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.

HPM-MVS++		89.02 5	89.15 5	88.63 1	95.01 3	76.03 1	92.38 15	92.85 36	80.26 14	87.78 15	94.27 19	75.89 8	96.81 10	87.45 10	96.44 2	93.05 69

SMA-MVS		89.08 4	89.23 4	88.61 2	94.25 19	73.73 7	92.40 14	93.63 10	74.77 92	92.29 1	95.97 2	74.28 18	97.24 3	88.58 4	96.91 1	94.87 5

3Dnovator+	77.84 4	85.48 46	84.47 54	88.51 3	91.08 67	73.49 14	93.18 4	93.78 8	80.79 11	76.66 154	93.37 37	60.40 165	96.75 13	77.20 84	93.73 47	95.29 1

CNVR-MVS		88.93 6	89.13 6	88.33 4	94.77 5	73.82 6	90.51 42	93.00 28	80.90 10	88.06 13	94.06 27	76.43 5	96.84 9	88.48 5	95.99 6	94.34 17

SteuartSystems-ACMMP		88.72 7	88.86 7	88.32 5	92.14 56	72.96 20	93.73 3	93.67 9	80.19 15	88.10 12	94.80 7	73.76 22	97.11 5	87.51 9	95.82 10	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
NCCC		88.06 9	88.01 12	88.24 6	94.41 15	73.62 8	91.22 33	92.83 37	81.50 7	85.79 25	93.47 36	73.02 26	97.00 8	84.90 19	94.94 26	94.10 23

region2R		87.42 19	87.20 20	88.09 7	94.63 7	73.55 10	93.03 7	93.12 24	76.73 55	84.45 43	94.52 10	69.09 57	96.70 14	84.37 28	94.83 30	94.03 27

ACMMPR		87.44 17	87.23 19	88.08 8	94.64 6	73.59 9	93.04 5	93.20 21	76.78 52	84.66 40	94.52 10	68.81 59	96.65 16	84.53 25	94.90 27	94.00 30

MVS_0304		86.37 37	85.81 41	88.02 9	90.13 82	72.39 35	89.66 65	92.75 40	81.64 6	82.66 69	92.04 57	64.44 92	97.35 2	84.76 23	94.25 43	94.33 18

ESAPD		89.48 1	89.98 1	88.01 10	94.80 4	72.69 27	91.59 26	94.10 1	75.90 70	92.29 1	95.66 3	81.67 1	97.38 1	87.44 11	96.34 4	93.95 32

XVS		87.18 23	86.91 25	88.00 11	94.42 13	73.33 17	92.78 9	92.99 30	79.14 21	83.67 55	94.17 22	67.45 68	96.60 20	83.06 38	94.50 35	94.07 25

X-MVStestdata		80.37 124	77.83 159	88.00 11	94.42 13	73.33 17	92.78 9	92.99 30	79.14 21	83.67 55	12.47 363	67.45 68	96.60 20	83.06 38	94.50 35	94.07 25

ACMMP_Plus		88.05 11	88.08 11	87.94 13	93.70 27	73.05 19	90.86 36	93.59 11	76.27 66	88.14 11	95.09 6	71.06 39	96.67 15	87.67 7	96.37 3	94.09 24

HFP-MVS		87.58 15	87.47 16	87.94 13	94.58 8	73.54 12	93.04 5	93.24 19	76.78 52	84.91 34	94.44 15	70.78 41	96.61 18	84.53 25	94.89 28	93.66 43

#test#		87.33 21	87.13 21	87.94 13	94.58 8	73.54 12	92.34 16	93.24 19	75.23 83	84.91 34	94.44 15	70.78 41	96.61 18	83.75 33	94.89 28	93.66 43

MP-MVS		87.71 13	87.64 14	87.93 16	94.36 17	73.88 4	92.71 13	92.65 44	77.57 35	83.84 52	94.40 18	72.24 33	96.28 27	85.65 15	95.30 23	93.62 50
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		87.53 16	87.41 17	87.90 17	94.18 23	74.25 2	90.23 50	92.02 64	79.45 19	85.88 22	94.80 7	68.07 61	96.21 29	86.69 12	95.34 19	93.23 61

MTAPA		87.23 22	87.00 22	87.90 17	94.18 23	74.25 2	86.58 169	92.02 64	79.45 19	85.88 22	94.80 7	68.07 61	96.21 29	86.69 12	95.34 19	93.23 61

PGM-MVS		86.68 29	86.27 32	87.90 17	94.22 21	73.38 16	90.22 51	93.04 25	75.53 77	83.86 51	94.42 17	67.87 65	96.64 17	82.70 43	94.57 34	93.66 43

HSP-MVS		89.28 2	89.76 2	87.85 20	94.28 18	73.46 15	92.90 8	92.73 41	80.27 13	91.35 5	94.16 23	78.35 3	96.77 11	89.59 1	94.22 44	93.33 59

DeepC-MVS_fast	79.65 3	86.91 27	86.62 28	87.76 21	93.52 32	72.37 37	91.26 30	93.04 25	76.62 57	84.22 48	93.36 38	71.44 37	96.76 12	80.82 56	95.33 21	94.16 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		89.15 3	89.63 3	87.73 22	94.49 11	71.69 44	93.83 2	93.96 5	75.70 73	91.06 6	96.03 1	76.84 4	97.03 7	89.09 2	95.65 15	94.47 13

MCST-MVS		87.37 20	87.25 18	87.73 22	94.53 10	72.46 34	89.82 57	93.82 7	73.07 130	84.86 39	92.89 48	76.22 6	96.33 25	84.89 21	95.13 24	94.40 14

TSAR-MVS + MP.		88.02 12	88.11 10	87.72 24	93.68 29	72.13 40	91.41 29	92.35 54	74.62 94	88.90 9	93.85 30	75.75 9	96.00 35	87.80 6	94.63 33	95.04 2

mPP-MVS		86.67 30	86.32 31	87.72 24	94.41 15	73.55 10	92.74 11	92.22 57	76.87 50	82.81 66	94.25 20	66.44 76	96.24 28	82.88 42	94.28 41	93.38 56

DeepC-MVS	79.81 2	87.08 26	86.88 26	87.69 26	91.16 66	72.32 38	90.31 48	93.94 6	77.12 44	82.82 64	94.23 21	72.13 34	97.09 6	84.83 22	95.37 18	93.65 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CP-MVS		87.11 24	86.92 24	87.68 27	94.20 22	73.86 5	93.98 1	92.82 39	76.62 57	83.68 54	94.46 14	67.93 63	95.95 37	84.20 31	94.39 38	93.23 61

MP-MVS-pluss		87.67 14	87.72 13	87.54 28	93.64 30	72.04 41	89.80 59	93.50 13	75.17 86	86.34 20	95.29 4	70.86 40	96.00 35	88.78 3	96.04 5	94.58 8
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CANet		86.45 32	86.10 36	87.51 29	90.09 84	70.94 52	89.70 63	92.59 45	81.78 4	81.32 81	91.43 74	70.34 44	97.23 4	84.26 29	93.36 48	94.37 15

HPM-MVS		87.11 24	86.98 23	87.50 30	93.88 26	72.16 39	92.19 19	93.33 18	76.07 69	83.81 53	93.95 29	69.77 51	96.01 34	85.15 16	94.66 32	94.32 19
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
ACMMP		85.89 42	85.39 44	87.38 31	93.59 31	72.63 29	92.74 11	93.18 23	76.78 52	80.73 90	93.82 31	64.33 93	96.29 26	82.67 44	90.69 69	93.23 61
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
PHI-MVS		86.43 33	86.17 35	87.24 32	90.88 72	70.96 50	92.27 18	94.07 4	72.45 142	85.22 30	91.90 61	69.47 54	96.42 24	83.28 36	95.94 7	94.35 16

APD-MVS		87.44 17	87.52 15	87.19 33	94.24 20	72.39 35	91.86 24	92.83 37	73.01 131	88.58 10	94.52 10	73.36 23	96.49 23	84.26 29	95.01 25	92.70 77
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CDPH-MVS		85.76 43	85.29 48	87.17 34	93.49 33	71.08 48	88.58 97	92.42 51	68.32 217	84.61 41	93.48 34	72.32 32	96.15 32	79.00 66	95.43 17	94.28 20

train_agg		86.43 33	86.20 33	87.13 35	93.26 37	72.96 20	88.75 91	91.89 73	68.69 206	85.00 32	93.10 42	74.43 14	95.41 50	84.97 17	95.71 12	93.02 70

agg_prior3		86.16 39	85.85 40	87.10 36	93.31 34	72.86 24	88.77 89	91.68 83	68.29 218	84.26 47	92.83 50	72.83 28	95.42 49	84.97 17	95.71 12	93.02 70

CSCG		86.41 35	86.19 34	87.07 37	92.91 44	72.48 33	90.81 37	93.56 12	73.95 102	83.16 60	91.07 81	75.94 7	95.19 57	79.94 63	94.38 39	93.55 52

Regformer-2		86.63 31	86.53 29	86.95 38	89.33 108	71.24 47	88.43 99	92.05 63	82.50 1	86.88 18	90.09 100	74.45 13	95.61 41	84.38 27	90.63 70	94.01 29

TSAR-MVS + GP.		85.71 44	85.33 45	86.84 39	91.34 64	72.50 32	89.07 80	87.28 213	76.41 59	85.80 24	90.22 98	74.15 21	95.37 54	81.82 47	91.88 57	92.65 80

test12				86.80 40	92.63 49	70.70 58		91.79 78		82.71 67		71.67 35	96.16 31		94.50 35	93.54 53

DeepPCF-MVS	80.84 1	88.10 8	88.56 8	86.73 41	92.24 54	69.03 83	89.57 67	93.39 17	77.53 39	89.79 8	94.12 25	78.98 2	96.58 22	85.66 14	95.72 11	94.58 8

SD-MVS		88.06 9	88.50 9	86.71 42	92.60 52	72.71 25	91.81 25	93.19 22	77.87 32	90.32 7	94.00 28	74.83 11	93.78 118	87.63 8	94.27 42	93.65 48

Regformer-1		86.41 35	86.33 30	86.64 43	89.33 108	70.93 53	88.43 99	91.39 94	82.14 3	86.65 19	90.09 100	74.39 16	95.01 66	83.97 32	90.63 70	93.97 31

agg_prior1		86.22 38	86.09 37	86.62 44	92.85 45	71.94 42	88.59 96	91.78 79	68.96 203	84.41 44	93.18 41	74.94 10	94.93 67	84.75 24	95.33 21	93.01 72

3Dnovator	76.31 5	83.38 68	82.31 75	86.59 45	87.94 157	72.94 23	90.64 40	92.14 61	77.21 42	75.47 181	92.83 50	58.56 172	94.72 80	73.24 125	92.71 53	92.13 97

HPM-MVS_fast		85.35 50	84.95 51	86.57 46	93.69 28	70.58 59	92.15 21	91.62 84	73.89 106	82.67 68	94.09 26	62.60 125	95.54 44	80.93 54	92.93 50	93.57 51

