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.

SED-MVS		90.08 1	90.85 1	87.77 23	95.30 2	70.98 65	93.57 5	94.06 10	77.24 47	93.10 1	95.72 6	82.99 1	97.44 2	89.07 6	96.63 2	94.88 7

DVP-MVS		89.60 2	90.35 2	87.33 42	95.27 5	71.25 59	93.49 7	92.73 58	77.33 45	92.12 8	95.78 4	80.98 7	97.40 4	89.08 4	96.41 8	93.33 75

MSP-MVS		89.51 3	89.91 4	88.30 7	94.28 27	73.46 16	92.90 14	94.11 6	80.27 12	91.35 11	94.16 39	78.35 10	96.77 20	89.59 1	94.22 60	94.67 16

DPE-MVS		89.48 4	89.98 3	88.01 12	94.80 9	72.69 30	91.59 40	94.10 8	75.90 81	92.29 6	95.66 8	81.67 4	97.38 6	87.44 17	96.34 11	93.95 44

APDe-MVS		89.15 5	89.63 5	87.73 27	94.49 18	71.69 55	93.83 2	93.96 14	75.70 85	91.06 12	96.03 1	76.84 12	97.03 12	89.09 3	95.65 28	94.47 23

SMA-MVS		89.08 6	89.23 6	88.61 3	94.25 28	73.73 8	92.40 20	93.63 20	74.77 102	92.29 6	95.97 2	74.28 31	97.24 8	88.58 10	96.91 1	94.87 9
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HPM-MVS++		89.02 7	89.15 7	88.63 2	95.01 8	76.03 1	92.38 23	92.85 53	80.26 13	87.78 26	94.27 34	75.89 16	96.81 19	87.45 16	96.44 7	93.05 86

CNVR-MVS		88.93 8	89.13 8	88.33 5	94.77 10	73.82 7	90.51 59	93.00 43	80.90 9	88.06 24	94.06 44	76.43 13	96.84 17	88.48 11	95.99 15	94.34 27

SteuartSystems-ACMMP		88.72 9	88.86 9	88.32 6	92.14 75	72.96 24	93.73 3	93.67 19	80.19 14	88.10 23	94.80 14	73.76 36	97.11 10	87.51 15	95.82 20	94.90 6
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 10	88.74 10	87.64 35	92.78 64	71.95 50	92.40 20	94.74 2	75.71 83	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

DeepPCF-MVS	80.84 1	88.10 11	88.56 11	86.73 53	92.24 73	69.03 105	89.57 86	93.39 30	77.53 42	89.79 14	94.12 41	78.98 9	96.58 33	85.66 24	95.72 25	94.58 19

ETH3D-3000-0.1		88.09 12	88.29 13	87.50 38	92.76 65	71.89 53	91.43 44	94.70 3	74.47 108	88.86 18	94.61 19	75.23 21	95.84 54	86.62 23	95.92 17	94.78 13

SD-MVS		88.06 13	88.50 12	86.71 54	92.60 71	72.71 28	91.81 38	93.19 35	77.87 33	90.32 13	94.00 46	74.83 24	93.78 138	87.63 14	94.27 59	93.65 62
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
NCCC		88.06 13	88.01 17	88.24 8	94.41 22	73.62 9	91.22 49	92.83 54	81.50 6	85.79 41	93.47 56	73.02 42	97.00 14	84.90 30	94.94 40	94.10 35

ACMMP_NAP		88.05 15	88.08 16	87.94 15	93.70 42	73.05 21	90.86 52	93.59 21	76.27 77	88.14 22	95.09 13	71.06 56	96.67 25	87.67 13	96.37 10	94.09 36

TSAR-MVS + MP.		88.02 16	88.11 15	87.72 29	93.68 44	72.13 47	91.41 45	92.35 73	74.62 106	88.90 17	93.85 49	75.75 17	96.00 50	87.80 12	94.63 49	95.04 3
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		87.94 17	87.85 19	88.20 9	94.39 24	73.33 18	93.03 12	93.81 17	76.81 60	85.24 47	94.32 33	71.76 51	96.93 15	85.53 26	95.79 21	94.32 28

xxxxxxxxxxxxxcwj		87.88 18	87.92 18	87.77 23	93.80 39	72.35 42	90.47 62	89.69 162	74.31 111	89.16 15	95.10 11	75.65 18	96.19 42	87.07 18	96.01 13	94.79 11

testtj		87.78 19	87.78 20	87.77 23	94.55 16	72.47 37	92.23 29	93.49 25	74.75 103	88.33 21	94.43 30	73.27 39	97.02 13	84.18 45	94.84 44	93.82 52

MP-MVS		87.71 20	87.64 22	87.93 18	94.36 26	73.88 5	92.71 19	92.65 63	77.57 38	83.84 73	94.40 32	72.24 47	96.28 38	85.65 25	95.30 36	93.62 64
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 45	72.04 49	89.80 80	93.50 24	75.17 96	86.34 36	95.29 10	70.86 57	96.00 50	88.78 9	96.04 12	94.58 19
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 22	87.47 24	87.94 15	94.58 14	73.54 13	93.04 10	93.24 32	76.78 62	84.91 52	94.44 28	70.78 58	96.61 29	84.53 37	94.89 42	93.66 57

zzz-MVS		87.53 23	87.41 26	87.90 19	94.18 32	74.25 3	90.23 69	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

ETH3 D test6400		87.50 24	87.44 25	87.70 32	93.71 41	71.75 54	90.62 57	94.05 13	70.80 169	87.59 29	93.51 53	77.57 11	96.63 28	83.31 50	95.77 22	94.72 15

ACMMPR		87.44 25	87.23 30	88.08 11	94.64 11	73.59 10	93.04 10	93.20 34	76.78 62	84.66 59	94.52 21	68.81 79	96.65 26	84.53 37	94.90 41	94.00 42

APD-MVS		87.44 25	87.52 23	87.19 44	94.24 29	72.39 40	91.86 37	92.83 54	73.01 139	88.58 19	94.52 21	73.36 37	96.49 34	84.26 42	95.01 38	92.70 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 27	87.26 28	87.89 22	94.12 34	72.97 23	92.39 22	93.43 28	76.89 58	84.68 58	93.99 47	70.67 61	96.82 18	84.18 45	95.01 38	93.90 47

region2R		87.42 27	87.20 31	88.09 10	94.63 12	73.55 11	93.03 12	93.12 38	76.73 65	84.45 62	94.52 21	69.09 76	96.70 23	84.37 40	94.83 46	94.03 39

MCST-MVS		87.37 29	87.25 29	87.73 27	94.53 17	72.46 38	89.82 78	93.82 16	73.07 137	84.86 57	92.89 68	76.22 14	96.33 36	84.89 32	95.13 37	94.40 24

#test#		87.33 30	87.13 32	87.94 15	94.58 14	73.54 13	92.34 25	93.24 32	75.23 93	84.91 52	94.44 28	70.78 58	96.61 29	83.75 49	94.89 42	93.66 57

ETH3D cwj APD-0.16		87.31 31	87.27 27	87.44 40	91.60 82	72.45 39	90.02 74	94.37 4	71.76 153	87.28 30	94.27 34	75.18 22	96.08 46	85.16 27	95.77 22	93.80 55

MTAPA		87.23 32	87.00 33	87.90 19	94.18 32	74.25 3	86.58 176	92.02 87	79.45 19	85.88 38	94.80 14	68.07 81	96.21 40	86.69 21	95.34 32	93.23 78

XVS		87.18 33	86.91 36	88.00 13	94.42 20	73.33 18	92.78 15	92.99 45	79.14 21	83.67 76	94.17 38	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

HPM-MVS		87.11 34	86.98 34	87.50 38	93.88 38	72.16 46	92.19 30	93.33 31	76.07 80	83.81 74	93.95 48	69.77 70	96.01 49	85.15 28	94.66 48	94.32 28
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 34	86.92 35	87.68 34	94.20 31	73.86 6	93.98 1	92.82 57	76.62 67	83.68 75	94.46 25	67.93 83	95.95 52	84.20 44	94.39 55	93.23 78

DeepC-MVS	79.81 2	87.08 36	86.88 37	87.69 33	91.16 86	72.32 44	90.31 67	93.94 15	77.12 52	82.82 86	94.23 37	72.13 49	97.09 11	84.83 33	95.37 31	93.65 62
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 37	86.62 40	87.76 26	93.52 47	72.37 41	91.26 46	93.04 39	76.62 67	84.22 67	93.36 58	71.44 54	96.76 21	80.82 75	95.33 34	94.16 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SR-MVS		86.73 38	86.67 39	86.91 49	94.11 35	72.11 48	92.37 24	92.56 66	74.50 107	86.84 33	94.65 18	67.31 90	95.77 57	84.80 34	92.85 68	92.84 94

test_prior3		86.73 38	86.86 38	86.33 60	92.61 69	69.59 95	88.85 104	92.97 48	75.41 89	84.91 52	93.54 51	74.28 31	95.48 66	83.31 50	95.86 18	93.91 45

PGM-MVS		86.68 40	86.27 44	87.90 19	94.22 30	73.38 17	90.22 71	93.04 39	75.53 87	83.86 72	94.42 31	67.87 85	96.64 27	82.70 62	94.57 51	93.66 57

mPP-MVS		86.67 41	86.32 43	87.72 29	94.41 22	73.55 11	92.74 17	92.22 80	76.87 59	82.81 87	94.25 36	66.44 97	96.24 39	82.88 59	94.28 58	93.38 72

Regformer-2		86.63 42	86.53 41	86.95 48	89.33 124	71.24 62	88.43 118	92.05 86	82.50 1	86.88 32	90.09 121	74.45 26	95.61 60	84.38 39	90.63 92	94.01 41

CANet		86.45 43	86.10 49	87.51 37	90.09 104	70.94 69	89.70 84	92.59 65	81.78 4	81.32 103	91.43 94	70.34 63	97.23 9	84.26 42	93.36 64	94.37 25

train_agg		86.43 44	86.20 46	87.13 46	93.26 51	72.96 24	88.75 108	91.89 96	68.69 216	85.00 50	93.10 62	74.43 27	95.41 71	84.97 29	95.71 26	93.02 88

PHI-MVS		86.43 44	86.17 48	87.24 43	90.88 92	70.96 67	92.27 28	94.07 9	72.45 142	85.22 48	91.90 81	69.47 72	96.42 35	83.28 53	95.94 16	94.35 26

Regformer-1		86.41 46	86.33 42	86.64 55	89.33 124	70.93 70	88.43 118	91.39 115	82.14 3	86.65 34	90.09 121	74.39 29	95.01 89	83.97 47	90.63 92	93.97 43

