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.

SED-MVS		98.05 1	97.99 1	98.24 8	99.42 6	95.30 16	98.25 30	98.27 28	95.13 17	99.19 1	98.89 4	95.54 4	99.85 15	97.52 3	99.66 9	99.56 24

test_241102_ONE					99.42 6	95.30 16		98.27 28	95.09 20	99.19 1	98.81 8	95.54 4	99.65 54

SD-MVS		97.41 9	97.53 6	97.06 72	98.57 73	94.46 31	97.92 58	98.14 53	94.82 30	99.01 3	98.55 19	94.18 12	97.41 304	96.94 12	99.64 12	99.32 62
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
test0726					99.45 2	95.36 11	98.31 24	98.29 24	94.92 24	98.99 4	98.92 2	95.08 6

IU-MVS					99.42 6	95.39 10		97.94 102	90.40 175	98.94 5				97.41 9	99.66 9	99.74 6

test_241102_TWO								98.27 28	95.13 17	98.93 6	98.89 4	94.99 9	99.85 15	97.52 3	99.65 11	99.74 6

PC_three_1452									90.77 158	98.89 7	98.28 53	96.24 1	98.35 202	95.76 56	99.58 21	99.59 18

SMA-MVS		97.35 12	97.03 14	98.30 7	99.06 40	95.42 9	97.94 56	98.18 46	90.57 171	98.85 8	98.94 1	93.33 18	99.83 23	96.72 21	99.68 4	99.63 12
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
DVP-MVS		97.91 2	97.81 2	98.22 10	99.45 2	95.36 11	98.21 37	97.85 112	94.92 24	98.73 9	98.87 6	95.08 6	99.84 20	97.52 3	99.67 6	99.48 43
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									94.78 33	98.73 9	98.87 6	95.87 3	99.84 20	97.45 7	99.72 2	99.77 1

DPE-MVS		97.86 3	97.65 4	98.47 4	99.17 32	95.78 6	97.21 134	98.35 19	95.16 16	98.71 11	98.80 9	95.05 8	99.89 3	96.70 22	99.73 1	99.73 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + MP.		97.42 8	97.33 9	97.69 43	99.25 27	94.24 40	98.07 45	97.85 112	93.72 60	98.57 12	98.35 38	93.69 16	99.40 110	97.06 10	99.46 40	99.44 49
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSP-MVS		97.59 7	97.54 5	97.73 39	99.40 11	93.77 59	98.53 10	98.29 24	95.55 5	98.56 13	97.81 83	93.90 13	99.65 54	96.62 23	99.21 71	99.77 1
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
APDe-MVS		97.82 4	97.73 3	98.08 16	99.15 33	94.82 26	98.81 3	98.30 23	94.76 34	98.30 14	98.90 3	93.77 15	99.68 48	97.93 1	99.69 3	99.75 4

xxxxxxxxxxxxxcwj		97.36 11	97.20 10	97.83 27	98.91 49	94.28 36	97.02 147	97.22 186	95.35 8	98.27 15	98.65 13	93.33 18	99.72 36	96.49 28	99.52 27	99.51 36

SF-MVS		97.39 10	97.13 11	98.17 12	99.02 43	95.28 18	98.23 34	98.27 28	92.37 110	98.27 15	98.65 13	93.33 18	99.72 36	96.49 28	99.52 27	99.51 36

SteuartSystems-ACMMP		97.62 6	97.53 6	97.87 25	98.39 81	94.25 39	98.43 18	98.27 28	95.34 10	98.11 17	98.56 17	94.53 10	99.71 39	96.57 26	99.62 14	99.65 10
Skip Steuart: Steuart Systems R&D Blog.
test_part2					99.28 25	95.74 7				98.10 18

APD-MVS		96.95 31	96.60 40	98.01 20	99.03 42	94.93 25	97.72 79	98.10 61	91.50 134	98.01 19	98.32 46	92.33 36	99.58 72	94.85 85	99.51 31	99.53 35
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepPCF-MVS	93.97 1	96.61 48	97.09 12	95.15 163	98.09 106	86.63 267	96.00 236	98.15 51	95.43 6	97.95 20	98.56 17	93.40 17	99.36 114	96.77 20	99.48 37	99.45 47

ACMMP_NAP		97.20 15	96.86 22	98.23 9	99.09 36	95.16 21	97.60 94	98.19 44	92.82 98	97.93 21	98.74 11	91.60 54	99.86 8	96.26 33	99.52 27	99.67 9

ETH3D-3000-0.1		97.07 22	96.71 36	98.14 14	98.90 51	95.33 15	97.68 83	98.24 34	91.57 132	97.90 22	98.37 36	92.61 30	99.66 53	95.59 68	99.51 31	99.43 51

9.14			96.75 33		98.93 47		97.73 76	98.23 38	91.28 146	97.88 23	98.44 28	93.00 22	99.65 54	95.76 56	99.47 38

CNVR-MVS		97.68 5	97.44 8	98.37 6	98.90 51	95.86 5	97.27 125	98.08 64	95.81 3	97.87 24	98.31 47	94.26 11	99.68 48	97.02 11	99.49 36	99.57 21

testtj		96.93 33	96.56 43	98.05 18	99.10 34	94.66 28	97.78 70	98.22 39	92.74 101	97.59 25	98.20 58	91.96 45	99.86 8	94.21 99	99.25 67	99.63 12

VNet		95.89 69	95.45 73	97.21 66	98.07 108	92.94 80	97.50 101	98.15 51	93.87 54	97.52 26	97.61 102	85.29 143	99.53 90	95.81 55	95.27 173	99.16 72

Regformer-2		97.16 18	96.99 16	97.67 44	98.32 87	93.84 54	96.83 167	98.10 61	95.24 11	97.49 27	98.25 55	92.57 31	99.61 63	96.80 17	99.29 59	99.56 24

Regformer-1		97.10 20	96.96 18	97.54 50	98.32 87	93.48 65	96.83 167	97.99 97	95.20 13	97.46 28	98.25 55	92.48 35	99.58 72	96.79 19	99.29 59	99.55 28

SR-MVS		97.01 28	96.86 22	97.47 52	99.09 36	93.27 72	97.98 50	98.07 70	93.75 59	97.45 29	98.48 25	91.43 57	99.59 69	96.22 36	99.27 63	99.54 31

APD-MVS_3200maxsize		96.81 40	96.71 36	97.12 70	99.01 46	92.31 98	97.98 50	98.06 73	93.11 84	97.44 30	98.55 19	90.93 69	99.55 85	96.06 44	99.25 67	99.51 36

TSAR-MVS + GP.		96.69 45	96.49 46	97.27 61	98.31 89	93.39 67	96.79 171	96.72 229	94.17 48	97.44 30	97.66 95	92.76 24	99.33 115	96.86 15	97.76 122	99.08 82

SR-MVS-dyc-post		96.88 36	96.80 29	97.11 71	99.02 43	92.34 95	97.98 50	98.03 84	93.52 69	97.43 32	98.51 22	91.40 58	99.56 82	96.05 45	99.26 65	99.43 51

RE-MVS-def			96.72 35		99.02 43	92.34 95	97.98 50	98.03 84	93.52 69	97.43 32	98.51 22	90.71 74		96.05 45	99.26 65	99.43 51

test1172		96.93 33	96.86 22	97.15 68	99.10 34	92.34 95	97.96 55	98.04 81	93.79 58	97.35 34	98.53 21	91.40 58	99.56 82	96.30 32	99.30 58	99.55 28

旧先验2							95.94 239		81.66 328	97.34 35			98.82 160	92.26 132

ETH3D cwj APD-0.16		96.56 50	96.06 59	98.05 18	98.26 93	95.19 19	96.99 152	98.05 80	89.85 185	97.26 36	98.22 57	91.80 48	99.69 45	94.84 86	99.28 61	99.27 68

MSLP-MVS++		96.94 32	97.06 13	96.59 84	98.72 59	91.86 113	97.67 84	98.49 12	94.66 37	97.24 37	98.41 34	92.31 38	98.94 151	96.61 24	99.46 40	98.96 94

abl_6		96.40 55	96.21 56	96.98 75	98.89 54	92.20 103	97.89 59	98.03 84	93.34 76	97.22 38	98.42 31	87.93 106	99.72 36	95.10 78	99.07 84	99.02 85

HFP-MVS		97.14 19	96.92 20	97.83 27	99.42 6	94.12 46	98.52 11	98.32 20	93.21 78	97.18 39	98.29 50	92.08 40	99.83 23	95.63 63	99.59 16	99.54 31

#test#		97.02 26	96.75 33	97.83 27	99.42 6	94.12 46	98.15 40	98.32 20	92.57 106	97.18 39	98.29 50	92.08 40	99.83 23	95.12 77	99.59 16	99.54 31

ACMMPR		97.07 22	96.84 25	97.79 33	99.44 5	93.88 53	98.52 11	98.31 22	93.21 78	97.15 41	98.33 44	91.35 60	99.86 8	95.63 63	99.59 16	99.62 14

region2R		97.07 22	96.84 25	97.77 36	99.46 1	93.79 56	98.52 11	98.24 34	93.19 81	97.14 42	98.34 41	91.59 55	99.87 7	95.46 71	99.59 16	99.64 11

Regformer-4		96.97 29	96.87 21	97.25 62	98.34 84	92.66 86	96.96 155	98.01 91	95.12 19	97.14 42	98.42 31	91.82 47	99.61 63	96.90 13	99.13 77	99.50 39

PGM-MVS		96.81 40	96.53 44	97.65 45	99.35 21	93.53 64	97.65 87	98.98 1	92.22 113	97.14 42	98.44 28	91.17 65	99.85 15	94.35 97	99.46 40	99.57 21

PHI-MVS		96.77 42	96.46 49	97.71 42	98.40 79	94.07 49	98.21 37	98.45 15	89.86 183	97.11 45	98.01 69	92.52 33	99.69 45	96.03 48	99.53 26	99.36 60

NCCC		97.30 14	97.03 14	98.11 15	98.77 57	95.06 23	97.34 117	98.04 81	95.96 2	97.09 46	97.88 75	93.18 21	99.71 39	95.84 54	99.17 74	99.56 24

Regformer-3		96.85 38	96.80 29	97.01 73	98.34 84	92.02 109	96.96 155	97.76 116	95.01 23	97.08 47	98.42 31	91.71 50	99.54 87	96.80 17	99.13 77	99.48 43

ZD-MVS					99.05 41	94.59 29		98.08 64	89.22 201	97.03 48	98.10 61	92.52 33	99.65 54	94.58 95	99.31 57

testdata				95.46 156	98.18 103	88.90 213		97.66 131	82.73 322	97.03 48	98.07 64	90.06 82	98.85 158	89.67 183	98.98 88	98.64 121

HPM-MVS_fast		96.51 51	96.27 54	97.22 65	99.32 23	92.74 83	98.74 5	98.06 73	90.57 171	96.77 50	98.35 38	90.21 80	99.53 90	94.80 90	99.63 13	99.38 58

hse-mvs3		94.15 112	93.52 120	96.04 119	97.81 120	90.22 168	97.62 93	97.58 141	95.19 14	96.74 51	97.45 111	83.67 165	99.61 63	95.85 52	79.73 334	98.29 149

hse-mvs2		93.45 139	92.99 135	94.81 180	97.02 155	88.59 219	96.69 181	96.47 247	95.19 14	96.74 51	96.16 181	83.67 165	98.48 194	95.85 52	79.13 338	97.35 190

