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.

APDe-MVS		89.15 5	89.63 5	87.73 28	94.49 18	71.69 56	93.83 2	93.96 14	75.70 90	91.06 13	96.03 1	76.84 13	97.03 13	89.09 4	95.65 30	94.47 24

SMA-MVS		89.08 6	89.23 6	88.61 4	94.25 29	73.73 9	92.40 20	93.63 20	74.77 107	92.29 6	95.97 2	74.28 32	97.24 9	88.58 11	96.91 1	94.87 10
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
test0726					95.27 5	71.25 60	93.60 4	94.11 6	77.33 47	92.81 3	95.79 3	80.98 8

DVP-MVS		89.60 2	90.35 2	87.33 43	95.27 5	71.25 60	93.49 7	92.73 61	77.33 47	92.12 8	95.78 4	80.98 8	97.40 5	89.08 5	96.41 10	93.33 77
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									78.38 33	92.12 8	95.78 4	81.46 6	97.40 5	89.42 3	96.57 6	94.67 17

SED-MVS		90.08 1	90.85 1	87.77 24	95.30 2	70.98 66	93.57 5	94.06 10	77.24 49	93.10 1	95.72 6	82.99 1	97.44 3	89.07 7	96.63 3	94.88 8

test_241102_TWO								94.06 10	77.24 49	92.78 4	95.72 6	81.26 7	97.44 3	89.07 7	96.58 5	94.26 33

DPE-MVS		89.48 4	89.98 3	88.01 13	94.80 9	72.69 31	91.59 40	94.10 8	75.90 86	92.29 6	95.66 8	81.67 5	97.38 7	87.44 18	96.34 13	93.95 46
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 53	93.10 1	95.39 9	82.99 1	97.27 8

MP-MVS-pluss		87.67 21	87.72 21	87.54 37	93.64 46	72.04 50	89.80 81	93.50 25	75.17 101	86.34 37	95.29 10	70.86 58	96.00 51	88.78 10	96.04 14	94.58 20
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
xxxxxxxxxxxxxcwj		87.88 18	87.92 18	87.77 24	93.80 40	72.35 43	90.47 64	89.69 164	74.31 116	89.16 16	95.10 11	75.65 19	96.19 43	87.07 19	96.01 15	94.79 12

SF-MVS		88.46 10	88.74 10	87.64 36	92.78 65	71.95 51	92.40 20	94.74 2	75.71 88	89.16 16	95.10 11	75.65 19	96.19 43	87.07 19	96.01 15	94.79 12

ACMMP_NAP		88.05 15	88.08 16	87.94 16	93.70 43	73.05 22	90.86 54	93.59 22	76.27 79	88.14 23	95.09 13	71.06 57	96.67 26	87.67 14	96.37 12	94.09 38

zzz-MVS		87.53 23	87.41 26	87.90 20	94.18 33	74.25 4	90.23 71	92.02 90	79.45 19	85.88 39	94.80 14	68.07 84	96.21 41	86.69 22	95.34 34	93.23 80

MTAPA		87.23 32	87.00 33	87.90 20	94.18 33	74.25 4	86.58 180	92.02 90	79.45 19	85.88 39	94.80 14	68.07 84	96.21 41	86.69 22	95.34 34	93.23 80

SteuartSystems-ACMMP		88.72 9	88.86 9	88.32 7	92.14 76	72.96 25	93.73 3	93.67 19	80.19 14	88.10 24	94.80 14	73.76 37	97.11 11	87.51 16	95.82 22	94.90 7
Skip Steuart: Steuart Systems R&D Blog.
9.14			88.26 14		92.84 64		91.52 43	94.75 1	73.93 126	88.57 21	94.67 17	75.57 21	95.79 57	86.77 21	95.76 26

SR-MVS		86.73 38	86.67 39	86.91 50	94.11 36	72.11 49	92.37 24	92.56 69	74.50 112	86.84 34	94.65 18	67.31 93	95.77 58	84.80 35	92.85 72	92.84 97

ETH3D-3000-0.1		88.09 12	88.29 13	87.50 39	92.76 66	71.89 54	91.43 44	94.70 3	74.47 113	88.86 19	94.61 19	75.23 22	95.84 56	86.62 24	95.92 19	94.78 14

test1172		86.20 49	86.22 46	86.12 68	93.95 38	69.89 92	91.79 39	92.28 78	75.07 102	86.40 36	94.58 20	65.00 118	95.56 64	84.34 43	92.60 75	92.90 95

region2R		87.42 27	87.20 31	88.09 11	94.63 12	73.55 12	93.03 12	93.12 39	76.73 68	84.45 65	94.52 21	69.09 78	96.70 24	84.37 42	94.83 48	94.03 41

ACMMPR		87.44 25	87.23 30	88.08 12	94.64 11	73.59 11	93.04 10	93.20 36	76.78 65	84.66 62	94.52 21	68.81 82	96.65 27	84.53 39	94.90 43	94.00 44

APD-MVS		87.44 25	87.52 23	87.19 45	94.24 30	72.39 41	91.86 37	92.83 57	73.01 144	88.58 20	94.52 21	73.36 38	96.49 35	84.26 44	95.01 40	92.70 99
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 51	85.88 52	86.22 65	92.69 68	69.53 99	91.93 34	92.99 46	73.54 134	85.94 38	94.51 24	65.80 110	95.61 61	83.04 60	92.51 77	93.53 72

CP-MVS		87.11 34	86.92 35	87.68 35	94.20 32	73.86 7	93.98 1	92.82 60	76.62 70	83.68 79	94.46 25	67.93 86	95.95 54	84.20 46	94.39 57	93.23 80

SR-MVS-dyc-post		85.77 54	85.61 55	86.23 64	93.06 58	70.63 78	91.88 35	92.27 79	73.53 135	85.69 43	94.45 26	65.00 118	95.56 64	82.75 63	91.87 81	92.50 106

RE-MVS-def			85.48 56		93.06 58	70.63 78	91.88 35	92.27 79	73.53 135	85.69 43	94.45 26	63.87 125		82.75 63	91.87 81	92.50 106

HFP-MVS		87.58 22	87.47 24	87.94 16	94.58 14	73.54 14	93.04 10	93.24 34	76.78 65	84.91 53	94.44 28	70.78 59	96.61 30	84.53 39	94.89 44	93.66 60

#test#		87.33 30	87.13 32	87.94 16	94.58 14	73.54 14	92.34 25	93.24 34	75.23 98	84.91 53	94.44 28	70.78 59	96.61 30	83.75 52	94.89 44	93.66 60

testtj		87.78 19	87.78 20	87.77 24	94.55 16	72.47 38	92.23 29	93.49 27	74.75 108	88.33 22	94.43 30	73.27 40	97.02 14	84.18 47	94.84 46	93.82 54

PGM-MVS		86.68 40	86.27 45	87.90 20	94.22 31	73.38 18	90.22 72	93.04 40	75.53 92	83.86 76	94.42 31	67.87 88	96.64 28	82.70 67	94.57 53	93.66 60

MP-MVS		87.71 20	87.64 22	87.93 19	94.36 26	73.88 6	92.71 19	92.65 66	77.57 40	83.84 77	94.40 32	72.24 48	96.28 39	85.65 26	95.30 38	93.62 67
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ZNCC-MVS		87.94 17	87.85 19	88.20 10	94.39 24	73.33 19	93.03 12	93.81 17	76.81 63	85.24 48	94.32 33	71.76 52	96.93 16	85.53 27	95.79 23	94.32 30

ETH3D cwj APD-0.16		87.31 31	87.27 27	87.44 41	91.60 83	72.45 40	90.02 75	94.37 4	71.76 158	87.28 31	94.27 34	75.18 23	96.08 47	85.16 28	95.77 24	93.80 57

HPM-MVS++		89.02 7	89.15 7	88.63 3	95.01 8	76.03 1	92.38 23	92.85 56	80.26 13	87.78 27	94.27 34	75.89 17	96.81 20	87.45 17	96.44 8	93.05 88

mPP-MVS		86.67 41	86.32 44	87.72 30	94.41 22	73.55 12	92.74 17	92.22 83	76.87 62	82.81 92	94.25 36	66.44 100	96.24 40	82.88 62	94.28 60	93.38 74

DeepC-MVS	79.81 2	87.08 36	86.88 37	87.69 34	91.16 87	72.32 45	90.31 69	93.94 15	77.12 55	82.82 91	94.23 37	72.13 50	97.09 12	84.83 34	95.37 33	93.65 65
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		87.18 33	86.91 36	88.00 14	94.42 20	73.33 19	92.78 15	92.99 46	79.14 22	83.67 80	94.17 38	67.45 91	96.60 32	83.06 58	94.50 54	94.07 39

MSP-MVS		89.51 3	89.91 4	88.30 8	94.28 28	73.46 17	92.90 14	94.11 6	80.27 12	91.35 12	94.16 39	78.35 11	96.77 21	89.59 2	94.22 62	94.67 17
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
abl_6		85.23 63	84.95 69	86.07 69	92.23 75	70.48 82	90.80 56	92.08 88	73.51 137	85.26 47	94.16 39	62.75 142	95.92 55	82.46 70	91.30 90	91.81 130

DeepPCF-MVS	80.84 1	88.10 11	88.56 11	86.73 54	92.24 74	69.03 106	89.57 87	93.39 32	77.53 44	89.79 15	94.12 41	78.98 10	96.58 34	85.66 25	95.72 27	94.58 20

HPM-MVS_fast		85.35 62	84.95 69	86.57 59	93.69 44	70.58 81	92.15 32	91.62 109	73.89 127	82.67 94	94.09 42	62.60 143	95.54 67	80.93 79	92.93 70	93.57 69

ZD-MVS					94.38 25	72.22 46		92.67 63	70.98 173	87.75 28	94.07 43	74.01 36	96.70 24	84.66 37	94.84 46

CNVR-MVS		88.93 8	89.13 8	88.33 6	94.77 10	73.82 8	90.51 61	93.00 44	80.90 9	88.06 25	94.06 44	76.43 14	96.84 18	88.48 12	95.99 17	94.34 29

OPU-MVS				89.06 2	94.62 13	75.42 3	93.57 5				94.02 45	82.45 3	96.87 17	83.77 51	96.48 7	94.88 8

PC_three_1452									68.21 228	92.02 10	94.00 46	82.09 4	95.98 53	84.58 38	96.68 2	94.95 5

SD-MVS		88.06 13	88.50 12	86.71 55	92.60 72	72.71 29	91.81 38	93.19 37	77.87 34	90.32 14	94.00 46	74.83 25	93.78 143	87.63 15	94.27 61	93.65 65
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
GST-MVS		87.42 27	87.26 28	87.89 23	94.12 35	72.97 24	92.39 22	93.43 30	76.89 61	84.68 59	93.99 48	70.67 62	96.82 19	84.18 47	95.01 40	93.90 49

