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.

SMA-MVS		80.28 5	80.39 6	79.95 3	86.60 22	61.95 22	86.33 11	85.75 25	62.49 63	82.20 13	92.28 1	56.53 32	89.70 13	79.85 3	91.48 1	88.19 13
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
APDe-MVS		80.16 6	80.59 5	78.86 26	86.64 20	60.02 51	88.12 1	86.42 16	62.94 52	82.40 12	92.12 2	59.64 16	89.76 12	78.70 9	88.32 34	86.79 59

DVP-MVS		80.84 3	81.64 2	78.42 34	87.75 7	59.07 68	87.85 3	85.03 36	64.26 30	83.82 7	92.00 3	64.82 7	90.75 6	78.66 11	90.61 9	85.45 107
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									65.04 20	83.82 7	92.00 3	64.69 9	90.75 6	79.48 4	90.63 8	88.09 16

test0726					87.75 7	59.07 68	87.86 2	86.83 10	64.26 30	84.19 6	91.92 5	64.82 7

SED-MVS		81.56 1	82.30 1	79.32 10	87.77 4	58.90 73	87.82 5	86.78 12	64.18 33	85.97 1	91.84 6	66.87 2	90.83 3	78.63 13	90.87 4	88.23 11

test_241102_TWO								86.73 14	64.18 33	84.26 5	91.84 6	65.19 5	90.83 3	78.63 13	90.70 6	87.65 32

DPE-MVS		80.56 4	80.98 4	79.29 12	87.27 12	60.56 46	85.71 24	86.42 16	63.28 45	83.27 11	91.83 8	64.96 6	90.47 8	76.41 25	89.67 20	86.84 56
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MP-MVS-pluss		78.35 21	78.46 17	78.03 41	84.96 56	59.52 59	82.93 63	85.39 29	62.15 68	76.41 33	91.51 9	52.47 77	86.78 69	80.66 2	89.64 21	87.80 26
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test_241102_ONE					87.77 4	58.90 73		86.78 12	64.20 32	85.97 1	91.34 10	66.87 2	90.78 5

ACMMP_NAP		78.77 14	78.78 14	78.74 28	85.44 48	61.04 36	83.84 50	85.16 33	62.88 54	78.10 25	91.26 11	52.51 75	88.39 29	79.34 5	90.52 11	86.78 60

SteuartSystems-ACMMP		79.48 9	79.31 10	79.98 2	83.01 78	62.18 19	87.60 7	85.83 23	66.69 10	78.03 28	90.98 12	54.26 55	90.06 10	78.42 15	89.02 25	87.69 30
Skip Steuart: Steuart Systems R&D Blog.
zzz-MVS		77.61 31	77.36 31	78.35 35	86.08 37	63.57 2	83.37 56	80.97 128	65.13 18	75.77 35	90.88 13	48.63 117	86.66 72	77.23 17	88.17 36	84.81 129

MTAPA		76.90 38	76.42 40	78.35 35	86.08 37	63.57 2	74.92 203	80.97 128	65.13 18	75.77 35	90.88 13	48.63 117	86.66 72	77.23 17	88.17 36	84.81 129

xxxxxxxxxxxxxcwj		78.37 20	78.25 22	78.76 27	86.17 33	61.30 31	83.98 46	79.95 143	59.00 126	79.16 17	90.75 15	57.96 23	87.09 60	77.08 21	90.18 13	87.87 22

SF-MVS		78.82 12	79.22 11	77.60 47	82.88 80	57.83 89	84.99 30	88.13 3	61.86 76	79.16 17	90.75 15	57.96 23	87.09 60	77.08 21	90.18 13	87.87 22

HPM-MVS++		79.88 7	80.14 7	79.10 18	88.17 1	64.80 1	86.59 10	83.70 63	65.37 15	78.78 22	90.64 17	58.63 21	87.24 53	79.00 8	90.37 12	85.26 117

MP-MVS		78.35 21	78.26 21	78.64 29	86.54 24	63.47 5	86.02 18	83.55 66	63.89 38	73.60 67	90.60 18	54.85 51	86.72 70	77.20 19	88.06 39	85.74 96
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
region2R		77.67 30	77.18 33	79.15 15	86.76 15	62.95 8	86.29 12	84.16 51	62.81 58	73.30 70	90.58 19	49.90 102	88.21 34	73.78 41	87.03 50	86.29 77

ACMMPR		77.71 28	77.23 32	79.16 14	86.75 16	62.93 9	86.29 12	84.24 49	62.82 56	73.55 68	90.56 20	49.80 104	88.24 33	74.02 39	87.03 50	86.32 74

MSP-MVS		81.06 2	81.40 3	80.02 1	86.21 31	62.73 12	86.09 15	86.83 10	65.51 14	83.81 9	90.51 21	63.71 10	89.23 18	81.51 1	88.44 30	88.09 16
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HFP-MVS		78.01 26	77.65 27	79.10 18	86.71 17	62.81 10	86.29 12	84.32 47	62.82 56	73.96 59	90.50 22	53.20 70	88.35 30	74.02 39	87.05 48	86.13 79

#test#		77.83 27	77.41 30	79.10 18	86.71 17	62.81 10	85.69 25	84.32 47	61.61 79	73.96 59	90.50 22	53.20 70	88.35 30	73.68 42	87.05 48	86.13 79

CP-MVS		77.12 36	76.68 37	78.43 33	86.05 39	63.18 7	87.55 8	83.45 69	62.44 65	72.68 82	90.50 22	48.18 123	87.34 52	73.59 44	85.71 63	84.76 133

ZNCC-MVS		78.82 12	78.67 16	79.30 11	86.43 27	62.05 21	86.62 9	86.01 22	63.32 44	75.08 40	90.47 25	53.96 59	88.68 26	76.48 24	89.63 22	87.16 49

9.14			78.75 15		83.10 74		84.15 41	88.26 2	59.90 110	78.57 24	90.36 26	57.51 29	86.86 66	77.39 16	89.52 23

ETH3D-3000-0.1		78.58 15	78.91 13	77.61 46	83.06 75	57.86 88	84.14 43	88.31 1	60.37 98	79.14 19	90.35 27	57.76 26	87.00 63	77.16 20	89.90 16	87.97 19

SR-MVS		76.13 45	75.70 47	77.40 52	85.87 41	61.20 33	85.52 26	82.19 93	59.99 109	75.10 39	90.35 27	47.66 129	86.52 79	71.64 56	82.99 79	84.47 139

PGM-MVS		76.77 40	76.06 42	78.88 25	86.14 36	62.73 12	82.55 72	83.74 62	61.71 77	72.45 88	90.34 29	48.48 121	88.13 35	72.32 50	86.85 54	85.78 90

APD-MVS		78.02 24	78.04 25	77.98 42	86.44 26	60.81 41	85.52 26	84.36 46	60.61 90	79.05 20	90.30 30	55.54 43	88.32 32	73.48 46	87.03 50	84.83 128
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
testtj		78.47 18	78.43 18	78.61 30	86.82 14	60.67 44	86.07 16	85.38 30	62.12 69	78.65 23	90.29 31	55.76 40	89.31 17	73.55 45	87.22 47	85.84 88

mPP-MVS		76.54 41	75.93 44	78.34 37	86.47 25	63.50 4	85.74 23	82.28 92	62.90 53	71.77 92	90.26 32	46.61 148	86.55 78	71.71 55	85.66 64	84.97 125

DeepC-MVS	69.38 2	78.56 17	78.14 23	79.83 5	83.60 68	61.62 26	84.17 40	86.85 9	63.23 47	73.84 64	90.25 33	57.68 27	89.96 11	74.62 33	89.03 24	87.89 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test1172		75.36 54	74.81 57	77.02 56	85.47 47	60.79 43	83.94 49	81.63 105	59.52 120	74.66 52	90.18 34	44.74 168	85.84 95	70.63 63	82.52 88	84.42 140

APD-MVS_3200maxsize		74.96 55	74.39 62	76.67 62	82.20 84	58.24 84	83.67 52	83.29 76	58.41 137	73.71 65	90.14 35	45.62 154	85.99 90	69.64 67	82.85 85	85.78 90

SR-MVS-dyc-post		74.57 64	73.90 67	76.58 64	83.49 70	59.87 55	84.29 35	81.36 112	58.07 143	73.14 73	90.07 36	44.74 168	85.84 95	68.20 76	81.76 95	84.03 150

RE-MVS-def			73.71 71		83.49 70	59.87 55	84.29 35	81.36 112	58.07 143	73.14 73	90.07 36	43.06 184		68.20 76	81.76 95	84.03 150

ZD-MVS					86.64 20	60.38 48		82.70 88	57.95 146	78.10 25	90.06 38	56.12 38	88.84 24	74.05 38	87.00 53

ETH3D cwj APD-0.16		78.02 24	78.13 24	77.71 45	82.10 85	58.65 78	82.72 68	87.55 5	58.33 140	78.05 27	90.06 38	58.35 22	87.65 49	76.15 26	89.86 17	86.82 57

CNVR-MVS		79.84 8	79.97 8	79.45 8	87.90 2	62.17 20	84.37 34	85.03 36	66.96 5	77.58 29	90.06 38	59.47 18	89.13 20	78.67 10	89.73 18	87.03 52

GST-MVS		78.14 23	77.85 26	78.99 23	86.05 39	61.82 25	85.84 19	85.21 32	63.56 42	74.29 56	90.03 41	52.56 74	88.53 28	74.79 32	88.34 32	86.63 63

DeepPCF-MVS	69.58 1	79.03 11	79.00 12	79.13 16	84.92 60	60.32 49	83.03 60	85.33 31	62.86 55	80.17 14	90.03 41	61.76 12	88.95 22	74.21 36	88.67 29	88.12 15

XVS		77.17 35	76.56 39	79.00 21	86.32 29	62.62 14	85.83 20	83.92 55	64.55 24	72.17 89	90.01 43	47.95 125	88.01 39	71.55 57	86.74 56	86.37 69

HPM-MVS		77.28 33	76.85 35	78.54 32	85.00 55	60.81 41	82.91 64	85.08 34	62.57 61	73.09 76	89.97 44	50.90 98	87.48 51	75.30 28	86.85 54	87.33 46
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
OPU-MVS				79.83 5	87.54 10	60.93 39	87.82 5				89.89 45	67.01 1	90.33 9	73.16 47	91.15 3	88.23 11

abl_6		74.34 66	73.50 72	76.86 58	82.43 82	60.16 50	83.48 55	81.86 99	58.81 130	73.95 61	89.86 46	41.87 195	86.62 74	67.98 80	81.23 100	83.80 163

PC_three_1452									55.09 200	84.46 4	89.84 47	66.68 4	89.41 15	74.24 35	91.38 2	88.42 7

HPM-MVS_fast		74.30 68	73.46 75	76.80 59	84.45 64	59.04 70	83.65 53	81.05 124	60.15 106	70.43 101	89.84 47	41.09 210	85.59 100	67.61 85	82.90 83	85.77 93

TSAR-MVS + MP.		78.44 19	78.28 20	78.90 24	84.96 56	61.41 29	84.03 44	83.82 61	59.34 123	79.37 16	89.76 49	59.84 14	87.62 50	76.69 23	86.74 56	87.68 31
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		76.02 46	75.33 50	78.07 39	85.20 52	61.91 23	85.49 28	84.44 44	63.04 50	69.80 117	89.74 50	45.43 161	87.16 57	72.01 53	82.87 84	85.14 118
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
ETH3 D test6400		79.14 10	79.32 9	78.61 30	86.34 28	58.11 85	84.65 32	87.66 4	58.56 135	78.87 21	89.54 51	63.67 11	89.57 14	74.60 34	89.98 15	88.14 14

