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.

DPE-MVS		89.48 3	89.98 2	88.01 10	94.80 6	72.69 28	91.59 33	94.10 8	75.90 77	92.29 2	95.66 6	81.67 1	97.38 4	87.44 14	96.34 7	93.95 38

test_0728_THIRD									78.38 32	92.12 4	95.78 4	81.46 2	97.40 2	89.42 2	96.57 2	94.67 11

DVP-MVS		89.60 1	90.35 1	87.33 38	95.27 2	71.25 55	93.49 5	92.73 53	77.33 45	92.12 4	95.78 4	80.98 3	97.40 2	89.08 4	96.41 4	93.33 69

test0726					95.27 2	71.25 55	93.60 4	94.11 6	77.33 45	92.81 1	95.79 3	80.98 3

DeepPCF-MVS	80.84 1	88.10 10	88.56 9	86.73 49	92.24 63	69.03 93	89.57 78	93.39 27	77.53 42	89.79 10	94.12 34	78.98 5	96.58 27	85.66 21	95.72 19	94.58 14

MSP-MVS		89.51 2	89.91 3	88.30 6	94.28 21	73.46 15	92.90 11	94.11 6	80.27 12	91.35 7	94.16 32	78.35 6	96.77 15	89.59 1	94.22 54	94.67 11

ETH3 D test6400		87.50 21	87.44 22	87.70 28	93.71 33	71.75 49	90.62 50	94.05 11	70.80 159	87.59 23	93.51 44	77.57 7	96.63 22	83.31 43	95.77 16	94.72 10

APDe-MVS		89.15 4	89.63 4	87.73 23	94.49 14	71.69 50	93.83 2	93.96 12	75.70 80	91.06 8	96.03 1	76.84 8	97.03 9	89.09 3	95.65 23	94.47 18

ETH3D LYJ0.05		88.29 9	88.55 10	87.52 32	92.79 54	71.69 50	91.68 32	94.80 1	75.66 82	88.82 12	94.42 24	76.51 9	95.79 48	86.27 19	95.96 10	95.07 3

CNVR-MVS		88.93 7	89.13 7	88.33 4	94.77 7	73.82 6	90.51 52	93.00 39	80.90 9	88.06 19	94.06 36	76.43 10	96.84 12	88.48 8	95.99 9	94.34 22

MCST-MVS		87.37 26	87.25 26	87.73 23	94.53 13	72.46 36	89.82 70	93.82 14	73.07 129	84.86 48	92.89 59	76.22 11	96.33 30	84.89 28	95.13 32	94.40 19

CSCG		86.41 43	86.19 43	87.07 43	92.91 50	72.48 34	90.81 45	93.56 19	73.95 113	83.16 72	91.07 93	75.94 12	95.19 70	79.94 74	94.38 50	93.55 61

HPM-MVS++		89.02 6	89.15 6	88.63 1	95.01 5	76.03 1	92.38 19	92.85 48	80.26 13	87.78 21	94.27 27	75.89 13	96.81 14	87.45 13	96.44 3	93.05 80

TSAR-MVS + MP.		88.02 14	88.11 13	87.72 25	93.68 36	72.13 43	91.41 37	92.35 67	74.62 101	88.90 11	93.85 40	75.75 14	96.00 43	87.80 9	94.63 43	95.04 4
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
9.14			88.26 12		92.84 53		91.52 36	94.75 2	73.93 115	88.57 14	94.67 13	75.57 15	95.79 48	86.77 15	95.76 18

ETH3D cwj APD-0.16		87.31 28	87.27 24	87.44 35	91.60 72	72.45 37	90.02 66	94.37 4	71.76 144	87.28 24	94.27 27	75.18 16	96.08 38	85.16 23	95.77 16	93.80 49

ETH3D LYJ0.16		87.72 16	87.83 16	87.41 36	92.28 62	71.92 48	90.80 46	94.47 3	74.17 110	87.15 25	94.69 12	75.15 17	96.03 39	86.06 20	95.70 21	94.27 25

agg_prior1		86.22 45	86.09 46	86.62 52	92.85 51	71.94 46	88.59 106	91.78 92	68.96 200	84.41 54	93.18 52	74.94 18	94.93 80	84.75 31	95.33 29	93.01 83

SD-MVS		88.06 11	88.50 11	86.71 50	92.60 60	72.71 26	91.81 31	93.19 32	77.87 33	90.32 9	94.00 37	74.83 19	93.78 129	87.63 11	94.27 53	93.65 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
DELS-MVS		85.41 55	85.30 54	85.77 65	88.49 147	67.93 122	85.52 197	93.44 24	78.70 28	83.63 69	89.03 139	74.57 20	95.71 52	80.26 72	94.04 55	93.66 51
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
Regformer-2		86.63 39	86.53 38	86.95 44	89.33 114	71.24 57	88.43 109	92.05 76	82.50 1	86.88 27	90.09 112	74.45 21	95.61 53	84.38 34	90.63 83	94.01 35

train_agg		86.43 41	86.20 42	87.13 42	93.26 43	72.96 22	88.75 100	91.89 86	68.69 205	85.00 41	93.10 53	74.43 22	95.41 61	84.97 25	95.71 20	93.02 82

test_8					93.13 45	72.57 32	88.68 104	91.84 89	68.69 205	84.87 47	93.10 53	74.43 22	95.16 71

Regformer-1		86.41 43	86.33 39	86.64 51	89.33 114	70.93 63	88.43 109	91.39 105	82.14 3	86.65 29	90.09 112	74.39 24	95.01 79	83.97 41	90.63 83	93.97 37

TEST9					93.26 43	72.96 22	88.75 100	91.89 86	68.44 209	85.00 41	93.10 53	74.36 25	95.41 61

SMA-MVS		89.08 5	89.23 5	88.61 2	94.25 22	73.73 7	92.40 17	93.63 17	74.77 97	92.29 2	95.97 2	74.28 26	97.24 5	88.58 7	96.91 1	94.87 8

test_prior3		86.73 35	86.86 35	86.33 56	92.61 58	69.59 84	88.85 96	92.97 44	75.41 85	84.91 43	93.54 42	74.28 26	95.48 57	83.31 43	95.86 12	93.91 39

test_prior2							88.85 96		75.41 85	84.91 43	93.54 42	74.28 26		83.31 43	95.86 12

TSAR-MVS + GP.		85.71 50	85.33 52	86.84 46	91.34 74	72.50 33	89.07 90	87.28 213	76.41 66	85.80 34	90.22 110	74.15 29	95.37 66	81.82 58	91.88 69	92.65 93

SteuartSystems-ACMMP		88.72 8	88.86 8	88.32 5	92.14 65	72.96 22	93.73 3	93.67 16	80.19 14	88.10 18	94.80 9	73.76 30	97.11 7	87.51 12	95.82 15	94.90 7
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		87.44 22	87.52 20	87.19 40	94.24 23	72.39 38	91.86 30	92.83 49	73.01 131	88.58 13	94.52 16	73.36 31	96.49 28	84.26 36	95.01 33	92.70 89
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
canonicalmvs		85.91 47	85.87 48	86.04 63	89.84 100	69.44 91	90.45 57	93.00 39	76.70 62	88.01 20	91.23 87	73.28 32	93.91 123	81.50 60	88.80 104	94.77 9

testtj		87.78 15	87.78 17	87.77 21	94.55 12	72.47 35	92.23 24	93.49 22	74.75 98	88.33 16	94.43 23	73.27 33	97.02 10	84.18 39	94.84 39	93.82 46

segment_acmp												73.08 34

DPM-MVS		84.93 62	84.29 66	86.84 46	90.20 92	73.04 20	87.12 149	93.04 35	69.80 178	82.85 76	91.22 88	73.06 35	96.02 41	76.72 102	94.63 43	91.46 127

NCCC		88.06 11	88.01 15	88.24 7	94.41 18	73.62 8	91.22 41	92.83 49	81.50 6	85.79 35	93.47 47	73.02 36	97.00 11	84.90 26	94.94 35	94.10 29

nrg030		83.88 67	83.53 68	84.96 82	86.77 195	69.28 92	90.46 56	92.67 55	74.79 96	82.95 73	91.33 86	72.70 37	93.09 160	80.79 68	79.28 218	92.50 96

Regformer-4		85.68 51	85.45 50	86.35 55	88.95 131	69.67 83	88.29 119	91.29 107	81.73 5	85.36 37	90.01 114	72.62 38	95.35 67	83.28 46	87.57 117	94.03 33

save filter2										88.41 15	94.67 13	72.46 39	94.59 94	86.67 18	95.86 12	91.94 113

Regformer-3		85.23 57	85.07 57	85.70 66	88.95 131	69.01 95	88.29 119	89.91 146	80.95 8	85.01 40	90.01 114	72.45 40	94.19 109	82.50 55	87.57 117	93.90 41

CDPH-MVS		85.76 49	85.29 55	87.17 41	93.49 40	71.08 58	88.58 107	92.42 65	68.32 210	84.61 51	93.48 45	72.32 41	96.15 37	79.00 77	95.43 25	94.28 24

MP-MVS		87.71 17	87.64 19	87.93 16	94.36 20	73.88 4	92.71 16	92.65 57	77.57 38	83.84 64	94.40 26	72.24 42	96.28 32	85.65 22	95.30 31	93.62 58
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
casdiffmvs		85.11 60	85.14 56	85.01 80	87.20 187	65.77 157	87.75 134	92.83 49	77.84 34	84.36 57	92.38 65	72.15 43	93.93 122	81.27 62	90.48 85	95.33 1

DeepC-MVS	79.81 2	87.08 33	86.88 34	87.69 29	91.16 76	72.32 41	90.31 59	93.94 13	77.12 49	82.82 77	94.23 30	72.13 44	97.09 8	84.83 29	95.37 26	93.65 56
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		84.93 62	84.98 58	84.80 89	87.30 185	65.39 164	87.30 145	92.88 46	77.62 36	84.04 62	92.26 66	71.81 45	93.96 116	81.31 61	90.30 87	95.03 5

test12				86.80 48	92.63 57	70.70 68		91.79 91		82.71 79		71.67 46	96.16 36		94.50 46	93.54 62

UniMVSNet_NR-MVSNet		81.88 96	81.54 95	82.92 153	88.46 149	63.46 202	87.13 148	92.37 66	80.19 14	78.38 127	89.14 135	71.66 47	93.05 162	70.05 156	76.46 245	92.25 104

DeepC-MVS_fast	79.65 3	86.91 34	86.62 37	87.76 22	93.52 39	72.37 39	91.26 38	93.04 35	76.62 63	84.22 58	93.36 49	71.44 48	96.76 16	80.82 66	95.33 29	94.16 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		85.14 59	84.75 62	86.32 58	91.65 71	72.70 27	85.98 182	90.33 134	76.11 75	82.08 84	91.61 79	71.36 49	94.17 111	81.02 63	92.58 66	92.08 110