HPM-MVS		87.11 34	86.98 34	87.50 39	93.88 39	72.16 47	92.19 30	93.33 33	76.07 82	83.81 78	93.95 49	69.77 72	96.01 50	85.15 29	94.66 50	94.32 30
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 16	88.11 15	87.72 30	93.68 45	72.13 48	91.41 45	92.35 76	74.62 111	88.90 18	93.85 50	75.75 18	96.00 51	87.80 13	94.63 51	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		85.89 53	85.39 58	87.38 42	93.59 47	72.63 33	92.74 17	93.18 38	76.78 65	80.73 117	93.82 51	64.33 121	96.29 38	82.67 68	90.69 95	93.23 80
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
test_prior3		86.73 38	86.86 38	86.33 61	92.61 70	69.59 97	88.85 106	92.97 50	75.41 94	84.91 53	93.54 52	74.28 32	95.48 69	83.31 53	95.86 20	93.91 47

test_prior2							88.85 106		75.41 94	84.91 53	93.54 52	74.28 32		83.31 53	95.86 20

ETH3 D test6400		87.50 24	87.44 25	87.70 33	93.71 42	71.75 55	90.62 59	94.05 13	70.80 175	87.59 30	93.51 54	77.57 12	96.63 29	83.31 53	95.77 24	94.72 16

VDDNet		81.52 114	80.67 117	84.05 124	90.44 101	64.13 205	89.73 84	85.91 248	71.11 170	83.18 84	93.48 55	50.54 264	93.49 158	73.40 146	88.25 125	94.54 23

CDPH-MVS		85.76 55	85.29 62	87.17 46	93.49 49	71.08 64	88.58 119	92.42 74	68.32 227	84.61 63	93.48 55	72.32 47	96.15 46	79.00 92	95.43 32	94.28 32

NCCC		88.06 13	88.01 17	88.24 9	94.41 22	73.62 10	91.22 49	92.83 57	81.50 6	85.79 42	93.47 57	73.02 43	97.00 15	84.90 31	94.94 42	94.10 37

3Dnovator+	77.84 4	85.48 58	84.47 73	88.51 5	91.08 88	73.49 16	93.18 9	93.78 18	80.79 10	76.66 181	93.37 58	60.40 186	96.75 23	77.20 112	93.73 66	95.29 2

DeepC-MVS_fast	79.65 3	86.91 37	86.62 40	87.76 27	93.52 48	72.37 42	91.26 46	93.04 40	76.62 70	84.22 70	93.36 59	71.44 55	96.76 22	80.82 81	95.33 36	94.16 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 91	82.36 92	84.96 89	91.02 90	66.40 160	88.91 103	88.11 208	77.57 40	84.39 68	93.29 60	52.19 241	93.91 138	77.05 114	88.70 119	94.57 22

UA-Net		85.08 67	84.96 68	85.45 76	92.07 77	68.07 133	89.78 82	90.86 133	82.48 2	84.60 64	93.20 61	69.35 75	95.22 85	71.39 162	90.88 94	93.07 87

agg_prior1		86.22 48	86.09 51	86.62 57	92.85 62	71.94 52	88.59 118	91.78 105	68.96 217	84.41 66	93.18 62	74.94 24	94.93 96	84.75 36	95.33 36	93.01 91

TEST9					93.26 52	72.96 25	88.75 111	91.89 99	68.44 226	85.00 51	93.10 63	74.36 31	95.41 75

train_agg		86.43 44	86.20 47	87.13 47	93.26 52	72.96 25	88.75 111	91.89 99	68.69 222	85.00 51	93.10 63	74.43 28	95.41 75	84.97 30	95.71 28	93.02 90

test_8					93.13 54	72.57 35	88.68 116	91.84 102	68.69 222	84.87 57	93.10 63	74.43 28	95.16 87

LFMVS		81.82 107	81.23 108	83.57 139	91.89 80	63.43 221	89.84 78	81.85 299	77.04 58	83.21 83	93.10 63	52.26 240	93.43 163	71.98 157	89.95 107	93.85 51

旧先验1					91.96 78	65.79 173		86.37 242			93.08 67	69.31 77			92.74 73	88.74 238

testdata				79.97 233	90.90 92	64.21 203		84.71 258	59.27 315	85.40 45	92.91 68	62.02 156	89.08 272	68.95 186	91.37 88	86.63 285

MCST-MVS		87.37 29	87.25 29	87.73 28	94.53 17	72.46 39	89.82 79	93.82 16	73.07 142	84.86 58	92.89 69	76.22 15	96.33 37	84.89 33	95.13 39	94.40 26

Vis-MVSNet		83.46 81	82.80 87	85.43 77	90.25 104	68.74 116	90.30 70	90.13 152	76.33 78	80.87 116	92.89 69	61.00 175	94.20 123	72.45 156	90.97 92	93.35 76
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CPTT-MVS		83.73 76	83.33 79	84.92 92	93.28 51	70.86 73	92.09 33	90.38 142	68.75 221	79.57 125	92.83 71	60.60 182	93.04 182	80.92 80	91.56 86	90.86 158

3Dnovator	76.31 5	83.38 84	82.31 93	86.59 58	87.94 181	72.94 28	90.64 58	92.14 87	77.21 51	75.47 206	92.83 71	58.56 193	94.72 108	73.24 149	92.71 74	92.13 121

MSLP-MVS++		85.43 60	85.76 54	84.45 107	91.93 79	70.24 83	90.71 57	92.86 55	77.46 46	84.22 70	92.81 73	67.16 95	92.94 184	80.36 86	94.35 59	90.16 182

MG-MVS		83.41 82	83.45 77	83.28 147	92.74 67	62.28 240	88.17 136	89.50 168	75.22 99	81.49 107	92.74 74	66.75 96	95.11 89	72.85 152	91.58 85	92.45 109

casdiffmvs		85.11 66	85.14 64	85.01 87	87.20 205	65.77 174	87.75 147	92.83 57	77.84 35	84.36 69	92.38 75	72.15 49	93.93 137	81.27 77	90.48 98	95.33 1

baseline		84.93 69	84.98 66	84.80 98	87.30 203	65.39 182	87.30 158	92.88 54	77.62 38	84.04 75	92.26 76	71.81 51	93.96 131	81.31 76	90.30 100	95.03 4

QAPM		80.88 123	79.50 139	85.03 86	88.01 180	68.97 110	91.59 40	92.00 93	66.63 243	75.15 220	92.16 77	57.70 199	95.45 71	63.52 225	88.76 118	90.66 164

IS-MVSNet		83.15 86	82.81 86	84.18 118	89.94 111	63.30 223	91.59 40	88.46 205	79.04 26	79.49 126	92.16 77	65.10 115	94.28 117	67.71 193	91.86 83	94.95 5

1121		80.84 125	79.77 132	84.05 124	93.11 56	70.78 75	84.66 225	85.42 252	57.37 329	81.76 106	92.02 79	63.41 129	94.12 127	67.28 198	92.93 70	87.26 269

新几何1				83.42 142	93.13 54	70.71 76		85.48 251	57.43 328	81.80 102	91.98 80	63.28 131	92.27 202	64.60 222	92.99 69	87.27 268

OpenMVS	72.83 10	79.77 152	78.33 165	84.09 121	85.17 233	69.91 90	90.57 60	90.97 129	66.70 239	72.17 252	91.91 81	54.70 221	93.96 131	61.81 244	90.95 93	88.41 246

PHI-MVS		86.43 44	86.17 49	87.24 44	90.88 93	70.96 68	92.27 28	94.07 9	72.45 147	85.22 49	91.90 82	69.47 74	96.42 36	83.28 56	95.94 18	94.35 28

VNet		82.21 99	82.41 90	81.62 197	90.82 94	60.93 254	84.47 231	89.78 160	76.36 77	84.07 74	91.88 83	64.71 120	90.26 252	70.68 167	88.89 115	93.66 60

DROMVSNet		86.01 50	86.38 42	84.91 93	89.31 132	66.27 163	92.32 26	93.63 20	79.37 21	84.17 72	91.88 83	69.04 81	95.43 73	83.93 50	93.77 65	93.01 91

OPM-MVS		83.50 80	82.95 84	85.14 83	88.79 154	70.95 69	89.13 98	91.52 112	77.55 43	80.96 115	91.75 85	60.71 178	94.50 113	79.67 91	86.51 148	89.97 198
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
XVG-OURS-SEG-HR		80.81 128	79.76 133	83.96 132	85.60 227	68.78 113	83.54 253	90.50 139	70.66 180	76.71 180	91.66 86	60.69 179	91.26 233	76.94 116	81.58 203	91.83 128

EPNet		83.72 77	82.92 85	86.14 67	84.22 248	69.48 100	91.05 52	85.27 253	81.30 7	76.83 176	91.65 87	66.09 105	95.56 64	76.00 124	93.85 64	93.38 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 94	81.97 101	84.85 95	88.75 156	67.42 144	87.98 140	90.87 132	74.92 105	79.72 124	91.65 87	62.19 153	93.96 131	75.26 131	86.42 149	93.16 85

test222					91.50 84	68.26 129	84.16 240	83.20 285	54.63 340	79.74 123	91.63 89	58.97 191			91.42 87	86.77 281

MVS_111021_HR		85.14 65	84.75 71	86.32 63	91.65 82	72.70 30	85.98 195	90.33 146	76.11 81	82.08 97	91.61 90	71.36 56	94.17 126	81.02 78	92.58 76	92.08 122

原ACMM1				84.35 112	93.01 60	68.79 112		92.44 71	63.96 277	81.09 113	91.57 91	66.06 106	95.45 71	67.19 201	94.82 49	88.81 235

LPG-MVS_test		82.08 101	81.27 107	84.50 104	89.23 137	68.76 114	90.22 72	91.94 97	75.37 96	76.64 182	91.51 92	54.29 224	94.91 98	78.44 99	83.78 174	89.83 203

LGP-MVS_train				84.50 104	89.23 137	68.76 114		91.94 97	75.37 96	76.64 182	91.51 92	54.29 224	94.91 98	78.44 99	83.78 174	89.83 203

XVG-OURS		80.41 140	79.23 146	83.97 131	85.64 226	69.02 107	83.03 261	90.39 141	71.09 171	77.63 160	91.49 94	54.62 223	91.35 231	75.71 125	83.47 181	91.54 135

alignmvs		85.48 58	85.32 60	85.96 72	89.51 120	69.47 101	89.74 83	92.47 70	76.17 80	87.73 29	91.46 95	70.32 65	93.78 143	81.51 74	88.95 114	94.63 19

CANet		86.45 43	86.10 50	87.51 38	90.09 107	70.94 70	89.70 85	92.59 68	81.78 4	81.32 108	91.43 96	70.34 64	97.23 10	84.26 44	93.36 68	94.37 27

hse-mvs3		83.15 86	82.19 94	86.02 71	90.56 98	70.85 74	88.15 138	89.16 180	76.02 83	84.67 60	91.39 97	61.54 161	95.50 68	82.71 65	75.48 274	91.72 132

nrg030		83.88 74	83.53 76	84.96 89	86.77 213	69.28 105	90.46 66	92.67 63	74.79 106	82.95 87	91.33 98	72.70 44	93.09 178	80.79 83	79.28 231	92.50 106

canonicalmvs		85.91 52	85.87 53	86.04 70	89.84 113	69.44 104	90.45 67	93.00 44	76.70 69	88.01 26	91.23 99	73.28 39	93.91 138	81.50 75	88.80 117	94.77 15