Regformer-4		85.68 45	85.45 43	86.35 47	88.95 124	69.67 73	88.29 109	91.29 96	81.73 5	85.36 28	90.01 103	72.62 30	95.35 55	83.28 36	87.57 105	94.03 27

test_prior3		86.73 28	86.86 27	86.33 48	92.61 50	69.59 75	88.85 86	92.97 33	75.41 79	84.91 34	93.54 32	74.28 18	95.48 45	83.31 34	95.86 8	93.91 33

test_prior				86.33 48	92.61 50	69.59 75		92.97 33					95.48 45			93.91 33

MVS_111021_HR		85.14 53	84.75 53	86.32 50	91.65 62	72.70 26	85.98 185	90.33 125	76.11 68	82.08 72	91.61 68	71.36 38	94.17 98	81.02 52	92.58 54	92.08 98

APD-MVS_3200maxsize		85.97 40	85.88 38	86.22 51	92.69 48	69.53 77	91.93 23	92.99 30	73.54 117	85.94 21	94.51 13	65.80 83	95.61 41	83.04 40	92.51 55	93.53 54

DP-MVS Recon		83.11 71	82.09 77	86.15 52	94.44 12	70.92 54	88.79 88	92.20 58	70.53 174	79.17 101	91.03 84	64.12 95	96.03 33	68.39 168	90.14 76	91.50 111

EPNet		83.72 61	82.92 67	86.14 53	84.22 227	69.48 78	91.05 35	85.27 233	81.30 8	76.83 150	91.65 65	66.09 79	95.56 43	76.00 95	93.85 46	93.38 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
casdiffmvs		83.96 57	83.25 61	86.07 54	88.48 141	69.60 74	89.26 72	92.40 52	68.07 219	82.82 64	90.03 102	69.77 51	94.86 75	81.79 48	86.64 121	93.75 41

abl_6		85.23 51	84.95 51	86.07 54	92.23 55	70.48 60	90.80 38	92.08 62	73.51 118	85.26 29	94.16 23	62.75 118	95.92 38	82.46 46	91.30 64	91.81 105

canonicalmvs		85.91 41	85.87 39	86.04 56	89.84 90	69.44 81	90.45 46	93.00 28	76.70 56	88.01 14	91.23 76	73.28 24	93.91 109	81.50 51	88.80 90	94.77 6

casdiffmvs1		84.76 55	84.33 55	86.04 56	89.40 105	68.78 90	89.67 64	92.54 46	66.43 235	85.41 26	90.75 89	72.88 27	94.76 78	81.64 49	90.24 75	94.57 10

alignmvs		85.48 46	85.32 46	85.96 58	89.51 102	69.47 79	89.74 61	92.47 47	76.17 67	87.73 16	91.46 73	70.32 45	93.78 118	81.51 50	88.95 86	94.63 7

DELS-MVS		85.41 49	85.30 47	85.77 59	88.49 140	67.93 112	85.52 207	93.44 15	78.70 28	83.63 57	89.03 127	74.57 12	95.71 40	80.26 61	94.04 45	93.66 43
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
Regformer-3		85.23 51	85.07 49	85.70 60	88.95 124	69.01 85	88.29 109	89.91 144	80.95 9	85.01 31	90.01 103	72.45 31	94.19 96	82.50 45	87.57 105	93.90 35

UA-Net		85.08 54	84.96 50	85.45 61	92.07 57	68.07 110	89.78 60	90.86 108	82.48 2	84.60 42	93.20 40	69.35 55	95.22 56	71.39 145	90.88 68	93.07 68

Vis-MVSNet		83.46 65	82.80 69	85.43 62	90.25 81	68.74 94	90.30 49	90.13 134	76.33 65	80.87 89	92.89 48	61.00 154	94.20 95	72.45 134	90.97 66	93.35 58
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EI-MVSNet-Vis-set		84.19 56	83.81 56	85.31 63	88.18 150	67.85 113	87.66 124	89.73 148	80.05 17	82.95 61	89.59 112	70.74 43	94.82 76	80.66 58	84.72 140	93.28 60

mvs-test1		80.88 102	79.40 121	85.29 64	85.13 214	69.75 72	89.28 70	88.10 198	74.99 87	76.44 160	86.72 189	57.27 182	94.26 94	73.53 121	83.18 164	91.87 102

MAR-MVS		81.84 87	80.70 94	85.27 65	91.32 65	71.53 46	89.82 57	90.92 105	69.77 184	78.50 110	86.21 215	62.36 132	94.52 84	65.36 191	92.05 56	89.77 187
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
Effi-MVS+		83.62 63	83.08 63	85.24 66	88.38 146	67.45 118	88.89 84	89.15 166	75.50 78	82.27 70	88.28 146	69.61 53	94.45 86	77.81 78	87.84 103	93.84 38

MVSFormer		82.85 74	82.05 78	85.24 66	87.35 180	70.21 62	90.50 43	90.38 120	68.55 208	81.32 81	89.47 115	61.68 139	93.46 136	78.98 67	90.26 73	92.05 99

OPM-MVS		83.50 64	82.95 66	85.14 68	88.79 132	70.95 51	89.13 79	91.52 88	77.55 38	80.96 88	91.75 63	60.71 157	94.50 85	79.67 64	86.51 124	89.97 176

HQP_MVS		83.64 62	83.14 62	85.14 68	90.08 85	68.71 96	91.25 31	92.44 48	79.12 23	78.92 104	91.00 85	60.42 163	95.38 52	78.71 69	86.32 126	91.33 115

EI-MVSNet-UG-set		83.81 59	83.38 59	85.09 70	87.87 158	67.53 117	87.44 136	89.66 149	79.74 18	82.23 71	89.41 121	70.24 46	94.74 79	79.95 62	83.92 146	92.99 73

QAPM		80.88 102	79.50 119	85.03 71	88.01 156	68.97 87	91.59 26	92.00 67	66.63 233	75.15 194	92.16 55	57.70 177	95.45 47	63.52 202	88.76 91	90.66 137

PCF-MVS	73.52 7	80.38 123	78.84 137	85.01 72	87.71 173	68.99 86	83.65 244	91.46 93	63.00 269	77.77 135	90.28 95	66.10 78	95.09 64	61.40 222	88.22 102	90.94 126
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
nrg030		83.88 58	83.53 57	84.96 73	86.77 193	69.28 82	90.46 45	92.67 42	74.79 91	82.95 61	91.33 75	72.70 29	93.09 154	80.79 57	79.28 215	92.50 84

VDD-MVS		83.01 73	82.36 74	84.96 73	91.02 69	66.40 134	88.91 83	88.11 197	77.57 35	84.39 46	93.29 39	52.19 223	93.91 109	77.05 87	88.70 92	94.57 10

PVSNet_Blended_VisFu		82.62 77	81.83 82	84.96 73	90.80 74	69.76 71	88.74 93	91.70 82	69.39 189	78.96 103	88.46 141	65.47 85	94.87 74	74.42 111	88.57 95	90.24 156

CPTT-MVS		83.73 60	83.33 60	84.92 76	93.28 36	70.86 55	92.09 22	90.38 120	68.75 205	79.57 97	92.83 50	60.60 161	93.04 158	80.92 55	91.56 61	90.86 128

OMC-MVS		82.69 75	81.97 81	84.85 77	88.75 134	67.42 119	87.98 116	90.87 107	74.92 90	79.72 96	91.65 65	62.19 136	93.96 104	75.26 107	86.42 125	93.16 66

PAPM_NR		83.02 72	82.41 72	84.82 78	92.47 53	66.37 135	87.93 120	91.80 77	73.82 111	77.32 142	90.66 91	67.90 64	94.90 71	70.37 150	89.48 83	93.19 65

lupinMVS		81.39 97	80.27 103	84.76 79	87.35 180	70.21 62	85.55 203	86.41 221	62.85 272	81.32 81	88.61 136	61.68 139	92.24 182	78.41 73	90.26 73	91.83 103

jason		81.39 97	80.29 102	84.70 80	86.63 194	69.90 69	85.95 186	86.77 217	63.24 266	81.07 87	89.47 115	61.08 153	92.15 183	78.33 74	90.07 78	92.05 99
jason: jason.
EPP-MVSNet		83.40 67	83.02 65	84.57 81	90.13 82	64.47 183	92.32 17	90.73 109	74.45 96	79.35 100	91.10 79	69.05 58	95.12 59	72.78 128	87.22 112	94.13 22

UGNet		80.83 106	79.59 114	84.54 82	88.04 154	68.09 109	89.42 68	88.16 196	76.95 48	76.22 166	89.46 117	49.30 272	93.94 106	68.48 166	90.31 72	91.60 107
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
LPG-MVS_test		82.08 82	81.27 86	84.50 83	89.23 116	68.76 92	90.22 51	91.94 71	75.37 81	76.64 155	91.51 70	54.29 206	94.91 69	78.44 71	83.78 147	89.83 180

LGP-MVS_train				84.50 83	89.23 116	68.76 92		91.94 71	75.37 81	76.64 155	91.51 70	54.29 206	94.91 69	78.44 71	83.78 147	89.83 180

MSLP-MVS++		85.43 48	85.76 42	84.45 85	91.93 59	70.24 61	90.71 39	92.86 35	77.46 41	84.22 48	92.81 53	67.16 72	92.94 161	80.36 59	94.35 40	90.16 158

Effi-MVS+-dtu		80.03 134	78.57 142	84.42 86	85.13 214	68.74 94	88.77 89	88.10 198	74.99 87	74.97 198	83.49 265	57.27 182	93.36 141	73.53 121	80.88 189	91.18 119

HQP-MVS		82.61 78	82.02 79	84.37 87	89.33 108	66.98 127	89.17 74	92.19 59	76.41 59	77.23 145	90.23 97	60.17 166	95.11 60	77.47 81	85.99 131	91.03 122

ACMP	74.13 6	81.51 95	80.57 96	84.36 88	89.42 104	68.69 99	89.97 55	91.50 92	74.46 95	75.04 197	90.41 94	53.82 211	94.54 82	77.56 80	82.91 167	89.86 179
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
原ACMM1				84.35 89	93.01 43	68.79 89		92.44 48	63.96 264	81.09 86	91.57 69	66.06 80	95.45 47	67.19 177	94.82 31	88.81 214

PS-MVSNAJss		82.07 83	81.31 85	84.34 90	86.51 196	67.27 123	89.27 71	91.51 89	71.75 154	79.37 99	90.22 98	63.15 106	94.27 90	77.69 79	82.36 176	91.49 112

CLD-MVS		82.31 80	81.65 83	84.29 91	88.47 142	67.73 116	85.81 194	92.35 54	75.78 71	78.33 118	86.58 202	64.01 96	94.35 87	76.05 94	87.48 110	90.79 129
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
API-MVS		81.99 85	81.23 87	84.26 92	90.94 70	70.18 67	91.10 34	89.32 159	71.51 160	78.66 108	88.28 146	65.26 86	95.10 63	64.74 198	91.23 65	87.51 250

114514_t		80.68 113	79.51 118	84.20 93	94.09 25	67.27 123	89.64 66	91.11 101	58.75 306	74.08 204	90.72 90	58.10 175	95.04 65	69.70 156	89.42 84	90.30 155

IS-MVSNet		83.15 69	82.81 68	84.18 94	89.94 88	63.30 210	91.59 26	88.46 194	79.04 25	79.49 98	92.16 55	65.10 88	94.28 89	67.71 169	91.86 58	94.95 3