ACMMP_NAP		88.05 13	88.08 14	87.94 13	93.70 34	73.05 19	90.86 44	93.59 18	76.27 73	88.14 17	95.09 8	71.06 50	96.67 19	87.67 10	96.37 6	94.09 30

MP-MVS-pluss		87.67 18	87.72 18	87.54 31	93.64 37	72.04 45	89.80 72	93.50 21	75.17 92	86.34 30	95.29 7	70.86 51	96.00 43	88.78 6	96.04 8	94.58 14
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 19	87.47 21	87.94 13	94.58 10	73.54 12	93.04 8	93.24 29	76.78 58	84.91 43	94.44 21	70.78 52	96.61 23	84.53 32	94.89 37	93.66 51

#test#		87.33 27	87.13 29	87.94 13	94.58 10	73.54 12	92.34 21	93.24 29	75.23 89	84.91 43	94.44 21	70.78 52	96.61 23	83.75 42	94.89 37	93.66 51

EI-MVSNet-Vis-set		84.19 66	83.81 67	85.31 70	88.18 156	67.85 123	87.66 136	89.73 151	80.05 16	82.95 73	89.59 124	70.74 54	94.82 88	80.66 69	84.72 152	93.28 71

GST-MVS		87.42 24	87.26 25	87.89 20	94.12 28	72.97 21	92.39 18	93.43 25	76.89 55	84.68 49	93.99 38	70.67 55	96.82 13	84.18 39	95.01 33	93.90 41

CS-MVS		84.76 65	84.61 64	85.22 75	89.66 102	66.43 144	90.23 61	93.56 19	76.52 65	82.59 81	85.93 219	70.41 56	95.80 47	79.93 75	92.68 65	93.42 65

CANet		86.45 40	86.10 45	87.51 33	90.09 94	70.94 62	89.70 76	92.59 59	81.78 4	81.32 94	91.43 85	70.34 57	97.23 6	84.26 36	93.36 58	94.37 20

alignmvs		85.48 52	85.32 53	85.96 64	89.51 108	69.47 88	89.74 74	92.47 61	76.17 74	87.73 22	91.46 84	70.32 58	93.78 129	81.51 59	88.95 101	94.63 13

EI-MVSNet-UG-set		83.81 68	83.38 70	85.09 78	87.87 165	67.53 127	87.44 142	89.66 152	79.74 18	82.23 83	89.41 133	70.24 59	94.74 91	79.95 73	83.92 160	92.99 84

MVS_Test		83.15 79	83.06 74	83.41 131	86.86 191	63.21 208	86.11 180	92.00 80	74.31 105	82.87 75	89.44 132	70.03 60	93.21 151	77.39 94	88.50 111	93.81 47

FC-MVSNet-test		81.52 103	82.02 89	80.03 216	88.42 151	55.97 296	87.95 129	93.42 26	77.10 50	77.38 147	90.98 99	69.96 61	91.79 201	68.46 171	84.50 154	92.33 100

FIs		82.07 93	82.42 81	81.04 199	88.80 138	58.34 260	88.26 121	93.49 22	76.93 54	78.47 126	91.04 94	69.92 62	92.34 184	69.87 159	84.97 149	92.44 98

UniMVSNet (Re)		81.60 102	81.11 100	83.09 144	88.38 152	64.41 183	87.60 137	93.02 38	78.42 31	78.56 123	88.16 161	69.78 63	93.26 150	69.58 162	76.49 244	91.60 120

HPM-MVS		87.11 31	86.98 31	87.50 34	93.88 31	72.16 42	92.19 25	93.33 28	76.07 76	83.81 65	93.95 39	69.77 64	96.01 42	85.15 24	94.66 42	94.32 23
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
Effi-MVS+		83.62 72	83.08 73	85.24 73	88.38 152	67.45 128	88.89 94	89.15 168	75.50 84	82.27 82	88.28 158	69.61 65	94.45 99	77.81 89	87.84 115	93.84 45

PHI-MVS		86.43 41	86.17 44	87.24 39	90.88 82	70.96 60	92.27 23	94.07 10	72.45 134	85.22 39	91.90 72	69.47 66	96.42 29	83.28 46	95.94 11	94.35 21

UA-Net		85.08 61	84.96 59	85.45 68	92.07 66	68.07 120	89.78 73	90.86 119	82.48 2	84.60 52	93.20 51	69.35 67	95.22 69	71.39 145	90.88 81	93.07 79

EIA-MVS		84.90 64	84.67 63	85.59 67	89.39 112	68.66 109	88.74 102	92.64 58	79.97 17	84.10 60	85.71 225	69.32 68	95.38 63	80.82 66	91.37 75	92.72 88

旧先验1					91.96 67	65.79 156		86.37 224			93.08 57	69.31 69			92.74 63	88.74 222

region2R		87.42 24	87.20 28	88.09 8	94.63 9	73.55 10	93.03 10	93.12 34	76.73 61	84.45 53	94.52 16	69.09 70	96.70 18	84.37 35	94.83 40	94.03 33

ETV-MVS		83.31 78	82.80 79	84.82 87	89.59 104	65.59 159	88.21 122	92.68 54	74.66 100	78.96 116	86.42 211	69.06 71	95.26 68	75.54 112	90.09 91	93.62 58

EPP-MVSNet		83.40 76	83.02 75	84.57 93	90.13 93	64.47 182	92.32 22	90.73 121	74.45 104	79.35 113	91.10 91	69.05 72	95.12 72	72.78 135	87.22 125	94.13 28

ACMMPR		87.44 22	87.23 27	88.08 9	94.64 8	73.59 9	93.04 8	93.20 31	76.78 58	84.66 50	94.52 16	68.81 73	96.65 20	84.53 32	94.90 36	94.00 36

mvs_anonymous		79.42 149	79.11 136	80.34 211	84.45 225	57.97 266	82.59 248	87.62 206	67.40 217	76.17 177	88.56 151	68.47 74	89.59 245	70.65 151	86.05 142	93.47 64

zzz-MVS		87.53 20	87.41 23	87.90 17	94.18 26	74.25 2	90.23 61	92.02 77	79.45 19	85.88 32	94.80 9	68.07 75	96.21 34	86.69 16	95.34 27	93.23 72

MTAPA		87.23 29	87.00 30	87.90 17	94.18 26	74.25 2	86.58 167	92.02 77	79.45 19	85.88 32	94.80 9	68.07 75	96.21 34	86.69 16	95.34 27	93.23 72

CP-MVS		87.11 31	86.92 32	87.68 30	94.20 25	73.86 5	93.98 1	92.82 52	76.62 63	83.68 66	94.46 20	67.93 77	95.95 45	84.20 38	94.39 49	93.23 72

PAPM_NR		83.02 82	82.41 82	84.82 87	92.47 61	66.37 146	87.93 131	91.80 90	73.82 117	77.32 149	90.66 102	67.90 78	94.90 84	70.37 153	89.48 98	93.19 76

PGM-MVS		86.68 37	86.27 41	87.90 17	94.22 24	73.38 16	90.22 63	93.04 35	75.53 83	83.86 63	94.42 24	67.87 79	96.64 21	82.70 53	94.57 45	93.66 51

PAPR		81.66 101	80.89 103	83.99 117	90.27 90	64.00 189	86.76 163	91.77 94	68.84 203	77.13 156	89.50 125	67.63 80	94.88 86	67.55 177	88.52 110	93.09 78

Fast-Effi-MVS+		80.81 117	79.92 117	83.47 127	88.85 133	64.51 179	85.53 195	89.39 158	70.79 160	78.49 125	85.06 241	67.54 81	93.58 137	67.03 186	86.58 134	92.32 101

XVS		87.18 30	86.91 33	88.00 11	94.42 16	73.33 17	92.78 12	92.99 41	79.14 21	83.67 67	94.17 31	67.45 82	96.60 25	83.06 48	94.50 46	94.07 31

X-MVStestdata		80.37 131	77.83 164	88.00 11	94.42 16	73.33 17	92.78 12	92.99 41	79.14 21	83.67 67	12.47 343	67.45 82	96.60 25	83.06 48	94.50 46	94.07 31

SR-MVS		86.73 35	86.67 36	86.91 45	94.11 29	72.11 44	92.37 20	92.56 60	74.50 102	86.84 28	94.65 15	67.31 84	95.77 50	84.80 30	92.85 62	92.84 87

NR-MVSNet		80.23 133	79.38 130	82.78 163	87.80 168	63.34 205	86.31 173	91.09 114	79.01 26	72.17 232	89.07 137	67.20 85	92.81 172	66.08 192	75.65 257	92.20 106

MSLP-MVS++		85.43 54	85.76 49	84.45 97	91.93 68	70.24 71	90.71 48	92.86 47	77.46 44	84.22 58	92.81 63	67.16 86	92.94 166	80.36 70	94.35 51	90.16 165

MG-MVS		83.41 75	83.45 69	83.28 134	92.74 55	62.28 222	88.17 124	89.50 156	75.22 90	81.49 93	92.74 64	66.75 87	95.11 73	72.85 134	91.58 72	92.45 97

EI-MVSNet		80.52 128	79.98 116	82.12 172	84.28 226	63.19 210	86.41 170	88.95 178	74.18 109	78.69 120	87.54 175	66.62 88	92.43 179	72.57 137	80.57 202	90.74 145

IterMVS-LS		80.06 136	79.38 130	82.11 173	85.89 203	63.20 209	86.79 160	89.34 159	74.19 108	75.45 189	86.72 195	66.62 88	92.39 181	72.58 136	76.86 238	90.75 144
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_ehance_all_eth		78.59 169	77.76 168	81.08 198	82.66 265	61.56 231	83.65 234	89.15 168	68.87 202	75.55 185	83.79 257	66.49 90	92.03 193	73.25 130	76.39 247	89.64 192

mPP-MVS		86.67 38	86.32 40	87.72 25	94.41 18	73.55 10	92.74 14	92.22 70	76.87 56	82.81 78	94.25 29	66.44 91	96.24 33	82.88 52	94.28 52	93.38 66

cl_fuxian		78.75 164	77.91 161	81.26 192	82.89 259	61.56 231	84.09 229	89.13 170	69.97 175	75.56 184	84.29 249	66.36 92	92.09 192	73.47 127	75.48 261	90.12 168

WR-MVS_H		78.51 170	78.49 147	78.56 243	88.02 162	56.38 291	88.43 109	92.67 55	77.14 48	73.89 215	87.55 174	66.25 93	89.24 251	58.92 250	73.55 284	90.06 175

