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.

ACMMP_Plus		78.77 7	78.78 7	78.74 20	85.44 29	61.04 31	83.84 28	85.16 11	62.88 42	78.10 8	91.26 3	52.51 48	88.39 13	79.34 3	90.52 1	86.78 39

HPM-MVS++		79.88 3	80.14 3	79.10 11	88.17 1	64.80 1	86.59 4	83.70 39	65.37 16	78.78 7	90.64 7	58.63 11	87.24 31	79.00 4	90.37 2	85.26 91

CNVR-MVS		79.84 4	79.97 4	79.45 4	87.90 2	62.17 20	84.37 20	85.03 13	66.96 6	77.58 10	90.06 20	59.47 8	89.13 8	78.67 6	89.73 3	87.03 33

PHI-MVS		75.87 37	75.36 36	77.41 36	80.62 85	55.91 95	84.28 21	85.78 8	56.08 162	73.41 43	86.58 65	50.94 64	88.54 12	70.79 40	89.71 4	87.79 13

MP-MVS-pluss		78.35 11	78.46 8	78.03 30	84.96 37	59.52 44	82.93 39	85.39 9	62.15 53	76.41 14	91.51 2	52.47 50	86.78 43	80.66 2	89.64 5	87.80 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
DeepC-MVS	69.38 2	78.56 9	78.14 12	79.83 2	83.60 49	61.62 24	84.17 24	86.85 2	63.23 35	73.84 37	90.25 18	57.68 12	89.96 2	74.62 18	89.03 6	87.89 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SteuartSystems-ACMMP		79.48 5	79.31 5	79.98 1	83.01 55	62.18 19	87.60 2	85.83 7	66.69 11	78.03 9	90.98 4	54.26 32	90.06 1	78.42 7	89.02 7	87.69 15
Skip Steuart: Steuart Systems R&D Blog.
test_prior3		76.89 27	76.96 21	76.69 44	84.20 46	57.27 71	81.75 59	84.88 15	60.37 78	75.01 20	89.06 33	56.22 17	86.43 54	72.19 32	88.96 8	86.38 41

test_prior2							81.75 59		60.37 78	75.01 20	89.06 33	56.22 17		72.19 32	88.96 8

DeepPCF-MVS	69.58 1	79.03 6	79.00 6	79.13 9	84.92 41	60.32 37	83.03 37	85.33 10	62.86 43	80.17 3	90.03 21	61.76 2	88.95 10	74.21 19	88.67 10	88.12 7

HSP-MVS		80.69 1	81.20 1	79.14 8	86.21 16	62.73 12	86.09 8	85.03 13	65.51 15	83.81 1	90.51 11	63.71 1	89.23 6	81.51 1	88.44 11	85.45 78

CDPH-MVS		76.31 32	75.67 35	78.22 27	85.35 32	59.14 49	81.31 69	84.02 28	56.32 156	74.05 31	88.98 36	53.34 43	87.92 24	69.23 44	88.42 12	87.59 18

train_agg		76.27 33	76.15 28	76.64 47	85.58 27	61.59 25	81.62 63	81.26 88	55.86 163	74.93 22	88.81 39	53.70 40	84.68 95	75.24 15	88.33 13	83.65 147

agg_prior3		76.13 34	75.89 34	76.85 42	85.76 23	62.02 21	81.65 61	81.01 96	55.51 172	73.73 38	88.60 43	53.23 44	84.90 88	75.24 15	88.33 13	83.65 147

APDe-MVS		80.16 2	80.59 2	78.86 18	86.64 9	60.02 39	88.12 1	86.42 5	62.94 40	82.40 2	92.12 1	59.64 6	89.76 3	78.70 5	88.32 15	86.79 38

test9_res														75.28 14	88.31 16	83.81 137

MPTG		77.61 19	77.36 18	78.35 24	86.08 20	63.57 2	83.37 33	80.97 97	65.13 18	75.77 16	90.88 5	48.63 100	86.66 45	77.23 8	88.17 17	84.81 104

MTAPA		76.90 26	76.42 27	78.35 24	86.08 20	63.57 2	74.92 184	80.97 97	65.13 18	75.77 16	90.88 5	48.63 100	86.66 45	77.23 8	88.17 17	84.81 104

test12				77.76 33	84.52 43	58.41 58		83.36 49		72.93 50		54.61 30	88.05 21		88.12 19	86.81 37

MP-MVS		78.35 11	78.26 11	78.64 21	86.54 11	63.47 5	86.02 9	83.55 42	63.89 31	73.60 41	90.60 8	54.85 28	86.72 44	77.20 10	88.06 20	85.74 66

DeepC-MVS_fast	68.24 3	77.25 22	76.63 25	79.12 10	86.15 18	60.86 33	84.71 18	84.85 17	61.98 59	73.06 48	88.88 38	53.72 39	89.06 9	68.27 47	88.04 21	87.42 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
原ACMM1				74.69 73	85.39 31	59.40 45		83.42 46	51.47 214	70.27 72	86.61 63	48.61 102	86.51 52	53.85 157	87.96 22	78.16 229

agg_prior2														73.09 29	87.93 23	84.33 115

agg_prior1		75.94 36	76.01 31	75.72 57	85.04 34	59.96 40	81.44 67	81.04 94	56.14 161	74.68 26	88.90 37	53.91 36	84.04 107	75.01 17	87.92 24	83.16 158

CSCG		76.92 25	76.75 23	77.41 36	83.96 48	59.60 43	82.95 38	86.50 4	60.78 71	75.27 19	84.83 91	60.76 3	86.56 50	67.86 51	87.87 25	86.06 55

MCST-MVS		77.48 20	77.45 16	77.54 34	86.67 8	58.36 59	83.22 35	86.93 1	56.91 141	74.91 24	88.19 44	59.15 9	87.68 27	73.67 25	87.45 26	86.57 40

NCCC		78.58 8	78.31 9	79.39 5	87.51 3	62.61 16	85.20 17	84.42 20	66.73 10	74.67 28	89.38 30	55.30 23	89.18 7	74.19 20	87.34 27	86.38 41

HFP-MVS		78.01 14	77.65 14	79.10 11	86.71 6	62.81 10	86.29 5	84.32 22	62.82 44	73.96 32	90.50 12	53.20 45	88.35 14	74.02 21	87.05 28	86.13 52

#test#		77.83 15	77.41 17	79.10 11	86.71 6	62.81 10	85.69 14	84.32 22	61.61 62	73.96 32	90.50 12	53.20 45	88.35 14	73.68 24	87.05 28	86.13 52

region2R		77.67 18	77.18 20	79.15 7	86.76 4	62.95 8	86.29 5	84.16 26	62.81 46	73.30 44	90.58 9	49.90 69	88.21 18	73.78 23	87.03 30	86.29 50

ACMMPR		77.71 16	77.23 19	79.16 6	86.75 5	62.93 9	86.29 5	84.24 24	62.82 44	73.55 42	90.56 10	49.80 71	88.24 17	74.02 21	87.03 30	86.32 48

APD-MVS		78.02 13	78.04 13	77.98 31	86.44 13	60.81 34	85.52 15	84.36 21	60.61 73	79.05 6	90.30 16	55.54 22	88.32 16	73.48 28	87.03 30	84.83 103
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
PGM-MVS		76.77 28	76.06 29	78.88 17	86.14 19	62.73 12	82.55 47	83.74 38	61.71 60	72.45 56	90.34 15	48.48 104	88.13 19	72.32 31	86.85 33	85.78 61

HPM-MVS		77.28 21	76.85 22	78.54 22	85.00 36	60.81 34	82.91 40	85.08 12	62.57 47	73.09 47	89.97 23	50.90 65	87.48 29	75.30 13	86.85 33	87.33 28

TSAR-MVS + MP.		78.44 10	78.28 10	78.90 16	84.96 37	61.41 27	84.03 26	83.82 37	59.34 113	79.37 5	89.76 26	59.84 4	87.62 28	76.69 11	86.74 35	87.68 16

XVS		77.17 23	76.56 26	79.00 14	86.32 14	62.62 14	85.83 10	83.92 31	64.55 22	72.17 57	90.01 22	47.95 109	88.01 22	71.55 36	86.74 35	86.37 44

X-MVStestdata		70.21 104	67.28 140	79.00 14	86.32 14	62.62 14	85.83 10	83.92 31	64.55 22	72.17 57	6.49 340	47.95 109	88.01 22	71.55 36	86.74 35	86.37 44

3Dnovator+	66.72 4	75.84 38	74.57 41	79.66 3	82.40 59	59.92 42	85.83 10	86.32 6	66.92 9	67.80 122	89.24 32	42.03 165	89.38 5	64.07 86	86.50 38	89.69 1

MVS_0304		76.73 29	76.04 30	78.78 19	81.32 72	58.89 53	82.50 49	84.07 27	67.73 5	72.08 59	87.28 54	49.49 73	89.57 4	73.52 27	86.40 39	87.87 11

EPNet		73.09 59	72.16 61	75.90 53	75.95 175	56.28 86	83.05 36	72.39 220	66.53 13	65.27 151	87.00 55	50.40 67	85.47 74	62.48 103	86.32 40	85.94 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DELS-MVS		74.76 43	74.46 42	75.65 60	77.84 137	52.25 139	75.59 168	84.17 25	63.76 32	73.15 46	82.79 121	59.58 7	86.80 41	67.24 56	86.04 41	87.89 9
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
CP-MVS		77.12 24	76.68 24	78.43 23	86.05 22	63.18 7	87.55 3	83.45 45	62.44 50	72.68 52	90.50 12	48.18 107	87.34 30	73.59 26	85.71 42	84.76 108

mPP-MVS		76.54 30	75.93 32	78.34 26	86.47 12	63.50 4	85.74 13	82.28 65	62.90 41	71.77 61	90.26 17	46.61 127	86.55 51	71.71 35	85.66 43	84.97 100

MSLP-MVS++		73.77 54	73.47 51	74.66 74	83.02 54	59.29 48	82.30 55	81.88 71	59.34 113	71.59 63	86.83 56	45.94 131	83.65 117	65.09 71	85.22 44	81.06 197

SD-MVS		77.70 17	77.62 15	77.93 32	84.47 44	61.88 23	84.55 19	83.87 35	60.37 78	79.89 4	89.38 30	54.97 25	85.58 70	76.12 12	84.94 45	86.33 47

3Dnovator	64.47 5	72.49 65	71.39 70	75.79 54	77.70 139	58.99 52	80.66 76	83.15 55	62.24 52	65.46 148	86.59 64	42.38 163	85.52 72	59.59 131	84.72 46	82.85 164