MVS_111021_LR		82.61 78	82.11 76	84.11 95	88.82 129	71.58 45	85.15 212	86.16 226	74.69 93	80.47 92	91.04 82	62.29 133	90.55 235	80.33 60	90.08 77	90.20 157

Anonymous20240529		80.19 129	78.89 136	84.10 96	90.60 75	64.75 170	88.95 82	90.90 106	65.97 242	80.59 91	91.17 78	49.97 264	93.73 127	69.16 162	82.70 172	93.81 39

OpenMVS	72.83 10	79.77 139	78.33 150	84.09 97	85.17 211	69.91 68	90.57 41	90.97 104	66.70 229	72.17 229	91.91 60	54.70 202	93.96 104	61.81 219	90.95 67	88.41 234

AdaColmap		80.58 117	79.42 120	84.06 98	93.09 42	68.91 88	89.36 69	88.97 176	69.27 193	75.70 180	89.69 108	57.20 185	95.77 39	63.06 206	88.41 100	87.50 251

1121		80.84 104	79.77 109	84.05 99	93.11 41	70.78 56	84.66 221	85.42 232	57.37 316	81.76 78	92.02 58	63.41 99	94.12 99	67.28 174	92.93 50	87.26 257

VDDNet		81.52 93	80.67 95	84.05 99	90.44 78	64.13 190	89.73 62	85.91 229	71.11 164	83.18 59	93.48 34	50.54 259	93.49 135	73.40 123	88.25 101	94.54 12

xiu_mvs_v1_base_debu		80.80 109	79.72 111	84.03 101	87.35 180	70.19 64	85.56 200	88.77 185	69.06 198	81.83 73	88.16 148	50.91 246	92.85 163	78.29 75	87.56 107	89.06 199

xiu_mvs_v1_base		80.80 109	79.72 111	84.03 101	87.35 180	70.19 64	85.56 200	88.77 185	69.06 198	81.83 73	88.16 148	50.91 246	92.85 163	78.29 75	87.56 107	89.06 199

xiu_mvs_v1_base_debi		80.80 109	79.72 111	84.03 101	87.35 180	70.19 64	85.56 200	88.77 185	69.06 198	81.83 73	88.16 148	50.91 246	92.85 163	78.29 75	87.56 107	89.06 199

PAPR		81.66 91	80.89 93	83.99 104	90.27 80	64.00 194	86.76 165	91.77 81	68.84 204	77.13 149	89.50 113	67.63 66	94.88 73	67.55 171	88.52 98	93.09 67

XVG-OURS		80.41 119	79.23 130	83.97 105	85.64 205	69.02 84	83.03 257	90.39 119	71.09 165	77.63 137	91.49 72	54.62 204	91.35 216	75.71 100	83.47 156	91.54 109

XVG-OURS-SEG-HR		80.81 107	79.76 110	83.96 106	85.60 206	68.78 90	83.54 247	90.50 117	70.66 172	76.71 153	91.66 64	60.69 158	91.26 218	76.94 88	81.58 183	91.83 103

HyFIR lowres test		77.53 189	75.40 209	83.94 107	89.59 97	66.62 131	80.36 277	88.64 190	56.29 322	76.45 157	85.17 242	57.64 178	93.28 143	61.34 224	83.10 166	91.91 101

tttt0517		79.40 148	77.91 157	83.90 108	88.10 152	63.84 197	88.37 106	84.05 244	71.45 161	76.78 151	89.12 124	49.93 267	94.89 72	70.18 151	83.18 164	92.96 74

test_normal		79.81 138	78.45 144	83.89 109	82.70 277	65.40 151	85.82 193	89.48 154	69.39 189	70.12 256	85.66 232	57.15 186	93.71 128	77.08 86	88.62 94	92.56 82

DI_MVS_plusplus_test		79.89 137	78.58 141	83.85 110	82.89 273	65.32 155	86.12 182	89.55 151	69.64 188	70.55 247	85.82 229	57.24 184	93.81 116	76.85 89	88.55 96	92.41 87

PS-MVSNAJ		81.69 89	81.02 92	83.70 111	89.51 102	68.21 108	84.28 236	90.09 135	70.79 168	81.26 85	85.62 234	63.15 106	94.29 88	75.62 102	88.87 89	88.59 226

Test4		77.83 182	75.90 200	83.62 112	80.24 307	65.25 157	85.27 208	90.67 110	69.03 201	66.48 297	83.75 261	43.07 305	93.00 160	75.93 96	88.66 93	92.62 81

xiu_mvs_v2_base		81.69 89	81.05 91	83.60 113	89.15 119	68.03 111	84.46 229	90.02 139	70.67 171	81.30 84	86.53 205	63.17 105	94.19 96	75.60 103	88.54 97	88.57 228

ACMM	73.20 8	80.78 112	79.84 108	83.58 114	89.31 113	68.37 103	89.99 54	91.60 85	70.28 178	77.25 143	89.66 109	53.37 214	93.53 134	74.24 114	82.85 168	88.85 212
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LFMVS		81.82 88	81.23 87	83.57 115	91.89 60	63.43 208	89.84 56	81.85 279	77.04 47	83.21 58	93.10 42	52.26 222	93.43 140	71.98 140	89.95 79	93.85 36

Fast-Effi-MVS+		80.81 107	79.92 106	83.47 116	88.85 126	64.51 177	85.53 205	89.39 157	70.79 168	78.49 111	85.06 245	67.54 67	93.58 130	67.03 180	86.58 122	92.32 89

CHOSEN 1792x2688		77.63 188	75.69 201	83.44 117	89.98 87	68.58 101	78.70 294	87.50 210	56.38 321	75.80 175	86.84 185	58.67 171	91.40 214	61.58 221	85.75 134	90.34 154

新几何1				83.42 118	93.13 39	70.71 57		85.48 231	57.43 315	81.80 76	91.98 59	63.28 101	92.27 180	64.60 199	92.99 49	87.27 256

DP-MVS		76.78 206	74.57 222	83.42 118	93.29 35	69.46 80	88.55 98	83.70 248	63.98 263	70.20 252	88.89 128	54.01 210	94.80 77	46.66 311	81.88 180	86.01 286

MVS_Test		83.15 69	83.06 64	83.41 120	86.86 190	63.21 213	86.11 183	92.00 67	74.31 97	82.87 63	89.44 120	70.03 47	93.21 145	77.39 83	88.50 99	93.81 39

LS3D		76.95 204	74.82 220	83.37 121	90.45 77	67.36 122	89.15 78	86.94 216	61.87 282	69.52 265	90.61 92	51.71 239	94.53 83	46.38 314	86.71 120	88.21 236

IB-MVS	68.01 15	75.85 226	73.36 234	83.31 122	84.76 218	66.03 138	83.38 248	85.06 235	70.21 180	69.40 266	81.05 294	45.76 293	94.66 81	65.10 194	75.49 262	89.25 196
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MG-MVS		83.41 66	83.45 58	83.28 123	92.74 47	62.28 227	88.17 113	89.50 153	75.22 84	81.49 79	92.74 54	66.75 73	95.11 60	72.85 127	91.58 60	92.45 85

jajsoiax		79.29 150	77.96 155	83.27 124	84.68 220	66.57 133	89.25 73	90.16 133	69.20 195	75.46 182	89.49 114	45.75 294	93.13 152	76.84 90	80.80 191	90.11 161

test_djsdf		80.30 125	79.32 124	83.27 124	83.98 244	65.37 154	90.50 43	90.38 120	68.55 208	76.19 167	88.70 132	56.44 189	93.46 136	78.98 67	80.14 202	90.97 125

0601test		81.17 99	80.47 99	83.24 126	89.13 120	63.62 199	86.21 180	89.95 141	72.43 145	81.78 77	89.61 111	57.50 180	93.58 130	70.75 146	86.90 117	92.52 83

mvs_tets		79.13 153	77.77 162	83.22 127	84.70 219	66.37 135	89.17 74	90.19 132	69.38 191	75.40 185	89.46 117	44.17 300	93.15 150	76.78 91	80.70 193	90.14 159

thisisatest0515		77.33 199	75.38 210	83.18 128	85.27 210	63.80 198	82.11 262	83.27 256	65.06 250	75.91 172	83.84 259	49.54 269	94.27 90	67.24 176	86.19 128	91.48 114

CDS-MVSNet		79.07 154	77.70 163	83.17 129	87.60 175	68.23 107	84.40 233	86.20 225	67.49 226	76.36 161	86.54 204	61.54 142	90.79 232	61.86 218	87.33 111	90.49 148
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v7		80.24 126	79.26 129	83.15 130	84.07 241	64.94 165	87.56 131	90.67 110	72.26 148	78.28 120	86.51 206	61.45 144	94.03 103	75.14 108	77.41 229	90.49 148

v7n		78.97 157	77.58 166	83.14 131	83.45 257	65.51 149	88.32 107	91.21 98	73.69 113	72.41 226	86.32 213	57.93 176	93.81 116	69.18 161	75.65 259	90.11 161

BH-RMVSNet		79.61 141	78.44 146	83.14 131	89.38 107	65.93 141	84.95 216	87.15 214	73.56 116	78.19 126	89.79 107	56.67 188	93.36 141	59.53 237	86.74 119	90.13 160

UniMVSNet (Re)		81.60 92	81.11 90	83.09 133	88.38 146	64.41 185	87.60 125	93.02 27	78.42 31	78.56 109	88.16 148	69.78 50	93.26 144	69.58 158	76.49 249	91.60 107

PLC	70.83 11	78.05 174	76.37 185	83.08 134	91.88 61	67.80 114	88.19 112	89.46 155	64.33 259	69.87 262	88.38 143	53.66 212	93.58 130	58.86 242	82.73 170	87.86 243
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v1192		79.59 142	78.43 147	83.07 135	83.55 255	64.52 175	86.93 157	90.58 114	70.83 167	77.78 134	85.90 225	59.15 169	93.94 106	73.96 116	77.19 233	90.76 130

v2v482		80.23 127	79.29 128	83.05 136	83.62 253	64.14 189	87.04 153	89.97 140	73.61 114	78.18 127	87.22 175	61.10 152	93.82 115	76.11 93	76.78 247	91.18 119