GST-MVS		96.85 38	96.52 45	97.82 30	99.36 19	94.14 45	98.29 26	98.13 54	92.72 102	96.70 53	98.06 65	91.35 60	99.86 8	94.83 87	99.28 61	99.47 46

xiu_mvs_v1_base_debu		95.01 91	94.76 89	95.75 132	96.58 175	91.71 114	96.25 221	97.35 177	92.99 88	96.70 53	96.63 156	82.67 187	99.44 105	96.22 36	97.46 126	96.11 222

xiu_mvs_v1_base		95.01 91	94.76 89	95.75 132	96.58 175	91.71 114	96.25 221	97.35 177	92.99 88	96.70 53	96.63 156	82.67 187	99.44 105	96.22 36	97.46 126	96.11 222

xiu_mvs_v1_base_debi		95.01 91	94.76 89	95.75 132	96.58 175	91.71 114	96.25 221	97.35 177	92.99 88	96.70 53	96.63 156	82.67 187	99.44 105	96.22 36	97.46 126	96.11 222

CDPH-MVS		95.97 67	95.38 76	97.77 36	98.93 47	94.44 32	96.35 211	97.88 106	86.98 267	96.65 57	97.89 73	91.99 44	99.47 101	92.26 132	99.46 40	99.39 56

ETH3 D test6400		96.16 62	95.52 70	98.07 17	98.90 51	95.06 23	97.03 144	98.21 40	88.16 237	96.64 58	97.70 90	91.18 64	99.67 50	92.44 131	99.47 38	99.48 43

DROMVSNet		96.42 54	96.47 47	96.26 109	97.01 156	91.52 123	98.89 1	97.75 117	94.42 42	96.64 58	97.68 92	89.32 88	98.60 180	97.45 7	99.11 82	98.67 120

UA-Net		95.95 68	95.53 69	97.20 67	97.67 127	92.98 79	97.65 87	98.13 54	94.81 31	96.61 60	98.35 38	88.87 93	99.51 95	90.36 171	97.35 133	99.11 80

HPM-MVS++		97.34 13	96.97 17	98.47 4	99.08 38	96.16 3	97.55 98	97.97 99	95.59 4	96.61 60	97.89 73	92.57 31	99.84 20	95.95 49	99.51 31	99.40 55

XVS		97.18 16	96.96 18	97.81 31	99.38 14	94.03 51	98.59 8	98.20 42	94.85 26	96.59 62	98.29 50	91.70 51	99.80 28	95.66 58	99.40 47	99.62 14

X-MVStestdata		91.71 199	89.67 258	97.81 31	99.38 14	94.03 51	98.59 8	98.20 42	94.85 26	96.59 62	32.69 365	91.70 51	99.80 28	95.66 58	99.40 47	99.62 14

DeepC-MVS_fast	93.89 2	96.93 33	96.64 39	97.78 34	98.64 68	94.30 35	97.41 109	98.04 81	94.81 31	96.59 62	98.37 36	91.24 62	99.64 62	95.16 75	99.52 27	99.42 54
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PS-MVSNAJ		95.37 81	95.33 78	95.49 152	97.35 137	90.66 156	95.31 265	97.48 150	93.85 55	96.51 65	95.70 208	88.65 97	99.65 54	94.80 90	98.27 107	96.17 217

EI-MVSNet-Vis-set		96.51 51	96.47 47	96.63 81	98.24 94	91.20 136	96.89 162	97.73 120	94.74 35	96.49 66	98.49 24	90.88 71	99.58 72	96.44 30	98.32 106	99.13 76

ETV-MVS		96.02 65	95.89 64	96.40 97	97.16 143	92.44 93	97.47 106	97.77 115	94.55 39	96.48 67	94.51 256	91.23 63	98.92 152	95.65 61	98.19 109	97.82 171

alignmvs		95.87 71	95.23 80	97.78 34	97.56 135	95.19 19	97.86 61	97.17 189	94.39 44	96.47 68	96.40 170	85.89 136	99.20 124	96.21 40	95.11 177	98.95 96

xiu_mvs_v2_base		95.32 83	95.29 79	95.40 157	97.22 139	90.50 161	95.44 259	97.44 165	93.70 62	96.46 69	96.18 178	88.59 100	99.53 90	94.79 92	97.81 119	96.17 217

CP-MVS		97.02 26	96.81 28	97.64 47	99.33 22	93.54 63	98.80 4	98.28 26	92.99 88	96.45 70	98.30 49	91.90 46	99.85 15	95.61 65	99.68 4	99.54 31

HPM-MVS		96.69 45	96.45 50	97.40 54	99.36 19	93.11 75	98.87 2	98.06 73	91.17 150	96.40 71	97.99 70	90.99 68	99.58 72	95.61 65	99.61 15	99.49 41
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ZNCC-MVS		96.96 30	96.67 38	97.85 26	99.37 16	94.12 46	98.49 15	98.18 46	92.64 105	96.39 72	98.18 59	91.61 53	99.88 4	95.59 68	99.55 23	99.57 21

diffmvs		95.25 85	95.13 83	95.63 140	96.43 188	89.34 198	95.99 237	97.35 177	92.83 97	96.31 73	97.37 115	86.44 128	98.67 174	96.26 33	97.19 139	98.87 105

LFMVS		93.60 134	92.63 147	96.52 86	98.13 105	91.27 131	97.94 56	93.39 338	90.57 171	96.29 74	98.31 47	69.00 325	99.16 129	94.18 101	95.87 162	99.12 79

canonicalmvs		96.02 65	95.45 73	97.75 38	97.59 133	95.15 22	98.28 27	97.60 138	94.52 40	96.27 75	96.12 182	87.65 110	99.18 127	96.20 41	94.82 181	98.91 100

MVSFormer		95.37 81	95.16 82	95.99 122	96.34 192	91.21 134	98.22 35	97.57 142	91.42 138	96.22 76	97.32 116	86.20 133	97.92 257	94.07 102	99.05 85	98.85 106

lupinMVS		94.99 95	94.56 95	96.29 107	96.34 192	91.21 134	95.83 244	96.27 255	88.93 211	96.22 76	96.88 138	86.20 133	98.85 158	95.27 73	99.05 85	98.82 109

EI-MVSNet-UG-set		96.34 57	96.30 53	96.47 92	98.20 99	90.93 147	96.86 163	97.72 124	94.67 36	96.16 78	98.46 26	90.43 77	99.58 72	96.23 35	97.96 116	98.90 101

zzz-MVS		97.07 22	96.77 32	97.97 23	99.37 16	94.42 33	97.15 140	98.08 64	95.07 21	96.11 79	98.59 15	90.88 71	99.90 1	96.18 42	99.50 34	99.58 19

MTAPA		97.08 21	96.78 31	97.97 23	99.37 16	94.42 33	97.24 127	98.08 64	95.07 21	96.11 79	98.59 15	90.88 71	99.90 1	96.18 42	99.50 34	99.58 19

MCST-MVS		97.18 16	96.84 25	98.20 11	99.30 24	95.35 13	97.12 142	98.07 70	93.54 68	96.08 81	97.69 91	93.86 14	99.71 39	96.50 27	99.39 49	99.55 28

CS-MVS		95.88 70	95.98 61	95.58 144	96.44 186	90.56 158	97.78 70	97.73 120	93.01 87	96.07 82	96.77 142	90.13 81	98.57 185	96.83 16	99.10 83	97.60 182

TEST9					98.70 60	94.19 41	96.41 203	98.02 88	88.17 235	96.03 83	97.56 107	92.74 25	99.59 69

train_agg		96.30 58	95.83 66	97.72 40	98.70 60	94.19 41	96.41 203	98.02 88	88.58 223	96.03 83	97.56 107	92.73 26	99.59 69	95.04 79	99.37 54	99.39 56

test_prior3		96.46 53	96.20 57	97.23 63	98.67 62	92.99 77	96.35 211	98.00 93	92.80 99	96.03 83	97.59 103	92.01 42	99.41 108	95.01 80	99.38 50	99.29 64

test_prior2							96.35 211		92.80 99	96.03 83	97.59 103	92.01 42		95.01 80	99.38 50

jason		94.84 100	94.39 103	96.18 113	95.52 225	90.93 147	96.09 230	96.52 245	89.28 199	96.01 87	97.32 116	84.70 150	98.77 165	95.15 76	98.91 92	98.85 106
jason: jason.
test_8					98.67 62	94.06 50	96.37 210	98.01 91	88.58 223	95.98 88	97.55 109	92.73 26	99.58 72

mPP-MVS		96.86 37	96.60 40	97.64 47	99.40 11	93.44 66	98.50 14	98.09 63	93.27 77	95.95 89	98.33 44	91.04 67	99.88 4	95.20 74	99.57 22	99.60 17

DELS-MVS		96.61 48	96.38 52	97.30 58	97.79 122	93.19 73	95.96 238	98.18 46	95.23 12	95.87 90	97.65 96	91.45 56	99.70 44	95.87 50	99.44 44	99.00 92
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
VDD-MVS		93.82 127	93.08 133	96.02 120	97.88 117	89.96 177	97.72 79	95.85 270	92.43 108	95.86 91	98.44 28	68.42 329	99.39 111	96.31 31	94.85 179	98.71 117

MVS_111021_HR		96.68 47	96.58 42	96.99 74	98.46 75	92.31 98	96.20 226	98.90 2	94.30 47	95.86 91	97.74 88	92.33 36	99.38 113	96.04 47	99.42 45	99.28 67

MVS_111021_LR		96.24 60	96.19 58	96.39 99	98.23 98	91.35 129	96.24 224	98.79 4	93.99 52	95.80 93	97.65 96	89.92 86	99.24 122	95.87 50	99.20 72	98.58 122

VDDNet		93.05 152	92.07 164	96.02 120	96.84 162	90.39 166	98.08 44	95.85 270	86.22 279	95.79 94	98.46 26	67.59 332	99.19 125	94.92 84	94.85 179	98.47 134

新几何1				97.32 57	98.60 69	93.59 62		97.75 117	81.58 329	95.75 95	97.85 79	90.04 83	99.67 50	86.50 248	99.13 77	98.69 118

test_yl		94.78 102	94.23 104	96.43 95	97.74 124	91.22 132	96.85 164	97.10 195	91.23 148	95.71 96	96.93 133	84.30 156	99.31 117	93.10 124	95.12 175	98.75 111

DCV-MVSNet		94.78 102	94.23 104	96.43 95	97.74 124	91.22 132	96.85 164	97.10 195	91.23 148	95.71 96	96.93 133	84.30 156	99.31 117	93.10 124	95.12 175	98.75 111

agg_prior1		96.22 61	95.77 67	97.56 49	98.67 62	93.79 56	96.28 219	98.00 93	88.76 220	95.68 98	97.55 109	92.70 28	99.57 80	95.01 80	99.32 55	99.32 62

agg_prior					98.67 62	93.79 56		98.00 93		95.68 98			99.57 80

1121		94.71 104	93.83 109	97.34 56	98.57 73	93.64 61	96.04 232	97.73 120	81.56 330	95.68 98	97.85 79	90.23 79	99.65 54	87.68 226	99.12 80	98.73 114