CSCG		86.41 46	86.19 47	87.07 47	92.91 60	72.48 36	90.81 53	93.56 22	73.95 119	83.16 81	91.07 102	75.94 15	95.19 80	79.94 83	94.38 56	93.55 67

agg_prior1		86.22 48	86.09 50	86.62 56	92.85 61	71.94 51	88.59 115	91.78 102	68.96 211	84.41 63	93.18 61	74.94 23	94.93 91	84.75 35	95.33 34	93.01 89

test1172		86.20 49	86.22 45	86.12 67	93.95 37	69.89 90	91.79 39	92.28 75	75.07 97	86.40 35	94.58 20	65.00 114	95.56 62	84.34 41	92.60 72	92.90 92

APD-MVS_3200maxsize		85.97 50	85.88 51	86.22 64	92.69 67	69.53 97	91.93 34	92.99 45	73.54 129	85.94 37	94.51 24	65.80 106	95.61 60	83.04 57	92.51 74	93.53 69

canonicalmvs		85.91 51	85.87 52	86.04 69	89.84 110	69.44 103	90.45 65	93.00 43	76.70 66	88.01 25	91.23 96	73.28 38	93.91 133	81.50 69	88.80 113	94.77 14

ACMMP		85.89 52	85.39 57	87.38 41	93.59 46	72.63 32	92.74 17	93.18 36	76.78 62	80.73 112	93.82 50	64.33 117	96.29 37	82.67 63	90.69 91	93.23 78
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
SR-MVS-dyc-post		85.77 53	85.61 54	86.23 63	93.06 57	70.63 76	91.88 35	92.27 76	73.53 130	85.69 42	94.45 26	65.00 114	95.56 62	82.75 60	91.87 77	92.50 103

CDPH-MVS		85.76 54	85.29 61	87.17 45	93.49 48	71.08 63	88.58 116	92.42 71	68.32 221	84.61 60	93.48 54	72.32 46	96.15 45	79.00 86	95.43 30	94.28 30

TSAR-MVS + GP.		85.71 55	85.33 58	86.84 50	91.34 84	72.50 35	89.07 98	87.28 224	76.41 70	85.80 40	90.22 119	74.15 34	95.37 76	81.82 67	91.88 76	92.65 100

Regformer-4		85.68 56	85.45 56	86.35 59	88.95 141	69.67 94	88.29 128	91.29 117	81.73 5	85.36 45	90.01 123	72.62 44	95.35 77	83.28 53	87.57 125	94.03 39

alignmvs		85.48 57	85.32 59	85.96 70	89.51 118	69.47 99	89.74 82	92.47 67	76.17 78	87.73 28	91.46 93	70.32 64	93.78 138	81.51 68	88.95 110	94.63 18

3Dnovator+	77.84 4	85.48 57	84.47 71	88.51 4	91.08 87	73.49 15	93.18 9	93.78 18	80.79 10	76.66 175	93.37 57	60.40 179	96.75 22	77.20 105	93.73 63	95.29 2

MSLP-MVS++		85.43 59	85.76 53	84.45 104	91.93 78	70.24 81	90.71 55	92.86 52	77.46 44	84.22 67	92.81 72	67.16 92	92.94 177	80.36 79	94.35 57	90.16 176

DELS-MVS		85.41 60	85.30 60	85.77 71	88.49 158	67.93 134	85.52 207	93.44 27	78.70 28	83.63 78	89.03 149	74.57 25	95.71 59	80.26 81	94.04 61	93.66 57
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
HPM-MVS_fast		85.35 61	84.95 66	86.57 58	93.69 43	70.58 79	92.15 32	91.62 106	73.89 122	82.67 89	94.09 42	62.60 138	95.54 65	80.93 73	92.93 66	93.57 66

Regformer-3		85.23 62	85.07 63	85.70 72	88.95 141	69.01 107	88.29 128	89.91 156	80.95 8	85.01 49	90.01 123	72.45 45	94.19 119	82.50 64	87.57 125	93.90 47

abl_6		85.23 62	84.95 66	86.07 68	92.23 74	70.48 80	90.80 54	92.08 85	73.51 132	85.26 46	94.16 39	62.75 137	95.92 53	82.46 65	91.30 86	91.81 125

MVS_111021_HR		85.14 64	84.75 68	86.32 62	91.65 81	72.70 29	85.98 191	90.33 144	76.11 79	82.08 93	91.61 88	71.36 55	94.17 121	81.02 72	92.58 73	92.08 118

casdiffmvs		85.11 65	85.14 62	85.01 87	87.20 198	65.77 170	87.75 143	92.83 54	77.84 34	84.36 66	92.38 74	72.15 48	93.93 132	81.27 71	90.48 94	95.33 1

UA-Net		85.08 66	84.96 65	85.45 74	92.07 76	68.07 132	89.78 81	90.86 130	82.48 2	84.60 61	93.20 60	69.35 73	95.22 79	71.39 156	90.88 90	93.07 85

DPM-MVS		84.93 67	84.29 72	86.84 50	90.20 102	73.04 22	87.12 158	93.04 39	69.80 188	82.85 85	91.22 97	73.06 41	96.02 48	76.72 112	94.63 49	91.46 135

baseline		84.93 67	84.98 64	84.80 96	87.30 196	65.39 177	87.30 154	92.88 51	77.62 36	84.04 71	92.26 75	71.81 50	93.96 126	81.31 70	90.30 96	95.03 4

ETV-MVS		84.90 69	84.67 69	85.59 73	89.39 122	68.66 121	88.74 110	92.64 64	79.97 17	84.10 69	85.71 237	69.32 74	95.38 73	80.82 75	91.37 84	92.72 95

CS-MVS		84.76 70	84.61 70	85.22 82	89.66 112	66.43 157	90.23 69	93.56 22	76.52 69	82.59 90	85.93 232	70.41 62	95.80 55	79.93 84	92.68 71	93.42 71

EI-MVSNet-Vis-set		84.19 71	83.81 73	85.31 77	88.18 167	67.85 135	87.66 145	89.73 161	80.05 16	82.95 82	89.59 133	70.74 60	94.82 99	80.66 78	84.72 160	93.28 77

nrg030		83.88 72	83.53 74	84.96 89	86.77 206	69.28 104	90.46 64	92.67 60	74.79 101	82.95 82	91.33 95	72.70 43	93.09 171	80.79 77	79.28 227	92.50 103

EI-MVSNet-UG-set		83.81 73	83.38 76	85.09 85	87.87 176	67.53 140	87.44 151	89.66 163	79.74 18	82.23 92	89.41 142	70.24 65	94.74 102	79.95 82	83.92 168	92.99 90

CPTT-MVS		83.73 74	83.33 77	84.92 92	93.28 50	70.86 72	92.09 33	90.38 140	68.75 215	79.57 119	92.83 70	60.60 175	93.04 175	80.92 74	91.56 82	90.86 151

EPNet		83.72 75	82.92 83	86.14 66	84.22 242	69.48 98	91.05 51	85.27 247	81.30 7	76.83 169	91.65 85	66.09 101	95.56 62	76.00 117	93.85 62	93.38 72
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP_MVS		83.64 76	83.14 78	85.14 83	90.08 105	68.71 117	91.25 47	92.44 68	79.12 23	78.92 127	91.00 106	60.42 177	95.38 73	78.71 89	86.32 146	91.33 137

Effi-MVS+		83.62 77	83.08 79	85.24 80	88.38 163	67.45 141	88.89 102	89.15 178	75.50 88	82.27 91	88.28 168	69.61 71	94.45 109	77.81 99	87.84 123	93.84 51

OPM-MVS		83.50 78	82.95 82	85.14 83	88.79 149	70.95 68	89.13 97	91.52 109	77.55 41	80.96 110	91.75 83	60.71 171	94.50 108	79.67 85	86.51 144	89.97 192

Vis-MVSNet		83.46 79	82.80 85	85.43 75	90.25 101	68.74 115	90.30 68	90.13 150	76.33 76	80.87 111	92.89 68	61.00 168	94.20 118	72.45 150	90.97 88	93.35 74
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 80	83.45 75	83.28 142	92.74 66	62.28 234	88.17 133	89.50 166	75.22 94	81.49 102	92.74 73	66.75 93	95.11 83	72.85 146	91.58 81	92.45 106

EPP-MVSNet		83.40 81	83.02 81	84.57 100	90.13 103	64.47 194	92.32 26	90.73 131	74.45 110	79.35 122	91.10 100	69.05 78	95.12 82	72.78 147	87.22 133	94.13 34

3Dnovator	76.31 5	83.38 82	82.31 91	86.59 57	87.94 175	72.94 27	90.64 56	92.14 84	77.21 49	75.47 199	92.83 70	58.56 186	94.72 103	73.24 143	92.71 70	92.13 117

EIA-MVS		83.31 83	82.80 85	84.82 94	89.59 114	65.59 172	88.21 131	92.68 59	74.66 105	78.96 125	86.42 224	69.06 77	95.26 78	75.54 122	90.09 100	93.62 64

MVS_Test		83.15 84	83.06 80	83.41 139	86.86 202	63.21 220	86.11 189	92.00 90	74.31 111	82.87 84	89.44 141	70.03 66	93.21 162	77.39 104	88.50 119	93.81 53

IS-MVSNet		83.15 84	82.81 84	84.18 115	89.94 108	63.30 218	91.59 40	88.46 201	79.04 25	79.49 120	92.16 76	65.10 111	94.28 112	67.71 186	91.86 79	94.95 5

DP-MVS Recon		83.11 86	82.09 93	86.15 65	94.44 19	70.92 71	88.79 106	92.20 81	70.53 176	79.17 123	91.03 105	64.12 119	96.03 47	68.39 183	90.14 99	91.50 132

PAPM_NR		83.02 87	82.41 88	84.82 94	92.47 72	66.37 159	87.93 140	91.80 100	73.82 123	77.32 159	90.66 111	67.90 84	94.90 95	70.37 164	89.48 107	93.19 82

VDD-MVS		83.01 88	82.36 90	84.96 89	91.02 89	66.40 158	88.91 101	88.11 204	77.57 38	84.39 65	93.29 59	52.19 235	93.91 133	77.05 107	88.70 115	94.57 21

MVSFormer		82.85 89	82.05 94	85.24 80	87.35 191	70.21 82	90.50 60	90.38 140	68.55 218	81.32 103	89.47 136	61.68 153	93.46 155	78.98 87	90.26 97	92.05 119

OMC-MVS		82.69 90	81.97 97	84.85 93	88.75 151	67.42 142	87.98 136	90.87 129	74.92 100	79.72 118	91.65 85	62.19 148	93.96 126	75.26 125	86.42 145	93.16 83

PVSNet_Blended_VisFu		82.62 91	81.83 99	84.96 89	90.80 94	69.76 92	88.74 110	91.70 105	69.39 195	78.96 125	88.46 163	65.47 108	94.87 98	74.42 128	88.57 116	90.24 174