UA-Net		73.13 79	72.93 79	73.76 121	83.58 69	51.66 180	78.75 125	77.66 189	67.75 4	72.61 84	89.42 52	49.82 103	83.29 150	53.61 192	83.14 76	86.32 74

VDDNet		71.81 97	71.33 96	73.26 143	82.80 81	47.60 236	78.74 126	75.27 222	59.59 119	72.94 78	89.40 53	41.51 204	83.91 139	58.75 157	82.99 79	88.26 9

SD-MVS		77.70 29	77.62 28	77.93 43	84.47 63	61.88 24	84.55 33	83.87 59	60.37 98	79.89 15	89.38 54	54.97 48	85.58 101	76.12 27	84.94 67	86.33 72
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
NCCC		78.58 15	78.31 19	79.39 9	87.51 11	62.61 16	85.20 29	84.42 45	66.73 9	74.67 51	89.38 54	55.30 44	89.18 19	74.19 37	87.34 46	86.38 66

3Dnovator+	66.72 4	75.84 49	74.57 59	79.66 7	82.40 83	59.92 54	85.83 20	86.32 18	66.92 8	67.80 154	89.24 56	42.03 192	89.38 16	64.07 113	86.50 59	89.69 1

test_prior3		76.89 39	76.96 34	76.69 60	84.20 65	57.27 97	81.75 83	84.88 39	60.37 98	75.01 41	89.06 57	56.22 36	86.43 82	72.19 51	88.96 26	86.38 66

test_prior2							81.75 83		60.37 98	75.01 41	89.06 57	56.22 36		72.19 51	88.96 26

VDD-MVS		72.50 86	72.09 85	73.75 123	81.58 93	49.69 209	77.76 143	77.63 190	63.21 48	73.21 71	89.02 59	42.14 191	83.32 149	61.72 136	82.50 89	88.25 10

CDPH-MVS		76.31 43	75.67 48	78.22 38	85.35 51	59.14 66	81.31 92	84.02 52	56.32 172	74.05 57	88.98 60	53.34 69	87.92 43	69.23 71	88.42 31	87.59 35

agg_prior1		75.94 47	76.01 43	75.72 77	85.04 53	59.96 52	81.44 90	81.04 125	56.14 178	74.68 49	88.90 61	53.91 61	84.04 134	75.01 31	87.92 43	83.16 187

DeepC-MVS_fast	68.24 3	77.25 34	76.63 38	79.12 17	86.15 35	60.86 40	84.71 31	84.85 41	61.98 75	73.06 77	88.88 62	53.72 64	89.06 21	68.27 75	88.04 40	87.42 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 45	61.59 27	81.62 86	81.26 119	55.65 190	74.93 43	88.81 63	53.70 65	84.68 122

train_agg		76.27 44	76.15 41	76.64 63	85.58 45	61.59 27	81.62 86	81.26 119	55.86 182	74.93 43	88.81 63	53.70 65	84.68 122	75.24 30	88.33 33	83.65 171

test_8					85.40 49	60.96 38	81.54 89	81.18 122	55.86 182	74.81 46	88.80 65	53.70 65	84.45 127

LFMVS		71.78 98	71.59 89	72.32 161	83.40 72	46.38 245	79.75 113	71.08 261	64.18 33	72.80 80	88.64 66	42.58 187	83.72 142	57.41 162	84.49 70	86.86 55

MCST-MVS		77.48 32	77.45 29	77.54 48	86.67 19	58.36 82	83.22 58	86.93 8	56.91 159	74.91 45	88.19 67	59.15 19	87.68 48	73.67 43	87.45 45	86.57 64

MG-MVS		73.96 72	73.89 68	74.16 113	85.65 43	49.69 209	81.59 88	81.29 118	61.45 80	71.05 98	88.11 68	51.77 86	87.73 47	61.05 141	83.09 77	85.05 122

Vis-MVSNet		72.18 92	71.37 95	74.61 102	81.29 100	55.41 133	80.90 95	78.28 181	60.73 89	69.23 128	88.09 69	44.36 174	82.65 170	57.68 160	81.75 97	85.77 93
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		72.78 82	72.08 86	74.87 95	84.88 61	61.41 29	84.15 41	77.86 185	55.27 195	67.51 159	88.08 70	41.93 194	81.85 183	69.04 74	80.01 115	81.35 216

casdiffmvs		74.80 58	74.89 55	74.53 106	75.59 222	50.37 197	78.17 137	85.06 35	62.80 59	74.40 54	87.86 71	57.88 25	83.61 145	69.46 70	82.79 86	89.59 2

旧先验1					83.04 76	53.15 156		67.52 286			87.85 72	44.08 175			80.76 102	78.03 263

baseline		74.61 63	74.70 58	74.34 110	75.70 218	49.99 204	77.54 148	84.63 43	62.73 60	73.98 58	87.79 73	57.67 28	83.82 141	69.49 68	82.74 87	89.20 3

OPM-MVS		74.73 61	74.25 63	76.19 69	80.81 108	59.01 71	82.60 71	83.64 64	63.74 40	72.52 85	87.49 74	47.18 139	85.88 94	69.47 69	80.78 101	83.66 170
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
testdata				64.66 269	81.52 94	52.93 159		65.29 301	46.09 294	73.88 63	87.46 75	38.08 235	66.26 314	53.31 195	78.48 140	74.78 298

IS-MVSNet		71.57 101	71.00 101	73.27 142	78.86 146	45.63 258	80.22 104	78.69 165	64.14 36	66.46 174	87.36 76	49.30 108	85.60 99	50.26 214	83.71 75	88.59 5

LPG-MVS_test		72.74 83	71.74 88	75.76 74	80.22 118	57.51 95	82.55 72	83.40 71	61.32 81	66.67 170	87.33 77	39.15 223	86.59 75	67.70 83	77.30 151	83.19 184

LGP-MVS_train				75.76 74	80.22 118	57.51 95		83.40 71	61.32 81	66.67 170	87.33 77	39.15 223	86.59 75	67.70 83	77.30 151	83.19 184

DROMVSNet		75.84 49	75.87 46	75.74 76	78.86 146	52.65 162	83.73 51	86.08 21	63.47 43	72.77 81	87.25 79	53.13 72	87.93 42	71.97 54	85.57 65	86.66 62

EPNet		73.09 80	72.16 84	75.90 72	75.95 214	56.28 114	83.05 59	72.39 254	66.53 12	65.27 195	87.00 80	50.40 100	85.47 107	62.48 129	86.32 60	85.94 84
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		73.77 75	73.47 74	74.66 99	83.02 77	59.29 63	82.30 79	81.88 98	59.34 123	71.59 96	86.83 81	45.94 152	83.65 144	65.09 107	85.22 66	81.06 223

DPM-MVS		75.47 52	75.00 51	76.88 57	81.38 99	59.16 64	79.94 108	85.71 26	56.59 167	72.46 86	86.76 82	56.89 30	87.86 45	66.36 94	88.91 28	83.64 172

Anonymous20240529		69.91 129	69.02 130	72.56 155	80.19 121	47.65 234	77.56 147	80.99 127	55.45 194	69.88 115	86.76 82	39.24 222	82.18 179	54.04 186	77.10 153	87.85 24

nrg030		72.96 81	73.01 78	72.84 149	75.41 225	50.24 198	80.02 106	82.89 86	58.36 139	74.44 53	86.73 84	58.90 20	80.83 206	65.84 99	74.46 171	87.44 41

FIs		70.82 111	71.43 92	68.98 223	78.33 161	38.14 314	76.96 162	83.59 65	61.02 85	67.33 161	86.73 84	55.07 47	81.64 187	54.61 184	79.22 127	87.14 50

alignmvs		73.86 74	73.99 66	73.45 135	78.20 164	50.50 196	78.57 130	82.43 90	59.40 121	76.57 31	86.71 86	56.42 35	81.23 197	65.84 99	81.79 93	88.62 4

1121		68.53 160	67.16 170	72.63 154	85.64 44	61.14 34	73.95 218	66.46 294	44.61 305	70.28 104	86.68 87	41.42 205	80.78 208	53.62 190	81.79 93	75.97 281

新几何1				70.76 193	85.66 42	61.13 35		66.43 295	44.68 304	70.29 103	86.64 88	41.29 207	75.23 274	49.72 218	81.75 97	75.93 283

VNet		69.68 134	70.19 112	68.16 232	79.73 130	41.63 293	70.53 268	77.38 195	60.37 98	70.69 99	86.63 89	51.08 94	77.09 262	53.61 192	81.69 99	85.75 95

原ACMM1				74.69 97	85.39 50	59.40 60		83.42 70	51.47 241	70.27 105	86.61 90	48.61 119	86.51 80	53.85 189	87.96 41	78.16 258

3Dnovator	64.47 5	72.49 87	71.39 94	75.79 73	77.70 177	58.99 72	80.66 99	83.15 80	62.24 67	65.46 192	86.59 91	42.38 190	85.52 103	59.59 153	84.72 68	82.85 193

PHI-MVS		75.87 48	75.36 49	77.41 50	80.62 112	55.91 123	84.28 37	85.78 24	56.08 180	73.41 69	86.58 92	50.94 97	88.54 27	70.79 61	89.71 19	87.79 27

canonicalmvs		74.67 62	74.98 53	73.71 125	78.94 145	50.56 195	80.23 103	83.87 59	60.30 104	77.15 30	86.56 93	59.65 15	82.00 181	66.01 97	82.12 91	88.58 6

FC-MVSNet-test		69.80 130	70.58 107	67.46 237	77.61 185	34.73 337	76.05 182	83.19 79	60.84 86	65.88 186	86.46 94	54.52 54	80.76 210	52.52 199	78.12 142	86.91 53

OMC-MVS		71.40 105	70.60 105	73.78 119	76.60 203	53.15 156	79.74 114	79.78 145	58.37 138	68.75 132	86.45 95	45.43 161	80.60 211	62.58 127	77.73 145	87.58 36

Anonymous202405211		66.84 193	65.99 192	69.40 218	80.19 121	42.21 285	71.11 261	71.31 260	58.80 131	67.90 147	86.39 96	29.83 306	79.65 222	49.60 221	78.78 135	86.33 72

CANet		76.46 42	75.93 44	78.06 40	81.29 100	57.53 94	82.35 74	83.31 75	67.78 3	70.09 106	86.34 97	54.92 49	88.90 23	72.68 49	84.55 69	87.76 29

QAPM		70.05 125	68.81 133	73.78 119	76.54 205	53.43 152	83.23 57	83.48 67	52.89 226	65.90 185	86.29 98	41.55 203	86.49 81	51.01 209	78.40 141	81.42 212

test_part1		74.74 60	74.42 61	75.70 79	81.69 92	51.26 182	83.98 46	87.05 7	65.31 16	73.10 75	86.20 99	53.94 60	88.06 37	65.32 104	73.17 192	87.77 28

ACMP	63.53 6	72.30 90	71.20 99	75.59 84	80.28 116	57.54 93	82.74 67	82.84 87	60.58 91	65.24 199	86.18 100	39.25 221	86.03 89	66.95 92	76.79 158	83.22 182
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test222					83.14 73	58.68 77	72.57 239	63.45 311	41.78 325	67.56 158	86.12 101	37.13 245			78.73 137	74.98 294

