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 22	94.34 27	71.25 55	95.06 1	94.23 3	78.38 31	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 8

SED-MVS		90.08 2	90.85 2	87.77 24	95.30 2	70.98 61	93.57 7	94.06 10	77.24 48	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 12

DVP-MVS		89.60 3	90.35 3	87.33 38	95.27 5	71.25 55	93.49 9	92.73 59	77.33 46	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 77
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 9	94.28 30	73.46 16	92.90 16	94.11 6	80.27 8	91.35 14	94.16 34	78.35 13	96.77 22	89.59 3	94.22 56	94.67 22
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 14	94.80 11	72.69 30	91.59 41	94.10 8	75.90 83	92.29 7	95.66 10	81.67 6	97.38 9	87.44 21	96.34 15	93.95 49
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		89.15 6	89.63 6	87.73 26	94.49 18	71.69 50	93.83 4	93.96 13	75.70 87	91.06 16	96.03 1	76.84 14	97.03 15	89.09 6	95.65 27	94.47 29

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 31	73.73 9	92.40 23	93.63 21	74.77 103	92.29 7	95.97 2	74.28 29	97.24 11	88.58 13	96.91 1	94.87 14
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 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 26	92.85 54	80.26 9	87.78 28	94.27 30	75.89 19	96.81 21	87.45 20	96.44 9	93.05 87

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 8	90.51 59	93.00 43	80.90 5	88.06 26	94.06 38	76.43 16	96.84 19	88.48 15	95.99 18	94.34 35

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 69	72.96 24	93.73 5	93.67 20	80.19 10	88.10 25	94.80 15	73.76 33	97.11 13	87.51 19	95.82 21	94.90 11
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 11	88.74 11	87.64 33	92.78 61	71.95 48	92.40 23	94.74 2	75.71 85	89.16 19	95.10 13	75.65 21	96.19 41	87.07 22	96.01 17	94.79 19

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 48	92.24 68	69.03 94	89.57 83	93.39 30	77.53 43	89.79 18	94.12 35	78.98 12	96.58 33	85.66 25	95.72 24	94.58 25

SD-MVS		88.06 13	88.50 13	86.71 49	92.60 66	72.71 28	91.81 40	93.19 35	77.87 34	90.32 17	94.00 40	74.83 23	93.78 134	87.63 18	94.27 55	93.65 64
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
NCCC		88.06 13	88.01 17	88.24 10	94.41 22	73.62 10	91.22 50	92.83 55	81.50 3	85.79 38	93.47 50	73.02 39	97.00 16	84.90 30	94.94 37	94.10 43

ACMMP_NAP		88.05 15	88.08 16	87.94 17	93.70 41	73.05 21	90.86 54	93.59 23	76.27 77	88.14 24	95.09 14	71.06 53	96.67 27	87.67 17	96.37 14	94.09 44

TSAR-MVS + MP.		88.02 16	88.11 15	87.72 28	93.68 43	72.13 45	91.41 45	92.35 74	74.62 107	88.90 20	93.85 45	75.75 20	96.00 47	87.80 16	94.63 45	95.04 6
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		87.94 17	87.85 18	88.20 11	94.39 24	73.33 18	93.03 14	93.81 17	76.81 61	85.24 43	94.32 29	71.76 46	96.93 17	85.53 27	95.79 22	94.32 36

MP-MVS		87.71 18	87.64 20	87.93 19	94.36 26	73.88 6	92.71 22	92.65 64	77.57 39	83.84 70	94.40 28	72.24 42	96.28 38	85.65 26	95.30 33	93.62 67
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MP-MVS-pluss		87.67 19	87.72 19	87.54 34	93.64 44	72.04 47	89.80 77	93.50 25	75.17 96	86.34 34	95.29 12	70.86 54	96.00 47	88.78 12	96.04 16	94.58 25
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 20	87.47 22	87.94 17	94.58 16	73.54 14	93.04 12	93.24 33	76.78 63	84.91 48	94.44 26	70.78 55	96.61 30	84.53 37	94.89 39	93.66 60

ACMMPR		87.44 21	87.23 25	88.08 13	94.64 13	73.59 11	93.04 12	93.20 34	76.78 63	84.66 55	94.52 19	68.81 76	96.65 28	84.53 37	94.90 38	94.00 48

APD-MVS		87.44 21	87.52 21	87.19 40	94.24 32	72.39 38	91.86 39	92.83 55	73.01 142	88.58 21	94.52 19	73.36 34	96.49 34	84.26 40	95.01 35	92.70 96
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 23	87.26 23	87.89 22	94.12 36	72.97 23	92.39 25	93.43 28	76.89 59	84.68 52	93.99 42	70.67 57	96.82 20	84.18 44	95.01 35	93.90 52

region2R		87.42 23	87.20 26	88.09 12	94.63 14	73.55 12	93.03 14	93.12 37	76.73 66	84.45 59	94.52 19	69.09 72	96.70 25	84.37 39	94.83 42	94.03 47

MCST-MVS		87.37 25	87.25 24	87.73 26	94.53 17	72.46 37	89.82 75	93.82 16	73.07 140	84.86 51	92.89 62	76.22 17	96.33 36	84.89 32	95.13 34	94.40 32

MTAPA		87.23 26	87.00 27	87.90 20	94.18 35	74.25 5	86.58 175	92.02 85	79.45 17	85.88 36	94.80 15	68.07 78	96.21 40	86.69 24	95.34 31	93.23 80

XVS		87.18 27	86.91 31	88.00 15	94.42 20	73.33 18	92.78 18	92.99 45	79.14 19	83.67 73	94.17 33	67.45 84	96.60 31	83.06 51	94.50 48	94.07 45

HPM-MVS		87.11 28	86.98 28	87.50 36	93.88 39	72.16 44	92.19 32	93.33 31	76.07 80	83.81 71	93.95 44	69.77 66	96.01 46	85.15 28	94.66 44	94.32 36
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 28	86.92 30	87.68 32	94.20 34	73.86 7	93.98 3	92.82 58	76.62 68	83.68 72	94.46 23	67.93 79	95.95 50	84.20 43	94.39 51	93.23 80

DeepC-MVS	79.81 2	87.08 30	86.88 32	87.69 31	91.16 80	72.32 42	90.31 66	93.94 14	77.12 53	82.82 84	94.23 32	72.13 44	97.09 14	84.83 33	95.37 30	93.65 64
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 31	86.62 34	87.76 25	93.52 46	72.37 40	91.26 46	93.04 38	76.62 68	84.22 63	93.36 52	71.44 50	96.76 23	80.82 73	95.33 32	94.16 41
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 32	86.67 33	86.91 44	94.11 37	72.11 46	92.37 27	92.56 67	74.50 108	86.84 32	94.65 18	67.31 86	95.77 52	84.80 34	92.85 65	92.84 94

CS-MVS		86.69 33	86.95 29	85.90 61	90.76 90	67.57 130	92.83 17	93.30 32	79.67 15	84.57 58	92.27 74	71.47 49	95.02 84	84.24 42	93.46 61	95.13 5

