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 4	89.15 4	88.63 1	95.01 3	76.03 1	92.38 14	92.85 34	80.26 14	87.78 13	94.27 18	75.89 9	96.81 10	87.45 9	96.44 1	93.05 64

test_part1								94.09 1				81.79 1			96.38 2	93.74 36

ESAPD		89.40 1	89.87 1	87.98 11	95.06 1	72.65 26	92.22 18	94.09 1	75.63 74	91.80 1	95.29 2	81.79 1	97.56 1	86.60 12	96.38 2	93.74 36

ACMMP_Plus		88.05 10	88.08 10	87.94 12	93.70 25	73.05 18	90.86 35	93.59 9	76.27 66	88.14 9	95.09 5	71.06 38	96.67 15	87.67 6	96.37 4	94.09 22

MP-MVS-pluss		87.67 13	87.72 12	87.54 27	93.64 28	72.04 40	89.80 57	93.50 11	75.17 85	86.34 18	95.29 2	70.86 39	96.00 35	88.78 3	96.04 5	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CNVR-MVS		88.93 5	89.13 5	88.33 3	94.77 4	73.82 6	90.51 41	93.00 26	80.90 10	88.06 11	94.06 26	76.43 6	96.84 9	88.48 4	95.99 6	94.34 15

PHI-MVS		86.43 32	86.17 34	87.24 31	90.88 69	70.96 49	92.27 17	94.07 3	72.45 139	85.22 27	91.90 60	69.47 52	96.42 24	83.28 36	95.94 7	94.35 14

test_prior3		86.73 27	86.86 26	86.33 47	92.61 48	69.59 73	88.85 81	92.97 31	75.41 78	84.91 31	93.54 31	74.28 19	95.48 45	83.31 34	95.86 8	93.91 30

test_prior2							88.85 81		75.41 78	84.91 31	93.54 31	74.28 19		83.31 34	95.86 8

SteuartSystems-ACMMP		88.72 6	88.86 6	88.32 4	92.14 54	72.96 19	93.73 3	93.67 8	80.19 15	88.10 10	94.80 6	73.76 22	97.11 5	87.51 8	95.82 10	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 7	88.56 7	86.73 40	92.24 52	69.03 81	89.57 64	93.39 15	77.53 39	89.79 6	94.12 24	78.98 3	96.58 22	85.66 14	95.72 11	94.58 7

train_agg		86.43 32	86.20 32	87.13 34	93.26 35	72.96 19	88.75 86	91.89 69	68.69 199	85.00 29	93.10 41	74.43 15	95.41 50	84.97 17	95.71 12	93.02 65

agg_prior3		86.16 38	85.85 39	87.10 35	93.31 32	72.86 23	88.77 84	91.68 79	68.29 211	84.26 44	92.83 49	72.83 27	95.42 49	84.97 17	95.71 12	93.02 65

test9_res														84.90 19	95.70 14	92.87 70

APDe-MVS		89.15 3	89.63 3	87.73 21	94.49 10	71.69 43	93.83 2	93.96 4	75.70 72	91.06 4	96.03 1	76.84 5	97.03 7	89.09 2	95.65 15	94.47 11

agg_prior2														82.91 41	95.45 16	92.70 71

CDPH-MVS		85.76 42	85.29 47	87.17 33	93.49 31	71.08 47	88.58 92	92.42 48	68.32 210	84.61 38	93.48 33	72.32 31	96.15 32	79.00 62	95.43 17	94.28 18

DeepC-MVS	79.81 2	87.08 25	86.88 25	87.69 25	91.16 64	72.32 37	90.31 47	93.94 5	77.12 44	82.82 61	94.23 20	72.13 33	97.09 6	84.83 22	95.37 18	93.65 43
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 15	87.41 16	87.90 16	94.18 21	74.25 2	90.23 49	92.02 60	79.45 19	85.88 20	94.80 6	68.07 59	96.21 29	86.69 10	95.34 19	93.23 56

MTAPA		87.23 21	87.00 21	87.90 16	94.18 21	74.25 2	86.58 162	92.02 60	79.45 19	85.88 20	94.80 6	68.07 59	96.21 29	86.69 10	95.34 19	93.23 56

agg_prior1		86.22 37	86.09 36	86.62 43	92.85 43	71.94 41	88.59 91	91.78 75	68.96 196	84.41 41	93.18 40	74.94 11	94.93 67	84.75 24	95.33 21	93.01 67

DeepC-MVS_fast	79.65 3	86.91 26	86.62 27	87.76 20	93.52 30	72.37 36	91.26 29	93.04 23	76.62 57	84.22 45	93.36 37	71.44 36	96.76 12	80.82 53	95.33 21	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 12	87.64 13	87.93 15	94.36 16	73.88 4	92.71 13	92.65 42	77.57 35	83.84 49	94.40 17	72.24 32	96.28 27	85.65 15	95.30 23	93.62 45

MCST-MVS		87.37 19	87.25 17	87.73 21	94.53 9	72.46 33	89.82 55	93.82 6	73.07 127	84.86 36	92.89 47	76.22 7	96.33 25	84.89 21	95.13 24	94.40 12

APD-MVS		87.44 16	87.52 14	87.19 32	94.24 18	72.39 34	91.86 24	92.83 35	73.01 128	88.58 8	94.52 9	73.36 23	96.49 23	84.26 29	95.01 25	92.70 71
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 8	88.01 11	88.24 5	94.41 14	73.62 7	91.22 32	92.83 35	81.50 7	85.79 23	93.47 35	73.02 26	97.00 8	84.90 19	94.94 26	94.10 21

ACMMPR		87.44 16	87.23 18	88.08 7	94.64 5	73.59 8	93.04 5	93.20 19	76.78 52	84.66 37	94.52 9	68.81 57	96.65 16	84.53 25	94.90 27	94.00 28

HFP-MVS		87.58 14	87.47 15	87.94 12	94.58 7	73.54 11	93.04 5	93.24 17	76.78 52	84.91 31	94.44 14	70.78 40	96.61 18	84.53 25	94.89 28	93.66 38

#test#		87.33 20	87.13 20	87.94 12	94.58 7	73.54 11	92.34 15	93.24 17	75.23 82	84.91 31	94.44 14	70.78 40	96.61 18	83.75 33	94.89 28	93.66 38

region2R		87.42 18	87.20 19	88.09 6	94.63 6	73.55 9	93.03 7	93.12 22	76.73 55	84.45 40	94.52 9	69.09 55	96.70 14	84.37 28	94.83 30	94.03 25

原ACMM1				84.35 86	93.01 41	68.79 87		92.44 45	63.96 253	81.09 80	91.57 68	66.06 77	95.45 47	67.19 169	94.82 31	88.81 204

HPM-MVS		87.11 23	86.98 22	87.50 29	93.88 24	72.16 38	92.19 20	93.33 16	76.07 69	83.81 50	93.95 28	69.77 50	96.01 34	85.15 16	94.66 32	94.32 17

TSAR-MVS + MP.		88.02 11	88.11 9	87.72 23	93.68 27	72.13 39	91.41 28	92.35 50	74.62 91	88.90 7	93.85 29	75.75 10	96.00 35	87.80 5	94.63 33	95.04 2

PGM-MVS		86.68 28	86.27 31	87.90 16	94.22 19	73.38 15	90.22 50	93.04 23	75.53 76	83.86 48	94.42 16	67.87 63	96.64 17	82.70 43	94.57 34	93.66 38

XVS		87.18 22	86.91 24	88.00 9	94.42 12	73.33 16	92.78 9	92.99 28	79.14 21	83.67 52	94.17 21	67.45 66	96.60 20	83.06 38	94.50 35	94.07 23

X-MVStestdata		80.37 118	77.83 152	88.00 9	94.42 12	73.33 16	92.78 9	92.99 28	79.14 21	83.67 52	12.47 351	67.45 66	96.60 20	83.06 38	94.50 35	94.07 23

test12				86.80 39	92.63 47	70.70 57		91.79 74		82.71 63		71.67 34	96.16 31		94.50 35	93.54 48

CP-MVS		87.11 23	86.92 23	87.68 26	94.20 20	73.86 5	93.98 1	92.82 37	76.62 57	83.68 51	94.46 13	67.93 61	95.95 37	84.20 31	94.39 38	93.23 56

CSCG		86.41 34	86.19 33	87.07 36	92.91 42	72.48 32	90.81 36	93.56 10	73.95 99	83.16 57	91.07 78	75.94 8	95.19 57	79.94 60	94.38 39	93.55 47

MSLP-MVS++		85.43 47	85.76 41	84.45 82	91.93 57	70.24 60	90.71 38	92.86 33	77.46 41	84.22 45	92.81 52	67.16 69	92.94 154	80.36 56	94.35 40	90.16 149

mPP-MVS		86.67 29	86.32 30	87.72 23	94.41 14	73.55 9	92.74 11	92.22 53	76.87 50	82.81 62	94.25 19	66.44 73	96.24 28	82.88 42	94.28 41	93.38 51

SD-MVS		88.06 8	88.50 8	86.71 41	92.60 50	72.71 24	91.81 25	93.19 20	77.87 32	90.32 5	94.00 27	74.83 12	93.78 114	87.63 7	94.27 42	93.65 43

MVS_0304		86.37 36	85.81 40	88.02 8	90.13 77	72.39 34	89.66 62	92.75 38	81.64 6	82.66 65	92.04 56	64.44 88	97.35 3	84.76 23	94.25 43	94.33 16

HSP-MVS		89.28 2	89.76 2	87.85 19	94.28 17	73.46 14	92.90 8	92.73 39	80.27 13	91.35 3	94.16 22	78.35 4	96.77 11	89.59 1	94.22 44	93.33 54

DELS-MVS		85.41 48	85.30 46	85.77 56	88.49 133	67.93 109	85.52 199	93.44 13	78.70 28	83.63 54	89.03 119	74.57 13	95.71 40	80.26 58	94.04 45	93.66 38
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 58	82.92 64	86.14 52	84.22 216	69.48 76	91.05 34	85.27 224	81.30 8	76.83 141	91.65 64	66.09 76	95.56 43	76.00 91	93.85 46	93.38 51
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	77.84 4	85.48 45	84.47 53	88.51 2	91.08 65	73.49 13	93.18 4	93.78 7	80.79 11	76.66 144	93.37 36	60.40 162	96.75 13	77.20 80	93.73 47	95.29 1

CANet		86.45 31	86.10 35	87.51 28	90.09 79	70.94 51	89.70 61	92.59 43	81.78 4	81.32 75	91.43 73	70.34 43	97.23 4	84.26 29	93.36 48	94.37 13

新几何1				83.42 113	93.13 37	70.71 56		85.48 222	57.43 302	81.80 72	91.98 58	63.28 98	92.27 173	64.60 190	92.99 49	87.27 246