HQP_MVS		74.31 67	73.73 70	76.06 70	81.41 97	56.31 112	84.22 38	84.01 53	64.52 26	69.27 125	86.10 102	45.26 165	87.21 55	68.16 78	80.58 105	84.65 134

plane_prior4											86.10 102

UniMVSNet_ETH3D		67.60 177	67.07 171	69.18 222	77.39 189	42.29 284	74.18 214	75.59 218	60.37 98	66.77 168	86.06 104	37.64 237	78.93 242	52.16 202	73.49 184	86.32 74

XVG-OURS-SEG-HR		68.81 149	67.47 157	72.82 151	74.40 241	56.87 109	70.59 267	79.04 157	54.77 208	66.99 165	86.01 105	39.57 218	78.21 247	62.54 128	73.33 187	83.37 177

MVS_111021_HR		74.02 70	73.46 75	75.69 80	83.01 78	60.63 45	77.29 155	78.40 179	61.18 84	70.58 100	85.97 106	54.18 57	84.00 138	67.52 86	82.98 81	82.45 199

hse-mvs3		72.71 84	71.49 91	76.40 66	81.99 88	59.58 58	76.92 164	76.74 205	60.40 95	74.81 46	85.95 107	45.54 157	85.76 98	70.41 64	70.61 222	83.86 158

PAPM_NR		72.63 85	71.80 87	75.13 91	81.72 91	53.42 153	79.91 110	83.28 77	59.14 125	66.31 178	85.90 108	51.86 85	86.06 87	57.45 161	80.62 103	85.91 86

EPP-MVSNet		72.16 94	71.31 97	74.71 96	78.68 152	49.70 207	82.10 80	81.65 104	60.40 95	65.94 183	85.84 109	51.74 87	86.37 84	55.93 169	79.55 122	88.07 18

VPNet		67.52 178	68.11 146	65.74 260	79.18 140	36.80 325	72.17 245	72.83 251	62.04 73	67.79 155	85.83 110	48.88 116	76.60 267	51.30 208	72.97 195	83.81 159

114514_t		70.83 110	69.56 120	74.64 101	86.21 31	54.63 140	82.34 75	81.81 101	48.22 272	63.01 225	85.83 110	40.92 211	87.10 59	57.91 159	79.79 116	82.18 202

XVG-OURS		68.76 153	67.37 160	72.90 148	74.32 242	57.22 99	70.09 273	78.81 161	55.24 196	67.79 155	85.81 112	36.54 251	78.28 246	62.04 133	75.74 165	83.19 184

PS-MVSNAJss		72.24 91	71.21 98	75.31 88	78.50 155	55.93 122	81.63 85	82.12 95	56.24 175	70.02 111	85.68 113	47.05 141	84.34 129	65.27 105	74.41 173	85.67 97

DP-MVS Recon		72.15 95	70.73 104	76.40 66	86.57 23	57.99 87	81.15 94	82.96 82	57.03 156	66.78 167	85.56 114	44.50 172	88.11 36	51.77 205	80.23 114	83.10 188

OpenMVS	61.03 9	68.85 148	67.56 152	72.70 153	74.26 243	53.99 144	81.21 93	81.34 116	52.70 227	62.75 228	85.55 115	38.86 226	84.14 132	48.41 229	83.01 78	79.97 239

NP-MVS					80.98 107	56.05 120					85.54 116

HQP-MVS		73.45 76	72.80 80	75.40 85	80.66 109	54.94 136	82.31 76	83.90 57	62.10 70	67.85 149	85.54 116	45.46 159	86.93 64	67.04 90	80.35 111	84.32 142

TranMVSNet+NR-MVSNet		70.36 120	70.10 115	71.17 186	78.64 153	42.97 280	76.53 170	81.16 123	66.95 6	68.53 136	85.42 118	51.61 88	83.07 154	52.32 200	69.70 241	87.46 40

PCF-MVS	61.88 8	70.95 109	69.49 122	75.35 87	77.63 180	55.71 125	76.04 183	81.81 101	50.30 253	69.66 118	85.40 119	52.51 75	84.89 118	51.82 204	80.24 113	85.45 107
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Vis-MVSNet (Re-imp)		63.69 228	63.88 212	63.14 279	74.75 233	31.04 350	71.16 259	63.64 310	56.32 172	59.80 259	84.99 120	44.51 171	75.46 273	39.12 295	80.62 103	82.92 190

TAPA-MVS	59.36 10	66.60 198	65.20 204	70.81 192	76.63 202	48.75 221	76.52 171	80.04 142	50.64 251	65.24 199	84.93 121	39.15 223	78.54 243	36.77 306	76.88 155	85.14 118
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		76.92 37	76.75 36	77.41 50	83.96 67	59.60 57	82.95 61	86.50 15	60.78 88	75.27 38	84.83 122	60.76 13	86.56 77	67.86 82	87.87 44	86.06 82

VPA-MVSNet		69.02 146	69.47 123	67.69 236	77.42 188	41.00 297	74.04 215	79.68 147	60.06 107	69.26 127	84.81 123	51.06 95	77.58 256	54.44 185	74.43 172	84.48 138

MVS_Test		72.45 88	72.46 83	72.42 160	74.88 230	48.50 224	76.28 176	83.14 81	59.40 121	72.46 86	84.68 124	55.66 42	81.12 198	65.98 98	79.66 119	87.63 33

MVS_111021_LR		69.50 139	68.78 134	71.65 171	78.38 158	59.33 62	74.82 205	70.11 269	58.08 142	67.83 153	84.68 124	41.96 193	76.34 270	65.62 102	77.54 146	79.30 250

LS3D		64.71 220	62.50 230	71.34 181	79.72 131	55.71 125	79.82 111	74.72 233	48.50 269	56.62 284	84.62 126	33.59 274	82.34 177	29.65 344	75.23 169	75.97 281

PAPR		71.72 100	70.82 103	74.41 109	81.20 104	51.17 183	79.55 117	83.33 74	55.81 185	66.93 166	84.61 127	50.95 96	86.06 87	55.79 172	79.20 128	86.00 83

UniMVSNet_NR-MVSNet		71.11 106	71.00 101	71.44 175	79.20 139	44.13 269	76.02 184	82.60 89	66.48 13	68.20 140	84.60 128	56.82 31	82.82 166	54.62 182	70.43 224	87.36 45

DU-MVS		70.01 126	69.53 121	71.44 175	78.05 170	44.13 269	75.01 200	81.51 108	64.37 29	68.20 140	84.52 129	49.12 114	82.82 166	54.62 182	70.43 224	87.37 43

NR-MVSNet		69.54 138	68.85 132	71.59 173	78.05 170	43.81 273	74.20 213	80.86 131	65.18 17	62.76 227	84.52 129	52.35 80	83.59 146	50.96 210	70.78 219	87.37 43

TSAR-MVS + GP.		74.90 57	74.15 65	77.17 54	82.00 87	58.77 76	81.80 82	78.57 168	58.58 133	74.32 55	84.51 131	55.94 39	87.22 54	67.11 89	84.48 71	85.52 103

CS-MVS-test		74.96 55	74.82 56	75.40 85	79.45 135	52.03 177	82.95 61	86.18 19	63.24 46	70.07 107	84.50 132	55.21 45	88.77 25	67.89 81	83.85 74	85.40 112

UGNet		68.81 149	67.39 159	73.06 145	78.33 161	54.47 141	79.77 112	75.40 221	60.45 94	63.22 222	84.40 133	32.71 286	80.91 205	51.71 206	80.56 107	83.81 159
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
ACMM	61.98 7	70.80 112	69.73 118	74.02 114	80.59 113	58.59 79	82.68 69	82.02 97	55.46 193	67.18 163	84.39 134	38.51 228	83.17 153	60.65 143	76.10 162	80.30 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-RMVSNet		68.81 149	67.42 158	72.97 146	80.11 123	52.53 166	74.26 212	76.29 208	58.48 136	68.38 138	84.20 135	42.59 186	83.83 140	46.53 240	75.91 163	82.56 195

AdaColmap		69.99 127	68.66 136	73.97 116	84.94 58	57.83 89	82.63 70	78.71 164	56.28 174	64.34 212	84.14 136	41.57 200	87.06 62	46.45 241	78.88 132	77.02 273

jajsoiax		68.25 165	66.45 178	73.66 127	75.62 220	55.49 131	80.82 96	78.51 171	52.33 231	64.33 213	84.11 137	28.28 316	81.81 185	63.48 122	70.62 221	83.67 168

mvs_tets		68.18 167	66.36 184	73.63 130	75.61 221	55.35 134	80.77 97	78.56 169	52.48 230	64.27 215	84.10 138	27.45 322	81.84 184	63.45 123	70.56 223	83.69 167

PEN-MVS		66.60 198	66.45 178	67.04 241	77.11 193	36.56 327	77.03 161	80.42 137	62.95 51	62.51 235	84.03 139	46.69 147	79.07 236	44.22 259	63.08 300	85.51 104

Anonymous20231211		69.28 142	68.47 139	71.73 168	80.28 116	47.18 240	79.98 107	82.37 91	54.61 209	67.24 162	84.01 140	39.43 219	82.41 176	55.45 176	72.83 196	85.62 101

PAPM		67.92 173	66.69 175	71.63 172	78.09 168	49.02 217	77.09 159	81.24 121	51.04 247	60.91 249	83.98 141	47.71 128	84.99 113	40.81 287	79.32 126	80.90 225

diffmvs		70.69 113	70.43 108	71.46 174	69.45 305	48.95 219	72.93 233	78.46 174	57.27 153	71.69 93	83.97 142	51.48 89	77.92 251	70.70 62	77.95 144	87.53 37

GeoE		71.01 108	70.15 113	73.60 131	79.57 133	52.17 172	78.93 124	78.12 182	58.02 145	67.76 157	83.87 143	52.36 79	82.72 168	56.90 164	75.79 164	85.92 85

test_yl		69.69 132	69.13 127	71.36 179	78.37 159	45.74 254	74.71 206	80.20 140	57.91 148	70.01 112	83.83 144	42.44 188	82.87 162	54.97 178	79.72 117	85.48 105

DCV-MVSNet		69.69 132	69.13 127	71.36 179	78.37 159	45.74 254	74.71 206	80.20 140	57.91 148	70.01 112	83.83 144	42.44 188	82.87 162	54.97 178	79.72 117	85.48 105

DTE-MVSNet		65.58 209	65.34 201	66.31 248	76.06 213	34.79 335	76.43 172	79.38 154	62.55 62	61.66 244	83.83 144	45.60 155	79.15 234	41.64 286	60.88 314	85.00 123

RRT_MVS		68.77 152	66.71 174	74.95 92	75.93 215	58.55 80	80.50 101	75.84 214	56.09 179	68.17 142	83.74 147	28.50 314	82.98 156	65.67 101	65.91 278	83.33 178

CS-MVS		74.01 71	74.24 64	73.32 141	76.47 207	48.51 223	79.19 122	86.17 20	60.56 92	71.62 95	83.71 148	55.16 46	87.94 41	69.21 72	86.11 61	83.51 175

PS-CasMVS		66.42 202	66.32 186	66.70 245	77.60 186	36.30 332	76.94 163	79.61 149	62.36 66	62.43 237	83.66 149	45.69 153	78.37 244	45.35 256	63.26 298	85.42 110