DPM-MVS		84.93 69	84.29 74	86.84 51	90.20 105	73.04 23	87.12 162	93.04 40	69.80 195	82.85 90	91.22 100	73.06 42	96.02 49	76.72 119	94.63 51	91.46 140

Anonymous202405211		78.25 187	77.01 195	81.99 191	91.03 89	60.67 258	84.77 223	83.90 271	70.65 181	80.00 122	91.20 101	41.08 327	91.43 229	65.21 216	85.26 160	93.85 51

Anonymous20240529		80.19 146	78.89 152	84.10 120	90.60 97	64.75 192	88.95 102	90.90 131	65.97 251	80.59 118	91.17 102	49.97 269	93.73 149	69.16 184	82.70 193	93.81 55

EPP-MVSNet		83.40 83	83.02 83	84.57 102	90.13 106	64.47 198	92.32 26	90.73 134	74.45 115	79.35 128	91.10 103	69.05 80	95.12 88	72.78 153	87.22 137	94.13 36

TAPA-MVS	73.13 9	79.15 167	77.94 172	82.79 175	89.59 116	62.99 233	88.16 137	91.51 113	65.77 252	77.14 173	91.09 104	60.91 176	93.21 168	50.26 316	87.05 139	92.17 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 46	86.19 48	87.07 48	92.91 61	72.48 37	90.81 55	93.56 23	73.95 124	83.16 85	91.07 105	75.94 16	95.19 86	79.94 90	94.38 58	93.55 70

FIs		82.07 102	82.42 89	81.04 215	88.80 153	58.34 278	88.26 133	93.49 27	76.93 60	78.47 143	91.04 106	69.92 70	92.34 201	69.87 177	84.97 162	92.44 110

MVS_111021_LR		82.61 96	82.11 95	84.11 119	88.82 151	71.58 57	85.15 215	86.16 245	74.69 109	80.47 120	91.04 106	62.29 150	90.55 250	80.33 87	90.08 105	90.20 181

DP-MVS Recon		83.11 89	82.09 96	86.15 66	94.44 19	70.92 72	88.79 109	92.20 84	70.53 182	79.17 129	91.03 108	64.12 123	96.03 48	68.39 190	90.14 103	91.50 137

HQP_MVS		83.64 78	83.14 80	85.14 83	90.08 108	68.71 118	91.25 47	92.44 71	79.12 24	78.92 133	91.00 109	60.42 184	95.38 78	78.71 95	86.32 150	91.33 142

plane_prior4											91.00 109

FC-MVSNet-test		81.52 114	82.02 99	80.03 232	88.42 168	55.97 314	87.95 142	93.42 31	77.10 56	77.38 164	90.98 111	69.96 69	91.79 219	68.46 189	84.50 167	92.33 111

Vis-MVSNet (Re-imp)		78.36 186	78.45 159	78.07 264	88.64 160	51.78 336	86.70 177	79.63 320	74.14 122	75.11 221	90.83 112	61.29 169	89.75 260	58.10 276	91.60 84	92.69 101

114514_t		80.68 134	79.51 138	84.20 117	94.09 37	67.27 148	89.64 86	91.11 127	58.75 320	74.08 234	90.72 113	58.10 195	95.04 94	69.70 178	89.42 112	90.30 178

PAPM_NR		83.02 90	82.41 90	84.82 96	92.47 73	66.37 161	87.93 144	91.80 103	73.82 128	77.32 166	90.66 114	67.90 87	94.90 100	70.37 170	89.48 111	93.19 84

LS3D		76.95 217	74.82 229	83.37 145	90.45 100	67.36 147	89.15 97	86.94 234	61.87 296	69.52 281	90.61 115	51.71 252	94.53 111	46.38 336	86.71 145	88.21 248

VPNet		78.69 178	78.66 155	78.76 253	88.31 171	55.72 316	84.45 234	86.63 238	76.79 64	78.26 147	90.55 116	59.30 189	89.70 262	66.63 205	77.05 249	90.88 157

UniMVSNet_ETH3D		79.10 169	78.24 167	81.70 196	86.85 210	60.24 264	87.28 159	88.79 194	74.25 119	76.84 175	90.53 117	49.48 275	91.56 225	67.98 191	82.15 197	93.29 78

ACMP	74.13 6	81.51 116	80.57 118	84.36 111	89.42 123	68.69 121	89.97 77	91.50 116	74.46 114	75.04 224	90.41 118	53.82 229	94.54 110	77.56 108	82.91 188	89.86 202
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 141	78.84 153	85.01 87	87.71 189	68.99 109	83.65 248	91.46 117	63.00 283	77.77 158	90.28 119	66.10 104	95.09 93	61.40 247	88.22 126	90.94 156
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 115	68.32 127					90.24 120

HQP-MVS		82.61 96	82.02 99	84.37 110	89.33 127	66.98 152	89.17 93	92.19 85	76.41 72	77.23 169	90.23 121	60.17 187	95.11 89	77.47 109	85.99 156	91.03 152

PS-MVSNAJss		82.07 102	81.31 106	84.34 113	86.51 216	67.27 148	89.27 91	91.51 113	71.75 159	79.37 127	90.22 122	63.15 136	94.27 118	77.69 107	82.36 196	91.49 138

TSAR-MVS + GP.		85.71 56	85.33 59	86.84 51	91.34 85	72.50 36	89.07 99	87.28 228	76.41 72	85.80 41	90.22 122	74.15 35	95.37 81	81.82 73	91.88 80	92.65 103

Regformer-1		86.41 46	86.33 43	86.64 56	89.33 127	70.93 71	88.43 121	91.39 118	82.14 3	86.65 35	90.09 124	74.39 30	95.01 95	83.97 49	90.63 96	93.97 45

Regformer-2		86.63 42	86.53 41	86.95 49	89.33 127	71.24 63	88.43 121	92.05 89	82.50 1	86.88 33	90.09 124	74.45 27	95.61 61	84.38 41	90.63 96	94.01 43

test_part1		82.78 93	82.08 97	84.89 94	90.66 96	66.97 154	90.96 53	92.93 53	77.19 52	80.53 119	90.04 126	63.44 128	95.39 77	76.04 123	76.90 251	92.31 113

Regformer-3		85.23 63	85.07 65	85.70 74	88.95 146	69.01 108	88.29 131	89.91 158	80.95 8	85.01 50	90.01 127	72.45 46	94.19 124	82.50 69	87.57 129	93.90 49

Regformer-4		85.68 57	85.45 57	86.35 60	88.95 146	69.67 96	88.29 131	91.29 120	81.73 5	85.36 46	90.01 127	72.62 45	95.35 82	83.28 56	87.57 129	94.03 41

CS-MVS-test		85.02 68	85.21 63	84.46 106	89.28 134	65.70 175	91.16 50	93.56 23	77.83 36	81.80 102	89.89 129	70.67 62	95.61 61	80.39 85	92.34 78	92.06 123

TranMVSNet+NR-MVSNet		80.84 125	80.31 124	82.42 183	87.85 183	62.33 238	87.74 148	91.33 119	80.55 11	77.99 154	89.86 130	65.23 114	92.62 190	67.05 203	75.24 284	92.30 114

diffmvs		82.10 100	81.88 102	82.76 178	83.00 274	63.78 211	83.68 247	89.76 161	72.94 145	82.02 98	89.85 131	65.96 109	90.79 246	82.38 71	87.30 136	93.71 59

BH-RMVSNet		79.61 154	78.44 160	83.14 156	89.38 126	65.93 169	84.95 220	87.15 231	73.56 133	78.19 149	89.79 132	56.67 210	93.36 164	59.53 261	86.74 144	90.13 184

GeoE		81.71 109	81.01 113	83.80 135	89.51 120	64.45 199	88.97 101	88.73 200	71.27 168	78.63 139	89.76 133	66.32 102	93.20 170	69.89 176	86.02 155	93.74 58

CS-MVS		84.53 72	84.97 67	83.23 152	87.54 197	63.27 224	88.82 108	93.50 25	75.98 85	83.07 86	89.73 134	70.29 66	95.23 84	82.07 72	93.70 67	91.18 146

AdaColmap		80.58 138	79.42 140	84.06 123	93.09 57	68.91 111	89.36 89	88.97 190	69.27 205	75.70 203	89.69 135	57.20 207	95.77 58	63.06 231	88.41 124	87.50 263

ACMM	73.20 8	80.78 133	79.84 131	83.58 138	89.31 132	68.37 126	89.99 76	91.60 110	70.28 186	77.25 167	89.66 136	53.37 232	93.53 157	74.24 137	82.85 189	88.85 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 193	76.79 202	81.97 192	90.40 102	71.07 65	87.59 151	84.55 261	66.03 250	72.38 250	89.64 137	57.56 201	86.04 301	59.61 260	83.35 182	88.79 236

test_yl		81.17 119	80.47 121	83.24 150	89.13 141	63.62 212	86.21 190	89.95 156	72.43 150	81.78 104	89.61 138	57.50 202	93.58 152	70.75 165	86.90 141	92.52 104

DCV-MVSNet		81.17 119	80.47 121	83.24 150	89.13 141	63.62 212	86.21 190	89.95 156	72.43 150	81.78 104	89.61 138	57.50 202	93.58 152	70.75 165	86.90 141	92.52 104

EI-MVSNet-Vis-set		84.19 73	83.81 75	85.31 78	88.18 173	67.85 136	87.66 149	89.73 163	80.05 16	82.95 87	89.59 140	70.74 61	94.82 104	80.66 84	84.72 165	93.28 79

PAPR		81.66 112	80.89 115	83.99 130	90.27 103	64.00 206	86.76 176	91.77 107	68.84 220	77.13 174	89.50 141	67.63 89	94.88 102	67.55 195	88.52 122	93.09 86

jajsoiax		79.29 164	77.96 171	83.27 148	84.68 242	66.57 159	89.25 92	90.16 151	69.20 209	75.46 208	89.49 142	45.75 302	93.13 176	76.84 117	80.80 211	90.11 186

MVSFormer		82.85 92	82.05 98	85.24 81	87.35 198	70.21 84	90.50 62	90.38 142	68.55 224	81.32 108	89.47 143	61.68 158	93.46 161	78.98 93	90.26 101	92.05 124

jason		81.39 117	80.29 125	84.70 100	86.63 215	69.90 91	85.95 196	86.77 236	63.24 279	81.07 114	89.47 143	61.08 174	92.15 208	78.33 102	90.07 106	92.05 124
jason: jason.
mvs_tets		79.13 168	77.77 179	83.22 153	84.70 241	66.37 161	89.17 93	90.19 150	69.38 203	75.40 211	89.46 145	44.17 310	93.15 174	76.78 118	80.70 213	90.14 183

UGNet		80.83 127	79.59 137	84.54 103	88.04 178	68.09 132	89.42 88	88.16 207	76.95 59	76.22 191	89.46 145	49.30 278	93.94 134	68.48 188	90.31 99	91.60 133
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
VPA-MVSNet		80.60 136	80.55 119	80.76 220	88.07 177	60.80 257	86.86 170	91.58 111	75.67 91	80.24 121	89.45 147	63.34 130	90.25 253	70.51 169	79.22 232	91.23 145

