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.

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

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

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
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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

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

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

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

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

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	12.47 339	67.45 64	96.60 18	83.06 36	94.50 33	94.07 23

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

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
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

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

#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

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

PHI-MVS		86.43 31	86.17 33	87.24 30	90.88 67	70.96 47	92.27 17	94.07 2	72.45 135	85.22 25	91.90 58	69.47 50	96.42 22	83.28 34	95.94 5	94.35 14

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

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 99	82.67 62	94.09 23	62.60 120	95.54 42	80.93 49	92.93 48	93.57 44

CPTT-MVS		83.73 56	83.33 57	84.92 72	93.28 32	70.86 52	92.09 20	90.38 112	68.75 194	79.57 87	92.83 47	60.60 156	93.04 150	80.92 50	91.56 59	90.86 117

APD-MVS_3200maxsize		85.97 38	85.88 36	86.22 49	92.69 44	69.53 73	91.93 21	92.99 27	73.54 110	85.94 17	94.51 10	65.80 78	95.61 39	83.04 38	92.51 53	93.53 47

APD-MVS		87.44 15	87.52 13	87.19 31	94.24 16	72.39 32	91.86 22	92.83 34	73.01 124	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
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

QAPM		80.88 95	79.50 112	85.03 67	88.01 143	68.97 83	91.59 24	92.00 62	66.63 214	75.15 179	92.16 52	57.70 172	95.45 45	63.52 191	88.76 87	90.66 126

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

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

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
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_prior2							91.25 28		79.12 23

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

API-MVS		81.99 80	81.23 82	84.26 88	90.94 65	70.18 64	91.10 31	89.32 149	71.51 151	78.66 98	88.28 135	65.26 80	95.10 61	64.74 187	91.23 63	87.51 232

EPNet		83.72 57	82.92 63	86.14 51	84.22 204	69.48 74	91.05 32	85.27 223	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
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

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

abl_6		85.23 49	84.95 49	86.07 52	92.23 51	70.48 57	90.80 35	92.08 57	73.51 111	85.26 24	94.16 20	62.75 113	95.92 36	82.46 44	91.30 62	91.81 96

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

3Dnovator	76.31 5	83.38 64	82.31 70	86.59 43	87.94 144	72.94 22	90.64 37	92.14 56	77.21 42	75.47 166	92.83 47	58.56 167	94.72 75	73.24 119	92.71 51	92.13 88

OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 188	69.91 65	90.57 38	90.97 97	66.70 210	72.17 207	91.91 57	54.70 196	93.96 98	61.81 208	90.95 65	88.41 216

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

MVSFormer		82.85 70	82.05 73	85.24 62	87.35 159	70.21 59	90.50 40	90.38 112	68.55 197	81.32 73	89.47 106	61.68 134	93.46 128	78.98 61	90.26 71	92.05 90

test_djsdf		80.30 118	79.32 117	83.27 118	83.98 221	65.37 149	90.50 40	90.38 112	68.55 197	76.19 153	88.70 121	56.44 183	93.46 128	78.98 61	80.14 188	90.97 114

nrg030		83.88 54	83.53 54	84.96 69	86.77 172	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	79.28 195	92.50 75

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

plane_prior						68.71 91	90.38 44		77.62 34						86.16 121

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
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
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

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

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 106	68.76 87	90.22 48	91.94 66	75.37 78	76.64 143	91.51 67	54.29 199	94.91 67	78.44 65	83.78 139	89.83 169

ACMM	73.20 8	80.78 105	79.84 101	83.58 108	89.31 103	68.37 98	89.99 50	91.60 80	70.28 167	77.25 132	89.66 101	53.37 207	93.53 126	74.24 108	82.85 157	88.85 194
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	74.13 6	81.51 90	80.57 90	84.36 84	89.42 95	68.69 94	89.97 51	91.50 86	74.46 90	75.04 182	90.41 88	53.82 204	94.54 77	77.56 74	82.91 156	89.86 168
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LFMVS		81.82 83	81.23 82	83.57 109	91.89 56	63.43 197	89.84 52	81.85 264	77.04 47	83.21 53	93.10 39	52.26 215	93.43 132	71.98 134	89.95 76	93.85 33

MCST-MVS		87.37 18	87.25 16	87.73 20	94.53 7	72.46 31	89.82 53	93.82 5	73.07 123	84.86 34	92.89 45	76.22 5	96.33 23	84.89 19	95.13 22	94.40 12

MAR-MVS		81.84 82	80.70 88	85.27 61	91.32 61	71.53 43	89.82 53	90.92 98	69.77 173	78.50 100	86.21 200	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
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
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

alignmvs		85.48 44	85.32 44	85.96 54	89.51 93	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

VDDNet		81.52 88	80.67 89	84.05 94	90.44 71	64.13 183	89.73 58	85.91 219	71.11 154	83.18 54	93.48 31	50.54 243	93.49 127	73.40 117	88.25 97	94.54 10

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

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

114514_t		80.68 106	79.51 111	84.20 89	94.09 21	67.27 118	89.64 61	91.11 95	58.75 281	74.08 189	90.72 84	58.10 170	95.04 63	69.70 149	89.42 81	90.30 144

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

UGNet		80.83 99	79.59 107	84.54 78	88.04 141	68.09 104	89.42 63	88.16 185	76.95 48	76.22 152	89.46 108	49.30 251	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
AdaColmap		80.58 110	79.42 113	84.06 93	93.09 38	68.91 84	89.36 64	88.97 166	69.27 182	75.70 165	89.69 100	57.20 179	95.77 37	63.06 195	88.41 96	87.50 233

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 191	69.75 69	89.28 65	88.10 188	74.99 84	76.44 148	86.72 175	57.27 176	94.26 88	73.53 115	83.18 154	91.87 93

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 174	67.27 118	89.27 66	91.51 84	71.75 145	79.37 89	90.22 92	63.15 101	94.27 85	77.69 73	82.36 164	91.49 103

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 197	66.57 128	89.25 67	90.16 125	69.20 184	75.46 167	89.49 105	45.75 271	93.13 144	76.84 84	80.80 177	90.11 150

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 196	66.37 130	89.17 68	90.19 124	69.38 180	75.40 170	89.46 108	44.17 277	93.15 142	76.78 85	80.70 179	90.14 148

HQP-NCC					89.33 98		89.17 68		76.41 59	77.23 134

ACMP_Plane					89.33 98		89.17 68		76.41 59	77.23 134

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 98	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

LS3D		76.95 192	74.82 206	83.37 115	90.45 70	67.36 117	89.15 72	86.94 206	61.87 259	69.52 241	90.61 86	51.71 230	94.53 78	46.38 298	86.71 114	88.21 218

OPM-MVS		83.50 60	82.95 62	85.14 64	88.79 121	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

TSAR-MVS + GP.		85.71 42	85.33 43	86.84 37	91.34 60	72.50 29	89.07 74	87.28 203	76.41 59	85.80 20	90.22 92	74.15 19	95.37 52	81.82 45	91.88 55	92.65 72

test_prior4						72.60 27	89.01 75

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	52.19 216	93.91 103	77.05 81	88.70 88	94.57 9

Effi-MVS+		83.62 59	83.08 59	85.24 62	88.38 134	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

ACMH+	68.96 14	76.01 210	74.01 215	82.03 163	88.60 126	65.31 151	88.86 78	87.55 198	70.25 168	67.75 260	87.47 156	41.27 292	93.19 140	58.37 236	75.94 233	87.60 230

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

DP-MVS Recon		83.11 67	82.09 72	86.15 50	94.44 9	70.92 51	88.79 81	92.20 53	70.53 163	79.17 91	91.03 79	64.12 89	96.03 31	68.39 159	90.14 73	91.50 102

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 191	68.74 89	88.77 82	88.10 188	74.99 84	74.97 183	83.49 242	57.27 176	93.36 133	73.53 115	80.88 175	91.18 109

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 30	72.86 23	88.77 82	91.68 78	68.29 201	84.26 42	92.83 47	72.83 25	95.42 47	84.97 15	95.71 10	93.02 63

TEST9					93.26 33	72.96 19	88.75 84	91.89 68	68.44 199	85.00 27	93.10 39	74.36 16	95.41 48

train_agg		86.43 31	86.20 31	87.13 33	93.26 33	72.96 19	88.75 84	91.89 68	68.69 195	85.00 27	93.10 39	74.43 13	95.41 48	84.97 15	95.71 10	93.02 63