TAMVS		78.89 159	77.51 167	83.03 137	87.80 168	67.79 115	84.72 220	85.05 236	67.63 222	76.75 152	87.70 161	62.25 134	90.82 231	58.53 246	87.13 113	90.49 148

v1144		80.03 134	79.03 133	83.01 138	83.78 251	64.51 177	87.11 151	90.57 115	71.96 153	78.08 130	86.20 216	61.41 145	93.94 106	74.93 109	77.23 231	90.60 140

cascas		76.72 207	74.64 221	82.99 139	85.78 203	65.88 143	82.33 260	89.21 165	60.85 288	72.74 214	81.02 295	47.28 282	93.75 122	67.48 172	85.02 136	89.34 194

anonymousdsp		78.60 162	77.15 172	82.98 140	80.51 305	67.08 125	87.24 144	89.53 152	65.66 245	75.16 193	87.19 177	52.52 216	92.25 181	77.17 85	79.34 214	89.61 191

v1neww		80.40 120	79.54 115	82.98 140	84.10 235	64.51 177	87.57 127	90.22 129	73.25 123	78.47 112	86.65 197	62.83 114	93.86 112	75.72 98	77.02 235	90.58 143

v7new		80.40 120	79.54 115	82.98 140	84.10 235	64.51 177	87.57 127	90.22 129	73.25 123	78.47 112	86.65 197	62.83 114	93.86 112	75.72 98	77.02 235	90.58 143

v6		80.40 120	79.54 115	82.98 140	84.09 237	64.50 181	87.57 127	90.22 129	73.25 123	78.47 112	86.63 199	62.84 113	93.86 112	75.73 97	77.02 235	90.58 143

v10		79.74 140	78.67 138	82.97 144	84.06 242	64.95 164	87.88 122	90.62 113	73.11 129	75.11 195	86.56 203	61.46 143	94.05 102	73.68 117	75.55 261	89.90 177

diffmvs1		82.63 76	82.51 70	82.96 145	83.87 246	63.47 205	85.19 209	89.42 156	75.58 76	81.38 80	89.89 105	67.42 70	91.69 203	81.01 53	88.88 88	93.71 42

UniMVSNet_NR-MVSNet		81.88 86	81.54 84	82.92 146	88.46 143	63.46 206	87.13 149	92.37 53	80.19 15	78.38 116	89.14 123	71.66 36	93.05 156	70.05 152	76.46 250	92.25 92

DU-MVS		81.12 100	80.52 98	82.90 147	87.80 168	63.46 206	87.02 154	91.87 75	79.01 26	78.38 116	89.07 125	65.02 89	93.05 156	70.05 152	76.46 250	92.20 94

PVSNet_Blended		80.98 101	80.34 100	82.90 147	88.85 126	65.40 151	84.43 231	92.00 67	67.62 223	78.11 128	85.05 246	66.02 81	94.27 90	71.52 143	89.50 82	89.01 206

testing_2		75.73 227	73.34 235	82.89 149	77.37 327	65.22 158	84.10 239	90.54 116	69.09 197	60.46 323	81.15 293	40.48 319	92.84 166	76.36 92	80.54 197	90.60 140

v1141		80.19 129	79.31 125	82.85 150	83.84 248	64.12 191	87.14 146	90.08 136	73.13 126	78.27 121	86.39 208	62.67 123	93.75 122	75.40 105	76.83 244	90.68 134

divwei89l23v2f112		80.19 129	79.31 125	82.85 150	83.84 248	64.11 193	87.13 149	90.08 136	73.13 126	78.27 121	86.39 208	62.69 121	93.75 122	75.40 105	76.82 245	90.68 134

v1		80.19 129	79.31 125	82.85 150	83.83 250	64.12 191	87.14 146	90.07 138	73.13 126	78.27 121	86.38 212	62.72 120	93.75 122	75.41 104	76.82 245	90.68 134

CANet_DTU		80.61 114	79.87 107	82.83 153	85.60 206	63.17 216	87.36 137	88.65 189	76.37 63	75.88 173	88.44 142	53.51 213	93.07 155	73.30 124	89.74 81	92.25 92

V42		79.38 149	78.24 152	82.83 153	81.10 299	65.50 150	85.55 203	89.82 145	71.57 159	78.21 125	86.12 217	60.66 159	93.18 149	75.64 101	75.46 263	89.81 182

Anonymous20231211		78.97 157	77.69 164	82.81 155	90.54 76	64.29 187	90.11 53	91.51 89	65.01 252	76.16 171	88.13 152	50.56 258	93.03 159	69.68 157	77.56 227	91.11 121

v1921920		79.22 151	78.03 154	82.80 156	83.30 260	63.94 196	86.80 161	90.33 125	69.91 182	77.48 139	85.53 236	58.44 173	93.75 122	73.60 120	76.85 242	90.71 133

v8		79.97 136	79.02 134	82.80 156	84.09 237	64.50 181	87.96 117	90.29 128	74.13 101	75.24 192	86.81 186	62.88 111	93.89 111	74.39 112	75.40 264	90.00 169

TAPA-MVS	73.13 9	79.15 152	77.94 156	82.79 158	89.59 97	62.99 220	88.16 114	91.51 89	65.77 243	77.14 148	91.09 80	60.91 155	93.21 145	50.26 288	87.05 114	92.17 96
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v144192		79.47 145	78.37 148	82.78 159	83.35 258	63.96 195	86.96 155	90.36 123	69.99 181	77.50 138	85.67 231	60.66 159	93.77 120	74.27 113	76.58 248	90.62 138

NR-MVSNet		80.23 127	79.38 122	82.78 159	87.80 168	63.34 209	86.31 177	91.09 102	79.01 26	72.17 229	89.07 125	67.20 71	92.81 167	66.08 186	75.65 259	92.20 94

v52		77.94 180	76.37 185	82.67 161	79.39 319	65.52 147	86.43 172	89.94 142	72.28 146	72.15 231	84.94 248	55.70 193	93.44 138	73.64 118	72.84 287	89.06 199

V4		77.95 178	76.37 185	82.67 161	79.40 318	65.52 147	86.43 172	89.94 142	72.28 146	72.14 232	84.95 247	55.72 192	93.44 138	73.64 118	72.86 286	89.05 203

v1240		78.99 156	77.78 161	82.64 163	83.21 262	63.54 202	86.62 168	90.30 127	69.74 187	77.33 141	85.68 230	57.04 187	93.76 121	73.13 126	76.92 238	90.62 138

Fast-Effi-MVS+-dtu		78.02 175	76.49 182	82.62 164	83.16 266	66.96 129	86.94 156	87.45 212	72.45 142	71.49 240	84.17 255	54.79 201	91.58 211	67.61 170	80.31 199	89.30 195

F-COLMAP		76.38 216	74.33 227	82.50 165	89.28 114	66.95 130	88.41 102	89.03 168	64.05 261	66.83 293	88.61 136	46.78 285	92.89 162	57.48 255	78.55 217	87.67 246

TranMVSNet+NR-MVSNet		80.84 104	80.31 101	82.42 166	87.85 159	62.33 225	87.74 123	91.33 95	80.55 12	77.99 131	89.86 106	65.23 87	92.62 169	67.05 179	75.24 268	92.30 90

MVSTER		79.01 155	77.88 158	82.38 167	83.07 267	64.80 168	84.08 240	88.95 178	69.01 202	78.69 106	87.17 178	54.70 202	92.43 174	74.69 110	80.57 195	89.89 178

PVSNet_BlendedMVS		80.60 115	80.02 104	82.36 168	88.85 126	65.40 151	86.16 181	92.00 67	69.34 192	78.11 128	86.09 218	66.02 81	94.27 90	71.52 143	82.06 177	87.39 252

diffmvs		81.48 96	81.21 89	82.31 169	83.28 261	62.72 222	85.09 213	88.63 191	74.99 87	78.31 119	88.81 131	65.80 83	91.36 215	79.03 65	86.95 116	92.84 76

EI-MVSNet		80.52 118	79.98 105	82.12 170	84.28 224	63.19 215	86.41 174	88.95 178	74.18 99	78.69 106	87.54 167	66.62 74	92.43 174	72.57 133	80.57 195	90.74 132

IterMVS-LS		80.06 133	79.38 122	82.11 171	85.89 201	63.20 214	86.79 162	89.34 158	74.19 98	75.45 183	86.72 189	66.62 74	92.39 176	72.58 132	76.86 241	90.75 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
BH-untuned		79.47 145	78.60 140	82.05 172	89.19 118	65.91 142	86.07 184	88.52 193	72.18 150	75.42 184	87.69 162	61.15 151	93.54 133	60.38 229	86.83 118	86.70 270

ACMH+	68.96 14	76.01 224	74.01 229	82.03 173	88.60 137	65.31 156	88.86 85	87.55 208	70.25 179	67.75 284	87.47 169	41.27 315	93.19 148	58.37 247	75.94 255	87.60 248

Anonymous202405211		78.25 167	77.01 174	81.99 174	91.03 68	60.67 239	84.77 219	83.90 246	70.65 173	80.00 94	91.20 77	41.08 317	91.43 213	65.21 192	85.26 135	93.85 36

GA-MVS		76.87 205	75.17 218	81.97 175	82.75 275	62.58 223	81.44 271	86.35 224	72.16 152	74.74 200	82.89 268	46.20 289	92.02 186	68.85 164	81.09 187	91.30 117

CNLPA		78.08 173	76.79 179	81.97 175	90.40 79	71.07 49	87.59 126	84.55 239	66.03 241	72.38 227	89.64 110	57.56 179	86.04 292	59.61 235	83.35 161	88.79 215

v748		77.97 177	76.65 181	81.92 177	82.29 283	63.28 211	87.53 132	90.35 124	73.50 119	70.76 246	85.55 235	58.28 174	92.81 167	68.81 165	72.76 288	89.67 189

MVS		78.19 171	76.99 175	81.78 178	85.66 204	66.99 126	84.66 221	90.47 118	55.08 326	72.02 234	85.27 241	63.83 97	94.11 101	66.10 185	89.80 80	84.24 304

v13		77.50 194	76.07 198	81.77 179	84.23 226	65.07 162	87.34 138	88.91 183	72.92 132	68.35 281	81.97 283	62.53 129	91.69 203	72.20 139	66.22 323	88.56 229

ACMH	67.68 16	75.89 225	73.93 230	81.77 179	88.71 135	66.61 132	88.62 95	89.01 171	69.81 183	66.78 294	86.70 194	41.95 314	91.51 212	55.64 265	78.14 223	87.17 259
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v12		77.51 192	76.09 197	81.76 181	84.22 227	64.99 163	87.30 141	88.93 182	72.92 132	68.48 280	81.97 283	62.54 128	91.70 202	72.24 138	66.21 324	88.58 227

V9		77.52 190	76.11 196	81.73 182	84.19 231	64.89 166	87.26 143	88.94 181	72.87 135	68.65 276	81.96 285	62.65 124	91.72 199	72.27 137	66.24 322	88.60 224

V14		77.52 190	76.12 193	81.70 183	84.15 232	64.77 169	87.21 145	88.95 178	72.80 136	68.79 273	81.94 286	62.69 121	91.72 199	72.31 136	66.27 321	88.60 224

v17		77.68 185	76.35 189	81.69 184	84.15 232	64.65 172	87.33 139	88.99 173	72.70 139	69.25 271	82.07 279	62.82 116	91.79 193	72.69 131	67.15 314	88.63 220

v16		77.69 184	76.36 188	81.68 185	84.15 232	64.63 174	87.33 139	88.99 173	72.69 140	69.31 270	82.08 278	62.80 117	91.79 193	72.70 130	67.23 312	88.63 220

v15		77.51 192	76.12 193	81.66 186	84.09 237	64.65 172	87.14 146	88.96 177	72.76 137	68.90 272	81.91 287	62.74 119	91.73 197	72.32 135	66.29 320	88.61 223

v18		77.67 187	76.35 189	81.64 187	84.09 237	64.47 183	87.27 142	89.01 171	72.59 141	69.39 267	82.04 280	62.85 112	91.80 192	72.72 129	67.20 313	88.63 220