PCF-MVS	73.52 7	80.38 130	78.84 141	85.01 80	87.71 172	68.99 96	83.65 234	91.46 104	63.00 262	77.77 141	90.28 107	66.10 94	95.09 77	61.40 230	88.22 114	90.94 139
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EPNet		83.72 70	82.92 77	86.14 61	84.22 228	69.48 87	91.05 43	85.27 234	81.30 7	76.83 158	91.65 76	66.09 95	95.56 55	76.00 107	93.85 56	93.38 66
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM1				84.35 101	93.01 49	68.79 99		92.44 62	63.96 257	81.09 99	91.57 80	66.06 96	95.45 59	67.19 183	94.82 41	88.81 219

PVSNet_BlendedMVS		80.60 125	80.02 115	82.36 171	88.85 133	65.40 162	86.16 178	92.00 80	69.34 188	78.11 134	86.09 218	66.02 97	94.27 103	71.52 142	82.06 185	87.39 247

PVSNet_Blended		80.98 111	80.34 111	82.90 154	88.85 133	65.40 162	84.43 220	92.00 80	67.62 213	78.11 134	85.05 242	66.02 97	94.27 103	71.52 142	89.50 97	89.01 209

diffmvs		82.10 91	81.88 92	82.76 165	83.00 254	63.78 194	83.68 233	89.76 149	72.94 132	82.02 85	89.85 117	65.96 99	90.79 228	82.38 57	87.30 124	93.71 50

APD-MVS_3200maxsize		85.97 46	85.88 47	86.22 59	92.69 56	69.53 86	91.93 29	92.99 41	73.54 123	85.94 31	94.51 19	65.80 100	95.61 53	83.04 50	92.51 67	93.53 63

miper_enhance_ethall		77.87 188	76.86 185	80.92 201	81.65 279	61.38 233	82.68 247	88.98 175	65.52 238	75.47 186	82.30 274	65.76 101	92.00 195	72.95 133	76.39 247	89.39 198

PVSNet_Blended_VisFu		82.62 86	81.83 93	84.96 82	90.80 84	69.76 81	88.74 102	91.70 95	69.39 186	78.96 116	88.46 153	65.47 102	94.87 87	74.42 116	88.57 107	90.24 163

API-MVS		81.99 95	81.23 98	84.26 105	90.94 80	70.18 77	91.10 42	89.32 160	71.51 151	78.66 122	88.28 158	65.26 103	95.10 76	64.74 204	91.23 78	87.51 245

TranMVSNet+NR-MVSNet		80.84 114	80.31 112	82.42 169	87.85 166	62.33 220	87.74 135	91.33 106	80.55 11	77.99 137	89.86 116	65.23 104	92.62 173	67.05 185	75.24 269	92.30 102

IS-MVSNet		83.15 79	82.81 78	84.18 107	89.94 98	63.30 206	91.59 33	88.46 190	79.04 25	79.49 111	92.16 67	65.10 105	94.28 102	67.71 175	91.86 70	94.95 6

DU-MVS		81.12 110	80.52 108	82.90 154	87.80 168	63.46 202	87.02 152	91.87 88	79.01 26	78.38 127	89.07 137	65.02 106	93.05 162	70.05 156	76.46 245	92.20 106

Baseline_NR-MVSNet		78.15 179	78.33 153	77.61 258	85.79 204	56.21 294	86.78 161	85.76 231	73.60 121	77.93 138	87.57 173	65.02 106	88.99 255	67.14 184	75.33 266	87.63 242

VNet		82.21 90	82.41 82	81.62 182	90.82 83	60.93 236	84.47 216	89.78 148	76.36 71	84.07 61	91.88 73	64.71 108	90.26 234	70.68 150	88.89 102	93.66 51

Test By Simon												64.33 109

ACMMP		85.89 48	85.39 51	87.38 37	93.59 38	72.63 30	92.74 14	93.18 33	76.78 58	80.73 103	93.82 41	64.33 109	96.29 31	82.67 54	90.69 82	93.23 72
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
DP-MVS Recon		83.11 81	82.09 87	86.15 60	94.44 15	70.92 64	88.79 98	92.20 71	70.53 166	79.17 114	91.03 96	64.12 111	96.03 39	68.39 172	90.14 90	91.50 124

CLD-MVS		82.31 89	81.65 94	84.29 104	88.47 148	67.73 126	85.81 188	92.35 67	75.78 78	78.33 129	86.58 206	64.01 112	94.35 100	76.05 106	87.48 122	90.79 142
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS		78.19 178	76.99 183	81.78 179	85.66 206	66.99 136	84.66 210	90.47 128	55.08 317	72.02 234	85.27 235	63.83 113	94.11 114	66.10 191	89.80 95	84.24 295

WR-MVS		79.49 146	79.22 135	80.27 213	88.79 139	58.35 259	85.06 202	88.61 188	78.56 29	77.65 142	88.34 156	63.81 114	90.66 231	64.98 202	77.22 233	91.80 119

1121		80.84 114	79.77 120	84.05 112	93.11 47	70.78 66	84.66 210	85.42 233	57.37 307	81.76 92	92.02 69	63.41 115	94.12 112	67.28 180	92.93 60	87.26 252

VPA-MVSNet		80.60 125	80.55 107	80.76 204	88.07 160	60.80 239	86.86 157	91.58 98	75.67 81	80.24 106	89.45 131	63.34 116	90.25 235	70.51 152	79.22 219	91.23 131

新几何1				83.42 129	93.13 45	70.71 67		85.48 232	57.43 306	81.80 89	91.98 70	63.28 117	92.27 185	64.60 205	92.99 59	87.27 251

HY-MVS	69.67 12	77.95 185	77.15 179	80.36 210	87.57 179	60.21 247	83.37 241	87.78 204	66.11 229	75.37 192	87.06 190	63.27 118	90.48 233	61.38 231	82.43 182	90.40 159

XXY-MVS		75.41 226	75.56 203	74.96 282	83.59 239	57.82 270	80.59 267	83.87 250	66.54 226	74.93 206	88.31 157	63.24 119	80.09 311	62.16 222	76.85 239	86.97 259

ab-mvs		79.51 145	78.97 139	81.14 196	88.46 149	60.91 237	83.84 231	89.24 165	70.36 168	79.03 115	88.87 142	63.23 120	90.21 236	65.12 199	82.57 181	92.28 103

xiu_mvs_v2_base		81.69 99	81.05 101	83.60 124	89.15 125	68.03 121	84.46 218	90.02 142	70.67 163	81.30 97	86.53 209	63.17 121	94.19 109	75.60 111	88.54 109	88.57 226

pcd_1.5k_mvsjas		5.26 323	7.02 325	0.00 334	0.00 351	0.00 352	0.00 345	0.00 353	0.00 347	0.00 349	0.00 349	63.15 122	0.00 350	0.00 347	0.00 347	0.00 346

PS-MVSNAJss		82.07 93	81.31 96	84.34 102	86.51 198	67.27 133	89.27 82	91.51 100	71.75 145	79.37 112	90.22 110	63.15 122	94.27 103	77.69 90	82.36 183	91.49 125

PS-MVSNAJ		81.69 99	81.02 102	83.70 123	89.51 108	68.21 118	84.28 224	90.09 141	70.79 160	81.26 98	85.62 229	63.15 122	94.29 101	75.62 110	88.87 103	88.59 225

WTY-MVS		75.65 222	75.68 202	75.57 277	86.40 199	56.82 282	77.92 293	82.40 270	65.10 241	76.18 175	87.72 168	63.13 125	80.90 308	60.31 238	81.96 186	89.00 211

TransMVSNet (Re)		75.39 227	74.56 217	77.86 252	85.50 210	57.10 279	86.78 161	86.09 229	72.17 140	71.53 239	87.34 178	63.01 126	89.31 250	56.84 269	61.83 320	87.17 254

v8		79.97 139	79.02 138	82.80 160	84.09 230	64.50 181	87.96 128	90.29 137	74.13 112	75.24 198	86.81 192	62.88 127	93.89 125	74.39 117	75.40 264	90.00 177

abl_6		85.23 57	84.95 60	86.07 62	92.23 64	70.48 70	90.80 46	92.08 75	73.51 124	85.26 38	94.16 32	62.75 128	95.92 46	82.46 56	91.30 77	91.81 118

HPM-MVS_fast		85.35 56	84.95 60	86.57 54	93.69 35	70.58 69	92.15 27	91.62 96	73.89 116	82.67 80	94.09 35	62.60 129	95.54 56	80.93 64	92.93 60	93.57 60

PAPM		77.68 192	76.40 197	81.51 185	87.29 186	61.85 227	83.78 232	89.59 153	64.74 246	71.23 241	88.70 144	62.59 130	93.66 136	52.66 284	87.03 128	89.01 209

1112_ss		77.40 197	76.43 196	80.32 212	89.11 130	60.41 245	83.65 234	87.72 205	62.13 273	73.05 222	86.72 195	62.58 131	89.97 239	62.11 224	80.80 198	90.59 152

LCM-MVSNet-Re		77.05 200	76.94 184	77.36 261	87.20 187	51.60 316	80.06 271	80.46 290	75.20 91	67.69 274	86.72 195	62.48 132	88.98 256	63.44 210	89.25 100	91.51 123

v148		78.72 165	77.80 165	81.47 186	82.73 263	61.96 226	86.30 174	88.08 196	73.26 127	76.18 175	85.47 232	62.46 133	92.36 183	71.92 141	73.82 282	90.09 171

baseline1		76.98 202	76.75 191	77.66 256	88.13 157	55.66 300	85.12 201	81.89 275	73.04 130	76.79 159	88.90 140	62.43 134	87.78 273	63.30 212	71.18 298	89.55 196

MAR-MVS		81.84 97	80.70 104	85.27 72	91.32 75	71.53 53	89.82 70	90.92 116	69.77 179	78.50 124	86.21 215	62.36 135	94.52 97	65.36 197	92.05 68	89.77 189
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
MVS_111021_LR		82.61 87	82.11 86	84.11 108	88.82 136	71.58 52	85.15 200	86.16 227	74.69 99	80.47 105	91.04 94	62.29 136	90.55 232	80.33 71	90.08 92	90.20 164

TAMVS		78.89 163	77.51 174	83.03 148	87.80 168	67.79 125	84.72 209	85.05 237	67.63 212	76.75 161	87.70 169	62.25 137	90.82 227	58.53 255	87.13 126	90.49 155

CP-MVSNet		78.22 175	78.34 152	77.84 253	87.83 167	54.54 304	87.94 130	91.17 111	77.65 35	73.48 217	88.49 152	62.24 138	88.43 265	62.19 221	74.07 277	90.55 153