CANet		76.46 31	75.93 32	78.06 29	81.29 73	57.53 68	82.35 50	83.31 51	67.78 3	70.09 73	86.34 69	54.92 26	88.90 11	72.68 30	84.55 47	87.76 14

LFMVS		71.78 76	71.59 66	72.32 148	83.40 51	46.38 221	79.75 87	71.08 224	64.18 28	72.80 51	88.64 42	42.58 162	83.72 115	57.41 138	84.49 48	86.86 36

TSAR-MVS + GP.		74.90 42	74.15 45	77.17 39	82.00 62	58.77 55	81.80 58	78.57 153	58.58 121	74.32 30	84.51 100	55.94 19	87.22 32	67.11 57	84.48 49	85.52 73

MAR-MVS		71.51 79	70.15 87	75.60 62	81.84 64	59.39 46	81.38 68	82.90 60	54.90 181	68.08 115	78.70 213	47.73 111	85.51 73	51.68 173	84.17 50	81.88 178
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
API-MVS		72.17 71	71.41 69	74.45 80	81.95 63	57.22 73	84.03 26	80.38 114	59.89 92	68.40 107	82.33 132	49.64 72	87.83 25	51.87 169	84.16 51	78.30 227

IS-MVSNet		71.57 78	71.00 77	73.27 118	78.86 110	45.63 225	80.22 80	78.69 151	64.14 29	66.46 136	87.36 51	49.30 77	85.60 68	50.26 180	83.71 52	88.59 3

UA-Net		73.13 58	72.93 56	73.76 91	83.58 50	51.66 145	78.75 96	77.66 170	67.75 4	72.61 53	89.42 28	49.82 70	83.29 124	53.61 160	83.14 53	86.32 48

MG-MVS		73.96 51	73.89 47	74.16 82	85.65 25	49.69 192	81.59 65	81.29 87	61.45 63	71.05 65	88.11 45	51.77 55	87.73 26	61.05 121	83.09 54	85.05 97

OpenMVS	61.03 9	68.85 127	67.56 129	72.70 132	74.26 200	53.99 114	81.21 70	81.34 85	52.70 199	62.75 179	85.55 84	38.86 199	84.14 105	48.41 195	83.01 55	79.97 211

VDDNet		71.81 75	71.33 72	73.26 119	82.80 57	47.60 213	78.74 97	75.27 196	59.59 106	72.94 49	89.40 29	41.51 177	83.91 112	58.75 133	82.99 56	88.26 5

MVS_111021_HR		74.02 50	73.46 52	75.69 59	83.01 55	60.63 36	77.29 138	78.40 162	61.18 67	70.58 67	85.97 76	54.18 34	84.00 111	67.52 55	82.98 57	82.45 170

HPM-MVS_fast		74.30 48	73.46 52	76.80 43	84.45 45	59.04 50	83.65 30	81.05 93	60.15 84	70.43 68	89.84 25	41.09 183	85.59 69	67.61 54	82.90 58	85.77 63

ACMMP		76.02 35	75.33 37	78.07 28	85.20 33	61.91 22	85.49 16	84.44 19	63.04 38	69.80 83	89.74 27	45.43 138	87.16 35	72.01 34	82.87 59	85.14 93
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
APD-MVS_3200maxsize		74.96 41	74.39 43	76.67 46	82.20 60	58.24 61	83.67 29	83.29 52	58.41 125	73.71 39	90.14 19	45.62 133	85.99 62	69.64 42	82.85 60	85.78 61

VDD-MVS		72.50 64	72.09 62	73.75 93	81.58 66	49.69 192	77.76 125	77.63 171	63.21 36	73.21 45	89.02 35	42.14 164	83.32 123	61.72 118	82.50 61	88.25 6

CLD-MVS		73.33 56	72.68 58	75.29 67	78.82 111	53.33 123	78.23 109	84.79 18	61.30 66	70.41 69	81.04 168	52.41 51	87.12 36	64.61 76	82.49 62	85.41 85
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
canonicalmvs		74.67 45	74.98 39	73.71 95	78.94 109	50.56 164	80.23 79	83.87 35	60.30 82	77.15 11	86.56 66	59.65 5	82.00 157	66.01 64	82.12 63	88.58 4

MVS		67.37 159	66.33 165	70.51 178	75.46 181	50.94 150	73.95 195	81.85 73	41.57 294	62.54 184	78.57 218	47.98 108	85.47 74	52.97 164	82.05 64	75.14 259

alignmvs		73.86 53	73.99 46	73.45 107	78.20 126	50.50 166	78.57 101	82.43 64	59.40 111	76.57 12	86.71 59	56.42 16	81.23 170	65.84 66	81.79 65	88.62 2

1121		68.53 138	67.16 145	72.63 133	85.64 26	61.14 29	73.95 195	66.46 261	44.61 272	70.28 71	86.68 60	41.42 178	80.78 179	53.62 158	81.79 65	75.97 249

新几何1				70.76 173	85.66 24	61.13 30		66.43 262	44.68 271	70.29 70	86.64 61	41.29 180	75.23 246	49.72 185	81.75 67	75.93 252

Vis-MVSNet		72.18 70	71.37 71	74.61 77	81.29 73	55.41 104	80.90 72	78.28 164	60.73 72	69.23 98	88.09 46	44.36 151	82.65 148	57.68 136	81.75 67	85.77 63
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
VNet		69.68 112	70.19 86	68.16 204	79.73 99	41.63 256	70.53 241	77.38 176	60.37 78	70.69 66	86.63 62	51.08 61	77.09 233	53.61 160	81.69 69	85.75 65

abl_6		74.34 46	73.50 49	76.86 41	82.43 58	60.16 38	83.48 32	81.86 72	58.81 119	73.95 34	89.86 24	41.87 168	86.62 47	67.98 50	81.23 70	83.80 140

OPM-MVS		74.73 44	74.25 44	76.19 50	80.81 81	59.01 51	82.60 46	83.64 40	63.74 33	72.52 54	87.49 49	47.18 119	85.88 66	69.47 43	80.78 71	83.66 146

旧先验1					83.04 53	53.15 125		67.52 254			87.85 48	44.08 152			80.76 72	78.03 231

PAPM_NR		72.63 63	71.80 64	75.13 68	81.72 65	53.42 122	79.91 84	83.28 53	59.14 115	66.31 140	85.90 77	51.86 54	86.06 59	57.45 137	80.62 73	85.91 58

Vis-MVSNet (Re-imp)		63.69 204	63.88 188	63.14 254	74.75 190	31.04 317	71.16 235	63.64 275	56.32 156	59.80 227	84.99 89	44.51 147	75.46 245	39.12 257	80.62 73	82.92 161

HQP_MVS		74.31 47	73.73 48	76.06 51	81.41 70	56.31 84	84.22 22	84.01 29	64.52 24	69.27 95	86.10 73	45.26 142	87.21 33	68.16 48	80.58 75	84.65 109

plane_prior5								84.01 29					87.21 33	68.16 48	80.58 75	84.65 109

UGNet		68.81 128	67.39 135	73.06 122	78.33 123	54.47 111	79.77 86	75.40 195	60.45 76	63.22 173	84.40 101	32.71 260	80.91 176	51.71 172	80.56 77	83.81 137
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
plane_prior						56.31 84	83.58 31		63.19 37						80.48 78

Regformer-1		75.47 40	74.93 40	77.09 40	80.43 87	57.70 66	79.50 92	82.13 66	67.84 1	75.73 18	80.75 180	56.50 14	86.07 58	71.07 39	80.38 79	87.50 20

Regformer-2		75.63 39	74.99 38	77.54 34	80.43 87	58.32 60	79.50 92	82.92 58	67.84 1	75.94 15	80.75 180	55.73 20	86.80 41	71.44 38	80.38 79	87.50 20

HQP3-MVS								83.90 33							80.35 81

HQP-MVS		73.45 55	72.80 57	75.40 64	80.66 82	54.94 106	82.31 52	83.90 33	62.10 54	67.85 117	85.54 85	45.46 136	86.93 39	67.04 58	80.35 81	84.32 116

PCF-MVS	61.88 8	70.95 84	69.49 100	75.35 65	77.63 142	55.71 97	76.04 163	81.81 74	50.30 223	69.66 84	85.40 88	52.51 48	84.89 89	51.82 170	80.24 83	85.45 78
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DP-MVS Recon		72.15 73	70.73 80	76.40 49	86.57 10	57.99 63	81.15 71	82.96 57	57.03 138	66.78 133	85.56 83	44.50 148	88.11 20	51.77 171	80.23 84	83.10 159

CPTT-MVS		72.78 61	72.08 63	74.87 71	84.88 42	61.41 27	84.15 25	77.86 166	55.27 174	67.51 126	88.08 47	41.93 167	81.85 159	69.04 46	80.01 85	81.35 191

Test4		67.77 156	65.97 168	73.19 121	68.64 275	50.58 161	74.80 187	80.48 112	54.13 190	59.11 235	79.07 211	33.89 246	83.12 129	63.61 94	79.98 86	85.87 59

test_normal		69.26 122	67.90 124	73.32 116	70.84 258	50.38 169	75.30 173	79.17 141	54.23 189	62.00 195	80.61 182	44.69 145	83.89 113	64.33 83	79.95 87	85.69 67

DI_MVS_plusplus_test		69.35 120	68.03 122	73.30 117	71.11 255	50.14 180	75.49 170	79.16 142	54.57 185	62.45 188	80.76 179	44.67 146	84.20 103	64.23 84	79.81 88	85.54 72

114514_t		70.83 85	69.56 95	74.64 76	86.21 16	54.63 110	82.34 51	81.81 74	48.22 240	63.01 176	85.83 79	40.92 186	87.10 37	57.91 135	79.79 89	82.18 173

MVS_Test		72.45 66	72.46 60	72.42 145	74.88 187	48.50 203	76.28 155	83.14 56	59.40 111	72.46 55	84.68 93	55.66 21	81.12 171	65.98 65	79.66 90	87.63 17

PS-MVSNAJ		70.51 94	69.70 91	72.93 124	81.52 67	55.79 96	74.92 184	79.00 145	55.04 179	69.88 79	78.66 214	47.05 120	82.19 154	61.61 119	79.58 91	80.83 201

PVSNet_Blended		68.59 133	67.72 126	71.19 166	77.03 161	50.57 162	72.51 216	81.52 77	51.91 207	64.22 169	77.77 228	49.13 93	82.87 137	55.82 144	79.58 91	80.14 210