PVSNet_Blended_VisFu		82.62 72	81.83 77	84.96 69	90.80 69	69.76 68	88.74 86	91.70 77	69.39 178	78.96 93	88.46 130	65.47 79	94.87 71	74.42 105	88.57 91	90.24 145

test_8					93.13 35	72.57 28	88.68 87	91.84 71	68.69 195	84.87 33	93.10 39	74.43 13	95.16 56

ACMH	67.68 16	75.89 211	73.93 216	81.77 168	88.71 124	66.61 127	88.62 88	89.01 161	69.81 172	66.78 270	86.70 180	41.95 291	91.51 200	55.64 253	78.14 203	87.17 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
agg_prior1		86.22 36	86.09 35	86.62 42	92.85 41	71.94 39	88.59 89	91.78 74	68.96 192	84.41 39	93.18 38	74.94 9	94.93 65	84.75 22	95.33 19	93.01 65

CDPH-MVS		85.76 41	85.29 46	87.17 32	93.49 29	71.08 45	88.58 90	92.42 47	68.32 200	84.61 36	93.48 31	72.32 29	96.15 30	79.00 60	95.43 15	94.28 18

DP-MVS		76.78 194	74.57 208	83.42 112	93.29 31	69.46 76	88.55 91	83.70 236	63.98 240	70.20 228	88.89 118	54.01 203	94.80 73	46.66 295	81.88 168	86.01 263

Regformer-1		86.41 33	86.33 28	86.64 41	89.33 98	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 98	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

WR-MVS_H		78.51 154	78.49 136	78.56 233	88.02 142	56.38 273	88.43 92	92.67 39	77.14 43	73.89 190	87.55 153	66.25 72	89.24 239	58.92 230	73.55 261	90.06 156

F-COLMAP		76.38 202	74.33 213	82.50 155	89.28 104	66.95 125	88.41 95	89.03 158	64.05 238	66.83 269	88.61 125	46.78 263	92.89 153	57.48 243	78.55 197	87.67 228

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 154	63.01 206	88.39 96	89.28 150	71.63 147	75.34 172	87.28 159	54.80 192	91.11 208	62.72 196	79.57 190	90.09 152

test1		78.40 155	77.40 159	81.40 183	87.60 154	63.01 206	88.39 96	89.28 150	71.63 147	75.34 172	87.28 159	54.80 192	91.11 208	62.72 196	79.57 190	90.09 152

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 171	63.01 206	88.39 96	89.28 150	70.49 164	74.39 188	87.28 159	49.06 254	91.11 208	60.91 215	78.52 198	90.09 152

v7n		78.97 149	77.58 157	83.14 123	83.45 233	65.51 144	88.32 99	91.21 92	73.69 106	72.41 204	86.32 199	57.93 171	93.81 110	69.18 153	75.65 237	90.11 150

COLMAP_ROB	66.92 17	73.01 238	70.41 245	80.81 193	87.13 166	65.63 141	88.30 100	84.19 233	62.96 247	63.80 289	87.69 149	38.04 304	92.56 163	46.66 295	74.91 248	84.24 279
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Regformer-3		85.23 49	85.07 47	85.70 56	88.95 113	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	88.95 113	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

FIs		82.07 78	82.42 66	81.04 190	88.80 120	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

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	64.33 236	69.87 238	88.38 132	53.66 205	93.58 123	58.86 231	82.73 159	87.86 225
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
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

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	65.77 222	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
PS-CasMVS		78.01 166	78.09 146	77.77 245	87.71 152	54.39 290	88.02 107	91.22 91	77.50 40	73.26 194	88.64 124	60.73 151	88.41 253	61.88 206	73.88 258	90.53 136

OMC-MVS		82.69 71	81.97 76	84.85 73	88.75 123	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

v8		79.97 128	79.02 127	82.80 146	84.09 214	64.50 175	87.96 109	90.29 120	74.13 96	75.24 177	86.81 172	62.88 106	93.89 105	74.39 106	75.40 242	90.00 158

FC-MVSNet-test		81.52 88	82.02 74	80.03 205	88.42 133	55.97 278	87.95 110	93.42 13	77.10 45	77.38 129	90.98 82	69.96 45	91.79 182	68.46 158	84.50 133	92.33 79

CP-MVSNet		78.22 158	78.34 142	77.84 243	87.83 147	54.54 288	87.94 111	91.17 94	77.65 33	73.48 192	88.49 129	62.24 130	88.43 252	62.19 202	74.07 254	90.55 135

PAPM_NR		83.02 68	82.41 67	84.82 74	92.47 49	66.37 130	87.93 112	91.80 72	73.82 104	77.32 131	90.66 85	67.90 60	94.90 69	70.37 144	89.48 80	93.19 58

PEN-MVS		77.73 173	77.69 156	77.84 243	87.07 167	53.91 292	87.91 113	91.18 93	77.56 37	73.14 196	88.82 120	61.23 144	89.17 240	59.95 221	72.37 267	90.43 140

v10		79.74 132	78.67 130	82.97 136	84.06 219	64.95 159	87.88 114	90.62 105	73.11 122	75.11 180	86.56 189	61.46 138	94.05 96	73.68 111	75.55 239	89.90 166

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 146	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	75.24 246	92.30 81

EI-MVSNet-Vis-set		84.19 53	83.81 53	85.31 59	88.18 138	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

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 134	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	76.49 227	91.60 98

CNLPA		78.08 163	76.79 169	81.97 164	90.40 72	71.07 46	87.59 118	84.55 229	66.03 221	72.38 205	89.64 102	57.56 174	86.04 270	59.61 224	83.35 151	88.79 197

v1neww		80.40 113	79.54 108	82.98 132	84.10 212	64.51 171	87.57 119	90.22 121	73.25 116	78.47 102	86.65 183	62.83 109	93.86 106	75.72 92	77.02 214	90.58 132

v7new		80.40 113	79.54 108	82.98 132	84.10 212	64.51 171	87.57 119	90.22 121	73.25 116	78.47 102	86.65 183	62.83 109	93.86 106	75.72 92	77.02 214	90.58 132

v6		80.40 113	79.54 108	82.98 132	84.09 214	64.50 175	87.57 119	90.22 121	73.25 116	78.47 102	86.63 185	62.84 108	93.86 106	75.73 91	77.02 214	90.58 132

v7		80.24 119	79.26 122	83.15 122	84.07 218	64.94 160	87.56 122	90.67 102	72.26 140	78.28 109	86.51 192	61.45 139	94.03 97	75.14 102	77.41 208	90.49 137

v748		77.97 167	76.65 171	81.92 166	82.29 259	63.28 200	87.53 123	90.35 116	73.50 112	70.76 222	85.55 214	58.28 169	92.81 158	68.81 156	72.76 266	89.67 174

DTE-MVSNet		76.99 191	76.80 168	77.54 249	86.24 176	53.06 296	87.52 124	90.66 104	77.08 46	72.50 202	88.67 123	60.48 157	89.52 234	57.33 246	70.74 278	90.05 157

无先验							87.48 125	88.98 165	60.00 271				94.12 93	67.28 165		88.97 193

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 154	62.89 210	87.47 126	89.02 159	71.63 147	75.29 176	87.28 159	54.80 192	91.10 211	62.38 200	79.38 193	89.61 175

EI-MVSNet-UG-set		83.81 55	83.38 56	85.09 66	87.87 145	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

CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 184	63.17 205	87.36 128	88.65 179	76.37 63	75.88 158	88.44 131	53.51 206	93.07 147	73.30 118	89.74 78	92.25 83

v13		77.50 184	76.07 187	81.77 168	84.23 203	65.07 157	87.34 129	88.91 173	72.92 125	68.35 257	81.97 260	62.53 124	91.69 192	72.20 133	66.22 299	88.56 211

v17		77.68 175	76.35 179	81.69 173	84.15 209	64.65 166	87.33 130	88.99 163	72.70 132	69.25 247	82.07 256	62.82 111	91.79 182	72.69 125	67.15 291	88.63 202

v16		77.69 174	76.36 178	81.68 174	84.15 209	64.63 168	87.33 130	88.99 163	72.69 133	69.31 246	82.08 255	62.80 112	91.79 182	72.70 124	67.23 289	88.63 202

v12		77.51 182	76.09 186	81.76 170	84.22 204	64.99 158	87.30 132	88.93 172	72.92 125	68.48 256	81.97 260	62.54 123	91.70 191	72.24 132	66.21 300	88.58 209

v18		77.67 177	76.35 179	81.64 176	84.09 214	64.47 177	87.27 133	89.01 161	72.59 134	69.39 243	82.04 257	62.85 107	91.80 181	72.72 123	67.20 290	88.63 202

