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.

HSP-MVS		80.69 1	81.20 1	79.14 8	86.21 17	62.73 12	86.09 8	85.03 14	65.51 15	83.81 1	90.51 11	63.71 2	89.23 6	81.51 1	88.44 12	85.45 79

APDe-MVS		80.16 2	80.59 2	78.86 18	86.64 10	60.02 40	88.12 1	86.42 6	62.94 40	82.40 3	92.12 1	59.64 7	89.76 3	78.70 5	88.32 16	86.79 38

HPM-MVS++		79.88 3	80.14 3	79.10 11	88.17 1	64.80 1	86.59 4	83.70 40	65.37 16	78.78 8	90.64 7	58.63 12	87.24 31	79.00 4	90.37 2	85.26 92

CNVR-MVS		79.84 4	79.97 4	79.45 4	87.90 2	62.17 20	84.37 20	85.03 14	66.96 6	77.58 11	90.06 20	59.47 9	89.13 8	78.67 6	89.73 4	87.03 33

SteuartSystems-ACMMP		79.48 5	79.31 5	79.98 1	83.01 56	62.18 19	87.60 2	85.83 8	66.69 11	78.03 10	90.98 4	54.26 33	90.06 1	78.42 7	89.02 8	87.69 15
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 6	79.00 6	79.13 9	84.92 42	60.32 38	83.03 37	85.33 11	62.86 43	80.17 4	90.03 21	61.76 3	88.95 10	74.21 19	88.67 11	88.12 7

ACMMP_Plus		78.77 7	78.78 7	78.74 20	85.44 30	61.04 31	83.84 28	85.16 12	62.88 42	78.10 9	91.26 3	52.51 49	88.39 13	79.34 3	90.52 1	86.78 39

NCCC		78.58 8	78.31 9	79.39 5	87.51 4	62.61 16	85.20 17	84.42 21	66.73 10	74.67 29	89.38 30	55.30 24	89.18 7	74.19 20	87.34 28	86.38 42

DeepC-MVS	69.38 2	78.56 9	78.14 12	79.83 2	83.60 50	61.62 24	84.17 24	86.85 2	63.23 35	73.84 38	90.25 18	57.68 13	89.96 2	74.62 18	89.03 7	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
TSAR-MVS + MP.		78.44 10	78.28 10	78.90 16	84.96 38	61.41 27	84.03 26	83.82 38	59.34 113	79.37 6	89.76 26	59.84 5	87.62 28	76.69 11	86.74 36	87.68 16

MP-MVS-pluss		78.35 11	78.46 8	78.03 30	84.96 38	59.52 45	82.93 39	85.39 10	62.15 53	76.41 15	91.51 2	52.47 51	86.78 43	80.66 2	89.64 6	87.80 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 11	78.26 11	78.64 21	86.54 12	63.47 5	86.02 9	83.55 43	63.89 31	73.60 42	90.60 8	54.85 29	86.72 44	77.20 10	88.06 21	85.74 67

APD-MVS		78.02 13	78.04 13	77.98 31	86.44 14	60.81 34	85.52 15	84.36 22	60.61 73	79.05 7	90.30 16	55.54 23	88.32 16	73.48 28	87.03 31	84.83 104
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 14	77.65 14	79.10 11	86.71 7	62.81 10	86.29 5	84.32 23	62.82 44	73.96 33	90.50 12	53.20 46	88.35 14	74.02 21	87.05 29	86.13 53

#test#		77.83 15	77.41 17	79.10 11	86.71 7	62.81 10	85.69 14	84.32 23	61.61 62	73.96 33	90.50 12	53.20 46	88.35 14	73.68 24	87.05 29	86.13 53

ACMMPR		77.71 16	77.23 19	79.16 6	86.75 6	62.93 9	86.29 5	84.24 25	62.82 44	73.55 43	90.56 10	49.80 72	88.24 17	74.02 21	87.03 31	86.32 49

SD-MVS		77.70 17	77.62 15	77.93 32	84.47 45	61.88 23	84.55 19	83.87 36	60.37 78	79.89 5	89.38 30	54.97 26	85.58 70	76.12 12	84.94 46	86.33 48

region2R		77.67 18	77.18 20	79.15 7	86.76 5	62.95 8	86.29 5	84.16 27	62.81 46	73.30 45	90.58 9	49.90 70	88.21 18	73.78 23	87.03 31	86.29 51

MPTG		77.61 19	77.36 18	78.35 24	86.08 21	63.57 2	83.37 33	80.97 98	65.13 18	75.77 17	90.88 5	48.63 101	86.66 45	77.23 8	88.17 18	84.81 105

MCST-MVS		77.48 20	77.45 16	77.54 34	86.67 9	58.36 60	83.22 35	86.93 1	56.91 141	74.91 25	88.19 44	59.15 10	87.68 27	73.67 25	87.45 27	86.57 41

HPM-MVS		77.28 21	76.85 22	78.54 22	85.00 37	60.81 34	82.91 40	85.08 13	62.57 47	73.09 48	89.97 23	50.90 66	87.48 29	75.30 13	86.85 34	87.33 28

DeepC-MVS_fast	68.24 3	77.25 22	76.63 25	79.12 10	86.15 19	60.86 33	84.71 18	84.85 18	61.98 59	73.06 49	88.88 38	53.72 40	89.06 9	68.27 47	88.04 22	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
XVS		77.17 23	76.56 26	79.00 14	86.32 15	62.62 14	85.83 10	83.92 32	64.55 22	72.17 58	90.01 22	47.95 110	88.01 22	71.55 36	86.74 36	86.37 45

CP-MVS		77.12 24	76.68 24	78.43 23	86.05 23	63.18 7	87.55 3	83.45 46	62.44 50	72.68 53	90.50 12	48.18 108	87.34 30	73.59 26	85.71 43	84.76 109

CSCG		76.92 25	76.75 23	77.41 36	83.96 49	59.60 44	82.95 38	86.50 5	60.78 71	75.27 20	84.83 91	60.76 4	86.56 50	67.86 51	87.87 26	86.06 56

MTAPA		76.90 26	76.42 27	78.35 24	86.08 21	63.57 2	74.92 184	80.97 98	65.13 18	75.77 17	90.88 5	48.63 101	86.66 45	77.23 8	88.17 18	84.81 105

test_prior3		76.89 27	76.96 21	76.69 44	84.20 47	57.27 72	81.75 59	84.88 16	60.37 78	75.01 21	89.06 33	56.22 18	86.43 54	72.19 32	88.96 9	86.38 42

PGM-MVS		76.77 28	76.06 29	78.88 17	86.14 20	62.73 12	82.55 47	83.74 39	61.71 60	72.45 57	90.34 15	48.48 105	88.13 19	72.32 31	86.85 34	85.78 62

MVS_0304		76.73 29	76.04 30	78.78 19	81.32 73	58.89 54	82.50 49	84.07 28	67.73 5	72.08 60	87.28 54	49.49 74	89.57 4	73.52 27	86.40 40	87.87 11

mPP-MVS		76.54 30	75.93 32	78.34 26	86.47 13	63.50 4	85.74 13	82.28 66	62.90 41	71.77 62	90.26 17	46.61 128	86.55 51	71.71 35	85.66 44	84.97 101

CANet		76.46 31	75.93 32	78.06 29	81.29 74	57.53 69	82.35 50	83.31 52	67.78 3	70.09 74	86.34 69	54.92 27	88.90 11	72.68 30	84.55 48	87.76 14

CDPH-MVS		76.31 32	75.67 35	78.22 27	85.35 33	59.14 50	81.31 69	84.02 29	56.32 156	74.05 32	88.98 36	53.34 44	87.92 24	69.23 44	88.42 13	87.59 18

train_agg		76.27 33	76.15 28	76.64 47	85.58 28	61.59 25	81.62 63	81.26 89	55.86 163	74.93 23	88.81 39	53.70 41	84.68 95	75.24 15	88.33 14	83.65 148

agg_prior3		76.13 34	75.89 34	76.85 42	85.76 24	62.02 21	81.65 61	81.01 97	55.51 172	73.73 39	88.60 43	53.23 45	84.90 88	75.24 15	88.33 14	83.65 148

ACMMP		76.02 35	75.33 37	78.07 28	85.20 34	61.91 22	85.49 16	84.44 20	63.04 38	69.80 84	89.74 27	45.43 139	87.16 35	72.01 34	82.87 60	85.14 94
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
agg_prior1		75.94 36	76.01 31	75.72 57	85.04 35	59.96 41	81.44 67	81.04 95	56.14 161	74.68 27	88.90 37	53.91 37	84.04 107	75.01 17	87.92 25	83.16 159

PHI-MVS		75.87 37	75.36 36	77.41 36	80.62 86	55.91 96	84.28 21	85.78 9	56.08 162	73.41 44	86.58 65	50.94 65	88.54 12	70.79 40	89.71 5	87.79 13

3Dnovator+	66.72 4	75.84 38	74.57 41	79.66 3	82.40 60	59.92 43	85.83 10	86.32 7	66.92 9	67.80 123	89.24 32	42.03 166	89.38 5	64.07 86	86.50 39	89.69 1

Regformer-2		75.63 39	74.99 38	77.54 34	80.43 88	58.32 61	79.50 92	82.92 59	67.84 1	75.94 16	80.75 180	55.73 21	86.80 41	71.44 38	80.38 80	87.50 20

Regformer-1		75.47 40	74.93 40	77.09 40	80.43 88	57.70 67	79.50 92	82.13 67	67.84 1	75.73 19	80.75 180	56.50 15	86.07 58	71.07 39	80.38 80	87.50 20

APD-MVS_3200maxsize		74.96 41	74.39 43	76.67 46	82.20 61	58.24 62	83.67 29	83.29 53	58.41 125	73.71 40	90.14 19	45.62 134	85.99 62	69.64 42	82.85 61	85.78 62

TSAR-MVS + GP.		74.90 42	74.15 45	77.17 39	82.00 63	58.77 56	81.80 58	78.57 154	58.58 121	74.32 31	84.51 100	55.94 20	87.22 32	67.11 57	84.48 50	85.52 74