EPP-MVSNet		72.16 72	71.31 73	74.71 72	78.68 116	49.70 190	82.10 56	81.65 76	60.40 77	65.94 143	85.84 78	51.74 56	86.37 56	55.93 143	79.55 93	88.07 8

xiu_mvs_v2_base		70.52 93	69.75 89	72.84 128	81.21 76	55.63 100	75.11 179	78.92 146	54.92 180	69.96 78	79.68 203	47.00 124	82.09 156	61.60 120	79.37 94	80.81 202

MVSFormer		71.50 80	70.38 84	74.88 70	78.76 113	57.15 78	82.79 41	78.48 157	51.26 217	69.49 90	83.22 117	43.99 154	83.24 125	66.06 62	79.37 94	84.23 123

lupinMVS		69.57 115	68.28 118	73.44 108	78.76 113	57.15 78	76.57 149	73.29 216	46.19 258	69.49 90	82.18 135	43.99 154	79.23 197	64.66 74	79.37 94	83.93 132

PAPM		67.92 155	66.69 157	71.63 157	78.09 130	49.02 199	77.09 141	81.24 90	51.04 219	60.91 215	83.98 108	47.71 112	84.99 80	40.81 250	79.32 97	80.90 200

FIs		70.82 86	71.43 68	68.98 196	78.33 123	38.14 279	76.96 144	83.59 41	61.02 68	67.33 128	86.73 57	55.07 24	81.64 162	54.61 154	79.22 98	87.14 31

jason		69.65 113	68.39 117	73.43 109	78.27 125	56.88 80	77.12 140	73.71 214	46.53 254	69.34 94	83.22 117	43.37 158	79.18 198	64.77 73	79.20 99	84.23 123
jason: jason.
PAPR		71.72 77	70.82 79	74.41 81	81.20 77	51.17 149	79.55 91	83.33 50	55.81 166	66.93 132	84.61 96	50.95 63	86.06 59	55.79 146	79.20 99	86.00 56

Effi-MVS+		73.31 57	72.54 59	75.62 61	77.87 136	53.64 117	79.62 90	79.61 123	61.63 61	72.02 60	82.61 126	56.44 15	85.97 64	63.99 89	79.07 101	87.25 29

gg-mvs-nofinetune		57.86 250	56.43 252	62.18 260	72.62 234	35.35 300	66.57 260	56.33 307	50.65 220	57.64 248	57.10 322	30.65 270	76.36 241	37.38 264	78.88 102	74.82 266

CDS-MVSNet		66.80 172	65.37 174	71.10 169	78.98 108	53.13 127	73.27 205	71.07 225	52.15 205	64.72 161	80.23 193	43.56 157	77.10 232	45.48 218	78.88 102	83.05 160
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
AdaColmap		69.99 108	68.66 112	73.97 85	84.94 39	57.83 64	82.63 45	78.71 150	56.28 158	64.34 165	84.14 104	41.57 173	87.06 38	46.45 205	78.88 102	77.02 242

CANet_DTU		68.18 151	67.71 128	69.59 189	74.83 188	46.24 222	78.66 99	76.85 182	59.60 103	63.45 172	82.09 141	35.25 234	77.41 229	59.88 128	78.76 105	85.14 93

test222					83.14 52	58.68 56	72.57 215	63.45 276	41.78 290	67.56 125	86.12 72	37.13 217			78.73 106	74.98 263

TAMVS		66.78 173	65.27 177	71.33 164	79.16 106	53.67 116	73.84 199	69.59 236	52.32 204	65.28 150	81.72 149	44.49 149	77.40 230	42.32 241	78.66 107	82.92 161

PVSNet_Blended_VisFu		71.45 81	70.39 83	74.65 75	82.01 61	58.82 54	79.93 83	80.35 116	55.09 177	65.82 147	82.16 138	49.17 92	82.64 149	60.34 125	78.62 108	82.50 169

testdata				64.66 246	81.52 67	52.93 128		65.29 266	46.09 259	73.88 36	87.46 50	38.08 208	66.26 279	53.31 163	78.48 109	74.78 267

QAPM		70.05 106	68.81 109	73.78 89	76.54 169	53.43 121	83.23 34	83.48 43	52.89 198	65.90 144	86.29 70	41.55 176	86.49 53	51.01 175	78.40 110	81.42 183

FC-MVSNet-test		69.80 110	70.58 82	67.46 209	77.61 147	34.73 303	76.05 162	83.19 54	60.84 69	65.88 145	86.46 67	54.52 31	80.76 181	52.52 166	78.12 111	86.91 34

LCM-MVSNet-Re		61.88 228	61.35 220	63.46 251	74.58 193	31.48 316	61.42 286	58.14 298	58.71 120	53.02 283	79.55 204	43.07 160	76.80 236	45.69 213	77.96 112	82.11 175

OMC-MVS		71.40 82	70.60 81	73.78 89	76.60 167	53.15 125	79.74 88	79.78 119	58.37 126	68.75 102	86.45 68	45.43 138	80.60 182	62.58 101	77.73 113	87.58 19

MVS_111021_LR		69.50 117	68.78 110	71.65 156	78.38 122	59.33 47	74.82 186	70.11 230	58.08 129	67.83 121	84.68 93	41.96 166	76.34 242	65.62 68	77.54 114	79.30 221

Fast-Effi-MVS+		70.28 103	69.12 106	73.73 94	78.50 119	51.50 148	75.01 181	79.46 136	56.16 160	68.59 103	79.55 204	53.97 35	84.05 106	53.34 162	77.53 115	85.65 70

xiu_mvs_v1_base_debu		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 209	70.04 74	81.41 162	32.79 256	79.02 206	63.81 91	77.31 116	81.22 193

xiu_mvs_v1_base		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 209	70.04 74	81.41 162	32.79 256	79.02 206	63.81 91	77.31 116	81.22 193

xiu_mvs_v1_base_debi		68.58 134	67.28 140	72.48 139	78.19 127	57.19 75	75.28 174	75.09 200	51.61 209	70.04 74	81.41 162	32.79 256	79.02 206	63.81 91	77.31 116	81.22 193

LPG-MVS_test		72.74 62	71.74 65	75.76 55	80.22 90	57.51 69	82.55 47	83.40 47	61.32 64	66.67 134	87.33 52	39.15 196	86.59 48	67.70 52	77.30 119	83.19 155

LGP-MVS_train				75.76 55	80.22 90	57.51 69		83.40 47	61.32 64	66.67 134	87.33 52	39.15 196	86.59 48	67.70 52	77.30 119	83.19 155

EPNet_dtu		61.90 226	61.97 211	61.68 262	72.89 230	39.78 264	75.85 166	65.62 264	55.09 177	54.56 270	79.36 206	37.59 211	67.02 275	39.80 256	76.95 121	78.25 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	59.36 10	66.60 176	65.20 178	70.81 172	76.63 166	48.75 201	76.52 151	80.04 117	50.64 221	65.24 153	84.93 90	39.15 196	78.54 212	36.77 267	76.88 122	85.14 93
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Regformer-3		73.89 52	73.28 54	75.71 58	79.75 96	55.48 103	78.54 103	79.93 118	66.58 12	73.62 40	80.30 190	54.87 27	84.54 98	69.09 45	76.84 123	87.10 32

Regformer-4		74.25 49	73.48 50	76.57 48	79.75 96	56.54 83	78.54 103	81.49 80	66.93 8	73.90 35	80.30 190	53.84 38	85.98 63	69.76 41	76.84 123	87.17 30

ACMP	63.53 6	72.30 68	71.20 75	75.59 63	80.28 89	57.54 67	82.74 43	82.84 62	60.58 74	65.24 153	86.18 71	39.25 195	86.03 61	66.95 60	76.79 125	83.22 153
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
cascas		65.98 184	63.42 194	73.64 99	77.26 157	52.58 133	72.26 220	77.21 178	48.56 235	61.21 211	74.60 267	32.57 264	85.82 67	50.38 179	76.75 126	82.52 168

BH-untuned		68.27 145	67.29 139	71.21 165	79.74 98	53.22 124	76.06 161	77.46 175	57.19 135	66.10 141	81.61 154	45.37 140	83.50 119	45.42 220	76.68 127	76.91 245

ACMM	61.98 7	70.80 87	69.73 90	74.02 83	80.59 86	58.59 57	82.68 44	82.02 70	55.46 173	67.18 129	84.39 102	38.51 201	83.17 127	60.65 122	76.10 128	80.30 207
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
BH-RMVSNet		68.81 128	67.42 134	72.97 123	80.11 93	52.53 134	74.26 192	76.29 185	58.48 124	68.38 108	84.20 103	42.59 161	83.83 114	46.53 204	75.91 129	82.56 166

XVG-OURS		68.76 131	67.37 136	72.90 125	74.32 199	57.22 73	70.09 247	78.81 148	55.24 175	67.79 123	85.81 81	36.54 229	78.28 219	62.04 113	75.74 130	83.19 155

mvs_anonymous		68.03 154	67.51 132	69.59 189	72.08 242	44.57 233	71.99 227	75.23 197	51.67 208	67.06 130	82.57 127	54.68 29	77.94 223	56.56 140	75.71 131	86.26 51

BH-w/o		66.85 171	65.83 170	69.90 186	79.29 101	52.46 136	74.66 188	76.65 183	54.51 186	64.85 160	78.12 220	45.59 135	82.95 133	43.26 234	75.54 132	74.27 271

LS3D		64.71 197	62.50 204	71.34 163	79.72 100	55.71 97	79.82 85	74.72 205	48.50 237	56.62 253	84.62 95	33.59 249	82.34 153	29.65 307	75.23 133	75.97 249

GG-mvs-BLEND				62.34 259	71.36 254	37.04 286	69.20 253	57.33 302		54.73 268	65.48 308	30.37 271	77.82 224	34.82 277	74.93 134	72.17 290

diffmvs		67.72 157	66.73 155	70.70 176	69.74 271	47.69 212	73.33 204	74.74 204	53.30 195	64.51 164	81.80 148	49.25 86	79.02 206	59.15 132	74.75 135	85.39 86

pcd1.5k->3k		30.06 311	30.56 312	28.55 326	78.81 112	0.00 348	0.00 340	82.07 69	0.00 344	0.00 345	0.00 346	39.61 192	0.00 345	0.00 344	74.56 136	85.66 69

nrg030		72.96 60	73.01 55	72.84 128	75.41 182	50.24 173	80.02 82	82.89 61	58.36 127	74.44 29	86.73 57	58.90 10	80.83 177	65.84 66	74.46 137	87.44 23

VPA-MVSNet		69.02 125	69.47 102	67.69 208	77.42 150	41.00 260	74.04 194	79.68 121	60.06 85	69.26 97	84.81 92	51.06 62	77.58 227	54.44 155	74.43 138	84.48 114