PGM-MVS		86.68 34	86.27 38	87.90 20	94.22 33	73.38 17	90.22 68	93.04 38	75.53 89	83.86 69	94.42 27	67.87 81	96.64 29	82.70 60	94.57 47	93.66 60

mPP-MVS		86.67 35	86.32 37	87.72 28	94.41 22	73.55 12	92.74 20	92.22 80	76.87 60	82.81 85	94.25 31	66.44 93	96.24 39	82.88 55	94.28 54	93.38 74

CANet		86.45 36	86.10 43	87.51 35	90.09 101	70.94 65	89.70 81	92.59 66	81.78 2	81.32 100	91.43 94	70.34 59	97.23 12	84.26 40	93.36 62	94.37 33

train_agg		86.43 37	86.20 39	87.13 42	93.26 50	72.96 24	88.75 106	91.89 93	68.69 219	85.00 46	93.10 55	74.43 26	95.41 65	84.97 29	95.71 25	93.02 89

PHI-MVS		86.43 37	86.17 41	87.24 39	90.88 87	70.96 63	92.27 31	94.07 9	72.45 145	85.22 44	91.90 80	69.47 68	96.42 35	83.28 50	95.94 19	94.35 34

CSCG		86.41 39	86.19 40	87.07 43	92.91 58	72.48 36	90.81 55	93.56 24	73.95 119	83.16 79	91.07 104	75.94 18	95.19 73	79.94 82	94.38 52	93.55 70

CS-MVS-test		86.29 40	86.48 35	85.71 63	91.02 83	67.21 140	92.36 28	93.78 18	78.97 26	83.51 76	91.20 99	70.65 58	95.15 75	81.96 64	94.89 39	94.77 20

EC-MVSNet		86.01 41	86.38 36	84.91 83	89.31 129	66.27 154	92.32 29	93.63 21	79.37 18	84.17 65	91.88 81	69.04 75	95.43 63	83.93 45	93.77 59	93.01 90

casdiffmvs_mvg		85.99 42	86.09 44	85.70 64	87.65 189	67.22 139	88.69 110	93.04 38	79.64 16	85.33 42	92.54 71	73.30 35	94.50 105	83.49 47	91.14 87	95.37 1
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 43	85.88 45	86.22 55	92.69 63	69.53 87	91.93 36	92.99 45	73.54 131	85.94 35	94.51 22	65.80 103	95.61 55	83.04 53	92.51 69	93.53 72

canonicalmvs		85.91 44	85.87 46	86.04 58	89.84 111	69.44 92	90.45 64	93.00 43	76.70 67	88.01 27	91.23 97	73.28 36	93.91 129	81.50 67	88.80 112	94.77 20

ACMMP		85.89 45	85.39 50	87.38 37	93.59 45	72.63 32	92.74 20	93.18 36	76.78 63	80.73 109	93.82 46	64.33 113	96.29 37	82.67 61	90.69 91	93.23 80
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 46	85.61 48	86.23 54	93.06 55	70.63 71	91.88 37	92.27 76	73.53 132	85.69 39	94.45 24	65.00 111	95.56 56	82.75 56	91.87 77	92.50 104

CDPH-MVS		85.76 47	85.29 55	87.17 41	93.49 47	71.08 59	88.58 114	92.42 72	68.32 225	84.61 56	93.48 48	72.32 41	96.15 43	79.00 86	95.43 29	94.28 38

TSAR-MVS + GP.		85.71 48	85.33 52	86.84 45	91.34 78	72.50 35	89.07 95	87.28 221	76.41 70	85.80 37	90.22 122	74.15 31	95.37 70	81.82 65	91.88 76	92.65 100

dcpmvs_2		85.63 49	86.15 42	84.06 114	91.71 75	64.94 185	86.47 178	91.87 95	73.63 127	86.60 33	93.02 60	76.57 15	91.87 211	83.36 48	92.15 73	95.35 2

alignmvs		85.48 50	85.32 53	85.96 60	89.51 118	69.47 89	89.74 79	92.47 68	76.17 78	87.73 30	91.46 93	70.32 60	93.78 134	81.51 66	88.95 109	94.63 24

3Dnovator+	77.84 4	85.48 50	84.47 63	88.51 6	91.08 81	73.49 15	93.18 11	93.78 18	80.79 6	76.66 183	93.37 51	60.40 175	96.75 24	77.20 105	93.73 60	95.29 4

MSLP-MVS++		85.43 52	85.76 47	84.45 97	91.93 72	70.24 74	90.71 56	92.86 53	77.46 45	84.22 63	92.81 66	67.16 88	92.94 175	80.36 78	94.35 53	90.16 179

DELS-MVS		85.41 53	85.30 54	85.77 62	88.49 158	67.93 122	85.52 207	93.44 27	78.70 27	83.63 75	89.03 150	74.57 24	95.71 54	80.26 80	94.04 57	93.66 60
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 54	84.95 59	86.57 51	93.69 42	70.58 73	92.15 34	91.62 103	73.89 122	82.67 87	94.09 36	62.60 132	95.54 58	80.93 71	92.93 64	93.57 69

test_fmvsm_n_1920		85.29 55	85.34 51	85.13 74	86.12 219	69.93 81	88.65 112	90.78 126	69.97 188	88.27 23	93.98 43	71.39 51	91.54 219	88.49 14	90.45 93	93.91 50

MVS_111021_HR		85.14 56	84.75 60	86.32 53	91.65 76	72.70 29	85.98 190	90.33 139	76.11 79	82.08 90	91.61 88	71.36 52	94.17 118	81.02 70	92.58 68	92.08 119

casdiffmvs		85.11 57	85.14 56	85.01 77	87.20 204	65.77 167	87.75 141	92.83 55	77.84 35	84.36 62	92.38 73	72.15 43	93.93 128	81.27 69	90.48 92	95.33 3
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 58	84.96 58	85.45 66	92.07 70	68.07 120	89.78 78	90.86 125	82.48 1	84.60 57	93.20 54	69.35 69	95.22 72	71.39 161	90.88 90	93.07 86

DPM-MVS		84.93 59	84.29 64	86.84 45	90.20 99	73.04 22	87.12 157	93.04 38	69.80 192	82.85 83	91.22 98	73.06 38	96.02 45	76.72 113	94.63 45	91.46 136

baseline		84.93 59	84.98 57	84.80 87	87.30 202	65.39 176	87.30 153	92.88 52	77.62 37	84.04 68	92.26 75	71.81 45	93.96 122	81.31 68	90.30 95	95.03 7

ETV-MVS		84.90 61	84.67 61	85.59 65	89.39 123	68.66 109	88.74 108	92.64 65	79.97 13	84.10 66	85.71 240	69.32 70	95.38 67	80.82 73	91.37 84	92.72 95

EI-MVSNet-Vis-set		84.19 62	83.81 65	85.31 68	88.18 168	67.85 123	87.66 143	89.73 156	80.05 12	82.95 80	89.59 134	70.74 56	94.82 93	80.66 77	84.72 160	93.28 79

nrg030		83.88 63	83.53 66	84.96 79	86.77 212	69.28 93	90.46 63	92.67 61	74.79 102	82.95 80	91.33 96	72.70 40	93.09 169	80.79 75	79.28 231	92.50 104

EI-MVSNet-UG-set		83.81 64	83.38 68	85.09 75	87.87 178	67.53 131	87.44 149	89.66 157	79.74 14	82.23 89	89.41 143	70.24 61	94.74 96	79.95 81	83.92 170	92.99 91