DELS-MVS		74.76 43	74.46 42	75.65 60	77.84 138	52.25 140	75.59 168	84.17 26	63.76 32	73.15 47	82.79 121	59.58 8	86.80 41	67.24 56	86.04 42	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
OPM-MVS		74.73 44	74.25 44	76.19 50	80.81 82	59.01 52	82.60 46	83.64 41	63.74 33	72.52 55	87.49 49	47.18 120	85.88 66	69.47 43	80.78 72	83.66 147

canonicalmvs		74.67 45	74.98 39	73.71 95	78.94 110	50.56 165	80.23 79	83.87 36	60.30 82	77.15 12	86.56 66	59.65 6	82.00 157	66.01 64	82.12 64	88.58 4

abl_6		74.34 46	73.50 49	76.86 41	82.43 59	60.16 39	83.48 32	81.86 73	58.81 119	73.95 35	89.86 24	41.87 169	86.62 47	67.98 50	81.23 71	83.80 141

HQP_MVS		74.31 47	73.73 48	76.06 51	81.41 71	56.31 85	84.22 22	84.01 30	64.52 24	69.27 96	86.10 73	45.26 143	87.21 33	68.16 48	80.58 76	84.65 110

HPM-MVS_fast		74.30 48	73.46 52	76.80 43	84.45 46	59.04 51	83.65 30	81.05 94	60.15 84	70.43 69	89.84 25	41.09 184	85.59 69	67.61 54	82.90 59	85.77 64

Regformer-4		74.25 49	73.48 50	76.57 48	79.75 97	56.54 84	78.54 103	81.49 81	66.93 8	73.90 36	80.30 190	53.84 39	85.98 63	69.76 41	76.84 124	87.17 30

MVS_111021_HR		74.02 50	73.46 52	75.69 59	83.01 56	60.63 36	77.29 138	78.40 163	61.18 67	70.58 68	85.97 76	54.18 35	84.00 111	67.52 55	82.98 58	82.45 171

MG-MVS		73.96 51	73.89 47	74.16 82	85.65 26	49.69 193	81.59 65	81.29 88	61.45 63	71.05 66	88.11 45	51.77 56	87.73 26	61.05 121	83.09 55	85.05 98

Regformer-3		73.89 52	73.28 54	75.71 58	79.75 97	55.48 104	78.54 103	79.93 119	66.58 12	73.62 41	80.30 190	54.87 28	84.54 98	69.09 45	76.84 124	87.10 32

alignmvs		73.86 53	73.99 46	73.45 107	78.20 127	50.50 167	78.57 101	82.43 65	59.40 111	76.57 13	86.71 59	56.42 17	81.23 170	65.84 66	81.79 66	88.62 2

MSLP-MVS++		73.77 54	73.47 51	74.66 74	83.02 55	59.29 49	82.30 55	81.88 72	59.34 113	71.59 64	86.83 56	45.94 132	83.65 117	65.09 71	85.22 45	81.06 198

HQP-MVS		73.45 55	72.80 57	75.40 64	80.66 83	54.94 107	82.31 52	83.90 34	62.10 54	67.85 118	85.54 85	45.46 137	86.93 39	67.04 58	80.35 82	84.32 117

CLD-MVS		73.33 56	72.68 58	75.29 67	78.82 112	53.33 124	78.23 109	84.79 19	61.30 66	70.41 70	81.04 168	52.41 52	87.12 36	64.61 76	82.49 63	85.41 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		73.31 57	72.54 59	75.62 61	77.87 137	53.64 118	79.62 90	79.61 124	61.63 61	72.02 61	82.61 126	56.44 16	85.97 64	63.99 89	79.07 102	87.25 29

UA-Net		73.13 58	72.93 56	73.76 91	83.58 51	51.66 146	78.75 96	77.66 171	67.75 4	72.61 54	89.42 28	49.82 71	83.29 124	53.61 160	83.14 54	86.32 49

EPNet		73.09 59	72.16 61	75.90 53	75.95 176	56.28 87	83.05 36	72.39 221	66.53 13	65.27 152	87.00 55	50.40 68	85.47 74	62.48 103	86.32 41	85.94 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
nrg030		72.96 60	73.01 55	72.84 128	75.41 183	50.24 174	80.02 82	82.89 62	58.36 127	74.44 30	86.73 57	58.90 11	80.83 177	65.84 66	74.46 138	87.44 23

CPTT-MVS		72.78 61	72.08 63	74.87 71	84.88 43	61.41 27	84.15 25	77.86 167	55.27 174	67.51 127	88.08 47	41.93 168	81.85 159	69.04 46	80.01 86	81.35 192

LPG-MVS_test		72.74 62	71.74 65	75.76 55	80.22 91	57.51 70	82.55 47	83.40 48	61.32 64	66.67 135	87.33 52	39.15 197	86.59 48	67.70 52	77.30 120	83.19 156

PAPM_NR		72.63 63	71.80 64	75.13 68	81.72 66	53.42 123	79.91 84	83.28 54	59.14 115	66.31 141	85.90 77	51.86 55	86.06 59	57.45 137	80.62 74	85.91 59

VDD-MVS		72.50 64	72.09 62	73.75 93	81.58 67	49.69 193	77.76 125	77.63 172	63.21 36	73.21 46	89.02 35	42.14 165	83.32 123	61.72 118	82.50 62	88.25 6

3Dnovator	64.47 5	72.49 65	71.39 70	75.79 54	77.70 140	58.99 53	80.66 76	83.15 56	62.24 52	65.46 149	86.59 64	42.38 164	85.52 72	59.59 131	84.72 47	82.85 165

MVS_Test		72.45 66	72.46 60	72.42 145	74.88 188	48.50 204	76.28 155	83.14 57	59.40 111	72.46 56	84.68 93	55.66 22	81.12 171	65.98 65	79.66 91	87.63 17

EI-MVSNet-Vis-set		72.42 67	71.59 66	74.91 69	78.47 122	54.02 114	77.05 142	79.33 141	65.03 20	71.68 63	79.35 210	52.75 48	84.89 89	66.46 61	74.23 141	85.83 61

ACMP	63.53 6	72.30 68	71.20 75	75.59 63	80.28 90	57.54 68	82.74 43	82.84 63	60.58 74	65.24 154	86.18 71	39.25 196	86.03 61	66.95 60	76.79 126	83.22 154
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 69	71.21 74	75.31 66	78.50 120	55.93 95	81.63 62	82.12 68	56.24 159	70.02 78	85.68 82	47.05 121	84.34 102	65.27 70	74.41 140	85.67 69

Vis-MVSNet		72.18 70	71.37 71	74.61 77	81.29 74	55.41 105	80.90 72	78.28 165	60.73 72	69.23 99	88.09 46	44.36 152	82.65 148	57.68 136	81.75 68	85.77 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		72.17 71	71.41 69	74.45 80	81.95 64	57.22 74	84.03 26	80.38 115	59.89 92	68.40 108	82.33 132	49.64 73	87.83 25	51.87 169	84.16 52	78.30 228

EPP-MVSNet		72.16 72	71.31 73	74.71 72	78.68 117	49.70 191	82.10 56	81.65 77	60.40 77	65.94 144	85.84 78	51.74 57	86.37 56	55.93 143	79.55 94	88.07 8

DP-MVS Recon		72.15 73	70.73 80	76.40 49	86.57 11	57.99 64	81.15 71	82.96 58	57.03 138	66.78 134	85.56 83	44.50 149	88.11 20	51.77 171	80.23 85	83.10 160

EI-MVSNet-UG-set		71.92 74	71.06 76	74.52 79	77.98 135	53.56 120	76.62 148	79.16 143	64.40 26	71.18 65	78.95 215	52.19 54	84.66 97	65.47 69	73.57 150	85.32 89

VDDNet		71.81 75	71.33 72	73.26 119	82.80 58	47.60 214	78.74 97	75.27 197	59.59 106	72.94 50	89.40 29	41.51 178	83.91 112	58.75 133	82.99 57	88.26 5

LFMVS		71.78 76	71.59 66	72.32 148	83.40 52	46.38 222	79.75 87	71.08 225	64.18 28	72.80 52	88.64 42	42.58 163	83.72 115	57.41 138	84.49 49	86.86 36

PAPR		71.72 77	70.82 79	74.41 81	81.20 78	51.17 150	79.55 91	83.33 51	55.81 166	66.93 133	84.61 96	50.95 64	86.06 59	55.79 146	79.20 100	86.00 57

IS-MVSNet		71.57 78	71.00 77	73.27 118	78.86 111	45.63 226	80.22 80	78.69 152	64.14 29	66.46 137	87.36 51	49.30 78	85.60 68	50.26 180	83.71 53	88.59 3

MAR-MVS		71.51 79	70.15 87	75.60 62	81.84 65	59.39 47	81.38 68	82.90 61	54.90 181	68.08 116	78.70 216	47.73 112	85.51 73	51.68 173	84.17 51	81.88 179
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
MVSFormer		71.50 80	70.38 84	74.88 70	78.76 114	57.15 79	82.79 41	78.48 158	51.26 219	69.49 91	83.22 117	43.99 155	83.24 125	66.06 62	79.37 95	84.23 124

PVSNet_Blended_VisFu		71.45 81	70.39 83	74.65 75	82.01 62	58.82 55	79.93 83	80.35 117	55.09 177	65.82 148	82.16 138	49.17 93	82.64 149	60.34 125	78.62 109	82.50 170

OMC-MVS		71.40 82	70.60 81	73.78 89	76.60 168	53.15 126	79.74 88	79.78 120	58.37 126	68.75 103	86.45 68	45.43 139	80.60 182	62.58 101	77.73 114	87.58 19

UniMVSNet_NR-MVSNet		71.11 83	71.00 77	71.44 159	79.20 104	44.13 237	76.02 164	82.60 64	66.48 14	68.20 111	84.60 97	56.82 14	82.82 139	54.62 152	70.43 193	87.36 27

PCF-MVS	61.88 8	70.95 84	69.49 100	75.35 65	77.63 143	55.71 98	76.04 163	81.81 75	50.30 227	69.66 85	85.40 88	52.51 49	84.89 89	51.82 170	80.24 84	85.45 79
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
114514_t		70.83 85	69.56 95	74.64 76	86.21 17	54.63 111	82.34 51	81.81 75	48.22 244	63.01 177	85.83 79	40.92 187	87.10 37	57.91 135	79.79 90	82.18 174

FIs		70.82 86	71.43 68	68.98 196	78.33 124	38.14 280	76.96 144	83.59 42	61.02 68	67.33 129	86.73 57	55.07 25	81.64 162	54.61 154	79.22 99	87.14 31

