97.04 236

MVS		94.67 227	93.54 264	98.08 128	96.88 275	96.56 128	98.19 210	98.50 171	78.05 357	92.69 289	98.02 191	91.07 154	99.63 132	90.09 291	98.36 152	98.04 209

WR-MVS_H		95.05 203	94.46 207	96.81 206	96.86 276	95.82 167	99.24 25	99.24 10	93.87 187	92.53 294	96.84 295	90.37 165	98.24 293	93.24 227	87.93 319	96.38 312

UniMVSNet_NR-MVSNet		95.71 165	95.15 175	97.40 174	96.84 277	96.97 108	98.74 119	99.24 10	95.16 131	93.88 248	97.72 222	91.68 136	98.31 284	95.81 146	87.25 327	96.92 244

USDC		93.33 280	92.71 281	95.21 287	96.83 278	90.83 306	96.91 308	97.50 293	93.84 188	90.72 319	98.14 184	77.69 334	98.82 229	89.51 305	93.21 256	95.97 325

test-LLR		95.10 200	94.87 189	95.80 270	96.77 279	89.70 319	96.91 308	95.21 348	95.11 135	94.83 207	95.72 329	87.71 226	98.97 205	93.06 232	98.50 144	98.72 178

test-mter		94.08 266	93.51 265	95.80 270	96.77 279	89.70 319	96.91 308	95.21 348	92.89 235	94.83 207	95.72 329	77.69 334	98.97 205	93.06 232	98.50 144	98.72 178

Patchmtry		93.22 283	92.35 287	95.84 269	96.77 279	93.09 273	94.66 350	97.56 284	87.37 335	92.90 282	96.24 315	88.15 215	97.90 317	87.37 322	90.10 290	96.53 297

gg-mvs-nofinetune		92.21 295	90.58 302	97.13 185	96.75 282	95.09 194	95.85 336	89.40 369	85.43 347	94.50 216	81.98 361	80.80 315	98.40 279	92.16 257	98.33 153	97.88 212

XXY-MVS		95.20 195	94.45 209	97.46 169	96.75 282	96.56 128	98.86 95	98.65 139	93.30 219	93.27 271	98.27 175	84.85 276	98.87 223	94.82 177	91.26 277	96.96 241

CP-MVSNet		94.94 212	94.30 216	96.83 205	96.72 284	95.56 174	99.11 47	98.95 34	93.89 185	92.42 300	97.90 203	87.19 236	98.12 300	94.32 195	88.21 316	96.82 261

PatchT		93.06 287	91.97 292	96.35 247	96.69 285	92.67 276	94.48 351	97.08 313	86.62 337	97.08 144	92.23 353	87.94 221	97.90 317	78.89 356	96.69 192	98.49 193

PS-CasMVS		94.67 227	93.99 236	96.71 211	96.68 286	95.26 187	99.13 44	99.03 25	93.68 202	92.33 301	97.95 199	85.35 268	98.10 301	93.59 218	88.16 318	96.79 262

WR-MVS		95.15 197	94.46 207	97.22 179	96.67 287	96.45 133	98.21 203	98.81 79	94.15 172	93.16 274	97.69 223	87.51 230	98.30 286	95.29 166	88.62 312	96.90 251

baseline2		95.11 199	94.52 203	96.87 203	96.65 288	93.56 253	98.27 199	94.10 362	93.45 212	92.02 308	97.43 246	87.45 234	99.19 174	93.88 209	97.41 181	97.87 213

test_0402		91.32 300	90.27 305	94.48 311	96.60 289	91.12 302	98.50 165	97.22 310	86.10 342	88.30 338	96.98 282	77.65 336	97.99 312	78.13 358	92.94 259	94.34 347

TransMVSNet (Re)		92.67 291	91.51 296	96.15 255	96.58 290	94.65 213	98.90 84	96.73 332	90.86 299	89.46 331	97.86 208	85.62 263	98.09 303	86.45 326	81.12 347	95.71 330