MVS_Test		83.15 86	83.06 82	83.41 144	86.86 209	63.21 226	86.11 193	92.00 93	74.31 116	82.87 89	89.44 148	70.03 68	93.21 168	77.39 111	88.50 123	93.81 55

EI-MVSNet-UG-set		83.81 75	83.38 78	85.09 85	87.87 182	67.53 142	87.44 155	89.66 165	79.74 18	82.23 96	89.41 149	70.24 67	94.74 107	79.95 89	83.92 173	92.99 93

RPSCF		73.23 259	71.46 260	78.54 257	82.50 286	59.85 266	82.18 267	82.84 290	58.96 317	71.15 262	89.41 149	45.48 304	84.77 311	58.82 269	71.83 312	91.02 154

RRT_MVS		79.88 151	78.38 162	84.38 109	85.42 230	70.60 80	88.71 115	88.75 199	72.30 152	78.83 135	89.14 151	44.44 308	92.18 207	78.50 98	79.33 230	90.35 176

UniMVSNet_NR-MVSNet		81.88 105	81.54 105	82.92 167	88.46 166	63.46 219	87.13 161	92.37 75	80.19 14	78.38 144	89.14 151	71.66 54	93.05 180	70.05 173	76.46 259	92.25 116

tttt0517		79.40 161	77.91 173	83.90 134	88.10 176	63.84 209	88.37 128	84.05 269	71.45 166	76.78 178	89.12 153	49.93 272	94.89 101	70.18 172	83.18 185	92.96 94

DU-MVS		81.12 121	80.52 120	82.90 168	87.80 185	63.46 219	87.02 165	91.87 101	79.01 27	78.38 144	89.07 154	65.02 116	93.05 180	70.05 173	76.46 259	92.20 118

NR-MVSNet		80.23 144	79.38 142	82.78 176	87.80 185	63.34 222	86.31 187	91.09 128	79.01 27	72.17 252	89.07 154	67.20 94	92.81 189	66.08 210	75.65 270	92.20 118

DELS-MVS		85.41 61	85.30 61	85.77 73	88.49 164	67.93 135	85.52 212	93.44 29	78.70 29	83.63 82	89.03 156	74.57 26	95.71 60	80.26 88	94.04 63	93.66 60
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
baseline1		76.98 216	76.75 205	77.66 269	88.13 174	55.66 317	85.12 216	81.89 297	73.04 143	76.79 177	88.90 157	62.43 148	87.78 290	63.30 229	71.18 316	89.55 212

DP-MVS		76.78 219	74.57 231	83.42 142	93.29 50	69.46 103	88.55 120	83.70 273	63.98 276	70.20 269	88.89 158	54.01 228	94.80 105	46.66 333	81.88 201	86.01 295

ab-mvs		79.51 156	78.97 151	81.14 212	88.46 166	60.91 255	83.84 245	89.24 177	70.36 184	79.03 130	88.87 159	63.23 134	90.21 254	65.12 217	82.57 194	92.28 115

PEN-MVS		77.73 202	77.69 183	77.84 266	87.07 208	53.91 326	87.91 145	91.18 124	77.56 42	73.14 241	88.82 160	61.23 170	89.17 270	59.95 257	72.37 307	90.43 173

test_djsdf		80.30 143	79.32 144	83.27 148	83.98 253	65.37 183	90.50 62	90.38 142	68.55 224	76.19 192	88.70 161	56.44 211	93.46 161	78.98 93	80.14 221	90.97 155

PAPM		77.68 205	76.40 211	81.51 200	87.29 204	61.85 245	83.78 246	89.59 166	64.74 264	71.23 260	88.70 161	62.59 144	93.66 151	52.66 303	87.03 140	89.01 225

DTE-MVSNet		76.99 215	76.80 201	77.54 273	86.24 218	53.06 333	87.52 152	90.66 135	77.08 57	72.50 247	88.67 163	60.48 183	89.52 264	57.33 283	70.74 318	90.05 193

PS-CasMVS		78.01 197	78.09 169	77.77 268	87.71 189	54.39 323	88.02 139	91.22 122	77.50 45	73.26 239	88.64 164	60.73 177	88.41 283	61.88 242	73.88 296	90.53 170

cdsmvs_eth3d_5k		19.96 334	26.61 336	0.00 353	0.00 374	0.00 375	0.00 364	89.26 176	0.00 370	0.00 371	88.61 165	61.62 160	0.00 372	0.00 370	0.00 370	0.00 368

lupinMVS		81.39 117	80.27 126	84.76 99	87.35 198	70.21 84	85.55 208	86.41 240	62.85 286	81.32 108	88.61 165	61.68 158	92.24 205	78.41 101	90.26 101	91.83 128

F-COLMAP		76.38 227	74.33 236	82.50 182	89.28 134	66.95 156	88.41 124	89.03 185	64.05 274	66.83 303	88.61 165	46.78 292	92.89 185	57.48 280	78.55 233	87.67 257

mvs_anonymous		79.42 160	79.11 148	80.34 227	84.45 245	57.97 284	82.59 263	87.62 221	67.40 234	76.17 195	88.56 168	68.47 83	89.59 263	70.65 168	86.05 154	93.47 73

CP-MVSNet		78.22 188	78.34 164	77.84 266	87.83 184	54.54 321	87.94 143	91.17 125	77.65 37	73.48 237	88.49 169	62.24 152	88.43 282	62.19 238	74.07 292	90.55 169

PVSNet_Blended_VisFu		82.62 95	81.83 103	84.96 89	90.80 95	69.76 94	88.74 113	91.70 108	69.39 202	78.96 131	88.46 170	65.47 112	94.87 103	74.42 134	88.57 120	90.24 180

CANet_DTU		80.61 135	79.87 130	82.83 170	85.60 227	63.17 229	87.36 156	88.65 201	76.37 76	75.88 200	88.44 171	53.51 231	93.07 179	73.30 147	89.74 109	92.25 116

PLC	70.83 11	78.05 195	76.37 212	83.08 159	91.88 81	67.80 137	88.19 135	89.46 169	64.33 270	69.87 278	88.38 172	53.66 230	93.58 152	58.86 268	82.73 191	87.86 254
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 157	79.22 147	80.27 229	88.79 154	58.35 277	85.06 217	88.61 203	78.56 30	77.65 159	88.34 173	63.81 127	90.66 249	64.98 219	77.22 247	91.80 131

XXY-MVS		75.41 239	75.56 217	74.96 296	83.59 259	57.82 288	80.59 283	83.87 272	66.54 244	74.93 226	88.31 174	63.24 133	80.09 332	62.16 239	76.85 254	86.97 277

Effi-MVS+		83.62 79	83.08 81	85.24 81	88.38 169	67.45 143	88.89 104	89.15 181	75.50 93	82.27 95	88.28 175	69.61 73	94.45 114	77.81 106	87.84 127	93.84 53

API-MVS		81.99 104	81.23 108	84.26 116	90.94 91	70.18 89	91.10 51	89.32 172	71.51 165	78.66 138	88.28 175	65.26 113	95.10 92	64.74 221	91.23 91	87.51 262

thisisatest0530		79.40 161	77.76 180	84.31 114	87.69 191	65.10 188	87.36 156	84.26 267	70.04 189	77.42 163	88.26 177	49.94 270	94.79 106	70.20 171	84.70 166	93.03 89

hse-mvs2		81.72 108	80.94 114	84.07 122	88.72 157	67.68 140	85.87 199	87.26 229	76.02 83	84.67 60	88.22 178	61.54 161	93.48 159	82.71 65	73.44 301	91.06 150

xiu_mvs_v1_base_debu		80.80 130	79.72 134	84.03 127	87.35 198	70.19 86	85.56 205	88.77 195	69.06 213	81.83 99	88.16 179	50.91 258	92.85 186	78.29 103	87.56 131	89.06 220

xiu_mvs_v1_base		80.80 130	79.72 134	84.03 127	87.35 198	70.19 86	85.56 205	88.77 195	69.06 213	81.83 99	88.16 179	50.91 258	92.85 186	78.29 103	87.56 131	89.06 220

xiu_mvs_v1_base_debi		80.80 130	79.72 134	84.03 127	87.35 198	70.19 86	85.56 205	88.77 195	69.06 213	81.83 99	88.16 179	50.91 258	92.85 186	78.29 103	87.56 131	89.06 220

UniMVSNet (Re)		81.60 113	81.11 110	83.09 158	88.38 169	64.41 200	87.60 150	93.02 43	78.42 32	78.56 140	88.16 179	69.78 71	93.26 167	69.58 180	76.49 258	91.60 133

AUN-MVS		79.21 166	77.60 185	84.05 124	88.71 158	67.61 141	85.84 201	87.26 229	69.08 212	77.23 169	88.14 183	53.20 234	93.47 160	75.50 130	73.45 300	91.06 150

Anonymous20231211		78.97 173	77.69 183	82.81 172	90.54 99	64.29 202	90.11 74	91.51 113	65.01 262	76.16 196	88.13 184	50.56 263	93.03 183	69.68 179	77.56 244	91.11 149

pm-mvs1		77.25 212	76.68 207	78.93 251	84.22 248	58.62 276	86.41 184	88.36 206	71.37 167	73.31 238	88.01 185	61.22 171	89.15 271	64.24 223	73.01 304	89.03 224

LTVRE_ROB	69.57 13	76.25 228	74.54 233	81.41 202	88.60 161	64.38 201	79.24 295	89.12 184	70.76 178	69.79 280	87.86 186	49.09 280	93.20 170	56.21 291	80.16 219	86.65 284
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
WTY-MVS		75.65 235	75.68 216	75.57 290	86.40 217	56.82 300	77.92 310	82.40 293	65.10 259	76.18 193	87.72 187	63.13 139	80.90 329	60.31 255	81.96 199	89.00 227

TAMVS		78.89 175	77.51 187	83.03 162	87.80 185	67.79 138	84.72 224	85.05 256	67.63 230	76.75 179	87.70 188	62.25 151	90.82 245	58.53 272	87.13 138	90.49 171

BH-untuned		79.47 158	78.60 156	82.05 189	89.19 139	65.91 170	86.07 194	88.52 204	72.18 153	75.42 210	87.69 189	61.15 172	93.54 156	60.38 254	86.83 143	86.70 283

COLMAP_ROB	66.92 17	73.01 261	70.41 272	80.81 219	87.13 207	65.63 176	88.30 130	84.19 268	62.96 284	63.80 327	87.69 189	38.04 337	92.56 193	46.66 333	74.91 286	84.24 314
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 246	72.42 253	79.80 237	83.76 257	59.59 269	85.92 198	86.64 237	66.39 245	66.96 300	87.58 191	39.46 331	91.60 223	65.76 213	69.27 322	88.22 247

Baseline_NR-MVSNet		78.15 192	78.33 165	77.61 271	85.79 223	56.21 312	86.78 174	85.76 249	73.60 132	77.93 155	87.57 192	65.02 116	88.99 273	67.14 202	75.33 280	87.63 258

WR-MVS_H		78.51 182	78.49 158	78.56 256	88.02 179	56.38 309	88.43 121	92.67 63	77.14 54	73.89 235	87.55 193	66.25 103	89.24 269	58.92 267	73.55 299	90.06 192