V9		77.52 180	76.11 185	81.73 171	84.19 208	64.89 161	87.26 134	88.94 171	72.87 128	68.65 252	81.96 262	62.65 119	91.72 188	72.27 131	66.24 298	88.60 206

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 281	67.08 120	87.24 135	89.53 143	65.66 224	75.16 178	87.19 165	52.52 209	92.25 172	77.17 79	79.34 194	89.61 175

V14		77.52 180	76.12 182	81.70 172	84.15 209	64.77 164	87.21 136	88.95 168	72.80 129	68.79 249	81.94 263	62.69 116	91.72 188	72.31 130	66.27 297	88.60 206

v1141		80.19 122	79.31 118	82.85 141	83.84 224	64.12 184	87.14 137	90.08 128	73.13 119	78.27 110	86.39 194	62.67 118	93.75 116	75.40 99	76.83 222	90.68 123

v15		77.51 182	76.12 182	81.66 175	84.09 214	64.65 166	87.14 137	88.96 167	72.76 130	68.90 248	81.91 264	62.74 114	91.73 186	72.32 129	66.29 296	88.61 205

v1		80.19 122	79.31 118	82.85 141	83.83 226	64.12 184	87.14 137	90.07 130	73.13 119	78.27 110	86.38 198	62.72 115	93.75 116	75.41 98	76.82 223	90.68 123

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 224	64.11 186	87.13 140	90.08 128	73.13 119	78.27 110	86.39 194	62.69 116	93.75 116	75.40 99	76.82 223	90.68 123

UniMVSNet_NR-MVSNet		81.88 81	81.54 79	82.92 137	88.46 131	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	76.46 228	92.25 83

v1144		80.03 126	79.03 126	83.01 130	83.78 227	64.51 171	87.11 142	90.57 107	71.96 144	78.08 119	86.20 201	61.41 140	93.94 100	74.93 103	77.23 210	90.60 129

v11		77.45 185	76.06 188	81.59 179	84.22 204	64.52 169	87.11 142	89.02 159	72.76 130	68.76 250	81.90 265	62.09 132	91.71 190	71.98 134	66.73 292	88.56 211

v2v482		80.23 120	79.29 121	83.05 128	83.62 229	64.14 182	87.04 144	89.97 132	73.61 107	78.18 116	87.22 163	61.10 147	93.82 109	76.11 87	76.78 225	91.18 109

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 148	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	76.46 228	92.20 85

v144192		79.47 138	78.37 141	82.78 149	83.35 234	63.96 188	86.96 146	90.36 115	69.99 170	77.50 127	85.67 210	60.66 154	93.77 114	74.27 107	76.58 226	90.62 127

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 242	66.96 124	86.94 147	87.45 202	72.45 135	71.49 217	84.17 233	54.79 195	91.58 199	67.61 161	80.31 185	89.30 179

v1192		79.59 134	78.43 140	83.07 127	83.55 231	64.52 169	86.93 148	90.58 106	70.83 157	77.78 123	85.90 204	59.15 164	93.94 100	73.96 110	77.19 212	90.76 119

EPNet_dtu		75.46 216	74.86 205	77.23 253	82.57 256	54.60 287	86.89 149	83.09 247	71.64 146	66.25 275	85.86 206	55.99 185	88.04 257	54.92 256	86.55 116	89.05 187
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
原ACMM2							86.86 150

VPA-MVSNet		80.60 108	80.55 91	80.76 194	88.07 140	60.80 226	86.86 150	91.58 81	75.67 73	80.24 84	89.45 110	63.34 95	90.25 224	70.51 143	79.22 196	91.23 108

v1921920		79.22 143	78.03 147	82.80 146	83.30 237	63.94 189	86.80 152	90.33 117	69.91 171	77.48 128	85.53 215	58.44 168	93.75 116	73.60 114	76.85 220	90.71 122

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 179	63.20 203	86.79 153	89.34 148	74.19 93	75.45 168	86.72 175	66.62 69	92.39 167	72.58 126	76.86 219	90.75 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		75.39 218	74.56 209	77.86 242	85.50 186	57.10 261	86.78 154	86.09 218	72.17 142	71.53 216	87.34 158	63.01 105	89.31 238	56.84 249	61.83 308	87.17 241

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 247	85.79 180	56.21 276	86.78 154	85.76 220	73.60 108	77.93 121	87.57 152	65.02 83	88.99 243	67.14 168	75.33 243	87.63 229

PAPR		81.66 86	80.89 87	83.99 99	90.27 73	64.00 187	86.76 156	91.77 76	68.84 193	77.13 138	89.50 104	67.63 62	94.88 70	67.55 162	88.52 94	93.09 60

Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 241	88.64 125	51.78 299	86.70 157	79.63 284	74.14 95	75.11 180	90.83 83	61.29 143	89.75 230	58.10 239	91.60 57	92.69 71

pmmvs6		74.69 220	73.39 219	78.61 232	81.38 270	57.48 257	86.64 158	87.95 192	64.99 230	70.18 229	86.61 186	50.43 244	89.52 234	62.12 204	70.18 280	88.83 195

v1240		78.99 148	77.78 153	82.64 153	83.21 238	63.54 192	86.62 159	90.30 119	69.74 176	77.33 130	85.68 209	57.04 181	93.76 115	73.13 120	76.92 217	90.62 127

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

旧先验2							86.56 161		58.10 283	87.04 13			88.98 244	74.07 109

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 170	62.36 213	86.52 162	88.77 175	71.13 153	75.34 172	86.66 182	54.07 202	91.10 211	62.72 196	79.57 190	89.45 177

v52		77.94 170	76.37 175	82.67 151	79.39 293	65.52 142	86.43 163	89.94 133	72.28 138	72.15 209	84.94 227	55.70 187	93.44 130	73.64 112	72.84 265	89.06 183

V4		77.95 168	76.37 175	82.67 151	79.40 292	65.52 142	86.43 163	89.94 133	72.28 138	72.14 210	84.95 226	55.72 186	93.44 130	73.64 112	72.86 264	89.05 187

pm-mvs1		77.25 189	76.68 170	78.93 227	84.22 204	58.62 239	86.41 165	88.36 184	71.37 152	73.31 193	88.01 141	61.22 145	89.15 241	64.24 189	73.01 263	89.03 189

EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 201	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	80.57 181	90.74 121

CVMVSNet		72.99 239	72.58 226	74.25 275	84.28 201	50.85 306	86.41 165	83.45 242	44.56 322	73.23 195	87.54 154	49.38 249	85.70 272	65.90 177	78.44 200	86.19 258

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 148	63.34 198	86.31 168	91.09 96	79.01 26	72.17 207	89.07 115	67.20 66	92.81 158	66.08 176	75.65 237	92.20 85

v148		78.72 151	77.80 152	81.47 181	82.73 252	61.96 219	86.30 169	88.08 190	73.26 115	76.18 154	85.47 217	62.46 126	92.36 169	71.92 136	73.82 259	90.09 152

新几何2							86.29 170

PVSNet_BlendedMVS		80.60 108	80.02 97	82.36 158	88.85 115	65.40 146	86.16 171	92.00 62	69.34 181	78.11 117	86.09 203	66.02 76	94.27 85	71.52 138	82.06 165	87.39 234

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 249	65.32 150	86.12 172	89.55 142	69.64 177	70.55 223	85.82 208	57.24 178	93.81 110	76.85 83	88.55 92	92.41 78

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 169	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

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 108	65.91 137	86.07 174	88.52 182	72.18 141	75.42 169	87.69 149	61.15 146	93.54 125	60.38 218	86.83 112	86.70 252