WR-MVS		68.47 161	68.47 139	68.44 230	80.20 120	39.84 300	73.75 225	76.07 211	64.68 23	68.11 145	83.63 150	50.39 101	79.14 235	49.78 215	69.66 242	86.34 71

UniMVSNet (Re)		70.63 114	70.20 111	71.89 164	78.55 154	45.29 260	75.94 185	82.92 83	63.68 41	68.16 143	83.59 151	53.89 62	83.49 148	53.97 187	71.12 217	86.89 54

CNLPA		65.43 212	64.02 210	69.68 212	78.73 151	58.07 86	77.82 142	70.71 265	51.49 240	61.57 246	83.58 152	38.23 233	70.82 291	43.90 264	70.10 232	80.16 235

ab-mvs		66.65 197	66.42 181	67.37 238	76.17 210	41.73 290	70.41 271	76.14 210	53.99 216	65.98 182	83.51 153	49.48 106	76.24 271	48.60 227	73.46 185	84.14 148

test_djsdf		69.45 141	67.74 148	74.58 104	74.57 237	54.92 138	82.79 65	78.48 172	51.26 245	65.41 193	83.49 154	38.37 230	83.24 151	66.06 95	69.25 248	85.56 102

CP-MVSNet		66.49 201	66.41 182	66.72 243	77.67 179	36.33 330	76.83 167	79.52 151	62.45 64	62.54 233	83.47 155	46.32 149	78.37 244	45.47 254	63.43 297	85.45 107

MVSFormer		71.50 103	70.38 110	74.88 94	78.76 149	57.15 104	82.79 65	78.48 172	51.26 245	69.49 120	83.22 156	43.99 177	83.24 151	66.06 95	79.37 123	84.23 145

jason		69.65 135	68.39 142	73.43 137	78.27 163	56.88 108	77.12 158	73.71 245	46.53 290	69.34 124	83.22 156	43.37 181	79.18 230	64.77 109	79.20 128	84.23 145
jason: jason.
pm-mvs1		65.24 215	64.97 206	66.04 255	72.38 265	39.40 305	72.62 238	75.63 217	55.53 192	62.35 239	83.18 158	47.45 134	76.47 268	49.06 224	66.54 274	82.24 201

Baseline_NR-MVSNet		67.05 188	67.56 152	65.50 262	75.65 219	37.70 318	75.42 190	74.65 234	59.90 110	68.14 144	83.15 159	49.12 114	77.20 260	52.23 201	69.78 238	81.60 210

baseline1		63.81 227	63.87 213	63.62 274	76.29 208	36.36 328	71.78 250	67.29 289	56.05 181	64.23 216	82.95 160	47.11 140	74.41 278	47.30 234	61.85 308	80.10 237

DELS-MVS		74.76 59	74.46 60	75.65 81	77.84 175	52.25 171	75.59 188	84.17 50	63.76 39	73.15 72	82.79 161	59.58 17	86.80 67	67.24 88	86.04 62	87.89 20
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
GBi-Net		67.21 182	66.55 176	69.19 219	77.63 180	43.33 276	77.31 152	77.83 186	56.62 164	65.04 203	82.70 162	41.85 196	80.33 216	47.18 235	72.76 198	83.92 154

test1		67.21 182	66.55 176	69.19 219	77.63 180	43.33 276	77.31 152	77.83 186	56.62 164	65.04 203	82.70 162	41.85 196	80.33 216	47.18 235	72.76 198	83.92 154

FMVSNet1		66.70 196	65.87 193	69.19 219	77.49 187	43.33 276	77.31 152	77.83 186	56.45 169	64.60 211	82.70 162	38.08 235	80.33 216	46.08 244	72.31 206	83.92 154

TransMVSNet (Re)		64.72 219	64.33 208	65.87 259	75.22 227	38.56 311	74.66 208	75.08 231	58.90 129	61.79 243	82.63 165	51.18 92	78.07 249	43.63 267	55.87 330	80.99 224

Effi-MVS+		73.31 78	72.54 82	75.62 82	77.87 174	53.64 147	79.62 116	79.61 149	61.63 78	72.02 91	82.61 166	56.44 34	85.97 92	63.99 116	79.07 131	87.25 47

mvs_anonymous		68.03 170	67.51 156	69.59 214	72.08 269	44.57 267	71.99 247	75.23 224	51.67 235	67.06 164	82.57 167	54.68 52	77.94 250	56.56 165	75.71 166	86.26 78

ACMH+	57.40 11	66.12 204	64.06 209	72.30 162	77.79 176	52.83 160	80.39 102	78.03 183	57.30 152	57.47 280	82.55 168	27.68 320	84.17 131	45.54 251	69.78 238	79.90 240

tttt0517		67.83 175	65.66 197	74.33 111	76.69 200	50.82 189	77.86 140	73.99 242	54.54 212	64.64 210	82.53 169	35.06 258	85.50 105	55.71 173	69.91 235	86.67 61

WR-MVS_H		67.02 189	66.92 172	67.33 240	77.95 173	37.75 317	77.57 146	82.11 96	62.03 74	62.65 230	82.48 170	50.57 99	79.46 225	42.91 274	64.01 291	84.79 131

LTVRE_ROB	55.42 16	63.15 236	61.23 245	68.92 224	76.57 204	47.80 231	59.92 323	76.39 207	54.35 215	58.67 270	82.46 171	29.44 309	81.49 191	42.12 280	71.14 216	77.46 266
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
DP-MVS		65.68 207	63.66 217	71.75 167	84.93 59	56.87 109	80.74 98	73.16 249	53.06 223	59.09 266	82.35 172	36.79 250	85.94 93	32.82 326	69.96 234	72.45 318

API-MVS		72.17 93	71.41 93	74.45 108	81.95 89	57.22 99	84.03 44	80.38 138	59.89 113	68.40 137	82.33 173	49.64 105	87.83 46	51.87 203	84.16 73	78.30 256

pmmvs6		63.69 228	62.82 227	66.27 250	70.63 287	39.27 306	73.13 231	75.47 220	52.69 228	59.75 260	82.30 174	39.71 217	77.03 263	47.40 233	64.35 290	82.53 196

RPSCF		55.80 287	54.22 295	60.53 294	65.13 332	42.91 281	64.30 306	57.62 334	36.84 343	58.05 277	82.28 175	28.01 317	56.24 347	37.14 304	58.61 322	82.44 200

cdsmvs_eth3d_5k		17.50 334	23.34 333	0.00 353	0.00 374	0.00 374	0.00 364	78.63 167	0.00 370	0.00 371	82.18 176	49.25 110	0.00 370	0.00 370	0.00 368	0.00 368

lupinMVS		69.57 137	68.28 143	73.44 136	78.76 149	57.15 104	76.57 169	73.29 248	46.19 293	69.49 120	82.18 176	43.99 177	79.23 229	64.66 110	79.37 123	83.93 153

FMVSNet2		66.93 191	66.31 187	68.79 226	77.63 180	42.98 279	76.11 179	77.47 192	56.62 164	65.22 201	82.17 178	41.85 196	80.18 219	47.05 238	72.72 201	83.20 183

PVSNet_Blended_VisFu		71.45 104	70.39 109	74.65 100	82.01 86	58.82 75	79.93 109	80.35 139	55.09 200	65.82 188	82.16 179	49.17 111	82.64 171	60.34 146	78.62 139	82.50 198

v2v482		70.50 117	69.45 124	73.66 127	72.62 261	50.03 203	77.58 145	80.51 136	59.90 110	69.52 119	82.14 180	47.53 132	84.88 120	65.07 108	70.17 230	86.09 81

v8		70.33 121	69.28 126	73.49 133	73.15 251	50.22 199	78.62 129	80.78 132	60.79 87	66.45 175	82.11 181	49.35 107	84.98 115	63.58 121	68.71 256	85.28 115

CANet_DTU		68.18 167	67.71 151	69.59 214	74.83 231	46.24 248	78.66 128	76.85 202	59.60 116	63.45 221	82.09 182	35.25 256	77.41 258	59.88 150	78.76 136	85.14 118

hse-mvs2		71.04 107	69.86 116	74.60 103	79.58 132	57.12 106	73.96 217	75.25 223	60.40 95	74.81 46	81.95 183	45.54 157	82.90 159	70.41 64	66.83 272	83.77 164

PLC	56.13 14	65.09 217	63.21 222	70.72 195	81.04 106	54.87 139	78.57 130	77.47 192	48.51 268	55.71 289	81.89 184	33.71 271	79.71 221	41.66 284	70.37 226	77.58 265
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
AUN-MVS		68.45 162	66.41 182	74.57 105	79.53 134	57.08 107	73.93 221	75.23 224	54.44 214	66.69 169	81.85 185	37.10 246	82.89 160	62.07 132	66.84 271	83.75 165

v10		70.21 123	69.02 130	73.81 118	73.51 248	50.92 187	78.74 126	81.39 111	60.05 108	66.39 176	81.83 186	47.58 131	85.41 110	62.80 126	68.86 255	85.09 121

thisisatest0530		67.92 173	65.78 195	74.33 111	76.29 208	51.03 184	76.89 165	74.25 239	53.67 219	65.59 190	81.76 187	35.15 257	85.50 105	55.94 168	72.47 202	86.47 65

TAMVS		66.78 195	65.27 203	71.33 182	79.16 142	53.67 146	73.84 224	69.59 274	52.32 232	65.28 194	81.72 188	44.49 173	77.40 259	42.32 278	78.66 138	82.92 190

v7n		69.01 147	67.36 161	73.98 115	72.51 264	52.65 162	78.54 132	81.30 117	60.26 105	62.67 229	81.62 189	43.61 179	84.49 126	57.01 163	68.70 257	84.79 131

BH-untuned		68.27 164	67.29 163	71.21 183	79.74 129	53.22 155	76.06 181	77.46 194	57.19 154	66.10 180	81.61 190	45.37 163	83.50 147	45.42 255	76.68 160	76.91 277

F-COLMAP		63.05 237	60.87 250	69.58 216	76.99 197	53.63 148	78.12 138	76.16 209	47.97 276	52.41 319	81.61 190	27.87 318	78.11 248	40.07 290	66.66 273	77.00 274

IterMVS-LS		69.22 145	68.48 138	71.43 177	74.44 240	49.40 213	76.23 177	77.55 191	59.60 116	65.85 187	81.59 192	51.28 91	81.58 190	59.87 151	69.90 236	83.30 179
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
COLMAP_ROB	52.97 17	61.27 256	58.81 258	68.64 227	74.63 235	52.51 167	78.42 135	73.30 247	49.92 258	50.96 324	81.51 193	23.06 340	79.40 226	31.63 334	65.85 279	74.01 306
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
xiu_mvs_v1_base_debu		68.58 156	67.28 164	72.48 157	78.19 165	57.19 101	75.28 192	75.09 228	51.61 236	70.04 108	81.41 194	32.79 282	79.02 237	63.81 117	77.31 148	81.22 218

xiu_mvs_v1_base		68.58 156	67.28 164	72.48 157	78.19 165	57.19 101	75.28 192	75.09 228	51.61 236	70.04 108	81.41 194	32.79 282	79.02 237	63.81 117	77.31 148	81.22 218

xiu_mvs_v1_base_debi		68.58 156	67.28 164	72.48 157	78.19 165	57.19 101	75.28 192	75.09 228	51.61 236	70.04 108	81.41 194	32.79 282	79.02 237	63.81 117	77.31 148	81.22 218