PS-MVSNAJss		72.24 69	71.21 74	75.31 66	78.50 119	55.93 94	81.63 62	82.12 67	56.24 159	70.02 77	85.68 82	47.05 120	84.34 102	65.27 70	74.41 139	85.67 68

EI-MVSNet-Vis-set		72.42 67	71.59 66	74.91 69	78.47 121	54.02 113	77.05 142	79.33 140	65.03 20	71.68 62	79.35 207	52.75 47	84.89 89	66.46 61	74.23 140	85.83 60

CHOSEN 1792x2688		65.08 195	62.84 200	71.82 153	81.49 69	56.26 87	66.32 263	74.20 210	40.53 300	63.16 175	78.65 215	41.30 179	77.80 225	45.80 212	74.09 141	81.40 184

testing_2		66.02 183	63.77 190	72.76 131	66.03 295	50.48 167	72.93 208	80.36 115	54.41 187	54.25 274	76.76 247	30.89 269	83.16 128	64.19 85	74.08 142	84.65 109

ACMMP++_ref															74.07 143

PVSNet_BlendedMVS		68.56 137	67.72 126	71.07 170	77.03 161	50.57 162	74.50 190	81.52 77	53.66 193	64.22 169	79.72 202	49.13 93	82.87 137	55.82 144	73.92 144	79.77 216

CMPMVS	42.80 21	57.81 251	55.97 255	63.32 252	60.98 315	47.38 215	64.66 274	69.50 237	32.06 321	46.83 302	77.80 227	29.50 278	71.36 258	48.68 192	73.75 145	71.21 295
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MS-PatchMatch		62.42 221	61.46 219	65.31 242	75.21 185	52.10 140	72.05 226	74.05 211	46.41 256	57.42 250	74.36 268	34.35 241	77.57 228	45.62 215	73.67 146	66.26 307

test-LLR		58.15 249	58.13 241	58.22 275	68.57 276	44.80 229	65.46 267	57.92 299	50.08 225	55.44 261	69.82 293	32.62 261	57.44 307	49.66 186	73.62 147	72.41 286

test-mter		56.42 257	55.82 256	58.22 275	68.57 276	44.80 229	65.46 267	57.92 299	39.94 304	55.44 261	69.82 293	21.92 312	57.44 307	49.66 186	73.62 147	72.41 286

EI-MVSNet-UG-set		71.92 74	71.06 76	74.52 79	77.98 134	53.56 119	76.62 148	79.16 142	64.40 26	71.18 64	78.95 212	52.19 53	84.66 97	65.47 69	73.57 149	85.32 88

TR-MVS		66.59 178	65.07 181	71.17 167	79.18 104	49.63 194	73.48 203	75.20 198	52.95 197	67.90 116	80.33 189	39.81 190	83.68 116	43.20 235	73.56 150	80.20 208

ab-mvs		66.65 175	66.42 162	67.37 210	76.17 171	41.73 254	70.41 244	76.14 187	53.99 191	65.98 142	83.51 114	49.48 74	76.24 243	48.60 193	73.46 151	84.14 130

EG-PatchMatch MVS		64.71 197	62.87 199	70.22 180	77.68 140	53.48 120	77.99 119	78.82 147	53.37 194	56.03 257	77.41 240	24.75 306	84.04 107	46.37 206	73.42 152	73.14 277

XVG-OURS-SEG-HR		68.81 128	67.47 133	72.82 130	74.40 198	56.87 81	70.59 240	79.04 144	54.77 182	66.99 131	86.01 75	39.57 193	78.21 220	62.54 102	73.33 153	83.37 150

thres200		62.20 223	61.16 223	65.34 241	75.38 183	39.99 262	69.60 250	69.29 241	55.64 170	61.87 198	76.99 242	37.07 218	78.96 211	31.28 297	73.28 154	77.06 241

conf200view11		63.38 206	62.41 205	66.29 222	77.31 151	38.66 274	72.65 211	69.11 243	57.07 136	62.45 188	81.03 169	37.01 219	79.17 199	31.84 288	73.25 155	81.03 198

thres100view900		63.28 209	62.41 205	65.89 231	77.31 151	38.66 274	72.65 211	69.11 243	57.07 136	62.45 188	81.03 169	37.01 219	79.17 199	31.84 288	73.25 155	79.83 213

tfpn200view9		63.18 211	62.18 209	66.21 224	76.85 164	39.62 265	71.96 228	69.44 239	56.63 148	62.61 182	79.83 198	37.18 214	79.17 199	31.84 288	73.25 155	79.83 213

thres400		63.31 207	62.18 209	66.72 215	76.85 164	39.62 265	71.96 228	69.44 239	56.63 148	62.61 182	79.83 198	37.18 214	79.17 199	31.84 288	73.25 155	81.36 185

TESTMET0.1,1		55.28 265	54.90 261	56.42 283	66.56 291	43.67 241	65.46 267	56.27 308	39.18 306	53.83 276	67.44 300	24.21 307	55.46 321	48.04 196	73.11 159	70.13 299

thres600view7		63.30 208	62.27 207	66.41 219	77.18 158	38.87 271	72.35 218	69.11 243	56.98 139	62.37 193	80.96 172	37.01 219	79.00 210	31.43 296	73.05 160	81.36 185

VPNet		67.52 158	68.11 121	65.74 233	79.18 104	36.80 291	72.17 221	72.83 218	62.04 57	67.79 123	85.83 79	48.88 99	76.60 239	51.30 174	72.97 161	83.81 137

mvs-test1		70.44 99	68.19 119	77.18 38	76.10 172	63.22 6	80.59 77	76.06 189	59.83 94	66.32 139	79.87 197	41.56 174	85.53 71	60.60 123	72.77 162	82.80 165

GBi-Net		67.21 162	66.55 158	69.19 193	77.63 142	43.33 243	77.31 135	77.83 167	56.62 150	65.04 156	82.70 122	41.85 169	80.33 185	47.18 199	72.76 163	83.92 133

test1		67.21 162	66.55 158	69.19 193	77.63 142	43.33 243	77.31 135	77.83 167	56.62 150	65.04 156	82.70 122	41.85 169	80.33 185	47.18 199	72.76 163	83.92 133

FMVSNet3		66.32 181	65.61 172	68.46 202	76.48 170	42.34 250	74.98 183	77.15 179	55.83 165	65.04 156	81.16 165	39.91 188	80.14 189	47.18 199	72.76 163	82.90 163

FMVSNet2		66.93 170	66.31 167	68.79 199	77.63 142	42.98 246	76.11 159	77.47 173	56.62 150	65.22 155	82.17 137	41.85 169	80.18 188	47.05 202	72.72 166	83.20 154

view600		62.77 215	61.84 212	65.55 235	77.28 153	36.87 287	72.15 222	67.78 250	56.79 142	61.46 206	81.92 142	36.88 222	78.42 213	29.86 302	72.46 167	81.36 185

view800		62.77 215	61.84 212	65.55 235	77.28 153	36.87 287	72.15 222	67.78 250	56.79 142	61.46 206	81.92 142	36.88 222	78.42 213	29.86 302	72.46 167	81.36 185

conf0.05thres1000		62.77 215	61.84 212	65.55 235	77.28 153	36.87 287	72.15 222	67.78 250	56.79 142	61.46 206	81.92 142	36.88 222	78.42 213	29.86 302	72.46 167	81.36 185

tfpn		62.77 215	61.84 212	65.55 235	77.28 153	36.87 287	72.15 222	67.78 250	56.79 142	61.46 206	81.92 142	36.88 222	78.42 213	29.86 302	72.46 167	81.36 185

PVSNet	50.76 19	58.40 246	57.39 244	61.42 264	75.53 180	44.04 238	61.43 285	63.45 276	47.04 252	56.91 251	73.61 273	27.00 294	64.76 284	39.12 257	72.40 171	75.47 257

MIMVSNet		57.35 252	57.07 246	58.22 275	74.21 201	37.18 284	62.46 281	60.88 290	48.88 233	55.29 264	75.99 257	31.68 267	62.04 293	31.87 287	72.35 172	75.43 258

1314		64.61 199	63.21 196	68.80 198	71.87 247	47.46 214	73.95 195	78.39 163	42.88 287	59.97 223	76.60 250	38.11 207	79.39 195	54.84 150	72.32 173	79.55 217

FMVSNet1		66.70 174	65.87 169	69.19 193	77.49 149	43.33 243	77.31 135	77.83 167	56.45 154	64.60 163	82.70 122	38.08 208	80.33 185	46.08 209	72.31 174	83.92 133

ACMMP++															72.16 175

MVP-Stereo		65.41 190	63.80 189	70.22 180	77.62 146	55.53 101	76.30 154	78.53 155	50.59 222	56.47 255	78.65 215	39.84 189	82.68 147	44.10 227	72.12 176	72.44 285
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
HyFIR lowres test		65.67 186	63.01 198	73.67 96	79.97 95	55.65 99	69.07 254	75.52 193	42.68 288	63.53 171	77.95 222	40.43 187	81.64 162	46.01 210	71.91 177	83.73 141

XVG-ACMP-BASELINE		64.36 201	62.23 208	70.74 174	72.35 238	52.45 137	70.80 239	78.45 159	53.84 192	59.87 225	81.10 167	16.24 319	79.32 196	55.64 148	71.76 178	80.47 204

HY-MVS	56.14 13	64.55 200	63.89 187	66.55 218	74.73 191	41.02 258	69.96 248	74.43 207	49.29 229	61.66 203	80.92 173	47.43 118	76.68 238	44.91 223	71.69 179	81.94 176

ACMH	55.70 15	65.20 193	63.57 193	70.07 183	78.07 131	52.01 144	79.48 94	79.69 120	55.75 167	56.59 254	80.98 171	27.12 292	80.94 174	42.90 239	71.58 180	77.25 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 164	65.58 173	71.88 152	70.37 263	49.70 190	70.25 246	78.45 159	51.52 212	69.16 99	80.37 186	38.45 202	82.50 150	60.19 126	71.46 181	83.44 149

EI-MVSNet		69.27 121	68.44 116	71.73 155	74.47 195	49.39 196	75.20 177	78.45 159	59.60 103	69.16 99	76.51 251	51.29 58	82.50 150	59.86 130	71.45 182	83.30 151

LTVRE_ROB	55.42 16	63.15 212	61.23 222	68.92 197	76.57 168	47.80 209	59.92 292	76.39 184	54.35 188	58.67 240	82.46 130	29.44 279	81.49 166	42.12 242	71.14 183	77.46 234
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
UniMVSNet (Re)		70.63 91	70.20 85	71.89 151	78.55 118	45.29 226	75.94 165	82.92 58	63.68 34	68.16 112	83.59 112	53.89 37	83.49 120	53.97 156	71.12 184	86.89 35

