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++		89.02 3	89.15 3	88.63 1	95.01 1	76.03 1	92.38 14	92.85 33	80.26 14	87.78 11	94.27 16	75.89 7	96.81 8	87.45 9	96.44 1	93.05 62

ACMMP_Plus		88.05 9	88.08 9	87.94 11	93.70 23	73.05 18	90.86 33	93.59 8	76.27 66	88.14 7	95.09 3	71.06 36	96.67 13	87.67 6	96.37 2	94.09 22

MP-MVS-pluss		87.67 12	87.72 11	87.54 26	93.64 26	72.04 38	89.80 55	93.50 10	75.17 83	86.34 16	95.29 2	70.86 37	96.00 33	88.78 3	96.04 3	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CNVR-MVS		88.93 4	89.13 4	88.33 3	94.77 2	73.82 6	90.51 39	93.00 25	80.90 10	88.06 9	94.06 24	76.43 4	96.84 7	88.48 4	95.99 4	94.34 15

PHI-MVS		86.43 31	86.17 33	87.24 30	90.88 67	70.96 47	92.27 17	94.07 2	72.45 133	85.22 25	91.90 58	69.47 50	96.42 22	83.28 34	95.94 5	94.35 14

test_prior3		86.73 26	86.86 25	86.33 46	92.61 46	69.59 71	88.85 79	92.97 30	75.41 76	84.91 29	93.54 29	74.28 17	95.48 43	83.31 32	95.86 6	93.91 30

test_prior2							88.85 79		75.41 76	84.91 29	93.54 29	74.28 17		83.31 32	95.86 6

SteuartSystems-ACMMP		88.72 5	88.86 5	88.32 4	92.14 52	72.96 19	93.73 3	93.67 7	80.19 15	88.10 8	94.80 4	73.76 20	97.11 3	87.51 8	95.82 8	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 6	88.56 6	86.73 39	92.24 50	69.03 79	89.57 62	93.39 14	77.53 39	89.79 4	94.12 22	78.98 1	96.58 20	85.66 12	95.72 9	94.58 7

train_agg		86.43 31	86.20 31	87.13 33	93.26 33	72.96 19	88.75 84	91.89 68	68.69 191	85.00 27	93.10 39	74.43 13	95.41 48	84.97 15	95.71 10	93.02 63

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 30	72.86 23	88.77 82	91.68 78	68.29 197	84.26 42	92.83 47	72.83 25	95.42 47	84.97 15	95.71 10	93.02 63

test9_res														84.90 17	95.70 12	92.87 68

APDe-MVS		89.15 2	89.63 2	87.73 20	94.49 8	71.69 41	93.83 2	93.96 3	75.70 72	91.06 2	96.03 1	76.84 3	97.03 5	89.09 2	95.65 13	94.47 11

agg_prior2														82.91 39	95.45 14	92.70 69

CDPH-MVS		85.76 41	85.29 46	87.17 32	93.49 29	71.08 45	88.58 90	92.42 47	68.32 196	84.61 36	93.48 31	72.32 29	96.15 30	79.00 60	95.43 15	94.28 18

DeepC-MVS	79.81 2	87.08 24	86.88 24	87.69 24	91.16 62	72.32 35	90.31 45	93.94 4	77.12 44	82.82 59	94.23 18	72.13 31	97.09 4	84.83 20	95.37 16	93.65 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MPTG		87.53 14	87.41 15	87.90 15	94.18 19	74.25 2	90.23 47	92.02 59	79.45 19	85.88 18	94.80 4	68.07 57	96.21 27	86.69 10	95.34 17	93.23 54

MTAPA		87.23 20	87.00 20	87.90 15	94.18 19	74.25 2	86.58 160	92.02 59	79.45 19	85.88 18	94.80 4	68.07 57	96.21 27	86.69 10	95.34 17	93.23 54

agg_prior1		86.22 36	86.09 35	86.62 42	92.85 41	71.94 39	88.59 89	91.78 74	68.96 188	84.41 39	93.18 38	74.94 9	94.93 65	84.75 22	95.33 19	93.01 65

DeepC-MVS_fast	79.65 3	86.91 25	86.62 26	87.76 19	93.52 28	72.37 34	91.26 27	93.04 22	76.62 57	84.22 43	93.36 35	71.44 34	96.76 10	80.82 51	95.33 19	94.16 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		87.71 11	87.64 12	87.93 14	94.36 14	73.88 4	92.71 13	92.65 41	77.57 35	83.84 47	94.40 15	72.24 30	96.28 25	85.65 13	95.30 21	93.62 43

MCST-MVS		87.37 18	87.25 16	87.73 20	94.53 7	72.46 31	89.82 53	93.82 5	73.07 121	84.86 34	92.89 45	76.22 5	96.33 23	84.89 19	95.13 22	94.40 12

APD-MVS		87.44 15	87.52 13	87.19 31	94.24 16	72.39 32	91.86 22	92.83 34	73.01 122	88.58 6	94.52 7	73.36 21	96.49 21	84.26 27	95.01 23	92.70 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 7	88.01 10	88.24 5	94.41 12	73.62 7	91.22 30	92.83 34	81.50 7	85.79 21	93.47 33	73.02 24	97.00 6	84.90 17	94.94 24	94.10 21

ACMMPR		87.44 15	87.23 17	88.08 7	94.64 3	73.59 8	93.04 5	93.20 18	76.78 52	84.66 35	94.52 7	68.81 55	96.65 14	84.53 23	94.90 25	94.00 28

HFP-MVS		87.58 13	87.47 14	87.94 11	94.58 5	73.54 11	93.04 5	93.24 16	76.78 52	84.91 29	94.44 12	70.78 38	96.61 16	84.53 23	94.89 26	93.66 36

#test#		87.33 19	87.13 19	87.94 11	94.58 5	73.54 11	92.34 15	93.24 16	75.23 80	84.91 29	94.44 12	70.78 38	96.61 16	83.75 31	94.89 26	93.66 36

region2R		87.42 17	87.20 18	88.09 6	94.63 4	73.55 9	93.03 7	93.12 21	76.73 55	84.45 38	94.52 7	69.09 53	96.70 12	84.37 26	94.83 28	94.03 25

原ACMM1				84.35 85	93.01 39	68.79 85		92.44 44	63.96 237	81.09 78	91.57 66	66.06 75	95.45 45	67.19 167	94.82 29	88.81 194

HPM-MVS		87.11 22	86.98 21	87.50 28	93.88 22	72.16 36	92.19 18	93.33 15	76.07 69	83.81 48	93.95 26	69.77 48	96.01 32	85.15 14	94.66 30	94.32 17

TSAR-MVS + MP.		88.02 10	88.11 8	87.72 22	93.68 25	72.13 37	91.41 26	92.35 49	74.62 89	88.90 5	93.85 27	75.75 8	96.00 33	87.80 5	94.63 31	95.04 2

PGM-MVS		86.68 27	86.27 30	87.90 15	94.22 17	73.38 15	90.22 48	93.04 22	75.53 74	83.86 46	94.42 14	67.87 61	96.64 15	82.70 41	94.57 32	93.66 36

XVS		87.18 21	86.91 23	88.00 9	94.42 10	73.33 16	92.78 9	92.99 27	79.14 21	83.67 50	94.17 19	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

X-MVStestdata		80.37 117	77.83 151	88.00 9	94.42 10	73.33 16	92.78 9	92.99 27	79.14 21	83.67 50	12.47 335	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

test12				86.80 38	92.63 45	70.70 55		91.79 73		82.71 61		71.67 32	96.16 29		94.50 33	93.54 46

CP-MVS		87.11 22	86.92 22	87.68 25	94.20 18	73.86 5	93.98 1	92.82 36	76.62 57	83.68 49	94.46 11	67.93 59	95.95 35	84.20 29	94.39 36	93.23 54

CSCG		86.41 33	86.19 32	87.07 35	92.91 40	72.48 30	90.81 34	93.56 9	73.95 97	83.16 55	91.07 76	75.94 6	95.19 55	79.94 58	94.38 37	93.55 45

MSLP-MVS++		85.43 46	85.76 40	84.45 81	91.93 55	70.24 58	90.71 36	92.86 32	77.46 41	84.22 43	92.81 50	67.16 67	92.94 152	80.36 54	94.35 38	90.16 147

mPP-MVS		86.67 28	86.32 29	87.72 22	94.41 12	73.55 9	92.74 11	92.22 52	76.87 50	82.81 60	94.25 17	66.44 71	96.24 26	82.88 40	94.28 39	93.38 49

SD-MVS		88.06 7	88.50 7	86.71 40	92.60 48	72.71 24	91.81 23	93.19 19	77.87 32	90.32 3	94.00 25	74.83 10	93.78 112	87.63 7	94.27 40	93.65 41

MVS_0304		86.37 35	85.81 39	88.02 8	90.13 75	72.39 32	89.66 60	92.75 37	81.64 6	82.66 63	92.04 54	64.44 86	97.35 1	84.76 21	94.25 41	94.33 16

HSP-MVS		89.28 1	89.76 1	87.85 18	94.28 15	73.46 14	92.90 8	92.73 38	80.27 13	91.35 1	94.16 20	78.35 2	96.77 9	89.59 1	94.22 42	93.33 52

DELS-MVS		85.41 47	85.30 45	85.77 55	88.49 127	67.93 107	85.52 194	93.44 12	78.70 28	83.63 52	89.03 117	74.57 11	95.71 38	80.26 56	94.04 43	93.66 36
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
EPNet		83.72 57	82.92 63	86.14 51	84.22 200	69.48 74	91.05 32	85.27 222	81.30 8	76.83 139	91.65 62	66.09 74	95.56 41	76.00 89	93.85 44	93.38 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	77.84 4	85.48 44	84.47 52	88.51 2	91.08 63	73.49 13	93.18 4	93.78 6	80.79 11	76.66 142	93.37 34	60.40 160	96.75 11	77.20 78	93.73 45	95.29 1

