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 10	86.21 21	62.73 12	86.09 10	85.03 19	65.51 15	83.81 2	90.51 15	63.71 3	89.23 8	81.51 1	88.44 14	85.45 92

ESAPD		80.56 2	80.98 2	79.29 7	87.27 7	60.56 40	85.71 18	86.42 7	63.28 38	83.27 5	91.83 4	64.96 1	90.47 1	76.41 13	89.67 6	86.84 42

SMA-MVS		80.28 3	80.39 4	79.95 2	86.60 14	61.95 22	86.33 6	85.75 12	62.49 53	82.20 7	92.28 1	56.53 18	89.70 5	79.85 3	91.48 1	88.19 8

APDe-MVS		80.16 4	80.59 3	78.86 21	86.64 13	60.02 44	88.12 1	86.42 7	62.94 44	82.40 6	92.12 2	59.64 8	89.76 4	78.70 6	88.32 19	86.79 44

HPM-MVS++		79.88 5	80.14 5	79.10 13	88.17 1	64.80 1	86.59 5	83.70 46	65.37 16	78.78 12	90.64 11	58.63 14	87.24 35	79.00 5	90.37 3	85.26 104

CNVR-MVS		79.84 6	79.97 6	79.45 5	87.90 2	62.17 20	84.37 26	85.03 19	66.96 6	77.58 15	90.06 25	59.47 10	89.13 10	78.67 7	89.73 4	87.03 38

SteuartSystems-ACMMP		79.48 7	79.31 7	79.98 1	83.01 62	62.18 19	87.60 3	85.83 10	66.69 11	78.03 14	90.98 8	54.26 38	90.06 2	78.42 8	89.02 9	87.69 19
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 8	79.00 8	79.13 11	84.92 48	60.32 42	83.03 43	85.33 15	62.86 47	80.17 8	90.03 26	61.76 4	88.95 12	74.21 22	88.67 13	88.12 9

ACMMP_NAP		78.77 9	78.78 9	78.74 23	85.44 36	61.04 34	83.84 34	85.16 17	62.88 46	78.10 13	91.26 7	52.51 56	88.39 16	79.34 4	90.52 2	86.78 45

NCCC		78.58 10	78.31 11	79.39 6	87.51 6	62.61 16	85.20 23	84.42 27	66.73 10	74.67 35	89.38 36	55.30 29	89.18 9	74.19 23	87.34 31	86.38 51

DeepC-MVS	69.38 2	78.56 11	78.14 14	79.83 3	83.60 56	61.62 26	84.17 30	86.85 2	63.23 39	73.84 45	90.25 23	57.68 15	89.96 3	74.62 21	89.03 8	87.89 11
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 12	78.28 12	78.90 19	84.96 44	61.41 29	84.03 32	83.82 44	59.34 121	79.37 10	89.76 32	59.84 6	87.62 32	76.69 12	86.74 39	87.68 20

MP-MVS-pluss		78.35 13	78.46 10	78.03 33	84.96 44	59.52 49	82.93 45	85.39 14	62.15 58	76.41 19	91.51 5	52.47 58	86.78 47	80.66 2	89.64 7	87.80 16
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 13	78.26 13	78.64 24	86.54 16	63.47 5	86.02 12	83.55 50	63.89 32	73.60 49	90.60 12	54.85 34	86.72 48	77.20 11	88.06 24	85.74 77
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 15	77.85 16	78.99 18	86.05 27	61.82 25	85.84 13	85.21 16	63.56 36	74.29 38	90.03 26	52.56 55	88.53 15	74.79 20	88.34 16	86.63 47

APD-MVS		78.02 16	78.04 15	77.98 34	86.44 18	60.81 37	85.52 20	84.36 28	60.61 79	79.05 11	90.30 21	55.54 28	88.32 19	73.48 31	87.03 34	84.83 116
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 17	77.65 17	79.10 13	86.71 10	62.81 10	86.29 7	84.32 29	62.82 48	73.96 40	90.50 16	53.20 51	88.35 17	74.02 24	87.05 32	86.13 63

#test#		77.83 18	77.41 20	79.10 13	86.71 10	62.81 10	85.69 19	84.32 29	61.61 67	73.96 40	90.50 16	53.20 51	88.35 17	73.68 27	87.05 32	86.13 63

ACMMPR		77.71 19	77.23 22	79.16 8	86.75 9	62.93 9	86.29 7	84.24 31	62.82 48	73.55 50	90.56 14	49.80 80	88.24 20	74.02 24	87.03 34	86.32 59

SD-MVS		77.70 20	77.62 18	77.93 35	84.47 51	61.88 24	84.55 25	83.87 42	60.37 84	79.89 9	89.38 36	54.97 31	85.58 77	76.12 14	84.94 49	86.33 57

region2R		77.67 21	77.18 23	79.15 9	86.76 8	62.95 8	86.29 7	84.16 33	62.81 50	73.30 53	90.58 13	49.90 78	88.21 21	73.78 26	87.03 34	86.29 61

zzz-MVS		77.61 22	77.36 21	78.35 27	86.08 25	63.57 2	83.37 39	80.97 108	65.13 18	75.77 22	90.88 9	48.63 108	86.66 49	77.23 9	88.17 21	84.81 117

MCST-MVS		77.48 23	77.45 19	77.54 37	86.67 12	58.36 66	83.22 41	86.93 1	56.91 153	74.91 31	88.19 50	59.15 12	87.68 31	73.67 28	87.45 30	86.57 48

HPM-MVS		77.28 24	76.85 25	78.54 25	85.00 43	60.81 37	82.91 46	85.08 18	62.57 51	73.09 56	89.97 29	50.90 74	87.48 33	75.30 15	86.85 37	87.33 33
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 25	76.63 28	79.12 12	86.15 23	60.86 36	84.71 24	84.85 23	61.98 64	73.06 57	88.88 44	53.72 45	89.06 11	68.27 54	88.04 25	87.42 28
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 26	76.56 29	79.00 16	86.32 19	62.62 14	85.83 14	83.92 38	64.55 22	72.17 67	90.01 28	47.95 117	88.01 25	71.55 40	86.74 39	86.37 54

CP-MVS		77.12 27	76.68 27	78.43 26	86.05 27	63.18 7	87.55 4	83.45 53	62.44 55	72.68 61	90.50 16	48.18 115	87.34 34	73.59 29	85.71 46	84.76 121

CSCG		76.92 28	76.75 26	77.41 39	83.96 55	59.60 48	82.95 44	86.50 6	60.78 77	75.27 25	84.83 103	60.76 5	86.56 55	67.86 59	87.87 29	86.06 66

MTAPA		76.90 29	76.42 30	78.35 27	86.08 25	63.57 2	74.92 197	80.97 108	65.13 18	75.77 22	90.88 9	48.63 108	86.66 49	77.23 9	88.17 21	84.81 117

test_prior3		76.89 30	76.96 24	76.69 50	84.20 53	57.27 78	81.75 67	84.88 21	60.37 84	75.01 27	89.06 39	56.22 23	86.43 60	72.19 35	88.96 10	86.38 51

PGM-MVS		76.77 31	76.06 32	78.88 20	86.14 24	62.73 12	82.55 54	83.74 45	61.71 65	72.45 66	90.34 20	48.48 112	88.13 22	72.32 34	86.85 37	85.78 72

MVS_0304		76.73 32	76.04 33	78.78 22	81.32 80	58.89 60	82.50 56	84.07 34	67.73 5	72.08 69	87.28 60	49.49 82	89.57 6	73.52 30	86.40 43	87.87 13

mPP-MVS		76.54 33	75.93 35	78.34 29	86.47 17	63.50 4	85.74 17	82.28 74	62.90 45	71.77 71	90.26 22	46.61 136	86.55 56	71.71 38	85.66 47	84.97 113

CANet		76.46 34	75.93 35	78.06 32	81.29 81	57.53 75	82.35 58	83.31 59	67.78 3	70.09 84	86.34 79	54.92 32	88.90 13	72.68 33	84.55 51	87.76 18

CDPH-MVS		76.31 35	75.67 39	78.22 30	85.35 39	59.14 55	81.31 77	84.02 35	56.32 169	74.05 39	88.98 42	53.34 49	87.92 27	69.23 51	88.42 15	87.59 22

train_agg		76.27 36	76.15 31	76.64 54	85.58 34	61.59 27	81.62 71	81.26 98	55.86 176	74.93 29	88.81 45	53.70 46	84.68 105	75.24 17	88.33 17	83.65 162

SR-MVS		76.13 37	75.70 38	77.40 41	85.87 29	61.20 31	85.52 20	82.19 75	59.99 96	75.10 26	90.35 19	47.66 121	86.52 57	71.64 39	82.99 60	84.47 128

agg_prior3		76.13 37	75.89 37	76.85 48	85.76 30	62.02 21	81.65 69	81.01 106	55.51 185	73.73 46	88.60 49	53.23 50	84.90 97	75.24 17	88.33 17	83.65 162

ACMMP		76.02 39	75.33 41	78.07 31	85.20 40	61.91 23	85.49 22	84.44 26	63.04 42	69.80 97	89.74 33	45.43 147	87.16 39	72.01 37	82.87 64	85.14 106
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 40	76.01 34	75.72 64	85.04 41	59.96 45	81.44 75	81.04 104	56.14 174	74.68 33	88.90 43	53.91 42	84.04 117	75.01 19	87.92 28	83.16 174

PHI-MVS		75.87 41	75.36 40	77.41 39	80.62 93	55.91 103	84.28 27	85.78 11	56.08 175	73.41 52	86.58 73	50.94 73	88.54 14	70.79 44	89.71 5	87.79 17

3Dnovator+	66.72 4	75.84 42	74.57 47	79.66 4	82.40 66	59.92 47	85.83 14	86.32 9	66.92 9	67.80 137	89.24 38	42.03 176	89.38 7	64.07 95	86.50 42	89.69 1

Regformer-2		75.63 43	74.99 43	77.54 37	80.43 95	58.32 67	79.50 102	82.92 66	67.84 1	75.94 21	80.75 198	55.73 26	86.80 45	71.44 42	80.38 85	87.50 24

DPM-MVS		75.47 44	75.00 42	76.88 45	81.38 79	59.16 54	79.94 92	85.71 13	56.59 165	72.46 64	86.76 63	56.89 16	87.86 28	66.36 70	88.91 12	83.64 164

Regformer-1		75.47 44	74.93 45	77.09 44	80.43 95	57.70 73	79.50 102	82.13 76	67.84 1	75.73 24	80.75 198	56.50 19	86.07 64	71.07 43	80.38 85	87.50 24

casdiffmvs1		75.24 46	74.76 46	76.69 50	80.32 97	55.61 109	82.80 47	83.60 48	52.54 219	76.15 20	86.48 75	59.44 11	85.78 74	69.78 47	81.70 74	88.69 2

APD-MVS_3200maxsize		74.96 47	74.39 49	76.67 53	82.20 67	58.24 68	83.67 35	83.29 60	58.41 134	73.71 47	90.14 24	45.62 142	85.99 68	69.64 49	82.85 65	85.78 72

TSAR-MVS + GP.		74.90 48	74.15 51	77.17 43	82.00 69	58.77 62	81.80 66	78.57 166	58.58 130	74.32 37	84.51 112	55.94 25	87.22 36	67.11 65	84.48 53	85.52 85

