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.

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 57	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 63	93.57 7	94.06 10	77.24 50	93.10 1	95.72 8	82.99 1	97.44 6	89.07 14	96.63 4	94.88 14

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 57	93.49 9	92.73 59	77.33 48	92.12 9	95.78 4	80.98 9	97.40 8	89.08 12	96.41 12	93.33 84
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 37	78.35 13	96.77 24	89.59 8	94.22 58	94.67 24
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 85	92.29 7	95.66 10	81.67 6	97.38 10	87.44 33	96.34 15	93.95 52
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MM		89.16 6	89.23 7	88.97 4	90.79 90	73.65 10	92.66 23	91.17 116	86.57 1	87.39 35	94.97 16	71.70 50	97.68 1	92.19 1	95.63 28	95.57 1

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 89	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 31
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 107	92.29 7	95.97 2	74.28 29	97.24 12	88.58 21	96.91 1	94.87 16
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 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 54	80.26 11	87.78 29	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 96

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 61	93.00 43	80.90 7	88.06 26	94.06 42	76.43 16	96.84 21	88.48 24	95.99 18	94.34 37

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 17	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 61	71.95 50	92.40 24	94.74 2	75.71 87	89.16 19	95.10 14	75.65 21	96.19 43	87.07 34	96.01 17	94.79 21

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 50	92.24 68	69.03 99	89.57 87	93.39 30	77.53 45	89.79 18	94.12 39	78.98 12	96.58 35	85.66 37	95.72 24	94.58 27

MVS_0304		88.08 14	88.08 17	88.08 14	89.67 114	72.04 48	92.26 33	89.26 173	84.19 2	85.01 55	95.18 13	69.93 69	97.20 14	91.63 2	95.60 29	94.99 9

SD-MVS		88.06 15	88.50 14	86.71 51	92.60 66	72.71 29	91.81 42	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 136	87.63 30	94.27 57	93.65 69
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 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 52	92.83 55	81.50 5	85.79 48	93.47 60	73.02 39	97.00 18	84.90 42	94.94 39	94.10 45

ACMMP_NAP		88.05 17	88.08 17	87.94 19	93.70 41	73.05 22	90.86 56	93.59 23	76.27 79	88.14 24	95.09 15	71.06 57	96.67 29	87.67 29	96.37 14	94.09 46

TSAR-MVS + MP.		88.02 18	88.11 16	87.72 30	93.68 43	72.13 46	91.41 47	92.35 74	74.62 111	88.90 20	93.85 52	75.75 20	96.00 49	87.80 28	94.63 47	95.04 7
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 19	87.85 20	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 63	85.24 53	94.32 31	71.76 48	96.93 19	85.53 39	95.79 22	94.32 38

MP-MVS		87.71 20	87.64 22	87.93 21	94.36 26	73.88 6	92.71 22	92.65 64	77.57 41	83.84 82	94.40 30	72.24 43	96.28 40	85.65 38	95.30 35	93.62 72
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 44	72.04 48	89.80 79	93.50 25	75.17 100	86.34 44	95.29 12	70.86 59	96.00 49	88.78 19	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 22	87.47 24	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 65	84.91 59	94.44 28	70.78 60	96.61 32	84.53 49	94.89 41	93.66 65

ACMMPR		87.44 23	87.23 27	88.08 14	94.64 13	73.59 12	93.04 12	93.20 34	76.78 65	84.66 66	94.52 21	68.81 84	96.65 30	84.53 49	94.90 40	94.00 50

APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 41	92.83 55	73.01 150	88.58 21	94.52 21	73.36 34	96.49 36	84.26 52	95.01 37	92.70 105
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 25	87.26 25	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 61	84.68 63	93.99 48	70.67 62	96.82 22	84.18 56	95.01 37	93.90 55

region2R		87.42 25	87.20 28	88.09 13	94.63 14	73.55 13	93.03 14	93.12 37	76.73 68	84.45 70	94.52 21	69.09 78	96.70 27	84.37 51	94.83 44	94.03 49

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 77	93.82 16	73.07 148	84.86 62	92.89 74	76.22 17	96.33 38	84.89 44	95.13 36	94.40 34

MTAPA		87.23 28	87.00 29	87.90 22	94.18 35	74.25 5	86.58 187	92.02 85	79.45 19	85.88 46	94.80 17	68.07 89	96.21 42	86.69 36	95.34 33	93.23 87

XVS		87.18 29	86.91 33	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	94.17 36	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

HPM-MVS		87.11 30	86.98 30	87.50 38	93.88 39	72.16 45	92.19 34	93.33 31	76.07 82	83.81 83	93.95 51	69.77 72	96.01 48	85.15 40	94.66 46	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 30	86.92 32	87.68 34	94.20 34	73.86 7	93.98 3	92.82 58	76.62 70	83.68 84	94.46 25	67.93 90	95.95 52	84.20 55	94.39 53	93.23 87

DeepC-MVS	79.81 2	87.08 32	86.88 34	87.69 33	91.16 80	72.32 43	90.31 68	93.94 14	77.12 55	82.82 96	94.23 35	72.13 45	97.09 16	84.83 45	95.37 32	93.65 69
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 33	86.62 36	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 70	84.22 74	93.36 63	71.44 54	96.76 25	80.82 85	95.33 34	94.16 43
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 34	86.67 35	86.91 46	94.11 37	72.11 47	92.37 28	92.56 67	74.50 112	86.84 42	94.65 20	67.31 97	95.77 54	84.80 46	92.85 67	92.84 103

CS-MVS		86.69 35	86.95 31	85.90 63	90.76 91	67.57 140	92.83 17	93.30 32	79.67 17	84.57 69	92.27 86	71.47 53	95.02 86	84.24 54	93.46 63	95.13 6

PGM-MVS		86.68 36	86.27 40	87.90 22	94.22 33	73.38 18	90.22 70	93.04 38	75.53 91	83.86 81	94.42 29	67.87 92	96.64 31	82.70 72	94.57 49	93.66 65

mPP-MVS		86.67 37	86.32 39	87.72 30	94.41 22	73.55 13	92.74 20	92.22 80	76.87 62	82.81 97	94.25 34	66.44 105	96.24 41	82.88 67	94.28 56	93.38 81

CANet		86.45 38	86.10 45	87.51 37	90.09 102	70.94 67	89.70 83	92.59 66	81.78 4	81.32 112	91.43 106	70.34 64	97.23 13	84.26 52	93.36 64	94.37 35

train_agg		86.43 39	86.20 41	87.13 44	93.26 50	72.96 25	88.75 115	91.89 93	68.69 236	85.00 57	93.10 67	74.43 26	95.41 67	84.97 41	95.71 25	93.02 98

PHI-MVS		86.43 39	86.17 43	87.24 41	90.88 87	70.96 65	92.27 32	94.07 9	72.45 153	85.22 54	91.90 92	69.47 74	96.42 37	83.28 62	95.94 19	94.35 36

CSCG		86.41 41	86.19 42	87.07 45	92.91 58	72.48 37	90.81 57	93.56 24	73.95 123	83.16 91	91.07 116	75.94 18	95.19 75	79.94 94	94.38 54	93.55 76

CS-MVS-test		86.29 42	86.48 37	85.71 65	91.02 83	67.21 152	92.36 29	93.78 18	78.97 28	83.51 88	91.20 111	70.65 63	95.15 77	81.96 76	94.89 41	94.77 22

EC-MVSNet		86.01 43	86.38 38	84.91 88	89.31 131	66.27 166	92.32 30	93.63 21	79.37 20	84.17 76	91.88 93	69.04 82	95.43 65	83.93 57	93.77 61	93.01 99

casdiffmvs_mvg		85.99 44	86.09 46	85.70 66	87.65 197	67.22 151	88.69 119	93.04 38	79.64 18	85.33 52	92.54 83	73.30 35	94.50 107	83.49 59	91.14 89	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS_3200maxsize		85.97 45	85.88 48	86.22 57	92.69 63	69.53 89	91.93 38	92.99 45	73.54 135	85.94 45	94.51 24	65.80 115	95.61 57	83.04 65	92.51 71	93.53 78

test_fmvsmconf_n		85.92 46	86.04 47	85.57 68	85.03 252	69.51 90	89.62 86	90.58 131	73.42 138	87.75 31	94.02 44	72.85 40	93.24 160	90.37 3	90.75 93	93.96 51

canonicalmvs		85.91 47	85.87 49	86.04 60	89.84 112	69.44 95	90.45 66	93.00 43	76.70 69	88.01 28	91.23 109	73.28 36	93.91 131	81.50 79	88.80 120	94.77 22

ACMMP		85.89 48	85.39 54	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 65	80.73 121	93.82 53	64.33 125	96.29 39	82.67 73	90.69 94	93.23 87
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 49	85.61 52	86.23 56	93.06 55	70.63 73	91.88 39	92.27 76	73.53 136	85.69 49	94.45 26	65.00 123	95.56 58	82.75 68	91.87 79	92.50 114

CDPH-MVS		85.76 50	85.29 59	87.17 43	93.49 47	71.08 61	88.58 123	92.42 72	68.32 243	84.61 67	93.48 58	72.32 42	96.15 45	79.00 98	95.43 31	94.28 40

TSAR-MVS + GP.		85.71 51	85.33 56	86.84 47	91.34 78	72.50 36	89.07 104	87.28 228	76.41 72	85.80 47	90.22 134	74.15 31	95.37 72	81.82 77	91.88 78	92.65 109

dcpmvs_2		85.63 52	86.15 44	84.06 125	91.71 75	64.94 197	86.47 190	91.87 95	73.63 131	86.60 43	93.02 72	76.57 15	91.87 216	83.36 60	92.15 75	95.35 3

test_fmvsmconf0.1_n		85.61 53	85.65 51	85.50 69	82.99 297	69.39 96	89.65 84	90.29 144	73.31 141	87.77 30	94.15 38	71.72 49	93.23 161	90.31 4	90.67 95	93.89 56

alignmvs		85.48 54	85.32 57	85.96 62	89.51 120	69.47 92	89.74 81	92.47 68	76.17 80	87.73 33	91.46 105	70.32 65	93.78 136	81.51 78	88.95 117	94.63 26

3Dnovator+	77.84 4	85.48 54	84.47 69	88.51 7	91.08 81	73.49 16	93.18 11	93.78 18	80.79 8	76.66 195	93.37 62	60.40 188	96.75 26	77.20 117	93.73 62	95.29 5

MSLP-MVS++		85.43 56	85.76 50	84.45 103	91.93 72	70.24 76	90.71 58	92.86 53	77.46 47	84.22 74	92.81 78	67.16 99	92.94 180	80.36 90	94.35 55	90.16 195

DELS-MVS		85.41 57	85.30 58	85.77 64	88.49 162	67.93 132	85.52 219	93.44 27	78.70 29	83.63 87	89.03 164	74.57 24	95.71 56	80.26 92	94.04 59	93.66 65
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 58	84.95 63	86.57 53	93.69 42	70.58 75	92.15 36	91.62 103	73.89 126	82.67 99	94.09 40	62.60 144	95.54 60	80.93 83	92.93 66	93.57 74

test_fmvsm_n_1920		85.29 59	85.34 55	85.13 79	86.12 231	69.93 83	88.65 121	90.78 127	69.97 203	88.27 23	93.98 49	71.39 55	91.54 228	88.49 23	90.45 97	93.91 53

MVS_111021_HR		85.14 60	84.75 64	86.32 55	91.65 76	72.70 30	85.98 202	90.33 141	76.11 81	82.08 102	91.61 100	71.36 56	94.17 120	81.02 82	92.58 70	92.08 131

casdiffmvs		85.11 61	85.14 60	85.01 82	87.20 213	65.77 179	87.75 153	92.83 55	77.84 37	84.36 73	92.38 85	72.15 44	93.93 130	81.27 81	90.48 96	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		85.08 62	84.96 62	85.45 70	92.07 70	68.07 129	89.78 80	90.86 126	82.48 3	84.60 68	93.20 66	69.35 75	95.22 74	71.39 174	90.88 92	93.07 95