1121		80.84 98	79.77 103	84.05 95	93.11 39	70.78 55	84.66 211	85.42 223	57.37 303	81.76 73	92.02 57	63.41 96	94.12 95	67.28 167	92.93 50	87.26 247

HPM-MVS_fast		85.35 49	84.95 50	86.57 45	93.69 26	70.58 58	92.15 21	91.62 80	73.89 103	82.67 64	94.09 25	62.60 122	95.54 44	80.93 51	92.93 50	93.57 46

旧先验1					91.96 56	65.79 142		86.37 214			93.08 45	69.31 54			92.74 52	88.74 207

3Dnovator	76.31 5	83.38 65	82.31 71	86.59 44	87.94 148	72.94 22	90.64 39	92.14 57	77.21 42	75.47 170	92.83 49	58.56 169	94.72 77	73.24 121	92.71 53	92.13 90

MVS_111021_HR		85.14 52	84.75 52	86.32 49	91.65 60	72.70 25	85.98 177	90.33 118	76.11 68	82.08 68	91.61 67	71.36 37	94.17 94	81.02 50	92.58 54	92.08 91

APD-MVS_3200maxsize		85.97 39	85.88 37	86.22 50	92.69 46	69.53 75	91.93 23	92.99 28	73.54 114	85.94 19	94.51 12	65.80 80	95.61 41	83.04 40	92.51 55	93.53 49

MAR-MVS		81.84 83	80.70 89	85.27 62	91.32 63	71.53 45	89.82 55	90.92 99	69.77 177	78.50 102	86.21 205	62.36 129	94.52 81	65.36 183	92.05 56	89.77 178
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 43	85.33 44	86.84 38	91.34 62	72.50 31	89.07 76	87.28 204	76.41 59	85.80 22	90.22 94	74.15 21	95.37 54	81.82 47	91.88 57	92.65 74

IS-MVSNet		83.15 66	82.81 65	84.18 91	89.94 83	63.30 201	91.59 26	88.46 184	79.04 25	79.49 90	92.16 54	65.10 84	94.28 86	67.71 162	91.86 58	94.95 3

Vis-MVSNet (Re-imp)		78.36 158	78.45 138	78.07 244	88.64 129	51.78 311	86.70 159	79.63 288	74.14 97	75.11 184	90.83 85	61.29 145	89.75 234	58.10 241	91.60 59	92.69 73

MG-MVS		83.41 63	83.45 56	83.28 118	92.74 45	62.28 218	88.17 107	89.50 145	75.22 83	81.49 74	92.74 53	66.75 70	95.11 60	72.85 123	91.58 60	92.45 78

CPTT-MVS		83.73 57	83.33 58	84.92 73	93.28 34	70.86 54	92.09 22	90.38 113	68.75 198	79.57 89	92.83 49	60.60 158	93.04 152	80.92 52	91.56 61	90.86 119

test222					91.50 61	68.26 103	84.16 228	83.20 249	54.63 314	79.74 87	91.63 66	58.97 167			91.42 62	86.77 258

testdata				79.97 207	90.90 68	64.21 183		84.71 228	59.27 289	85.40 24	92.91 46	62.02 135	89.08 253	68.95 156	91.37 63	86.63 262

abl_6		85.23 50	84.95 50	86.07 53	92.23 53	70.48 59	90.80 37	92.08 58	73.51 115	85.26 26	94.16 22	62.75 115	95.92 38	82.46 46	91.30 64	91.81 98

API-MVS		81.99 81	81.23 83	84.26 89	90.94 67	70.18 66	91.10 33	89.32 150	71.51 155	78.66 100	88.28 137	65.26 82	95.10 63	64.74 189	91.23 65	87.51 240

Vis-MVSNet		83.46 62	82.80 66	85.43 59	90.25 76	68.74 91	90.30 48	90.13 127	76.33 65	80.87 83	92.89 47	61.00 151	94.20 91	72.45 130	90.97 66	93.35 53
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	72.83 10	79.77 132	78.33 144	84.09 93	85.17 200	69.91 67	90.57 40	90.97 98	66.70 222	72.17 218	91.91 59	54.70 198	93.96 100	61.81 210	90.95 67	88.41 224

UA-Net		85.08 53	84.96 49	85.45 58	92.07 55	68.07 107	89.78 58	90.86 101	82.48 2	84.60 39	93.20 39	69.35 53	95.22 56	71.39 142	90.88 68	93.07 63

ACMMP		85.89 41	85.39 43	87.38 30	93.59 29	72.63 28	92.74 11	93.18 21	76.78 52	80.73 84	93.82 30	64.33 89	96.29 26	82.67 44	90.69 69	93.23 56
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 34	86.33 29	86.64 42	89.33 102	70.93 52	88.43 94	91.39 89	82.14 3	86.65 17	90.09 96	74.39 17	95.01 66	83.97 32	90.63 70	93.97 29

Regformer-2		86.63 30	86.53 28	86.95 37	89.33 102	71.24 46	88.43 94	92.05 59	82.50 1	86.88 16	90.09 96	74.45 14	95.61 41	84.38 27	90.63 70	94.01 27

UGNet		80.83 100	79.59 108	84.54 79	88.04 145	68.09 106	89.42 65	88.16 186	76.95 48	76.22 156	89.46 110	49.30 263	93.94 102	68.48 159	90.31 72	91.60 100
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 71	82.05 74	85.24 63	87.35 171	70.21 61	90.50 42	90.38 113	68.55 201	81.32 75	89.47 108	61.68 136	93.46 130	78.98 63	90.26 73	92.05 92

lupinMVS		81.39 92	80.27 97	84.76 76	87.35 171	70.21 61	85.55 195	86.41 212	62.85 261	81.32 75	88.61 127	61.68 136	92.24 175	78.41 69	90.26 73	91.83 96

DP-MVS Recon		83.11 68	82.09 73	86.15 51	94.44 11	70.92 53	88.79 83	92.20 54	70.53 167	79.17 93	91.03 81	64.12 91	96.03 33	68.39 161	90.14 75	91.50 104

MVS_111021_LR		82.61 74	82.11 72	84.11 92	88.82 122	71.58 44	85.15 204	86.16 217	74.69 90	80.47 85	91.04 79	62.29 130	90.55 225	80.33 57	90.08 76	90.20 148

jason		81.39 92	80.29 96	84.70 77	86.63 185	69.90 68	85.95 178	86.77 208	63.24 255	81.07 81	89.47 108	61.08 150	92.15 176	78.33 70	90.07 77	92.05 92
jason: jason.
LFMVS		81.82 84	81.23 83	83.57 110	91.89 58	63.43 199	89.84 54	81.85 267	77.04 47	83.21 55	93.10 41	52.26 217	93.43 134	71.98 136	89.95 78	93.85 33

MVS		78.19 162	76.99 166	81.78 168	85.66 194	66.99 123	84.66 211	90.47 111	55.08 313	72.02 223	85.27 231	63.83 94	94.11 97	66.10 177	89.80 79	84.24 293

CANet_DTU		80.61 108	79.87 101	82.83 145	85.60 196	63.17 207	87.36 130	88.65 180	76.37 63	75.88 162	88.44 133	53.51 208	93.07 149	73.30 120	89.74 80	92.25 85

PVSNet_Blended		80.98 95	80.34 94	82.90 139	88.85 119	65.40 148	84.43 221	92.00 63	67.62 216	78.11 119	85.05 236	66.02 78	94.27 87	71.52 140	89.50 81	89.01 196

PAPM_NR		83.02 69	82.41 68	84.82 75	92.47 51	66.37 132	87.93 114	91.80 73	73.82 108	77.32 133	90.66 87	67.90 62	94.90 71	70.37 146	89.48 82	93.19 60

114514_t		80.68 107	79.51 112	84.20 90	94.09 23	67.27 120	89.64 63	91.11 96	58.75 293	74.08 193	90.72 86	58.10 172	95.04 65	69.70 151	89.42 83	90.30 146

LCM-MVSNet-Re		77.05 191	76.94 167	77.36 255	87.20 177	51.60 312	80.06 269	80.46 279	75.20 84	67.69 273	86.72 179	62.48 127	88.98 256	63.44 194	89.25 84	91.51 103

alignmvs		85.48 45	85.32 45	85.96 55	89.51 97	69.47 77	89.74 59	92.47 44	76.17 67	87.73 14	91.46 72	70.32 44	93.78 114	81.51 48	88.95 85	94.63 6

VNet		82.21 77	82.41 68	81.62 178	90.82 70	60.93 225	84.47 217	89.78 138	76.36 64	84.07 47	91.88 61	64.71 87	90.26 227	70.68 143	88.89 86	93.66 38

PS-MVSNAJ		81.69 85	81.02 87	83.70 106	89.51 97	68.21 105	84.28 226	90.09 128	70.79 162	81.26 79	85.62 224	63.15 103	94.29 85	75.62 98	88.87 87	88.59 216

canonicalmvs		85.91 40	85.87 38	86.04 54	89.84 85	69.44 79	90.45 45	93.00 26	76.70 56	88.01 12	91.23 75	73.28 24	93.91 105	81.50 49	88.80 88	94.77 5

QAPM		80.88 96	79.50 113	85.03 68	88.01 147	68.97 85	91.59 26	92.00 63	66.63 226	75.15 183	92.16 54	57.70 174	95.45 47	63.52 193	88.76 89	90.66 128

VDD-MVS		83.01 70	82.36 70	84.96 70	91.02 66	66.40 131	88.91 78	88.11 187	77.57 35	84.39 43	93.29 38	52.19 218	93.91 105	77.05 83	88.70 90	94.57 9

Test4		77.83 173	75.90 190	83.62 107	80.24 295	65.25 154	85.27 201	90.67 103	69.03 194	66.48 285	83.75 250	43.07 294	93.00 153	75.93 92	88.66 91	92.62 75

test_normal		79.81 131	78.45 138	83.89 104	82.70 265	65.40 148	85.82 185	89.48 146	69.39 182	70.12 244	85.66 222	57.15 182	93.71 123	77.08 82	88.62 92	92.56 76

PVSNet_Blended_VisFu		82.62 73	81.83 78	84.96 70	90.80 71	69.76 70	88.74 88	91.70 78	69.39 182	78.96 95	88.46 132	65.47 81	94.87 73	74.42 107	88.57 93	90.24 147

DI_MVS_plusplus_test		79.89 130	78.58 135	83.85 105	82.89 261	65.32 152	86.12 174	89.55 143	69.64 181	70.55 235	85.82 219	57.24 180	93.81 112	76.85 85	88.55 94	92.41 80

xiu_mvs_v2_base		81.69 85	81.05 86	83.60 108	89.15 113	68.03 108	84.46 219	90.02 132	70.67 165	81.30 78	86.53 195	63.17 102	94.19 92	75.60 99	88.54 95	88.57 218