DELS-MVS		74.76 49	74.46 48	75.65 67	77.84 152	52.25 148	75.59 181	84.17 32	63.76 33	73.15 55	82.79 137	59.58 9	86.80 45	67.24 64	86.04 45	87.89 11
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 50	74.25 50	76.19 57	80.81 89	59.01 58	82.60 53	83.64 47	63.74 34	72.52 63	87.49 55	47.18 128	85.88 72	69.47 50	80.78 77	83.66 161

canonicalmvs		74.67 51	74.98 44	73.71 104	78.94 122	50.56 175	80.23 87	83.87 42	60.30 89	77.15 16	86.56 74	59.65 7	82.00 171	66.01 73	82.12 68	88.58 5

casdiffmvs		74.55 52	73.78 54	76.87 46	79.00 120	56.18 96	82.36 57	84.45 25	53.88 208	73.46 51	85.76 92	56.38 22	86.59 52	70.70 45	78.04 122	87.83 15

abl_6		74.34 53	73.50 56	76.86 47	82.43 65	60.16 43	83.48 38	81.86 82	58.81 127	73.95 42	89.86 30	41.87 179	86.62 51	67.98 57	81.23 76	83.80 155

HQP_MVS		74.31 54	73.73 55	76.06 58	81.41 77	56.31 91	84.22 28	84.01 36	64.52 24	69.27 109	86.10 83	45.26 151	87.21 37	68.16 55	80.58 81	84.65 122

HPM-MVS_fast		74.30 55	73.46 59	76.80 49	84.45 52	59.04 57	83.65 36	81.05 103	60.15 91	70.43 79	89.84 31	41.09 194	85.59 76	67.61 62	82.90 63	85.77 74

Regformer-4		74.25 56	73.48 57	76.57 55	79.75 108	56.54 90	78.54 113	81.49 90	66.93 8	73.90 43	80.30 208	53.84 44	85.98 69	69.76 48	76.84 136	87.17 35

MVS_111021_HR		74.02 57	73.46 59	75.69 66	83.01 62	60.63 39	77.29 150	78.40 175	61.18 72	70.58 78	85.97 86	54.18 40	84.00 121	67.52 63	82.98 62	82.45 186

MG-MVS		73.96 58	73.89 53	74.16 91	85.65 32	49.69 203	81.59 73	81.29 97	61.45 68	71.05 76	88.11 51	51.77 63	87.73 30	61.05 130	83.09 58	85.05 110

Regformer-3		73.89 59	73.28 61	75.71 65	79.75 108	55.48 112	78.54 113	79.93 131	66.58 12	73.62 48	80.30 208	54.87 33	84.54 108	69.09 52	76.84 136	87.10 37

alignmvs		73.86 60	73.99 52	73.45 116	78.20 141	50.50 177	78.57 111	82.43 72	59.40 119	76.57 17	86.71 67	56.42 21	81.23 184	65.84 75	81.79 70	88.62 3

MSLP-MVS++		73.77 61	73.47 58	74.66 81	83.02 61	59.29 53	82.30 63	81.88 81	59.34 121	71.59 73	86.83 62	45.94 140	83.65 127	65.09 80	85.22 48	81.06 213

HQP-MVS		73.45 62	72.80 64	75.40 71	80.66 90	54.94 115	82.31 60	83.90 40	62.10 59	67.85 132	85.54 96	45.46 145	86.93 43	67.04 66	80.35 87	84.32 130

CLD-MVS		73.33 63	72.68 65	75.29 74	78.82 124	53.33 132	78.23 119	84.79 24	61.30 71	70.41 80	81.04 185	52.41 59	87.12 40	64.61 85	82.49 67	85.41 99
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 64	72.54 66	75.62 68	77.87 151	53.64 126	79.62 100	79.61 136	61.63 66	72.02 70	82.61 142	56.44 20	85.97 70	63.99 98	79.07 110	87.25 34

UA-Net		73.13 65	72.93 63	73.76 100	83.58 57	51.66 154	78.75 106	77.66 183	67.75 4	72.61 62	89.42 34	49.82 79	83.29 134	53.61 174	83.14 57	86.32 59

EPNet		73.09 66	72.16 68	75.90 60	75.95 193	56.28 93	83.05 42	72.39 237	66.53 13	65.27 169	87.00 61	50.40 76	85.47 83	62.48 112	86.32 44	85.94 68
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
nrg030		72.96 67	73.01 62	72.84 137	75.41 200	50.24 184	80.02 90	82.89 69	58.36 136	74.44 36	86.73 65	58.90 13	80.83 191	65.84 75	74.46 150	87.44 27

CPTT-MVS		72.78 68	72.08 70	74.87 78	84.88 49	61.41 29	84.15 31	77.86 179	55.27 188	67.51 142	88.08 53	41.93 178	81.85 173	69.04 53	80.01 91	81.35 207

LPG-MVS_test		72.74 69	71.74 72	75.76 62	80.22 100	57.51 76	82.55 54	83.40 55	61.32 69	66.67 151	87.33 58	39.15 209	86.59 52	67.70 60	77.30 131	83.19 171

PAPM_NR		72.63 70	71.80 71	75.13 75	81.72 72	53.42 131	79.91 94	83.28 61	59.14 123	66.31 157	85.90 87	51.86 62	86.06 65	57.45 145	80.62 79	85.91 69

VDD-MVS		72.50 71	72.09 69	73.75 102	81.58 73	49.69 203	77.76 136	77.63 184	63.21 40	73.21 54	89.02 41	42.14 175	83.32 133	61.72 127	82.50 66	88.25 7

3Dnovator	64.47 5	72.49 72	71.39 78	75.79 61	77.70 154	58.99 59	80.66 84	83.15 63	62.24 57	65.46 166	86.59 72	42.38 174	85.52 79	59.59 140	84.72 50	82.85 180

MVS_Test		72.45 73	72.46 67	72.42 156	74.88 205	48.50 217	76.28 168	83.14 64	59.40 119	72.46 64	84.68 105	55.66 27	81.12 185	65.98 74	79.66 98	87.63 21

EI-MVSNet-Vis-set		72.42 74	71.59 73	74.91 76	78.47 134	54.02 122	77.05 154	79.33 153	65.03 20	71.68 72	79.35 232	52.75 53	84.89 98	66.46 69	74.23 153	85.83 71

ACMP	63.53 6	72.30 75	71.20 83	75.59 70	80.28 98	57.54 74	82.74 50	82.84 70	60.58 80	65.24 171	86.18 81	39.25 207	86.03 67	66.95 68	76.79 138	83.22 169
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 76	71.21 82	75.31 73	78.50 132	55.93 102	81.63 70	82.12 77	56.24 172	70.02 88	85.68 93	47.05 129	84.34 112	65.27 79	74.41 152	85.67 79

Vis-MVSNet		72.18 77	71.37 79	74.61 84	81.29 81	55.41 113	80.90 80	78.28 177	60.73 78	69.23 112	88.09 52	44.36 160	82.65 160	57.68 144	81.75 72	85.77 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		72.17 78	71.41 76	74.45 87	81.95 70	57.22 80	84.03 32	80.38 125	59.89 100	68.40 121	82.33 149	49.64 81	87.83 29	51.87 184	84.16 55	78.30 246

EPP-MVSNet		72.16 79	71.31 81	74.71 79	78.68 129	49.70 201	82.10 64	81.65 86	60.40 83	65.94 160	85.84 88	51.74 64	86.37 62	55.93 152	79.55 101	88.07 10

DP-MVS Recon		72.15 80	70.73 88	76.40 56	86.57 15	57.99 70	81.15 79	82.96 65	57.03 150	66.78 150	85.56 94	44.50 157	88.11 23	51.77 186	80.23 90	83.10 175

EI-MVSNet-UG-set		71.92 81	71.06 84	74.52 86	77.98 149	53.56 128	76.62 161	79.16 155	64.40 26	71.18 74	78.95 237	52.19 61	84.66 107	65.47 78	73.57 162	85.32 101

VDDNet		71.81 82	71.33 80	73.26 128	82.80 64	47.60 228	78.74 107	75.27 211	59.59 114	72.94 58	89.40 35	41.51 188	83.91 122	58.75 141	82.99 60	88.26 6

LFMVS		71.78 83	71.59 73	72.32 159	83.40 58	46.38 237	79.75 97	71.08 242	64.18 29	72.80 60	88.64 48	42.58 171	83.72 125	57.41 146	84.49 52	86.86 41

PAPR		71.72 84	70.82 87	74.41 88	81.20 85	51.17 158	79.55 101	83.33 58	55.81 179	66.93 149	84.61 108	50.95 72	86.06 65	55.79 155	79.20 108	86.00 67

IS-MVSNet		71.57 85	71.00 85	73.27 127	78.86 123	45.63 243	80.22 88	78.69 164	64.14 30	66.46 153	87.36 57	49.30 86	85.60 75	50.26 195	83.71 56	88.59 4

diffmvs1		71.56 86	71.40 77	72.04 161	71.20 282	48.71 215	74.80 200	77.10 193	60.84 74	71.10 75	85.28 100	52.65 54	80.01 204	70.26 46	79.35 105	87.40 29

MAR-MVS		71.51 87	70.15 95	75.60 69	81.84 71	59.39 51	81.38 76	82.90 68	54.90 195	68.08 129	78.70 238	47.73 119	85.51 80	51.68 188	84.17 54	81.88 194
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 88	70.38 92	74.88 77	78.76 126	57.15 85	82.79 48	78.48 170	51.26 241	69.49 104	83.22 133	43.99 163	83.24 135	66.06 71	79.37 102	84.23 137

PVSNet_Blended_VisFu		71.45 89	70.39 91	74.65 82	82.01 68	58.82 61	79.93 93	80.35 127	55.09 191	65.82 164	82.16 155	49.17 100	82.64 161	60.34 134	78.62 118	82.50 185

OMC-MVS		71.40 90	70.60 89	73.78 98	76.60 184	53.15 134	79.74 98	79.78 132	58.37 135	68.75 116	86.45 77	45.43 147	80.60 196	62.58 110	77.73 124	87.58 23

UniMVSNet_NR-MVSNet		71.11 91	71.00 85	71.44 173	79.20 115	44.13 255	76.02 177	82.60 71	66.48 14	68.20 124	84.60 109	56.82 17	82.82 151	54.62 165	70.43 208	87.36 32

PCF-MVS	61.88 8	70.95 92	69.49 109	75.35 72	77.63 157	55.71 105	76.04 176	81.81 84	50.30 249	69.66 98	85.40 99	52.51 56	84.89 98	51.82 185	80.24 89	85.45 92
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
114514_t		70.83 93	69.56 104	74.64 83	86.21 21	54.63 119	82.34 59	81.81 84	48.22 267	63.01 195	85.83 89	40.92 197	87.10 41	57.91 143	79.79 95	82.18 189

FIs		70.82 94	71.43 75	68.98 212	78.33 138	38.14 300	76.96 156	83.59 49	61.02 73	67.33 144	86.73 65	55.07 30	81.64 176	54.61 167	79.22 107	87.14 36