DPM-MVS		84.93 63	84.29 70	86.84 47	90.20 100	73.04 23	87.12 169	93.04 38	69.80 207	82.85 95	91.22 110	73.06 38	96.02 47	76.72 126	94.63 47	91.46 149

baseline		84.93 63	84.98 61	84.80 92	87.30 211	65.39 188	87.30 165	92.88 52	77.62 39	84.04 79	92.26 87	71.81 47	93.96 124	81.31 80	90.30 99	95.03 8

ETV-MVS		84.90 65	84.67 65	85.59 67	89.39 125	68.66 116	88.74 117	92.64 65	79.97 15	84.10 77	85.71 254	69.32 76	95.38 69	80.82 85	91.37 86	92.72 104

test_fmvsmconf0.01_n		84.73 66	84.52 68	85.34 72	80.25 337	69.03 99	89.47 88	89.65 161	73.24 145	86.98 40	94.27 32	66.62 101	93.23 161	90.26 5	89.95 107	93.78 62

fmvsm_l_conf0.5_n		84.47 67	84.54 66	84.27 113	85.42 241	68.81 105	88.49 125	87.26 229	68.08 245	88.03 27	93.49 57	72.04 46	91.77 218	88.90 17	89.14 116	92.24 125

EI-MVSNet-Vis-set		84.19 68	83.81 73	85.31 73	88.18 172	67.85 133	87.66 155	89.73 159	80.05 14	82.95 92	89.59 148	70.74 61	94.82 95	80.66 89	84.72 170	93.28 86

fmvsm_l_conf0.5_n_a		84.13 69	84.16 71	84.06 125	85.38 242	68.40 120	88.34 132	86.85 237	67.48 252	87.48 34	93.40 61	70.89 58	91.61 222	88.38 25	89.22 115	92.16 129

test_fmvsmvis_n_1920		84.02 70	83.87 72	84.49 101	84.12 268	69.37 97	88.15 140	87.96 212	70.01 201	83.95 80	93.23 65	68.80 85	91.51 231	88.61 20	89.96 106	92.57 110

nrg030		83.88 71	83.53 75	84.96 84	86.77 221	69.28 98	90.46 65	92.67 61	74.79 106	82.95 92	91.33 108	72.70 41	93.09 174	80.79 87	79.28 250	92.50 114

EI-MVSNet-UG-set		83.81 72	83.38 78	85.09 80	87.87 185	67.53 141	87.44 161	89.66 160	79.74 16	82.23 101	89.41 157	70.24 66	94.74 98	79.95 93	83.92 184	92.99 100

fmvsm_s_conf0.5_n		83.80 73	83.71 74	84.07 123	86.69 223	67.31 147	89.46 89	83.07 292	71.09 178	86.96 41	93.70 55	69.02 83	91.47 233	88.79 18	84.62 172	93.44 80

CPTT-MVS		83.73 74	83.33 80	84.92 87	93.28 49	70.86 69	92.09 37	90.38 137	68.75 235	79.57 132	92.83 76	60.60 184	93.04 178	80.92 84	91.56 84	90.86 168

EPNet		83.72 75	82.92 87	86.14 59	84.22 266	69.48 91	91.05 55	85.27 257	81.30 6	76.83 190	91.65 97	66.09 110	95.56 58	76.00 132	93.85 60	93.38 81
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
patch_mono-2		83.65 76	84.54 66	80.99 220	90.06 107	65.83 175	84.21 247	88.74 198	71.60 168	85.01 55	92.44 84	74.51 25	83.50 332	82.15 75	92.15 75	93.64 71

HQP_MVS		83.64 77	83.14 81	85.14 77	90.08 103	68.71 112	91.25 50	92.44 69	79.12 23	78.92 141	91.00 120	60.42 186	95.38 69	78.71 102	86.32 151	91.33 150

fmvsm_s_conf0.5_n_a		83.63 78	83.41 77	84.28 111	86.14 230	68.12 127	89.43 90	82.87 296	70.27 197	87.27 37	93.80 54	69.09 78	91.58 224	88.21 26	83.65 192	93.14 93

Effi-MVS+		83.62 79	83.08 82	85.24 75	88.38 167	67.45 142	88.89 109	89.15 179	75.50 92	82.27 100	88.28 186	69.61 73	94.45 109	77.81 111	87.84 130	93.84 59

fmvsm_s_conf0.1_n		83.56 80	83.38 78	84.10 118	84.86 254	67.28 148	89.40 93	83.01 293	70.67 186	87.08 38	93.96 50	68.38 87	91.45 234	88.56 22	84.50 173	93.56 75

OPM-MVS		83.50 81	82.95 86	85.14 77	88.79 152	70.95 66	89.13 103	91.52 106	77.55 44	80.96 119	91.75 95	60.71 179	94.50 107	79.67 95	86.51 149	89.97 211
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 82	82.80 89	85.43 71	90.25 99	68.74 110	90.30 69	90.13 148	76.33 78	80.87 120	92.89 74	61.00 176	94.20 118	72.45 168	90.97 90	93.35 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 83	83.45 76	83.28 152	92.74 62	62.28 248	88.17 138	89.50 164	75.22 96	81.49 111	92.74 82	66.75 100	95.11 80	72.85 162	91.58 83	92.45 117

EPP-MVSNet		83.40 84	83.02 84	84.57 96	90.13 101	64.47 207	92.32 30	90.73 128	74.45 115	79.35 135	91.10 114	69.05 81	95.12 78	72.78 163	87.22 138	94.13 44

3Dnovator	76.31 5	83.38 85	82.31 95	86.59 52	87.94 183	72.94 28	90.64 59	92.14 84	77.21 52	75.47 221	92.83 76	58.56 195	94.72 99	73.24 159	92.71 69	92.13 130

fmvsm_s_conf0.1_n_a		83.32 86	82.99 85	84.28 111	83.79 275	68.07 129	89.34 95	82.85 297	69.80 207	87.36 36	94.06 42	68.34 88	91.56 226	87.95 27	83.46 198	93.21 90

EIA-MVS		83.31 87	82.80 89	84.82 90	89.59 116	65.59 181	88.21 136	92.68 60	74.66 109	78.96 139	86.42 241	69.06 80	95.26 73	75.54 138	90.09 103	93.62 72

h-mvs33		83.15 88	82.19 96	86.02 61	90.56 93	70.85 70	88.15 140	89.16 178	76.02 83	84.67 64	91.39 107	61.54 162	95.50 61	82.71 70	75.48 297	91.72 139

MVS_Test		83.15 88	83.06 83	83.41 149	86.86 217	63.21 234	86.11 200	92.00 87	74.31 116	82.87 94	89.44 156	70.03 67	93.21 163	77.39 116	88.50 126	93.81 60

IS-MVSNet		83.15 88	82.81 88	84.18 116	89.94 110	63.30 232	91.59 43	88.46 204	79.04 25	79.49 133	92.16 88	65.10 120	94.28 112	67.71 209	91.86 81	94.95 10

DP-MVS Recon		83.11 91	82.09 98	86.15 58	94.44 19	70.92 68	88.79 113	92.20 81	70.53 191	79.17 137	91.03 119	64.12 127	96.03 46	68.39 206	90.14 102	91.50 145

PAPM_NR		83.02 92	82.41 92	84.82 90	92.47 67	66.37 164	87.93 148	91.80 98	73.82 127	77.32 179	90.66 125	67.90 91	94.90 91	70.37 183	89.48 112	93.19 91

VDD-MVS		83.01 93	82.36 94	84.96 84	91.02 83	66.40 163	88.91 108	88.11 207	77.57 41	84.39 72	93.29 64	52.19 247	93.91 131	77.05 119	88.70 122	94.57 29

MVSFormer		82.85 94	82.05 99	85.24 75	87.35 205	70.21 77	90.50 62	90.38 137	68.55 238	81.32 112	89.47 151	61.68 159	93.46 153	78.98 99	90.26 100	92.05 132

OMC-MVS		82.69 95	81.97 102	84.85 89	88.75 154	67.42 143	87.98 144	90.87 125	74.92 103	79.72 130	91.65 97	62.19 154	93.96 124	75.26 140	86.42 150	93.16 92

PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 84	90.80 89	69.76 87	88.74 117	91.70 102	69.39 215	78.96 139	88.46 181	65.47 117	94.87 94	74.42 145	88.57 123	90.24 193

MVS_111021_LR		82.61 97	82.11 97	84.11 117	88.82 149	71.58 53	85.15 222	86.16 247	74.69 108	80.47 123	91.04 117	62.29 151	90.55 256	80.33 91	90.08 104	90.20 194

HQP-MVS		82.61 97	82.02 100	84.37 105	89.33 128	66.98 155	89.17 98	92.19 82	76.41 72	77.23 182	90.23 133	60.17 189	95.11 80	77.47 114	85.99 159	91.03 162

CLD-MVS		82.31 99	81.65 105	84.29 110	88.47 163	67.73 136	85.81 210	92.35 74	75.78 86	78.33 156	86.58 236	64.01 128	94.35 110	76.05 131	87.48 135	90.79 169
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 100	82.41 92	81.62 200	90.82 88	60.93 262	84.47 238	89.78 156	76.36 77	84.07 78	91.88 93	64.71 124	90.26 258	70.68 180	88.89 118	93.66 65

diffmvs		82.10 101	81.88 103	82.76 182	83.00 295	63.78 220	83.68 254	89.76 157	72.94 151	82.02 103	89.85 140	65.96 114	90.79 252	82.38 74	87.30 137	93.71 64
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		82.08 102	81.27 108	84.50 99	89.23 135	68.76 108	90.22 70	91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 185	89.83 216

FIs		82.07 103	82.42 91	81.04 219	88.80 151	58.34 288	88.26 135	93.49 26	76.93 60	78.47 153	91.04 117	69.92 70	92.34 199	69.87 190	84.97 167	92.44 118

PS-MVSNAJss		82.07 103	81.31 107	84.34 108	86.51 226	67.27 149	89.27 96	91.51 107	71.75 162	79.37 134	90.22 134	63.15 138	94.27 113	77.69 112	82.36 212	91.49 146

API-MVS		81.99 105	81.23 109	84.26 114	90.94 85	70.18 82	91.10 53	89.32 169	71.51 170	78.66 147	88.28 186	65.26 118	95.10 83	64.74 236	91.23 88	87.51 277

UniMVSNet_NR-MVSNet		81.88 106	81.54 106	82.92 171	88.46 164	63.46 228	87.13 168	92.37 73	80.19 12	78.38 154	89.14 160	71.66 52	93.05 176	70.05 186	76.46 280	92.25 123

MAR-MVS		81.84 107	80.70 118	85.27 74	91.32 79	71.53 54	89.82 77	90.92 122	69.77 209	78.50 151	86.21 245	62.36 150	94.52 106	65.36 230	92.05 77	89.77 219
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		81.82 108	81.23 109	83.57 144	91.89 73	63.43 230	89.84 76	81.85 307	77.04 58	83.21 89	93.10 67	52.26 246	93.43 155	71.98 169	89.95 107	93.85 57

hse-mvs2		81.72 109	80.94 115	84.07 123	88.72 155	67.68 138	85.87 206	87.26 229	76.02 83	84.67 64	88.22 189	61.54 162	93.48 151	82.71 70	73.44 325	91.06 160