EI-MVSNet		80.52 139	79.98 128	82.12 186	84.28 246	63.19 228	86.41 184	88.95 191	74.18 121	78.69 136	87.54 194	66.62 97	92.43 196	72.57 155	80.57 215	90.74 162

CVMVSNet		72.99 262	72.58 251	74.25 303	84.28 246	50.85 342	86.41 184	83.45 280	44.56 350	73.23 240	87.54 194	49.38 276	85.70 303	65.90 211	78.44 236	86.19 290

ACMH+	68.96 14	76.01 231	74.01 238	82.03 190	88.60 161	65.31 184	88.86 105	87.55 222	70.25 187	67.75 292	87.47 196	41.27 325	93.19 172	58.37 273	75.94 267	87.60 259

RRT_test8_iter05		78.38 185	77.40 188	81.34 206	86.00 221	58.86 273	86.55 182	91.26 121	72.13 156	75.91 198	87.42 197	44.97 305	93.73 149	77.02 115	75.30 281	91.45 141

TransMVSNet (Re)		75.39 240	74.56 232	77.86 265	85.50 229	57.10 297	86.78 174	86.09 247	72.17 154	71.53 258	87.34 198	63.01 140	89.31 268	56.84 287	61.83 340	87.17 271

GBi-Net		78.40 183	77.40 188	81.40 203	87.60 192	63.01 230	88.39 125	89.28 173	71.63 161	75.34 213	87.28 199	54.80 217	91.11 236	62.72 232	79.57 224	90.09 188

test1		78.40 183	77.40 188	81.40 203	87.60 192	63.01 230	88.39 125	89.28 173	71.63 161	75.34 213	87.28 199	54.80 217	91.11 236	62.72 232	79.57 224	90.09 188

FMVSNet2		78.20 190	77.21 192	81.20 210	87.60 192	62.89 234	87.47 154	89.02 186	71.63 161	75.29 217	87.28 199	54.80 217	91.10 239	62.38 236	79.38 228	89.61 210

FMVSNet1		77.44 208	76.12 214	81.40 203	86.81 212	63.01 230	88.39 125	89.28 173	70.49 183	74.39 231	87.28 199	49.06 281	91.11 236	60.91 251	78.52 234	90.09 188

v2v482		80.23 144	79.29 145	83.05 161	83.62 258	64.14 204	87.04 164	89.97 155	73.61 131	78.18 150	87.22 203	61.10 173	93.82 141	76.11 121	76.78 256	91.18 146

ITE_SJBPF				78.22 261	81.77 296	60.57 259		83.30 281	69.25 206	67.54 294	87.20 204	36.33 342	87.28 294	54.34 296	74.62 289	86.80 280

anonymousdsp		78.60 180	77.15 193	82.98 165	80.51 315	67.08 150	87.24 160	89.53 167	65.66 254	75.16 219	87.19 205	52.52 235	92.25 204	77.17 113	79.34 229	89.61 210

MVSTER		79.01 171	77.88 175	82.38 184	83.07 271	64.80 191	84.08 244	88.95 191	69.01 216	78.69 136	87.17 206	54.70 221	92.43 196	74.69 133	80.57 215	89.89 201

thres100view900		76.50 222	75.55 218	79.33 245	89.52 119	56.99 298	85.83 202	83.23 283	73.94 125	76.32 189	87.12 207	51.89 249	91.95 214	48.33 324	83.75 176	89.07 218

thres600view7		76.50 222	75.44 220	79.68 239	89.40 124	57.16 295	85.53 210	83.23 283	73.79 129	76.26 190	87.09 208	51.89 249	91.89 217	48.05 329	83.72 179	90.00 194

XVG-ACMP-BASELINE		76.11 230	74.27 237	81.62 197	83.20 267	64.67 193	83.60 251	89.75 162	69.75 197	71.85 255	87.09 208	32.78 349	92.11 209	69.99 175	80.43 217	88.09 249

HY-MVS	69.67 12	77.95 198	77.15 193	80.36 226	87.57 196	60.21 265	83.37 255	87.78 219	66.11 247	75.37 212	87.06 210	63.27 132	90.48 251	61.38 248	82.43 195	90.40 175

CHOSEN 1792x2688		77.63 206	75.69 215	83.44 141	89.98 110	68.58 124	78.70 302	87.50 224	56.38 334	75.80 202	86.84 211	58.67 192	91.40 230	61.58 246	85.75 159	90.34 177

v8		79.97 150	79.02 150	82.80 173	84.09 250	64.50 197	87.96 141	90.29 149	74.13 123	75.24 218	86.81 212	62.88 141	93.89 140	74.39 135	75.40 278	90.00 194

AllTest		70.96 275	68.09 287	79.58 242	85.15 234	63.62 212	84.58 230	79.83 318	62.31 292	60.32 337	86.73 213	32.02 350	88.96 276	50.28 314	71.57 314	86.15 291

TestCases				79.58 242	85.15 234	63.62 212		79.83 318	62.31 292	60.32 337	86.73 213	32.02 350	88.96 276	50.28 314	71.57 314	86.15 291

mvs-test1		80.88 123	79.40 141	85.29 79	85.13 236	69.75 95	89.28 90	88.10 209	74.99 103	76.44 187	86.72 215	57.27 205	94.26 122	73.53 142	83.18 185	91.87 127

LCM-MVSNet-Re		77.05 214	76.94 198	77.36 274	87.20 205	51.60 337	80.06 287	80.46 312	75.20 100	67.69 293	86.72 215	62.48 146	88.98 274	63.44 227	89.25 113	91.51 136

1112_ss		77.40 210	76.43 210	80.32 228	89.11 145	60.41 263	83.65 248	87.72 220	62.13 294	73.05 242	86.72 215	62.58 145	89.97 257	62.11 241	80.80 211	90.59 168

ab-mvs-re		7.23 337	9.64 340	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	86.72 215	0.00 376	0.00 372	0.00 370	0.00 370	0.00 368

IterMVS-LS		80.06 147	79.38 142	82.11 187	85.89 222	63.20 227	86.79 173	89.34 171	74.19 120	75.45 209	86.72 215	66.62 97	92.39 198	72.58 154	76.86 253	90.75 161
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 232	73.93 239	81.77 195	88.71 158	66.61 158	88.62 117	89.01 187	69.81 194	66.78 304	86.70 220	41.95 324	91.51 228	55.64 292	78.14 239	87.17 271
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 226	75.44 220	79.27 246	89.28 134	58.09 280	81.69 272	87.07 232	59.53 313	72.48 248	86.67 221	61.30 168	89.33 267	60.81 253	80.15 220	90.41 174

FMVSNet3		77.88 200	76.85 200	80.97 216	86.84 211	62.36 237	86.52 183	88.77 195	71.13 169	75.34 213	86.66 222	54.07 227	91.10 239	62.72 232	79.57 224	89.45 213

pmmvs6		74.69 243	73.39 244	78.61 255	81.38 303	57.48 293	86.64 178	87.95 214	64.99 263	70.18 270	86.61 223	50.43 265	89.52 264	62.12 240	70.18 320	88.83 234

ET-MVSNet_ETH3D		78.63 179	76.63 208	84.64 101	86.73 214	69.47 101	85.01 218	84.61 260	69.54 200	66.51 309	86.59 224	50.16 267	91.75 220	76.26 120	84.24 171	92.69 101

testgi		66.67 304	66.53 302	67.08 333	75.62 343	41.69 358	75.93 317	76.50 334	66.11 247	65.20 319	86.59 224	35.72 344	74.71 352	43.71 343	73.38 302	84.84 308

CLD-MVS		82.31 98	81.65 104	84.29 115	88.47 165	67.73 139	85.81 203	92.35 76	75.78 87	78.33 146	86.58 226	64.01 124	94.35 115	76.05 122	87.48 134	90.79 159
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 153	78.67 154	82.97 166	84.06 251	64.95 189	87.88 146	90.62 136	73.11 141	75.11 221	86.56 227	61.46 164	94.05 130	73.68 140	75.55 272	89.90 200

CDS-MVSNet		79.07 170	77.70 182	83.17 155	87.60 192	68.23 130	84.40 237	86.20 244	67.49 233	76.36 188	86.54 228	61.54 161	90.79 246	61.86 243	87.33 135	90.49 171
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
xiu_mvs_v2_base		81.69 110	81.05 111	83.60 137	89.15 140	68.03 134	84.46 233	90.02 154	70.67 179	81.30 111	86.53 229	63.17 135	94.19 124	75.60 128	88.54 121	88.57 242

TR-MVS		77.44 208	76.18 213	81.20 210	88.24 172	63.24 225	84.61 229	86.40 241	67.55 232	77.81 156	86.48 230	54.10 226	93.15 174	57.75 279	82.72 192	87.20 270

EIA-MVS		83.31 85	82.80 87	84.82 96	89.59 116	65.59 177	88.21 134	92.68 62	74.66 110	78.96 131	86.42 231	69.06 79	95.26 83	75.54 129	90.09 104	93.62 67

tfpn200view9		76.42 225	75.37 224	79.55 244	89.13 141	57.65 290	85.17 213	83.60 274	73.41 138	76.45 184	86.39 232	52.12 242	91.95 214	48.33 324	83.75 176	89.07 218

thres400		76.50 222	75.37 224	79.86 235	89.13 141	57.65 290	85.17 213	83.60 274	73.41 138	76.45 184	86.39 232	52.12 242	91.95 214	48.33 324	83.75 176	90.00 194

v7n		78.97 173	77.58 186	83.14 156	83.45 261	65.51 178	88.32 129	91.21 123	73.69 130	72.41 249	86.32 234	57.93 196	93.81 142	69.18 183	75.65 270	90.11 186

MAR-MVS		81.84 106	80.70 116	85.27 80	91.32 86	71.53 58	89.82 79	90.92 130	69.77 196	78.50 141	86.21 235	62.36 149	94.52 112	65.36 215	92.05 79	89.77 206
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
v1144		80.03 148	79.03 149	83.01 163	83.78 256	64.51 195	87.11 163	90.57 138	71.96 157	78.08 153	86.20 236	61.41 165	93.94 134	74.93 132	77.23 246	90.60 167

V42		79.38 163	78.24 167	82.83 170	81.10 309	65.50 179	85.55 208	89.82 159	71.57 164	78.21 148	86.12 237	60.66 180	93.18 173	75.64 126	75.46 276	89.81 205

PVSNet_BlendedMVS		80.60 136	80.02 127	82.36 185	88.85 148	65.40 180	86.16 192	92.00 93	69.34 204	78.11 151	86.09 238	66.02 107	94.27 118	71.52 159	82.06 198	87.39 264

v1192		79.59 155	78.43 161	83.07 160	83.55 260	64.52 194	86.93 168	90.58 137	70.83 174	77.78 157	85.90 239	59.15 190	93.94 134	73.96 139	77.19 248	90.76 160

SixPastTwentyTwo		73.37 255	71.26 265	79.70 238	85.08 238	57.89 286	85.57 204	83.56 276	71.03 172	65.66 313	85.88 240	42.10 322	92.57 192	59.11 265	63.34 339	88.65 240

