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.

APDe-MVS		89.15 1	89.63 1	87.73 15	94.49 8	71.69 34	93.83 2	93.96 2	75.70 64	91.06 1	96.03 1	76.84 2	97.03 3	89.09 1	95.65 11	94.47 9

SteuartSystems-ACMMP		88.72 4	88.86 4	88.32 4	92.14 46	72.96 15	93.73 3	93.67 6	80.19 12	88.10 5	94.80 2	73.76 18	97.11 1	87.51 4	95.82 6	94.90 3
Skip Steuart: Steuart Systems R&D Blog.
region2R		87.42 12	87.20 13	88.09 6	94.63 4	73.55 7	93.03 7	93.12 18	76.73 49	84.45 32	94.52 3	69.09 47	96.70 9	84.37 19	94.83 24	94.03 20

ACMMPR		87.44 10	87.23 12	88.08 7	94.64 3	73.59 6	93.04 5	93.20 15	76.78 46	84.66 29	94.52 3	68.81 49	96.65 10	84.53 16	94.90 21	94.00 23

APD-MVS		87.44 10	87.52 9	87.19 23	94.24 15	72.39 28	91.86 21	92.83 31	73.01 102	88.58 4	94.52 3	73.36 19	96.49 17	84.26 20	95.01 19	92.70 60
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
APD-MVS_3200maxsize		85.97 30	85.88 29	86.22 41	92.69 38	69.53 60	91.93 20	92.99 24	73.54 91	85.94 13	94.51 6	65.80 69	95.61 30	83.04 30	92.51 45	93.53 41

CP-MVS		87.11 16	86.92 16	87.68 19	94.20 17	73.86 3	93.98 1	92.82 33	76.62 51	83.68 43	94.46 7	67.93 51	95.95 27	84.20 21	94.39 31	93.23 47

HFP-MVS		87.58 9	87.47 10	87.94 10	94.58 5	73.54 9	93.04 5	93.24 13	76.78 46	84.91 23	94.44 8	70.78 34	96.61 12	84.53 16	94.89 22	93.66 30

#test#		87.33 14	87.13 14	87.94 10	94.58 5	73.54 9	92.34 14	93.24 13	75.23 71	84.91 23	94.44 8	70.78 34	96.61 12	83.75 23	94.89 22	93.66 30

PGM-MVS		86.68 21	86.27 24	87.90 13	94.22 16	73.38 12	90.22 43	93.04 19	75.53 66	83.86 40	94.42 10	67.87 53	96.64 11	82.70 33	94.57 27	93.66 30

MP-MVS		87.71 8	87.64 8	87.93 12	94.36 14	73.88 2	92.71 12	92.65 36	77.57 30	83.84 41	94.40 11	72.24 28	96.28 21	85.65 7	95.30 17	93.62 37

HPM-MVS++		89.02 2	89.15 2	88.63 1	95.01 1	76.03 1	92.38 13	92.85 30	80.26 11	87.78 8	94.27 12	75.89 6	96.81 6	87.45 5	96.44 1	93.05 53

mPP-MVS		86.67 22	86.32 23	87.72 17	94.41 12	73.55 7	92.74 10	92.22 45	76.87 44	82.81 54	94.25 13	66.44 62	96.24 22	82.88 32	94.28 34	93.38 43

DeepC-MVS	79.81 2	87.08 18	86.88 18	87.69 18	91.16 56	72.32 30	90.31 41	93.94 3	77.12 38	82.82 53	94.23 14	72.13 29	97.09 2	84.83 14	95.37 14	93.65 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		87.18 15	86.91 17	88.00 8	94.42 10	73.33 13	92.78 8	92.99 24	79.14 16	83.67 44	94.17 15	67.45 55	96.60 14	83.06 28	94.50 28	94.07 18

abl_6					92.23 45	70.48 48		92.08 50		85.26 18	94.16 16	62.75 102				91.81 83

DeepPCF-MVS	80.84 1	88.10 5	88.56 5	86.73 31	92.24 44	69.03 66	89.57 54	93.39 11	77.53 33	89.79 3	94.12 17	78.98 1	96.58 16	85.66 6	95.72 7	94.58 6

HPM-MVS_fast		85.35 40	84.95 41	86.57 36	93.69 20	70.58 47	92.15 18	91.62 69	73.89 85	82.67 56	94.09 18	62.60 109	95.54 33	80.93 40	92.93 40	93.57 38

CNVR-MVS		88.93 3	89.13 3	88.33 3	94.77 2	73.82 4	90.51 35	93.00 22	80.90 8	88.06 6	94.06 19	76.43 3	96.84 5	88.48 2	95.99 2	94.34 12

SD-MVS		88.06 6	88.50 6	86.71 32	92.60 42	72.71 20	91.81 22	93.19 16	77.87 27	90.32 2	94.00 20	74.83 8	93.78 99	87.63 3	94.27 35	93.65 35

HPM-MVS		87.11 16	86.98 15	87.50 20	93.88 19	72.16 31	92.19 17	93.33 12	76.07 61	83.81 42	93.95 21	69.77 43	96.01 26	85.15 8	94.66 26	94.32 13

ACMMP		85.89 32	85.39 34	87.38 21	93.59 21	72.63 22	92.74 10	93.18 17	76.78 46	80.73 68	93.82 22	64.33 77	96.29 20	82.67 34	90.69 58	93.23 47
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
test_prior3		86.73 20	86.86 19	86.33 38	92.61 40	69.59 58	88.85 68	92.97 27	75.41 67	84.91 23	93.54 23	74.28 15	95.48 34	83.31 24	95.86 4	93.91 25

test_prior2							88.85 68		75.41 67	84.91 23	93.54 23	74.28 15		83.31 24	95.86 4

VDDNet		81.52 76	80.67 77	84.05 81	90.44 65	64.13 161	89.73 52	85.91 192	71.11 129	83.18 48	93.48 25	50.54 204	93.49 113	73.40 99	88.25 86	94.54 8

CDPH-MVS		85.76 33	85.29 38	87.17 24	93.49 23	71.08 38	88.58 78	92.42 41	68.32 168	84.61 30	93.48 25	72.32 27	96.15 24	79.00 50	95.43 13	94.28 14

NCCC		88.06 6	88.01 7	88.24 5	94.41 12	73.62 5	91.22 28	92.83 31	81.50 5	85.79 15	93.47 27	73.02 22	97.00 4	84.90 11	94.94 20	94.10 17

3Dnovator+	77.84 4	85.48 36	84.47 43	88.51 2	91.08 57	73.49 11	93.18 4	93.78 5	80.79 9	76.66 127	93.37 28	60.40 147	96.75 8	77.20 64	93.73 38	95.29 1

DeepC-MVS_fast	79.65 3	86.91 19	86.62 20	87.76 14	93.52 22	72.37 29	91.26 25	93.04 19	76.62 51	84.22 37	93.36 29	71.44 32	96.76 7	80.82 42	95.33 15	94.16 15
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		83.01 58	82.36 58	84.96 57	91.02 58	66.40 109	88.91 66	88.11 165	77.57 30	84.39 35	93.29 30	52.19 195	93.91 90	77.05 66	88.70 78	94.57 7

UA-Net		85.08 43	84.96 40	85.45 48	92.07 47	68.07 90	89.78 50	90.86 89	82.48 2	84.60 31	93.20 31	69.35 45	95.22 45	71.39 121	90.88 57	93.07 52

agg_prior1		86.22 28	86.09 28	86.62 34	92.85 35	71.94 32	88.59 77	91.78 64	68.96 160	84.41 33	93.18 32	74.94 7	94.93 56	84.75 15	95.33 15	93.01 56

TEST9					93.26 27	72.96 15	88.75 72	91.89 58	68.44 167	85.00 21	93.10 33	74.36 14	95.41 39

train_agg		86.43 24	86.20 25	87.13 25	93.26 27	72.96 15	88.75 72	91.89 58	68.69 163	85.00 21	93.10 33	74.43 11	95.41 39	84.97 9	95.71 8	93.02 54

test_8					93.13 29	72.57 24	88.68 75	91.84 61	68.69 163	84.87 27	93.10 33	74.43 11	95.16 47

LFMVS		81.82 72	81.23 71	83.57 91	91.89 50	63.43 174	89.84 47	81.85 226	77.04 41	83.21 47	93.10 33	52.26 194	93.43 118	71.98 115	89.95 67	93.85 28

旧先验1					91.96 48	65.79 120		86.37 186			93.08 37	69.31 46			92.74 42	88.74 169

testdata				79.97 182	90.90 60	64.21 159		84.71 200	59.27 240	85.40 16	92.91 38	62.02 122	89.08 207	68.95 134	91.37 53	86.63 218

MCST-MVS		87.37 13	87.25 11	87.73 15	94.53 7	72.46 27	89.82 48	93.82 4	73.07 101	84.86 28	92.89 39	76.22 4	96.33 19	84.89 13	95.13 18	94.40 10

Vis-MVSNet		83.46 50	82.80 54	85.43 49	90.25 68	68.74 76	90.30 42	90.13 115	76.33 58	80.87 67	92.89 39	61.00 137	94.20 77	72.45 109	90.97 55	93.35 45
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
agg_prior3		86.16 29	85.85 31	87.10 26	93.31 24	72.86 19	88.77 71	91.68 68	68.29 169	84.26 36	92.83 41	72.83 23	95.42 38	84.97 9	95.71 8	93.02 54