CPTT-MVS		83.73 65	83.33 69	84.92 82	93.28 49	70.86 67	92.09 35	90.38 135	68.75 218	79.57 120	92.83 64	60.60 171	93.04 173	80.92 72	91.56 82	90.86 155

EPNet		83.72 66	82.92 75	86.14 57	84.22 247	69.48 88	91.05 53	85.27 247	81.30 4	76.83 178	91.65 85	66.09 98	95.56 56	76.00 119	93.85 58	93.38 74
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
patch_mono-2		83.65 67	84.54 62	80.99 208	90.06 106	65.83 163	84.21 235	88.74 192	71.60 160	85.01 45	92.44 72	74.51 25	83.50 313	82.15 63	92.15 73	93.64 66

HQP_MVS		83.64 68	83.14 70	85.14 72	90.08 102	68.71 105	91.25 48	92.44 69	79.12 21	78.92 129	91.00 108	60.42 173	95.38 67	78.71 90	86.32 143	91.33 137

Effi-MVS+		83.62 69	83.08 71	85.24 70	88.38 163	67.45 132	88.89 100	89.15 173	75.50 90	82.27 88	88.28 172	69.61 67	94.45 107	77.81 99	87.84 122	93.84 55

OPM-MVS		83.50 70	82.95 74	85.14 72	88.79 148	70.95 64	89.13 94	91.52 106	77.55 42	80.96 107	91.75 83	60.71 167	94.50 105	79.67 83	86.51 141	89.97 195
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 71	82.80 77	85.43 67	90.25 98	68.74 103	90.30 67	90.13 145	76.33 76	80.87 108	92.89 62	61.00 164	94.20 116	72.45 155	90.97 88	93.35 76
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 72	83.45 67	83.28 140	92.74 62	62.28 235	88.17 127	89.50 160	75.22 94	81.49 99	92.74 70	66.75 89	95.11 78	72.85 149	91.58 81	92.45 107

EPP-MVSNet		83.40 73	83.02 73	84.57 91	90.13 100	64.47 194	92.32 29	90.73 127	74.45 111	79.35 123	91.10 102	69.05 74	95.12 76	72.78 150	87.22 130	94.13 42

3Dnovator	76.31 5	83.38 74	82.31 83	86.59 50	87.94 177	72.94 27	90.64 57	92.14 84	77.21 50	75.47 206	92.83 64	58.56 182	94.72 97	73.24 146	92.71 67	92.13 118

EIA-MVS		83.31 75	82.80 77	84.82 85	89.59 114	65.59 169	88.21 125	92.68 60	74.66 105	78.96 127	86.42 227	69.06 73	95.26 71	75.54 125	90.09 99	93.62 67

h-mvs33		83.15 76	82.19 84	86.02 59	90.56 92	70.85 68	88.15 129	89.16 172	76.02 81	84.67 53	91.39 95	61.54 150	95.50 59	82.71 58	75.48 275	91.72 127

MVS_Test		83.15 76	83.06 72	83.41 137	86.86 208	63.21 221	86.11 188	92.00 87	74.31 112	82.87 82	89.44 142	70.03 62	93.21 158	77.39 104	88.50 118	93.81 56

IS-MVSNet		83.15 76	82.81 76	84.18 107	89.94 109	63.30 219	91.59 41	88.46 198	79.04 23	79.49 121	92.16 76	65.10 108	94.28 110	67.71 196	91.86 79	94.95 8

DP-MVS Recon		83.11 79	82.09 86	86.15 56	94.44 19	70.92 66	88.79 104	92.20 81	70.53 179	79.17 125	91.03 107	64.12 115	96.03 44	68.39 193	90.14 98	91.50 133

PAPM_NR		83.02 80	82.41 80	84.82 85	92.47 67	66.37 152	87.93 136	91.80 98	73.82 123	77.32 167	90.66 113	67.90 80	94.90 89	70.37 170	89.48 106	93.19 83

VDD-MVS		83.01 81	82.36 82	84.96 79	91.02 83	66.40 151	88.91 99	88.11 201	77.57 39	84.39 61	93.29 53	52.19 231	93.91 129	77.05 107	88.70 114	94.57 27

MVSFormer		82.85 82	82.05 87	85.24 70	87.35 197	70.21 75	90.50 60	90.38 135	68.55 221	81.32 100	89.47 137	61.68 147	93.46 151	78.98 87	90.26 96	92.05 120

OMC-MVS		82.69 83	81.97 90	84.85 84	88.75 150	67.42 133	87.98 132	90.87 124	74.92 99	79.72 118	91.65 85	62.19 142	93.96 122	75.26 127	86.42 142	93.16 84

PVSNet_Blended_VisFu		82.62 84	81.83 92	84.96 79	90.80 89	69.76 85	88.74 108	91.70 102	69.39 199	78.96 127	88.46 167	65.47 105	94.87 92	74.42 132	88.57 115	90.24 177

MVS_111021_LR		82.61 85	82.11 85	84.11 108	88.82 145	71.58 51	85.15 210	86.16 238	74.69 104	80.47 111	91.04 105	62.29 139	90.55 244	80.33 79	90.08 100	90.20 178

HQP-MVS		82.61 85	82.02 88	84.37 99	89.33 126	66.98 143	89.17 89	92.19 82	76.41 70	77.23 170	90.23 121	60.17 176	95.11 78	77.47 102	85.99 150	91.03 149

CLD-MVS		82.31 87	81.65 93	84.29 104	88.47 159	67.73 126	85.81 198	92.35 74	75.78 84	78.33 144	86.58 222	64.01 116	94.35 108	76.05 118	87.48 127	90.79 156
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 88	82.41 80	81.62 188	90.82 88	60.93 249	84.47 226	89.78 153	76.36 75	84.07 67	91.88 81	64.71 112	90.26 246	70.68 167	88.89 110	93.66 60

diffmvs		82.10 89	81.88 91	82.76 170	83.00 274	63.78 207	83.68 243	89.76 154	72.94 143	82.02 91	89.85 126	65.96 102	90.79 240	82.38 62	87.30 129	93.71 59
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 90	81.27 96	84.50 94	89.23 133	68.76 101	90.22 68	91.94 91	75.37 92	76.64 184	91.51 90	54.29 214	94.91 86	78.44 92	83.78 171	89.83 200

FIs		82.07 91	82.42 79	81.04 207	88.80 147	58.34 274	88.26 124	93.49 26	76.93 58	78.47 141	91.04 105	69.92 64	92.34 194	69.87 177	84.97 157	92.44 108

PS-MVSNAJss		82.07 91	81.31 95	84.34 102	86.51 215	67.27 137	89.27 87	91.51 107	71.75 154	79.37 122	90.22 122	63.15 126	94.27 111	77.69 100	82.36 193	91.49 134

API-MVS		81.99 93	81.23 97	84.26 105	90.94 85	70.18 80	91.10 51	89.32 164	71.51 162	78.66 135	88.28 172	65.26 106	95.10 81	64.74 223	91.23 86	87.51 258

UniMVSNet_NR-MVSNet		81.88 94	81.54 94	82.92 159	88.46 160	63.46 215	87.13 156	92.37 73	80.19 10	78.38 142	89.14 146	71.66 48	93.05 171	70.05 173	76.46 260	92.25 113