Effi-MVS+-dtu		69.64 114	67.53 131	75.95 52	76.10 172	62.29 18	80.20 81	76.06 189	59.83 94	65.26 152	77.09 241	41.56 174	84.02 110	60.60 123	71.09 185	81.53 181

NR-MVSNet		69.54 116	68.85 108	71.59 158	78.05 132	43.81 240	74.20 193	80.86 100	65.18 17	62.76 178	84.52 98	52.35 52	83.59 118	50.96 176	70.78 186	87.37 25

v1144		70.42 100	69.31 104	73.76 91	73.22 213	50.64 159	77.83 123	81.43 81	58.58 121	69.40 93	81.16 165	47.53 115	85.29 79	64.01 88	70.64 187	85.34 87

jajsoiax		68.25 147	66.45 160	73.66 97	75.62 178	55.49 102	80.82 73	78.51 156	52.33 203	64.33 166	84.11 105	28.28 284	81.81 161	63.48 96	70.62 188	83.67 145

v7		70.57 92	69.48 101	73.85 86	73.50 205	50.92 152	78.27 107	81.43 81	58.93 116	69.61 85	81.49 158	47.56 114	85.43 76	63.94 90	70.62 188	85.21 92

mvs_tets		68.18 151	66.36 164	73.63 100	75.61 179	55.35 105	80.77 74	78.56 154	52.48 202	64.27 168	84.10 106	27.45 290	81.84 160	63.45 97	70.56 190	83.69 142

UniMVSNet_NR-MVSNet		71.11 83	71.00 77	71.44 159	79.20 103	44.13 236	76.02 164	82.60 63	66.48 14	68.20 110	84.60 97	56.82 13	82.82 139	54.62 152	70.43 191	87.36 27

DU-MVS		70.01 107	69.53 96	71.44 159	78.05 132	44.13 236	75.01 181	81.51 79	64.37 27	68.20 110	84.52 98	49.12 95	82.82 139	54.62 152	70.43 191	87.37 25

v6		70.66 88	69.70 91	73.53 102	73.14 220	50.21 177	78.11 112	80.67 104	59.65 100	69.82 82	81.65 152	49.29 79	84.96 84	64.55 77	70.39 193	85.42 81

v1neww		70.66 88	69.70 91	73.53 102	73.15 217	50.22 174	78.11 112	80.68 102	59.65 100	69.83 80	81.67 150	49.29 79	84.96 84	64.55 77	70.38 194	85.42 81

v7new		70.66 88	69.70 91	73.53 102	73.15 217	50.22 174	78.11 112	80.68 102	59.65 100	69.83 80	81.67 150	49.29 79	84.96 84	64.55 77	70.38 194	85.42 81

v1192		69.97 109	68.68 111	73.85 86	73.19 216	50.94 150	77.68 129	81.36 84	57.51 133	68.95 101	80.85 176	45.28 141	85.33 78	62.97 99	70.37 196	85.27 90

PLC	56.13 14	65.09 194	63.21 196	70.72 175	81.04 79	54.87 109	78.57 101	77.47 173	48.51 236	55.71 258	81.89 146	33.71 247	79.71 190	41.66 246	70.37 196	77.58 233
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GA-MVS		65.53 188	63.70 191	71.02 171	70.87 257	48.10 206	70.48 242	74.40 208	56.69 146	64.70 162	76.77 246	33.66 248	81.10 172	55.42 149	70.32 198	83.87 136

divwei89l23v2f112		70.50 95	69.53 96	73.41 111	72.91 228	50.00 184	77.69 126	80.59 107	59.50 108	69.60 86	81.43 159	49.26 84	84.77 92	64.48 81	70.31 199	85.47 75

v1141		70.50 95	69.53 96	73.41 111	72.92 227	50.00 184	77.69 126	80.60 106	59.50 108	69.60 86	81.43 159	49.24 89	84.77 92	64.48 81	70.30 200	85.46 77

v1		70.50 95	69.53 96	73.42 110	72.91 228	50.00 184	77.69 126	80.59 107	59.50 108	69.59 88	81.42 161	49.26 84	84.77 92	64.49 80	70.30 200	85.47 75

Fast-Effi-MVS+-dtu		67.37 159	65.33 176	73.48 106	72.94 226	57.78 65	77.47 133	76.88 181	57.60 132	61.97 196	76.85 245	39.31 194	80.49 183	54.72 151	70.28 202	82.17 174

v2v482		70.50 95	69.45 103	73.66 97	72.62 234	50.03 183	77.58 130	80.51 111	59.90 90	69.52 89	82.14 139	47.53 115	84.88 91	65.07 72	70.17 203	86.09 54

IB-MVS	56.42 12	65.40 191	62.73 202	73.40 113	74.89 186	52.78 130	73.09 207	75.13 199	55.69 168	58.48 244	73.73 272	32.86 255	86.32 57	50.63 177	70.11 204	81.10 196
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
CNLPA		65.43 189	64.02 186	69.68 187	78.73 115	58.07 62	77.82 124	70.71 227	51.49 213	61.57 205	83.58 113	38.23 206	70.82 260	43.90 228	70.10 205	80.16 209

1112_ss		64.00 203	63.36 195	65.93 230	79.28 102	42.58 249	71.35 231	72.36 221	46.41 256	60.55 219	77.89 225	46.27 130	73.28 252	46.18 207	69.97 206	81.92 177

DP-MVS		65.68 185	63.66 192	71.75 154	84.93 40	56.87 81	80.74 75	73.16 217	53.06 196	59.09 236	82.35 131	36.79 227	85.94 65	32.82 284	69.96 207	72.45 284

IterMVS-LS		69.22 124	68.48 114	71.43 161	74.44 197	49.40 195	76.23 157	77.55 172	59.60 103	65.85 146	81.59 156	51.28 59	81.58 165	59.87 129	69.90 208	83.30 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		69.47 118	68.17 120	73.36 114	73.06 223	50.10 182	77.39 134	80.56 109	56.58 153	68.59 103	80.37 186	44.72 144	84.98 82	62.47 104	69.82 209	85.00 98

Baseline_NR-MVSNet		67.05 167	67.56 129	65.50 239	75.65 177	37.70 283	75.42 171	74.65 206	59.90 90	68.14 113	83.15 120	49.12 95	77.20 231	52.23 168	69.78 210	81.60 180

ACMH+	57.40 11	66.12 182	64.06 185	72.30 149	77.79 138	52.83 129	80.39 78	78.03 165	57.30 134	57.47 249	82.55 128	27.68 288	84.17 104	45.54 216	69.78 210	79.90 212

v1240		69.24 123	67.91 123	73.25 120	73.02 225	49.82 187	77.21 139	80.54 110	56.43 155	68.34 109	80.51 184	43.33 159	84.99 80	62.03 114	69.77 212	84.95 101

TranMVSNet+NR-MVSNet		70.36 101	70.10 88	71.17 167	78.64 117	42.97 247	76.53 150	81.16 92	66.95 7	68.53 106	85.42 87	51.61 57	83.07 130	52.32 167	69.70 213	87.46 22

v144192		69.71 111	68.51 113	73.33 115	73.10 222	50.13 181	77.54 132	80.64 105	56.65 147	68.57 105	80.55 183	46.87 125	84.96 84	62.98 98	69.66 214	84.89 102

WR-MVS		68.47 139	68.47 115	68.44 203	80.20 92	39.84 263	73.75 200	76.07 188	64.68 21	68.11 114	83.63 111	50.39 68	79.14 204	49.78 182	69.66 214	86.34 46

WTY-MVS		59.75 239	60.39 227	57.85 278	72.32 239	37.83 281	61.05 290	64.18 273	45.95 263	61.91 197	79.11 210	47.01 123	60.88 296	42.50 240	69.49 216	74.83 265

test_djsdf		69.45 119	67.74 125	74.58 78	74.57 194	54.92 108	82.79 41	78.48 157	51.26 217	65.41 149	83.49 115	38.37 203	83.24 125	66.06 62	69.25 217	85.56 71

CostFormer		64.04 202	62.51 203	68.61 201	71.88 246	45.77 224	71.30 232	70.60 228	47.55 247	64.31 167	76.61 249	41.63 172	79.62 192	49.74 184	69.00 218	80.42 205

tpm2		62.07 225	60.10 229	67.99 205	72.79 231	43.86 239	71.05 236	66.85 259	43.14 285	62.77 177	75.39 261	38.32 204	80.80 178	41.69 245	68.88 219	79.32 220

v10		70.21 104	69.02 107	73.81 88	73.51 204	50.92 152	78.74 97	81.39 83	60.05 86	66.39 138	81.83 147	47.58 113	85.41 77	62.80 100	68.86 220	85.09 96

v8		70.33 102	69.28 105	73.49 105	73.15 217	50.22 174	78.62 100	80.78 101	60.79 70	66.45 137	82.11 140	49.35 75	84.98 82	63.58 95	68.71 221	85.28 89

v7n		69.01 126	67.36 137	73.98 84	72.51 236	52.65 131	78.54 103	81.30 86	60.26 83	62.67 180	81.62 153	43.61 156	84.49 99	57.01 139	68.70 222	84.79 106

Test_1112_low_res		62.32 222	61.77 216	64.00 250	79.08 107	39.53 267	68.17 256	70.17 229	43.25 283	59.03 237	79.90 196	44.08 152	71.24 259	43.79 230	68.42 223	81.25 192

PMMVS		53.96 269	53.26 273	56.04 284	62.60 308	50.92 152	61.17 289	56.09 309	32.81 319	53.51 281	66.84 302	34.04 243	59.93 300	44.14 226	68.18 224	57.27 322

tfpnnormal		62.47 220	61.63 218	64.99 244	74.81 189	39.01 270	71.22 233	73.72 213	55.22 176	60.21 221	80.09 195	41.26 182	76.98 235	30.02 301	68.09 225	78.97 225

DWT-MVSNet_test		61.90 226	59.93 230	67.83 206	71.98 245	46.09 223	71.03 237	69.71 232	50.09 224	58.51 243	70.62 287	30.21 274	77.63 226	49.28 188	67.91 226	79.78 215

Anonymous20231206		55.10 267	55.30 259	54.48 292	69.81 270	33.94 307	62.91 280	62.13 287	41.08 295	55.18 265	75.65 259	32.75 259	56.59 313	30.32 300	67.86 227	72.91 278

