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.

HPM-MVS++		95.14 8	94.91 10	95.83 1	98.25 25	89.65 1	95.92 50	96.96 44	91.75 7	94.02 27	96.83 39	88.12 15	99.55 9	93.41 20	98.94 9	98.28 35

SMA-MVS		95.20 6	95.07 8	95.59 2	98.14 31	88.48 5	96.26 33	97.28 23	85.90 123	97.67 1	98.10 2	88.41 13	99.56 4	94.66 7	99.19 1	98.71 8

3Dnovator+	87.14 4	92.42 62	91.37 70	95.55 3	95.63 106	88.73 3	97.07 11	96.77 62	90.84 16	84.02 213	96.62 51	75.95 139	99.34 27	87.77 97	97.68 62	98.59 13

CNVR-MVS		95.40 5	95.37 5	95.50 4	98.11 32	88.51 4	95.29 77	96.96 44	92.09 3	95.32 14	97.08 31	89.49 8	99.33 31	95.10 5	98.85 12	98.66 10

ACMMP_NAP		94.74 12	94.56 13	95.28 5	98.02 37	87.70 6	95.68 59	97.34 15	88.28 73	95.30 15	97.67 8	85.90 39	99.54 13	93.91 14	98.95 8	98.60 12

DPE-MVS		95.57 2	95.67 2	95.25 6	98.36 21	87.28 11	95.56 66	97.51 4	89.13 50	97.14 3	97.91 6	91.64 2	99.62 1	94.61 8	99.17 2	98.86 5

MCST-MVS		94.45 15	94.20 23	95.19 7	98.46 14	87.50 9	95.00 95	97.12 33	87.13 98	92.51 59	96.30 62	89.24 10	99.34 27	93.46 17	98.62 36	98.73 7

NCCC		94.81 11	94.69 12	95.17 8	97.83 40	87.46 10	95.66 61	96.93 47	92.34 2	93.94 28	96.58 53	87.74 19	99.44 24	92.83 27	98.40 43	98.62 11

DPM-MVS		92.58 59	91.74 66	95.08 9	96.19 84	89.31 2	92.66 213	96.56 80	83.44 174	91.68 78	95.04 103	86.60 33	98.99 65	85.60 122	97.92 58	96.93 106

test_0728_SECOND				95.01 10	98.79 1	86.43 34	97.09 9	97.49 5					99.61 2	95.62 3	99.08 4	98.99 4

testtj		94.39 20	94.18 24	95.00 11	98.24 27	86.77 22	96.16 36	97.23 26	87.28 95	94.85 18	97.04 32	86.99 28	99.52 17	91.54 54	98.33 46	98.71 8

zzz-MVS		94.47 14	94.30 17	95.00 11	98.42 16	86.95 13	95.06 93	96.97 41	91.07 13	93.14 43	97.56 9	84.30 57	99.56 4	93.43 18	98.75 20	98.47 19

MTAPA		94.42 19	94.22 20	95.00 11	98.42 16	86.95 13	94.36 141	96.97 41	91.07 13	93.14 43	97.56 9	84.30 57	99.56 4	93.43 18	98.75 20	98.47 19

MSP-MVS		95.42 4	95.56 4	94.98 14	98.49 12	86.52 31	96.91 18	97.47 7	91.73 8	96.10 9	96.69 46	89.90 5	99.30 34	94.70 6	98.04 54	99.13 1

region2R		94.43 17	94.27 19	94.92 15	98.65 4	86.67 26	96.92 17	97.23 26	88.60 65	93.58 35	97.27 19	85.22 46	99.54 13	92.21 36	98.74 22	98.56 14

APDe-MVS		95.46 3	95.64 3	94.91 16	98.26 24	86.29 42	97.46 2	97.40 13	89.03 53	96.20 8	98.10 2	89.39 9	99.34 27	95.88 1	99.03 6	99.10 3

ACMMPR		94.43 17	94.28 18	94.91 16	98.63 5	86.69 24	96.94 13	97.32 20	88.63 63	93.53 38	97.26 21	85.04 49	99.54 13	92.35 34	98.78 17	98.50 15

GST-MVS		94.21 28	93.97 32	94.90 18	98.41 18	86.82 17	96.54 26	97.19 28	88.24 74	93.26 39	96.83 39	85.48 43	99.59 3	91.43 58	98.40 43	98.30 31

HFP-MVS		94.52 13	94.40 15	94.86 19	98.61 6	86.81 18	96.94 13	97.34 15	88.63 63	93.65 31	97.21 24	86.10 35	99.49 20	92.35 34	98.77 18	98.30 31

#test#		94.32 23	94.14 26	94.86 19	98.61 6	86.81 18	96.43 27	97.34 15	87.51 92	93.65 31	97.21 24	86.10 35	99.49 20	91.68 52	98.77 18	98.30 31

MP-MVS-pluss		94.21 28	94.00 31	94.85 21	98.17 30	86.65 27	94.82 106	97.17 31	86.26 117	92.83 47	97.87 7	85.57 42	99.56 4	94.37 11	98.92 10	98.34 28
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
canonicalmvs		93.27 49	92.75 54	94.85 21	95.70 104	87.66 7	96.33 30	96.41 86	90.00 30	94.09 25	94.60 120	82.33 75	98.62 92	92.40 33	92.86 145	98.27 37

XVS		94.45 15	94.32 16	94.85 21	98.54 9	86.60 29	96.93 15	97.19 28	90.66 22	92.85 45	97.16 29	85.02 50	99.49 20	91.99 43	98.56 39	98.47 19

X-MVStestdata		88.31 149	86.13 186	94.85 21	98.54 9	86.60 29	96.93 15	97.19 28	90.66 22	92.85 45	23.41 332	85.02 50	99.49 20	91.99 43	98.56 39	98.47 19

SteuartSystems-ACMMP		95.20 6	95.32 7	94.85 21	96.99 63	86.33 38	97.33 3	97.30 21	91.38 11	95.39 13	97.46 12	88.98 12	99.40 25	94.12 12	98.89 11	98.82 6
Skip Steuart: Steuart Systems R&D Blog.
alignmvs		93.08 53	92.50 58	94.81 26	95.62 107	87.61 8	95.99 46	96.07 109	89.77 34	94.12 24	94.87 108	80.56 93	98.66 88	92.42 32	93.10 140	98.15 46

DeepC-MVS_fast	89.43 2	94.04 31	93.79 35	94.80 27	97.48 49	86.78 20	95.65 63	96.89 50	89.40 42	92.81 48	96.97 34	85.37 45	99.24 36	90.87 68	98.69 26	98.38 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		94.25 24	94.07 29	94.77 28	98.47 13	86.31 40	96.71 23	96.98 40	89.04 52	91.98 68	97.19 26	85.43 44	99.56 4	92.06 42	98.79 15	98.44 24
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
APD-MVS		94.24 25	94.07 29	94.75 29	98.06 35	86.90 16	95.88 51	96.94 46	85.68 129	95.05 17	97.18 27	87.31 24	99.07 49	91.90 50	98.61 37	98.28 35
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		94.34 21	94.21 22	94.74 30	98.39 19	86.64 28	97.60 1	97.24 24	88.53 67	92.73 52	97.23 22	85.20 47	99.32 32	92.15 39	98.83 14	98.25 40

Regformer-2		94.33 22	94.22 20	94.68 31	95.54 109	86.75 23	94.57 122	96.70 68	91.84 6	94.41 19	96.56 55	87.19 25	99.13 45	93.50 15	97.65 64	98.16 45

PGM-MVS		93.96 34	93.72 38	94.68 31	98.43 15	86.22 43	95.30 74	97.78 1	87.45 93	93.26 39	97.33 16	84.62 55	99.51 18	90.75 70	98.57 38	98.32 30

DVP-MVS		95.67 1	96.02 1	94.64 33	98.78 2	85.93 47	97.09 9	96.73 64	90.27 25	97.04 4	98.05 4	91.47 3	99.55 9	95.62 3	99.08 4	98.45 23

mPP-MVS		93.99 33	93.78 36	94.63 34	98.50 11	85.90 52	96.87 19	96.91 48	88.70 61	91.83 74	97.17 28	83.96 61	99.55 9	91.44 57	98.64 35	98.43 25

PHI-MVS		93.89 36	93.65 39	94.62 35	96.84 66	86.43 34	96.69 24	97.49 5	85.15 142	93.56 37	96.28 63	85.60 41	99.31 33	92.45 30	98.79 15	98.12 49

TSAR-MVS + MP.		94.85 10	94.94 9	94.58 36	98.25 25	86.33 38	96.11 41	96.62 75	88.14 78	96.10 9	96.96 35	89.09 11	98.94 72	94.48 9	98.68 28	98.48 17
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		93.54 42	93.20 46	94.55 37	95.65 105	85.73 56	94.94 98	96.69 70	91.89 5	90.69 91	95.88 80	81.99 84	99.54 13	93.14 25	97.95 57	98.39 26

train_agg		93.44 44	93.08 47	94.52 38	97.53 45	86.49 32	94.07 157	96.78 60	81.86 209	92.77 49	96.20 67	87.63 21	99.12 46	92.14 40	98.69 26	97.94 61

Regformer-1		94.22 27	94.13 27	94.51 39	95.54 109	86.36 37	94.57 122	96.44 83	91.69 9	94.32 22	96.56 55	87.05 27	99.03 55	93.35 21	97.65 64	98.15 46

CDPH-MVS		92.83 55	92.30 60	94.44 40	97.79 41	86.11 45	94.06 159	96.66 72	80.09 230	92.77 49	96.63 50	86.62 30	99.04 54	87.40 102	98.66 32	98.17 44

3Dnovator	86.66 5	91.73 72	90.82 82	94.44 40	94.59 146	86.37 36	97.18 7	97.02 38	89.20 47	84.31 209	96.66 49	73.74 169	99.17 41	86.74 112	97.96 56	97.79 71

SR-MVS		94.23 26	94.17 25	94.43 42	98.21 29	85.78 54	96.40 29	96.90 49	88.20 76	94.33 21	97.40 13	84.75 54	99.03 55	93.35 21	97.99 55	98.48 17