ACMM	61.98 7	70.80 87	69.73 90	74.02 83	80.59 87	58.59 58	82.68 44	82.02 71	55.46 173	67.18 130	84.39 102	38.51 202	83.17 127	60.65 122	76.10 129	80.30 208
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1neww		70.66 88	69.70 91	73.53 102	73.15 222	50.22 175	78.11 112	80.68 103	59.65 100	69.83 81	81.67 150	49.29 80	84.96 84	64.55 77	70.38 196	85.42 82

v7new		70.66 88	69.70 91	73.53 102	73.15 222	50.22 175	78.11 112	80.68 103	59.65 100	69.83 81	81.67 150	49.29 80	84.96 84	64.55 77	70.38 196	85.42 82

v6		70.66 88	69.70 91	73.53 102	73.14 225	50.21 178	78.11 112	80.67 105	59.65 100	69.82 83	81.65 152	49.29 80	84.96 84	64.55 77	70.39 195	85.42 82

UniMVSNet (Re)		70.63 91	70.20 85	71.89 151	78.55 119	45.29 227	75.94 165	82.92 59	63.68 34	68.16 113	83.59 112	53.89 38	83.49 120	53.97 156	71.12 185	86.89 35

v7		70.57 92	69.48 101	73.85 86	73.50 210	50.92 153	78.27 107	81.43 82	58.93 116	69.61 86	81.49 158	47.56 115	85.43 76	63.94 90	70.62 189	85.21 93

xiu_mvs_v2_base		70.52 93	69.75 89	72.84 128	81.21 77	55.63 101	75.11 179	78.92 147	54.92 180	69.96 79	79.68 203	47.00 125	82.09 156	61.60 120	79.37 95	80.81 203

PS-MVSNAJ		70.51 94	69.70 91	72.93 124	81.52 68	55.79 97	74.92 184	79.00 146	55.04 179	69.88 80	78.66 217	47.05 121	82.19 154	61.61 119	79.58 92	80.83 202

v1141		70.50 95	69.53 96	73.41 111	72.92 232	50.00 185	77.69 126	80.60 107	59.50 108	69.60 87	81.43 159	49.24 90	84.77 92	64.48 81	70.30 202	85.46 78

divwei89l23v2f112		70.50 95	69.53 96	73.41 111	72.91 233	50.00 185	77.69 126	80.59 108	59.50 108	69.60 87	81.43 159	49.26 85	84.77 92	64.48 81	70.31 201	85.47 76

v2v482		70.50 95	69.45 103	73.66 97	72.62 239	50.03 184	77.58 130	80.51 112	59.90 90	69.52 90	82.14 139	47.53 116	84.88 91	65.07 72	70.17 205	86.09 55

v1		70.50 95	69.53 96	73.42 110	72.91 233	50.00 185	77.69 126	80.59 108	59.50 108	69.59 89	81.42 161	49.26 85	84.77 92	64.49 80	70.30 202	85.47 76

mvs-test1		70.44 99	68.19 119	77.18 38	76.10 173	63.22 6	80.59 77	76.06 190	59.83 94	66.32 140	79.87 197	41.56 175	85.53 71	60.60 123	72.77 163	82.80 166

v1144		70.42 100	69.31 104	73.76 91	73.22 218	50.64 160	77.83 123	81.43 82	58.58 121	69.40 94	81.16 165	47.53 116	85.29 79	64.01 88	70.64 188	85.34 88

TranMVSNet+NR-MVSNet		70.36 101	70.10 88	71.17 167	78.64 118	42.97 248	76.53 150	81.16 93	66.95 7	68.53 107	85.42 87	51.61 58	83.07 130	52.32 167	69.70 216	87.46 22

v8		70.33 102	69.28 105	73.49 105	73.15 222	50.22 175	78.62 100	80.78 102	60.79 70	66.45 138	82.11 140	49.35 76	84.98 82	63.58 95	68.71 224	85.28 90

Fast-Effi-MVS+		70.28 103	69.12 106	73.73 94	78.50 120	51.50 149	75.01 181	79.46 137	56.16 160	68.59 104	79.55 206	53.97 36	84.05 106	53.34 162	77.53 116	85.65 71

X-MVStestdata		70.21 104	67.28 140	79.00 14	86.32 15	62.62 14	85.83 10	83.92 32	64.55 22	72.17 58	6.49 344	47.95 110	88.01 22	71.55 36	86.74 36	86.37 45

v10		70.21 104	69.02 107	73.81 88	73.51 209	50.92 153	78.74 97	81.39 84	60.05 86	66.39 139	81.83 147	47.58 114	85.41 77	62.80 100	68.86 223	85.09 97

QAPM		70.05 106	68.81 109	73.78 89	76.54 170	53.43 122	83.23 34	83.48 44	52.89 198	65.90 145	86.29 70	41.55 177	86.49 53	51.01 175	78.40 111	81.42 184

DU-MVS		70.01 107	69.53 96	71.44 159	78.05 133	44.13 237	75.01 181	81.51 80	64.37 27	68.20 111	84.52 98	49.12 96	82.82 139	54.62 152	70.43 193	87.37 25

AdaColmap		69.99 108	68.66 112	73.97 85	84.94 40	57.83 65	82.63 45	78.71 151	56.28 158	64.34 166	84.14 104	41.57 174	87.06 38	46.45 205	78.88 103	77.02 243

v1192		69.97 109	68.68 111	73.85 86	73.19 221	50.94 151	77.68 129	81.36 85	57.51 133	68.95 102	80.85 176	45.28 142	85.33 78	62.97 99	70.37 198	85.27 91

FC-MVSNet-test		69.80 110	70.58 82	67.46 209	77.61 148	34.73 305	76.05 162	83.19 55	60.84 69	65.88 146	86.46 67	54.52 32	80.76 181	52.52 166	78.12 112	86.91 34

v144192		69.71 111	68.51 113	73.33 115	73.10 227	50.13 182	77.54 132	80.64 106	56.65 147	68.57 106	80.55 183	46.87 126	84.96 84	62.98 98	69.66 217	84.89 103

VNet		69.68 112	70.19 86	68.16 204	79.73 100	41.63 257	70.53 241	77.38 177	60.37 78	70.69 67	86.63 62	51.08 62	77.09 233	53.61 160	81.69 70	85.75 66

jason		69.65 113	68.39 117	73.43 109	78.27 126	56.88 81	77.12 140	73.71 215	46.53 258	69.34 95	83.22 117	43.37 159	79.18 198	64.77 73	79.20 100	84.23 124
jason: jason.
Effi-MVS+-dtu		69.64 114	67.53 131	75.95 52	76.10 173	62.29 18	80.20 81	76.06 190	59.83 94	65.26 153	77.09 245	41.56 175	84.02 110	60.60 123	71.09 186	81.53 182

lupinMVS		69.57 115	68.28 118	73.44 108	78.76 114	57.15 79	76.57 149	73.29 217	46.19 262	69.49 91	82.18 135	43.99 155	79.23 197	64.66 74	79.37 95	83.93 133

NR-MVSNet		69.54 116	68.85 108	71.59 158	78.05 133	43.81 241	74.20 193	80.86 101	65.18 17	62.76 179	84.52 98	52.35 53	83.59 118	50.96 176	70.78 187	87.37 25

MVS_111021_LR		69.50 117	68.78 110	71.65 156	78.38 123	59.33 48	74.82 186	70.11 231	58.08 129	67.83 122	84.68 93	41.96 167	76.34 242	65.62 68	77.54 115	79.30 222

v1921920		69.47 118	68.17 120	73.36 114	73.06 228	50.10 183	77.39 134	80.56 110	56.58 153	68.59 104	80.37 186	44.72 145	84.98 82	62.47 104	69.82 212	85.00 99

test_djsdf		69.45 119	67.74 125	74.58 78	74.57 195	54.92 109	82.79 41	78.48 158	51.26 219	65.41 150	83.49 115	38.37 204	83.24 125	66.06 62	69.25 220	85.56 72

DI_MVS_plusplus_test		69.35 120	68.03 122	73.30 117	71.11 260	50.14 181	75.49 170	79.16 143	54.57 185	62.45 189	80.76 179	44.67 147	84.20 103	64.23 84	79.81 89	85.54 73

EI-MVSNet		69.27 121	68.44 116	71.73 155	74.47 196	49.39 197	75.20 177	78.45 160	59.60 103	69.16 100	76.51 255	51.29 59	82.50 150	59.86 130	71.45 183	83.30 152

test_normal		69.26 122	67.90 124	73.32 116	70.84 263	50.38 170	75.30 173	79.17 142	54.23 189	62.00 196	80.61 182	44.69 146	83.89 113	64.33 83	79.95 88	85.69 68

v1240		69.24 123	67.91 123	73.25 120	73.02 230	49.82 188	77.21 139	80.54 111	56.43 155	68.34 110	80.51 184	43.33 160	84.99 80	62.03 114	69.77 215	84.95 102

IterMVS-LS		69.22 124	68.48 114	71.43 161	74.44 198	49.40 196	76.23 157	77.55 173	59.60 103	65.85 147	81.59 156	51.28 60	81.58 165	59.87 129	69.90 211	83.30 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet		69.02 125	69.47 102	67.69 208	77.42 151	41.00 261	74.04 194	79.68 122	60.06 85	69.26 98	84.81 92	51.06 63	77.58 227	54.44 155	74.43 139	84.48 115

v7n		69.01 126	67.36 137	73.98 84	72.51 241	52.65 132	78.54 103	81.30 87	60.26 83	62.67 181	81.62 153	43.61 157	84.49 99	57.01 139	68.70 225	84.79 107

OpenMVS	61.03 9	68.85 127	67.56 129	72.70 132	74.26 201	53.99 115	81.21 70	81.34 86	52.70 199	62.75 180	85.55 84	38.86 200	84.14 105	48.41 195	83.01 56	79.97 212

XVG-OURS-SEG-HR		68.81 128	67.47 133	72.82 130	74.40 199	56.87 82	70.59 240	79.04 145	54.77 182	66.99 132	86.01 75	39.57 194	78.21 220	62.54 102	73.33 154	83.37 151

BH-RMVSNet		68.81 128	67.42 134	72.97 123	80.11 94	52.53 135	74.26 192	76.29 186	58.48 124	68.38 109	84.20 103	42.59 162	83.83 114	46.53 204	75.91 130	82.56 167

UGNet		68.81 128	67.39 135	73.06 122	78.33 124	54.47 112	79.77 86	75.40 196	60.45 76	63.22 174	84.40 101	32.71 265	80.91 176	51.71 172	80.56 78	83.81 138
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
XVG-OURS		68.76 131	67.37 136	72.90 125	74.32 200	57.22 74	70.09 247	78.81 149	55.24 175	67.79 124	85.81 81	36.54 230	78.28 219	62.04 113	75.74 131	83.19 156