VNet		82.21 81	82.41 72	81.62 188	90.82 73	60.93 235	84.47 227	89.78 146	76.36 64	84.07 50	91.88 62	64.71 91	90.26 237	70.68 147	88.89 87	93.66 43

XVG-ACMP-BASELINE		76.11 223	74.27 228	81.62 188	83.20 263	64.67 171	83.60 246	89.75 147	69.75 185	71.85 235	87.09 182	32.78 338	92.11 184	69.99 154	80.43 198	88.09 238

v11		77.45 195	76.06 199	81.59 190	84.22 227	64.52 175	87.11 151	89.02 169	72.76 137	68.76 274	81.90 288	62.09 137	91.71 201	71.98 140	66.73 315	88.56 229

PAPM		77.68 185	76.40 184	81.51 191	87.29 185	61.85 231	83.78 243	89.59 150	64.74 254	71.23 241	88.70 132	62.59 126	93.66 129	52.66 278	87.03 115	89.01 206

v148		78.72 160	77.80 160	81.47 192	82.73 276	61.96 230	86.30 178	88.08 200	73.26 122	76.18 168	85.47 238	62.46 131	92.36 178	71.92 142	73.82 281	90.09 163

LTVRE_ROB	69.57 13	76.25 217	74.54 224	81.41 193	88.60 137	64.38 186	79.24 287	89.12 167	70.76 170	69.79 264	87.86 155	49.09 274	93.20 147	56.21 264	80.16 200	86.65 271
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
GBi-Net		78.40 164	77.40 168	81.40 194	87.60 175	63.01 217	88.39 103	89.28 160	71.63 156	75.34 187	87.28 171	54.80 198	91.11 222	62.72 207	79.57 210	90.09 163

test1		78.40 164	77.40 168	81.40 194	87.60 175	63.01 217	88.39 103	89.28 160	71.63 156	75.34 187	87.28 171	54.80 198	91.11 222	62.72 207	79.57 210	90.09 163

FMVSNet1		77.44 196	76.12 193	81.40 194	86.81 192	63.01 217	88.39 103	89.28 160	70.49 175	74.39 203	87.28 171	49.06 275	91.11 222	60.91 226	78.52 218	90.09 163

FMVSNet2		78.20 170	77.21 171	81.20 197	87.60 175	62.89 221	87.47 135	89.02 169	71.63 156	75.29 191	87.28 171	54.80 198	91.10 225	62.38 211	79.38 213	89.61 191

TR-MVS		77.44 196	76.18 192	81.20 197	88.24 149	63.24 212	84.61 225	86.40 222	67.55 225	77.81 133	86.48 207	54.10 208	93.15 150	57.75 254	82.72 171	87.20 258

ab-mvs		79.51 143	78.97 135	81.14 199	88.46 143	60.91 236	83.84 242	89.24 164	70.36 176	79.03 102	88.87 129	63.23 104	90.21 239	65.12 193	82.57 174	92.28 91

MVP-Stereo		76.12 222	74.46 226	81.13 200	85.37 209	69.79 70	84.42 232	87.95 202	65.03 251	67.46 287	85.33 240	53.28 215	91.73 197	58.01 252	83.27 162	81.85 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FIs		82.07 83	82.42 71	81.04 201	88.80 131	58.34 256	88.26 111	93.49 14	76.93 49	78.47 112	91.04 82	69.92 49	92.34 179	69.87 155	84.97 137	92.44 86

FMVSNet3		77.88 181	76.85 177	80.97 202	86.84 191	62.36 224	86.52 171	88.77 185	71.13 163	75.34 187	86.66 196	54.07 209	91.10 225	62.72 207	79.57 210	89.45 193

BH-w/o		78.21 169	77.33 170	80.84 203	88.81 130	65.13 161	84.87 217	87.85 204	69.75 185	74.52 202	84.74 252	61.34 146	93.11 153	58.24 250	85.84 133	84.27 303

COLMAP_ROB	66.92 17	73.01 260	70.41 267	80.81 204	87.13 187	65.63 146	88.30 108	84.19 243	62.96 270	63.80 314	87.69 162	38.04 328	92.56 172	46.66 311	74.91 270	84.24 304
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
VPA-MVSNet		80.60 115	80.55 97	80.76 205	88.07 153	60.80 238	86.86 159	91.58 86	75.67 74	80.24 93	89.45 119	63.34 100	90.25 238	70.51 149	79.22 216	91.23 118

EG-PatchMatch MVS		74.04 239	71.82 255	80.71 206	84.92 217	67.42 119	85.86 189	88.08 200	66.04 240	64.22 311	83.85 258	35.10 337	92.56 172	57.44 256	80.83 190	82.16 322

MSDG		73.36 256	70.99 263	80.49 207	84.51 222	65.80 144	80.71 274	86.13 227	65.70 244	65.46 302	83.74 262	44.60 297	90.91 230	51.13 283	76.89 239	84.74 300

pmmvs4		74.03 240	71.91 252	80.39 208	81.96 286	68.32 104	81.45 270	82.14 272	59.32 300	69.87 262	85.13 243	52.40 219	88.13 278	60.21 231	74.74 272	84.73 301

HY-MVS	69.67 12	77.95 178	77.15 172	80.36 209	87.57 179	60.21 243	83.37 255	87.78 205	66.11 238	75.37 186	87.06 184	63.27 102	90.48 236	61.38 223	82.43 175	90.40 153

mvs_anonymous		79.42 147	79.11 132	80.34 210	84.45 223	57.97 262	82.59 258	87.62 207	67.40 228	76.17 170	88.56 139	68.47 60	89.59 247	70.65 148	86.05 130	93.47 55

1112_ss		77.40 198	76.43 183	80.32 211	89.11 123	60.41 242	83.65 244	87.72 206	62.13 280	73.05 212	86.72 189	62.58 127	89.97 241	62.11 216	80.80 191	90.59 142

tpmp4_e23		73.45 248	71.17 262	80.31 212	83.55 255	59.56 247	81.88 263	82.33 269	57.94 311	70.51 249	81.62 289	51.19 244	91.63 209	53.96 272	77.51 228	89.75 188

WR-MVS		79.49 144	79.22 131	80.27 213	88.79 132	58.35 255	85.06 214	88.61 192	78.56 29	77.65 136	88.34 144	63.81 98	90.66 234	64.98 196	77.22 232	91.80 106

1314		76.53 210	75.30 213	80.21 214	83.93 245	62.32 226	84.66 221	88.81 184	60.23 292	70.16 255	84.07 257	55.30 196	90.73 233	67.37 173	83.21 163	87.59 249

semantic-postprocess				80.11 215	82.69 278	64.85 167		83.47 253	69.16 196	70.49 250	84.15 256	50.83 250	88.15 277	69.23 160	72.14 292	87.34 254

FC-MVSNet-test		81.52 93	82.02 79	80.03 216	88.42 145	55.97 295	87.95 118	93.42 16	77.10 45	77.38 140	90.98 87	69.96 48	91.79 193	68.46 167	84.50 141	92.33 88

testdata				79.97 217	90.90 71	64.21 188		84.71 237	59.27 301	85.40 27	92.91 47	62.02 138	89.08 263	68.95 163	91.37 63	86.63 272

thres400		76.50 211	75.37 211	79.86 218	89.13 120	57.65 268	85.17 210	83.60 249	73.41 120	76.45 157	86.39 208	52.12 224	91.95 187	48.33 296	83.75 149	90.00 169

test_0402		72.79 263	70.44 266	79.84 219	88.13 151	65.99 140	85.93 187	84.29 241	65.57 246	67.40 289	85.49 237	46.92 284	92.61 170	35.88 341	74.38 275	80.94 326

OurMVSNet-221017-0		74.26 238	72.42 244	79.80 220	83.76 252	59.59 245	85.92 188	86.64 218	66.39 236	66.96 292	87.58 164	39.46 322	91.60 210	65.76 189	69.27 304	88.22 235

SixPastTwentyTwo		73.37 254	71.26 261	79.70 221	85.08 216	57.89 264	85.57 199	83.56 251	71.03 166	65.66 301	85.88 226	42.10 312	92.57 171	59.11 240	63.34 329	88.65 219

thres600view7		76.50 211	75.44 207	79.68 222	89.40 105	57.16 273	85.53 205	83.23 257	73.79 112	76.26 165	87.09 182	51.89 230	91.89 191	48.05 302	83.72 154	90.00 169

CR-MVSNet		73.37 254	71.27 260	79.67 223	81.32 297	65.19 159	75.92 309	80.30 293	59.92 295	72.73 215	81.19 291	52.50 217	86.69 286	59.84 233	77.71 224	87.11 262

RPMNet		71.62 268	68.94 275	79.67 223	81.32 297	65.19 159	75.92 309	78.30 313	57.60 314	72.73 215	76.45 325	52.30 221	86.69 286	48.14 301	77.71 224	87.11 262

tfpn111		76.54 209	75.51 206	79.61 225	89.52 99	56.99 276	85.83 190	83.23 257	73.94 103	76.32 162	87.12 179	51.89 230	92.06 185	48.04 303	83.73 153	89.78 183

AllTest		70.96 273	68.09 283	79.58 226	85.15 212	63.62 199	84.58 226	79.83 298	62.31 278	60.32 324	86.73 187	32.02 339	88.96 268	50.28 286	71.57 296	86.15 281

TestCases				79.58 226	85.15 212	63.62 199		79.83 298	62.31 278	60.32 324	86.73 187	32.02 339	88.96 268	50.28 286	71.57 296	86.15 281

conf200view11		76.55 208	75.55 204	79.57 228	89.52 99	56.99 276	85.83 190	83.23 257	73.94 103	76.32 162	87.12 179	51.89 230	91.95 187	48.33 296	83.75 149	89.78 183

tfpn200view9		76.42 214	75.37 211	79.55 229	89.13 120	57.65 268	85.17 210	83.60 249	73.41 120	76.45 157	86.39 208	52.12 224	91.95 187	48.33 296	83.75 149	89.07 197

thres100view900		76.50 211	75.55 204	79.33 230	89.52 99	56.99 276	85.83 190	83.23 257	73.94 103	76.32 162	87.12 179	51.89 230	91.95 187	48.33 296	83.75 149	89.07 197

CostFormer		75.24 233	73.90 231	79.27 231	82.65 279	58.27 257	80.80 272	82.73 266	61.57 283	75.33 190	83.13 267	55.52 194	91.07 228	64.98 196	78.34 222	88.45 232

Test_1112_low_res		76.40 215	75.44 207	79.27 231	89.28 114	58.09 258	81.69 267	87.07 215	59.53 299	72.48 219	86.67 195	61.30 147	89.33 252	60.81 228	80.15 201	90.41 152

DWT-MVSNet_test		73.70 242	71.86 253	79.21 233	82.91 272	58.94 250	82.34 259	82.17 271	65.21 247	71.05 245	78.31 313	44.21 299	90.17 240	63.29 205	77.28 230	88.53 231