MAR-MVS		81.84 95	80.70 106	85.27 69	91.32 79	71.53 52	89.82 75	90.92 121	69.77 193	78.50 139	86.21 231	62.36 138	94.52 104	65.36 217	92.05 75	89.77 203
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 96	81.23 97	83.57 132	91.89 73	63.43 217	89.84 74	81.85 294	77.04 56	83.21 77	93.10 55	52.26 230	93.43 153	71.98 156	89.95 102	93.85 53

hse-mvs2		81.72 97	80.94 103	84.07 113	88.72 151	67.68 128	85.87 194	87.26 222	76.02 81	84.67 53	88.22 175	61.54 150	93.48 149	82.71 58	73.44 302	91.06 147

GeoE		81.71 98	81.01 102	83.80 127	89.51 118	64.45 195	88.97 97	88.73 193	71.27 165	78.63 136	89.76 128	66.32 95	93.20 161	69.89 176	86.02 149	93.74 58

xiu_mvs_v2_base		81.69 99	81.05 100	83.60 130	89.15 136	68.03 121	84.46 228	90.02 147	70.67 176	81.30 103	86.53 225	63.17 125	94.19 117	75.60 124	88.54 116	88.57 240

PS-MVSNAJ		81.69 99	81.02 101	83.70 129	89.51 118	68.21 118	84.28 234	90.09 146	70.79 173	81.26 104	85.62 245	63.15 126	94.29 109	75.62 123	88.87 111	88.59 239

mvsmamba		81.69 99	80.74 105	84.56 92	87.45 196	66.72 147	91.26 46	85.89 242	74.66 105	78.23 147	90.56 115	54.33 213	94.91 86	80.73 76	83.54 178	92.04 122

PAPR		81.66 102	80.89 104	83.99 121	90.27 97	64.00 202	86.76 171	91.77 101	68.84 217	77.13 176	89.50 135	67.63 82	94.88 91	67.55 198	88.52 117	93.09 85

UniMVSNet (Re)		81.60 103	81.11 99	83.09 150	88.38 163	64.41 196	87.60 144	93.02 42	78.42 30	78.56 138	88.16 176	69.78 65	93.26 157	69.58 180	76.49 259	91.60 128

FC-MVSNet-test		81.52 104	82.02 88	80.03 227	88.42 162	55.97 311	87.95 134	93.42 29	77.10 54	77.38 165	90.98 110	69.96 63	91.79 212	68.46 192	84.50 162	92.33 109

VDDNet		81.52 104	80.67 107	84.05 116	90.44 95	64.13 201	89.73 80	85.91 241	71.11 168	83.18 78	93.48 48	50.54 254	93.49 148	73.40 143	88.25 120	94.54 28

ACMP	74.13 6	81.51 106	80.57 108	84.36 100	89.42 121	68.69 108	89.97 72	91.50 110	74.46 110	75.04 225	90.41 118	53.82 219	94.54 102	77.56 101	82.91 185	89.86 199
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 107	80.29 115	84.70 89	86.63 214	69.90 83	85.95 191	86.77 229	63.24 276	81.07 106	89.47 137	61.08 163	92.15 200	78.33 95	90.07 101	92.05 120
jason: jason.
lupinMVS		81.39 107	80.27 116	84.76 88	87.35 197	70.21 75	85.55 203	86.41 233	62.85 283	81.32 100	88.61 162	61.68 147	92.24 198	78.41 94	90.26 96	91.83 124

test_yl		81.17 109	80.47 111	83.24 143	89.13 137	63.62 208	86.21 185	89.95 150	72.43 148	81.78 96	89.61 132	57.50 192	93.58 142	70.75 165	86.90 134	92.52 102

DCV-MVSNet		81.17 109	80.47 111	83.24 143	89.13 137	63.62 208	86.21 185	89.95 150	72.43 148	81.78 96	89.61 132	57.50 192	93.58 142	70.75 165	86.90 134	92.52 102

DU-MVS		81.12 111	80.52 110	82.90 160	87.80 182	63.46 215	87.02 160	91.87 95	79.01 24	78.38 142	89.07 148	65.02 109	93.05 171	70.05 173	76.46 260	92.20 115

PVSNet_Blended		80.98 112	80.34 113	82.90 160	88.85 142	65.40 174	84.43 230	92.00 87	67.62 229	78.11 151	85.05 259	66.02 100	94.27 111	71.52 158	89.50 105	89.01 223

FA-MVS(test-final)		80.96 113	79.91 120	84.10 109	88.30 166	65.01 183	84.55 225	90.01 148	73.25 137	79.61 119	87.57 189	58.35 184	94.72 97	71.29 162	86.25 145	92.56 101

QAPM		80.88 114	79.50 129	85.03 76	88.01 176	68.97 97	91.59 41	92.00 87	66.63 241	75.15 221	92.16 76	57.70 189	95.45 61	63.52 227	88.76 113	90.66 162

TranMVSNet+NR-MVSNet		80.84 115	80.31 114	82.42 175	87.85 179	62.33 233	87.74 142	91.33 112	80.55 7	77.99 155	89.86 125	65.23 107	92.62 181	67.05 205	75.24 284	92.30 111

UGNet		80.83 116	79.59 127	84.54 93	88.04 174	68.09 119	89.42 84	88.16 200	76.95 57	76.22 193	89.46 139	49.30 268	93.94 125	68.48 191	90.31 94	91.60 128
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Fast-Effi-MVS+		80.81 117	79.92 119	83.47 133	88.85 142	64.51 191	85.53 205	89.39 162	70.79 173	78.49 140	85.06 258	67.54 83	93.58 142	67.03 206	86.58 139	92.32 110

XVG-OURS-SEG-HR		80.81 117	79.76 123	83.96 123	85.60 226	68.78 100	83.54 249	90.50 132	70.66 177	76.71 182	91.66 84	60.69 168	91.26 227	76.94 108	81.58 201	91.83 124

xiu_mvs_v1_base_debu		80.80 119	79.72 124	84.03 118	87.35 197	70.19 77	85.56 200	88.77 188	69.06 211	81.83 92	88.16 176	50.91 248	92.85 177	78.29 96	87.56 124	89.06 218

xiu_mvs_v1_base		80.80 119	79.72 124	84.03 118	87.35 197	70.19 77	85.56 200	88.77 188	69.06 211	81.83 92	88.16 176	50.91 248	92.85 177	78.29 96	87.56 124	89.06 218

xiu_mvs_v1_base_debi		80.80 119	79.72 124	84.03 118	87.35 197	70.19 77	85.56 200	88.77 188	69.06 211	81.83 92	88.16 176	50.91 248	92.85 177	78.29 96	87.56 124	89.06 218

ACMM	73.20 8	80.78 122	79.84 122	83.58 131	89.31 129	68.37 113	89.99 71	91.60 104	70.28 183	77.25 168	89.66 130	53.37 222	93.53 147	74.24 135	82.85 186	88.85 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		80.68 123	79.51 128	84.20 106	94.09 38	67.27 137	89.64 82	91.11 118	58.75 318	74.08 236	90.72 112	58.10 185	95.04 83	69.70 178	89.42 107	90.30 175

iter_conf_final		80.63 124	79.35 133	84.46 96	89.36 125	67.70 127	89.85 73	84.49 257	73.19 138	78.30 145	88.94 151	45.98 292	94.56 100	79.59 84	84.48 164	91.11 144