CANet		86.45 30	86.10 34	87.51 27	90.09 77	70.94 49	89.70 59	92.59 42	81.78 4	81.32 73	91.43 71	70.34 41	97.23 2	84.26 27	93.36 46	94.37 13

新几何1				83.42 112	93.13 35	70.71 54		85.48 220	57.43 286	81.80 70	91.98 56	63.28 96	92.27 171	64.60 188	92.99 47	87.27 236

1121		80.84 97	79.77 102	84.05 94	93.11 37	70.78 53	84.66 205	85.42 221	57.37 287	81.76 71	92.02 55	63.41 94	94.12 93	67.28 165	92.93 48	87.26 237

HPM-MVS_fast		85.35 48	84.95 49	86.57 44	93.69 24	70.58 56	92.15 19	91.62 79	73.89 98	82.67 62	94.09 23	62.60 120	95.54 42	80.93 49	92.93 48	93.57 44

旧先验1					91.96 54	65.79 140		86.37 212			93.08 43	69.31 52			92.74 50	88.74 197

3Dnovator	76.31 5	83.38 64	82.31 70	86.59 43	87.94 142	72.94 22	90.64 37	92.14 56	77.21 42	75.47 163	92.83 47	58.56 167	94.72 75	73.24 119	92.71 51	92.13 88

MVS_111021_HR		85.14 51	84.75 51	86.32 48	91.65 58	72.70 25	85.98 175	90.33 117	76.11 68	82.08 66	91.61 65	71.36 35	94.17 92	81.02 48	92.58 52	92.08 89

APD-MVS_3200maxsize		85.97 38	85.88 36	86.22 49	92.69 44	69.53 73	91.93 21	92.99 27	73.54 109	85.94 17	94.51 10	65.80 78	95.61 39	83.04 38	92.51 53	93.53 47

MAR-MVS		81.84 82	80.70 88	85.27 61	91.32 61	71.53 43	89.82 53	90.92 98	69.77 169	78.50 100	86.21 198	62.36 127	94.52 79	65.36 181	92.05 54	89.77 172
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
TSAR-MVS + GP.		85.71 42	85.33 43	86.84 37	91.34 60	72.50 29	89.07 74	87.28 202	76.41 59	85.80 20	90.22 92	74.15 19	95.37 52	81.82 45	91.88 55	92.65 72

IS-MVSNet		83.15 65	82.81 64	84.18 90	89.94 81	63.30 199	91.59 24	88.46 183	79.04 25	79.49 88	92.16 52	65.10 82	94.28 84	67.71 160	91.86 56	94.95 3

Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 237	88.64 123	51.78 295	86.70 157	79.63 280	74.14 95	75.11 177	90.83 83	61.29 143	89.75 226	58.10 239	91.60 57	92.69 71

MG-MVS		83.41 62	83.45 55	83.28 117	92.74 43	62.28 216	88.17 105	89.50 144	75.22 81	81.49 72	92.74 51	66.75 68	95.11 58	72.85 121	91.58 58	92.45 76

CPTT-MVS		83.73 56	83.33 57	84.92 72	93.28 32	70.86 52	92.09 20	90.38 112	68.75 190	79.57 87	92.83 47	60.60 156	93.04 150	80.92 50	91.56 59	90.86 117

test222					91.50 59	68.26 101	84.16 220	83.20 242	54.63 298	79.74 85	91.63 64	58.97 165			91.42 60	86.77 247

testdata				79.97 206	90.90 66	64.21 181		84.71 226	59.27 273	85.40 22	92.91 44	62.02 133	89.08 238	68.95 154	91.37 61	86.63 251

abl_6		85.23 49	84.95 49	86.07 52	92.23 51	70.48 57	90.80 35	92.08 57	73.51 110	85.26 24	94.16 20	62.75 113	95.92 36	82.46 44	91.30 62	91.81 96

API-MVS		81.99 80	81.23 82	84.26 88	90.94 65	70.18 64	91.10 31	89.32 149	71.51 148	78.66 98	88.28 135	65.26 80	95.10 61	64.74 187	91.23 63	87.51 230

Vis-MVSNet		83.46 61	82.80 65	85.43 58	90.25 74	68.74 89	90.30 46	90.13 126	76.33 65	80.87 81	92.89 45	61.00 149	94.20 89	72.45 128	90.97 64	93.35 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 184	69.91 65	90.57 38	90.97 97	66.70 206	72.17 204	91.91 57	54.70 195	93.96 98	61.81 208	90.95 65	88.41 214

UA-Net		85.08 52	84.96 48	85.45 57	92.07 53	68.07 105	89.78 56	90.86 100	82.48 2	84.60 37	93.20 37	69.35 51	95.22 54	71.39 140	90.88 66	93.07 61

ACMMP		85.89 40	85.39 42	87.38 29	93.59 27	72.63 26	92.74 11	93.18 20	76.78 52	80.73 82	93.82 28	64.33 87	96.29 24	82.67 42	90.69 67	93.23 54
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
Regformer-1		86.41 33	86.33 28	86.64 41	89.33 97	70.93 50	88.43 92	91.39 88	82.14 3	86.65 15	90.09 94	74.39 15	95.01 64	83.97 30	90.63 68	93.97 29

Regformer-2		86.63 29	86.53 27	86.95 36	89.33 97	71.24 44	88.43 92	92.05 58	82.50 1	86.88 14	90.09 94	74.45 12	95.61 39	84.38 25	90.63 68	94.01 27

UGNet		80.83 99	79.59 107	84.54 78	88.04 139	68.09 104	89.42 63	88.16 185	76.95 48	76.22 150	89.46 108	49.30 247	93.94 100	68.48 157	90.31 70	91.60 98
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
MVSFormer		82.85 70	82.05 73	85.24 62	87.35 156	70.21 59	90.50 40	90.38 112	68.55 193	81.32 73	89.47 106	61.68 134	93.46 128	78.98 61	90.26 71	92.05 90

lupinMVS		81.39 91	80.27 96	84.76 75	87.35 156	70.21 59	85.55 190	86.41 210	62.85 245	81.32 73	88.61 125	61.68 134	92.24 173	78.41 67	90.26 71	91.83 94

DP-MVS Recon		83.11 67	82.09 72	86.15 50	94.44 9	70.92 51	88.79 81	92.20 53	70.53 160	79.17 91	91.03 79	64.12 89	96.03 31	68.39 159	90.14 73	91.50 102

MVS_111021_LR		82.61 73	82.11 71	84.11 91	88.82 116	71.58 42	85.15 198	86.16 215	74.69 88	80.47 83	91.04 77	62.29 128	90.55 218	80.33 55	90.08 74	90.20 146

jason		81.39 91	80.29 95	84.70 76	86.63 170	69.90 66	85.95 176	86.77 206	63.24 239	81.07 79	89.47 106	61.08 148	92.15 174	78.33 68	90.07 75	92.05 90
jason: jason.
LFMVS		81.82 83	81.23 82	83.57 109	91.89 56	63.43 197	89.84 52	81.85 260	77.04 47	83.21 53	93.10 39	52.26 214	93.43 132	71.98 134	89.95 76	93.85 33

MVS		78.19 161	76.99 165	81.78 167	85.66 178	66.99 121	84.66 205	90.47 110	55.08 297	72.02 208	85.27 217	63.83 92	94.11 95	66.10 175	89.80 77	84.24 275

CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 180	63.17 205	87.36 128	88.65 179	76.37 63	75.88 156	88.44 131	53.51 205	93.07 147	73.30 118	89.74 78	92.25 83

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 113	65.40 146	84.43 214	92.00 62	67.62 201	78.11 117	85.05 222	66.02 76	94.27 85	71.52 138	89.50 79	89.01 188

PAPM_NR		83.02 68	82.41 67	84.82 74	92.47 49	66.37 130	87.93 112	91.80 72	73.82 103	77.32 131	90.66 85	67.90 60	94.90 69	70.37 144	89.48 80	93.19 58

114514_t		80.68 106	79.51 111	84.20 89	94.09 21	67.27 118	89.64 61	91.11 95	58.75 277	74.08 186	90.72 84	58.10 170	95.04 63	69.70 149	89.42 81	90.30 144

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 246	87.20 162	51.60 296	80.06 253	80.46 272	75.20 82	67.69 257	86.72 174	62.48 125	88.98 240	63.44 192	89.25 82	91.51 101

alignmvs		85.48 44	85.32 44	85.96 54	89.51 92	69.47 75	89.74 57	92.47 43	76.17 67	87.73 12	91.46 70	70.32 42	93.78 112	81.51 46	88.95 83	94.63 6

VNet		82.21 76	82.41 67	81.62 177	90.82 68	60.93 223	84.47 210	89.78 137	76.36 64	84.07 45	91.88 59	64.71 85	90.26 220	70.68 141	88.89 84	93.66 36

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 92	68.21 103	84.28 218	90.09 127	70.79 155	81.26 77	85.62 210	63.15 101	94.29 83	75.62 96	88.87 85	88.59 206

canonicalmvs		85.91 39	85.87 37	86.04 53	89.84 83	69.44 77	90.45 43	93.00 25	76.70 56	88.01 10	91.23 73	73.28 22	93.91 103	81.50 47	88.80 86	94.77 5

QAPM		80.88 95	79.50 112	85.03 67	88.01 141	68.97 83	91.59 24	92.00 62	66.63 210	75.15 176	92.16 52	57.70 172	95.45 45	63.52 191	88.76 87	90.66 126

VDD-MVS		83.01 69	82.36 69	84.96 69	91.02 64	66.40 129	88.91 76	88.11 186	77.57 35	84.39 41	93.29 36	52.19 215	93.91 103	77.05 81	88.70 88	94.57 9