V42		68.65 132	67.35 138	72.56 136	68.93 279	50.18 179	72.90 209	79.47 136	56.92 140	69.45 93	80.26 192	46.29 130	82.99 131	64.07 86	67.82 233	84.53 113

PVSNet_Blended		68.59 133	67.72 126	71.19 166	77.03 162	50.57 163	72.51 216	81.52 78	51.91 207	64.22 170	77.77 231	49.13 94	82.87 137	55.82 144	79.58 92	80.14 211

xiu_mvs_v1_base_debu		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 211	70.04 75	81.41 162	32.79 261	79.02 206	63.81 91	77.31 117	81.22 194

xiu_mvs_v1_base		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 211	70.04 75	81.41 162	32.79 261	79.02 206	63.81 91	77.31 117	81.22 194

xiu_mvs_v1_base_debi		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 211	70.04 75	81.41 162	32.79 261	79.02 206	63.81 91	77.31 117	81.22 194

PVSNet_BlendedMVS		68.56 137	67.72 126	71.07 170	77.03 162	50.57 163	74.50 190	81.52 78	53.66 193	64.22 170	79.72 202	49.13 94	82.87 137	55.82 144	73.92 145	79.77 217

1121		68.53 138	67.16 145	72.63 133	85.64 27	61.14 29	73.95 195	66.46 262	44.61 276	70.28 72	86.68 60	41.42 179	80.78 179	53.62 158	81.79 66	75.97 250

WR-MVS		68.47 139	68.47 115	68.44 203	80.20 93	39.84 264	73.75 200	76.07 189	64.68 21	68.11 115	83.63 111	50.39 69	79.14 204	49.78 182	69.66 217	86.34 47

v16		68.38 140	67.01 146	72.47 143	73.22 218	50.29 172	78.10 115	79.59 129	59.71 98	61.72 202	77.60 237	49.28 83	82.89 135	62.36 106	61.54 275	84.23 124

v17		68.37 141	67.00 147	72.48 139	73.22 218	50.31 171	78.10 115	79.58 131	59.71 98	61.67 203	77.60 237	49.31 77	82.89 135	62.37 105	61.48 278	84.23 124

v18		68.33 142	66.96 148	72.42 145	73.13 226	50.16 180	77.97 120	79.57 133	59.57 107	61.80 200	77.50 242	49.30 78	82.90 134	62.31 107	61.50 276	84.20 130

v13		68.29 143	66.84 150	72.63 133	73.50 210	50.83 156	78.25 108	79.58 131	60.05 86	60.76 218	77.68 234	49.11 99	82.77 141	62.17 110	60.45 289	84.30 119

v12		68.28 144	66.83 152	72.60 135	73.43 212	50.74 158	78.18 110	79.59 129	60.01 88	60.89 217	77.66 235	49.12 96	82.77 141	62.18 108	60.46 288	84.29 120

V9		68.27 145	66.84 150	72.56 136	73.39 215	50.63 161	78.10 115	79.60 126	59.94 89	61.05 215	77.62 236	49.18 92	82.77 141	62.17 110	60.48 287	84.27 121

BH-untuned		68.27 145	67.29 139	71.21 165	79.74 99	53.22 125	76.06 161	77.46 176	57.19 135	66.10 142	81.61 154	45.37 141	83.50 119	45.42 220	76.68 128	76.91 246

jajsoiax		68.25 147	66.45 160	73.66 97	75.62 179	55.49 103	80.82 73	78.51 157	52.33 203	64.33 167	84.11 105	28.28 289	81.81 161	63.48 96	70.62 189	83.67 146

V14		68.25 147	66.82 153	72.52 138	73.33 216	50.53 166	78.02 118	79.60 126	59.83 94	61.16 213	77.57 239	49.19 91	82.77 141	62.18 108	60.50 286	84.26 122

v148		68.24 149	67.19 144	71.40 162	70.43 266	47.77 212	75.76 167	77.03 181	58.91 117	67.36 128	80.10 194	48.60 104	81.89 158	60.01 127	66.52 241	84.53 113

v15		68.22 150	66.81 154	72.47 143	73.25 217	50.40 169	77.92 122	79.60 126	59.77 97	61.28 211	77.52 241	49.25 87	82.77 141	62.16 112	60.51 285	84.24 123

CANet_DTU		68.18 151	67.71 128	69.59 189	74.83 189	46.24 223	78.66 99	76.85 183	59.60 103	63.45 173	82.09 141	35.25 237	77.41 229	59.88 128	78.76 106	85.14 94

mvs_tets		68.18 151	66.36 164	73.63 100	75.61 180	55.35 106	80.77 74	78.56 155	52.48 202	64.27 169	84.10 106	27.45 295	81.84 160	63.45 97	70.56 192	83.69 143

v11		68.15 153	66.73 155	72.42 145	73.43 212	50.28 173	77.94 121	79.65 123	59.88 93	61.11 214	77.55 240	48.25 107	82.75 146	61.88 117	60.85 282	84.23 124

mvs_anonymous		68.03 154	67.51 132	69.59 189	72.08 247	44.57 234	71.99 227	75.23 198	51.67 210	67.06 131	82.57 127	54.68 30	77.94 223	56.56 140	75.71 132	86.26 52

PAPM		67.92 155	66.69 157	71.63 157	78.09 131	49.02 200	77.09 141	81.24 91	51.04 222	60.91 216	83.98 108	47.71 113	84.99 80	40.81 250	79.32 98	80.90 201

Test4		67.77 156	65.97 168	73.19 121	68.64 280	50.58 162	74.80 187	80.48 113	54.13 190	59.11 236	79.07 214	33.89 251	83.12 129	63.61 94	79.98 87	85.87 60

diffmvs		67.72 157	66.73 155	70.70 176	69.74 276	47.69 213	73.33 204	74.74 205	53.30 195	64.51 165	81.80 148	49.25 87	79.02 206	59.15 132	74.75 136	85.39 87

VPNet		67.52 158	68.11 121	65.74 233	79.18 105	36.80 292	72.17 221	72.83 219	62.04 57	67.79 124	85.83 79	48.88 100	76.60 239	51.30 174	72.97 162	83.81 138

Fast-Effi-MVS+-dtu		67.37 159	65.33 176	73.48 106	72.94 231	57.78 66	77.47 133	76.88 182	57.60 132	61.97 197	76.85 249	39.31 195	80.49 183	54.72 151	70.28 204	82.17 175

MVS		67.37 159	66.33 165	70.51 178	75.46 182	50.94 151	73.95 195	81.85 74	41.57 298	62.54 185	78.57 221	47.98 109	85.47 74	52.97 164	82.05 65	75.14 260

v748		67.26 161	65.67 171	72.02 150	69.90 274	49.77 190	76.24 156	79.57 133	58.58 121	60.49 221	80.38 185	44.47 151	82.17 155	56.16 142	65.26 249	84.12 132

GBi-Net		67.21 162	66.55 158	69.19 193	77.63 143	43.33 244	77.31 135	77.83 168	56.62 150	65.04 157	82.70 122	41.85 170	80.33 185	47.18 199	72.76 164	83.92 134

test1		67.21 162	66.55 158	69.19 193	77.63 143	43.33 244	77.31 135	77.83 168	56.62 150	65.04 157	82.70 122	41.85 170	80.33 185	47.18 199	72.76 164	83.92 134

MVSTER		67.16 164	65.58 173	71.88 152	70.37 268	49.70 191	70.25 246	78.45 160	51.52 214	69.16 100	80.37 186	38.45 203	82.50 150	60.19 126	71.46 182	83.44 150

v52		67.09 165	65.16 179	72.87 126	66.77 294	51.60 147	73.69 201	79.45 138	57.88 130	62.46 188	78.57 221	40.95 186	83.34 121	61.99 115	64.70 254	83.68 144

V4		67.09 165	65.16 179	72.87 126	66.76 295	51.60 147	73.69 201	79.45 138	57.88 130	62.45 189	78.58 220	40.96 185	83.34 121	61.99 115	64.71 252	83.68 144

Baseline_NR-MVSNet		67.05 167	67.56 129	65.50 239	75.65 178	37.70 284	75.42 171	74.65 207	59.90 90	68.14 114	83.15 120	49.12 96	77.20 231	52.23 168	69.78 213	81.60 181

WR-MVS_H		67.02 168	66.92 149	67.33 212	77.95 136	37.75 283	77.57 131	82.11 69	62.03 58	62.65 182	82.48 129	50.57 67	79.46 193	42.91 238	64.01 258	84.79 107

anonymousdsp		67.00 169	64.82 183	73.57 101	70.09 270	56.13 90	76.35 153	77.35 178	48.43 242	64.99 160	80.84 177	33.01 258	80.34 184	64.66 74	67.64 236	84.23 124

FMVSNet2		66.93 170	66.31 167	68.79 199	77.63 143	42.98 247	76.11 159	77.47 174	56.62 150	65.22 156	82.17 137	41.85 170	80.18 188	47.05 202	72.72 167	83.20 155

BH-w/o		66.85 171	65.83 170	69.90 186	79.29 102	52.46 137	74.66 188	76.65 184	54.51 186	64.85 161	78.12 223	45.59 136	82.95 133	43.26 234	75.54 133	74.27 272

CDS-MVSNet		66.80 172	65.37 174	71.10 169	78.98 109	53.13 128	73.27 205	71.07 226	52.15 205	64.72 162	80.23 193	43.56 158	77.10 232	45.48 218	78.88 103	83.05 161
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 173	65.27 177	71.33 164	79.16 107	53.67 117	73.84 199	69.59 237	52.32 204	65.28 151	81.72 149	44.49 150	77.40 230	42.32 241	78.66 108	82.92 162

FMVSNet1		66.70 174	65.87 169	69.19 193	77.49 150	43.33 244	77.31 135	77.83 168	56.45 154	64.60 164	82.70 122	38.08 209	80.33 185	46.08 209	72.31 175	83.92 134

ab-mvs		66.65 175	66.42 162	67.37 210	76.17 172	41.73 255	70.41 244	76.14 188	53.99 191	65.98 143	83.51 114	49.48 75	76.24 243	48.60 193	73.46 152	84.14 131

PEN-MVS		66.60 176	66.45 160	67.04 213	77.11 160	36.56 294	77.03 143	80.42 114	62.95 39	62.51 187	84.03 107	46.69 127	79.07 205	44.22 224	63.08 266	85.51 75