HPM-MVS		94.02 32	93.88 33	94.43 42	98.39 19	85.78 54	97.25 5	97.07 37	86.90 106	92.62 56	96.80 43	84.85 53	99.17 41	92.43 31	98.65 34	98.33 29
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + GP.		93.66 40	93.41 42	94.41 44	96.59 72	86.78 20	94.40 134	93.93 216	89.77 34	94.21 23	95.59 89	87.35 23	98.61 93	92.72 28	96.15 90	97.83 69

test12				94.34 45	97.13 61	86.15 44		96.29 92		91.04 89		85.08 48	99.01 61		98.13 51	97.86 67

ACMMP		93.24 50	92.88 53	94.30 46	98.09 34	85.33 60	96.86 20	97.45 10	88.33 71	90.15 97	97.03 33	81.44 87	99.51 18	90.85 69	95.74 93	98.04 55
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
agg_prior1		93.29 48	92.97 51	94.26 47	97.38 51	85.92 49	93.92 167	96.72 66	81.96 203	92.16 64	96.23 65	87.85 16	98.97 68	91.95 46	98.55 41	97.90 66

DeepC-MVS	88.79 3	93.31 47	92.99 50	94.26 47	96.07 91	85.83 53	94.89 101	96.99 39	89.02 54	89.56 102	97.37 15	82.51 72	99.38 26	92.20 37	98.30 47	97.57 78
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Regformer-4		93.91 35	93.81 34	94.19 49	95.36 114	85.47 58	94.68 114	96.41 86	91.60 10	93.75 30	96.71 44	85.95 38	99.10 48	93.21 24	96.65 82	98.01 58

EPNet		91.79 69	91.02 78	94.10 50	90.10 283	85.25 61	96.03 45	92.05 251	92.83 1	87.39 138	95.78 83	79.39 109	99.01 61	88.13 92	97.48 67	98.05 54
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		93.43 45	93.25 44	93.97 51	95.42 113	85.04 62	93.06 203	97.13 32	90.74 20	91.84 72	95.09 102	86.32 34	99.21 38	91.22 60	98.45 42	97.65 73
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
DP-MVS Recon		91.95 66	91.28 72	93.96 52	98.33 23	85.92 49	94.66 117	96.66 72	82.69 192	90.03 99	95.82 82	82.30 76	99.03 55	84.57 134	96.48 87	96.91 107

HPM-MVS_fast		93.40 46	93.22 45	93.94 53	98.36 21	84.83 64	97.15 8	96.80 59	85.77 126	92.47 60	97.13 30	82.38 73	99.07 49	90.51 72	98.40 43	97.92 65

SD-MVS		94.96 9	95.33 6	93.88 54	97.25 60	86.69 24	96.19 35	97.11 35	90.42 24	96.95 6	97.27 19	89.53 7	96.91 219	94.38 10	98.85 12	98.03 56
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
MVS_111021_HR		93.45 43	93.31 43	93.84 55	96.99 63	84.84 63	93.24 196	97.24 24	88.76 59	91.60 79	95.85 81	86.07 37	98.66 88	91.91 47	98.16 50	98.03 56

test_prior3		93.60 41	93.53 41	93.82 56	97.29 56	84.49 71	94.12 150	96.88 51	87.67 89	92.63 54	96.39 60	86.62 30	98.87 74	91.50 55	98.67 30	98.11 50

test_prior				93.82 56	97.29 56	84.49 71		96.88 51					98.87 74			98.11 50

Regformer-3		93.68 39	93.64 40	93.81 58	95.36 114	84.61 67	94.68 114	95.83 127	91.27 12	93.60 34	96.71 44	85.75 40	98.86 77	92.87 26	96.65 82	97.96 60

APD-MVS_3200maxsize		93.78 37	93.77 37	93.80 59	97.92 38	84.19 82	96.30 31	96.87 53	86.96 102	93.92 29	97.47 11	83.88 62	98.96 71	92.71 29	97.87 59	98.26 39

CSCG		93.23 51	93.05 48	93.76 60	98.04 36	84.07 84	96.22 34	97.37 14	84.15 158	90.05 98	95.66 87	87.77 18	99.15 44	89.91 75	98.27 48	98.07 52

UA-Net		92.83 55	92.54 57	93.68 61	96.10 89	84.71 66	95.66 61	96.39 88	91.92 4	93.22 41	96.49 57	83.16 66	98.87 74	84.47 135	95.47 98	97.45 83

QAPM		89.51 116	88.15 136	93.59 62	94.92 132	84.58 68	96.82 21	96.70 68	78.43 249	83.41 228	96.19 70	73.18 177	99.30 34	77.11 231	96.54 85	96.89 108

abl_6		93.18 52	93.05 48	93.57 63	97.52 47	84.27 81	95.53 67	96.67 71	87.85 84	93.20 42	97.22 23	80.35 94	99.18 40	91.91 47	97.21 71	97.26 88

EI-MVSNet-Vis-set		93.01 54	92.92 52	93.29 64	95.01 125	83.51 98	94.48 126	95.77 131	90.87 15	92.52 58	96.67 48	84.50 56	99.00 64	91.99 43	94.44 118	97.36 84

Vis-MVSNet		91.75 71	91.23 73	93.29 64	95.32 117	83.78 90	96.14 38	95.98 114	89.89 31	90.45 93	96.58 53	75.09 147	98.31 116	84.75 132	96.90 76	97.78 72
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VNet		92.24 64	91.91 64	93.24 66	96.59 72	83.43 99	94.84 105	96.44 83	89.19 48	94.08 26	95.90 79	77.85 125	98.17 122	88.90 84	93.38 134	98.13 48

1121		90.42 97	89.49 102	93.20 67	97.27 58	84.46 74	92.63 214	95.51 152	71.01 308	91.20 87	96.21 66	82.92 68	99.05 51	80.56 192	98.07 53	96.10 131

VDD-MVS		90.74 87	89.92 98	93.20 67	96.27 82	83.02 110	95.73 56	93.86 217	88.42 70	92.53 57	96.84 38	62.09 274	98.64 90	90.95 66	92.62 148	97.93 64

nrg030		91.08 84	90.39 85	93.17 69	93.07 199	86.91 15	96.41 28	96.26 94	88.30 72	88.37 119	94.85 111	82.19 79	97.64 158	91.09 61	82.95 251	94.96 169

EI-MVSNet-UG-set		92.74 57	92.62 55	93.12 70	94.86 136	83.20 104	94.40 134	95.74 134	90.71 21	92.05 67	96.60 52	84.00 60	98.99 65	91.55 53	93.63 127	97.17 94

新几何1				93.10 71	97.30 55	84.35 80		95.56 147	71.09 307	91.26 86	96.24 64	82.87 69	98.86 77	79.19 211	98.10 52	96.07 133

OMC-MVS		91.23 80	90.62 84	93.08 72	96.27 82	84.07 84	93.52 181	95.93 117	86.95 103	89.51 103	96.13 73	78.50 117	98.35 112	85.84 120	92.90 144	96.83 109

OpenMVS	83.78 11	88.74 139	87.29 153	93.08 72	92.70 209	85.39 59	96.57 25	96.43 85	78.74 246	80.85 255	96.07 74	69.64 218	99.01 61	78.01 222	96.65 82	94.83 176

MAR-MVS		90.30 98	89.37 106	93.07 74	96.61 71	84.48 73	95.68 59	95.67 138	82.36 196	87.85 127	92.85 176	76.63 134	98.80 84	80.01 200	96.68 81	95.91 138
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
lupinMVS		90.92 85	90.21 88	93.03 75	93.86 175	83.88 88	92.81 210	93.86 217	79.84 233	91.76 75	94.29 129	77.92 122	98.04 136	90.48 73	97.11 72	97.17 94

Effi-MVS+		91.59 75	91.11 75	93.01 76	94.35 159	83.39 101	94.60 119	95.10 178	87.10 99	90.57 92	93.10 171	81.43 88	98.07 134	89.29 80	94.48 116	97.59 77

MVS_111021_LR		92.47 61	92.29 61	92.98 77	95.99 94	84.43 78	93.08 201	96.09 107	88.20 76	91.12 88	95.72 86	81.33 89	97.76 151	91.74 51	97.37 70	96.75 111

LFMVS		90.08 102	89.13 112	92.95 78	96.71 68	82.32 132	96.08 42	89.91 301	86.79 107	92.15 66	96.81 41	62.60 271	98.34 113	87.18 106	93.90 123	98.19 43

UGNet		89.95 106	88.95 116	92.95 78	94.51 149	83.31 102	95.70 58	95.23 171	89.37 43	87.58 133	93.94 143	64.00 266	98.78 85	83.92 139	96.31 89	96.74 112
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
jason		90.80 86	90.10 91	92.90 80	93.04 201	83.53 97	93.08 201	94.15 211	80.22 227	91.41 83	94.91 106	76.87 128	97.93 145	90.28 74	96.90 76	97.24 89
jason: jason.
DP-MVS		87.25 187	85.36 207	92.90 80	97.65 43	83.24 103	94.81 107	92.00 253	74.99 279	81.92 245	95.00 104	72.66 182	99.05 51	66.92 294	92.33 152	96.40 118

CANet_DTU		90.26 100	89.41 105	92.81 82	93.46 189	83.01 111	93.48 182	94.47 201	89.43 41	87.76 131	94.23 133	70.54 208	99.03 55	84.97 127	96.39 88	96.38 119

MVSFormer		91.68 74	91.30 71	92.80 83	93.86 175	83.88 88	95.96 48	95.90 121	84.66 153	91.76 75	94.91 106	77.92 122	97.30 187	89.64 77	97.11 72	97.24 89

PVSNet_Blended_VisFu		91.38 77	90.91 80	92.80 83	96.39 79	83.17 105	94.87 103	96.66 72	83.29 178	89.27 107	94.46 124	80.29 96	99.17 41	87.57 100	95.37 101	96.05 135

VDDNet		89.56 115	88.49 127	92.76 85	95.07 124	82.09 135	96.30 31	93.19 228	81.05 222	91.88 70	96.86 37	61.16 284	98.33 114	88.43 89	92.49 151	97.84 68

casdiffmvs		92.51 60	92.43 59	92.74 86	94.41 155	81.98 138	94.54 124	96.23 98	89.57 38	91.96 69	96.17 71	82.58 71	98.01 138	90.95 66	95.45 100	98.23 41