Test4		77.83 172	75.90 189	83.62 106	80.24 279	65.25 152	85.27 196	90.67 102	69.03 186	66.48 269	83.75 234	43.07 278	93.00 151	75.93 90	88.66 89	92.62 73

test_normal		79.81 130	78.45 137	83.89 103	82.70 249	65.40 146	85.82 180	89.48 145	69.39 174	70.12 228	85.66 209	57.15 180	93.71 121	77.08 80	88.62 90	92.56 74

PVSNet_Blended_VisFu		82.62 72	81.83 77	84.96 69	90.80 69	69.76 68	88.74 86	91.70 77	69.39 174	78.96 93	88.46 130	65.47 79	94.87 71	74.42 105	88.57 91	90.24 145

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 245	65.32 150	86.12 172	89.55 142	69.64 173	70.55 219	85.82 206	57.24 178	93.81 110	76.85 83	88.55 92	92.41 78

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 108	68.03 106	84.46 212	90.02 131	70.67 158	81.30 76	86.53 190	63.17 100	94.19 90	75.60 97	88.54 93	88.57 208

PAPR		81.66 86	80.89 87	83.99 99	90.27 73	64.00 187	86.76 156	91.77 76	68.84 189	77.13 138	89.50 104	67.63 62	94.88 70	67.55 162	88.52 94	93.09 60

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 166	63.21 202	86.11 173	92.00 62	74.31 92	82.87 58	89.44 111	70.03 44	93.21 137	77.39 77	88.50 95	93.81 35

AdaColmap		80.58 110	79.42 113	84.06 93	93.09 38	68.91 84	89.36 64	88.97 166	69.27 178	75.70 162	89.69 100	57.20 179	95.77 37	63.06 195	88.41 96	87.50 231

VDDNet		81.52 88	80.67 89	84.05 94	90.44 71	64.13 183	89.73 58	85.91 218	71.11 151	83.18 54	93.48 31	50.54 239	93.49 127	73.40 117	88.25 97	94.54 10

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 149	68.99 82	83.65 226	91.46 87	63.00 242	77.77 124	90.28 89	66.10 73	95.09 62	61.40 211	88.22 98	90.94 115
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+		83.62 59	83.08 59	85.24 62	88.38 132	67.45 113	88.89 77	89.15 156	75.50 75	82.27 64	88.28 135	69.61 49	94.45 81	77.81 72	87.84 99	93.84 34

gg-mvs-nofinetune		69.95 256	67.96 258	75.94 256	83.07 239	54.51 285	77.23 276	70.29 317	63.11 240	70.32 223	62.33 319	43.62 275	88.69 246	53.88 261	87.76 100	84.62 273

Regformer-3		85.23 49	85.07 47	85.70 56	88.95 111	69.01 81	88.29 101	89.91 135	80.95 9	85.01 26	90.01 96	72.45 28	94.19 90	82.50 43	87.57 101	93.90 32

Regformer-4		85.68 43	85.45 41	86.35 45	88.95 111	69.67 70	88.29 101	91.29 90	81.73 5	85.36 23	90.01 96	72.62 27	95.35 53	83.28 34	87.57 101	94.03 25

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 156	70.19 61	85.56 187	88.77 175	69.06 183	81.83 67	88.16 137	50.91 233	92.85 154	78.29 69	87.56 103	89.06 181

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 156	70.19 61	85.56 187	88.77 175	69.06 183	81.83 67	88.16 137	50.91 233	92.85 154	78.29 69	87.56 103	89.06 181

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 156	70.19 61	85.56 187	88.77 175	69.06 183	81.83 67	88.16 137	50.91 233	92.85 154	78.29 69	87.56 103	89.06 181

CLD-MVS		82.31 75	81.65 78	84.29 87	88.47 128	67.73 111	85.81 181	92.35 49	75.78 70	78.33 108	86.58 187	64.01 90	94.35 82	76.05 88	87.48 106	90.79 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 151	68.23 102	84.40 216	86.20 214	67.49 203	76.36 148	86.54 189	61.54 137	90.79 215	61.86 207	87.33 107	90.49 137
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPP-MVSNet		83.40 63	83.02 61	84.57 77	90.13 75	64.47 177	92.32 16	90.73 101	74.45 91	79.35 90	91.10 74	69.05 54	95.12 57	72.78 122	87.22 108	94.13 20

TAMVS		78.89 150	77.51 158	83.03 129	87.80 146	67.79 110	84.72 204	85.05 225	67.63 200	76.75 140	87.70 148	62.25 129	90.82 214	58.53 235	87.13 109	90.49 137

TAPA-MVS	73.13 9	79.15 144	77.94 149	82.79 148	89.59 90	62.99 209	88.16 106	91.51 84	65.77 218	77.14 137	91.09 75	60.91 150	93.21 137	50.26 275	87.05 110	92.17 87
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM		77.68 175	76.40 174	81.51 180	87.29 161	61.85 220	83.78 225	89.59 141	64.74 227	71.23 215	88.70 121	62.59 121	93.66 122	52.66 266	87.03 111	89.01 188

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 107	65.91 137	86.07 174	88.52 182	72.18 138	75.42 166	87.69 149	61.15 146	93.54 125	60.38 218	86.83 112	86.70 249

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 96	65.93 136	84.95 201	87.15 203	73.56 108	78.19 115	89.79 99	56.67 182	93.36 133	59.53 226	86.74 113	90.13 149

LS3D		76.95 192	74.82 204	83.37 115	90.45 70	67.36 117	89.15 72	86.94 205	61.87 255	69.52 237	90.61 86	51.71 226	94.53 78	46.38 295	86.71 114	88.21 216

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 113	64.51 171	85.53 192	89.39 147	70.79 155	78.49 101	85.06 221	67.54 63	93.58 123	67.03 170	86.58 115	92.32 80

EPNet_dtu		75.46 213	74.86 203	77.23 249	82.57 252	54.60 283	86.89 149	83.09 243	71.64 143	66.25 271	85.86 204	55.99 185	88.04 253	54.92 256	86.55 116	89.05 185
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		83.50 60	82.95 62	85.14 64	88.79 119	70.95 48	89.13 73	91.52 83	77.55 38	80.96 80	91.75 60	60.71 152	94.50 80	79.67 59	86.51 117	89.97 165

OMC-MVS		82.69 71	81.97 76	84.85 73	88.75 121	67.42 114	87.98 108	90.87 99	74.92 86	79.72 86	91.65 62	62.19 131	93.96 98	75.26 101	86.42 118	93.16 59

HQP_MVS		83.64 58	83.14 58	85.14 64	90.08 78	68.71 91	91.25 28	92.44 44	79.12 23	78.92 94	91.00 80	60.42 158	95.38 50	78.71 63	86.32 119	91.33 105

plane_prior5								92.44 44					95.38 50	78.71 63	86.32 119	91.33 105

plane_prior						68.71 91	90.38 44		77.62 34						86.16 121

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 196	57.97 247	82.59 233	87.62 197	67.40 205	76.17 154	88.56 128	68.47 56	89.59 229	70.65 142	86.05 122	93.47 48

HQP3-MVS								92.19 54							85.99 123

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 97	66.98 122	89.17 68	92.19 54	76.41 59	77.23 134	90.23 91	60.17 161	95.11 58	77.47 75	85.99 123	91.03 111

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 117	65.13 156	84.87 202	87.85 194	69.75 170	74.52 184	84.74 227	61.34 141	93.11 145	58.24 238	85.84 125	84.27 274

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 80	68.58 96	78.70 267	87.50 199	56.38 292	75.80 157	86.84 170	58.67 166	91.40 198	61.58 210	85.75 126	90.34 143

cascas		76.72 195	74.64 205	82.99 131	85.78 177	65.88 138	82.33 235	89.21 155	60.85 261	72.74 196	81.02 268	47.28 256	93.75 116	67.48 163	85.02 127	89.34 178

FIs		82.07 78	82.42 66	81.04 190	88.80 118	58.34 242	88.26 103	93.49 11	76.93 49	78.47 102	91.04 77	69.92 46	92.34 170	69.87 148	84.97 128	92.44 77

test-LLR		72.94 236	72.43 223	74.48 268	81.35 267	58.04 245	78.38 268	77.46 289	66.66 207	69.95 232	79.00 283	48.06 253	79.24 294	66.13 173	84.83 129	86.15 255

test-mter		71.41 244	70.39 242	74.48 268	81.35 267	58.04 245	78.38 268	77.46 289	60.32 264	69.95 232	79.00 283	36.08 307	79.24 294	66.13 173	84.83 129	86.15 255

diffmvs		79.51 135	78.59 133	82.25 159	83.31 232	62.66 211	84.17 219	88.11 186	67.64 199	76.09 155	87.47 156	64.01 90	91.15 204	71.71 137	84.82 131	92.94 67

EI-MVSNet-Vis-set		84.19 53	83.81 53	85.31 59	88.18 136	67.85 108	87.66 116	89.73 139	80.05 17	82.95 56	89.59 103	70.74 40	94.82 72	80.66 53	84.72 132	93.28 53

GG-mvs-BLEND				75.38 262	81.59 262	55.80 279	79.32 260	69.63 319		67.19 262	73.67 306	43.24 276	88.90 245	50.41 272	84.50 133	81.45 296

FC-MVSNet-test		81.52 88	82.02 74	80.03 205	88.42 131	55.97 274	87.95 110	93.42 13	77.10 45	77.38 129	90.98 82	69.96 45	91.79 180	68.46 158	84.50 133	92.33 79

PVSNet	64.34 18	72.08 241	70.87 239	75.69 258	86.21 174	56.44 267	74.37 291	80.73 269	62.06 254	70.17 226	82.23 249	42.86 280	83.31 281	54.77 257	84.45 135	87.32 235

MS-PatchMatch		73.83 223	72.67 221	77.30 248	83.87 219	66.02 134	81.82 238	84.66 227	61.37 259	68.61 250	82.82 243	47.29 255	88.21 250	59.27 227	84.32 136	77.68 307