CPTT-MVS		83.73 47	83.33 48	84.92 60	93.28 26	70.86 43	92.09 19	90.38 101	68.75 162	79.57 73	92.83 41	60.60 143	93.04 133	80.92 41	91.56 51	90.86 103

3Dnovator	76.31 5	83.38 53	82.31 59	86.59 35	87.94 123	72.94 18	90.64 33	92.14 49	77.21 36	75.47 139	92.83 41	58.56 154	94.72 66	73.24 100	92.71 43	92.13 77

MSLP-MVS		85.43 38	85.76 32	84.45 69	91.93 49	70.24 49	90.71 32	92.86 29	77.46 35	84.22 37	92.81 44	67.16 58	92.94 135	80.36 45	94.35 33	90.16 133

MG-MVS		83.41 51	83.45 46	83.28 98	92.74 37	62.28 192	88.17 93	89.50 131	75.22 72	81.49 61	92.74 45	66.75 59	95.11 49	72.85 102	91.58 50	92.45 67

QAPM		80.88 83	79.50 95	85.03 55	88.01 122	68.97 70	91.59 23	92.00 52	66.63 182	75.15 151	92.16 46	57.70 159	95.45 36	63.52 168	88.76 77	90.66 112

IS-MVSNet		83.15 54	82.81 53	84.18 77	89.94 73	63.30 176	91.59 23	88.46 163	79.04 20	79.49 74	92.16 46	65.10 73	94.28 73	67.71 140	91.86 48	94.95 2

liao		80.84 84	79.77 88	84.05 81	93.11 31	70.78 44	84.66 176	85.42 195	57.37 253	81.76 60	92.02 48	63.41 84	94.12 80	67.28 144	92.93 40	87.26 204

新几何1				83.42 93	93.13 29	70.71 45		85.48 194	57.43 252	81.80 59	91.98 49	63.28 86	92.27 151	64.60 166	92.99 39	87.27 203

OpenMVS	72.83 10	79.77 112	78.33 124	84.09 79	85.17 161	69.91 53	90.57 34	90.97 86	66.70 178	72.17 176	91.91 50	54.70 178	93.96 85	61.81 184	90.95 56	88.41 185

PHI-MVS		86.43 24	86.17 27	87.24 22	90.88 61	70.96 40	92.27 16	94.07 1	72.45 113	85.22 19	91.90 51	69.47 44	96.42 18	83.28 26	95.94 3	94.35 11

VNet		82.21 65	82.41 56	81.62 155	90.82 62	60.93 198	84.47 181	89.78 125	76.36 57	84.07 39	91.88 52	64.71 76	90.26 192	70.68 122	88.89 74	93.66 30

XVG-OURS-SEG-HR		80.81 87	79.76 89	83.96 85	85.60 158	68.78 73	83.54 199	90.50 98	70.66 135	76.71 126	91.66 53	60.69 140	91.26 173	76.94 67	81.58 141	91.83 81

EPNet		83.72 48	82.92 52	86.14 43	84.22 175	69.48 61	91.05 30	85.27 196	81.30 6	76.83 124	91.65 54	66.09 65	95.56 32	76.00 74	93.85 37	93.38 43
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OMC-MVS		82.69 60	81.97 65	84.85 61	88.75 103	67.42 97	87.98 96	90.87 88	74.92 74	79.72 72	91.65 54	62.19 120	93.96 85	75.26 85	86.42 101	93.16 50

test222					91.50 53	68.26 86	84.16 190	83.20 213	54.63 264	79.74 71	91.63 56	58.97 152			91.42 52	86.77 214

MVS_111021_HR		85.14 42	84.75 42	86.32 40	91.65 52	72.70 21	85.98 157	90.33 106	76.11 60	82.08 58	91.61 57	71.36 33	94.17 79	81.02 39	92.58 44	92.08 78

原ACMM1				84.35 72	93.01 33	68.79 72		92.44 38	63.96 206	81.09 65	91.57 58	66.06 66	95.45 36	67.19 146	94.82 25	88.81 166

LPG-MVS_test		82.08 66	81.27 70	84.50 67	89.23 91	68.76 74	90.22 43	91.94 56	75.37 69	76.64 128	91.51 59	54.29 181	94.91 58	78.44 55	83.78 121	89.83 148

LGP-MVS_train				84.50 67	89.23 91	68.76 74		91.94 56	75.37 69	76.64 128	91.51 59	54.29 181	94.91 58	78.44 55	83.78 121	89.83 148

XVG-OURS		80.41 94	79.23 105	83.97 84	85.64 157	69.02 67	83.03 202	90.39 100	71.09 130	77.63 111	91.49 61	54.62 180	91.35 171	75.71 79	83.47 123	91.54 87

alignmvs		85.48 36	85.32 36	85.96 45	89.51 79	69.47 62	89.74 51	92.47 37	76.17 59	87.73 9	91.46 62	70.32 37	93.78 99	81.51 37	88.95 73	94.63 5

nrg030		83.88 45	83.53 45	84.96 57	86.77 147	69.28 65	90.46 38	92.67 34	74.79 75	82.95 50	91.33 63	72.70 24	93.09 130	80.79 43	79.28 162	92.50 66

canonicalmvs		85.91 31	85.87 30	86.04 44	89.84 75	69.44 64	90.45 39	93.00 22	76.70 50	88.01 7	91.23 64	73.28 20	93.91 90	81.50 38	88.80 76	94.77 4

EPP-MVSNet		83.40 52	83.02 51	84.57 65	90.13 69	64.47 155	92.32 15	90.73 90	74.45 78	79.35 76	91.10 65	69.05 48	95.12 48	72.78 103	87.22 93	94.13 16

TAPA-MVS	73.13 9	79.15 125	77.94 130	82.79 127	89.59 78	62.99 185	88.16 94	91.51 73	65.77 189	77.14 122	91.09 66	60.91 138	93.21 121	50.26 244	87.05 95	92.17 76
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CSCG		86.41 26	86.19 26	87.07 27	92.91 34	72.48 26	90.81 31	93.56 7	73.95 84	83.16 49	91.07 67	75.94 5	95.19 46	79.94 49	94.38 32	93.55 39

FIs		82.07 67	82.42 55	81.04 168	88.80 101	58.34 218	88.26 91	93.49 8	76.93 43	78.47 87	91.04 68	69.92 41	92.34 150	69.87 129	84.97 111	92.44 68

MVS_111021_LR		82.61 62	82.11 60	84.11 78	88.82 99	71.58 35	85.15 169	86.16 189	74.69 76	80.47 69	91.04 68	62.29 117	90.55 190	80.33 46	90.08 65	90.20 132

DP-MVS Recon		83.11 56	82.09 61	86.15 42	94.44 9	70.92 42	88.79 70	92.20 46	70.53 136	79.17 77	91.03 70	64.12 79	96.03 25	68.39 139	90.14 64	91.50 89

HQP_MVS		83.64 49	83.14 49	85.14 53	90.08 70	68.71 77	91.25 26	92.44 38	79.12 18	78.92 80	91.00 71	60.42 145	95.38 41	78.71 53	86.32 102	91.33 92

plane_prior4											91.00 71

FC-MVSNet-test		81.52 76	82.02 63	80.03 181	88.42 113	55.97 246	87.95 98	93.42 10	77.10 39	77.38 114	90.98 73	69.96 40	91.79 157	68.46 138	84.50 116	92.33 70

Vis-MVSNet (Re-imp)		78.36 137	78.45 118	78.07 204	88.64 105	51.78 262	86.70 142	79.63 244	74.14 82	75.11 152	90.83 74	61.29 132	89.75 198	58.10 212	91.60 49	92.69 62

114514_t		80.68 89	79.51 94	84.20 76	94.09 18	67.27 101	89.64 53	91.11 84	58.75 244	74.08 160	90.72 75	58.10 157	95.04 54	69.70 130	89.42 71	90.30 130

PAPM_NR		83.02 57	82.41 56	84.82 62	92.47 43	66.37 110	87.93 100	91.80 62	73.82 86	77.32 116	90.66 76	67.90 52	94.90 60	70.37 125	89.48 70	93.19 49

LS3D		76.95 169	74.82 175	83.37 96	90.45 64	67.36 100	89.15 63	86.94 179	61.87 224	69.52 206	90.61 77	51.71 199	94.53 69	46.38 258	86.71 99	88.21 187

VPNet		78.69 133	78.66 113	78.76 196	88.31 115	55.72 247	84.45 183	86.63 182	76.79 45	78.26 98	90.55 78	59.30 150	89.70 199	66.63 149	77.05 179	90.88 102

ACMP	74.13 6	81.51 78	80.57 78	84.36 71	89.42 81	68.69 80	89.97 46	91.50 75	74.46 77	75.04 154	90.41 79	53.82 186	94.54 68	77.56 60	82.91 127	89.86 147
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PCF-MVS	73.52 7	80.38 98	78.84 111	85.01 56	87.71 130	68.99 69	83.65 196	91.46 76	63.00 211	77.77 109	90.28 80	66.10 64	95.09 53	61.40 187	88.22 87	90.94 101
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
NP-MVS					89.62 77	68.32 85					90.24 81

