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 30	71.25 63	95.06 1	94.23 6	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

SED-MVS		90.08 2	90.85 2	87.77 27	95.30 2	70.98 70	93.57 8	94.06 14	77.24 60	93.10 1	95.72 8	82.99 1	97.44 7	89.07 24	96.63 4	94.88 16

DVP-MVS		89.60 3	90.35 3	87.33 44	95.27 5	71.25 63	93.49 10	92.73 68	77.33 57	92.12 11	95.78 4	80.98 10	97.40 9	89.08 22	96.41 12	93.33 115
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 33	73.46 17	92.90 20	94.11 10	80.27 10	91.35 16	94.16 52	78.35 14	96.77 27	89.59 17	94.22 65	94.67 32
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 50	94.10 12	75.90 100	92.29 7	95.66 10	81.67 6	97.38 13	87.44 46	96.34 15	93.95 77
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TestfortrainingZip a		89.27 6	89.82 6	87.60 38	94.57 17	70.90 76	93.28 12	94.36 3	75.24 118	92.25 9	95.03 20	81.59 7	97.39 11	86.12 55	95.96 19	94.52 47

MM		89.16 7	89.23 9	88.97 4	90.79 101	73.65 10	92.66 27	91.17 141	86.57 1	87.39 56	94.97 24	71.70 60	97.68 1	92.19 1	95.63 31	95.57 1

APDe-MVS		89.15 8	89.63 7	87.73 30	94.49 21	71.69 54	93.83 4	93.96 17	75.70 106	91.06 18	96.03 1	76.84 16	97.03 20	89.09 21	95.65 30	94.47 49
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 9	89.23 9	88.61 6	94.25 34	73.73 9	92.40 28	93.63 25	74.77 138	92.29 7	95.97 2	74.28 32	97.24 15	88.58 32	96.91 1	94.87 18
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 10	89.15 11	88.63 5	95.01 9	76.03 1	92.38 31	92.85 63	80.26 11	87.78 47	94.27 46	75.89 21	96.81 26	87.45 45	96.44 9	93.05 133

ME-MVS		88.98 11	89.39 8	87.75 29	94.54 19	71.43 60	91.61 48	94.25 5	76.30 92	90.62 20	95.03 20	78.06 15	97.07 19	88.15 38	95.96 19	94.75 29

CNVR-MVS		88.93 12	89.13 12	88.33 8	94.77 12	73.82 8	90.51 69	93.00 50	80.90 7	88.06 42	94.06 57	76.43 18	96.84 24	88.48 35	95.99 18	94.34 56

SteuartSystems-ACMMP		88.72 13	88.86 13	88.32 9	92.14 77	72.96 25	93.73 5	93.67 24	80.19 12	88.10 41	94.80 26	73.76 36	97.11 17	87.51 44	95.82 24	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 14	88.74 14	87.64 37	92.78 69	71.95 51	92.40 28	94.74 2	75.71 104	89.16 28	95.10 18	75.65 23	96.19 50	87.07 47	96.01 17	94.79 23

DeepPCF-MVS	80.84 1	88.10 15	88.56 16	86.73 58	92.24 76	69.03 109	89.57 97	93.39 34	77.53 53	89.79 24	94.12 54	78.98 13	96.58 38	85.66 56	95.72 27	94.58 40

lecture		88.09 16	88.59 15	86.58 61	93.26 55	69.77 95	93.70 6	94.16 8	77.13 65	89.76 25	95.52 14	72.26 51	96.27 47	86.87 48	94.65 51	93.70 93

SD-MVS		88.06 17	88.50 17	86.71 59	92.60 74	72.71 29	91.81 45	93.19 39	77.87 42	90.32 22	94.00 61	74.83 25	93.78 157	87.63 43	94.27 64	93.65 98
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 17	88.01 21	88.24 11	94.41 25	73.62 11	91.22 61	92.83 64	81.50 5	85.79 70	93.47 78	73.02 44	97.00 21	84.90 62	94.94 43	94.10 68

ACMMP_NAP		88.05 19	88.08 20	87.94 19	93.70 44	73.05 22	90.86 64	93.59 27	76.27 93	88.14 40	95.09 19	71.06 70	96.67 32	87.67 42	96.37 14	94.09 69

TSAR-MVS + MP.		88.02 20	88.11 19	87.72 32	93.68 46	72.13 48	91.41 57	92.35 86	74.62 142	88.90 31	93.85 69	75.75 22	96.00 58	87.80 41	94.63 53	95.04 10
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 21	87.85 23	88.20 12	94.39 27	73.33 19	93.03 18	93.81 21	76.81 74	85.24 75	94.32 43	71.76 58	96.93 22	85.53 59	95.79 25	94.32 58

MP-MVS		87.71 22	87.64 25	87.93 21	94.36 29	73.88 6	92.71 26	92.65 74	77.57 49	83.84 107	94.40 40	72.24 52	96.28 46	85.65 57	95.30 38	93.62 101
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MGCNet		87.69 23	87.55 28	88.12 13	89.45 137	71.76 53	91.47 56	89.54 197	82.14 3	86.65 64	94.28 45	68.28 111	97.46 6	90.81 6	95.31 37	95.15 8

MP-MVS-pluss		87.67 24	87.72 24	87.54 39	93.64 47	72.04 50	89.80 88	93.50 29	75.17 126	86.34 66	95.29 17	70.86 72	96.00 58	88.78 30	96.04 16	94.58 40
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 25	87.47 30	87.94 19	94.58 16	73.54 15	93.04 16	93.24 37	76.78 76	84.91 80	94.44 38	70.78 73	96.61 35	84.53 70	94.89 45	93.66 94

reproduce-ours		87.47 26	87.61 26	87.07 49	93.27 53	71.60 55	91.56 53	93.19 39	74.98 129	88.96 29	95.54 12	71.20 68	96.54 39	86.28 52	93.49 70	93.06 131

our_new_method		87.47 26	87.61 26	87.07 49	93.27 53	71.60 55	91.56 53	93.19 39	74.98 129	88.96 29	95.54 12	71.20 68	96.54 39	86.28 52	93.49 70	93.06 131

ACMMPR		87.44 28	87.23 35	88.08 15	94.64 13	73.59 12	93.04 16	93.20 38	76.78 76	84.66 87	94.52 31	68.81 102	96.65 33	84.53 70	94.90 44	94.00 74

APD-MVS		87.44 28	87.52 29	87.19 46	94.24 35	72.39 41	91.86 44	92.83 64	73.01 189	88.58 33	94.52 31	73.36 37	96.49 41	84.26 73	95.01 40	92.70 147
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 30	87.26 33	87.89 24	94.12 39	72.97 24	92.39 30	93.43 32	76.89 72	84.68 84	93.99 63	70.67 75	96.82 25	84.18 77	95.01 40	93.90 80

region2R		87.42 30	87.20 36	88.09 14	94.63 14	73.55 13	93.03 18	93.12 44	76.73 79	84.45 92	94.52 31	69.09 96	96.70 30	84.37 72	94.83 48	94.03 72

fmvsm_s_conf0.5_n_9		87.39 32	87.95 22	85.70 80	89.48 136	67.88 152	88.59 144	89.05 225	80.19 12	90.70 19	95.40 15	74.56 27	93.92 150	91.54 2	92.07 90	95.31 5

MCST-MVS		87.37 33	87.25 34	87.73 30	94.53 20	72.46 40	89.82 86	93.82 20	73.07 187	84.86 83	92.89 93	76.22 19	96.33 44	84.89 64	95.13 39	94.40 52

reproduce_model		87.28 34	87.39 32	86.95 53	93.10 61	71.24 67	91.60 49	93.19 39	74.69 139	88.80 32	95.61 11	70.29 79	96.44 42	86.20 54	93.08 74	93.16 125

MTAPA		87.23 35	87.00 38	87.90 22	94.18 38	74.25 5	86.58 222	92.02 103	79.45 22	85.88 68	94.80 26	68.07 113	96.21 49	86.69 50	95.34 35	93.23 118

XVS		87.18 36	86.91 43	88.00 17	94.42 23	73.33 19	92.78 22	92.99 53	79.14 26	83.67 111	94.17 51	67.45 120	96.60 36	83.06 85	94.50 56	94.07 70

HPM-MVS		87.11 37	86.98 40	87.50 42	93.88 42	72.16 47	92.19 37	93.33 35	76.07 97	83.81 108	93.95 66	69.77 87	96.01 57	85.15 60	94.66 50	94.32 58
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 37	86.92 42	87.68 36	94.20 37	73.86 7	93.98 3	92.82 67	76.62 82	83.68 110	94.46 35	67.93 115	95.95 61	84.20 76	94.39 60	93.23 118

DeepC-MVS	79.81 2	87.08 39	86.88 44	87.69 35	91.16 90	72.32 45	90.31 78	93.94 18	77.12 66	82.82 126	94.23 49	72.13 54	97.09 18	84.83 65	95.37 34	93.65 98
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 40	86.62 48	87.76 28	93.52 49	72.37 43	91.26 58	93.04 45	76.62 82	84.22 98	93.36 82	71.44 64	96.76 28	80.82 111	95.33 36	94.16 64
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
balanced_conf03		86.78 41	86.99 39	86.15 69	91.24 89	67.61 161	90.51 69	92.90 60	77.26 59	87.44 55	91.63 127	71.27 67	96.06 53	85.62 58	95.01 40	94.78 24

SR-MVS		86.73 42	86.67 46	86.91 54	94.11 40	72.11 49	92.37 32	92.56 79	74.50 143	86.84 63	94.65 30	67.31 122	95.77 63	84.80 66	92.85 77	92.84 145

CS-MVS		86.69 43	86.95 41	85.90 77	90.76 102	67.57 163	92.83 21	93.30 36	79.67 19	84.57 91	92.27 105	71.47 63	95.02 99	84.24 75	93.46 72	95.13 9

PGM-MVS		86.68 44	86.27 53	87.90 22	94.22 36	73.38 18	90.22 80	93.04 45	75.53 109	83.86 106	94.42 39	67.87 117	96.64 34	82.70 96	94.57 55	93.66 94

mPP-MVS		86.67 45	86.32 51	87.72 32	94.41 25	73.55 13	92.74 24	92.22 93	76.87 73	82.81 127	94.25 48	66.44 133	96.24 48	82.88 90	94.28 63	93.38 111

fmvsm_s_conf0.5_n_8		86.56 46	87.17 37	84.73 119	87.76 219	65.62 210	89.20 112	92.21 95	79.94 17	89.74 26	94.86 25	68.63 105	94.20 135	90.83 5	91.39 102	94.38 53

CANet		86.45 47	86.10 59	87.51 41	90.09 114	70.94 74	89.70 92	92.59 78	81.78 4	81.32 149	91.43 137	70.34 77	97.23 16	84.26 73	93.36 73	94.37 54

train_agg		86.43 48	86.20 54	87.13 48	93.26 55	72.96 25	88.75 136	91.89 111	68.69 293	85.00 78	93.10 86	74.43 29	95.41 79	84.97 61	95.71 28	93.02 135

PHI-MVS		86.43 48	86.17 57	87.24 45	90.88 98	70.96 72	92.27 36	94.07 13	72.45 195	85.22 76	91.90 115	69.47 90	96.42 43	83.28 84	95.94 22	94.35 55

CSCG		86.41 50	86.19 56	87.07 49	92.91 66	72.48 37	90.81 65	93.56 28	73.95 158	83.16 119	91.07 149	75.94 20	95.19 88	79.94 122	94.38 61	93.55 106

fmvsm_s_conf0.5_n_10		86.38 51	86.76 45	85.24 94	87.33 237	67.30 173	89.50 99	90.98 146	76.25 94	90.56 21	94.75 28	68.38 108	94.24 134	90.80 7	92.32 87	94.19 63

fmvsm_s_conf0.5_n_3		86.36 52	87.46 31	83.09 198	87.08 251	65.21 219	89.09 121	90.21 174	79.67 19	89.98 23	95.02 23	73.17 41	91.71 260	91.30 3	91.60 97	92.34 164

NormalMVS		86.29 53	85.88 63	87.52 40	93.26 55	72.47 38	91.65 46	92.19 97	79.31 24	84.39 94	92.18 107	64.64 155	95.53 70	80.70 114	94.65 51	94.56 44

SPE-MVS-test		86.29 53	86.48 49	85.71 79	91.02 94	67.21 179	92.36 33	93.78 22	78.97 33	83.51 114	91.20 144	70.65 76	95.15 90	81.96 100	94.89 45	94.77 25

fmvsm_l_conf0.5_n_3		86.02 55	86.32 51	85.14 97	87.20 242	68.54 129	89.57 97	90.44 163	75.31 117	87.49 53	94.39 41	72.86 46	92.72 216	89.04 26	90.56 116	94.16 64

EC-MVSNet		86.01 56	86.38 50	84.91 111	89.31 146	66.27 193	92.32 34	93.63 25	79.37 23	84.17 100	91.88 116	69.04 100	95.43 76	83.93 79	93.77 68	93.01 136

MVSMamba_PlusPlus		85.99 57	85.96 62	86.05 72	91.09 91	67.64 160	89.63 95	92.65 74	72.89 192	84.64 88	91.71 122	71.85 56	96.03 54	84.77 67	94.45 59	94.49 48

casdiffmvs_mvg		85.99 57	86.09 60	85.70 80	87.65 225	67.22 178	88.69 140	93.04 45	79.64 21	85.33 74	92.54 102	73.30 38	94.50 123	83.49 81	91.14 106	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 59	85.88 63	86.22 66	92.69 71	69.53 98	91.93 41	92.99 53	73.54 171	85.94 67	94.51 34	65.80 145	95.61 66	83.04 87	92.51 82	93.53 108

test_fmvsmconf_n		85.92 60	86.04 61	85.57 86	85.03 307	69.51 99	89.62 96	90.58 158	73.42 175	87.75 49	94.02 59	72.85 47	93.24 186	90.37 8	90.75 113	93.96 75

sasdasda		85.91 61	85.87 65	86.04 73	89.84 124	69.44 104	90.45 75	93.00 50	76.70 80	88.01 44	91.23 141	73.28 39	93.91 151	81.50 103	88.80 148	94.77 25

canonicalmvs		85.91 61	85.87 65	86.04 73	89.84 124	69.44 104	90.45 75	93.00 50	76.70 80	88.01 44	91.23 141	73.28 39	93.91 151	81.50 103	88.80 148	94.77 25