TESTMET0.1,1		69.89 257	69.00 248	72.55 278	79.27 291	56.85 259	78.38 268	74.71 305	57.64 284	68.09 254	77.19 294	37.75 301	76.70 305	63.92 190	84.09 137	84.10 278

EI-MVSNet-UG-set		83.81 55	83.38 56	85.09 66	87.87 143	67.53 112	87.44 127	89.66 140	79.74 18	82.23 65	89.41 112	70.24 43	94.74 74	79.95 57	83.92 138	92.99 66

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 105	68.76 87	90.22 48	91.94 66	75.37 78	76.64 143	91.51 67	54.29 198	94.91 67	78.44 65	83.78 139	89.83 169

LGP-MVS_train				84.50 79	89.23 105	68.76 87		91.94 66	75.37 78	76.64 143	91.51 67	54.29 198	94.91 67	78.44 65	83.78 139	89.83 169

thres400		76.50 197	75.37 196	79.86 207	89.13 109	57.65 252	85.17 197	83.60 236	73.41 112	76.45 145	86.39 193	52.12 216	91.95 177	48.33 282	83.75 141	90.00 158

thres600view7		76.50 197	75.44 193	79.68 211	89.40 95	57.16 256	85.53 192	83.23 241	73.79 104	76.26 149	87.09 167	51.89 220	91.89 178	48.05 285	83.72 142	90.00 158

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 179	69.02 80	83.03 232	90.39 111	71.09 152	77.63 126	91.49 69	54.62 197	91.35 199	75.71 94	83.47 143	91.54 100

view600		76.20 202	75.21 198	79.16 220	89.64 85	55.82 275	85.74 182	82.06 255	73.88 99	75.74 158	87.85 143	51.84 221	91.66 190	46.75 288	83.42 144	90.00 158

view800		76.20 202	75.21 198	79.16 220	89.64 85	55.82 275	85.74 182	82.06 255	73.88 99	75.74 158	87.85 143	51.84 221	91.66 190	46.75 288	83.42 144	90.00 158

conf0.05thres1000		76.20 202	75.21 198	79.16 220	89.64 85	55.82 275	85.74 182	82.06 255	73.88 99	75.74 158	87.85 143	51.84 221	91.66 190	46.75 288	83.42 144	90.00 158

tfpn		76.20 202	75.21 198	79.16 220	89.64 85	55.82 275	85.74 182	82.06 255	73.88 99	75.74 158	87.85 143	51.84 221	91.66 190	46.75 288	83.42 144	90.00 158

CNLPA		78.08 163	76.79 169	81.97 164	90.40 72	71.07 46	87.59 118	84.55 228	66.03 217	72.38 202	89.64 102	57.56 174	86.04 266	59.61 224	83.35 148	88.79 195

MVP-Stereo		76.12 206	74.46 209	81.13 189	85.37 183	69.79 67	84.42 215	87.95 192	65.03 225	67.46 259	85.33 216	53.28 207	91.73 183	58.01 240	83.27 149	81.85 294
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		76.53 196	75.30 197	80.21 203	83.93 218	62.32 215	84.66 205	88.81 174	60.23 265	70.16 227	84.07 231	55.30 190	90.73 216	67.37 164	83.21 150	87.59 229

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 187	69.75 69	89.28 65	88.10 188	74.99 84	76.44 147	86.72 174	57.27 176	94.26 88	73.53 115	83.18 151	91.87 93

HyFIR lowres test		77.53 179	75.40 195	83.94 102	89.59 90	66.62 126	80.36 251	88.64 180	56.29 293	76.45 145	85.17 218	57.64 173	93.28 135	61.34 213	83.10 152	91.91 92

ACMP	74.13 6	81.51 90	80.57 90	84.36 84	89.42 94	68.69 94	89.97 51	91.50 86	74.46 90	75.04 179	90.41 88	53.82 203	94.54 77	77.56 74	82.91 153	89.86 168
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	73.20 8	80.78 105	79.84 101	83.58 108	89.31 102	68.37 98	89.99 50	91.60 80	70.28 163	77.25 132	89.66 101	53.37 206	93.53 126	74.24 108	82.85 154	88.85 192
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMMVS		69.34 259	68.67 250	71.35 285	75.67 304	62.03 218	75.17 285	73.46 310	50.00 314	68.68 247	79.05 281	52.07 217	78.13 299	61.16 214	82.77 155	73.90 315

PLC	70.83 11	78.05 164	76.37 175	83.08 126	91.88 57	67.80 109	88.19 104	89.46 146	64.33 232	69.87 234	88.38 132	53.66 204	93.58 123	58.86 231	82.73 156	87.86 223
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 186	76.18 181	81.20 186	88.24 135	63.24 201	84.61 208	86.40 211	67.55 202	77.81 122	86.48 192	54.10 200	93.15 142	57.75 242	82.72 157	87.20 238

pcd1.5k->3k		34.07 309	35.26 309	30.50 324	86.92 165	0.00 344	0.00 335	91.58 81	0.00 339	0.00 340	0.00 341	56.23 184	0.00 342	0.00 339	82.60 158	91.49 103

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 129	60.91 224	83.84 224	89.24 154	70.36 162	79.03 92	88.87 119	63.23 99	90.21 222	65.12 182	82.57 159	92.28 82

HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 155	60.21 230	83.37 231	87.78 195	66.11 214	75.37 168	87.06 169	63.27 97	90.48 219	61.38 212	82.43 160	90.40 142

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 171	67.27 118	89.27 66	91.51 84	71.75 142	79.37 89	90.22 92	63.15 101	94.27 85	77.69 73	82.36 161	91.49 103

PVSNet_BlendedMVS		80.60 108	80.02 97	82.36 158	88.85 113	65.40 146	86.16 171	92.00 62	69.34 177	78.11 117	86.09 201	66.02 76	94.27 85	71.52 138	82.06 162	87.39 232

WTY-MVS		75.65 212	75.68 191	75.57 260	86.40 172	56.82 260	77.92 273	82.40 249	65.10 224	76.18 152	87.72 147	63.13 104	80.90 288	60.31 219	81.96 163	89.00 190

ACMMP++_ref															81.95 164

DP-MVS		76.78 194	74.57 206	83.42 112	93.29 31	69.46 76	88.55 91	83.70 235	63.98 236	70.20 224	88.89 118	54.01 202	94.80 73	46.66 292	81.88 165	86.01 259

CMPMVS	51.72 21	70.19 255	68.16 255	76.28 254	73.15 313	57.55 253	79.47 259	83.92 233	48.02 316	56.48 308	84.81 225	43.13 277	86.42 264	62.67 199	81.81 166	84.89 269
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 180	68.78 86	83.54 229	90.50 109	70.66 159	76.71 141	91.66 61	60.69 153	91.26 201	76.94 82	81.58 167	91.83 94

MIMVSNet		70.69 249	69.30 245	74.88 265	84.52 194	56.35 270	75.87 283	79.42 281	64.59 228	67.76 255	82.41 246	41.10 289	81.54 287	46.64 294	81.34 168	86.75 248

ACMMP++															81.25 169

GA-MVS		76.87 193	75.17 202	81.97 164	82.75 247	62.58 212	81.44 245	86.35 213	72.16 140	74.74 182	82.89 241	46.20 262	92.02 176	68.85 155	81.09 170	91.30 107

sss		73.60 225	73.64 215	73.51 275	82.80 246	55.01 282	76.12 279	81.69 261	62.47 250	74.68 183	85.85 205	57.32 175	78.11 300	60.86 216	80.93 171	87.39 232

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 187	68.74 89	88.77 82	88.10 188	74.99 84	74.97 180	83.49 238	57.27 176	93.36 133	73.53 115	80.88 172	91.18 109

EG-PatchMatch MVS		74.04 221	71.82 229	80.71 195	84.92 190	67.42 114	85.86 179	88.08 190	66.04 216	64.22 282	83.85 232	35.10 309	92.56 163	57.44 244	80.83 173	82.16 293

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 193	66.57 128	89.25 67	90.16 125	69.20 180	75.46 164	89.49 105	45.75 267	93.13 144	76.84 84	80.80 174	90.11 150

1112_ss		77.40 188	76.43 173	80.32 200	89.11 110	60.41 229	83.65 226	87.72 196	62.13 253	73.05 194	86.72 174	62.58 122	89.97 224	62.11 205	80.80 174	90.59 131

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 192	66.37 130	89.17 68	90.19 124	69.38 176	75.40 167	89.46 108	44.17 273	93.15 142	76.78 85	80.70 176	90.14 148

PatchMatch-RL		72.38 239	70.90 238	76.80 252	88.60 124	67.38 116	79.53 258	76.17 295	62.75 247	69.36 240	82.00 255	45.51 268	84.89 274	53.62 262	80.58 177	78.12 305

EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 197	63.19 204	86.41 165	88.95 168	74.18 94	78.69 96	87.54 154	66.62 69	92.43 165	72.57 127	80.57 178	90.74 121

MVSTER		79.01 147	77.88 150	82.38 157	83.07 239	64.80 163	84.08 223	88.95 168	69.01 187	78.69 96	87.17 166	54.70 195	92.43 165	74.69 104	80.57 178	89.89 167

testing_2		75.73 211	73.34 218	82.89 140	77.37 297	65.22 153	84.10 222	90.54 108	69.09 182	60.46 293	81.15 266	40.48 291	92.84 157	76.36 86	80.54 180	90.60 129

XVG-ACMP-BASELINE		76.11 207	74.27 211	81.62 177	83.20 235	64.67 165	83.60 228	89.75 138	69.75 170	71.85 209	87.09 167	32.78 310	92.11 175	69.99 147	80.43 181	88.09 218

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 238	66.96 124	86.94 147	87.45 201	72.45 133	71.49 214	84.17 229	54.79 194	91.58 196	67.61 161	80.31 182	89.30 179