CANet_DTU		80.61 125	79.87 121	82.83 162	85.60 226	63.17 224	87.36 150	88.65 194	76.37 74	75.88 200	88.44 168	53.51 221	93.07 170	73.30 144	89.74 104	92.25 113

VPA-MVSNet		80.60 126	80.55 109	80.76 213	88.07 173	60.80 252	86.86 165	91.58 105	75.67 88	80.24 113	89.45 141	63.34 120	90.25 247	70.51 169	79.22 232	91.23 141

PVSNet_BlendedMVS		80.60 126	80.02 117	82.36 177	88.85 142	65.40 174	86.16 187	92.00 87	69.34 201	78.11 151	86.09 235	66.02 100	94.27 111	71.52 158	82.06 195	87.39 260

AdaColmap		80.58 128	79.42 130	84.06 114	93.09 54	68.91 98	89.36 86	88.97 182	69.27 202	75.70 203	89.69 129	57.20 196	95.77 52	63.06 232	88.41 119	87.50 259

EI-MVSNet		80.52 129	79.98 118	82.12 178	84.28 245	63.19 223	86.41 179	88.95 183	74.18 116	78.69 133	87.54 192	66.62 90	92.43 188	72.57 153	80.57 214	90.74 160

XVG-OURS		80.41 130	79.23 137	83.97 122	85.64 225	69.02 95	83.03 259	90.39 134	71.09 169	77.63 161	91.49 92	54.62 212	91.35 225	75.71 121	83.47 179	91.54 130

PCF-MVS	73.52 7	80.38 131	78.84 146	85.01 77	87.71 186	68.99 96	83.65 244	91.46 111	63.00 280	77.77 159	90.28 119	66.10 97	95.09 82	61.40 249	88.22 121	90.94 153
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 132	77.83 169	88.00 15	94.42 20	73.33 18	92.78 18	92.99 45	79.14 19	83.67 73	12.47 376	67.45 84	96.60 31	83.06 51	94.50 48	94.07 45

RRT_MVS		80.35 133	79.22 138	83.74 128	87.63 190	65.46 173	91.08 52	88.92 185	73.82 123	76.44 189	90.03 124	49.05 273	94.25 115	76.84 109	79.20 233	91.51 131

test_djsdf		80.30 134	79.32 134	83.27 141	83.98 252	65.37 177	90.50 60	90.38 135	68.55 221	76.19 194	88.70 158	56.44 200	93.46 151	78.98 87	80.14 220	90.97 152

v2v482		80.23 135	79.29 135	83.05 153	83.62 257	64.14 200	87.04 159	89.97 149	73.61 128	78.18 150	87.22 200	61.10 162	93.82 132	76.11 116	76.78 257	91.18 142

NR-MVSNet		80.23 135	79.38 131	82.78 168	87.80 182	63.34 218	86.31 182	91.09 119	79.01 24	72.17 255	89.07 148	67.20 87	92.81 180	66.08 212	75.65 271	92.20 115

Anonymous20240529		80.19 137	78.89 145	84.10 109	90.60 91	64.75 188	88.95 98	90.90 122	65.97 249	80.59 110	91.17 101	49.97 259	93.73 140	69.16 184	82.70 190	93.81 56

IterMVS-LS		80.06 138	79.38 131	82.11 179	85.89 221	63.20 222	86.79 168	89.34 163	74.19 115	75.45 209	86.72 212	66.62 90	92.39 190	72.58 152	76.86 254	90.75 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 139	78.57 151	84.42 98	85.13 235	68.74 103	88.77 105	88.10 202	74.99 98	74.97 226	83.49 281	57.27 195	93.36 154	73.53 140	80.88 208	91.18 142

v1144		80.03 139	79.03 142	83.01 155	83.78 255	64.51 191	87.11 158	90.57 131	71.96 153	78.08 153	86.20 232	61.41 154	93.94 125	74.93 128	77.23 248	90.60 165

iter_conf05		80.00 141	78.70 147	83.91 125	87.84 180	65.83 163	88.84 103	84.92 252	71.61 159	78.70 132	88.94 151	43.88 307	94.56 100	79.28 85	84.28 167	91.33 137

v8		79.97 142	79.02 143	82.80 165	84.09 249	64.50 193	87.96 133	90.29 142	74.13 118	75.24 219	86.81 209	62.88 131	93.89 131	74.39 133	75.40 279	90.00 191

OpenMVS	72.83 10	79.77 143	78.33 158	84.09 111	85.17 231	69.91 82	90.57 58	90.97 120	66.70 237	72.17 255	91.91 79	54.70 210	93.96 122	61.81 246	90.95 89	88.41 243

v10		79.74 144	78.67 148	82.97 158	84.06 250	64.95 184	87.88 139	90.62 129	73.11 139	75.11 222	86.56 223	61.46 153	94.05 121	73.68 138	75.55 273	89.90 197

ECVR-MVS		79.61 145	79.26 136	80.67 215	90.08 102	54.69 322	87.89 138	77.44 329	74.88 100	80.27 112	92.79 67	48.96 275	92.45 187	68.55 190	92.50 70	94.86 15

BH-RMVSNet		79.61 145	78.44 154	83.14 148	89.38 124	65.93 160	84.95 215	87.15 224	73.56 130	78.19 149	89.79 127	56.67 199	93.36 154	59.53 263	86.74 137	90.13 181

v1192		79.59 147	78.43 155	83.07 152	83.55 259	64.52 190	86.93 163	90.58 130	70.83 172	77.78 158	85.90 236	59.15 179	93.94 125	73.96 137	77.19 250	90.76 158

ab-mvs		79.51 148	78.97 144	81.14 204	88.46 160	60.91 250	83.84 241	89.24 169	70.36 181	79.03 126	88.87 155	63.23 124	90.21 248	65.12 219	82.57 191	92.28 112

WR-MVS		79.49 149	79.22 138	80.27 223	88.79 148	58.35 273	85.06 212	88.61 196	78.56 28	77.65 160	88.34 170	63.81 119	90.66 243	64.98 221	77.22 249	91.80 126

v144192		79.47 150	78.37 156	82.78 168	83.35 262	63.96 203	86.96 161	90.36 138	69.99 187	77.50 162	85.67 243	60.66 169	93.77 136	74.27 134	76.58 258	90.62 163

BH-untuned		79.47 150	78.60 150	82.05 180	89.19 135	65.91 161	86.07 189	88.52 197	72.18 150	75.42 210	87.69 186	61.15 161	93.54 146	60.38 256	86.83 136	86.70 279

test1111		79.43 152	79.18 140	80.15 225	89.99 107	53.31 335	87.33 152	77.05 332	75.04 97	80.23 114	92.77 69	48.97 274	92.33 195	68.87 187	92.40 72	94.81 18

mvs_anonymous		79.42 153	79.11 141	80.34 221	84.45 244	57.97 280	82.59 261	87.62 214	67.40 232	76.17 197	88.56 165	68.47 77	89.59 256	70.65 168	86.05 148	93.47 73

thisisatest0530		79.40 154	77.76 174	84.31 103	87.69 188	65.10 182	87.36 150	84.26 263	70.04 186	77.42 164	88.26 174	49.94 260	94.79 95	70.20 171	84.70 161	93.03 88

tttt0517		79.40 154	77.91 166	83.90 126	88.10 171	63.84 205	88.37 121	84.05 265	71.45 163	76.78 180	89.12 147	49.93 262	94.89 90	70.18 172	83.18 183	92.96 92