V42		68.65 132	67.35 138	72.56 136	68.93 274	50.18 178	72.90 209	79.47 135	56.92 140	69.45 92	80.26 192	46.29 129	82.99 131	64.07 86	67.82 228	84.53 112

MDTV_nov1_ep13			57.00 247		72.73 232	38.26 278	65.02 273	64.73 270	44.74 270	55.46 260	72.48 277	32.61 263	70.47 263	37.47 263	67.75 229

tpmp4_e23		62.71 219	60.13 228	70.45 179	73.40 209	48.39 204	72.82 210	69.49 238	44.88 268	59.91 224	74.99 263	37.79 210	81.47 167	40.22 252	67.71 230	81.48 182

anonymousdsp		67.00 169	64.82 183	73.57 101	70.09 265	56.13 89	76.35 153	77.35 177	48.43 238	64.99 159	80.84 177	33.01 253	80.34 184	64.66 74	67.64 231	84.23 123

OpenMVS_ROB	52.78 18	60.03 236	58.14 240	65.69 234	70.47 260	44.82 228	75.33 172	70.86 226	45.04 267	56.06 256	76.00 255	26.89 295	79.65 191	35.36 276	67.29 232	72.60 281

XXY-MVS		60.68 234	61.67 217	57.70 280	70.43 261	38.45 277	64.19 276	66.47 260	48.05 243	63.22 173	80.86 175	49.28 82	60.47 297	45.25 222	67.28 233	74.19 272

F-COLMAP		63.05 213	60.87 225	69.58 191	76.99 163	53.63 118	78.12 111	76.16 186	47.97 244	52.41 284	81.61 154	27.87 286	78.11 221	40.07 253	66.66 234	77.00 243

pm-mvs1		65.24 192	64.97 182	66.04 227	72.38 237	39.40 268	72.62 214	75.63 192	55.53 171	62.35 194	83.18 119	47.45 117	76.47 240	49.06 190	66.54 235	82.24 172

v148		68.24 149	67.19 144	71.40 162	70.43 261	47.77 211	75.76 167	77.03 180	58.91 117	67.36 127	80.10 194	48.60 103	81.89 158	60.01 127	66.52 236	84.53 112

sss		56.17 260	56.57 250	54.96 289	66.93 287	36.32 296	57.94 298	61.69 288	41.67 292	58.64 241	75.32 262	38.72 200	56.25 316	42.04 243	66.19 237	72.31 289

COLMAP_ROB	52.97 17	61.27 233	58.81 234	68.64 200	74.63 192	52.51 135	78.42 106	73.30 215	49.92 227	50.96 289	81.51 157	23.06 309	79.40 194	31.63 293	65.85 238	74.01 274
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		59.63 241	59.14 233	61.08 267	74.47 195	38.84 272	75.20 177	68.74 246	31.15 322	58.24 245	76.51 251	32.39 265	68.58 270	49.77 183	65.84 239	75.81 253

MSDG		61.81 229	59.23 232	69.55 192	72.64 233	52.63 132	70.45 243	75.81 191	51.38 215	53.70 277	76.11 254	29.52 277	81.08 173	37.70 262	65.79 240	74.93 264

PatchFormer-LS_test		62.20 223	60.59 226	67.04 213	72.18 241	46.82 219	70.36 245	68.62 247	51.92 206	59.19 234	70.23 290	36.86 226	75.07 247	50.23 181	65.68 241	79.23 222

FMVSNet5		55.86 262	54.93 260	58.66 274	71.05 256	36.35 294	64.18 277	62.48 284	46.76 253	50.66 292	74.73 266	25.80 300	64.04 286	33.11 283	65.57 242	75.59 256

pmmvs5		56.47 256	55.68 257	58.86 272	61.41 312	36.71 292	66.37 262	62.75 282	40.38 301	53.70 277	76.62 248	34.56 237	67.05 274	40.02 255	65.27 243	72.83 279

v748		67.26 161	65.67 171	72.02 150	69.90 269	49.77 189	76.24 156	79.57 132	58.58 121	60.49 220	80.38 185	44.47 150	82.17 155	56.16 142	65.26 244	84.12 131

tpm		57.34 253	58.16 239	54.86 290	71.80 248	34.77 302	67.47 259	56.04 310	48.20 241	60.10 222	76.92 243	37.17 216	53.41 324	40.76 251	65.01 245	76.40 248

pmmvs4		61.48 231	59.39 231	67.76 207	71.57 249	53.86 115	71.42 230	65.34 265	44.20 276	59.46 229	77.92 224	35.90 230	74.71 249	43.87 229	64.87 246	74.71 268

V4		67.09 165	65.16 179	72.87 126	66.76 290	51.60 146	73.69 201	79.45 137	57.88 130	62.45 188	78.58 217	40.96 184	83.34 121	61.99 115	64.71 247	83.68 143

test_0402		63.25 210	61.01 224	69.96 184	80.00 94	54.37 112	76.86 146	72.02 222	54.58 184	58.71 239	80.79 178	35.00 235	84.36 101	26.41 316	64.71 247	71.15 296

v52		67.09 165	65.16 179	72.87 126	66.77 289	51.60 146	73.69 201	79.45 137	57.88 130	62.46 187	78.57 218	40.95 185	83.34 121	61.99 115	64.70 249	83.68 143

CR-MVSNet		59.91 237	57.90 243	65.96 228	69.96 267	52.07 141	65.31 270	63.15 279	42.48 289	59.36 230	74.84 264	35.83 231	70.75 261	45.50 217	64.65 250	75.06 260

RPMNet		58.70 244	56.29 254	65.96 228	69.96 267	52.07 141	65.31 270	62.15 286	43.20 284	59.36 230	70.15 292	35.37 233	70.75 261	36.42 273	64.65 250	75.06 260

pmmvs6		63.69 204	62.82 201	66.27 223	70.63 259	39.27 269	73.13 206	75.47 194	52.69 200	59.75 228	82.30 133	39.71 191	77.03 234	47.40 198	64.35 252	82.53 167

WR-MVS_H		67.02 168	66.92 149	67.33 212	77.95 135	37.75 282	77.57 131	82.11 68	62.03 58	62.65 181	82.48 129	50.57 66	79.46 193	42.91 238	64.01 253	84.79 106

test0.0.03 1		53.32 274	53.59 271	52.50 300	62.81 307	29.45 320	59.51 293	54.11 318	50.08 225	54.40 272	74.31 269	32.62 261	55.92 318	30.50 299	63.95 254	72.15 291

PatchMatch-RL		56.25 259	54.55 263	61.32 266	77.06 160	56.07 91	65.57 266	54.10 319	44.13 278	53.49 282	71.27 285	25.20 303	66.78 276	36.52 272	63.66 255	61.12 316

PatchmatchNet		59.84 238	58.24 238	64.65 247	73.05 224	46.70 220	69.42 252	62.18 285	47.55 247	58.88 238	71.96 280	34.49 239	69.16 267	42.99 237	63.60 256	78.07 230
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
semantic-postprocess				65.40 240	71.99 244	50.80 156		69.63 235	45.71 265	60.61 218	77.93 223	36.56 228	65.99 281	55.67 147	63.50 257	79.42 219

CP-MVSNet		66.49 179	66.41 163	66.72 215	77.67 141	36.33 295	76.83 147	79.52 134	62.45 49	62.54 184	83.47 116	46.32 128	78.37 217	45.47 219	63.43 258	85.45 78

PS-CasMVS		66.42 180	66.32 166	66.70 217	77.60 148	36.30 297	76.94 145	79.61 123	62.36 51	62.43 192	83.66 110	45.69 132	78.37 217	45.35 221	63.26 259	85.42 81

IterMVS		62.79 214	61.27 221	67.35 211	69.37 272	52.04 143	71.17 234	68.24 249	52.63 201	59.82 226	76.91 244	37.32 213	72.36 255	52.80 165	63.19 260	77.66 232
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PEN-MVS		66.60 176	66.45 160	67.04 213	77.11 159	36.56 293	77.03 143	80.42 113	62.95 39	62.51 186	84.03 107	46.69 126	79.07 205	44.22 224	63.08 261	85.51 74

tpmrst		58.24 247	58.70 236	56.84 282	66.97 286	34.32 305	69.57 251	61.14 289	47.17 251	58.58 242	71.60 281	41.28 181	60.41 298	49.20 189	62.84 262	75.78 254

testgi		51.90 278	52.37 275	50.51 305	60.39 318	23.55 334	58.42 296	58.15 297	49.03 232	51.83 286	79.21 209	22.39 310	55.59 319	29.24 308	62.64 263	72.40 288

Patchmatch-test1		59.75 239	58.00 242	64.98 245	74.14 202	48.06 207	63.35 278	63.23 278	49.13 231	59.33 233	71.46 282	37.45 212	69.59 265	41.39 249	62.57 264	77.30 236

EPMVS		53.96 269	53.69 269	54.79 291	66.12 294	31.96 315	62.34 283	49.05 326	44.42 275	55.54 259	71.33 284	30.22 273	56.70 311	41.65 247	62.54 265	75.71 255

ITE_SJBPF				62.09 261	66.16 293	44.55 234		64.32 272	47.36 249	55.31 263	80.34 188	19.27 315	62.68 291	36.29 274	62.39 266	79.04 223

MIMVSNet1		55.17 266	54.31 265	57.77 279	70.03 266	32.01 314	65.68 265	64.81 268	49.19 230	46.75 303	76.00 255	25.53 302	64.04 286	28.65 310	62.13 267	77.26 239

USDC		56.35 258	54.24 266	62.69 258	64.74 300	40.31 261	65.05 272	73.83 212	43.93 279	47.58 298	77.71 229	15.36 321	75.05 248	38.19 261	61.81 268	72.70 280

PatchT		53.17 275	53.44 272	52.33 301	68.29 280	25.34 331	58.21 297	54.41 316	44.46 274	54.56 270	69.05 295	33.32 251	60.94 295	36.93 266	61.76 269	70.73 298

v16		68.38 140	67.01 146	72.47 143	73.22 213	50.29 171	78.10 115	79.59 128	59.71 98	61.72 201	77.60 233	49.28 82	82.89 135	62.36 106	61.54 270	84.23 123

v18		68.33 142	66.96 148	72.42 145	73.13 221	50.16 179	77.97 120	79.57 132	59.57 107	61.80 199	77.50 238	49.30 77	82.90 134	62.31 107	61.50 271	84.20 129

tpm cat1		59.25 242	56.95 248	66.15 225	72.19 240	46.96 217	68.09 257	65.76 263	40.03 303	57.81 247	70.56 288	38.32 204	74.51 250	38.26 260	61.50 271	77.00 243

v17		68.37 141	67.00 147	72.48 139	73.22 213	50.31 170	78.10 115	79.58 130	59.71 98	61.67 202	77.60 233	49.31 76	82.89 135	62.37 105	61.48 273	84.23 123

tpmvs		58.47 245	56.95 248	63.03 256	70.20 264	41.21 257	67.90 258	67.23 257	49.62 228	54.73 268	70.84 286	34.14 242	76.24 243	36.64 270	61.29 274	71.64 292