ACMM	61.98 7	70.80 95	69.73 99	74.02 92	80.59 94	58.59 64	82.68 51	82.02 80	55.46 186	67.18 146	84.39 114	38.51 214	83.17 137	60.65 131	76.10 141	80.30 226
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1neww		70.66 96	69.70 100	73.53 111	73.15 244	50.22 185	78.11 122	80.68 113	59.65 108	69.83 94	81.67 167	49.29 88	84.96 93	64.55 86	70.38 211	85.42 95

v7new		70.66 96	69.70 100	73.53 111	73.15 244	50.22 185	78.11 122	80.68 113	59.65 108	69.83 94	81.67 167	49.29 88	84.96 93	64.55 86	70.38 211	85.42 95

v6		70.66 96	69.70 100	73.53 111	73.14 247	50.21 188	78.11 122	80.67 115	59.65 108	69.82 96	81.65 169	49.29 88	84.96 93	64.55 86	70.39 210	85.42 95

UniMVSNet (Re)		70.63 99	70.20 93	71.89 163	78.55 131	45.29 245	75.94 178	82.92 66	63.68 35	68.16 126	83.59 128	53.89 43	83.49 130	53.97 170	71.12 200	86.89 40

v7		70.57 100	69.48 110	73.85 95	73.50 232	50.92 162	78.27 117	81.43 91	58.93 124	69.61 99	81.49 175	47.56 123	85.43 85	63.94 99	70.62 204	85.21 105

xiu_mvs_v2_base		70.52 101	69.75 98	72.84 137	81.21 84	55.63 108	75.11 192	78.92 159	54.92 194	69.96 91	79.68 221	47.00 133	82.09 170	61.60 129	79.37 102	80.81 221

PS-MVSNAJ		70.51 102	69.70 100	72.93 133	81.52 74	55.79 104	74.92 197	79.00 158	55.04 193	69.88 92	78.66 239	47.05 129	82.19 167	61.61 128	79.58 99	80.83 220

v1141		70.50 103	69.53 105	73.41 120	72.92 254	50.00 195	77.69 137	80.60 117	59.50 116	69.60 100	81.43 176	49.24 97	84.77 102	64.48 90	70.30 217	85.46 91

divwei89l23v2f112		70.50 103	69.53 105	73.41 120	72.91 255	50.00 195	77.69 137	80.59 118	59.50 116	69.60 100	81.43 176	49.26 93	84.77 102	64.48 90	70.31 216	85.47 89

v2v482		70.50 103	69.45 112	73.66 106	72.62 261	50.03 194	77.58 141	80.51 122	59.90 98	69.52 103	82.14 156	47.53 124	84.88 100	65.07 81	70.17 220	86.09 65

v1		70.50 103	69.53 105	73.42 119	72.91 255	50.00 195	77.69 137	80.59 118	59.50 116	69.59 102	81.42 178	49.26 93	84.77 102	64.49 89	70.30 217	85.47 89

mvs-test1		70.44 107	68.19 132	77.18 42	76.10 190	63.22 6	80.59 85	76.06 204	59.83 102	66.32 156	79.87 215	41.56 185	85.53 78	60.60 132	72.77 177	82.80 181

v1144		70.42 108	69.31 113	73.76 100	73.22 240	50.64 170	77.83 134	81.43 91	58.58 130	69.40 107	81.16 182	47.53 124	85.29 88	64.01 97	70.64 203	85.34 100

diffmvs		70.36 109	69.99 97	71.46 172	70.48 288	48.19 220	74.59 206	76.30 199	60.36 88	67.75 140	83.81 125	51.22 68	79.77 205	67.92 58	77.50 127	86.42 50

TranMVSNet+NR-MVSNet		70.36 109	70.10 96	71.17 183	78.64 130	42.97 266	76.53 163	81.16 102	66.95 7	68.53 120	85.42 98	51.61 65	83.07 140	52.32 182	69.70 232	87.46 26

v8		70.33 111	69.28 114	73.49 114	73.15 244	50.22 185	78.62 110	80.78 112	60.79 76	66.45 154	82.11 157	49.35 84	84.98 91	63.58 104	68.71 240	85.28 102

Fast-Effi-MVS+		70.28 112	69.12 117	73.73 103	78.50 132	51.50 157	75.01 194	79.46 149	56.16 173	68.59 117	79.55 228	53.97 41	84.05 116	53.34 176	77.53 126	85.65 81

X-MVStestdata		70.21 113	67.28 153	79.00 16	86.32 19	62.62 14	85.83 14	83.92 38	64.55 22	72.17 67	6.49 368	47.95 117	88.01 25	71.55 40	86.74 39	86.37 54

v10		70.21 113	69.02 118	73.81 97	73.51 231	50.92 162	78.74 107	81.39 93	60.05 93	66.39 155	81.83 164	47.58 122	85.41 86	62.80 109	68.86 239	85.09 109

QAPM		70.05 115	68.81 121	73.78 98	76.54 186	53.43 130	83.23 40	83.48 51	52.89 215	65.90 161	86.29 80	41.55 187	86.49 59	51.01 190	78.40 120	81.42 199

DU-MVS		70.01 116	69.53 105	71.44 173	78.05 147	44.13 255	75.01 194	81.51 89	64.37 27	68.20 124	84.52 110	49.12 103	82.82 151	54.62 165	70.43 208	87.37 30

AdaColmap		69.99 117	68.66 124	73.97 94	84.94 46	57.83 71	82.63 52	78.71 163	56.28 171	64.34 183	84.14 116	41.57 184	87.06 42	46.45 221	78.88 111	77.02 263

v1192		69.97 118	68.68 123	73.85 95	73.19 243	50.94 160	77.68 140	81.36 94	57.51 144	68.95 115	80.85 194	45.28 150	85.33 87	62.97 108	70.37 213	85.27 103

Anonymous20240529		69.91 119	69.02 118	72.56 146	80.19 103	47.65 227	77.56 143	80.99 107	55.45 187	69.88 92	86.76 63	39.24 208	82.18 168	54.04 169	77.10 133	87.85 14

FC-MVSNet-test		69.80 120	70.58 90	67.46 225	77.61 162	34.73 324	76.05 175	83.19 62	60.84 74	65.88 162	86.46 76	54.52 37	80.76 195	52.52 181	78.12 121	86.91 39

v144192		69.71 121	68.51 125	73.33 124	73.10 249	50.13 192	77.54 144	80.64 116	56.65 159	68.57 119	80.55 201	46.87 134	84.96 93	62.98 107	69.66 233	84.89 115

test_yl		69.69 122	69.13 115	71.36 177	78.37 136	45.74 241	74.71 202	80.20 128	57.91 139	70.01 89	83.83 122	42.44 172	82.87 147	54.97 161	79.72 96	85.48 87

DCV-MVSNet		69.69 122	69.13 115	71.36 177	78.37 136	45.74 241	74.71 202	80.20 128	57.91 139	70.01 89	83.83 122	42.44 172	82.87 147	54.97 161	79.72 96	85.48 87

VNet		69.68 124	70.19 94	68.16 220	79.73 111	41.63 276	70.53 260	77.38 189	60.37 84	70.69 77	86.63 70	51.08 70	77.09 250	53.61 174	81.69 75	85.75 76

jason		69.65 125	68.39 130	73.43 118	78.27 140	56.88 87	77.12 152	73.71 230	46.53 281	69.34 108	83.22 133	43.37 167	79.18 216	64.77 82	79.20 108	84.23 137
jason: jason.
Effi-MVS+-dtu		69.64 126	67.53 144	75.95 59	76.10 190	62.29 18	80.20 89	76.06 204	59.83 102	65.26 170	77.09 267	41.56 185	84.02 120	60.60 132	71.09 201	81.53 197

lupinMVS		69.57 127	68.28 131	73.44 117	78.76 126	57.15 85	76.57 162	73.29 232	46.19 284	69.49 104	82.18 152	43.99 163	79.23 214	64.66 83	79.37 102	83.93 147

NR-MVSNet		69.54 128	68.85 120	71.59 171	78.05 147	43.81 259	74.20 209	80.86 111	65.18 17	62.76 197	84.52 110	52.35 60	83.59 128	50.96 191	70.78 202	87.37 30

MVS_111021_LR		69.50 129	68.78 122	71.65 169	78.38 135	59.33 52	74.82 199	70.11 248	58.08 138	67.83 136	84.68 105	41.96 177	76.34 258	65.62 77	77.54 125	79.30 240

v1921920		69.47 130	68.17 133	73.36 123	73.06 250	50.10 193	77.39 146	80.56 120	56.58 166	68.59 117	80.37 204	44.72 153	84.98 91	62.47 113	69.82 228	85.00 111

test_djsdf		69.45 131	67.74 138	74.58 85	74.57 212	54.92 117	82.79 48	78.48 170	51.26 241	65.41 167	83.49 131	38.37 216	83.24 135	66.06 71	69.25 236	85.56 83

DI_MVS_plusplus_test		69.35 132	68.03 135	73.30 126	71.11 283	50.14 191	75.49 183	79.16 155	54.57 200	62.45 207	80.76 197	44.67 155	84.20 113	64.23 93	79.81 94	85.54 84

Anonymous20231211		69.28 133	68.47 127	71.73 167	80.28 98	47.18 232	79.98 91	82.37 73	54.61 197	67.24 145	84.01 120	39.43 205	82.41 165	55.45 159	72.83 176	85.62 82

EI-MVSNet		69.27 134	68.44 129	71.73 167	74.47 213	49.39 208	75.20 190	78.45 172	59.60 111	69.16 113	76.51 277	51.29 66	82.50 162	59.86 139	71.45 198	83.30 167

test_normal		69.26 135	67.90 137	73.32 125	70.84 286	50.38 180	75.30 186	79.17 154	54.23 205	62.00 215	80.61 200	44.69 154	83.89 123	64.33 92	79.95 93	85.69 78

v1240		69.24 136	67.91 136	73.25 129	73.02 252	49.82 198	77.21 151	80.54 121	56.43 168	68.34 123	80.51 202	43.33 168	84.99 89	62.03 123	69.77 231	84.95 114

IterMVS-LS		69.22 137	68.48 126	71.43 175	74.44 215	49.40 207	76.23 170	77.55 185	59.60 111	65.85 163	81.59 173	51.28 67	81.58 179	59.87 138	69.90 227	83.30 167
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet		69.02 138	69.47 111	67.69 224	77.42 165	41.00 280	74.04 210	79.68 134	60.06 92	69.26 111	84.81 104	51.06 71	77.58 244	54.44 168	74.43 151	84.48 127

v7n		69.01 139	67.36 150	73.98 93	72.51 263	52.65 140	78.54 113	81.30 96	60.26 90	62.67 199	81.62 170	43.61 165	84.49 109	57.01 147	68.70 241	84.79 119

OpenMVS	61.03 9	68.85 140	67.56 142	72.70 142	74.26 218	53.99 123	81.21 78	81.34 95	52.70 216	62.75 198	85.55 95	38.86 212	84.14 115	48.41 211	83.01 59	79.97 230

XVG-OURS-SEG-HR		68.81 141	67.47 146	72.82 139	74.40 216	56.87 88	70.59 259	79.04 157	54.77 196	66.99 148	86.01 85	39.57 204	78.21 237	62.54 111	73.33 166	83.37 166

BH-RMVSNet		68.81 141	67.42 147	72.97 132	80.11 105	52.53 143	74.26 208	76.29 200	58.48 133	68.38 122	84.20 115	42.59 170	83.83 124	46.53 220	75.91 142	82.56 182