MG-MVS		95.61 76	95.38 76	96.31 104	98.42 78	90.53 160	96.04 232	97.48 150	93.47 72	95.67 101	98.10 61	89.17 90	99.25 121	91.27 159	98.77 94	99.13 76

baseline		95.58 77	95.42 75	96.08 115	96.78 166	90.41 165	97.16 138	97.45 161	93.69 63	95.65 102	97.85 79	87.29 118	98.68 173	95.66 58	97.25 137	99.13 76

MVS_Test		94.89 98	94.62 93	95.68 138	96.83 164	89.55 187	96.70 179	97.17 189	91.17 150	95.60 103	96.11 185	87.87 107	98.76 166	93.01 128	97.17 140	98.72 115

DPM-MVS		95.69 73	94.92 86	98.01 20	98.08 107	95.71 8	95.27 268	97.62 137	90.43 174	95.55 104	97.07 129	91.72 49	99.50 98	89.62 185	98.94 90	98.82 109

MP-MVS-pluss		96.70 44	96.27 54	97.98 22	99.23 30	94.71 27	96.96 155	98.06 73	90.67 162	95.55 104	98.78 10	91.07 66	99.86 8	96.58 25	99.55 23	99.38 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		96.77 42	96.45 50	97.72 40	99.39 13	93.80 55	98.41 19	98.06 73	93.37 73	95.54 106	98.34 41	90.59 76	99.88 4	94.83 87	99.54 25	99.49 41
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CS-MVS-test		95.86 72	95.88 65	95.80 129	96.76 169	90.59 157	98.40 20	97.65 133	93.52 69	95.53 107	96.79 140	89.98 84	98.59 184	95.59 68	98.69 97	98.23 150

test12				97.65 45	98.46 75	94.26 38		97.66 131		95.52 108		90.89 70	99.46 102		99.25 67	99.22 69

casdiffmvs		95.64 75	95.49 71	96.08 115	96.76 169	90.45 163	97.29 124	97.44 165	94.00 51	95.46 109	97.98 71	87.52 114	98.73 168	95.64 62	97.33 134	99.08 82

test222					98.24 94	92.21 101	95.33 263	97.60 138	79.22 342	95.25 110	97.84 82	88.80 95			99.15 75	98.72 115

原ACMM1				96.38 100	98.59 70	91.09 142		97.89 104	87.41 259	95.22 111	97.68 92	90.25 78	99.54 87	87.95 216	99.12 80	98.49 131

CPTT-MVS		95.57 78	95.19 81	96.70 78	99.27 26	91.48 124	98.33 23	98.11 59	87.79 248	95.17 112	98.03 67	87.09 121	99.61 63	93.51 115	99.42 45	99.02 85

DP-MVS Recon		95.68 74	95.12 84	97.37 55	99.19 31	94.19 41	97.03 144	98.08 64	88.35 230	95.09 113	97.65 96	89.97 85	99.48 100	92.08 141	98.59 101	98.44 139

Vis-MVSNet		95.23 86	94.81 88	96.51 89	97.18 142	91.58 121	98.26 29	98.12 56	94.38 45	94.90 114	98.15 60	82.28 197	98.92 152	91.45 156	98.58 102	99.01 89
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CANet		96.39 56	96.02 60	97.50 51	97.62 130	93.38 68	97.02 147	97.96 100	95.42 7	94.86 115	97.81 83	87.38 117	99.82 26	96.88 14	99.20 72	99.29 64

API-MVS		94.84 100	94.49 99	95.90 125	97.90 116	92.00 110	97.80 68	97.48 150	89.19 202	94.81 116	96.71 145	88.84 94	99.17 128	88.91 203	98.76 95	96.53 208

OMC-MVS		95.09 90	94.70 92	96.25 111	98.46 75	91.28 130	96.43 201	97.57 142	92.04 122	94.77 117	97.96 72	87.01 122	99.09 138	91.31 158	96.77 145	98.36 146

WTY-MVS		94.71 104	94.02 106	96.79 77	97.71 126	92.05 107	96.59 194	97.35 177	90.61 168	94.64 118	96.93 133	86.41 129	99.39 111	91.20 161	94.71 185	98.94 97

ACMMP		96.27 59	95.93 62	97.28 60	99.24 28	92.62 88	98.25 30	98.81 3	92.99 88	94.56 119	98.39 35	88.96 92	99.85 15	94.57 96	97.63 123	99.36 60
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
Effi-MVS+		94.93 96	94.45 101	96.36 102	96.61 172	91.47 125	96.41 203	97.41 170	91.02 155	94.50 120	95.92 191	87.53 113	98.78 163	93.89 108	96.81 144	98.84 108

sss		94.51 106	93.80 110	96.64 79	97.07 148	91.97 111	96.32 215	98.06 73	88.94 210	94.50 120	96.78 141	84.60 151	99.27 120	91.90 142	96.02 158	98.68 119

PVSNet_BlendedMVS		94.06 118	93.92 107	94.47 195	98.27 90	89.46 193	96.73 175	98.36 16	90.17 177	94.36 122	95.24 227	88.02 103	99.58 72	93.44 117	90.72 240	94.36 310

PVSNet_Blended		94.87 99	94.56 95	95.81 128	98.27 90	89.46 193	95.47 258	98.36 16	88.84 214	94.36 122	96.09 186	88.02 103	99.58 72	93.44 117	98.18 110	98.40 142

PMMVS		92.86 162	92.34 158	94.42 199	94.92 262	86.73 263	94.53 282	96.38 251	84.78 301	94.27 124	95.12 232	83.13 175	98.40 197	91.47 155	96.49 154	98.12 155

EPP-MVSNet		95.22 87	95.04 85	95.76 130	97.49 136	89.56 186	98.67 6	97.00 208	90.69 161	94.24 125	97.62 101	89.79 87	98.81 161	93.39 120	96.49 154	98.92 99

PVSNet_Blended_VisFu		95.27 84	94.91 87	96.38 100	98.20 99	90.86 149	97.27 125	98.25 33	90.21 176	94.18 126	97.27 118	87.48 115	99.73 33	93.53 114	97.77 121	98.55 123

thisisatest0530		93.03 153	92.21 162	95.49 152	97.07 148	89.11 209	97.49 105	92.19 346	90.16 178	94.09 127	96.41 169	76.43 285	99.05 144	90.38 170	95.68 168	98.31 148

XVG-OURS-SEG-HR		93.86 126	93.55 117	94.81 180	97.06 151	88.53 222	95.28 266	97.45 161	91.68 130	94.08 128	97.68 92	82.41 195	98.90 155	93.84 110	92.47 210	96.98 195

XVG-OURS		93.72 131	93.35 128	94.80 183	97.07 148	88.61 218	94.79 276	97.46 155	91.97 125	93.99 129	97.86 78	81.74 208	98.88 157	92.64 130	92.67 208	96.92 199

IS-MVSNet		94.90 97	94.52 98	96.05 118	97.67 127	90.56 158	98.44 17	96.22 258	93.21 78	93.99 129	97.74 88	85.55 141	98.45 195	89.98 174	97.86 117	99.14 75

CSCG		96.05 64	95.91 63	96.46 94	99.24 28	90.47 162	98.30 25	98.57 11	89.01 206	93.97 131	97.57 105	92.62 29	99.76 31	94.66 93	99.27 63	99.15 74

EIA-MVS		95.53 79	95.47 72	95.71 137	97.06 151	89.63 182	97.82 66	97.87 108	93.57 64	93.92 132	95.04 233	90.61 75	98.95 150	94.62 94	98.68 98	98.54 124

tttt0517		92.96 156	92.33 159	94.87 177	97.11 146	87.16 255	97.97 54	92.09 347	90.63 166	93.88 133	97.01 132	76.50 282	99.06 143	90.29 173	95.45 170	98.38 144

HyFIR lowres test		93.66 132	92.92 138	95.87 126	98.24 94	89.88 178	94.58 280	98.49 12	85.06 296	93.78 134	95.78 202	82.86 183	98.67 174	91.77 146	95.71 167	99.07 84

CHOSEN 1792x2688		94.15 112	93.51 121	96.06 117	98.27 90	89.38 196	95.18 272	98.48 14	85.60 287	93.76 135	97.11 127	83.15 174	99.61 63	91.33 157	98.72 96	99.19 70

Anonymous202405211		92.07 191	90.83 210	95.76 130	98.19 101	88.75 215	97.58 95	95.00 306	86.00 282	93.64 136	97.45 111	66.24 341	99.53 90	90.68 168	92.71 206	99.01 89

CDS-MVSNet		94.14 115	93.54 118	95.93 123	96.18 199	91.46 126	96.33 214	97.04 204	88.97 209	93.56 137	96.51 163	87.55 112	97.89 261	89.80 179	95.95 160	98.44 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MDTV_nov1_ep13_2view						70.35 357	93.10 322		83.88 311	93.55 138		82.47 194		86.25 251		98.38 144

Anonymous20240529		91.98 193	90.73 214	95.73 135	98.14 104	89.40 195	97.99 49	97.72 124	79.63 340	93.54 139	97.41 114	69.94 323	99.56 82	91.04 162	91.11 233	98.22 151

CANet_DTU		94.37 107	93.65 115	96.55 85	96.46 185	92.13 105	96.21 225	96.67 237	94.38 45	93.53 140	97.03 131	79.34 246	99.71 39	90.76 165	98.45 104	97.82 171

tpmrst		91.44 213	91.32 190	91.79 291	95.15 250	79.20 343	93.42 315	95.37 288	88.55 226	93.49 141	93.67 297	82.49 193	98.27 207	90.41 169	89.34 254	97.90 164

TAMVS		94.01 121	93.46 123	95.64 139	96.16 201	90.45 163	96.71 178	96.89 219	89.27 200	93.46 142	96.92 136	87.29 118	97.94 254	88.70 207	95.74 165	98.53 125

thisisatest0515		92.29 181	91.30 192	95.25 160	96.60 173	88.90 213	94.36 289	92.32 345	87.92 242	93.43 143	94.57 255	77.28 278	99.00 147	89.42 189	95.86 163	97.86 167

DeepC-MVS	93.07 3	96.06 63	95.66 68	97.29 59	97.96 110	93.17 74	97.30 123	98.06 73	93.92 53	93.38 144	98.66 12	86.83 123	99.73 33	95.60 67	99.22 70	98.96 94
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
thres600view7		92.49 172	91.60 180	95.18 162	97.91 115	89.47 191	97.65 87	94.66 317	92.18 119	93.33 145	94.91 237	78.06 271	99.10 135	81.61 300	94.06 193	96.98 195

thres100view900		92.43 173	91.58 181	94.98 171	97.92 114	89.37 197	97.71 81	94.66 317	92.20 115	93.31 146	94.90 238	78.06 271	99.08 140	81.40 303	94.08 190	96.48 211

thres200		92.23 185	91.39 187	94.75 187	97.61 131	89.03 210	96.60 193	95.09 303	92.08 121	93.28 147	94.00 284	78.39 265	99.04 146	81.26 307	94.18 189	96.19 216

tfpn200view9		92.38 176	91.52 184	94.95 174	97.85 118	89.29 201	97.41 109	94.88 312	92.19 117	93.27 148	94.46 261	78.17 267	99.08 140	81.40 303	94.08 190	96.48 211

thres400		92.42 174	91.52 184	95.12 166	97.85 118	89.29 201	97.41 109	94.88 312	92.19 117	93.27 148	94.46 261	78.17 267	99.08 140	81.40 303	94.08 190	96.98 195

ab-mvs		93.57 136	92.55 151	96.64 79	97.28 138	91.96 112	95.40 260	97.45 161	89.81 187	93.22 150	96.28 175	79.62 243	99.46 102	90.74 166	93.11 202	98.50 129