EPNet_dtu		75.46 237	74.86 228	77.23 278	82.57 285	54.60 320	86.89 169	83.09 287	71.64 160	66.25 311	85.86 241	55.99 212	88.04 287	54.92 294	86.55 147	89.05 223
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 253	73.64 243	73.51 307	82.80 279	55.01 319	76.12 316	81.69 300	62.47 291	74.68 229	85.85 242	57.32 204	78.11 339	60.86 252	80.93 208	87.39 264

ETV-MVS		84.90 71	84.67 72	85.59 75	89.39 125	68.66 122	88.74 113	92.64 67	79.97 17	84.10 73	85.71 243	69.32 76	95.38 78	80.82 81	91.37 88	92.72 98

v1240		78.99 172	77.78 178	82.64 179	83.21 266	63.54 216	86.62 179	90.30 148	69.74 199	77.33 165	85.68 244	57.04 208	93.76 146	73.13 150	76.92 250	90.62 165

v144192		79.47 158	78.37 163	82.78 176	83.35 262	63.96 207	86.96 166	90.36 145	69.99 190	77.50 161	85.67 245	60.66 180	93.77 145	74.27 136	76.58 257	90.62 165

tfpnnormal		74.39 244	73.16 247	78.08 263	86.10 220	58.05 281	84.65 228	87.53 223	70.32 185	71.22 261	85.63 246	54.97 216	89.86 258	43.03 345	75.02 285	86.32 287

PS-MVSNAJ		81.69 110	81.02 112	83.70 136	89.51 120	68.21 131	84.28 239	90.09 153	70.79 176	81.26 112	85.62 247	63.15 136	94.29 116	75.62 127	88.87 116	88.59 241

v1921920		79.22 165	78.03 170	82.80 173	83.30 264	63.94 208	86.80 172	90.33 146	69.91 193	77.48 162	85.53 248	58.44 194	93.75 147	73.60 141	76.85 254	90.71 163

test_0402		72.79 264	70.44 271	79.84 236	88.13 174	65.99 167	85.93 197	84.29 265	65.57 255	67.40 297	85.49 249	46.92 291	92.61 191	35.88 354	74.38 291	80.94 338

v148		78.72 177	77.80 177	81.47 201	82.73 281	61.96 244	86.30 188	88.08 211	73.26 140	76.18 193	85.47 250	62.46 147	92.36 200	71.92 158	73.82 297	90.09 188

USDC		70.33 281	68.37 282	76.21 285	80.60 313	56.23 311	79.19 297	86.49 239	60.89 301	61.29 334	85.47 250	31.78 352	89.47 266	53.37 300	76.21 265	82.94 329

MVP-Stereo		76.12 229	74.46 235	81.13 213	85.37 231	69.79 93	84.42 236	87.95 214	65.03 261	67.46 295	85.33 252	53.28 233	91.73 222	58.01 277	83.27 183	81.85 333
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MVS		78.19 191	76.99 197	81.78 194	85.66 225	66.99 151	84.66 225	90.47 140	55.08 339	72.02 254	85.27 253	63.83 126	94.11 129	66.10 209	89.80 108	84.24 314

cl-mvsnet1		77.72 203	76.76 203	80.58 222	82.48 288	60.48 261	83.09 258	87.86 217	69.22 207	74.38 232	85.24 254	62.10 154	91.53 226	71.09 163	75.40 278	89.74 207

cl-mvsnet____		77.72 203	76.76 203	80.58 222	82.49 287	60.48 261	83.09 258	87.87 216	69.22 207	74.38 232	85.22 255	62.10 154	91.53 226	71.09 163	75.41 277	89.73 208

HyFIR lowres test		77.53 207	75.40 222	83.94 133	89.59 116	66.62 157	80.36 284	88.64 202	56.29 335	76.45 184	85.17 256	57.64 200	93.28 166	61.34 249	83.10 187	91.91 126

pmmvs4		74.03 250	71.91 256	80.39 225	81.96 294	68.32 127	81.45 275	82.14 295	59.32 314	69.87 278	85.13 257	52.40 238	88.13 286	60.21 256	74.74 288	84.73 310

TDRefinement		67.49 298	64.34 307	76.92 280	73.47 352	61.07 253	84.86 222	82.98 288	59.77 310	58.30 343	85.13 257	26.06 355	87.89 288	47.92 330	60.59 344	81.81 334

Fast-Effi-MVS+		80.81 128	79.92 129	83.47 140	88.85 148	64.51 195	85.53 210	89.39 170	70.79 176	78.49 142	85.06 259	67.54 90	93.58 152	67.03 204	86.58 146	92.32 112

PVSNet_Blended		80.98 122	80.34 123	82.90 168	88.85 148	65.40 180	84.43 235	92.00 93	67.62 231	78.11 151	85.05 260	66.02 107	94.27 118	71.52 159	89.50 110	89.01 225

CMPMVS	51.72 21	70.19 283	68.16 285	76.28 284	73.15 354	57.55 292	79.47 293	83.92 270	48.02 349	56.48 348	84.81 261	43.13 314	86.42 299	62.67 235	81.81 202	84.89 307
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 295	67.61 295	71.31 320	78.51 333	47.01 351	84.47 231	84.27 266	42.27 351	66.44 310	84.79 262	40.44 329	83.76 316	58.76 270	68.54 327	83.17 323

BH-w/o		78.21 189	77.33 191	80.84 218	88.81 152	65.13 187	84.87 221	87.85 218	69.75 197	74.52 230	84.74 263	61.34 167	93.11 177	58.24 275	85.84 158	84.27 313

pmmvs5		71.55 271	70.20 274	75.61 289	77.83 334	56.39 308	81.74 271	80.89 304	57.76 325	67.46 295	84.49 264	49.26 279	85.32 307	57.08 285	75.29 282	85.11 306

thres200		75.55 236	74.47 234	78.82 252	87.78 188	57.85 287	83.07 260	83.51 277	72.44 149	75.84 201	84.42 265	52.08 244	91.75 220	47.41 331	83.64 180	86.86 279

eth_miper_zixun_eth		77.92 199	76.69 206	81.61 199	83.00 274	61.98 243	83.15 257	89.20 179	69.52 201	74.86 227	84.35 266	61.76 157	92.56 193	71.50 161	72.89 305	90.28 179

cl_fuxian		78.75 176	77.91 173	81.26 208	82.89 278	61.56 249	84.09 243	89.13 183	69.97 191	75.56 204	84.29 267	66.36 101	92.09 210	73.47 145	75.48 274	90.12 185

Fast-Effi-MVS+-dtu		78.02 196	76.49 209	82.62 180	83.16 270	66.96 155	86.94 167	87.45 226	72.45 147	71.49 259	84.17 268	54.79 220	91.58 224	67.61 194	80.31 218	89.30 216

IterMVS-SCA-FT		75.43 238	73.87 241	80.11 231	82.69 282	64.85 190	81.57 274	83.47 279	69.16 210	70.49 266	84.15 269	51.95 247	88.15 285	69.23 182	72.14 310	87.34 266

1314		76.53 221	75.30 226	80.21 230	83.93 254	62.32 239	84.66 225	88.81 193	60.23 306	70.16 272	84.07 270	55.30 215	90.73 248	67.37 197	83.21 184	87.59 261

cl-mvsnet2		78.07 194	77.01 195	81.23 209	82.37 290	61.83 246	83.55 252	87.98 213	68.96 217	75.06 223	83.87 271	61.40 166	91.88 218	73.53 142	76.39 261	89.98 197

EG-PatchMatch MVS		74.04 249	71.82 258	80.71 221	84.92 239	67.42 144	85.86 200	88.08 211	66.04 249	64.22 323	83.85 272	35.10 345	92.56 193	57.44 281	80.83 210	82.16 332

thisisatest0515		77.33 211	75.38 223	83.18 154	85.27 232	63.80 210	82.11 268	83.27 282	65.06 260	75.91 198	83.84 273	49.54 274	94.27 118	67.24 200	86.19 152	91.48 139

test20.03		67.45 299	66.95 300	68.94 327	75.48 344	44.84 354	77.50 311	77.67 328	66.66 240	63.01 329	83.80 274	47.02 290	78.40 337	42.53 347	68.86 326	83.58 320

miper_ehance_all_eth		78.59 181	77.76 180	81.08 214	82.66 283	61.56 249	83.65 248	89.15 181	68.87 219	75.55 205	83.79 275	66.49 99	92.03 211	73.25 148	76.39 261	89.64 209

MSDG		73.36 257	70.99 266	80.49 224	84.51 244	65.80 172	80.71 281	86.13 246	65.70 253	65.46 314	83.74 276	44.60 306	90.91 244	51.13 309	76.89 252	84.74 309

IterMVS		74.29 245	72.94 249	78.35 260	81.53 300	63.49 218	81.58 273	82.49 292	68.06 229	69.99 275	83.69 277	51.66 253	85.54 304	65.85 212	71.64 313	86.01 295
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 268	71.71 259	74.35 302	82.19 292	52.00 334	79.22 296	77.29 331	64.56 266	72.95 243	83.68 278	51.35 254	83.26 321	58.33 274	75.80 268	87.81 255

Effi-MVS+-dtu		80.03 148	78.57 157	84.42 108	85.13 236	68.74 116	88.77 110	88.10 209	74.99 103	74.97 225	83.49 279	57.27 205	93.36 164	73.53 142	80.88 209	91.18 146

baseline2		75.70 234	73.83 242	81.30 207	83.26 265	61.79 247	82.57 264	80.65 308	66.81 236	66.88 301	83.42 280	57.86 198	92.19 206	63.47 226	79.57 224	89.91 199

bset_n11_16_dypcd		77.12 213	75.47 219	82.06 188	81.12 308	65.99 167	81.37 277	83.20 285	69.94 192	76.09 197	83.38 281	47.75 286	92.26 203	78.51 97	77.91 240	87.95 250

TinyColmap		67.30 301	64.81 305	74.76 299	81.92 295	56.68 304	80.29 286	81.49 302	60.33 304	56.27 349	83.22 282	24.77 356	87.66 291	45.52 339	69.47 321	79.95 342

CostFormer		75.24 241	73.90 240	79.27 246	82.65 284	58.27 279	80.80 278	82.73 291	61.57 297	75.33 216	83.13 283	55.52 213	91.07 242	64.98 219	78.34 238	88.45 244

miper_lstm_enhance		74.11 248	73.11 248	77.13 279	80.11 318	59.62 268	72.23 332	86.92 235	66.76 238	70.40 267	82.92 284	56.93 209	82.92 322	69.06 185	72.63 306	88.87 232

GA-MVS		76.87 218	75.17 227	81.97 192	82.75 280	62.58 235	81.44 276	86.35 243	72.16 155	74.74 228	82.89 285	46.20 297	92.02 212	68.85 187	81.09 207	91.30 144

K. test v3		71.19 273	68.51 281	79.21 248	83.04 273	57.78 289	84.35 238	76.91 333	72.90 146	62.99 330	82.86 286	39.27 332	91.09 241	61.65 245	52.66 352	88.75 237