ACMMP		85.89 63	85.39 74	87.38 43	93.59 48	72.63 33	92.74 24	93.18 43	76.78 76	80.73 163	93.82 70	64.33 157	96.29 45	82.67 97	90.69 114	93.23 118
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
fmvsm_l_conf0.5_n_9		85.84 64	86.63 47	83.46 181	87.12 250	66.01 197	88.56 146	89.43 201	75.59 108	89.32 27	94.32 43	72.89 45	91.21 285	90.11 11	92.33 86	93.16 125

SR-MVS-dyc-post		85.77 65	85.61 70	86.23 65	93.06 63	70.63 81	91.88 42	92.27 88	73.53 172	85.69 71	94.45 36	65.00 153	95.56 67	82.75 92	91.87 93	92.50 157

CDPH-MVS		85.76 66	85.29 79	87.17 47	93.49 50	71.08 68	88.58 145	92.42 84	68.32 300	84.61 89	93.48 76	72.32 50	96.15 52	79.00 131	95.43 33	94.28 60

TSAR-MVS + GP.		85.71 67	85.33 76	86.84 55	91.34 87	72.50 36	89.07 122	87.28 276	76.41 85	85.80 69	90.22 177	74.15 34	95.37 84	81.82 101	91.88 92	92.65 151

dcpmvs_2		85.63 68	86.15 58	84.06 156	91.71 83	64.94 230	86.47 225	91.87 113	73.63 167	86.60 65	93.02 91	76.57 17	91.87 254	83.36 82	92.15 88	95.35 3

test_fmvsmconf0.1_n		85.61 69	85.65 69	85.50 87	82.99 359	69.39 106	89.65 93	90.29 172	73.31 179	87.77 48	94.15 53	71.72 59	93.23 187	90.31 9	90.67 115	93.89 81

fmvsm_s_conf0.5_n_6		85.55 70	86.20 54	83.60 176	87.32 239	65.13 222	88.86 128	91.63 125	75.41 113	88.23 39	93.45 79	68.56 106	92.47 227	89.52 18	92.78 78	93.20 123

alignmvs		85.48 71	85.32 77	85.96 76	89.51 133	69.47 101	89.74 90	92.47 80	76.17 95	87.73 51	91.46 136	70.32 78	93.78 157	81.51 102	88.95 145	94.63 37

3Dnovator+	77.84 4	85.48 71	84.47 90	88.51 7	91.08 92	73.49 16	93.18 15	93.78 22	80.79 8	76.66 245	93.37 81	60.40 227	96.75 29	77.20 153	93.73 69	95.29 6

MSLP-MVS++		85.43 73	85.76 67	84.45 127	91.93 80	70.24 84	90.71 66	92.86 62	77.46 55	84.22 98	92.81 97	67.16 124	92.94 207	80.36 117	94.35 62	90.16 249

DELS-MVS		85.41 74	85.30 78	85.77 78	88.49 181	67.93 151	85.52 259	93.44 31	78.70 34	83.63 113	89.03 210	74.57 26	95.71 65	80.26 119	94.04 66	93.66 94
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
fmvsm_s_conf0.5_n_4		85.39 75	85.75 68	84.30 136	86.70 262	65.83 203	88.77 134	89.78 186	75.46 112	88.35 35	93.73 72	69.19 95	93.06 202	91.30 3	88.44 157	94.02 73

SymmetryMVS		85.38 76	84.81 84	87.07 49	91.47 86	72.47 38	91.65 46	88.06 256	79.31 24	84.39 94	92.18 107	64.64 155	95.53 70	80.70 114	90.91 111	93.21 121

HPM-MVS_fast		85.35 77	84.95 83	86.57 62	93.69 45	70.58 83	92.15 39	91.62 126	73.89 161	82.67 129	94.09 55	62.60 179	95.54 69	80.93 109	92.93 76	93.57 104

test_fmvsm_n_1920		85.29 78	85.34 75	85.13 100	86.12 277	69.93 91	88.65 142	90.78 154	69.97 260	88.27 37	93.98 64	71.39 65	91.54 270	88.49 34	90.45 118	93.91 78

fmvsm_s_conf0.5_n_5		85.22 79	85.55 71	84.25 143	86.26 271	67.40 169	89.18 113	89.31 210	72.50 194	88.31 36	93.86 68	69.66 88	91.96 248	89.81 13	91.05 107	93.38 111

MVS_111021_HR		85.14 80	84.75 85	86.32 64	91.65 84	72.70 30	85.98 241	90.33 169	76.11 96	82.08 136	91.61 130	71.36 66	94.17 138	81.02 108	92.58 81	92.08 180

casdiffmvs		85.11 81	85.14 80	85.01 104	87.20 242	65.77 207	87.75 178	92.83 64	77.84 43	84.36 97	92.38 104	72.15 53	93.93 149	81.27 107	90.48 117	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 82	84.96 82	85.45 88	92.07 78	68.07 144	89.78 89	90.86 152	82.48 2	84.60 90	93.20 85	69.35 92	95.22 87	71.39 225	90.88 112	93.07 130

MGCFI-Net		85.06 83	85.51 72	83.70 174	89.42 138	63.01 282	89.43 102	92.62 77	76.43 84	87.53 52	91.34 139	72.82 48	93.42 179	81.28 106	88.74 151	94.66 35

DPM-MVS		84.93 84	84.29 91	86.84 55	90.20 112	73.04 23	87.12 198	93.04 45	69.80 264	82.85 125	91.22 143	73.06 43	96.02 56	76.72 165	94.63 53	91.46 201

baseline		84.93 84	84.98 81	84.80 116	87.30 240	65.39 216	87.30 194	92.88 61	77.62 47	84.04 103	92.26 106	71.81 57	93.96 143	81.31 105	90.30 120	95.03 11

ETV-MVS		84.90 86	84.67 86	85.59 85	89.39 141	68.66 126	88.74 138	92.64 76	79.97 16	84.10 101	85.71 306	69.32 93	95.38 81	80.82 111	91.37 103	92.72 146

test_fmvsmconf0.01_n		84.73 87	84.52 89	85.34 91	80.25 401	69.03 109	89.47 100	89.65 193	73.24 183	86.98 61	94.27 46	66.62 129	93.23 187	90.26 10	89.95 128	93.78 90

fmvsm_l_conf0.5_n		84.47 88	84.54 87	84.27 140	85.42 294	68.81 115	88.49 148	87.26 278	68.08 302	88.03 43	93.49 75	72.04 55	91.77 256	88.90 28	89.14 144	92.24 171

BP-MVS1		84.32 89	83.71 100	86.17 67	87.84 212	67.85 153	89.38 107	89.64 194	77.73 45	83.98 104	92.12 112	56.89 257	95.43 76	84.03 78	91.75 96	95.24 7

EI-MVSNet-Vis-set		84.19 90	83.81 97	85.31 92	88.18 193	67.85 153	87.66 180	89.73 191	80.05 15	82.95 122	89.59 195	70.74 74	94.82 107	80.66 116	84.72 222	93.28 117

fmvsm_l_conf0.5_n_a		84.13 91	84.16 92	84.06 156	85.38 295	68.40 132	88.34 156	86.85 288	67.48 309	87.48 54	93.40 80	70.89 71	91.61 261	88.38 36	89.22 141	92.16 178

fmvsm_s_conf0.5_n_2		84.04 92	84.11 93	83.81 172	86.17 275	65.00 227	86.96 204	87.28 276	74.35 147	88.25 38	94.23 49	61.82 195	92.60 219	89.85 12	88.09 162	93.84 84

test_fmvsmvis_n_1920		84.02 93	83.87 95	84.49 126	84.12 325	69.37 107	88.15 164	87.96 259	70.01 258	83.95 105	93.23 84	68.80 103	91.51 273	88.61 31	89.96 127	92.57 152

E3		84.00 94	83.87 95	84.39 129	87.70 223	64.95 228	86.40 230	92.23 91	75.85 101	83.21 116	91.78 120	70.09 82	93.55 169	79.52 126	88.05 163	94.66 35

viewcassd2359sk11		83.89 95	83.74 99	84.34 133	87.76 219	64.91 233	86.30 233	92.22 93	75.47 111	83.04 121	91.52 132	70.15 81	93.53 171	79.26 127	87.96 164	94.57 42

nrg030		83.88 96	83.53 104	84.96 106	86.77 260	69.28 108	90.46 74	92.67 71	74.79 137	82.95 122	91.33 140	72.70 49	93.09 200	80.79 113	79.28 304	92.50 157

EI-MVSNet-UG-set		83.81 97	83.38 107	85.09 102	87.87 210	67.53 165	87.44 189	89.66 192	79.74 18	82.23 133	89.41 204	70.24 80	94.74 113	79.95 121	83.92 237	92.99 138

fmvsm_s_conf0.1_n_2		83.80 98	83.79 98	83.83 170	85.62 288	64.94 230	87.03 201	86.62 294	74.32 148	87.97 46	94.33 42	60.67 219	92.60 219	89.72 14	87.79 167	93.96 75

fmvsm_s_conf0.5_n		83.80 98	83.71 100	84.07 153	86.69 263	67.31 172	89.46 101	83.07 348	71.09 225	86.96 62	93.70 73	69.02 101	91.47 275	88.79 29	84.62 224	93.44 110

viewmacassd2359aftdt		83.76 100	83.66 102	84.07 153	86.59 266	64.56 238	86.88 209	91.82 116	75.72 103	83.34 115	92.15 111	68.24 112	92.88 210	79.05 128	89.15 143	94.77 25

CPTT-MVS		83.73 101	83.33 109	84.92 110	93.28 52	70.86 77	92.09 40	90.38 165	68.75 292	79.57 178	92.83 95	60.60 223	93.04 205	80.92 110	91.56 100	90.86 219

EPNet		83.72 102	82.92 116	86.14 71	84.22 323	69.48 100	91.05 63	85.27 312	81.30 6	76.83 240	91.65 125	66.09 140	95.56 67	76.00 172	93.85 67	93.38 111
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmanbaseed2359cas		83.66 103	83.55 103	84.00 164	86.81 258	64.53 239	86.65 219	91.75 121	74.89 133	83.15 120	91.68 123	68.74 104	92.83 214	79.02 129	89.24 140	94.63 37

patch_mono-2		83.65 104	84.54 87	80.99 266	90.06 119	65.83 203	84.21 293	88.74 241	71.60 213	85.01 77	92.44 103	74.51 28	83.50 393	82.15 99	92.15 88	93.64 100

HQP_MVS		83.64 105	83.14 110	85.14 97	90.08 115	68.71 122	91.25 59	92.44 81	79.12 28	78.92 190	91.00 154	60.42 225	95.38 81	78.71 135	86.32 193	91.33 202

fmvsm_s_conf0.5_n_a		83.63 106	83.41 106	84.28 138	86.14 276	68.12 142	89.43 102	82.87 353	70.27 253	87.27 58	93.80 71	69.09 96	91.58 263	88.21 37	83.65 245	93.14 128

Effi-MVS+		83.62 107	83.08 111	85.24 94	88.38 187	67.45 166	88.89 127	89.15 221	75.50 110	82.27 132	88.28 235	69.61 89	94.45 126	77.81 145	87.84 166	93.84 84

fmvsm_s_conf0.1_n		83.56 108	83.38 107	84.10 147	84.86 309	67.28 174	89.40 106	83.01 349	70.67 237	87.08 59	93.96 65	68.38 108	91.45 276	88.56 33	84.50 225	93.56 105

GDP-MVS		83.52 109	82.64 121	86.16 68	88.14 196	68.45 131	89.13 119	92.69 69	72.82 193	83.71 109	91.86 118	55.69 264	95.35 85	80.03 120	89.74 132	94.69 31

OPM-MVS		83.50 110	82.95 115	85.14 97	88.79 171	70.95 73	89.13 119	91.52 130	77.55 52	80.96 157	91.75 121	60.71 217	94.50 123	79.67 125	86.51 191	89.97 265
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 111	82.80 118	85.43 89	90.25 111	68.74 120	90.30 79	90.13 177	76.33 91	80.87 160	92.89 93	61.00 214	94.20 135	72.45 217	90.97 109	93.35 114
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 112	83.45 105	83.28 188	92.74 70	62.28 299	88.17 162	89.50 199	75.22 120	81.49 147	92.74 101	66.75 127	95.11 93	72.85 207	91.58 99	92.45 161

EPP-MVSNet		83.40 113	83.02 113	84.57 122	90.13 113	64.47 244	92.32 34	90.73 155	74.45 146	79.35 184	91.10 147	69.05 99	95.12 91	72.78 208	87.22 177	94.13 66

3Dnovator	76.31 5	83.38 114	82.31 128	86.59 60	87.94 207	72.94 28	90.64 67	92.14 102	77.21 62	75.47 271	92.83 95	58.56 239	94.72 114	73.24 204	92.71 80	92.13 179

viewdifsd2359ckpt09		83.34 115	82.55 123	85.70 80	87.64 226	67.72 158	88.43 149	91.68 123	71.91 207	81.65 145	90.68 161	67.10 125	94.75 112	76.17 168	87.70 169	94.62 39

fmvsm_s_conf0.5_n_7		83.34 115	84.03 94	81.28 257	85.73 285	65.13 222	85.40 260	89.90 184	74.96 131	82.13 135	93.89 67	66.65 128	87.92 347	86.56 51	91.05 107	90.80 220

fmvsm_s_conf0.1_n_a		83.32 117	82.99 114	84.28 138	83.79 333	68.07 144	89.34 109	82.85 354	69.80 264	87.36 57	94.06 57	68.34 110	91.56 266	87.95 40	83.46 251	93.21 121

KinetiMVS		83.31 118	82.61 122	85.39 90	87.08 251	67.56 164	88.06 166	91.65 124	77.80 44	82.21 134	91.79 119	57.27 252	94.07 141	77.77 146	89.89 130	94.56 44

EIA-MVS		83.31 118	82.80 118	84.82 114	89.59 129	65.59 211	88.21 160	92.68 70	74.66 141	78.96 188	86.42 293	69.06 98	95.26 86	75.54 179	90.09 124	93.62 101

h-mvs33		83.15 120	82.19 131	86.02 75	90.56 104	70.85 78	88.15 164	89.16 220	76.02 98	84.67 85	91.39 138	61.54 200	95.50 72	82.71 94	75.48 356	91.72 191