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

jason		81.39 91	80.29 95	84.70 76	86.63 173	69.90 66	85.95 176	86.77 207	63.24 243	81.07 79	89.47 106	61.08 148	92.15 174	78.33 68	90.07 75	92.05 90
jason: jason.
test_0402		72.79 241	70.44 244	79.84 208	88.13 139	65.99 135	85.93 177	84.29 231	65.57 225	67.40 265	85.49 216	46.92 262	92.61 161	35.88 318	74.38 253	80.94 301

OurMVSNet-221017-0		74.26 224	72.42 228	79.80 209	83.76 228	59.59 232	85.92 178	86.64 208	66.39 216	66.96 268	87.58 151	39.46 298	91.60 198	65.76 179	69.27 282	88.22 217

EG-PatchMatch MVS		74.04 225	71.82 233	80.71 195	84.92 194	67.42 114	85.86 179	88.08 190	66.04 220	64.22 286	83.85 236	35.10 313	92.56 163	57.44 244	80.83 176	82.16 297

thres100view900		76.50 197	75.55 193	79.33 217	89.52 92	56.99 262	85.83 180	83.23 244	73.94 98	76.32 150	87.12 167	51.89 223	91.95 177	48.33 282	83.75 141	89.07 181

test_normal		79.81 130	78.45 137	83.89 103	82.70 253	65.40 146	85.82 181	89.48 145	69.39 178	70.12 232	85.66 211	57.15 180	93.71 121	77.08 80	88.62 90	92.56 74

CLD-MVS		82.31 75	81.65 78	84.29 87	88.47 130	67.73 111	85.81 182	92.35 49	75.78 70	78.33 108	86.58 188	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
view600		76.20 204	75.21 200	79.16 222	89.64 85	55.82 279	85.74 183	82.06 259	73.88 100	75.74 161	87.85 143	51.84 225	91.66 193	46.75 291	83.42 147	90.00 158

view800		76.20 204	75.21 200	79.16 222	89.64 85	55.82 279	85.74 183	82.06 259	73.88 100	75.74 161	87.85 143	51.84 225	91.66 193	46.75 291	83.42 147	90.00 158

conf0.05thres1000		76.20 204	75.21 200	79.16 222	89.64 85	55.82 279	85.74 183	82.06 259	73.88 100	75.74 161	87.85 143	51.84 225	91.66 193	46.75 291	83.42 147	90.00 158

tfpn		76.20 204	75.21 200	79.16 222	89.64 85	55.82 279	85.74 183	82.06 259	73.88 100	75.74 161	87.85 143	51.84 225	91.66 193	46.75 291	83.42 147	90.00 158

SixPastTwentyTwo		73.37 232	71.26 239	79.70 210	85.08 193	57.89 250	85.57 187	83.56 239	71.03 156	65.66 277	85.88 205	42.10 289	92.57 162	59.11 229	63.34 305	88.65 201

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 159	70.19 61	85.56 188	88.77 175	69.06 187	81.83 67	88.16 137	50.91 237	92.85 154	78.29 69	87.56 103	89.06 183

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 159	70.19 61	85.56 188	88.77 175	69.06 187	81.83 67	88.16 137	50.91 237	92.85 154	78.29 69	87.56 103	89.06 183

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 159	70.19 61	85.56 188	88.77 175	69.06 187	81.83 67	88.16 137	50.91 237	92.85 154	78.29 69	87.56 103	89.06 183

V42		79.38 141	78.24 145	82.83 144	81.10 275	65.50 145	85.55 191	89.82 136	71.57 150	78.21 114	86.12 202	60.66 154	93.18 141	75.64 95	75.46 241	89.81 171

lupinMVS		81.39 91	80.27 96	84.76 75	87.35 159	70.21 59	85.55 191	86.41 211	62.85 249	81.32 73	88.61 125	61.68 134	92.24 173	78.41 67	90.26 71	91.83 94

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 115	64.51 171	85.53 193	89.39 147	70.79 158	78.49 101	85.06 224	67.54 63	93.58 123	67.03 170	86.58 115	92.32 80

thres600view7		76.50 197	75.44 194	79.68 211	89.40 96	57.16 259	85.53 193	83.23 244	73.79 105	76.26 151	87.09 168	51.89 223	91.89 180	48.05 287	83.72 144	90.00 158

DELS-MVS		85.41 47	85.30 45	85.77 55	88.49 129	67.93 107	85.52 195	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
Anonymous20231211		64.82 283	61.79 287	73.91 278	77.11 303	50.92 305	85.29 196	81.53 266	54.19 303	57.98 305	78.03 292	26.90 320	87.83 260	37.92 315	57.12 316	82.99 292

Test4		77.83 172	75.90 189	83.62 106	80.24 283	65.25 152	85.27 197	90.67 102	69.03 190	66.48 273	83.75 238	43.07 282	93.00 151	75.93 90	88.66 89	92.62 73

tfpn200view9		76.42 200	75.37 197	79.55 216	89.13 110	57.65 254	85.17 198	83.60 237	73.41 113	76.45 145	86.39 194	52.12 217	91.95 177	48.33 282	83.75 141	89.07 181

thres400		76.50 197	75.37 197	79.86 207	89.13 110	57.65 254	85.17 198	83.60 237	73.41 113	76.45 145	86.39 194	52.12 217	91.95 177	48.33 282	83.75 141	90.00 158

MVS_111021_LR		82.61 73	82.11 71	84.11 91	88.82 118	71.58 42	85.15 200	86.16 216	74.69 88	80.47 83	91.04 77	62.29 128	90.55 221	80.33 55	90.08 74	90.20 146

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 121	58.35 241	85.06 201	88.61 181	78.56 29	77.65 125	88.34 133	63.81 93	90.66 220	64.98 185	77.22 211	91.80 97

OpenMVS_ROB	64.09 19	70.56 255	68.19 258	77.65 246	80.26 282	59.41 236	85.01 202	82.96 249	58.76 280	65.43 279	82.33 251	37.63 307	91.23 206	45.34 303	76.03 232	82.32 295

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 97	65.93 136	84.95 203	87.15 204	73.56 109	78.19 115	89.79 99	56.67 182	93.36 133	59.53 226	86.74 113	90.13 149

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 119	65.13 156	84.87 204	87.85 194	69.75 174	74.52 187	84.74 230	61.34 141	93.11 145	58.24 238	85.84 125	84.27 278

TDRefinement		67.49 270	64.34 277	76.92 254	73.47 315	61.07 222	84.86 205	82.98 248	59.77 273	58.30 304	85.13 222	26.06 322	87.89 258	47.92 288	60.59 313	81.81 299

TAMVS		78.89 150	77.51 158	83.03 129	87.80 148	67.79 110	84.72 206	85.05 226	67.63 204	76.75 140	87.70 148	62.25 129	90.82 217	58.53 235	87.13 109	90.49 137

1314		76.53 196	75.30 199	80.21 203	83.93 222	62.32 215	84.66 207	88.81 174	60.23 269	70.16 231	84.07 235	55.30 190	90.73 219	67.37 164	83.21 153	87.59 231

1121		80.84 97	79.77 102	84.05 94	93.11 37	70.78 53	84.66 207	85.42 222	57.37 291	81.76 71	92.02 55	63.41 94	94.12 93	67.28 165	92.93 48	87.26 239

MVS		78.19 161	76.99 165	81.78 167	85.66 182	66.99 121	84.66 207	90.47 110	55.08 301	72.02 211	85.27 220	63.83 92	94.11 95	66.10 175	89.80 77	84.24 279

tfpnnormal		74.39 222	73.16 222	78.08 240	86.10 178	58.05 245	84.65 210	87.53 199	70.32 166	71.22 219	85.63 212	54.97 191	89.86 228	43.03 307	75.02 247	86.32 256

TR-MVS		77.44 186	76.18 181	81.20 186	88.24 137	63.24 201	84.61 211	86.40 212	67.55 206	77.81 122	86.48 193	54.10 201	93.15 142	57.75 242	82.72 160	87.20 240

AllTest		70.96 251	68.09 261	79.58 214	85.15 189	63.62 190	84.58 212	79.83 282	62.31 255	60.32 298	86.73 173	32.02 315	88.96 246	50.28 273	71.57 274	86.15 259

EU-MVSNet		68.53 267	67.61 269	71.31 290	78.51 297	47.01 316	84.47 213	84.27 232	42.27 323	66.44 274	84.79 229	40.44 296	83.76 281	58.76 233	68.54 288	83.17 287