XVG-ACMP-BASELINE		94.54 236	94.14 226	95.75 273	96.55 291	91.65 292	98.11 222	98.44 180	94.96 144	94.22 233	97.90 203	79.18 324	99.11 185	94.05 206	93.85 241	96.48 307

DU-MVS		95.42 179	94.76 192	97.40 174	96.53 292	96.97 108	98.66 141	98.99 29	95.43 115	93.88 248	97.69 223	88.57 204	98.31 284	95.81 146	87.25 327	96.92 244

NR-MVSNet		94.98 208	94.16 224	97.44 170	96.53 292	97.22 102	98.74 119	98.95 34	94.96 144	89.25 332	97.69 223	89.32 183	98.18 295	94.59 186	87.40 325	96.92 244

tpm		94.13 261	93.80 248	95.12 290	96.50 294	87.91 345	97.44 270	95.89 344	92.62 242	96.37 181	96.30 314	84.13 290	98.30 286	93.24 227	91.66 271	99.14 146

pm-mvs1		93.94 271	93.06 275	96.59 224	96.49 295	95.16 189	98.95 77	98.03 259	92.32 255	91.08 316	97.84 211	84.54 282	98.41 273	92.16 257	86.13 338	96.19 320

JIA-IIPM		93.35 278	92.49 285	95.92 264	96.48 296	90.65 310	95.01 344	96.96 321	85.93 343	96.08 187	87.33 358	87.70 228	98.78 233	91.35 275	95.58 221	98.34 199

TranMVSNet+NR-MVSNet		95.14 198	94.48 205	97.11 187	96.45 297	96.36 138	99.03 60	99.03 25	95.04 140	93.58 258	97.93 201	88.27 211	98.03 308	94.13 201	86.90 332	96.95 243

testgi		93.06 287	92.45 286	94.88 298	96.43 298	89.90 316	98.75 116	97.54 290	95.60 106	91.63 312	97.91 202	74.46 350	97.02 337	86.10 328	93.67 243	97.72 219

v10		94.29 251	93.55 263	96.51 234	96.39 299	94.80 210	98.99 69	98.19 223	91.35 285	93.02 280	96.99 281	88.09 217	98.41 273	90.50 287	88.41 314	96.33 315

v8		94.47 242	93.77 251	96.57 227	96.36 300	94.83 208	99.05 56	98.19 223	91.92 267	93.16 274	96.97 283	88.82 201	98.48 257	91.69 271	87.79 320	96.39 311

GG-mvs-BLEND				96.59 224	96.34 301	94.98 200	96.51 328	88.58 370		93.10 279	94.34 345	80.34 318	98.05 307	89.53 304	96.99 186	96.74 268

V42		94.78 220	94.14 226	96.70 213	96.33 302	95.22 188	98.97 73	98.09 248	92.32 255	94.31 228	97.06 273	88.39 209	98.55 252	92.90 239	88.87 310	96.34 313

PEN-MVS		94.42 244	93.73 255	96.49 235	96.28 303	94.84 206	99.17 38	99.00 27	93.51 209	92.23 303	97.83 214	86.10 256	97.90 317	92.55 250	86.92 331	96.74 268

v1144		94.59 232	93.92 239	96.60 223	96.21 304	94.78 212	98.59 148	98.14 236	91.86 270	94.21 234	97.02 278	87.97 220	98.41 273	91.72 270	89.57 296	96.61 285

Baseline_NR-MVSNet		94.35 247	93.81 247	95.96 263	96.20 305	94.05 238	98.61 147	96.67 336	91.44 281	93.85 250	97.60 232	88.57 204	98.14 298	94.39 191	86.93 330	95.68 331

MS-PatchMatch		93.84 272	93.63 260	94.46 313	96.18 306	89.45 323	97.76 254	98.27 213	92.23 259	92.13 305	97.49 240	79.50 321	98.69 237	89.75 299	99.38 106	95.25 336

v2v482		94.69 222	94.03 230	96.65 216	96.17 307	94.79 211	98.67 138	98.08 250	92.72 239	94.00 244	97.16 261	87.69 229	98.45 262	92.91 238	88.87 310	96.72 271