TAPA-MVS	59.36 10	66.60 176	65.20 178	70.81 172	76.63 167	48.75 202	76.52 151	80.04 118	50.64 225	65.24 154	84.93 90	39.15 197	78.54 212	36.77 267	76.88 123	85.14 94
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 178	65.07 181	71.17 167	79.18 105	49.63 195	73.48 203	75.20 199	52.95 197	67.90 117	80.33 189	39.81 191	83.68 116	43.20 235	73.56 151	80.20 209

CP-MVSNet		66.49 179	66.41 163	66.72 215	77.67 142	36.33 296	76.83 147	79.52 135	62.45 49	62.54 185	83.47 116	46.32 129	78.37 217	45.47 219	63.43 263	85.45 79

PS-CasMVS		66.42 180	66.32 166	66.70 217	77.60 149	36.30 298	76.94 145	79.61 124	62.36 51	62.43 193	83.66 110	45.69 133	78.37 217	45.35 221	63.26 264	85.42 82

FMVSNet3		66.32 181	65.61 172	68.46 202	76.48 171	42.34 251	74.98 183	77.15 180	55.83 165	65.04 157	81.16 165	39.91 189	80.14 189	47.18 199	72.76 164	82.90 164

ACMH+	57.40 11	66.12 182	64.06 185	72.30 149	77.79 139	52.83 130	80.39 78	78.03 166	57.30 134	57.47 253	82.55 128	27.68 293	84.17 104	45.54 216	69.78 213	79.90 213

testing_2		66.02 183	63.77 190	72.76 131	66.03 300	50.48 168	72.93 208	80.36 116	54.41 187	54.25 279	76.76 251	30.89 274	83.16 128	64.19 85	74.08 143	84.65 110

cascas		65.98 184	63.42 194	73.64 99	77.26 158	52.58 134	72.26 220	77.21 179	48.56 239	61.21 212	74.60 271	32.57 269	85.82 67	50.38 179	76.75 127	82.52 169

DP-MVS		65.68 185	63.66 192	71.75 154	84.93 41	56.87 82	80.74 75	73.16 218	53.06 196	59.09 237	82.35 131	36.79 228	85.94 65	32.82 284	69.96 210	72.45 288

HyFIR lowres test		65.67 186	63.01 198	73.67 96	79.97 96	55.65 100	69.07 255	75.52 194	42.68 292	63.53 172	77.95 225	40.43 188	81.64 162	46.01 210	71.91 178	83.73 142

DTE-MVSNet		65.58 187	65.34 175	66.31 220	76.06 175	34.79 303	76.43 152	79.38 140	62.55 48	61.66 204	83.83 109	45.60 135	79.15 203	41.64 248	60.88 281	85.00 99

GA-MVS		65.53 188	63.70 191	71.02 171	70.87 262	48.10 207	70.48 242	74.40 209	56.69 146	64.70 163	76.77 250	33.66 253	81.10 172	55.42 149	70.32 200	83.87 137

CNLPA		65.43 189	64.02 186	69.68 187	78.73 116	58.07 63	77.82 124	70.71 228	51.49 215	61.57 206	83.58 113	38.23 207	70.82 264	43.90 228	70.10 207	80.16 210

MVP-Stereo		65.41 190	63.80 189	70.22 180	77.62 147	55.53 102	76.30 154	78.53 156	50.59 226	56.47 260	78.65 218	39.84 190	82.68 147	44.10 227	72.12 177	72.44 289
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 191	62.73 202	73.40 113	74.89 187	52.78 131	73.09 207	75.13 200	55.69 168	58.48 245	73.73 276	32.86 260	86.32 57	50.63 177	70.11 206	81.10 197
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
pm-mvs1		65.24 192	64.97 182	66.04 227	72.38 242	39.40 269	72.62 214	75.63 193	55.53 171	62.35 195	83.18 119	47.45 118	76.47 240	49.06 190	66.54 240	82.24 173

ACMH	55.70 15	65.20 193	63.57 193	70.07 183	78.07 132	52.01 145	79.48 94	79.69 121	55.75 167	56.59 259	80.98 171	27.12 297	80.94 174	42.90 239	71.58 181	77.25 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 194	63.21 196	70.72 175	81.04 80	54.87 110	78.57 101	77.47 174	48.51 240	55.71 263	81.89 146	33.71 252	79.71 190	41.66 246	70.37 198	77.58 234
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 195	62.84 200	71.82 153	81.49 70	56.26 88	66.32 267	74.20 211	40.53 304	63.16 176	78.65 218	41.30 180	77.80 225	45.80 212	74.09 142	81.40 185

TransMVSNet (Re)		64.72 196	64.33 184	65.87 232	75.22 185	38.56 277	74.66 188	75.08 204	58.90 118	61.79 201	82.63 125	51.18 61	78.07 222	43.63 231	55.87 300	80.99 200

EG-PatchMatch MVS		64.71 197	62.87 199	70.22 180	77.68 141	53.48 121	77.99 119	78.82 148	53.37 194	56.03 262	77.41 244	24.75 311	84.04 107	46.37 206	73.42 153	73.14 281

LS3D		64.71 197	62.50 204	71.34 163	79.72 101	55.71 98	79.82 85	74.72 206	48.50 241	56.62 258	84.62 95	33.59 254	82.34 153	29.65 308	75.23 134	75.97 250

1314		64.61 199	63.21 196	68.80 198	71.87 252	47.46 215	73.95 195	78.39 164	42.88 291	59.97 224	76.60 254	38.11 208	79.39 195	54.84 150	72.32 174	79.55 218

HY-MVS	56.14 13	64.55 200	63.89 187	66.55 218	74.73 192	41.02 259	69.96 248	74.43 208	49.29 233	61.66 204	80.92 173	47.43 119	76.68 238	44.91 223	71.69 180	81.94 177

XVG-ACMP-BASELINE		64.36 201	62.23 208	70.74 174	72.35 243	52.45 138	70.80 239	78.45 160	53.84 192	59.87 226	81.10 167	16.24 324	79.32 196	55.64 148	71.76 179	80.47 205

CostFormer		64.04 202	62.51 203	68.61 201	71.88 251	45.77 225	71.30 232	70.60 229	47.55 251	64.31 168	76.61 253	41.63 173	79.62 192	49.74 184	69.00 221	80.42 206

1112_ss		64.00 203	63.36 195	65.93 230	79.28 103	42.58 250	71.35 231	72.36 222	46.41 260	60.55 220	77.89 228	46.27 131	73.28 256	46.18 207	69.97 209	81.92 178

pmmvs6		63.69 204	62.82 201	66.27 223	70.63 264	39.27 270	73.13 206	75.47 195	52.69 200	59.75 229	82.30 133	39.71 192	77.03 234	47.40 198	64.35 257	82.53 168

Vis-MVSNet (Re-imp)		63.69 204	63.88 188	63.14 254	74.75 191	31.04 322	71.16 235	63.64 280	56.32 156	59.80 228	84.99 89	44.51 148	75.46 246	39.12 257	80.62 74	82.92 162

conf200view11		63.38 206	62.41 205	66.29 222	77.31 152	38.66 275	72.65 211	69.11 244	57.07 136	62.45 189	81.03 169	37.01 220	79.17 199	31.84 288	73.25 156	81.03 199

thres400		63.31 207	62.18 209	66.72 215	76.85 165	39.62 266	71.96 228	69.44 240	56.63 148	62.61 183	79.83 198	37.18 215	79.17 199	31.84 288	73.25 156	81.36 186

thres600view7		63.30 208	62.27 207	66.41 219	77.18 159	38.87 272	72.35 218	69.11 244	56.98 139	62.37 194	80.96 172	37.01 220	79.00 210	31.43 296	73.05 161	81.36 186

thres100view900		63.28 209	62.41 205	65.89 231	77.31 152	38.66 275	72.65 211	69.11 244	57.07 136	62.45 189	81.03 169	37.01 220	79.17 199	31.84 288	73.25 156	79.83 214

test_0402		63.25 210	61.01 224	69.96 184	80.00 95	54.37 113	76.86 146	72.02 223	54.58 184	58.71 240	80.79 178	35.00 238	84.36 101	26.41 320	64.71 252	71.15 301

tfpn200view9		63.18 211	62.18 209	66.21 224	76.85 165	39.62 266	71.96 228	69.44 240	56.63 148	62.61 183	79.83 198	37.18 215	79.17 199	31.84 288	73.25 156	79.83 214

LTVRE_ROB	55.42 16	63.15 212	61.23 222	68.92 197	76.57 169	47.80 210	59.92 296	76.39 185	54.35 188	58.67 241	82.46 130	29.44 284	81.49 166	42.12 242	71.14 184	77.46 235
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
F-COLMAP		63.05 213	60.87 225	69.58 191	76.99 164	53.63 119	78.12 111	76.16 187	47.97 248	52.41 289	81.61 154	27.87 291	78.11 221	40.07 253	66.66 239	77.00 244

IterMVS		62.79 214	61.27 221	67.35 211	69.37 277	52.04 144	71.17 234	68.24 250	52.63 201	59.82 227	76.91 248	37.32 214	72.36 259	52.80 165	63.19 265	77.66 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view600		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

view800		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

conf0.05thres1000		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

tfpn		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

tpmp4_e23		62.71 219	60.13 228	70.45 179	73.40 214	48.39 205	72.82 210	69.49 239	44.88 272	59.91 225	74.99 267	37.79 211	81.47 167	40.22 252	67.71 235	81.48 183

tfpnnormal		62.47 220	61.63 218	64.99 244	74.81 190	39.01 271	71.22 233	73.72 214	55.22 176	60.21 222	80.09 195	41.26 183	76.98 235	30.02 302	68.09 230	78.97 226

MS-PatchMatch		62.42 221	61.46 219	65.31 242	75.21 186	52.10 141	72.05 226	74.05 212	46.41 260	57.42 254	74.36 272	34.35 246	77.57 228	45.62 215	73.67 147	66.26 312

Test_1112_low_res		62.32 222	61.77 216	64.00 250	79.08 108	39.53 268	68.17 260	70.17 230	43.25 287	59.03 238	79.90 196	44.08 153	71.24 263	43.79 230	68.42 226	81.25 193

thres200		62.20 223	61.16 223	65.34 241	75.38 184	39.99 263	69.60 250	69.29 242	55.64 170	61.87 199	76.99 246	37.07 219	78.96 211	31.28 297	73.28 155	77.06 242