RRT_test8_iter05		68.17 169	66.86 173	72.07 163	75.81 216	46.33 246	76.41 173	81.81 101	56.43 170	66.52 172	81.30 197	31.90 294	84.25 130	63.77 120	67.83 264	85.64 100

v1144		70.42 119	69.31 125	73.76 121	73.22 249	50.64 192	77.83 141	81.43 110	58.58 133	69.40 123	81.16 198	47.53 132	85.29 112	64.01 115	70.64 220	85.34 113

FMVSNet3		66.32 203	65.61 198	68.46 229	76.48 206	42.34 283	74.98 202	77.15 199	55.83 184	65.04 203	81.16 198	39.91 214	80.14 220	47.18 235	72.76 198	82.90 192

XVG-ACMP-BASELINE		64.36 224	62.23 233	70.74 194	72.35 266	52.45 169	70.80 266	78.45 175	53.84 218	59.87 257	81.10 200	16.24 352	79.32 228	55.64 175	71.76 210	80.47 230

CLD-MVS		73.33 77	72.68 81	75.29 90	78.82 148	53.33 154	78.23 136	84.79 42	61.30 83	70.41 102	81.04 201	52.41 78	87.12 58	64.61 112	82.49 90	85.41 111
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres100view900		63.28 233	62.41 231	65.89 258	77.31 190	38.66 310	72.65 236	69.11 280	57.07 155	62.45 236	81.03 202	37.01 248	79.17 231	31.84 330	73.25 189	79.83 242

ACMH	55.70 15	65.20 216	63.57 218	70.07 205	78.07 169	52.01 178	79.48 120	79.69 146	55.75 187	56.59 285	80.98 203	27.12 324	80.94 203	42.90 275	71.58 213	77.25 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres600view7		63.30 232	62.27 232	66.41 247	77.18 192	38.87 308	72.35 242	69.11 280	56.98 157	62.37 238	80.96 204	37.01 248	79.00 240	31.43 337	73.05 194	81.36 214

OurMVSNet-221017-0		61.37 255	58.63 261	69.61 213	72.05 270	48.06 229	73.93 221	72.51 253	47.23 286	54.74 301	80.92 205	21.49 347	81.24 196	48.57 228	56.22 329	79.53 247

HY-MVS	56.14 13	64.55 223	63.89 211	66.55 246	74.73 234	41.02 295	69.96 274	74.43 235	49.29 261	61.66 244	80.92 205	47.43 135	76.68 266	44.91 258	71.69 211	81.94 206

XXY-MVS		60.68 257	61.67 238	57.70 310	70.43 290	38.45 312	64.19 307	66.47 293	48.05 275	63.22 222	80.86 207	49.28 109	60.47 330	45.25 257	67.28 269	74.19 304

v1192		69.97 128	68.68 135	73.85 117	73.19 250	50.94 185	77.68 144	81.36 112	57.51 151	68.95 131	80.85 208	45.28 164	85.33 111	62.97 125	70.37 226	85.27 116

anonymousdsp		67.00 190	64.82 207	73.57 132	70.09 296	56.13 117	76.35 174	77.35 196	48.43 270	64.99 206	80.84 209	33.01 279	80.34 215	64.66 110	67.64 267	84.23 145

test_0402		63.25 234	61.01 247	69.96 206	80.00 124	54.37 142	76.86 166	72.02 256	54.58 211	58.71 269	80.79 210	35.00 259	84.36 128	26.41 352	64.71 287	71.15 330

Regformer-1		75.47 52	74.93 54	77.09 55	80.43 114	57.70 92	79.50 118	82.13 94	67.84 1	75.73 37	80.75 211	56.50 33	86.07 86	71.07 60	80.38 109	87.50 38

Regformer-2		75.63 51	74.99 52	77.54 48	80.43 114	58.32 83	79.50 118	82.92 83	67.84 1	75.94 34	80.75 211	55.73 41	86.80 67	71.44 59	80.38 109	87.50 38

v144192		69.71 131	68.51 137	73.33 140	73.10 252	50.13 201	77.54 148	80.64 133	56.65 161	68.57 135	80.55 213	46.87 146	84.96 117	62.98 124	69.66 242	84.89 127

v1240		69.24 144	67.91 147	73.25 144	73.02 255	49.82 205	77.21 157	80.54 135	56.43 170	68.34 139	80.51 214	43.33 182	84.99 113	62.03 134	69.77 240	84.95 126

v1921920		69.47 140	68.17 145	73.36 139	73.06 253	50.10 202	77.39 151	80.56 134	56.58 168	68.59 133	80.37 215	44.72 170	84.98 115	62.47 130	69.82 237	85.00 123

MVSTER		67.16 186	65.58 199	71.88 165	70.37 292	49.70 207	70.25 272	78.45 175	51.52 239	69.16 129	80.37 215	38.45 229	82.50 173	60.19 147	71.46 214	83.44 176

ITE_SJBPF				62.09 286	66.16 327	44.55 268		64.32 307	47.36 283	55.31 294	80.34 217	19.27 348	62.68 324	36.29 314	62.39 305	79.04 251

TR-MVS		66.59 200	65.07 205	71.17 186	79.18 140	49.63 211	73.48 227	75.20 226	52.95 224	67.90 147	80.33 218	39.81 216	83.68 143	43.20 271	73.56 183	80.20 234

Regformer-3		73.89 73	73.28 77	75.71 78	79.75 127	55.48 132	78.54 132	79.93 144	66.58 11	73.62 66	80.30 219	54.87 50	84.54 125	69.09 73	76.84 156	87.10 51

Regformer-4		74.25 69	73.48 73	76.57 65	79.75 127	56.54 111	78.54 132	81.49 109	66.93 7	73.90 62	80.30 219	53.84 63	85.98 91	69.76 66	76.84 156	87.17 48

V42		68.65 154	67.35 162	72.56 155	68.93 310	50.18 200	72.90 234	79.47 152	56.92 158	69.45 122	80.26 221	46.29 150	82.99 155	64.07 113	67.82 265	84.53 136

CDS-MVSNet		66.80 194	65.37 200	71.10 188	78.98 144	53.13 158	73.27 230	71.07 262	52.15 233	64.72 208	80.23 222	43.56 180	77.10 261	45.48 253	78.88 132	83.05 189
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
ET-MVSNet_ETH3D		67.96 172	65.72 196	74.68 98	76.67 201	55.62 129	75.11 197	74.74 232	52.91 225	60.03 254	80.12 223	33.68 272	82.64 171	61.86 135	76.34 161	85.78 90

v148		68.24 166	67.19 169	71.40 178	70.43 290	47.77 233	75.76 187	77.03 200	58.91 128	67.36 160	80.10 224	48.60 120	81.89 182	60.01 149	66.52 275	84.53 136

tfpnnormal		62.47 240	61.63 239	64.99 268	74.81 232	39.01 307	71.22 257	73.72 244	55.22 197	60.21 252	80.09 225	41.26 209	76.98 264	30.02 342	68.09 261	78.97 253

Test_1112_low_res		62.32 242	61.77 237	64.00 273	79.08 143	39.53 304	68.17 283	70.17 268	43.25 318	59.03 267	79.90 226	44.08 175	71.24 290	43.79 266	68.42 259	81.25 217

mvs-test1		70.44 118	68.19 144	77.18 53	76.10 211	63.22 6	80.59 100	76.06 212	59.83 114	66.32 177	79.87 227	41.56 201	85.53 102	60.60 144	72.77 197	82.80 194

tfpn200view9		63.18 235	62.18 234	66.21 251	76.85 198	39.62 302	71.96 248	69.44 276	56.63 162	62.61 231	79.83 228	37.18 242	79.17 231	31.84 330	73.25 189	79.83 242

thres400		63.31 231	62.18 234	66.72 243	76.85 198	39.62 302	71.96 248	69.44 276	56.63 162	62.61 231	79.83 228	37.18 242	79.17 231	31.84 330	73.25 189	81.36 214

AllTest		57.08 279	54.65 288	64.39 271	71.44 278	49.03 215	69.92 275	67.30 287	45.97 296	47.16 337	79.77 230	17.47 349	67.56 307	33.65 323	59.16 320	76.57 278

TestCases				64.39 271	71.44 278	49.03 215		67.30 287	45.97 296	47.16 337	79.77 230	17.47 349	67.56 307	33.65 323	59.16 320	76.57 278

PVSNet_BlendedMVS		68.56 159	67.72 149	71.07 189	77.03 195	50.57 193	74.50 210	81.52 106	53.66 220	64.22 217	79.72 232	49.13 112	82.87 162	55.82 170	73.92 177	79.77 245

xiu_mvs_v2_base		70.52 115	69.75 117	72.84 149	81.21 103	55.63 128	75.11 197	78.92 159	54.92 206	69.96 114	79.68 233	47.00 145	82.09 180	61.60 138	79.37 123	80.81 227

cl-mvsnet1		67.18 184	66.26 189	69.94 207	70.20 293	45.74 254	73.29 229	76.83 203	55.10 198	65.27 195	79.58 234	47.38 137	80.53 212	59.43 155	69.22 249	83.54 173

cl-mvsnet____		67.18 184	66.26 189	69.94 207	70.20 293	45.74 254	73.30 228	76.83 203	55.10 198	65.27 195	79.57 235	47.39 136	80.53 212	59.41 156	69.22 249	83.53 174

Fast-Effi-MVS+		70.28 122	69.12 129	73.73 124	78.50 155	51.50 181	75.01 200	79.46 153	56.16 177	68.59 133	79.55 236	53.97 58	84.05 133	53.34 194	77.53 147	85.65 99

LCM-MVSNet-Re		61.88 249	61.35 242	63.46 275	74.58 236	31.48 349	61.42 317	58.14 331	58.71 132	53.02 318	79.55 236	43.07 183	76.80 265	45.69 248	77.96 143	82.11 205

ETV-MVS		74.46 65	73.84 69	76.33 68	79.27 138	55.24 135	79.22 121	85.00 38	64.97 22	72.65 83	79.46 238	53.65 68	87.87 44	67.45 87	82.91 82	85.89 87

EIA-MVS		71.78 98	70.60 105	75.30 89	79.85 126	53.54 150	77.27 156	83.26 78	57.92 147	66.49 173	79.39 239	52.07 83	86.69 71	60.05 148	79.14 130	85.66 98

EPNet_dtu		61.90 247	61.97 236	61.68 288	72.89 257	39.78 301	75.85 186	65.62 298	55.09 200	54.56 304	79.36 240	37.59 238	67.02 310	39.80 293	76.95 154	78.25 257
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EI-MVSNet-Vis-set		72.42 89	71.59 89	74.91 93	78.47 157	54.02 143	77.05 160	79.33 155	65.03 21	71.68 94	79.35 241	52.75 73	84.89 118	66.46 93	74.23 174	85.83 89

SixPastTwentyTwo		61.65 251	58.80 259	70.20 203	75.80 217	47.22 239	75.59 188	69.68 272	54.61 209	54.11 308	79.26 242	27.07 325	82.96 157	43.27 269	49.79 344	80.41 232

testgi		51.90 306	52.37 303	50.51 333	60.39 352	23.55 362	58.42 327	58.15 330	49.03 264	51.83 321	79.21 243	22.39 342	55.59 349	29.24 345	62.64 302	72.40 322