MS-PatchMatch		73.83 251	72.67 250	77.30 276	83.87 255	66.02 166	81.82 269	84.66 259	61.37 300	68.61 288	82.82 287	47.29 288	88.21 284	59.27 262	84.32 170	77.68 347

lessismore_v0				78.97 250	81.01 310	57.15 296		65.99 356		61.16 335	82.82 287	39.12 333	91.34 232	59.67 259	46.92 357	88.43 245

D2MVS		74.82 242	73.21 246	79.64 241	79.81 322	62.56 236	80.34 285	87.35 227	64.37 269	68.86 285	82.66 289	46.37 294	90.10 256	67.91 192	81.24 206	86.25 288

Anonymous20231206		68.60 293	67.80 292	71.02 321	80.23 317	50.75 343	78.30 306	80.47 311	56.79 332	66.11 312	82.63 290	46.35 295	78.95 335	43.62 344	75.70 269	83.36 322

MIMVSNet		70.69 277	69.30 276	74.88 297	84.52 243	56.35 310	75.87 320	79.42 321	64.59 265	67.76 291	82.41 291	41.10 326	81.54 327	46.64 335	81.34 204	86.75 282

OpenMVS_ROB	64.09 19	70.56 279	68.19 284	77.65 270	80.26 316	59.41 271	85.01 218	82.96 289	58.76 319	65.43 315	82.33 292	37.63 339	91.23 235	45.34 341	76.03 266	82.32 330

miper_enhance_ethall		77.87 201	76.86 199	80.92 217	81.65 297	61.38 251	82.68 262	88.98 188	65.52 256	75.47 206	82.30 293	65.76 111	92.00 213	72.95 151	76.39 261	89.39 214

test0.0.03 1		68.00 297	67.69 294	68.90 328	77.55 335	47.43 349	75.70 321	72.95 345	66.66 240	66.56 305	82.29 294	48.06 284	75.87 348	44.97 342	74.51 290	83.41 321

PVSNet	64.34 18	72.08 270	70.87 269	75.69 288	86.21 219	56.44 307	74.37 328	80.73 307	62.06 295	70.17 271	82.23 295	42.86 316	83.31 320	54.77 295	84.45 169	87.32 267

MIMVSNet1		68.58 294	66.78 301	73.98 305	80.07 319	51.82 335	80.77 279	84.37 262	64.40 268	59.75 340	82.16 296	36.47 341	83.63 318	42.73 346	70.33 319	86.48 286

CL-MVSNet_2432*1600		72.37 268	71.46 260	75.09 295	79.49 328	53.53 328	80.76 280	85.01 257	69.12 211	70.51 265	82.05 297	57.92 197	84.13 314	52.27 304	66.00 333	87.60 259

tpm2		73.26 258	71.46 260	78.63 254	83.34 263	56.71 303	80.65 282	80.40 313	56.63 333	73.55 236	82.02 298	51.80 251	91.24 234	56.35 290	78.42 237	87.95 250

PatchMatch-RL		72.38 267	70.90 267	76.80 282	88.60 161	67.38 146	79.53 292	76.17 335	62.75 288	69.36 283	82.00 299	45.51 303	84.89 310	53.62 299	80.58 214	78.12 346

FMVSNet5		69.50 287	67.96 288	74.15 304	82.97 276	55.35 318	80.01 288	82.12 296	62.56 290	63.02 328	81.53 300	36.92 340	81.92 325	48.42 323	74.06 293	85.17 305

CR-MVSNet		73.37 255	71.27 264	79.67 240	81.32 306	65.19 185	75.92 318	80.30 314	59.92 309	72.73 245	81.19 301	52.50 236	86.69 296	59.84 258	77.71 241	87.11 275

Patchmtry		70.74 276	69.16 278	75.49 292	80.72 311	54.07 325	74.94 327	80.30 314	58.34 321	70.01 273	81.19 301	52.50 236	86.54 297	53.37 300	71.09 317	85.87 298

IB-MVS	68.01 15	75.85 233	73.36 245	83.31 146	84.76 240	66.03 165	83.38 254	85.06 255	70.21 188	69.40 282	81.05 303	45.76 301	94.66 109	65.10 218	75.49 273	89.25 217
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
cascas		76.72 220	74.64 230	82.99 164	85.78 224	65.88 171	82.33 266	89.21 178	60.85 302	72.74 244	81.02 304	47.28 289	93.75 147	67.48 196	85.02 161	89.34 215

LF4IMVS		64.02 315	62.19 318	69.50 326	70.90 356	53.29 332	76.13 315	77.18 332	52.65 344	58.59 341	80.98 305	23.55 357	76.52 345	53.06 302	66.66 330	78.68 345

Anonymous20240521		68.80 292	67.22 298	73.55 306	74.33 347	54.11 324	83.18 256	85.61 250	58.15 322	61.68 333	80.94 306	30.71 353	81.27 328	57.00 286	73.34 303	85.28 302

gm-plane-assit					81.40 302	53.83 327			62.72 289		80.94 306		92.39 198	63.40 228

UnsupCasMVSNet_eth		67.33 300	65.99 303	71.37 317	73.48 351	51.47 339	75.16 323	85.19 254	65.20 258	60.78 336	80.93 308	42.35 318	77.20 343	57.12 284	53.69 351	85.44 300

MVS_0304		72.48 265	70.89 268	77.24 277	82.20 291	59.68 267	84.11 242	83.49 278	67.10 235	66.87 302	80.59 309	35.00 346	87.40 292	59.07 266	79.58 223	84.63 311

MDTV_nov1_ep13			69.97 275		83.18 268	53.48 329	77.10 314	80.18 317	60.45 303	69.33 284	80.44 310	48.89 282	86.90 295	51.60 307	78.51 235

pmmvs-eth3d		70.50 280	67.83 291	78.52 258	77.37 337	66.18 164	81.82 269	81.51 301	58.90 318	63.90 326	80.42 311	42.69 317	86.28 300	58.56 271	65.30 335	83.11 325

PM-MVS		66.41 306	64.14 308	73.20 310	73.92 349	56.45 306	78.97 299	64.96 359	63.88 278	64.72 320	80.24 312	19.84 360	83.44 319	66.24 206	64.52 337	79.71 343

SCA		74.22 247	72.33 254	79.91 234	84.05 252	62.17 241	79.96 289	79.29 322	66.30 246	72.38 250	80.13 313	51.95 247	88.60 280	59.25 263	77.67 243	88.96 229

Patchmatch-test		64.82 313	63.24 313	69.57 325	79.42 329	49.82 346	63.49 353	69.05 353	51.98 346	59.95 339	80.13 313	50.91 258	70.98 357	40.66 350	73.57 298	87.90 253

tpmrst		72.39 266	72.13 255	73.18 311	80.54 314	49.91 345	79.91 290	79.08 323	63.11 281	71.69 257	79.95 315	55.32 214	82.77 323	65.66 214	73.89 295	86.87 278

DSMNet-mixed		57.77 321	56.90 323	60.38 337	67.70 358	35.61 361	69.18 342	53.97 363	32.30 360	57.49 345	79.88 316	40.39 330	68.57 359	38.78 352	72.37 307	76.97 348

MDA-MVSNet-bldmvs		66.68 303	63.66 311	75.75 287	79.28 330	60.56 260	73.92 329	78.35 325	64.43 267	50.13 354	79.87 317	44.02 311	83.67 317	46.10 337	56.86 347	83.03 327

PatchmatchNet		73.12 260	71.33 263	78.49 259	83.18 268	60.85 256	79.63 291	78.57 324	64.13 271	71.73 256	79.81 318	51.20 256	85.97 302	57.40 282	76.36 264	88.66 239
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ppachtmachnet_test		70.04 284	67.34 297	78.14 262	79.80 323	61.13 252	79.19 297	80.59 309	59.16 316	65.27 316	79.29 319	46.75 293	87.29 293	49.33 320	66.72 329	86.00 297

EPMVS		69.02 290	68.16 285	71.59 315	79.61 326	49.80 347	77.40 312	66.93 355	62.82 287	70.01 273	79.05 320	45.79 300	77.86 341	56.58 288	75.26 283	87.13 274

PMMVS		69.34 288	68.67 280	71.35 319	75.67 342	62.03 242	75.17 322	73.46 343	50.00 348	68.68 286	79.05 320	52.07 245	78.13 338	61.16 250	82.77 190	73.90 350

test-LLR		72.94 263	72.43 252	74.48 300	81.35 304	58.04 282	78.38 303	77.46 329	66.66 240	69.95 276	79.00 322	48.06 284	79.24 333	66.13 207	84.83 163	86.15 291

test-mter		71.41 272	70.39 273	74.48 300	81.35 304	58.04 282	78.38 303	77.46 329	60.32 305	69.95 276	79.00 322	36.08 343	79.24 333	66.13 207	84.83 163	86.15 291

DIV-MVS_2432*1600		68.81 291	67.59 296	72.46 313	74.29 348	45.45 352	77.93 309	87.00 233	63.12 280	63.99 325	78.99 324	42.32 319	84.77 311	56.55 289	64.09 338	87.16 273

KD-MVS_2432*1600		66.22 308	63.89 309	73.21 308	75.47 345	53.42 330	70.76 337	84.35 263	64.10 272	66.52 307	78.52 325	34.55 347	84.98 308	50.40 312	50.33 355	81.23 336

miper_refine_blended		66.22 308	63.89 309	73.21 308	75.47 345	53.42 330	70.76 337	84.35 263	64.10 272	66.52 307	78.52 325	34.55 347	84.98 308	50.40 312	50.33 355	81.23 336

DWT-MVSNet_test		73.70 252	71.86 257	79.21 248	82.91 277	58.94 272	82.34 265	82.17 294	65.21 257	71.05 263	78.31 327	44.21 309	90.17 255	63.29 230	77.28 245	88.53 243

tpmvs		71.09 274	69.29 277	76.49 283	82.04 293	56.04 313	78.92 300	81.37 303	64.05 274	67.18 299	78.28 328	49.74 273	89.77 259	49.67 319	72.37 307	83.67 319

our_test_3		69.14 289	67.00 299	75.57 290	79.80 323	58.80 274	77.96 308	77.81 327	59.55 312	62.90 331	78.25 329	47.43 287	83.97 315	51.71 306	67.58 328	83.93 318

MDA-MVSNet_test_wron		65.03 311	62.92 314	71.37 317	75.93 340	56.73 301	69.09 345	74.73 340	57.28 330	54.03 351	77.89 330	45.88 298	74.39 354	49.89 318	61.55 341	82.99 328

YYNet1		65.03 311	62.91 315	71.38 316	75.85 341	56.60 305	69.12 344	74.66 342	57.28 330	54.12 350	77.87 331	45.85 299	74.48 353	49.95 317	61.52 342	83.05 326

ambc				75.24 294	73.16 353	50.51 344	63.05 354	87.47 225		64.28 322	77.81 332	17.80 361	89.73 261	57.88 278	60.64 343	85.49 299

tpm cat1		70.57 278	68.31 283	77.35 275	82.41 289	57.95 285	78.08 307	80.22 316	52.04 345	68.54 289	77.66 333	52.00 246	87.84 289	51.77 305	72.07 311	86.25 288

dp		66.80 302	65.43 304	70.90 322	79.74 325	48.82 348	75.12 325	74.77 339	59.61 311	64.08 324	77.23 334	42.89 315	80.72 330	48.86 322	66.58 331	83.16 324