VNet		82.21 76	82.41 67	81.62 177	90.82 68	60.93 223	84.47 213	89.78 137	76.36 64	84.07 45	91.88 59	64.71 85	90.26 223	70.68 141	88.89 84	93.66 36

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 109	68.03 106	84.46 215	90.02 131	70.67 161	81.30 76	86.53 191	63.17 100	94.19 90	75.60 97	88.54 93	88.57 210

VPNet		78.69 152	78.66 131	78.76 230	88.31 136	55.72 284	84.45 216	86.63 209	76.79 51	78.26 113	90.55 87	59.30 163	89.70 232	66.63 171	77.05 213	90.88 116

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 115	65.40 146	84.43 217	92.00 62	67.62 205	78.11 117	85.05 225	66.02 76	94.27 85	71.52 138	89.50 79	89.01 190

MVP-Stereo		76.12 208	74.46 212	81.13 189	85.37 187	69.79 67	84.42 218	87.95 192	65.03 229	67.46 263	85.33 219	53.28 208	91.73 186	58.01 240	83.27 152	81.85 298
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 154	68.23 102	84.40 219	86.20 215	67.49 207	76.36 149	86.54 190	61.54 137	90.79 218	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
K. test v3		71.19 249	68.51 255	79.21 220	83.04 245	57.78 253	84.35 220	76.91 297	72.90 127	62.99 292	82.86 246	39.27 299	91.09 213	61.65 209	52.66 323	88.75 198

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 93	68.21 103	84.28 221	90.09 127	70.79 158	81.26 77	85.62 213	63.15 101	94.29 83	75.62 96	88.87 85	88.59 208

diffmvs		79.51 135	78.59 133	82.25 159	83.31 236	62.66 211	84.17 222	88.11 186	67.64 203	76.09 157	87.47 156	64.01 90	91.15 207	71.71 137	84.82 131	92.94 67

test222					91.50 59	68.26 101	84.16 223	83.20 246	54.63 302	79.74 85	91.63 64	58.97 165			91.42 60	86.77 250

testdata1							84.14 224		75.71 71

testing_2		75.73 213	73.34 221	82.89 140	77.37 301	65.22 153	84.10 225	90.54 108	69.09 186	60.46 297	81.15 270	40.48 295	92.84 157	76.36 86	80.54 183	90.60 129

MVSTER		79.01 147	77.88 150	82.38 157	83.07 243	64.80 163	84.08 226	88.95 168	69.01 191	78.69 96	87.17 166	54.70 196	92.43 165	74.69 104	80.57 181	89.89 167

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 131	60.91 224	83.84 227	89.24 154	70.36 165	79.03 92	88.87 119	63.23 99	90.21 225	65.12 182	82.57 162	92.28 82

PAPM		77.68 175	76.40 174	81.51 180	87.29 164	61.85 220	83.78 228	89.59 141	64.74 231	71.23 218	88.70 121	62.59 121	93.66 122	52.66 266	87.03 111	89.01 190

1112_ss		77.40 188	76.43 173	80.32 200	89.11 112	60.41 229	83.65 229	87.72 196	62.13 257	73.05 197	86.72 175	62.58 122	89.97 227	62.11 205	80.80 177	90.59 131

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 152	68.99 82	83.65 229	91.46 87	63.00 246	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
XVG-ACMP-BASELINE		76.11 209	74.27 214	81.62 177	83.20 239	64.67 165	83.60 231	89.75 138	69.75 174	71.85 212	87.09 168	32.78 314	92.11 175	69.99 147	80.43 184	88.09 220

XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 184	68.78 86	83.54 232	90.50 109	70.66 162	76.71 141	91.66 61	60.69 153	91.26 204	76.94 82	81.58 170	91.83 94

IB-MVS	68.01 15	75.85 212	73.36 220	83.31 116	84.76 195	66.03 133	83.38 233	85.06 225	70.21 169	69.40 242	81.05 271	45.76 270	94.66 76	65.10 183	75.49 240	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
HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 158	60.21 230	83.37 234	87.78 195	66.11 218	75.37 171	87.06 170	63.27 97	90.48 222	61.38 212	82.43 163	90.40 142

thres200		75.55 215	74.47 211	78.82 229	87.78 151	57.85 251	83.07 235	83.51 240	72.44 137	75.84 159	84.42 232	52.08 219	91.75 185	47.41 289	83.64 145	86.86 248

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 183	69.02 80	83.03 236	90.39 111	71.09 155	77.63 126	91.49 69	54.62 198	91.35 202	75.71 94	83.47 146	91.54 100

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 200	57.97 248	82.59 237	87.62 197	67.40 209	76.17 156	88.56 128	68.47 56	89.59 233	70.65 142	86.05 122	93.47 48

DWT-MVSNet_test		73.70 228	71.86 231	79.21 220	82.91 248	58.94 237	82.34 238	82.17 256	65.21 226	71.05 221	78.31 289	44.21 276	90.17 226	63.29 194	77.28 209	88.53 213

cascas		76.72 195	74.64 207	82.99 131	85.78 181	65.88 138	82.33 239	89.21 155	60.85 265	72.74 199	81.02 272	47.28 260	93.75 116	67.48 163	85.02 127	89.34 178

RPSCF		73.23 236	71.46 235	78.54 234	82.50 257	59.85 231	82.18 240	82.84 250	58.96 278	71.15 220	89.41 112	45.48 273	84.77 279	58.82 232	71.83 272	91.02 113

tpmp4_e23		73.45 231	71.17 240	80.31 201	83.55 231	59.56 234	81.88 241	82.33 254	57.94 286	70.51 225	81.62 266	51.19 235	91.63 197	53.96 260	77.51 207	89.75 173

pmmvs-eth3d		70.50 256	67.83 265	78.52 235	77.37 301	66.18 132	81.82 242	81.51 267	58.90 279	63.90 288	80.42 277	42.69 285	86.28 269	58.56 234	65.30 302	83.11 289

MS-PatchMatch		73.83 227	72.67 225	77.30 252	83.87 223	66.02 134	81.82 242	84.66 228	61.37 263	68.61 254	82.82 247	47.29 259	88.21 254	59.27 227	84.32 136	77.68 311

pmmvs5		71.55 247	70.20 247	75.61 263	77.83 298	56.39 272	81.74 244	80.89 270	57.76 287	67.46 263	84.49 231	49.26 252	85.32 276	57.08 248	75.29 244	85.11 272

Test_1112_low_res		76.40 201	75.44 194	79.27 218	89.28 104	58.09 244	81.69 245	87.07 205	59.53 275	72.48 203	86.67 181	61.30 142	89.33 237	60.81 217	80.15 187	90.41 141

PatchFormer-LS_test		74.50 221	73.05 223	78.86 228	82.95 247	59.55 235	81.65 246	82.30 255	67.44 208	71.62 215	78.15 291	52.34 213	88.92 248	65.05 184	75.90 234	88.12 219

IterMVS		74.29 223	72.94 224	78.35 238	81.53 267	63.49 194	81.58 247	82.49 252	68.06 202	69.99 235	83.69 240	51.66 231	85.54 273	65.85 178	71.64 273	86.01 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
pmmvs4		74.03 226	71.91 230	80.39 197	81.96 262	68.32 99	81.45 248	82.14 257	59.32 276	69.87 238	85.13 222	52.40 212	88.13 256	60.21 220	74.74 250	84.73 276

GA-MVS		76.87 193	75.17 204	81.97 164	82.75 251	62.58 212	81.44 249	86.35 214	72.16 143	74.74 185	82.89 245	46.20 266	92.02 176	68.85 155	81.09 173	91.30 107

CostFormer		75.24 219	73.90 217	79.27 218	82.65 255	58.27 243	80.80 250	82.73 251	61.57 260	75.33 175	83.13 244	55.52 188	91.07 214	64.98 185	78.34 202	88.45 214

MIMVSNet1		68.58 266	66.78 271	73.98 277	80.07 285	51.82 298	80.77 251	84.37 230	64.40 235	59.75 301	82.16 254	36.47 309	83.63 283	42.73 308	70.33 279	86.48 255