Vis-MVSNet (Re-imp)		94.15 112	93.88 108	94.95 174	97.61 131	87.92 238	98.10 42	95.80 272	92.22 113	93.02 151	97.45 111	84.53 153	97.91 260	88.24 211	97.97 115	99.02 85

114514_t		93.95 122	93.06 134	96.63 81	99.07 39	91.61 118	97.46 108	97.96 100	77.99 346	93.00 152	97.57 105	86.14 135	99.33 115	89.22 196	99.15 75	98.94 97

UGNet		94.04 120	93.28 130	96.31 104	96.85 161	91.19 137	97.88 60	97.68 129	94.40 43	93.00 152	96.18 178	73.39 305	99.61 63	91.72 147	98.46 103	98.13 154
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
HY-MVS	89.66 9	93.87 125	92.95 137	96.63 81	97.10 147	92.49 92	95.64 252	96.64 238	89.05 205	93.00 152	95.79 201	85.77 139	99.45 104	89.16 200	94.35 187	97.96 160

PVSNet	86.66 18	92.24 184	91.74 177	93.73 228	97.77 123	83.69 308	92.88 324	96.72 229	87.91 243	93.00 152	94.86 240	78.51 261	99.05 144	86.53 246	97.45 130	98.47 134

MAR-MVS		94.22 110	93.46 123	96.51 89	98.00 109	92.19 104	97.67 84	97.47 153	88.13 239	93.00 152	95.84 195	84.86 149	99.51 95	87.99 215	98.17 111	97.83 170
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
PAPM_NR		95.01 91	94.59 94	96.26 109	98.89 54	90.68 155	97.24 127	97.73 120	91.80 127	92.93 157	96.62 159	89.13 91	99.14 132	89.21 197	97.78 120	98.97 93

MDTV_nov1_ep13			90.76 212		95.22 247	80.33 332	93.03 323	95.28 293	88.14 238	92.84 158	93.83 288	81.34 212	98.08 230	82.86 291	94.34 188

CostFormer		91.18 231	90.70 215	92.62 272	94.84 268	81.76 321	94.09 299	94.43 322	84.15 307	92.72 159	93.77 292	79.43 245	98.20 213	90.70 167	92.18 216	97.90 164

EPNet		95.20 88	94.56 95	97.14 69	92.80 325	92.68 85	97.85 64	94.87 315	96.64 1	92.46 160	97.80 85	86.23 130	99.65 54	93.72 112	98.62 100	99.10 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		90.82 243	89.77 254	93.95 218	94.45 284	87.19 253	90.23 344	95.68 278	86.89 269	92.40 161	92.36 320	80.91 218	97.05 314	81.09 308	93.95 194	97.60 182

RPMNet		88.98 276	87.05 291	94.77 185	94.45 284	87.19 253	90.23 344	98.03 84	77.87 348	92.40 161	87.55 350	80.17 232	99.51 95	68.84 354	93.95 194	97.60 182

EPMVS		90.70 249	89.81 252	93.37 247	94.73 273	84.21 300	93.67 310	88.02 358	89.50 193	92.38 163	93.49 301	77.82 275	97.78 271	86.03 258	92.68 207	98.11 158

baseline1		92.82 165	91.90 171	95.55 147	97.20 141	90.77 153	97.19 135	94.58 320	92.20 115	92.36 164	96.34 173	84.16 159	98.21 211	89.20 198	83.90 317	97.68 176

PatchT		88.87 280	87.42 285	93.22 253	94.08 295	85.10 290	89.51 348	94.64 319	81.92 326	92.36 164	88.15 347	80.05 234	97.01 318	72.43 346	93.65 197	97.54 186

PAPR		94.18 111	93.42 127	96.48 91	97.64 129	91.42 128	95.55 254	97.71 128	88.99 207	92.34 166	95.82 197	89.19 89	99.11 134	86.14 254	97.38 131	98.90 101

mvs-test1		93.63 133	93.69 113	93.46 243	96.02 208	84.61 297	97.24 127	96.72 229	93.85 55	92.30 167	95.76 203	83.08 176	98.89 156	91.69 150	96.54 152	96.87 201

SCA		91.84 196	91.18 199	93.83 224	95.59 221	84.95 293	94.72 277	95.58 282	90.82 156	92.25 168	93.69 294	75.80 288	98.10 225	86.20 252	95.98 159	98.45 136

CVMVSNet		91.23 225	91.75 175	89.67 324	95.77 216	74.69 352	96.44 199	94.88 312	85.81 284	92.18 169	97.64 99	79.07 249	95.58 341	88.06 214	95.86 163	98.74 113

AUN-MVS		91.76 198	90.75 213	94.81 180	97.00 157	88.57 220	96.65 185	96.49 246	89.63 190	92.15 170	96.12 182	78.66 259	98.50 190	90.83 164	79.18 337	97.36 189

AdaColmap		94.34 108	93.68 114	96.31 104	98.59 70	91.68 117	96.59 194	97.81 114	89.87 182	92.15 170	97.06 130	83.62 167	99.54 87	89.34 191	98.07 113	97.70 175

GeoE		93.89 124	93.28 130	95.72 136	96.96 159	89.75 181	98.24 33	96.92 216	89.47 194	92.12 172	97.21 122	84.42 154	98.39 200	87.71 222	96.50 153	99.01 89

PatchmatchNet		91.91 194	91.35 188	93.59 236	95.38 231	84.11 302	93.15 320	95.39 286	89.54 191	92.10 173	93.68 296	82.82 185	98.13 220	84.81 273	95.32 172	98.52 126
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		93.24 145	92.48 156	95.51 149	95.70 219	92.39 94	97.86 61	98.66 9	92.30 111	92.09 174	95.37 222	80.49 225	98.40 197	93.95 105	85.86 285	95.75 239

tpm		90.25 259	89.74 257	91.76 294	93.92 298	79.73 339	93.98 300	93.54 335	88.28 231	91.99 175	93.25 306	77.51 277	97.44 301	87.30 237	87.94 265	98.12 155

CNLPA		94.28 109	93.53 119	96.52 86	98.38 82	92.55 90	96.59 194	96.88 220	90.13 179	91.91 176	97.24 120	85.21 144	99.09 138	87.64 229	97.83 118	97.92 163

BH-RMVSNet		92.72 168	91.97 169	94.97 172	97.16 143	87.99 237	96.15 228	95.60 280	90.62 167	91.87 177	97.15 126	78.41 264	98.57 185	83.16 288	97.60 124	98.36 146

PatchMatch-RL		92.90 160	92.02 167	95.56 145	98.19 101	90.80 151	95.27 268	97.18 187	87.96 241	91.86 178	95.68 209	80.44 226	98.99 148	84.01 282	97.54 125	96.89 200

OPM-MVS		93.28 144	92.76 141	94.82 178	94.63 278	90.77 153	96.65 185	97.18 187	93.72 60	91.68 179	97.26 119	79.33 247	98.63 177	92.13 138	92.28 212	95.07 274
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tpm2		89.96 265	89.21 266	92.23 280	94.91 265	81.25 324	93.78 306	94.42 323	80.62 336	91.56 180	93.44 303	76.44 284	97.94 254	85.60 264	92.08 220	97.49 187

TAPA-MVS	90.10 7	92.30 180	91.22 197	95.56 145	98.33 86	89.60 184	96.79 171	97.65 133	81.83 327	91.52 181	97.23 121	87.94 105	98.91 154	71.31 350	98.37 105	98.17 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		91.48 212	90.59 219	94.16 207	96.40 189	87.33 247	95.67 249	95.34 292	87.68 253	91.46 182	95.52 218	76.77 281	98.35 202	82.85 292	93.61 199	96.79 204

RPSCF		90.75 246	90.86 206	90.42 317	96.84 162	76.29 350	95.61 253	96.34 252	83.89 310	91.38 183	97.87 76	76.45 283	98.78 163	87.16 241	92.23 213	96.20 215

PLC	91.00 6	94.11 116	93.43 125	96.13 114	98.58 72	91.15 141	96.69 181	97.39 171	87.29 262	91.37 184	96.71 145	88.39 101	99.52 94	87.33 236	97.13 141	97.73 173
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		93.12 149	92.72 145	94.34 202	96.71 171	87.27 249	90.29 343	97.72 124	86.61 274	91.34 185	95.29 224	84.29 158	98.41 196	93.25 122	98.94 90	97.35 190

HQP_MVS		93.78 129	93.43 125	94.82 178	96.21 196	89.99 173	97.74 74	97.51 148	94.85 26	91.34 185	96.64 152	81.32 213	98.60 180	93.02 126	92.23 213	95.86 228

plane_prior3						90.00 171			94.46 41	91.34 185

Fast-Effi-MVS+		93.46 138	92.75 143	95.59 143	96.77 167	90.03 170	96.81 170	97.13 192	88.19 233	91.30 188	94.27 272	86.21 132	98.63 177	87.66 228	96.46 156	98.12 155

EI-MVSNet		93.03 153	92.88 139	93.48 241	95.77 216	86.98 258	96.44 199	97.12 193	90.66 164	91.30 188	97.64 99	86.56 125	98.05 236	89.91 176	90.55 242	95.41 253

MVSTER		93.20 147	92.81 140	94.37 200	96.56 178	89.59 185	97.06 143	97.12 193	91.24 147	91.30 188	95.96 189	82.02 202	98.05 236	93.48 116	90.55 242	95.47 249

RRT_MVS		93.21 146	92.32 160	95.91 124	94.92 262	94.15 44	96.92 159	96.86 223	91.42 138	91.28 191	96.43 167	79.66 242	98.10 225	93.29 121	90.06 247	95.46 250

ADS-MVSNet2		89.45 272	88.59 274	92.03 283	95.86 211	82.26 319	90.93 339	94.32 327	83.23 319	91.28 191	91.81 327	79.01 254	95.99 332	79.52 315	91.39 229	97.84 168

ADS-MVSNet		89.89 267	88.68 273	93.53 239	95.86 211	84.89 294	90.93 339	95.07 304	83.23 319	91.28 191	91.81 327	79.01 254	97.85 263	79.52 315	91.39 229	97.84 168

nrg030		94.05 119	93.31 129	96.27 108	95.22 247	94.59 29	98.34 22	97.46 155	92.93 95	91.21 194	96.64 152	87.23 120	98.22 210	94.99 83	85.80 286	95.98 226

Effi-MVS+-dtu		93.08 150	93.21 132	92.68 271	96.02 208	83.25 311	97.14 141	96.72 229	93.85 55	91.20 195	93.44 303	83.08 176	98.30 206	91.69 150	95.73 166	96.50 210