Patchmtry		57.16 254	56.47 251	59.23 269	69.17 273	34.58 304	62.98 279	63.15 279	44.53 273	56.83 252	74.84 264	35.83 231	68.71 269	40.03 254	60.91 275	74.39 270

DTE-MVSNet		65.58 187	65.34 175	66.31 220	76.06 174	34.79 301	76.43 152	79.38 139	62.55 48	61.66 203	83.83 109	45.60 134	79.15 203	41.64 248	60.88 276	85.00 98

v11		68.15 153	66.73 155	72.42 145	73.43 207	50.28 172	77.94 121	79.65 122	59.88 93	61.11 213	77.55 236	48.25 106	82.75 146	61.88 117	60.85 277	84.23 123

CHOSEN 280x420		47.83 288	46.36 289	52.24 302	67.37 285	49.78 188	38.91 332	43.11 335	35.00 316	43.27 315	63.30 314	28.95 281	49.19 329	36.53 271	60.80 278	57.76 321

Patchmatch-test		49.08 285	48.28 285	51.50 303	64.40 302	30.85 318	45.68 324	48.46 329	35.60 315	46.10 306	72.10 279	34.47 240	46.37 330	27.08 314	60.65 279	77.27 238

v15		68.22 150	66.81 154	72.47 143	73.25 212	50.40 168	77.92 122	79.60 125	59.77 97	61.28 210	77.52 237	49.25 86	82.77 141	62.16 112	60.51 280	84.24 122

V14		68.25 147	66.82 153	72.52 138	73.33 211	50.53 165	78.02 118	79.60 125	59.83 94	61.16 212	77.57 235	49.19 90	82.77 141	62.18 108	60.50 281	84.26 121

V9		68.27 145	66.84 150	72.56 136	73.39 210	50.63 160	78.10 115	79.60 125	59.94 89	61.05 214	77.62 232	49.18 91	82.77 141	62.17 110	60.48 282	84.27 120

v12		68.28 144	66.83 152	72.60 135	73.43 207	50.74 157	78.18 110	79.59 128	60.01 88	60.89 216	77.66 231	49.12 95	82.77 141	62.18 108	60.46 283	84.29 119

v13		68.29 143	66.84 150	72.63 133	73.50 205	50.83 155	78.25 108	79.58 130	60.05 86	60.76 217	77.68 230	49.11 98	82.77 141	62.17 110	60.45 284	84.30 118

test20.03		53.87 271	54.02 268	53.41 296	61.47 311	28.11 323	61.30 287	59.21 294	51.34 216	52.09 285	77.43 239	33.29 252	58.55 304	29.76 306	60.27 285	73.58 276

MVS-HIRNet		45.52 293	44.48 295	48.65 308	68.49 278	34.05 306	59.41 295	44.50 334	27.03 326	37.96 324	50.47 329	26.16 299	64.10 285	26.74 315	59.52 286	47.82 326

Patchmatch-RL test		58.16 248	55.49 258	66.15 225	67.92 282	48.89 200	60.66 291	51.07 323	47.86 245	59.36 230	62.71 315	34.02 244	72.27 256	56.41 141	59.40 287	77.30 236

AllTest		57.08 255	54.65 262	64.39 248	71.44 250	49.03 197	69.92 249	67.30 255	45.97 261	47.16 300	79.77 200	17.47 316	67.56 272	33.65 281	59.16 288	76.57 246

TestCases				64.39 248	71.44 250	49.03 197		67.30 255	45.97 261	47.16 300	79.77 200	17.47 316	67.56 272	33.65 281	59.16 288	76.57 246

RPSCF		55.80 263	54.22 267	60.53 268	65.13 299	42.91 248	64.30 275	57.62 301	36.84 313	58.05 246	82.28 134	28.01 285	56.24 317	37.14 265	58.61 290	82.44 171

EU-MVSNet		55.61 264	54.41 264	59.19 270	65.41 298	33.42 309	72.44 217	71.91 223	28.81 324	51.27 287	73.87 271	24.76 305	69.08 268	43.04 236	58.20 291	75.06 260

pmmvs-eth3d		58.81 243	56.31 253	66.30 221	67.61 283	52.42 138	72.30 219	64.76 269	43.55 281	54.94 266	74.19 270	28.95 281	72.60 254	43.31 232	57.21 292	73.88 275

TinyColmap		54.14 268	51.72 276	61.40 265	66.84 288	41.97 251	66.52 261	68.51 248	44.81 269	42.69 317	75.77 258	11.66 329	72.94 253	31.96 286	56.77 293	69.27 303

OurMVSNet-221017-0		61.37 232	58.63 237	69.61 188	72.05 243	48.06 207	73.93 198	72.51 219	47.23 250	54.74 267	80.92 173	21.49 313	81.24 169	48.57 194	56.22 294	79.53 218

TransMVSNet (Re)		64.72 196	64.33 184	65.87 232	75.22 184	38.56 276	74.66 188	75.08 203	58.90 118	61.79 200	82.63 125	51.18 60	78.07 222	43.63 231	55.87 295	80.99 199

test2356		45.61 292	44.66 294	48.47 309	60.15 319	28.08 324	52.44 312	52.83 322	38.01 309	46.13 305	60.98 317	15.08 322	55.54 320	20.43 328	55.85 296	61.78 314

FPMVS		42.18 300	41.11 300	45.39 312	58.03 324	41.01 259	49.50 318	53.81 320	30.07 323	33.71 325	64.03 309	11.69 328	52.08 327	14.01 334	55.11 297	43.09 329

dp		51.89 279	51.60 277	52.77 299	68.44 279	32.45 312	62.36 282	54.57 315	44.16 277	49.31 295	67.91 297	28.87 283	56.61 312	33.89 280	54.89 298	69.24 304

ADS-MVSNet2		51.33 281	48.76 284	59.07 271	66.02 296	44.60 232	50.90 316	59.76 293	36.90 311	50.74 290	66.18 306	26.38 296	63.11 288	27.17 312	54.76 299	69.50 301

ADS-MVSNet		48.48 287	47.77 286	50.63 304	66.02 296	29.92 319	50.90 316	50.87 325	36.90 311	50.74 290	66.18 306	26.38 296	52.47 326	27.17 312	54.76 299	69.50 301

PM-MVS		52.33 277	50.19 280	58.75 273	62.10 309	45.14 227	65.75 264	40.38 336	43.60 280	53.52 280	72.65 276	9.16 335	65.87 282	50.41 178	54.18 301	65.24 309

JIA-IIPM		51.56 280	47.68 288	63.21 253	64.61 301	50.73 158	47.71 321	58.77 296	42.90 286	48.46 297	51.72 326	24.97 304	70.24 264	36.06 275	53.89 302	68.64 305

ambc				65.13 243	63.72 305	37.07 285	47.66 322	78.78 149		54.37 273	71.42 283	11.24 331	80.94 174	45.64 214	53.85 303	77.38 235

DSMNet-mixed		39.30 305	38.72 304	41.03 318	51.22 330	19.66 336	45.53 325	31.35 341	15.83 336	39.80 323	67.42 301	22.19 311	45.13 333	22.43 320	52.69 304	58.31 320

testus		44.59 295	43.87 297	46.76 311	59.85 321	24.65 332	53.86 308	55.82 311	36.26 314	43.97 313	63.42 312	8.39 336	53.14 325	20.70 327	52.52 305	62.51 312

N_pmnet		39.35 304	40.28 301	36.54 321	63.76 304	1.62 346	49.37 319	0.76 348	34.62 317	43.61 314	66.38 305	26.25 298	42.57 336	26.02 318	51.77 306	65.44 308

TDRefinement		53.44 273	50.72 279	61.60 263	64.31 303	46.96 217	70.89 238	65.27 267	41.78 290	44.61 309	77.98 221	11.52 330	66.36 278	28.57 311	51.59 307	71.49 293

Gipuma		34.77 309	31.91 311	43.33 316	62.05 310	37.87 280	20.39 337	67.03 258	23.23 330	18.41 334	25.84 335	4.24 341	62.73 290	14.71 333	51.32 308	29.38 334
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		55.92 261	53.63 270	62.77 257	68.22 281	35.56 299	74.48 191	69.89 231	46.42 255	49.07 296	73.45 274	21.13 314	76.77 237	28.74 309	51.30 309	75.97 249

YYNet1		50.73 282	48.96 281	56.03 285	61.10 314	41.78 253	51.94 314	56.44 306	40.94 297	44.84 307	67.80 299	30.08 275	55.08 322	36.77 267	50.71 310	71.22 294

MDA-MVSNet_test_wron		50.71 283	48.95 282	56.00 286	61.17 313	41.84 252	51.90 315	56.45 305	40.96 296	44.79 308	67.84 298	30.04 276	55.07 323	36.71 269	50.69 311	71.11 297

testpf		44.11 297	45.40 292	40.26 319	60.52 317	27.34 325	33.26 334	54.33 317	45.87 264	41.08 319	60.26 318	16.46 318	59.14 302	46.09 208	50.68 312	34.31 332

SixPastTwentyTwo		61.65 230	58.80 235	70.20 182	75.80 176	47.22 216	75.59 168	69.68 234	54.61 183	54.11 275	79.26 208	27.07 293	82.96 132	43.27 233	49.79 313	80.41 206

1111		44.40 296	45.00 293	42.61 317	57.55 325	17.33 339	53.82 310	57.05 303	40.78 298	44.11 311	66.57 303	13.37 324	45.77 331	22.15 321	49.58 314	64.73 311

test1235678		45.66 291	44.46 296	49.26 306	59.88 320	28.68 322	56.36 303	55.54 313	39.12 307	40.89 320	63.40 313	14.41 323	57.32 309	21.05 325	49.47 315	61.78 314

new-patchmatchnet		47.56 289	47.73 287	47.06 310	58.81 323	9.37 342	48.78 320	59.21 294	43.28 282	44.22 310	68.66 296	25.67 301	57.20 310	31.57 295	49.35 316	74.62 269

LF4IMVS		42.95 298	42.26 298	45.04 313	48.30 333	32.50 311	54.80 305	48.49 328	28.03 325	40.51 322	70.16 291	9.24 334	43.89 334	31.63 293	49.18 317	58.72 319