PAPR		81.66 87	80.89 88	83.99 100	90.27 75	64.00 189	86.76 158	91.77 77	68.84 197	77.13 140	89.50 106	67.63 64	94.88 72	67.55 164	88.52 96	93.09 62

MVS_Test		83.15 66	83.06 61	83.41 115	86.86 181	63.21 204	86.11 175	92.00 63	74.31 94	82.87 60	89.44 113	70.03 46	93.21 139	77.39 79	88.50 97	93.81 35

AdaColmap		80.58 111	79.42 114	84.06 94	93.09 40	68.91 86	89.36 66	88.97 167	69.27 186	75.70 169	89.69 102	57.20 181	95.77 39	63.06 197	88.41 98	87.50 241

VDDNet		81.52 89	80.67 90	84.05 95	90.44 73	64.13 185	89.73 60	85.91 220	71.11 158	83.18 56	93.48 33	50.54 253	93.49 129	73.40 119	88.25 99	94.54 10

PCF-MVS	73.52 7	80.38 117	78.84 130	85.01 69	87.71 164	68.99 84	83.65 235	91.46 88	63.00 258	77.77 126	90.28 91	66.10 75	95.09 64	61.40 213	88.22 100	90.94 117
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+		83.62 60	83.08 60	85.24 63	88.38 138	67.45 115	88.89 79	89.15 157	75.50 77	82.27 66	88.28 137	69.61 51	94.45 83	77.81 74	87.84 101	93.84 34

gg-mvs-nofinetune		69.95 271	67.96 273	75.94 271	83.07 255	54.51 294	77.23 292	70.29 332	63.11 256	70.32 239	62.33 335	43.62 291	88.69 262	53.88 263	87.76 102	84.62 291

Regformer-3		85.23 50	85.07 48	85.70 57	88.95 117	69.01 83	88.29 103	89.91 136	80.95 9	85.01 28	90.01 98	72.45 30	94.19 92	82.50 45	87.57 103	93.90 32

Regformer-4		85.68 44	85.45 42	86.35 46	88.95 117	69.67 72	88.29 103	91.29 91	81.73 5	85.36 25	90.01 98	72.62 29	95.35 55	83.28 36	87.57 103	94.03 25

xiu_mvs_v1_base_debu		80.80 103	79.72 105	84.03 97	87.35 171	70.19 63	85.56 192	88.77 176	69.06 191	81.83 69	88.16 139	50.91 241	92.85 156	78.29 71	87.56 105	89.06 189

xiu_mvs_v1_base		80.80 103	79.72 105	84.03 97	87.35 171	70.19 63	85.56 192	88.77 176	69.06 191	81.83 69	88.16 139	50.91 241	92.85 156	78.29 71	87.56 105	89.06 189

xiu_mvs_v1_base_debi		80.80 103	79.72 105	84.03 97	87.35 171	70.19 63	85.56 192	88.77 176	69.06 191	81.83 69	88.16 139	50.91 241	92.85 156	78.29 71	87.56 105	89.06 189

CLD-MVS		82.31 76	81.65 79	84.29 88	88.47 134	67.73 113	85.81 186	92.35 50	75.78 70	78.33 110	86.58 192	64.01 92	94.35 84	76.05 90	87.48 108	90.79 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CDS-MVSNet		79.07 147	77.70 156	83.17 122	87.60 166	68.23 104	84.40 223	86.20 216	67.49 219	76.36 151	86.54 194	61.54 139	90.79 222	61.86 209	87.33 109	90.49 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPP-MVSNet		83.40 64	83.02 62	84.57 78	90.13 77	64.47 179	92.32 16	90.73 102	74.45 93	79.35 92	91.10 76	69.05 56	95.12 59	72.78 124	87.22 110	94.13 20

TAMVS		78.89 151	77.51 159	83.03 130	87.80 159	67.79 112	84.72 210	85.05 227	67.63 215	76.75 142	87.70 150	62.25 131	90.82 221	58.53 237	87.13 111	90.49 139

TAPA-MVS	73.13 9	79.15 145	77.94 150	82.79 149	89.59 92	62.99 211	88.16 108	91.51 85	65.77 234	77.14 139	91.09 77	60.91 152	93.21 139	50.26 277	87.05 112	92.17 89
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM		77.68 176	76.40 175	81.51 181	87.29 176	61.85 222	83.78 234	89.59 142	64.74 243	71.23 230	88.70 123	62.59 123	93.66 124	52.66 268	87.03 113	89.01 196

BH-untuned		79.47 139	78.60 133	82.05 163	89.19 112	65.91 139	86.07 176	88.52 183	72.18 145	75.42 173	87.69 151	61.15 148	93.54 127	60.38 220	86.83 114	86.70 260

BH-RMVSNet		79.61 134	78.44 140	83.14 124	89.38 101	65.93 138	84.95 207	87.15 205	73.56 113	78.19 117	89.79 101	56.67 184	93.36 135	59.53 228	86.74 115	90.13 151

LS3D		76.95 193	74.82 209	83.37 116	90.45 72	67.36 119	89.15 74	86.94 207	61.87 271	69.52 253	90.61 88	51.71 234	94.53 80	46.38 302	86.71 116	88.21 226

Fast-Effi-MVS+		80.81 101	79.92 100	83.47 111	88.85 119	64.51 173	85.53 197	89.39 148	70.79 162	78.49 103	85.06 235	67.54 65	93.58 125	67.03 172	86.58 117	92.32 82

EPNet_dtu		75.46 219	74.86 208	77.23 258	82.57 268	54.60 292	86.89 151	83.09 250	71.64 150	66.25 287	85.86 217	55.99 187	88.04 269	54.92 258	86.55 118	89.05 193
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		83.50 61	82.95 63	85.14 65	88.79 125	70.95 50	89.13 75	91.52 84	77.55 38	80.96 82	91.75 62	60.71 154	94.50 82	79.67 61	86.51 119	89.97 167

OMC-MVS		82.69 72	81.97 77	84.85 74	88.75 127	67.42 116	87.98 110	90.87 100	74.92 88	79.72 88	91.65 64	62.19 133	93.96 100	75.26 103	86.42 120	93.16 61

HQP_MVS		83.64 59	83.14 59	85.14 65	90.08 80	68.71 93	91.25 30	92.44 45	79.12 23	78.92 96	91.00 82	60.42 160	95.38 52	78.71 65	86.32 121	91.33 107

plane_prior5								92.44 45					95.38 52	78.71 65	86.32 121	91.33 107

plane_prior						68.71 93	90.38 46		77.62 34						86.16 123

mvs_anonymous		79.42 141	79.11 126	80.34 200	84.45 212	57.97 250	82.59 249	87.62 198	67.40 221	76.17 160	88.56 130	68.47 58	89.59 237	70.65 144	86.05 124	93.47 50

HQP3-MVS								92.19 55							85.99 125

HQP-MVS		82.61 74	82.02 75	84.37 84	89.33 102	66.98 124	89.17 70	92.19 55	76.41 59	77.23 136	90.23 93	60.17 163	95.11 60	77.47 77	85.99 125	91.03 113

BH-w/o		78.21 160	77.33 162	80.84 193	88.81 123	65.13 158	84.87 208	87.85 195	69.75 178	74.52 191	84.74 242	61.34 143	93.11 147	58.24 240	85.84 127	84.27 292

CHOSEN 1792x2688		77.63 179	75.69 191	83.44 112	89.98 82	68.58 98	78.70 283	87.50 201	56.38 308	75.80 164	86.84 175	58.67 168	91.40 205	61.58 212	85.75 128	90.34 145

cascas		76.72 196	74.64 210	82.99 132	85.78 193	65.88 140	82.33 251	89.21 156	60.85 277	72.74 203	81.02 284	47.28 272	93.75 118	67.48 165	85.02 129	89.34 184

FIs		82.07 79	82.42 67	81.04 191	88.80 124	58.34 244	88.26 105	93.49 12	76.93 49	78.47 104	91.04 79	69.92 48	92.34 172	69.87 150	84.97 130	92.44 79

test-LLR		72.94 251	72.43 232	74.48 283	81.35 283	58.04 248	78.38 284	77.46 304	66.66 223	69.95 248	79.00 299	48.06 269	79.24 310	66.13 175	84.83 131	86.15 271

test-mter		71.41 259	70.39 257	74.48 283	81.35 283	58.04 248	78.38 284	77.46 304	60.32 280	69.95 248	79.00 299	36.08 323	79.24 310	66.13 175	84.83 131	86.15 271

diffmvs		79.51 136	78.59 134	82.25 160	83.31 248	62.66 213	84.17 227	88.11 187	67.64 214	76.09 161	87.47 158	64.01 92	91.15 211	71.71 139	84.82 133	92.94 69

EI-MVSNet-Vis-set		84.19 54	83.81 54	85.31 60	88.18 142	67.85 110	87.66 118	89.73 140	80.05 17	82.95 58	89.59 105	70.74 42	94.82 74	80.66 55	84.72 134	93.28 55

GG-mvs-BLEND				75.38 277	81.59 278	55.80 287	79.32 276	69.63 334		67.19 278	73.67 322	43.24 292	88.90 261	50.41 274	84.50 135	81.45 314

FC-MVSNet-test		81.52 89	82.02 75	80.03 206	88.42 137	55.97 282	87.95 112	93.42 14	77.10 45	77.38 131	90.98 84	69.96 47	91.79 186	68.46 160	84.50 135	92.33 81

PVSNet	64.34 18	72.08 256	70.87 254	75.69 273	86.21 189	56.44 275	74.37 307	80.73 276	62.06 270	70.17 242	82.23 265	42.86 296	83.31 297	54.77 259	84.45 137	87.32 245

MS-PatchMatch		73.83 230	72.67 229	77.30 257	83.87 235	66.02 136	81.82 254	84.66 229	61.37 275	68.61 266	82.82 259	47.29 271	88.21 266	59.27 229	84.32 138	77.68 325

TESTMET0.1,1		69.89 272	69.00 263	72.55 293	79.27 307	56.85 267	78.38 284	74.71 320	57.64 300	68.09 270	77.19 310	37.75 317	76.70 321	63.92 192	84.09 139	84.10 296

EI-MVSNet-UG-set		83.81 56	83.38 57	85.09 67	87.87 149	67.53 114	87.44 129	89.66 141	79.74 18	82.23 67	89.41 114	70.24 45	94.74 76	79.95 59	83.92 140	92.99 68

LPG-MVS_test		82.08 78	81.27 82	84.50 80	89.23 110	68.76 89	90.22 50	91.94 67	75.37 80	76.64 145	91.51 69	54.29 201	94.91 69	78.44 67	83.78 141	89.83 171