CS-MVS		92.60 58	92.56 56	92.73 87	95.55 108	82.35 131	96.14 38	96.85 54	88.71 60	91.44 82	91.51 225	84.13 59	98.48 99	91.27 59	97.47 68	97.34 85

test_yl		90.69 89	90.02 96	92.71 88	95.72 102	82.41 129	94.11 152	95.12 176	85.63 130	91.49 80	94.70 114	74.75 151	98.42 108	86.13 118	92.53 149	97.31 86

DCV-MVSNet		90.69 89	90.02 96	92.71 88	95.72 102	82.41 129	94.11 152	95.12 176	85.63 130	91.49 80	94.70 114	74.75 151	98.42 108	86.13 118	92.53 149	97.31 86

PCF-MVS	84.11 10	87.74 164	86.08 190	92.70 90	94.02 166	84.43 78	89.27 274	95.87 124	73.62 291	84.43 203	94.33 126	78.48 118	98.86 77	70.27 272	94.45 117	94.81 177
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline		92.39 63	92.29 61	92.69 91	94.46 152	81.77 142	94.14 149	96.27 93	89.22 46	91.88 70	96.00 75	82.35 74	97.99 140	91.05 62	95.27 105	98.30 31

ETV-MVS		91.88 68	91.62 68	92.65 92	95.49 111	82.25 133	95.30 74	96.49 81	87.15 96	89.82 100	91.03 241	83.53 64	98.48 99	88.12 93	97.65 64	97.22 92

MSLP-MVS++		93.72 38	94.08 28	92.65 92	97.31 54	83.43 99	95.79 54	97.33 18	90.03 29	93.58 35	96.96 35	84.87 52	97.76 151	92.19 38	98.66 32	96.76 110

ab-mvs		89.41 122	88.35 129	92.60 94	95.15 123	82.65 124	92.20 229	95.60 145	83.97 162	88.55 115	93.70 156	74.16 162	98.21 121	82.46 158	89.37 184	96.94 105

LS3D		87.89 160	86.32 182	92.59 95	96.07 91	82.92 114	95.23 81	94.92 188	75.66 272	82.89 233	95.98 76	72.48 185	99.21 38	68.43 286	95.23 106	95.64 148

Anonymous20240529		88.09 156	86.59 175	92.58 96	96.53 75	81.92 140	95.99 46	95.84 126	74.11 287	89.06 111	95.21 98	61.44 279	98.81 83	83.67 142	87.47 213	97.01 102

CPTT-MVS		91.99 65	91.80 65	92.55 97	98.24 27	81.98 138	96.76 22	96.49 81	81.89 208	90.24 95	96.44 59	78.59 115	98.61 93	89.68 76	97.85 60	97.06 99

114514_t		89.51 116	88.50 125	92.54 98	98.11 32	81.99 137	95.16 87	96.36 90	70.19 310	85.81 163	95.25 96	76.70 132	98.63 91	82.07 165	96.86 78	97.00 103

PAPM_NR		91.22 81	90.78 83	92.52 99	97.60 44	81.46 150	94.37 140	96.24 97	86.39 115	87.41 135	94.80 113	82.06 82	98.48 99	82.80 154	95.37 101	97.61 75

DeepPCF-MVS	89.96 1	94.20 30	94.77 11	92.49 100	96.52 76	80.00 191	94.00 164	97.08 36	90.05 28	95.65 11	97.29 17	89.66 6	98.97 68	93.95 13	98.71 23	98.50 15

IS-MVSNet		91.43 76	91.09 77	92.46 101	95.87 99	81.38 153	96.95 12	93.69 222	89.72 36	89.50 104	95.98 76	78.57 116	97.77 150	83.02 148	96.50 86	98.22 42

API-MVS		90.66 91	90.07 92	92.45 102	96.36 80	84.57 69	96.06 44	95.22 173	82.39 194	89.13 108	94.27 132	80.32 95	98.46 103	80.16 199	96.71 80	94.33 198

xiu_mvs_v1_base_debu		90.64 92	90.05 93	92.40 103	93.97 172	84.46 74	93.32 186	95.46 154	85.17 139	92.25 61	94.03 135	70.59 204	98.57 95	90.97 63	94.67 109	94.18 201

xiu_mvs_v1_base		90.64 92	90.05 93	92.40 103	93.97 172	84.46 74	93.32 186	95.46 154	85.17 139	92.25 61	94.03 135	70.59 204	98.57 95	90.97 63	94.67 109	94.18 201

xiu_mvs_v1_base_debi		90.64 92	90.05 93	92.40 103	93.97 172	84.46 74	93.32 186	95.46 154	85.17 139	92.25 61	94.03 135	70.59 204	98.57 95	90.97 63	94.67 109	94.18 201

AdaColmap		89.89 109	89.07 113	92.37 106	97.41 50	83.03 109	94.42 133	95.92 118	82.81 189	86.34 156	94.65 118	73.89 165	99.02 59	80.69 189	95.51 96	95.05 163

CNLPA		89.07 130	87.98 139	92.34 107	96.87 65	84.78 65	94.08 156	93.24 227	81.41 217	84.46 201	95.13 101	75.57 143	96.62 228	77.21 229	93.84 125	95.61 149

ET-MVSNet_ETH3D		87.51 177	85.91 196	92.32 108	93.70 183	83.93 86	92.33 224	90.94 282	84.16 157	72.09 309	92.52 187	69.90 213	95.85 265	89.20 81	88.36 202	97.17 94

Anonymous202405211		87.68 165	86.13 186	92.31 109	96.66 69	80.74 170	94.87 103	91.49 268	80.47 226	89.46 105	95.44 90	54.72 307	98.23 118	82.19 163	89.89 176	97.97 59

CHOSEN 1792x2688		88.84 136	87.69 144	92.30 110	96.14 85	81.42 152	90.01 264	95.86 125	74.52 284	87.41 135	93.94 143	75.46 144	98.36 110	80.36 195	95.53 95	97.12 98

HY-MVS	83.01 12	89.03 132	87.94 141	92.29 111	94.86 136	82.77 116	92.08 234	94.49 200	81.52 216	86.93 143	92.79 182	78.32 120	98.23 118	79.93 201	90.55 166	95.88 140

CDS-MVSNet		89.45 119	88.51 124	92.29 111	93.62 184	83.61 96	93.01 204	94.68 197	81.95 204	87.82 129	93.24 165	78.69 113	96.99 213	80.34 196	93.23 138	96.28 122
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PAPR		90.02 103	89.27 110	92.29 111	95.78 100	80.95 164	92.68 212	96.22 99	81.91 206	86.66 149	93.75 155	82.23 77	98.44 107	79.40 210	94.79 108	97.48 81

PLC	84.53 7	89.06 131	88.03 138	92.15 114	97.27 58	82.69 123	94.29 142	95.44 159	79.71 235	84.01 214	94.18 134	76.68 133	98.75 86	77.28 228	93.41 133	95.02 164
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DI_MVS_plusplus_test		88.15 154	86.82 164	92.14 115	90.67 271	81.07 160	93.01 204	94.59 198	83.83 165	77.78 283	90.63 248	68.51 235	98.16 123	88.02 95	94.37 119	97.17 94

EPP-MVSNet		91.70 73	91.56 69	92.13 116	95.88 97	80.50 177	97.33 3	95.25 170	86.15 119	89.76 101	95.60 88	83.42 65	98.32 115	87.37 104	93.25 137	97.56 79

原ACMM1				92.01 117	97.34 53	81.05 161		96.81 58	78.89 241	90.45 93	95.92 78	82.65 70	98.84 82	80.68 190	98.26 49	96.14 126

UniMVSNet (Re)		89.80 110	89.07 113	92.01 117	93.60 185	84.52 70	94.78 109	97.47 7	89.26 45	86.44 154	92.32 193	82.10 80	97.39 184	84.81 131	80.84 277	94.12 205

MG-MVS		91.77 70	91.70 67	92.00 119	97.08 62	80.03 189	93.60 179	95.18 174	87.85 84	90.89 90	96.47 58	82.06 82	98.36 110	85.07 126	97.04 75	97.62 74

EIA-MVS		91.95 66	91.94 63	91.98 120	95.16 122	80.01 190	95.36 69	96.73 64	88.44 68	89.34 106	92.16 198	83.82 63	98.45 106	89.35 79	97.06 74	97.48 81

PVSNet_Blended		90.73 88	90.32 87	91.98 120	96.12 86	81.25 155	92.55 218	96.83 55	82.04 202	89.10 109	92.56 186	81.04 91	98.85 80	86.72 115	95.91 91	95.84 142

PS-MVSNAJ		91.18 82	90.92 79	91.96 122	95.26 120	82.60 126	92.09 233	95.70 136	86.27 116	91.84 72	92.46 188	79.70 104	98.99 65	89.08 82	95.86 92	94.29 199

TAMVS		89.21 127	88.29 133	91.96 122	93.71 181	82.62 125	93.30 190	94.19 209	82.22 198	87.78 130	93.94 143	78.83 111	96.95 216	77.70 224	92.98 143	96.32 120

MVS_Test		91.31 79	91.11 75	91.93 124	94.37 156	80.14 182	93.46 184	95.80 129	86.46 113	91.35 85	93.77 153	82.21 78	98.09 132	87.57 100	94.95 107	97.55 80

NR-MVSNet		88.58 144	87.47 149	91.93 124	93.04 201	84.16 83	94.77 110	96.25 96	89.05 51	80.04 269	93.29 163	79.02 110	97.05 210	81.71 174	80.05 287	94.59 184

HyFIR lowres test		88.09 156	86.81 165	91.93 124	96.00 93	80.63 172	90.01 264	95.79 130	73.42 292	87.68 132	92.10 204	73.86 166	97.96 142	80.75 188	91.70 155	97.19 93

thisisatest0530		88.67 140	87.61 146	91.86 127	94.87 135	80.07 185	94.63 118	89.90 302	84.00 161	88.46 117	93.78 152	66.88 246	98.46 103	83.30 144	92.65 147	97.06 99

xiu_mvs_v2_base		91.13 83	90.89 81	91.86 127	94.97 128	82.42 127	92.24 227	95.64 143	86.11 122	91.74 77	93.14 169	79.67 107	98.89 73	89.06 83	95.46 99	94.28 200