MVS_111021_LR		82.61 92	82.11 92	84.11 116	88.82 146	71.58 56	85.15 210	86.16 240	74.69 104	80.47 114	91.04 103	62.29 145	90.55 243	80.33 80	90.08 101	90.20 175

HQP-MVS		82.61 92	82.02 95	84.37 107	89.33 124	66.98 150	89.17 92	92.19 82	76.41 70	77.23 162	90.23 118	60.17 180	95.11 83	77.47 102	85.99 151	91.03 145

CLD-MVS		82.31 94	81.65 100	84.29 112	88.47 159	67.73 138	85.81 198	92.35 73	75.78 82	78.33 139	86.58 219	64.01 120	94.35 110	76.05 116	87.48 130	90.79 152
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 95	82.41 88	81.62 191	90.82 93	60.93 248	84.47 226	89.78 158	76.36 75	84.07 70	91.88 82	64.71 116	90.26 245	70.68 161	88.89 111	93.66 57

diffmvs		82.10 96	81.88 98	82.76 173	83.00 268	63.78 206	83.68 243	89.76 159	72.94 140	82.02 94	89.85 126	65.96 105	90.79 239	82.38 66	87.30 132	93.71 56

LPG-MVS_test		82.08 97	81.27 103	84.50 102	89.23 132	68.76 113	90.22 71	91.94 94	75.37 91	76.64 176	91.51 90	54.29 218	94.91 93	78.44 92	83.78 169	89.83 197

FIs		82.07 98	82.42 87	81.04 209	88.80 148	58.34 272	88.26 130	93.49 25	76.93 57	78.47 136	91.04 103	69.92 68	92.34 195	69.87 170	84.97 157	92.44 107

PS-MVSNAJss		82.07 98	81.31 102	84.34 110	86.51 210	67.27 146	89.27 90	91.51 110	71.75 154	79.37 121	90.22 119	63.15 131	94.27 113	77.69 100	82.36 191	91.49 133

API-MVS		81.99 100	81.23 104	84.26 113	90.94 90	70.18 87	91.10 50	89.32 170	71.51 160	78.66 132	88.28 168	65.26 109	95.10 86	64.74 214	91.23 87	87.51 255

UniMVSNet_NR-MVSNet		81.88 101	81.54 101	82.92 161	88.46 160	63.46 214	87.13 157	92.37 72	80.19 14	78.38 137	89.14 144	71.66 53	93.05 173	70.05 167	76.46 253	92.25 112

MAR-MVS		81.84 102	80.70 110	85.27 79	91.32 85	71.53 57	89.82 78	90.92 127	69.77 189	78.50 134	86.21 228	62.36 144	94.52 107	65.36 208	92.05 75	89.77 200
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
LFMVS		81.82 103	81.23 104	83.57 134	91.89 79	63.43 216	89.84 77	81.85 292	77.04 55	83.21 79	93.10 62	52.26 234	93.43 157	71.98 151	89.95 103	93.85 49

xiu_mvs_v2_base		81.69 104	81.05 107	83.60 132	89.15 135	68.03 133	84.46 228	90.02 152	70.67 173	81.30 106	86.53 222	63.17 130	94.19 119	75.60 121	88.54 117	88.57 236

PS-MVSNAJ		81.69 104	81.02 108	83.70 131	89.51 118	68.21 130	84.28 234	90.09 151	70.79 170	81.26 107	85.62 241	63.15 131	94.29 111	75.62 120	88.87 112	88.59 235

PAPR		81.66 106	80.89 109	83.99 126	90.27 100	64.00 201	86.76 172	91.77 104	68.84 214	77.13 167	89.50 134	67.63 86	94.88 97	67.55 188	88.52 118	93.09 84

UniMVSNet (Re)		81.60 107	81.11 106	83.09 152	88.38 163	64.41 195	87.60 146	93.02 42	78.42 31	78.56 133	88.16 171	69.78 69	93.26 161	69.58 173	76.49 252	91.60 128

FC-MVSNet-test		81.52 108	82.02 95	80.03 226	88.42 162	55.97 308	87.95 138	93.42 29	77.10 53	77.38 157	90.98 108	69.96 67	91.79 212	68.46 182	84.50 162	92.33 108

VDDNet		81.52 108	80.67 111	84.05 120	90.44 98	64.13 200	89.73 83	85.91 243	71.11 164	83.18 80	93.48 54	50.54 258	93.49 153	73.40 140	88.25 121	94.54 22

ACMP	74.13 6	81.51 110	80.57 112	84.36 108	89.42 120	68.69 120	89.97 76	91.50 113	74.46 109	75.04 217	90.41 115	53.82 223	94.54 105	77.56 101	82.91 183	89.86 196
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 111	80.29 119	84.70 98	86.63 209	69.90 89	85.95 192	86.77 231	63.24 273	81.07 109	89.47 136	61.08 167	92.15 201	78.33 95	90.07 102	92.05 119
jason: jason.
lupinMVS		81.39 111	80.27 120	84.76 97	87.35 191	70.21 82	85.55 203	86.41 235	62.85 280	81.32 103	88.61 158	61.68 153	92.24 198	78.41 94	90.26 97	91.83 123

test_yl		81.17 113	80.47 115	83.24 145	89.13 136	63.62 207	86.21 186	89.95 154	72.43 145	81.78 99	89.61 131	57.50 195	93.58 147	70.75 159	86.90 137	92.52 101

DCV-MVSNet		81.17 113	80.47 115	83.24 145	89.13 136	63.62 207	86.21 186	89.95 154	72.43 145	81.78 99	89.61 131	57.50 195	93.58 147	70.75 159	86.90 137	92.52 101

DU-MVS		81.12 115	80.52 114	82.90 162	87.80 179	63.46 214	87.02 161	91.87 98	79.01 26	78.38 137	89.07 147	65.02 112	93.05 173	70.05 167	76.46 253	92.20 114

PVSNet_Blended		80.98 116	80.34 117	82.90 162	88.85 143	65.40 175	84.43 230	92.00 90	67.62 224	78.11 144	85.05 254	66.02 103	94.27 113	71.52 153	89.50 106	89.01 219

mvs-test1		80.88 117	79.40 135	85.29 78	85.13 230	69.75 93	89.28 89	88.10 205	74.99 98	76.44 181	86.72 208	57.27 198	94.26 117	73.53 136	83.18 180	91.87 122

QAPM		80.88 117	79.50 133	85.03 86	88.01 174	68.97 109	91.59 40	92.00 90	66.63 237	75.15 213	92.16 76	57.70 192	95.45 68	63.52 218	88.76 114	90.66 157

1121		80.84 119	79.77 126	84.05 120	93.11 55	70.78 73	84.66 220	85.42 246	57.37 322	81.76 101	92.02 78	63.41 124	94.12 122	67.28 191	92.93 66	87.26 262

TranMVSNet+NR-MVSNet		80.84 119	80.31 118	82.42 178	87.85 177	62.33 232	87.74 144	91.33 116	80.55 11	77.99 147	89.86 125	65.23 110	92.62 184	67.05 196	75.24 278	92.30 110

UGNet		80.83 121	79.59 131	84.54 101	88.04 172	68.09 131	89.42 87	88.16 203	76.95 56	76.22 185	89.46 138	49.30 272	93.94 129	68.48 181	90.31 95	91.60 128
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
Fast-Effi-MVS+		80.81 122	79.92 123	83.47 135	88.85 143	64.51 191	85.53 205	89.39 168	70.79 170	78.49 135	85.06 253	67.54 87	93.58 147	67.03 197	86.58 142	92.32 109

XVG-OURS-SEG-HR		80.81 122	79.76 127	83.96 128	85.60 221	68.78 112	83.54 249	90.50 137	70.66 174	76.71 173	91.66 84	60.69 172	91.26 226	76.94 109	81.58 198	91.83 123

xiu_mvs_v1_base_debu		80.80 124	79.72 128	84.03 123	87.35 191	70.19 84	85.56 200	88.77 192	69.06 207	81.83 95	88.16 171	50.91 252	92.85 179	78.29 96	87.56 127	89.06 214

xiu_mvs_v1_base		80.80 124	79.72 128	84.03 123	87.35 191	70.19 84	85.56 200	88.77 192	69.06 207	81.83 95	88.16 171	50.91 252	92.85 179	78.29 96	87.56 127	89.06 214

xiu_mvs_v1_base_debi		80.80 124	79.72 128	84.03 123	87.35 191	70.19 84	85.56 200	88.77 192	69.06 207	81.83 95	88.16 171	50.91 252	92.85 179	78.29 96	87.56 127	89.06 214

ACMM	73.20 8	80.78 127	79.84 125	83.58 133	89.31 129	68.37 125	89.99 75	91.60 107	70.28 180	77.25 160	89.66 129	53.37 226	93.53 152	74.24 131	82.85 184	88.85 227
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		80.68 128	79.51 132	84.20 114	94.09 36	67.27 146	89.64 85	91.11 124	58.75 314	74.08 227	90.72 110	58.10 188	95.04 88	69.70 171	89.42 108	90.30 172

CANet_DTU		80.61 129	79.87 124	82.83 165	85.60 221	63.17 223	87.36 152	88.65 197	76.37 74	75.88 193	88.44 164	53.51 225	93.07 172	73.30 141	89.74 105	92.25 112

VPA-MVSNet		80.60 130	80.55 113	80.76 214	88.07 171	60.80 251	86.86 166	91.58 108	75.67 86	80.24 115	89.45 140	63.34 125	90.25 246	70.51 163	79.22 228	91.23 140

PVSNet_BlendedMVS		80.60 130	80.02 121	82.36 180	88.85 143	65.40 175	86.16 188	92.00 90	69.34 197	78.11 144	86.09 231	66.02 103	94.27 113	71.52 153	82.06 193	87.39 257

test_part1		80.58 132	78.97 145	85.40 76	86.75 207	69.46 101	92.32 26	93.13 37	66.72 232	76.67 174	87.81 179	56.73 203	95.01 89	75.34 124	75.27 276	91.73 127

AdaColmap		80.58 132	79.42 134	84.06 119	93.09 56	68.91 110	89.36 88	88.97 187	69.27 198	75.70 196	89.69 128	57.20 200	95.77 57	63.06 224	88.41 120	87.50 256

EI-MVSNet		80.52 134	79.98 122	82.12 181	84.28 240	63.19 222	86.41 180	88.95 188	74.18 116	78.69 130	87.54 187	66.62 94	92.43 190	72.57 149	80.57 210	90.74 155

XVG-OURS		80.41 135	79.23 140	83.97 127	85.64 220	69.02 106	83.03 256	90.39 139	71.09 165	77.63 153	91.49 92	54.62 217	91.35 224	75.71 118	83.47 176	91.54 130