GeoE		81.71 110	81.01 114	83.80 139	89.51 120	64.45 208	88.97 106	88.73 199	71.27 174	78.63 148	89.76 142	66.32 107	93.20 166	69.89 189	86.02 158	93.74 63

xiu_mvs_v2_base		81.69 111	81.05 112	83.60 142	89.15 138	68.03 131	84.46 240	90.02 150	70.67 186	81.30 115	86.53 239	63.17 137	94.19 119	75.60 137	88.54 124	88.57 259

PS-MVSNAJ		81.69 111	81.02 113	83.70 141	89.51 120	68.21 126	84.28 246	90.09 149	70.79 183	81.26 116	85.62 259	63.15 138	94.29 111	75.62 136	88.87 119	88.59 258

mvsmamba		81.69 111	80.74 117	84.56 97	87.45 204	66.72 159	91.26 48	85.89 251	74.66 109	78.23 159	90.56 127	54.33 227	94.91 88	80.73 88	83.54 196	92.04 134

PAPR		81.66 114	80.89 116	83.99 133	90.27 98	64.00 215	86.76 183	91.77 101	68.84 234	77.13 188	89.50 149	67.63 93	94.88 93	67.55 211	88.52 125	93.09 94

UniMVSNet (Re)		81.60 115	81.11 111	83.09 162	88.38 167	64.41 209	87.60 156	93.02 42	78.42 32	78.56 150	88.16 190	69.78 71	93.26 159	69.58 193	76.49 279	91.60 140

FC-MVSNet-test		81.52 116	82.02 100	80.03 240	88.42 166	55.97 326	87.95 146	93.42 29	77.10 56	77.38 177	90.98 122	69.96 68	91.79 217	68.46 205	84.50 173	92.33 119

VDDNet		81.52 116	80.67 119	84.05 128	90.44 96	64.13 214	89.73 82	85.91 250	71.11 177	83.18 90	93.48 58	50.54 271	93.49 150	73.40 156	88.25 128	94.54 30

ACMP	74.13 6	81.51 118	80.57 120	84.36 106	89.42 123	68.69 115	89.97 74	91.50 110	74.46 114	75.04 241	90.41 130	53.82 233	94.54 104	77.56 113	82.91 204	89.86 215
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 119	80.29 127	84.70 94	86.63 225	69.90 85	85.95 203	86.77 238	63.24 299	81.07 118	89.47 151	61.08 175	92.15 205	78.33 107	90.07 105	92.05 132
jason: jason.
lupinMVS		81.39 119	80.27 128	84.76 93	87.35 205	70.21 77	85.55 215	86.41 242	62.85 306	81.32 112	88.61 176	61.68 159	92.24 203	78.41 106	90.26 100	91.83 136

test_yl		81.17 121	80.47 123	83.24 155	89.13 139	63.62 221	86.21 197	89.95 153	72.43 156	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

DCV-MVSNet		81.17 121	80.47 123	83.24 155	89.13 139	63.62 221	86.21 197	89.95 153	72.43 156	81.78 108	89.61 146	57.50 205	93.58 144	70.75 178	86.90 142	92.52 112

DU-MVS		81.12 123	80.52 122	82.90 172	87.80 189	63.46 228	87.02 172	91.87 95	79.01 26	78.38 154	89.07 162	65.02 121	93.05 176	70.05 186	76.46 280	92.20 126

PVSNet_Blended		80.98 124	80.34 125	82.90 172	88.85 146	65.40 186	84.43 242	92.00 87	67.62 249	78.11 163	85.05 273	66.02 112	94.27 113	71.52 171	89.50 111	89.01 241

FA-MVS(test-final)		80.96 125	79.91 133	84.10 118	88.30 170	65.01 195	84.55 237	90.01 151	73.25 144	79.61 131	87.57 203	58.35 197	94.72 99	71.29 175	86.25 153	92.56 111

QAPM		80.88 126	79.50 142	85.03 81	88.01 182	68.97 103	91.59 43	92.00 87	66.63 263	75.15 237	92.16 88	57.70 202	95.45 63	63.52 241	88.76 121	90.66 175

TranMVSNet+NR-MVSNet		80.84 127	80.31 126	82.42 187	87.85 186	62.33 246	87.74 154	91.33 112	80.55 9	77.99 167	89.86 139	65.23 119	92.62 186	67.05 218	75.24 307	92.30 121

UGNet		80.83 128	79.59 140	84.54 98	88.04 180	68.09 128	89.42 91	88.16 206	76.95 59	76.22 207	89.46 153	49.30 286	93.94 127	68.48 204	90.31 98	91.60 140
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+		80.81 129	79.92 132	83.47 145	88.85 146	64.51 204	85.53 217	89.39 167	70.79 183	78.49 152	85.06 272	67.54 94	93.58 144	67.03 219	86.58 147	92.32 120

XVG-OURS-SEG-HR		80.81 129	79.76 136	83.96 135	85.60 238	68.78 107	83.54 260	90.50 134	70.66 189	76.71 194	91.66 96	60.69 180	91.26 239	76.94 120	81.58 221	91.83 136

xiu_mvs_v1_base_debu		80.80 131	79.72 137	84.03 130	87.35 205	70.19 79	85.56 212	88.77 194	69.06 228	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 236

xiu_mvs_v1_base		80.80 131	79.72 137	84.03 130	87.35 205	70.19 79	85.56 212	88.77 194	69.06 228	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 236

xiu_mvs_v1_base_debi		80.80 131	79.72 137	84.03 130	87.35 205	70.19 79	85.56 212	88.77 194	69.06 228	81.83 104	88.16 190	50.91 265	92.85 182	78.29 108	87.56 132	89.06 236

ACMM	73.20 8	80.78 134	79.84 135	83.58 143	89.31 131	68.37 121	89.99 73	91.60 104	70.28 196	77.25 180	89.66 144	53.37 238	93.53 149	74.24 148	82.85 205	88.85 249
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		80.68 135	79.51 141	84.20 115	94.09 38	67.27 149	89.64 85	91.11 119	58.75 343	74.08 253	90.72 124	58.10 198	95.04 85	69.70 191	89.42 113	90.30 191

iter_conf_final		80.63 136	79.35 146	84.46 102	89.36 127	67.70 137	89.85 75	84.49 267	73.19 146	78.30 157	88.94 165	45.98 312	94.56 102	79.59 96	84.48 176	91.11 157

CANet_DTU		80.61 137	79.87 134	82.83 174	85.60 238	63.17 237	87.36 162	88.65 200	76.37 76	75.88 214	88.44 182	53.51 236	93.07 175	73.30 157	89.74 110	92.25 123

VPA-MVSNet		80.60 138	80.55 121	80.76 226	88.07 179	60.80 265	86.86 177	91.58 105	75.67 90	80.24 125	89.45 155	63.34 132	90.25 259	70.51 182	79.22 251	91.23 154

PVSNet_BlendedMVS		80.60 138	80.02 130	82.36 189	88.85 146	65.40 186	86.16 199	92.00 87	69.34 217	78.11 163	86.09 249	66.02 112	94.27 113	71.52 171	82.06 215	87.39 279

AdaColmap		80.58 140	79.42 143	84.06 125	93.09 54	68.91 104	89.36 94	88.97 188	69.27 218	75.70 217	89.69 143	57.20 209	95.77 54	63.06 246	88.41 127	87.50 278

EI-MVSNet		80.52 141	79.98 131	82.12 190	84.28 264	63.19 236	86.41 191	88.95 189	74.18 120	78.69 145	87.54 206	66.62 101	92.43 193	72.57 166	80.57 234	90.74 173

XVG-OURS		80.41 142	79.23 150	83.97 134	85.64 237	69.02 101	83.03 271	90.39 136	71.09 178	77.63 173	91.49 104	54.62 226	91.35 237	75.71 134	83.47 197	91.54 142

SDMVSNet		80.38 143	80.18 129	80.99 220	89.03 144	64.94 197	80.45 302	89.40 166	75.19 98	76.61 198	89.98 137	60.61 183	87.69 300	76.83 123	83.55 194	90.33 189

PCF-MVS	73.52 7	80.38 143	78.84 159	85.01 82	87.71 194	68.99 102	83.65 255	91.46 111	63.00 303	77.77 171	90.28 131	66.10 109	95.09 84	61.40 265	88.22 129	90.94 166
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 145	77.83 182	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	12.47 402	67.45 95	96.60 33	83.06 63	94.50 50	94.07 47

RRT_MVS		80.35 146	79.22 151	83.74 140	87.63 198	65.46 185	91.08 54	88.92 191	73.82 127	76.44 203	90.03 136	49.05 291	94.25 117	76.84 121	79.20 252	91.51 143

test_djsdf		80.30 147	79.32 147	83.27 153	83.98 272	65.37 189	90.50 62	90.38 137	68.55 238	76.19 208	88.70 172	56.44 213	93.46 153	78.98 99	80.14 240	90.97 165

v2v482		80.23 148	79.29 148	83.05 165	83.62 278	64.14 213	87.04 171	89.97 152	73.61 132	78.18 162	87.22 214	61.10 174	93.82 134	76.11 129	76.78 277	91.18 155

NR-MVSNet		80.23 148	79.38 144	82.78 180	87.80 189	63.34 231	86.31 194	91.09 120	79.01 26	72.17 275	89.07 162	67.20 98	92.81 185	66.08 225	75.65 293	92.20 126

Anonymous20240529		80.19 150	78.89 158	84.10 118	90.60 92	64.75 201	88.95 107	90.90 123	65.97 271	80.59 122	91.17 113	49.97 276	93.73 142	69.16 197	82.70 209	93.81 60

IterMVS-LS		80.06 151	79.38 144	82.11 191	85.89 233	63.20 235	86.79 180	89.34 168	74.19 119	75.45 224	86.72 226	66.62 101	92.39 195	72.58 165	76.86 274	90.75 172
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 152	78.57 164	84.42 104	85.13 249	68.74 110	88.77 114	88.10 208	74.99 102	74.97 242	83.49 300	57.27 208	93.36 156	73.53 153	80.88 228	91.18 155

v1144		80.03 152	79.03 155	83.01 167	83.78 276	64.51 204	87.11 170	90.57 133	71.96 161	78.08 165	86.20 246	61.41 166	93.94 127	74.93 141	77.23 268	90.60 178

iter_conf05		80.00 154	78.70 160	83.91 137	87.84 187	65.83 175	88.84 112	84.92 262	71.61 167	78.70 144	88.94 165	43.88 328	94.56 102	79.28 97	84.28 180	91.33 150

v8		79.97 155	79.02 156	82.80 177	84.09 269	64.50 206	87.96 145	90.29 144	74.13 122	75.24 234	86.81 223	62.88 143	93.89 133	74.39 146	75.40 302	90.00 207

OpenMVS	72.83 10	79.77 156	78.33 171	84.09 121	85.17 245	69.91 84	90.57 60	90.97 121	66.70 257	72.17 275	91.91 91	54.70 224	93.96 124	61.81 262	90.95 91	88.41 262

v10		79.74 157	78.67 161	82.97 170	84.06 270	64.95 196	87.88 151	90.62 130	73.11 147	75.11 238	86.56 237	61.46 165	94.05 123	73.68 151	75.55 295	89.90 213

ECVR-MVS		79.61 158	79.26 149	80.67 228	90.08 103	54.69 339	87.89 150	77.44 347	74.88 104	80.27 124	92.79 79	48.96 293	92.45 192	68.55 203	92.50 72	94.86 17

BH-RMVSNet		79.61 158	78.44 167	83.14 160	89.38 126	65.93 172	84.95 227	87.15 232	73.56 134	78.19 161	89.79 141	56.67 212	93.36 156	59.53 279	86.74 145	90.13 197

v1192		79.59 160	78.43 168	83.07 164	83.55 280	64.52 203	86.93 175	90.58 131	70.83 182	77.78 170	85.90 250	59.15 192	93.94 127	73.96 150	77.19 270	90.76 171

ab-mvs		79.51 161	78.97 157	81.14 216	88.46 164	60.91 263	83.84 252	89.24 175	70.36 193	79.03 138	88.87 169	63.23 136	90.21 260	65.12 232	82.57 210	92.28 122