V42		79.38 156	78.24 160	82.83 162	81.10 307	65.50 171	85.55 203	89.82 152	71.57 161	78.21 148	86.12 234	60.66 169	93.18 164	75.64 122	75.46 277	89.81 202

jajsoiax		79.29 157	77.96 164	83.27 141	84.68 241	66.57 150	89.25 88	90.16 144	69.20 206	75.46 208	89.49 136	45.75 297	93.13 167	76.84 109	80.80 210	90.11 183

v1921920		79.22 158	78.03 163	82.80 165	83.30 264	63.94 204	86.80 167	90.33 139	69.91 190	77.48 163	85.53 246	58.44 183	93.75 138	73.60 139	76.85 255	90.71 161

AUN-MVS		79.21 159	77.60 179	84.05 116	88.71 152	67.61 129	85.84 196	87.26 222	69.08 210	77.23 170	88.14 180	53.20 224	93.47 150	75.50 126	73.45 301	91.06 147

TAPA-MVS	73.13 9	79.15 160	77.94 165	82.79 167	89.59 114	62.99 228	88.16 128	91.51 107	65.77 250	77.14 175	91.09 103	60.91 165	93.21 158	50.26 323	87.05 132	92.17 117
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 161	77.77 173	83.22 145	84.70 240	66.37 152	89.17 89	90.19 143	69.38 200	75.40 211	89.46 139	44.17 305	93.15 165	76.78 111	80.70 212	90.14 180

UniMVSNet_ETH3D		79.10 162	78.24 160	81.70 187	86.85 209	60.24 261	87.28 154	88.79 187	74.25 114	76.84 177	90.53 117	49.48 265	91.56 218	67.98 194	82.15 194	93.29 78

CDS-MVSNet		79.07 163	77.70 176	83.17 147	87.60 191	68.23 117	84.40 232	86.20 237	67.49 231	76.36 190	86.54 224	61.54 150	90.79 240	61.86 245	87.33 128	90.49 169
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 164	77.88 168	82.38 176	83.07 271	64.80 187	84.08 240	88.95 183	69.01 214	78.69 133	87.17 203	54.70 210	92.43 188	74.69 129	80.57 214	89.89 198

v1240		78.99 165	77.78 172	82.64 171	83.21 266	63.54 212	86.62 174	90.30 141	69.74 196	77.33 166	85.68 242	57.04 197	93.76 137	73.13 147	76.92 252	90.62 163

Anonymous20231211		78.97 166	77.69 177	82.81 164	90.54 93	64.29 198	90.11 70	91.51 107	65.01 259	76.16 198	88.13 181	50.56 253	93.03 174	69.68 179	77.56 247	91.11 144

v7n		78.97 166	77.58 180	83.14 148	83.45 261	65.51 170	88.32 122	91.21 114	73.69 126	72.41 252	86.32 230	57.93 186	93.81 133	69.18 183	75.65 271	90.11 183

TAMVS		78.89 168	77.51 181	83.03 154	87.80 182	67.79 125	84.72 219	85.05 250	67.63 228	76.75 181	87.70 185	62.25 140	90.82 239	58.53 275	87.13 131	90.49 169

c3_l		78.75 169	77.91 166	81.26 199	82.89 277	61.56 244	84.09 239	89.13 175	69.97 188	75.56 204	84.29 269	66.36 94	92.09 202	73.47 142	75.48 275	90.12 182

tt0805		78.73 170	77.83 169	81.43 193	85.17 231	60.30 260	89.41 85	90.90 122	71.21 166	77.17 174	88.73 157	46.38 287	93.21 158	72.57 153	78.96 234	90.79 156

v148		78.72 171	77.80 171	81.47 192	82.73 280	61.96 239	86.30 183	88.08 203	73.26 136	76.18 195	85.47 248	62.46 136	92.36 192	71.92 157	73.82 298	90.09 185

VPNet		78.69 172	78.66 149	78.76 248	88.31 165	55.72 313	84.45 229	86.63 231	76.79 62	78.26 146	90.55 116	59.30 178	89.70 255	66.63 207	77.05 251	90.88 154

ET-MVSNet_ETH3D		78.63 173	76.63 201	84.64 90	86.73 213	69.47 89	85.01 213	84.61 255	69.54 197	66.51 313	86.59 220	50.16 257	91.75 213	76.26 115	84.24 168	92.69 98

anonymousdsp		78.60 174	77.15 186	82.98 157	80.51 313	67.08 141	87.24 155	89.53 159	65.66 252	75.16 220	87.19 202	52.52 225	92.25 197	77.17 106	79.34 230	89.61 207

miper_ehance_all_eth		78.59 175	77.76 174	81.08 206	82.66 282	61.56 244	83.65 244	89.15 173	68.87 216	75.55 205	83.79 277	66.49 92	92.03 203	73.25 145	76.39 262	89.64 206

WR-MVS_H		78.51 176	78.49 152	78.56 251	88.02 175	56.38 306	88.43 116	92.67 61	77.14 52	73.89 237	87.55 191	66.25 96	89.24 262	58.92 270	73.55 300	90.06 189

GBi-Net		78.40 177	77.40 182	81.40 195	87.60 191	63.01 225	88.39 118	89.28 165	71.63 156	75.34 213	87.28 196	54.80 206	91.11 230	62.72 234	79.57 225	90.09 185

test1		78.40 177	77.40 182	81.40 195	87.60 191	63.01 225	88.39 118	89.28 165	71.63 156	75.34 213	87.28 196	54.80 206	91.11 230	62.72 234	79.57 225	90.09 185

Vis-MVSNet (Re-imp)		78.36 179	78.45 153	78.07 259	88.64 154	51.78 342	86.70 172	79.63 316	74.14 117	75.11 222	90.83 111	61.29 158	89.75 253	58.10 279	91.60 80	92.69 98

Anonymous202405211		78.25 180	77.01 188	81.99 182	91.03 82	60.67 254	84.77 218	83.90 267	70.65 178	80.00 116	91.20 99	41.08 324	91.43 223	65.21 218	85.26 155	93.85 53

CP-MVSNet		78.22 181	78.34 157	77.84 261	87.83 181	54.54 324	87.94 135	91.17 116	77.65 36	73.48 240	88.49 166	62.24 141	88.43 276	62.19 240	74.07 293	90.55 167

BH-w/o		78.21 182	77.33 184	80.84 211	88.81 146	65.13 181	84.87 216	87.85 210	69.75 194	74.52 232	84.74 263	61.34 156	93.11 168	58.24 278	85.84 152	84.27 313

FMVSNet2		78.20 183	77.21 185	81.20 202	87.60 191	62.89 229	87.47 148	89.02 178	71.63 156	75.29 218	87.28 196	54.80 206	91.10 233	62.38 238	79.38 229	89.61 207