LTVRE_ROB	69.57 13	76.25 201	74.54 208	81.41 182	88.60 124	64.38 180	79.24 261	89.12 157	70.76 157	69.79 236	87.86 142	49.09 249	93.20 139	56.21 252	80.16 183	86.65 250
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_1112_low_res		76.40 199	75.44 193	79.27 216	89.28 103	58.09 244	81.69 241	87.07 204	59.53 271	72.48 200	86.67 180	61.30 142	89.33 233	60.81 217	80.15 184	90.41 141

test_djsdf		80.30 118	79.32 117	83.27 118	83.98 217	65.37 149	90.50 40	90.38 112	68.55 193	76.19 151	88.70 121	56.44 183	93.46 128	78.98 61	80.14 185	90.97 114

CHOSEN 280x420		66.51 273	64.71 272	71.90 280	81.45 264	63.52 193	57.98 325	68.95 324	53.57 304	62.59 289	76.70 295	46.22 261	75.29 312	55.25 255	79.68 186	76.88 313

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 151	63.01 206	88.39 96	89.28 150	71.63 144	75.34 169	87.28 159	54.80 191	91.11 205	62.72 196	79.57 187	90.09 152

test1		78.40 155	77.40 159	81.40 183	87.60 151	63.01 206	88.39 96	89.28 150	71.63 144	75.34 169	87.28 159	54.80 191	91.11 205	62.72 196	79.57 187	90.09 152

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 167	62.36 213	86.52 162	88.77 175	71.13 150	75.34 169	86.66 181	54.07 201	91.10 208	62.72 196	79.57 187	89.45 177

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 151	62.89 210	87.47 126	89.02 159	71.63 144	75.29 173	87.28 159	54.80 191	91.10 208	62.38 200	79.38 190	89.61 175

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 277	67.08 120	87.24 135	89.53 143	65.66 220	75.16 175	87.19 165	52.52 208	92.25 172	77.17 79	79.34 191	89.61 175

nrg030		83.88 54	83.53 54	84.96 69	86.77 169	69.28 78	90.46 42	92.67 39	74.79 87	82.95 56	91.33 72	72.70 26	93.09 146	80.79 52	79.28 192	92.50 75

VPA-MVSNet		80.60 108	80.55 91	80.76 194	88.07 138	60.80 226	86.86 150	91.58 81	75.67 73	80.24 84	89.45 110	63.34 95	90.25 221	70.51 143	79.22 193	91.23 108

F-COLMAP		76.38 200	74.33 210	82.50 155	89.28 103	66.95 125	88.41 95	89.03 158	64.05 234	66.83 265	88.61 125	46.78 259	92.89 153	57.48 243	78.55 194	87.67 226

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 168	63.01 206	88.39 96	89.28 150	70.49 161	74.39 185	87.28 159	49.06 250	91.11 205	60.91 215	78.52 195	90.09 152

MDTV_nov1_ep13			69.97 244		83.18 236	53.48 290	77.10 277	80.18 277	60.45 262	69.33 241	80.44 272	48.89 251	86.90 259	51.60 268	78.51 196

CVMVSNet		72.99 235	72.58 222	74.25 271	84.28 197	50.85 302	86.41 165	83.45 239	44.56 318	73.23 192	87.54 154	49.38 245	85.70 268	65.90 177	78.44 197	86.19 254

tpm2		73.26 231	71.46 231	78.63 228	83.34 231	56.71 263	80.65 249	80.40 273	56.63 291	73.55 188	82.02 254	51.80 225	91.24 202	56.35 251	78.42 198	87.95 220

CostFormer		75.24 216	73.90 214	79.27 216	82.65 251	58.27 243	80.80 246	82.73 247	61.57 256	75.33 172	83.13 240	55.52 188	91.07 211	64.98 185	78.34 199	88.45 212

ACMH	67.68 16	75.89 209	73.93 213	81.77 168	88.71 122	66.61 127	88.62 88	89.01 161	69.81 168	66.78 266	86.70 179	41.95 287	91.51 197	55.64 253	78.14 200	87.17 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CR-MVSNet		73.37 228	71.27 234	79.67 212	81.32 269	65.19 154	75.92 281	80.30 274	59.92 268	72.73 197	81.19 264	52.50 209	86.69 260	59.84 222	77.71 201	87.11 242

RPMNet		71.62 242	68.94 249	79.67 212	81.32 269	65.19 154	75.92 281	78.30 286	57.60 285	72.73 197	76.45 297	52.30 213	86.69 260	48.14 284	77.71 201	87.11 242

Patchmatch-test1		73.49 226	71.85 228	78.41 234	84.05 216	62.17 217	79.96 255	79.29 282	66.30 213	72.38 202	79.58 280	51.95 219	85.08 273	55.46 254	77.67 203	87.99 219

tpmp4_e23		73.45 227	71.17 236	80.31 201	83.55 227	59.56 234	81.88 237	82.33 250	57.94 282	70.51 221	81.62 262	51.19 231	91.63 194	53.96 260	77.51 204	89.75 173

v7		80.24 119	79.26 122	83.15 122	84.07 214	64.94 160	87.56 122	90.67 102	72.26 137	78.28 109	86.51 191	61.45 139	94.03 97	75.14 102	77.41 205	90.49 137

DWT-MVSNet_test		73.70 224	71.86 227	79.21 218	82.91 244	58.94 237	82.34 234	82.17 252	65.21 222	71.05 217	78.31 285	44.21 272	90.17 223	63.29 194	77.28 206	88.53 211

v1144		80.03 126	79.03 126	83.01 130	83.78 223	64.51 171	87.11 142	90.57 107	71.96 141	78.08 119	86.20 199	61.41 140	93.94 100	74.93 103	77.23 207	90.60 129

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 119	58.35 241	85.06 199	88.61 181	78.56 29	77.65 125	88.34 133	63.81 93	90.66 217	64.98 185	77.22 208	91.80 97

v1192		79.59 134	78.43 140	83.07 127	83.55 227	64.52 169	86.93 148	90.58 106	70.83 154	77.78 123	85.90 202	59.15 164	93.94 100	73.96 110	77.19 209	90.76 119

VPNet		78.69 152	78.66 131	78.76 227	88.31 134	55.72 280	84.45 213	86.63 208	76.79 51	78.26 113	90.55 87	59.30 163	89.70 228	66.63 171	77.05 210	90.88 116

v1neww		80.40 113	79.54 108	82.98 132	84.10 208	64.51 171	87.57 119	90.22 121	73.25 114	78.47 102	86.65 182	62.83 109	93.86 106	75.72 92	77.02 211	90.58 132

v7new		80.40 113	79.54 108	82.98 132	84.10 208	64.51 171	87.57 119	90.22 121	73.25 114	78.47 102	86.65 182	62.83 109	93.86 106	75.72 92	77.02 211	90.58 132

v6		80.40 113	79.54 108	82.98 132	84.09 210	64.50 175	87.57 119	90.22 121	73.25 114	78.47 102	86.63 184	62.84 108	93.86 106	75.73 91	77.02 211	90.58 132

v1240		78.99 148	77.78 153	82.64 153	83.21 234	63.54 192	86.62 159	90.30 119	69.74 172	77.33 130	85.68 207	57.04 181	93.76 115	73.13 120	76.92 214	90.62 127

MSDG		73.36 230	70.99 237	80.49 196	84.51 195	65.80 139	80.71 248	86.13 216	65.70 219	65.46 274	83.74 235	44.60 270	90.91 213	51.13 270	76.89 215	84.74 271

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 175	63.20 203	86.79 153	89.34 148	74.19 93	75.45 165	86.72 174	66.62 69	92.39 167	72.58 126	76.86 216	90.75 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		79.22 143	78.03 147	82.80 146	83.30 233	63.94 189	86.80 152	90.33 117	69.91 167	77.48 128	85.53 212	58.44 168	93.75 116	73.60 114	76.85 217	90.71 122

XXY-MVS		75.41 214	75.56 192	74.96 264	83.59 226	57.82 250	80.59 250	83.87 234	66.54 211	74.93 181	88.31 134	63.24 98	80.09 292	62.16 203	76.85 217	86.97 244

v1141		80.19 122	79.31 118	82.85 141	83.84 220	64.12 184	87.14 137	90.08 128	73.13 117	78.27 110	86.39 193	62.67 118	93.75 116	75.40 99	76.83 219	90.68 123

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 220	64.11 186	87.13 140	90.08 128	73.13 117	78.27 110	86.39 193	62.69 116	93.75 116	75.40 99	76.82 220	90.68 123

v1		80.19 122	79.31 118	82.85 141	83.83 222	64.12 184	87.14 137	90.07 130	73.13 117	78.27 110	86.38 196	62.72 115	93.75 116	75.41 98	76.82 220	90.68 123

v2v482		80.23 120	79.29 121	83.05 128	83.62 225	64.14 182	87.04 144	89.97 132	73.61 106	78.18 116	87.22 163	61.10 147	93.82 109	76.11 87	76.78 222	91.18 109

v144192		79.47 138	78.37 141	82.78 149	83.35 230	63.96 188	86.96 146	90.36 115	69.99 166	77.50 127	85.67 208	60.66 154	93.77 114	74.27 107	76.58 223	90.62 127

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 132	64.41 179	87.60 117	93.02 24	78.42 31	78.56 99	88.16 137	69.78 47	93.26 136	69.58 150	76.49 224	91.60 98

UniMVSNet_NR-MVSNet		81.88 81	81.54 79	82.92 137	88.46 129	63.46 195	87.13 140	92.37 48	80.19 15	78.38 106	89.14 114	71.66 33	93.05 148	70.05 145	76.46 225	92.25 83

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 146	63.46 195	87.02 145	91.87 70	79.01 26	78.38 106	89.07 115	65.02 83	93.05 148	70.05 145	76.46 225	92.20 85