VPNet		92.23 185	91.31 191	94.99 169	95.56 223	90.96 145	97.22 133	97.86 111	92.96 94	90.96 196	96.62 159	75.06 293	98.20 213	91.90 142	83.65 319	95.80 234

JIA-IIPM		88.26 288	87.04 292	91.91 285	93.52 310	81.42 323	89.38 349	94.38 324	80.84 334	90.93 197	80.74 355	79.22 248	97.92 257	82.76 293	91.62 224	96.38 213

test-LLR		91.42 214	91.19 198	92.12 281	94.59 279	80.66 327	94.29 293	92.98 340	91.11 152	90.76 198	92.37 317	79.02 252	98.07 233	88.81 204	96.74 146	97.63 177

test-mter		90.19 262	89.54 261	92.12 281	94.59 279	80.66 327	94.29 293	92.98 340	87.68 253	90.76 198	92.37 317	67.67 331	98.07 233	88.81 204	96.74 146	97.63 177

ACMM	89.79 8	92.96 156	92.50 155	94.35 201	96.30 194	88.71 216	97.58 95	97.36 176	91.40 141	90.53 200	96.65 151	79.77 239	98.75 167	91.24 160	91.64 223	95.59 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
F-COLMAP		93.58 135	92.98 136	95.37 158	98.40 79	88.98 211	97.18 136	97.29 182	87.75 251	90.49 201	97.10 128	85.21 144	99.50 98	86.70 245	96.72 148	97.63 177

DWT-MVSNet_test		90.76 244	89.89 248	93.38 246	95.04 256	83.70 307	95.85 243	94.30 328	88.19 233	90.46 202	92.80 310	73.61 303	98.50 190	88.16 212	90.58 241	97.95 162

TESTMET0.1,1		90.06 264	89.42 262	91.97 284	94.41 286	80.62 329	94.29 293	91.97 349	87.28 263	90.44 203	92.47 316	68.79 326	97.67 279	88.50 210	96.60 151	97.61 181

FIs		94.09 117	93.70 112	95.27 159	95.70 219	92.03 108	98.10 42	98.68 7	93.36 75	90.39 204	96.70 147	87.63 111	97.94 254	92.25 134	90.50 244	95.84 231

GA-MVS		91.38 216	90.31 229	94.59 189	94.65 276	87.62 245	94.34 290	96.19 260	90.73 160	90.35 205	93.83 288	71.84 308	97.96 251	87.22 238	93.61 199	98.21 152

LS3D		93.57 136	92.61 149	96.47 92	97.59 133	91.61 118	97.67 84	97.72 124	85.17 294	90.29 206	98.34 41	84.60 151	99.73 33	83.85 286	98.27 107	98.06 159

FC-MVSNet-test		93.94 123	93.57 116	95.04 167	95.48 227	91.45 127	98.12 41	98.71 5	93.37 73	90.23 207	96.70 147	87.66 109	97.85 263	91.49 154	90.39 245	95.83 232

bset_n11_16_dypcd		91.55 207	90.59 219	94.44 196	91.51 337	90.25 167	92.70 327	93.42 337	92.27 112	90.22 208	94.74 247	78.42 263	97.80 268	94.19 100	87.86 267	95.29 269

HQP-NCC					95.86 211		96.65 185		93.55 65	90.14 209

ACMP_Plane					95.86 211		96.65 185		93.55 65	90.14 209

HQP4-MVS										90.14 209			98.50 190			95.78 235

HQP-MVS		93.19 148	92.74 144	94.54 194	95.86 211	89.33 199	96.65 185	97.39 171	93.55 65	90.14 209	95.87 193	80.95 216	98.50 190	92.13 138	92.10 218	95.78 235

UniMVSNet_NR-MVSNet		93.37 141	92.67 146	95.47 155	95.34 236	92.83 81	97.17 137	98.58 10	92.98 93	90.13 213	95.80 198	88.37 102	97.85 263	91.71 148	83.93 314	95.73 241

DU-MVS		92.90 160	92.04 165	95.49 152	94.95 260	92.83 81	97.16 138	98.24 34	93.02 86	90.13 213	95.71 206	83.47 168	97.85 263	91.71 148	83.93 314	95.78 235

LPG-MVS_test		92.94 158	92.56 150	94.10 208	96.16 201	88.26 228	97.65 87	97.46 155	91.29 143	90.12 215	97.16 124	79.05 250	98.73 168	92.25 134	91.89 221	95.31 262

LGP-MVS_train				94.10 208	96.16 201	88.26 228		97.46 155	91.29 143	90.12 215	97.16 124	79.05 250	98.73 168	92.25 134	91.89 221	95.31 262

UniMVSNet (Re)		93.31 143	92.55 151	95.61 142	95.39 230	93.34 71	97.39 113	98.71 5	93.14 83	90.10 217	94.83 242	87.71 108	98.03 240	91.67 152	83.99 313	95.46 250

mvs_anonymous		93.82 127	93.74 111	94.06 210	96.44 186	85.41 285	95.81 245	97.05 202	89.85 185	90.09 218	96.36 172	87.44 116	97.75 274	93.97 104	96.69 149	99.02 85

test_djsdf		93.07 151	92.76 141	94.00 213	93.49 312	88.70 217	98.22 35	97.57 142	91.42 138	90.08 219	95.55 216	82.85 184	97.92 257	94.07 102	91.58 225	95.40 256

dp		88.90 279	88.26 279	90.81 310	94.58 281	76.62 349	92.85 325	94.93 310	85.12 295	90.07 220	93.07 307	75.81 287	98.12 223	80.53 310	87.42 272	97.71 174

PS-MVSNAJss		93.74 130	93.51 121	94.44 196	93.91 299	89.28 203	97.75 73	97.56 145	92.50 107	89.94 221	96.54 162	88.65 97	98.18 216	93.83 111	90.90 238	95.86 228

UniMVSNet_ETH3D		91.34 221	90.22 237	94.68 188	94.86 267	87.86 241	97.23 132	97.46 155	87.99 240	89.90 222	96.92 136	66.35 339	98.23 209	90.30 172	90.99 236	97.96 160

CLD-MVS		92.98 155	92.53 153	94.32 203	96.12 205	89.20 205	95.28 266	97.47 153	92.66 103	89.90 222	95.62 211	80.58 223	98.40 197	92.73 129	92.40 211	95.38 258
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
gg-mvs-nofinetune		87.82 291	85.61 300	94.44 196	94.46 283	89.27 204	91.21 338	84.61 363	80.88 333	89.89 224	74.98 357	71.50 310	97.53 293	85.75 263	97.21 138	96.51 209

1112_ss		93.37 141	92.42 157	96.21 112	97.05 153	90.99 143	96.31 216	96.72 229	86.87 270	89.83 225	96.69 149	86.51 127	99.14 132	88.12 213	93.67 196	98.50 129

BH-untuned		92.94 158	92.62 148	93.92 222	97.22 139	86.16 276	96.40 206	96.25 257	90.06 180	89.79 226	96.17 180	83.19 172	98.35 202	87.19 239	97.27 136	97.24 192

V42		91.58 205	90.87 205	93.73 228	94.05 296	88.50 223	97.32 120	96.97 209	88.80 219	89.71 227	94.33 267	82.54 191	98.05 236	89.01 201	85.07 298	94.64 304

Baseline_NR-MVSNet		91.20 227	90.62 217	92.95 262	93.83 302	88.03 236	97.01 151	95.12 302	88.42 228	89.70 228	95.13 231	83.47 168	97.44 301	89.66 184	83.24 322	93.37 328

v144192		91.06 233	90.28 231	93.39 245	93.66 307	87.23 252	96.83 167	97.07 199	87.43 258	89.69 229	94.28 271	81.48 211	98.00 243	87.18 240	84.92 302	94.93 282

v1144		91.37 218	90.60 218	93.68 233	93.89 300	88.23 230	96.84 166	97.03 206	88.37 229	89.69 229	94.39 263	82.04 201	97.98 244	87.80 219	85.37 291	94.84 288

Test_1112_low_res		92.84 164	91.84 173	95.85 127	97.04 154	89.97 176	95.53 256	96.64 238	85.38 290	89.65 231	95.18 228	85.86 137	99.10 135	87.70 223	93.58 201	98.49 131

v1192		91.07 232	90.23 235	93.58 237	93.70 305	87.82 242	96.73 175	97.07 199	87.77 249	89.58 232	94.32 269	80.90 220	97.97 247	86.52 247	85.48 289	94.95 278

v1240		90.70 249	89.85 250	93.23 252	93.51 311	86.80 261	96.61 191	97.02 207	87.16 265	89.58 232	94.31 270	79.55 244	97.98 244	85.52 265	85.44 290	94.90 285

TranMVSNet+NR-MVSNet		92.50 170	91.63 179	95.14 164	94.76 271	92.07 106	97.53 99	98.11 59	92.90 96	89.56 234	96.12 182	83.16 173	97.60 287	89.30 192	83.20 323	95.75 239

v2v482		91.59 203	90.85 208	93.80 226	93.87 301	88.17 233	96.94 158	96.88 220	89.54 191	89.53 235	94.90 238	81.70 209	98.02 241	89.25 195	85.04 300	95.20 271

v1921920		90.85 242	90.03 245	93.29 250	93.55 308	86.96 260	96.74 174	97.04 204	87.36 260	89.52 236	94.34 266	80.23 231	97.97 247	86.27 250	85.21 295	94.94 280

IterMVS-LS		92.29 181	91.94 170	93.34 248	96.25 195	86.97 259	96.57 197	97.05 202	90.67 162	89.50 237	94.80 244	86.59 124	97.64 282	89.91 176	86.11 284	95.40 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
cascas		91.20 227	90.08 241	94.58 193	94.97 258	89.16 208	93.65 311	97.59 140	79.90 339	89.40 238	92.92 309	75.36 292	98.36 201	92.14 137	94.75 183	96.23 214

XVG-ACMP-BASELINE		90.93 240	90.21 238	93.09 257	94.31 290	85.89 278	95.33 263	97.26 183	91.06 154	89.38 239	95.44 221	68.61 327	98.60 180	89.46 188	91.05 234	94.79 296

GBi-Net		91.35 219	90.27 232	94.59 189	96.51 181	91.18 138	97.50 101	96.93 212	88.82 216	89.35 240	94.51 256	73.87 299	97.29 310	86.12 255	88.82 257	95.31 262

test1		91.35 219	90.27 232	94.59 189	96.51 181	91.18 138	97.50 101	96.93 212	88.82 216	89.35 240	94.51 256	73.87 299	97.29 310	86.12 255	88.82 257	95.31 262

FMVSNet3		91.78 197	90.69 216	95.03 168	96.53 180	92.27 100	97.02 147	96.93 212	89.79 188	89.35 240	94.65 252	77.01 279	97.47 298	86.12 255	88.82 257	95.35 260

WR-MVS		92.34 177	91.53 183	94.77 185	95.13 252	90.83 150	96.40 206	97.98 98	91.88 126	89.29 243	95.54 217	82.50 192	97.80 268	89.79 180	85.27 294	95.69 242

DP-MVS		92.76 167	91.51 186	96.52 86	98.77 57	90.99 143	97.38 115	96.08 263	82.38 323	89.29 243	97.87 76	83.77 163	99.69 45	81.37 306	96.69 149	98.89 103