MVS_Test		83.15 120	83.06 112	83.41 185	86.86 255	63.21 278	86.11 239	92.00 105	74.31 149	82.87 124	89.44 203	70.03 83	93.21 189	77.39 152	88.50 156	93.81 86

IS-MVSNet		83.15 120	82.81 117	84.18 145	89.94 122	63.30 276	91.59 50	88.46 249	79.04 30	79.49 179	92.16 109	65.10 150	94.28 129	67.71 263	91.86 95	94.95 12

DP-MVS Recon		83.11 123	82.09 134	86.15 69	94.44 22	70.92 75	88.79 133	92.20 96	70.53 242	79.17 186	91.03 152	64.12 159	96.03 54	68.39 260	90.14 123	91.50 197

PAPM_NR		83.02 124	82.41 125	84.82 114	92.47 75	66.37 191	87.93 172	91.80 117	73.82 162	77.32 228	90.66 162	67.90 116	94.90 103	70.37 235	89.48 137	93.19 124

VDD-MVS		83.01 125	82.36 127	84.96 106	91.02 94	66.40 190	88.91 126	88.11 252	77.57 49	84.39 94	93.29 83	52.19 298	93.91 151	77.05 156	88.70 152	94.57 42

viewdifsd2359ckpt13		82.91 126	82.29 129	84.77 117	86.96 254	66.90 186	87.47 185	91.62 126	72.19 200	81.68 144	90.71 160	66.92 126	93.28 182	75.90 173	87.15 179	94.12 67

MVSFormer		82.85 127	82.05 135	85.24 94	87.35 232	70.21 85	90.50 71	90.38 165	68.55 295	81.32 149	89.47 198	61.68 197	93.46 176	78.98 132	90.26 121	92.05 181

viewdifsd2359ckpt07		82.83 128	82.78 120	82.99 205	86.51 268	62.58 290	85.09 268	90.83 153	75.22 120	82.28 131	91.63 127	69.43 91	92.03 244	77.71 147	86.32 193	94.34 56

OMC-MVS		82.69 129	81.97 138	84.85 113	88.75 173	67.42 167	87.98 168	90.87 151	74.92 132	79.72 176	91.65 125	62.19 189	93.96 143	75.26 183	86.42 192	93.16 125

PVSNet_Blended_VisFu		82.62 130	81.83 140	84.96 106	90.80 100	69.76 96	88.74 138	91.70 122	69.39 272	78.96 188	88.46 230	65.47 147	94.87 106	74.42 190	88.57 153	90.24 247

MVS_111021_LR		82.61 131	82.11 132	84.11 146	88.82 165	71.58 57	85.15 265	86.16 302	74.69 139	80.47 168	91.04 150	62.29 186	90.55 303	80.33 118	90.08 125	90.20 248

HQP-MVS		82.61 131	82.02 136	84.37 130	89.33 143	66.98 182	89.17 114	92.19 97	76.41 85	77.23 231	90.23 176	60.17 228	95.11 93	77.47 150	85.99 202	91.03 212

RRT-MVS		82.60 133	82.10 133	84.10 147	87.98 206	62.94 287	87.45 188	91.27 137	77.42 56	79.85 174	90.28 173	56.62 260	94.70 116	79.87 123	88.15 161	94.67 32

diffmvs_AUTHOR		82.38 134	82.27 130	82.73 224	83.26 347	63.80 258	83.89 300	89.76 188	73.35 178	82.37 130	90.84 157	66.25 136	90.79 297	82.77 91	87.93 165	93.59 103

CLD-MVS		82.31 135	81.65 141	84.29 137	88.47 182	67.73 157	85.81 249	92.35 86	75.78 102	78.33 205	86.58 288	64.01 160	94.35 127	76.05 171	87.48 173	90.79 221
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 136	82.41 125	81.62 246	90.82 99	60.93 315	84.47 284	89.78 186	76.36 90	84.07 102	91.88 116	64.71 154	90.26 305	70.68 232	88.89 146	93.66 94

diffmvs		82.10 137	81.88 139	82.76 222	83.00 357	63.78 260	83.68 305	89.76 188	72.94 190	82.02 137	89.85 182	65.96 144	90.79 297	82.38 98	87.30 176	93.71 92
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		82.08 138	81.27 144	84.50 124	89.23 151	68.76 118	90.22 80	91.94 109	75.37 115	76.64 246	91.51 133	54.29 277	94.91 101	78.44 137	83.78 238	89.83 270

FIs		82.07 139	82.42 124	81.04 265	88.80 170	58.34 344	88.26 159	93.49 30	76.93 71	78.47 202	91.04 150	69.92 85	92.34 235	69.87 244	84.97 218	92.44 162

PS-MVSNAJss		82.07 139	81.31 143	84.34 133	86.51 268	67.27 175	89.27 110	91.51 131	71.75 208	79.37 183	90.22 177	63.15 171	94.27 130	77.69 148	82.36 266	91.49 198

API-MVS		81.99 141	81.23 145	84.26 142	90.94 96	70.18 90	91.10 62	89.32 209	71.51 215	78.66 195	88.28 235	65.26 148	95.10 96	64.74 290	91.23 105	87.51 337

SSM_0404		81.91 142	80.84 153	85.13 100	89.24 150	68.26 136	87.84 177	89.25 215	71.06 227	80.62 164	90.39 170	59.57 230	94.65 118	72.45 217	87.19 178	92.47 160

UniMVSNet_NR-MVSNet		81.88 143	81.54 142	82.92 209	88.46 183	63.46 272	87.13 197	92.37 85	80.19 12	78.38 203	89.14 206	71.66 62	93.05 203	70.05 240	76.46 339	92.25 169

MAR-MVS		81.84 144	80.70 154	85.27 93	91.32 88	71.53 58	89.82 86	90.92 148	69.77 266	78.50 199	86.21 297	62.36 185	94.52 122	65.36 284	92.05 91	89.77 273
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		81.82 145	81.23 145	83.57 179	91.89 81	63.43 274	89.84 85	81.85 365	77.04 69	83.21 116	93.10 86	52.26 297	93.43 178	71.98 220	89.95 128	93.85 82

hse-mvs2		81.72 146	80.94 151	84.07 153	88.72 174	67.68 159	85.87 245	87.26 278	76.02 98	84.67 85	88.22 238	61.54 200	93.48 174	82.71 94	73.44 384	91.06 210

GeoE		81.71 147	81.01 150	83.80 173	89.51 133	64.45 245	88.97 124	88.73 242	71.27 221	78.63 196	89.76 188	66.32 135	93.20 192	69.89 243	86.02 201	93.74 91

xiu_mvs_v2_base		81.69 148	81.05 148	83.60 176	89.15 154	68.03 146	84.46 286	90.02 179	70.67 237	81.30 152	86.53 291	63.17 170	94.19 137	75.60 178	88.54 154	88.57 315

PS-MVSNAJ		81.69 148	81.02 149	83.70 174	89.51 133	68.21 141	84.28 292	90.09 178	70.79 234	81.26 153	85.62 311	63.15 171	94.29 128	75.62 177	88.87 147	88.59 314

PAPR		81.66 150	80.89 152	83.99 165	90.27 110	64.00 252	86.76 216	91.77 120	68.84 291	77.13 238	89.50 196	67.63 118	94.88 105	67.55 265	88.52 155	93.09 129

UniMVSNet (Re)		81.60 151	81.11 147	83.09 198	88.38 187	64.41 246	87.60 181	93.02 49	78.42 37	78.56 198	88.16 239	69.78 86	93.26 185	69.58 247	76.49 338	91.60 192

SSM_0407		81.58 152	80.48 160	84.87 112	88.81 166	67.96 148	87.37 190	89.25 215	71.06 227	79.48 180	90.39 170	59.57 230	94.48 125	72.45 217	85.93 204	92.18 174

Elysia		81.53 153	80.16 168	85.62 83	85.51 291	68.25 138	88.84 131	92.19 97	71.31 218	80.50 166	89.83 183	46.89 358	94.82 107	76.85 158	89.57 134	93.80 88

StellarMVS		81.53 153	80.16 168	85.62 83	85.51 291	68.25 138	88.84 131	92.19 97	71.31 218	80.50 166	89.83 183	46.89 358	94.82 107	76.85 158	89.57 134	93.80 88

FC-MVSNet-test		81.52 155	82.02 136	80.03 288	88.42 186	55.97 383	87.95 170	93.42 33	77.10 67	77.38 226	90.98 156	69.96 84	91.79 255	68.46 259	84.50 225	92.33 165

VDDNet		81.52 155	80.67 155	84.05 159	90.44 107	64.13 251	89.73 91	85.91 305	71.11 224	83.18 118	93.48 76	50.54 324	93.49 173	73.40 201	88.25 159	94.54 46

ACMP	74.13 6	81.51 157	80.57 157	84.36 131	89.42 138	68.69 125	89.97 84	91.50 134	74.46 145	75.04 293	90.41 169	53.82 283	94.54 120	77.56 149	82.91 258	89.86 269
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 158	80.29 165	84.70 120	86.63 265	69.90 93	85.95 242	86.77 289	63.24 362	81.07 155	89.47 198	61.08 213	92.15 241	78.33 140	90.07 126	92.05 181
jason: jason.
lupinMVS		81.39 158	80.27 166	84.76 118	87.35 232	70.21 85	85.55 255	86.41 296	62.85 369	81.32 149	88.61 225	61.68 197	92.24 239	78.41 139	90.26 121	91.83 184

test_yl		81.17 160	80.47 161	83.24 191	89.13 155	63.62 261	86.21 236	89.95 182	72.43 198	81.78 142	89.61 193	57.50 249	93.58 165	70.75 230	86.90 183	92.52 155

DCV-MVSNet		81.17 160	80.47 161	83.24 191	89.13 155	63.62 261	86.21 236	89.95 182	72.43 198	81.78 142	89.61 193	57.50 249	93.58 165	70.75 230	86.90 183	92.52 155

guyue		81.13 162	80.64 156	82.60 227	86.52 267	63.92 256	86.69 218	87.73 267	73.97 157	80.83 162	89.69 189	56.70 258	91.33 281	78.26 144	85.40 215	92.54 154

DU-MVS		81.12 163	80.52 159	82.90 210	87.80 214	63.46 272	87.02 202	91.87 113	79.01 31	78.38 203	89.07 208	65.02 151	93.05 203	70.05 240	76.46 339	92.20 172

PVSNet_Blended		80.98 164	80.34 163	82.90 210	88.85 162	65.40 214	84.43 288	92.00 105	67.62 306	78.11 210	85.05 327	66.02 142	94.27 130	71.52 222	89.50 136	89.01 295

FA-MVS(test-final)		80.96 165	79.91 175	84.10 147	88.30 190	65.01 226	84.55 283	90.01 180	73.25 182	79.61 177	87.57 255	58.35 241	94.72 114	71.29 226	86.25 196	92.56 153

QAPM		80.88 166	79.50 189	85.03 103	88.01 205	68.97 113	91.59 50	92.00 105	66.63 322	75.15 289	92.16 109	57.70 246	95.45 74	63.52 296	88.76 150	90.66 228

TranMVSNet+NR-MVSNet		80.84 167	80.31 164	82.42 230	87.85 211	62.33 297	87.74 179	91.33 136	80.55 9	77.99 214	89.86 181	65.23 149	92.62 217	67.05 272	75.24 366	92.30 167

UGNet		80.83 168	79.59 187	84.54 123	88.04 202	68.09 143	89.42 104	88.16 251	76.95 70	76.22 257	89.46 200	49.30 341	93.94 146	68.48 258	90.31 119	91.60 192
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
AstraMVS		80.81 169	80.14 170	82.80 216	86.05 280	63.96 253	86.46 226	85.90 306	73.71 165	80.85 161	90.56 166	54.06 281	91.57 265	79.72 124	83.97 236	92.86 143

Fast-Effi-MVS+		80.81 169	79.92 174	83.47 180	88.85 162	64.51 241	85.53 257	89.39 203	70.79 234	78.49 200	85.06 326	67.54 119	93.58 165	67.03 273	86.58 189	92.32 166

XVG-OURS-SEG-HR		80.81 169	79.76 180	83.96 167	85.60 289	68.78 117	83.54 312	90.50 161	70.66 240	76.71 244	91.66 124	60.69 218	91.26 282	76.94 157	81.58 274	91.83 184

IMVS_0403		80.80 172	80.12 171	82.87 212	87.13 245	63.59 265	85.19 262	89.33 205	70.51 243	78.49 200	89.03 210	63.26 167	93.27 184	72.56 213	85.56 211	91.74 187

xiu_mvs_v1_base_debu		80.80 172	79.72 183	84.03 161	87.35 232	70.19 87	85.56 252	88.77 237	69.06 285	81.83 138	88.16 239	50.91 318	92.85 211	78.29 141	87.56 170	89.06 290

xiu_mvs_v1_base		80.80 172	79.72 183	84.03 161	87.35 232	70.19 87	85.56 252	88.77 237	69.06 285	81.83 138	88.16 239	50.91 318	92.85 211	78.29 141	87.56 170	89.06 290

xiu_mvs_v1_base_debi		80.80 172	79.72 183	84.03 161	87.35 232	70.19 87	85.56 252	88.77 237	69.06 285	81.83 138	88.16 239	50.91 318	92.85 211	78.29 141	87.56 170	89.06 290

ACMM	73.20 8	80.78 176	79.84 178	83.58 178	89.31 146	68.37 133	89.99 83	91.60 128	70.28 252	77.25 229	89.66 191	53.37 288	93.53 171	74.24 193	82.85 259	88.85 303
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
LuminaMVS		80.68 177	79.62 186	83.83 170	85.07 306	68.01 147	86.99 203	88.83 234	70.36 248	81.38 148	87.99 246	50.11 329	92.51 226	79.02 129	86.89 185	90.97 215

114514_t		80.68 177	79.51 188	84.20 144	94.09 41	67.27 175	89.64 94	91.11 144	58.75 409	74.08 308	90.72 159	58.10 242	95.04 98	69.70 245	89.42 138	90.30 245

IMVS_0407		80.61 179	79.90 176	82.75 223	87.13 245	63.59 265	85.33 261	89.33 205	70.51 243	77.82 216	89.03 210	61.84 193	92.91 208	72.56 213	85.56 211	91.74 187