PatchmatchNet		73.12 233	71.33 233	78.49 233	83.18 236	60.85 225	79.63 257	78.57 284	64.13 233	71.73 210	79.81 279	51.20 230	85.97 267	57.40 245	76.36 227	88.66 198
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		70.33 253	68.37 252	76.21 255	80.60 275	56.23 271	79.19 263	86.49 209	60.89 260	61.29 290	85.47 214	31.78 313	89.47 232	53.37 263	76.21 228	82.94 290

OpenMVS_ROB	64.09 19	70.56 251	68.19 254	77.65 242	80.26 278	59.41 236	85.01 200	82.96 245	58.76 276	65.43 275	82.33 247	37.63 303	91.23 203	45.34 300	76.03 229	82.32 291

ACMH+	68.96 14	76.01 208	74.01 212	82.03 163	88.60 124	65.31 151	88.86 78	87.55 198	70.25 164	67.75 256	87.47 156	41.27 288	93.19 140	58.37 236	75.94 230	87.60 228

PatchFormer-LS_test		74.50 218	73.05 219	78.86 226	82.95 243	59.55 235	81.65 242	82.30 251	67.44 204	71.62 212	78.15 287	52.34 212	88.92 244	65.05 184	75.90 231	88.12 217

tpm		72.37 240	71.71 230	74.35 270	82.19 256	52.00 293	79.22 262	77.29 291	64.56 229	72.95 195	83.68 237	51.35 228	83.26 282	58.33 237	75.80 232	87.81 224

Anonymous20231206		68.60 261	67.80 262	71.02 287	80.23 280	50.75 303	78.30 271	80.47 271	56.79 290	66.11 272	82.63 245	46.35 260	78.95 296	43.62 303	75.70 233	83.36 282

v7n		78.97 149	77.58 157	83.14 123	83.45 229	65.51 144	88.32 99	91.21 92	73.69 105	72.41 201	86.32 197	57.93 171	93.81 110	69.18 153	75.65 234	90.11 150

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 146	63.34 198	86.31 168	91.09 96	79.01 26	72.17 204	89.07 115	67.20 66	92.81 158	66.08 176	75.65 234	92.20 85

v10		79.74 132	78.67 130	82.97 136	84.06 215	64.95 159	87.88 114	90.62 105	73.11 120	75.11 177	86.56 188	61.46 138	94.05 96	73.68 111	75.55 236	89.90 166

IB-MVS	68.01 15	75.85 210	73.36 217	83.31 116	84.76 191	66.03 133	83.38 230	85.06 224	70.21 165	69.40 238	81.05 267	45.76 266	94.66 76	65.10 183	75.49 237	89.25 180
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
V42		79.38 141	78.24 145	82.83 144	81.10 271	65.50 145	85.55 190	89.82 136	71.57 147	78.21 114	86.12 200	60.66 154	93.18 141	75.64 95	75.46 238	89.81 171

v8		79.97 128	79.02 127	82.80 146	84.09 210	64.50 175	87.96 109	90.29 120	74.13 96	75.24 174	86.81 171	62.88 106	93.89 105	74.39 106	75.40 239	90.00 158

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 243	85.79 176	56.21 272	86.78 154	85.76 219	73.60 107	77.93 121	87.57 152	65.02 83	88.99 239	67.14 168	75.33 240	87.63 227

pmmvs5		71.55 243	70.20 243	75.61 259	77.83 294	56.39 268	81.74 240	80.89 266	57.76 283	67.46 259	84.49 228	49.26 248	85.32 272	57.08 248	75.29 241	85.11 268

EPMVS		69.02 260	68.16 255	71.59 281	79.61 285	49.80 308	77.40 275	66.93 326	62.82 246	70.01 229	79.05 281	45.79 265	77.86 302	56.58 250	75.26 242	87.13 241

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 144	62.33 214	87.74 115	91.33 89	80.55 12	77.99 120	89.86 98	65.23 81	92.62 160	67.05 169	75.24 243	92.30 81

COLMAP_ROB	66.92 17	73.01 234	70.41 241	80.81 193	87.13 163	65.63 141	88.30 100	84.19 232	62.96 243	63.80 285	87.69 149	38.04 300	92.56 163	46.66 292	74.91 244	84.24 275
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		68.46 264	67.85 260	70.29 289	80.70 274	43.93 316	72.47 294	74.88 301	60.15 266	70.55 219	76.57 296	49.94 243	81.59 286	50.58 271	74.83 245	85.34 264

pmmvs4		74.03 222	71.91 226	80.39 197	81.96 258	68.32 99	81.45 244	82.14 253	59.32 272	69.87 234	85.13 219	52.40 211	88.13 252	60.21 220	74.74 246	84.73 272

ITE_SJBPF				78.22 236	81.77 260	60.57 227		83.30 240	69.25 179	67.54 258	87.20 164	36.33 306	87.28 258	54.34 258	74.62 247	86.80 246

test0.0.03 1		68.00 265	67.69 264	68.90 294	77.55 295	47.43 310	75.70 284	72.95 312	66.66 207	66.56 267	82.29 248	48.06 253	75.87 309	44.97 301	74.51 248	83.41 281

test_0402		72.79 237	70.44 240	79.84 208	88.13 137	65.99 135	85.93 177	84.29 230	65.57 221	67.40 261	85.49 213	46.92 258	92.61 161	35.88 314	74.38 249	80.94 297

CP-MVSNet		78.22 158	78.34 142	77.84 239	87.83 145	54.54 284	87.94 111	91.17 94	77.65 33	73.48 189	88.49 129	62.24 130	88.43 248	62.19 202	74.07 250	90.55 135

FMVSNet5		69.50 258	67.96 258	74.15 272	82.97 242	55.35 281	80.01 254	82.12 254	62.56 249	63.02 286	81.53 263	36.92 304	81.92 285	48.42 281	74.06 251	85.17 267

MVS-HIRNet		59.14 288	57.67 290	63.57 304	81.65 261	43.50 317	71.73 295	65.06 329	39.59 323	51.43 317	57.73 323	38.34 299	82.58 284	39.53 309	73.95 252	64.62 322

tpmrst		72.39 238	72.13 225	73.18 277	80.54 276	49.91 306	79.91 256	79.08 283	63.11 240	71.69 211	79.95 276	55.32 189	82.77 283	65.66 180	73.89 253	86.87 245

PS-CasMVS		78.01 166	78.09 146	77.77 241	87.71 149	54.39 286	88.02 107	91.22 91	77.50 40	73.26 191	88.64 124	60.73 151	88.41 249	61.88 206	73.88 254	90.53 136

v148		78.72 151	77.80 152	81.47 181	82.73 248	61.96 219	86.30 169	88.08 190	73.26 113	76.18 152	85.47 214	62.46 126	92.36 169	71.92 136	73.82 255	90.09 152

Patchmatch-test		64.82 279	63.24 277	69.57 291	79.42 287	49.82 307	63.49 320	69.05 323	51.98 310	59.95 296	80.13 275	50.91 233	70.98 323	40.66 308	73.57 256	87.90 222

WR-MVS_H		78.51 154	78.49 136	78.56 230	88.02 140	56.38 269	88.43 92	92.67 39	77.14 43	73.89 187	87.55 153	66.25 72	89.24 235	58.92 230	73.55 257	90.06 156

testgi		66.67 272	66.53 268	67.08 299	75.62 305	41.69 321	75.93 280	76.50 294	66.11 214	65.20 278	86.59 186	35.72 308	74.71 313	43.71 302	73.38 258	84.84 270

pm-mvs1		77.25 189	76.68 170	78.93 225	84.22 200	58.62 239	86.41 165	88.36 184	71.37 149	73.31 190	88.01 141	61.22 145	89.15 237	64.24 189	73.01 259	89.03 187

V4		77.95 168	76.37 175	82.67 151	79.40 288	65.52 142	86.43 163	89.94 133	72.28 135	72.14 207	84.95 223	55.72 186	93.44 130	73.64 112	72.86 260	89.05 185

v52		77.94 170	76.37 175	82.67 151	79.39 289	65.52 142	86.43 163	89.94 133	72.28 135	72.15 206	84.94 224	55.70 187	93.44 130	73.64 112	72.84 261	89.06 181

v748		77.97 167	76.65 171	81.92 166	82.29 255	63.28 200	87.53 123	90.35 116	73.50 111	70.76 218	85.55 211	58.28 169	92.81 158	68.81 156	72.76 262	89.67 174

tpmvs		71.09 246	69.29 246	76.49 253	82.04 257	56.04 273	78.92 265	81.37 265	64.05 234	67.18 263	78.28 286	49.74 244	89.77 225	49.67 278	72.37 263	83.67 279

PEN-MVS		77.73 173	77.69 156	77.84 239	87.07 164	53.91 288	87.91 113	91.18 93	77.56 37	73.14 193	88.82 120	61.23 144	89.17 236	59.95 221	72.37 263	90.43 140

DSMNet-mixed		57.77 292	56.90 292	60.38 308	67.70 323	35.61 327	69.18 305	53.97 333	32.30 329	57.49 304	79.88 277	40.39 293	68.57 327	38.78 310	72.37 263	76.97 310

semantic-postprocess				80.11 204	82.69 250	64.85 162		83.47 238	69.16 181	70.49 222	84.15 230	50.83 237	88.15 251	69.23 152	72.14 266	87.34 234

tpm cat1		70.57 250	68.31 253	77.35 247	82.41 254	57.95 248	78.08 272	80.22 276	52.04 309	68.54 251	77.66 292	52.00 218	87.84 255	51.77 267	72.07 267	86.25 253

RPSCF		73.23 232	71.46 231	78.54 231	82.50 253	59.85 231	82.18 236	82.84 246	58.96 274	71.15 216	89.41 112	45.48 269	84.77 275	58.82 232	71.83 268	91.02 113