UGNet		68.81 141	67.39 148	73.06 131	78.33 138	54.47 120	79.77 96	75.40 210	60.45 82	63.22 192	84.40 113	32.71 286	80.91 190	51.71 187	80.56 83	83.81 152
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		68.76 144	67.37 149	72.90 134	74.32 217	57.22 80	70.09 266	78.81 161	55.24 189	67.79 138	85.81 91	36.54 243	78.28 236	62.04 122	75.74 143	83.19 171

V42		68.65 145	67.35 151	72.56 146	68.93 304	50.18 189	72.90 224	79.47 148	56.92 152	69.45 106	80.26 210	46.29 138	82.99 141	64.07 95	67.82 253	84.53 125

PVSNet_Blended		68.59 146	67.72 139	71.19 182	77.03 177	50.57 173	72.51 232	81.52 87	51.91 225	64.22 187	77.77 253	49.13 101	82.87 147	55.82 153	79.58 99	80.14 229

xiu_mvs_v1_base_debu		68.58 147	67.28 153	72.48 150	78.19 142	57.19 82	75.28 187	75.09 215	51.61 233	70.04 85	81.41 179	32.79 282	79.02 224	63.81 100	77.31 128	81.22 209

xiu_mvs_v1_base		68.58 147	67.28 153	72.48 150	78.19 142	57.19 82	75.28 187	75.09 215	51.61 233	70.04 85	81.41 179	32.79 282	79.02 224	63.81 100	77.31 128	81.22 209

xiu_mvs_v1_base_debi		68.58 147	67.28 153	72.48 150	78.19 142	57.19 82	75.28 187	75.09 215	51.61 233	70.04 85	81.41 179	32.79 282	79.02 224	63.81 100	77.31 128	81.22 209

PVSNet_BlendedMVS		68.56 150	67.72 139	71.07 186	77.03 177	50.57 173	74.50 207	81.52 87	53.66 211	64.22 187	79.72 220	49.13 101	82.87 147	55.82 153	73.92 157	79.77 235

1121		68.53 151	67.16 158	72.63 143	85.64 33	61.14 32	73.95 211	66.46 279	44.61 298	70.28 82	86.68 68	41.42 189	80.78 193	53.62 172	81.79 70	75.97 271

WR-MVS		68.47 152	68.47 127	68.44 219	80.20 102	39.84 283	73.75 216	76.07 203	64.68 21	68.11 128	83.63 127	50.39 77	79.14 222	49.78 197	69.66 233	86.34 56

v16		68.38 153	67.01 159	72.47 154	73.22 240	50.29 182	78.10 125	79.59 141	59.71 106	61.72 221	77.60 259	49.28 91	82.89 145	62.36 115	61.54 295	84.23 137

v17		68.37 154	67.00 160	72.48 150	73.22 240	50.31 181	78.10 125	79.58 143	59.71 106	61.67 222	77.60 259	49.31 85	82.89 145	62.37 114	61.48 298	84.23 137

v18		68.33 155	66.96 161	72.42 156	73.13 248	50.16 190	77.97 130	79.57 145	59.57 115	61.80 219	77.50 264	49.30 86	82.90 144	62.31 116	61.50 296	84.20 143

v13		68.29 156	66.84 163	72.63 143	73.50 232	50.83 165	78.25 118	79.58 143	60.05 93	60.76 237	77.68 256	49.11 106	82.77 153	62.17 119	60.45 309	84.30 132

v12		68.28 157	66.83 165	72.60 145	73.43 234	50.74 168	78.18 120	79.59 141	60.01 95	60.89 236	77.66 257	49.12 103	82.77 153	62.18 117	60.46 308	84.29 133

V9		68.27 158	66.84 163	72.56 146	73.39 237	50.63 171	78.10 125	79.60 138	59.94 97	61.05 234	77.62 258	49.18 99	82.77 153	62.17 119	60.48 307	84.27 134

BH-untuned		68.27 158	67.29 152	71.21 181	79.74 110	53.22 133	76.06 174	77.46 188	57.19 146	66.10 158	81.61 171	45.37 149	83.50 129	45.42 236	76.68 140	76.91 267

jajsoiax		68.25 160	66.45 172	73.66 106	75.62 196	55.49 111	80.82 81	78.51 169	52.33 221	64.33 184	84.11 117	28.28 312	81.81 175	63.48 105	70.62 204	83.67 160

V14		68.25 160	66.82 166	72.52 149	73.33 238	50.53 176	78.02 128	79.60 138	59.83 102	61.16 232	77.57 261	49.19 98	82.77 153	62.18 117	60.50 306	84.26 135

v148		68.24 162	67.19 157	71.40 176	70.43 290	47.77 226	75.76 180	77.03 194	58.91 125	67.36 143	80.10 212	48.60 111	81.89 172	60.01 136	66.52 261	84.53 125

v15		68.22 163	66.81 167	72.47 154	73.25 239	50.40 179	77.92 132	79.60 138	59.77 105	61.28 230	77.52 263	49.25 95	82.77 153	62.16 121	60.51 305	84.24 136

CANet_DTU		68.18 164	67.71 141	69.59 204	74.83 206	46.24 238	78.66 109	76.85 196	59.60 111	63.45 191	82.09 158	35.25 254	77.41 246	59.88 137	78.76 115	85.14 106

mvs_tets		68.18 164	66.36 176	73.63 109	75.61 197	55.35 114	80.77 82	78.56 167	52.48 220	64.27 186	84.10 118	27.45 318	81.84 174	63.45 106	70.56 207	83.69 157

v11		68.15 166	66.73 168	72.42 156	73.43 234	50.28 183	77.94 131	79.65 135	59.88 101	61.11 233	77.55 262	48.25 114	82.75 158	61.88 126	60.85 302	84.23 137

mvs_anonymous		68.03 167	67.51 145	69.59 204	72.08 269	44.57 252	71.99 243	75.23 212	51.67 232	67.06 147	82.57 143	54.68 35	77.94 240	56.56 148	75.71 144	86.26 62

thisisatest0530		67.92 168	65.78 184	74.33 89	76.29 188	51.03 159	76.89 158	74.25 224	53.67 210	65.59 165	81.76 165	35.15 255	85.50 81	55.94 151	72.47 182	86.47 49

PAPM		67.92 168	66.69 169	71.63 170	78.09 145	49.02 211	77.09 153	81.24 100	51.04 244	60.91 235	83.98 121	47.71 120	84.99 89	40.81 266	79.32 106	80.90 219

tttt0517		67.83 170	65.66 186	74.33 89	76.69 182	50.82 166	77.86 133	73.99 227	54.54 201	64.64 181	82.53 145	35.06 256	85.50 81	55.71 156	69.91 226	86.67 46

Test4		67.77 171	65.97 181	73.19 130	68.64 305	50.58 172	74.80 200	80.48 123	54.13 206	59.11 255	79.07 236	33.89 271	83.12 139	63.61 103	79.98 92	85.87 70

VPNet		67.52 172	68.11 134	65.74 252	79.18 116	36.80 312	72.17 237	72.83 235	62.04 62	67.79 138	85.83 89	48.88 107	76.60 255	51.30 189	72.97 175	83.81 152

Fast-Effi-MVS+-dtu		67.37 173	65.33 191	73.48 115	72.94 253	57.78 72	77.47 145	76.88 195	57.60 143	61.97 216	76.85 271	39.31 206	80.49 197	54.72 164	70.28 219	82.17 190

MVS		67.37 173	66.33 177	70.51 193	75.46 199	50.94 160	73.95 211	81.85 83	41.57 322	62.54 203	78.57 243	47.98 116	85.47 83	52.97 179	82.05 69	75.14 280

v748		67.26 175	65.67 185	72.02 162	69.90 298	49.77 200	76.24 169	79.57 145	58.58 130	60.49 240	80.38 203	44.47 159	82.17 169	56.16 150	65.26 269	84.12 146

GBi-Net		67.21 176	66.55 170	69.19 209	77.63 157	43.33 262	77.31 147	77.83 180	56.62 162	65.04 174	82.70 138	41.85 180	80.33 199	47.18 215	72.76 178	83.92 148

test1		67.21 176	66.55 170	69.19 209	77.63 157	43.33 262	77.31 147	77.83 180	56.62 162	65.04 174	82.70 138	41.85 180	80.33 199	47.18 215	72.76 178	83.92 148

MVSTER		67.16 178	65.58 188	71.88 164	70.37 292	49.70 201	70.25 265	78.45 172	51.52 236	69.16 113	80.37 204	38.45 215	82.50 162	60.19 135	71.46 197	83.44 165

v52		67.09 179	65.16 194	72.87 135	66.77 318	51.60 155	73.69 217	79.45 150	57.88 141	62.46 206	78.57 243	40.95 196	83.34 131	61.99 124	64.70 274	83.68 158

V4		67.09 179	65.16 194	72.87 135	66.76 319	51.60 155	73.69 217	79.45 150	57.88 141	62.45 207	78.58 242	40.96 195	83.34 131	61.99 124	64.71 272	83.68 158

Baseline_NR-MVSNet		67.05 181	67.56 142	65.50 258	75.65 195	37.70 304	75.42 184	74.65 220	59.90 98	68.14 127	83.15 136	49.12 103	77.20 248	52.23 183	69.78 229	81.60 196

WR-MVS_H		67.02 182	66.92 162	67.33 228	77.95 150	37.75 303	77.57 142	82.11 78	62.03 63	62.65 200	82.48 146	50.57 75	79.46 210	42.91 255	64.01 278	84.79 119

anonymousdsp		67.00 183	64.82 198	73.57 110	70.09 294	56.13 97	76.35 166	77.35 190	48.43 265	64.99 177	80.84 195	33.01 279	80.34 198	64.66 83	67.64 256	84.23 137

FMVSNet2		66.93 184	66.31 179	68.79 215	77.63 157	42.98 265	76.11 172	77.47 186	56.62 162	65.22 173	82.17 154	41.85 180	80.18 202	47.05 218	72.72 181	83.20 170

BH-w/o		66.85 185	65.83 183	69.90 201	79.29 113	52.46 145	74.66 204	76.65 197	54.51 202	64.85 178	78.12 245	45.59 144	82.95 143	43.26 251	75.54 145	74.27 293

Anonymous202405211		66.84 186	65.99 180	69.40 208	80.19 103	42.21 270	71.11 254	71.31 241	58.80 128	67.90 130	86.39 78	29.83 304	79.65 207	49.60 203	78.78 114	86.33 57

CDS-MVSNet		66.80 187	65.37 189	71.10 185	78.98 121	53.13 136	73.27 220	71.07 243	52.15 223	64.72 179	80.23 211	43.56 166	77.10 249	45.48 234	78.88 111	83.05 176
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 188	65.27 192	71.33 180	79.16 118	53.67 125	73.84 215	69.59 253	52.32 222	65.28 168	81.72 166	44.49 158	77.40 247	42.32 258	78.66 117	82.92 177

FMVSNet1		66.70 189	65.87 182	69.19 209	77.49 164	43.33 262	77.31 147	77.83 180	56.45 167	64.60 182	82.70 138	38.08 221	80.33 199	46.08 225	72.31 190	83.92 148

ab-mvs		66.65 190	66.42 174	67.37 226	76.17 189	41.73 274	70.41 263	76.14 202	53.99 207	65.98 159	83.51 130	49.48 83	76.24 259	48.60 209	73.46 164	84.14 144

PEN-MVS		66.60 191	66.45 172	67.04 229	77.11 175	36.56 314	77.03 155	80.42 124	62.95 43	62.51 205	84.03 119	46.69 135	79.07 223	44.22 240	63.08 286	85.51 86