PatchFormer-LS_test		62.20 223	60.59 226	67.04 213	72.18 246	46.82 220	70.36 245	68.62 248	51.92 206	59.19 235	70.23 294	36.86 227	75.07 251	50.23 181	65.68 246	79.23 223

tpm2		62.07 225	60.10 229	67.99 205	72.79 236	43.86 240	71.05 236	66.85 260	43.14 289	62.77 178	75.39 265	38.32 205	80.80 178	41.69 245	68.88 222	79.32 221

DWT-MVSNet_test		61.90 226	59.93 230	67.83 206	71.98 250	46.09 224	71.03 237	69.71 233	50.09 228	58.51 244	70.62 291	30.21 279	77.63 226	49.28 188	67.91 231	79.78 216

EPNet_dtu		61.90 226	61.97 211	61.68 262	72.89 235	39.78 265	75.85 166	65.62 265	55.09 177	54.56 275	79.36 209	37.59 212	67.02 279	39.80 256	76.95 122	78.25 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 228	61.35 220	63.46 251	74.58 194	31.48 321	61.42 290	58.14 303	58.71 120	53.02 288	79.55 206	43.07 161	76.80 236	45.69 213	77.96 113	82.11 176

MSDG		61.81 229	59.23 232	69.55 192	72.64 238	52.63 133	70.45 243	75.81 192	51.38 217	53.70 282	76.11 258	29.52 282	81.08 173	37.70 262	65.79 245	74.93 265

SixPastTwentyTwo		61.65 230	58.80 238	70.20 182	75.80 177	47.22 217	75.59 168	69.68 235	54.61 183	54.11 280	79.26 211	27.07 298	82.96 132	43.27 233	49.79 318	80.41 207

pmmvs4		61.48 231	59.39 231	67.76 207	71.57 254	53.86 116	71.42 230	65.34 267	44.20 280	59.46 230	77.92 227	35.90 231	74.71 253	43.87 229	64.87 251	74.71 269

OurMVSNet-221017-0		61.37 232	58.63 241	69.61 188	72.05 248	48.06 208	73.93 198	72.51 220	47.23 254	54.74 272	80.92 173	21.49 318	81.24 169	48.57 194	56.22 299	79.53 219

COLMAP_ROB	52.97 17	61.27 233	58.81 235	68.64 200	74.63 193	52.51 136	78.42 106	73.30 216	49.92 231	50.96 294	81.51 157	23.06 314	79.40 194	31.63 293	65.85 243	74.01 277
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 234	61.67 217	57.70 284	70.43 266	38.45 278	64.19 280	66.47 261	48.05 247	63.22 174	80.86 175	49.28 83	60.47 301	45.25 222	67.28 238	74.19 275

K. test v3		60.47 235	57.11 249	70.56 177	73.74 208	48.22 206	75.10 180	62.55 288	58.27 128	53.62 284	76.31 257	27.81 292	81.59 164	47.42 197	39.18 330	81.88 179

OpenMVS_ROB	52.78 18	60.03 236	58.14 244	65.69 234	70.47 265	44.82 229	75.33 172	70.86 227	45.04 271	56.06 261	76.00 259	26.89 300	79.65 191	35.36 276	67.29 237	72.60 285

CR-MVSNet		59.91 237	57.90 247	65.96 228	69.96 272	52.07 142	65.31 274	63.15 284	42.48 293	59.36 231	74.84 268	35.83 232	70.75 265	45.50 217	64.65 255	75.06 261

PatchmatchNet		59.84 238	58.24 242	64.65 247	73.05 229	46.70 221	69.42 252	62.18 290	47.55 251	58.88 239	71.96 284	34.49 244	69.16 271	42.99 237	63.60 261	78.07 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test1		59.75 239	58.00 246	64.98 245	74.14 205	48.06 208	63.35 282	63.23 283	49.13 235	59.33 234	71.46 286	37.45 213	69.59 269	41.39 249	62.57 269	77.30 237

WTY-MVS		59.75 239	60.39 227	57.85 282	72.32 244	37.83 282	61.05 294	64.18 277	45.95 267	61.91 198	79.11 213	47.01 124	60.88 300	42.50 240	69.49 219	74.83 266

CVMVSNet		59.63 241	59.14 233	61.08 270	74.47 196	38.84 273	75.20 177	68.74 247	31.15 326	58.24 246	76.51 255	32.39 270	68.58 274	49.77 183	65.84 244	75.81 254

tfpn_ndepth		59.57 242	59.02 234	61.23 267	73.81 206	35.60 300	69.40 253	65.59 266	50.96 223	57.96 250	77.72 232	34.81 239	75.91 245	30.36 300	70.57 191	72.18 294

tfpnconf		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

tfpnview11		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

tpm cat1		59.25 245	56.95 252	66.15 225	72.19 245	46.96 218	68.09 261	65.76 264	40.03 307	57.81 251	70.56 292	38.32 205	74.51 254	38.26 260	61.50 276	77.00 244

tfpn1000		59.24 246	58.70 239	60.86 271	73.75 207	33.99 309	68.86 256	63.98 278	51.25 221	57.29 255	79.51 208	34.58 241	75.26 249	29.08 312	69.99 208	73.32 280

pmmvs-eth3d		58.81 247	56.31 257	66.30 221	67.61 288	52.42 139	72.30 219	64.76 271	43.55 285	54.94 271	74.19 274	28.95 286	72.60 258	43.31 232	57.21 297	73.88 278

RPMNet		58.70 248	56.29 258	65.96 228	69.96 272	52.07 142	65.31 274	62.15 291	43.20 288	59.36 231	70.15 296	35.37 236	70.75 265	36.42 273	64.65 255	75.06 261

tpmvs		58.47 249	56.95 252	63.03 256	70.20 269	41.21 258	67.90 262	67.23 258	49.62 232	54.73 273	70.84 290	34.14 247	76.24 243	36.64 270	61.29 279	71.64 297

PVSNet	50.76 19	58.40 250	57.39 248	61.42 264	75.53 181	44.04 239	61.43 289	63.45 281	47.04 256	56.91 256	73.61 277	27.00 299	64.76 288	39.12 257	72.40 172	75.47 258

tpmrst		58.24 251	58.70 239	56.84 286	66.97 291	34.32 307	69.57 251	61.14 294	47.17 255	58.58 243	71.60 285	41.28 182	60.41 302	49.20 189	62.84 267	75.78 255

Patchmatch-RL test		58.16 252	55.49 262	66.15 225	67.92 287	48.89 201	60.66 295	51.07 328	47.86 249	59.36 231	62.71 319	34.02 249	72.27 260	56.41 141	59.40 292	77.30 237

test-LLR		58.15 253	58.13 245	58.22 279	68.57 281	44.80 230	65.46 271	57.92 304	50.08 229	55.44 266	69.82 297	32.62 266	57.44 311	49.66 186	73.62 148	72.41 290

gg-mvs-nofinetune		57.86 254	56.43 256	62.18 260	72.62 239	35.35 302	66.57 264	56.33 312	50.65 224	57.64 252	57.10 326	30.65 275	76.36 241	37.38 264	78.88 103	74.82 267

CMPMVS	42.80 21	57.81 255	55.97 259	63.32 252	60.98 320	47.38 216	64.66 278	69.50 238	32.06 325	46.83 307	77.80 230	29.50 283	71.36 262	48.68 192	73.75 146	71.21 300
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 256	57.07 250	58.22 279	74.21 202	37.18 285	62.46 285	60.88 295	48.88 237	55.29 269	75.99 261	31.68 272	62.04 297	31.87 287	72.35 173	75.43 259

tpm		57.34 257	58.16 243	54.86 294	71.80 253	34.77 304	67.47 263	56.04 315	48.20 245	60.10 223	76.92 247	37.17 217	53.41 328	40.76 251	65.01 250	76.40 249

Patchmtry		57.16 258	56.47 255	59.23 273	69.17 278	34.58 306	62.98 283	63.15 284	44.53 277	56.83 257	74.84 268	35.83 232	68.71 273	40.03 254	60.91 280	74.39 271

AllTest		57.08 259	54.65 266	64.39 248	71.44 255	49.03 198	69.92 249	67.30 256	45.97 265	47.16 305	79.77 200	17.47 321	67.56 276	33.65 281	59.16 293	76.57 247

pmmvs5		56.47 260	55.68 261	58.86 276	61.41 317	36.71 293	66.37 266	62.75 287	40.38 305	53.70 282	76.62 252	34.56 242	67.05 278	40.02 255	65.27 248	72.83 283

test-mter		56.42 261	55.82 260	58.22 279	68.57 281	44.80 230	65.46 271	57.92 304	39.94 308	55.44 266	69.82 297	21.92 317	57.44 311	49.66 186	73.62 148	72.41 290

USDC		56.35 262	54.24 270	62.69 258	64.74 305	40.31 262	65.05 276	73.83 213	43.93 283	47.58 303	77.71 233	15.36 326	75.05 252	38.19 261	61.81 273	72.70 284

PatchMatch-RL		56.25 263	54.55 267	61.32 266	77.06 161	56.07 92	65.57 270	54.10 324	44.13 282	53.49 287	71.27 289	25.20 308	66.78 280	36.52 272	63.66 260	61.12 321

sss		56.17 264	56.57 254	54.96 293	66.93 292	36.32 297	57.94 302	61.69 293	41.67 296	58.64 242	75.32 266	38.72 201	56.25 320	42.04 243	66.19 242	72.31 293

Anonymous20231211		55.92 265	53.63 274	62.77 257	68.22 286	35.56 301	74.48 191	69.89 232	46.42 259	49.07 301	73.45 278	21.13 319	76.77 237	28.74 313	51.30 314	75.97 250

FMVSNet5		55.86 266	54.93 264	58.66 278	71.05 261	36.35 295	64.18 281	62.48 289	46.76 257	50.66 297	74.73 270	25.80 305	64.04 290	33.11 283	65.57 247	75.59 257

RPSCF		55.80 267	54.22 271	60.53 272	65.13 304	42.91 249	64.30 279	57.62 306	36.84 317	58.05 247	82.28 134	28.01 290	56.24 321	37.14 265	58.61 295	82.44 172

EU-MVSNet		55.61 268	54.41 268	59.19 274	65.41 303	33.42 314	72.44 217	71.91 224	28.81 328	51.27 292	73.87 275	24.76 310	69.08 272	43.04 236	58.20 296	75.06 261

TESTMET0.1,1		55.28 269	54.90 265	56.42 287	66.56 296	43.67 242	65.46 271	56.27 313	39.18 310	53.83 281	67.44 304	24.21 312	55.46 325	48.04 196	73.11 160	70.13 304