MSDG		73.36 234	70.99 241	80.49 196	84.51 199	65.80 139	80.71 252	86.13 217	65.70 223	65.46 278	83.74 239	44.60 274	90.91 216	51.13 270	76.89 218	84.74 275

tpm2		73.26 235	71.46 235	78.63 231	83.34 235	56.71 267	80.65 253	80.40 277	56.63 295	73.55 191	82.02 258	51.80 229	91.24 205	56.35 251	78.42 201	87.95 222

XXY-MVS		75.41 217	75.56 192	74.96 268	83.59 230	57.82 252	80.59 254	83.87 235	66.54 215	74.93 184	88.31 134	63.24 98	80.09 296	62.16 203	76.85 220	86.97 246

HyFIR lowres test		77.53 179	75.40 196	83.94 102	89.59 90	66.62 126	80.36 255	88.64 180	56.29 297	76.45 145	85.17 221	57.64 173	93.28 135	61.34 213	83.10 155	91.91 92

TinyColmap		67.30 273	64.81 275	74.76 271	81.92 263	56.68 268	80.29 256	81.49 268	60.33 267	56.27 313	83.22 243	24.77 324	87.66 261	45.52 301	69.47 281	79.95 305

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 250	87.20 165	51.60 300	80.06 257	80.46 276	75.20 82	67.69 261	86.72 175	62.48 125	88.98 244	63.44 192	89.25 82	91.51 101

FMVSNet5		69.50 262	67.96 262	74.15 276	82.97 246	55.35 285	80.01 258	82.12 258	62.56 253	63.02 290	81.53 267	36.92 308	81.92 289	48.42 281	74.06 255	85.17 271

Patchmatch-test1		73.49 230	71.85 232	78.41 237	84.05 220	62.17 217	79.96 259	79.29 286	66.30 217	72.38 205	79.58 284	51.95 222	85.08 277	55.46 254	77.67 206	87.99 221

tpmrst		72.39 242	72.13 229	73.18 281	80.54 280	49.91 310	79.91 260	79.08 287	63.11 244	71.69 214	79.95 280	55.32 189	82.77 287	65.66 180	73.89 257	86.87 247

PatchmatchNet		73.12 237	71.33 237	78.49 236	83.18 240	60.85 225	79.63 261	78.57 288	64.13 237	71.73 213	79.81 283	51.20 234	85.97 271	57.40 245	76.36 230	88.66 200
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.38 243	70.90 242	76.80 256	88.60 126	67.38 116	79.53 262	76.17 299	62.75 251	69.36 244	82.00 259	45.51 272	84.89 278	53.62 262	80.58 180	78.12 309

CMPMVS	51.72 21	70.19 259	68.16 259	76.28 258	73.15 317	57.55 256	79.47 263	83.92 234	48.02 320	56.48 312	84.81 228	43.13 281	86.42 268	62.67 199	81.81 169	84.89 273
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
GG-mvs-BLEND				75.38 266	81.59 266	55.80 283	79.32 264	69.63 323		67.19 266	73.67 310	43.24 280	88.90 249	50.41 272	84.50 133	81.45 300

LTVRE_ROB	69.57 13	76.25 203	74.54 210	81.41 182	88.60 126	64.38 180	79.24 265	89.12 157	70.76 160	69.79 240	87.86 142	49.09 253	93.20 139	56.21 252	80.16 186	86.65 253
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
tpm		72.37 244	71.71 234	74.35 274	82.19 260	52.00 297	79.22 266	77.29 295	64.56 233	72.95 198	83.68 241	51.35 232	83.26 286	58.33 237	75.80 235	87.81 226

USDC		70.33 257	68.37 256	76.21 259	80.60 279	56.23 275	79.19 267	86.49 210	60.89 264	61.29 294	85.47 217	31.78 317	89.47 236	53.37 263	76.21 231	82.94 294

PM-MVS		66.41 278	64.14 278	73.20 280	73.92 312	56.45 270	78.97 268	64.96 334	63.88 242	64.72 283	80.24 278	19.84 330	83.44 284	66.24 172	64.52 304	79.71 306

tpmvs		71.09 250	69.29 250	76.49 257	82.04 261	56.04 277	78.92 269	81.37 269	64.05 238	67.18 267	78.28 290	49.74 248	89.77 229	49.67 278	72.37 267	83.67 283

test_post1							78.90 270				5.43 341	48.81 256	85.44 275	59.25 228

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 80	68.58 96	78.70 271	87.50 200	56.38 296	75.80 160	86.84 171	58.67 166	91.40 201	61.58 210	85.75 126	90.34 143

test-LLR		72.94 240	72.43 227	74.48 272	81.35 271	58.04 246	78.38 272	77.46 293	66.66 211	69.95 236	79.00 287	48.06 257	79.24 298	66.13 173	84.83 129	86.15 259

TESTMET0.1,1		69.89 261	69.00 252	72.55 282	79.27 295	56.85 263	78.38 272	74.71 309	57.64 288	68.09 258	77.19 298	37.75 305	76.70 309	63.92 190	84.09 137	84.10 282

test-mter		71.41 248	70.39 246	74.48 272	81.35 271	58.04 246	78.38 272	77.46 293	60.32 268	69.95 236	79.00 287	36.08 311	79.24 298	66.13 173	84.83 129	86.15 259

Anonymous20231206		68.60 265	67.80 266	71.02 291	80.23 284	50.75 307	78.30 275	80.47 275	56.79 294	66.11 276	82.63 249	46.35 264	78.95 300	43.62 306	75.70 236	83.36 286

tpm cat1		70.57 254	68.31 257	77.35 251	82.41 258	57.95 249	78.08 276	80.22 280	52.04 313	68.54 255	77.66 296	52.00 221	87.84 259	51.77 267	72.07 271	86.25 257

WTY-MVS		75.65 214	75.68 191	75.57 264	86.40 175	56.82 264	77.92 277	82.40 253	65.10 228	76.18 154	87.72 147	63.13 104	80.90 292	60.31 219	81.96 166	89.00 192

test20.03		67.45 271	66.95 270	68.94 297	75.48 311	44.84 318	77.50 278	77.67 292	66.66 211	63.01 291	83.80 237	47.02 261	78.40 302	42.53 309	68.86 286	83.58 284

EPMVS		69.02 264	68.16 259	71.59 285	79.61 289	49.80 312	77.40 279	66.93 330	62.82 250	70.01 233	79.05 285	45.79 269	77.86 306	56.58 250	75.26 245	87.13 243

gg-mvs-nofinetune		69.95 260	67.96 262	75.94 260	83.07 243	54.51 289	77.23 280	70.29 321	63.11 244	70.32 227	62.33 323	43.62 279	88.69 250	53.88 261	87.76 100	84.62 277

MDTV_nov1_ep13			69.97 248		83.18 240	53.48 294	77.10 281	80.18 281	60.45 266	69.33 245	80.44 276	48.89 255	86.90 263	51.60 268	78.51 199

LF4IMVS		64.02 286	62.19 286	69.50 296	70.90 322	53.29 295	76.13 282	77.18 296	52.65 312	58.59 302	80.98 273	23.55 325	76.52 310	53.06 265	66.66 293	78.68 308

sss		73.60 229	73.64 218	73.51 279	82.80 250	55.01 286	76.12 283	81.69 265	62.47 254	74.68 186	85.85 207	57.32 175	78.11 304	60.86 216	80.93 174	87.39 234

testgi		66.67 276	66.53 272	67.08 303	75.62 309	41.69 325	75.93 284	76.50 298	66.11 218	65.20 282	86.59 187	35.72 312	74.71 317	43.71 305	73.38 262	84.84 274

CR-MVSNet		73.37 232	71.27 238	79.67 212	81.32 273	65.19 154	75.92 285	80.30 278	59.92 272	72.73 200	81.19 268	52.50 210	86.69 264	59.84 222	77.71 204	87.11 244

RPMNet		71.62 246	68.94 253	79.67 212	81.32 273	65.19 154	75.92 285	78.30 290	57.60 289	72.73 200	76.45 301	52.30 214	86.69 264	48.14 286	77.71 204	87.11 244

MIMVSNet		70.69 253	69.30 249	74.88 269	84.52 198	56.35 274	75.87 287	79.42 285	64.59 232	67.76 259	82.41 250	41.10 293	81.54 291	46.64 297	81.34 171	86.75 251

test0.0.03 1		68.00 269	67.69 268	68.90 298	77.55 299	47.43 314	75.70 288	72.95 316	66.66 211	66.56 271	82.29 252	48.06 257	75.87 313	44.97 304	74.51 252	83.41 285