TAPA-MVS	59.36 10	66.60 191	65.20 193	70.81 188	76.63 183	48.75 214	76.52 164	80.04 130	50.64 247	65.24 171	84.93 102	39.15 209	78.54 229	36.77 284	76.88 135	85.14 106
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 193	65.07 196	71.17 183	79.18 116	49.63 205	73.48 219	75.20 213	52.95 214	67.90 130	80.33 207	39.81 201	83.68 126	43.20 252	73.56 163	80.20 227

CP-MVSNet		66.49 194	66.41 175	66.72 232	77.67 156	36.33 316	76.83 160	79.52 147	62.45 54	62.54 203	83.47 132	46.32 137	78.37 234	45.47 235	63.43 283	85.45 92

PS-CasMVS		66.42 195	66.32 178	66.70 234	77.60 163	36.30 318	76.94 157	79.61 136	62.36 56	62.43 212	83.66 126	45.69 141	78.37 234	45.35 237	63.26 284	85.42 95

FMVSNet3		66.32 196	65.61 187	68.46 218	76.48 187	42.34 269	74.98 196	77.15 192	55.83 178	65.04 174	81.16 182	39.91 199	80.14 203	47.18 215	72.76 178	82.90 179

ACMH+	57.40 11	66.12 197	64.06 200	72.30 160	77.79 153	52.83 138	80.39 86	78.03 178	57.30 145	57.47 276	82.55 144	27.68 316	84.17 114	45.54 232	69.78 229	79.90 231

testing_2		66.02 198	63.77 205	72.76 141	66.03 324	50.48 178	72.93 223	80.36 126	54.41 203	54.25 302	76.76 273	30.89 296	83.16 138	64.19 94	74.08 155	84.65 122

cascas		65.98 199	63.42 210	73.64 108	77.26 173	52.58 142	72.26 236	77.21 191	48.56 262	61.21 231	74.60 294	32.57 290	85.82 73	50.38 194	76.75 139	82.52 184

thisisatest0515		65.83 200	63.50 209	72.82 139	73.75 228	49.50 206	71.32 248	73.12 234	49.39 255	63.82 189	76.50 279	34.95 258	84.84 101	53.20 178	75.49 146	84.13 145

DP-MVS		65.68 201	63.66 207	71.75 166	84.93 47	56.87 88	80.74 83	73.16 233	53.06 213	59.09 256	82.35 148	36.79 242	85.94 71	32.82 302	69.96 225	72.45 311

HyFIR lowres test		65.67 202	63.01 214	73.67 105	79.97 107	55.65 107	69.07 274	75.52 208	42.68 316	63.53 190	77.95 247	40.43 198	81.64 176	46.01 226	71.91 193	83.73 156

DTE-MVSNet		65.58 203	65.34 190	66.31 237	76.06 192	34.79 322	76.43 165	79.38 152	62.55 52	61.66 223	83.83 122	45.60 143	79.15 221	41.64 265	60.88 301	85.00 111

GA-MVS		65.53 204	63.70 206	71.02 187	70.87 285	48.10 221	70.48 261	74.40 222	56.69 158	64.70 180	76.77 272	33.66 273	81.10 186	55.42 160	70.32 215	83.87 151

CNLPA		65.43 205	64.02 201	69.68 202	78.73 128	58.07 69	77.82 135	70.71 245	51.49 237	61.57 225	83.58 129	38.23 219	70.82 286	43.90 245	70.10 222	80.16 228

MVP-Stereo		65.41 206	63.80 204	70.22 195	77.62 161	55.53 110	76.30 167	78.53 168	50.59 248	56.47 283	78.65 240	39.84 200	82.68 159	44.10 244	72.12 192	72.44 312
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 207	62.73 218	73.40 122	74.89 204	52.78 139	73.09 222	75.13 214	55.69 181	58.48 264	73.73 299	32.86 281	86.32 63	50.63 192	70.11 221	81.10 212
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		65.24 208	64.97 197	66.04 246	72.38 264	39.40 288	72.62 230	75.63 207	55.53 184	62.35 214	83.18 135	47.45 126	76.47 256	49.06 206	66.54 260	82.24 188

ACMH	55.70 15	65.20 209	63.57 208	70.07 198	78.07 146	52.01 153	79.48 104	79.69 133	55.75 180	56.59 282	80.98 189	27.12 320	80.94 188	42.90 256	71.58 196	77.25 261
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 210	63.21 212	70.72 191	81.04 87	54.87 118	78.57 111	77.47 186	48.51 263	55.71 286	81.89 163	33.71 272	79.71 206	41.66 263	70.37 213	77.58 255
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 211	62.84 216	71.82 165	81.49 76	56.26 94	66.32 291	74.20 225	40.53 328	63.16 194	78.65 240	41.30 190	77.80 242	45.80 228	74.09 154	81.40 200

TransMVSNet (Re)		64.72 212	64.33 199	65.87 251	75.22 202	38.56 297	74.66 204	75.08 218	58.90 126	61.79 220	82.63 141	51.18 69	78.07 239	43.63 248	55.87 322	80.99 218

EG-PatchMatch MVS		64.71 213	62.87 215	70.22 195	77.68 155	53.48 129	77.99 129	78.82 160	53.37 212	56.03 285	77.41 266	24.75 334	84.04 117	46.37 222	73.42 165	73.14 304

LS3D		64.71 213	62.50 220	71.34 179	79.72 112	55.71 105	79.82 95	74.72 219	48.50 264	56.62 281	84.62 107	33.59 274	82.34 166	29.65 327	75.23 147	75.97 271

1314		64.61 215	63.21 212	68.80 214	71.87 274	47.46 229	73.95 211	78.39 176	42.88 315	59.97 243	76.60 276	38.11 220	79.39 212	54.84 163	72.32 189	79.55 236

HY-MVS	56.14 13	64.55 216	63.89 202	66.55 235	74.73 209	41.02 278	69.96 267	74.43 221	49.29 256	61.66 223	80.92 191	47.43 127	76.68 254	44.91 239	71.69 195	81.94 192

XVG-ACMP-BASELINE		64.36 217	62.23 225	70.74 190	72.35 265	52.45 146	70.80 258	78.45 172	53.84 209	59.87 245	81.10 184	16.24 346	79.32 213	55.64 158	71.76 194	80.47 223

CostFormer		64.04 218	62.51 219	68.61 217	71.88 273	45.77 240	71.30 249	70.60 246	47.55 274	64.31 185	76.61 275	41.63 183	79.62 209	49.74 199	69.00 237	80.42 224

1112_ss		64.00 219	63.36 211	65.93 249	79.28 114	42.58 268	71.35 247	72.36 238	46.41 282	60.55 239	77.89 250	46.27 139	73.28 277	46.18 223	69.97 224	81.92 193

pmmvs6		63.69 220	62.82 217	66.27 241	70.63 287	39.27 289	73.13 221	75.47 209	52.69 217	59.75 248	82.30 150	39.71 202	77.03 251	47.40 214	64.35 277	82.53 183

Vis-MVSNet (Re-imp)		63.69 220	63.88 203	63.14 274	74.75 208	31.04 345	71.16 252	63.64 302	56.32 169	59.80 247	84.99 101	44.51 156	75.46 262	39.12 273	80.62 79	82.92 177

conf200view11		63.38 222	62.41 221	66.29 240	77.31 166	38.66 294	72.65 226	69.11 260	57.07 147	62.45 207	81.03 186	37.01 233	79.17 217	31.84 306	73.25 168	81.03 214

tfpn111		63.33 223	62.34 223	66.30 238	77.31 166	38.66 294	72.65 226	69.11 260	57.07 147	62.45 207	81.03 186	37.01 233	79.23 214	31.38 315	73.09 173	81.03 214

thres400		63.31 224	62.18 226	66.72 232	76.85 180	39.62 285	71.96 244	69.44 256	56.63 160	62.61 201	79.83 216	37.18 228	79.17 217	31.84 306	73.25 168	81.36 201

thres600view7		63.30 225	62.27 224	66.41 236	77.18 174	38.87 291	72.35 234	69.11 260	56.98 151	62.37 213	80.96 190	37.01 233	79.00 227	31.43 314	73.05 174	81.36 201

thres100view900		63.28 226	62.41 221	65.89 250	77.31 166	38.66 294	72.65 226	69.11 260	57.07 147	62.45 207	81.03 186	37.01 233	79.17 217	31.84 306	73.25 168	79.83 232

test_0402		63.25 227	61.01 242	69.96 199	80.00 106	54.37 121	76.86 159	72.02 239	54.58 199	58.71 259	80.79 196	35.00 257	84.36 111	26.41 342	64.71 272	71.15 324

tfpn200view9		63.18 228	62.18 226	66.21 242	76.85 180	39.62 285	71.96 244	69.44 256	56.63 160	62.61 201	79.83 216	37.18 228	79.17 217	31.84 306	73.25 168	79.83 232

LTVRE_ROB	55.42 16	63.15 229	61.23 240	68.92 213	76.57 185	47.80 224	59.92 321	76.39 198	54.35 204	58.67 260	82.46 147	29.44 307	81.49 180	42.12 259	71.14 199	77.46 256
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		63.05 230	60.87 243	69.58 206	76.99 179	53.63 127	78.12 121	76.16 201	47.97 271	52.41 313	81.61 171	27.87 314	78.11 238	40.07 269	66.66 259	77.00 264

IterMVS		62.79 231	61.27 239	67.35 227	69.37 302	52.04 152	71.17 251	68.24 267	52.63 218	59.82 246	76.91 270	37.32 227	72.36 280	52.80 180	63.19 285	77.66 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view600		62.77 232	61.84 229	65.55 254	77.28 169	36.87 308	72.15 238	67.78 268	56.79 154	61.46 226	81.92 159	36.88 237	78.42 230	29.86 322	72.46 183	81.36 201

view800		62.77 232	61.84 229	65.55 254	77.28 169	36.87 308	72.15 238	67.78 268	56.79 154	61.46 226	81.92 159	36.88 237	78.42 230	29.86 322	72.46 183	81.36 201

conf0.05thres1000		62.77 232	61.84 229	65.55 254	77.28 169	36.87 308	72.15 238	67.78 268	56.79 154	61.46 226	81.92 159	36.88 237	78.42 230	29.86 322	72.46 183	81.36 201

tfpn		62.77 232	61.84 229	65.55 254	77.28 169	36.87 308	72.15 238	67.78 268	56.79 154	61.46 226	81.92 159	36.88 237	78.42 230	29.86 322	72.46 183	81.36 201

tpmp4_e23		62.71 236	60.13 246	70.45 194	73.40 236	48.39 218	72.82 225	69.49 255	44.88 294	59.91 244	74.99 290	37.79 223	81.47 181	40.22 268	67.71 255	81.48 198

IterMVS-SCA-FT		62.49 237	61.52 236	65.40 259	71.99 271	50.80 167	71.15 253	69.63 252	45.71 291	60.61 238	77.93 248	37.45 225	65.99 307	55.67 157	63.50 282	79.42 238

tfpnnormal		62.47 238	61.63 235	64.99 263	74.81 207	39.01 290	71.22 250	73.72 229	55.22 190	60.21 241	80.09 213	41.26 193	76.98 252	30.02 321	68.09 250	78.97 244