K. test v3		71.19 271	68.51 277	79.21 233	83.04 269	57.78 267	84.35 234	76.91 321	72.90 134	62.99 317	82.86 269	39.27 323	91.09 227	61.65 220	52.66 346	88.75 216

view600		76.20 218	75.21 214	79.16 235	89.64 92	55.82 296	85.74 195	82.06 274	73.88 107	75.74 176	87.85 156	51.84 234	91.66 205	46.75 307	83.42 157	90.00 169

view800		76.20 218	75.21 214	79.16 235	89.64 92	55.82 296	85.74 195	82.06 274	73.88 107	75.74 176	87.85 156	51.84 234	91.66 205	46.75 307	83.42 157	90.00 169

conf0.05thres1000		76.20 218	75.21 214	79.16 235	89.64 92	55.82 296	85.74 195	82.06 274	73.88 107	75.74 176	87.85 156	51.84 234	91.66 205	46.75 307	83.42 157	90.00 169

tfpn		76.20 218	75.21 214	79.16 235	89.64 92	55.82 296	85.74 195	82.06 274	73.88 107	75.74 176	87.85 156	51.84 234	91.66 205	46.75 307	83.42 157	90.00 169

Anonymous20240521		76.96 203	76.26 191	79.07 239	86.63 194	56.37 290	87.57 127	91.09 102	72.19 149	71.23 241	88.10 153	54.30 205	91.20 221	58.34 248	76.89 239	89.65 190

lessismore_v0				78.97 240	81.01 300	57.15 274		65.99 355		61.16 321	82.82 270	39.12 324	91.34 217	59.67 234	46.92 350	88.43 233

pm-mvs1		77.25 200	76.68 180	78.93 241	84.22 227	58.62 253	86.41 174	88.36 195	71.37 162	73.31 208	88.01 154	61.22 150	89.15 262	64.24 200	73.01 285	89.03 205

PatchFormer-LS_test		74.50 235	73.05 237	78.86 242	82.95 271	59.55 248	81.65 268	82.30 270	67.44 227	71.62 238	78.15 316	52.34 220	88.92 270	65.05 195	75.90 256	88.12 237

thres200		75.55 229	74.47 225	78.82 243	87.78 171	57.85 265	83.07 256	83.51 252	72.44 144	75.84 174	84.42 254	52.08 226	91.75 196	47.41 305	83.64 155	86.86 266

VPNet		78.69 161	78.66 139	78.76 244	88.31 148	55.72 301	84.45 230	86.63 219	76.79 51	78.26 124	90.55 93	59.30 168	89.70 246	66.63 181	77.05 234	90.88 127

tpm2		73.26 257	71.46 257	78.63 245	83.34 259	56.71 283	80.65 275	80.40 292	56.63 320	73.55 206	82.02 281	51.80 238	91.24 219	56.35 263	78.42 221	87.95 240

pmmvs6		74.69 234	73.39 233	78.61 246	81.38 294	57.48 271	86.64 167	87.95 202	64.99 253	70.18 253	86.61 200	50.43 260	89.52 248	62.12 215	70.18 302	88.83 213

WR-MVS_H		78.51 163	78.49 143	78.56 247	88.02 155	56.38 289	88.43 99	92.67 42	77.14 43	73.89 205	87.55 166	66.25 77	89.24 254	58.92 241	73.55 283	90.06 167

RPSCF		73.23 258	71.46 257	78.54 248	82.50 281	59.85 244	82.18 261	82.84 265	58.96 303	71.15 244	89.41 121	45.48 296	84.77 301	58.82 243	71.83 294	91.02 124

pmmvs-eth3d		70.50 278	67.83 287	78.52 249	77.37 327	66.18 137	81.82 264	81.51 281	58.90 304	63.90 313	80.42 300	42.69 308	86.28 291	58.56 245	65.30 326	83.11 315

PatchmatchNet		73.12 259	71.33 259	78.49 250	83.18 264	60.85 237	79.63 283	78.57 311	64.13 260	71.73 236	79.81 306	51.20 243	85.97 293	57.40 257	76.36 252	88.66 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test1		73.49 247	71.85 254	78.41 251	84.05 243	62.17 228	79.96 281	79.29 302	66.30 237	72.38 227	79.58 307	51.95 229	85.08 299	55.46 266	77.67 226	87.99 239

IterMVS		74.29 237	72.94 238	78.35 252	81.53 291	63.49 204	81.58 269	82.49 267	68.06 220	69.99 259	83.69 263	51.66 240	85.54 295	65.85 188	71.64 295	86.01 286
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ITE_SJBPF				78.22 253	81.77 288	60.57 240		83.30 255	69.25 194	67.54 286	87.20 176	36.33 334	87.28 284	54.34 270	74.62 273	86.80 267

ppachtmachnet_test		70.04 282	67.34 292	78.14 254	79.80 312	61.13 233	79.19 289	80.59 288	59.16 302	65.27 304	79.29 308	46.75 286	87.29 283	49.33 292	66.72 316	86.00 288

tfpnnormal		74.39 236	73.16 236	78.08 255	86.10 200	58.05 259	84.65 224	87.53 209	70.32 177	71.22 243	85.63 233	54.97 197	89.86 242	43.03 331	75.02 269	86.32 278

Vis-MVSNet (Re-imp)		78.36 166	78.45 144	78.07 256	88.64 136	51.78 324	86.70 166	79.63 300	74.14 100	75.11 195	90.83 88	61.29 148	89.75 244	58.10 251	91.60 59	92.69 79

TransMVSNet (Re)		75.39 232	74.56 223	77.86 257	85.50 208	57.10 275	86.78 163	86.09 228	72.17 151	71.53 239	87.34 170	63.01 110	89.31 253	56.84 261	61.83 332	87.17 259

PEN-MVS		77.73 183	77.69 164	77.84 258	87.07 188	53.91 310	87.91 121	91.18 99	77.56 37	73.14 211	88.82 130	61.23 149	89.17 261	59.95 232	72.37 289	90.43 151

CP-MVSNet		78.22 168	78.34 149	77.84 258	87.83 166	54.54 306	87.94 119	91.17 100	77.65 33	73.48 207	88.49 140	62.24 135	88.43 274	62.19 213	74.07 276	90.55 146

PS-CasMVS		78.01 176	78.09 153	77.77 260	87.71 173	54.39 308	88.02 115	91.22 97	77.50 40	73.26 209	88.64 135	60.73 156	88.41 275	61.88 217	73.88 280	90.53 147

OpenMVS_ROB	64.09 19	70.56 277	68.19 280	77.65 261	80.26 306	59.41 249	85.01 215	82.96 264	58.76 305	65.43 303	82.33 274	37.63 331	91.23 220	45.34 320	76.03 254	82.32 320

Patchmatch-RL test		70.24 280	67.78 289	77.61 262	77.43 326	59.57 246	71.16 324	70.33 344	62.94 271	68.65 276	72.77 335	50.62 251	85.49 296	69.58 158	66.58 318	87.77 245

Baseline_NR-MVSNet		78.15 172	78.33 150	77.61 262	85.79 202	56.21 293	86.78 163	85.76 230	73.60 115	77.93 132	87.57 165	65.02 89	88.99 265	67.14 178	75.33 265	87.63 247

DTE-MVSNet		76.99 202	76.80 178	77.54 264	86.24 198	53.06 321	87.52 133	90.66 112	77.08 46	72.50 217	88.67 134	60.48 162	89.52 248	57.33 258	70.74 300	90.05 168

conf0.01		73.67 244	72.42 244	77.42 265	87.85 159	53.28 315	83.38 248	79.08 303	68.40 211	72.45 220	86.08 219	50.60 252	89.19 255	44.25 322	79.66 204	89.78 183

conf0.002		73.67 244	72.42 244	77.42 265	87.85 159	53.28 315	83.38 248	79.08 303	68.40 211	72.45 220	86.08 219	50.60 252	89.19 255	44.25 322	79.66 204	89.78 183

LCM-MVSNet-Re		77.05 201	76.94 176	77.36 267	87.20 186	51.60 325	80.06 279	80.46 291	75.20 85	67.69 285	86.72 189	62.48 130	88.98 266	63.44 203	89.25 85	91.51 110

tpm cat1		70.57 276	68.31 279	77.35 268	82.41 282	57.95 263	78.08 299	80.22 296	52.04 337	68.54 279	77.66 320	52.00 228	87.84 281	51.77 279	72.07 293	86.25 279

MS-PatchMatch		73.83 241	72.67 240	77.30 269	83.87 246	66.02 139	81.82 264	84.66 238	61.37 286	68.61 278	82.82 270	47.29 281	88.21 276	59.27 238	84.32 144	77.68 336

EPNet_dtu		75.46 230	74.86 219	77.23 270	82.57 280	54.60 305	86.89 158	83.09 262	71.64 155	66.25 299	85.86 227	55.99 191	88.04 279	54.92 268	86.55 123	89.05 203
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TDRefinement		67.49 294	64.34 301	76.92 271	73.47 340	61.07 234	84.86 218	82.98 263	59.77 296	58.30 330	85.13 243	26.06 345	87.89 280	47.92 304	60.59 337	81.81 324

JIA-IIPM		66.32 303	62.82 309	76.82 272	77.09 329	61.72 232	65.34 345	75.38 325	58.04 310	64.51 309	62.32 348	42.05 313	86.51 289	51.45 282	69.22 305	82.21 321

PatchMatch-RL		72.38 265	70.90 264	76.80 273	88.60 137	67.38 121	79.53 284	76.17 323	62.75 274	69.36 268	82.00 282	45.51 295	84.89 300	53.62 274	80.58 194	78.12 334

tfpn_ndepth		73.70 242	72.75 239	76.52 274	87.78 171	54.92 304	84.32 235	80.28 295	67.57 224	72.50 217	84.82 249	50.12 262	89.44 251	45.73 317	81.66 182	85.20 293

tpmvs		71.09 272	69.29 272	76.49 275	82.04 285	56.04 294	78.92 292	81.37 283	64.05 261	67.18 291	78.28 314	49.74 268	89.77 243	49.67 291	72.37 289	83.67 309

thresconf0.02		73.39 250	72.42 244	76.31 276	87.85 159	53.28 315	83.38 248	79.08 303	68.40 211	72.45 220	86.08 219	50.60 252	89.19 255	44.25 322	79.66 204	86.48 273

tfpn_n400		73.39 250	72.42 244	76.31 276	87.85 159	53.28 315	83.38 248	79.08 303	68.40 211	72.45 220	86.08 219	50.60 252	89.19 255	44.25 322	79.66 204	86.48 273

tfpnconf		73.39 250	72.42 244	76.31 276	87.85 159	53.28 315	83.38 248	79.08 303	68.40 211	72.45 220	86.08 219	50.60 252	89.19 255	44.25 322	79.66 204	86.48 273

tfpnview11		73.39 250	72.42 244	76.31 276	87.85 159	53.28 315	83.38 248	79.08 303	68.40 211	72.45 220	86.08 219	50.60 252	89.19 255	44.25 322	79.66 204	86.48 273

tfpn1000		73.44 249	72.49 242	76.29 280	87.81 167	53.69 312	84.05 241	78.81 310	67.99 221	72.09 233	86.27 214	49.95 265	89.04 264	44.09 328	81.38 184	86.15 281