HQP-MVS		82.61 62	82.02 63	84.37 70	89.33 83	66.98 104	89.17 59	92.19 47	76.41 53	77.23 119	90.23 82	60.17 148	95.11 49	77.47 61	85.99 106	91.03 97

PS-MVSNAJss		82.07 67	81.31 69	84.34 73	86.51 149	67.27 101	89.27 57	91.51 73	71.75 121	79.37 75	90.22 83	63.15 90	94.27 74	77.69 59	82.36 135	91.49 90

TSAR-MVS		85.71 34	85.33 35	86.84 29	91.34 54	72.50 25	89.07 64	87.28 176	76.41 53	85.80 14	90.22 83	74.15 17	95.37 43	81.82 36	91.88 47	92.65 63

Regformer-1		86.41 26	86.33 22	86.64 33	89.33 83	70.93 41	88.43 80	91.39 77	82.14 3	86.65 12	90.09 85	74.39 13	95.01 55	83.97 22	90.63 59	93.97 24

Regformer-2		86.63 23	86.53 21	86.95 28	89.33 83	71.24 37	88.43 80	92.05 51	82.50 1	86.88 11	90.09 85	74.45 10	95.61 30	84.38 18	90.63 59	94.01 22

Regformer-3		85.23 41	85.07 39	85.70 47	88.95 95	69.01 68	88.29 89	89.91 123	80.95 7	85.01 20	90.01 87	72.45 26	94.19 78	82.50 35	87.57 89	93.90 27

Regformer-4		85.68 35	85.45 33	86.35 37	88.95 95	69.67 57	88.29 89	91.29 79	81.73 4	85.36 17	90.01 87	72.62 25	95.35 44	83.28 26	87.57 89	94.03 20

TranMVSNet+NR-MVSNet		80.84 84	80.31 82	82.42 134	87.85 125	62.33 190	87.74 103	91.33 78	80.55 10	77.99 105	89.86 89	65.23 72	92.62 140	67.05 148	75.24 211	92.30 71

BH-RMVSNet		79.61 114	78.44 120	83.14 104	89.38 82	65.93 116	84.95 172	87.15 177	73.56 90	78.19 100	89.79 90	56.67 166	93.36 119	59.53 200	86.74 98	90.13 135

AdaColmap		80.58 92	79.42 96	84.06 80	93.09 32	68.91 71	89.36 56	88.97 151	69.27 154	75.70 138	89.69 91	57.20 163	95.77 28	63.06 172	88.41 85	87.50 199

ACMM	73.20 8	80.78 88	79.84 87	83.58 90	89.31 88	68.37 84	89.99 45	91.60 70	70.28 139	77.25 117	89.66 92	53.37 188	93.53 112	74.24 92	82.85 128	88.85 165
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CNLPA		78.08 143	76.79 149	81.97 142	90.40 66	71.07 39	87.59 106	84.55 202	66.03 188	72.38 175	89.64 93	57.56 160	86.04 230	59.61 198	83.35 124	88.79 167

EI-MVSNet-Vis-set		84.19 44	83.81 44	85.31 50	88.18 117	67.85 92	87.66 104	89.73 127	80.05 14	82.95 50	89.59 94	70.74 36	94.82 63	80.66 44	84.72 115	93.28 46

PAPR		81.66 74	80.89 75	83.99 83	90.27 67	64.00 165	86.76 141	91.77 66	68.84 161	77.13 123	89.50 95	67.63 54	94.88 61	67.55 141	88.52 83	93.09 51

jajsoiax		79.29 123	77.96 129	83.27 99	84.68 168	66.57 108	89.25 58	90.16 114	69.20 156	75.46 140	89.49 96	45.75 227	93.13 128	76.84 69	80.80 147	90.11 136

MVSFormer		82.85 59	82.05 62	85.24 52	87.35 137	70.21 50	90.50 36	90.38 101	68.55 165	81.32 62	89.47 97	61.68 123	93.46 114	78.98 51	90.26 62	92.05 79

jason		81.39 79	80.29 83	84.70 64	86.63 148	69.90 54	85.95 158	86.77 180	63.24 208	81.07 66	89.47 97	61.08 136	92.15 153	78.33 58	90.07 66	92.05 79
jason: jason.
mvs_tets		79.13 126	77.77 135	83.22 101	84.70 167	66.37 110	89.17 59	90.19 113	69.38 152	75.40 143	89.46 99	44.17 233	93.15 126	76.78 70	80.70 149	90.14 134

UGNet		80.83 86	79.59 90	84.54 66	88.04 120	68.09 89	89.42 55	88.16 164	76.95 42	76.22 131	89.46 99	49.30 209	93.94 87	68.48 137	90.31 61	91.60 85
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
VPA-MVSNet		80.60 90	80.55 79	80.76 172	88.07 119	60.80 201	86.86 135	91.58 71	75.67 65	80.24 70	89.45 101	63.34 85	90.25 193	70.51 124	79.22 163	91.23 95

MVS_Test		83.15 54	83.06 50	83.41 95	86.86 144	63.21 179	86.11 155	92.00 52	74.31 79	82.87 52	89.44 102	70.03 39	93.21 121	77.39 63	88.50 84	93.81 29

EI-MVSNet-UG-set		83.81 46	83.38 47	85.09 54	87.87 124	67.53 96	87.44 115	89.66 128	79.74 15	82.23 57	89.41 103	70.24 38	94.74 65	79.95 48	83.92 120	92.99 57

RPSCF		73.23 200	71.46 199	78.54 199	82.50 224	59.85 206	82.18 206	82.84 217	58.96 241	71.15 187	89.41 103	45.48 229	84.77 237	58.82 205	71.83 233	91.02 99

UniMVSNet_NR-MVSNet		81.88 70	81.54 68	82.92 117	88.46 111	63.46 172	87.13 126	92.37 42	80.19 12	78.38 91	89.14 105	71.66 31	93.05 131	70.05 126	76.46 194	92.25 73

DU-MVS		81.12 81	80.52 80	82.90 118	87.80 127	63.46 172	87.02 131	91.87 60	79.01 21	78.38 91	89.07 106	65.02 74	93.05 131	70.05 126	76.46 194	92.20 74

NR-MVSNet		80.23 102	79.38 97	82.78 128	87.80 127	63.34 175	86.31 150	91.09 85	79.01 21	72.17 176	89.07 106	67.20 57	92.81 138	66.08 154	75.65 203	92.20 74

DELS-MVS		85.41 39	85.30 37	85.77 46	88.49 109	67.93 91	85.52 167	93.44 9	78.70 23	83.63 46	89.03 108	74.57 9	95.71 29	80.26 47	94.04 36	93.66 30
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
DP-MVS		76.78 171	74.57 177	83.42 93	93.29 25	69.46 63	88.55 79	83.70 209	63.98 205	70.20 194	88.89 109	54.01 185	94.80 64	46.66 255	81.88 139	86.01 226

ab-mvs		79.51 116	78.97 110	81.14 166	88.46 111	60.91 199	83.84 194	89.24 140	70.36 138	79.03 78	88.87 110	63.23 89	90.21 194	65.12 160	82.57 133	92.28 72

PEN-MVS		77.73 152	77.69 137	77.84 206	87.07 142	53.91 255	87.91 101	91.18 82	77.56 32	73.14 166	88.82 111	61.23 133	89.17 206	59.95 195	72.37 229	90.43 126

test_djsdf		80.30 100	79.32 99	83.27 99	83.98 190	65.37 129	90.50 36	90.38 101	68.55 165	76.19 132	88.70 112	56.44 167	93.46 114	78.98 51	80.14 157	90.97 100

PAPM		77.68 154	76.40 152	81.51 158	87.29 139	61.85 195	83.78 195	89.59 129	64.74 196	71.23 186	88.70 112	62.59 110	93.66 109	52.66 236	87.03 96	89.01 161

DTE-MVSNet		76.99 168	76.80 148	77.54 211	86.24 151	53.06 259	87.52 112	90.66 93	77.08 40	72.50 172	88.67 114	60.48 144	89.52 201	57.33 218	70.74 239	90.05 143

PS-CasMVS		78.01 145	78.09 127	77.77 208	87.71 130	54.39 253	88.02 95	91.22 80	77.50 34	73.26 164	88.64 115	60.73 139	88.41 216	61.88 182	73.88 222	90.53 122

cdsmvs_eth3d_5k		19.96 278	26.61 278	0.00 292	0.00 309	0.00 309	0.00 298	89.26 139	0.00 303	0.00 304	88.61 116	61.62 125	0.00 301	0.00 300	0.00 303	0.00 301

lupinMVS		81.39 79	80.27 84	84.76 63	87.35 137	70.21 50	85.55 165	86.41 184	62.85 214	81.32 62	88.61 116	61.68 123	92.24 152	78.41 57	90.26 62	91.83 81