MS-PatchMatch		62.42 239	61.46 237	65.31 261	75.21 203	52.10 149	72.05 242	74.05 226	46.41 282	57.42 277	74.36 295	34.35 266	77.57 245	45.62 231	73.67 159	66.26 335

Test_1112_low_res		62.32 240	61.77 233	64.00 268	79.08 119	39.53 287	68.17 283	70.17 247	43.25 311	59.03 257	79.90 214	44.08 161	71.24 285	43.79 247	68.42 242	81.25 208

thres200		62.20 241	61.16 241	65.34 260	75.38 201	39.99 282	69.60 269	69.29 258	55.64 183	61.87 218	76.99 268	37.07 232	78.96 228	31.28 316	73.28 167	77.06 262

PatchFormer-LS_test		62.20 241	60.59 244	67.04 229	72.18 268	46.82 235	70.36 264	68.62 265	51.92 224	59.19 254	70.23 317	36.86 241	75.07 271	50.23 196	65.68 266	79.23 241

tpm2		62.07 243	60.10 247	67.99 221	72.79 258	43.86 258	71.05 255	66.85 277	43.14 313	62.77 196	75.39 288	38.32 217	80.80 192	41.69 262	68.88 238	79.32 239

DWT-MVSNet_test		61.90 244	59.93 248	67.83 222	71.98 272	46.09 239	71.03 256	69.71 249	50.09 250	58.51 263	70.62 314	30.21 301	77.63 243	49.28 204	67.91 251	79.78 234

EPNet_dtu		61.90 244	61.97 228	61.68 282	72.89 257	39.78 284	75.85 179	65.62 283	55.09 191	54.56 298	79.36 231	37.59 224	67.02 301	39.80 272	76.95 134	78.25 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 246	61.35 238	63.46 269	74.58 211	31.48 344	61.42 315	58.14 326	58.71 129	53.02 312	79.55 228	43.07 169	76.80 253	45.69 229	77.96 123	82.11 191

MSDG		61.81 247	59.23 250	69.55 207	72.64 260	52.63 141	70.45 262	75.81 206	51.38 239	53.70 306	76.11 281	29.52 305	81.08 187	37.70 279	65.79 265	74.93 285

SixPastTwentyTwo		61.65 248	58.80 260	70.20 197	75.80 194	47.22 231	75.59 181	69.68 251	54.61 197	54.11 303	79.26 233	27.07 321	82.96 142	43.27 250	49.79 339	80.41 225

pmmvs4		61.48 249	59.39 249	67.76 223	71.57 276	53.86 124	71.42 246	65.34 285	44.20 303	59.46 249	77.92 249	35.90 244	74.71 273	43.87 246	64.87 271	74.71 289

OurMVSNet-221017-0		61.37 250	58.63 263	69.61 203	72.05 270	48.06 222	73.93 214	72.51 236	47.23 277	54.74 295	80.92 191	21.49 341	81.24 183	48.57 210	56.22 321	79.53 237

COLMAP_ROB	52.97 17	61.27 251	58.81 253	68.64 216	74.63 210	52.51 144	78.42 116	73.30 231	49.92 253	50.96 318	81.51 174	23.06 337	79.40 211	31.63 311	65.85 263	74.01 300
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 252	61.67 234	57.70 306	70.43 290	38.45 298	64.19 305	66.47 278	48.05 270	63.22 192	80.86 193	49.28 91	60.47 323	45.25 238	67.28 258	74.19 298

SCA		60.49 253	58.38 264	66.80 231	74.14 226	48.06 222	63.35 307	63.23 305	49.13 258	59.33 253	72.10 305	37.45 225	74.27 275	44.17 241	62.57 289	78.05 250

K. test v3		60.47 254	57.11 271	70.56 192	73.74 230	48.22 219	75.10 193	62.55 310	58.27 137	53.62 308	76.31 280	27.81 315	81.59 178	47.42 213	39.18 351	81.88 194

OpenMVS_ROB	52.78 18	60.03 255	58.14 267	65.69 253	70.47 289	44.82 247	75.33 185	70.86 244	45.04 293	56.06 284	76.00 282	26.89 323	79.65 207	35.36 294	67.29 257	72.60 308

conf0.01		59.97 256	58.81 253	63.42 270	74.15 220	33.83 330	68.32 277	64.22 293	51.79 226	58.04 267	79.57 222	35.41 247	75.41 263	29.57 328	68.26 243	81.03 214

conf0.002		59.97 256	58.81 253	63.42 270	74.15 220	33.83 330	68.32 277	64.22 293	51.79 226	58.04 267	79.57 222	35.41 247	75.41 263	29.57 328	68.26 243	81.03 214

CR-MVSNet		59.91 258	57.90 269	65.96 247	69.96 296	52.07 150	65.31 299	63.15 306	42.48 317	59.36 250	74.84 291	35.83 245	70.75 287	45.50 233	64.65 275	75.06 281

PatchmatchNet		59.84 259	58.24 265	64.65 265	73.05 251	46.70 236	69.42 271	62.18 312	47.55 274	58.88 258	71.96 307	34.49 264	69.16 292	42.99 254	63.60 281	78.07 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WTY-MVS		59.75 260	60.39 245	57.85 304	72.32 266	37.83 302	61.05 319	64.18 299	45.95 289	61.91 217	79.11 235	47.01 132	60.88 322	42.50 257	69.49 235	74.83 286

CVMVSNet		59.63 261	59.14 251	61.08 292	74.47 213	38.84 292	75.20 190	68.74 264	31.15 350	58.24 265	76.51 277	32.39 291	68.58 295	49.77 198	65.84 264	75.81 274

tfpn_ndepth		59.57 262	59.02 252	61.23 287	73.81 227	35.60 320	69.40 272	65.59 284	50.96 245	57.96 273	77.72 254	34.81 259	75.91 261	30.36 319	70.57 206	72.18 317

thresconf0.02		59.40 263	58.81 253	61.17 288	74.15 220	33.83 330	68.32 277	64.22 293	51.79 226	58.04 267	79.57 222	35.41 247	75.41 263	29.57 328	68.26 243	74.25 294

tfpn_n400		59.40 263	58.81 253	61.17 288	74.15 220	33.83 330	68.32 277	64.22 293	51.79 226	58.04 267	79.57 222	35.41 247	75.41 263	29.57 328	68.26 243	74.25 294

tfpnconf		59.40 263	58.81 253	61.17 288	74.15 220	33.83 330	68.32 277	64.22 293	51.79 226	58.04 267	79.57 222	35.41 247	75.41 263	29.57 328	68.26 243	74.25 294

tfpnview11		59.40 263	58.81 253	61.17 288	74.15 220	33.83 330	68.32 277	64.22 293	51.79 226	58.04 267	79.57 222	35.41 247	75.41 263	29.57 328	68.26 243	74.25 294

tpm cat1		59.25 267	56.95 274	66.15 243	72.19 267	46.96 233	68.09 284	65.76 282	40.03 331	57.81 274	70.56 315	38.32 217	74.51 274	38.26 277	61.50 296	77.00 264

tfpn1000		59.24 268	58.70 261	60.86 293	73.75 228	33.99 328	68.86 275	63.98 300	51.25 243	57.29 278	79.51 230	34.58 261	75.26 269	29.08 335	69.99 223	73.32 303

pmmvs-eth3d		58.81 269	56.31 279	66.30 238	67.61 312	52.42 147	72.30 235	64.76 289	43.55 309	54.94 294	74.19 297	28.95 309	72.60 279	43.31 249	57.21 317	73.88 301

RPMNet		58.70 270	56.29 280	65.96 247	69.96 296	52.07 150	65.31 299	62.15 313	43.20 312	59.36 250	70.15 319	35.37 253	70.75 287	36.42 291	64.65 275	75.06 281

tpmvs		58.47 271	56.95 274	63.03 276	70.20 293	41.21 277	67.90 286	67.23 275	49.62 254	54.73 296	70.84 312	34.14 267	76.24 259	36.64 288	61.29 299	71.64 320

PVSNet	50.76 19	58.40 272	57.39 270	61.42 284	75.53 198	44.04 257	61.43 314	63.45 303	47.04 279	56.91 279	73.61 300	27.00 322	64.76 310	39.12 273	72.40 187	75.47 278

tpmrst		58.24 273	58.70 261	56.84 308	66.97 315	34.32 326	69.57 270	61.14 317	47.17 278	58.58 262	71.60 308	41.28 192	60.41 324	49.20 205	62.84 287	75.78 275

Patchmatch-RL test		58.16 274	55.49 284	66.15 243	67.92 311	48.89 213	60.66 320	51.07 351	47.86 272	59.36 250	62.71 343	34.02 269	72.27 282	56.41 149	59.40 312	77.30 258

test-LLR		58.15 275	58.13 268	58.22 301	68.57 306	44.80 248	65.46 296	57.92 327	50.08 251	55.44 289	69.82 320	32.62 287	57.44 333	49.66 201	73.62 160	72.41 313

ppachtmachnet_test		58.06 276	55.38 285	66.10 245	69.51 300	48.99 212	68.01 285	66.13 281	44.50 300	54.05 304	70.74 313	32.09 293	72.34 281	36.68 287	56.71 320	76.99 266

gg-mvs-nofinetune		57.86 277	56.43 278	62.18 280	72.62 261	35.35 321	66.57 288	56.33 335	50.65 246	57.64 275	57.10 350	30.65 297	76.36 257	37.38 281	78.88 111	74.82 287

CMPMVS	42.80 21	57.81 278	55.97 281	63.32 272	60.98 344	47.38 230	64.66 303	69.50 254	32.06 349	46.83 331	77.80 252	29.50 306	71.36 284	48.68 208	73.75 158	71.21 323
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 279	57.07 272	58.22 301	74.21 219	37.18 305	62.46 310	60.88 318	48.88 260	55.29 292	75.99 284	31.68 294	62.04 319	31.87 305	72.35 188	75.43 279

tpm		57.34 280	58.16 266	54.86 316	71.80 275	34.77 323	67.47 287	56.04 338	48.20 268	60.10 242	76.92 269	37.17 230	53.41 350	40.76 267	65.01 270	76.40 270

Patchmtry		57.16 281	56.47 277	59.23 295	69.17 303	34.58 325	62.98 308	63.15 306	44.53 299	56.83 280	74.84 291	35.83 245	68.71 294	40.03 270	60.91 300	74.39 292

AllTest		57.08 282	54.65 289	64.39 266	71.44 277	49.03 209	69.92 268	67.30 273	45.97 287	47.16 329	79.77 218	17.47 343	67.56 298	33.65 299	59.16 313	76.57 268

our_test_3		56.49 283	54.42 291	62.68 278	69.51 300	45.48 244	66.08 292	61.49 316	44.11 306	50.73 321	69.60 322	33.05 278	68.15 296	38.38 276	56.86 318	74.40 291

pmmvs5		56.47 284	55.68 283	58.86 298	61.41 341	36.71 313	66.37 290	62.75 309	40.38 329	53.70 306	76.62 274	34.56 262	67.05 300	40.02 271	65.27 268	72.83 306

test-mter		56.42 285	55.82 282	58.22 301	68.57 306	44.80 248	65.46 296	57.92 327	39.94 332	55.44 289	69.82 320	21.92 340	57.44 333	49.66 201	73.62 160	72.41 313

USDC		56.35 286	54.24 294	62.69 277	64.74 329	40.31 281	65.05 301	73.83 228	43.93 307	47.58 327	77.71 255	15.36 348	75.05 272	38.19 278	61.81 293	72.70 307