PCF-MVS	73.52 7	80.38 136	78.84 148	85.01 87	87.71 183	68.99 108	83.65 244	91.46 114	63.00 277	77.77 151	90.28 116	66.10 100	95.09 87	61.40 240	88.22 122	90.94 149
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 137	77.83 171	88.00 13	94.42 20	73.33 18	92.78 15	92.99 45	79.14 21	83.67 76	12.47 357	67.45 88	96.60 31	83.06 55	94.50 52	94.07 37

test_djsdf		80.30 138	79.32 138	83.27 143	83.98 247	65.37 178	90.50 60	90.38 140	68.55 218	76.19 186	88.70 154	56.44 205	93.46 155	78.98 87	80.14 217	90.97 148

v2v482		80.23 139	79.29 139	83.05 155	83.62 252	64.14 199	87.04 160	89.97 153	73.61 126	78.18 143	87.22 196	61.10 166	93.82 136	76.11 115	76.78 250	91.18 141

NR-MVSNet		80.23 139	79.38 136	82.78 171	87.80 179	63.34 217	86.31 183	91.09 125	79.01 26	72.17 245	89.07 147	67.20 91	92.81 183	66.08 203	75.65 264	92.20 114

Anonymous20240529		80.19 141	78.89 147	84.10 117	90.60 95	64.75 188	88.95 100	90.90 128	65.97 245	80.59 113	91.17 99	49.97 263	93.73 144	69.16 177	82.70 188	93.81 53

IterMVS-LS		80.06 142	79.38 136	82.11 182	85.89 216	63.20 221	86.79 169	89.34 169	74.19 115	75.45 202	86.72 208	66.62 94	92.39 192	72.58 148	76.86 247	90.75 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 143	78.57 152	84.42 105	85.13 230	68.74 115	88.77 107	88.10 205	74.99 98	74.97 218	83.49 273	57.27 198	93.36 158	73.53 136	80.88 204	91.18 141

v1144		80.03 143	79.03 143	83.01 157	83.78 250	64.51 191	87.11 159	90.57 135	71.96 152	78.08 146	86.20 229	61.41 158	93.94 129	74.93 126	77.23 241	90.60 160

v8		79.97 145	79.02 144	82.80 168	84.09 244	64.50 193	87.96 137	90.29 147	74.13 118	75.24 211	86.81 205	62.88 136	93.89 135	74.39 129	75.40 271	90.00 188

RRT_MVS		79.88 146	78.38 157	84.38 106	85.42 224	70.60 78	88.71 112	88.75 196	72.30 147	78.83 129	89.14 144	44.44 301	92.18 200	78.50 91	79.33 226	90.35 170

OpenMVS	72.83 10	79.77 147	78.33 160	84.09 118	85.17 227	69.91 88	90.57 58	90.97 126	66.70 233	72.17 245	91.91 80	54.70 215	93.96 126	61.81 237	90.95 89	88.41 240

v10		79.74 148	78.67 149	82.97 160	84.06 245	64.95 185	87.88 142	90.62 133	73.11 136	75.11 214	86.56 220	61.46 157	94.05 125	73.68 134	75.55 266	89.90 194

BH-RMVSNet		79.61 149	78.44 155	83.14 150	89.38 123	65.93 165	84.95 215	87.15 226	73.56 128	78.19 142	89.79 127	56.67 204	93.36 158	59.53 254	86.74 140	90.13 178

v1192		79.59 150	78.43 156	83.07 154	83.55 254	64.52 190	86.93 164	90.58 134	70.83 168	77.78 150	85.90 233	59.15 183	93.94 129	73.96 133	77.19 243	90.76 153

ab-mvs		79.51 151	78.97 145	81.14 206	88.46 160	60.91 249	83.84 241	89.24 175	70.36 178	79.03 124	88.87 152	63.23 129	90.21 247	65.12 210	82.57 189	92.28 111

WR-MVS		79.49 152	79.22 141	80.27 223	88.79 149	58.35 271	85.06 212	88.61 199	78.56 29	77.65 152	88.34 166	63.81 123	90.66 242	64.98 212	77.22 242	91.80 126

v144192		79.47 153	78.37 158	82.78 171	83.35 256	63.96 202	86.96 162	90.36 143	69.99 184	77.50 154	85.67 239	60.66 173	93.77 140	74.27 130	76.58 251	90.62 158

BH-untuned		79.47 153	78.60 151	82.05 183	89.19 134	65.91 166	86.07 190	88.52 200	72.18 148	75.42 203	87.69 182	61.15 165	93.54 151	60.38 247	86.83 139	86.70 276

mvs_anonymous		79.42 155	79.11 142	80.34 221	84.45 239	57.97 278	82.59 258	87.62 217	67.40 227	76.17 189	88.56 161	68.47 80	89.59 256	70.65 162	86.05 150	93.47 70

thisisatest0530		79.40 156	77.76 175	84.31 111	87.69 185	65.10 184	87.36 152	84.26 261	70.04 183	77.42 156	88.26 170	49.94 264	94.79 101	70.20 165	84.70 161	93.03 87

tttt0517		79.40 156	77.91 168	83.90 130	88.10 170	63.84 204	88.37 125	84.05 263	71.45 161	76.78 171	89.12 146	49.93 266	94.89 96	70.18 166	83.18 180	92.96 91

V42		79.38 158	78.24 162	82.83 165	81.10 302	65.50 174	85.55 203	89.82 157	71.57 159	78.21 141	86.12 230	60.66 173	93.18 166	75.64 119	75.46 269	89.81 199

jajsoiax		79.29 159	77.96 166	83.27 143	84.68 236	66.57 156	89.25 91	90.16 149	69.20 202	75.46 201	89.49 135	45.75 295	93.13 169	76.84 110	80.80 206	90.11 180

v1921920		79.22 160	78.03 165	82.80 168	83.30 258	63.94 203	86.80 168	90.33 144	69.91 186	77.48 155	85.53 242	58.44 187	93.75 142	73.60 135	76.85 248	90.71 156

AUN-MVS		79.21 161	77.60 180	84.05 120	88.71 152	67.61 139	85.84 196	87.26 225	69.08 206	77.23 162	88.14 175	53.20 228	93.47 154	75.50 123	73.45 294	91.06 144

TAPA-MVS	73.13 9	79.15 162	77.94 167	82.79 170	89.59 114	62.99 227	88.16 134	91.51 110	65.77 246	77.14 166	91.09 101	60.91 169	93.21 162	50.26 308	87.05 135	92.17 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 163	77.77 174	83.22 147	84.70 235	66.37 159	89.17 92	90.19 148	69.38 196	75.40 204	89.46 138	44.17 303	93.15 167	76.78 111	80.70 208	90.14 177

UniMVSNet_ETH3D		79.10 164	78.24 162	81.70 190	86.85 203	60.24 258	87.28 155	88.79 191	74.25 114	76.84 168	90.53 114	49.48 269	91.56 218	67.98 184	82.15 192	93.29 76

CDS-MVSNet		79.07 165	77.70 177	83.17 149	87.60 186	68.23 129	84.40 232	86.20 239	67.49 226	76.36 182	86.54 221	61.54 156	90.79 239	61.86 236	87.33 131	90.49 165
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 166	77.88 170	82.38 179	83.07 265	64.80 187	84.08 240	88.95 188	69.01 210	78.69 130	87.17 199	54.70 215	92.43 190	74.69 127	80.57 210	89.89 195

v1240		78.99 167	77.78 173	82.64 174	83.21 260	63.54 211	86.62 175	90.30 146	69.74 192	77.33 158	85.68 238	57.04 201	93.76 141	73.13 144	76.92 245	90.62 158

Anonymous20231211		78.97 168	77.69 178	82.81 167	90.54 96	64.29 197	90.11 73	91.51 110	65.01 256	76.16 190	88.13 176	50.56 257	93.03 176	69.68 172	77.56 239	91.11 143

v7n		78.97 168	77.58 181	83.14 150	83.45 255	65.51 173	88.32 126	91.21 120	73.69 125	72.41 242	86.32 227	57.93 189	93.81 137	69.18 176	75.65 264	90.11 180

TAMVS		78.89 170	77.51 182	83.03 156	87.80 179	67.79 137	84.72 219	85.05 250	67.63 223	76.75 172	87.70 181	62.25 146	90.82 238	58.53 265	87.13 134	90.49 165

cl_fuxian		78.75 171	77.91 168	81.26 202	82.89 272	61.56 243	84.09 239	89.13 180	69.97 185	75.56 197	84.29 261	66.36 98	92.09 203	73.47 139	75.48 268	90.12 179

v148		78.72 172	77.80 172	81.47 195	82.73 275	61.96 238	86.30 184	88.08 207	73.26 135	76.18 187	85.47 244	62.46 142	92.36 194	71.92 152	73.82 291	90.09 182

VPNet		78.69 173	78.66 150	78.76 247	88.31 165	55.72 310	84.45 229	86.63 233	76.79 61	78.26 140	90.55 113	59.30 182	89.70 255	66.63 198	77.05 244	90.88 150

ET-MVSNet_ETH3D		78.63 174	76.63 203	84.64 99	86.73 208	69.47 99	85.01 213	84.61 254	69.54 193	66.51 302	86.59 217	50.16 261	91.75 213	76.26 114	84.24 166	92.69 98

anonymousdsp		78.60 175	77.15 188	82.98 159	80.51 308	67.08 148	87.24 156	89.53 165	65.66 248	75.16 212	87.19 198	52.52 229	92.25 197	77.17 106	79.34 225	89.61 204

miper_ehance_all_eth		78.59 176	77.76 175	81.08 208	82.66 277	61.56 243	83.65 244	89.15 178	68.87 213	75.55 198	83.79 269	66.49 96	92.03 204	73.25 142	76.39 255	89.64 203

WR-MVS_H		78.51 177	78.49 153	78.56 250	88.02 173	56.38 303	88.43 118	92.67 60	77.14 51	73.89 228	87.55 186	66.25 99	89.24 262	58.92 260	73.55 293	90.06 186

GBi-Net		78.40 178	77.40 183	81.40 197	87.60 186	63.01 224	88.39 122	89.28 171	71.63 156	75.34 206	87.28 192	54.80 211	91.11 229	62.72 225	79.57 220	90.09 182

test1		78.40 178	77.40 183	81.40 197	87.60 186	63.01 224	88.39 122	89.28 171	71.63 156	75.34 206	87.28 192	54.80 211	91.11 229	62.72 225	79.57 220	90.09 182

RRT_test8_iter05		78.38 180	77.40 183	81.34 200	86.00 215	58.86 267	86.55 178	91.26 118	72.13 151	75.91 191	87.42 190	44.97 298	93.73 144	77.02 108	75.30 274	91.45 136

Vis-MVSNet (Re-imp)		78.36 181	78.45 154	78.07 258	88.64 154	51.78 329	86.70 173	79.63 313	74.14 117	75.11 214	90.83 109	61.29 162	89.75 253	58.10 269	91.60 80	92.69 98