DU-MVS		89.34 126	88.50 125	91.85 129	93.04 201	83.72 91	94.47 129	96.59 77	89.50 39	86.46 151	93.29 163	77.25 126	97.23 197	84.92 128	81.02 273	94.59 184

OPM-MVS		90.12 101	89.56 101	91.82 130	93.14 196	83.90 87	94.16 148	95.74 134	88.96 55	87.86 126	95.43 92	72.48 185	97.91 146	88.10 94	90.18 171	93.65 234

HQP_MVS		90.60 95	90.19 89	91.82 130	94.70 142	82.73 120	95.85 52	96.22 99	90.81 17	86.91 144	94.86 109	74.23 158	98.12 124	88.15 90	89.99 172	94.63 181

UniMVSNet_NR-MVSNet		89.92 108	89.29 108	91.81 132	93.39 190	83.72 91	94.43 132	97.12 33	89.80 33	86.46 151	93.32 160	83.16 66	97.23 197	84.92 128	81.02 273	94.49 193

diffmvs		91.37 78	91.23 73	91.77 133	93.09 198	80.27 179	92.36 223	95.52 151	87.03 101	91.40 84	94.93 105	80.08 98	97.44 172	92.13 41	94.56 114	97.61 75

1112_ss		88.42 145	87.33 152	91.72 134	94.92 132	80.98 162	92.97 207	94.54 199	78.16 254	83.82 217	93.88 148	78.78 112	97.91 146	79.45 206	89.41 183	96.26 123

Fast-Effi-MVS+		89.41 122	88.64 122	91.71 135	94.74 138	80.81 168	93.54 180	95.10 178	83.11 181	86.82 147	90.67 247	79.74 103	97.75 154	80.51 194	93.55 128	96.57 116

WTY-MVS		89.60 113	88.92 117	91.67 136	95.47 112	81.15 159	92.38 222	94.78 195	83.11 181	89.06 111	94.32 127	78.67 114	96.61 230	81.57 175	90.89 165	97.24 89

TAPA-MVS	84.62 6	88.16 153	87.01 160	91.62 137	96.64 70	80.65 171	94.39 136	96.21 102	76.38 265	86.19 159	95.44 90	79.75 102	98.08 133	62.75 309	95.29 103	96.13 127
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
VPA-MVSNet		89.62 112	88.96 115	91.60 138	93.86 175	82.89 115	95.46 68	97.33 18	87.91 81	88.43 118	93.31 161	74.17 161	97.40 181	87.32 105	82.86 253	94.52 189

XVG-OURS		89.40 124	88.70 121	91.52 139	94.06 164	81.46 150	91.27 250	96.07 109	86.14 120	88.89 113	95.77 84	68.73 232	97.26 193	87.39 103	89.96 174	95.83 143

TranMVSNet+NR-MVSNet		88.84 136	87.95 140	91.49 140	92.68 210	83.01 111	94.92 100	96.31 91	89.88 32	85.53 171	93.85 150	76.63 134	96.96 215	81.91 169	79.87 290	94.50 191

XVG-OURS-SEG-HR		89.95 106	89.45 103	91.47 141	94.00 170	81.21 158	91.87 236	96.06 111	85.78 125	88.55 115	95.73 85	74.67 154	97.27 191	88.71 86	89.64 181	95.91 138

MVS		87.44 180	86.10 189	91.44 142	92.61 211	83.62 95	92.63 214	95.66 140	67.26 314	81.47 247	92.15 199	77.95 121	98.22 120	79.71 203	95.48 97	92.47 267

F-COLMAP		87.95 159	86.80 166	91.40 143	96.35 81	80.88 166	94.73 112	95.45 157	79.65 236	82.04 243	94.61 119	71.13 195	98.50 98	76.24 239	91.05 163	94.80 178

thisisatest0515		87.33 183	85.99 192	91.37 144	93.49 187	79.55 198	90.63 255	89.56 308	80.17 228	87.56 134	90.86 243	67.07 243	98.28 117	81.50 176	93.02 142	96.29 121

HQP-MVS		89.80 110	89.28 109	91.34 145	94.17 161	81.56 144	94.39 136	96.04 112	88.81 56	85.43 180	93.97 142	73.83 167	97.96 142	87.11 109	89.77 179	94.50 191

FMVSNet3		87.40 182	86.11 188	91.30 146	93.79 180	83.64 94	94.20 147	94.81 194	83.89 163	84.37 204	91.87 213	68.45 236	96.56 231	78.23 219	85.36 229	93.70 233

FMVSNet2		87.19 191	85.82 198	91.30 146	94.01 167	83.67 93	94.79 108	94.94 184	83.57 169	83.88 215	92.05 208	66.59 251	96.51 234	77.56 226	85.01 233	93.73 231

IB-MVS	80.51 15	85.24 228	83.26 239	91.19 148	92.13 218	79.86 194	91.75 239	91.29 273	83.28 179	80.66 258	88.49 278	61.28 280	98.46 103	80.99 184	79.46 293	95.25 159
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
CLD-MVS		89.47 118	88.90 118	91.18 149	94.22 160	82.07 136	92.13 231	96.09 107	87.90 82	85.37 186	92.45 189	74.38 156	97.56 162	87.15 107	90.43 167	93.93 214
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
LPG-MVS_test		89.45 119	88.90 118	91.12 150	94.47 150	81.49 148	95.30 74	96.14 104	86.73 109	85.45 177	95.16 99	69.89 214	98.10 126	87.70 98	89.23 188	93.77 228

LGP-MVS_train				91.12 150	94.47 150	81.49 148		96.14 104	86.73 109	85.45 177	95.16 99	69.89 214	98.10 126	87.70 98	89.23 188	93.77 228

ACMM	84.12 9	89.14 128	88.48 128	91.12 150	94.65 145	81.22 157	95.31 72	96.12 106	85.31 138	85.92 162	94.34 125	70.19 212	98.06 135	85.65 121	88.86 193	94.08 209
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tttt0517		88.61 142	87.78 143	91.11 153	94.96 129	77.81 240	95.35 70	89.69 305	85.09 144	88.05 124	94.59 121	66.93 244	98.48 99	83.27 145	92.13 154	97.03 101

GBi-Net		87.26 185	85.98 193	91.08 154	94.01 167	83.10 106	95.14 88	94.94 184	83.57 169	84.37 204	91.64 217	66.59 251	96.34 246	78.23 219	85.36 229	93.79 224

test1		87.26 185	85.98 193	91.08 154	94.01 167	83.10 106	95.14 88	94.94 184	83.57 169	84.37 204	91.64 217	66.59 251	96.34 246	78.23 219	85.36 229	93.79 224

FMVSNet1		85.85 216	84.11 228	91.08 154	92.81 207	83.10 106	95.14 88	94.94 184	81.64 212	82.68 235	91.64 217	59.01 295	96.34 246	75.37 246	83.78 241	93.79 224

Test_1112_low_res		87.65 167	86.51 177	91.08 154	94.94 131	79.28 209	91.77 238	94.30 207	76.04 270	83.51 226	92.37 191	77.86 124	97.73 155	78.69 215	89.13 190	96.22 124

PS-MVSNAJss		89.97 105	89.62 100	91.02 158	91.90 221	80.85 167	95.26 80	95.98 114	86.26 117	86.21 158	94.29 129	79.70 104	97.65 156	88.87 85	88.10 205	94.57 186

BH-RMVSNet		88.37 147	87.48 148	91.02 158	95.28 118	79.45 201	92.89 209	93.07 230	85.45 135	86.91 144	94.84 112	70.35 209	97.76 151	73.97 257	94.59 113	95.85 141

UniMVSNet_ETH3D		87.53 176	86.37 179	91.00 160	92.44 213	78.96 214	94.74 111	95.61 144	84.07 160	85.36 187	94.52 123	59.78 292	97.34 186	82.93 149	87.88 210	96.71 113

FIs		90.51 96	90.35 86	90.99 161	93.99 171	80.98 162	95.73 56	97.54 3	89.15 49	86.72 148	94.68 116	81.83 86	97.24 195	85.18 125	88.31 203	94.76 179

ACMP	84.23 8	89.01 134	88.35 129	90.99 161	94.73 139	81.27 154	95.07 91	95.89 123	86.48 112	83.67 221	94.30 128	69.33 221	97.99 140	87.10 111	88.55 195	93.72 232
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20231211		86.59 204	85.13 210	90.98 163	96.52 76	81.50 146	96.14 38	96.16 103	73.78 289	83.65 222	92.15 199	63.26 269	97.37 185	82.82 153	81.74 265	94.06 210

sss		88.93 135	88.26 135	90.94 164	94.05 165	80.78 169	91.71 241	95.38 164	81.55 215	88.63 114	93.91 147	75.04 148	95.47 282	82.47 157	91.61 156	96.57 116

PVSNet_BlendedMVS		89.98 104	89.70 99	90.82 165	96.12 86	81.25 155	93.92 167	96.83 55	83.49 173	89.10 109	92.26 196	81.04 91	98.85 80	86.72 115	87.86 211	92.35 272

cascas		86.43 207	84.98 214	90.80 166	92.10 219	80.92 165	90.24 259	95.91 120	73.10 295	83.57 225	88.39 279	65.15 261	97.46 169	84.90 130	91.43 157	94.03 212

GA-MVS		86.61 202	85.27 208	90.66 167	91.33 243	78.71 216	90.40 257	93.81 220	85.34 137	85.12 190	89.57 265	61.25 281	97.11 205	80.99 184	89.59 182	96.15 125

thres600view7		87.65 167	86.67 171	90.59 168	96.08 90	78.72 215	94.88 102	91.58 264	87.06 100	88.08 122	92.30 194	68.91 229	98.10 126	70.05 279	91.10 159	94.96 169

thres400		87.62 172	86.64 172	90.57 169	95.99 94	78.64 217	94.58 120	91.98 255	86.94 104	88.09 120	91.77 214	69.18 226	98.10 126	70.13 276	91.10 159	94.96 169

baseline1		88.10 155	87.28 154	90.57 169	94.96 129	80.07 185	94.27 143	91.29 273	86.74 108	87.41 135	94.00 140	76.77 131	96.20 250	80.77 187	79.31 294	95.44 153

testing_2		83.40 250	81.02 256	90.56 171	85.06 315	80.51 176	91.37 248	95.57 146	82.92 186	67.06 317	85.54 304	49.47 317	97.24 195	86.74 112	85.44 228	93.93 214