LGP-MVS_train				84.50 80	89.23 110	68.76 89		91.94 67	75.37 80	76.64 145	91.51 69	54.29 201	94.91 69	78.44 67	83.78 141	89.83 171

conf200view11		76.55 197	75.55 194	79.57 218	89.52 94	56.99 264	85.83 182	83.23 245	73.94 100	76.32 152	87.12 169	51.89 225	91.95 180	48.33 284	83.75 143	89.78 174

thres100view900		76.50 200	75.55 194	79.33 220	89.52 94	56.99 264	85.83 182	83.23 245	73.94 100	76.32 152	87.12 169	51.89 225	91.95 180	48.33 284	83.75 143	89.07 187

tfpn200view9		76.42 203	75.37 200	79.55 219	89.13 114	57.65 256	85.17 202	83.60 238	73.41 117	76.45 147	86.39 198	52.12 219	91.95 180	48.33 284	83.75 143	89.07 187

thres400		76.50 200	75.37 200	79.86 208	89.13 114	57.65 256	85.17 202	83.60 238	73.41 117	76.45 147	86.39 198	52.12 219	91.95 180	48.33 284	83.75 143	90.00 160

tfpn111		76.54 198	75.51 196	79.61 215	89.52 94	56.99 264	85.83 182	83.23 245	73.94 100	76.32 152	87.12 169	51.89 225	92.06 178	48.04 291	83.73 147	89.78 174

thres600view7		76.50 200	75.44 197	79.68 212	89.40 100	57.16 261	85.53 197	83.23 245	73.79 109	76.26 155	87.09 172	51.89 225	91.89 184	48.05 290	83.72 148	90.00 160

thres200		75.55 218	74.47 214	78.82 232	87.78 162	57.85 253	83.07 247	83.51 241	72.44 141	75.84 163	84.42 244	52.08 221	91.75 189	47.41 293	83.64 149	86.86 256

XVG-OURS		80.41 113	79.23 124	83.97 101	85.64 195	69.02 82	83.03 248	90.39 112	71.09 159	77.63 128	91.49 71	54.62 200	91.35 206	75.71 96	83.47 150	91.54 102

view600		76.20 207	75.21 203	79.16 225	89.64 87	55.82 283	85.74 187	82.06 262	73.88 104	75.74 165	87.85 145	51.84 229	91.66 197	46.75 295	83.42 151	90.00 160

view800		76.20 207	75.21 203	79.16 225	89.64 87	55.82 283	85.74 187	82.06 262	73.88 104	75.74 165	87.85 145	51.84 229	91.66 197	46.75 295	83.42 151	90.00 160

conf0.05thres1000		76.20 207	75.21 203	79.16 225	89.64 87	55.82 283	85.74 187	82.06 262	73.88 104	75.74 165	87.85 145	51.84 229	91.66 197	46.75 295	83.42 151	90.00 160

tfpn		76.20 207	75.21 203	79.16 225	89.64 87	55.82 283	85.74 187	82.06 262	73.88 104	75.74 165	87.85 145	51.84 229	91.66 197	46.75 295	83.42 151	90.00 160

CNLPA		78.08 164	76.79 170	81.97 165	90.40 74	71.07 48	87.59 120	84.55 230	66.03 233	72.38 216	89.64 104	57.56 176	86.04 282	59.61 226	83.35 155	88.79 205

MVP-Stereo		76.12 211	74.46 215	81.13 190	85.37 199	69.79 69	84.42 222	87.95 193	65.03 241	67.46 275	85.33 230	53.28 210	91.73 190	58.01 242	83.27 156	81.85 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		76.53 199	75.30 202	80.21 204	83.93 234	62.32 217	84.66 211	88.81 175	60.23 281	70.16 243	84.07 247	55.30 192	90.73 223	67.37 166	83.21 157	87.59 239

mvs-test1		80.88 96	79.40 115	85.29 61	85.13 203	69.75 71	89.28 67	88.10 189	74.99 86	76.44 150	86.72 179	57.27 178	94.26 90	73.53 117	83.18 158	91.87 95

HyFIR lowres test		77.53 180	75.40 199	83.94 103	89.59 92	66.62 128	80.36 267	88.64 181	56.29 309	76.45 147	85.17 232	57.64 175	93.28 137	61.34 215	83.10 159	91.91 94

ACMP	74.13 6	81.51 91	80.57 91	84.36 85	89.42 99	68.69 96	89.97 53	91.50 87	74.46 92	75.04 186	90.41 90	53.82 206	94.54 79	77.56 76	82.91 160	89.86 170
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	73.20 8	80.78 106	79.84 102	83.58 109	89.31 107	68.37 100	89.99 52	91.60 81	70.28 171	77.25 134	89.66 103	53.37 209	93.53 128	74.24 110	82.85 161	88.85 202
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMMVS		69.34 274	68.67 265	71.35 300	75.67 320	62.03 220	75.17 301	73.46 325	50.00 330	68.68 263	79.05 297	52.07 222	78.13 315	61.16 216	82.77 162	73.90 333

PLC	70.83 11	78.05 165	76.37 176	83.08 127	91.88 59	67.80 111	88.19 106	89.46 147	64.33 248	69.87 250	88.38 134	53.66 207	93.58 125	58.86 233	82.73 163	87.86 233
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 187	76.18 182	81.20 187	88.24 141	63.24 203	84.61 215	86.40 213	67.55 218	77.81 124	86.48 197	54.10 203	93.15 144	57.75 244	82.72 164	87.20 248

pcd1.5k->3k		34.07 324	35.26 324	30.50 339	86.92 180	0.00 360	0.00 351	91.58 82	0.00 355	0.00 356	0.00 357	56.23 186	0.00 358	0.00 355	82.60 165	91.49 105

ab-mvs		79.51 136	78.97 129	81.14 189	88.46 135	60.91 226	83.84 233	89.24 155	70.36 169	79.03 94	88.87 121	63.23 101	90.21 229	65.12 184	82.57 166	92.28 84

HY-MVS	69.67 12	77.95 169	77.15 164	80.36 199	87.57 170	60.21 232	83.37 246	87.78 196	66.11 230	75.37 175	87.06 174	63.27 99	90.48 226	61.38 214	82.43 167	90.40 144

PS-MVSNAJss		82.07 79	81.31 81	84.34 87	86.51 186	67.27 120	89.27 68	91.51 85	71.75 149	79.37 91	90.22 94	63.15 103	94.27 87	77.69 75	82.36 168	91.49 105

PVSNet_BlendedMVS		80.60 109	80.02 98	82.36 159	88.85 119	65.40 148	86.16 173	92.00 63	69.34 185	78.11 119	86.09 208	66.02 78	94.27 87	71.52 140	82.06 169	87.39 242

WTY-MVS		75.65 217	75.68 192	75.57 275	86.40 187	56.82 268	77.92 289	82.40 256	65.10 240	76.18 158	87.72 149	63.13 106	80.90 304	60.31 221	81.96 170	89.00 198

ACMMP++_ref															81.95 171

DP-MVS		76.78 195	74.57 211	83.42 113	93.29 33	69.46 78	88.55 93	83.70 237	63.98 252	70.20 240	88.89 120	54.01 205	94.80 75	46.66 299	81.88 172	86.01 276

CMPMVS	51.72 21	70.19 270	68.16 270	76.28 269	73.15 329	57.55 258	79.47 275	83.92 235	48.02 332	56.48 324	84.81 240	43.13 293	86.42 280	62.67 201	81.81 173	84.89 287
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn_ndepth		73.70 231	72.75 228	76.52 262	87.78 162	54.92 291	84.32 225	80.28 283	67.57 217	72.50 206	84.82 239	50.12 256	89.44 241	45.73 305	81.66 174	85.20 282

XVG-OURS-SEG-HR		80.81 101	79.76 104	83.96 102	85.60 196	68.78 88	83.54 238	90.50 110	70.66 166	76.71 143	91.66 63	60.69 155	91.26 208	76.94 84	81.58 175	91.83 96

tfpn1000		73.44 238	72.49 231	76.29 268	87.81 158	53.69 299	84.05 232	78.81 298	67.99 213	72.09 222	86.27 204	49.95 258	89.04 254	44.09 316	81.38 176	86.15 271

MIMVSNet		70.69 264	69.30 260	74.88 280	84.52 210	56.35 278	75.87 299	79.42 289	64.59 244	67.76 271	82.41 262	41.10 305	81.54 303	46.64 301	81.34 177	86.75 259

ACMMP++															81.25 178

GA-MVS		76.87 194	75.17 207	81.97 165	82.75 263	62.58 214	81.44 261	86.35 215	72.16 147	74.74 189	82.89 257	46.20 278	92.02 179	68.85 157	81.09 179	91.30 109

sss		73.60 235	73.64 221	73.51 290	82.80 262	55.01 290	76.12 295	81.69 268	62.47 266	74.68 190	85.85 218	57.32 177	78.11 316	60.86 218	80.93 180	87.39 242

Effi-MVS+-dtu		80.03 127	78.57 136	84.42 83	85.13 203	68.74 91	88.77 84	88.10 189	74.99 86	74.97 187	83.49 254	57.27 178	93.36 135	73.53 117	80.88 181	91.18 111

EG-PatchMatch MVS		74.04 228	71.82 244	80.71 196	84.92 206	67.42 116	85.86 181	88.08 191	66.04 232	64.22 298	83.85 248	35.10 325	92.56 165	57.44 246	80.83 182	82.16 311

jajsoiax		79.29 143	77.96 149	83.27 119	84.68 209	66.57 130	89.25 69	90.16 126	69.20 188	75.46 171	89.49 107	45.75 283	93.13 146	76.84 86	80.80 183	90.11 152

1112_ss		77.40 189	76.43 174	80.32 201	89.11 116	60.41 231	83.65 235	87.72 197	62.13 269	73.05 201	86.72 179	62.58 124	89.97 231	62.11 207	80.80 183	90.59 133

mvs_tets		79.13 146	77.77 155	83.22 121	84.70 208	66.37 132	89.17 70	90.19 125	69.38 184	75.40 174	89.46 110	44.17 289	93.15 144	76.78 87	80.70 185	90.14 150

PatchMatch-RL		72.38 254	70.90 253	76.80 261	88.60 130	67.38 118	79.53 274	76.17 310	62.75 263	69.36 256	82.00 271	45.51 284	84.89 290	53.62 264	80.58 186	78.12 323

EI-MVSNet		80.52 112	79.98 99	82.12 161	84.28 213	63.19 206	86.41 167	88.95 169	74.18 96	78.69 98	87.54 156	66.62 71	92.43 167	72.57 129	80.57 187	90.74 123