MVS		78.19 184	76.99 190	81.78 185	85.66 224	66.99 142	84.66 220	90.47 133	55.08 337	72.02 257	85.27 251	63.83 118	94.11 120	66.10 211	89.80 103	84.24 314

Baseline_NR-MVSNet		78.15 185	78.33 158	77.61 266	85.79 222	56.21 309	86.78 169	85.76 243	73.60 129	77.93 156	87.57 189	65.02 109	88.99 266	67.14 204	75.33 281	87.63 254

CNLPA		78.08 186	76.79 195	81.97 183	90.40 96	71.07 60	87.59 145	84.55 256	66.03 248	72.38 253	89.64 131	57.56 191	86.04 295	59.61 262	83.35 180	88.79 234

cl22		78.07 187	77.01 188	81.23 200	82.37 289	61.83 241	83.55 248	87.98 205	68.96 215	75.06 224	83.87 273	61.40 155	91.88 210	73.53 140	76.39 262	89.98 194

PLC	70.83 11	78.05 188	76.37 206	83.08 151	91.88 74	67.80 124	88.19 126	89.46 161	64.33 267	69.87 280	88.38 169	53.66 220	93.58 142	58.86 271	82.73 188	87.86 250
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 189	76.49 202	82.62 172	83.16 270	66.96 145	86.94 162	87.45 219	72.45 145	71.49 262	84.17 270	54.79 209	91.58 217	67.61 197	80.31 217	89.30 214

PS-CasMVS		78.01 190	78.09 162	77.77 263	87.71 186	54.39 326	88.02 131	91.22 113	77.50 44	73.26 242	88.64 161	60.73 166	88.41 277	61.88 244	73.88 297	90.53 168

HY-MVS	69.67 12	77.95 191	77.15 186	80.36 220	87.57 195	60.21 262	83.37 251	87.78 212	66.11 245	75.37 212	87.06 207	63.27 122	90.48 245	61.38 250	82.43 192	90.40 173

eth_miper_zixun_eth		77.92 192	76.69 199	81.61 190	83.00 274	61.98 238	83.15 254	89.20 171	69.52 198	74.86 228	84.35 268	61.76 146	92.56 184	71.50 160	72.89 306	90.28 176

FMVSNet3		77.88 193	76.85 193	80.97 209	86.84 210	62.36 232	86.52 177	88.77 188	71.13 167	75.34 213	86.66 218	54.07 217	91.10 233	62.72 234	79.57 225	89.45 210

miper_enhance_ethall		77.87 194	76.86 192	80.92 210	81.65 296	61.38 246	82.68 260	88.98 180	65.52 254	75.47 206	82.30 296	65.76 104	92.00 205	72.95 148	76.39 262	89.39 211

FE-MVS		77.78 195	75.68 212	84.08 112	88.09 172	66.00 158	83.13 255	87.79 211	68.42 224	78.01 154	85.23 253	45.50 299	95.12 76	59.11 267	85.83 153	91.11 144

PEN-MVS		77.73 196	77.69 177	77.84 261	87.07 207	53.91 329	87.91 137	91.18 115	77.56 41	73.14 244	88.82 156	61.23 159	89.17 263	59.95 259	72.37 308	90.43 171

cl____		77.72 197	76.76 196	80.58 216	82.49 286	60.48 257	83.09 256	87.87 208	69.22 204	74.38 234	85.22 254	62.10 143	91.53 220	71.09 163	75.41 278	89.73 205

DIV-MVS_self_test		77.72 197	76.76 196	80.58 216	82.48 287	60.48 257	83.09 256	87.86 209	69.22 204	74.38 234	85.24 252	62.10 143	91.53 220	71.09 163	75.40 279	89.74 204

PAPM		77.68 199	76.40 205	81.51 191	87.29 203	61.85 240	83.78 242	89.59 158	64.74 261	71.23 263	88.70 158	62.59 133	93.66 141	52.66 310	87.03 133	89.01 223

CHOSEN 1792x2688		77.63 200	75.69 211	83.44 134	89.98 108	68.58 111	78.70 304	87.50 217	56.38 332	75.80 202	86.84 208	58.67 181	91.40 224	61.58 248	85.75 154	90.34 174

HyFIR lowres test		77.53 201	75.40 218	83.94 124	89.59 114	66.62 148	80.36 285	88.64 195	56.29 333	76.45 186	85.17 255	57.64 190	93.28 156	61.34 251	83.10 184	91.91 123

FMVSNet1		77.44 202	76.12 208	81.40 195	86.81 211	63.01 225	88.39 118	89.28 165	70.49 180	74.39 233	87.28 196	49.06 272	91.11 230	60.91 253	78.52 237	90.09 185

TR-MVS		77.44 202	76.18 207	81.20 202	88.24 167	63.24 220	84.61 223	86.40 234	67.55 230	77.81 157	86.48 226	54.10 216	93.15 165	57.75 282	82.72 189	87.20 265

1112_ss		77.40 204	76.43 204	80.32 222	89.11 141	60.41 259	83.65 244	87.72 213	62.13 292	73.05 245	86.72 212	62.58 134	89.97 250	62.11 243	80.80 210	90.59 166

thisisatest0515		77.33 205	75.38 219	83.18 146	85.27 230	63.80 206	82.11 265	83.27 278	65.06 257	75.91 199	83.84 275	49.54 264	94.27 111	67.24 202	86.19 146	91.48 135

test2506		77.30 206	76.49 202	79.74 233	90.08 102	52.02 338	87.86 140	63.10 366	74.88 100	80.16 115	92.79 67	38.29 334	92.35 193	68.74 189	92.50 70	94.86 15

bld_raw_dy_0_64		77.29 207	75.98 209	81.22 201	85.04 237	65.47 172	88.14 130	77.56 326	69.20 206	73.77 238	89.40 145	42.24 318	88.85 272	76.78 111	81.64 200	89.33 213

pm-mvs1		77.25 208	76.68 200	78.93 246	84.22 247	58.62 272	86.41 179	88.36 199	71.37 164	73.31 241	88.01 182	61.22 160	89.15 264	64.24 225	73.01 305	89.03 222

LCM-MVSNet-Re		77.05 209	76.94 191	77.36 269	87.20 204	51.60 343	80.06 288	80.46 307	75.20 95	67.69 296	86.72 212	62.48 135	88.98 267	63.44 229	89.25 108	91.51 131

DTE-MVSNet		76.99 210	76.80 194	77.54 268	86.24 217	53.06 337	87.52 146	90.66 128	77.08 55	72.50 250	88.67 160	60.48 172	89.52 257	57.33 286	70.74 319	90.05 190

baseline1		76.98 211	76.75 198	77.66 264	88.13 169	55.66 314	85.12 211	81.89 292	73.04 141	76.79 179	88.90 153	62.43 137	87.78 284	63.30 231	71.18 317	89.55 209

LS3D		76.95 212	74.82 225	83.37 138	90.45 94	67.36 136	89.15 93	86.94 227	61.87 294	69.52 283	90.61 114	51.71 242	94.53 103	46.38 343	86.71 138	88.21 245

GA-MVS		76.87 213	75.17 223	81.97 183	82.75 279	62.58 230	81.44 274	86.35 236	72.16 152	74.74 229	82.89 288	46.20 291	92.02 204	68.85 188	81.09 206	91.30 140