PMMVS		69.34 263	68.67 254	71.35 289	75.67 308	62.03 218	75.17 289	73.46 314	50.00 318	68.68 251	79.05 285	52.07 220	78.13 303	61.16 214	82.77 158	73.90 319

UnsupCasMVSNet_eth		67.33 272	65.99 273	71.37 287	73.48 314	51.47 302	75.16 290	85.19 224	65.20 227	60.78 296	80.93 275	42.35 286	77.20 308	57.12 247	53.69 322	85.44 267

MDTV_nov1_ep13_2view						37.79 330	75.16 290		55.10 300	66.53 272		49.34 250		53.98 259		87.94 223

pmmvs3		57.79 295	54.26 299	68.37 301	64.02 329	56.72 266	75.12 292	65.17 332	40.20 325	52.93 319	69.86 318	20.36 329	75.48 315	45.45 302	55.25 321	72.90 320

dp		66.80 274	65.43 274	70.90 292	79.74 288	48.82 313	75.12 292	74.77 307	59.61 274	64.08 287	77.23 297	42.89 283	80.72 293	48.86 280	66.58 294	83.16 288

Patchmtry		70.74 252	69.16 251	75.49 265	80.72 277	54.07 291	74.94 294	80.30 278	58.34 282	70.01 233	81.19 268	52.50 210	86.54 266	53.37 263	71.09 276	85.87 265

PVSNet	64.34 18	72.08 245	70.87 243	75.69 262	86.21 177	56.44 271	74.37 295	80.73 273	62.06 258	70.17 230	82.23 253	42.86 284	83.31 285	54.77 257	84.45 135	87.32 237

MDA-MVSNet-bldmvs		66.68 275	63.66 279	75.75 261	79.28 294	60.56 228	73.92 296	78.35 289	64.43 234	50.13 323	79.87 282	44.02 278	83.67 282	46.10 299	56.86 317	83.03 291

UnsupCasMVSNet_bld		63.70 287	61.53 289	70.21 294	73.69 313	51.39 303	72.82 297	81.89 263	55.63 299	57.81 306	71.80 313	38.67 301	78.61 301	49.26 279	52.21 324	80.63 302

PatchT		68.46 268	67.85 264	70.29 293	80.70 278	43.93 320	72.47 298	74.88 305	60.15 270	70.55 223	76.57 300	49.94 247	81.59 290	50.58 271	74.83 249	85.34 268

MVS-HIRNet		59.14 292	57.67 294	63.57 308	81.65 265	43.50 321	71.73 299	65.06 333	39.59 327	51.43 321	57.73 327	38.34 303	82.58 288	39.53 313	73.95 256	64.62 326

Patchmatch-RL test		70.24 258	67.78 267	77.61 247	77.43 300	59.57 233	71.16 300	70.33 320	62.94 248	68.65 252	72.77 311	50.62 242	85.49 274	69.58 150	66.58 294	87.77 227

test123		6.12 321	8.11 322	0.14 332	0.06 346	0.09 346	71.05 301	0.03 349	0.04 342	0.25 343	1.30 344	0.05 349	0.03 345	0.21 342	0.01 343	0.29 339

test1235678		58.74 294	56.89 297	64.30 305	69.70 323	41.87 324	71.05 301	74.87 306	54.06 304	50.63 322	71.53 314	25.30 323	74.10 320	31.80 326	63.10 306	76.93 315

ANet_high		50.57 305	46.10 307	63.99 306	48.67 339	39.13 327	70.99 303	80.85 271	61.39 262	31.18 331	57.70 328	17.02 334	73.65 322	31.22 327	15.89 338	79.18 307

testmv		53.85 300	51.03 302	62.31 309	61.46 331	38.88 329	70.95 304	74.69 310	51.11 317	41.26 325	66.85 320	14.28 336	72.13 325	29.19 328	49.51 326	75.93 318

testmvs		6.04 322	8.02 323	0.10 333	0.08 345	0.03 347	69.74 305	0.04 348	0.05 341	0.31 342	1.68 343	0.02 350	0.04 344	0.24 341	0.02 341	0.25 340

testus		59.00 293	57.91 292	62.25 310	72.25 319	39.09 328	69.74 305	75.02 304	53.04 311	57.21 309	73.72 309	18.76 332	70.33 328	32.86 321	68.57 287	77.35 312

N_pmnet		52.79 302	53.26 300	51.40 321	78.99 296	7.68 345	69.52 307	3.89 346	51.63 316	57.01 310	74.98 305	40.83 294	65.96 334	37.78 316	64.67 303	80.56 304

FPMVS		53.68 301	51.64 301	59.81 313	65.08 328	51.03 304	69.48 308	69.58 324	41.46 324	40.67 326	72.32 312	16.46 335	70.00 329	24.24 333	65.42 301	58.40 328

DSMNet-mixed		57.77 296	56.90 296	60.38 312	67.70 327	35.61 331	69.18 309	53.97 337	32.30 333	57.49 308	79.88 281	40.39 297	68.57 331	38.78 314	72.37 267	76.97 314

new-patchmatchnet		61.73 288	61.73 288	61.70 311	72.74 318	24.50 341	69.16 310	78.03 291	61.40 261	56.72 311	75.53 304	38.42 302	76.48 311	45.95 300	57.67 315	84.13 281

YYNet1		65.03 281	62.91 283	71.38 286	75.85 307	56.60 269	69.12 311	74.66 311	57.28 292	54.12 315	77.87 294	45.85 268	74.48 318	49.95 276	61.52 310	83.05 290

MDA-MVSNet_test_wron		65.03 281	62.92 282	71.37 287	75.93 306	56.73 265	69.09 312	74.73 308	57.28 292	54.03 316	77.89 293	45.88 267	74.39 319	49.89 277	61.55 309	82.99 292

1111		57.11 297	56.82 298	57.97 315	69.10 324	28.28 336	68.90 313	74.54 312	54.01 305	53.71 317	74.51 306	23.09 326	67.90 332	32.28 323	61.26 311	77.73 310

.test1245		45.55 307	50.02 304	32.14 327	69.10 324	28.28 336	68.90 313	74.54 312	54.01 305	53.71 317	74.51 306	23.09 326	67.90 332	32.28 323	0.02 341	0.25 340

PVSNet_0	57.27 20	61.67 289	59.27 290	68.85 299	79.61 289	57.44 258	68.01 315	73.44 315	55.93 298	58.54 303	70.41 316	44.58 275	77.55 307	47.01 290	35.91 329	71.55 321

test2356		59.50 291	58.08 291	63.74 307	71.23 321	41.88 323	67.59 316	72.42 318	53.72 307	57.65 307	70.74 315	26.31 321	72.40 324	32.03 325	71.06 277	76.93 315

ADS-MVSNet2		66.20 280	63.33 280	74.82 270	79.92 286	58.75 238	67.55 317	75.19 303	53.37 309	65.25 280	75.86 302	42.32 287	80.53 294	41.57 310	68.91 284	85.18 269

ADS-MVSNet		64.36 285	62.88 284	68.78 300	79.92 286	47.17 315	67.55 317	71.18 319	53.37 309	65.25 280	75.86 302	42.32 287	73.99 321	41.57 310	68.91 284	85.18 269

LP		61.36 290	57.78 293	72.09 283	75.54 310	58.53 240	67.16 319	75.22 302	51.90 315	54.13 314	69.97 317	37.73 306	80.45 295	32.74 322	55.63 319	77.29 313

LCM-MVSNet		54.25 299	49.68 305	67.97 302	53.73 336	45.28 317	66.85 320	80.78 272	35.96 329	39.45 328	62.23 325	8.70 342	78.06 305	48.24 285	51.20 325	80.57 303

JIA-IIPM		66.32 279	62.82 285	76.82 255	77.09 304	61.72 221	65.34 321	75.38 301	58.04 285	64.51 284	62.32 324	42.05 290	86.51 267	51.45 269	69.22 283	82.21 296