CMPMVS	51.72 21	70.19 281	68.16 281	76.28 281	73.15 342	57.55 270	79.47 285	83.92 245	48.02 344	56.48 337	84.81 250	43.13 304	86.42 290	62.67 210	81.81 181	84.89 298
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
USDC		70.33 279	68.37 278	76.21 282	80.60 303	56.23 292	79.19 289	86.49 220	60.89 287	61.29 320	85.47 238	31.78 341	89.47 250	53.37 275	76.21 253	82.94 319

gg-mvs-nofinetune		69.95 283	67.96 284	75.94 283	83.07 267	54.51 307	77.23 304	70.29 345	63.11 267	70.32 251	62.33 347	43.62 302	88.69 272	53.88 273	87.76 104	84.62 302

MDA-MVSNet-bldmvs		66.68 299	63.66 303	75.75 284	79.28 320	60.56 241	73.92 320	78.35 312	64.43 257	50.13 348	79.87 305	44.02 301	83.67 305	46.10 315	56.86 340	83.03 317

PVSNet	64.34 18	72.08 267	70.87 265	75.69 285	86.21 199	56.44 287	74.37 319	80.73 287	62.06 281	70.17 254	82.23 276	42.86 307	83.31 308	54.77 269	84.45 143	87.32 255

pmmvs5		71.55 269	70.20 269	75.61 286	77.83 324	56.39 288	81.74 266	80.89 284	57.76 312	67.46 287	84.49 253	49.26 273	85.32 298	57.08 260	75.29 266	85.11 297

our_test_3		69.14 287	67.00 293	75.57 287	79.80 312	58.80 251	77.96 300	77.81 315	59.55 298	62.90 318	78.25 315	47.43 280	83.97 303	51.71 280	67.58 311	83.93 308

WTY-MVS		75.65 228	75.68 202	75.57 287	86.40 197	56.82 280	77.92 301	82.40 268	65.10 249	76.18 168	87.72 160	63.13 109	80.90 315	60.31 230	81.96 178	89.00 208

Patchmtry		70.74 274	69.16 273	75.49 289	80.72 301	54.07 309	74.94 318	80.30 293	58.34 307	70.01 257	81.19 291	52.50 217	86.54 288	53.37 275	71.09 298	85.87 289

GG-mvs-BLEND				75.38 290	81.59 290	55.80 300	79.32 286	69.63 347		67.19 290	73.67 334	43.24 303	88.90 271	50.41 285	84.50 141	81.45 325

ambc				75.24 291	73.16 341	50.51 332	63.05 349	87.47 211		64.28 310	77.81 319	17.80 356	89.73 245	57.88 253	60.64 336	85.49 290

XXY-MVS		75.41 231	75.56 203	74.96 292	83.59 254	57.82 266	80.59 276	83.87 247	66.54 234	74.93 199	88.31 145	63.24 103	80.09 319	62.16 214	76.85 242	86.97 264

MIMVSNet		70.69 275	69.30 271	74.88 293	84.52 221	56.35 291	75.87 311	79.42 301	64.59 255	67.76 283	82.41 273	41.10 316	81.54 314	46.64 313	81.34 185	86.75 269

ADS-MVSNet2		66.20 304	63.33 304	74.82 294	79.92 310	58.75 252	67.55 341	75.19 327	53.37 333	65.25 305	75.86 326	42.32 310	80.53 317	41.57 334	68.91 306	85.18 294

TinyColmap		67.30 297	64.81 299	74.76 295	81.92 287	56.68 284	80.29 278	81.49 282	60.33 290	56.27 338	83.22 266	24.77 347	87.66 282	45.52 318	69.47 303	79.95 330

test-LLR		72.94 262	72.43 243	74.48 296	81.35 295	58.04 260	78.38 295	77.46 317	66.66 230	69.95 260	79.00 311	48.06 278	79.24 321	66.13 183	84.83 138	86.15 281

test-mter		71.41 270	70.39 268	74.48 296	81.35 295	58.04 260	78.38 295	77.46 317	60.32 291	69.95 260	79.00 311	36.08 335	79.24 321	66.13 183	84.83 138	86.15 281

tpm		72.37 266	71.71 256	74.35 298	82.19 284	52.00 322	79.22 288	77.29 319	64.56 256	72.95 213	83.68 264	51.35 241	83.26 309	58.33 249	75.80 257	87.81 244

CVMVSNet		72.99 261	72.58 241	74.25 299	84.28 224	50.85 330	86.41 174	83.45 254	44.56 346	73.23 210	87.54 167	49.38 270	85.70 294	65.90 187	78.44 220	86.19 280

FMVSNet5		69.50 285	67.96 284	74.15 300	82.97 270	55.35 302	80.01 280	82.12 273	62.56 276	63.02 315	81.53 290	36.92 332	81.92 312	48.42 295	74.06 277	85.17 296

MIMVSNet1		68.58 290	66.78 295	73.98 301	80.07 309	51.82 323	80.77 273	84.37 240	64.40 258	59.75 327	82.16 277	36.47 333	83.63 306	42.73 332	70.33 301	86.48 273

sss		73.60 246	73.64 232	73.51 302	82.80 274	55.01 303	76.12 307	81.69 280	62.47 277	74.68 201	85.85 228	57.32 181	78.11 327	60.86 227	80.93 188	87.39 252

PM-MVS		66.41 302	64.14 302	73.20 303	73.92 337	56.45 286	78.97 291	64.96 358	63.88 265	64.72 308	80.24 301	19.84 353	83.44 307	66.24 182	64.52 328	79.71 331

tpmrst		72.39 264	72.13 251	73.18 304	80.54 304	49.91 334	79.91 282	79.08 303	63.11 267	71.69 237	79.95 303	55.32 195	82.77 310	65.66 190	73.89 279	86.87 265

TESTMET0.1,1		69.89 284	69.00 274	72.55 305	79.27 321	56.85 279	78.38 295	74.71 333	57.64 313	68.09 282	77.19 322	37.75 329	76.70 332	63.92 201	84.09 145	84.10 307

LP		61.36 313	57.78 316	72.09 306	75.54 335	58.53 254	67.16 343	75.22 326	51.90 339	54.13 339	69.97 341	37.73 330	80.45 318	32.74 345	55.63 342	77.29 338

CHOSEN 280x420		66.51 301	64.71 300	71.90 307	81.45 292	63.52 203	57.98 353	68.95 352	53.57 332	62.59 319	76.70 323	46.22 288	75.29 339	55.25 267	79.68 203	76.88 342

EPMVS		69.02 288	68.16 281	71.59 308	79.61 315	49.80 336	77.40 303	66.93 354	62.82 273	70.01 257	79.05 309	45.79 292	77.86 329	56.58 262	75.26 267	87.13 261

YYNet1		65.03 305	62.91 307	71.38 309	75.85 332	56.60 285	69.12 335	74.66 335	57.28 317	54.12 340	77.87 318	45.85 291	74.48 341	49.95 289	61.52 334	83.05 316

MDA-MVSNet_test_wron		65.03 305	62.92 306	71.37 310	75.93 331	56.73 281	69.09 336	74.73 332	57.28 317	54.03 341	77.89 317	45.88 290	74.39 342	49.89 290	61.55 333	82.99 318

UnsupCasMVSNet_eth		67.33 296	65.99 297	71.37 310	73.48 339	51.47 327	75.16 314	85.19 234	65.20 248	60.78 322	80.93 298	42.35 309	77.20 331	57.12 259	53.69 345	85.44 291

PMMVS		69.34 286	68.67 276	71.35 312	75.67 333	62.03 229	75.17 313	73.46 338	50.00 342	68.68 275	79.05 309	52.07 227	78.13 326	61.16 225	82.77 169	73.90 344

EU-MVSNet		68.53 291	67.61 291	71.31 313	78.51 323	47.01 340	84.47 227	84.27 242	42.27 347	66.44 298	84.79 251	40.44 320	83.76 304	58.76 244	68.54 310	83.17 313

Anonymous20231206		68.60 289	67.80 288	71.02 314	80.23 308	50.75 331	78.30 298	80.47 290	56.79 319	66.11 300	82.63 272	46.35 287	78.95 323	43.62 330	75.70 258	83.36 312

dp		66.80 298	65.43 298	70.90 315	79.74 314	48.82 337	75.12 316	74.77 331	59.61 297	64.08 312	77.23 321	42.89 306	80.72 316	48.86 294	66.58 318	83.16 314

PatchT		68.46 292	67.85 286	70.29 316	80.70 302	43.93 344	72.47 322	74.88 329	60.15 293	70.55 247	76.57 324	49.94 266	81.59 313	50.58 284	74.83 271	85.34 292

UnsupCasMVSNet_bld		63.70 310	61.53 312	70.21 317	73.69 338	51.39 328	72.82 321	81.89 278	55.63 324	57.81 331	71.80 337	38.67 325	78.61 324	49.26 293	52.21 347	80.63 327

Patchmatch-test		64.82 307	63.24 305	69.57 318	79.42 317	49.82 335	63.49 348	69.05 351	51.98 338	59.95 326	80.13 302	50.91 246	70.98 350	40.66 336	73.57 282	87.90 242

LF4IMVS		64.02 309	62.19 310	69.50 319	70.90 347	53.29 314	76.13 306	77.18 320	52.65 336	58.59 328	80.98 296	23.55 348	76.52 333	53.06 277	66.66 317	78.68 333

test20.03		67.45 295	66.95 294	68.94 320	75.48 336	44.84 342	77.50 302	77.67 316	66.66 230	63.01 316	83.80 260	47.02 283	78.40 325	42.53 333	68.86 308	83.58 310

test0.0.03 1		68.00 293	67.69 290	68.90 321	77.55 325	47.43 338	75.70 312	72.95 340	66.66 230	66.56 295	82.29 275	48.06 278	75.87 336	44.97 321	74.51 274	83.41 311

PVSNet_0	57.27 20	61.67 312	59.27 313	68.85 322	79.61 315	57.44 272	68.01 339	73.44 339	55.93 323	58.54 329	70.41 340	44.58 298	77.55 330	47.01 306	35.91 352	71.55 346

ADS-MVSNet		64.36 308	62.88 308	68.78 323	79.92 310	47.17 339	67.55 341	71.18 343	53.37 333	65.25 305	75.86 326	42.32 310	73.99 344	41.57 334	68.91 306	85.18 294

pmmvs3		57.79 318	54.26 322	68.37 324	64.02 354	56.72 282	75.12 316	65.17 356	40.20 349	52.93 344	69.86 342	20.36 352	75.48 338	45.45 319	55.25 344	72.90 345

LCM-MVSNet		54.25 322	49.68 329	67.97 325	53.73 361	45.28 341	66.85 344	80.78 286	35.96 353	39.45 353	62.23 349	8.70 365	78.06 328	48.24 300	51.20 348	80.57 328

testgi		66.67 300	66.53 296	67.08 326	75.62 334	41.69 349	75.93 308	76.50 322	66.11 238	65.20 307	86.59 201	35.72 336	74.71 340	43.71 329	73.38 284	84.84 299

no-one		51.08 326	45.79 332	66.95 327	57.92 359	50.49 333	59.63 352	76.04 324	48.04 343	31.85 354	56.10 354	19.12 354	80.08 320	36.89 340	26.52 354	70.29 347

test1235678		58.74 317	56.89 320	64.30 328	69.70 348	41.87 348	71.05 325	74.87 330	54.06 328	50.63 347	71.53 338	25.30 346	74.10 343	31.80 349	63.10 330	76.93 340