IterMVS		74.29 219	72.94 220	78.35 235	81.53 263	63.49 194	81.58 243	82.49 248	68.06 198	69.99 231	83.69 236	51.66 227	85.54 269	65.85 178	71.64 269	86.01 259
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		70.96 247	68.09 257	79.58 214	85.15 185	63.62 190	84.58 209	79.83 278	62.31 251	60.32 294	86.73 172	32.02 311	88.96 242	50.28 273	71.57 270	86.15 255

TestCases				79.58 214	85.15 185	63.62 190		79.83 278	62.31 251	60.32 294	86.73 172	32.02 311	88.96 242	50.28 273	71.57 270	86.15 255

Patchmtry		70.74 248	69.16 247	75.49 261	80.72 273	54.07 287	74.94 290	80.30 274	58.34 278	70.01 229	81.19 264	52.50 209	86.54 262	53.37 263	71.09 272	85.87 261

test2356		59.50 287	58.08 287	63.74 303	71.23 317	41.88 319	67.59 312	72.42 314	53.72 303	57.65 303	70.74 311	26.31 317	72.40 320	32.03 321	71.06 273	76.93 311

DTE-MVSNet		76.99 191	76.80 168	77.54 245	86.24 173	53.06 292	87.52 124	90.66 104	77.08 46	72.50 199	88.67 123	60.48 157	89.52 230	57.33 246	70.74 274	90.05 157

MIMVSNet1		68.58 262	66.78 267	73.98 273	80.07 281	51.82 294	80.77 247	84.37 229	64.40 231	59.75 297	82.16 250	36.47 305	83.63 279	42.73 304	70.33 275	86.48 252

pmmvs6		74.69 217	73.39 216	78.61 229	81.38 266	57.48 254	86.64 158	87.95 192	64.99 226	70.18 225	86.61 185	50.43 240	89.52 230	62.12 204	70.18 276	88.83 193

TinyColmap		67.30 269	64.81 271	74.76 267	81.92 259	56.68 264	80.29 252	81.49 264	60.33 263	56.27 309	83.22 239	24.77 320	87.66 257	45.52 298	69.47 277	79.95 301

OurMVSNet-221017-0		74.26 220	72.42 224	79.80 209	83.76 224	59.59 232	85.92 178	86.64 207	66.39 212	66.96 264	87.58 151	39.46 294	91.60 195	65.76 179	69.27 278	88.22 215

JIA-IIPM		66.32 275	62.82 281	76.82 251	77.09 300	61.72 221	65.34 317	75.38 297	58.04 281	64.51 280	62.32 320	42.05 286	86.51 263	51.45 269	69.22 279	82.21 292

ADS-MVSNet2		66.20 276	63.33 276	74.82 266	79.92 282	58.75 238	67.55 313	75.19 299	53.37 305	65.25 276	75.86 298	42.32 283	80.53 290	41.57 306	68.91 280	85.18 265

ADS-MVSNet		64.36 281	62.88 280	68.78 296	79.92 282	47.17 311	67.55 313	71.18 315	53.37 305	65.25 276	75.86 298	42.32 283	73.99 317	41.57 306	68.91 280	85.18 265

test20.03		67.45 267	66.95 266	68.94 293	75.48 307	44.84 314	77.50 274	77.67 288	66.66 207	63.01 287	83.80 233	47.02 257	78.40 298	42.53 305	68.86 282	83.58 280

testus		59.00 289	57.91 288	62.25 306	72.25 315	39.09 324	69.74 301	75.02 300	53.04 307	57.21 305	73.72 305	18.76 328	70.33 324	32.86 317	68.57 283	77.35 308

EU-MVSNet		68.53 263	67.61 265	71.31 286	78.51 293	47.01 312	84.47 210	84.27 231	42.27 319	66.44 270	84.79 226	40.44 292	83.76 277	58.76 233	68.54 284	83.17 283

v16		77.69 174	76.36 178	81.68 174	84.15 205	64.63 168	87.33 130	88.99 163	72.69 131	69.31 242	82.08 251	62.80 112	91.79 180	72.70 124	67.23 285	88.63 200

v18		77.67 177	76.35 179	81.64 176	84.09 210	64.47 177	87.27 133	89.01 161	72.59 132	69.39 239	82.04 253	62.85 107	91.80 179	72.72 123	67.20 286	88.63 200

v17		77.68 175	76.35 179	81.69 173	84.15 205	64.65 166	87.33 130	88.99 163	72.70 130	69.25 243	82.07 252	62.82 111	91.79 180	72.69 125	67.15 287	88.63 200

v11		77.45 185	76.06 188	81.59 179	84.22 200	64.52 169	87.11 142	89.02 159	72.76 128	68.76 246	81.90 261	62.09 132	91.71 187	71.98 134	66.73 288	88.56 209

LF4IMVS		64.02 282	62.19 282	69.50 292	70.90 318	53.29 291	76.13 278	77.18 292	52.65 308	58.59 298	80.98 269	23.55 321	76.52 306	53.06 265	66.66 289	78.68 304

Patchmatch-RL test		70.24 254	67.78 263	77.61 243	77.43 296	59.57 233	71.16 296	70.33 316	62.94 244	68.65 248	72.77 307	50.62 238	85.49 270	69.58 150	66.58 290	87.77 225

dp		66.80 270	65.43 270	70.90 288	79.74 284	48.82 309	75.12 288	74.77 303	59.61 270	64.08 283	77.23 293	42.89 279	80.72 289	48.86 280	66.58 290	83.16 284

v15		77.51 182	76.12 182	81.66 175	84.09 210	64.65 166	87.14 137	88.96 167	72.76 128	68.90 244	81.91 260	62.74 114	91.73 183	72.32 129	66.29 292	88.61 203

V14		77.52 180	76.12 182	81.70 172	84.15 205	64.77 164	87.21 136	88.95 168	72.80 127	68.79 245	81.94 259	62.69 116	91.72 185	72.31 130	66.27 293	88.60 204

V9		77.52 180	76.11 185	81.73 171	84.19 204	64.89 161	87.26 134	88.94 171	72.87 126	68.65 248	81.96 258	62.65 119	91.72 185	72.27 131	66.24 294	88.60 204

v13		77.50 184	76.07 187	81.77 168	84.23 199	65.07 157	87.34 129	88.91 173	72.92 123	68.35 253	81.97 256	62.53 124	91.69 189	72.20 133	66.22 295	88.56 209

v12		77.51 182	76.09 186	81.76 170	84.22 200	64.99 158	87.30 132	88.93 172	72.92 123	68.48 252	81.97 256	62.54 123	91.70 188	72.24 132	66.21 296	88.58 207

FPMVS		53.68 297	51.64 297	59.81 309	65.08 324	51.03 300	69.48 304	69.58 320	41.46 320	40.67 322	72.32 308	16.46 331	70.00 325	24.24 329	65.42 297	58.40 324

pmmvs-eth3d		70.50 252	67.83 261	78.52 232	77.37 297	66.18 132	81.82 238	81.51 263	58.90 275	63.90 284	80.42 273	42.69 281	86.28 265	58.56 234	65.30 298	83.11 285

N_pmnet		52.79 298	53.26 296	51.40 317	78.99 292	7.68 341	69.52 303	3.89 342	51.63 312	57.01 306	74.98 301	40.83 290	65.96 330	37.78 312	64.67 299	80.56 300

PM-MVS		66.41 274	64.14 274	73.20 276	73.92 308	56.45 266	78.97 264	64.96 330	63.88 238	64.72 279	80.24 274	19.84 326	83.44 280	66.24 172	64.52 300	79.71 302

SixPastTwentyTwo		73.37 228	71.26 235	79.70 210	85.08 189	57.89 249	85.57 186	83.56 237	71.03 153	65.66 273	85.88 203	42.10 285	92.57 162	59.11 229	63.34 301	88.65 199

test1235678		58.74 290	56.89 293	64.30 301	69.70 319	41.87 320	71.05 297	74.87 302	54.06 300	50.63 318	71.53 310	25.30 319	74.10 316	31.80 322	63.10 302	76.93 311

testpf		56.51 294	57.58 291	53.30 314	71.99 316	41.19 322	46.89 330	69.32 322	58.06 280	52.87 316	69.45 315	27.99 315	72.73 319	59.59 225	62.07 303	45.98 327

TransMVSNet (Re)		75.39 215	74.56 207	77.86 238	85.50 182	57.10 258	86.78 154	86.09 217	72.17 139	71.53 213	87.34 158	63.01 105	89.31 234	56.84 249	61.83 304	87.17 239

MDA-MVSNet_test_wron		65.03 277	62.92 278	71.37 283	75.93 302	56.73 261	69.09 308	74.73 304	57.28 288	54.03 312	77.89 289	45.88 263	74.39 315	49.89 277	61.55 305	82.99 288

YYNet1		65.03 277	62.91 279	71.38 282	75.85 303	56.60 265	69.12 307	74.66 307	57.28 288	54.12 311	77.87 290	45.85 264	74.48 314	49.95 276	61.52 306	83.05 286

1111		57.11 293	56.82 294	57.97 311	69.10 320	28.28 332	68.90 309	74.54 308	54.01 301	53.71 313	74.51 302	23.09 322	67.90 328	32.28 319	61.26 307	77.73 306

ambc				75.24 263	73.16 312	50.51 304	63.05 321	87.47 200		64.28 281	77.81 291	17.80 329	89.73 227	57.88 241	60.64 308	85.49 262

TDRefinement		67.49 266	64.34 273	76.92 250	73.47 311	61.07 222	84.86 203	82.98 244	59.77 269	58.30 300	85.13 219	26.06 318	87.89 254	47.92 286	60.59 309	81.81 295

Gipuma		45.18 304	41.86 305	55.16 313	77.03 301	51.52 297	32.50 333	80.52 270	32.46 327	27.12 328	35.02 330	9.52 337	75.50 310	22.31 330	60.21 310	38.45 329
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new-patchmatchnet		61.73 284	61.73 284	61.70 307	72.74 314	24.50 337	69.16 306	78.03 287	61.40 257	56.72 307	75.53 300	38.42 298	76.48 307	45.95 297	57.67 311	84.13 277