Anonymous202405211		78.25 182	77.01 190	81.99 185	91.03 88	60.67 252	84.77 218	83.90 265	70.65 175	80.00 116	91.20 98	41.08 320	91.43 222	65.21 209	85.26 155	93.85 49

CP-MVSNet		78.22 183	78.34 159	77.84 260	87.83 178	54.54 315	87.94 139	91.17 122	77.65 35	73.48 230	88.49 162	62.24 147	88.43 275	62.19 231	74.07 286	90.55 163

BH-w/o		78.21 184	77.33 186	80.84 212	88.81 147	65.13 183	84.87 216	87.85 214	69.75 190	74.52 223	84.74 257	61.34 160	93.11 170	58.24 268	85.84 153	84.27 305

FMVSNet2		78.20 185	77.21 187	81.20 204	87.60 186	62.89 228	87.47 150	89.02 183	71.63 156	75.29 210	87.28 192	54.80 211	91.10 232	62.38 229	79.38 224	89.61 204

MVS		78.19 186	76.99 192	81.78 188	85.66 219	66.99 149	84.66 220	90.47 138	55.08 332	72.02 247	85.27 247	63.83 122	94.11 124	66.10 202	89.80 104	84.24 306

Baseline_NR-MVSNet		78.15 187	78.33 160	77.61 265	85.79 217	56.21 306	86.78 170	85.76 244	73.60 127	77.93 148	87.57 185	65.02 112	88.99 266	67.14 195	75.33 273	87.63 251

CNLPA		78.08 188	76.79 197	81.97 186	90.40 99	71.07 64	87.59 147	84.55 255	66.03 244	72.38 243	89.64 130	57.56 194	86.04 294	59.61 253	83.35 177	88.79 230

cl-mvsnet2		78.07 189	77.01 190	81.23 203	82.37 284	61.83 240	83.55 248	87.98 209	68.96 211	75.06 216	83.87 265	61.40 159	91.88 211	73.53 136	76.39 255	89.98 191

PLC	70.83 11	78.05 190	76.37 207	83.08 153	91.88 80	67.80 136	88.19 132	89.46 167	64.33 264	69.87 271	88.38 165	53.66 224	93.58 147	58.86 261	82.73 186	87.86 247
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 191	76.49 204	82.62 175	83.16 264	66.96 152	86.94 163	87.45 222	72.45 142	71.49 252	84.17 262	54.79 214	91.58 217	67.61 187	80.31 214	89.30 210

PS-CasMVS		78.01 192	78.09 164	77.77 262	87.71 183	54.39 317	88.02 135	91.22 119	77.50 43	73.26 232	88.64 157	60.73 170	88.41 276	61.88 235	73.88 290	90.53 164

HY-MVS	69.67 12	77.95 193	77.15 188	80.36 220	87.57 190	60.21 259	83.37 251	87.78 215	66.11 241	75.37 205	87.06 203	63.27 127	90.48 244	61.38 241	82.43 190	90.40 169

eth_miper_zixun_eth		77.92 194	76.69 201	81.61 193	83.00 268	61.98 237	83.15 252	89.20 177	69.52 194	74.86 220	84.35 260	61.76 152	92.56 187	71.50 155	72.89 297	90.28 173

FMVSNet3		77.88 195	76.85 195	80.97 210	86.84 204	62.36 231	86.52 179	88.77 192	71.13 163	75.34 206	86.66 215	54.07 221	91.10 232	62.72 225	79.57 220	89.45 207

miper_enhance_ethall		77.87 196	76.86 194	80.92 211	81.65 291	61.38 245	82.68 257	88.98 185	65.52 250	75.47 199	82.30 286	65.76 107	92.00 206	72.95 145	76.39 255	89.39 208

PEN-MVS		77.73 197	77.69 178	77.84 260	87.07 201	53.91 319	87.91 141	91.18 121	77.56 40	73.14 234	88.82 153	61.23 163	89.17 263	59.95 250	72.37 299	90.43 167

cl-mvsnet_		77.72 198	76.76 198	80.58 216	82.49 281	60.48 255	83.09 253	87.87 212	69.22 200	74.38 225	85.22 249	62.10 149	91.53 219	71.09 157	75.41 270	89.73 202

cl-mvsnet1		77.72 198	76.76 198	80.58 216	82.48 282	60.48 255	83.09 253	87.86 213	69.22 200	74.38 225	85.24 248	62.10 149	91.53 219	71.09 157	75.40 271	89.74 201

PAPM		77.68 200	76.40 206	81.51 194	87.29 197	61.85 239	83.78 242	89.59 164	64.74 258	71.23 253	88.70 154	62.59 139	93.66 146	52.66 295	87.03 136	89.01 219

CHOSEN 1792x2688		77.63 201	75.69 210	83.44 136	89.98 107	68.58 123	78.70 296	87.50 220	56.38 327	75.80 195	86.84 204	58.67 185	91.40 223	61.58 239	85.75 154	90.34 171

HyFIR lowres test		77.53 202	75.40 216	83.94 129	89.59 114	66.62 154	80.36 278	88.64 198	56.29 328	76.45 178	85.17 250	57.64 193	93.28 160	61.34 242	83.10 182	91.91 121

FMVSNet1		77.44 203	76.12 209	81.40 197	86.81 205	63.01 224	88.39 122	89.28 171	70.49 177	74.39 224	87.28 192	49.06 275	91.11 229	60.91 244	78.52 230	90.09 182

TR-MVS		77.44 203	76.18 208	81.20 204	88.24 166	63.24 219	84.61 224	86.40 236	67.55 225	77.81 149	86.48 223	54.10 220	93.15 167	57.75 272	82.72 187	87.20 263

1112_ss		77.40 205	76.43 205	80.32 222	89.11 140	60.41 257	83.65 244	87.72 216	62.13 288	73.05 235	86.72 208	62.58 140	89.97 250	62.11 234	80.80 206	90.59 162

thisisatest0515		77.33 206	75.38 217	83.18 148	85.27 226	63.80 205	82.11 263	83.27 276	65.06 254	75.91 191	83.84 267	49.54 268	94.27 113	67.24 193	86.19 148	91.48 134

pm-mvs1		77.25 207	76.68 202	78.93 245	84.22 242	58.62 270	86.41 180	88.36 202	71.37 162	73.31 231	88.01 177	61.22 164	89.15 264	64.24 216	73.01 296	89.03 218

LCM-MVSNet-Re		77.05 208	76.94 193	77.36 268	87.20 198	51.60 330	80.06 281	80.46 305	75.20 95	67.69 286	86.72 208	62.48 141	88.98 267	63.44 220	89.25 109	91.51 131

DTE-MVSNet		76.99 209	76.80 196	77.54 267	86.24 212	53.06 326	87.52 148	90.66 132	77.08 54	72.50 240	88.67 156	60.48 176	89.52 257	57.33 276	70.74 310	90.05 187

baseline1		76.98 210	76.75 200	77.66 263	88.13 168	55.66 311	85.12 211	81.89 290	73.04 138	76.79 170	88.90 150	62.43 143	87.78 283	63.30 222	71.18 308	89.55 206

LS3D		76.95 211	74.82 223	83.37 140	90.45 97	67.36 145	89.15 96	86.94 229	61.87 290	69.52 274	90.61 112	51.71 246	94.53 106	46.38 328	86.71 141	88.21 242

GA-MVS		76.87 212	75.17 221	81.97 186	82.75 274	62.58 229	81.44 271	86.35 238	72.16 150	74.74 221	82.89 278	46.20 290	92.02 205	68.85 180	81.09 202	91.30 139

DP-MVS		76.78 213	74.57 225	83.42 137	93.29 49	69.46 101	88.55 117	83.70 267	63.98 270	70.20 262	88.89 151	54.01 222	94.80 100	46.66 325	81.88 196	86.01 288

cascas		76.72 214	74.64 224	82.99 158	85.78 218	65.88 167	82.33 261	89.21 176	60.85 296	72.74 237	81.02 298	47.28 282	93.75 142	67.48 189	85.02 156	89.34 209

1314		76.53 215	75.30 220	80.21 224	83.93 248	62.32 233	84.66 220	88.81 190	60.23 300	70.16 265	84.07 264	55.30 209	90.73 241	67.37 190	83.21 179	87.59 254

thres100view900		76.50 216	75.55 213	79.33 239	89.52 117	56.99 292	85.83 197	83.23 277	73.94 120	76.32 183	87.12 200	51.89 243	91.95 207	48.33 316	83.75 171	89.07 212

thres600view7		76.50 216	75.44 214	79.68 233	89.40 121	57.16 289	85.53 205	83.23 277	73.79 124	76.26 184	87.09 201	51.89 243	91.89 210	48.05 321	83.72 174	90.00 188

thres400		76.50 216	75.37 218	79.86 229	89.13 136	57.65 284	85.17 208	83.60 268	73.41 133	76.45 178	86.39 225	52.12 236	91.95 207	48.33 316	83.75 171	90.00 188

tfpn200view9		76.42 219	75.37 218	79.55 238	89.13 136	57.65 284	85.17 208	83.60 268	73.41 133	76.45 178	86.39 225	52.12 236	91.95 207	48.33 316	83.75 171	89.07 212

Test_1112_low_res		76.40 220	75.44 214	79.27 240	89.28 130	58.09 274	81.69 267	87.07 227	59.53 307	72.48 241	86.67 214	61.30 161	89.33 260	60.81 246	80.15 216	90.41 168

F-COLMAP		76.38 221	74.33 230	82.50 177	89.28 130	66.95 153	88.41 121	89.03 182	64.05 268	66.83 296	88.61 158	46.78 285	92.89 178	57.48 273	78.55 229	87.67 250

LTVRE_ROB	69.57 13	76.25 222	74.54 227	81.41 196	88.60 155	64.38 196	79.24 289	89.12 181	70.76 172	69.79 273	87.86 178	49.09 274	93.20 164	56.21 283	80.16 215	86.65 277
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
MVP-Stereo		76.12 223	74.46 229	81.13 207	85.37 225	69.79 91	84.42 231	87.95 210	65.03 255	67.46 288	85.33 246	53.28 227	91.73 215	58.01 270	83.27 178	81.85 325
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 224	74.27 231	81.62 191	83.20 261	64.67 189	83.60 247	89.75 160	69.75 190	71.85 248	87.09 201	32.78 343	92.11 202	69.99 169	80.43 213	88.09 243

ACMH+	68.96 14	76.01 225	74.01 232	82.03 184	88.60 155	65.31 179	88.86 103	87.55 218	70.25 181	67.75 285	87.47 189	41.27 318	93.19 165	58.37 266	75.94 261	87.60 252