F-COLMAP		76.38 175	74.33 181	82.50 133	89.28 89	66.95 106	88.41 83	89.03 143	64.05 203	66.83 233	88.61 116	46.78 220	92.89 136	57.48 215	78.55 164	87.67 194

mvs_anonymous		79.42 121	79.11 107	80.34 176	84.45 171	57.97 223	82.59 203	87.62 173	67.40 177	76.17 135	88.56 119	68.47 50	89.59 200	70.65 123	86.05 105	93.47 42

CP-MVSNet		78.22 138	78.34 123	77.84 206	87.83 126	54.54 251	87.94 99	91.17 83	77.65 28	73.48 163	88.49 120	62.24 119	88.43 215	62.19 179	74.07 218	90.55 121

PVSNet_Blended_VisFu		82.62 61	81.83 66	84.96 57	90.80 63	69.76 56	88.74 74	91.70 67	69.39 150	78.96 79	88.46 121	65.47 70	94.87 62	74.42 89	88.57 81	90.24 131

PLC	70.83 11	78.05 144	76.37 153	83.08 107	91.88 51	67.80 93	88.19 92	89.46 133	64.33 201	69.87 204	88.38 122	53.66 187	93.58 110	58.86 204	82.73 130	87.86 192
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
WR-MVS		79.49 118	79.22 106	80.27 179	88.79 102	58.35 217	85.06 170	88.61 161	78.56 24	77.65 110	88.34 123	63.81 83	90.66 189	64.98 163	77.22 177	91.80 84

XXY-MVS		75.41 185	75.56 170	74.96 229	83.59 199	57.82 226	80.59 219	83.87 208	66.54 183	74.93 155	88.31 124	63.24 88	80.09 253	62.16 180	76.85 186	86.97 211

API-MVS		81.99 69	81.23 71	84.26 75	90.94 59	70.18 52	91.10 29	89.32 135	71.51 127	78.66 84	88.28 125	65.26 71	95.10 52	64.74 165	91.23 54	87.51 198

UniMVSNet (Re)		81.60 75	81.11 73	83.09 106	88.38 114	64.41 157	87.60 105	93.02 21	78.42 26	78.56 85	88.16 126	69.78 42	93.26 120	69.58 131	76.49 193	91.60 85

LTVRE_ROB	69.57 13	76.25 176	74.54 179	81.41 160	88.60 106	64.38 158	79.24 227	89.12 142	70.76 134	69.79 205	87.86 127	49.09 210	93.20 123	56.21 223	80.16 155	86.65 217
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
WTY-MVS		75.65 183	75.68 169	75.57 226	86.40 150	56.82 233	77.92 238	82.40 220	65.10 194	76.18 133	87.72 128	63.13 93	80.90 249	60.31 194	81.96 137	89.00 163

TAMVS		78.89 131	77.51 139	83.03 110	87.80 127	67.79 94	84.72 175	85.05 199	67.63 172	76.75 125	87.70 129	62.25 118	90.82 186	58.53 208	87.13 94	90.49 123

BH-untuned		79.47 119	78.60 114	82.05 140	89.19 93	65.91 117	86.07 156	88.52 162	72.18 117	75.42 142	87.69 130	61.15 134	93.54 111	60.38 193	86.83 97	86.70 216

COLMAP_ROB	66.92 17	73.01 202	70.41 209	80.81 171	87.13 141	65.63 121	88.30 88	84.19 206	62.96 212	63.80 252	87.69 130	38.04 258	92.56 143	46.66 255	74.91 212	84.24 240
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		74.26 190	72.42 194	79.80 184	83.76 197	59.59 207	85.92 160	86.64 181	66.39 184	66.96 232	87.58 132	39.46 253	91.60 168	65.76 157	69.27 242	88.22 186

Baseline_NR-MVSNet		78.15 142	78.33 124	77.61 210	85.79 154	56.21 244	86.78 139	85.76 193	73.60 89	77.93 106	87.57 133	65.02 74	88.99 208	67.14 147	75.33 209	87.63 195

WR-MVS_H		78.51 134	78.49 117	78.56 198	88.02 121	56.38 241	88.43 80	92.67 34	77.14 37	73.89 161	87.55 134	66.25 63	89.24 205	58.92 203	73.55 224	90.06 142

EI-MVSNet		80.52 93	79.98 86	82.12 138	84.28 172	63.19 181	86.41 148	88.95 153	74.18 81	78.69 82	87.54 135	66.62 60	92.43 145	72.57 108	80.57 151	90.74 107

CVMVSNet		72.99 203	72.58 192	74.25 236	84.28 172	50.85 268	86.41 148	83.45 211	44.56 281	73.23 165	87.54 135	49.38 207	85.70 232	65.90 155	78.44 167	86.19 221

diffmvs		79.51 116	78.59 115	82.25 137	83.31 205	62.66 187	84.17 189	88.11 165	67.64 171	76.09 136	87.47 137	64.01 80	91.15 176	71.71 118	84.82 114	92.94 58

ACMH+	68.96 14	76.01 179	74.01 183	82.03 141	88.60 106	65.31 131	88.86 67	87.55 174	70.25 140	67.75 225	87.47 137	41.27 247	93.19 124	58.37 209	75.94 199	87.60 196

TransMVSNet (Re)		75.39 186	74.56 178	77.86 205	85.50 159	57.10 231	86.78 139	86.09 191	72.17 118	71.53 185	87.34 139	63.01 94	89.31 204	56.84 220	61.83 270	87.17 206

GBi-Net		78.40 135	77.40 140	81.40 161	87.60 132	63.01 182	88.39 84	89.28 136	71.63 123	75.34 145	87.28 140	54.80 175	91.11 177	62.72 173	79.57 158	90.09 138

test1		78.40 135	77.40 140	81.40 161	87.60 132	63.01 182	88.39 84	89.28 136	71.63 123	75.34 145	87.28 140	54.80 175	91.11 177	62.72 173	79.57 158	90.09 138

FMVSNet2		78.20 140	77.21 143	81.20 164	87.60 132	62.89 186	87.47 114	89.02 144	71.63 123	75.29 149	87.28 140	54.80 175	91.10 180	62.38 177	79.38 161	89.61 154

FMVSNet1		77.44 164	76.12 160	81.40 161	86.81 146	63.01 182	88.39 84	89.28 136	70.49 137	74.39 159	87.28 140	49.06 211	91.11 177	60.91 190	78.52 165	90.09 138

v2v482		80.23 102	79.29 103	83.05 109	83.62 198	64.14 160	87.04 130	89.97 120	73.61 88	78.18 101	87.22 144	61.10 135	93.82 96	76.11 72	76.78 191	91.18 96

ITE_SJBPF				78.22 203	81.77 230	60.57 202		83.30 212	69.25 155	67.54 227	87.20 145	36.33 264	87.28 222	54.34 227	74.62 214	86.80 213

MVSTER		79.01 128	77.88 131	82.38 135	83.07 211	64.80 142	84.08 193	88.95 153	69.01 159	78.69 82	87.17 146	54.70 178	92.43 145	74.69 88	80.57 151	89.89 146

XVG-ACMP-BASELINE		76.11 178	74.27 182	81.62 155	83.20 208	64.67 144	83.60 198	89.75 126	69.75 146	71.85 181	87.09 147	32.78 269	92.11 154	69.99 128	80.43 154	88.09 189

HY-MVS	69.67 12	77.95 147	77.15 144	80.36 175	87.57 136	60.21 205	83.37 201	87.78 171	66.11 185	75.37 144	87.06 148	63.27 87	90.48 191	61.38 188	82.43 134	90.40 128

HyFIR		77.63 157	75.69 168	83.44 92	89.98 72	68.58 82	78.70 232	87.50 175	56.38 258	75.80 137	86.84 149	58.67 153	91.40 170	61.58 186	85.75 109	90.34 129

v8		79.97 109	79.02 109	82.80 125	84.09 184	64.50 153	87.96 97	90.29 109	74.13 83	75.24 150	86.81 150	62.88 95	93.89 92	74.39 90	75.40 208	90.00 144

AllTest		70.96 214	68.09 225	79.58 188	85.15 162	63.62 168	84.58 180	79.83 242	62.31 220	60.32 260	86.73 151	32.02 270	88.96 211	50.28 242	71.57 235	86.15 222

TestCases				79.58 188	85.15 162	63.62 168		79.83 242	62.31 220	60.32 260	86.73 151	32.02 270	88.96 211	50.28 242	71.57 235	86.15 222

LCM-MVSNet-Re		77.05 167	76.94 146	77.36 212	87.20 140	51.60 263	80.06 221	80.46 236	75.20 73	67.69 226	86.72 153	62.48 114	88.98 209	63.44 169	89.25 72	91.51 88

1112_ss		77.40 166	76.43 151	80.32 177	89.11 94	60.41 204	83.65 196	87.72 172	62.13 222	73.05 167	86.72 153	62.58 111	89.97 196	62.11 181	80.80 147	90.59 117

ab-mvs-re		7.23 281	9.64 282	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	86.72 153	0.00 306	0.00 301	0.00 300	0.00 303	0.00 301