CANet_DTU		80.61 179	79.87 177	82.83 213	85.60 289	63.17 281	87.36 191	88.65 245	76.37 89	75.88 264	88.44 231	53.51 286	93.07 201	73.30 202	89.74 132	92.25 169

VPA-MVSNet		80.60 181	80.55 158	80.76 272	88.07 201	60.80 318	86.86 210	91.58 129	75.67 107	80.24 170	89.45 202	63.34 164	90.25 306	70.51 234	79.22 305	91.23 205

mvsmamba		80.60 181	79.38 191	84.27 140	89.74 127	67.24 177	87.47 185	86.95 284	70.02 257	75.38 277	88.93 215	51.24 315	92.56 222	75.47 181	89.22 141	93.00 137

PVSNet_BlendedMVS		80.60 181	80.02 172	82.36 232	88.85 162	65.40 214	86.16 238	92.00 105	69.34 274	78.11 210	86.09 301	66.02 142	94.27 130	71.52 222	82.06 269	87.39 339

AdaColmap		80.58 184	79.42 190	84.06 156	93.09 62	68.91 114	89.36 108	88.97 231	69.27 276	75.70 267	89.69 189	57.20 254	95.77 63	63.06 301	88.41 158	87.50 338

EI-MVSNet		80.52 185	79.98 173	82.12 235	84.28 321	63.19 280	86.41 227	88.95 232	74.18 154	78.69 193	87.54 258	66.62 129	92.43 229	72.57 211	80.57 288	90.74 225

viewmambaseed2359dif		80.41 186	79.84 178	82.12 235	82.95 361	62.50 293	83.39 313	88.06 256	67.11 311	80.98 156	90.31 172	66.20 138	91.01 293	74.62 187	84.90 219	92.86 143

XVG-OURS		80.41 186	79.23 197	83.97 166	85.64 287	69.02 111	83.03 325	90.39 164	71.09 225	77.63 222	91.49 135	54.62 276	91.35 279	75.71 175	83.47 250	91.54 195

SDMVSNet		80.38 188	80.18 167	80.99 266	89.03 160	64.94 230	80.45 357	89.40 202	75.19 124	76.61 248	89.98 179	60.61 222	87.69 351	76.83 161	83.55 247	90.33 243

PCF-MVS	73.52 7	80.38 188	78.84 206	85.01 104	87.71 221	68.99 112	83.65 306	91.46 135	63.00 366	77.77 220	90.28 173	66.10 139	95.09 97	61.40 320	88.22 160	90.94 217
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
viewdifsd2359ckpt11		80.37 190	79.73 181	82.30 233	83.70 337	62.39 294	84.20 294	86.67 290	73.22 184	80.90 158	90.62 163	63.00 176	91.56 266	76.81 162	78.44 311	92.95 140

viewmsd2359difaftdt		80.37 190	79.73 181	82.30 233	83.70 337	62.39 294	84.20 294	86.67 290	73.22 184	80.90 158	90.62 163	63.00 176	91.56 266	76.81 162	78.44 311	92.95 140

X-MVStestdata		80.37 190	77.83 230	88.00 17	94.42 23	73.33 19	92.78 22	92.99 53	79.14 26	83.67 111	12.47 473	67.45 120	96.60 36	83.06 85	94.50 56	94.07 70

test_djsdf		80.30 193	79.32 194	83.27 189	83.98 329	65.37 217	90.50 71	90.38 165	68.55 295	76.19 258	88.70 221	56.44 261	93.46 176	78.98 132	80.14 294	90.97 215

v2v482		80.23 194	79.29 195	83.05 202	83.62 339	64.14 250	87.04 200	89.97 181	73.61 168	78.18 209	87.22 266	61.10 212	93.82 155	76.11 169	76.78 335	91.18 206

NR-MVSNet		80.23 194	79.38 191	82.78 220	87.80 214	63.34 275	86.31 232	91.09 145	79.01 31	72.17 334	89.07 208	67.20 123	92.81 215	66.08 279	75.65 352	92.20 172

Anonymous20240529		80.19 196	78.89 205	84.10 147	90.60 103	64.75 236	88.95 125	90.90 149	65.97 330	80.59 165	91.17 146	49.97 331	93.73 163	69.16 251	82.70 263	93.81 86

IterMVS-LS		80.06 197	79.38 191	82.11 237	85.89 281	63.20 279	86.79 213	89.34 204	74.19 153	75.45 274	86.72 278	66.62 129	92.39 231	72.58 210	76.86 332	90.75 224
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 198	78.57 210	84.42 128	85.13 304	68.74 120	88.77 134	88.10 253	74.99 128	74.97 295	83.49 362	57.27 252	93.36 180	73.53 198	80.88 282	91.18 206

v1144		80.03 198	79.03 201	83.01 204	83.78 334	64.51 241	87.11 199	90.57 160	71.96 206	78.08 212	86.20 298	61.41 204	93.94 146	74.93 185	77.23 326	90.60 231

v8		79.97 200	79.02 202	82.80 216	84.09 326	64.50 243	87.96 169	90.29 172	74.13 156	75.24 286	86.81 275	62.88 178	93.89 154	74.39 191	75.40 361	90.00 261

OpenMVS	72.83 10	79.77 201	78.33 217	84.09 151	85.17 300	69.91 92	90.57 68	90.97 147	66.70 316	72.17 334	91.91 114	54.70 274	93.96 143	61.81 317	90.95 110	88.41 319

v10		79.74 202	78.67 207	82.97 208	84.06 327	64.95 228	87.88 175	90.62 157	73.11 186	75.11 290	86.56 289	61.46 203	94.05 142	73.68 196	75.55 354	89.90 267

ECVR-MVS		79.61 203	79.26 196	80.67 274	90.08 115	54.69 398	87.89 174	77.44 412	74.88 134	80.27 169	92.79 98	48.96 347	92.45 228	68.55 257	92.50 83	94.86 19

BH-RMVSNet		79.61 203	78.44 213	83.14 196	89.38 142	65.93 200	84.95 272	87.15 281	73.56 170	78.19 208	89.79 187	56.67 259	93.36 180	59.53 336	86.74 187	90.13 251

v1192		79.59 205	78.43 214	83.07 201	83.55 341	64.52 240	86.93 207	90.58 158	70.83 233	77.78 219	85.90 302	59.15 234	93.94 146	73.96 195	77.19 328	90.76 223

ab-mvs		79.51 206	78.97 203	81.14 262	88.46 183	60.91 316	83.84 301	89.24 217	70.36 248	79.03 187	88.87 218	63.23 169	90.21 307	65.12 286	82.57 264	92.28 168

WR-MVS		79.49 207	79.22 198	80.27 283	88.79 171	58.35 343	85.06 269	88.61 247	78.56 35	77.65 221	88.34 233	63.81 163	90.66 302	64.98 288	77.22 327	91.80 186

v144192		79.47 208	78.37 215	82.78 220	83.35 344	63.96 253	86.96 204	90.36 168	69.99 259	77.50 223	85.67 309	60.66 220	93.77 159	74.27 192	76.58 336	90.62 229

BH-untuned		79.47 208	78.60 209	82.05 238	89.19 153	65.91 201	86.07 240	88.52 248	72.18 201	75.42 275	87.69 252	61.15 211	93.54 170	60.38 328	86.83 186	86.70 360

test1111		79.43 210	79.18 199	80.15 286	89.99 120	53.31 411	87.33 193	77.05 416	75.04 127	80.23 171	92.77 100	48.97 346	92.33 236	68.87 254	92.40 85	94.81 22

mvs_anonymous		79.42 211	79.11 200	80.34 281	84.45 320	57.97 350	82.59 327	87.62 269	67.40 310	76.17 261	88.56 228	68.47 107	89.59 318	70.65 233	86.05 200	93.47 109

thisisatest0530		79.40 212	77.76 235	84.31 135	87.69 224	65.10 225	87.36 191	84.26 328	70.04 256	77.42 225	88.26 237	49.94 332	94.79 111	70.20 238	84.70 223	93.03 134

tttt0517		79.40 212	77.91 226	83.90 169	88.10 199	63.84 257	88.37 155	84.05 330	71.45 216	76.78 242	89.12 207	49.93 334	94.89 104	70.18 239	83.18 256	92.96 139

V42		79.38 214	78.24 219	82.83 213	81.10 393	65.50 213	85.55 255	89.82 185	71.57 214	78.21 207	86.12 300	60.66 220	93.18 195	75.64 176	75.46 358	89.81 272

mamba_0408		79.37 215	77.52 242	84.93 109	88.81 166	67.96 148	65.03 457	88.66 243	70.96 231	79.48 180	89.80 185	58.69 236	94.65 118	70.35 236	85.93 204	92.18 174

jajsoiax		79.29 216	77.96 224	83.27 189	84.68 314	66.57 189	89.25 111	90.16 176	69.20 281	75.46 273	89.49 197	45.75 374	93.13 198	76.84 160	80.80 284	90.11 253

v1921920		79.22 217	78.03 223	82.80 216	83.30 346	63.94 255	86.80 212	90.33 169	69.91 262	77.48 224	85.53 313	58.44 240	93.75 161	73.60 197	76.85 333	90.71 227

AUN-MVS		79.21 218	77.60 240	84.05 159	88.71 175	67.61 161	85.84 247	87.26 278	69.08 284	77.23 231	88.14 243	53.20 290	93.47 175	75.50 180	73.45 383	91.06 210

TAPA-MVS	73.13 9	79.15 219	77.94 225	82.79 219	89.59 129	62.99 286	88.16 163	91.51 131	65.77 331	77.14 237	91.09 148	60.91 215	93.21 189	50.26 404	87.05 181	92.17 177
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 220	77.77 234	83.22 193	84.70 313	66.37 191	89.17 114	90.19 175	69.38 273	75.40 276	89.46 200	44.17 386	93.15 196	76.78 164	80.70 286	90.14 250

UniMVSNet_ETH3D		79.10 221	78.24 219	81.70 245	86.85 256	60.24 327	87.28 195	88.79 236	74.25 152	76.84 239	90.53 168	49.48 337	91.56 266	67.98 261	82.15 267	93.29 116

CDS-MVSNet		79.07 222	77.70 237	83.17 195	87.60 227	68.23 140	84.40 290	86.20 301	67.49 308	76.36 254	86.54 290	61.54 200	90.79 297	61.86 316	87.33 175	90.49 236
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 223	77.88 229	82.38 231	83.07 354	64.80 235	84.08 299	88.95 232	69.01 288	78.69 193	87.17 269	54.70 274	92.43 229	74.69 186	80.57 288	89.89 268

v1240		78.99 224	77.78 233	82.64 225	83.21 349	63.54 269	86.62 221	90.30 171	69.74 269	77.33 227	85.68 308	57.04 255	93.76 160	73.13 205	76.92 330	90.62 229

Anonymous20231211		78.97 225	77.69 238	82.81 215	90.54 105	64.29 248	90.11 82	91.51 131	65.01 342	76.16 262	88.13 244	50.56 323	93.03 206	69.68 246	77.56 325	91.11 208

v7n		78.97 225	77.58 241	83.14 196	83.45 343	65.51 212	88.32 157	91.21 139	73.69 166	72.41 330	86.32 296	57.93 243	93.81 156	69.18 250	75.65 352	90.11 253

icg_test_0407_2		78.92 227	78.93 204	78.90 311	87.13 245	63.59 265	76.58 404	89.33 205	70.51 243	77.82 216	89.03 210	61.84 193	81.38 408	72.56 213	85.56 211	91.74 187

TAMVS		78.89 228	77.51 244	83.03 203	87.80 214	67.79 156	84.72 276	85.05 317	67.63 305	76.75 243	87.70 251	62.25 187	90.82 296	58.53 347	87.13 180	90.49 236

c3_l		78.75 229	77.91 226	81.26 258	82.89 362	61.56 308	84.09 298	89.13 223	69.97 260	75.56 269	84.29 341	66.36 134	92.09 243	73.47 200	75.48 356	90.12 252

tt0805		78.73 230	77.83 230	81.43 251	85.17 300	60.30 326	89.41 105	90.90 149	71.21 222	77.17 236	88.73 220	46.38 363	93.21 189	72.57 211	78.96 306	90.79 221

v148		78.72 231	77.80 232	81.47 250	82.73 365	61.96 303	86.30 233	88.08 254	73.26 181	76.18 259	85.47 315	62.46 183	92.36 233	71.92 221	73.82 380	90.09 255

VPNet		78.69 232	78.66 208	78.76 313	88.31 189	55.72 387	84.45 287	86.63 293	76.79 75	78.26 206	90.55 167	59.30 233	89.70 317	66.63 274	77.05 329	90.88 218

ET-MVSNet_ETH3D		78.63 233	76.63 265	84.64 121	86.73 261	69.47 101	85.01 270	84.61 321	69.54 270	66.51 400	86.59 286	50.16 328	91.75 257	76.26 167	84.24 233	92.69 149

anonymousdsp		78.60 234	77.15 250	82.98 207	80.51 399	67.08 180	87.24 196	89.53 198	65.66 333	75.16 288	87.19 268	52.52 292	92.25 238	77.17 154	79.34 303	89.61 277

miper_ehance_all_eth		78.59 235	77.76 235	81.08 264	82.66 367	61.56 308	83.65 306	89.15 221	68.87 290	75.55 270	83.79 353	66.49 132	92.03 244	73.25 203	76.39 341	89.64 276

VortexMVS		78.57 236	77.89 228	80.59 275	85.89 281	62.76 289	85.61 250	89.62 195	72.06 204	74.99 294	85.38 317	55.94 263	90.77 300	74.99 184	76.58 336	88.23 321

WR-MVS_H		78.51 237	78.49 211	78.56 318	88.02 203	56.38 377	88.43 149	92.67 71	77.14 64	73.89 310	87.55 257	66.25 136	89.24 325	58.92 342	73.55 382	90.06 259

GBi-Net		78.40 238	77.40 245	81.40 253	87.60 227	63.01 282	88.39 152	89.28 211	71.63 210	75.34 279	87.28 262	54.80 270	91.11 286	62.72 303	79.57 298	90.09 255

test1		78.40 238	77.40 245	81.40 253	87.60 227	63.01 282	88.39 152	89.28 211	71.63 210	75.34 279	87.28 262	54.80 270	91.11 286	62.72 303	79.57 298	90.09 255