ACMH	67.68 16	75.89 226	73.93 233	81.77 189	88.71 152	66.61 155	88.62 114	89.01 184	69.81 187	66.78 297	86.70 213	41.95 317	91.51 221	55.64 284	78.14 235	87.17 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 227	73.36 239	83.31 141	84.76 234	66.03 162	83.38 250	85.06 249	70.21 182	69.40 275	81.05 297	45.76 294	94.66 104	65.10 211	75.49 267	89.25 211
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
testing_2		75.73 228	73.34 240	82.89 164	77.37 330	65.22 180	84.10 238	90.54 136	69.09 205	60.46 329	81.15 296	40.48 322	92.84 182	76.36 113	80.54 212	90.60 160

baseline2		75.70 229	73.83 236	81.30 201	83.26 259	61.79 241	82.57 259	80.65 301	66.81 229	66.88 294	83.42 274	57.86 191	92.19 199	63.47 219	79.57 220	89.91 193

WTY-MVS		75.65 230	75.68 211	75.57 284	86.40 211	56.82 294	77.92 304	82.40 286	65.10 253	76.18 187	87.72 180	63.13 134	80.90 321	60.31 248	81.96 194	89.00 221

thres200		75.55 231	74.47 228	78.82 246	87.78 182	57.85 281	83.07 255	83.51 271	72.44 144	75.84 194	84.42 259	52.08 238	91.75 213	47.41 323	83.64 175	86.86 272

EPNet_dtu		75.46 232	74.86 222	77.23 272	82.57 279	54.60 314	86.89 165	83.09 280	71.64 155	66.25 304	85.86 235	55.99 206	88.04 280	54.92 286	86.55 143	89.05 217
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 233	73.87 235	80.11 225	82.69 276	64.85 186	81.57 269	83.47 273	69.16 203	70.49 259	84.15 263	51.95 241	88.15 278	69.23 175	72.14 302	87.34 259

XXY-MVS		75.41 234	75.56 212	74.96 290	83.59 253	57.82 282	80.59 277	83.87 266	66.54 238	74.93 219	88.31 167	63.24 128	80.09 324	62.16 232	76.85 248	86.97 270

TransMVSNet (Re)		75.39 235	74.56 226	77.86 259	85.50 223	57.10 291	86.78 170	86.09 242	72.17 149	71.53 251	87.34 191	63.01 135	89.31 261	56.84 279	61.83 332	87.17 264

CostFormer		75.24 236	73.90 234	79.27 240	82.65 278	58.27 273	80.80 272	82.73 284	61.57 291	75.33 209	83.13 276	55.52 207	91.07 235	64.98 212	78.34 234	88.45 238

D2MVS		74.82 237	73.21 241	79.64 235	79.81 315	62.56 230	80.34 279	87.35 223	64.37 263	68.86 278	82.66 282	46.37 287	90.10 249	67.91 185	81.24 201	86.25 281

pmmvs6		74.69 238	73.39 238	78.61 249	81.38 297	57.48 287	86.64 174	87.95 210	64.99 257	70.18 263	86.61 216	50.43 259	89.52 257	62.12 233	70.18 312	88.83 228

tfpnnormal		74.39 239	73.16 242	78.08 257	86.10 214	58.05 275	84.65 223	87.53 219	70.32 179	71.22 254	85.63 240	54.97 210	89.86 251	43.03 337	75.02 279	86.32 280

IterMVS		74.29 240	72.94 244	78.35 254	81.53 294	63.49 213	81.58 268	82.49 285	68.06 222	69.99 268	83.69 271	51.66 247	85.54 297	65.85 205	71.64 305	86.01 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 241	72.42 248	79.80 231	83.76 251	59.59 263	85.92 194	86.64 232	66.39 239	66.96 293	87.58 184	39.46 325	91.60 216	65.76 206	69.27 314	88.22 241

SCA		74.22 242	72.33 249	79.91 228	84.05 246	62.17 235	79.96 283	79.29 315	66.30 240	72.38 243	80.13 306	51.95 241	88.60 273	59.25 256	77.67 238	88.96 223

miper_lstm_enhance		74.11 243	73.11 243	77.13 273	80.11 311	59.62 262	72.23 326	86.92 230	66.76 231	70.40 260	82.92 277	56.93 202	82.92 315	69.06 178	72.63 298	88.87 226

EG-PatchMatch MVS		74.04 244	71.82 253	80.71 215	84.92 233	67.42 142	85.86 195	88.08 207	66.04 243	64.22 316	83.85 266	35.10 339	92.56 187	57.44 274	80.83 205	82.16 324

pmmvs4		74.03 245	71.91 251	80.39 219	81.96 288	68.32 126	81.45 270	82.14 288	59.32 308	69.87 271	85.13 251	52.40 232	88.13 279	60.21 249	74.74 282	84.73 302

MS-PatchMatch		73.83 246	72.67 245	77.30 270	83.87 249	66.02 163	81.82 264	84.66 253	61.37 294	68.61 281	82.82 280	47.29 281	88.21 277	59.27 255	84.32 165	77.68 339

DWT-MVSNet_test		73.70 247	71.86 252	79.21 242	82.91 271	58.94 266	82.34 260	82.17 287	65.21 251	71.05 256	78.31 320	44.21 302	90.17 248	63.29 223	77.28 240	88.53 237

sss		73.60 248	73.64 237	73.51 300	82.80 273	55.01 313	76.12 310	81.69 293	62.47 285	74.68 222	85.85 236	57.32 197	78.11 331	60.86 245	80.93 203	87.39 257

RPMNet		73.51 249	70.49 265	82.58 176	81.32 300	65.19 181	75.92 312	92.27 76	57.60 320	72.73 238	76.45 331	52.30 233	95.43 70	48.14 320	77.71 236	87.11 268

SixPastTwentyTwo		73.37 250	71.26 260	79.70 232	85.08 232	57.89 280	85.57 199	83.56 270	71.03 166	65.66 306	85.88 234	42.10 315	92.57 186	59.11 258	63.34 331	88.65 234

CR-MVSNet		73.37 250	71.27 259	79.67 234	81.32 300	65.19 181	75.92 312	80.30 307	59.92 303	72.73 238	81.19 294	52.50 230	86.69 289	59.84 251	77.71 236	87.11 268

MSDG		73.36 252	70.99 261	80.49 218	84.51 238	65.80 168	80.71 275	86.13 241	65.70 247	65.46 307	83.74 270	44.60 299	90.91 237	51.13 301	76.89 246	84.74 301

tpm2		73.26 253	71.46 255	78.63 248	83.34 257	56.71 297	80.65 276	80.40 306	56.63 326	73.55 229	82.02 291	51.80 245	91.24 227	56.35 282	78.42 233	87.95 244

RPSCF		73.23 254	71.46 255	78.54 251	82.50 280	59.85 260	82.18 262	82.84 283	58.96 311	71.15 255	89.41 142	45.48 297	84.77 304	58.82 262	71.83 304	91.02 147

PatchmatchNet		73.12 255	71.33 258	78.49 253	83.18 262	60.85 250	79.63 285	78.57 317	64.13 265	71.73 249	79.81 311	51.20 250	85.97 295	57.40 275	76.36 258	88.66 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	66.92 17	73.01 256	70.41 267	80.81 213	87.13 200	65.63 171	88.30 127	84.19 262	62.96 278	63.80 320	87.69 182	38.04 331	92.56 187	46.66 325	74.91 280	84.24 306
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 257	72.58 246	74.25 297	84.28 240	50.85 335	86.41 180	83.45 274	44.56 343	73.23 233	87.54 187	49.38 270	85.70 296	65.90 204	78.44 232	86.19 283

test-LLR		72.94 258	72.43 247	74.48 294	81.35 298	58.04 276	78.38 297	77.46 322	66.66 234	69.95 269	79.00 315	48.06 278	79.24 325	66.13 200	84.83 158	86.15 284

test_0402		72.79 259	70.44 266	79.84 230	88.13 168	65.99 164	85.93 193	84.29 259	65.57 249	67.40 290	85.49 243	46.92 284	92.61 185	35.88 346	74.38 285	80.94 330

MVS_0304		72.48 260	70.89 263	77.24 271	82.20 285	59.68 261	84.11 237	83.49 272	67.10 228	66.87 295	80.59 302	35.00 340	87.40 285	59.07 259	79.58 219	84.63 303

tpmrst		72.39 261	72.13 250	73.18 304	80.54 307	49.91 338	79.91 284	79.08 316	63.11 275	71.69 250	79.95 308	55.32 208	82.77 316	65.66 207	73.89 289	86.87 271

PatchMatch-RL		72.38 262	70.90 262	76.80 276	88.60 155	67.38 144	79.53 286	76.17 328	62.75 282	69.36 276	82.00 292	45.51 296	84.89 303	53.62 291	80.58 209	78.12 338

CL-MVSNet_2432*1600		72.37 263	71.46 255	75.09 289	79.49 321	53.53 321	80.76 274	85.01 251	69.12 204	70.51 258	82.05 290	57.92 190	84.13 307	52.27 296	66.00 325	87.60 252

tpm		72.37 263	71.71 254	74.35 296	82.19 286	52.00 327	79.22 290	77.29 324	64.56 260	72.95 236	83.68 272	51.35 248	83.26 314	58.33 267	75.80 262	87.81 248

PVSNet	64.34 18	72.08 265	70.87 264	75.69 282	86.21 213	56.44 301	74.37 322	80.73 300	62.06 289	70.17 264	82.23 288	42.86 309	83.31 313	54.77 287	84.45 164	87.32 260

pmmvs5		71.55 266	70.20 269	75.61 283	77.83 327	56.39 302	81.74 266	80.89 297	57.76 318	67.46 288	84.49 258	49.26 273	85.32 300	57.08 278	75.29 275	85.11 298

test-mter		71.41 267	70.39 268	74.48 294	81.35 298	58.04 276	78.38 297	77.46 322	60.32 299	69.95 269	79.00 315	36.08 337	79.24 325	66.13 200	84.83 158	86.15 284

K. test v3		71.19 268	68.51 276	79.21 242	83.04 267	57.78 283	84.35 233	76.91 326	72.90 141	62.99 323	82.86 279	39.27 326	91.09 234	61.65 238	52.66 344	88.75 231

tpmvs		71.09 269	69.29 272	76.49 277	82.04 287	56.04 307	78.92 294	81.37 296	64.05 268	67.18 292	78.28 321	49.74 267	89.77 252	49.67 311	72.37 299	83.67 311

AllTest		70.96 270	68.09 282	79.58 236	85.15 228	63.62 207	84.58 225	79.83 311	62.31 286	60.32 330	86.73 206	32.02 344	88.96 269	50.28 306	71.57 306	86.15 284

Patchmtry		70.74 271	69.16 273	75.49 286	80.72 304	54.07 318	74.94 321	80.30 307	58.34 315	70.01 266	81.19 294	52.50 230	86.54 290	53.37 292	71.09 309	85.87 291