EPNet_dtu		95.21 194	94.95 186	95.99 260	96.17 307	90.45 313	98.16 216	97.27 308	96.77 59	93.14 277	98.33 166	90.34 166	98.42 266	85.57 332	98.81 131	99.09 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		95.69 168	95.33 167	96.76 208	96.16 309	94.63 215	98.43 174	98.39 191	96.64 65	95.02 201	98.78 117	85.15 271	99.05 192	95.21 170	94.20 229	96.60 286
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
v1192		94.32 249	93.58 262	96.53 232	96.10 310	94.45 224	98.50 165	98.17 231	91.54 278	94.19 235	97.06 273	86.95 242	98.43 265	90.14 290	89.57 296	96.70 275

v148		94.29 251	93.76 253	95.91 265	96.10 310	92.93 274	98.58 150	97.97 262	92.59 244	93.47 265	96.95 287	88.53 207	98.32 282	92.56 249	87.06 329	96.49 306

v144192		94.39 246	93.70 257	96.48 236	96.06 312	94.35 230	98.58 150	98.16 233	91.45 280	94.33 227	97.02 278	87.50 232	98.45 262	91.08 277	89.11 305	96.63 283

DTE-MVSNet		93.98 270	93.26 273	96.14 256	96.06 312	94.39 228	99.20 34	98.86 63	93.06 227	91.78 309	97.81 216	85.87 260	97.58 328	90.53 286	86.17 336	96.46 309

v1240		94.06 268	93.29 272	96.34 248	96.03 314	93.90 241	98.44 172	98.17 231	91.18 295	94.13 238	97.01 280	86.05 257	98.42 266	89.13 311	89.50 300	96.70 275

v1921920		94.20 256	93.47 267	96.40 244	95.98 315	94.08 237	98.52 160	98.15 234	91.33 286	94.25 231	97.20 260	86.41 251	98.42 266	90.04 295	89.39 302	96.69 280

EU-MVSNet		93.66 273	94.14 226	92.25 333	95.96 316	83.38 356	98.52 160	98.12 238	94.69 154	92.61 291	98.13 185	87.36 235	96.39 350	91.82 267	90.00 291	96.98 239

v7n		94.19 257	93.43 268	96.47 237	95.90 317	94.38 229	99.26 23	98.34 200	91.99 265	92.76 286	97.13 262	88.31 210	98.52 255	89.48 306	87.70 321	96.52 300

gm-plane-assit					95.88 318	87.47 347			89.74 319		96.94 288		99.19 174	93.32 226

LF4IMVS		93.14 286	92.79 280	94.20 316	95.88 318	88.67 335	97.66 261	97.07 314	93.81 191	91.71 310	97.65 227	77.96 333	98.81 230	91.47 274	91.92 268	95.12 339

PS-MVSNAJss		96.43 136	96.26 132	96.92 201	95.84 320	95.08 195	99.16 39	98.50 171	95.87 95	93.84 251	98.34 165	94.51 88	98.61 245	96.88 106	93.45 250	97.06 235

pmmvs4		94.69 222	93.99 236	96.81 206	95.74 321	95.94 158	97.40 273	97.67 277	90.42 306	93.37 268	97.59 233	89.08 191	98.20 294	92.97 236	91.67 270	96.30 317

test_djsdf		96.00 152	95.69 152	96.93 198	95.72 322	95.49 178	99.47 5	98.40 188	94.98 142	94.58 213	97.86 208	89.16 188	98.41 273	96.91 100	94.12 234	96.88 253

SixPastTwentyTwo		93.34 279	92.86 278	94.75 303	95.67 323	89.41 325	98.75 116	96.67 336	93.89 185	90.15 325	98.25 177	80.87 313	98.27 292	90.90 281	90.64 284	96.57 290

K. test v3		92.55 292	91.91 294	94.48 311	95.64 324	89.24 326	99.07 54	94.88 352	94.04 176	86.78 343	97.59 233	77.64 337	97.64 326	92.08 259	89.43 301	96.57 290