Vis-MVSNet (Re-imp)		78.36 240	78.45 212	78.07 330	88.64 177	51.78 422	86.70 217	79.63 394	74.14 155	75.11 290	90.83 158	61.29 208	89.75 315	58.10 352	91.60 97	92.69 149

Anonymous202405211		78.25 241	77.01 252	81.99 240	91.03 93	60.67 320	84.77 275	83.90 332	70.65 241	80.00 173	91.20 144	41.08 407	91.43 277	65.21 285	85.26 216	93.85 82

CP-MVSNet		78.22 242	78.34 216	77.84 334	87.83 213	54.54 400	87.94 171	91.17 141	77.65 46	73.48 316	88.49 229	62.24 188	88.43 341	62.19 311	74.07 375	90.55 233

BH-w/o		78.21 243	77.33 248	80.84 270	88.81 166	65.13 222	84.87 273	87.85 264	69.75 267	74.52 303	84.74 333	61.34 206	93.11 199	58.24 351	85.84 207	84.27 398

FMVSNet2		78.20 244	77.21 249	81.20 260	87.60 227	62.89 288	87.47 185	89.02 227	71.63 210	75.29 285	87.28 262	54.80 270	91.10 289	62.38 308	79.38 302	89.61 277

MVS		78.19 245	76.99 254	81.78 243	85.66 286	66.99 181	84.66 278	90.47 162	55.08 430	72.02 336	85.27 319	63.83 162	94.11 140	66.10 278	89.80 131	84.24 399

Baseline_NR-MVSNet		78.15 246	78.33 217	77.61 339	85.79 283	56.21 381	86.78 214	85.76 308	73.60 169	77.93 215	87.57 255	65.02 151	88.99 330	67.14 271	75.33 363	87.63 333

CNLPA		78.08 247	76.79 259	81.97 241	90.40 108	71.07 69	87.59 182	84.55 322	66.03 329	72.38 331	89.64 192	57.56 248	86.04 368	59.61 335	83.35 252	88.79 306

cl22		78.07 248	77.01 252	81.23 259	82.37 374	61.83 305	83.55 310	87.98 258	68.96 289	75.06 292	83.87 349	61.40 205	91.88 253	73.53 198	76.39 341	89.98 264

PLC	70.83 11	78.05 249	76.37 271	83.08 200	91.88 82	67.80 155	88.19 161	89.46 200	64.33 350	69.87 361	88.38 232	53.66 284	93.58 165	58.86 343	82.73 261	87.86 329
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 250	76.49 266	82.62 226	83.16 353	66.96 184	86.94 206	87.45 274	72.45 195	71.49 342	84.17 346	54.79 273	91.58 263	67.61 264	80.31 291	89.30 286

PS-CasMVS		78.01 251	78.09 222	77.77 336	87.71 221	54.39 402	88.02 167	91.22 138	77.50 54	73.26 318	88.64 224	60.73 216	88.41 342	61.88 315	73.88 379	90.53 234

HY-MVS	69.67 12	77.95 252	77.15 250	80.36 280	87.57 231	60.21 328	83.37 315	87.78 266	66.11 326	75.37 278	87.06 273	63.27 166	90.48 304	61.38 321	82.43 265	90.40 240

eth_miper_zixun_eth		77.92 253	76.69 263	81.61 248	83.00 357	61.98 302	83.15 319	89.20 219	69.52 271	74.86 297	84.35 340	61.76 196	92.56 222	71.50 224	72.89 388	90.28 246

FMVSNet3		77.88 254	76.85 257	80.97 268	86.84 257	62.36 296	86.52 224	88.77 237	71.13 223	75.34 279	86.66 284	54.07 280	91.10 289	62.72 303	79.57 298	89.45 281

miper_enhance_ethall		77.87 255	76.86 256	80.92 269	81.65 381	61.38 310	82.68 326	88.98 229	65.52 335	75.47 271	82.30 382	65.76 146	92.00 247	72.95 206	76.39 341	89.39 283

FE-MVS		77.78 256	75.68 277	84.08 152	88.09 200	66.00 198	83.13 320	87.79 265	68.42 299	78.01 213	85.23 321	45.50 377	95.12 91	59.11 340	85.83 208	91.11 208

PEN-MVS		77.73 257	77.69 238	77.84 334	87.07 253	53.91 405	87.91 173	91.18 140	77.56 51	73.14 320	88.82 219	61.23 209	89.17 327	59.95 331	72.37 390	90.43 238

cl____		77.72 258	76.76 260	80.58 276	82.49 371	60.48 323	83.09 321	87.87 262	69.22 279	74.38 306	85.22 322	62.10 190	91.53 271	71.09 227	75.41 360	89.73 275

DIV-MVS_self_test		77.72 258	76.76 260	80.58 276	82.48 372	60.48 323	83.09 321	87.86 263	69.22 279	74.38 306	85.24 320	62.10 190	91.53 271	71.09 227	75.40 361	89.74 274

sd_testset		77.70 260	77.40 245	78.60 316	89.03 160	60.02 329	79.00 378	85.83 307	75.19 124	76.61 248	89.98 179	54.81 269	85.46 376	62.63 307	83.55 247	90.33 243

PAPM		77.68 261	76.40 270	81.51 249	87.29 241	61.85 304	83.78 302	89.59 196	64.74 344	71.23 344	88.70 221	62.59 180	93.66 164	52.66 388	87.03 182	89.01 295

SSM_04072		77.67 262	77.52 242	78.12 328	88.81 166	67.96 148	65.03 457	88.66 243	70.96 231	79.48 180	89.80 185	58.69 236	74.23 449	70.35 236	85.93 204	92.18 174

CHOSEN 1792x2688		77.63 263	75.69 276	83.44 182	89.98 121	68.58 128	78.70 383	87.50 272	56.38 425	75.80 266	86.84 274	58.67 238	91.40 278	61.58 319	85.75 209	90.34 242

HyFIR lowres test		77.53 264	75.40 284	83.94 168	89.59 129	66.62 187	80.36 358	88.64 246	56.29 426	76.45 251	85.17 323	57.64 247	93.28 182	61.34 322	83.10 257	91.91 183

FMVSNet1		77.44 265	76.12 273	81.40 253	86.81 258	63.01 282	88.39 152	89.28 211	70.49 247	74.39 305	87.28 262	49.06 345	91.11 286	60.91 324	78.52 309	90.09 255

TR-MVS		77.44 265	76.18 272	81.20 260	88.24 191	63.24 277	84.61 281	86.40 297	67.55 307	77.81 218	86.48 292	54.10 279	93.15 196	57.75 355	82.72 262	87.20 345

1112_ss		77.40 267	76.43 268	80.32 282	89.11 159	60.41 325	83.65 306	87.72 268	62.13 379	73.05 321	86.72 278	62.58 181	89.97 311	62.11 314	80.80 284	90.59 232

thisisatest0515		77.33 268	75.38 285	83.18 194	85.27 299	63.80 258	82.11 332	83.27 342	65.06 340	75.91 263	83.84 351	49.54 336	94.27 130	67.24 269	86.19 197	91.48 199

test2506		77.30 269	76.49 266	79.74 294	90.08 115	52.02 416	87.86 176	63.10 459	74.88 134	80.16 172	92.79 98	38.29 422	92.35 234	68.74 256	92.50 83	94.86 19

pm-mvs1		77.25 270	76.68 264	78.93 310	84.22 323	58.62 341	86.41 227	88.36 250	71.37 217	73.31 317	88.01 245	61.22 210	89.15 328	64.24 294	73.01 387	89.03 294

IMVS_0404		77.16 271	76.42 269	79.37 302	87.13 245	63.59 265	77.12 402	89.33 205	70.51 243	66.22 403	89.03 210	50.36 326	82.78 398	72.56 213	85.56 211	91.74 187

LCM-MVSNet-Re		77.05 272	76.94 255	77.36 343	87.20 242	51.60 423	80.06 363	80.46 382	75.20 123	67.69 380	86.72 278	62.48 182	88.98 331	63.44 298	89.25 139	91.51 196

DTE-MVSNet		76.99 273	76.80 258	77.54 342	86.24 272	53.06 414	87.52 183	90.66 156	77.08 68	72.50 328	88.67 223	60.48 224	89.52 319	57.33 359	70.74 402	90.05 260

baseline1		76.98 274	76.75 262	77.66 337	88.13 197	55.66 388	85.12 266	81.89 363	73.04 188	76.79 241	88.90 216	62.43 184	87.78 350	63.30 300	71.18 400	89.55 279

LS3D		76.95 275	74.82 293	83.37 186	90.45 106	67.36 171	89.15 118	86.94 285	61.87 382	69.52 364	90.61 165	51.71 311	94.53 121	46.38 426	86.71 188	88.21 323

GA-MVS		76.87 276	75.17 290	81.97 241	82.75 364	62.58 290	81.44 341	86.35 299	72.16 203	74.74 298	82.89 373	46.20 368	92.02 246	68.85 255	81.09 279	91.30 204

mamv4		76.81 277	78.23 221	72.54 396	86.12 277	65.75 208	78.76 382	82.07 362	64.12 352	72.97 322	91.02 153	67.97 114	68.08 461	83.04 87	78.02 318	83.80 406

DP-MVS		76.78 278	74.57 296	83.42 183	93.29 51	69.46 103	88.55 147	83.70 334	63.98 357	70.20 352	88.89 217	54.01 282	94.80 110	46.66 423	81.88 272	86.01 372

cascas		76.72 279	74.64 295	82.99 205	85.78 284	65.88 202	82.33 329	89.21 218	60.85 388	72.74 324	81.02 393	47.28 354	93.75 161	67.48 266	85.02 217	89.34 285

testing91		76.54 280	75.66 279	79.18 307	88.43 185	55.89 384	81.08 344	83.00 350	73.76 164	75.34 279	84.29 341	46.20 368	90.07 309	64.33 292	84.50 225	91.58 194

1314		76.53 281	75.30 288	80.21 285	83.93 330	62.32 298	84.66 278	88.81 235	60.23 393	70.16 355	84.07 348	55.30 267	90.73 301	67.37 267	83.21 255	87.59 336

thres100view900		76.50 282	75.55 281	79.33 303	89.52 132	56.99 366	85.83 248	83.23 343	73.94 159	76.32 255	87.12 270	51.89 307	91.95 249	48.33 414	83.75 241	89.07 288

thres600view7		76.50 282	75.44 282	79.68 296	89.40 140	57.16 363	85.53 257	83.23 343	73.79 163	76.26 256	87.09 271	51.89 307	91.89 252	48.05 419	83.72 244	90.00 261

thres400		76.50 282	75.37 286	79.86 291	89.13 155	57.65 357	85.17 263	83.60 335	73.41 176	76.45 251	86.39 294	52.12 299	91.95 249	48.33 414	83.75 241	90.00 261

MonoMVSNet		76.49 285	75.80 274	78.58 317	81.55 384	58.45 342	86.36 231	86.22 300	74.87 136	74.73 299	83.73 355	51.79 310	88.73 336	70.78 229	72.15 393	88.55 316

tfpn200view9		76.42 286	75.37 286	79.55 301	89.13 155	57.65 357	85.17 263	83.60 335	73.41 176	76.45 251	86.39 294	52.12 299	91.95 249	48.33 414	83.75 241	89.07 288

Test_1112_low_res		76.40 287	75.44 282	79.27 304	89.28 148	58.09 346	81.69 336	87.07 282	59.53 400	72.48 329	86.67 283	61.30 207	89.33 322	60.81 326	80.15 293	90.41 239

F-COLMAP		76.38 288	74.33 302	82.50 229	89.28 148	66.95 185	88.41 151	89.03 226	64.05 355	66.83 392	88.61 225	46.78 360	92.89 209	57.48 356	78.55 308	87.67 332

LTVRE_ROB	69.57 13	76.25 289	74.54 298	81.41 252	88.60 178	64.38 247	79.24 373	89.12 224	70.76 236	69.79 363	87.86 248	49.09 344	93.20 192	56.21 371	80.16 292	86.65 361
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		76.12 290	74.46 300	81.13 263	85.37 296	69.79 94	84.42 289	87.95 260	65.03 341	67.46 383	85.33 318	53.28 289	91.73 259	58.01 353	83.27 254	81.85 426
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 291	74.27 303	81.62 246	83.20 350	64.67 237	83.60 309	89.75 190	69.75 267	71.85 337	87.09 271	32.78 437	92.11 242	69.99 242	80.43 290	88.09 325

testing99		76.09 292	75.12 291	79.00 308	88.16 194	55.50 390	80.79 348	81.40 370	73.30 180	75.17 287	84.27 344	44.48 383	90.02 310	64.28 293	84.22 234	91.48 199

ACMH+	68.96 14	76.01 293	74.01 304	82.03 239	88.60 178	65.31 218	88.86 128	87.55 270	70.25 254	67.75 379	87.47 260	41.27 405	93.19 194	58.37 349	75.94 349	87.60 334

ACMH	67.68 16	75.89 294	73.93 306	81.77 244	88.71 175	66.61 188	88.62 143	89.01 228	69.81 263	66.78 393	86.70 282	41.95 402	91.51 273	55.64 372	78.14 317	87.17 346
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 295	73.36 315	83.31 187	84.76 312	66.03 195	83.38 314	85.06 316	70.21 255	69.40 365	81.05 392	45.76 373	94.66 117	65.10 287	75.49 355	89.25 287
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		75.70 296	73.83 309	81.30 256	83.26 347	61.79 306	82.57 328	80.65 377	66.81 313	66.88 391	83.42 363	57.86 245	92.19 240	63.47 297	79.57 298	89.91 266

WTY-MVS		75.65 297	75.68 277	75.57 359	86.40 270	56.82 368	77.92 396	82.40 358	65.10 339	76.18 259	87.72 250	63.13 174	80.90 411	60.31 329	81.96 270	89.00 297

thres200		75.55 298	74.47 299	78.82 312	87.78 217	57.85 353	83.07 323	83.51 338	72.44 197	75.84 265	84.42 336	52.08 302	91.75 257	47.41 421	83.64 246	86.86 356

test_vis1_n_1920		75.52 299	75.78 275	74.75 373	79.84 407	57.44 361	83.26 317	85.52 310	62.83 370	79.34 185	86.17 299	45.10 379	79.71 415	78.75 134	81.21 278	87.10 352