MIMVSNet1		55.17 270	54.31 269	57.77 283	70.03 271	32.01 319	65.68 269	64.81 270	49.19 234	46.75 308	76.00 259	25.53 307	64.04 290	28.65 314	62.13 272	77.26 240

Anonymous20231206		55.10 271	55.30 263	54.48 296	69.81 275	33.94 310	62.91 284	62.13 292	41.08 299	55.18 270	75.65 263	32.75 264	56.59 317	30.32 301	67.86 232	72.91 282

TinyColmap		54.14 272	51.72 280	61.40 265	66.84 293	41.97 252	66.52 265	68.51 249	44.81 273	42.69 322	75.77 262	11.66 334	72.94 257	31.96 286	56.77 298	69.27 308

EPMVS		53.96 273	53.69 273	54.79 295	66.12 299	31.96 320	62.34 287	49.05 331	44.42 279	55.54 264	71.33 288	30.22 278	56.70 315	41.65 247	62.54 270	75.71 256

PMMVS		53.96 273	53.26 277	56.04 288	62.60 313	50.92 153	61.17 293	56.09 314	32.81 323	53.51 286	66.84 306	34.04 248	59.93 304	44.14 226	68.18 229	57.27 327

test20.03		53.87 275	54.02 272	53.41 300	61.47 316	28.11 328	61.30 291	59.21 299	51.34 218	52.09 290	77.43 243	33.29 257	58.55 308	29.76 307	60.27 290	73.58 279

MDA-MVSNet-bldmvs		53.87 275	50.81 282	63.05 255	66.25 297	48.58 203	56.93 305	63.82 279	48.09 246	41.22 323	70.48 293	30.34 277	68.00 275	34.24 279	45.92 325	72.57 286

TDRefinement		53.44 277	50.72 283	61.60 263	64.31 308	46.96 218	70.89 238	65.27 269	41.78 294	44.61 314	77.98 224	11.52 335	66.36 282	28.57 315	51.59 312	71.49 298

test0.0.03 1		53.32 278	53.59 275	52.50 304	62.81 312	29.45 325	59.51 297	54.11 323	50.08 229	54.40 277	74.31 273	32.62 266	55.92 322	30.50 299	63.95 259	72.15 296

PatchT		53.17 279	53.44 276	52.33 305	68.29 285	25.34 336	58.21 301	54.41 321	44.46 278	54.56 275	69.05 299	33.32 256	60.94 299	36.93 266	61.76 274	70.73 303

UnsupCasMVSNet_eth		53.16 280	52.47 278	55.23 291	59.45 327	33.39 315	59.43 298	69.13 243	45.98 264	50.35 299	72.32 282	29.30 285	58.26 309	42.02 244	44.30 327	74.05 276

PM-MVS		52.33 281	50.19 284	58.75 277	62.10 314	45.14 228	65.75 268	40.38 341	43.60 284	53.52 285	72.65 280	9.16 340	65.87 286	50.41 178	54.18 306	65.24 314

testgi		51.90 282	52.37 279	50.51 309	60.39 323	23.55 339	58.42 300	58.15 302	49.03 236	51.83 291	79.21 212	22.39 315	55.59 323	29.24 311	62.64 268	72.40 292

dp		51.89 283	51.60 281	52.77 303	68.44 284	32.45 317	62.36 286	54.57 320	44.16 281	49.31 300	67.91 301	28.87 288	56.61 316	33.89 280	54.89 303	69.24 309

JIA-IIPM		51.56 284	47.68 292	63.21 253	64.61 306	50.73 159	47.71 325	58.77 301	42.90 290	48.46 302	51.72 330	24.97 309	70.24 268	36.06 275	53.89 307	68.64 310

ADS-MVSNet2		51.33 285	48.76 288	59.07 275	66.02 301	44.60 233	50.90 320	59.76 298	36.90 315	50.74 295	66.18 310	26.38 301	63.11 292	27.17 316	54.76 304	69.50 306

YYNet1		50.73 286	48.96 285	56.03 289	61.10 319	41.78 254	51.94 318	56.44 311	40.94 301	44.84 312	67.80 303	30.08 280	55.08 326	36.77 267	50.71 315	71.22 299

MDA-MVSNet_test_wron		50.71 287	48.95 286	56.00 290	61.17 318	41.84 253	51.90 319	56.45 310	40.96 300	44.79 313	67.84 302	30.04 281	55.07 327	36.71 269	50.69 316	71.11 302

UnsupCasMVSNet_bld		50.07 288	48.87 287	53.66 298	60.97 321	33.67 313	57.62 303	64.56 273	39.47 309	47.38 304	64.02 315	27.47 294	59.32 305	34.69 278	43.68 328	67.98 311

Patchmatch-test		49.08 289	48.28 289	51.50 307	64.40 307	30.85 323	45.68 328	48.46 334	35.60 319	46.10 311	72.10 283	34.47 245	46.37 334	27.08 318	60.65 284	77.27 239

LP		48.51 290	45.51 295	57.52 285	62.86 311	44.53 236	52.38 317	59.84 297	38.11 312	42.81 321	61.02 320	23.23 313	63.02 293	24.10 323	45.24 326	65.02 315

ADS-MVSNet		48.48 291	47.77 290	50.63 308	66.02 301	29.92 324	50.90 320	50.87 330	36.90 315	50.74 295	66.18 310	26.38 301	52.47 330	27.17 316	54.76 304	69.50 306

CHOSEN 280x420		47.83 292	46.36 293	52.24 306	67.37 290	49.78 189	38.91 336	43.11 340	35.00 320	43.27 320	63.30 318	28.95 286	49.19 333	36.53 271	60.80 283	57.76 326

new-patchmatchnet		47.56 293	47.73 291	47.06 314	58.81 328	9.37 347	48.78 324	59.21 299	43.28 286	44.22 315	68.66 300	25.67 306	57.20 314	31.57 295	49.35 321	74.62 270

PVSNet_0	43.31 20	47.46 294	45.64 294	52.92 302	67.60 289	44.65 232	54.06 311	54.64 319	41.59 297	46.15 309	58.75 325	30.99 273	58.66 307	32.18 285	24.81 336	55.46 328

test1235678		45.66 295	44.46 300	49.26 310	59.88 325	28.68 327	56.36 307	55.54 318	39.12 311	40.89 325	63.40 317	14.41 328	57.32 313	21.05 329	49.47 320	61.78 319

test2356		45.61 296	44.66 298	48.47 313	60.15 324	28.08 329	52.44 316	52.83 327	38.01 313	46.13 310	60.98 321	15.08 327	55.54 324	20.43 332	55.85 301	61.78 319

MVS-HIRNet		45.52 297	44.48 299	48.65 312	68.49 283	34.05 308	59.41 299	44.50 339	27.03 330	37.96 329	50.47 333	26.16 304	64.10 289	26.74 319	59.52 291	47.82 331

pmmvs3		44.92 298	41.95 303	53.86 297	52.58 334	43.55 243	62.11 288	46.90 338	26.05 332	40.63 326	60.19 323	11.08 337	57.91 310	31.83 292	46.15 324	60.11 323

testus		44.59 299	43.87 301	46.76 315	59.85 326	24.65 337	53.86 312	55.82 316	36.26 318	43.97 318	63.42 316	8.39 341	53.14 329	20.70 331	52.52 310	62.51 317

1111		44.40 300	45.00 297	42.61 321	57.55 330	17.33 344	53.82 314	57.05 308	40.78 302	44.11 316	66.57 307	13.37 329	45.77 335	22.15 325	49.58 319	64.73 316

testpf		44.11 301	45.40 296	40.26 323	60.52 322	27.34 330	33.26 338	54.33 322	45.87 268	41.08 324	60.26 322	16.46 323	59.14 306	46.09 208	50.68 317	34.31 337

LF4IMVS		42.95 302	42.26 302	45.04 317	48.30 338	32.50 316	54.80 309	48.49 333	28.03 329	40.51 327	70.16 295	9.24 339	43.89 338	31.63 293	49.18 322	58.72 324

testmv		42.25 303	40.11 306	48.66 311	53.23 332	27.02 331	56.62 306	55.74 317	37.25 314	33.10 331	59.52 324	7.78 342	56.58 318	19.61 333	38.13 332	62.40 318

FPMVS		42.18 304	41.11 304	45.39 316	58.03 329	41.01 260	49.50 322	53.81 325	30.07 327	33.71 330	64.03 313	11.69 333	52.08 331	14.01 338	55.11 302	43.09 334

ANet_high		41.38 305	37.47 309	53.11 301	39.73 344	24.45 338	56.94 304	69.69 234	47.65 250	26.04 335	52.32 329	12.44 331	62.38 296	21.80 328	10.61 344	72.49 287

no-one		40.85 306	36.09 310	55.14 292	48.55 337	38.72 274	42.15 334	62.92 286	34.60 322	23.55 336	49.74 334	12.21 332	66.16 284	26.27 321	24.84 335	60.54 322

LCM-MVSNet		40.30 307	35.88 312	53.57 299	42.24 341	29.15 326	45.21 330	60.53 296	22.23 336	28.02 334	50.98 332	3.72 348	61.78 298	31.22 298	38.76 331	69.78 305

N_pmnet		39.35 308	40.28 305	36.54 325	63.76 309	1.62 351	49.37 323	0.76 353	34.62 321	43.61 319	66.38 309	26.25 303	42.57 340	26.02 322	51.77 311	65.44 313

DSMNet-mixed		39.30 309	38.72 308	41.03 322	51.22 335	19.66 341	45.53 329	31.35 346	15.83 340	39.80 328	67.42 305	22.19 316	45.13 337	22.43 324	52.69 309	58.31 325

PMVS	28.69 22	36.22 310	33.29 314	45.02 318	36.82 346	35.98 299	54.68 310	48.74 332	26.31 331	21.02 337	51.61 331	2.88 350	60.10 303	9.99 342	47.58 323	38.99 336
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		36.16 311	35.94 311	36.83 324	50.82 336	8.52 348	44.84 331	53.49 326	32.72 324	30.11 333	55.08 327	7.11 344	49.47 332	16.60 335	32.68 334	52.50 329

.test1245		34.88 312	39.49 307	21.04 333	57.55 330	17.33 344	53.82 314	57.05 308	40.78 302	44.11 316	66.57 307	13.37 329	45.77 335	22.15 325	0.00 347	0.03 346