WR-MVS		79.49 162	79.22 151	80.27 236	88.79 152	58.35 287	85.06 224	88.61 202	78.56 30	77.65 172	88.34 184	63.81 131	90.66 255	64.98 234	77.22 269	91.80 138

v144192		79.47 163	78.37 169	82.78 180	83.35 283	63.96 216	86.96 173	90.36 140	69.99 202	77.50 174	85.67 257	60.66 181	93.77 138	74.27 147	76.58 278	90.62 176

BH-untuned		79.47 163	78.60 163	82.05 192	89.19 137	65.91 173	86.07 201	88.52 203	72.18 158	75.42 225	87.69 200	61.15 173	93.54 148	60.38 272	86.83 144	86.70 298

test1111		79.43 165	79.18 153	80.15 238	89.99 108	53.31 352	87.33 164	77.05 350	75.04 101	80.23 126	92.77 81	48.97 292	92.33 200	68.87 200	92.40 74	94.81 20

mvs_anonymous		79.42 166	79.11 154	80.34 234	84.45 263	57.97 294	82.59 273	87.62 221	67.40 253	76.17 211	88.56 179	68.47 86	89.59 270	70.65 181	86.05 157	93.47 79

thisisatest0530		79.40 167	77.76 187	84.31 109	87.69 196	65.10 194	87.36 162	84.26 273	70.04 200	77.42 176	88.26 188	49.94 277	94.79 97	70.20 184	84.70 171	93.03 97

tttt0517		79.40 167	77.91 179	83.90 138	88.10 177	63.84 218	88.37 131	84.05 275	71.45 171	76.78 192	89.12 161	49.93 279	94.89 92	70.18 185	83.18 202	92.96 101

V42		79.38 169	78.24 173	82.83 174	81.10 329	65.50 183	85.55 215	89.82 155	71.57 169	78.21 160	86.12 248	60.66 181	93.18 169	75.64 135	75.46 299	89.81 218

jajsoiax		79.29 170	77.96 177	83.27 153	84.68 257	66.57 162	89.25 97	90.16 147	69.20 223	75.46 223	89.49 150	45.75 317	93.13 172	76.84 121	80.80 230	90.11 199

v1921920		79.22 171	78.03 176	82.80 177	83.30 285	63.94 217	86.80 179	90.33 141	69.91 205	77.48 175	85.53 260	58.44 196	93.75 140	73.60 152	76.85 275	90.71 174

AUN-MVS		79.21 172	77.60 192	84.05 128	88.71 156	67.61 139	85.84 208	87.26 229	69.08 227	77.23 182	88.14 194	53.20 240	93.47 152	75.50 139	73.45 324	91.06 160

TAPA-MVS	73.13 9	79.15 173	77.94 178	82.79 179	89.59 116	62.99 241	88.16 139	91.51 107	65.77 272	77.14 187	91.09 115	60.91 177	93.21 163	50.26 340	87.05 140	92.17 128
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 174	77.77 186	83.22 157	84.70 256	66.37 164	89.17 98	90.19 146	69.38 216	75.40 226	89.46 153	44.17 326	93.15 170	76.78 124	80.70 232	90.14 196

UniMVSNet_ETH3D		79.10 175	78.24 173	81.70 199	86.85 218	60.24 274	87.28 166	88.79 193	74.25 118	76.84 189	90.53 129	49.48 282	91.56 226	67.98 207	82.15 213	93.29 85

CDS-MVSNet		79.07 176	77.70 189	83.17 159	87.60 199	68.23 125	84.40 244	86.20 246	67.49 251	76.36 204	86.54 238	61.54 162	90.79 252	61.86 261	87.33 136	90.49 183
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 177	77.88 181	82.38 188	83.07 292	64.80 200	84.08 251	88.95 189	69.01 231	78.69 145	87.17 217	54.70 224	92.43 193	74.69 142	80.57 234	89.89 214

v1240		78.99 178	77.78 185	82.64 183	83.21 287	63.54 225	86.62 186	90.30 143	69.74 212	77.33 178	85.68 256	57.04 210	93.76 139	73.13 160	76.92 272	90.62 176

Anonymous20231211		78.97 179	77.69 190	82.81 176	90.54 94	64.29 211	90.11 72	91.51 107	65.01 281	76.16 212	88.13 195	50.56 270	93.03 179	69.68 192	77.56 267	91.11 157

v7n		78.97 179	77.58 193	83.14 160	83.45 282	65.51 182	88.32 133	91.21 114	73.69 130	72.41 272	86.32 244	57.93 199	93.81 135	69.18 196	75.65 293	90.11 199

TAMVS		78.89 181	77.51 194	83.03 166	87.80 189	67.79 135	84.72 231	85.05 260	67.63 248	76.75 193	87.70 199	62.25 152	90.82 251	58.53 290	87.13 139	90.49 183

c3_l		78.75 182	77.91 179	81.26 211	82.89 299	61.56 257	84.09 250	89.13 181	69.97 203	75.56 219	84.29 284	66.36 106	92.09 207	73.47 155	75.48 297	90.12 198

tt0805		78.73 183	77.83 182	81.43 205	85.17 245	60.30 273	89.41 92	90.90 123	71.21 175	77.17 186	88.73 171	46.38 306	93.21 163	72.57 166	78.96 253	90.79 169

v148		78.72 184	77.80 184	81.47 204	82.73 302	61.96 252	86.30 195	88.08 209	73.26 143	76.18 209	85.47 262	62.46 148	92.36 197	71.92 170	73.82 321	90.09 201

VPNet		78.69 185	78.66 162	78.76 262	88.31 169	55.72 328	84.45 241	86.63 240	76.79 64	78.26 158	90.55 128	59.30 191	89.70 269	66.63 220	77.05 271	90.88 167

ET-MVSNet_ETH3D		78.63 186	76.63 215	84.64 95	86.73 222	69.47 92	85.01 225	84.61 265	69.54 213	66.51 336	86.59 234	50.16 274	91.75 219	76.26 128	84.24 181	92.69 107

anonymousdsp		78.60 187	77.15 200	82.98 169	80.51 335	67.08 153	87.24 167	89.53 163	65.66 274	75.16 236	87.19 216	52.52 241	92.25 202	77.17 118	79.34 249	89.61 223

miper_ehance_all_eth		78.59 188	77.76 187	81.08 218	82.66 304	61.56 257	83.65 255	89.15 179	68.87 233	75.55 220	83.79 294	66.49 104	92.03 208	73.25 158	76.39 282	89.64 222

WR-MVS_H		78.51 189	78.49 165	78.56 266	88.02 181	56.38 320	88.43 126	92.67 61	77.14 54	73.89 254	87.55 205	66.25 108	89.24 276	58.92 285	73.55 323	90.06 205

GBi-Net		78.40 190	77.40 195	81.40 207	87.60 199	63.01 238	88.39 128	89.28 170	71.63 164	75.34 228	87.28 210	54.80 220	91.11 242	62.72 248	79.57 244	90.09 201

test1		78.40 190	77.40 195	81.40 207	87.60 199	63.01 238	88.39 128	89.28 170	71.63 164	75.34 228	87.28 210	54.80 220	91.11 242	62.72 248	79.57 244	90.09 201

Vis-MVSNet (Re-imp)		78.36 192	78.45 166	78.07 276	88.64 158	51.78 361	86.70 184	79.63 331	74.14 121	75.11 238	90.83 123	61.29 170	89.75 267	58.10 294	91.60 82	92.69 107

Anonymous202405211		78.25 193	77.01 202	81.99 194	91.03 82	60.67 267	84.77 230	83.90 277	70.65 190	80.00 128	91.20 111	41.08 346	91.43 235	65.21 231	85.26 165	93.85 57

CP-MVSNet		78.22 194	78.34 170	77.84 278	87.83 188	54.54 341	87.94 147	91.17 116	77.65 38	73.48 259	88.49 180	62.24 153	88.43 291	62.19 256	74.07 316	90.55 180

BH-w/o		78.21 195	77.33 198	80.84 224	88.81 150	65.13 193	84.87 228	87.85 217	69.75 210	74.52 249	84.74 277	61.34 168	93.11 173	58.24 293	85.84 161	84.27 333

FMVSNet2		78.20 196	77.21 199	81.20 214	87.60 199	62.89 242	87.47 160	89.02 184	71.63 164	75.29 233	87.28 210	54.80 220	91.10 245	62.38 253	79.38 248	89.61 223

MVS		78.19 197	76.99 204	81.78 197	85.66 236	66.99 154	84.66 232	90.47 135	55.08 363	72.02 277	85.27 265	63.83 130	94.11 122	66.10 224	89.80 109	84.24 334

Baseline_NR-MVSNet		78.15 198	78.33 171	77.61 283	85.79 234	56.21 324	86.78 181	85.76 253	73.60 133	77.93 168	87.57 203	65.02 121	88.99 280	67.14 217	75.33 304	87.63 273

CNLPA		78.08 199	76.79 209	81.97 195	90.40 97	71.07 62	87.59 157	84.55 266	66.03 270	72.38 273	89.64 145	57.56 204	86.04 311	59.61 278	83.35 199	88.79 252

cl22		78.07 200	77.01 202	81.23 212	82.37 311	61.83 254	83.55 259	87.98 211	68.96 232	75.06 240	83.87 290	61.40 167	91.88 215	73.53 153	76.39 282	89.98 210

PLC	70.83 11	78.05 201	76.37 220	83.08 163	91.88 74	67.80 134	88.19 137	89.46 165	64.33 289	69.87 300	88.38 183	53.66 234	93.58 144	58.86 286	82.73 207	87.86 269
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 202	76.49 216	82.62 184	83.16 291	66.96 157	86.94 174	87.45 226	72.45 153	71.49 282	84.17 287	54.79 223	91.58 224	67.61 210	80.31 237	89.30 232

PS-CasMVS		78.01 203	78.09 175	77.77 280	87.71 194	54.39 343	88.02 143	91.22 113	77.50 46	73.26 261	88.64 175	60.73 178	88.41 292	61.88 260	73.88 320	90.53 181

HY-MVS	69.67 12	77.95 204	77.15 200	80.36 233	87.57 203	60.21 275	83.37 262	87.78 219	66.11 267	75.37 227	87.06 221	63.27 134	90.48 257	61.38 266	82.43 211	90.40 187

eth_miper_zixun_eth		77.92 205	76.69 213	81.61 202	83.00 295	61.98 251	83.15 265	89.20 177	69.52 214	74.86 244	84.35 283	61.76 158	92.56 189	71.50 173	72.89 329	90.28 192

FMVSNet3		77.88 206	76.85 207	80.97 222	86.84 219	62.36 245	86.52 189	88.77 194	71.13 176	75.34 228	86.66 232	54.07 231	91.10 245	62.72 248	79.57 244	89.45 227

miper_enhance_ethall		77.87 207	76.86 206	80.92 223	81.65 318	61.38 259	82.68 272	88.98 186	65.52 276	75.47 221	82.30 317	65.76 116	92.00 210	72.95 161	76.39 282	89.39 228

FE-MVS		77.78 208	75.68 226	84.08 122	88.09 178	66.00 170	83.13 266	87.79 218	68.42 242	78.01 166	85.23 267	45.50 319	95.12 78	59.11 283	85.83 162	91.11 157

PEN-MVS		77.73 209	77.69 190	77.84 278	87.07 216	53.91 346	87.91 149	91.18 115	77.56 43	73.14 263	88.82 170	61.23 171	89.17 277	59.95 275	72.37 331	90.43 185

cl____		77.72 210	76.76 210	80.58 229	82.49 308	60.48 270	83.09 267	87.87 215	69.22 221	74.38 251	85.22 268	62.10 155	91.53 229	71.09 176	75.41 301	89.73 221