PMVS	28.69 22	36.22 306	33.29 310	45.02 314	36.82 341	35.98 298	54.68 306	48.74 327	26.31 327	21.02 332	51.61 327	2.88 345	60.10 299	9.99 338	47.58 318	38.99 331
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 294	41.95 299	53.86 293	52.58 329	43.55 242	62.11 284	46.90 333	26.05 328	40.63 321	60.19 319	11.08 332	57.91 306	31.83 292	46.15 319	60.11 318

MDA-MVSNet-bldmvs		53.87 271	50.81 278	63.05 255	66.25 292	48.58 202	56.93 301	63.82 274	48.09 242	41.22 318	70.48 289	30.34 272	68.00 271	34.24 279	45.92 320	72.57 282

LP		48.51 286	45.51 291	57.52 281	62.86 306	44.53 235	52.38 313	59.84 292	38.11 308	42.81 316	61.02 316	23.23 308	63.02 289	24.10 319	45.24 321	65.02 310

UnsupCasMVSNet_eth		53.16 276	52.47 274	55.23 287	59.45 322	33.39 310	59.43 294	69.13 242	45.98 260	50.35 294	72.32 278	29.30 280	58.26 305	42.02 244	44.30 322	74.05 273

UnsupCasMVSNet_bld		50.07 284	48.87 283	53.66 294	60.97 316	33.67 308	57.62 299	64.56 271	39.47 305	47.38 299	64.02 311	27.47 289	59.32 301	34.69 278	43.68 323	67.98 306

new_pmnet		34.13 310	34.29 309	33.64 322	52.63 328	18.23 338	44.43 328	33.90 339	22.81 331	30.89 327	53.18 324	10.48 333	35.72 340	20.77 326	39.51 324	46.98 327

K. test v3		60.47 235	57.11 245	70.56 177	73.74 203	48.22 205	75.10 180	62.55 283	58.27 128	53.62 279	76.31 253	27.81 287	81.59 164	47.42 197	39.18 325	81.88 178

LCM-MVSNet		40.30 303	35.88 308	53.57 295	42.24 336	29.15 321	45.21 326	60.53 291	22.23 332	28.02 329	50.98 328	3.72 343	61.78 294	31.22 298	38.76 326	69.78 300

testmv		42.25 299	40.11 302	48.66 307	53.23 327	27.02 326	56.62 302	55.74 312	37.25 310	33.10 326	59.52 320	7.78 337	56.58 314	19.61 329	38.13 327	62.40 313

lessismore_v0				69.91 185	71.42 252	47.80 209		50.90 324		50.39 293	75.56 260	27.43 291	81.33 168	45.91 211	34.10 328	80.59 203

test12356		36.16 307	35.94 307	36.83 320	50.82 331	8.52 343	44.84 327	53.49 321	32.72 320	30.11 328	55.08 323	7.11 339	49.47 328	16.60 331	32.68 329	52.50 324

no-one		40.85 302	36.09 306	55.14 288	48.55 332	38.72 273	42.15 330	62.92 281	34.60 318	23.55 331	49.74 330	12.21 327	66.16 280	26.27 317	24.84 330	60.54 317

PVSNet_0	43.31 20	47.46 290	45.64 290	52.92 298	67.60 284	44.65 231	54.06 307	54.64 314	41.59 293	46.15 304	58.75 321	30.99 268	58.66 303	32.18 285	24.81 331	55.46 323

PMMVS2		27.40 314	25.91 314	31.87 325	39.46 340	6.57 344	31.17 335	28.52 342	23.96 329	20.45 333	48.94 331	4.20 342	37.94 339	16.51 332	19.97 332	51.09 325

MVE	17.77 23	21.41 317	17.77 319	32.34 324	34.34 343	25.44 330	16.11 338	24.11 343	11.19 338	13.22 337	31.92 333	1.58 346	30.95 341	10.47 336	17.03 333	40.62 330
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		23.77 315	22.73 316	26.90 327	42.02 337	20.67 335	42.66 329	35.70 337	17.43 334	10.28 339	25.05 336	6.42 340	42.39 337	10.28 337	14.71 334	17.63 335

wuykxyi23d		28.12 312	22.54 317	44.87 315	34.97 342	32.11 313	37.96 333	47.31 331	13.32 337	9.29 341	23.72 337	0.45 348	56.58 314	21.85 323	13.98 335	45.93 328

EMVS		22.97 316	21.84 318	26.36 328	40.20 338	19.53 337	41.95 331	34.64 338	17.09 335	9.73 340	22.83 338	7.29 338	42.22 338	9.18 339	13.66 336	17.32 336

PNet_i23d		27.88 313	25.99 313	33.55 323	47.54 334	25.89 328	47.24 323	32.91 340	21.44 333	15.90 335	38.09 332	0.85 347	42.76 335	16.90 330	13.03 337	32.00 333

wuyk23d		13.32 319	12.52 320	15.71 330	47.54 334	26.27 327	31.06 336	1.98 347	4.93 340	5.18 342	1.94 343	0.45 348	18.54 342	6.81 341	12.83 338	2.33 339

ANet_high		41.38 301	37.47 305	53.11 297	39.73 339	24.45 333	56.94 300	69.69 233	47.65 246	26.04 330	52.32 325	12.44 326	62.38 292	21.80 324	10.61 339	72.49 283

tmp_tt		9.43 320	11.14 321	4.30 332	2.38 345	4.40 345	13.62 339	16.08 345	0.39 341	15.89 336	13.06 339	15.80 320	5.54 344	12.63 335	10.46 340	2.95 338

DeepMVS_CX				12.03 331	17.97 344	10.91 341		10.60 346	7.46 339	11.07 338	28.36 334	3.28 344	11.29 343	8.01 340	9.74 341	13.89 337

cdsmvs_eth3d_5k		17.50 318	23.34 315	0.00 335	0.00 348	0.00 348	0.00 340	78.63 152	0.00 344	0.00 345	82.18 135	49.25 86	0.00 345	0.00 344	0.00 342	0.00 343

pcd_1.5k_mvsjas		3.92 324	5.23 325	0.00 335	0.00 348	0.00 348	0.00 340	0.00 349	0.00 344	0.00 345	0.00 346	47.05 120	0.00 345	0.00 344	0.00 342	0.00 343

sosnet-low-res		0.00 325	0.00 326	0.00 335	0.00 348	0.00 348	0.00 340	0.00 349	0.00 344	0.00 345	0.00 346	0.00 351	0.00 345	0.00 344	0.00 342	0.00 343

sosnet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 348	0.00 340	0.00 349	0.00 344	0.00 345	0.00 346	0.00 351	0.00 345	0.00 344	0.00 342	0.00 343

uncertanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 348	0.00 340	0.00 349	0.00 344	0.00 345	0.00 346	0.00 351	0.00 345	0.00 344	0.00 342	0.00 343

Regformer		0.00 325	0.00 326	0.00 335	0.00 348	0.00 348	0.00 340	0.00 349	0.00 344	0.00 345	0.00 346	0.00 351	0.00 345	0.00 344	0.00 342	0.00 343

.test1245		34.88 308	39.49 303	21.04 329	57.55 325	17.33 339	53.82 310	57.05 303	40.78 298	44.11 311	66.57 303	13.37 324	45.77 331	22.15 321	0.00 342	0.03 341

testmvs		4.52 323	6.03 324	0.01 334	0.01 346	0.00 348	53.86 308	0.00 349	0.01 342	0.04 343	0.27 344	0.00 351	0.00 345	0.04 342	0.00 342	0.03 341

test123		4.73 322	6.30 323	0.02 333	0.01 346	0.01 347	56.36 303	0.00 349	0.01 342	0.04 343	0.21 345	0.01 350	0.00 345	0.03 343	0.00 342	0.04 340

ab-mvs-re		6.49 321	8.65 322	0.00 335	0.00 348	0.00 348	0.00 340	0.00 349	0.00 344	0.00 345	77.89 225	0.00 351	0.00 345	0.00 344	0.00 342	0.00 343

uanet		0.00 325	0.00 326	0.00 335	0.00 348	0.00 348	0.00 340	0.00 349	0.00 344	0.00 345	0.00 346	0.00 351	0.00 345	0.00 344	0.00 342	0.00 343

ESAPD								86.76 3

sam_mvs1												34.74 236

sam_mvs												33.43 250

MTGPA								80.97 97

test_post1							68.67 255				3.64 341	32.39 265	69.49 266	44.17 225

test_post											3.55 342	33.90 245	66.52 277

patchmatchnet-post											64.03 309	34.50 238	74.27 251

MTMP								17.08 344

gm-plane-assit					71.40 253	41.72 255			48.85 234		73.31 275		82.48 152	48.90 191

TEST9					85.58 27	61.59 25	81.62 63	81.26 88	55.65 169	74.93 22	88.81 39	53.70 40	84.68 95

test_8					85.40 30	60.96 32	81.54 66	81.18 91	55.86 163	74.81 25	88.80 41	53.70 40	84.45 100

agg_prior					85.04 34	59.96 40		81.04 94		74.68 26			84.04 107

test_prior4						62.51 17	82.08 57

test_prior				76.69 44	84.20 46	57.27 71		84.88 15					86.43 54			86.38 41

旧先验2							76.08 160		45.32 266	76.55 13			65.56 283	58.75 133

新几何2							76.12 158

无先验							79.66 89	74.30 209	48.40 239				80.78 179	53.62 158		79.03 224

原ACMM2							79.02 95

testdata2													72.18 257	46.95 203

segment_acmp												54.23 33

testdata1							72.65 211		60.50 75

plane_prior7					81.41 70	55.96 93

plane_prior6					81.20 77	56.24 88						45.26 142

plane_prior4											86.10 73

plane_prior3						56.09 90			63.92 30	69.27 95

plane_prior2							84.22 22		64.52 24

plane_prior1					81.27 75

n2								0.00 349

nn								0.00 349

door-mid								47.19 332

test11								83.47 44

door								47.60 330

HQP5-MVS						54.94 106

HQP-NCC					80.66 82		82.31 52		62.10 54	67.85 117

ACMP_Plane					80.66 82		82.31 52		62.10 54	67.85 117

BP-MVS														67.04 58

HQP4-MVS										67.85 117			86.93 39			84.32 116

HQP2-MVS												45.46 136

NP-MVS					80.98 80	56.05 92					85.54 85

MDTV_nov1_ep13_2view						25.89 328	61.22 288		40.10 302	51.10 288		32.97 254		38.49 259		78.61 226

Test By Simon												48.33 105

High-resolution multi-view benchmark