MVSTER		79.01 148	77.88 151	82.38 158	83.07 255	64.80 165	84.08 231	88.95 169	69.01 195	78.69 98	87.17 168	54.70 198	92.43 167	74.69 106	80.57 187	89.89 169

testing_2		75.73 216	73.34 224	82.89 141	77.37 313	65.22 155	84.10 230	90.54 109	69.09 190	60.46 309	81.15 282	40.48 307	92.84 159	76.36 88	80.54 189	90.60 131

XVG-ACMP-BASELINE		76.11 212	74.27 217	81.62 178	83.20 251	64.67 167	83.60 237	89.75 139	69.75 178	71.85 224	87.09 172	32.78 326	92.11 177	69.99 149	80.43 190	88.09 228

Fast-Effi-MVS+-dtu		78.02 166	76.49 173	82.62 155	83.16 254	66.96 126	86.94 149	87.45 203	72.45 139	71.49 229	84.17 245	54.79 197	91.58 203	67.61 163	80.31 191	89.30 185

LTVRE_ROB	69.57 13	76.25 206	74.54 213	81.41 183	88.60 130	64.38 182	79.24 277	89.12 158	70.76 164	69.79 252	87.86 144	49.09 265	93.20 141	56.21 254	80.16 192	86.65 261
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		76.40 204	75.44 197	79.27 221	89.28 108	58.09 246	81.69 257	87.07 206	59.53 287	72.48 208	86.67 185	61.30 144	89.33 242	60.81 219	80.15 193	90.41 143

test_djsdf		80.30 119	79.32 118	83.27 119	83.98 233	65.37 151	90.50 42	90.38 113	68.55 201	76.19 157	88.70 123	56.44 185	93.46 130	78.98 63	80.14 194	90.97 116

CHOSEN 280x420		66.51 288	64.71 287	71.90 295	81.45 280	63.52 195	57.98 341	68.95 339	53.57 320	62.59 305	76.70 311	46.22 277	75.29 328	55.25 257	79.68 195	76.88 331

conf0.01		73.67 233	72.42 233	77.42 253	87.85 150	53.28 302	83.38 239	79.08 291	68.40 204	72.45 209	86.08 209	50.60 247	89.19 245	44.25 310	79.66 196	89.78 174

conf0.002		73.67 233	72.42 233	77.42 253	87.85 150	53.28 302	83.38 239	79.08 291	68.40 204	72.45 209	86.08 209	50.60 247	89.19 245	44.25 310	79.66 196	89.78 174

thresconf0.02		73.39 239	72.42 233	76.31 264	87.85 150	53.28 302	83.38 239	79.08 291	68.40 204	72.45 209	86.08 209	50.60 247	89.19 245	44.25 310	79.66 196	86.48 263

tfpn_n400		73.39 239	72.42 233	76.31 264	87.85 150	53.28 302	83.38 239	79.08 291	68.40 204	72.45 209	86.08 209	50.60 247	89.19 245	44.25 310	79.66 196	86.48 263

tfpnconf		73.39 239	72.42 233	76.31 264	87.85 150	53.28 302	83.38 239	79.08 291	68.40 204	72.45 209	86.08 209	50.60 247	89.19 245	44.25 310	79.66 196	86.48 263

tfpnview11		73.39 239	72.42 233	76.31 264	87.85 150	53.28 302	83.38 239	79.08 291	68.40 204	72.45 209	86.08 209	50.60 247	89.19 245	44.25 310	79.66 196	86.48 263

GBi-Net		78.40 156	77.40 160	81.40 184	87.60 166	63.01 208	88.39 98	89.28 151	71.63 151	75.34 176	87.28 161	54.80 194	91.11 212	62.72 198	79.57 202	90.09 154

test1		78.40 156	77.40 160	81.40 184	87.60 166	63.01 208	88.39 98	89.28 151	71.63 151	75.34 176	87.28 161	54.80 194	91.11 212	62.72 198	79.57 202	90.09 154

FMVSNet3		77.88 172	76.85 168	80.97 192	86.84 182	62.36 215	86.52 164	88.77 176	71.13 157	75.34 176	86.66 186	54.07 204	91.10 215	62.72 198	79.57 202	89.45 183

FMVSNet2		78.20 161	77.21 163	81.20 187	87.60 166	62.89 212	87.47 128	89.02 160	71.63 151	75.29 180	87.28 161	54.80 194	91.10 215	62.38 202	79.38 205	89.61 181

anonymousdsp		78.60 154	77.15 164	82.98 133	80.51 293	67.08 122	87.24 137	89.53 144	65.66 236	75.16 182	87.19 167	52.52 211	92.25 174	77.17 81	79.34 206	89.61 181

nrg030		83.88 55	83.53 55	84.96 70	86.77 184	69.28 80	90.46 44	92.67 40	74.79 89	82.95 58	91.33 74	72.70 28	93.09 148	80.79 54	79.28 207	92.50 77

VPA-MVSNet		80.60 109	80.55 92	80.76 195	88.07 144	60.80 228	86.86 152	91.58 82	75.67 73	80.24 86	89.45 112	63.34 97	90.25 228	70.51 145	79.22 208	91.23 110

F-COLMAP		76.38 205	74.33 216	82.50 156	89.28 108	66.95 127	88.41 97	89.03 159	64.05 250	66.83 281	88.61 127	46.78 275	92.89 155	57.48 245	78.55 209	87.67 236

FMVSNet1		77.44 187	76.12 183	81.40 184	86.81 183	63.01 208	88.39 98	89.28 151	70.49 168	74.39 192	87.28 161	49.06 266	91.11 212	60.91 217	78.52 210	90.09 154

MDTV_nov1_ep13			69.97 259		83.18 252	53.48 300	77.10 293	80.18 285	60.45 278	69.33 257	80.44 288	48.89 267	86.90 275	51.60 270	78.51 211

CVMVSNet		72.99 250	72.58 230	74.25 286	84.28 213	50.85 318	86.41 167	83.45 243	44.56 334	73.23 199	87.54 156	49.38 261	85.70 284	65.90 179	78.44 212	86.19 270

tpm2		73.26 246	71.46 246	78.63 234	83.34 247	56.71 271	80.65 265	80.40 280	56.63 307	73.55 195	82.02 270	51.80 233	91.24 209	56.35 253	78.42 213	87.95 230

CostFormer		75.24 222	73.90 220	79.27 221	82.65 267	58.27 245	80.80 262	82.73 254	61.57 272	75.33 179	83.13 256	55.52 190	91.07 218	64.98 187	78.34 214	88.45 222

ACMH	67.68 16	75.89 214	73.93 219	81.77 169	88.71 128	66.61 129	88.62 90	89.01 162	69.81 176	66.78 282	86.70 184	41.95 303	91.51 204	55.64 255	78.14 215	87.17 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CR-MVSNet		73.37 243	71.27 249	79.67 213	81.32 285	65.19 156	75.92 297	80.30 281	59.92 284	72.73 204	81.19 280	52.50 212	86.69 276	59.84 224	77.71 216	87.11 252

RPMNet		71.62 257	68.94 264	79.67 213	81.32 285	65.19 156	75.92 297	78.30 301	57.60 301	72.73 204	76.45 313	52.30 216	86.69 276	48.14 289	77.71 216	87.11 252

Patchmatch-test1		73.49 236	71.85 243	78.41 240	84.05 232	62.17 219	79.96 271	79.29 290	66.30 229	72.38 216	79.58 296	51.95 224	85.08 289	55.46 256	77.67 218	87.99 229

tpmp4_e23		73.45 237	71.17 251	80.31 202	83.55 243	59.56 236	81.88 253	82.33 257	57.94 298	70.51 237	81.62 278	51.19 239	91.63 201	53.96 262	77.51 219	89.75 179

v7		80.24 120	79.26 123	83.15 123	84.07 230	64.94 162	87.56 124	90.67 103	72.26 144	78.28 111	86.51 196	61.45 141	94.03 99	75.14 104	77.41 220	90.49 139

DWT-MVSNet_test		73.70 231	71.86 242	79.21 223	82.91 260	58.94 239	82.34 250	82.17 259	65.21 238	71.05 233	78.31 301	44.21 288	90.17 230	63.29 196	77.28 221	88.53 221

v1144		80.03 127	79.03 127	83.01 131	83.78 239	64.51 173	87.11 144	90.57 108	71.96 148	78.08 121	86.20 206	61.41 142	93.94 102	74.93 105	77.23 222	90.60 131

WR-MVS		79.49 138	79.22 125	80.27 203	88.79 125	58.35 243	85.06 205	88.61 182	78.56 29	77.65 127	88.34 135	63.81 95	90.66 224	64.98 187	77.22 223	91.80 99

v1192		79.59 135	78.43 141	83.07 128	83.55 243	64.52 171	86.93 150	90.58 107	70.83 161	77.78 125	85.90 215	59.15 166	93.94 102	73.96 112	77.19 224	90.76 121

VPNet		78.69 153	78.66 132	78.76 233	88.31 140	55.72 288	84.45 220	86.63 210	76.79 51	78.26 115	90.55 89	59.30 165	89.70 236	66.63 173	77.05 225	90.88 118

v1neww		80.40 114	79.54 109	82.98 133	84.10 224	64.51 173	87.57 121	90.22 122	73.25 120	78.47 104	86.65 187	62.83 111	93.86 108	75.72 94	77.02 226	90.58 134

v7new		80.40 114	79.54 109	82.98 133	84.10 224	64.51 173	87.57 121	90.22 122	73.25 120	78.47 104	86.65 187	62.83 111	93.86 108	75.72 94	77.02 226	90.58 134

v6		80.40 114	79.54 109	82.98 133	84.09 226	64.50 177	87.57 121	90.22 122	73.25 120	78.47 104	86.63 189	62.84 110	93.86 108	75.73 93	77.02 226	90.58 134

v1240		78.99 149	77.78 154	82.64 154	83.21 250	63.54 194	86.62 161	90.30 120	69.74 180	77.33 132	85.68 220	57.04 183	93.76 117	73.13 122	76.92 229	90.62 129

MSDG		73.36 245	70.99 252	80.49 197	84.51 211	65.80 141	80.71 264	86.13 218	65.70 235	65.46 290	83.74 251	44.60 286	90.91 220	51.13 272	76.89 230	84.74 289

IterMVS-LS		80.06 126	79.38 116	82.11 162	85.89 191	63.20 205	86.79 155	89.34 149	74.19 95	75.45 172	86.72 179	66.62 71	92.39 169	72.58 128	76.86 231	90.75 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		79.22 144	78.03 148	82.80 147	83.30 249	63.94 191	86.80 154	90.33 118	69.91 175	77.48 130	85.53 226	58.44 170	93.75 118	73.60 116	76.85 232	90.71 124