BH-w/o		92.14 190	91.75 175	93.31 249	96.99 158	85.73 280	95.67 249	95.69 276	88.73 221	89.26 245	94.82 243	82.97 181	98.07 233	85.26 269	96.32 157	96.13 221

3Dnovator	91.36 5	95.19 89	94.44 102	97.44 53	96.56 178	93.36 70	98.65 7	98.36 16	94.12 49	89.25 246	98.06 65	82.20 199	99.77 30	93.41 119	99.32 55	99.18 71

miper_enhance_ethall		91.54 209	91.01 202	93.15 255	95.35 235	87.07 257	93.97 301	96.90 217	86.79 271	89.17 247	93.43 305	86.55 126	97.64 282	89.97 175	86.93 275	94.74 300

Fast-Effi-MVS+-dtu		92.29 181	91.99 168	93.21 254	95.27 243	85.52 283	97.03 144	96.63 241	92.09 120	89.11 248	95.14 230	80.33 229	98.08 230	87.54 232	94.74 184	96.03 225

RRT_test8_iter05		91.19 230	90.78 211	92.41 276	95.76 218	83.14 312	97.32 120	97.46 155	91.37 142	89.07 249	95.57 213	70.33 318	98.21 211	93.56 113	86.62 280	95.89 227

XXY-MVS		92.16 188	91.23 196	94.95 174	94.75 272	90.94 146	97.47 106	97.43 168	89.14 203	88.90 250	96.43 167	79.71 240	98.24 208	89.56 186	87.68 268	95.67 244

PCF-MVS	89.48 11	91.56 206	89.95 246	96.36 102	96.60 173	92.52 91	92.51 330	97.26 183	79.41 341	88.90 250	96.56 161	84.04 161	99.55 85	77.01 332	97.30 135	97.01 194
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
miper_ehance_all_eth		91.59 203	91.13 200	92.97 261	95.55 224	86.57 268	94.47 283	96.88 220	87.77 249	88.88 252	94.01 283	86.22 131	97.54 291	89.49 187	86.93 275	94.79 296

jajsoiax		92.42 174	91.89 172	94.03 212	93.33 317	88.50 223	97.73 76	97.53 146	92.00 124	88.85 253	96.50 164	75.62 291	98.11 224	93.88 109	91.56 226	95.48 247

eth_miper_zixun_eth		91.02 235	90.59 219	92.34 278	95.33 239	84.35 298	94.10 298	96.90 217	88.56 225	88.84 254	94.33 267	84.08 160	97.60 287	88.77 206	84.37 309	95.06 275

cl_fuxian		91.38 216	90.89 204	92.88 264	95.58 222	86.30 271	94.68 278	96.84 225	88.17 235	88.83 255	94.23 275	85.65 140	97.47 298	89.36 190	84.63 304	94.89 286

test_part1		92.21 187	91.10 201	95.51 149	97.80 121	92.66 86	98.02 48	97.68 129	89.79 188	88.80 256	96.02 187	76.85 280	98.18 216	90.86 163	84.11 312	95.69 242

mvs_tets		92.31 179	91.76 174	93.94 220	93.41 314	88.29 226	97.63 92	97.53 146	92.04 122	88.76 257	96.45 166	74.62 295	98.09 229	93.91 107	91.48 227	95.45 252

v148		90.99 236	90.38 226	92.81 267	93.83 302	85.80 279	96.78 173	96.68 235	89.45 195	88.75 258	93.93 287	82.96 182	97.82 267	87.83 218	83.25 321	94.80 294

FMVSNet2		91.31 222	90.08 241	94.99 169	96.51 181	92.21 101	97.41 109	96.95 210	88.82 216	88.62 259	94.75 246	73.87 299	97.42 303	85.20 270	88.55 262	95.35 260

PAPM		91.52 210	90.30 230	95.20 161	95.30 242	89.83 179	93.38 316	96.85 224	86.26 278	88.59 260	95.80 198	84.88 148	98.15 219	75.67 336	95.93 161	97.63 177

cl-mvsnet2		91.21 226	90.56 222	93.14 256	96.09 207	86.80 261	94.41 287	96.58 244	87.80 247	88.58 261	93.99 285	80.85 221	97.62 285	89.87 178	86.93 275	94.99 277

3Dnovator+	91.43 4	95.40 80	94.48 100	98.16 13	96.90 160	95.34 14	98.48 16	97.87 108	94.65 38	88.53 262	98.02 68	83.69 164	99.71 39	93.18 123	98.96 89	99.44 49

anonymousdsp		92.16 188	91.55 182	93.97 216	92.58 329	89.55 187	97.51 100	97.42 169	89.42 196	88.40 263	94.84 241	80.66 222	97.88 262	91.87 144	91.28 231	94.48 306

WR-MVS_H		92.00 192	91.35 188	93.95 218	95.09 254	89.47 191	98.04 47	98.68 7	91.46 136	88.34 264	94.68 250	85.86 137	97.56 289	85.77 262	84.24 310	94.82 291

v8		91.29 224	90.53 223	93.57 238	94.15 292	88.12 235	97.34 117	97.06 201	88.99 207	88.32 265	94.26 274	83.08 176	98.01 242	87.62 230	83.92 316	94.57 305

ACMP	89.59 10	92.62 169	92.14 163	94.05 211	96.40 189	88.20 231	97.36 116	97.25 185	91.52 133	88.30 266	96.64 152	78.46 262	98.72 171	91.86 145	91.48 227	95.23 270
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v10		91.04 234	90.23 235	93.49 240	94.12 293	88.16 234	97.32 120	97.08 198	88.26 232	88.29 267	94.22 277	82.17 200	97.97 247	86.45 249	84.12 311	94.33 311

QAPM		93.45 139	92.27 161	96.98 75	96.77 167	92.62 88	98.39 21	98.12 56	84.50 304	88.27 268	97.77 86	82.39 196	99.81 27	85.40 267	98.81 93	98.51 128

Anonymous20231211		90.63 251	89.42 262	94.27 204	98.24 94	89.19 207	98.05 46	97.89 104	79.95 338	88.25 269	94.96 234	72.56 306	98.13 220	89.70 182	85.14 296	95.49 246

CP-MVSNet		91.89 195	91.24 195	93.82 225	95.05 255	88.57 220	97.82 66	98.19 44	91.70 129	88.21 270	95.76 203	81.96 203	97.52 295	87.86 217	84.65 303	95.37 259

cl-mvsnet1		90.97 238	90.33 227	92.88 264	95.36 234	86.19 275	94.46 285	96.63 241	87.82 245	88.18 271	94.23 275	82.99 179	97.53 293	87.72 220	85.57 288	94.93 282

cl-mvsnet____		90.96 239	90.32 228	92.89 263	95.37 233	86.21 274	94.46 285	96.64 238	87.82 245	88.15 272	94.18 278	82.98 180	97.54 291	87.70 223	85.59 287	94.92 284

tpmvs		89.83 270	89.15 268	91.89 286	94.92 262	80.30 333	93.11 321	95.46 285	86.28 277	88.08 273	92.65 312	80.44 226	98.52 189	81.47 302	89.92 249	96.84 202

PS-CasMVS		91.55 207	90.84 209	93.69 232	94.96 259	88.28 227	97.84 65	98.24 34	91.46 136	88.04 274	95.80 198	79.67 241	97.48 297	87.02 242	84.54 307	95.31 262

MVS_0304		88.79 281	87.57 283	92.46 273	94.65 276	86.15 277	96.40 206	97.17 189	86.44 275	88.02 275	91.71 329	56.68 356	97.03 315	84.47 278	92.58 209	94.19 316

MIMVSNet		88.50 285	86.76 293	93.72 230	94.84 268	87.77 243	91.39 334	94.05 330	86.41 276	87.99 276	92.59 314	63.27 348	95.82 337	77.44 326	92.84 205	97.57 185

GG-mvs-BLEND				93.62 234	93.69 306	89.20 205	92.39 332	83.33 364		87.98 277	89.84 340	71.00 314	96.87 322	82.08 299	95.40 171	94.80 294

miper_lstm_enhance		90.50 255	90.06 244	91.83 288	95.33 239	83.74 304	93.86 304	96.70 234	87.56 256	87.79 278	93.81 291	83.45 170	96.92 321	87.39 234	84.62 305	94.82 291

PEN-MVS		91.20 227	90.44 224	93.48 241	94.49 282	87.91 240	97.76 72	98.18 46	91.29 143	87.78 279	95.74 205	80.35 228	97.33 308	85.46 266	82.96 324	95.19 272

ITE_SJBPF				92.43 275	95.34 236	85.37 286		95.92 266	91.47 135	87.75 280	96.39 171	71.00 314	97.96 251	82.36 297	89.86 250	93.97 320

v7n		90.76 244	89.86 249	93.45 244	93.54 309	87.60 246	97.70 82	97.37 174	88.85 213	87.65 281	94.08 282	81.08 215	98.10 225	84.68 275	83.79 318	94.66 303

Patchmtry		88.64 284	87.25 287	92.78 268	94.09 294	86.64 264	89.82 347	95.68 278	80.81 335	87.63 282	92.36 320	80.91 218	97.03 315	78.86 321	85.12 297	94.67 302

pmmvs4		90.93 240	89.85 250	94.17 206	93.34 316	90.79 152	94.60 279	96.02 264	84.62 302	87.45 283	95.15 229	81.88 206	97.45 300	87.70 223	87.87 266	94.27 315

tpm cat1		88.36 286	87.21 289	91.81 290	95.13 252	80.55 330	92.58 329	95.70 275	74.97 350	87.45 283	91.96 325	78.01 273	98.17 218	80.39 311	88.74 260	96.72 206

FMVSNet1		89.88 268	88.31 277	94.59 189	95.41 229	91.18 138	97.50 101	96.93 212	86.62 273	87.41 285	94.51 256	65.94 343	97.29 310	83.04 290	87.43 271	95.31 262

IterMVS-SCA-FT		90.31 257	89.81 252	91.82 289	95.52 225	84.20 301	94.30 292	96.15 261	90.61 168	87.39 286	94.27 272	75.80 288	96.44 327	87.34 235	86.88 279	94.82 291

MVS		91.71 199	90.44 224	95.51 149	95.20 249	91.59 120	96.04 232	97.45 161	73.44 353	87.36 287	95.60 212	85.42 142	99.10 135	85.97 259	97.46 126	95.83 232

EU-MVSNet		88.72 283	88.90 270	88.20 329	93.15 320	74.21 353	96.63 190	94.22 329	85.18 293	87.32 288	95.97 188	76.16 286	94.98 345	85.27 268	86.17 282	95.41 253

IterMVS		90.15 263	89.67 258	91.61 296	95.48 227	83.72 305	94.33 291	96.12 262	89.99 181	87.31 289	94.15 280	75.78 290	96.27 330	86.97 243	86.89 278	94.83 289
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs5		89.86 269	88.87 271	92.82 266	92.86 323	86.23 273	96.26 220	95.39 286	84.24 306	87.12 290	94.51 256	74.27 297	97.36 307	87.61 231	87.57 269	94.86 287