WTY-MVS		59.75 263	60.39 251	57.85 308	72.32 267	37.83 316	61.05 321	64.18 308	45.95 298	61.91 241	79.11 244	47.01 144	60.88 329	42.50 277	69.49 244	74.83 296

EI-MVSNet-UG-set		71.92 96	71.06 100	74.52 107	77.98 172	53.56 149	76.62 168	79.16 156	64.40 28	71.18 97	78.95 245	52.19 81	84.66 124	65.47 103	73.57 182	85.32 114

MAR-MVS		71.51 102	70.15 113	75.60 83	81.84 90	59.39 61	81.38 91	82.90 85	54.90 207	68.08 146	78.70 246	47.73 127	85.51 104	51.68 207	84.17 72	81.88 208
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		70.51 116	69.70 119	72.93 147	81.52 94	55.79 124	74.92 203	79.00 158	55.04 205	69.88 115	78.66 247	47.05 141	82.19 178	61.61 137	79.58 120	80.83 226

MVP-Stereo		65.41 213	63.80 214	70.22 201	77.62 184	55.53 130	76.30 175	78.53 170	50.59 252	56.47 286	78.65 248	39.84 215	82.68 169	44.10 263	72.12 208	72.44 319
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CHOSEN 1792x2688		65.08 218	62.84 226	71.82 166	81.49 96	56.26 115	66.32 292	74.20 240	40.53 334	63.16 224	78.65 248	41.30 206	77.80 253	45.80 247	74.09 175	81.40 213

eth_miper_zixun_eth		67.63 176	66.28 188	71.67 170	71.60 276	48.33 226	73.68 226	77.88 184	55.80 186	65.91 184	78.62 250	47.35 138	82.88 161	59.45 154	66.25 276	83.81 159

MVS		67.37 180	66.33 185	70.51 199	75.46 224	50.94 185	73.95 218	81.85 100	41.57 329	62.54 233	78.57 251	47.98 124	85.47 107	52.97 197	82.05 92	75.14 290

cl_fuxian		68.33 163	67.56 152	70.62 196	70.87 284	46.21 249	74.47 211	78.80 162	56.22 176	66.19 179	78.53 252	51.88 84	81.40 192	62.08 131	69.04 251	84.25 144

BH-w/o		66.85 192	65.83 194	69.90 210	79.29 136	52.46 168	74.66 208	76.65 206	54.51 213	64.85 207	78.12 253	45.59 156	82.95 158	43.26 270	75.54 167	74.27 303

TDRefinement		53.44 301	50.72 309	61.60 289	64.31 336	46.96 241	70.89 265	65.27 302	41.78 325	44.61 345	77.98 254	11.52 358	66.36 313	28.57 347	51.59 340	71.49 327

HyFIR lowres test		65.67 208	63.01 224	73.67 126	79.97 125	55.65 127	69.07 281	75.52 219	42.68 323	63.53 220	77.95 255	40.43 212	81.64 187	46.01 245	71.91 209	83.73 166

IterMVS-SCA-FT		62.49 239	61.52 240	65.40 264	71.99 271	50.80 190	71.15 260	69.63 273	45.71 299	60.61 250	77.93 256	37.45 239	65.99 315	55.67 174	63.50 296	79.42 248

cl-mvsnet2		67.47 179	66.45 178	70.54 198	69.85 301	46.49 244	73.85 223	77.35 196	55.07 203	65.51 191	77.92 257	47.64 130	81.10 199	61.58 139	69.32 245	84.01 152

pmmvs4		61.48 254	59.39 255	67.76 235	71.57 277	53.86 145	71.42 252	65.34 300	44.20 310	59.46 261	77.92 257	35.90 252	74.71 276	43.87 265	64.87 286	74.71 299

1112_ss		64.00 226	63.36 221	65.93 257	79.28 137	42.58 282	71.35 254	72.36 255	46.41 291	60.55 251	77.89 259	46.27 151	73.28 281	46.18 243	69.97 233	81.92 207

ab-mvs-re		6.49 337	8.65 340	0.00 353	0.00 374	0.00 374	0.00 364	0.00 373	0.00 370	0.00 371	77.89 259	0.00 375	0.00 370	0.00 370	0.00 368	0.00 368

miper_ehance_all_eth		68.03 170	67.24 168	70.40 200	70.54 288	46.21 249	73.98 216	78.68 166	55.07 203	66.05 181	77.80 261	52.16 82	81.31 194	61.53 140	69.32 245	83.67 168

CMPMVS	42.80 21	57.81 275	55.97 280	63.32 276	60.98 349	47.38 238	64.66 305	69.50 275	32.06 348	46.83 339	77.80 261	29.50 308	71.36 289	48.68 226	73.75 178	71.21 329
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PVSNet_Blended		68.59 155	67.72 149	71.19 184	77.03 195	50.57 193	72.51 240	81.52 106	51.91 234	64.22 217	77.77 263	49.13 112	82.87 162	55.82 170	79.58 120	80.14 236

USDC		56.35 283	54.24 294	62.69 282	64.74 333	40.31 298	65.05 303	73.83 243	43.93 314	47.58 335	77.71 264	15.36 354	75.05 275	38.19 300	61.81 309	72.70 314

test20.03		53.87 297	54.02 296	53.41 324	61.47 345	28.11 355	61.30 318	59.21 327	51.34 244	52.09 320	77.43 265	33.29 277	58.55 337	29.76 343	60.27 317	73.58 309

EG-PatchMatch MVS		64.71 220	62.87 225	70.22 201	77.68 178	53.48 151	77.99 139	78.82 160	53.37 221	56.03 288	77.41 266	24.75 338	84.04 134	46.37 242	73.42 186	73.14 310

Effi-MVS+-dtu		69.64 136	67.53 155	75.95 71	76.10 211	62.29 18	80.20 105	76.06 212	59.83 114	65.26 198	77.09 267	41.56 201	84.02 137	60.60 144	71.09 218	81.53 211

thres200		62.20 244	61.16 246	65.34 265	75.38 226	39.99 299	69.60 276	69.29 278	55.64 191	61.87 242	76.99 268	37.07 247	78.96 241	31.28 338	73.28 188	77.06 272

tpm		57.34 277	58.16 265	54.86 318	71.80 275	34.77 336	67.47 288	56.04 341	48.20 273	60.10 253	76.92 269	37.17 244	53.41 353	40.76 288	65.01 285	76.40 280

IterMVS		62.79 238	61.27 243	67.35 239	69.37 306	52.04 176	71.17 258	68.24 285	52.63 229	59.82 258	76.91 270	37.32 241	72.36 284	52.80 198	63.19 299	77.66 264
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		67.37 180	65.33 202	73.48 134	72.94 256	57.78 91	77.47 150	76.88 201	57.60 150	61.97 240	76.85 271	39.31 220	80.49 214	54.72 181	70.28 229	82.17 204

GA-MVS		65.53 211	63.70 215	71.02 190	70.87 284	48.10 228	70.48 269	74.40 236	56.69 160	64.70 209	76.77 272	33.66 273	81.10 199	55.42 177	70.32 228	83.87 157

CL-MVSNet_2432*1600		61.53 252	60.94 248	63.30 277	68.95 309	36.93 324	67.60 287	72.80 252	55.67 189	59.95 256	76.63 273	45.01 167	72.22 287	39.74 294	62.09 307	80.74 228

pmmvs5		56.47 281	55.68 282	58.86 301	61.41 346	36.71 326	66.37 291	62.75 316	40.38 335	53.70 311	76.62 274	34.56 262	67.05 309	40.02 292	65.27 283	72.83 313

CostFormer		64.04 225	62.51 229	68.61 228	71.88 273	45.77 253	71.30 256	70.60 266	47.55 280	64.31 214	76.61 275	41.63 199	79.62 224	49.74 217	69.00 253	80.42 231

1314		64.61 222	63.21 222	68.80 225	71.87 274	47.46 237	73.95 218	78.39 180	42.88 322	59.97 255	76.60 276	38.11 234	79.39 227	54.84 180	72.32 205	79.55 246

EI-MVSNet		69.27 143	68.44 141	71.73 168	74.47 238	49.39 214	75.20 195	78.45 175	59.60 116	69.16 129	76.51 277	51.29 90	82.50 173	59.86 152	71.45 215	83.30 179

CVMVSNet		59.63 264	59.14 257	61.08 293	74.47 238	38.84 309	75.20 195	68.74 282	31.15 349	58.24 275	76.51 277	32.39 291	68.58 303	49.77 216	65.84 280	75.81 284

thisisatest0515		65.83 206	63.50 219	72.82 151	73.75 246	49.50 212	71.32 255	73.12 250	49.39 260	63.82 219	76.50 279	34.95 260	84.84 121	53.20 196	75.49 168	84.13 149

bset_n11_16_dypcd		65.57 210	63.69 216	71.19 184	70.84 286	51.79 179	71.37 253	70.48 267	53.33 222	65.19 202	76.41 280	31.46 296	81.76 186	65.12 106	69.04 251	80.01 238

K. test v3		60.47 259	57.11 271	70.56 197	73.74 247	48.22 227	75.10 199	62.55 317	58.27 141	53.62 313	76.31 281	27.81 319	81.59 189	47.42 232	39.18 355	81.88 208

MSDG		61.81 250	59.23 256	69.55 217	72.64 260	52.63 164	70.45 270	75.81 215	51.38 242	53.70 311	76.11 282	29.52 307	81.08 201	37.70 301	65.79 281	74.93 295

MIMVSNet1		55.17 292	54.31 293	57.77 309	70.03 298	32.01 347	65.68 296	64.81 303	49.19 262	46.75 340	76.00 283	25.53 334	64.04 320	28.65 346	62.13 306	77.26 270

OpenMVS_ROB	52.78 18	60.03 260	58.14 266	65.69 261	70.47 289	44.82 262	75.33 191	70.86 264	45.04 301	56.06 287	76.00 283	26.89 327	79.65 222	35.36 318	67.29 268	72.60 315

MIMVSNet		57.35 276	57.07 272	58.22 304	74.21 244	37.18 319	62.46 312	60.88 324	48.88 265	55.29 295	75.99 285	31.68 295	62.04 326	31.87 329	72.35 204	75.43 289

miper_enhance_ethall		67.11 187	66.09 191	70.17 204	69.21 307	45.98 252	72.85 235	78.41 178	51.38 242	65.65 189	75.98 286	51.17 93	81.25 195	60.82 142	69.32 245	83.29 181

TinyColmap		54.14 294	51.72 304	61.40 291	66.84 322	41.97 286	66.52 290	68.51 283	44.81 302	42.69 349	75.77 287	11.66 357	72.94 282	31.96 328	56.77 327	69.27 339

Anonymous20231206		55.10 293	55.30 285	54.48 320	69.81 302	33.94 341	62.91 311	62.13 320	41.08 331	55.18 296	75.65 288	32.75 285	56.59 345	30.32 341	67.86 263	72.91 311

lessismore_v0				69.91 209	71.42 280	47.80 231		50.90 349		50.39 330	75.56 289	27.43 323	81.33 193	45.91 246	34.10 357	80.59 229

baseline2		63.42 230	61.26 244	69.89 211	72.55 263	47.62 235	71.54 251	68.38 284	50.11 254	54.82 300	75.55 290	43.06 184	80.96 202	48.13 230	67.16 270	81.11 221

miper_lstm_enhance		62.03 246	60.88 249	65.49 263	66.71 323	46.25 247	56.29 335	75.70 216	50.68 249	61.27 247	75.48 291	40.21 213	68.03 305	56.31 167	65.25 284	82.18 202

tpm2		62.07 245	60.10 253	67.99 233	72.79 258	43.86 272	71.05 263	66.85 292	43.14 320	62.77 226	75.39 292	38.32 231	80.80 207	41.69 283	68.88 254	79.32 249

sss		56.17 285	56.57 276	54.96 317	66.93 321	36.32 331	57.94 329	61.69 321	41.67 327	58.64 271	75.32 293	38.72 227	56.25 346	42.04 281	66.19 277	72.31 323