MIMVSNet		70.69 272	69.30 271	74.88 291	84.52 237	56.35 304	75.87 314	79.42 314	64.59 259	67.76 284	82.41 284	41.10 319	81.54 320	46.64 327	81.34 199	86.75 275

tpm cat1		70.57 273	68.31 278	77.35 269	82.41 283	57.95 279	78.08 301	80.22 309	52.04 338	68.54 282	77.66 326	52.00 240	87.84 282	51.77 297	72.07 303	86.25 281

OpenMVS_ROB	64.09 19	70.56 274	68.19 279	77.65 264	80.26 309	59.41 265	85.01 213	82.96 282	58.76 313	65.43 308	82.33 285	37.63 333	91.23 228	45.34 333	76.03 260	82.32 322

pmmvs-eth3d		70.50 275	67.83 286	78.52 252	77.37 330	66.18 161	81.82 264	81.51 294	58.90 312	63.90 319	80.42 304	42.69 310	86.28 293	58.56 264	65.30 327	83.11 317

USDC		70.33 276	68.37 277	76.21 279	80.60 306	56.23 305	79.19 291	86.49 234	60.89 295	61.29 326	85.47 244	31.78 346	89.47 259	53.37 292	76.21 259	82.94 321

Patchmatch-RL test		70.24 277	67.78 288	77.61 265	77.43 329	59.57 264	71.16 328	70.33 340	62.94 279	68.65 280	72.77 338	50.62 256	85.49 298	69.58 173	66.58 323	87.77 249

CMPMVS	51.72 21	70.19 278	68.16 280	76.28 278	73.15 347	57.55 286	79.47 287	83.92 264	48.02 342	56.48 341	84.81 255	43.13 307	86.42 292	62.67 228	81.81 197	84.89 299
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		70.04 279	67.34 292	78.14 256	79.80 316	61.13 246	79.19 291	80.59 302	59.16 310	65.27 309	79.29 312	46.75 286	87.29 286	49.33 312	66.72 321	86.00 290

gg-mvs-nofinetune		69.95 280	67.96 283	75.94 280	83.07 265	54.51 316	77.23 307	70.29 341	63.11 275	70.32 261	62.33 344	43.62 305	88.69 272	53.88 290	87.76 124	84.62 304

TESTMET0.1,1		69.89 281	69.00 274	72.55 305	79.27 324	56.85 293	78.38 297	74.71 334	57.64 319	68.09 283	77.19 328	37.75 332	76.70 336	63.92 217	84.09 167	84.10 309

FMVSNet5		69.50 282	67.96 283	74.15 298	82.97 270	55.35 312	80.01 282	82.12 289	62.56 284	63.02 321	81.53 293	36.92 334	81.92 318	48.42 315	74.06 287	85.17 297

PMMVS		69.34 283	68.67 275	71.35 312	75.67 336	62.03 236	75.17 316	73.46 336	50.00 341	68.68 279	79.05 313	52.07 239	78.13 330	61.16 243	82.77 185	73.90 342

our_test_3		69.14 284	67.00 293	75.57 284	79.80 316	58.80 268	77.96 302	77.81 320	59.55 306	62.90 324	78.25 322	47.43 280	83.97 308	51.71 298	67.58 320	83.93 310

EPMVS		69.02 285	68.16 280	71.59 308	79.61 319	49.80 340	77.40 306	66.93 348	62.82 281	70.01 266	79.05 313	45.79 293	77.86 333	56.58 280	75.26 277	87.13 267

DIV-MVS_2432*1600		68.81 286	67.59 291	72.46 306	74.29 341	45.45 345	77.93 303	87.00 228	63.12 274	63.99 318	78.99 317	42.32 312	84.77 304	56.55 281	64.09 330	87.16 266

Anonymous20231206		68.60 287	67.80 287	71.02 314	80.23 310	50.75 336	78.30 300	80.47 304	56.79 325	66.11 305	82.63 283	46.35 288	78.95 327	43.62 336	75.70 263	83.36 314

MIMVSNet1		68.58 288	66.78 295	73.98 299	80.07 312	51.82 328	80.77 273	84.37 256	64.40 262	59.75 333	82.16 289	36.47 335	83.63 311	42.73 338	70.33 311	86.48 279

EU-MVSNet		68.53 289	67.61 290	71.31 313	78.51 326	47.01 344	84.47 226	84.27 260	42.27 344	66.44 303	84.79 256	40.44 323	83.76 309	58.76 263	68.54 319	83.17 315

PatchT		68.46 290	67.85 285	70.29 316	80.70 305	43.93 348	72.47 325	74.88 331	60.15 301	70.55 257	76.57 330	49.94 264	81.59 319	50.58 302	74.83 281	85.34 294

test0.0.03 1		68.00 291	67.69 289	68.90 321	77.55 328	47.43 342	75.70 315	72.95 338	66.66 234	66.56 298	82.29 287	48.06 278	75.87 340	44.97 334	74.51 284	83.41 313

TDRefinement		67.49 292	64.34 301	76.92 274	73.47 345	61.07 247	84.86 217	82.98 281	59.77 304	58.30 336	85.13 251	26.06 348	87.89 281	47.92 322	60.59 336	81.81 326

test20.03		67.45 293	66.95 294	68.94 320	75.48 338	44.84 347	77.50 305	77.67 321	66.66 234	63.01 322	83.80 268	47.02 283	78.40 329	42.53 339	68.86 318	83.58 312

UnsupCasMVSNet_eth		67.33 294	65.99 297	71.37 310	73.48 344	51.47 332	75.16 317	85.19 248	65.20 252	60.78 328	80.93 301	42.35 311	77.20 335	57.12 277	53.69 343	85.44 293

TinyColmap		67.30 295	64.81 299	74.76 293	81.92 289	56.68 298	80.29 280	81.49 295	60.33 298	56.27 342	83.22 275	24.77 349	87.66 284	45.52 331	69.47 313	79.95 334

dp		66.80 296	65.43 298	70.90 315	79.74 318	48.82 341	75.12 319	74.77 332	59.61 305	64.08 317	77.23 327	42.89 308	80.72 322	48.86 314	66.58 323	83.16 316

MDA-MVSNet-bldmvs		66.68 297	63.66 305	75.75 281	79.28 323	60.56 254	73.92 323	78.35 318	64.43 261	50.13 347	79.87 310	44.02 304	83.67 310	46.10 329	56.86 339	83.03 319

testgi		66.67 298	66.53 296	67.08 326	75.62 337	41.69 351	75.93 311	76.50 327	66.11 241	65.20 312	86.59 217	35.72 338	74.71 344	43.71 335	73.38 295	84.84 300

CHOSEN 280x420		66.51 299	64.71 300	71.90 307	81.45 295	63.52 212	57.98 349	68.95 347	53.57 334	62.59 325	76.70 329	46.22 289	75.29 343	55.25 285	79.68 218	76.88 341

PM-MVS		66.41 300	64.14 302	73.20 303	73.92 342	56.45 300	78.97 293	64.96 352	63.88 272	64.72 313	80.24 305	19.84 353	83.44 312	66.24 199	64.52 329	79.71 335

JIA-IIPM		66.32 301	62.82 311	76.82 275	77.09 332	61.72 242	65.34 344	75.38 329	58.04 317	64.51 314	62.32 345	42.05 316	86.51 291	51.45 300	69.22 315	82.21 323

KD-MVS_2432*1600		66.22 302	63.89 303	73.21 301	75.47 339	53.42 323	70.76 331	84.35 257	64.10 266	66.52 300	78.52 318	34.55 341	84.98 301	50.40 304	50.33 347	81.23 328

miper_refine_blended		66.22 302	63.89 303	73.21 301	75.47 339	53.42 323	70.76 331	84.35 257	64.10 266	66.52 300	78.52 318	34.55 341	84.98 301	50.40 304	50.33 347	81.23 328

ADS-MVSNet2		66.20 304	63.33 306	74.82 292	79.92 313	58.75 269	67.55 341	75.19 330	53.37 335	65.25 310	75.86 332	42.32 312	80.53 323	41.57 340	68.91 316	85.18 295

YYNet1		65.03 305	62.91 309	71.38 309	75.85 335	56.60 299	69.12 338	74.66 335	57.28 323	54.12 343	77.87 324	45.85 292	74.48 345	49.95 309	61.52 334	83.05 318

MDA-MVSNet_test_wron		65.03 305	62.92 308	71.37 310	75.93 334	56.73 295	69.09 339	74.73 333	57.28 323	54.03 344	77.89 323	45.88 291	74.39 346	49.89 310	61.55 333	82.99 320

Patchmatch-test		64.82 307	63.24 307	69.57 318	79.42 322	49.82 339	63.49 347	69.05 346	51.98 339	59.95 332	80.13 306	50.91 252	70.98 349	40.66 342	73.57 292	87.90 246

ADS-MVSNet		64.36 308	62.88 310	68.78 323	79.92 313	47.17 343	67.55 341	71.18 339	53.37 335	65.25 310	75.86 332	42.32 312	73.99 347	41.57 340	68.91 316	85.18 295

LF4IMVS		64.02 309	62.19 312	69.50 319	70.90 349	53.29 325	76.13 309	77.18 325	52.65 337	58.59 334	80.98 299	23.55 350	76.52 337	53.06 294	66.66 322	78.68 337

UnsupCasMVSNet_bld		63.70 310	61.53 314	70.21 317	73.69 343	51.39 333	72.82 324	81.89 290	55.63 330	57.81 337	71.80 340	38.67 328	78.61 328	49.26 313	52.21 345	80.63 331

new-patchmatchnet		61.73 311	61.73 313	61.70 329	72.74 348	24.50 361	69.16 337	78.03 319	61.40 292	56.72 340	75.53 334	38.42 329	76.48 338	45.95 330	57.67 338	84.13 308

PVSNet_0	57.27 20	61.67 312	59.27 315	68.85 322	79.61 319	57.44 288	68.01 340	73.44 337	55.93 329	58.54 335	70.41 341	44.58 300	77.55 334	47.01 324	35.91 350	71.55 344

MVS-HIRNet		59.14 313	57.67 316	63.57 328	81.65 291	43.50 349	71.73 327	65.06 351	39.59 348	51.43 346	57.73 348	38.34 330	82.58 317	39.53 343	73.95 288	64.62 347

pmmvs3		57.79 314	54.26 318	68.37 324	64.02 353	56.72 296	75.12 319	65.17 350	40.20 346	52.93 345	69.86 342	20.36 352	75.48 342	45.45 332	55.25 342	72.90 343

DSMNet-mixed		57.77 315	56.90 317	60.38 330	67.70 351	35.61 354	69.18 336	53.97 355	32.30 353	57.49 338	79.88 309	40.39 324	68.57 351	38.78 344	72.37 299	76.97 340

LCM-MVSNet		54.25 316	49.68 322	67.97 325	53.73 356	45.28 346	66.85 343	80.78 299	35.96 350	39.45 350	62.23 346	8.70 361	78.06 332	48.24 319	51.20 346	80.57 332