IterMVS-LS		80.06 107	79.38 97	82.11 139	85.89 153	63.20 180	86.79 138	89.34 134	74.19 80	75.45 141	86.72 153	66.62 60	92.39 147	72.58 107	76.86 185	90.75 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	67.68 16	75.89 180	73.93 184	81.77 146	88.71 104	66.61 107	88.62 76	89.01 146	69.81 144	66.78 234	86.70 157	41.95 246	91.51 169	55.64 224	78.14 170	87.17 206
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test_1112_low_res		76.40 174	75.44 171	79.27 190	89.28 89	58.09 220	81.69 210	87.07 178	59.53 239	72.48 173	86.67 158	61.30 131	89.33 203	60.81 192	80.15 156	90.41 127

FMVSNet3		77.88 150	76.85 147	80.97 169	86.84 145	62.36 189	86.52 145	88.77 160	71.13 128	75.34 145	86.66 159	54.07 184	91.10 180	62.72 173	79.57 158	89.45 155

v1neww		80.40 95	79.54 91	82.98 113	84.10 182	64.51 150	87.57 107	90.22 110	73.25 94	78.47 87	86.65 160	62.83 98	93.86 93	75.72 77	77.02 180	90.58 118

v7new		80.40 95	79.54 91	82.98 113	84.10 182	64.51 150	87.57 107	90.22 110	73.25 94	78.47 87	86.65 160	62.83 98	93.86 93	75.72 77	77.02 180	90.58 118

v6		80.40 95	79.54 91	82.98 113	84.09 184	64.50 153	87.57 107	90.22 110	73.25 94	78.47 87	86.63 162	62.84 97	93.86 93	75.73 76	77.02 180	90.58 118

testgi		66.67 240	66.53 236	67.08 261	75.62 271	41.69 286	75.93 245	76.50 257	66.11 185	65.20 246	86.59 163	35.72 266	74.71 272	43.71 264	73.38 225	84.84 236

CLD-MVS		82.31 64	81.65 67	84.29 74	88.47 110	67.73 95	85.81 163	92.35 43	75.78 62	78.33 93	86.58 164	64.01 80	94.35 71	76.05 73	87.48 91	90.79 104
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v10		79.74 113	78.67 112	82.97 116	84.06 189	64.95 139	87.88 102	90.62 94	73.11 100	75.11 152	86.56 165	61.46 127	94.05 83	73.68 95	75.55 205	89.90 145

CDS-MVSNet		79.07 127	77.70 136	83.17 102	87.60 132	68.23 87	84.40 186	86.20 188	67.49 175	76.36 130	86.54 166	61.54 126	90.79 187	61.86 183	87.33 92	90.49 123
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v7		80.24 101	79.26 104	83.15 103	84.07 188	64.94 140	87.56 110	90.67 91	72.26 116	78.28 94	86.51 167	61.45 128	94.03 84	75.14 86	77.41 174	90.49 123

TR-MVS		77.44 164	76.18 159	81.20 164	88.24 116	63.24 178	84.61 179	86.40 185	67.55 174	77.81 107	86.48 168	54.10 183	93.15 126	57.75 214	82.72 131	87.20 205

v1141		80.19 104	79.31 100	82.85 121	83.84 193	64.12 162	87.14 123	90.08 117	73.13 97	78.27 95	86.39 169	62.67 107	93.75 103	75.40 83	76.83 188	90.68 109

divwei89l23v2f112		80.19 104	79.31 100	82.85 121	83.84 193	64.11 164	87.13 126	90.08 117	73.13 97	78.27 95	86.39 169	62.69 105	93.75 103	75.40 83	76.82 189	90.68 109

v1		80.19 104	79.31 100	82.85 121	83.83 195	64.12 162	87.14 123	90.07 119	73.13 97	78.27 95	86.38 171	62.72 104	93.75 103	75.41 82	76.82 189	90.68 109

v7n		78.97 130	77.58 138	83.14 104	83.45 202	65.51 124	88.32 87	91.21 81	73.69 87	72.41 174	86.32 172	57.93 158	93.81 97	69.18 133	75.65 203	90.11 136

MAR-MVS		81.84 71	80.70 76	85.27 51	91.32 55	71.53 36	89.82 48	90.92 87	69.77 145	78.50 86	86.21 173	62.36 116	94.52 70	65.36 159	92.05 46	89.77 151
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
v1144		80.03 108	79.03 108	83.01 111	83.78 196	64.51 150	87.11 128	90.57 96	71.96 120	78.08 104	86.20 174	61.41 129	93.94 87	74.93 87	77.23 176	90.60 115

V42		79.38 122	78.24 126	82.83 124	81.10 238	65.50 125	85.55 165	89.82 124	71.57 126	78.21 99	86.12 175	60.66 141	93.18 125	75.64 80	75.46 207	89.81 150

PVSNet_BlendedMVS		80.60 90	80.02 85	82.36 136	88.85 97	65.40 126	86.16 153	92.00 52	69.34 153	78.11 102	86.09 176	66.02 67	94.27 74	71.52 119	82.06 136	87.39 200

v1192		79.59 115	78.43 121	83.07 108	83.55 200	64.52 148	86.93 133	90.58 95	70.83 132	77.78 108	85.90 177	59.15 151	93.94 87	73.96 94	77.19 178	90.76 105

SixPastTwentyTwo		73.37 196	71.26 203	79.70 185	85.08 164	57.89 225	85.57 164	83.56 210	71.03 131	65.66 241	85.88 178	42.10 244	92.57 142	59.11 202	63.34 267	88.65 171

EPNet_dtu		75.46 184	74.86 174	77.23 215	82.57 223	54.60 250	86.89 134	83.09 214	71.64 122	66.25 239	85.86 179	55.99 169	88.04 218	54.92 225	86.55 100	89.05 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
sss		73.60 194	73.64 186	73.51 239	82.80 218	55.01 249	76.12 244	81.69 227	62.47 219	74.68 157	85.85 180	57.32 161	78.11 261	60.86 191	80.93 145	87.39 200

DI_MVS_test_dynamic		79.89 110	78.58 116	83.85 87	82.89 217	65.32 130	86.12 154	89.55 130	69.64 149	70.55 190	85.82 181	57.24 162	93.81 97	76.85 68	88.55 82	92.41 69

v1240		78.99 129	77.78 134	82.64 132	83.21 207	63.54 170	86.62 143	90.30 108	69.74 148	77.33 115	85.68 182	57.04 165	93.76 102	73.13 101	76.92 183	90.62 113

v144192		79.47 119	78.37 122	82.78 128	83.35 203	63.96 166	86.96 132	90.36 104	69.99 142	77.50 112	85.67 183	60.66 141	93.77 101	74.27 91	76.58 192	90.62 113

DI_MVS_test_normal		79.81 111	78.45 118	83.89 86	82.70 221	65.40 126	85.82 162	89.48 132	69.39 150	70.12 197	85.66 184	57.15 164	93.71 108	77.08 65	88.62 80	92.56 65

PS-MVSNAJ		81.69 73	81.02 74	83.70 88	89.51 79	68.21 88	84.28 188	90.09 116	70.79 133	81.26 64	85.62 185	63.15 90	94.29 72	75.62 81	88.87 75	88.59 178

v748		77.97 146	76.65 150	81.92 144	82.29 226	63.28 177	87.53 111	90.35 105	73.50 92	70.76 189	85.55 186	58.28 156	92.81 138	68.81 136	72.76 228	89.67 153

v1921920		79.22 124	78.03 128	82.80 125	83.30 206	63.94 167	86.80 137	90.33 106	69.91 143	77.48 113	85.53 187	58.44 155	93.75 103	73.60 98	76.85 186	90.71 108

test_0402		72.79 205	70.44 208	79.84 183	88.13 118	65.99 115	85.93 159	84.29 204	65.57 191	67.40 229	85.49 188	46.92 219	92.61 141	35.88 275	74.38 217	80.94 260

v148		78.72 132	77.80 133	81.47 159	82.73 220	61.96 194	86.30 151	88.08 167	73.26 93	76.18 133	85.47 189	62.46 115	92.36 149	71.92 117	73.82 223	90.09 138

USDC		70.33 221	68.37 220	76.21 222	80.60 242	56.23 243	79.19 229	86.49 183	60.89 228	61.29 256	85.47 189	31.78 272	89.47 202	53.37 232	76.21 197	82.94 253

MVP-PatchMatch		76.12 177	74.46 180	81.13 167	85.37 160	69.79 55	84.42 185	87.95 169	65.03 195	67.46 228	85.33 191	53.28 189	91.73 160	58.01 213	83.27 125	81.85 257

HC-MVS		78.19 141	76.99 145	81.78 145	85.66 156	66.99 103	84.66 176	90.47 99	55.08 263	72.02 180	85.27 192	63.83 82	94.11 82	66.10 153	89.80 68	84.24 240

TDRefinement		67.49 234	64.34 240	76.92 216	73.47 277	61.07 197	84.86 174	82.98 215	59.77 237	58.30 265	85.13 193	26.06 277	87.89 219	47.92 253	60.59 274	81.81 258

PVSNet_Blended		80.98 82	80.34 81	82.90 118	88.85 97	65.40 126	84.43 184	92.00 52	67.62 173	78.11 102	85.05 194	66.02 67	94.27 74	71.52 119	89.50 69	89.01 161