FC-MVSNet-test		90.27 99	90.18 90	90.53 172	93.71 181	79.85 195	95.77 55	97.59 2	89.31 44	86.27 157	94.67 117	81.93 85	97.01 212	84.26 137	88.09 207	94.71 180

PAPM		86.68 201	85.39 205	90.53 172	93.05 200	79.33 208	89.79 267	94.77 196	78.82 243	81.95 244	93.24 165	76.81 129	97.30 187	66.94 292	93.16 139	94.95 172

WR-MVS		88.38 146	87.67 145	90.52 174	93.30 193	80.18 180	93.26 193	95.96 116	88.57 66	85.47 176	92.81 180	76.12 136	96.91 219	81.24 178	82.29 256	94.47 196

MVSTER		88.84 136	88.29 133	90.51 175	92.95 205	80.44 178	93.73 173	95.01 181	84.66 153	87.15 139	93.12 170	72.79 181	97.21 199	87.86 96	87.36 216	93.87 219

testdata				90.49 176	96.40 78	77.89 237		95.37 166	72.51 300	93.63 33	96.69 46	82.08 81	97.65 156	83.08 146	97.39 69	95.94 137

jajsoiax		88.24 151	87.50 147	90.48 177	90.89 262	80.14 182	95.31 72	95.65 142	84.97 146	84.24 211	94.02 138	65.31 260	97.42 174	88.56 87	88.52 197	93.89 216

PatchMatch-RL		86.77 200	85.54 201	90.47 178	95.88 97	82.71 122	90.54 256	92.31 243	79.82 234	84.32 208	91.57 224	68.77 231	96.39 242	73.16 262	93.48 132	92.32 273

tfpn200view9		87.58 174	86.64 172	90.41 179	95.99 94	78.64 217	94.58 120	91.98 255	86.94 104	88.09 120	91.77 214	69.18 226	98.10 126	70.13 276	91.10 159	94.48 194

VPNet		88.20 152	87.47 149	90.39 180	93.56 186	79.46 200	94.04 160	95.54 150	88.67 62	86.96 142	94.58 122	69.33 221	97.15 201	84.05 138	80.53 282	94.56 187

ACMH	80.38 17	85.36 223	83.68 234	90.39 180	94.45 153	80.63 172	94.73 112	94.85 191	82.09 200	77.24 287	92.65 184	60.01 290	97.58 160	72.25 265	84.87 234	92.96 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres100view900		87.63 170	86.71 169	90.38 182	96.12 86	78.55 219	95.03 94	91.58 264	87.15 96	88.06 123	92.29 195	68.91 229	98.10 126	70.13 276	91.10 159	94.48 194

mvs-test1		89.45 119	89.14 111	90.38 182	93.33 191	77.63 246	94.95 97	94.36 204	87.70 87	87.10 141	92.81 180	73.45 172	98.03 137	85.57 123	93.04 141	95.48 151

mvs_tets		88.06 158	87.28 154	90.38 182	90.94 258	79.88 193	95.22 82	95.66 140	85.10 143	84.21 212	93.94 143	63.53 268	97.40 181	88.50 88	88.40 201	93.87 219

1314		87.51 177	86.57 176	90.34 185	92.42 214	79.74 197	92.63 214	95.35 168	78.35 250	80.14 266	91.62 221	74.05 163	97.15 201	81.05 179	93.53 129	94.12 205

LTVRE_ROB	82.13 13	86.26 209	84.90 217	90.34 185	94.44 154	81.50 146	92.31 226	94.89 189	83.03 183	79.63 273	92.67 183	69.69 217	97.79 149	71.20 268	86.26 223	91.72 282
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
test_djsdf		89.03 132	88.64 122	90.21 187	90.74 268	79.28 209	95.96 48	95.90 121	84.66 153	85.33 188	92.94 175	74.02 164	97.30 187	89.64 77	88.53 196	94.05 211

v2v482		87.84 161	87.06 158	90.17 188	90.99 254	79.23 212	94.00 164	95.13 175	84.87 147	85.53 171	92.07 207	74.45 155	97.45 170	84.71 133	81.75 264	93.85 222

pmmvs4		85.43 222	83.86 232	90.16 189	90.02 286	82.97 113	90.27 258	92.67 238	75.93 271	80.73 256	91.74 216	71.05 196	95.73 271	78.85 213	83.46 248	91.78 281

V42		87.68 165	86.86 162	90.15 190	90.58 274	80.14 182	94.24 145	95.28 169	83.66 168	85.67 166	91.33 227	74.73 153	97.41 179	84.43 136	81.83 262	92.89 256

MSDG		84.86 235	83.09 241	90.14 191	93.80 178	80.05 187	89.18 277	93.09 229	78.89 241	78.19 279	91.91 211	65.86 259	97.27 191	68.47 285	88.45 199	93.11 250

anonymousdsp		87.84 161	87.09 157	90.12 192	89.13 293	80.54 175	94.67 116	95.55 148	82.05 201	83.82 217	92.12 201	71.47 193	97.15 201	87.15 107	87.80 212	92.67 261

thres200		87.21 190	86.24 185	90.12 192	95.36 114	78.53 220	93.26 193	92.10 248	86.42 114	88.00 125	91.11 237	69.24 225	98.00 139	69.58 280	91.04 164	93.83 223

CR-MVSNet		85.35 224	83.76 233	90.12 192	90.58 274	79.34 205	85.24 308	91.96 257	78.27 251	85.55 169	87.87 289	71.03 197	95.61 272	73.96 258	89.36 185	95.40 155

RPMNet		83.18 252	80.87 259	90.12 192	90.58 274	79.34 205	85.24 308	90.78 287	71.44 305	85.55 169	82.97 312	70.87 199	95.61 272	61.01 313	89.36 185	95.40 155

v1144		87.61 173	86.79 167	90.06 196	91.01 253	79.34 205	93.95 166	95.42 163	83.36 177	85.66 167	91.31 230	74.98 149	97.42 174	83.37 143	82.06 258	93.42 242

XXY-MVS		87.65 167	86.85 163	90.03 197	92.14 217	80.60 174	93.76 172	95.23 171	82.94 185	84.60 197	94.02 138	74.27 157	95.49 281	81.04 180	83.68 244	94.01 213

Vis-MVSNet (Re-imp)		89.59 114	89.44 104	90.03 197	95.74 101	75.85 262	95.61 64	90.80 286	87.66 91	87.83 128	95.40 93	76.79 130	96.46 239	78.37 216	96.73 79	97.80 70

BH-untuned		88.60 143	88.13 137	90.01 199	95.24 121	78.50 222	93.29 191	94.15 211	84.75 151	84.46 201	93.40 157	75.76 140	97.40 181	77.59 225	94.52 115	94.12 205

v1192		87.25 187	86.33 181	90.00 200	90.76 267	79.04 213	93.80 170	95.48 153	82.57 193	85.48 175	91.18 233	73.38 176	97.42 174	82.30 160	82.06 258	93.53 237

v7n		86.81 196	85.76 199	89.95 201	90.72 269	79.25 211	95.07 91	95.92 118	84.45 156	82.29 238	90.86 243	72.60 184	97.53 164	79.42 209	80.52 283	93.08 252

v8		87.50 179	86.71 169	89.89 202	91.37 240	79.40 202	94.50 125	95.38 164	84.81 149	83.60 224	91.33 227	76.05 137	97.42 174	82.84 152	80.51 284	92.84 258

v10		87.25 187	86.38 178	89.85 203	91.19 246	79.50 199	94.48 126	95.45 157	83.79 166	83.62 223	91.19 232	75.13 146	97.42 174	81.94 168	80.60 279	92.63 263

baseline2		86.50 205	85.39 205	89.84 204	91.12 250	76.70 254	91.88 235	88.58 310	82.35 197	79.95 270	90.95 242	73.42 174	97.63 159	80.27 198	89.95 175	95.19 160

pm-mvs1		86.61 202	85.54 201	89.82 205	91.44 234	80.18 180	95.28 79	94.85 191	83.84 164	81.66 246	92.62 185	72.45 187	96.48 236	79.67 204	78.06 297	92.82 259

TR-MVS		86.78 198	85.76 199	89.82 205	94.37 156	78.41 224	92.47 219	92.83 233	81.11 221	86.36 155	92.40 190	68.73 232	97.48 167	73.75 260	89.85 178	93.57 236

ACMH+	81.04 14	85.05 231	83.46 238	89.82 205	94.66 144	79.37 203	94.44 131	94.12 213	82.19 199	78.04 281	92.82 179	58.23 297	97.54 163	73.77 259	82.90 252	92.54 264

EI-MVSNet		89.10 129	88.86 120	89.80 208	91.84 223	78.30 227	93.70 176	95.01 181	85.73 127	87.15 139	95.28 94	79.87 101	97.21 199	83.81 141	87.36 216	93.88 218

v144192		87.19 191	86.35 180	89.74 209	90.64 272	78.24 229	93.92 167	95.43 161	81.93 205	85.51 173	91.05 239	74.21 160	97.45 170	82.86 151	81.56 266	93.53 237

COLMAP_ROB	80.39 16	83.96 243	82.04 249	89.74 209	95.28 118	79.75 196	94.25 144	92.28 244	75.17 277	78.02 282	93.77 153	58.60 296	97.84 148	65.06 302	85.92 224	91.63 284
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
SCA		86.32 208	85.18 209	89.73 211	92.15 216	76.60 255	91.12 253	91.69 262	83.53 172	85.50 174	88.81 272	66.79 247	96.48 236	76.65 234	90.35 169	96.12 128

IterMVS-LS		88.36 148	87.91 142	89.70 212	93.80 178	78.29 228	93.73 173	95.08 180	85.73 127	84.75 195	91.90 212	79.88 100	96.92 218	83.83 140	82.51 254	93.89 216
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		86.97 195	86.06 191	89.69 213	90.53 277	78.11 232	93.80 170	95.43 161	81.90 207	85.33 188	91.05 239	72.66 182	97.41 179	82.05 166	81.80 263	93.53 237