OMC-MVS		82.69 85	81.97 91	84.85 86	88.75 141	67.42 129	87.98 127	90.87 118	74.92 95	79.72 109	91.65 76	62.19 139	93.96 116	75.26 113	86.42 137	93.16 77

cl-mvsnet_		77.72 190	76.76 189	80.58 206	82.49 269	60.48 243	83.09 243	87.87 201	69.22 191	74.38 212	85.22 237	62.10 140	91.53 208	71.09 146	75.41 263	89.73 191

cl-mvsnet1		77.72 190	76.76 189	80.58 206	82.48 270	60.48 243	83.09 243	87.86 202	69.22 191	74.38 212	85.24 236	62.10 140	91.53 208	71.09 146	75.40 264	89.74 190

testdata				79.97 217	90.90 81	64.21 186		84.71 238	59.27 294	85.40 36	92.91 58	62.02 142	89.08 254	68.95 168	91.37 75	86.63 267

eth_miper_zixun_eth		77.92 186	76.69 192	81.61 184	83.00 254	61.98 225	83.15 242	89.20 167	69.52 185	74.86 207	84.35 248	61.76 143	92.56 176	71.50 144	72.89 287	90.28 162

MVSFormer		82.85 84	82.05 88	85.24 73	87.35 180	70.21 72	90.50 53	90.38 130	68.55 207	81.32 94	89.47 127	61.68 144	93.46 144	78.98 78	90.26 88	92.05 111

lupinMVS		81.39 106	80.27 114	84.76 90	87.35 180	70.21 72	85.55 193	86.41 222	62.85 265	81.32 94	88.61 148	61.68 144	92.24 188	78.41 84	90.26 88	91.83 116

cdsmvs_eth3d_5k		19.96 317	26.61 318	0.00 334	0.00 351	0.00 352	0.00 345	89.26 164	0.00 347	0.00 349	88.61 148	61.62 146	0.00 350	0.00 347	0.00 347	0.00 346

CDS-MVSNet		79.07 158	77.70 170	83.17 141	87.60 175	68.23 117	84.40 222	86.20 226	67.49 215	76.36 170	86.54 208	61.54 147	90.79 228	61.86 226	87.33 123	90.49 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v10		79.74 142	78.67 142	82.97 152	84.06 231	64.95 173	87.88 133	90.62 123	73.11 128	75.11 201	86.56 207	61.46 148	94.05 115	73.68 122	75.55 259	89.90 183

v1144		80.03 137	79.03 137	83.01 149	83.78 236	64.51 179	87.11 150	90.57 125	71.96 143	78.08 136	86.20 216	61.41 149	93.94 119	74.93 114	77.23 232	90.60 150

cl-mvsnet2		78.07 181	77.01 181	81.23 193	82.37 272	61.83 228	83.55 238	87.98 198	68.96 200	75.06 203	83.87 253	61.40 150	91.88 200	73.53 124	76.39 247	89.98 180

BH-w/o		78.21 176	77.33 177	80.84 202	88.81 137	65.13 171	84.87 206	87.85 203	69.75 180	74.52 210	84.74 245	61.34 151	93.11 159	58.24 258	85.84 145	84.27 294

Test_1112_low_res		76.40 212	75.44 205	79.27 231	89.28 120	58.09 262	81.69 257	87.07 215	59.53 292	72.48 228	86.67 201	61.30 152	89.33 249	60.81 236	80.15 208	90.41 158

Vis-MVSNet (Re-imp)		78.36 173	78.45 148	78.07 251	88.64 143	51.78 315	86.70 164	79.63 298	74.14 111	75.11 201	90.83 100	61.29 153	89.75 242	58.10 259	91.60 71	92.69 91

PEN-MVS		77.73 189	77.69 171	77.84 253	87.07 190	53.91 308	87.91 132	91.18 110	77.56 40	73.14 221	88.82 143	61.23 154	89.17 252	59.95 240	72.37 289	90.43 157

pm-mvs1		77.25 199	76.68 193	78.93 237	84.22 228	58.62 258	86.41 170	88.36 191	71.37 153	73.31 218	88.01 166	61.22 155	89.15 253	64.24 206	73.01 286	89.03 208

BH-untuned		79.47 147	78.60 144	82.05 174	89.19 124	65.91 153	86.07 181	88.52 189	72.18 139	75.42 190	87.69 170	61.15 156	93.54 141	60.38 237	86.83 131	86.70 265

v2v482		80.23 133	79.29 133	83.05 147	83.62 238	64.14 187	87.04 151	89.97 143	73.61 120	78.18 133	87.22 183	61.10 157	93.82 126	76.11 105	76.78 242	91.18 132

jason		81.39 106	80.29 113	84.70 91	86.63 197	69.90 79	85.95 183	86.77 218	63.24 259	81.07 100	89.47 127	61.08 158	92.15 190	78.33 85	90.07 93	92.05 111
jason: jason.
Vis-MVSNet		83.46 74	82.80 79	85.43 69	90.25 91	68.74 103	90.30 60	90.13 140	76.33 72	80.87 102	92.89 59	61.00 159	94.20 108	72.45 138	90.97 79	93.35 68
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	73.13 9	79.15 155	77.94 160	82.79 162	89.59 104	62.99 215	88.16 125	91.51 100	65.77 234	77.14 155	91.09 92	60.91 160	93.21 151	50.26 294	87.05 127	92.17 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PS-CasMVS		78.01 184	78.09 157	77.77 255	87.71 172	54.39 306	88.02 126	91.22 108	77.50 43	73.26 219	88.64 147	60.73 161	88.41 266	61.88 225	73.88 281	90.53 154

OPM-MVS		83.50 73	82.95 76	85.14 76	88.79 139	70.95 61	89.13 89	91.52 99	77.55 41	80.96 101	91.75 74	60.71 162	94.50 98	79.67 76	86.51 136	89.97 181

XVG-OURS-SEG-HR		80.81 117	79.76 121	83.96 119	85.60 208	68.78 100	83.54 239	90.50 127	70.66 164	76.71 162	91.66 75	60.69 163	91.26 215	76.94 98	81.58 190	91.83 116

v144192		79.47 147	78.37 151	82.78 163	83.35 242	63.96 190	86.96 153	90.36 133	69.99 174	77.50 144	85.67 227	60.66 164	93.77 131	74.27 118	76.58 243	90.62 148

V42		79.38 152	78.24 155	82.83 157	81.10 290	65.50 161	85.55 193	89.82 147	71.57 150	78.21 131	86.12 217	60.66 164	93.18 155	75.64 109	75.46 262	89.81 188

CPTT-MVS		83.73 69	83.33 71	84.92 85	93.28 42	70.86 65	92.09 28	90.38 130	68.75 204	79.57 110	92.83 61	60.60 166	93.04 164	80.92 65	91.56 73	90.86 141

DTE-MVSNet		76.99 201	76.80 187	77.54 260	86.24 200	53.06 312	87.52 139	90.66 122	77.08 51	72.50 227	88.67 146	60.48 167	89.52 246	57.33 266	70.74 300	90.05 176

HQP_MVS		83.64 71	83.14 72	85.14 76	90.08 95	68.71 105	91.25 39	92.44 62	79.12 23	78.92 118	91.00 97	60.42 168	95.38 63	78.71 80	86.32 138	91.33 128

plane_prior6					89.84 100	68.70 107						60.42 168

3Dnovator+	77.84 4	85.48 52	84.47 65	88.51 3	91.08 77	73.49 14	93.18 7	93.78 15	80.79 10	76.66 163	93.37 48	60.40 170	96.75 17	77.20 95	93.73 57	95.29 2

HQP2-MVS												60.17 171

HQP-MVS		82.61 87	82.02 89	84.37 99	89.33 114	66.98 137	89.17 84	92.19 72	76.41 66	77.23 152	90.23 109	60.17 171	95.11 73	77.47 92	85.99 143	91.03 135

VPNet		78.69 166	78.66 143	78.76 240	88.31 154	55.72 298	84.45 219	86.63 220	76.79 57	78.26 130	90.55 104	59.30 173	89.70 244	66.63 187	77.05 235	90.88 140

v1192		79.59 144	78.43 150	83.07 146	83.55 240	64.52 178	86.93 155	90.58 124	70.83 158	77.78 140	85.90 220	59.15 174	93.94 119	73.96 121	77.19 234	90.76 143

test222					91.50 73	68.26 116	84.16 225	83.20 263	54.63 318	79.74 108	91.63 78	58.97 175			91.42 74	86.77 263

CHOSEN 1792x2688		77.63 193	75.69 201	83.44 128	89.98 97	68.58 111	78.70 286	87.50 209	56.38 312	75.80 182	86.84 191	58.67 176	91.40 212	61.58 229	85.75 146	90.34 160

3Dnovator	76.31 5	83.38 77	82.31 85	86.59 53	87.94 164	72.94 25	90.64 49	92.14 74	77.21 47	75.47 186	92.83 61	58.56 177	94.72 92	73.24 131	92.71 64	92.13 109

v1921920		79.22 154	78.03 158	82.80 160	83.30 244	63.94 191	86.80 159	90.33 134	69.91 176	77.48 145	85.53 230	58.44 178	93.75 133	73.60 123	76.85 239	90.71 146

114514_t		80.68 123	79.51 126	84.20 106	94.09 30	67.27 133	89.64 77	91.11 113	58.75 299	74.08 214	90.72 101	58.10 179	95.04 78	69.70 160	89.42 99	90.30 161

v7n		78.97 161	77.58 173	83.14 142	83.45 241	65.51 160	88.32 117	91.21 109	73.69 119	72.41 229	86.32 214	57.93 180	93.81 127	69.18 165	75.65 257	90.11 169

baseline2		75.70 221	73.83 227	81.30 191	83.26 245	61.79 229	82.57 249	80.65 286	66.81 219	66.88 282	83.42 262	57.86 181	92.19 189	63.47 209	79.57 212	89.91 182

QAPM		80.88 112	79.50 127	85.03 79	88.01 163	68.97 97	91.59 33	92.00 80	66.63 225	75.15 200	92.16 67	57.70 182	95.45 59	63.52 208	88.76 105	90.66 147

HyFIR lowres test		77.53 194	75.40 207	83.94 120	89.59 104	66.62 141	80.36 268	88.64 187	56.29 313	76.45 166	85.17 238	57.64 183	93.28 149	61.34 232	83.10 174	91.91 114

CNLPA		78.08 180	76.79 188	81.97 177	90.40 89	71.07 59	87.59 138	84.55 241	66.03 232	72.38 230	89.64 121	57.56 184	86.04 285	59.61 243	83.35 169	88.79 220

test_yl		81.17 108	80.47 109	83.24 137	89.13 126	63.62 195	86.21 176	89.95 144	72.43 137	81.78 90	89.61 122	57.50 185	93.58 137	70.75 148	86.90 129	92.52 94