EPNet_dtu		75.46 300	74.86 292	77.23 346	82.57 369	54.60 399	86.89 208	83.09 347	71.64 209	66.25 402	85.86 304	55.99 262	88.04 346	54.92 376	86.55 190	89.05 293
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 301	73.87 308	80.11 287	82.69 366	64.85 234	81.57 338	83.47 339	69.16 282	70.49 349	84.15 347	51.95 305	88.15 344	69.23 249	72.14 394	87.34 341

XXY-MVS		75.41 302	75.56 280	74.96 368	83.59 340	57.82 354	80.59 354	83.87 333	66.54 323	74.93 296	88.31 234	63.24 168	80.09 414	62.16 312	76.85 333	86.97 354

reproduce_monomvs		75.40 303	74.38 301	78.46 323	83.92 331	57.80 355	83.78 302	86.94 285	73.47 174	72.25 333	84.47 335	38.74 418	89.27 324	75.32 182	70.53 403	88.31 320

TransMVSNet (Re)		75.39 304	74.56 297	77.86 333	85.50 293	57.10 365	86.78 214	86.09 304	72.17 202	71.53 341	87.34 261	63.01 175	89.31 323	56.84 365	61.83 432	87.17 346

CostFormer		75.24 305	73.90 307	79.27 304	82.65 368	58.27 345	80.80 347	82.73 356	61.57 383	75.33 283	83.13 368	55.52 265	91.07 292	64.98 288	78.34 316	88.45 317

testing11		75.14 306	74.01 304	78.53 320	88.16 194	56.38 377	80.74 351	80.42 384	70.67 237	72.69 327	83.72 356	43.61 390	89.86 312	62.29 310	83.76 240	89.36 284

testing3-2		75.12 307	75.19 289	74.91 369	90.40 108	45.09 452	80.29 360	78.42 404	78.37 40	76.54 250	87.75 249	44.36 384	87.28 356	57.04 362	83.49 249	92.37 163

D2MVS		74.82 308	73.21 316	79.64 298	79.81 408	62.56 292	80.34 359	87.35 275	64.37 349	68.86 370	82.66 377	46.37 364	90.10 308	67.91 262	81.24 277	86.25 365

pmmvs6		74.69 309	73.39 313	78.61 315	81.38 388	57.48 360	86.64 220	87.95 260	64.99 343	70.18 353	86.61 285	50.43 325	89.52 319	62.12 313	70.18 405	88.83 304

SD_0403		74.65 310	74.77 294	74.29 377	86.20 274	47.42 441	83.71 304	85.12 314	69.30 275	68.50 375	87.95 247	59.40 232	86.05 367	49.38 408	83.35 252	89.40 282

tfpnnormal		74.39 311	73.16 317	78.08 329	86.10 279	58.05 347	84.65 280	87.53 271	70.32 251	71.22 345	85.63 310	54.97 268	89.86 312	43.03 438	75.02 368	86.32 364

IterMVS		74.29 312	72.94 320	78.35 324	81.53 385	63.49 271	81.58 337	82.49 357	68.06 303	69.99 358	83.69 357	51.66 312	85.54 374	65.85 281	71.64 397	86.01 372
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 313	72.42 326	79.80 293	83.76 335	59.59 334	85.92 244	86.64 292	66.39 324	66.96 390	87.58 254	39.46 413	91.60 262	65.76 282	69.27 408	88.22 322

SCA		74.22 314	72.33 327	79.91 290	84.05 328	62.17 300	79.96 366	79.29 398	66.30 325	72.38 331	80.13 405	51.95 305	88.60 339	59.25 338	77.67 324	88.96 299

mmtdpeth		74.16 315	73.01 319	77.60 341	83.72 336	61.13 311	85.10 267	85.10 315	72.06 204	77.21 235	80.33 402	43.84 388	85.75 370	77.14 155	52.61 451	85.91 375

miper_lstm_enhance		74.11 316	73.11 318	77.13 347	80.11 403	59.62 333	72.23 428	86.92 287	66.76 315	70.40 350	82.92 372	56.93 256	82.92 397	69.06 252	72.63 389	88.87 302

testing222		74.04 317	72.66 323	78.19 326	87.89 209	55.36 391	81.06 345	79.20 399	71.30 220	74.65 301	83.57 361	39.11 417	88.67 338	51.43 396	85.75 209	90.53 234

EG-PatchMatch MVS		74.04 317	71.82 331	80.71 273	84.92 308	67.42 167	85.86 246	88.08 254	66.04 328	64.22 415	83.85 350	35.10 433	92.56 222	57.44 357	80.83 283	82.16 424

pmmvs4		74.03 319	71.91 330	80.39 279	81.96 377	68.32 134	81.45 340	82.14 360	59.32 401	69.87 361	85.13 324	52.40 295	88.13 345	60.21 330	74.74 371	84.73 395

MS-PatchMatch		73.83 320	72.67 322	77.30 345	83.87 332	66.02 196	81.82 333	84.66 320	61.37 386	68.61 373	82.82 375	47.29 353	88.21 343	59.27 337	84.32 232	77.68 441

test_cas_vis1_n_1920		73.76 321	73.74 310	73.81 383	75.90 429	59.77 331	80.51 355	82.40 358	58.30 411	81.62 146	85.69 307	44.35 385	76.41 433	76.29 166	78.61 307	85.23 385

myMVS_eth3d28		73.62 322	73.53 312	73.90 382	88.20 192	47.41 442	78.06 393	79.37 396	74.29 151	73.98 309	84.29 341	44.67 380	83.54 392	51.47 394	87.39 174	90.74 225

sss		73.60 323	73.64 311	73.51 385	82.80 363	55.01 396	76.12 406	81.69 366	62.47 375	74.68 300	85.85 305	57.32 251	78.11 422	60.86 325	80.93 280	87.39 339

RPMNet		73.51 324	70.49 347	82.58 228	81.32 391	65.19 220	75.92 408	92.27 88	57.60 418	72.73 325	76.45 433	52.30 296	95.43 76	48.14 418	77.71 321	87.11 350

WBMVS		73.43 325	72.81 321	75.28 365	87.91 208	50.99 429	78.59 386	81.31 372	65.51 337	74.47 304	84.83 330	46.39 362	86.68 360	58.41 348	77.86 319	88.17 324

SixPastTwentyTwo		73.37 326	71.26 340	79.70 295	85.08 305	57.89 352	85.57 251	83.56 337	71.03 229	65.66 405	85.88 303	42.10 400	92.57 221	59.11 340	63.34 427	88.65 312

CR-MVSNet		73.37 326	71.27 339	79.67 297	81.32 391	65.19 220	75.92 408	80.30 386	59.92 396	72.73 325	81.19 390	52.50 293	86.69 359	59.84 332	77.71 321	87.11 350

MSDG		73.36 328	70.99 342	80.49 278	84.51 319	65.80 205	80.71 352	86.13 303	65.70 332	65.46 406	83.74 354	44.60 381	90.91 295	51.13 397	76.89 331	84.74 394

SSC-MVS3.2		73.35 329	73.39 313	73.23 386	85.30 298	49.01 437	74.58 421	81.57 367	75.21 122	73.68 313	85.58 312	52.53 291	82.05 403	54.33 380	77.69 323	88.63 313

tpm2		73.26 330	71.46 335	78.63 314	83.34 345	56.71 371	80.65 353	80.40 385	56.63 424	73.55 315	82.02 387	51.80 309	91.24 283	56.35 370	78.42 314	87.95 326

RPSCF		73.23 331	71.46 335	78.54 319	82.50 370	59.85 330	82.18 331	82.84 355	58.96 405	71.15 346	89.41 204	45.48 378	84.77 383	58.82 344	71.83 396	91.02 214

PatchmatchNet		73.12 332	71.33 338	78.49 322	83.18 351	60.85 317	79.63 368	78.57 403	64.13 351	71.73 338	79.81 410	51.20 316	85.97 369	57.40 358	76.36 346	88.66 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UBG		73.08 333	72.27 328	75.51 361	88.02 203	51.29 427	78.35 390	77.38 413	65.52 335	73.87 311	82.36 380	45.55 375	86.48 363	55.02 375	84.39 231	88.75 308

COLMAP_ROB	66.92 17	73.01 334	70.41 349	80.81 271	87.13 245	65.63 209	88.30 158	84.19 329	62.96 367	63.80 420	87.69 252	38.04 423	92.56 222	46.66 423	74.91 369	84.24 399
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 335	72.58 324	74.25 378	84.28 321	50.85 430	86.41 227	83.45 340	44.56 450	73.23 319	87.54 258	49.38 339	85.70 371	65.90 280	78.44 311	86.19 367

test-LLR		72.94 336	72.43 325	74.48 374	81.35 389	58.04 348	78.38 387	77.46 410	66.66 317	69.95 359	79.00 417	48.06 350	79.24 416	66.13 276	84.83 220	86.15 368

test_0402		72.79 337	70.44 348	79.84 292	88.13 197	65.99 199	85.93 243	84.29 326	65.57 334	67.40 386	85.49 314	46.92 357	92.61 218	35.88 452	74.38 374	80.94 431

tpmrst		72.39 338	72.13 329	73.18 390	80.54 398	49.91 434	79.91 367	79.08 400	63.11 364	71.69 339	79.95 407	55.32 266	82.77 399	65.66 283	73.89 378	86.87 355

PatchMatch-RL		72.38 339	70.90 343	76.80 350	88.60 178	67.38 170	79.53 369	76.17 422	62.75 372	69.36 366	82.00 388	45.51 376	84.89 382	53.62 383	80.58 287	78.12 440

CL-MVSNet_self_test		72.37 340	71.46 335	75.09 367	79.49 414	53.53 407	80.76 350	85.01 318	69.12 283	70.51 348	82.05 386	57.92 244	84.13 387	52.27 390	66.00 421	87.60 334

tpm		72.37 340	71.71 332	74.35 376	82.19 375	52.00 417	79.22 374	77.29 414	64.56 346	72.95 323	83.68 358	51.35 313	83.26 396	58.33 350	75.80 350	87.81 330

ETVMVS		72.25 342	71.05 341	75.84 355	87.77 218	51.91 419	79.39 371	74.98 425	69.26 277	73.71 312	82.95 371	40.82 409	86.14 366	46.17 427	84.43 230	89.47 280

sc_t1		72.19 343	69.51 354	80.23 284	84.81 310	61.09 313	84.68 277	80.22 388	60.70 389	71.27 343	83.58 360	36.59 428	89.24 325	60.41 327	63.31 428	90.37 241

UWE-MVS		72.13 344	71.49 334	74.03 380	86.66 264	47.70 439	81.40 342	76.89 418	63.60 361	75.59 268	84.22 345	39.94 412	85.62 373	48.98 411	86.13 199	88.77 307

PVSNet	64.34 18	72.08 345	70.87 344	75.69 357	86.21 273	56.44 375	74.37 422	80.73 376	62.06 380	70.17 354	82.23 384	42.86 394	83.31 395	54.77 377	84.45 229	87.32 342

WB-MVSnew		71.96 346	71.65 333	72.89 392	84.67 317	51.88 420	82.29 330	77.57 409	62.31 376	73.67 314	83.00 370	53.49 287	81.10 410	45.75 430	82.13 268	85.70 378

pmmvs5		71.55 347	70.20 352	75.61 358	77.83 422	56.39 376	81.74 335	80.89 373	57.76 416	67.46 383	84.49 334	49.26 342	85.32 378	57.08 361	75.29 364	85.11 389

test-mter		71.41 348	70.39 350	74.48 374	81.35 389	58.04 348	78.38 387	77.46 410	60.32 392	69.95 359	79.00 417	36.08 431	79.24 416	66.13 276	84.83 220	86.15 368

K. test v3		71.19 349	68.51 361	79.21 306	83.04 356	57.78 356	84.35 291	76.91 417	72.90 191	62.99 423	82.86 374	39.27 414	91.09 291	61.65 318	52.66 450	88.75 308

dmvs_re		71.14 350	70.58 345	72.80 393	81.96 377	59.68 332	75.60 412	79.34 397	68.55 295	69.27 368	80.72 398	49.42 338	76.54 430	52.56 389	77.79 320	82.19 423

tpmvs		71.09 351	69.29 356	76.49 351	82.04 376	56.04 382	78.92 380	81.37 371	64.05 355	67.18 388	78.28 423	49.74 335	89.77 314	49.67 407	72.37 390	83.67 407

AllTest		70.96 352	68.09 367	79.58 299	85.15 302	63.62 261	84.58 282	79.83 391	62.31 376	60.32 433	86.73 276	32.02 438	88.96 333	50.28 402	71.57 398	86.15 368

test_fmvs1		70.93 353	70.52 346	72.16 398	73.71 441	55.05 395	80.82 346	78.77 402	51.21 442	78.58 197	84.41 337	31.20 442	76.94 428	75.88 174	80.12 295	84.47 397

test_fmvs1_n		70.86 354	70.24 351	72.73 394	72.51 452	55.28 393	81.27 343	79.71 393	51.49 441	78.73 192	84.87 329	27.54 447	77.02 427	76.06 170	79.97 296	85.88 376

Patchmtry		70.74 355	69.16 358	75.49 362	80.72 395	54.07 404	74.94 419	80.30 386	58.34 410	70.01 356	81.19 390	52.50 293	86.54 361	53.37 385	71.09 401	85.87 377

MIMVSNet		70.69 356	69.30 355	74.88 370	84.52 318	56.35 379	75.87 410	79.42 395	64.59 345	67.76 378	82.41 379	41.10 406	81.54 406	46.64 425	81.34 275	86.75 359

tpm cat1		70.57 357	68.31 363	77.35 344	82.41 373	57.95 351	78.08 392	80.22 388	52.04 437	68.54 374	77.66 428	52.00 304	87.84 349	51.77 391	72.07 395	86.25 365

OpenMVS_ROB	64.09 19	70.56 358	68.19 364	77.65 338	80.26 400	59.41 337	85.01 270	82.96 352	58.76 408	65.43 407	82.33 381	37.63 425	91.23 284	45.34 433	76.03 348	82.32 421

pmmvs-eth3d		70.50 359	67.83 373	78.52 321	77.37 425	66.18 194	81.82 333	81.51 368	58.90 406	63.90 419	80.42 400	42.69 395	86.28 365	58.56 346	65.30 423	83.11 413

tt0320		70.49 360	68.03 368	77.89 332	84.78 311	59.12 338	83.55 310	80.44 383	58.13 413	67.43 385	80.41 401	39.26 415	87.54 353	55.12 374	63.18 429	86.99 353