OurMVSNet-221017-0		94.21 255	94.00 234	94.85 299	95.60 325	89.22 327	98.89 88	97.43 300	95.29 124	92.18 304	98.52 145	82.86 301	98.59 249	93.46 221	91.76 269	96.74 268

mvs_tets		95.41 181	95.00 182	96.65 216	95.58 326	94.42 226	99.00 67	98.55 157	95.73 100	93.21 273	98.38 158	83.45 300	98.63 244	97.09 92	94.00 237	96.91 249

Gipuma		78.40 327	76.75 330	83.38 343	95.54 327	80.43 361	79.42 364	97.40 302	64.67 362	73.46 360	80.82 362	45.65 366	93.14 361	66.32 363	87.43 324	76.56 364
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test0.0.03 1		94.08 266	93.51 265	95.80 270	95.53 328	92.89 275	97.38 275	95.97 341	95.11 135	92.51 296	96.66 301	87.71 226	96.94 339	87.03 323	93.67 243	97.57 222

pmmvs5		93.65 275	92.97 277	95.68 274	95.49 329	92.37 278	98.20 206	97.28 307	89.66 320	92.58 292	97.26 254	82.14 303	98.09 303	93.18 230	90.95 282	96.58 288

N_pmnet		87.12 323	87.77 322	85.17 342	95.46 330	61.92 369	97.37 277	70.66 375	85.83 344	88.73 337	96.04 323	85.33 270	97.76 324	80.02 351	90.48 285	95.84 327

our_test_3		93.65 275	93.30 271	94.69 304	95.45 331	89.68 321	96.91 308	97.65 278	91.97 266	91.66 311	96.88 291	89.67 177	97.93 316	88.02 318	91.49 272	96.48 307

ppachtmachnet_test		93.22 283	92.63 283	94.97 295	95.45 331	90.84 305	96.88 314	97.88 269	90.60 301	92.08 306	97.26 254	88.08 218	97.86 322	85.12 336	90.33 286	96.22 318

jajsoiax		95.45 177	95.03 181	96.73 210	95.42 333	94.63 215	99.14 41	98.52 163	95.74 99	93.22 272	98.36 160	83.87 296	98.65 243	96.95 99	94.04 235	96.91 249

MDA-MVSNet-bldmvs		89.97 313	88.35 318	94.83 301	95.21 334	91.34 296	97.64 262	97.51 292	88.36 331	71.17 363	96.13 321	79.22 323	96.63 347	83.65 343	86.27 335	96.52 300

anonymousdsp		95.42 179	94.91 187	96.94 197	95.10 335	95.90 164	99.14 41	98.41 186	93.75 192	93.16 274	97.46 242	87.50 232	98.41 273	95.63 157	94.03 236	96.50 305

EPNet		97.28 103	96.87 106	98.51 95	94.98 336	96.14 147	98.90 84	97.02 319	98.28 1	95.99 190	99.11 70	91.36 145	99.89 38	96.98 95	99.19 115	99.50 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVP-Stereo		94.28 253	93.92 239	95.35 284	94.95 337	92.60 277	97.97 234	97.65 278	91.61 277	90.68 320	97.09 267	86.32 253	98.42 266	89.70 301	99.34 108	95.02 343
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
lessismore_v0				94.45 314	94.93 338	88.44 339		91.03 367		86.77 344	97.64 229	76.23 342	98.42 266	90.31 289	85.64 339	96.51 303

MDA-MVSNet_test_wron		90.71 307	89.38 312	94.68 305	94.83 339	90.78 308	97.19 291	97.46 296	87.60 333	72.41 362	95.72 329	86.51 247	96.71 345	85.92 330	86.80 333	96.56 292

YYNet1		90.70 308	89.39 311	94.62 307	94.79 340	90.65 310	97.20 290	97.46 296	87.54 334	72.54 361	95.74 326	86.51 247	96.66 346	86.00 329	86.76 334	96.54 295