PatchMatch-RL		56.25 287	54.55 290	61.32 286	77.06 176	56.07 99	65.57 295	54.10 347	44.13 305	53.49 311	71.27 311	25.20 331	66.78 302	36.52 290	63.66 280	61.12 344

sss		56.17 288	56.57 276	54.96 315	66.93 316	36.32 317	57.94 327	61.69 315	41.67 320	58.64 261	75.32 289	38.72 213	56.25 342	42.04 260	66.19 262	72.31 316

FMVSNet5		55.86 289	54.93 287	58.66 300	71.05 284	36.35 315	64.18 306	62.48 311	46.76 280	50.66 322	74.73 293	25.80 328	64.04 312	33.11 301	65.57 267	75.59 277

RPSCF		55.80 290	54.22 295	60.53 294	65.13 328	42.91 267	64.30 304	57.62 329	36.84 341	58.05 266	82.28 151	28.01 313	56.24 343	37.14 282	58.61 315	82.44 187

EU-MVSNet		55.61 291	54.41 292	59.19 296	65.41 327	33.42 337	72.44 233	71.91 240	28.81 352	51.27 316	73.87 298	24.76 333	69.08 293	43.04 253	58.20 316	75.06 281

TESTMET0.1,1		55.28 292	54.90 288	56.42 309	66.56 320	43.67 260	65.46 296	56.27 336	39.18 334	53.83 305	67.44 328	24.21 335	55.46 347	48.04 212	73.11 172	70.13 327

MIMVSNet1		55.17 293	54.31 293	57.77 305	70.03 295	32.01 342	65.68 294	64.81 288	49.19 257	46.75 332	76.00 282	25.53 330	64.04 312	28.65 336	62.13 292	77.26 260

Anonymous20231206		55.10 294	55.30 286	54.48 318	69.81 299	33.94 329	62.91 309	62.13 314	41.08 323	55.18 293	75.65 286	32.75 285	56.59 339	30.32 320	67.86 252	72.91 305

TinyColmap		54.14 295	51.72 303	61.40 285	66.84 317	41.97 271	66.52 289	68.51 266	44.81 295	42.69 346	75.77 285	11.66 356	72.94 278	31.96 304	56.77 319	69.27 331

EPMVS		53.96 296	53.69 297	54.79 317	66.12 323	31.96 343	62.34 312	49.05 354	44.42 302	55.54 287	71.33 310	30.22 300	56.70 337	41.65 264	62.54 290	75.71 276

PMMVS		53.96 296	53.26 300	56.04 310	62.60 337	50.92 162	61.17 318	56.09 337	32.81 347	53.51 310	66.84 330	34.04 268	59.93 326	44.14 243	68.18 249	57.27 350

test20.03		53.87 298	54.02 296	53.41 322	61.47 340	28.11 351	61.30 316	59.21 322	51.34 240	52.09 314	77.43 265	33.29 277	58.55 330	29.76 326	60.27 310	73.58 302

MDA-MVSNet-bldmvs		53.87 298	50.81 305	63.05 275	66.25 321	48.58 216	56.93 330	63.82 301	48.09 269	41.22 347	70.48 316	30.34 299	68.00 297	34.24 297	45.92 346	72.57 309

TDRefinement		53.44 300	50.72 306	61.60 283	64.31 332	46.96 233	70.89 257	65.27 287	41.78 318	44.61 338	77.98 246	11.52 357	66.36 304	28.57 337	51.59 334	71.49 321

test0.0.03 1		53.32 301	53.59 298	52.50 326	62.81 336	29.45 348	59.51 322	54.11 346	50.08 251	54.40 300	74.31 296	32.62 287	55.92 344	30.50 318	63.95 279	72.15 319

PatchT		53.17 302	53.44 299	52.33 327	68.29 310	25.34 359	58.21 326	54.41 344	44.46 301	54.56 298	69.05 323	33.32 276	60.94 321	36.93 283	61.76 294	70.73 326

UnsupCasMVSNet_eth		53.16 303	52.47 301	55.23 313	59.45 351	33.39 338	59.43 323	69.13 259	45.98 286	50.35 324	72.32 304	29.30 308	58.26 331	42.02 261	44.30 348	74.05 299

PM-MVS		52.33 304	50.19 307	58.75 299	62.10 338	45.14 246	65.75 293	40.38 364	43.60 308	53.52 309	72.65 302	9.16 362	65.87 308	50.41 193	54.18 328	65.24 337

testgi		51.90 305	52.37 302	50.51 331	60.39 347	23.55 362	58.42 325	58.15 325	49.03 259	51.83 315	79.21 234	22.39 338	55.59 345	29.24 334	62.64 288	72.40 315

dp		51.89 306	51.60 304	52.77 325	68.44 309	32.45 340	62.36 311	54.57 343	44.16 304	49.31 325	67.91 325	28.87 311	56.61 338	33.89 298	54.89 325	69.24 332

JIA-IIPM		51.56 307	47.68 315	63.21 273	64.61 330	50.73 169	47.71 350	58.77 324	42.90 314	48.46 326	51.72 354	24.97 332	70.24 290	36.06 293	53.89 329	68.64 333

ADS-MVSNet2		51.33 308	48.76 311	59.07 297	66.02 325	44.60 251	50.90 345	59.76 321	36.90 339	50.74 319	66.18 334	26.38 324	63.11 314	27.17 338	54.76 326	69.50 329

YYNet1		50.73 309	48.96 308	56.03 311	61.10 343	41.78 273	51.94 343	56.44 334	40.94 325	44.84 336	67.80 327	30.08 302	55.08 348	36.77 284	50.71 336	71.22 322

MDA-MVSNet_test_wron		50.71 310	48.95 309	56.00 312	61.17 342	41.84 272	51.90 344	56.45 333	40.96 324	44.79 337	67.84 326	30.04 303	55.07 349	36.71 286	50.69 337	71.11 325

UnsupCasMVSNet_bld		50.07 311	48.87 310	53.66 320	60.97 345	33.67 336	57.62 328	64.56 291	39.47 333	47.38 328	64.02 339	27.47 317	59.32 327	34.69 296	43.68 349	67.98 334

Patchmatch-test		49.08 312	48.28 312	51.50 329	64.40 331	30.85 346	45.68 353	48.46 357	35.60 343	46.10 335	72.10 305	34.47 265	46.37 356	27.08 340	60.65 304	77.27 259

LP		48.51 313	45.51 319	57.52 307	62.86 335	44.53 254	52.38 342	59.84 320	38.11 336	42.81 345	61.02 344	23.23 336	63.02 315	24.10 345	45.24 347	65.02 338

ADS-MVSNet		48.48 314	47.77 313	50.63 330	66.02 325	29.92 347	50.90 345	50.87 353	36.90 339	50.74 319	66.18 334	26.38 324	52.47 352	27.17 338	54.76 326	69.50 329

CHOSEN 280x420		47.83 315	46.36 316	52.24 328	67.37 314	49.78 199	38.91 361	43.11 363	35.00 344	43.27 344	63.30 342	28.95 309	49.19 355	36.53 289	60.80 303	57.76 349

new-patchmatchnet		47.56 316	47.73 314	47.06 336	58.81 352	9.37 370	48.78 349	59.21 322	43.28 310	44.22 339	68.66 324	25.67 329	57.20 336	31.57 313	49.35 342	74.62 290

PVSNet_0	43.31 20	47.46 317	45.64 318	52.92 324	67.60 313	44.65 250	54.06 336	54.64 342	41.59 321	46.15 333	58.75 349	30.99 295	58.66 329	32.18 303	24.81 357	55.46 351

test1235678		45.66 318	44.46 324	49.26 332	59.88 349	28.68 350	56.36 332	55.54 341	39.12 335	40.89 349	63.40 341	14.41 350	57.32 335	21.05 351	49.47 341	61.78 342

test2356		45.61 319	44.66 322	48.47 335	60.15 348	28.08 352	52.44 341	52.83 350	38.01 337	46.13 334	60.98 345	15.08 349	55.54 346	20.43 354	55.85 323	61.78 342

MVS-HIRNet		45.52 320	44.48 323	48.65 334	68.49 308	34.05 327	59.41 324	44.50 362	27.03 354	37.96 353	50.47 357	26.16 327	64.10 311	26.74 341	59.52 311	47.82 354

pmmvs3		44.92 321	41.95 327	53.86 319	52.58 358	43.55 261	62.11 313	46.90 361	26.05 356	40.63 350	60.19 347	11.08 359	57.91 332	31.83 310	46.15 345	60.11 346

testus		44.59 322	43.87 325	46.76 337	59.85 350	24.65 360	53.86 337	55.82 339	36.26 342	43.97 342	63.42 340	8.39 363	53.14 351	20.70 353	52.52 332	62.51 340

1111		44.40 323	45.00 321	42.61 343	57.55 354	17.33 367	53.82 339	57.05 331	40.78 326	44.11 340	66.57 331	13.37 351	45.77 357	22.15 347	49.58 340	64.73 339

testpf		44.11 324	45.40 320	40.26 345	60.52 346	27.34 353	33.26 363	54.33 345	45.87 290	41.08 348	60.26 346	16.46 345	59.14 328	46.09 224	50.68 338	34.31 360

LF4IMVS		42.95 325	42.26 326	45.04 339	48.30 362	32.50 339	54.80 334	48.49 356	28.03 353	40.51 351	70.16 318	9.24 361	43.89 360	31.63 311	49.18 343	58.72 347

testmv		42.25 326	40.11 330	48.66 333	53.23 356	27.02 354	56.62 331	55.74 340	37.25 338	33.10 355	59.52 348	7.78 364	56.58 340	19.61 355	38.13 353	62.40 341

FPMVS		42.18 327	41.11 328	45.39 338	58.03 353	41.01 279	49.50 347	53.81 348	30.07 351	33.71 354	64.03 337	11.69 355	52.08 353	14.01 360	55.11 324	43.09 357

ANet_high		41.38 328	37.47 333	53.11 323	39.73 368	24.45 361	56.94 329	69.69 250	47.65 273	26.04 359	52.32 353	12.44 353	62.38 318	21.80 350	10.61 365	72.49 310

no-one		40.85 329	36.09 334	55.14 314	48.55 361	38.72 293	42.15 359	62.92 308	34.60 346	23.55 360	49.74 358	12.21 354	66.16 306	26.27 343	24.84 356	60.54 345

LCM-MVSNet		40.30 330	35.88 336	53.57 321	42.24 365	29.15 349	45.21 355	60.53 319	22.23 360	28.02 358	50.98 356	3.72 370	61.78 320	31.22 317	38.76 352	69.78 328

N_pmnet		39.35 331	40.28 329	36.54 347	63.76 333	1.62 374	49.37 348	0.76 376	34.62 345	43.61 343	66.38 333	26.25 326	42.57 362	26.02 344	51.77 333	65.44 336

DSMNet-mixed		39.30 332	38.72 332	41.03 344	51.22 359	19.66 364	45.53 354	31.35 369	15.83 364	39.80 352	67.42 329	22.19 339	45.13 359	22.43 346	52.69 331	58.31 348

PMVS	28.69 22	36.22 333	33.29 338	45.02 340	36.82 370	35.98 319	54.68 335	48.74 355	26.31 355	21.02 361	51.61 355	2.88 372	60.10 325	9.99 364	47.58 344	38.99 359
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		36.16 334	35.94 335	36.83 346	50.82 360	8.52 371	44.84 356	53.49 349	32.72 348	30.11 357	55.08 351	7.11 366	49.47 354	16.60 357	32.68 355	52.50 352