DTE-MVSNet		90.56 252	89.75 256	93.01 259	93.95 297	87.25 250	97.64 91	97.65 133	90.74 159	87.12 290	95.68 209	79.97 236	97.00 319	83.33 287	81.66 329	94.78 298

Patchmatch-test		89.42 273	87.99 280	93.70 231	95.27 243	85.11 289	88.98 350	94.37 325	81.11 331	87.10 292	93.69 294	82.28 197	97.50 296	74.37 340	94.76 182	98.48 133

IB-MVS	87.33 17	89.91 266	88.28 278	94.79 184	95.26 246	87.70 244	95.12 274	93.95 333	89.35 198	87.03 293	92.49 315	70.74 316	99.19 125	89.18 199	81.37 330	97.49 187
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
EPNet_dtu		91.71 199	91.28 193	92.99 260	93.76 304	83.71 306	96.69 181	95.28 293	93.15 82	87.02 294	95.95 190	83.37 171	97.38 306	79.46 318	96.84 143	97.88 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
baseline2		91.63 202	90.86 206	93.94 220	94.33 288	86.32 270	95.92 240	91.64 351	89.37 197	86.94 295	94.69 249	81.62 210	98.69 172	88.64 208	94.57 186	96.81 203

MSDG		91.42 214	90.24 234	94.96 173	97.15 145	88.91 212	93.69 309	96.32 253	85.72 286	86.93 296	96.47 165	80.24 230	98.98 149	80.57 309	95.05 178	96.98 195

test0.0.03 1		89.37 274	88.70 272	91.41 301	92.47 330	85.63 281	95.22 271	92.70 343	91.11 152	86.91 297	93.65 298	79.02 252	93.19 355	78.00 325	89.18 255	95.41 253

COLMAP_ROB	87.81 15	90.40 256	89.28 265	93.79 227	97.95 111	87.13 256	96.92 159	95.89 269	82.83 321	86.88 298	97.18 123	73.77 302	99.29 119	78.44 323	93.62 198	94.95 278
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
D2MVS		91.30 223	90.95 203	92.35 277	94.71 274	85.52 283	96.18 227	98.21 40	88.89 212	86.60 299	93.82 290	79.92 237	97.95 253	89.29 193	90.95 237	93.56 324

OurMVSNet-221017-0		90.51 254	90.19 239	91.44 300	93.41 314	81.25 324	96.98 154	96.28 254	91.68 130	86.55 300	96.30 174	74.20 298	97.98 244	88.96 202	87.40 273	95.09 273

MS-PatchMatch		90.27 258	89.77 254	91.78 292	94.33 288	84.72 296	95.55 254	96.73 228	86.17 280	86.36 301	95.28 226	71.28 312	97.80 268	84.09 281	98.14 112	92.81 333

1314		92.81 166	92.03 166	95.14 164	95.33 239	89.52 190	96.04 232	97.44 165	87.72 252	86.25 302	95.33 223	83.84 162	98.79 162	89.26 194	97.05 142	97.11 193

tfpnnormal		89.70 271	88.40 276	93.60 235	95.15 250	90.10 169	97.56 97	98.16 50	87.28 263	86.16 303	94.63 253	77.57 276	98.05 236	74.48 338	84.59 306	92.65 336

pm-mvs1		90.72 248	89.65 260	93.96 217	94.29 291	89.63 182	97.79 69	96.82 226	89.07 204	86.12 304	95.48 220	78.61 260	97.78 271	86.97 243	81.67 328	94.46 307

OpenMVS	89.19 12	92.86 162	91.68 178	96.40 97	95.34 236	92.73 84	98.27 28	98.12 56	84.86 299	85.78 305	97.75 87	78.89 257	99.74 32	87.50 233	98.65 99	96.73 205

LTVRE_ROB	88.41 13	90.99 236	89.92 247	94.19 205	96.18 199	89.55 187	96.31 216	97.09 197	87.88 244	85.67 306	95.91 192	78.79 258	98.57 185	81.50 301	89.98 248	94.44 308
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
testgi		87.97 289	87.21 289	90.24 319	92.86 323	80.76 326	96.67 184	94.97 308	91.74 128	85.52 307	95.83 196	62.66 350	94.47 349	76.25 333	88.36 263	95.48 247

AllTest		90.23 260	88.98 269	93.98 214	97.94 112	86.64 264	96.51 198	95.54 283	85.38 290	85.49 308	96.77 142	70.28 319	99.15 130	80.02 313	92.87 203	96.15 219

TestCases				93.98 214	97.94 112	86.64 264		95.54 283	85.38 290	85.49 308	96.77 142	70.28 319	99.15 130	80.02 313	92.87 203	96.15 219

DSMNet-mixed		86.34 302	86.12 298	87.00 334	89.88 347	70.43 356	94.93 275	90.08 356	77.97 347	85.42 310	92.78 311	74.44 296	93.96 351	74.43 339	95.14 174	96.62 207

ppachtmachnet_test		88.35 287	87.29 286	91.53 297	92.45 331	83.57 309	93.75 307	95.97 265	84.28 305	85.32 311	94.18 278	79.00 256	96.93 320	75.71 335	84.99 301	94.10 317

CL-MVSNet_2432*1600		86.31 303	85.15 305	89.80 323	88.83 352	81.74 322	93.93 303	96.22 258	86.67 272	85.03 312	90.80 333	78.09 270	94.50 347	74.92 337	71.86 351	93.15 329

our_test_3		88.78 282	87.98 281	91.20 305	92.45 331	82.53 315	93.61 313	95.69 276	85.77 285	84.88 313	93.71 293	79.99 235	96.78 325	79.47 317	86.24 281	94.28 314

MVP-Stereo		90.74 247	90.08 241	92.71 269	93.19 319	88.20 231	95.86 242	96.27 255	86.07 281	84.86 314	94.76 245	77.84 274	97.75 274	83.88 285	98.01 114	92.17 344
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMH+	87.92 14	90.20 261	89.18 267	93.25 251	96.48 184	86.45 269	96.99 152	96.68 235	88.83 215	84.79 315	96.22 177	70.16 321	98.53 188	84.42 280	88.04 264	94.77 299

NR-MVSNet		92.34 177	91.27 194	95.53 148	94.95 260	93.05 76	97.39 113	98.07 70	92.65 104	84.46 316	95.71 206	85.00 147	97.77 273	89.71 181	83.52 320	95.78 235

LF4IMVS		87.94 290	87.25 287	89.98 321	92.38 333	80.05 337	94.38 288	95.25 296	87.59 255	84.34 317	94.74 247	64.31 346	97.66 281	84.83 272	87.45 270	92.23 341

LCM-MVSNet-Re		92.50 170	92.52 154	92.44 274	96.82 165	81.89 320	96.92 159	93.71 334	92.41 109	84.30 318	94.60 254	85.08 146	97.03 315	91.51 153	97.36 132	98.40 142

TransMVSNet (Re)		88.94 277	87.56 284	93.08 258	94.35 287	88.45 225	97.73 76	95.23 297	87.47 257	84.26 319	95.29 224	79.86 238	97.33 308	79.44 319	74.44 347	93.45 327

Anonymous20231206		87.09 296	86.14 297	89.93 322	91.22 339	80.35 331	96.11 229	95.35 289	83.57 316	84.16 320	93.02 308	73.54 304	95.61 339	72.16 347	86.14 283	93.84 322

SixPastTwentyTwo		89.15 275	88.54 275	90.98 307	93.49 312	80.28 334	96.70 179	94.70 316	90.78 157	84.15 321	95.57 213	71.78 309	97.71 277	84.63 276	85.07 298	94.94 280

TDRefinement		86.53 299	84.76 309	91.85 287	82.23 360	84.25 299	96.38 209	95.35 289	84.97 298	84.09 322	94.94 235	65.76 344	98.34 205	84.60 277	74.52 346	92.97 330

DIV-MVS_2432*1600		85.95 307	84.95 306	88.96 326	89.55 350	79.11 344	95.13 273	96.42 249	85.91 283	84.07 323	90.48 334	70.03 322	94.82 346	80.04 312	72.94 350	92.94 331

pmmvs6		87.81 292	86.19 296	92.69 270	91.32 338	86.30 271	97.34 117	96.41 250	80.59 337	84.05 324	94.37 265	67.37 334	97.67 279	84.75 274	79.51 336	94.09 319

ACMH	87.59 16	90.53 253	89.42 262	93.87 223	96.21 196	87.92 238	97.24 127	96.94 211	88.45 227	83.91 325	96.27 176	71.92 307	98.62 179	84.43 279	89.43 253	95.05 276
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet5		87.29 295	85.79 299	91.78 292	94.80 270	87.28 248	95.49 257	95.28 293	84.09 308	83.85 326	91.82 326	62.95 349	94.17 350	78.48 322	85.34 293	93.91 321

USDC		88.94 277	87.83 282	92.27 279	94.66 275	84.96 292	93.86 304	95.90 268	87.34 261	83.40 327	95.56 215	67.43 333	98.19 215	82.64 296	89.67 252	93.66 323

Anonymous20240521		86.42 301	85.44 301	89.34 325	90.33 343	79.79 338	96.73 175	95.92 266	83.71 314	83.25 328	91.36 332	63.92 347	96.01 331	78.39 324	85.36 292	92.22 342

KD-MVS_2432*1600		84.81 314	82.64 317	91.31 302	91.07 340	85.34 287	91.22 336	95.75 273	85.56 288	83.09 329	90.21 336	67.21 335	95.89 333	77.18 330	62.48 358	92.69 334

miper_refine_blended		84.81 314	82.64 317	91.31 302	91.07 340	85.34 287	91.22 336	95.75 273	85.56 288	83.09 329	90.21 336	67.21 335	95.89 333	77.18 330	62.48 358	92.69 334

PVSNet_0	82.17 19	85.46 311	83.64 314	90.92 308	95.27 243	79.49 340	90.55 342	95.60 280	83.76 313	83.00 331	89.95 338	71.09 313	97.97 247	82.75 294	60.79 360	95.31 262

test_0402		86.46 300	84.79 308	91.45 299	95.02 257	85.55 282	96.29 218	94.89 311	80.90 332	82.21 332	93.97 286	68.21 330	97.29 310	62.98 358	88.68 261	91.51 347

Patchmatch-RL test		87.38 294	86.24 295	90.81 310	88.74 353	78.40 347	88.12 352	93.17 339	87.11 266	82.17 333	89.29 342	81.95 204	95.60 340	88.64 208	77.02 341	98.41 141

TinyColmap		86.82 298	85.35 304	91.21 304	94.91 265	82.99 313	93.94 302	94.02 332	83.58 315	81.56 334	94.68 250	62.34 351	98.13 220	75.78 334	87.35 274	92.52 338

test20.03		86.14 305	85.40 303	88.35 327	90.12 344	80.06 336	95.90 241	95.20 298	88.59 222	81.29 335	93.62 299	71.43 311	92.65 356	71.26 351	81.17 331	92.34 340

N_pmnet		78.73 323	78.71 325	78.79 339	92.80 325	46.50 368	94.14 297	43.71 371	78.61 344	80.83 336	91.66 330	74.94 294	96.36 328	67.24 355	84.45 308	93.50 325