Gipuma		34.77 313	31.91 315	43.33 320	62.05 315	37.87 281	20.39 341	67.03 259	23.23 334	18.41 339	25.84 339	4.24 346	62.73 294	14.71 337	51.32 313	29.38 339
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		34.13 314	34.29 313	33.64 326	52.63 333	18.23 343	44.43 332	33.90 344	22.81 335	30.89 332	53.18 328	10.48 338	35.72 344	20.77 330	39.51 329	46.98 332

pcd1.5k->3k		30.06 315	30.56 316	28.55 330	78.81 113	0.00 353	0.00 344	82.07 70	0.00 348	0.00 350	0.00 350	39.61 193	0.00 349	0.00 348	74.56 137	85.66 70

wuykxyi23d		28.12 316	22.54 321	44.87 319	34.97 347	32.11 318	37.96 337	47.31 336	13.32 341	9.29 346	23.72 341	0.45 353	56.58 318	21.85 327	13.98 340	45.93 333

PNet_i23d		27.88 317	25.99 317	33.55 327	47.54 339	25.89 333	47.24 327	32.91 345	21.44 337	15.90 340	38.09 336	0.85 352	42.76 339	16.90 334	13.03 342	32.00 338

PMMVS2		27.40 318	25.91 318	31.87 329	39.46 345	6.57 349	31.17 339	28.52 347	23.96 333	20.45 338	48.94 335	4.20 347	37.94 343	16.51 336	19.97 337	51.09 330

E-PMN		23.77 319	22.73 320	26.90 331	42.02 342	20.67 340	42.66 333	35.70 342	17.43 338	10.28 344	25.05 340	6.42 345	42.39 341	10.28 341	14.71 339	17.63 340

EMVS		22.97 320	21.84 322	26.36 332	40.20 343	19.53 342	41.95 335	34.64 343	17.09 339	9.73 345	22.83 342	7.29 343	42.22 342	9.18 343	13.66 341	17.32 341

MVE	17.77 23	21.41 321	17.77 323	32.34 328	34.34 348	25.44 335	16.11 342	24.11 348	11.19 342	13.22 342	31.92 337	1.58 351	30.95 345	10.47 340	17.03 338	40.62 335
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		17.50 322	23.34 319	0.00 339	0.00 353	0.00 353	0.00 344	78.63 153	0.00 348	0.00 350	82.18 135	49.25 87	0.00 349	0.00 348	0.00 347	0.00 348

wuyk23d		13.32 323	12.52 324	15.71 334	47.54 339	26.27 332	31.06 340	1.98 352	4.93 344	5.18 347	1.94 347	0.45 353	18.54 346	6.81 345	12.83 343	2.33 344

tmp_tt		9.43 324	11.14 325	4.30 336	2.38 350	4.40 350	13.62 343	16.08 350	0.39 345	15.89 341	13.06 343	15.80 325	5.54 348	12.63 339	10.46 345	2.95 343

ab-mvs-re		6.49 325	8.65 326	0.00 339	0.00 353	0.00 353	0.00 344	0.00 354	0.00 348	0.00 350	77.89 228	0.00 356	0.00 349	0.00 348	0.00 347	0.00 348

test123		4.73 326	6.30 327	0.02 337	0.01 351	0.01 352	56.36 307	0.00 354	0.01 346	0.04 348	0.21 349	0.01 355	0.00 349	0.03 347	0.00 347	0.04 345

testmvs		4.52 327	6.03 328	0.01 338	0.01 351	0.00 353	53.86 312	0.00 354	0.01 346	0.04 348	0.27 348	0.00 356	0.00 349	0.04 346	0.00 347	0.03 346

pcd_1.5k_mvsjas		3.92 328	5.23 329	0.00 339	0.00 353	0.00 353	0.00 344	0.00 354	0.00 348	0.00 350	0.00 350	47.05 121	0.00 349	0.00 348	0.00 347	0.00 348

sosnet-low-res		0.00 329	0.00 330	0.00 339	0.00 353	0.00 353	0.00 344	0.00 354	0.00 348	0.00 350	0.00 350	0.00 356	0.00 349	0.00 348	0.00 347	0.00 348

sosnet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 353	0.00 344	0.00 354	0.00 348	0.00 350	0.00 350	0.00 356	0.00 349	0.00 348	0.00 347	0.00 348

uncertanet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 353	0.00 344	0.00 354	0.00 348	0.00 350	0.00 350	0.00 356	0.00 349	0.00 348	0.00 347	0.00 348

Regformer		0.00 329	0.00 330	0.00 339	0.00 353	0.00 353	0.00 344	0.00 354	0.00 348	0.00 350	0.00 350	0.00 356	0.00 349	0.00 348	0.00 347	0.00 348

uanet		0.00 329	0.00 330	0.00 339	0.00 353	0.00 353	0.00 344	0.00 354	0.00 348	0.00 350	0.00 350	0.00 356	0.00 349	0.00 348	0.00 347	0.00 348

test_part2					87.58 3	60.47 37				83.42 2

test_part1								86.64 4				65.59 1			90.06 3	86.78 39

test_full								86.76 3

sam_mvs1												34.74 240

sam_mvs												33.43 255

semantic-postprocess				65.40 240	71.99 249	50.80 157		69.63 236	45.71 269	60.61 219	77.93 226	36.56 229	65.99 285	55.67 147	63.50 262	79.42 220

ambc				65.13 243	63.72 310	37.07 286	47.66 326	78.78 150		54.37 278	71.42 287	11.24 336	80.94 174	45.64 214	53.85 308	77.38 236

MTGPA								80.97 98

test_post1							68.67 257				3.64 345	32.39 270	69.49 270	44.17 225

test_post											3.55 346	33.90 250	66.52 281

patchmatchnet-post											64.03 313	34.50 243	74.27 255

GG-mvs-BLEND				62.34 259	71.36 259	37.04 287	69.20 254	57.33 307		54.73 273	65.48 312	30.37 276	77.82 224	34.82 277	74.93 135	72.17 295

MTMP								17.08 349

gm-plane-assit					71.40 258	41.72 256			48.85 238		73.31 279		82.48 152	48.90 191

test9_res														75.28 14	88.31 17	83.81 138

TEST9					85.58 28	61.59 25	81.62 63	81.26 89	55.65 169	74.93 23	88.81 39	53.70 41	84.68 95

test_8					85.40 31	60.96 32	81.54 66	81.18 92	55.86 163	74.81 26	88.80 41	53.70 41	84.45 100

agg_prior2														73.09 29	87.93 24	84.33 116

agg_prior					85.04 35	59.96 41		81.04 95		74.68 27			84.04 107

TestCases				64.39 248	71.44 255	49.03 198		67.30 256	45.97 265	47.16 305	79.77 200	17.47 321	67.56 276	33.65 281	59.16 293	76.57 247

test_prior4						62.51 17	82.08 57

test_prior2							81.75 59		60.37 78	75.01 21	89.06 33	56.22 18		72.19 32	88.96 9

test_prior				76.69 44	84.20 47	57.27 72		84.88 16					86.43 54			86.38 42

旧先验2							76.08 160		45.32 270	76.55 14			65.56 287	58.75 133

新几何2							76.12 158

新几何1				70.76 173	85.66 25	61.13 30		66.43 263	44.68 275	70.29 71	86.64 61	41.29 181	75.23 250	49.72 185	81.75 68	75.93 253

旧先验1					83.04 54	53.15 126		67.52 255			87.85 48	44.08 153			80.76 73	78.03 232

无先验							79.66 89	74.30 210	48.40 243				80.78 179	53.62 158		79.03 225

原ACMM2							79.02 95

原ACMM1				74.69 73	85.39 32	59.40 46		83.42 47	51.47 216	70.27 73	86.61 63	48.61 103	86.51 52	53.85 157	87.96 23	78.16 230

test222					83.14 53	58.68 57	72.57 215	63.45 281	41.78 294	67.56 126	86.12 72	37.13 218			78.73 107	74.98 264

testdata2													72.18 261	46.95 203

segment_acmp												54.23 34

testdata				64.66 246	81.52 68	52.93 129		65.29 268	46.09 263	73.88 37	87.46 50	38.08 209	66.26 283	53.31 163	78.48 110	74.78 268

testdata1							72.65 211		60.50 75

test12				77.76 33	84.52 44	58.41 59		83.36 50		72.93 51		54.61 31	88.05 21		88.12 20	86.81 37

plane_prior7					81.41 71	55.96 94

plane_prior6					81.20 78	56.24 89						45.26 143

plane_prior5								84.01 30					87.21 33	68.16 48	80.58 76	84.65 110

plane_prior4											86.10 73

plane_prior3						56.09 91			63.92 30	69.27 96

plane_prior2							84.22 22		64.52 24

plane_prior1					81.27 76

plane_prior						56.31 85	83.58 31		63.19 37						80.48 79

n2								0.00 354

nn								0.00 354

door-mid								47.19 337

lessismore_v0				69.91 185	71.42 257	47.80 210		50.90 329		50.39 298	75.56 264	27.43 296	81.33 168	45.91 211	34.10 333	80.59 204

LGP-MVS_train				75.76 55	80.22 91	57.51 70		83.40 48	61.32 64	66.67 135	87.33 52	39.15 197	86.59 48	67.70 52	77.30 120	83.19 156

test11								83.47 45

door								47.60 335

HQP5-MVS						54.94 107

HQP-NCC					80.66 83		82.31 52		62.10 54	67.85 118

ACMP_Plane					80.66 83		82.31 52		62.10 54	67.85 118

BP-MVS														67.04 58

HQP4-MVS										67.85 118			86.93 39			84.32 117

HQP3-MVS								83.90 34							80.35 82

HQP2-MVS												45.46 137

NP-MVS					80.98 81	56.05 93					85.54 85

MDTV_nov1_ep13_2view						25.89 333	61.22 292		40.10 306	51.10 293		32.97 259		38.49 259		78.61 227

MDTV_nov1_ep13			57.00 251		72.73 237	38.26 279	65.02 277	64.73 272	44.74 274	55.46 265	72.48 281	32.61 268	70.47 267	37.47 263	67.75 234

ACMMP++_ref															74.07 144

ACMMP++															72.16 176

Test By Simon												48.33 106

ITE_SJBPF				62.09 261	66.16 298	44.55 235		64.32 274	47.36 253	55.31 268	80.34 188	19.27 320	62.68 295	36.29 274	62.39 271	79.04 224

DeepMVS_CX				12.03 335	17.97 349	10.91 346		10.60 351	7.46 343	11.07 343	28.36 338	3.28 349	11.29 347	8.01 344	9.74 346	13.89 342

High-resolution multi-view benchmark