DIV-MVS_self_test		77.72 210	76.76 210	80.58 229	82.48 309	60.48 270	83.09 267	87.86 216	69.22 221	74.38 251	85.24 266	62.10 155	91.53 229	71.09 176	75.40 302	89.74 220

sd_testset		77.70 212	77.40 195	78.60 265	89.03 144	60.02 276	79.00 320	85.83 252	75.19 98	76.61 198	89.98 137	54.81 219	85.46 318	62.63 252	83.55 194	90.33 189

PAPM		77.68 213	76.40 219	81.51 203	87.29 212	61.85 253	83.78 253	89.59 162	64.74 283	71.23 283	88.70 172	62.59 145	93.66 143	52.66 325	87.03 141	89.01 241

CHOSEN 1792x2688		77.63 214	75.69 225	83.44 146	89.98 109	68.58 118	78.70 324	87.50 224	56.38 358	75.80 216	86.84 222	58.67 194	91.40 236	61.58 264	85.75 163	90.34 188

HyFIR lowres test		77.53 215	75.40 232	83.94 136	89.59 116	66.62 160	80.36 303	88.64 201	56.29 359	76.45 200	85.17 269	57.64 203	93.28 158	61.34 267	83.10 203	91.91 135

FMVSNet1		77.44 216	76.12 222	81.40 207	86.81 220	63.01 238	88.39 128	89.28 170	70.49 192	74.39 250	87.28 210	49.06 290	91.11 242	60.91 269	78.52 256	90.09 201

TR-MVS		77.44 216	76.18 221	81.20 214	88.24 171	63.24 233	84.61 235	86.40 243	67.55 250	77.81 169	86.48 240	54.10 230	93.15 170	57.75 297	82.72 208	87.20 284

1112_ss		77.40 218	76.43 218	80.32 235	89.11 143	60.41 272	83.65 255	87.72 220	62.13 316	73.05 264	86.72 226	62.58 146	89.97 263	62.11 259	80.80 230	90.59 179

thisisatest0515		77.33 219	75.38 233	83.18 158	85.27 244	63.80 219	82.11 278	83.27 287	65.06 279	75.91 213	83.84 292	49.54 281	94.27 113	67.24 215	86.19 154	91.48 147

test2506		77.30 220	76.49 216	79.74 246	90.08 103	52.02 355	87.86 152	63.10 390	74.88 104	80.16 127	92.79 79	38.29 359	92.35 198	68.74 202	92.50 72	94.86 17

bld_raw_dy_0_64		77.29 221	75.98 223	81.22 213	85.04 251	65.47 184	88.14 142	77.56 344	69.20 223	73.77 255	89.40 159	42.24 340	88.85 286	76.78 124	81.64 220	89.33 231

pm-mvs1		77.25 222	76.68 214	78.93 260	84.22 266	58.62 286	86.41 191	88.36 205	71.37 172	73.31 260	88.01 196	61.22 172	89.15 278	64.24 239	73.01 328	89.03 240

LCM-MVSNet-Re		77.05 223	76.94 205	77.36 286	87.20 213	51.60 362	80.06 306	80.46 321	75.20 97	67.69 318	86.72 226	62.48 147	88.98 281	63.44 243	89.25 114	91.51 143

DTE-MVSNet		76.99 224	76.80 208	77.54 285	86.24 228	53.06 354	87.52 158	90.66 129	77.08 57	72.50 270	88.67 174	60.48 185	89.52 271	57.33 301	70.74 342	90.05 206

baseline1		76.98 225	76.75 212	77.66 281	88.13 175	55.66 329	85.12 223	81.89 305	73.04 149	76.79 191	88.90 167	62.43 149	87.78 299	63.30 245	71.18 340	89.55 225

LS3D		76.95 226	74.82 240	83.37 150	90.45 95	67.36 146	89.15 102	86.94 235	61.87 318	69.52 303	90.61 126	51.71 259	94.53 105	46.38 361	86.71 146	88.21 264

GA-MVS		76.87 227	75.17 237	81.97 195	82.75 301	62.58 243	81.44 287	86.35 245	72.16 160	74.74 245	82.89 309	46.20 311	92.02 209	68.85 201	81.09 226	91.30 153

DP-MVS		76.78 228	74.57 242	83.42 147	93.29 48	69.46 94	88.55 124	83.70 279	63.98 295	70.20 291	88.89 168	54.01 232	94.80 96	46.66 358	81.88 218	86.01 310

cascas		76.72 229	74.64 241	82.99 168	85.78 235	65.88 174	82.33 275	89.21 176	60.85 324	72.74 266	81.02 328	47.28 300	93.75 140	67.48 212	85.02 166	89.34 230

1314		76.53 230	75.30 236	80.21 237	83.93 273	62.32 247	84.66 232	88.81 192	60.23 328	70.16 294	84.07 289	55.30 217	90.73 254	67.37 213	83.21 201	87.59 276

thres100view900		76.50 231	75.55 229	79.33 254	89.52 119	56.99 309	85.83 209	83.23 288	73.94 124	76.32 205	87.12 218	51.89 256	91.95 211	48.33 349	83.75 188	89.07 234

thres600view7		76.50 231	75.44 230	79.68 248	89.40 124	57.16 306	85.53 217	83.23 288	73.79 129	76.26 206	87.09 219	51.89 256	91.89 214	48.05 354	83.72 191	90.00 207

thres400		76.50 231	75.37 234	79.86 243	89.13 139	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 248	91.95 211	48.33 349	83.75 188	90.00 207

tfpn200view9		76.42 234	75.37 234	79.55 253	89.13 139	57.65 300	85.17 220	83.60 280	73.41 139	76.45 200	86.39 242	52.12 248	91.95 211	48.33 349	83.75 188	89.07 234

Test_1112_low_res		76.40 235	75.44 230	79.27 255	89.28 133	58.09 290	81.69 282	87.07 233	59.53 335	72.48 271	86.67 231	61.30 169	89.33 274	60.81 271	80.15 239	90.41 186

F-COLMAP		76.38 236	74.33 247	82.50 186	89.28 133	66.95 158	88.41 127	89.03 183	64.05 293	66.83 328	88.61 176	46.78 304	92.89 181	57.48 298	78.55 255	87.67 272

LTVRE_ROB	69.57 13	76.25 237	74.54 244	81.41 206	88.60 159	64.38 210	79.24 316	89.12 182	70.76 185	69.79 302	87.86 197	49.09 289	93.20 166	56.21 311	80.16 238	86.65 299
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		76.12 238	74.46 246	81.13 217	85.37 243	69.79 86	84.42 243	87.95 213	65.03 280	67.46 321	85.33 264	53.28 239	91.73 221	58.01 295	83.27 200	81.85 358
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 239	74.27 248	81.62 200	83.20 288	64.67 202	83.60 258	89.75 158	69.75 210	71.85 278	87.09 219	32.78 371	92.11 206	69.99 188	80.43 236	88.09 265

testing99		76.09 240	75.12 238	79.00 258	88.16 173	55.50 331	80.79 293	81.40 311	73.30 142	75.17 235	84.27 285	44.48 324	90.02 262	64.28 238	84.22 182	91.48 147

ACMH+	68.96 14	76.01 241	74.01 249	82.03 193	88.60 159	65.31 190	88.86 110	87.55 222	70.25 198	67.75 317	87.47 208	41.27 344	93.19 168	58.37 291	75.94 290	87.60 274

ACMH	67.68 16	75.89 242	73.93 251	81.77 198	88.71 156	66.61 161	88.62 122	89.01 185	69.81 206	66.78 329	86.70 230	41.95 343	91.51 231	55.64 312	78.14 262	87.17 285
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 243	73.36 258	83.31 151	84.76 255	66.03 168	83.38 261	85.06 259	70.21 199	69.40 304	81.05 327	45.76 316	94.66 101	65.10 233	75.49 296	89.25 233
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
baseline2		75.70 244	73.83 254	81.30 210	83.26 286	61.79 255	82.57 274	80.65 317	66.81 254	66.88 327	83.42 301	57.86 201	92.19 204	63.47 242	79.57 244	89.91 212

WTY-MVS		75.65 245	75.68 226	75.57 302	86.40 227	56.82 311	77.92 334	82.40 301	65.10 278	76.18 209	87.72 198	63.13 141	80.90 347	60.31 273	81.96 216	89.00 243

thres200		75.55 246	74.47 245	78.82 261	87.78 192	57.85 297	83.07 269	83.51 283	72.44 155	75.84 215	84.42 279	52.08 251	91.75 219	47.41 356	83.64 193	86.86 294

test_vis1_n_1920		75.52 247	75.78 224	74.75 312	79.84 343	57.44 304	83.26 263	85.52 255	62.83 307	79.34 136	86.17 247	45.10 321	79.71 351	78.75 101	81.21 225	87.10 291

EPNet_dtu		75.46 248	74.86 239	77.23 289	82.57 306	54.60 340	86.89 176	83.09 291	71.64 163	66.25 338	85.86 252	55.99 214	88.04 296	54.92 314	86.55 148	89.05 239
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 249	73.87 253	80.11 239	82.69 303	64.85 199	81.57 284	83.47 284	69.16 225	70.49 288	84.15 288	51.95 254	88.15 294	69.23 195	72.14 334	87.34 281

XXY-MVS		75.41 250	75.56 228	74.96 308	83.59 279	57.82 298	80.59 299	83.87 278	66.54 264	74.93 243	88.31 185	63.24 135	80.09 350	62.16 257	76.85 275	86.97 292

TransMVSNet (Re)		75.39 251	74.56 243	77.86 277	85.50 240	57.10 308	86.78 181	86.09 249	72.17 159	71.53 281	87.34 209	63.01 142	89.31 275	56.84 306	61.83 368	87.17 285

CostFormer		75.24 252	73.90 252	79.27 255	82.65 305	58.27 289	80.80 292	82.73 299	61.57 319	75.33 231	83.13 305	55.52 215	91.07 248	64.98 234	78.34 261	88.45 260

testing11		75.14 253	74.01 249	78.53 268	88.16 173	56.38 320	80.74 296	80.42 322	70.67 186	72.69 269	83.72 296	43.61 330	89.86 264	62.29 255	83.76 187	89.36 229

D2MVS		74.82 254	73.21 259	79.64 250	79.81 344	62.56 244	80.34 304	87.35 227	64.37 288	68.86 309	82.66 313	46.37 307	90.10 261	67.91 208	81.24 224	86.25 303

pmmvs6		74.69 255	73.39 257	78.61 264	81.38 324	57.48 303	86.64 185	87.95 213	64.99 282	70.18 292	86.61 233	50.43 272	89.52 271	62.12 258	70.18 344	88.83 250

tfpnnormal		74.39 256	73.16 260	78.08 275	86.10 232	58.05 291	84.65 234	87.53 223	70.32 195	71.22 284	85.63 258	54.97 218	89.86 264	43.03 372	75.02 309	86.32 302

IterMVS		74.29 257	72.94 262	78.35 271	81.53 321	63.49 227	81.58 283	82.49 300	68.06 246	69.99 297	83.69 297	51.66 260	85.54 316	65.85 227	71.64 337	86.01 310
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 258	72.42 267	79.80 245	83.76 277	59.59 281	85.92 205	86.64 239	66.39 265	66.96 326	87.58 202	39.46 352	91.60 223	65.76 228	69.27 347	88.22 263