USDC		70.33 361	68.37 362	76.21 353	80.60 397	56.23 380	79.19 375	86.49 295	60.89 387	61.29 428	85.47 315	31.78 440	89.47 321	53.37 385	76.21 347	82.94 417

Patchmatch-RL test		70.24 362	67.78 375	77.61 339	77.43 424	59.57 335	71.16 432	70.33 439	62.94 368	68.65 372	72.77 445	50.62 322	85.49 375	69.58 247	66.58 418	87.77 331

CMPMVS	51.72 21	70.19 363	68.16 365	76.28 352	73.15 448	57.55 359	79.47 370	83.92 331	48.02 446	56.48 446	84.81 331	43.13 392	86.42 364	62.67 306	81.81 273	84.89 392
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tt0320-xc		70.11 364	67.45 381	78.07 330	85.33 297	59.51 336	83.28 316	78.96 401	58.77 407	67.10 389	80.28 403	36.73 427	87.42 354	56.83 366	59.77 439	87.29 343

ppachtmachnet_test		70.04 365	67.34 383	78.14 327	79.80 409	61.13 311	79.19 375	80.59 378	59.16 403	65.27 408	79.29 414	46.75 361	87.29 355	49.33 409	66.72 416	86.00 374

gg-mvs-nofinetune		69.95 366	67.96 369	75.94 354	83.07 354	54.51 401	77.23 401	70.29 440	63.11 364	70.32 351	62.33 454	43.62 389	88.69 337	53.88 382	87.76 168	84.62 396

TESTMET0.1,1		69.89 367	69.00 359	72.55 395	79.27 417	56.85 367	78.38 387	74.71 429	57.64 417	68.09 377	77.19 430	37.75 424	76.70 429	63.92 295	84.09 235	84.10 402

test_vis1_n		69.85 368	69.21 357	71.77 400	72.66 451	55.27 394	81.48 339	76.21 421	52.03 438	75.30 284	83.20 367	28.97 445	76.22 435	74.60 188	78.41 315	83.81 405

FMVSNet5		69.50 369	67.96 369	74.15 379	82.97 360	55.35 392	80.01 365	82.12 361	62.56 374	63.02 421	81.53 389	36.92 426	81.92 404	48.42 413	74.06 376	85.17 388

mvs5depth		69.45 370	67.45 381	75.46 363	73.93 439	55.83 385	79.19 375	83.23 343	66.89 312	71.63 340	83.32 364	33.69 436	85.09 379	59.81 333	55.34 447	85.46 381

PMMVS		69.34 371	68.67 360	71.35 405	75.67 432	62.03 301	75.17 414	73.46 432	50.00 443	68.68 371	79.05 415	52.07 303	78.13 421	61.16 323	82.77 260	73.90 447

our_test_3		69.14 372	67.00 385	75.57 359	79.80 409	58.80 339	77.96 394	77.81 407	59.55 399	62.90 424	78.25 424	47.43 352	83.97 388	51.71 392	67.58 415	83.93 404

EPMVS		69.02 373	68.16 365	71.59 401	79.61 412	49.80 436	77.40 399	66.93 450	62.82 371	70.01 356	79.05 415	45.79 372	77.86 424	56.58 368	75.26 365	87.13 349

KD-MVS_self_test		68.81 374	67.59 379	72.46 397	74.29 438	45.45 447	77.93 395	87.00 283	63.12 363	63.99 418	78.99 419	42.32 397	84.77 383	56.55 369	64.09 426	87.16 348

Anonymous20240521		68.80 375	67.22 384	73.55 384	74.33 437	54.11 403	83.18 318	85.61 309	58.15 412	61.68 427	80.94 395	30.71 443	81.27 409	57.00 363	73.34 386	85.28 384

Anonymous20231206		68.60 376	67.80 374	71.02 408	80.23 402	50.75 431	78.30 391	80.47 381	56.79 423	66.11 404	82.63 378	46.35 365	78.95 418	43.62 436	75.70 351	83.36 410

MIMVSNet1		68.58 377	66.78 387	73.98 381	80.07 404	51.82 421	80.77 349	84.37 323	64.40 348	59.75 436	82.16 385	36.47 429	83.63 391	42.73 439	70.33 404	86.48 363

testing3		68.56 378	67.67 377	71.22 407	87.33 237	42.87 457	83.06 324	71.54 437	70.36 248	69.08 369	84.38 338	30.33 444	85.69 372	37.50 450	75.45 359	85.09 390

EU-MVSNet		68.53 379	67.61 378	71.31 406	78.51 421	47.01 444	84.47 284	84.27 327	42.27 453	66.44 401	84.79 332	40.44 410	83.76 389	58.76 345	68.54 413	83.17 411

PatchT		68.46 380	67.85 371	70.29 411	80.70 396	43.93 455	72.47 427	74.88 426	60.15 394	70.55 347	76.57 432	49.94 332	81.59 405	50.58 398	74.83 370	85.34 383

test_fmvs2		68.35 381	67.48 380	70.98 409	69.50 455	51.95 418	80.05 364	76.38 420	49.33 444	74.65 301	84.38 338	23.30 456	75.40 444	74.51 189	75.17 367	85.60 379

Syy-MVS		68.05 382	67.85 371	68.67 420	84.68 314	40.97 463	78.62 384	73.08 434	66.65 320	66.74 394	79.46 412	52.11 301	82.30 401	32.89 455	76.38 344	82.75 418

test0.0.03 1		68.00 383	67.69 376	68.90 417	77.55 423	47.43 440	75.70 411	72.95 436	66.66 317	66.56 396	82.29 383	48.06 350	75.87 439	44.97 434	74.51 373	83.41 409

TDRefinement		67.49 384	64.34 396	76.92 348	73.47 445	61.07 314	84.86 274	82.98 351	59.77 397	58.30 440	85.13 324	26.06 448	87.89 348	47.92 420	60.59 437	81.81 427

test20.03		67.45 385	66.95 386	68.94 416	75.48 434	44.84 453	77.50 398	77.67 408	66.66 317	63.01 422	83.80 352	47.02 356	78.40 420	42.53 441	68.86 412	83.58 408

UnsupCasMVSNet_eth		67.33 386	65.99 390	71.37 403	73.48 444	51.47 425	75.16 415	85.19 313	65.20 338	60.78 430	80.93 397	42.35 396	77.20 426	57.12 360	53.69 449	85.44 382

TinyColmap		67.30 387	64.81 394	74.76 372	81.92 379	56.68 372	80.29 360	81.49 369	60.33 391	56.27 447	83.22 365	24.77 452	87.66 352	45.52 431	69.47 407	79.95 436

FE-MVSNET		67.25 388	65.33 392	73.02 391	75.86 430	52.54 415	80.26 362	80.56 379	63.80 360	60.39 431	79.70 411	41.41 404	84.66 385	43.34 437	62.62 430	81.86 425

myMVS_eth3d		67.02 389	66.29 389	69.21 415	84.68 314	42.58 458	78.62 384	73.08 434	66.65 320	66.74 394	79.46 412	31.53 441	82.30 401	39.43 447	76.38 344	82.75 418

dp		66.80 390	65.43 391	70.90 410	79.74 411	48.82 438	75.12 417	74.77 427	59.61 398	64.08 417	77.23 429	42.89 393	80.72 412	48.86 412	66.58 418	83.16 412

MDA-MVSNet-bldmvs		66.68 391	63.66 401	75.75 356	79.28 416	60.56 322	73.92 424	78.35 405	64.43 347	50.13 455	79.87 409	44.02 387	83.67 390	46.10 428	56.86 441	83.03 415

testgi		66.67 392	66.53 388	67.08 427	75.62 433	41.69 462	75.93 407	76.50 419	66.11 326	65.20 411	86.59 286	35.72 432	74.71 446	43.71 435	73.38 385	84.84 393

CHOSEN 280x420		66.51 393	64.71 395	71.90 399	81.45 386	63.52 270	57.98 464	68.95 446	53.57 433	62.59 425	76.70 431	46.22 367	75.29 445	55.25 373	79.68 297	76.88 443

PM-MVS		66.41 394	64.14 397	73.20 389	73.92 440	56.45 374	78.97 379	64.96 456	63.88 359	64.72 412	80.24 404	19.84 460	83.44 394	66.24 275	64.52 425	79.71 437

JIA-IIPM		66.32 395	62.82 407	76.82 349	77.09 426	61.72 307	65.34 455	75.38 423	58.04 415	64.51 413	62.32 455	42.05 401	86.51 362	51.45 395	69.22 409	82.21 422

KD-MVS_2432*1600		66.22 396	63.89 399	73.21 387	75.47 435	53.42 409	70.76 435	84.35 324	64.10 353	66.52 398	78.52 421	34.55 434	84.98 380	50.40 400	50.33 454	81.23 429

miper_refine_blended		66.22 396	63.89 399	73.21 387	75.47 435	53.42 409	70.76 435	84.35 324	64.10 353	66.52 398	78.52 421	34.55 434	84.98 380	50.40 400	50.33 454	81.23 429

ADS-MVSNet2		66.20 398	63.33 402	74.82 371	79.92 405	58.75 340	67.55 447	75.19 424	53.37 434	65.25 409	75.86 436	42.32 397	80.53 413	41.57 442	68.91 410	85.18 386

UWE-MVS-28		65.32 399	64.93 393	66.49 428	78.70 419	38.55 465	77.86 397	64.39 457	62.00 381	64.13 416	83.60 359	41.44 403	76.00 437	31.39 457	80.89 281	84.92 391

YYNet1		65.03 400	62.91 405	71.38 402	75.85 431	56.60 373	69.12 443	74.66 430	57.28 421	54.12 449	77.87 426	45.85 371	74.48 447	49.95 405	61.52 434	83.05 414

MDA-MVSNet_test_wron		65.03 400	62.92 404	71.37 403	75.93 428	56.73 369	69.09 444	74.73 428	57.28 421	54.03 450	77.89 425	45.88 370	74.39 448	49.89 406	61.55 433	82.99 416

Patchmatch-test		64.82 402	63.24 403	69.57 413	79.42 415	49.82 435	63.49 461	69.05 445	51.98 439	59.95 435	80.13 405	50.91 318	70.98 454	40.66 444	73.57 381	87.90 328

ADS-MVSNet		64.36 403	62.88 406	68.78 419	79.92 405	47.17 443	67.55 447	71.18 438	53.37 434	65.25 409	75.86 436	42.32 397	73.99 450	41.57 442	68.91 410	85.18 386

LF4IMVS		64.02 404	62.19 408	69.50 414	70.90 453	53.29 412	76.13 405	77.18 415	52.65 436	58.59 438	80.98 394	23.55 455	76.52 431	53.06 387	66.66 417	78.68 439

UnsupCasMVSNet_bld		63.70 405	61.53 411	70.21 412	73.69 442	51.39 426	72.82 426	81.89 363	55.63 428	57.81 442	71.80 447	38.67 419	78.61 419	49.26 410	52.21 452	80.63 433

test_fmvs3		63.36 406	61.82 409	67.98 424	62.51 464	46.96 445	77.37 400	74.03 431	45.24 449	67.50 382	78.79 420	12.16 468	72.98 453	72.77 209	66.02 420	83.99 403

dmvs_testset		62.63 407	64.11 398	58.19 438	78.55 420	24.76 476	75.28 413	65.94 453	67.91 304	60.34 432	76.01 435	53.56 285	73.94 451	31.79 456	67.65 414	75.88 445

mvsany_test1		62.30 408	61.26 412	65.41 430	69.52 454	54.86 397	66.86 449	49.78 470	46.65 447	68.50 375	83.21 366	49.15 343	66.28 462	56.93 364	60.77 435	75.11 446

new-patchmatchnet		61.73 409	61.73 410	61.70 434	72.74 450	24.50 477	69.16 442	78.03 406	61.40 384	56.72 445	75.53 439	38.42 420	76.48 432	45.95 429	57.67 440	84.13 401

PVSNet_0	57.27 20	61.67 410	59.27 413	68.85 418	79.61 412	57.44 361	68.01 445	73.44 433	55.93 427	58.54 439	70.41 450	44.58 382	77.55 425	47.01 422	35.91 462	71.55 450

test_vis1_rt		60.28 411	58.42 414	65.84 429	67.25 458	55.60 389	70.44 437	60.94 462	44.33 451	59.00 437	66.64 452	24.91 451	68.67 459	62.80 302	69.48 406	73.25 448

ttmdpeth		59.91 412	57.10 416	68.34 422	67.13 459	46.65 446	74.64 420	67.41 449	48.30 445	62.52 426	85.04 328	20.40 458	75.93 438	42.55 440	45.90 460	82.44 420

MVS-HIRNet		59.14 413	57.67 415	63.57 432	81.65 381	43.50 456	71.73 429	65.06 455	39.59 457	51.43 452	57.73 460	38.34 421	82.58 400	39.53 445	73.95 377	64.62 456

pmmvs3		57.79 414	54.26 419	68.37 421	64.02 463	56.72 370	75.12 417	65.17 454	40.20 455	52.93 451	69.86 451	20.36 459	75.48 442	45.45 432	55.25 448	72.90 449

DSMNet-mixed		57.77 415	56.90 417	60.38 436	67.70 457	35.61 467	69.18 441	53.97 468	32.30 466	57.49 443	79.88 408	40.39 411	68.57 460	38.78 448	72.37 390	76.97 442

MVStest1		56.63 416	52.76 422	68.25 423	61.67 465	53.25 413	71.67 430	68.90 447	38.59 458	50.59 454	83.05 369	25.08 450	70.66 455	36.76 451	38.56 461	80.83 432

WB-MVS		54.94 417	54.72 418	55.60 444	73.50 443	20.90 478	74.27 423	61.19 461	59.16 403	50.61 453	74.15 441	47.19 355	75.78 440	17.31 469	35.07 463	70.12 451

LCM-MVSNet		54.25 418	49.68 428	67.97 425	53.73 473	45.28 450	66.85 450	80.78 375	35.96 462	39.45 463	62.23 456	8.70 472	78.06 423	48.24 417	51.20 453	80.57 434

mvsany_test3		53.99 419	51.45 424	61.61 435	55.51 469	44.74 454	63.52 460	45.41 474	43.69 452	58.11 441	76.45 433	17.99 461	63.76 465	54.77 377	47.59 456	76.34 444