TESTMET0.1,1		69.89 286	69.00 279	72.55 312	79.27 331	56.85 299	78.38 303	74.71 341	57.64 326	68.09 290	77.19 335	37.75 338	76.70 344	63.92 224	84.09 172	84.10 317

CHOSEN 280x420		66.51 305	64.71 306	71.90 314	81.45 301	63.52 217	57.98 355	68.95 354	53.57 341	62.59 332	76.70 336	46.22 296	75.29 351	55.25 293	79.68 222	76.88 349

PatchT		68.46 296	67.85 290	70.29 323	80.70 312	43.93 355	72.47 331	74.88 338	60.15 307	70.55 264	76.57 337	49.94 270	81.59 326	50.58 310	74.83 287	85.34 301

RPMNet		73.51 254	70.49 270	82.58 181	81.32 306	65.19 185	75.92 318	92.27 79	57.60 327	72.73 245	76.45 338	52.30 239	95.43 73	48.14 328	77.71 241	87.11 275

ADS-MVSNet2		66.20 310	63.33 312	74.82 298	79.92 320	58.75 275	67.55 347	75.19 337	53.37 342	65.25 317	75.86 339	42.32 319	80.53 331	41.57 348	68.91 324	85.18 303

ADS-MVSNet		64.36 314	62.88 316	68.78 330	79.92 320	47.17 350	67.55 347	71.18 346	53.37 342	65.25 317	75.86 339	42.32 319	73.99 355	41.57 348	68.91 324	85.18 303

new-patchmatchnet		61.73 317	61.73 319	61.70 336	72.74 355	24.50 368	69.16 343	78.03 326	61.40 298	56.72 347	75.53 341	38.42 335	76.48 346	45.95 338	57.67 346	84.13 316

N_pmnet		52.79 324	53.26 325	51.40 342	78.99 332	7.68 371	69.52 340	3.89 371	51.63 347	57.01 346	74.98 342	40.83 328	65.96 360	37.78 353	64.67 336	80.56 341

patchmatchnet-post											74.00 343	51.12 257	88.60 280

GG-mvs-BLEND				75.38 293	81.59 299	55.80 315	79.32 294	69.63 350		67.19 298	73.67 344	43.24 313	88.90 278	50.41 311	84.50 167	81.45 335

Patchmatch-RL test		70.24 282	67.78 293	77.61 271	77.43 336	59.57 270	71.16 334	70.33 347	62.94 285	68.65 287	72.77 345	50.62 262	85.49 305	69.58 180	66.58 331	87.77 256

FPMVS		53.68 323	51.64 326	59.81 338	65.08 359	51.03 341	69.48 341	69.58 351	41.46 352	40.67 356	72.32 346	16.46 363	70.00 358	24.24 360	65.42 334	58.40 357

UnsupCasMVSNet_bld		63.70 316	61.53 320	70.21 324	73.69 350	51.39 340	72.82 330	81.89 297	55.63 337	57.81 344	71.80 347	38.67 334	78.61 336	49.26 321	52.21 353	80.63 339

PVSNet_0	57.27 20	61.67 318	59.27 321	68.85 329	79.61 326	57.44 294	68.01 346	73.44 344	55.93 336	58.54 342	70.41 348	44.58 307	77.55 342	47.01 332	35.91 358	71.55 352

pmmvs3		57.79 320	54.26 324	68.37 331	64.02 360	56.72 302	75.12 325	65.17 357	40.20 353	52.93 352	69.86 349	20.36 359	75.48 350	45.45 340	55.25 350	72.90 351

new_pmnet		50.91 325	50.29 327	52.78 341	68.58 357	34.94 363	63.71 352	56.63 362	39.73 354	44.95 355	65.47 350	21.93 358	58.48 361	34.98 355	56.62 348	64.92 354

gg-mvs-nofinetune		69.95 285	67.96 288	75.94 286	83.07 271	54.51 322	77.23 313	70.29 348	63.11 281	70.32 268	62.33 351	43.62 312	88.69 279	53.88 298	87.76 128	84.62 312

JIA-IIPM		66.32 307	62.82 317	76.82 281	77.09 338	61.72 248	65.34 350	75.38 336	58.04 324	64.51 321	62.32 352	42.05 323	86.51 298	51.45 308	69.22 323	82.21 331

LCM-MVSNet		54.25 322	49.68 328	67.97 332	53.73 363	45.28 353	66.85 349	80.78 306	35.96 357	39.45 357	62.23 353	8.70 369	78.06 340	48.24 327	51.20 354	80.57 340

PMMVS2		40.82 329	38.86 332	46.69 343	53.84 362	16.45 369	48.61 358	49.92 364	37.49 356	31.67 358	60.97 354	8.14 370	56.42 362	28.42 357	30.72 360	67.19 353

MVS-HIRNet		59.14 319	57.67 322	63.57 335	81.65 297	43.50 356	71.73 333	65.06 358	39.59 355	51.43 353	57.73 355	38.34 336	82.58 324	39.53 351	73.95 294	64.62 355

ANet_high		50.57 326	46.10 329	63.99 334	48.67 366	39.13 359	70.99 336	80.85 305	61.39 299	31.18 359	57.70 356	17.02 362	73.65 356	31.22 356	15.89 365	79.18 344

PMVS	37.38 22	44.16 328	40.28 331	55.82 339	40.82 368	42.54 357	65.12 351	63.99 360	34.43 358	24.48 361	57.12 357	3.92 371	76.17 347	17.10 363	55.52 349	48.75 358
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		31.52 331	29.28 335	38.23 345	27.03 370	6.50 372	20.94 363	62.21 361	4.05 366	22.35 364	52.50 358	13.33 364	47.58 365	27.04 359	34.04 359	60.62 356

DeepMVS_CX				27.40 348	40.17 369	26.90 366		24.59 370	17.44 364	23.95 362	48.61 359	9.77 367	26.48 367	18.06 362	24.47 361	28.83 361

MVE	26.22 23	30.37 333	25.89 337	43.81 344	44.55 367	35.46 362	28.87 362	39.07 367	18.20 363	18.58 365	40.18 360	2.68 372	47.37 366	17.07 364	23.78 362	48.60 359
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 327	41.86 330	55.16 340	77.03 339	51.52 338	32.50 361	80.52 310	32.46 359	27.12 360	35.02 361	9.52 368	75.50 349	22.31 361	60.21 345	38.45 360
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		31.77 330	30.64 333	35.15 346	52.87 364	27.67 365	57.09 356	47.86 365	24.64 361	16.40 366	33.05 362	11.23 366	54.90 363	14.46 365	18.15 363	22.87 362

EMVS		30.81 332	29.65 334	34.27 347	50.96 365	25.95 367	56.58 357	46.80 366	24.01 362	15.53 367	30.68 363	12.47 365	54.43 364	12.81 366	17.05 364	22.43 363

tmp_tt		18.61 335	21.40 338	10.23 350	4.82 371	10.11 370	34.70 360	30.74 369	1.48 367	23.91 363	26.07 364	28.42 354	13.41 369	27.12 358	15.35 366	7.17 364

X-MVStestdata		80.37 142	77.83 176	88.00 14	94.42 20	73.33 19	92.78 15	92.99 46	79.14 22	83.67 80	12.47 365	67.45 91	96.60 32	83.06 58	94.50 54	94.07 39

test_post											5.46 366	50.36 266	84.24 313

test_post1							78.90 301				5.43 367	48.81 283	85.44 306	59.25 263

wuyk23d		16.82 336	15.94 339	19.46 349	58.74 361	31.45 364	39.22 359	3.74 372	6.84 365	6.04 368	2.70 368	1.27 373	24.29 368	10.54 367	14.40 367	2.63 365

testmvs		6.04 339	8.02 342	0.10 352	0.08 372	0.03 374	69.74 339	0.04 373	0.05 368	0.31 369	1.68 369	0.02 375	0.04 370	0.24 368	0.02 368	0.25 367

test123		6.12 338	8.11 341	0.14 351	0.06 373	0.09 373	71.05 335	0.03 374	0.04 369	0.25 370	1.30 370	0.05 374	0.03 371	0.21 369	0.01 369	0.29 366

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

pcd_1.5k_mvsjas		5.26 340	7.02 343	0.00 353	0.00 374	0.00 375	0.00 364	0.00 375	0.00 370	0.00 371	0.00 371	63.15 136	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

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				89.16 1	94.34 27	75.53 2		92.99 46					97.53 1	89.67 1	96.44 8	94.41 25

eth-test2					0.00 374

eth-test					0.00 374

IU-MVS					95.30 2	71.25 60		92.95 52	66.81 236	92.39 5				88.94 9	96.63 3	94.85 11

save fliter					93.80 40	72.35 43	90.47 64	91.17 125	74.31 116

test_0728_SECOND				87.71 32	95.34 1	71.43 59	93.49 7	94.23 5					97.49 2	89.08 5	96.41 10	94.21 34

GSMVS																88.96 229

test_part2					95.06 7	72.65 32				91.80 11

sam_mvs1												51.32 255				88.96 229

sam_mvs												50.01 268

MTGPA								92.02 90

MTMP							92.18 31	32.83 368

test9_res														84.90 31	95.70 29	92.87 96

agg_prior2														82.91 61	95.45 31	92.70 99

agg_prior					92.85 62	71.94 52		91.78 105		84.41 66			94.93 96

test_prior4						72.60 34	89.01 100

test_prior				86.33 61	92.61 70	69.59 97		92.97 50					95.48 69			93.91 47

旧先验2							86.56 181		58.10 323	87.04 32			88.98 274	74.07 138

新几何2							86.29 189

无先验							87.48 153	88.98 188	60.00 308				94.12 127	67.28 198		88.97 228

原ACMM2							86.86 170

testdata2													91.01 243	62.37 237

segment_acmp												73.08 41

testdata1							84.14 241		75.71 88

test12				86.80 53	92.63 69	70.70 77		91.79 104		82.71 93		71.67 53	96.16 45		94.50 54	93.54 71

plane_prior7					90.08 108	68.51 125

plane_prior6					89.84 113	68.70 120						60.42 184

plane_prior5								92.44 71					95.38 78	78.71 95	86.32 150	91.33 142

plane_prior3						68.60 123			78.44 31	78.92 133

plane_prior2							91.25 47		79.12 24

plane_prior1					89.90 112

plane_prior						68.71 118	90.38 68		77.62 38						86.16 153

n2								0.00 375

nn								0.00 375

door-mid								69.98 349

test11								92.23 82

door								69.44 352

HQP5-MVS						66.98 152

HQP-NCC					89.33 127		89.17 93		76.41 72	77.23 169

ACMP_Plane					89.33 127		89.17 93		76.41 72	77.23 169

BP-MVS														77.47 109

HQP4-MVS										77.24 168			95.11 89			91.03 152

HQP3-MVS								92.19 85							85.99 156

HQP2-MVS												60.17 187

MDTV_nov1_ep13_2view						37.79 360	75.16 323		55.10 338	66.53 306		49.34 277		53.98 297		87.94 252

ACMMP++_ref															81.95 200

ACMMP++															81.25 205

Test By Simon												64.33 121

High-resolution multi-view benchmark