PMVS	37.38 22	44.16 309	40.28 310	55.82 316	40.82 342	42.54 322	65.12 322	63.99 335	34.43 330	24.48 333	57.12 329	3.92 344	76.17 312	17.10 336	55.52 320	48.75 329
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		50.91 304	50.29 303	52.78 319	68.58 326	34.94 334	63.71 323	56.63 336	39.73 326	44.95 324	65.47 322	21.93 328	58.48 336	34.98 319	56.62 318	64.92 325

Patchmatch-test		64.82 283	63.24 281	69.57 295	79.42 291	49.82 311	63.49 324	69.05 327	51.98 314	59.95 300	80.13 279	50.91 237	70.98 327	40.66 312	73.57 260	87.90 224

ambc				75.24 267	73.16 316	50.51 308	63.05 325	87.47 201		64.28 285	77.81 295	17.80 333	89.73 231	57.88 241	60.64 312	85.49 266

test12356		49.28 306	48.51 306	51.59 320	62.06 330	19.11 342	60.40 326	72.45 317	47.60 321	40.64 327	65.68 321	13.84 337	68.72 330	27.29 330	46.67 328	66.94 324

PNet_i23d		38.26 312	35.42 312	46.79 322	58.74 332	35.48 332	59.65 327	51.25 338	32.45 332	23.44 336	47.53 332	2.04 346	58.96 335	25.60 332	18.09 336	45.92 332

no-one		51.08 303	45.79 308	66.95 304	57.92 334	50.49 309	59.63 328	76.04 300	48.04 319	31.85 329	56.10 330	19.12 331	80.08 297	36.89 317	26.52 331	70.29 322

CHOSEN 280x420		66.51 277	64.71 276	71.90 284	81.45 268	63.52 193	57.98 329	68.95 328	53.57 308	62.59 293	76.70 299	46.22 265	75.29 316	55.25 255	79.68 189	76.88 317

wuykxyi23d		39.76 311	33.18 314	59.51 314	46.98 340	44.01 319	57.70 330	67.74 329	24.13 335	13.98 340	34.33 335	1.27 347	71.33 326	34.23 320	18.23 334	63.18 327

E-PMN		31.77 314	30.64 315	35.15 325	52.87 337	27.67 338	57.09 331	47.86 340	24.64 334	16.40 338	33.05 336	11.23 339	54.90 338	14.46 338	18.15 335	22.87 335

EMVS		30.81 315	29.65 316	34.27 326	50.96 338	25.95 340	56.58 332	46.80 341	24.01 336	15.53 339	30.68 337	12.47 338	54.43 339	12.81 339	17.05 337	22.43 336

PMMVS2		40.82 310	38.86 311	46.69 323	53.84 335	16.45 343	48.61 333	49.92 339	37.49 328	31.67 330	60.97 326	8.14 343	56.42 337	28.42 329	30.72 330	67.19 323

testpf		56.51 298	57.58 295	53.30 318	71.99 320	41.19 326	46.89 334	69.32 326	58.06 284	52.87 320	69.45 319	27.99 319	72.73 323	59.59 225	62.07 307	45.98 331

wuyk23d		16.82 319	15.94 320	19.46 330	58.74 332	31.45 335	39.22 335	3.74 347	6.84 339	6.04 341	2.70 342	1.27 347	24.29 342	10.54 340	14.40 340	2.63 338

tmp_tt		18.61 318	21.40 319	10.23 331	4.82 344	10.11 344	34.70 336	30.74 344	1.48 340	23.91 335	26.07 338	28.42 318	13.41 343	27.12 331	15.35 339	7.17 337

Gipuma		45.18 308	41.86 309	55.16 317	77.03 305	51.52 301	32.50 337	80.52 274	32.46 331	27.12 332	35.02 334	9.52 341	75.50 314	22.31 334	60.21 314	38.45 333
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	26.22 23	30.37 316	25.89 318	43.81 324	44.55 341	35.46 333	28.87 338	39.07 342	18.20 337	18.58 337	40.18 333	2.68 345	47.37 340	17.07 337	23.78 333	48.60 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 317	26.61 317	0.00 334	0.00 347	0.00 348	0.00 339	89.26 153	0.00 343	0.00 344	88.61 125	61.62 136	0.00 346	0.00 343	0.00 344	0.00 342

pcd_1.5k_mvsjas		5.26 323	7.02 324	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	63.15 101	0.00 346	0.00 343	0.00 344	0.00 342

pcd1.5k->3k		34.07 313	35.26 313	30.50 328	86.92 168	0.00 348	0.00 339	91.58 81	0.00 343	0.00 344	0.00 345	56.23 184	0.00 346	0.00 343	82.60 161	91.49 103

sosnet-low-res		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

sosnet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

uncertanet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

Regformer		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

ab-mvs-re		7.23 320	9.64 321	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	86.72 175	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

uanet		0.00 324	0.00 325	0.00 334	0.00 347	0.00 348	0.00 339	0.00 350	0.00 343	0.00 344	0.00 345	0.00 351	0.00 346	0.00 343	0.00 344	0.00 342

ESAPD								94.22 1

sam_mvs1												51.32 233

sam_mvs												50.01 246

semantic-postprocess				80.11 204	82.69 254	64.85 162		83.47 241	69.16 185	70.49 226	84.15 234	50.83 241	88.15 255	69.23 152	72.14 270	87.34 236

MTGPA								92.02 59

test_post											5.46 340	50.36 245	84.24 280

patchmatchnet-post											74.00 308	51.12 236	88.60 251

MTMP								32.83 343

gm-plane-assit					81.40 269	53.83 293			62.72 252		80.94 274		92.39 167	63.40 193

test9_res														84.90 17	95.70 12	92.87 68

agg_prior2														82.91 39	95.45 14	92.70 69

agg_prior					92.85 41	71.94 39		91.78 74		84.41 39			94.93 65

TestCases				79.58 214	85.15 189	63.62 190		79.83 282	62.31 255	60.32 298	86.73 173	32.02 315	88.96 246	50.28 273	71.57 274	86.15 259

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

新几何1				83.42 112	93.13 35	70.71 54		85.48 221	57.43 290	81.80 70	91.98 56	63.28 96	92.27 171	64.60 188	92.99 47	87.27 238

旧先验1					91.96 54	65.79 140		86.37 213			93.08 43	69.31 52			92.74 50	88.74 199

原ACMM1				84.35 85	93.01 39	68.79 85		92.44 44	63.96 241	81.09 78	91.57 66	66.06 75	95.45 45	67.19 167	94.82 29	88.81 196

testdata2													91.01 215	62.37 201

segment_acmp												73.08 23

testdata				79.97 206	90.90 66	64.21 181		84.71 227	59.27 277	85.40 22	92.91 44	62.02 133	89.08 242	68.95 154	91.37 61	86.63 254

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

plane_prior7					90.08 78	68.51 97

plane_prior6					89.84 83	68.70 93						60.42 158

plane_prior5								92.44 44					95.38 50	78.71 63	86.32 119	91.33 105

plane_prior4											91.00 80

plane_prior3						68.60 95			78.44 30	78.92 94

plane_prior1					89.90 82

n2								0.00 350

nn								0.00 350

door-mid								69.98 322

lessismore_v0				78.97 226	81.01 276	57.15 260		65.99 331		61.16 295	82.82 247	39.12 300	91.34 203	59.67 223	46.92 327	88.43 215

LGP-MVS_train				84.50 79	89.23 106	68.76 87		91.94 66	75.37 78	76.64 143	91.51 67	54.29 199	94.91 67	78.44 65	83.78 139	89.83 169

test11								92.23 51

door								69.44 325

HQP5-MVS						66.98 122

BP-MVS														77.47 75

HQP4-MVS										77.24 133			95.11 58			91.03 111

HQP3-MVS								92.19 54							85.99 123

HQP2-MVS												60.17 161

NP-MVS					89.62 89	68.32 99					90.24 90

ACMMP++_ref															81.95 167

ACMMP++															81.25 172

Test By Simon												64.33 87

ITE_SJBPF				78.22 239	81.77 264	60.57 227		83.30 243	69.25 183	67.54 262	87.20 164	36.33 310	87.28 262	54.34 258	74.62 251	86.80 249

DeepMVS_CX				27.40 329	40.17 343	26.90 339		24.59 345	17.44 338	23.95 334	48.61 331	9.77 340	26.48 341	18.06 335	24.47 332	28.83 334

High-resolution multi-view benchmark