Anonymous20231211		64.82 279	61.79 283	73.91 274	77.11 299	50.92 301	85.29 195	81.53 262	54.19 299	57.98 301	78.03 288	26.90 316	87.83 256	37.92 311	57.12 312	82.99 288

MDA-MVSNet-bldmvs		66.68 271	63.66 275	75.75 257	79.28 290	60.56 228	73.92 292	78.35 285	64.43 230	50.13 319	79.87 278	44.02 274	83.67 278	46.10 296	56.86 313	83.03 287

new_pmnet		50.91 300	50.29 299	52.78 315	68.58 322	34.94 330	63.71 319	56.63 332	39.73 322	44.95 320	65.47 318	21.93 324	58.48 332	34.98 315	56.62 314	64.92 321

LP		61.36 286	57.78 289	72.09 279	75.54 306	58.53 240	67.16 315	75.22 298	51.90 311	54.13 310	69.97 313	37.73 302	80.45 291	32.74 318	55.63 315	77.29 309

PMVS	37.38 22	44.16 305	40.28 306	55.82 312	40.82 338	42.54 318	65.12 318	63.99 331	34.43 326	24.48 329	57.12 325	3.92 340	76.17 308	17.10 332	55.52 316	48.75 325
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		57.79 291	54.26 295	68.37 297	64.02 325	56.72 262	75.12 288	65.17 328	40.20 321	52.93 315	69.86 314	20.36 325	75.48 311	45.45 299	55.25 317	72.90 316

UnsupCasMVSNet_eth		67.33 268	65.99 269	71.37 283	73.48 310	51.47 298	75.16 286	85.19 223	65.20 223	60.78 292	80.93 271	42.35 282	77.20 304	57.12 247	53.69 318	85.44 263

K. test v3		71.19 245	68.51 251	79.21 218	83.04 241	57.78 251	84.35 217	76.91 293	72.90 125	62.99 288	82.86 242	39.27 295	91.09 210	61.65 209	52.66 319	88.75 196

UnsupCasMVSNet_bld		63.70 283	61.53 285	70.21 290	73.69 309	51.39 299	72.82 293	81.89 259	55.63 295	57.81 302	71.80 309	38.67 297	78.61 297	49.26 279	52.21 320	80.63 298

LCM-MVSNet		54.25 295	49.68 301	67.97 298	53.73 332	45.28 313	66.85 316	80.78 268	35.96 325	39.45 324	62.23 321	8.70 338	78.06 301	48.24 283	51.20 321	80.57 299

testmv		53.85 296	51.03 298	62.31 305	61.46 327	38.88 325	70.95 300	74.69 306	51.11 313	41.26 321	66.85 316	14.28 332	72.13 321	29.19 324	49.51 322	75.93 314

lessismore_v0				78.97 224	81.01 272	57.15 257		65.99 327		61.16 291	82.82 243	39.12 296	91.34 200	59.67 223	46.92 323	88.43 213

test12356		49.28 302	48.51 302	51.59 316	62.06 326	19.11 338	60.40 322	72.45 313	47.60 317	40.64 323	65.68 317	13.84 333	68.72 326	27.29 326	46.67 324	66.94 320

PVSNet_0	57.27 20	61.67 285	59.27 286	68.85 295	79.61 285	57.44 255	68.01 311	73.44 311	55.93 294	58.54 299	70.41 312	44.58 271	77.55 303	47.01 287	35.91 325	71.55 317

PMMVS2		40.82 306	38.86 307	46.69 319	53.84 331	16.45 339	48.61 329	49.92 335	37.49 324	31.67 326	60.97 322	8.14 339	56.42 333	28.42 325	30.72 326	67.19 319

no-one		51.08 299	45.79 304	66.95 300	57.92 330	50.49 305	59.63 324	76.04 296	48.04 315	31.85 325	56.10 326	19.12 327	80.08 293	36.89 313	26.52 327	70.29 318

DeepMVS_CX				27.40 325	40.17 339	26.90 335		24.59 341	17.44 334	23.95 330	48.61 327	9.77 336	26.48 337	18.06 331	24.47 328	28.83 330

MVE	26.22 23	30.37 312	25.89 314	43.81 320	44.55 337	35.46 329	28.87 334	39.07 338	18.20 333	18.58 333	40.18 329	2.68 341	47.37 336	17.07 333	23.78 329	48.60 326
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		39.76 307	33.18 310	59.51 310	46.98 336	44.01 315	57.70 326	67.74 325	24.13 331	13.98 336	34.33 331	1.27 343	71.33 322	34.23 316	18.23 330	63.18 323

E-PMN		31.77 310	30.64 311	35.15 321	52.87 333	27.67 334	57.09 327	47.86 336	24.64 330	16.40 334	33.05 332	11.23 335	54.90 334	14.46 334	18.15 331	22.87 331

PNet_i23d		38.26 308	35.42 308	46.79 318	58.74 328	35.48 328	59.65 323	51.25 334	32.45 328	23.44 332	47.53 328	2.04 342	58.96 331	25.60 328	18.09 332	45.92 328

EMVS		30.81 311	29.65 312	34.27 322	50.96 334	25.95 336	56.58 328	46.80 337	24.01 332	15.53 335	30.68 333	12.47 334	54.43 335	12.81 335	17.05 333	22.43 332

ANet_high		50.57 301	46.10 303	63.99 302	48.67 335	39.13 323	70.99 299	80.85 267	61.39 258	31.18 327	57.70 324	17.02 330	73.65 318	31.22 323	15.89 334	79.18 303

tmp_tt		18.61 314	21.40 315	10.23 327	4.82 340	10.11 340	34.70 332	30.74 340	1.48 336	23.91 331	26.07 334	28.42 314	13.41 339	27.12 327	15.35 335	7.17 333

wuyk23d		16.82 315	15.94 316	19.46 326	58.74 328	31.45 331	39.22 331	3.74 343	6.84 335	6.04 337	2.70 338	1.27 343	24.29 338	10.54 336	14.40 336	2.63 334

.test1245		45.55 303	50.02 300	32.14 323	69.10 320	28.28 332	68.90 309	74.54 308	54.01 301	53.71 313	74.51 302	23.09 322	67.90 328	32.28 319	0.02 337	0.25 336

testmvs		6.04 318	8.02 319	0.10 329	0.08 341	0.03 343	69.74 301	0.04 344	0.05 337	0.31 338	1.68 339	0.02 346	0.04 340	0.24 337	0.02 337	0.25 336

test123		6.12 317	8.11 318	0.14 328	0.06 342	0.09 342	71.05 297	0.03 345	0.04 338	0.25 339	1.30 340	0.05 345	0.03 341	0.21 338	0.01 339	0.29 335

cdsmvs_eth3d_5k		19.96 313	26.61 313	0.00 330	0.00 343	0.00 344	0.00 335	89.26 153	0.00 339	0.00 340	88.61 125	61.62 136	0.00 342	0.00 339	0.00 340	0.00 338

pcd_1.5k_mvsjas		5.26 319	7.02 320	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	63.15 101	0.00 342	0.00 339	0.00 340	0.00 338

sosnet-low-res		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

sosnet		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

uncertanet		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

Regformer		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

ab-mvs-re		7.23 316	9.64 317	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	86.72 174	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

uanet		0.00 320	0.00 321	0.00 330	0.00 343	0.00 344	0.00 335	0.00 346	0.00 339	0.00 340	0.00 341	0.00 347	0.00 342	0.00 339	0.00 340	0.00 338

ESAPD								94.22 1

sam_mvs1												51.32 229

sam_mvs												50.01 242

MTGPA								92.02 59

test_post1							78.90 266				5.43 337	48.81 252	85.44 271	59.25 228

test_post											5.46 336	50.36 241	84.24 276

patchmatchnet-post											74.00 304	51.12 232	88.60 247

MTMP								32.83 339

gm-plane-assit					81.40 265	53.83 289			62.72 248		80.94 270		92.39 167	63.40 193

TEST9					93.26 33	72.96 19	88.75 84	91.89 68	68.44 195	85.00 27	93.10 39	74.36 16	95.41 48

test_8					93.13 35	72.57 28	88.68 87	91.84 71	68.69 191	84.87 33	93.10 39	74.43 13	95.16 56

agg_prior					92.85 41	71.94 39		91.78 74		84.41 39			94.93 65

test_prior4						72.60 27	89.01 75

test_prior				86.33 46	92.61 46	69.59 71		92.97 30					95.48 43			93.91 30

旧先验2							86.56 161		58.10 279	87.04 13			88.98 240	74.07 109

新几何2							86.29 170

无先验							87.48 125	88.98 165	60.00 267				94.12 93	67.28 165		88.97 191

原ACMM2							86.86 150

testdata2													91.01 212	62.37 201

segment_acmp												73.08 23

testdata1							84.14 221		75.71 71

plane_prior7					90.08 78	68.51 97

plane_prior6					89.84 83	68.70 93						60.42 158

plane_prior4											91.00 80

plane_prior3						68.60 95			78.44 30	78.92 94

plane_prior2							91.25 28		79.12 23

plane_prior1					89.90 82

n2								0.00 346

nn								0.00 346

door-mid								69.98 318

test11								92.23 51

door								69.44 321

HQP5-MVS						66.98 122

HQP-NCC					89.33 97		89.17 68		76.41 59	77.23 134

ACMP_Plane					89.33 97		89.17 68		76.41 59	77.23 134

BP-MVS														77.47 75

HQP4-MVS										77.24 133			95.11 58			91.03 111

HQP2-MVS												60.17 161

NP-MVS					89.62 89	68.32 99					90.24 90

MDTV_nov1_ep13_2view						37.79 326	75.16 286		55.10 296	66.53 268		49.34 246		53.98 259		87.94 221

Test By Simon												64.33 87

High-resolution multi-view benchmark