Fast-Effi-MVS+-dtu		87.44 180	86.72 168	89.63 214	92.04 220	77.68 245	94.03 161	93.94 215	85.81 124	82.42 237	91.32 229	70.33 210	97.06 209	80.33 197	90.23 170	94.14 204

v1240		86.78 198	85.85 197	89.56 215	90.45 278	77.79 241	93.61 178	95.37 166	81.65 211	85.43 180	91.15 235	71.50 192	97.43 173	81.47 177	82.05 260	93.47 241

Effi-MVS+-dtu		88.65 141	88.35 129	89.54 216	93.33 191	76.39 257	94.47 129	94.36 204	87.70 87	85.43 180	89.56 266	73.45 172	97.26 193	85.57 123	91.28 158	94.97 166

AllTest		83.42 248	81.39 253	89.52 217	95.01 125	77.79 241	93.12 198	90.89 284	77.41 257	76.12 292	93.34 158	54.08 310	97.51 165	68.31 287	84.27 238	93.26 244

TestCases				89.52 217	95.01 125	77.79 241		90.89 284	77.41 257	76.12 292	93.34 158	54.08 310	97.51 165	68.31 287	84.27 238	93.26 244

mvs_anonymous		89.37 125	89.32 107	89.51 219	93.47 188	74.22 270	91.65 244	94.83 193	82.91 187	85.45 177	93.79 151	81.23 90	96.36 245	86.47 117	94.09 121	97.94 61

XVG-ACMP-BASELINE		86.00 212	84.84 219	89.45 220	91.20 245	78.00 233	91.70 242	95.55 148	85.05 145	82.97 232	92.25 197	54.49 308	97.48 167	82.93 149	87.45 215	92.89 256

MVP-Stereo		85.97 213	84.86 218	89.32 221	90.92 260	82.19 134	92.11 232	94.19 209	78.76 245	78.77 278	91.63 220	68.38 237	96.56 231	75.01 251	93.95 122	89.20 308
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PatchmatchNet		85.85 216	84.70 221	89.29 222	91.76 226	75.54 264	88.49 285	91.30 272	81.63 213	85.05 191	88.70 276	71.71 189	96.24 249	74.61 254	89.05 191	96.08 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v148		87.04 194	86.32 182	89.21 223	90.94 258	77.26 249	93.71 175	94.43 202	84.84 148	84.36 207	90.80 245	76.04 138	97.05 210	82.12 164	79.60 292	93.31 243

tfpnnormal		84.72 237	83.23 240	89.20 224	92.79 208	80.05 187	94.48 126	95.81 128	82.38 195	81.08 253	91.21 231	69.01 228	96.95 216	61.69 311	80.59 280	90.58 302

BH-w/o		87.57 175	87.05 159	89.12 225	94.90 134	77.90 236	92.41 220	93.51 224	82.89 188	83.70 220	91.34 226	75.75 141	97.07 208	75.49 244	93.49 130	92.39 270

WR-MVS_H		87.80 163	87.37 151	89.10 226	93.23 194	78.12 231	95.61 64	97.30 21	87.90 82	83.72 219	92.01 209	79.65 108	96.01 258	76.36 236	80.54 281	93.16 248

gg-mvs-nofinetune		81.77 262	79.37 273	88.99 227	90.85 264	77.73 244	86.29 302	79.63 328	74.88 282	83.19 231	69.05 323	60.34 287	96.11 254	75.46 245	94.64 112	93.11 250

pmmvs6		83.42 248	81.60 252	88.87 228	88.01 306	77.87 238	94.96 96	94.24 208	74.67 283	78.80 277	91.09 238	60.17 289	96.49 235	77.06 233	75.40 305	92.23 276

DWT-MVSNet_test		84.95 233	83.68 234	88.77 229	91.43 237	73.75 275	91.74 240	90.98 280	80.66 225	83.84 216	87.36 293	62.44 272	97.11 205	78.84 214	85.81 225	95.46 152

MIMVSNet		82.59 256	80.53 260	88.76 230	91.51 233	78.32 226	86.57 301	90.13 295	79.32 237	80.70 257	88.69 277	52.98 312	93.07 309	66.03 297	88.86 193	94.90 173

CP-MVSNet		87.63 170	87.26 156	88.74 231	93.12 197	76.59 256	95.29 77	96.58 78	88.43 69	83.49 227	92.98 174	75.28 145	95.83 266	78.97 212	81.15 270	93.79 224

PatchFormer-LS_test		86.02 211	85.13 210	88.70 232	91.52 232	74.12 273	91.19 252	92.09 249	82.71 191	84.30 210	87.24 295	70.87 199	96.98 214	81.04 180	85.17 232	95.00 165

CHOSEN 280x420		85.15 229	83.99 230	88.65 233	92.47 212	78.40 225	79.68 323	92.76 235	74.90 281	81.41 249	89.59 264	69.85 216	95.51 278	79.92 202	95.29 103	92.03 278

PS-CasMVS		87.32 184	86.88 161	88.63 234	92.99 204	76.33 259	95.33 71	96.61 76	88.22 75	83.30 230	93.07 172	73.03 179	95.79 269	78.36 217	81.00 275	93.75 230

TransMVSNet (Re)		84.43 240	83.06 242	88.54 235	91.72 227	78.44 223	95.18 85	92.82 234	82.73 190	79.67 272	92.12 201	73.49 171	95.96 260	71.10 271	68.73 317	91.21 293

EG-PatchMatch MVS		82.37 258	80.34 262	88.46 236	90.27 280	79.35 204	92.80 211	94.33 206	77.14 261	73.26 306	90.18 254	47.47 322	96.72 223	70.25 273	87.32 218	89.30 306

PEN-MVS		86.80 197	86.27 184	88.40 237	92.32 215	75.71 263	95.18 85	96.38 89	87.97 79	82.82 234	93.15 168	73.39 175	95.92 261	76.15 240	79.03 296	93.59 235

Baseline_NR-MVSNet		87.07 193	86.63 174	88.40 237	91.44 234	77.87 238	94.23 146	92.57 240	84.12 159	85.74 165	92.08 205	77.25 126	96.04 255	82.29 161	79.94 288	91.30 290

D2MVS		85.90 214	85.09 212	88.35 239	90.79 265	77.42 248	91.83 237	95.70 136	80.77 224	80.08 268	90.02 256	66.74 249	96.37 243	81.88 170	87.97 209	91.26 291

pmmvs5		84.21 241	82.84 246	88.34 240	88.95 295	76.94 252	92.41 220	91.91 259	75.63 273	80.28 263	91.18 233	64.59 264	95.57 274	77.09 232	83.47 247	92.53 265

LCM-MVSNet-Re		88.30 150	88.32 132	88.27 241	94.71 141	72.41 289	93.15 197	90.98 280	87.77 86	79.25 276	91.96 210	78.35 119	95.75 270	83.04 147	95.62 94	96.65 114

CostFormer		85.77 218	84.94 216	88.26 242	91.16 249	72.58 288	89.47 272	91.04 279	76.26 268	86.45 153	89.97 258	70.74 202	96.86 222	82.35 159	87.07 221	95.34 158

ITE_SJBPF				88.24 243	91.88 222	77.05 251		92.92 231	85.54 133	80.13 267	93.30 162	57.29 299	96.20 250	72.46 264	84.71 235	91.49 286

PVSNet	78.82 18	85.55 220	84.65 222	88.23 244	94.72 140	71.93 290	87.12 298	92.75 236	78.80 244	84.95 193	90.53 250	64.43 265	96.71 225	74.74 252	93.86 124	96.06 134

IterMVS-SCA-FT		85.45 221	84.53 225	88.18 245	91.71 228	76.87 253	90.19 261	92.65 239	85.40 136	81.44 248	90.54 249	66.79 247	95.00 290	81.04 180	81.05 271	92.66 262

EPNet_dtu		86.49 206	85.94 195	88.14 246	90.24 281	72.82 282	94.11 152	92.20 246	86.66 111	79.42 275	92.36 192	73.52 170	95.81 268	71.26 267	93.66 126	95.80 145
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		83.46 247	81.92 250	88.10 247	90.63 273	77.49 247	93.26 193	93.75 221	80.04 231	80.44 262	87.24 295	47.94 320	95.55 275	75.79 242	88.16 204	91.26 291

Patchmtry		82.71 254	80.93 258	88.06 248	90.05 285	76.37 258	84.74 310	91.96 257	72.28 302	81.32 251	87.87 289	71.03 197	95.50 280	68.97 282	80.15 286	92.32 273

DTE-MVSNet		86.11 210	85.48 203	87.98 249	91.65 231	74.92 266	94.93 99	95.75 133	87.36 94	82.26 239	93.04 173	72.85 180	95.82 267	74.04 256	77.46 301	93.20 246

PMMVS		85.71 219	84.96 215	87.95 250	88.90 296	77.09 250	88.68 283	90.06 297	72.32 301	86.47 150	90.76 246	72.15 188	94.40 293	81.78 173	93.49 130	92.36 271

GG-mvs-BLEND				87.94 251	89.73 291	77.91 235	87.80 291	78.23 330		80.58 259	83.86 307	59.88 291	95.33 284	71.20 268	92.22 153	90.60 301

pmmvs-eth3d		80.97 274	78.72 280	87.74 252	84.99 316	79.97 192	90.11 263	91.65 263	75.36 274	73.51 304	86.03 301	59.45 293	93.96 299	75.17 248	72.21 310	89.29 307

MS-PatchMatch		85.05 231	84.16 227	87.73 253	91.42 238	78.51 221	91.25 251	93.53 223	77.50 256	80.15 265	91.58 222	61.99 275	95.51 278	75.69 243	94.35 120	89.16 309

test_0402		81.30 271	79.17 276	87.67 254	93.19 195	78.17 230	92.98 206	91.71 260	75.25 276	76.02 294	90.31 252	59.23 294	96.37 243	50.22 323	83.63 245	88.47 315

IterMVS		84.88 234	83.98 231	87.60 255	91.44 234	76.03 261	90.18 262	92.41 242	83.24 180	81.06 254	90.42 251	66.60 250	94.28 296	79.46 205	80.98 276	92.48 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test		81.37 269	79.30 274	87.58 256	90.92 260	74.16 272	80.99 321	87.68 315	70.52 309	76.63 290	88.81 272	71.21 194	92.76 310	60.01 317	86.93 222	95.83 143