SCA		74.22 259	72.33 268	79.91 242	84.05 271	62.17 249	79.96 309	79.29 334	66.30 266	72.38 273	80.13 337	51.95 254	88.60 289	59.25 281	77.67 266	88.96 245

miper_lstm_enhance		74.11 260	73.11 261	77.13 290	80.11 339	59.62 280	72.23 361	86.92 236	66.76 256	70.40 289	82.92 308	56.93 211	82.92 336	69.06 198	72.63 330	88.87 248

testing222		74.04 261	72.66 264	78.19 273	87.89 184	55.36 332	81.06 290	79.20 335	71.30 173	74.65 247	83.57 299	39.11 355	88.67 288	51.43 332	85.75 163	90.53 181

EG-PatchMatch MVS		74.04 261	71.82 271	80.71 227	84.92 253	67.42 143	85.86 207	88.08 209	66.04 269	64.22 350	83.85 291	35.10 368	92.56 189	57.44 299	80.83 229	82.16 357

pmmvs4		74.03 263	71.91 270	80.39 232	81.96 314	68.32 122	81.45 286	82.14 303	59.32 336	69.87 300	85.13 270	52.40 244	88.13 295	60.21 274	74.74 312	84.73 330

MS-PatchMatch		73.83 264	72.67 263	77.30 288	83.87 274	66.02 169	81.82 279	84.66 264	61.37 322	68.61 312	82.82 311	47.29 299	88.21 293	59.27 280	84.32 179	77.68 372

test_cas_vis1_n_1920		73.76 265	73.74 255	73.81 320	75.90 364	59.77 278	80.51 300	82.40 301	58.30 345	81.62 110	85.69 255	44.35 325	76.41 369	76.29 127	78.61 254	85.23 321

sss		73.60 266	73.64 256	73.51 322	82.80 300	55.01 337	76.12 341	81.69 308	62.47 312	74.68 246	85.85 253	57.32 207	78.11 358	60.86 270	80.93 227	87.39 279

RPMNet		73.51 267	70.49 287	82.58 185	81.32 327	65.19 191	75.92 343	92.27 76	57.60 351	72.73 267	76.45 364	52.30 245	95.43 65	48.14 353	77.71 264	87.11 289

SixPastTwentyTwo		73.37 268	71.26 280	79.70 247	85.08 250	57.89 296	85.57 211	83.56 282	71.03 180	65.66 340	85.88 251	42.10 341	92.57 188	59.11 283	63.34 366	88.65 257

CR-MVSNet		73.37 268	71.27 279	79.67 249	81.32 327	65.19 191	75.92 343	80.30 324	59.92 331	72.73 267	81.19 325	52.50 242	86.69 305	59.84 276	77.71 264	87.11 289

MSDG		73.36 270	70.99 282	80.49 231	84.51 262	65.80 177	80.71 297	86.13 248	65.70 273	65.46 341	83.74 295	44.60 322	90.91 250	51.13 333	76.89 273	84.74 329

tpm2		73.26 271	71.46 275	78.63 263	83.34 284	56.71 314	80.65 298	80.40 323	56.63 357	73.55 258	82.02 322	51.80 258	91.24 240	56.35 310	78.42 259	87.95 266

RPSCF		73.23 272	71.46 275	78.54 267	82.50 307	59.85 277	82.18 277	82.84 298	58.96 340	71.15 285	89.41 157	45.48 320	84.77 324	58.82 287	71.83 336	91.02 164

PatchmatchNet		73.12 273	71.33 278	78.49 270	83.18 289	60.85 264	79.63 311	78.57 338	64.13 290	71.73 279	79.81 342	51.20 263	85.97 312	57.40 300	76.36 287	88.66 256
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	66.92 17	73.01 274	70.41 289	80.81 225	87.13 215	65.63 180	88.30 134	84.19 274	62.96 304	63.80 354	87.69 200	38.04 360	92.56 189	46.66 358	74.91 310	84.24 334
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 275	72.58 265	74.25 316	84.28 264	50.85 367	86.41 191	83.45 285	44.56 380	73.23 262	87.54 206	49.38 284	85.70 313	65.90 226	78.44 258	86.19 305

test-LLR		72.94 276	72.43 266	74.48 313	81.35 325	58.04 292	78.38 327	77.46 345	66.66 258	69.95 298	79.00 348	48.06 296	79.24 352	66.13 222	84.83 168	86.15 306

test_0402		72.79 277	70.44 288	79.84 244	88.13 175	65.99 171	85.93 204	84.29 271	65.57 275	67.40 323	85.49 261	46.92 303	92.61 187	35.88 384	74.38 315	80.94 363

tpmrst		72.39 278	72.13 269	73.18 326	80.54 334	49.91 371	79.91 310	79.08 336	63.11 301	71.69 280	79.95 339	55.32 216	82.77 337	65.66 229	73.89 319	86.87 293

PatchMatch-RL		72.38 279	70.90 283	76.80 293	88.60 159	67.38 145	79.53 312	76.17 356	62.75 309	69.36 305	82.00 323	45.51 318	84.89 323	53.62 320	80.58 233	78.12 371

CL-MVSNet_self_test		72.37 280	71.46 275	75.09 307	79.49 350	53.53 348	80.76 295	85.01 261	69.12 226	70.51 287	82.05 321	57.92 200	84.13 327	52.27 327	66.00 360	87.60 274

tpm		72.37 280	71.71 272	74.35 315	82.19 312	52.00 356	79.22 317	77.29 348	64.56 285	72.95 265	83.68 298	51.35 261	83.26 335	58.33 292	75.80 291	87.81 270

ETVMVS		72.25 282	71.05 281	75.84 298	87.77 193	51.91 358	79.39 314	74.98 359	69.26 219	73.71 256	82.95 307	40.82 348	86.14 310	46.17 362	84.43 178	89.47 226

UWE-MVS		72.13 283	71.49 274	74.03 318	86.66 224	47.70 375	81.40 288	76.89 352	63.60 298	75.59 218	84.22 286	39.94 351	85.62 315	48.98 346	86.13 156	88.77 253

PVSNet	64.34 18	72.08 284	70.87 284	75.69 300	86.21 229	56.44 318	74.37 355	80.73 316	62.06 317	70.17 293	82.23 319	42.86 334	83.31 334	54.77 315	84.45 177	87.32 282

WB-MVSnew		71.96 285	71.65 273	72.89 327	84.67 260	51.88 359	82.29 276	77.57 343	62.31 313	73.67 257	83.00 306	53.49 237	81.10 346	45.75 365	82.13 214	85.70 315

pmmvs5		71.55 286	70.20 292	75.61 301	77.83 357	56.39 319	81.74 281	80.89 313	57.76 349	67.46 321	84.49 278	49.26 287	85.32 320	57.08 303	75.29 305	85.11 325

test-mter		71.41 287	70.39 290	74.48 313	81.35 325	58.04 292	78.38 327	77.46 345	60.32 327	69.95 298	79.00 348	36.08 366	79.24 352	66.13 222	84.83 168	86.15 306

K. test v3		71.19 288	68.51 300	79.21 257	83.04 294	57.78 299	84.35 245	76.91 351	72.90 152	62.99 357	82.86 310	39.27 353	91.09 247	61.65 263	52.66 384	88.75 254

dmvs_re		71.14 289	70.58 285	72.80 328	81.96 314	59.68 279	75.60 347	79.34 333	68.55 238	69.27 307	80.72 333	49.42 283	76.54 366	52.56 326	77.79 263	82.19 356

tpmvs		71.09 290	69.29 295	76.49 294	82.04 313	56.04 325	78.92 322	81.37 312	64.05 293	67.18 325	78.28 354	49.74 280	89.77 266	49.67 343	72.37 331	83.67 341

AllTest		70.96 291	68.09 306	79.58 251	85.15 247	63.62 221	84.58 236	79.83 328	62.31 313	60.32 365	86.73 224	32.02 372	88.96 283	50.28 338	71.57 338	86.15 306

test_fmvs1		70.93 292	70.52 286	72.16 332	73.71 374	55.05 336	80.82 291	78.77 337	51.21 374	78.58 149	84.41 280	31.20 376	76.94 364	75.88 133	80.12 241	84.47 332

test_fmvs1_n		70.86 293	70.24 291	72.73 329	72.51 384	55.28 334	81.27 289	79.71 330	51.49 373	78.73 143	84.87 274	27.54 381	77.02 363	76.06 130	79.97 242	85.88 313

Patchmtry		70.74 294	69.16 297	75.49 304	80.72 331	54.07 345	74.94 354	80.30 324	58.34 344	70.01 295	81.19 325	52.50 242	86.54 306	53.37 322	71.09 341	85.87 314

MIMVSNet		70.69 295	69.30 294	74.88 309	84.52 261	56.35 322	75.87 345	79.42 332	64.59 284	67.76 316	82.41 315	41.10 345	81.54 343	46.64 360	81.34 222	86.75 297

tpm cat1		70.57 296	68.31 302	77.35 287	82.41 310	57.95 295	78.08 331	80.22 326	52.04 369	68.54 313	77.66 359	52.00 253	87.84 298	51.77 328	72.07 335	86.25 303

OpenMVS_ROB	64.09 19	70.56 297	68.19 303	77.65 282	80.26 336	59.41 283	85.01 225	82.96 295	58.76 342	65.43 342	82.33 316	37.63 362	91.23 241	45.34 368	76.03 289	82.32 354

pmmvs-eth3d		70.50 298	67.83 311	78.52 269	77.37 360	66.18 167	81.82 279	81.51 309	58.90 341	63.90 353	80.42 335	42.69 335	86.28 309	58.56 289	65.30 362	83.11 347

USDC		70.33 299	68.37 301	76.21 296	80.60 333	56.23 323	79.19 318	86.49 241	60.89 323	61.29 361	85.47 262	31.78 374	89.47 273	53.37 322	76.21 288	82.94 351

Patchmatch-RL test		70.24 300	67.78 313	77.61 283	77.43 359	59.57 282	71.16 364	70.33 373	62.94 305	68.65 311	72.77 376	50.62 269	85.49 317	69.58 193	66.58 357	87.77 271

CMPMVS	51.72 21	70.19 301	68.16 304	76.28 295	73.15 380	57.55 302	79.47 313	83.92 276	48.02 377	56.48 378	84.81 275	43.13 332	86.42 308	62.67 251	81.81 219	84.89 327
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		70.04 302	67.34 319	78.14 274	79.80 345	61.13 260	79.19 318	80.59 318	59.16 338	65.27 343	79.29 345	46.75 305	87.29 302	49.33 344	66.72 355	86.00 312

gg-mvs-nofinetune		69.95 303	67.96 307	75.94 297	83.07 292	54.51 342	77.23 338	70.29 374	63.11 301	70.32 290	62.33 385	43.62 329	88.69 287	53.88 319	87.76 131	84.62 331

TESTMET0.1,1		69.89 304	69.00 298	72.55 330	79.27 353	56.85 310	78.38 327	74.71 363	57.64 350	68.09 315	77.19 361	37.75 361	76.70 365	63.92 240	84.09 183	84.10 337

test_vis1_n		69.85 305	69.21 296	71.77 334	72.66 383	55.27 335	81.48 285	76.21 355	52.03 370	75.30 232	83.20 304	28.97 379	76.22 371	74.60 143	78.41 260	83.81 340

FMVSNet5		69.50 306	67.96 307	74.15 317	82.97 298	55.35 333	80.01 308	82.12 304	62.56 311	63.02 355	81.53 324	36.92 363	81.92 341	48.42 348	74.06 317	85.17 324

PMMVS		69.34 307	68.67 299	71.35 339	75.67 366	62.03 250	75.17 349	73.46 366	50.00 375	68.68 310	79.05 346	52.07 252	78.13 357	61.16 268	82.77 206	73.90 378

our_test_3		69.14 308	67.00 321	75.57 302	79.80 345	58.80 284	77.96 332	77.81 341	59.55 334	62.90 358	78.25 355	47.43 298	83.97 328	51.71 329	67.58 354	83.93 339