DCV-MVSNet		81.17 108	80.47 109	83.24 137	89.13 126	63.62 195	86.21 176	89.95 144	72.43 137	81.78 90	89.61 122	57.50 185	93.58 137	70.75 148	86.90 129	92.52 94

sss		73.60 241	73.64 228	73.51 292	82.80 261	55.01 302	76.12 299	81.69 278	62.47 270	74.68 209	85.85 223	57.32 187	78.11 318	60.86 235	80.93 195	87.39 247

Effi-MVS+-dtu		80.03 137	78.57 146	84.42 98	85.13 216	68.74 103	88.77 99	88.10 194	74.99 93	74.97 205	83.49 261	57.27 188	93.36 147	73.53 124	80.88 196	91.18 132

mvs-test1		80.88 112	79.40 129	85.29 71	85.13 216	69.75 82	89.28 81	88.10 194	74.99 93	76.44 169	86.72 195	57.27 188	94.26 107	73.53 124	83.18 172	91.87 115

DI_MVS_plusplus_test		79.89 140	78.58 145	83.85 122	82.89 259	65.32 166	86.12 179	89.55 154	69.64 183	70.55 245	85.82 224	57.24 190	93.81 127	76.85 99	88.55 108	92.41 99

AdaColmap		80.58 127	79.42 128	84.06 111	93.09 48	68.91 98	89.36 80	88.97 177	69.27 189	75.70 183	89.69 119	57.20 191	95.77 50	63.06 214	88.41 112	87.50 246

v1240		78.99 160	77.78 166	82.64 166	83.21 246	63.54 199	86.62 166	90.30 136	69.74 182	77.33 148	85.68 226	57.04 192	93.76 132	73.13 132	76.92 236	90.62 148

miper_lstm_enhance		74.11 236	73.11 234	77.13 266	80.11 299	59.62 250	72.23 315	86.92 217	66.76 220	70.40 248	82.92 265	56.93 193	82.92 302	69.06 167	72.63 288	88.87 216

BH-RMVSNet		79.61 143	78.44 149	83.14 142	89.38 113	65.93 152	84.95 205	87.15 214	73.56 122	78.19 132	89.79 118	56.67 194	93.36 147	59.53 244	86.74 132	90.13 167

test_djsdf		80.30 132	79.32 132	83.27 135	83.98 233	65.37 165	90.50 53	90.38 130	68.55 207	76.19 174	88.70 144	56.44 195	93.46 144	78.98 78	80.14 209	90.97 138

EPNet_dtu		75.46 224	74.86 213	77.23 265	82.57 267	54.60 303	86.89 156	83.09 264	71.64 146	66.25 290	85.86 222	55.99 196	88.04 270	54.92 275	86.55 135	89.05 207
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CostFormer		75.24 228	73.90 225	79.27 231	82.65 266	58.27 261	80.80 263	82.73 268	61.57 276	75.33 196	83.13 264	55.52 197	91.07 224	64.98 202	78.34 225	88.45 228

tpmrst		72.39 253	72.13 242	73.18 294	80.54 295	49.91 324	79.91 274	79.08 301	63.11 260	71.69 237	79.95 295	55.32 198	82.77 303	65.66 196	73.89 280	86.87 260

1314		76.53 207	75.30 211	80.21 214	83.93 234	62.32 221	84.66 210	88.81 180	60.23 285	70.16 253	84.07 252	55.30 199	90.73 230	67.37 179	83.21 171	87.59 244

tfpnnormal		74.39 232	73.16 233	78.08 250	86.10 202	58.05 263	84.65 213	87.53 208	70.32 169	71.22 242	85.63 228	54.97 200	89.86 240	43.03 323	75.02 270	86.32 269

GBi-Net		78.40 171	77.40 175	81.40 188	87.60 175	63.01 212	88.39 113	89.28 161	71.63 147	75.34 193	87.28 179	54.80 201	91.11 218	62.72 215	79.57 212	90.09 171

test1		78.40 171	77.40 175	81.40 188	87.60 175	63.01 212	88.39 113	89.28 161	71.63 147	75.34 193	87.28 179	54.80 201	91.11 218	62.72 215	79.57 212	90.09 171

FMVSNet2		78.20 177	77.21 178	81.20 194	87.60 175	62.89 216	87.47 141	89.02 173	71.63 147	75.29 197	87.28 179	54.80 201	91.10 221	62.38 219	79.38 216	89.61 193

Fast-Effi-MVS+-dtu		78.02 183	76.49 195	82.62 167	83.16 250	66.96 139	86.94 154	87.45 211	72.45 134	71.49 240	84.17 250	54.79 204	91.58 206	67.61 176	80.31 206	89.30 200

MVSTER		79.01 159	77.88 163	82.38 170	83.07 251	64.80 175	84.08 230	88.95 178	69.01 199	78.69 120	87.17 186	54.70 205	92.43 179	74.69 115	80.57 202	89.89 184

OpenMVS	72.83 10	79.77 141	78.33 153	84.09 110	85.17 213	69.91 78	90.57 51	90.97 115	66.70 221	72.17 232	91.91 71	54.70 205	93.96 116	61.81 227	90.95 80	88.41 230

XVG-OURS		80.41 129	79.23 134	83.97 118	85.64 207	69.02 94	83.03 246	90.39 129	71.09 156	77.63 143	91.49 83	54.62 207	91.35 213	75.71 108	83.47 168	91.54 122

LPG-MVS_test		82.08 92	81.27 97	84.50 95	89.23 122	68.76 101	90.22 63	91.94 84	75.37 87	76.64 164	91.51 81	54.29 208	94.91 82	78.44 82	83.78 161	89.83 186

LGP-MVS_train				84.50 95	89.23 122	68.76 101		91.94 84	75.37 87	76.64 164	91.51 81	54.29 208	94.91 82	78.44 82	83.78 161	89.83 186

TR-MVS		77.44 195	76.18 199	81.20 194	88.24 155	63.24 207	84.61 214	86.40 223	67.55 214	77.81 139	86.48 210	54.10 210	93.15 156	57.75 262	82.72 179	87.20 253

FMVSNet3		77.88 187	76.85 186	80.97 200	86.84 193	62.36 219	86.52 169	88.77 182	71.13 154	75.34 193	86.66 202	54.07 211	91.10 221	62.72 215	79.57 212	89.45 197

DP-MVS		76.78 205	74.57 216	83.42 129	93.29 41	69.46 90	88.55 108	83.70 251	63.98 256	70.20 250	88.89 141	54.01 212	94.80 89	46.66 311	81.88 188	86.01 277

ACMP	74.13 6	81.51 105	80.57 106	84.36 100	89.42 110	68.69 108	89.97 68	91.50 103	74.46 103	75.04 204	90.41 106	53.82 213	94.54 95	77.56 91	82.91 175	89.86 185
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PLC	70.83 11	78.05 182	76.37 198	83.08 145	91.88 70	67.80 124	88.19 123	89.46 157	64.33 252	69.87 259	88.38 155	53.66 214	93.58 137	58.86 251	82.73 178	87.86 238
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CANet_DTU		80.61 124	79.87 118	82.83 157	85.60 208	63.17 211	87.36 143	88.65 186	76.37 70	75.88 180	88.44 154	53.51 215	93.07 161	73.30 129	89.74 96	92.25 104

ACMM	73.20 8	80.78 122	79.84 119	83.58 125	89.31 119	68.37 113	89.99 67	91.60 97	70.28 170	77.25 150	89.66 120	53.37 216	93.53 142	74.24 119	82.85 176	88.85 217
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVP-Stereo		76.12 215	74.46 220	81.13 197	85.37 211	69.79 80	84.42 221	87.95 199	65.03 243	67.46 276	85.33 234	53.28 217	91.73 204	58.01 260	83.27 170	81.85 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
anonymousdsp		78.60 168	77.15 179	82.98 151	80.51 296	67.08 135	87.24 147	89.53 155	65.66 236	75.16 199	87.19 185	52.52 218	92.25 186	77.17 96	79.34 217	89.61 193

CR-MVSNet		73.37 242	71.27 250	79.67 224	81.32 288	65.19 169	75.92 301	80.30 292	59.92 288	72.73 225	81.19 281	52.50 219	86.69 279	59.84 241	77.71 227	87.11 257

Patchmtry		70.74 263	69.16 263	75.49 279	80.72 292	54.07 307	74.94 310	80.30 292	58.34 300	70.01 254	81.19 281	52.50 219	86.54 281	53.37 281	71.09 299	85.87 280

pmmvs4		74.03 238	71.91 243	80.39 209	81.96 276	68.32 114	81.45 261	82.14 273	59.32 293	69.87 259	85.13 239	52.40 221	88.13 269	60.21 239	74.74 273	84.73 291

PatchFormer-LS_test		74.50 231	73.05 235	78.86 238	82.95 257	59.55 253	81.65 258	82.30 271	67.44 216	71.62 238	78.15 307	52.34 222	88.92 260	65.05 201	75.90 254	88.12 233

RPMNet		71.62 257	68.94 265	79.67 224	81.32 288	65.19 169	75.92 301	78.30 304	57.60 305	72.73 225	76.45 316	52.30 223	86.69 279	48.14 306	77.71 227	87.11 257

LFMVS		81.82 98	81.23 98	83.57 126	91.89 69	63.43 204	89.84 69	81.85 277	77.04 52	83.21 70	93.10 53	52.26 224	93.43 146	71.98 140	89.95 94	93.85 43

VDD-MVS		83.01 83	82.36 84	84.96 82	91.02 79	66.40 145	88.91 93	88.11 193	77.57 38	84.39 56	93.29 50	52.19 225	93.91 123	77.05 97	88.70 106	94.57 16

tfpn200view9		76.42 211	75.37 209	79.55 229	89.13 126	57.65 272	85.17 198	83.60 252	73.41 125	76.45 166	86.39 212	52.12 226	91.95 196	48.33 302	83.75 163	89.07 202

thres400		76.50 208	75.37 209	79.86 219	89.13 126	57.65 272	85.17 198	83.60 252	73.41 125	76.45 166	86.39 212	52.12 226	91.95 196	48.33 302	83.75 163	90.00 177

thres200		75.55 223	74.47 219	78.82 239	87.78 171	57.85 269	83.07 245	83.51 255	72.44 136	75.84 181	84.42 247	52.08 228	91.75 202	47.41 309	83.64 167	86.86 261

PMMVS		69.34 275	68.67 266	71.35 301	75.67 323	62.03 224	75.17 305	73.46 322	50.00 326	68.68 267	79.05 300	52.07 229	78.13 317	61.16 233	82.77 177	73.90 329

tpm cat1		70.57 265	68.31 269	77.35 262	82.41 271	57.95 267	78.08 291	80.22 294	52.04 323	68.54 270	77.66 311	52.00 230	87.84 272	51.77 285	72.07 293	86.25 270

IterMVS-SCA-FT		75.43 225	73.87 226	80.11 215	82.69 264	64.85 174	81.57 260	83.47 257	69.16 194	70.49 247	84.15 251	51.95 231	88.15 268	69.23 164	72.14 292	87.34 249