EPMVS		83.90 245	82.70 247	87.51 257	90.23 282	72.67 284	88.62 284	81.96 325	81.37 218	85.01 192	88.34 280	66.31 254	94.45 292	75.30 247	87.12 219	95.43 154

ADS-MVSNet2		81.66 264	79.71 271	87.50 258	91.35 241	74.19 271	83.33 315	88.48 311	72.90 297	82.24 240	85.77 302	64.98 262	93.20 307	64.57 303	83.74 242	95.12 161

OurMVSNet-221017-0		85.35 224	84.64 223	87.49 259	90.77 266	72.59 287	94.01 163	94.40 203	84.72 152	79.62 274	93.17 167	61.91 276	96.72 223	81.99 167	81.16 268	93.16 248

tpm2		84.08 242	82.94 243	87.48 260	91.39 239	71.27 294	89.23 276	90.37 291	71.95 303	84.64 196	89.33 267	67.30 239	96.55 233	75.17 248	87.09 220	94.63 181

RPSCF		85.07 230	84.27 226	87.48 260	92.91 206	70.62 302	91.69 243	92.46 241	76.20 269	82.67 236	95.22 97	63.94 267	97.29 190	77.51 227	85.80 226	94.53 188

miper_lstm_enhance		85.27 227	84.59 224	87.31 262	91.28 244	74.63 267	87.69 294	94.09 214	81.20 220	81.36 250	89.85 261	74.97 150	94.30 295	81.03 183	79.84 291	93.01 253

FMVSNet5		81.52 267	79.60 272	87.27 263	91.17 247	77.95 234	91.49 246	92.26 245	76.87 262	76.16 291	87.91 288	51.67 313	92.34 311	67.74 291	81.16 268	91.52 285

USDC		82.76 253	81.26 255	87.26 264	91.17 247	74.55 268	89.27 274	93.39 226	78.26 252	75.30 296	92.08 205	54.43 309	96.63 227	71.64 266	85.79 227	90.61 299

test-LLR		85.87 215	85.41 204	87.25 265	90.95 256	71.67 292	89.55 268	89.88 303	83.41 175	84.54 199	87.95 286	67.25 240	95.11 287	81.82 171	93.37 135	94.97 166

test-mter		84.54 239	83.64 236	87.25 265	90.95 256	71.67 292	89.55 268	89.88 303	79.17 238	84.54 199	87.95 286	55.56 303	95.11 287	81.82 171	93.37 135	94.97 166

JIA-IIPM		81.04 272	78.98 279	87.25 265	88.64 297	73.48 277	81.75 320	89.61 307	73.19 294	82.05 242	73.71 319	66.07 258	95.87 264	71.18 270	84.60 236	92.41 269

TDRefinement		79.81 280	77.34 283	87.22 268	79.24 325	75.48 265	93.12 198	92.03 252	76.45 264	75.01 297	91.58 222	49.19 318	96.44 240	70.22 275	69.18 314	89.75 305

tpmvs		83.35 251	82.07 248	87.20 269	91.07 252	71.00 299	88.31 288	91.70 261	78.91 240	80.49 261	87.18 297	69.30 224	97.08 207	68.12 290	83.56 246	93.51 240

ppachtmachnet_test		81.84 261	80.07 267	87.15 270	88.46 300	74.43 269	89.04 279	92.16 247	75.33 275	77.75 284	88.99 269	66.20 255	95.37 283	65.12 301	77.60 299	91.65 283

tpm cat1		81.96 259	80.27 263	87.01 271	91.09 251	71.02 298	87.38 297	91.53 267	66.25 315	80.17 264	86.35 300	68.22 238	96.15 253	69.16 281	82.29 256	93.86 221

OpenMVS_ROB	74.94 19	79.51 282	77.03 287	86.93 272	87.00 309	76.23 260	92.33 224	90.74 288	68.93 312	74.52 300	88.23 283	49.58 316	96.62 228	57.64 319	84.29 237	87.94 317

SixPastTwentyTwo		83.91 244	82.90 244	86.92 273	90.99 254	70.67 301	93.48 182	91.99 254	85.54 133	77.62 286	92.11 203	60.59 286	96.87 221	76.05 241	77.75 298	93.20 246

ADS-MVSNet		81.56 266	79.78 269	86.90 274	91.35 241	71.82 291	83.33 315	89.16 309	72.90 297	82.24 240	85.77 302	64.98 262	93.76 300	64.57 303	83.74 242	95.12 161

PatchT		82.68 255	81.27 254	86.89 275	90.09 284	70.94 300	84.06 312	90.15 294	74.91 280	85.63 168	83.57 309	69.37 220	94.87 291	65.19 299	88.50 198	94.84 175

tpm		84.73 236	84.02 229	86.87 276	90.33 279	68.90 310	89.06 278	89.94 300	80.85 223	85.75 164	89.86 260	68.54 234	95.97 259	77.76 223	84.05 240	95.75 146

Patchmatch-RL test		81.67 263	79.96 268	86.81 277	85.42 313	71.23 295	82.17 319	87.50 316	78.47 248	77.19 288	82.50 313	70.81 201	93.48 303	82.66 155	72.89 309	95.71 147

MDA-MVSNet-bldmvs		78.85 286	76.31 288	86.46 278	89.76 290	73.88 274	88.79 281	90.42 290	79.16 239	59.18 322	88.33 281	60.20 288	94.04 298	62.00 310	68.96 315	91.48 287

tpmrst		85.35 224	84.99 213	86.43 279	90.88 263	67.88 313	88.71 282	91.43 270	80.13 229	86.08 161	88.80 274	73.05 178	96.02 257	82.48 156	83.40 250	95.40 155

TESTMET0.1,1		83.74 246	82.85 245	86.42 280	89.96 287	71.21 296	89.55 268	87.88 312	77.41 257	83.37 229	87.31 294	56.71 300	93.65 302	80.62 191	92.85 146	94.40 197

our_test_3		81.93 260	80.46 261	86.33 281	88.46 300	73.48 277	88.46 286	91.11 275	76.46 263	76.69 289	88.25 282	66.89 245	94.36 294	68.75 283	79.08 295	91.14 295

test_normal		75.12 292	71.44 295	86.17 282	81.33 323	69.54 308	50.52 332	95.44 159	84.80 150	55.21 323	70.88 322	41.07 326	96.66 226	82.25 162	81.48 267	92.30 275

lessismore_v0				86.04 283	88.46 300	68.78 311		80.59 326		73.01 307	90.11 255	55.39 304	96.43 241	75.06 250	65.06 319	92.90 255

TinyColmap		79.76 281	77.69 282	85.97 284	91.71 228	73.12 279	89.55 268	90.36 292	75.03 278	72.03 310	90.19 253	46.22 323	96.19 252	63.11 307	81.03 272	88.59 314

K. test v3		81.59 265	80.15 266	85.91 285	89.89 289	69.42 309	92.57 217	87.71 314	85.56 132	73.44 305	89.71 263	55.58 302	95.52 277	77.17 230	69.76 313	92.78 260

MIMVSNet1		79.38 283	77.28 284	85.69 286	86.35 311	73.67 276	91.61 245	92.75 236	78.11 255	72.64 308	88.12 284	48.16 319	91.97 315	60.32 314	77.49 300	91.43 288

UnsupCasMVSNet_eth		80.07 278	78.27 281	85.46 287	85.24 314	72.63 286	88.45 287	94.87 190	82.99 184	71.64 312	88.07 285	56.34 301	91.75 316	73.48 261	63.36 322	92.01 279

MDA-MVSNet_test_wron		79.21 285	77.19 286	85.29 288	88.22 304	72.77 283	85.87 304	90.06 297	74.34 285	62.62 321	87.56 292	66.14 256	91.99 314	66.90 295	73.01 307	91.10 297

YYNet1		79.22 284	77.20 285	85.28 289	88.20 305	72.66 285	85.87 304	90.05 299	74.33 286	62.70 320	87.61 291	66.09 257	92.03 313	66.94 292	72.97 308	91.15 294

dp		81.47 268	80.23 264	85.17 290	89.92 288	65.49 319	86.74 299	90.10 296	76.30 267	81.10 252	87.12 298	62.81 270	95.92 261	68.13 289	79.88 289	94.09 208

UnsupCasMVSNet_bld		76.23 291	73.27 293	85.09 291	83.79 318	72.92 280	85.65 307	93.47 225	71.52 304	68.84 315	79.08 317	49.77 315	93.21 306	66.81 296	60.52 324	89.13 311

Anonymous20231206		81.03 273	79.77 270	84.82 292	87.85 308	70.26 304	91.42 247	92.08 250	73.67 290	77.75 284	89.25 268	62.43 273	93.08 308	61.50 312	82.00 261	91.12 296

test0.0.03 1		82.41 257	81.69 251	84.59 293	88.23 303	72.89 281	90.24 259	87.83 313	83.41 175	79.86 271	89.78 262	67.25 240	88.99 322	65.18 300	83.42 249	91.90 280

CMPMVS	59.16 21	80.52 276	79.20 275	84.48 294	83.98 317	67.63 315	89.95 266	93.84 219	64.79 317	66.81 318	91.14 236	57.93 298	95.17 285	76.25 238	88.10 205	90.65 298
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		84.69 238	84.79 220	84.37 295	91.84 223	64.92 320	93.70 176	91.47 269	66.19 316	86.16 160	95.28 94	67.18 242	93.33 305	80.89 186	90.42 168	94.88 174

PVSNet_0	73.20 20	77.22 288	74.83 292	84.37 295	90.70 270	71.10 297	83.09 317	89.67 306	72.81 299	73.93 303	83.13 311	60.79 285	93.70 301	68.54 284	50.84 326	88.30 316

LF4IMVS		80.37 277	79.07 278	84.27 297	86.64 310	69.87 307	89.39 273	91.05 278	76.38 265	74.97 298	90.00 257	47.85 321	94.25 297	74.55 255	80.82 278	88.69 313

PM-MVS		78.11 287	76.12 290	84.09 298	83.54 319	70.08 305	88.97 280	85.27 320	79.93 232	74.73 299	86.43 299	34.70 328	93.48 303	79.43 208	72.06 311	88.72 312

testgi		80.94 275	80.20 265	83.18 299	87.96 307	66.29 316	91.28 249	90.70 289	83.70 167	78.12 280	92.84 177	51.37 314	90.82 319	63.34 306	82.46 255	92.43 268

ambc				83.06 300	79.99 324	63.51 322	77.47 324	92.86 232		74.34 302	84.45 306	28.74 329	95.06 289	73.06 263	68.89 316	90.61 299

test20.03		79.95 279	79.08 277	82.55 301	85.79 312	67.74 314	91.09 254	91.08 276	81.23 219	74.48 301	89.96 259	61.63 277	90.15 320	60.08 315	76.38 303	89.76 304