V4		77.95 147	76.37 153	82.67 130	79.40 254	65.52 122	86.43 146	89.94 121	72.28 114	72.14 179	84.95 195	55.72 170	93.44 116	73.64 96	72.86 226	89.05 159

v52		77.94 149	76.37 153	82.67 130	79.39 255	65.52 122	86.43 146	89.94 121	72.28 114	72.15 178	84.94 196	55.70 171	93.44 116	73.64 96	72.84 227	89.06 158

CMPMVS	51.72 21	70.19 222	68.16 223	76.28 221	73.15 278	57.55 228	79.47 226	83.92 207	48.02 279	56.48 271	84.81 197	43.13 236	86.42 228	62.67 176	81.81 140	84.89 235
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
EU-MVSNet		68.53 231	67.61 232	71.31 249	78.51 260	47.01 277	84.47 181	84.27 205	42.27 282	66.44 238	84.79 198	40.44 251	83.76 238	58.76 206	68.54 248	83.17 247

BH-w/o		78.21 139	77.33 142	80.84 170	88.81 100	65.13 136	84.87 173	87.85 170	69.75 146	74.52 158	84.74 199	61.34 130	93.11 129	58.24 211	85.84 108	84.27 239

liao1		76.53 173	75.30 172	80.21 180	83.93 191	62.32 191	84.66 176	88.81 159	60.23 233	70.16 196	84.07 200	55.30 174	90.73 188	67.37 143	83.21 126	87.59 197

EG-PatchMatch MVS		74.04 191	71.82 197	80.71 173	84.92 165	67.42 97	85.86 161	88.08 167	66.04 187	64.22 249	83.85 201	35.10 267	92.56 143	57.44 216	80.83 146	82.16 256

test20.03		67.45 235	66.95 233	68.94 255	75.48 273	44.84 279	77.50 239	77.67 251	66.66 179	63.01 254	83.80 202	47.02 218	78.40 259	42.53 267	68.86 246	83.58 244

Test4		77.83 151	75.90 167	83.62 89	80.24 245	65.25 132	85.27 168	90.67 91	69.03 158	66.48 237	83.75 203	43.07 237	93.00 134	75.93 75	88.66 79	92.62 64

MSDG		73.36 198	70.99 205	80.49 174	84.51 170	65.80 119	80.71 216	86.13 190	65.70 190	65.46 242	83.74 204	44.60 230	90.91 185	51.13 240	76.89 184	84.74 237

IterMVS		74.29 189	72.94 190	78.35 202	81.53 232	63.49 171	81.58 212	82.49 219	68.06 170	69.99 201	83.69 205	51.66 200	85.54 233	65.85 156	71.64 234	86.01 226
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm		72.37 208	71.71 198	74.35 235	82.19 227	52.00 260	79.22 228	77.29 254	64.56 198	72.95 168	83.68 206	51.35 201	83.26 243	58.33 210	75.80 201	87.81 193

pm-mvs1		70.87 215	69.03 215	76.39 220	79.41 253	58.92 213	80.64 218	78.89 247	55.71 260	67.14 231	83.58 207	48.48 213	85.25 235	52.94 235	74.48 216	84.99 234

TinyColmap		67.30 237	64.81 239	74.76 232	81.92 229	56.68 237	80.29 220	81.49 229	60.33 231	56.27 272	83.22 208	24.77 279	87.66 221	45.52 260	69.47 241	79.95 264

CostFormer		75.24 187	73.90 185	79.27 190	82.65 222	58.27 219	80.80 214	82.73 218	61.57 225	75.33 148	83.13 209	55.52 172	91.07 183	64.98 163	78.34 169	88.45 183

GA-MVS		76.87 170	75.17 173	81.97 142	82.75 219	62.58 188	81.44 213	86.35 187	72.16 119	74.74 156	82.89 210	46.20 222	92.02 155	68.85 135	81.09 144	91.30 94

K. test v3		71.19 212	68.51 219	79.21 192	83.04 213	57.78 227	84.35 187	76.91 256	72.90 105	62.99 255	82.86 211	39.27 254	91.09 182	61.65 185	52.66 282	88.75 168

MS-PatchMatch		73.83 192	72.67 191	77.30 214	83.87 192	66.02 114	81.82 208	84.66 201	61.37 227	68.61 219	82.82 212	47.29 216	88.21 217	59.27 201	84.32 118	77.68 270

lessismore_v0				78.97 194	81.01 239	57.15 230		65.99 288		61.16 257	82.82 212	39.12 255	91.34 172	59.67 197	46.92 286	88.43 184

Anonymous20231206		68.60 229	67.80 230	71.02 250	80.23 246	50.75 269	78.30 236	80.47 235	56.79 256	66.11 240	82.63 214	46.35 221	78.95 257	43.62 265	75.70 202	83.36 246

MIMVSNet		70.69 217	69.30 212	74.88 230	84.52 169	56.35 242	75.87 248	79.42 245	64.59 197	67.76 224	82.41 215	41.10 248	81.54 248	46.64 257	81.34 142	86.75 215

OpenMVS_ROB	64.09 19	70.56 219	68.19 222	77.65 209	80.26 244	59.41 211	85.01 171	82.96 216	58.76 243	65.43 243	82.33 216	37.63 261	91.23 175	45.34 262	76.03 198	82.32 254

test0.0.03 1		68.00 233	67.69 231	68.90 256	77.55 261	47.43 275	75.70 249	72.95 276	66.66 179	66.56 235	82.29 217	48.06 214	75.87 269	44.97 263	74.51 215	83.41 245

PVSNet	64.34 18	72.08 209	70.87 207	75.69 225	86.21 152	56.44 240	74.37 256	80.73 233	62.06 223	70.17 195	82.23 218	42.86 239	83.31 242	54.77 226	84.45 117	87.32 202

MIMVSNet1		68.58 230	66.78 234	73.98 238	80.07 247	51.82 261	80.77 215	84.37 203	64.40 200	59.75 262	82.16 219	36.47 263	83.63 240	42.73 266	70.33 240	86.48 219

v16		77.69 153	76.36 156	81.68 152	84.15 179	64.63 147	87.33 117	88.99 148	72.69 111	69.31 211	82.08 220	62.80 101	91.79 157	72.70 105	67.23 249	88.63 172

v17		77.68 154	76.35 157	81.69 151	84.15 179	64.65 145	87.33 117	88.99 148	72.70 110	69.25 212	82.07 221	62.82 100	91.79 157	72.69 106	67.15 251	88.63 172

v18		77.67 156	76.35 157	81.64 154	84.09 184	64.47 155	87.27 120	89.01 146	72.59 112	69.39 208	82.04 222	62.85 96	91.80 156	72.72 104	67.20 250	88.63 172

tpm2		73.26 199	71.46 199	78.63 197	83.34 204	56.71 236	80.65 217	80.40 237	56.63 257	73.55 162	82.02 223	51.80 198	91.24 174	56.35 222	78.42 168	87.95 190

PatchMatch-RL		72.38 207	70.90 206	76.80 218	88.60 106	67.38 99	79.53 225	76.17 258	62.75 216	69.36 209	82.00 224	45.51 228	84.89 236	53.62 231	80.58 150	78.12 268

v13		77.50 162	76.07 165	81.77 146	84.23 174	65.07 137	87.34 116	88.91 158	72.92 103	68.35 222	81.97 225	62.53 113	91.69 166	72.20 114	66.22 259	88.56 180

v12		77.51 160	76.09 164	81.76 148	84.22 175	64.99 138	87.30 119	88.93 157	72.92 103	68.48 221	81.97 225	62.54 112	91.70 165	72.24 113	66.21 260	88.58 179

V9		77.52 158	76.11 163	81.73 149	84.19 178	64.89 141	87.26 121	88.94 156	72.87 106	68.65 217	81.96 227	62.65 108	91.72 162	72.27 112	66.24 258	88.60 176

V14		77.52 158	76.12 160	81.70 150	84.15 179	64.77 143	87.21 122	88.95 153	72.80 107	68.79 214	81.94 228	62.69 105	91.72 162	72.31 111	66.27 257	88.60 176

v15		77.51 160	76.12 160	81.66 153	84.09 184	64.65 145	87.14 123	88.96 152	72.76 108	68.90 213	81.91 229	62.74 103	91.73 160	72.32 110	66.29 256	88.61 175

v11		77.45 163	76.06 166	81.59 157	84.22 175	64.52 148	87.11 128	89.02 144	72.76 108	68.76 215	81.90 230	62.09 121	91.71 164	71.98 115	66.73 252	88.56 180

Patchmatch-RL test		68.82 228	66.60 235	75.47 228	77.43 262	59.57 208	71.16 261	70.33 280	62.94 213	68.65 217	81.66 231	31.06 273	85.49 234	69.58 131	66.58 254	81.34 259

tpmp4_e23		73.45 195	71.17 204	80.31 178	83.55 200	59.56 209	81.88 207	82.33 221	57.94 249	70.51 192	81.62 232	51.19 203	91.63 167	53.96 229	77.51 173	89.75 152

FMVSNet5		69.50 225	67.96 226	74.15 237	82.97 214	55.35 248	80.01 222	82.12 224	62.56 218	63.02 253	81.53 233	36.92 262	81.92 246	48.42 250	74.06 219	85.17 233