SCA		74.22 235	72.33 241	79.91 218	84.05 232	62.17 223	79.96 273	79.29 300	66.30 228	72.38 230	80.13 293	51.95 231	88.60 263	59.25 246	77.67 229	88.96 213

thres100view900		76.50 208	75.55 204	79.33 230	89.52 107	56.99 280	85.83 187	83.23 261	73.94 114	76.32 171	87.12 187	51.89 233	91.95 196	48.33 302	83.75 163	89.07 202

thres600view7		76.50 208	75.44 205	79.68 223	89.40 111	57.16 277	85.53 195	83.23 261	73.79 118	76.26 172	87.09 188	51.89 233	91.89 199	48.05 307	83.72 166	90.00 177

tpm2		73.26 245	71.46 247	78.63 241	83.34 243	56.71 285	80.65 266	80.40 291	56.63 311	73.55 216	82.02 278	51.80 235	91.24 216	56.35 271	78.42 224	87.95 235

LS3D		76.95 203	74.82 214	83.37 132	90.45 87	67.36 132	89.15 88	86.94 216	61.87 275	69.52 262	90.61 103	51.71 236	94.53 96	46.38 314	86.71 133	88.21 232

IterMVS		74.29 233	72.94 236	78.35 247	81.53 282	63.49 201	81.58 259	82.49 269	68.06 211	69.99 256	83.69 259	51.66 237	85.54 288	65.85 194	71.64 295	86.01 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 255	71.71 246	74.35 288	82.19 274	52.00 313	79.22 280	77.29 310	64.56 248	72.95 223	83.68 260	51.35 238	83.26 301	58.33 257	75.80 255	87.81 239

sam_mvs1												51.32 239				88.96 213

PatchmatchNet		73.12 247	71.33 249	78.49 246	83.18 248	60.85 238	79.63 275	78.57 302	64.13 253	71.73 236	79.81 298	51.20 240	85.97 286	57.40 265	76.36 250	88.66 223
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
patchmatchnet-post											74.00 321	51.12 241	88.60 263

xiu_mvs_v1_base_debu		80.80 119	79.72 122	84.03 114	87.35 180	70.19 74	85.56 190	88.77 182	69.06 196	81.83 86	88.16 161	50.91 242	92.85 168	78.29 86	87.56 119	89.06 204

xiu_mvs_v1_base		80.80 119	79.72 122	84.03 114	87.35 180	70.19 74	85.56 190	88.77 182	69.06 196	81.83 86	88.16 161	50.91 242	92.85 168	78.29 86	87.56 119	89.06 204

xiu_mvs_v1_base_debi		80.80 119	79.72 122	84.03 114	87.35 180	70.19 74	85.56 190	88.77 182	69.06 196	81.83 86	88.16 161	50.91 242	92.85 168	78.29 86	87.56 119	89.06 204

Patchmatch-test		64.82 297	63.24 296	69.57 307	79.42 309	49.82 325	63.49 334	69.05 332	51.98 324	59.95 317	80.13 293	50.91 242	70.98 336	40.66 328	73.57 283	87.90 237

Patchmatch-RL test		70.24 269	67.78 279	77.61 258	77.43 316	59.57 252	71.16 317	70.33 326	62.94 264	68.65 268	72.77 323	50.62 246	85.49 289	69.58 162	66.58 313	87.77 240

Anonymous20231211		78.97 161	77.69 171	82.81 159	90.54 86	64.29 185	90.11 65	91.51 100	65.01 244	76.16 178	88.13 165	50.56 247	93.03 165	69.68 161	77.56 230	91.11 134

VDDNet		81.52 103	80.67 105	84.05 112	90.44 88	64.13 188	89.73 75	85.91 230	71.11 155	83.18 71	93.48 45	50.54 248	93.49 143	73.40 128	88.25 113	94.54 17

pmmvs6		74.69 230	73.39 229	78.61 242	81.38 285	57.48 275	86.64 165	87.95 199	64.99 245	70.18 251	86.61 203	50.43 249	89.52 246	62.12 223	70.18 302	88.83 218

test_post											5.46 344	50.36 250	84.24 294

ET-MVSNet_ETH3D		78.63 167	76.63 194	84.64 92	86.73 196	69.47 88	85.01 203	84.61 240	69.54 184	66.51 288	86.59 204	50.16 251	91.75 202	76.26 104	84.24 158	92.69 91

sam_mvs												50.01 252

Anonymous20240529		80.19 135	78.89 140	84.10 109	90.60 85	64.75 176	88.95 92	90.90 117	65.97 233	80.59 104	91.17 90	49.97 253	93.73 135	69.16 166	82.70 180	93.81 47

thisisatest0530		79.40 150	77.76 168	84.31 103	87.69 174	65.10 172	87.36 143	84.26 245	70.04 173	77.42 146	88.26 160	49.94 254	94.79 90	70.20 154	84.70 153	93.03 81

PatchT		68.46 281	67.85 276	70.29 305	80.70 293	43.93 333	72.47 314	74.88 317	60.15 286	70.55 245	76.57 315	49.94 254	81.59 306	50.58 290	74.83 272	85.34 283

tttt0517		79.40 150	77.91 161	83.90 121	88.10 159	63.84 192	88.37 116	84.05 247	71.45 152	76.78 160	89.12 136	49.93 256	94.89 85	70.18 155	83.18 172	92.96 85

tpmvs		71.09 261	69.29 262	76.49 270	82.04 275	56.04 295	78.92 284	81.37 281	64.05 254	67.18 280	78.28 305	49.74 257	89.77 241	49.67 297	72.37 289	83.67 300

thisisatest0515		77.33 198	75.38 208	83.18 140	85.27 212	63.80 193	82.11 253	83.27 260	65.06 242	75.91 179	83.84 255	49.54 258	94.27 103	67.24 182	86.19 140	91.48 126

UniMVSNet_ETH3D		79.10 157	78.24 155	81.70 181	86.85 192	60.24 246	87.28 146	88.79 181	74.25 107	76.84 157	90.53 105	49.48 259	91.56 207	67.98 173	82.15 184	93.29 70

CVMVSNet		72.99 249	72.58 238	74.25 289	84.28 226	50.85 321	86.41 170	83.45 258	44.56 328	73.23 220	87.54 175	49.38 260	85.70 287	65.90 193	78.44 223	86.19 272

MDTV_nov1_ep13_2view						37.79 338	75.16 306		55.10 316	66.53 287		49.34 261		53.98 278		87.94 236

UGNet		80.83 116	79.59 125	84.54 94	88.04 161	68.09 119	89.42 79	88.16 192	76.95 53	76.22 173	89.46 129	49.30 262	93.94 119	68.48 170	90.31 86	91.60 120
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
pmmvs5		71.55 258	70.20 259	75.61 276	77.83 314	56.39 290	81.74 256	80.89 282	57.76 303	67.46 276	84.49 246	49.26 263	85.32 291	57.08 268	75.29 267	85.11 287

LTVRE_ROB	69.57 13	76.25 214	74.54 218	81.41 187	88.60 144	64.38 184	79.24 279	89.12 171	70.76 162	69.79 261	87.86 167	49.09 264	93.20 153	56.21 272	80.16 207	86.65 266
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
FMVSNet1		77.44 195	76.12 200	81.40 188	86.81 194	63.01 212	88.39 113	89.28 161	70.49 167	74.39 211	87.28 179	49.06 265	91.11 218	60.91 234	78.52 221	90.09 171

MDTV_nov1_ep13			69.97 260		83.18 248	53.48 310	77.10 297	80.18 295	60.45 282	69.33 265	80.44 290	48.89 266	86.90 278	51.60 287	78.51 222

test_post1							78.90 285				5.43 345	48.81 267	85.44 290	59.25 246

test-LLR		72.94 250	72.43 239	74.48 286	81.35 286	58.04 264	78.38 287	77.46 308	66.66 222	69.95 257	79.00 302	48.06 268	79.24 312	66.13 189	84.83 150	86.15 273

test0.0.03 1		68.00 282	67.69 280	68.90 310	77.55 315	47.43 328	75.70 304	72.95 324	66.66 222	66.56 286	82.29 275	48.06 268	75.87 327	44.97 320	74.51 275	83.41 302

our_test_3		69.14 276	67.00 283	75.57 277	79.80 304	58.80 256	77.96 292	77.81 306	59.55 291	62.90 309	78.25 306	47.43 270	83.97 295	51.71 286	67.58 310	83.93 299

MS-PatchMatch		73.83 239	72.67 237	77.30 263	83.87 235	66.02 150	81.82 254	84.66 239	61.37 279	68.61 269	82.82 268	47.29 271	88.21 267	59.27 245	84.32 157	77.68 326

cascas		76.72 206	74.64 215	82.99 150	85.78 205	65.88 154	82.33 251	89.21 166	60.85 281	72.74 224	81.02 285	47.28 272	93.75 133	67.48 178	85.02 148	89.34 199

test20.03		67.45 285	66.95 284	68.94 309	75.48 325	44.84 332	77.50 294	77.67 307	66.66 222	63.01 307	83.80 256	47.02 273	78.40 316	42.53 325	68.86 308	83.58 301

test_0402		72.79 251	70.44 256	79.84 220	88.13 157	65.99 151	85.93 184	84.29 243	65.57 237	67.40 278	85.49 231	46.92 274	92.61 174	35.88 332	74.38 276	80.94 317

F-COLMAP		76.38 213	74.33 221	82.50 168	89.28 120	66.95 140	88.41 112	89.03 172	64.05 254	66.83 284	88.61 148	46.78 275	92.89 167	57.48 263	78.55 220	87.67 241

ppachtmachnet_test		70.04 271	67.34 282	78.14 249	79.80 304	61.13 234	79.19 281	80.59 287	59.16 295	65.27 295	79.29 299	46.75 276	87.29 276	49.33 298	66.72 311	86.00 279

D2MVS		74.82 229	73.21 232	79.64 226	79.81 303	62.56 218	80.34 269	87.35 212	64.37 251	68.86 266	82.66 270	46.37 277	90.10 238	67.91 174	81.24 193	86.25 270

Anonymous20231206		68.60 278	67.80 278	71.02 303	80.23 298	50.75 322	78.30 290	80.47 289	56.79 310	66.11 291	82.63 271	46.35 278	78.95 314	43.62 322	75.70 256	83.36 303

CHOSEN 280x420		66.51 291	64.71 290	71.90 296	81.45 283	63.52 200	57.98 336	68.95 333	53.57 319	62.59 310	76.70 314	46.22 279	75.29 330	55.25 274	79.68 210	76.88 328

GA-MVS		76.87 204	75.17 212	81.97 177	82.75 262	62.58 217	81.44 262	86.35 225	72.16 141	74.74 208	82.89 266	46.20 280	92.02 194	68.85 169	81.09 194	91.30 130