EU-MVSNet		81.32 270	80.95 257	82.42 302	88.50 299	63.67 321	93.32 186	91.33 271	64.02 318	80.57 260	92.83 178	61.21 283	92.27 312	76.34 237	80.38 285	91.32 289

pmmvs3		71.81 295	68.71 297	81.11 303	75.86 326	70.42 303	86.74 299	83.66 322	58.95 321	68.64 316	80.89 315	36.93 327	89.52 321	63.10 308	63.59 321	83.39 319

new-patchmatchnet		76.41 290	75.17 291	80.13 304	82.65 322	59.61 323	87.66 295	91.08 276	78.23 253	69.85 313	83.22 310	54.76 306	91.63 318	64.14 305	64.89 320	89.16 309

DSMNet-mixed		76.94 289	76.29 289	78.89 305	83.10 320	56.11 328	87.78 292	79.77 327	60.65 320	75.64 295	88.71 275	61.56 278	88.34 323	60.07 316	89.29 187	92.21 277

new_pmnet		72.15 294	70.13 296	78.20 306	82.95 321	65.68 317	83.91 313	82.40 324	62.94 319	64.47 319	79.82 316	42.85 325	86.26 325	57.41 320	74.44 306	82.65 321

MVS-HIRNet		73.70 293	72.20 294	78.18 307	91.81 225	56.42 327	82.94 318	82.58 323	55.24 322	68.88 314	66.48 324	55.32 305	95.13 286	58.12 318	88.42 200	83.01 320

LCM-MVSNet		66.00 297	62.16 300	77.51 308	64.51 332	58.29 324	83.87 314	90.90 283	48.17 325	54.69 324	73.31 320	16.83 337	86.75 324	65.47 298	61.67 323	87.48 318

ANet_high		58.88 300	54.22 303	72.86 309	56.50 335	56.67 326	80.75 322	86.00 317	73.09 296	37.39 329	64.63 326	22.17 332	79.49 330	43.51 325	23.96 330	82.43 322

FPMVS		64.63 298	62.55 299	70.88 310	70.80 328	56.71 325	84.42 311	84.42 321	51.78 324	49.57 325	81.61 314	23.49 331	81.48 328	40.61 327	76.25 304	74.46 324

N_pmnet		68.89 296	68.44 298	70.23 311	89.07 294	28.79 337	88.06 289	19.50 338	69.47 311	71.86 311	84.93 305	61.24 282	91.75 316	54.70 321	77.15 302	90.15 303

PMMVS2		59.60 299	56.40 301	69.21 312	68.83 329	46.58 332	73.02 328	77.48 331	55.07 323	49.21 326	72.95 321	17.43 336	80.04 329	49.32 324	44.33 327	80.99 323

Gipuma		57.99 301	54.91 302	67.24 313	88.51 298	65.59 318	52.21 331	90.33 293	43.58 327	42.84 328	51.18 329	20.29 334	85.07 326	34.77 328	70.45 312	51.05 328
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	47.18 22	52.22 302	48.46 304	63.48 314	45.72 336	46.20 333	73.41 327	78.31 329	41.03 328	30.06 331	65.68 325	6.05 338	83.43 327	30.04 329	65.86 318	60.80 325
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	39.65 23	43.39 303	38.59 308	57.77 315	56.52 334	48.77 331	55.38 330	58.64 335	29.33 331	28.96 332	52.65 328	4.68 339	64.62 333	28.11 330	33.07 328	59.93 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				56.31 316	74.23 327	51.81 330		56.67 336	44.85 326	48.54 327	75.16 318	27.87 330	58.74 334	40.92 326	52.22 325	58.39 327

E-PMN		43.23 304	42.29 305	46.03 317	65.58 331	37.41 334	73.51 326	64.62 332	33.99 329	28.47 333	47.87 330	19.90 335	67.91 331	22.23 331	24.45 329	32.77 329

EMVS		42.07 305	41.12 306	44.92 318	63.45 333	35.56 336	73.65 325	63.48 333	33.05 330	26.88 334	45.45 331	21.27 333	67.14 332	19.80 332	23.02 331	32.06 330

tmp_tt		35.64 306	39.24 307	24.84 319	14.87 337	23.90 338	62.71 329	51.51 337	6.58 333	36.66 330	62.08 327	44.37 324	30.34 336	52.40 322	22.00 332	20.27 331

wuyk23d		21.27 308	20.48 310	23.63 320	68.59 330	36.41 335	49.57 333	6.85 339	9.37 332	7.89 335	4.46 337	4.03 340	31.37 335	17.47 333	16.07 333	3.12 332

test123		8.76 310	11.22 312	1.39 321	0.85 339	0.97 339	85.76 306	0.35 341	0.54 335	2.45 337	8.14 336	0.60 341	0.48 337	2.16 335	0.17 335	2.71 333

testmvs		8.92 309	11.52 311	1.12 322	1.06 338	0.46 340	86.02 303	0.65 340	0.62 334	2.74 336	9.52 335	0.31 342	0.45 338	2.38 334	0.39 334	2.46 334

test_part1				0.00 323		0.00 341	0.00 334	97.45 10				0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

cdsmvs_eth3d_5k		22.14 307	29.52 309	0.00 323	0.00 340	0.00 341	0.00 334	95.76 132	0.00 336	0.00 338	94.29 129	75.66 142	0.00 339	0.00 336	0.00 336	0.00 335

pcd_1.5k_mvsjas		6.64 312	8.86 314	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	79.70 104	0.00 339	0.00 336	0.00 336	0.00 335

sosnet-low-res		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

sosnet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uncertanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

Regformer		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

ab-mvs-re		7.82 311	10.43 313	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	93.88 148	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

uanet		0.00 313	0.00 315	0.00 323	0.00 340	0.00 341	0.00 334	0.00 342	0.00 336	0.00 338	0.00 338	0.00 343	0.00 339	0.00 336	0.00 336	0.00 335

9.14			94.47 14		97.79 41		96.08 42	97.44 12	86.13 121	95.10 16	97.40 13	88.34 14	99.22 37	93.25 23	98.70 25

save filter2										95.61 12	97.28 18	87.84 17	99.34 27	93.50 15	99.00 7	97.94 61

save fliter					97.85 39	85.63 57	95.21 83	96.82 57	89.44 40

test_0728_THIRD									90.75 19	97.04 4	98.05 4	92.09 1	99.55 9	95.64 2	99.13 3	99.13 1

test0726					98.78 2	85.93 47	97.19 6	97.47 7	90.27 25	97.64 2	98.13 1	91.47 3

GSMVS																96.12 128

test_part2					98.55 8	87.22 12				96.40 7

sam_mvs1												71.70 190				96.12 128

sam_mvs												70.60 203

MTGPA								96.97 41

test_post1							88.00 290				9.81 334	69.31 223	95.53 276	76.65 234

test_post											10.29 333	70.57 207	95.91 263

patchmatchnet-post											83.76 308	71.53 191	96.48 236

MTMP							96.16 36	60.64 334

gm-plane-assit					89.60 292	68.00 312			77.28 260		88.99 269		97.57 161	79.44 207

test9_res														91.91 47	98.71 23	98.07 52

TEST9					97.53 45	86.49 32	94.07 157	96.78 60	81.61 214	92.77 49	96.20 67	87.71 20	99.12 46

test_8					97.49 48	86.30 41	94.02 162	96.76 63	81.86 209	92.70 53	96.20 67	87.63 21	99.02 59

agg_prior2														90.54 71	98.68 28	98.27 37

agg_prior					97.38 51	85.92 49		96.72 66		92.16 64			98.97 68

test_prior4						85.96 46	94.11 152

test_prior2							94.12 150		87.67 89	92.63 54	96.39 60	86.62 30		91.50 55	98.67 30

旧先验2							93.36 185		71.25 306	94.37 20			97.13 204	86.74 112

新几何2							93.11 200

旧先验1					96.79 67	81.81 141		95.67 138			96.81 41	86.69 29			97.66 63	96.97 104

无先验							93.28 192	96.26 94	73.95 288				99.05 51	80.56 192		96.59 115

原ACMM2							92.94 208

test222					96.55 74	81.70 143	92.22 228	95.01 181	68.36 313	90.20 96	96.14 72	80.26 97			97.80 61	96.05 135

testdata2													98.75 86	78.30 218

segment_acmp												87.16 26

testdata1							92.15 230		87.94 80

plane_prior7					94.70 142	82.74 119

plane_prior6					94.52 148	82.75 117						74.23 158

plane_prior5								96.22 99					98.12 124	88.15 90	89.99 172	94.63 181

plane_prior4											94.86 109

plane_prior3						82.75 117			90.26 27	86.91 144

plane_prior2							95.85 52		90.81 17

plane_prior1					94.59 146

plane_prior						82.73 120	95.21 83		89.66 37						89.88 177

n2								0.00 342

nn								0.00 342

door-mid								85.49 318

test11								96.57 79

door								85.33 319

HQP5-MVS						81.56 144

HQP-NCC					94.17 161		94.39 136		88.81 56	85.43 180

ACMP_Plane					94.17 161		94.39 136		88.81 56	85.43 180

BP-MVS														87.11 109

HQP4-MVS										85.43 180			97.96 142			94.51 190

HQP3-MVS								96.04 112							89.77 179

HQP2-MVS												73.83 167

NP-MVS					94.37 156	82.42 127					93.98 141

MDTV_nov1_ep13_2view						55.91 329	87.62 296		73.32 293	84.59 198		70.33 210		74.65 253		95.50 150

MDTV_nov1_ep13			83.56 237		91.69 230	69.93 306	87.75 293	91.54 266	78.60 247	84.86 194	88.90 271	69.54 219	96.03 256	70.25 273	88.93 192

ACMMP++_ref															87.47 213

ACMMP++															88.01 208

Test By Simon												80.02 99

High-resolution multi-view benchmark