EG-PatchMatch MVS		91.13 303	90.12 306	94.17 318	94.73 341	89.00 331	98.13 219	97.81 271	89.22 326	85.32 350	96.46 309	67.71 357	98.42 266	87.89 320	93.82 242	95.08 341

pmmvs6		91.77 297	90.63 301	95.17 289	94.69 342	91.24 301	98.67 138	97.92 267	86.14 341	89.62 328	97.56 237	75.79 344	98.34 280	90.75 284	84.56 340	95.94 326

bset_n11_16_dypcd		94.89 214	94.27 217	96.76 208	94.41 343	95.15 191	95.67 339	95.64 346	95.53 109	94.65 211	97.52 239	87.10 237	98.29 289	96.58 121	91.35 273	96.83 260

new_pmnet		90.06 312	89.00 316	93.22 328	94.18 344	88.32 341	96.42 330	96.89 327	86.19 340	85.67 349	93.62 347	77.18 339	97.10 336	81.61 348	89.29 303	94.23 348

DSMNet-mixed		92.52 293	92.58 284	92.33 332	94.15 345	82.65 358	98.30 194	94.26 359	89.08 327	92.65 290	95.73 327	85.01 273	95.76 352	86.24 327	97.76 171	98.59 189

UnsupCasMVSNet_eth		90.99 305	89.92 308	94.19 317	94.08 346	89.83 317	97.13 297	98.67 132	93.69 200	85.83 348	96.19 320	75.15 346	96.74 342	89.14 310	79.41 351	96.00 324

KD-MVS_2432*1600		89.61 316	87.96 320	94.54 308	94.06 347	91.59 293	95.59 341	97.63 280	89.87 316	88.95 334	94.38 343	78.28 329	96.82 340	84.83 337	68.05 361	95.21 337

miper_refine_blended		89.61 316	87.96 320	94.54 308	94.06 347	91.59 293	95.59 341	97.63 280	89.87 316	88.95 334	94.38 343	78.28 329	96.82 340	84.83 337	68.05 361	95.21 337

Anonymous20231206		91.66 298	91.10 298	93.33 325	94.02 349	87.35 348	98.58 150	97.26 309	90.48 303	90.16 324	96.31 313	83.83 297	96.53 348	79.36 354	89.90 292	96.12 321

Anonymous20240521		91.18 302	90.44 303	93.42 322	93.70 350	88.47 338	98.94 79	97.56 284	88.46 330	89.56 330	95.08 338	77.15 340	96.97 338	83.92 342	89.55 298	94.82 345

test20.03		90.89 306	90.38 304	92.43 331	93.48 351	88.14 343	98.33 186	97.56 284	93.40 214	87.96 339	96.71 300	80.69 316	94.13 360	79.15 355	86.17 336	95.01 344

CMPMVS	66.06 21	89.70 314	89.67 310	89.78 337	93.19 352	76.56 362	97.00 302	98.35 198	80.97 354	81.57 355	97.75 219	74.75 348	98.61 245	89.85 297	93.63 245	94.17 349
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
OpenMVS_ROB	86.42 20	89.00 319	87.43 324	93.69 320	93.08 353	89.42 324	97.91 239	96.89 327	78.58 356	85.86 347	94.69 340	69.48 356	98.29 289	77.13 359	93.29 255	93.36 356

DIV-MVS_2432*1600		90.38 309	89.38 312	93.40 324	92.85 354	88.94 332	97.95 235	97.94 265	90.35 308	90.25 323	93.96 346	79.82 319	95.94 351	84.62 341	76.69 355	95.33 335

MIMVSNet1		89.67 315	88.28 319	93.82 319	92.81 355	91.08 303	98.01 230	97.45 298	87.95 332	87.90 340	95.87 325	67.63 358	94.56 359	78.73 357	88.18 317	95.83 328

UnsupCasMVSNet_bld		87.17 322	85.12 326	93.31 326	91.94 356	88.77 333	94.92 347	98.30 210	84.30 350	82.30 354	90.04 355	63.96 362	97.25 334	85.85 331	74.47 359	93.93 354