XXY-MVS		75.41 220	75.56 193	74.96 279	83.59 242	57.82 254	80.59 266	83.87 236	66.54 227	74.93 188	88.31 136	63.24 100	80.09 308	62.16 205	76.85 232	86.97 254

v1141		80.19 123	79.31 119	82.85 142	83.84 236	64.12 186	87.14 139	90.08 129	73.13 123	78.27 112	86.39 198	62.67 120	93.75 118	75.40 101	76.83 234	90.68 125

divwei89l23v2f112		80.19 123	79.31 119	82.85 142	83.84 236	64.11 188	87.13 142	90.08 129	73.13 123	78.27 112	86.39 198	62.69 118	93.75 118	75.40 101	76.82 235	90.68 125

v1		80.19 123	79.31 119	82.85 142	83.83 238	64.12 186	87.14 139	90.07 131	73.13 123	78.27 112	86.38 202	62.72 117	93.75 118	75.41 100	76.82 235	90.68 125

v2v482		80.23 121	79.29 122	83.05 129	83.62 241	64.14 184	87.04 146	89.97 133	73.61 111	78.18 118	87.22 165	61.10 149	93.82 111	76.11 89	76.78 237	91.18 111

v144192		79.47 139	78.37 142	82.78 150	83.35 246	63.96 190	86.96 148	90.36 116	69.99 174	77.50 129	85.67 221	60.66 156	93.77 116	74.27 109	76.58 238	90.62 129

UniMVSNet (Re)		81.60 88	81.11 85	83.09 126	88.38 138	64.41 181	87.60 119	93.02 25	78.42 31	78.56 101	88.16 139	69.78 49	93.26 138	69.58 152	76.49 239	91.60 100

UniMVSNet_NR-MVSNet		81.88 82	81.54 80	82.92 138	88.46 135	63.46 197	87.13 142	92.37 49	80.19 15	78.38 108	89.14 116	71.66 35	93.05 150	70.05 147	76.46 240	92.25 85

DU-MVS		81.12 94	80.52 93	82.90 139	87.80 159	63.46 197	87.02 147	91.87 71	79.01 26	78.38 108	89.07 117	65.02 85	93.05 150	70.05 147	76.46 240	92.20 87

PatchmatchNet		73.12 248	71.33 248	78.49 239	83.18 252	60.85 227	79.63 273	78.57 299	64.13 249	71.73 225	79.81 295	51.20 238	85.97 283	57.40 247	76.36 242	88.66 208
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		70.33 268	68.37 267	76.21 270	80.60 291	56.23 279	79.19 279	86.49 211	60.89 276	61.29 306	85.47 228	31.78 329	89.47 240	53.37 265	76.21 243	82.94 308

OpenMVS_ROB	64.09 19	70.56 266	68.19 269	77.65 249	80.26 294	59.41 238	85.01 206	82.96 252	58.76 292	65.43 291	82.33 263	37.63 319	91.23 210	45.34 308	76.03 244	82.32 309

ACMH+	68.96 14	76.01 213	74.01 218	82.03 164	88.60 130	65.31 153	88.86 80	87.55 199	70.25 172	67.75 272	87.47 158	41.27 304	93.19 142	58.37 238	75.94 245	87.60 238

PatchFormer-LS_test		74.50 224	73.05 226	78.86 231	82.95 259	59.55 237	81.65 258	82.30 258	67.44 220	71.62 227	78.15 303	52.34 215	88.92 260	65.05 186	75.90 246	88.12 227

tpm		72.37 255	71.71 245	74.35 285	82.19 272	52.00 309	79.22 278	77.29 306	64.56 245	72.95 202	83.68 253	51.35 236	83.26 298	58.33 239	75.80 247	87.81 234

Anonymous20231206		68.60 276	67.80 277	71.02 302	80.23 296	50.75 319	78.30 287	80.47 278	56.79 306	66.11 288	82.63 261	46.35 276	78.95 312	43.62 318	75.70 248	83.36 300

v7n		78.97 150	77.58 158	83.14 124	83.45 245	65.51 146	88.32 101	91.21 93	73.69 110	72.41 215	86.32 203	57.93 173	93.81 112	69.18 155	75.65 249	90.11 152

NR-MVSNet		80.23 121	79.38 116	82.78 150	87.80 159	63.34 200	86.31 170	91.09 97	79.01 26	72.17 218	89.07 117	67.20 68	92.81 160	66.08 178	75.65 249	92.20 87

v10		79.74 133	78.67 131	82.97 137	84.06 231	64.95 161	87.88 116	90.62 106	73.11 126	75.11 184	86.56 193	61.46 140	94.05 98	73.68 113	75.55 251	89.90 168

IB-MVS	68.01 15	75.85 215	73.36 223	83.31 117	84.76 207	66.03 135	83.38 239	85.06 226	70.21 173	69.40 254	81.05 283	45.76 282	94.66 78	65.10 185	75.49 252	89.25 186
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		79.38 142	78.24 146	82.83 145	81.10 287	65.50 147	85.55 195	89.82 137	71.57 154	78.21 116	86.12 207	60.66 156	93.18 143	75.64 97	75.46 253	89.81 173

v8		79.97 129	79.02 128	82.80 147	84.09 226	64.50 177	87.96 111	90.29 121	74.13 98	75.24 181	86.81 176	62.88 108	93.89 107	74.39 108	75.40 254	90.00 160

Baseline_NR-MVSNet		78.15 163	78.33 144	77.61 250	85.79 192	56.21 280	86.78 156	85.76 221	73.60 112	77.93 123	87.57 154	65.02 85	88.99 255	67.14 170	75.33 255	87.63 237

pmmvs5		71.55 258	70.20 258	75.61 274	77.83 310	56.39 276	81.74 256	80.89 273	57.76 299	67.46 275	84.49 243	49.26 264	85.32 288	57.08 250	75.29 256	85.11 286

EPMVS		69.02 275	68.16 270	71.59 296	79.61 301	49.80 324	77.40 291	66.93 341	62.82 262	70.01 245	79.05 297	45.79 281	77.86 318	56.58 252	75.26 257	87.13 251

TranMVSNet+NR-MVSNet		80.84 98	80.31 95	82.42 157	87.85 150	62.33 216	87.74 117	91.33 90	80.55 12	77.99 122	89.86 100	65.23 83	92.62 162	67.05 171	75.24 258	92.30 83

tfpnnormal		74.39 225	73.16 225	78.08 243	86.10 190	58.05 247	84.65 214	87.53 200	70.32 170	71.22 231	85.63 223	54.97 193	89.86 232	43.03 319	75.02 259	86.32 268

COLMAP_ROB	66.92 17	73.01 249	70.41 256	80.81 194	87.13 178	65.63 143	88.30 102	84.19 234	62.96 259	63.80 301	87.69 151	38.04 316	92.56 165	46.66 299	74.91 260	84.24 293
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		68.46 279	67.85 275	70.29 304	80.70 290	43.93 332	72.47 310	74.88 316	60.15 282	70.55 235	76.57 312	49.94 259	81.59 302	50.58 273	74.83 261	85.34 281

pmmvs4		74.03 229	71.91 241	80.39 198	81.96 274	68.32 101	81.45 260	82.14 260	59.32 288	69.87 250	85.13 233	52.40 214	88.13 268	60.21 222	74.74 262	84.73 290

ITE_SJBPF				78.22 242	81.77 276	60.57 229		83.30 244	69.25 187	67.54 274	87.20 166	36.33 322	87.28 274	54.34 260	74.62 263	86.80 257

test0.0.03 1		68.00 280	67.69 279	68.90 309	77.55 311	47.43 326	75.70 300	72.95 327	66.66 223	66.56 283	82.29 264	48.06 269	75.87 325	44.97 309	74.51 264	83.41 299

test_0402		72.79 252	70.44 255	79.84 209	88.13 143	65.99 137	85.93 179	84.29 232	65.57 237	67.40 277	85.49 227	46.92 274	92.61 163	35.88 330	74.38 265	80.94 315

CP-MVSNet		78.22 159	78.34 143	77.84 246	87.83 157	54.54 293	87.94 113	91.17 95	77.65 33	73.48 196	88.49 131	62.24 132	88.43 264	62.19 204	74.07 266	90.55 137

FMVSNet5		69.50 273	67.96 273	74.15 287	82.97 258	55.35 289	80.01 270	82.12 261	62.56 265	63.02 302	81.53 279	36.92 320	81.92 301	48.42 283	74.06 267	85.17 285

MVS-HIRNet		59.14 303	57.67 305	63.57 319	81.65 277	43.50 333	71.73 311	65.06 344	39.59 339	51.43 333	57.73 339	38.34 315	82.58 300	39.53 325	73.95 268	64.62 340

tpmrst		72.39 253	72.13 240	73.18 292	80.54 292	49.91 322	79.91 272	79.08 291	63.11 256	71.69 226	79.95 292	55.32 191	82.77 299	65.66 182	73.89 269	86.87 255

PS-CasMVS		78.01 167	78.09 147	77.77 248	87.71 164	54.39 295	88.02 109	91.22 92	77.50 40	73.26 198	88.64 126	60.73 153	88.41 265	61.88 208	73.88 270	90.53 138

v148		78.72 152	77.80 153	81.47 182	82.73 264	61.96 221	86.30 171	88.08 191	73.26 119	76.18 158	85.47 228	62.46 128	92.36 171	71.92 138	73.82 271	90.09 154

Patchmatch-test		64.82 294	63.24 292	69.57 306	79.42 303	49.82 323	63.49 336	69.05 338	51.98 326	59.95 312	80.13 291	50.91 241	70.98 339	40.66 324	73.57 272	87.90 232

WR-MVS_H		78.51 155	78.49 137	78.56 236	88.02 146	56.38 277	88.43 94	92.67 40	77.14 43	73.89 194	87.55 155	66.25 74	89.24 244	58.92 232	73.55 273	90.06 158

testgi		66.67 287	66.53 283	67.08 314	75.62 321	41.69 337	75.93 296	76.50 309	66.11 230	65.20 294	86.59 191	35.72 324	74.71 329	43.71 317	73.38 274	84.84 288

pm-mvs1		77.25 190	76.68 171	78.93 230	84.22 216	58.62 241	86.41 167	88.36 185	71.37 156	73.31 197	88.01 143	61.22 147	89.15 252	64.24 191	73.01 275	89.03 195

V4		77.95 169	76.37 176	82.67 152	79.40 304	65.52 144	86.43 165	89.94 134	72.28 142	72.14 221	84.95 237	55.72 188	93.44 132	73.64 114	72.86 276	89.05 193

v52		77.94 171	76.37 176	82.67 152	79.39 305	65.52 144	86.43 165	89.94 134	72.28 142	72.15 220	84.94 238	55.70 189	93.44 132	73.64 114	72.84 277	89.06 189

v748		77.97 168	76.65 172	81.92 167	82.29 271	63.28 202	87.53 125	90.35 117	73.50 116	70.76 234	85.55 225	58.28 171	92.81 160	68.81 158	72.76 278	89.67 180

tpmvs		71.09 261	69.29 261	76.49 263	82.04 273	56.04 281	78.92 281	81.37 272	64.05 250	67.18 279	78.28 302	49.74 260	89.77 233	49.67 280	72.37 279	83.67 297