EPMVS		69.02 309	68.16 304	71.59 335	79.61 348	49.80 373	77.40 336	66.93 382	62.82 308	70.01 295	79.05 346	45.79 315	77.86 360	56.58 308	75.26 306	87.13 288

KD-MVS_self_test		68.81 310	67.59 317	72.46 331	74.29 372	45.45 380	77.93 333	87.00 234	63.12 300	63.99 352	78.99 350	42.32 337	84.77 324	56.55 309	64.09 365	87.16 287

Anonymous20240521		68.80 311	67.22 320	73.55 321	74.33 371	54.11 344	83.18 264	85.61 254	58.15 346	61.68 360	80.94 330	30.71 377	81.27 345	57.00 304	73.34 327	85.28 320

Anonymous20231206		68.60 312	67.80 312	71.02 342	80.23 338	50.75 368	78.30 330	80.47 320	56.79 356	66.11 339	82.63 314	46.35 308	78.95 354	43.62 371	75.70 292	83.36 344

MIMVSNet1		68.58 313	66.78 323	73.98 319	80.07 340	51.82 360	80.77 294	84.37 268	64.40 287	59.75 368	82.16 320	36.47 364	83.63 331	42.73 373	70.33 343	86.48 301

testing3		68.56 314	67.67 315	71.22 341	87.33 210	42.87 389	83.06 270	71.54 371	70.36 193	69.08 308	84.38 281	30.33 378	85.69 314	37.50 383	75.45 300	85.09 326

EU-MVSNet		68.53 315	67.61 316	71.31 340	78.51 356	47.01 378	84.47 238	84.27 272	42.27 383	66.44 337	84.79 276	40.44 349	83.76 329	58.76 288	68.54 352	83.17 345

PatchT		68.46 316	67.85 309	70.29 345	80.70 332	43.93 387	72.47 360	74.88 360	60.15 329	70.55 286	76.57 363	49.94 277	81.59 342	50.58 334	74.83 311	85.34 319

test_fmvs2		68.35 317	67.48 318	70.98 343	69.50 387	51.95 357	80.05 307	76.38 354	49.33 376	74.65 247	84.38 281	23.30 387	75.40 378	74.51 144	75.17 308	85.60 316

Syy-MVS		68.05 318	67.85 309	68.67 354	84.68 257	40.97 395	78.62 325	73.08 368	66.65 261	66.74 330	79.46 343	52.11 250	82.30 339	32.89 387	76.38 285	82.75 352

test0.0.03 1		68.00 319	67.69 314	68.90 351	77.55 358	47.43 376	75.70 346	72.95 370	66.66 258	66.56 332	82.29 318	48.06 296	75.87 373	44.97 369	74.51 314	83.41 343

TDRefinement		67.49 320	64.34 330	76.92 291	73.47 378	61.07 261	84.86 229	82.98 294	59.77 332	58.30 372	85.13 270	26.06 382	87.89 297	47.92 355	60.59 373	81.81 359

test20.03		67.45 321	66.95 322	68.94 350	75.48 368	44.84 385	77.50 335	77.67 342	66.66 258	63.01 356	83.80 293	47.02 302	78.40 356	42.53 374	68.86 351	83.58 342

UnsupCasMVSNet_eth		67.33 322	65.99 326	71.37 337	73.48 377	51.47 364	75.16 350	85.19 258	65.20 277	60.78 363	80.93 332	42.35 336	77.20 362	57.12 302	53.69 383	85.44 318

TinyColmap		67.30 323	64.81 328	74.76 311	81.92 316	56.68 315	80.29 305	81.49 310	60.33 326	56.27 379	83.22 302	24.77 384	87.66 301	45.52 366	69.47 346	79.95 367

myMVS_eth3d		67.02 324	66.29 325	69.21 349	84.68 257	42.58 390	78.62 325	73.08 368	66.65 261	66.74 330	79.46 343	31.53 375	82.30 339	39.43 380	76.38 285	82.75 352

dp		66.80 325	65.43 327	70.90 344	79.74 347	48.82 374	75.12 352	74.77 361	59.61 333	64.08 351	77.23 360	42.89 333	80.72 348	48.86 347	66.58 357	83.16 346

MDA-MVSNet-bldmvs		66.68 326	63.66 335	75.75 299	79.28 352	60.56 269	73.92 357	78.35 339	64.43 286	50.13 386	79.87 341	44.02 327	83.67 330	46.10 363	56.86 376	83.03 349

testgi		66.67 327	66.53 324	67.08 359	75.62 367	41.69 394	75.93 342	76.50 353	66.11 267	65.20 346	86.59 234	35.72 367	74.71 380	43.71 370	73.38 326	84.84 328

CHOSEN 280x420		66.51 328	64.71 329	71.90 333	81.45 322	63.52 226	57.98 392	68.95 380	53.57 365	62.59 359	76.70 362	46.22 310	75.29 379	55.25 313	79.68 243	76.88 374

PM-MVS		66.41 329	64.14 331	73.20 325	73.92 373	56.45 317	78.97 321	64.96 388	63.88 297	64.72 347	80.24 336	19.84 390	83.44 333	66.24 221	64.52 364	79.71 368

JIA-IIPM		66.32 330	62.82 341	76.82 292	77.09 361	61.72 256	65.34 385	75.38 357	58.04 348	64.51 348	62.32 386	42.05 342	86.51 307	51.45 331	69.22 348	82.21 355

KD-MVS_2432*1600		66.22 331	63.89 333	73.21 323	75.47 369	53.42 350	70.76 367	84.35 269	64.10 291	66.52 334	78.52 352	34.55 369	84.98 321	50.40 336	50.33 387	81.23 361

miper_refine_blended		66.22 331	63.89 333	73.21 323	75.47 369	53.42 350	70.76 367	84.35 269	64.10 291	66.52 334	78.52 352	34.55 369	84.98 321	50.40 336	50.33 387	81.23 361

ADS-MVSNet2		66.20 333	63.33 336	74.82 310	79.92 341	58.75 285	67.55 378	75.19 358	53.37 366	65.25 344	75.86 367	42.32 337	80.53 349	41.57 375	68.91 349	85.18 322

YYNet1		65.03 334	62.91 339	71.38 336	75.85 365	56.60 316	69.12 375	74.66 364	57.28 354	54.12 381	77.87 357	45.85 314	74.48 381	49.95 341	61.52 370	83.05 348

MDA-MVSNet_test_wron		65.03 334	62.92 338	71.37 337	75.93 363	56.73 312	69.09 376	74.73 362	57.28 354	54.03 382	77.89 356	45.88 313	74.39 382	49.89 342	61.55 369	82.99 350

Patchmatch-test		64.82 336	63.24 337	69.57 347	79.42 351	49.82 372	63.49 389	69.05 379	51.98 371	59.95 367	80.13 337	50.91 265	70.98 387	40.66 377	73.57 322	87.90 268

ADS-MVSNet		64.36 337	62.88 340	68.78 353	79.92 341	47.17 377	67.55 378	71.18 372	53.37 366	65.25 344	75.86 367	42.32 337	73.99 383	41.57 375	68.91 349	85.18 322

LF4IMVS		64.02 338	62.19 342	69.50 348	70.90 385	53.29 353	76.13 340	77.18 349	52.65 368	58.59 370	80.98 329	23.55 386	76.52 367	53.06 324	66.66 356	78.68 370

UnsupCasMVSNet_bld		63.70 339	61.53 345	70.21 346	73.69 375	51.39 365	72.82 359	81.89 305	55.63 361	57.81 374	71.80 378	38.67 356	78.61 355	49.26 345	52.21 385	80.63 364

test_fmvs3		63.36 340	61.82 343	67.98 356	62.51 394	46.96 379	77.37 337	74.03 365	45.24 379	67.50 320	78.79 351	12.16 398	72.98 386	72.77 164	66.02 359	83.99 338

dmvs_testset		62.63 341	64.11 332	58.19 369	78.55 355	24.76 405	75.28 348	65.94 385	67.91 247	60.34 364	76.01 366	53.56 235	73.94 384	31.79 388	67.65 353	75.88 376

mvsany_test1		62.30 342	61.26 346	65.41 361	69.52 386	54.86 338	66.86 380	49.78 401	46.65 378	68.50 314	83.21 303	49.15 288	66.28 393	56.93 305	60.77 371	75.11 377

new-patchmatchnet		61.73 343	61.73 344	61.70 365	72.74 382	24.50 406	69.16 374	78.03 340	61.40 320	56.72 377	75.53 370	38.42 357	76.48 368	45.95 364	57.67 375	84.13 336

PVSNet_0	57.27 20	61.67 344	59.27 347	68.85 352	79.61 348	57.44 304	68.01 377	73.44 367	55.93 360	58.54 371	70.41 381	44.58 323	77.55 361	47.01 357	35.91 393	71.55 381

test_vis1_rt		60.28 345	58.42 348	65.84 360	67.25 390	55.60 330	70.44 369	60.94 393	44.33 381	59.00 369	66.64 383	24.91 383	68.67 391	62.80 247	69.48 345	73.25 379

MVS-HIRNet		59.14 346	57.67 349	63.57 363	81.65 318	43.50 388	71.73 362	65.06 387	39.59 387	51.43 384	57.73 391	38.34 358	82.58 338	39.53 378	73.95 318	64.62 387

pmmvs3		57.79 347	54.26 352	68.37 355	64.02 393	56.72 313	75.12 352	65.17 386	40.20 385	52.93 383	69.86 382	20.36 389	75.48 376	45.45 367	55.25 382	72.90 380

DSMNet-mixed		57.77 348	56.90 350	60.38 367	67.70 389	35.61 398	69.18 373	53.97 399	32.30 395	57.49 375	79.88 340	40.39 350	68.57 392	38.78 381	72.37 331	76.97 373

WB-MVS		54.94 349	54.72 351	55.60 375	73.50 376	20.90 407	74.27 356	61.19 392	59.16 338	50.61 385	74.15 372	47.19 301	75.78 374	17.31 399	35.07 394	70.12 382

LCM-MVSNet		54.25 350	49.68 360	67.97 357	53.73 402	45.28 383	66.85 381	80.78 315	35.96 391	39.45 392	62.23 387	8.70 402	78.06 359	48.24 352	51.20 386	80.57 365

mvsany_test3		53.99 351	51.45 356	61.61 366	55.51 398	44.74 386	63.52 388	45.41 405	43.69 382	58.11 373	76.45 364	17.99 391	63.76 396	54.77 315	47.59 389	76.34 375

SSC-MVS		53.88 352	53.59 353	54.75 377	72.87 381	19.59 408	73.84 358	60.53 394	57.58 352	49.18 387	73.45 375	46.34 309	75.47 377	16.20 402	32.28 396	69.20 383

FPMVS		53.68 353	51.64 355	59.81 368	65.08 392	51.03 366	69.48 372	69.58 377	41.46 384	40.67 390	72.32 377	16.46 394	70.00 390	24.24 396	65.42 361	58.40 392

APD_test1		53.31 354	49.93 359	63.42 364	65.68 391	50.13 370	71.59 363	66.90 383	34.43 392	40.58 391	71.56 379	8.65 403	76.27 370	34.64 386	55.36 381	63.86 388

N_pmnet		52.79 355	53.26 354	51.40 379	78.99 354	7.68 411	69.52 371	3.89 410	51.63 372	57.01 376	74.98 371	40.83 347	65.96 394	37.78 382	64.67 363	80.56 366

test_f		52.09 356	50.82 357	55.90 373	53.82 401	42.31 393	59.42 391	58.31 397	36.45 390	56.12 380	70.96 380	12.18 397	57.79 398	53.51 321	56.57 378	67.60 384