CL-MVSNet_2432*1600		90.11 311	89.14 314	93.02 329	91.86 357	88.23 342	96.51 328	98.07 252	90.49 302	90.49 322	94.41 341	84.75 278	95.34 354	80.79 350	74.95 357	95.50 333

Patchmatch-RL test		91.49 299	90.85 300	93.41 323	91.37 358	84.40 353	92.81 355	95.93 343	91.87 269	87.25 341	94.87 339	88.99 193	96.53 348	92.54 251	82.00 343	99.30 125

pmmvs-eth3d		90.36 310	89.05 315	94.32 315	91.10 359	92.12 280	97.63 264	96.95 322	88.86 328	84.91 351	93.13 349	78.32 328	96.74 342	88.70 313	81.81 345	94.09 351

PM-MVS		87.77 321	86.55 325	91.40 336	91.03 360	83.36 357	96.92 306	95.18 350	91.28 290	86.48 346	93.42 348	53.27 364	96.74 342	89.43 307	81.97 344	94.11 350

new-patchmatchnet		88.50 320	87.45 323	91.67 335	90.31 361	85.89 352	97.16 295	97.33 304	89.47 322	83.63 353	92.77 350	76.38 341	95.06 357	82.70 345	77.29 354	94.06 352

pmmvs3		86.67 324	84.86 327	92.11 334	88.16 362	87.19 350	96.63 324	94.75 354	79.88 355	87.22 342	92.75 351	66.56 359	95.20 356	81.24 349	76.56 356	93.96 353

ambc				89.49 338	86.66 363	75.78 363	92.66 356	96.72 333		86.55 345	92.50 352	46.01 365	97.90 317	90.32 288	82.09 342	94.80 346

test_method		79.03 325	78.17 328	81.63 344	86.06 364	54.40 374	82.75 363	96.89 327	39.54 368	80.98 356	95.57 334	58.37 363	94.73 358	84.74 340	78.61 352	95.75 329

TDRefinement		91.06 304	89.68 309	95.21 287	85.35 365	91.49 295	98.51 164	97.07 314	91.47 279	88.83 336	97.84 211	77.31 338	99.09 189	92.79 242	77.98 353	95.04 342

PMMVS2		77.95 328	75.44 332	85.46 341	82.54 366	74.95 364	94.23 353	93.08 364	72.80 360	74.68 359	87.38 357	36.36 370	91.56 363	73.95 361	63.94 363	89.87 358

E-PMN		64.94 333	64.25 335	67.02 350	82.28 367	59.36 372	91.83 358	85.63 371	52.69 365	60.22 366	77.28 364	41.06 368	80.12 368	46.15 367	41.14 365	61.57 366

EMVS		64.07 334	63.26 337	66.53 351	81.73 368	58.81 373	91.85 357	84.75 372	51.93 367	59.09 367	75.13 365	43.32 367	79.09 369	42.03 368	39.47 366	61.69 365

FPMVS		77.62 329	77.14 329	79.05 346	79.25 369	60.97 370	95.79 337	95.94 342	65.96 361	67.93 364	94.40 342	37.73 369	88.88 365	68.83 362	88.46 313	87.29 359

wuyk23d		30.17 336	30.18 340	30.16 352	78.61 370	43.29 376	66.79 365	14.21 376	17.31 370	14.82 373	11.93 372	11.55 375	41.43 371	37.08 369	19.30 369	5.76 369

LCM-MVSNet		78.70 326	76.24 331	86.08 340	77.26 371	71.99 366	94.34 352	96.72 333	61.62 363	76.53 358	89.33 356	33.91 371	92.78 362	81.85 347	74.60 358	93.46 355

MVE	62.14 22	63.28 335	59.38 338	74.99 347	74.33 372	65.47 368	85.55 361	80.50 374	52.02 366	51.10 368	75.00 366	10.91 376	80.50 367	51.60 366	53.40 364	78.99 362
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
ANet_high		69.08 330	65.37 334	80.22 345	65.99 373	71.96 367	90.91 359	90.09 368	82.62 351	49.93 369	78.39 363	29.36 372	81.75 366	62.49 364	38.52 367	86.95 361