MVS-HIRNet		82.47 320	81.21 322	86.26 336	95.38 231	69.21 359	88.96 351	89.49 357	66.28 355	80.79 337	74.08 359	68.48 328	97.39 305	71.93 348	95.47 169	92.18 343

PM-MVS		83.48 317	81.86 321	88.31 328	87.83 356	77.59 348	93.43 314	91.75 350	86.91 268	80.63 338	89.91 339	44.42 361	95.84 336	85.17 271	76.73 343	91.50 348

ambc				86.56 335	83.60 358	70.00 358	85.69 354	94.97 308		80.60 339	88.45 343	37.42 363	96.84 323	82.69 295	75.44 345	92.86 332

MIMVSNet1		84.93 313	83.05 315	90.56 315	89.56 349	84.84 295	95.40 260	95.35 289	83.91 309	80.38 340	92.21 324	57.23 354	93.34 354	70.69 353	82.75 327	93.50 325

lessismore_v0				90.45 316	91.96 336	79.09 345		87.19 361		80.32 341	94.39 263	66.31 340	97.55 290	84.00 283	76.84 342	94.70 301

K. test v3		87.64 293	86.75 294	90.32 318	93.02 322	79.48 341	96.61 191	92.08 348	90.66 164	80.25 342	94.09 281	67.21 335	96.65 326	85.96 260	80.83 332	94.83 289

OpenMVS_ROB	81.14 20	84.42 316	82.28 319	90.83 309	90.06 345	84.05 303	95.73 248	94.04 331	73.89 352	80.17 343	91.53 331	59.15 353	97.64 282	66.92 356	89.05 256	90.80 351

EG-PatchMatch MVS		87.02 297	85.44 301	91.76 294	92.67 327	85.00 291	96.08 231	96.45 248	83.41 318	79.52 344	93.49 301	57.10 355	97.72 276	79.34 320	90.87 239	92.56 337

pmmvs-eth3d		86.22 304	84.45 310	91.53 297	88.34 354	87.25 250	94.47 283	95.01 305	83.47 317	79.51 345	89.61 341	69.75 324	95.71 338	83.13 289	76.73 343	91.64 345

pmmvs3		79.97 322	77.50 326	87.39 332	82.80 359	79.38 342	92.70 327	90.75 355	70.69 354	78.66 346	87.47 351	51.34 359	93.40 353	73.39 344	69.65 354	89.38 354

UnsupCasMVSNet_eth		85.99 306	84.45 310	90.62 314	89.97 346	82.40 318	93.62 312	97.37 174	89.86 183	78.59 347	92.37 317	65.25 345	95.35 344	82.27 298	70.75 352	94.10 317

new-patchmatchnet		83.18 318	81.87 320	87.11 333	86.88 357	75.99 351	93.70 308	95.18 299	85.02 297	77.30 348	88.40 344	65.99 342	93.88 352	74.19 342	70.18 353	91.47 349

UnsupCasMVSNet_bld		82.13 321	79.46 324	90.14 320	88.00 355	82.47 316	90.89 341	96.62 243	78.94 343	75.61 349	84.40 353	56.63 357	96.31 329	77.30 329	66.77 356	91.63 346

ET-MVSNet_ETH3D		91.49 211	90.11 240	95.63 140	96.40 189	91.57 122	95.34 262	93.48 336	90.60 170	75.58 350	95.49 219	80.08 233	96.79 324	94.25 98	89.76 251	98.52 126

new_pmnet		82.89 319	81.12 323	88.18 330	89.63 348	80.18 335	91.77 333	92.57 344	76.79 349	75.56 351	88.23 346	61.22 352	94.48 348	71.43 349	82.92 325	89.87 353

CMPMVS	62.92 21	85.62 310	84.92 307	87.74 331	89.14 351	73.12 355	94.17 296	96.80 227	73.98 351	73.65 352	94.93 236	66.36 338	97.61 286	83.95 284	91.28 231	92.48 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
YYNet1		85.87 308	84.23 312	90.78 313	92.38 333	82.46 317	93.17 318	95.14 301	82.12 325	67.69 353	92.36 320	78.16 269	95.50 343	77.31 328	79.73 334	94.39 309

MDA-MVSNet_test_wron		85.87 308	84.23 312	90.80 312	92.38 333	82.57 314	93.17 318	95.15 300	82.15 324	67.65 354	92.33 323	78.20 266	95.51 342	77.33 327	79.74 333	94.31 313

DeepMVS_CX				74.68 343	90.84 342	64.34 363		81.61 366	65.34 356	67.47 355	88.01 349	48.60 360	80.13 364	62.33 359	73.68 349	79.58 358

LCM-MVSNet		72.55 324	69.39 328	82.03 337	70.81 367	65.42 362	90.12 346	94.36 326	55.02 359	65.88 356	81.72 354	24.16 370	89.96 357	74.32 341	68.10 355	90.71 352

test_method		66.11 328	64.89 331	69.79 344	72.62 365	35.23 372	65.19 362	92.83 342	20.35 365	65.20 357	88.08 348	43.14 362	82.70 362	73.12 345	63.46 357	91.45 350

MDA-MVSNet-bldmvs		85.00 312	82.95 316	91.17 306	93.13 321	83.33 310	94.56 281	95.00 306	84.57 303	65.13 358	92.65 312	70.45 317	95.85 335	73.57 343	77.49 340	94.33 311

PMMVS2		70.19 326	66.92 329	80.01 338	76.35 361	65.67 361	86.22 353	87.58 360	64.83 357	62.38 359	80.29 356	26.78 368	88.49 359	63.79 357	54.07 361	85.88 355

FPMVS		71.27 325	69.85 327	75.50 341	74.64 362	59.03 364	91.30 335	91.50 352	58.80 358	57.92 360	88.28 345	29.98 366	85.53 361	53.43 360	82.84 326	81.95 357

Gipuma		67.86 327	65.41 330	75.18 342	92.66 328	73.45 354	66.50 361	94.52 321	53.33 360	57.80 361	66.07 361	30.81 364	89.20 358	48.15 362	78.88 339	62.90 361
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
tmp_tt		51.94 334	53.82 334	46.29 349	33.73 371	45.30 370	78.32 359	67.24 370	18.02 366	50.93 362	87.05 352	52.99 358	53.11 368	70.76 352	25.29 366	40.46 364

ANet_high		63.94 329	59.58 332	77.02 340	61.24 369	66.06 360	85.66 355	87.93 359	78.53 345	42.94 363	71.04 360	25.42 369	80.71 363	52.60 361	30.83 364	84.28 356

E-PMN		53.28 331	52.56 335	55.43 347	74.43 363	47.13 367	83.63 357	76.30 367	42.23 362	42.59 364	62.22 363	28.57 367	74.40 365	31.53 365	31.51 363	44.78 362

EMVS		52.08 333	51.31 336	54.39 348	72.62 365	45.39 369	83.84 356	75.51 368	41.13 363	40.77 365	59.65 364	30.08 365	73.60 366	28.31 366	29.90 365	44.18 363

MVE	50.73 23	53.25 332	48.81 337	66.58 346	65.34 368	57.50 365	72.49 360	70.94 369	40.15 364	39.28 366	63.51 362	6.89 373	73.48 367	38.29 364	42.38 362	68.76 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	53.92 22	58.58 330	55.40 333	68.12 345	51.00 370	48.64 366	78.86 358	87.10 362	46.77 361	35.84 367	74.28 358	8.76 371	86.34 360	42.07 363	73.91 348	69.38 359
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuyk23d		25.11 335	24.57 339	26.74 350	73.98 364	39.89 371	57.88 363	9.80 372	12.27 367	10.39 368	6.97 370	7.03 372	36.44 369	25.43 367	17.39 367	3.89 367

testmvs		13.36 337	16.33 340	4.48 352	5.04 372	2.26 374	93.18 317	3.28 373	2.70 368	8.24 369	21.66 366	2.29 375	2.19 370	7.58 368	2.96 368	9.00 366

test123		13.04 338	15.66 341	5.18 351	4.51 373	3.45 373	92.50 331	1.81 374	2.50 369	7.58 370	20.15 367	3.67 374	2.18 371	7.13 369	1.07 369	9.90 365

uanet_test		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

cdsmvs_eth3d_5k		23.24 336	30.99 338	0.00 353	0.00 374	0.00 375	0.00 364	97.63 136	0.00 370	0.00 371	96.88 138	84.38 155	0.00 372	0.00 370	0.00 370	0.00 368

pcd_1.5k_mvsjas		7.39 340	9.85 343	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	88.65 97	0.00 372	0.00 370	0.00 370	0.00 368

sosnet-low-res		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

sosnet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

uncertanet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

Regformer		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

ab-mvs-re		8.06 339	10.74 342	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	96.69 149	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

uanet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

No_MVS				98.86 1	98.67 62	96.94 1		97.93 103					99.86 8	97.68 2	99.67 6	99.77 1

eth-test2					0.00 374

eth-test					0.00 374

OPU-MVS				98.55 2	98.82 56	96.86 2	98.25 30				98.26 54	96.04 2	99.24 122	95.36 72	99.59 16	99.56 24

save fliter					98.91 49	94.28 36	97.02 147	98.02 88	95.35 8

test_0728_SECOND				98.51 3	99.45 2	95.93 4	98.21 37	98.28 26					99.86 8	97.52 3	99.67 6	99.75 4

GSMVS																98.45 136

sam_mvs1												82.76 186				98.45 136

sam_mvs												81.94 205

MTGPA								98.08 64

test_post1							92.81 326				16.58 369	80.53 224	97.68 278	86.20 252

test_post											17.58 368	81.76 207	98.08 230

patchmatchnet-post											90.45 335	82.65 190	98.10 225

MTMP							97.86 61	82.03 365

gm-plane-assit					93.22 318	78.89 346			84.82 300		93.52 300		98.64 176	87.72 220

test9_res														94.81 89	99.38 50	99.45 47

agg_prior2														93.94 106	99.38 50	99.50 39

test_prior4						93.66 60	96.42 202

test_prior				97.23 63	98.67 62	92.99 77		98.00 93					99.41 108			99.29 64

新几何2							95.79 246

旧先验1					98.38 82	93.38 68		97.75 117			98.09 63	92.30 39			99.01 87	99.16 72

无先验							95.79 246	97.87 108	83.87 312				99.65 54	87.68 226		98.89 103

原ACMM2							95.67 249

testdata2													99.67 50	85.96 260

segment_acmp												92.89 23

testdata1							95.26 270		93.10 85

plane_prior7					96.21 196	89.98 175

plane_prior6					96.10 206	90.00 171						81.32 213

plane_prior5								97.51 148					98.60 180	93.02 126	92.23 213	95.86 228

plane_prior4											96.64 152

plane_prior2							97.74 74		94.85 26

plane_prior1					96.14 204

plane_prior						89.99 173	97.24 127		94.06 50						92.16 217

n2								0.00 375

nn								0.00 375

door-mid								91.06 354

test11								97.88 106

door								91.13 353

HQP5-MVS						89.33 199

BP-MVS														92.13 138

HQP3-MVS								97.39 171							92.10 218

HQP2-MVS												80.95 216

NP-MVS					95.99 210	89.81 180					95.87 193

ACMMP++_ref															90.30 246

ACMMP++															91.02 235

Test By Simon												88.73 96

High-resolution multi-view benchmark