MDA-MVSNet_test_wron		65.03 295	62.92 297	71.37 299	75.93 321	56.73 283	69.09 326	74.73 319	57.28 308	54.03 329	77.89 308	45.88 281	74.39 333	49.89 296	61.55 321	82.99 309

YYNet1		65.03 295	62.91 298	71.38 298	75.85 322	56.60 287	69.12 325	74.66 321	57.28 308	54.12 328	77.87 309	45.85 282	74.48 332	49.95 295	61.52 322	83.05 307

EPMVS		69.02 277	68.16 271	71.59 297	79.61 307	49.80 326	77.40 295	66.93 334	62.82 266	70.01 254	79.05 300	45.79 283	77.86 320	56.58 270	75.26 268	87.13 256

IB-MVS	68.01 15	75.85 219	73.36 230	83.31 133	84.76 220	66.03 149	83.38 240	85.06 236	70.21 172	69.40 263	81.05 284	45.76 284	94.66 93	65.10 200	75.49 260	89.25 201
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
jajsoiax		79.29 153	77.96 159	83.27 135	84.68 222	66.57 143	89.25 83	90.16 139	69.20 193	75.46 188	89.49 126	45.75 285	93.13 158	76.84 100	80.80 198	90.11 169

PatchMatch-RL		72.38 254	70.90 253	76.80 269	88.60 144	67.38 131	79.53 276	76.17 314	62.75 267	69.36 264	82.00 279	45.51 286	84.89 292	53.62 280	80.58 201	78.12 325

RPSCF		73.23 246	71.46 247	78.54 244	82.50 268	59.85 248	82.18 252	82.84 267	58.96 296	71.15 243	89.41 133	45.48 287	84.77 293	58.82 252	71.83 294	91.02 137

MSDG		73.36 244	70.99 252	80.49 208	84.51 224	65.80 155	80.71 265	86.13 228	65.70 235	65.46 293	83.74 258	44.60 288	90.91 226	51.13 289	76.89 237	84.74 290

PVSNet_0	57.27 20	61.67 302	59.27 304	68.85 311	79.61 307	57.44 276	68.01 327	73.44 323	55.93 314	58.54 320	70.41 326	44.58 289	77.55 321	47.01 310	35.91 336	71.55 331

DWT-MVSNet_test		73.70 240	71.86 244	79.21 233	82.91 258	58.94 255	82.34 250	82.17 272	65.21 239	71.05 244	78.31 304	44.21 290	90.17 237	63.29 213	77.28 231	88.53 227

mvs_tets		79.13 156	77.77 167	83.22 139	84.70 221	66.37 146	89.17 84	90.19 138	69.38 187	75.40 191	89.46 129	44.17 291	93.15 156	76.78 101	80.70 200	90.14 166

MDA-MVSNet-bldmvs		66.68 289	63.66 293	75.75 274	79.28 310	60.56 242	73.92 312	78.35 303	64.43 249	50.13 332	79.87 297	44.02 292	83.67 297	46.10 315	56.86 327	83.03 308

gg-mvs-nofinetune		69.95 272	67.96 274	75.94 273	83.07 251	54.51 305	77.23 296	70.29 327	63.11 260	70.32 249	62.33 330	43.62 293	88.69 262	53.88 279	87.76 116	84.62 293

GG-mvs-BLEND				75.38 280	81.59 281	55.80 297	79.32 278	69.63 329		67.19 279	73.67 322	43.24 294	88.90 261	50.41 291	84.50 154	81.45 316

CMPMVS	51.72 21	70.19 270	68.16 271	76.28 271	73.15 331	57.55 274	79.47 277	83.92 248	48.02 327	56.48 326	84.81 243	43.13 295	86.42 283	62.67 218	81.81 189	84.89 288
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
dp		66.80 288	65.43 288	70.90 304	79.74 306	48.82 327	75.12 308	74.77 318	59.61 290	64.08 303	77.23 312	42.89 296	80.72 309	48.86 300	66.58 313	83.16 305

PVSNet	64.34 18	72.08 256	70.87 255	75.69 275	86.21 201	56.44 289	74.37 311	80.73 285	62.06 274	70.17 252	82.23 276	42.86 297	83.31 300	54.77 276	84.45 156	87.32 250

pmmvs-eth3d		70.50 267	67.83 277	78.52 245	77.37 317	66.18 148	81.82 254	81.51 279	58.90 297	63.90 304	80.42 291	42.69 298	86.28 284	58.56 254	65.30 316	83.11 306

UnsupCasMVSNet_eth		67.33 286	65.99 287	71.37 299	73.48 328	51.47 318	75.16 306	85.19 235	65.20 240	60.78 313	80.93 288	42.35 299	77.20 322	57.12 267	53.69 331	85.44 282

ADS-MVSNet2		66.20 294	63.33 295	74.82 284	79.92 301	58.75 257	67.55 328	75.19 316	53.37 320	65.25 296	75.86 317	42.32 300	80.53 310	41.57 326	68.91 306	85.18 284

ADS-MVSNet		64.36 298	62.88 299	68.78 312	79.92 301	47.17 329	67.55 328	71.18 325	53.37 320	65.25 296	75.86 317	42.32 300	73.99 334	41.57 326	68.91 306	85.18 284

SixPastTwentyTwo		73.37 242	71.26 251	79.70 222	85.08 218	57.89 268	85.57 189	83.56 254	71.03 157	65.66 292	85.88 221	42.10 302	92.57 175	59.11 248	63.34 319	88.65 224

JIA-IIPM		66.32 293	62.82 300	76.82 268	77.09 319	61.72 230	65.34 331	75.38 315	58.04 302	64.51 300	62.32 331	42.05 303	86.51 282	51.45 288	69.22 305	82.21 312

ACMH	67.68 16	75.89 218	73.93 224	81.77 180	88.71 142	66.61 142	88.62 105	89.01 174	69.81 177	66.78 285	86.70 200	41.95 304	91.51 210	55.64 273	78.14 226	87.17 254
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 217	74.01 223	82.03 175	88.60 144	65.31 167	88.86 95	87.55 207	70.25 171	67.75 273	87.47 177	41.27 305	93.19 154	58.37 256	75.94 253	87.60 243

MIMVSNet		70.69 264	69.30 261	74.88 283	84.52 223	56.35 292	75.87 303	79.42 299	64.59 247	67.76 272	82.41 272	41.10 306	81.54 307	46.64 313	81.34 191	86.75 264

Anonymous202405211		78.25 174	77.01 181	81.99 176	91.03 78	60.67 240	84.77 208	83.90 249	70.65 165	80.00 107	91.20 89	41.08 307	91.43 211	65.21 198	85.26 147	93.85 43

N_pmnet		52.79 308	53.26 308	51.40 324	78.99 312	7.68 349	69.52 321	3.89 349	51.63 325	57.01 324	74.98 320	40.83 308	65.96 339	37.78 331	64.67 317	80.56 320

testing_2		75.73 220	73.34 231	82.89 156	77.37 317	65.22 168	84.10 228	90.54 126	69.09 195	60.46 314	81.15 283	40.48 309	92.84 171	76.36 103	80.54 204	90.60 150

EU-MVSNet		68.53 280	67.61 281	71.31 302	78.51 313	47.01 330	84.47 216	84.27 244	42.27 329	66.44 289	84.79 244	40.44 310	83.76 296	58.76 253	68.54 309	83.17 304

DSMNet-mixed		57.77 305	56.90 306	60.38 319	67.70 336	35.61 339	69.18 323	53.97 341	32.30 338	57.49 323	79.88 296	40.39 311	68.57 338	38.78 330	72.37 289	76.97 327

OurMVSNet-221017-0		74.26 234	72.42 240	79.80 221	83.76 237	59.59 251	85.92 185	86.64 219	66.39 227	66.96 281	87.58 172	39.46 312	91.60 205	65.76 195	69.27 304	88.22 231

K. test v3		71.19 260	68.51 267	79.21 233	83.04 253	57.78 271	84.35 223	76.91 312	72.90 133	62.99 308	82.86 267	39.27 313	91.09 223	61.65 228	52.66 332	88.75 221

lessismore_v0				78.97 236	81.01 291	57.15 278		65.99 335		61.16 312	82.82 268	39.12 314	91.34 214	59.67 242	46.92 335	88.43 229

UnsupCasMVSNet_bld		63.70 300	61.53 303	70.21 306	73.69 327	51.39 319	72.82 313	81.89 275	55.63 315	57.81 322	71.80 325	38.67 315	78.61 315	49.26 299	52.21 333	80.63 318

new-patchmatchnet		61.73 301	61.73 302	61.70 318	72.74 333	24.50 346	69.16 324	78.03 305	61.40 277	56.72 325	75.53 319	38.42 316	76.48 325	45.95 316	57.67 326	84.13 297

MVS-HIRNet		59.14 303	57.67 305	63.57 317	81.65 279	43.50 334	71.73 316	65.06 337	39.59 333	51.43 331	57.73 334	38.34 317	82.58 304	39.53 329	73.95 279	64.62 334

COLMAP_ROB	66.92 17	73.01 248	70.41 257	80.81 203	87.13 189	65.63 158	88.30 118	84.19 246	62.96 263	63.80 305	87.69 170	38.04 318	92.56 176	46.66 311	74.91 271	84.24 295
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
TESTMET0.1,1		69.89 273	69.00 264	72.55 295	79.27 311	56.85 281	78.38 287	74.71 320	57.64 304	68.09 271	77.19 313	37.75 319	76.70 323	63.92 207	84.09 159	84.10 298

OpenMVS_ROB	64.09 19	70.56 266	68.19 270	77.65 257	80.26 297	59.41 254	85.01 203	82.96 266	58.76 298	65.43 294	82.33 273	37.63 320	91.23 217	45.34 319	76.03 252	82.32 311

FMVSNet5		69.50 274	67.96 274	74.15 290	82.97 256	55.35 301	80.01 272	82.12 274	62.56 269	63.02 306	81.53 280	36.92 321	81.92 305	48.42 301	74.06 278	85.17 286

MIMVSNet1		68.58 279	66.78 285	73.98 291	80.07 300	51.82 314	80.77 264	84.37 242	64.40 250	59.75 318	82.16 277	36.47 322	83.63 298	42.73 324	70.33 301	86.48 268

ITE_SJBPF				78.22 248	81.77 278	60.57 241		83.30 259	69.25 190	67.54 275	87.20 184	36.33 323	87.28 277	54.34 277	74.62 274	86.80 262

test-mter		71.41 259	70.39 258	74.48 286	81.35 286	58.04 264	78.38 287	77.46 308	60.32 284	69.95 257	79.00 302	36.08 324	79.24 312	66.13 189	84.83 150	86.15 273

testgi		66.67 290	66.53 286	67.08 315	75.62 324	41.69 336	75.93 300	76.50 313	66.11 229	65.20 298	86.59 204	35.72 325	74.71 331	43.71 321	73.38 285	84.84 289