FPMVS		53.68 317	51.64 320	59.81 331	65.08 352	51.03 334	69.48 335	69.58 344	41.46 345	40.67 349	72.32 339	16.46 356	70.00 350	24.24 351	65.42 326	58.40 348

N_pmnet		52.79 318	53.26 319	51.40 335	78.99 325	7.68 364	69.52 334	3.89 363	51.63 340	57.01 339	74.98 335	40.83 321	65.96 352	37.78 345	64.67 328	80.56 333

new_pmnet		50.91 319	50.29 321	52.78 334	68.58 350	34.94 356	63.71 346	56.63 354	39.73 347	44.95 348	65.47 343	21.93 351	58.48 353	34.98 347	56.62 340	64.92 346

ANet_high		50.57 320	46.10 323	63.99 327	48.67 359	39.13 352	70.99 330	80.85 298	61.39 293	31.18 352	57.70 349	17.02 355	73.65 348	31.22 348	15.89 356	79.18 336

Gipuma		45.18 321	41.86 324	55.16 333	77.03 333	51.52 331	32.50 355	80.52 303	32.46 352	27.12 353	35.02 353	9.52 360	75.50 341	22.31 352	60.21 337	38.45 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 322	40.28 325	55.82 332	40.82 361	42.54 350	65.12 345	63.99 353	34.43 351	24.48 354	57.12 350	3.92 363	76.17 339	17.10 354	55.52 341	48.75 349
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 323	38.86 326	46.69 336	53.84 355	16.45 362	48.61 352	49.92 356	37.49 349	31.67 351	60.97 347	8.14 362	56.42 354	28.42 349	30.72 351	67.19 345

E-PMN		31.77 324	30.64 327	35.15 338	52.87 357	27.67 358	57.09 350	47.86 357	24.64 354	16.40 358	33.05 354	11.23 358	54.90 355	14.46 356	18.15 354	22.87 353

EMVS		30.81 325	29.65 328	34.27 339	50.96 358	25.95 360	56.58 351	46.80 358	24.01 355	15.53 359	30.68 355	12.47 357	54.43 356	12.81 357	17.05 355	22.43 354

MVE	26.22 23	30.37 326	25.89 330	43.81 337	44.55 360	35.46 355	28.87 356	39.07 359	18.20 356	18.58 357	40.18 352	2.68 364	47.37 357	17.07 355	23.78 353	48.60 350
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 327	26.61 329	0.00 345	0.00 366	0.00 367	0.00 357	89.26 174	0.00 362	0.00 363	88.61 158	61.62 155	0.00 363	0.00 361	0.00 361	0.00 359

tmp_tt		18.61 328	21.40 331	10.23 342	4.82 363	10.11 363	34.70 354	30.74 361	1.48 359	23.91 356	26.07 356	28.42 347	13.41 360	27.12 350	15.35 357	7.17 355

wuyk23d		16.82 329	15.94 332	19.46 341	58.74 354	31.45 357	39.22 353	3.74 364	6.84 358	6.04 360	2.70 360	1.27 365	24.29 359	10.54 358	14.40 358	2.63 356

ab-mvs-re		7.23 330	9.64 333	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	86.72 208	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

test123		6.12 331	8.11 334	0.14 343	0.06 365	0.09 365	71.05 329	0.03 366	0.04 361	0.25 362	1.30 362	0.05 366	0.03 362	0.21 360	0.01 360	0.29 357

testmvs		6.04 332	8.02 335	0.10 344	0.08 364	0.03 366	69.74 333	0.04 365	0.05 360	0.31 361	1.68 361	0.02 367	0.04 361	0.24 359	0.02 359	0.25 358

pcd_1.5k_mvsjas		5.26 333	7.02 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	63.15 131	0.00 363	0.00 361	0.00 361	0.00 359

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 361	0.00 359

ZD-MVS					94.38 25	72.22 45		92.67 60	70.98 167	87.75 27	94.07 43	74.01 35	96.70 23	84.66 36	94.84 44

RE-MVS-def			85.48 55		93.06 57	70.63 76	91.88 35	92.27 76	73.53 130	85.69 42	94.45 26	63.87 121		82.75 60	91.87 77	92.50 103

IU-MVS					95.30 2	71.25 59		92.95 50	66.81 229	92.39 5				88.94 8	96.63 2	94.85 10

OPU-MVS				89.06 1	94.62 13	75.42 2	93.57 5				94.02 45	82.45 3	96.87 16	83.77 48	96.48 6	94.88 7

test_241102_TWO								94.06 10	77.24 47	92.78 4	95.72 6	81.26 6	97.44 2	89.07 6	96.58 4	94.26 31

test_241102_ONE					95.30 2	70.98 65		94.06 10	77.17 50	93.10 1	95.39 9	82.99 1	97.27 7

9.14			88.26 14		92.84 63		91.52 43	94.75 1	73.93 121	88.57 20	94.67 17	75.57 20	95.79 56	86.77 20	95.76 24

save fliter					93.80 39	72.35 42	90.47 62	91.17 122	74.31 111

test_0728_THIRD									78.38 32	92.12 8	95.78 4	81.46 5	97.40 4	89.42 2	96.57 5	94.67 16

test_0728_SECOND				87.71 31	95.34 1	71.43 58	93.49 7	94.23 5					97.49 1	89.08 4	96.41 8	94.21 32

test0726					95.27 5	71.25 59	93.60 4	94.11 6	77.33 45	92.81 3	95.79 3	80.98 7

GSMVS																88.96 223

test_part2					95.06 7	72.65 31				91.80 10

sam_mvs1												51.32 249				88.96 223

sam_mvs												50.01 262

ambc				75.24 288	73.16 346	50.51 337	63.05 348	87.47 221		64.28 315	77.81 325	17.80 354	89.73 254	57.88 271	60.64 335	85.49 292

MTGPA								92.02 87

test_post1							78.90 295				5.43 359	48.81 277	85.44 299	59.25 256

test_post											5.46 358	50.36 260	84.24 306

patchmatchnet-post											74.00 336	51.12 251	88.60 273

GG-mvs-BLEND				75.38 287	81.59 293	55.80 309	79.32 288	69.63 343		67.19 291	73.67 337	43.24 306	88.90 271	50.41 303	84.50 162	81.45 327

MTMP							92.18 31	32.83 360

gm-plane-assit					81.40 296	53.83 320			62.72 283		80.94 300		92.39 192	63.40 221

test9_res														84.90 30	95.70 27	92.87 93

TEST9					93.26 51	72.96 24	88.75 108	91.89 96	68.44 220	85.00 50	93.10 62	74.36 30	95.41 71

test_8					93.13 53	72.57 34	88.68 113	91.84 99	68.69 216	84.87 56	93.10 62	74.43 27	95.16 81

agg_prior2														82.91 58	95.45 29	92.70 96

agg_prior					92.85 61	71.94 51		91.78 102		84.41 63			94.93 91

TestCases				79.58 236	85.15 228	63.62 207		79.83 311	62.31 286	60.32 330	86.73 206	32.02 344	88.96 269	50.28 306	71.57 306	86.15 284

test_prior4						72.60 33	89.01 99

test_prior2							88.85 104		75.41 89	84.91 52	93.54 51	74.28 31		83.31 50	95.86 18

test_prior				86.33 60	92.61 69	69.59 95		92.97 48					95.48 66			93.91 45

旧先验2							86.56 177		58.10 316	87.04 31			88.98 267	74.07 132

新几何2							86.29 185

新几何1				83.42 137	93.13 53	70.71 74		85.48 245	57.43 321	81.80 98	91.98 79	63.28 126	92.27 196	64.60 215	92.99 65	87.27 261

旧先验1					91.96 77	65.79 169		86.37 237			93.08 66	69.31 75			92.74 69	88.74 232

无先验							87.48 149	88.98 185	60.00 302				94.12 122	67.28 191		88.97 222

原ACMM2							86.86 166

原ACMM1				84.35 109	93.01 59	68.79 111		92.44 68	63.96 271	81.09 108	91.57 89	66.06 102	95.45 68	67.19 194	94.82 47	88.81 229

test222					91.50 83	68.26 128	84.16 235	83.20 279	54.63 333	79.74 117	91.63 87	58.97 184			91.42 83	86.77 274

testdata2													91.01 236	62.37 230

segment_acmp												73.08 40

testdata				79.97 227	90.90 91	64.21 198		84.71 252	59.27 309	85.40 44	92.91 67	62.02 151	89.08 265	68.95 179	91.37 84	86.63 278

testdata1							84.14 236		75.71 83

test12				86.80 52	92.63 68	70.70 75		91.79 101		82.71 88		71.67 52	96.16 44		94.50 52	93.54 68

plane_prior7					90.08 105	68.51 124

plane_prior6					89.84 110	68.70 119						60.42 177

plane_prior5								92.44 68					95.38 73	78.71 89	86.32 146	91.33 137

plane_prior4											91.00 106

plane_prior3						68.60 122			78.44 30	78.92 127

plane_prior2							91.25 47		79.12 23

plane_prior1					89.90 109

plane_prior						68.71 117	90.38 66		77.62 36						86.16 149

n2								0.00 367

nn								0.00 367

door-mid								69.98 342

lessismore_v0				78.97 244	81.01 303	57.15 290		65.99 349		61.16 327	82.82 280	39.12 327	91.34 225	59.67 252	46.92 349	88.43 239

LGP-MVS_train				84.50 102	89.23 132	68.76 113		91.94 94	75.37 91	76.64 176	91.51 90	54.29 218	94.91 93	78.44 92	83.78 169	89.83 197

test11								92.23 79

door								69.44 345

HQP5-MVS						66.98 150

HQP-NCC					89.33 124		89.17 92		76.41 70	77.23 162

ACMP_Plane					89.33 124		89.17 92		76.41 70	77.23 162

BP-MVS														77.47 102

HQP4-MVS										77.24 161			95.11 83			91.03 145

HQP3-MVS								92.19 82							85.99 151

HQP2-MVS												60.17 180

NP-MVS					89.62 113	68.32 126					90.24 117

MDTV_nov1_ep13_2view						37.79 353	75.16 317		55.10 331	66.53 299		49.34 271		53.98 289		87.94 245

MDTV_nov1_ep13			69.97 270		83.18 262	53.48 322	77.10 308	80.18 310	60.45 297	69.33 277	80.44 303	48.89 276	86.90 288	51.60 299	78.51 231

ACMMP++_ref															81.95 195

ACMMP++															81.25 200

Test By Simon												64.33 117

ITE_SJBPF				78.22 255	81.77 290	60.57 253		83.30 275	69.25 199	67.54 287	87.20 197	36.33 336	87.28 287	54.34 288	74.62 283	86.80 273

DeepMVS_CX				27.40 340	40.17 362	26.90 359		24.59 362	17.44 357	23.95 355	48.61 351	9.77 359	26.48 358	18.06 353	24.47 352	28.83 352

High-resolution multi-view benchmark