D2MVS		62.30 243	60.29 252	68.34 231	66.46 325	48.42 225	65.70 295	73.42 246	47.71 278	58.16 276	75.02 294	30.51 299	77.71 254	53.96 188	71.68 212	78.90 254

CR-MVSNet		59.91 261	57.90 268	65.96 256	69.96 299	52.07 174	65.31 301	63.15 314	42.48 324	59.36 262	74.84 295	35.83 253	70.75 292	45.50 252	64.65 288	75.06 291

Patchmtry		57.16 278	56.47 277	59.23 297	69.17 308	34.58 338	62.98 310	63.15 314	44.53 306	56.83 283	74.84 295	35.83 253	68.71 302	40.03 291	60.91 313	74.39 302

FMVSNet5		55.86 286	54.93 286	58.66 303	71.05 283	36.35 329	64.18 308	62.48 318	46.76 289	50.66 329	74.73 297	25.80 332	64.04 320	33.11 325	65.57 282	75.59 287

cascas		65.98 205	63.42 220	73.64 129	77.26 191	52.58 165	72.26 244	77.21 198	48.56 267	61.21 248	74.60 298	32.57 290	85.82 97	50.38 213	76.75 159	82.52 197

MS-PatchMatch		62.42 241	61.46 241	65.31 266	75.21 228	52.10 173	72.05 246	74.05 241	46.41 291	57.42 281	74.36 299	34.35 266	77.57 257	45.62 250	73.67 179	66.26 343

test0.0.03 1		53.32 302	53.59 299	52.50 328	62.81 341	29.45 353	59.51 324	54.11 345	50.08 256	54.40 306	74.31 300	32.62 287	55.92 348	30.50 340	63.95 293	72.15 325

pmmvs-eth3d		58.81 266	56.31 279	66.30 249	67.61 317	52.42 170	72.30 243	64.76 304	43.55 316	54.94 299	74.19 301	28.95 311	72.60 283	43.31 268	57.21 325	73.88 307

MVS_0304		58.51 267	57.36 270	61.96 287	70.04 297	41.83 288	69.40 279	65.46 299	50.73 248	53.30 317	74.06 302	22.65 341	70.18 298	42.16 279	68.44 258	73.86 308

EU-MVSNet		55.61 288	54.41 291	59.19 299	65.41 331	33.42 343	72.44 241	71.91 257	28.81 351	51.27 322	73.87 303	24.76 337	69.08 301	43.04 272	58.20 323	75.06 291

IB-MVS	56.42 12	65.40 214	62.73 228	73.40 138	74.89 229	52.78 161	73.09 232	75.13 227	55.69 188	58.48 274	73.73 304	32.86 281	86.32 85	50.63 211	70.11 231	81.10 222
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
PVSNet	50.76 19	58.40 269	57.39 269	61.42 290	75.53 223	44.04 271	61.43 316	63.45 311	47.04 288	56.91 282	73.61 305	27.00 326	64.76 318	39.12 295	72.40 203	75.47 288

Anonymous20240521		55.30 289	54.41 291	57.96 307	60.92 351	41.73 290	71.09 262	71.06 263	41.18 330	48.65 333	73.31 306	16.93 351	59.25 335	42.54 276	64.01 291	72.90 312

gm-plane-assit					71.40 281	41.72 292			48.85 266		73.31 306		82.48 175	48.90 225

PM-MVS		52.33 305	50.19 310	58.75 302	62.10 343	45.14 261	65.75 294	40.38 361	43.60 315	53.52 314	72.65 308	9.16 363	65.87 316	50.41 212	54.18 335	65.24 345

MDTV_nov1_ep13			57.00 273		72.73 259	38.26 313	65.02 304	64.73 305	44.74 303	55.46 291	72.48 309	32.61 289	70.47 293	37.47 302	67.75 266

UnsupCasMVSNet_eth		53.16 304	52.47 302	55.23 316	59.45 353	33.39 344	59.43 325	69.13 279	45.98 295	50.35 331	72.32 310	29.30 310	58.26 338	42.02 282	44.30 350	74.05 305

SCA		60.49 258	58.38 263	66.80 242	74.14 245	48.06 229	63.35 309	63.23 313	49.13 263	59.33 265	72.10 311	37.45 239	74.27 279	44.17 260	62.57 303	78.05 260

Patchmatch-test		49.08 313	48.28 315	51.50 331	64.40 335	30.85 351	45.68 351	48.46 354	35.60 344	46.10 342	72.10 311	34.47 265	46.37 357	27.08 350	60.65 316	77.27 269

PatchmatchNet		59.84 262	58.24 264	64.65 270	73.05 254	46.70 243	69.42 278	62.18 319	47.55 280	58.88 268	71.96 313	34.49 264	69.16 300	42.99 273	63.60 295	78.07 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpmrst		58.24 270	58.70 260	56.84 311	66.97 320	34.32 339	69.57 277	61.14 323	47.17 287	58.58 272	71.60 314	41.28 208	60.41 331	49.20 223	62.84 301	75.78 285

ambc				65.13 267	63.72 338	37.07 322	47.66 350	78.78 163		54.37 307	71.42 315	11.24 359	80.94 203	45.64 249	53.85 337	77.38 267

EPMVS		53.96 295	53.69 298	54.79 319	66.12 328	31.96 348	62.34 314	49.05 351	44.42 309	55.54 290	71.33 316	30.22 302	56.70 343	41.65 285	62.54 304	75.71 286

PatchMatch-RL		56.25 284	54.55 289	61.32 292	77.06 194	56.07 119	65.57 297	54.10 346	44.13 312	53.49 316	71.27 317	25.20 335	66.78 311	36.52 312	63.66 294	61.12 346

tpmvs		58.47 268	56.95 274	63.03 281	70.20 293	41.21 294	67.90 286	67.23 290	49.62 259	54.73 302	70.84 318	34.14 267	76.24 271	36.64 310	61.29 312	71.64 326

ppachtmachnet_test		58.06 273	55.38 284	66.10 254	69.51 303	48.99 218	68.01 285	66.13 296	44.50 307	54.05 309	70.74 319	32.09 293	72.34 285	36.68 309	56.71 328	76.99 276

DWT-MVSNet_test		61.90 247	59.93 254	67.83 234	71.98 272	46.09 251	71.03 264	69.71 270	50.09 255	58.51 273	70.62 320	30.21 303	77.63 255	49.28 222	67.91 262	79.78 244

tpm cat1		59.25 265	56.95 274	66.15 252	72.19 268	46.96 241	68.09 284	65.76 297	40.03 337	57.81 278	70.56 321	38.32 231	74.51 277	38.26 299	61.50 311	77.00 274

KD-MVS_2432*1600		53.45 299	51.50 306	59.30 295	62.82 339	37.14 320	55.33 336	71.79 258	47.34 284	55.09 297	70.52 322	21.91 345	70.45 294	35.72 316	42.97 352	70.31 333

miper_refine_blended		53.45 299	51.50 306	59.30 295	62.82 339	37.14 320	55.33 336	71.79 258	47.34 284	55.09 297	70.52 322	21.91 345	70.45 294	35.72 316	42.97 352	70.31 333

MDA-MVSNet-bldmvs		53.87 297	50.81 308	63.05 280	66.25 326	48.58 222	56.93 333	63.82 309	48.09 274	41.22 350	70.48 324	30.34 301	68.00 306	34.24 321	45.92 349	72.57 316

LF4IMVS		42.95 320	42.26 322	45.04 337	48.30 360	32.50 345	54.80 338	48.49 353	28.03 352	40.51 352	70.16 325	9.24 362	43.89 359	31.63 334	49.18 346	58.72 348

RPMNet		61.53 252	58.42 262	70.86 191	69.96 299	52.07 174	65.31 301	81.36 112	43.20 319	59.36 262	70.15 326	35.37 255	85.47 107	36.42 313	64.65 288	75.06 291

DIV-MVS_2432*1600		55.22 291	53.89 297	59.21 298	57.80 356	27.47 356	57.75 330	74.32 237	47.38 282	50.90 325	70.00 327	28.45 315	70.30 296	40.44 289	57.92 324	79.87 241

test-LLR		58.15 272	58.13 267	58.22 304	68.57 311	44.80 263	65.46 298	57.92 332	50.08 256	55.44 292	69.82 328	32.62 287	57.44 340	49.66 219	73.62 180	72.41 320

test-mter		56.42 282	55.82 281	58.22 304	68.57 311	44.80 263	65.46 298	57.92 332	39.94 338	55.44 292	69.82 328	21.92 344	57.44 340	49.66 219	73.62 180	72.41 320

our_test_3		56.49 280	54.42 290	62.68 283	69.51 303	45.48 259	66.08 293	61.49 322	44.11 313	50.73 328	69.60 330	33.05 278	68.15 304	38.38 298	56.86 326	74.40 301

PatchT		53.17 303	53.44 300	52.33 329	68.29 315	25.34 360	58.21 328	54.41 344	44.46 308	54.56 304	69.05 331	33.32 276	60.94 328	36.93 305	61.76 310	70.73 332

new-patchmatchnet		47.56 316	47.73 317	47.06 335	58.81 354	9.37 368	48.78 348	59.21 327	43.28 317	44.22 346	68.66 332	25.67 333	57.20 342	31.57 336	49.35 345	74.62 300

dp		51.89 307	51.60 305	52.77 327	68.44 314	32.45 346	62.36 313	54.57 343	44.16 311	49.31 332	67.91 333	28.87 313	56.61 344	33.89 322	54.89 332	69.24 340

MDA-MVSNet_test_wron		50.71 311	48.95 312	56.00 315	61.17 347	41.84 287	51.90 343	56.45 336	40.96 332	44.79 344	67.84 334	30.04 305	55.07 352	36.71 308	50.69 343	71.11 331

YYNet1		50.73 310	48.96 311	56.03 314	61.10 348	41.78 289	51.94 342	56.44 337	40.94 333	44.84 343	67.80 335	30.08 304	55.08 351	36.77 306	50.71 342	71.22 328

TESTMET0.1,1		55.28 290	54.90 287	56.42 312	66.56 324	43.67 274	65.46 298	56.27 339	39.18 340	53.83 310	67.44 336	24.21 339	55.46 350	48.04 231	73.11 193	70.13 335

DSMNet-mixed		39.30 325	38.72 326	41.03 340	51.22 359	19.66 364	45.53 352	31.35 365	15.83 362	39.80 353	67.42 337	22.19 343	45.13 358	22.43 354	52.69 338	58.31 349

PMMVS		53.96 295	53.26 301	56.04 313	62.60 342	50.92 187	61.17 320	56.09 340	32.81 347	53.51 315	66.84 338	34.04 268	59.93 333	44.14 262	68.18 260	57.27 351