.test1245		34.88 335	39.49 331	21.04 355	57.55 354	17.33 367	53.82 339	57.05 331	40.78 326	44.11 340	66.57 331	13.37 351	45.77 357	22.15 347	0.00 368	0.03 369

Gipuma		34.77 336	31.91 339	43.33 342	62.05 339	37.87 301	20.39 366	67.03 276	23.23 358	18.41 363	25.84 363	4.24 368	62.73 316	14.71 359	51.32 335	29.38 362
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
v1.0		34.38 337	45.84 317	0.00 361	87.58 4	0.00 376	0.00 369	86.64 4	63.49 37	83.42 3	91.40 6	0.00 378	0.00 371	0.00 370	0.00 368	0.00 371

new_pmnet		34.13 338	34.29 337	33.64 348	52.63 357	18.23 366	44.43 357	33.90 367	22.81 359	30.89 356	53.18 352	10.48 360	35.72 366	20.77 352	39.51 350	46.98 355

pcd1.5k->3k		30.06 339	30.56 340	28.55 352	78.81 125	0.00 376	0.00 369	82.07 79	0.00 372	0.00 374	0.00 374	39.61 203	0.00 371	0.00 370	74.56 149	85.66 80

wuykxyi23d		28.12 340	22.54 345	44.87 341	34.97 371	32.11 341	37.96 362	47.31 359	13.32 365	9.29 370	23.72 365	0.45 375	56.58 340	21.85 349	13.98 361	45.93 356

PNet_i23d		27.88 341	25.99 341	33.55 349	47.54 363	25.89 356	47.24 352	32.91 368	21.44 361	15.90 364	38.09 360	0.85 374	42.76 361	16.90 356	13.03 363	32.00 361

PMMVS2		27.40 342	25.91 342	31.87 351	39.46 369	6.57 372	31.17 364	28.52 370	23.96 357	20.45 362	48.94 359	4.20 369	37.94 365	16.51 358	19.97 358	51.09 353

E-PMN		23.77 343	22.73 344	26.90 353	42.02 366	20.67 363	42.66 358	35.70 365	17.43 362	10.28 368	25.05 364	6.42 367	42.39 363	10.28 363	14.71 360	17.63 363

EMVS		22.97 344	21.84 346	26.36 354	40.20 367	19.53 365	41.95 360	34.64 366	17.09 363	9.73 369	22.83 366	7.29 365	42.22 364	9.18 365	13.66 362	17.32 364

MVE	17.77 23	21.41 345	17.77 347	32.34 350	34.34 372	25.44 358	16.11 367	24.11 371	11.19 366	13.22 366	31.92 361	1.58 373	30.95 367	10.47 362	17.03 359	40.62 358
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		17.50 346	23.34 343	0.00 361	0.00 377	0.00 376	0.00 369	78.63 165	0.00 372	0.00 374	82.18 152	49.25 95	0.00 371	0.00 370	0.00 368	0.00 371

wuyk23d		13.32 347	12.52 348	15.71 356	47.54 363	26.27 355	31.06 365	1.98 375	4.93 368	5.18 371	1.94 371	0.45 375	18.54 368	6.81 367	12.83 364	2.33 367

tmp_tt		9.43 348	11.14 349	4.30 358	2.38 374	4.40 373	13.62 368	16.08 373	0.39 369	15.89 365	13.06 367	15.80 347	5.54 370	12.63 361	10.46 366	2.95 366

ab-mvs-re		6.49 349	8.65 350	0.00 361	0.00 377	0.00 376	0.00 369	0.00 377	0.00 372	0.00 374	77.89 250	0.00 378	0.00 371	0.00 370	0.00 368	0.00 371

test123		4.73 350	6.30 351	0.02 359	0.01 375	0.01 375	56.36 332	0.00 377	0.01 370	0.04 372	0.21 373	0.01 377	0.00 371	0.03 369	0.00 368	0.04 368

testmvs		4.52 351	6.03 352	0.01 360	0.01 375	0.00 376	53.86 337	0.00 377	0.01 370	0.04 372	0.27 372	0.00 378	0.00 371	0.04 368	0.00 368	0.03 369

pcd_1.5k_mvsjas		3.92 352	5.23 353	0.00 361	0.00 377	0.00 376	0.00 369	0.00 377	0.00 372	0.00 374	0.00 374	47.05 129	0.00 371	0.00 370	0.00 368	0.00 371

sosnet-low-res		0.00 353	0.00 354	0.00 361	0.00 377	0.00 376	0.00 369	0.00 377	0.00 372	0.00 374	0.00 374	0.00 378	0.00 371	0.00 370	0.00 368	0.00 371

sosnet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 376	0.00 369	0.00 377	0.00 372	0.00 374	0.00 374	0.00 378	0.00 371	0.00 370	0.00 368	0.00 371

uncertanet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 376	0.00 369	0.00 377	0.00 372	0.00 374	0.00 374	0.00 378	0.00 371	0.00 370	0.00 368	0.00 371

Regformer		0.00 353	0.00 354	0.00 361	0.00 377	0.00 376	0.00 369	0.00 377	0.00 372	0.00 374	0.00 374	0.00 378	0.00 371	0.00 370	0.00 368	0.00 371

uanet		0.00 353	0.00 354	0.00 361	0.00 377	0.00 376	0.00 369	0.00 377	0.00 372	0.00 374	0.00 374	0.00 378	0.00 371	0.00 370	0.00 368	0.00 371

test0726					87.75 3	59.07 56	87.86 2	86.83 3	64.26 28	84.19 1	91.92 3	64.82 2

GSMVS																78.05 250

test_part2					87.58 4	60.47 41				83.42 3

test_part1				0.00 361		0.00 376	0.00 369	86.64 4				0.00 378	0.00 371	0.00 370	0.00 368	0.00 371

sam_mvs1												34.74 260				78.05 250

sam_mvs												33.43 275

ambc				65.13 262	63.72 334	37.07 306	47.66 351	78.78 162		54.37 301	71.42 309	11.24 358	80.94 188	45.64 230	53.85 330	77.38 257

MTGPA								80.97 108

test_post1							68.67 276				3.64 369	32.39 291	69.49 291	44.17 241

test_post											3.55 370	33.90 270	66.52 303

patchmatchnet-post											64.03 337	34.50 263	74.27 275

GG-mvs-BLEND				62.34 279	71.36 281	37.04 307	69.20 273	57.33 330		54.73 296	65.48 336	30.37 298	77.82 241	34.82 295	74.93 148	72.17 318

MTMP							86.03 11	17.08 372

gm-plane-assit					71.40 280	41.72 275			48.85 261		73.31 301		82.48 164	48.90 207

test9_res														75.28 16	88.31 20	83.81 152

TEST9					85.58 34	61.59 27	81.62 71	81.26 98	55.65 182	74.93 29	88.81 45	53.70 46	84.68 105

test_8					85.40 37	60.96 35	81.54 74	81.18 101	55.86 176	74.81 32	88.80 47	53.70 46	84.45 110

agg_prior2														73.09 32	87.93 27	84.33 129

agg_prior					85.04 41	59.96 45		81.04 104		74.68 33			84.04 117

TestCases				64.39 266	71.44 277	49.03 209		67.30 273	45.97 287	47.16 329	79.77 218	17.47 343	67.56 298	33.65 299	59.16 313	76.57 268

test_prior4						62.51 17	82.08 65

test_prior2							81.75 67		60.37 84	75.01 27	89.06 39	56.22 23		72.19 35	88.96 10

test_prior				76.69 50	84.20 53	57.27 78		84.88 21					86.43 60			86.38 51

旧先验2							76.08 173		45.32 292	76.55 18			65.56 309	58.75 141

新几何2							76.12 171

新几何1				70.76 189	85.66 31	61.13 33		66.43 280	44.68 297	70.29 81	86.64 69	41.29 191	75.23 270	49.72 200	81.75 72	75.93 273

旧先验1					83.04 60	53.15 134		67.52 272			87.85 54	44.08 161			80.76 78	78.03 253

无先验							79.66 99	74.30 223	48.40 266				80.78 193	53.62 172		79.03 243

原ACMM2							79.02 105

原ACMM1				74.69 80	85.39 38	59.40 50		83.42 54	51.47 238	70.27 83	86.61 71	48.61 110	86.51 58	53.85 171	87.96 26	78.16 248

test222					83.14 59	58.68 63	72.57 231	63.45 303	41.78 318	67.56 141	86.12 82	37.13 231			78.73 116	74.98 284

testdata2													72.18 283	46.95 219

segment_acmp												54.23 39

testdata				64.66 264	81.52 74	52.93 137		65.29 286	46.09 285	73.88 44	87.46 56	38.08 221	66.26 305	53.31 177	78.48 119	74.78 288

testdata1							72.65 226		60.50 81

test12				77.76 36	84.52 50	58.41 65		83.36 57		72.93 59		54.61 36	88.05 24		88.12 23	86.81 43

plane_prior7					81.41 77	55.96 101

plane_prior6					81.20 85	56.24 95						45.26 151

plane_prior5								84.01 36					87.21 37	68.16 55	80.58 81	84.65 122

plane_prior4											86.10 83

plane_prior3						56.09 98			63.92 31	69.27 109

plane_prior2							84.22 28		64.52 24

plane_prior1					81.27 83

plane_prior						56.31 91	83.58 37		63.19 41						80.48 84

n2								0.00 377

nn								0.00 377

door-mid								47.19 360

lessismore_v0				69.91 200	71.42 279	47.80 224		50.90 352		50.39 323	75.56 287	27.43 319	81.33 182	45.91 227	34.10 354	80.59 222

LGP-MVS_train				75.76 62	80.22 100	57.51 76		83.40 55	61.32 69	66.67 151	87.33 58	39.15 209	86.59 52	67.70 60	77.30 131	83.19 171

test11								83.47 52

door								47.60 358

HQP5-MVS						54.94 115

HQP-NCC					80.66 90		82.31 60		62.10 59	67.85 132

ACMP_Plane					80.66 90		82.31 60		62.10 59	67.85 132

BP-MVS														67.04 66

HQP4-MVS										67.85 132			86.93 43			84.32 130

HQP3-MVS								83.90 40							80.35 87

HQP2-MVS												45.46 145

NP-MVS					80.98 88	56.05 100					85.54 96

MDTV_nov1_ep13_2view						25.89 356	61.22 317		40.10 330	51.10 317		32.97 280		38.49 275		78.61 245

MDTV_nov1_ep13			57.00 273		72.73 259	38.26 299	65.02 302	64.73 290	44.74 296	55.46 288	72.48 303	32.61 289	70.47 289	37.47 280	67.75 254

ACMMP++_ref															74.07 156

ACMMP++															72.16 191

Test By Simon												48.33 113

ITE_SJBPF				62.09 281	66.16 322	44.55 253		64.32 292	47.36 276	55.31 291	80.34 206	19.27 342	62.68 317	36.29 292	62.39 291	79.04 242

DeepMVS_CX				12.03 357	17.97 373	10.91 369		10.60 374	7.46 367	11.07 367	28.36 362	3.28 371	11.29 369	8.01 366	9.74 367	13.89 365

High-resolution multi-view benchmark