CR-MVSNet		73.37 196	71.27 202	79.67 186	81.32 236	65.19 134	75.92 246	80.30 238	59.92 236	72.73 170	81.19 234	52.50 190	86.69 224	59.84 196	77.71 171	87.11 209

Patchmtry		70.74 216	69.16 214	75.49 227	80.72 240	54.07 254	74.94 255	80.30 238	58.34 245	70.01 198	81.19 234	52.50 190	86.54 226	53.37 232	71.09 237	85.87 228

testing_2		75.73 182	73.34 188	82.89 120	77.37 263	65.22 133	84.10 192	90.54 97	69.09 157	60.46 259	81.15 236	40.48 250	92.84 137	76.36 71	80.54 153	90.60 115

IB-MVS	68.01 15	75.85 181	73.36 187	83.31 97	84.76 166	66.03 113	83.38 200	85.06 198	70.21 141	69.40 207	81.05 237	45.76 226	94.66 67	65.10 161	75.49 206	89.25 157
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
cascas		76.72 172	74.64 176	82.99 112	85.78 155	65.88 118	82.33 205	89.21 141	60.85 229	72.74 169	81.02 238	47.28 217	93.75 103	67.48 142	85.02 110	89.34 156

LF4IMVS		64.02 247	62.19 248	69.50 254	70.90 282	53.29 258	76.13 243	77.18 255	52.65 272	58.59 263	80.98 239	23.55 280	76.52 267	53.06 234	66.66 253	78.68 267

gm-plane-assit					81.40 233	53.83 256			62.72 217		80.94 240		92.39 147	63.40 170

UnsupCasMVSNet_eth		67.33 236	65.99 237	71.37 246	73.48 276	51.47 265	75.16 251	85.19 197	65.20 193	60.78 258	80.93 241	42.35 241	77.20 265	57.12 219	53.69 281	85.44 229

MDTV_nov1_ep13			69.97 211		83.18 209	53.48 257	77.10 242	80.18 241	60.45 230	69.33 210	80.44 242	48.89 212	86.90 223	51.60 238	78.51 166

pmmvs-eth3d		70.50 220	67.83 229	78.52 200	77.37 263	66.18 112	81.82 208	81.51 228	58.90 242	63.90 251	80.42 243	42.69 240	86.28 229	58.56 207	65.30 263	83.11 249

PM-MVS		66.41 241	64.14 241	73.20 240	73.92 274	56.45 239	78.97 230	64.96 291	63.88 207	64.72 247	80.24 244	19.84 286	83.44 241	66.24 150	64.52 265	79.71 265

Patchmatch-test1					78.56 259	58.71 215	64.97 283	73.71 273		70.00 200	80.13 245	33.36 268			65.60 261

tpmrst		72.39 206	72.13 195	73.18 241	80.54 243	49.91 271	79.91 223	79.08 246	63.11 209	71.69 183	79.95 246	55.32 173	82.77 244	65.66 158	73.89 221	86.87 212

DSMNet-mixed		57.77 256	56.90 256	60.38 269	67.70 288	35.61 293	69.18 270	53.97 294	32.30 293	57.49 268	79.88 247	40.39 252	68.57 286	38.78 272	72.37 229	76.97 273

MDA-MVSNet-bldmvs		66.68 239	63.66 242	75.75 224	79.28 256	60.56 203	73.92 257	78.35 249	64.43 199	50.13 282	79.87 248	44.02 234	83.67 239	46.10 259	56.86 276	83.03 251

PatchmatchNet		73.12 201	71.33 201	78.49 201	83.18 209	60.85 200	79.63 224	78.57 248	64.13 202	71.73 182	79.81 249	51.20 202	85.97 231	57.40 217	76.36 196	88.66 170
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
EPMVS		69.02 227	68.16 223	71.59 244	79.61 251	49.80 272	77.40 240	66.93 287	62.82 215	70.01 198	79.05 250	45.79 225	77.86 263	56.58 221	75.26 210	87.13 208

PMMVS		69.34 226	68.67 218	71.35 248	75.67 270	62.03 193	75.17 250	73.46 274	50.00 277	68.68 216	79.05 250	52.07 196	78.13 260	61.16 189	82.77 129	73.90 277

test-LLR		72.94 204	72.43 193	74.48 233	81.35 234	58.04 221	78.38 233	77.46 252	66.66 179	69.95 202	79.00 252	48.06 214	79.24 255	66.13 151	84.83 112	86.15 222

test-mter		71.41 211	70.39 210	74.48 233	81.35 234	58.04 221	78.38 233	77.46 252	60.32 232	69.95 202	79.00 252	36.08 265	79.24 255	66.13 151	84.83 112	86.15 222

DWT-MVSNet_test		73.70 193	71.86 196	79.21 192	82.91 216	58.94 212	82.34 204	82.17 223	65.21 192	71.05 188	78.31 254	44.21 232	90.17 195	63.29 171	77.28 175	88.53 182

tpmvs		71.09 213	69.29 213	76.49 219	82.04 228	56.04 245	78.92 231	81.37 230	64.05 203	67.18 230	78.28 255	49.74 206	89.77 197	49.67 247	72.37 229	83.67 243

PatchFormer-LS_test		74.50 188	73.05 189	78.86 195	82.95 215	59.55 210	81.65 211	82.30 222	67.44 176	71.62 184	78.15 256	52.34 192	88.92 213	65.05 162	75.90 200	88.12 188

MDA-MVSNet_test_wron		65.03 244	62.92 244	71.37 246	75.93 267	56.73 234	69.09 272	74.73 267	57.28 254	54.03 275	77.89 257	45.88 223	74.39 274	49.89 246	61.55 271	82.99 252

YYNet1		65.03 244	62.91 245	71.38 245	75.85 268	56.60 238	69.12 271	74.66 270	57.28 254	54.12 274	77.87 258	45.85 224	74.48 273	49.95 245	61.52 272	83.05 250

tpm cat1		70.57 218	68.31 221	77.35 213	82.41 225	57.95 224	78.08 237	80.22 240	52.04 273	68.54 220	77.66 259	52.00 197	87.84 220	51.77 237	72.07 232	86.25 220

dp		66.80 238	65.43 238	70.90 251	79.74 250	48.82 274	75.12 253	74.77 266	59.61 238	64.08 250	77.23 260	42.89 238	80.72 250	48.86 249	66.58 254	83.16 248

TESTMET0.1,1		69.89 224	69.00 216	72.55 242	79.27 257	56.85 232	78.38 233	74.71 268	57.64 250	68.09 223	77.19 261	37.75 259	76.70 266	63.92 167	84.09 119	84.10 242

PatchT		68.46 232	67.85 228	70.29 252	80.70 241	43.93 281	72.47 259	74.88 264	60.15 234	70.55 190	76.57 262	49.94 205	81.59 247	50.58 241	74.83 213	85.34 230

RPMNet		71.62 210	68.94 217	79.67 186	81.32 236	65.19 134	75.92 246	78.30 250	57.60 251	72.73 170	76.45 263	52.30 193	86.69 224	48.14 252	77.71 171	87.11 209

ADS-MVSNet2		66.20 243	63.33 243	74.82 231	79.92 248	58.75 214	67.55 277	75.19 262	53.37 269	65.25 244	75.86 264	42.32 242	80.53 251	41.57 268	68.91 244	85.18 231

ADS-MVSNet		64.36 246	62.88 246	68.78 258	79.92 248	47.17 276	67.55 277	71.18 279	53.37 269	65.25 244	75.86 264	42.32 242	73.99 276	41.57 268	68.91 244	85.18 231

N_pmnet		52.79 262	53.26 260	51.40 279	78.99 258	7.68 306	69.52 268	3.89 303	51.63 275	57.01 270	74.98 266	40.83 249	65.96 289	37.78 273	64.67 264	80.56 263

1111		57.11 257	56.82 258	57.97 272	69.10 284	28.28 298	68.90 273	74.54 271	54.01 266	53.71 276	74.51 267	23.09 281	67.90 287	32.28 280	61.26 273	77.73 269

.test1245		45.55 268	50.02 264	32.14 285	69.10 284	28.28 298	68.90 273	74.54 271	54.01 266	53.71 276	74.51 267	23.09 281	67.90 287	32.28 280	0.02 300	0.25 299

testus		59.00 253	57.91 252	62.25 268	72.25 279	39.09 289	69.74 266	75.02 263	53.04 271	57.21 269	73.72 269	18.76 288	70.33 283	32.86 278	68.57 247	77.35 271

Patchmatch-test					75.77 269	36.09 292					72.41 270

FPMVS		53.68 261	51.64 261	59.81 270	65.08 289	51.03 267	69.48 269	69.58 283	41.46 283	40.67 286	72.32 271	16.46 290	70.00 284	24.24 290	65.42 262	58.40 286

UnsupCasMVSNet_bld		63.70 248	61.53 249	70.21 253	73.69 275	51.39 266	72.82 258	81.89 225	55.63 261	57.81 266	71.80 272	38.67 256	78.61 258	49.26 248	52.21 283	80.63 261

test1235678		58.74 254	56.89 257	64.30 263	69.70 283	41.87 285	71.05 262	74.87 265	54.06 265	50.63 281	71.53 273	25.30 278	74.10 275	31.80 283	63.10 268	76.93 274

test2356		59.50 251	58.08 251	63.74 265	71.23 281	41.88 284	67.59 276	72.42 278	53.72 268	57.65 267	70.74 274	26.31 276	72.40 279	32.03 282	71.06 238	76.93 274