EGC-MVSNET		52.07 357	47.05 361	67.14 358	83.51 281	60.71 266	80.50 301	67.75 381	0.07 405	0.43 406	75.85 369	24.26 385	81.54 343	28.82 390	62.25 367	59.16 390

new_pmnet		50.91 358	50.29 358	52.78 378	68.58 388	34.94 400	63.71 387	56.63 398	39.73 386	44.95 388	65.47 384	21.93 388	58.48 397	34.98 385	56.62 377	64.92 386

ANet_high		50.57 359	46.10 363	63.99 362	48.67 405	39.13 396	70.99 366	80.85 314	61.39 321	31.18 394	57.70 392	17.02 393	73.65 385	31.22 389	15.89 402	79.18 369

test_vis3_rt		49.26 360	47.02 362	56.00 372	54.30 399	45.27 384	66.76 382	48.08 402	36.83 389	44.38 389	53.20 394	7.17 405	64.07 395	56.77 307	55.66 379	58.65 391

testf1		45.72 361	41.96 364	57.00 370	56.90 396	45.32 381	66.14 383	59.26 395	26.19 396	30.89 395	60.96 389	4.14 406	70.64 388	26.39 394	46.73 391	55.04 393

APD_test2		45.72 361	41.96 364	57.00 370	56.90 396	45.32 381	66.14 383	59.26 395	26.19 396	30.89 395	60.96 389	4.14 406	70.64 388	26.39 394	46.73 391	55.04 393

Gipuma		45.18 363	41.86 366	55.16 376	77.03 362	51.52 363	32.50 398	80.52 319	32.46 394	27.12 397	35.02 398	9.52 401	75.50 375	22.31 397	60.21 374	38.45 397
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 364	40.28 367	55.82 374	40.82 407	42.54 392	65.12 386	63.99 389	34.43 392	24.48 398	57.12 393	3.92 408	76.17 372	17.10 400	55.52 380	48.75 395
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 365	38.86 368	46.69 380	53.84 400	16.45 409	48.61 395	49.92 400	37.49 388	31.67 393	60.97 388	8.14 404	56.42 399	28.42 391	30.72 397	67.19 385

E-PMN		31.77 366	30.64 369	35.15 383	52.87 403	27.67 402	57.09 393	47.86 403	24.64 398	16.40 403	33.05 399	11.23 399	54.90 400	14.46 403	18.15 400	22.87 399

test_method		31.52 367	29.28 371	38.23 382	27.03 409	6.50 412	20.94 400	62.21 391	4.05 403	22.35 401	52.50 395	13.33 395	47.58 402	27.04 393	34.04 395	60.62 389

EMVS		30.81 368	29.65 370	34.27 384	50.96 404	25.95 404	56.58 394	46.80 404	24.01 399	15.53 404	30.68 400	12.47 396	54.43 401	12.81 404	17.05 401	22.43 400

MVE	26.22 23	30.37 369	25.89 373	43.81 381	44.55 406	35.46 399	28.87 399	39.07 406	18.20 400	18.58 402	40.18 397	2.68 409	47.37 403	17.07 401	23.78 399	48.60 396
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 370	26.61 372	0.00 390	0.00 413	0.00 415	0.00 401	89.26 173	0.00 408	0.00 409	88.61 176	61.62 161	0.00 409	0.00 408	0.00 407	0.00 405

tmp_tt		18.61 371	21.40 374	10.23 387	4.82 410	10.11 410	34.70 397	30.74 408	1.48 404	23.91 400	26.07 401	28.42 380	13.41 406	27.12 392	15.35 403	7.17 401

wuyk23d		16.82 372	15.94 375	19.46 386	58.74 395	31.45 401	39.22 396	3.74 411	6.84 402	6.04 405	2.70 405	1.27 410	24.29 405	10.54 405	14.40 404	2.63 402

ab-mvs-re		7.23 373	9.64 376	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	86.72 226	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

test123		6.12 374	8.11 377	0.14 388	0.06 412	0.09 413	71.05 365	0.03 413	0.04 407	0.25 408	1.30 407	0.05 411	0.03 408	0.21 407	0.01 406	0.29 403

testmvs		6.04 375	8.02 378	0.10 389	0.08 411	0.03 414	69.74 370	0.04 412	0.05 406	0.31 407	1.68 406	0.02 412	0.04 407	0.24 406	0.02 405	0.25 404

pcd_1.5k_mvsjas		5.26 376	7.02 379	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	63.15 138	0.00 409	0.00 408	0.00 407	0.00 405

test_blank		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

uanet_test		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

DCPMVS		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

sosnet-low-res		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

sosnet		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

uncertanet		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

Regformer		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

uanet		0.00 377	0.00 380	0.00 390	0.00 413	0.00 415	0.00 401	0.00 414	0.00 408	0.00 409	0.00 408	0.00 413	0.00 409	0.00 408	0.00 407	0.00 405

WAC-MVS						42.58 390								39.46 379

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 113	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

PC_three_1452									68.21 244	92.02 12	94.00 46	82.09 5	95.98 51	84.58 48	96.68 2	94.95 10

No_MVS				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

test_one_0601					95.07 7	71.46 55		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

eth-test2					0.00 413

eth-test					0.00 413

ZD-MVS					94.38 25	72.22 44		92.67 61	70.98 181	87.75 31	94.07 41	74.01 32	96.70 27	84.66 47	94.84 43

RE-MVS-def			85.48 53		93.06 55	70.63 73	91.88 39	92.27 76	73.53 136	85.69 49	94.45 26	63.87 129		82.75 68	91.87 79	92.50 114

IU-MVS					95.30 2	71.25 57		92.95 51	66.81 254	92.39 6				88.94 16	96.63 4	94.85 19

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 44	82.45 3	96.87 20	83.77 58	96.48 8	94.88 14

test_241102_TWO								94.06 10	77.24 50	92.78 4	95.72 8	81.26 8	97.44 6	89.07 14	96.58 6	94.26 41

test_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 53	93.10 1	95.39 11	82.99 1	97.27 11

9.14			88.26 15		92.84 60		91.52 46	94.75 1	73.93 125	88.57 22	94.67 19	75.57 22	95.79 53	86.77 35	95.76 23

save fliter					93.80 40	72.35 42	90.47 64	91.17 116	74.31 116

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 8	89.42 9	96.57 7	94.67 24

test_0728_SECOND				87.71 32	95.34 1	71.43 56	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 42

test0726					95.27 5	71.25 57	93.60 6	94.11 6	77.33 48	92.81 3	95.79 3	80.98 9

GSMVS																88.96 245

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 262				88.96 245

sam_mvs												50.01 275

ambc				75.24 306	73.16 379	50.51 369	63.05 390	87.47 225		64.28 349	77.81 358	17.80 392	89.73 268	57.88 296	60.64 372	85.49 317

MTGPA								92.02 85

test_post1							78.90 323				5.43 404	48.81 295	85.44 319	59.25 281

test_post											5.46 403	50.36 273	84.24 326

patchmatchnet-post											74.00 373	51.12 264	88.60 289

GG-mvs-BLEND				75.38 305	81.59 320	55.80 327	79.32 315	69.63 376		67.19 324	73.67 374	43.24 331	88.90 285	50.41 335	84.50 173	81.45 360

MTMP							92.18 35	32.83 407

gm-plane-assit					81.40 323	53.83 347			62.72 310		80.94 330		92.39 195	63.40 244

test9_res														84.90 42	95.70 26	92.87 102

TEST9					93.26 50	72.96 25	88.75 115	91.89 93	68.44 241	85.00 57	93.10 67	74.36 28	95.41 67

test_8					93.13 52	72.57 35	88.68 120	91.84 97	68.69 236	84.87 61	93.10 67	74.43 26	95.16 76

agg_prior2														82.91 66	95.45 30	92.70 105

agg_prior					92.85 59	71.94 51		91.78 100		84.41 71			94.93 87

TestCases				79.58 251	85.15 247	63.62 221		79.83 328	62.31 313	60.32 365	86.73 224	32.02 372	88.96 283	50.28 338	71.57 338	86.15 306

test_prior4						72.60 34	89.01 105

test_prior2							88.85 111		75.41 93	84.91 59	93.54 56	74.28 29		83.31 61	95.86 20

test_prior				86.33 54	92.61 65	69.59 88		92.97 50					95.48 62			93.91 53

旧先验2							86.56 188		58.10 347	87.04 39			88.98 281	74.07 149

新几何2							86.29 196

新几何1				83.42 147	93.13 52	70.71 71		85.48 256	57.43 353	81.80 107	91.98 90	63.28 133	92.27 201	64.60 237	92.99 65	87.27 283

旧先验1					91.96 71	65.79 178		86.37 244			93.08 71	69.31 77			92.74 68	88.74 255

无先验							87.48 159	88.98 186	60.00 330				94.12 121	67.28 214		88.97 244

原ACMM2							86.86 177

原ACMM1				84.35 107	93.01 57	68.79 106		92.44 69	63.96 296	81.09 117	91.57 101	66.06 111	95.45 63	67.19 216	94.82 45	88.81 251

test222					91.50 77	68.26 124	84.16 248	83.20 290	54.63 364	79.74 129	91.63 99	58.97 193			91.42 85	86.77 296

testdata2													91.01 249	62.37 254

segment_acmp												73.08 37

testdata				79.97 241	90.90 86	64.21 212		84.71 263	59.27 337	85.40 51	92.91 73	62.02 157	89.08 279	68.95 199	91.37 86	86.63 300

testdata1							84.14 249		75.71 87

test12				86.80 49	92.63 64	70.70 72		91.79 99		82.71 98		71.67 51	96.16 44		94.50 50	93.54 77

plane_prior7					90.08 103	68.51 119

plane_prior6					89.84 112	68.70 114						60.42 186

plane_prior5								92.44 69					95.38 69	78.71 102	86.32 151	91.33 150

plane_prior4											91.00 120

plane_prior3						68.60 117			78.44 31	78.92 141

plane_prior2							91.25 50		79.12 23

plane_prior1					89.90 111

plane_prior						68.71 112	90.38 67		77.62 39						86.16 155

n2								0.00 414

nn								0.00 414

door-mid								69.98 375

lessismore_v0				78.97 259	81.01 330	57.15 307		65.99 384		61.16 362	82.82 311	39.12 354	91.34 238	59.67 277	46.92 390	88.43 261

LGP-MVS_train				84.50 99	89.23 135	68.76 108		91.94 91	75.37 94	76.64 196	91.51 102	54.29 228	94.91 88	78.44 104	83.78 185	89.83 216

test11								92.23 79

door								69.44 378

HQP5-MVS						66.98 155

HQP-NCC					89.33 128		89.17 98		76.41 72	77.23 182

ACMP_Plane					89.33 128		89.17 98		76.41 72	77.23 182

BP-MVS														77.47 114

HQP4-MVS										77.24 181			95.11 80			91.03 162

HQP3-MVS								92.19 82							85.99 159

HQP2-MVS												60.17 189

NP-MVS					89.62 115	68.32 122					90.24 132

MDTV_nov1_ep13_2view						37.79 397	75.16 350		55.10 362	66.53 333		49.34 285		53.98 318		87.94 267

MDTV_nov1_ep13			69.97 293		83.18 289	53.48 349	77.10 339	80.18 327	60.45 325	69.33 306	80.44 334	48.89 294	86.90 304	51.60 330	78.51 257

ACMMP++_ref															81.95 217

ACMMP++															81.25 223

Test By Simon												64.33 125

ITE_SJBPF				78.22 272	81.77 317	60.57 268		83.30 286	69.25 220	67.54 319	87.20 215	36.33 365	87.28 303	54.34 317	74.62 313	86.80 295

DeepMVS_CX				27.40 385	40.17 408	26.90 403		24.59 409	17.44 401	23.95 399	48.61 396	9.77 400	26.48 404	18.06 398	24.47 398	28.83 398

High-resolution multi-view benchmark