PEN-MVS		77.73 174	77.69 157	77.84 246	87.07 179	53.91 297	87.91 115	91.18 94	77.56 37	73.14 200	88.82 122	61.23 146	89.17 251	59.95 223	72.37 279	90.43 142

DSMNet-mixed		57.77 307	56.90 307	60.38 323	67.70 339	35.61 343	69.18 321	53.97 348	32.30 345	57.49 320	79.88 293	40.39 309	68.57 343	38.78 326	72.37 279	76.97 328

semantic-postprocess				80.11 205	82.69 266	64.85 164		83.47 242	69.16 189	70.49 238	84.15 246	50.83 245	88.15 267	69.23 154	72.14 282	87.34 244

tpm cat1		70.57 265	68.31 268	77.35 256	82.41 270	57.95 251	78.08 288	80.22 284	52.04 325	68.54 267	77.66 308	52.00 223	87.84 271	51.77 269	72.07 283	86.25 269

RPSCF		73.23 247	71.46 246	78.54 237	82.50 269	59.85 233	82.18 252	82.84 253	58.96 290	71.15 232	89.41 114	45.48 285	84.77 291	58.82 234	71.83 284	91.02 115

IterMVS		74.29 226	72.94 227	78.35 241	81.53 279	63.49 196	81.58 259	82.49 255	68.06 212	69.99 247	83.69 252	51.66 235	85.54 285	65.85 180	71.64 285	86.01 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		70.96 262	68.09 272	79.58 216	85.15 201	63.62 192	84.58 216	79.83 286	62.31 267	60.32 310	86.73 177	32.02 327	88.96 258	50.28 275	71.57 286	86.15 271

TestCases				79.58 216	85.15 201	63.62 192		79.83 286	62.31 267	60.32 310	86.73 177	32.02 327	88.96 258	50.28 275	71.57 286	86.15 271

Patchmtry		70.74 263	69.16 262	75.49 276	80.72 289	54.07 296	74.94 306	80.30 281	58.34 294	70.01 245	81.19 280	52.50 212	86.54 278	53.37 265	71.09 288	85.87 278

test2356		59.50 302	58.08 302	63.74 318	71.23 333	41.88 335	67.59 328	72.42 329	53.72 319	57.65 319	70.74 327	26.31 333	72.40 336	32.03 337	71.06 289	76.93 329

DTE-MVSNet		76.99 192	76.80 169	77.54 252	86.24 188	53.06 308	87.52 126	90.66 105	77.08 46	72.50 206	88.67 125	60.48 159	89.52 238	57.33 248	70.74 290	90.05 159

MIMVSNet1		68.58 277	66.78 282	73.98 288	80.07 297	51.82 310	80.77 263	84.37 231	64.40 247	59.75 313	82.16 266	36.47 321	83.63 295	42.73 320	70.33 291	86.48 263

pmmvs6		74.69 223	73.39 222	78.61 235	81.38 282	57.48 259	86.64 160	87.95 193	64.99 242	70.18 241	86.61 190	50.43 254	89.52 238	62.12 206	70.18 292	88.83 203

TinyColmap		67.30 284	64.81 286	74.76 282	81.92 275	56.68 272	80.29 268	81.49 271	60.33 279	56.27 325	83.22 255	24.77 336	87.66 273	45.52 306	69.47 293	79.95 319

OurMVSNet-221017-0		74.26 227	72.42 233	79.80 210	83.76 240	59.59 234	85.92 180	86.64 209	66.39 228	66.96 280	87.58 153	39.46 310	91.60 202	65.76 181	69.27 294	88.22 225

JIA-IIPM		66.32 290	62.82 296	76.82 260	77.09 316	61.72 223	65.34 333	75.38 312	58.04 297	64.51 296	62.32 336	42.05 302	86.51 279	51.45 271	69.22 295	82.21 310

ADS-MVSNet2		66.20 291	63.33 291	74.82 281	79.92 298	58.75 240	67.55 329	75.19 314	53.37 321	65.25 292	75.86 314	42.32 299	80.53 306	41.57 322	68.91 296	85.18 283

ADS-MVSNet		64.36 296	62.88 295	68.78 311	79.92 298	47.17 327	67.55 329	71.18 330	53.37 321	65.25 292	75.86 314	42.32 299	73.99 333	41.57 322	68.91 296	85.18 283

test20.03		67.45 282	66.95 281	68.94 308	75.48 323	44.84 330	77.50 290	77.67 303	66.66 223	63.01 303	83.80 249	47.02 273	78.40 314	42.53 321	68.86 298	83.58 298

testus		59.00 304	57.91 303	62.25 321	72.25 331	39.09 340	69.74 317	75.02 315	53.04 323	57.21 321	73.72 321	18.76 344	70.33 340	32.86 333	68.57 299	77.35 326

EU-MVSNet		68.53 278	67.61 280	71.31 301	78.51 309	47.01 328	84.47 217	84.27 233	42.27 335	66.44 286	84.79 241	40.44 308	83.76 293	58.76 235	68.54 300	83.17 301

v16		77.69 175	76.36 179	81.68 175	84.15 221	64.63 170	87.33 132	88.99 164	72.69 137	69.31 258	82.08 267	62.80 114	91.79 186	72.70 126	67.23 301	88.63 210

v18		77.67 178	76.35 180	81.64 177	84.09 226	64.47 179	87.27 135	89.01 162	72.59 138	69.39 255	82.04 269	62.85 109	91.80 185	72.72 125	67.20 302	88.63 210

v17		77.68 176	76.35 180	81.69 174	84.15 221	64.65 168	87.33 132	88.99 164	72.70 136	69.25 259	82.07 268	62.82 113	91.79 186	72.69 127	67.15 303	88.63 210

v11		77.45 186	76.06 189	81.59 180	84.22 216	64.52 171	87.11 144	89.02 160	72.76 134	68.76 262	81.90 277	62.09 134	91.71 194	71.98 136	66.73 304	88.56 219

LF4IMVS		64.02 297	62.19 297	69.50 307	70.90 334	53.29 301	76.13 294	77.18 307	52.65 324	58.59 314	80.98 285	23.55 337	76.52 322	53.06 267	66.66 305	78.68 322

Patchmatch-RL test		70.24 269	67.78 278	77.61 250	77.43 312	59.57 235	71.16 312	70.33 331	62.94 260	68.65 264	72.77 323	50.62 246	85.49 286	69.58 152	66.58 306	87.77 235

dp		66.80 285	65.43 285	70.90 303	79.74 300	48.82 325	75.12 304	74.77 318	59.61 286	64.08 299	77.23 309	42.89 295	80.72 305	48.86 282	66.58 306	83.16 302

v15		77.51 183	76.12 183	81.66 176	84.09 226	64.65 168	87.14 139	88.96 168	72.76 134	68.90 260	81.91 276	62.74 116	91.73 190	72.32 131	66.29 308	88.61 213

V14		77.52 181	76.12 183	81.70 173	84.15 221	64.77 166	87.21 138	88.95 169	72.80 133	68.79 261	81.94 275	62.69 118	91.72 192	72.31 132	66.27 309	88.60 214

V9		77.52 181	76.11 186	81.73 172	84.19 220	64.89 163	87.26 136	88.94 172	72.87 132	68.65 264	81.96 274	62.65 121	91.72 192	72.27 133	66.24 310	88.60 214

v13		77.50 185	76.07 188	81.77 169	84.23 215	65.07 159	87.34 131	88.91 174	72.92 129	68.35 269	81.97 272	62.53 126	91.69 196	72.20 135	66.22 311	88.56 219

v12		77.51 183	76.09 187	81.76 171	84.22 216	64.99 160	87.30 134	88.93 173	72.92 129	68.48 268	81.97 272	62.54 125	91.70 195	72.24 134	66.21 312	88.58 217

FPMVS		53.68 312	51.64 312	59.81 324	65.08 340	51.03 316	69.48 320	69.58 335	41.46 336	40.67 338	72.32 324	16.46 347	70.00 341	24.24 345	65.42 313	58.40 342

pmmvs-eth3d		70.50 267	67.83 276	78.52 238	77.37 313	66.18 134	81.82 254	81.51 270	58.90 291	63.90 300	80.42 289	42.69 297	86.28 281	58.56 236	65.30 314	83.11 303

N_pmnet		52.79 313	53.26 311	51.40 332	78.99 308	7.68 357	69.52 319	3.89 357	51.63 328	57.01 322	74.98 317	40.83 306	65.96 346	37.78 328	64.67 315	80.56 318

PM-MVS		66.41 289	64.14 289	73.20 291	73.92 324	56.45 274	78.97 280	64.96 345	63.88 254	64.72 295	80.24 290	19.84 342	83.44 296	66.24 174	64.52 316	79.71 320

SixPastTwentyTwo		73.37 243	71.26 250	79.70 211	85.08 205	57.89 252	85.57 191	83.56 240	71.03 160	65.66 289	85.88 216	42.10 301	92.57 164	59.11 231	63.34 317	88.65 209

test1235678		58.74 305	56.89 308	64.30 316	69.70 335	41.87 336	71.05 313	74.87 317	54.06 316	50.63 334	71.53 326	25.30 335	74.10 332	31.80 338	63.10 318	76.93 329

testpf		56.51 309	57.58 306	53.30 329	71.99 332	41.19 338	46.89 346	69.32 337	58.06 296	52.87 332	69.45 331	27.99 331	72.73 335	59.59 227	62.07 319	45.98 345

TransMVSNet (Re)		75.39 221	74.56 212	77.86 245	85.50 198	57.10 263	86.78 156	86.09 219	72.17 146	71.53 228	87.34 160	63.01 107	89.31 243	56.84 251	61.83 320	87.17 249

MDA-MVSNet_test_wron		65.03 292	62.92 293	71.37 298	75.93 318	56.73 269	69.09 324	74.73 319	57.28 304	54.03 328	77.89 305	45.88 279	74.39 331	49.89 279	61.55 321	82.99 306

YYNet1		65.03 292	62.91 294	71.38 297	75.85 319	56.60 273	69.12 323	74.66 322	57.28 304	54.12 327	77.87 306	45.85 280	74.48 330	49.95 278	61.52 322	83.05 304