ANet_high		50.57 328	46.10 331	63.99 329	48.67 364	39.13 351	70.99 327	80.85 285	61.39 285	31.18 356	57.70 352	17.02 357	73.65 345	31.22 350	15.89 361	79.18 332

test2356		59.50 314	58.08 314	63.74 330	71.23 346	41.88 347	67.59 340	72.42 342	53.72 331	57.65 332	70.74 339	26.31 344	72.40 347	32.03 348	71.06 299	76.93 340

MVS-HIRNet		59.14 315	57.67 317	63.57 331	81.65 289	43.50 345	71.73 323	65.06 357	39.59 351	51.43 346	57.73 351	38.34 327	82.58 311	39.53 337	73.95 278	64.62 351

testmv		53.85 323	51.03 325	62.31 332	61.46 356	38.88 353	70.95 328	74.69 334	51.11 341	41.26 350	66.85 344	14.28 359	72.13 348	29.19 351	49.51 349	75.93 343

testus		59.00 316	57.91 315	62.25 333	72.25 344	39.09 352	69.74 329	75.02 328	53.04 335	57.21 334	73.72 333	18.76 355	70.33 351	32.86 344	68.57 309	77.35 337

new-patchmatchnet		61.73 311	61.73 311	61.70 334	72.74 343	24.50 365	69.16 334	78.03 314	61.40 284	56.72 336	75.53 328	38.42 326	76.48 334	45.95 316	57.67 339	84.13 306

DSMNet-mixed		57.77 319	56.90 319	60.38 335	67.70 352	35.61 355	69.18 333	53.97 361	32.30 357	57.49 333	79.88 304	40.39 321	68.57 354	38.78 338	72.37 289	76.97 339

FPMVS		53.68 324	51.64 324	59.81 336	65.08 353	51.03 329	69.48 332	69.58 348	41.46 348	40.67 351	72.32 336	16.46 358	70.00 352	24.24 356	65.42 325	58.40 353

wuykxyi23d		39.76 334	33.18 338	59.51 337	46.98 365	44.01 343	57.70 354	67.74 353	24.13 359	13.98 365	34.33 359	1.27 370	71.33 349	34.23 343	18.23 357	63.18 352

1111		57.11 320	56.82 321	57.97 338	69.10 349	28.28 360	68.90 337	74.54 336	54.01 329	53.71 342	74.51 330	23.09 349	67.90 355	32.28 346	61.26 335	77.73 335

PMVS	37.38 22	44.16 332	40.28 334	55.82 339	40.82 367	42.54 346	65.12 346	63.99 359	34.43 354	24.48 358	57.12 353	3.92 367	76.17 335	17.10 359	55.52 343	48.75 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 331	41.86 333	55.16 340	77.03 330	51.52 326	32.50 361	80.52 289	32.46 355	27.12 357	35.02 358	9.52 364	75.50 337	22.31 357	60.21 338	38.45 358
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
testpf		56.51 321	57.58 318	53.30 341	71.99 345	41.19 350	46.89 358	69.32 350	58.06 309	52.87 345	69.45 343	27.99 343	72.73 346	59.59 236	62.07 331	45.98 356

new_pmnet		50.91 327	50.29 326	52.78 342	68.58 351	34.94 358	63.71 347	56.63 360	39.73 350	44.95 349	65.47 346	21.93 351	58.48 359	34.98 342	56.62 341	64.92 350

test12356		49.28 329	48.51 330	51.59 343	62.06 355	19.11 366	60.40 350	72.45 341	47.60 345	40.64 352	65.68 345	13.84 360	68.72 353	27.29 353	46.67 351	66.94 349

N_pmnet		52.79 325	53.26 323	51.40 344	78.99 322	7.68 369	69.52 331	3.89 370	51.63 340	57.01 335	74.98 329	40.83 318	65.96 357	37.78 339	64.67 327	80.56 329

PNet_i23d		38.26 335	35.42 336	46.79 345	58.74 357	35.48 356	59.65 351	51.25 362	32.45 356	23.44 361	47.53 356	2.04 369	58.96 358	25.60 355	18.09 359	45.92 357

PMMVS2		40.82 333	38.86 335	46.69 346	53.84 360	16.45 367	48.61 357	49.92 363	37.49 352	31.67 355	60.97 350	8.14 366	56.42 360	28.42 352	30.72 353	67.19 348

MVE	26.22 23	30.37 340	25.89 342	43.81 347	44.55 366	35.46 357	28.87 362	39.07 366	18.20 361	18.58 362	40.18 357	2.68 368	47.37 363	17.07 360	23.78 356	48.60 355
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		31.77 338	30.64 339	35.15 348	52.87 362	27.67 362	57.09 355	47.86 364	24.64 358	16.40 363	33.05 360	11.23 362	54.90 361	14.46 361	18.15 358	22.87 360

EMVS		30.81 339	29.65 340	34.27 349	50.96 363	25.95 364	56.58 356	46.80 365	24.01 360	15.53 364	30.68 361	12.47 361	54.43 362	12.81 362	17.05 360	22.43 361

.test1245		45.55 330	50.02 328	32.14 350	69.10 349	28.28 360	68.90 337	74.54 336	54.01 329	53.71 342	74.51 330	23.09 349	67.90 355	32.28 346	0.02 364	0.25 365

pcd1.5k->3k		34.07 337	35.26 337	30.50 351	86.92 189	0.00 372	0.00 363	91.58 86	0.00 367	0.00 369	0.00 369	56.23 190	0.00 369	0.00 366	82.60 173	91.49 112

DeepMVS_CX				27.40 352	40.17 368	26.90 363		24.59 369	17.44 362	23.95 359	48.61 355	9.77 363	26.48 364	18.06 358	24.47 355	28.83 359

wuyk23d		16.82 343	15.94 344	19.46 353	58.74 357	31.45 359	39.22 359	3.74 371	6.84 363	6.04 366	2.70 366	1.27 370	24.29 365	10.54 363	14.40 363	2.63 363

tmp_tt		18.61 342	21.40 343	10.23 354	4.82 369	10.11 368	34.70 360	30.74 368	1.48 364	23.91 360	26.07 362	28.42 342	13.41 366	27.12 354	15.35 362	7.17 362

test123		6.12 345	8.11 346	0.14 355	0.06 371	0.09 370	71.05 325	0.03 373	0.04 366	0.25 368	1.30 368	0.05 372	0.03 368	0.21 365	0.01 366	0.29 364

testmvs		6.04 346	8.02 347	0.10 356	0.08 370	0.03 371	69.74 329	0.04 372	0.05 365	0.31 367	1.68 367	0.02 373	0.04 367	0.24 364	0.02 364	0.25 365

test_part1				0.00 357		0.00 372	0.00 363	94.09 2				0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

v1.0		37.66 336	50.21 327	0.00 357	95.06 1	0.00 372	0.00 363	94.09 2	75.63 75	91.80 3	95.29 4	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

cdsmvs_eth3d_5k		19.96 341	26.61 341	0.00 357	0.00 372	0.00 372	0.00 363	89.26 163	0.00 367	0.00 369	88.61 136	61.62 141	0.00 369	0.00 366	0.00 367	0.00 367

pcd_1.5k_mvsjas		5.26 347	7.02 348	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	63.15 106	0.00 369	0.00 366	0.00 367	0.00 367

sosnet-low-res		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

sosnet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uncertanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

Regformer		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

ab-mvs-re		7.23 344	9.64 345	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	86.72 189	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

uanet		0.00 348	0.00 349	0.00 357	0.00 372	0.00 372	0.00 363	0.00 374	0.00 367	0.00 369	0.00 369	0.00 374	0.00 369	0.00 366	0.00 367	0.00 367

GSMVS																88.96 210

test_part2					95.06 1	72.65 28				91.80 3

sam_mvs1												51.32 242				88.96 210

sam_mvs												50.01 263

MTGPA								92.02 64

test_post1							78.90 293				5.43 365	48.81 277	85.44 297	59.25 239

test_post											5.46 364	50.36 261	84.24 302

patchmatchnet-post											74.00 332	51.12 245	88.60 273

MTMP							92.18 20	32.83 367

gm-plane-assit					81.40 293	53.83 311			62.72 275		80.94 297		92.39 176	63.40 204

test9_res														84.90 19	95.70 14	92.87 75

TEST9					93.26 37	72.96 20	88.75 91	91.89 73	68.44 210	85.00 32	93.10 42	74.36 17	95.41 50

test_8					93.13 39	72.57 31	88.68 94	91.84 76	68.69 206	84.87 38	93.10 42	74.43 14	95.16 58

agg_prior2														82.91 41	95.45 16	92.70 77

agg_prior					92.85 45	71.94 42		91.78 79		84.41 44			94.93 67

test_prior4						72.60 30	89.01 81

test_prior2							88.85 86		75.41 79	84.91 34	93.54 32	74.28 18		83.31 34	95.86 8

旧先验2							86.56 170		58.10 308	87.04 17			88.98 266	74.07 115

新几何2							86.29 179

旧先验1					91.96 58	65.79 145		86.37 223			93.08 46	69.31 56			92.74 52	88.74 217

无先验							87.48 134	88.98 175	60.00 294				94.12 99	67.28 174		88.97 209

原ACMM2							86.86 159

test222					91.50 63	68.26 106	84.16 237	83.20 261	54.63 327	79.74 95	91.63 67	58.97 170			91.42 62	86.77 268

testdata2													91.01 229	62.37 212

segment_acmp												73.08 25

testdata1							84.14 238		75.71 72

plane_prior7					90.08 85	68.51 102

plane_prior6					89.84 90	68.70 98						60.42 163

plane_prior5								92.44 48					95.38 52	78.71 69	86.32 126	91.33 115

plane_prior4											91.00 85

plane_prior3						68.60 100			78.44 30	78.92 104

plane_prior2							91.25 31		79.12 23

plane_prior1					89.90 89

plane_prior						68.71 96	90.38 47		77.62 34						86.16 129

n2								0.00 374

nn								0.00 374

door-mid								69.98 346

test11								92.23 56

door								69.44 349

HQP5-MVS						66.98 127

HQP-NCC					89.33 108		89.17 74		76.41 59	77.23 145

ACMP_Plane					89.33 108		89.17 74		76.41 59	77.23 145

BP-MVS														77.47 81

HQP4-MVS										77.24 144			95.11 60			91.03 122

HQP3-MVS								92.19 59							85.99 131

HQP2-MVS												60.17 166

NP-MVS					89.62 96	68.32 104					90.24 96

MDTV_nov1_ep13_2view						37.79 354	75.16 314		55.10 325	66.53 296		49.34 271		53.98 271		87.94 241

MDTV_nov1_ep13			69.97 270		83.18 264	53.48 313	77.10 305	80.18 297	60.45 289	69.33 269	80.44 299	48.89 276	86.90 285	51.60 281	78.51 219

ACMMP++_ref															81.95 179

ACMMP++															81.25 186

Test By Simon												64.33 93

High-resolution multi-view benchmark