SSC-MVS		53.88 420	53.59 420	54.75 446	72.87 449	19.59 479	73.84 425	60.53 463	57.58 419	49.18 457	73.45 444	46.34 366	75.47 443	16.20 472	32.28 465	69.20 452

FPMVS		53.68 421	51.64 423	59.81 437	65.08 461	51.03 428	69.48 440	69.58 443	41.46 454	40.67 461	72.32 446	16.46 464	70.00 458	24.24 465	65.42 422	58.40 461

APD_test1		53.31 422	49.93 427	63.42 433	65.68 460	50.13 433	71.59 431	66.90 451	34.43 463	40.58 462	71.56 448	8.65 473	76.27 434	34.64 454	55.36 446	63.86 457

N_pmnet		52.79 423	53.26 421	51.40 448	78.99 418	7.68 482	69.52 439	3.89 481	51.63 440	57.01 444	74.98 440	40.83 408	65.96 463	37.78 449	64.67 424	80.56 435

test_f		52.09 424	50.82 425	55.90 442	53.82 472	42.31 461	59.42 463	58.31 466	36.45 461	56.12 448	70.96 449	12.18 467	57.79 468	53.51 384	56.57 443	67.60 453

EGC-MVSNET		52.07 425	47.05 429	67.14 426	83.51 342	60.71 319	80.50 356	67.75 448	0.07 476	0.43 477	75.85 438	24.26 453	81.54 406	28.82 459	62.25 431	59.16 459

new_pmnet		50.91 426	50.29 426	52.78 447	68.58 456	34.94 469	63.71 459	56.63 467	39.73 456	44.95 458	65.47 453	21.93 457	58.48 467	34.98 453	56.62 442	64.92 455

ANet_high		50.57 427	46.10 431	63.99 431	48.67 476	39.13 464	70.99 434	80.85 374	61.39 385	31.18 465	57.70 461	17.02 463	73.65 452	31.22 458	15.89 473	79.18 438

test_vis3_rt		49.26 428	47.02 430	56.00 441	54.30 470	45.27 451	66.76 451	48.08 471	36.83 460	44.38 459	53.20 464	7.17 475	64.07 464	56.77 367	55.66 444	58.65 460

testf1		45.72 429	41.96 433	57.00 439	56.90 467	45.32 448	66.14 452	59.26 464	26.19 467	30.89 466	60.96 458	4.14 476	70.64 456	26.39 463	46.73 458	55.04 462

APD_test2		45.72 429	41.96 433	57.00 439	56.90 467	45.32 448	66.14 452	59.26 464	26.19 467	30.89 466	60.96 458	4.14 476	70.64 456	26.39 463	46.73 458	55.04 462

dongtai		45.42 431	45.38 432	45.55 450	73.36 446	26.85 474	67.72 446	34.19 476	54.15 432	49.65 456	56.41 463	25.43 449	62.94 466	19.45 467	28.09 467	46.86 466

Gipuma		45.18 432	41.86 435	55.16 445	77.03 427	51.52 424	32.50 470	80.52 380	32.46 465	27.12 468	35.02 469	9.52 471	75.50 441	22.31 466	60.21 438	38.45 468
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 433	40.28 437	55.82 443	40.82 478	42.54 460	65.12 456	63.99 458	34.43 463	24.48 469	57.12 462	3.92 478	76.17 436	17.10 470	55.52 445	48.75 464
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 434	38.86 438	46.69 449	53.84 471	16.45 480	48.61 467	49.92 469	37.49 459	31.67 464	60.97 457	8.14 474	56.42 469	28.42 460	30.72 466	67.19 454

kuosan		39.70 435	40.40 436	37.58 453	64.52 462	26.98 472	65.62 454	33.02 477	46.12 448	42.79 460	48.99 466	24.10 454	46.56 474	12.16 475	26.30 468	39.20 467

E-PMN		31.77 436	30.64 439	35.15 454	52.87 474	27.67 471	57.09 465	47.86 472	24.64 469	16.40 474	33.05 470	11.23 469	54.90 470	14.46 473	18.15 471	22.87 470

test_method		31.52 437	29.28 441	38.23 452	27.03 480	6.50 483	20.94 472	62.21 460	4.05 474	22.35 472	52.50 465	13.33 465	47.58 472	27.04 462	34.04 464	60.62 458

EMVS		30.81 438	29.65 440	34.27 455	50.96 475	25.95 475	56.58 466	46.80 473	24.01 470	15.53 475	30.68 471	12.47 466	54.43 471	12.81 474	17.05 472	22.43 471

MVE	26.22 23	30.37 439	25.89 443	43.81 451	44.55 477	35.46 468	28.87 471	39.07 475	18.20 471	18.58 473	40.18 468	2.68 479	47.37 473	17.07 471	23.78 470	48.60 465
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 440	26.61 442	0.00 461	0.00 484	0.00 486	0.00 473	89.26 214	0.00 479	0.00 480	88.61 225	61.62 199	0.00 480	0.00 479	0.00 478	0.00 476

tmp_tt		18.61 441	21.40 444	10.23 458	4.82 481	10.11 481	34.70 469	30.74 479	1.48 475	23.91 471	26.07 472	28.42 446	13.41 477	27.12 461	15.35 474	7.17 472

wuyk23d		16.82 442	15.94 445	19.46 457	58.74 466	31.45 470	39.22 468	3.74 482	6.84 473	6.04 476	2.70 476	1.27 480	24.29 476	10.54 476	14.40 475	2.63 473

ab-mvs-re		7.23 443	9.64 446	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	86.72 278	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

test123		6.12 444	8.11 447	0.14 459	0.06 483	0.09 484	71.05 433	0.03 484	0.04 478	0.25 479	1.30 478	0.05 481	0.03 479	0.21 478	0.01 477	0.29 474

testmvs		6.04 445	8.02 448	0.10 460	0.08 482	0.03 485	69.74 438	0.04 483	0.05 477	0.31 478	1.68 477	0.02 482	0.04 478	0.24 477	0.02 476	0.25 475

pcd_1.5k_mvsjas		5.26 446	7.02 449	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	63.15 171	0.00 480	0.00 479	0.00 478	0.00 476

mmdepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

monomultidepth		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

test_blank		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uanet_test		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

DCPMVS		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

sosnet-low-res		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

sosnet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uncertanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

Regformer		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

uanet		0.00 447	0.00 450	0.00 461	0.00 484	0.00 486	0.00 473	0.00 485	0.00 479	0.00 480	0.00 479	0.00 483	0.00 480	0.00 479	0.00 478	0.00 476

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 118	92.25 9	95.03 20		97.39 11	88.15 38	95.96 19	94.75 29

TestfortrainingZip							93.28 12

WAC-MVS						42.58 458								39.46 446

FOURS1					95.00 10	72.39 41	95.06 1	93.84 19	74.49 144	91.30 17

MSC_two_6792asdad				89.16 1	94.34 30	75.53 2		92.99 53					97.53 2	89.67 15	96.44 9	94.41 50

PC_three_1452									68.21 301	92.02 14	94.00 61	82.09 5	95.98 60	84.58 69	96.68 2	94.95 12

No_MVS				89.16 1	94.34 30	75.53 2		92.99 53					97.53 2	89.67 15	96.44 9	94.41 50

test_one_0601					95.07 7	71.46 59		94.14 9	78.27 41	92.05 13	95.74 6	80.83 12

eth-test2					0.00 484

eth-test					0.00 484

ZD-MVS					94.38 28	72.22 46		92.67 71	70.98 230	87.75 49	94.07 56	74.01 35	96.70 30	84.66 68	94.84 47

RE-MVS-def			85.48 73		93.06 63	70.63 81	91.88 42	92.27 88	73.53 172	85.69 71	94.45 36	63.87 161		82.75 92	91.87 93	92.50 157

IU-MVS					95.30 2	71.25 63		92.95 59	66.81 313	92.39 6				88.94 27	96.63 4	94.85 21

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 59	82.45 3	96.87 23	83.77 80	96.48 8	94.88 16

test_241102_TWO								94.06 14	77.24 60	92.78 4	95.72 8	81.26 9	97.44 7	89.07 24	96.58 6	94.26 61

test_241102_ONE					95.30 2	70.98 70		94.06 14	77.17 63	93.10 1	95.39 16	82.99 1	97.27 14

9.14			88.26 18		92.84 68		91.52 55	94.75 1	73.93 160	88.57 34	94.67 29	75.57 24	95.79 62	86.77 49	95.76 26

save fliter					93.80 43	72.35 44	90.47 73	91.17 141	74.31 149

test_0728_THIRD									78.38 38	92.12 11	95.78 4	81.46 8	97.40 9	89.42 19	96.57 7	94.67 32

test_0728_SECOND				87.71 34	95.34 1	71.43 60	93.49 10	94.23 6					97.49 4	89.08 22	96.41 12	94.21 62

test0726					95.27 5	71.25 63	93.60 7	94.11 10	77.33 57	92.81 3	95.79 3	80.98 10

GSMVS																88.96 299

test_part2					95.06 8	72.65 32				91.80 15

sam_mvs1												51.32 314				88.96 299

sam_mvs												50.01 330

ambc				75.24 366	73.16 447	50.51 432	63.05 462	87.47 273		64.28 414	77.81 427	17.80 462	89.73 316	57.88 354	60.64 436	85.49 380

MTGPA								92.02 103

test_post1							78.90 381				5.43 475	48.81 349	85.44 377	59.25 338

test_post											5.46 474	50.36 326	84.24 386

patchmatchnet-post											74.00 442	51.12 317	88.60 339

GG-mvs-BLEND				75.38 364	81.59 383	55.80 386	79.32 372	69.63 442		67.19 387	73.67 443	43.24 391	88.90 335	50.41 399	84.50 225	81.45 428

MTMP							92.18 38	32.83 478

gm-plane-assit					81.40 387	53.83 406			62.72 373		80.94 395		92.39 231	63.40 299

test9_res														84.90 62	95.70 29	92.87 142

TEST9					93.26 55	72.96 25	88.75 136	91.89 111	68.44 298	85.00 78	93.10 86	74.36 31	95.41 79

test_8					93.13 59	72.57 35	88.68 141	91.84 115	68.69 293	84.87 82	93.10 86	74.43 29	95.16 89

agg_prior2														82.91 89	95.45 32	92.70 147

agg_prior					92.85 67	71.94 52		91.78 119		84.41 93			94.93 100

TestCases				79.58 299	85.15 302	63.62 261		79.83 391	62.31 376	60.32 433	86.73 276	32.02 438	88.96 333	50.28 402	71.57 398	86.15 368

test_prior4						72.60 34	89.01 123

test_prior2							88.85 130		75.41 113	84.91 80	93.54 74	74.28 32		83.31 83	95.86 23

test_prior				86.33 63	92.61 73	69.59 97		92.97 58					95.48 73			93.91 78

旧先验2							86.56 223		58.10 414	87.04 60			88.98 331	74.07 194

新几何2							86.29 235

新几何1				83.42 183	93.13 59	70.71 79		85.48 311	57.43 420	81.80 141	91.98 113	63.28 165	92.27 237	64.60 291	92.99 75	87.27 344

旧先验1					91.96 79	65.79 206		86.37 298			93.08 90	69.31 94			92.74 79	88.74 310

无先验							87.48 184	88.98 229	60.00 395				94.12 139	67.28 268		88.97 298

原ACMM2							86.86 210

原ACMM1				84.35 132	93.01 65	68.79 116		92.44 81	63.96 358	81.09 154	91.57 131	66.06 141	95.45 74	67.19 270	94.82 49	88.81 305

test222					91.50 85	68.26 136	84.16 296	83.20 346	54.63 431	79.74 175	91.63 127	58.97 235			91.42 101	86.77 358

testdata2													91.01 293	62.37 309

segment_acmp												73.08 42

testdata				79.97 289	90.90 97	64.21 249		84.71 319	59.27 402	85.40 73	92.91 92	62.02 192	89.08 329	68.95 253	91.37 103	86.63 362

testdata1							84.14 297		75.71 104

test12				86.80 57	92.63 72	70.70 80		91.79 118		82.71 128		71.67 61	96.16 51		94.50 56	93.54 107

plane_prior7					90.08 115	68.51 130

plane_prior6					89.84 124	68.70 124						60.42 225

plane_prior5								92.44 81					95.38 81	78.71 135	86.32 193	91.33 202

plane_prior4											91.00 154

plane_prior3						68.60 127			78.44 36	78.92 190

plane_prior2							91.25 59		79.12 28

plane_prior1					89.90 123

plane_prior						68.71 122	90.38 77		77.62 47						86.16 198

n2								0.00 485

nn								0.00 485

door-mid								69.98 441

lessismore_v0				78.97 309	81.01 394	57.15 364		65.99 452		61.16 429	82.82 375	39.12 416	91.34 280	59.67 334	46.92 457	88.43 318

LGP-MVS_train				84.50 124	89.23 151	68.76 118		91.94 109	75.37 115	76.64 246	91.51 133	54.29 277	94.91 101	78.44 137	83.78 238	89.83 270

test11								92.23 91

door								69.44 444

HQP5-MVS						66.98 182

HQP-NCC					89.33 143		89.17 114		76.41 85	77.23 231

ACMP_Plane					89.33 143		89.17 114		76.41 85	77.23 231

BP-MVS														77.47 150

HQP4-MVS										77.24 230			95.11 93			91.03 212

HQP3-MVS								92.19 97							85.99 202

HQP2-MVS												60.17 228

NP-MVS					89.62 128	68.32 134					90.24 175

MDTV_nov1_ep13_2view						37.79 466	75.16 415		55.10 429	66.53 397		49.34 340		53.98 381		87.94 327

MDTV_nov1_ep13			69.97 353		83.18 351	53.48 408	77.10 403	80.18 390	60.45 390	69.33 367	80.44 399	48.89 348	86.90 358	51.60 393	78.51 310

ACMMP++_ref															81.95 271

ACMMP++															81.25 276

Test By Simon												64.33 157

ITE_SJBPF				78.22 325	81.77 380	60.57 321		83.30 341	69.25 278	67.54 381	87.20 267	36.33 430	87.28 356	54.34 379	74.62 372	86.80 357

DeepMVS_CX				27.40 456	40.17 479	26.90 473		24.59 480	17.44 472	23.95 470	48.61 467	9.77 470	26.48 475	18.06 468	24.47 469	28.83 469

High-resolution multi-view benchmark