1111		57.11 308	56.82 309	57.97 326	69.10 336	28.28 348	68.90 325	74.54 323	54.01 317	53.71 329	74.51 318	23.09 338	67.90 344	32.28 335	61.26 323	77.73 324

ambc				75.24 278	73.16 328	50.51 320	63.05 337	87.47 202		64.28 297	77.81 307	17.80 345	89.73 235	57.88 243	60.64 324	85.49 279

TDRefinement		67.49 281	64.34 288	76.92 259	73.47 327	61.07 224	84.86 209	82.98 251	59.77 285	58.30 316	85.13 233	26.06 334	87.89 270	47.92 292	60.59 325	81.81 313

Gipuma		45.18 319	41.86 320	55.16 328	77.03 317	51.52 313	32.50 349	80.52 277	32.46 343	27.12 344	35.02 346	9.52 353	75.50 326	22.31 346	60.21 326	38.45 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new-patchmatchnet		61.73 299	61.73 299	61.70 322	72.74 330	24.50 353	69.16 322	78.03 302	61.40 273	56.72 323	75.53 316	38.42 314	76.48 323	45.95 304	57.67 327	84.13 295

Anonymous20231211		64.82 294	61.79 298	73.91 289	77.11 315	50.92 317	85.29 200	81.53 269	54.19 315	57.98 317	78.03 304	26.90 332	87.83 272	37.92 327	57.12 328	82.99 306

MDA-MVSNet-bldmvs		66.68 286	63.66 290	75.75 272	79.28 306	60.56 230	73.92 308	78.35 300	64.43 246	50.13 335	79.87 294	44.02 290	83.67 294	46.10 303	56.86 329	83.03 305

new_pmnet		50.91 315	50.29 314	52.78 330	68.58 338	34.94 346	63.71 335	56.63 347	39.73 338	44.95 336	65.47 334	21.93 340	58.48 348	34.98 331	56.62 330	64.92 339

LP		61.36 301	57.78 304	72.09 294	75.54 322	58.53 242	67.16 331	75.22 313	51.90 327	54.13 326	69.97 329	37.73 318	80.45 307	32.74 334	55.63 331	77.29 327

PMVS	37.38 22	44.16 320	40.28 321	55.82 327	40.82 354	42.54 334	65.12 334	63.99 346	34.43 342	24.48 345	57.12 341	3.92 356	76.17 324	17.10 348	55.52 332	48.75 343
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		57.79 306	54.26 310	68.37 312	64.02 341	56.72 270	75.12 304	65.17 343	40.20 337	52.93 331	69.86 330	20.36 341	75.48 327	45.45 307	55.25 333	72.90 334

UnsupCasMVSNet_eth		67.33 283	65.99 284	71.37 298	73.48 326	51.47 314	75.16 302	85.19 225	65.20 239	60.78 308	80.93 287	42.35 298	77.20 320	57.12 249	53.69 334	85.44 280

K. test v3		71.19 260	68.51 266	79.21 223	83.04 257	57.78 255	84.35 224	76.91 308	72.90 131	62.99 304	82.86 258	39.27 311	91.09 217	61.65 211	52.66 335	88.75 206

UnsupCasMVSNet_bld		63.70 298	61.53 300	70.21 305	73.69 325	51.39 315	72.82 309	81.89 266	55.63 311	57.81 318	71.80 325	38.67 313	78.61 313	49.26 281	52.21 336	80.63 316

LCM-MVSNet		54.25 310	49.68 316	67.97 313	53.73 348	45.28 329	66.85 332	80.78 275	35.96 341	39.45 340	62.23 337	8.70 354	78.06 317	48.24 288	51.20 337	80.57 317

testmv		53.85 311	51.03 313	62.31 320	61.46 343	38.88 341	70.95 316	74.69 321	51.11 329	41.26 337	66.85 332	14.28 348	72.13 337	29.19 340	49.51 338	75.93 332

lessismore_v0				78.97 229	81.01 288	57.15 262		65.99 342		61.16 307	82.82 259	39.12 312	91.34 207	59.67 225	46.92 339	88.43 223

test12356		49.28 317	48.51 317	51.59 331	62.06 342	19.11 354	60.40 338	72.45 328	47.60 333	40.64 339	65.68 333	13.84 349	68.72 342	27.29 342	46.67 340	66.94 338

PVSNet_0	57.27 20	61.67 300	59.27 301	68.85 310	79.61 301	57.44 260	68.01 327	73.44 326	55.93 310	58.54 315	70.41 328	44.58 287	77.55 319	47.01 294	35.91 341	71.55 335

PMMVS2		40.82 321	38.86 322	46.69 334	53.84 347	16.45 355	48.61 345	49.92 350	37.49 340	31.67 342	60.97 338	8.14 355	56.42 349	28.42 341	30.72 342	67.19 337

no-one		51.08 314	45.79 319	66.95 315	57.92 346	50.49 321	59.63 340	76.04 311	48.04 331	31.85 341	56.10 342	19.12 343	80.08 309	36.89 329	26.52 343	70.29 336

DeepMVS_CX				27.40 340	40.17 355	26.90 351		24.59 356	17.44 350	23.95 346	48.61 343	9.77 352	26.48 353	18.06 347	24.47 344	28.83 348

MVE	26.22 23	30.37 327	25.89 329	43.81 335	44.55 353	35.46 345	28.87 350	39.07 353	18.20 349	18.58 349	40.18 345	2.68 357	47.37 352	17.07 349	23.78 345	48.60 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		39.76 322	33.18 325	59.51 325	46.98 352	44.01 331	57.70 342	67.74 340	24.13 347	13.98 352	34.33 347	1.27 359	71.33 338	34.23 332	18.23 346	63.18 341

E-PMN		31.77 325	30.64 326	35.15 336	52.87 349	27.67 350	57.09 343	47.86 351	24.64 346	16.40 350	33.05 348	11.23 351	54.90 350	14.46 350	18.15 347	22.87 349

PNet_i23d		38.26 323	35.42 323	46.79 333	58.74 344	35.48 344	59.65 339	51.25 349	32.45 344	23.44 348	47.53 344	2.04 358	58.96 347	25.60 344	18.09 348	45.92 346

EMVS		30.81 326	29.65 327	34.27 337	50.96 350	25.95 352	56.58 344	46.80 352	24.01 348	15.53 351	30.68 349	12.47 350	54.43 351	12.81 351	17.05 349	22.43 350

ANet_high		50.57 316	46.10 318	63.99 317	48.67 351	39.13 339	70.99 315	80.85 274	61.39 274	31.18 343	57.70 340	17.02 346	73.65 334	31.22 339	15.89 350	79.18 321

tmp_tt		18.61 329	21.40 330	10.23 342	4.82 356	10.11 356	34.70 348	30.74 355	1.48 352	23.91 347	26.07 350	28.42 330	13.41 355	27.12 343	15.35 351	7.17 351

wuyk23d		16.82 330	15.94 331	19.46 341	58.74 344	31.45 347	39.22 347	3.74 358	6.84 351	6.04 353	2.70 354	1.27 359	24.29 354	10.54 352	14.40 352	2.63 352

.test1245		45.55 318	50.02 315	32.14 338	69.10 336	28.28 348	68.90 325	74.54 323	54.01 317	53.71 329	74.51 318	23.09 338	67.90 344	32.28 335	0.02 353	0.25 354

testmvs		6.04 333	8.02 334	0.10 344	0.08 357	0.03 359	69.74 317	0.04 359	0.05 353	0.31 354	1.68 355	0.02 362	0.04 356	0.24 353	0.02 353	0.25 354

test123		6.12 332	8.11 333	0.14 343	0.06 358	0.09 358	71.05 313	0.03 360	0.04 354	0.25 355	1.30 356	0.05 361	0.03 357	0.21 354	0.01 355	0.29 353

cdsmvs_eth3d_5k		19.96 328	26.61 328	0.00 345	0.00 359	0.00 360	0.00 351	89.26 154	0.00 355	0.00 356	88.61 127	61.62 138	0.00 358	0.00 355	0.00 356	0.00 356

pcd_1.5k_mvsjas		5.26 334	7.02 335	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	63.15 103	0.00 358	0.00 355	0.00 356	0.00 356

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

ab-mvs-re		7.23 331	9.64 332	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	86.72 179	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 359	0.00 360	0.00 351	0.00 361	0.00 355	0.00 356	0.00 357	0.00 363	0.00 358	0.00 355	0.00 356	0.00 356

GSMVS																88.96 200

test_part3							92.22 18		75.63 74		95.29 2		97.56 1	86.60 12

test_part2					95.06 1	72.65 26				91.80 1

sam_mvs1												51.32 237				88.96 200

sam_mvs												50.01 257

MTGPA								92.02 60

test_post1							78.90 282				5.43 353	48.81 268	85.44 287	59.25 230

test_post											5.46 352	50.36 255	84.24 292

patchmatchnet-post											74.00 320	51.12 240	88.60 263

MTMP								32.83 354

gm-plane-assit					81.40 281	53.83 298			62.72 264		80.94 286		92.39 169	63.40 195

TEST9					93.26 35	72.96 19	88.75 86	91.89 69	68.44 203	85.00 29	93.10 41	74.36 18	95.41 50

test_8					93.13 37	72.57 30	88.68 89	91.84 72	68.69 199	84.87 35	93.10 41	74.43 15	95.16 58

agg_prior					92.85 43	71.94 41		91.78 75		84.41 41			94.93 67

test_prior4						72.60 29	89.01 77

test_prior				86.33 47	92.61 48	69.59 73		92.97 31					95.48 45			93.91 30

旧先验2							86.56 163		58.10 295	87.04 15			88.98 256	74.07 111

新几何2							86.29 172

无先验							87.48 127	88.98 166	60.00 283				94.12 95	67.28 167		88.97 199

原ACMM2							86.86 152

testdata2													91.01 219	62.37 203

segment_acmp												73.08 25

testdata1							84.14 229		75.71 71

plane_prior7					90.08 80	68.51 99

plane_prior6					89.84 85	68.70 95						60.42 160

plane_prior4											91.00 82

plane_prior3						68.60 97			78.44 30	78.92 96

plane_prior2							91.25 30		79.12 23

plane_prior1					89.90 84

n2								0.00 361

nn								0.00 361

door-mid								69.98 333

test11								92.23 52

door								69.44 336

HQP5-MVS						66.98 124

HQP-NCC					89.33 102		89.17 70		76.41 59	77.23 136

ACMP_Plane					89.33 102		89.17 70		76.41 59	77.23 136

BP-MVS														77.47 77

HQP4-MVS										77.24 135			95.11 60			91.03 113

HQP2-MVS												60.17 163

NP-MVS					89.62 91	68.32 101					90.24 92

MDTV_nov1_ep13_2view						37.79 342	75.16 302		55.10 312	66.53 284		49.34 262		53.98 261		87.94 231

Test By Simon												64.33 89

High-resolution multi-view benchmark