N_pmnet		39.35 324	40.28 325	36.54 341	63.76 337	1.62 372	49.37 347	0.76 372	34.62 346	43.61 347	66.38 339	26.25 330	42.57 360	26.02 353	51.77 339	65.44 344

ADS-MVSNet2		51.33 309	48.76 314	59.07 300	66.02 329	44.60 266	50.90 344	59.76 326	36.90 341	50.74 326	66.18 340	26.38 328	63.11 322	27.17 348	54.76 333	69.50 337

ADS-MVSNet		48.48 314	47.77 316	50.63 332	66.02 329	29.92 352	50.90 344	50.87 350	36.90 341	50.74 326	66.18 340	26.38 328	52.47 354	27.17 348	54.76 333	69.50 337

GG-mvs-BLEND				62.34 284	71.36 282	37.04 323	69.20 280	57.33 335		54.73 302	65.48 342	30.37 300	77.82 252	34.82 319	74.93 170	72.17 324

patchmatchnet-post											64.03 343	34.50 263	74.27 279

FPMVS		42.18 321	41.11 324	45.39 336	58.03 355	41.01 296	49.50 346	53.81 347	30.07 350	33.71 355	64.03 343	11.69 356	52.08 355	14.01 360	55.11 331	43.09 356

UnsupCasMVSNet_bld		50.07 312	48.87 313	53.66 322	60.97 350	33.67 342	57.62 331	64.56 306	39.47 339	47.38 336	64.02 345	27.47 321	59.32 334	34.69 320	43.68 351	67.98 342

CHOSEN 280x420		47.83 315	46.36 319	52.24 330	67.37 319	49.78 206	38.91 357	43.11 359	35.00 345	43.27 348	63.30 346	28.95 311	49.19 356	36.53 311	60.80 315	57.76 350

Patchmatch-RL test		58.16 271	55.49 283	66.15 252	67.92 316	48.89 220	60.66 322	51.07 348	47.86 277	59.36 262	62.71 347	34.02 269	72.27 286	56.41 166	59.40 319	77.30 268

pmmvs3		44.92 319	41.95 323	53.86 321	52.58 358	43.55 275	62.11 315	46.90 357	26.05 355	40.63 351	60.19 348	11.08 360	57.91 339	31.83 333	46.15 348	60.11 347

PVSNet_0	43.31 20	47.46 317	45.64 320	52.92 326	67.60 318	44.65 265	54.06 340	54.64 342	41.59 328	46.15 341	58.75 349	30.99 297	58.66 336	32.18 327	24.81 358	55.46 352

gg-mvs-nofinetune		57.86 274	56.43 278	62.18 285	72.62 261	35.35 334	66.57 289	56.33 338	50.65 250	57.64 279	57.10 350	30.65 298	76.36 269	37.38 303	78.88 132	74.82 297

new_pmnet		34.13 328	34.29 329	33.64 342	52.63 357	18.23 366	44.43 354	33.90 364	22.81 358	30.89 356	53.18 351	10.48 361	35.72 364	20.77 356	39.51 354	46.98 355

ANet_high		41.38 322	37.47 327	53.11 325	39.73 365	24.45 361	56.94 332	69.69 271	47.65 279	26.04 358	52.32 352	12.44 355	62.38 325	21.80 355	10.61 365	72.49 317

JIA-IIPM		51.56 308	47.68 318	63.21 278	64.61 334	50.73 191	47.71 349	58.77 329	42.90 321	48.46 334	51.72 353	24.97 336	70.24 297	36.06 315	53.89 336	68.64 341

PMVS	28.69 22	36.22 326	33.29 330	45.02 338	36.82 367	35.98 333	54.68 339	48.74 352	26.31 354	21.02 359	51.61 354	2.88 371	60.10 332	9.99 364	47.58 347	38.99 359
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LCM-MVSNet		40.30 323	35.88 328	53.57 323	42.24 362	29.15 354	45.21 353	60.53 325	22.23 359	28.02 357	50.98 355	3.72 369	61.78 327	31.22 339	38.76 356	69.78 336

MVS-HIRNet		45.52 318	44.48 321	48.65 334	68.49 313	34.05 340	59.41 326	44.50 358	27.03 353	37.96 354	50.47 356	26.16 331	64.10 319	26.74 351	59.52 318	47.82 354

PMMVS2		27.40 329	25.91 332	31.87 344	39.46 366	6.57 369	31.17 358	28.52 366	23.96 356	20.45 360	48.94 357	4.20 368	37.94 363	16.51 357	19.97 360	51.09 353

test_method		19.68 333	18.10 336	24.41 347	13.68 370	3.11 371	12.06 363	42.37 360	2.00 366	11.97 364	36.38 358	5.77 366	29.35 366	15.06 358	23.65 359	40.76 357

MVE	17.77 23	21.41 332	17.77 337	32.34 343	34.34 368	25.44 359	16.11 361	24.11 367	11.19 363	13.22 363	31.92 359	1.58 372	30.95 365	10.47 362	17.03 361	40.62 358
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 349	17.97 369	10.91 367		10.60 370	7.46 364	11.07 365	28.36 360	3.28 370	11.29 368	8.01 366	9.74 367	13.89 363

Gipuma		34.77 327	31.91 331	43.33 339	62.05 344	37.87 315	20.39 360	67.03 291	23.23 357	18.41 361	25.84 361	4.24 367	62.73 323	14.71 359	51.32 341	29.38 360
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		23.77 330	22.73 334	26.90 345	42.02 363	20.67 363	42.66 355	35.70 362	17.43 360	10.28 366	25.05 362	6.42 365	42.39 361	10.28 363	14.71 362	17.63 361

EMVS		22.97 331	21.84 335	26.36 346	40.20 364	19.53 365	41.95 356	34.64 363	17.09 361	9.73 367	22.83 363	7.29 364	42.22 362	9.18 365	13.66 363	17.32 362

tmp_tt		9.43 336	11.14 339	4.30 350	2.38 371	4.40 370	13.62 362	16.08 369	0.39 367	15.89 362	13.06 364	15.80 353	5.54 369	12.63 361	10.46 366	2.95 364

X-MVStestdata		70.21 123	67.28 164	79.00 21	86.32 29	62.62 14	85.83 20	83.92 55	64.55 24	72.17 89	6.49 365	47.95 125	88.01 39	71.55 57	86.74 56	86.37 69

test_post1							68.67 282				3.64 366	32.39 291	69.49 299	44.17 260

test_post											3.55 367	33.90 270	66.52 312

wuyk23d		13.32 335	12.52 338	15.71 348	47.54 361	26.27 357	31.06 359	1.98 371	4.93 365	5.18 368	1.94 368	0.45 373	18.54 367	6.81 367	12.83 364	2.33 365

testmvs		4.52 339	6.03 342	0.01 352	0.01 372	0.00 374	53.86 341	0.00 373	0.01 368	0.04 369	0.27 369	0.00 375	0.00 370	0.04 368	0.00 368	0.03 367

test123		4.73 338	6.30 341	0.02 351	0.01 372	0.01 373	56.36 334	0.00 373	0.01 368	0.04 369	0.21 370	0.01 374	0.00 370	0.03 369	0.00 368	0.04 366

uanet_test		0.00 341	0.00 344	0.00 353	0.00 374	0.00 374	0.00 364	0.00 373	0.00 370	0.00 371	0.00 371	0.00 375	0.00 370	0.00 370	0.00 368	0.00 368

pcd_1.5k_mvsjas		3.92 340	5.23 343	0.00 353	0.00 374	0.00 374	0.00 364	0.00 373	0.00 370	0.00 371	0.00 371	47.05 141	0.00 370	0.00 370	0.00 368	0.00 368

sosnet-low-res		0.00 341	0.00 344	0.00 353	0.00 374	0.00 374	0.00 364	0.00 373	0.00 370	0.00 371	0.00 371	0.00 375	0.00 370	0.00 370	0.00 368	0.00 368

sosnet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 374	0.00 364	0.00 373	0.00 370	0.00 371	0.00 371	0.00 375	0.00 370	0.00 370	0.00 368	0.00 368

uncertanet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 374	0.00 364	0.00 373	0.00 370	0.00 371	0.00 371	0.00 375	0.00 370	0.00 370	0.00 368	0.00 368

Regformer		0.00 341	0.00 344	0.00 353	0.00 374	0.00 374	0.00 364	0.00 373	0.00 370	0.00 371	0.00 371	0.00 375	0.00 370	0.00 370	0.00 368	0.00 368

uanet		0.00 341	0.00 344	0.00 353	0.00 374	0.00 374	0.00 364	0.00 373	0.00 370	0.00 371	0.00 371	0.00 375	0.00 370	0.00 370	0.00 368	0.00 368

No_MVS				79.95 3	87.24 13	61.04 36		85.62 27					90.96 1	79.31 6	90.65 7	87.85 24

eth-test2					0.00 374

eth-test					0.00 374

IU-MVS					87.77 4	59.15 65		85.53 28	53.93 217	84.64 3				79.07 7	90.87 4	88.37 8

save fliter					86.17 33	61.30 31	83.98 46	79.66 148	59.00 126

test_0728_SECOND				79.19 13	87.82 3	59.11 67	87.85 3	87.15 6					90.84 2	78.66 11	90.61 9	87.62 34

GSMVS																78.05 260

test_part2					87.58 9	60.47 47				83.42 10

sam_mvs1												34.74 261				78.05 260

sam_mvs												33.43 275

MTGPA								80.97 128

MTMP							86.03 17	17.08 368

test9_res														75.28 29	88.31 35	83.81 159

agg_prior2														73.09 48	87.93 42	84.33 141

agg_prior					85.04 53	59.96 52		81.04 125		74.68 49			84.04 134

test_prior4						62.51 17	82.08 81

test_prior				76.69 60	84.20 65	57.27 97		84.88 39					86.43 82			86.38 66

旧先验2							76.08 180		45.32 300	76.55 32			65.56 317	58.75 157

新几何2							76.12 178

无先验							79.66 115	74.30 238	48.40 271				80.78 208	53.62 190		79.03 252

原ACMM2							79.02 123

testdata2													72.18 288	46.95 239

segment_acmp												54.23 56

testdata1							72.65 236		60.50 93

test12				77.76 44	84.52 62	58.41 81		83.36 73		72.93 79		54.61 53	88.05 38		88.12 38	86.81 58

plane_prior7					81.41 97	55.96 121

plane_prior6					81.20 104	56.24 116						45.26 165

plane_prior5								84.01 53					87.21 55	68.16 78	80.58 105	84.65 134

plane_prior3						56.09 118			63.92 37	69.27 125

plane_prior2							84.22 38		64.52 26

plane_prior1					81.27 102

plane_prior						56.31 112	83.58 54		63.19 49						80.48 108

n2								0.00 373

nn								0.00 373

door-mid								47.19 356

test11								83.47 68

door								47.60 355

HQP5-MVS						54.94 136

HQP-NCC					80.66 109		82.31 76		62.10 70	67.85 149

ACMP_Plane					80.66 109		82.31 76		62.10 70	67.85 149

BP-MVS														67.04 90

HQP4-MVS										67.85 149			86.93 64			84.32 142

HQP3-MVS								83.90 57							80.35 111

HQP2-MVS												45.46 159

MDTV_nov1_ep13_2view						25.89 358	61.22 319		40.10 336	51.10 323		32.97 280		38.49 297		78.61 255

ACMMP++_ref															74.07 176

ACMMP++															72.16 207

Test By Simon												48.33 122

High-resolution multi-view benchmark