EG-PatchMatch MVS		74.04 237	71.82 245	80.71 205	84.92 219	67.42 129	85.86 186	88.08 196	66.04 231	64.22 302	83.85 254	35.10 326	92.56 176	57.44 264	80.83 197	82.16 313

MVS_0304		72.48 252	70.89 254	77.24 264	82.20 273	59.68 249	84.11 227	83.49 256	67.10 218	66.87 283	80.59 289	35.00 327	87.40 275	59.07 249	79.58 211	84.63 292

XVG-ACMP-BASELINE		76.11 216	74.27 222	81.62 182	83.20 247	64.67 177	83.60 237	89.75 150	69.75 180	71.85 235	87.09 188	32.78 328	92.11 191	69.99 158	80.43 205	88.09 234

test_normal		67.47 284	63.56 294	79.18 235	72.78 332	55.71 299	40.72 340	90.78 120	72.12 142	48.43 333	65.82 328	32.32 329	92.25 186	72.25 139	76.85 239	89.59 195

AllTest		70.96 262	68.09 273	79.58 227	85.15 214	63.62 195	84.58 215	79.83 296	62.31 271	60.32 315	86.73 193	32.02 330	88.96 258	50.28 292	71.57 296	86.15 273

TestCases				79.58 227	85.15 214	63.62 195		79.83 296	62.31 271	60.32 315	86.73 193	32.02 330	88.96 258	50.28 292	71.57 296	86.15 273

USDC		70.33 268	68.37 268	76.21 272	80.60 294	56.23 293	79.19 281	86.49 221	60.89 280	61.29 311	85.47 232	31.78 332	89.47 248	53.37 281	76.21 251	82.94 310

tmp_tt		18.61 318	21.40 320	10.23 331	4.82 348	10.11 348	34.70 342	30.74 347	1.48 344	23.91 342	26.07 342	28.42 333	13.41 347	27.12 336	15.35 343	7.17 342

TDRefinement		67.49 283	64.34 291	76.92 267	73.47 329	61.07 235	84.86 207	82.98 265	59.77 289	58.30 321	85.13 239	26.06 334	87.89 271	47.92 308	60.59 324	81.81 315

TinyColmap		67.30 287	64.81 289	74.76 285	81.92 277	56.68 286	80.29 270	81.49 280	60.33 283	56.27 327	83.22 263	24.77 335	87.66 274	45.52 317	69.47 303	79.95 321

LF4IMVS		64.02 299	62.19 301	69.50 308	70.90 334	53.29 311	76.13 298	77.18 311	52.65 322	58.59 319	80.98 286	23.55 336	76.52 324	53.06 283	66.66 312	78.68 324

new_pmnet		50.91 309	50.29 310	52.78 323	68.58 335	34.94 341	63.71 333	56.63 340	39.73 332	44.95 334	65.47 329	21.93 337	58.48 340	34.98 333	56.62 328	64.92 333

pmmvs3		57.79 304	54.26 307	68.37 313	64.02 338	56.72 284	75.12 308	65.17 336	40.20 331	52.93 330	69.86 327	20.36 338	75.48 329	45.45 318	55.25 330	72.90 330

PM-MVS		66.41 292	64.14 292	73.20 293	73.92 326	56.45 288	78.97 283	64.96 338	63.88 258	64.72 299	80.24 292	19.84 339	83.44 299	66.24 188	64.52 318	79.71 322

ambc				75.24 281	73.16 330	50.51 323	63.05 335	87.47 210		64.28 301	77.81 310	17.80 340	89.73 243	57.88 261	60.64 323	85.49 281

ANet_high		50.57 310	46.10 312	63.99 316	48.67 344	39.13 337	70.99 319	80.85 283	61.39 278	31.18 338	57.70 335	17.02 341	73.65 335	31.22 334	15.89 342	79.18 323

FPMVS		53.68 307	51.64 309	59.81 320	65.08 337	51.03 320	69.48 322	69.58 330	41.46 330	40.67 335	72.32 324	16.46 342	70.00 337	24.24 337	65.42 315	58.40 335

EMVS		30.81 315	29.65 317	34.27 328	50.96 343	25.95 345	56.58 338	46.80 344	24.01 340	15.53 345	30.68 341	12.47 343	54.43 343	12.81 343	17.05 341	22.43 341

E-PMN		31.77 314	30.64 316	35.15 327	52.87 342	27.67 343	57.09 337	47.86 343	24.64 339	16.40 344	33.05 340	11.23 344	54.90 342	14.46 342	18.15 340	22.87 340

DeepMVS_CX				27.40 329	40.17 347	26.90 344		24.59 348	17.44 342	23.95 341	48.61 337	9.77 345	26.48 345	18.06 339	24.47 338	28.83 339

Gipuma		45.18 311	41.86 313	55.16 322	77.03 320	51.52 317	32.50 343	80.52 288	32.46 337	27.12 339	35.02 339	9.52 346	75.50 328	22.31 338	60.21 325	38.45 338
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		54.25 306	49.68 311	67.97 314	53.73 341	45.28 331	66.85 330	80.78 284	35.96 335	39.45 336	62.23 332	8.70 347	78.06 319	48.24 305	51.20 334	80.57 319

PMMVS2		40.82 313	38.86 315	46.69 325	53.84 340	16.45 347	48.61 339	49.92 342	37.49 334	31.67 337	60.97 333	8.14 348	56.42 341	28.42 335	30.72 337	67.19 332

PMVS	37.38 22	44.16 312	40.28 314	55.82 321	40.82 346	42.54 335	65.12 332	63.99 339	34.43 336	24.48 340	57.12 336	3.92 349	76.17 326	17.10 340	55.52 329	48.75 336
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	26.22 23	30.37 316	25.89 319	43.81 326	44.55 345	35.46 340	28.87 344	39.07 345	18.20 341	18.58 343	40.18 338	2.68 350	47.37 344	17.07 341	23.78 339	48.60 337
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		16.82 319	15.94 321	19.46 330	58.74 339	31.45 342	39.22 341	3.74 350	6.84 343	6.04 346	2.70 346	1.27 351	24.29 346	10.54 344	14.40 344	2.63 343

test123		6.12 321	8.11 323	0.14 332	0.06 350	0.09 350	71.05 318	0.03 352	0.04 346	0.25 348	1.30 348	0.05 352	0.03 349	0.21 346	0.01 346	0.29 344

testmvs		6.04 322	8.02 324	0.10 333	0.08 349	0.03 351	69.74 320	0.04 351	0.05 345	0.31 347	1.68 347	0.02 353	0.04 348	0.24 345	0.02 345	0.25 345

uanet_test		0.00 324	0.00 326	0.00 334	0.00 351	0.00 352	0.00 345	0.00 353	0.00 347	0.00 349	0.00 349	0.00 354	0.00 350	0.00 347	0.00 347	0.00 346

test_part1				0.00 334		0.00 352	0.00 345	94.09 9				0.00 354	0.00 350	0.00 347	0.00 347	0.00 346

sosnet-low-res		0.00 324	0.00 326	0.00 334	0.00 351	0.00 352	0.00 345	0.00 353	0.00 347	0.00 349	0.00 349	0.00 354	0.00 350	0.00 347	0.00 347	0.00 346

sosnet		0.00 324	0.00 326	0.00 334	0.00 351	0.00 352	0.00 345	0.00 353	0.00 347	0.00 349	0.00 349	0.00 354	0.00 350	0.00 347	0.00 347	0.00 346

uncertanet		0.00 324	0.00 326	0.00 334	0.00 351	0.00 352	0.00 345	0.00 353	0.00 347	0.00 349	0.00 349	0.00 354	0.00 350	0.00 347	0.00 347	0.00 346

Regformer		0.00 324	0.00 326	0.00 334	0.00 351	0.00 352	0.00 345	0.00 353	0.00 347	0.00 349	0.00 349	0.00 354	0.00 350	0.00 347	0.00 347	0.00 346

ab-mvs-re		7.23 320	9.64 322	0.00 334	0.00 351	0.00 352	0.00 345	0.00 353	0.00 347	0.00 349	86.72 195	0.00 354	0.00 350	0.00 347	0.00 347	0.00 346

uanet		0.00 324	0.00 326	0.00 334	0.00 351	0.00 352	0.00 345	0.00 353	0.00 347	0.00 349	0.00 349	0.00 354	0.00 350	0.00 347	0.00 347	0.00 346

save fliter					93.80 32	72.35 40	90.47 55	91.17 111	74.31 105

test_0728_SECOND				87.71 27	95.34 1	71.43 54	93.49 5	94.23 5					97.49 1	89.08 4	96.41 4	94.21 26

GSMVS																88.96 213

test_part2					95.06 4	72.65 29				91.80 6

MTGPA								92.02 77

MTMP							92.18 26	32.83 346

gm-plane-assit					81.40 284	53.83 309			62.72 268		80.94 287		92.39 181	63.40 211

test9_res														84.90 26	95.70 21	92.87 86

agg_prior2														82.91 51	95.45 24	92.70 89

agg_prior					92.85 51	71.94 46		91.78 92		84.41 54			94.93 80

test_prior4						72.60 31	89.01 91

test_prior				86.33 56	92.61 58	69.59 84		92.97 44					95.48 57			93.91 39

旧先验2							86.56 168		58.10 301	87.04 26			88.98 256	74.07 120

新几何2							86.29 175

无先验							87.48 140	88.98 175	60.00 287				94.12 112	67.28 180		88.97 212

原ACMM2							86.86 157

testdata2													91.01 225	62.37 220

testdata1							84.14 226		75.71 79

plane_prior7					90.08 95	68.51 112

plane_prior5								92.44 62					95.38 63	78.71 80	86.32 138	91.33 128

plane_prior4											91.00 97

plane_prior3						68.60 110			78.44 30	78.92 118

plane_prior2							91.25 39		79.12 23

plane_prior1					89.90 99

plane_prior						68.71 105	90.38 58		77.62 36						86.16 141

n2								0.00 353

nn								0.00 353

door-mid								69.98 328

test11								92.23 69

door								69.44 331

HQP5-MVS						66.98 137

HQP-NCC					89.33 114		89.17 84		76.41 66	77.23 152

ACMP_Plane					89.33 114		89.17 84		76.41 66	77.23 152

BP-MVS														77.47 92

HQP4-MVS										77.24 151			95.11 73			91.03 135

HQP3-MVS								92.19 72							85.99 143

NP-MVS					89.62 103	68.32 114					90.24 108

ACMMP++_ref															81.95 187

ACMMP++															81.25 192

High-resolution multi-view benchmark