PVSNet_0	57.27 20	61.67 249	59.27 250	68.85 257	79.61 251	57.44 229	68.01 275	73.44 275	55.93 259	58.54 264	70.41 275	44.58 231	77.55 264	47.01 254	35.91 288	71.55 279

LP		61.36 250	57.78 253	72.09 243	75.54 272	58.53 216	67.16 279	75.22 261	51.90 274	54.13 273	69.97 276	37.73 260	80.45 252	32.74 279	55.63 278	77.29 272

pmmvs3		57.79 255	54.26 259	68.37 259	64.02 290	56.72 235	75.12 253	65.17 289	40.20 284	52.93 278	69.86 277	20.36 284	75.48 271	45.45 261	55.25 280	72.90 278

testpf		56.51 258	57.58 255	53.30 276	71.99 280	41.19 287	46.89 293	69.32 285	58.06 247	52.87 279	69.45 278	27.99 275	72.73 278	59.59 199	62.07 269	45.98 290

testmv		53.85 260	51.03 262	62.31 267	61.46 292	38.88 290	70.95 265	74.69 269	51.11 276	41.26 285	66.85 279	14.28 291	72.13 280	29.19 285	49.51 285	75.93 276

test12356		49.28 267	48.51 267	51.59 278	62.06 291	19.11 303	60.40 286	72.45 277	47.60 280	40.64 287	65.68 280	13.84 292	68.72 285	27.29 287	46.67 287	66.94 282

new_pmnet		50.91 265	50.29 263	52.78 277	68.58 287	34.94 296	63.71 284	56.63 293	39.73 285	44.95 283	65.47 281	21.93 283	58.48 291	34.98 276	56.62 277	64.92 283

HyFIR lowres test		51.79 263	50.01 265	57.11 273	68.82 286	49.21 273	60.50 285	53.26 295	34.52 289	43.77 284	64.94 282	20.34 285	71.75 281	39.87 270	64.06 266	50.39 287

gg-mvs-nofinetune		69.95 223	67.96 226	75.94 223	83.07 211	54.51 252	77.23 241	70.29 281	63.11 209	70.32 193	62.33 283	43.62 235	88.69 214	53.88 230	87.76 88	84.62 238

JIA-IIPM		66.32 242	62.82 247	76.82 217	77.09 265	61.72 196	65.34 281	75.38 260	58.04 248	64.51 248	62.32 284	42.05 245	86.51 227	51.45 239	69.22 243	82.21 255

LCM-MVSNet		54.25 259	49.68 266	67.97 260	53.73 297	45.28 278	66.85 280	80.78 232	35.96 288	39.45 288	62.23 285	8.70 297	78.06 262	48.24 251	51.20 284	80.57 262

PMMVS2		40.82 271	38.86 272	46.69 281	53.84 296	16.45 304	48.61 292	49.92 297	37.49 287	31.67 290	60.97 286	8.14 298	56.42 292	28.42 286	30.72 289	67.19 281

MVS-HIRNet		59.14 252	57.67 254	63.57 266	81.65 231	43.50 282	71.73 260	65.06 290	39.59 286	51.43 280	57.73 287	38.34 257	82.58 245	39.53 271	73.95 220	64.62 284

ANet_high		50.57 266	46.10 268	63.99 264	48.67 301	39.13 288	70.99 264	80.85 231	61.39 226	31.18 291	57.70 288	17.02 289	73.65 277	31.22 284	15.89 297	79.18 266

PMVS	37.38 22	44.16 270	40.28 271	55.82 274	40.82 304	42.54 283	65.12 282	63.99 292	34.43 290	24.48 293	57.12 289	3.92 299	76.17 268	17.10 293	55.52 279	48.75 288
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		51.08 264	45.79 269	66.95 262	57.92 295	50.49 270	59.63 288	76.04 259	48.04 278	31.85 289	56.10 290	19.12 287	80.08 254	36.89 274	26.52 290	70.29 280

DeepMVS_CX				27.40 287	40.17 305	26.90 301		24.59 302	17.44 298	23.95 294	48.61 291	9.77 295	26.48 296	18.06 292	24.47 291	28.83 293

PNet_i23d		38.26 273	35.42 273	46.79 280	58.74 293	35.48 294	59.65 287	51.25 296	32.45 292	23.44 296	47.53 292	2.04 301	58.96 290	25.60 289	18.09 295	45.92 291

MVE	26.22 23	30.37 277	25.89 279	43.81 282	44.55 303	35.46 295	28.87 297	39.07 300	18.20 297	18.58 297	40.18 293	2.68 300	47.37 295	17.07 294	23.78 292	48.60 289
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		45.18 269	41.86 270	55.16 275	77.03 266	51.52 264	32.50 296	80.52 234	32.46 291	27.12 292	35.02 294	9.52 296	75.50 270	22.31 291	60.21 275	38.45 292
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
wuykxyi23d		39.76 272	33.18 275	59.51 271	46.98 302	44.01 280	57.70 289	67.74 286	24.13 295	13.98 300	34.33 295	1.27 302	71.33 282	34.23 277	18.23 293	63.18 285

E-PMN		31.77 275	30.64 276	35.15 283	52.87 298	27.67 300	57.09 290	47.86 298	24.64 294	16.40 298	33.05 296	11.23 294	54.90 293	14.46 295	18.15 294	22.87 294

EMVS		30.81 276	29.65 277	34.27 284	50.96 300	25.95 302	56.58 291	46.80 299	24.01 296	15.53 299	30.68 297	12.47 293	54.43 294	12.81 296	17.05 296	22.43 295

DUST3R		18.61 279	21.40 280	10.23 289	4.82 306	10.11 305	34.70 295	30.74 301	1.48 300	23.91 295	26.07 298	28.42 274	13.41 298	27.12 288	15.35 298	7.17 296

X-MVStestdata		80.37 99	77.83 132	88.00 8	94.42 10	73.33 13	92.78 8	92.99 24	79.14 16	83.67 44	12.47 299	67.45 55	96.60 14	83.06 28	94.50 28	94.07 18

wuyk23d		16.82 280	15.94 281	19.46 288	58.74 293	31.45 297	39.22 294	3.74 304	6.84 299	6.04 301	2.70 300	1.27 302	24.29 297	10.54 297	14.40 299	2.63 297

testmvs		6.04 283	8.02 284	0.10 291	0.08 307	0.03 308	69.74 266	0.04 305	0.05 301	0.31 302	1.68 301	0.02 305	0.04 299	0.24 298	0.02 300	0.25 299

test123		6.12 282	8.11 283	0.14 290	0.06 308	0.09 307	71.05 262	0.03 306	0.04 302	0.25 303	1.30 302	0.05 304	0.03 300	0.21 299	0.01 302	0.29 298

pcd_1.5k_mvsjas		5.26 284	7.02 285	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	63.15 90	0.00 301	0.00 300	0.00 303	0.00 301

pcd1.5k->3k		34.07 274	35.26 274	30.50 286	86.92 143	0.00 309	0.00 298	91.58 71	0.00 303	0.00 304	0.00 303	56.23 168	0.00 301	0.00 300	82.60 132	91.49 90

sosnet-low-res		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 303	0.00 301

sosnet		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 303	0.00 301

uncertanet		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 303	0.00 301

Regformer		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 303	0.00 301

uanet		0.00 285	0.00 286	0.00 292	0.00 309	0.00 309	0.00 298	0.00 307	0.00 303	0.00 304	0.00 303	0.00 306	0.00 301	0.00 300	0.00 303	0.00 301

MTAPA					52.35 299

test9_res														84.90 11	95.70 10	92.87 59

agg_prior2														82.91 31	95.45 12	92.70 60

agg_prior					92.85 35	71.94 32		91.78 64		84.41 33			94.93 56

test_prior4						72.60 23	89.01 65

test_prior				86.33 38	92.61 40	69.59 58		92.97 27					95.48 34			93.91 25

旧先验2							86.56 144		58.10 246	87.04 10			88.98 209	74.07 93

新几何2							86.29 152

无先验							87.48 113	88.98 150	60.00 235				94.12 80	67.28 144		88.97 164

原ACMM2							86.86 135

testdata2													91.01 184	62.37 178

segment_acmp												73.08 21

testdata1							84.14 191		75.71 63

test12				86.80 30	92.63 39	70.70 46		91.79 63		82.71 55		71.67 30	96.16 23		94.50 28	93.54 40

plane_prior7					90.08 70	68.51 83

plane_prior6					89.84 75	68.70 79						60.42 145

plane_prior5								92.44 38					95.38 41	78.71 53	86.32 102	91.33 92

plane_prior3						68.60 81			78.44 25	78.92 80

plane_prior2							91.25 26		79.12 18

plane_prior1					89.90 74

plane_prior						68.71 77	90.38 40		77.62 29						86.16 104

n2								0.00 307

nn								0.00 307

door-mid								69.98 282

test11								92.23 44

High-resolution multi-view benchmark