PMVS	61.03 23	65.95 332	63.57 336	73.09 349	57.90 374	51.22 375	85.05 362	93.93 363	54.45 364	44.32 370	83.57 359	13.22 373	89.15 364	58.68 365	81.00 348	78.91 363
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		68.90 331	66.97 333	74.68 348	50.78 375	59.95 371	87.13 360	83.47 373	38.80 369	62.21 365	96.23 317	64.70 361	76.91 370	88.91 312	30.49 368	87.19 360

testmvs		21.48 338	24.95 341	11.09 354	14.89 376	6.47 378	96.56 326	9.87 377	7.55 371	17.93 371	39.02 368	9.43 377	5.90 373	16.56 371	12.72 370	20.91 368

test123		20.95 339	23.72 342	12.64 353	13.54 377	8.19 377	96.55 327	6.13 378	7.48 372	16.74 372	37.98 369	12.97 374	6.05 372	16.69 370	5.43 371	23.68 367

eth-test2					0.00 378

eth-test					0.00 378

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

cdsmvs_eth3d_5k		23.98 337	31.98 339	0.00 355	0.00 378	0.00 379	0.00 366	98.59 147	0.00 373	0.00 374	98.61 133	90.60 162	0.00 374	0.00 372	0.00 372	0.00 370

pcd_1.5k_mvsjas		7.88 341	10.50 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	94.51 88	0.00 374	0.00 372	0.00 372	0.00 370

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

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

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

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

ab-mvs-re		8.20 340	10.94 343	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	98.43 151	0.00 378	0.00 374	0.00 372	0.00 372	0.00 370

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

PC_three_1452									95.08 139	99.60 5	99.16 63	97.86 2	98.47 260	97.52 79	99.72 52	99.74 35

test_241102_TWO								98.87 57	97.65 9	99.53 9	99.48 6	97.34 11	99.94 3	98.43 23	99.80 17	99.83 7

test_0728_THIRD									97.32 31	99.45 11	99.46 11	97.88 1	99.94 3	98.47 19	99.86 1	99.85 4

GSMVS																99.20 134

sam_mvs1												89.45 180				99.20 134

sam_mvs												88.99 193

MTGPA								98.74 107

test_post1							96.68 323				30.43 371	87.85 225	98.69 237	92.59 247

test_post											31.83 370	88.83 200	98.91 216

patchmatchnet-post											95.10 337	89.42 181	98.89 220

MTMP							98.89 88	94.14 361

test9_res														96.39 129	99.57 79	99.69 55

agg_prior2														95.87 145	99.57 79	99.68 61

test_prior4						98.01 67	97.86 246

test_prior2							97.80 251		96.12 87	97.89 114	98.69 125	95.96 39		96.89 103	99.60 72

旧先验2							97.57 267		91.30 288	98.67 63			99.80 83	95.70 155

新几何2							97.64 262

无先验							97.58 266	98.72 113	91.38 282				99.87 47	93.36 224		99.60 82

原ACMM2							97.67 260

testdata2													99.89 38	91.65 272

segment_acmp												96.85 14

testdata1							97.32 283		96.34 77

plane_prior5								98.56 155					99.03 197	96.07 135	94.27 226	96.92 244

plane_prior4											98.28 172

plane_prior3						94.61 218			97.02 52	95.34 195

plane_prior2							98.80 110		97.28 34

plane_prior						94.60 220	98.44 172		96.74 61						94.22 228

n2								0.00 379

nn								0.00 379

door-mid								94.37 357

test11								98.66 135

door								94.64 355

HQP5-MVS						94.25 234

BP-MVS														95.30 164

HQP4-MVS										94.45 218			98.96 209			96.87 255

HQP3-MVS								98.46 176							94.18 230

HQP2-MVS												86.75 244

MDTV_nov1_ep13_2view						84.26 354	96.89 313		90.97 298	97.90 113		89.89 173		93.91 208		99.18 142

ACMMP++_ref															92.97 258

ACMMP++															93.61 246

Test By Simon												94.64 83

High-resolution multi-view benchmark