DP-MVS		76.78 214	74.57 227	83.42 135	93.29 48	69.46 91	88.55 115	83.70 269	63.98 273	70.20 271	88.89 154	54.01 218	94.80 94	46.66 340	81.88 198	86.01 291

cascas		76.72 215	74.64 226	82.99 156	85.78 223	65.88 162	82.33 263	89.21 170	60.85 300	72.74 247	81.02 307	47.28 282	93.75 138	67.48 199	85.02 156	89.34 212

1314		76.53 216	75.30 222	80.21 224	83.93 253	62.32 234	84.66 220	88.81 186	60.23 304	70.16 274	84.07 272	55.30 204	90.73 242	67.37 200	83.21 182	87.59 257

thres100view900		76.50 217	75.55 215	79.33 241	89.52 117	56.99 295	85.83 197	83.23 279	73.94 120	76.32 191	87.12 204	51.89 239	91.95 206	48.33 331	83.75 173	89.07 216

thres600view7		76.50 217	75.44 216	79.68 235	89.40 122	57.16 292	85.53 205	83.23 279	73.79 125	76.26 192	87.09 205	51.89 239	91.89 209	48.05 336	83.72 176	90.00 191

thres400		76.50 217	75.37 220	79.86 230	89.13 137	57.65 286	85.17 208	83.60 270	73.41 134	76.45 186	86.39 228	52.12 232	91.95 206	48.33 331	83.75 173	90.00 191

tfpn200view9		76.42 220	75.37 220	79.55 240	89.13 137	57.65 286	85.17 208	83.60 270	73.41 134	76.45 186	86.39 228	52.12 232	91.95 206	48.33 331	83.75 173	89.07 216

Test_1112_low_res		76.40 221	75.44 216	79.27 242	89.28 131	58.09 276	81.69 269	87.07 225	59.53 311	72.48 251	86.67 217	61.30 157	89.33 260	60.81 255	80.15 219	90.41 172

F-COLMAP		76.38 222	74.33 232	82.50 174	89.28 131	66.95 146	88.41 117	89.03 177	64.05 271	66.83 307	88.61 162	46.78 285	92.89 176	57.48 283	78.55 236	87.67 253

LTVRE_ROB	69.57 13	76.25 223	74.54 229	81.41 194	88.60 155	64.38 197	79.24 297	89.12 176	70.76 175	69.79 282	87.86 183	49.09 271	93.20 161	56.21 296	80.16 218	86.65 280
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		76.12 224	74.46 231	81.13 205	85.37 229	69.79 84	84.42 231	87.95 206	65.03 258	67.46 299	85.33 250	53.28 223	91.73 215	58.01 280	83.27 181	81.85 335
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 225	74.27 233	81.62 188	83.20 267	64.67 189	83.60 247	89.75 155	69.75 194	71.85 258	87.09 205	32.78 347	92.11 201	69.99 175	80.43 216	88.09 246

ACMH+	68.96 14	76.01 226	74.01 234	82.03 181	88.60 155	65.31 178	88.86 101	87.55 215	70.25 184	67.75 295	87.47 194	41.27 322	93.19 163	58.37 276	75.94 268	87.60 255

ACMH	67.68 16	75.89 227	73.93 235	81.77 186	88.71 152	66.61 149	88.62 113	89.01 179	69.81 191	66.78 308	86.70 216	41.95 321	91.51 222	55.64 297	78.14 243	87.17 266
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 228	73.36 242	83.31 139	84.76 239	66.03 156	83.38 250	85.06 249	70.21 185	69.40 284	81.05 306	45.76 296	94.66 99	65.10 220	75.49 274	89.25 215
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		75.70 229	73.83 238	81.30 198	83.26 265	61.79 242	82.57 262	80.65 303	66.81 234	66.88 305	83.42 282	57.86 188	92.19 199	63.47 228	79.57 225	89.91 196

WTY-MVS		75.65 230	75.68 212	75.57 285	86.40 216	56.82 297	77.92 312	82.40 288	65.10 256	76.18 195	87.72 184	63.13 129	80.90 325	60.31 257	81.96 196	89.00 225

thres200		75.55 231	74.47 230	78.82 247	87.78 185	57.85 283	83.07 258	83.51 273	72.44 147	75.84 201	84.42 265	52.08 234	91.75 213	47.41 338	83.64 177	86.86 275

test_vis1_n_1920		75.52 232	75.78 210	74.75 295	79.84 320	57.44 290	83.26 252	85.52 245	62.83 284	79.34 124	86.17 233	45.10 301	79.71 329	78.75 89	81.21 205	87.10 272

EPNet_dtu		75.46 233	74.86 224	77.23 273	82.57 284	54.60 323	86.89 164	83.09 282	71.64 155	66.25 315	85.86 238	55.99 201	88.04 281	54.92 299	86.55 140	89.05 221
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 234	73.87 237	80.11 226	82.69 281	64.85 186	81.57 271	83.47 275	69.16 208	70.49 268	84.15 271	51.95 237	88.15 279	69.23 182	72.14 311	87.34 262

XXY-MVS		75.41 235	75.56 214	74.96 291	83.59 258	57.82 284	80.59 282	83.87 268	66.54 242	74.93 227	88.31 171	63.24 123	80.09 328	62.16 241	76.85 255	86.97 273

TransMVSNet (Re)		75.39 236	74.56 228	77.86 260	85.50 228	57.10 294	86.78 169	86.09 240	72.17 151	71.53 261	87.34 195	63.01 130	89.31 261	56.84 291	61.83 344	87.17 266

CostFormer		75.24 237	73.90 236	79.27 242	82.65 283	58.27 275	80.80 277	82.73 286	61.57 295	75.33 216	83.13 286	55.52 202	91.07 236	64.98 221	78.34 242	88.45 241

D2MVS		74.82 238	73.21 243	79.64 237	79.81 321	62.56 231	80.34 286	87.35 220	64.37 266	68.86 287	82.66 292	46.37 288	90.10 249	67.91 195	81.24 204	86.25 284

pmmvs6		74.69 239	73.39 241	78.61 250	81.38 302	57.48 289	86.64 173	87.95 206	64.99 260	70.18 272	86.61 219	50.43 255	89.52 257	62.12 242	70.18 321	88.83 232

tfpnnormal		74.39 240	73.16 244	78.08 258	86.10 220	58.05 277	84.65 222	87.53 216	70.32 182	71.22 264	85.63 244	54.97 205	89.86 251	43.03 352	75.02 286	86.32 283

IterMVS		74.29 241	72.94 246	78.35 255	81.53 299	63.49 214	81.58 270	82.49 287	68.06 227	69.99 277	83.69 279	51.66 243	85.54 298	65.85 214	71.64 314	86.01 291
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 242	72.42 250	79.80 232	83.76 256	59.59 267	85.92 193	86.64 230	66.39 243	66.96 304	87.58 188	39.46 328	91.60 216	65.76 215	69.27 324	88.22 244

SCA		74.22 243	72.33 251	79.91 229	84.05 251	62.17 236	79.96 291	79.29 318	66.30 244	72.38 253	80.13 316	51.95 237	88.60 274	59.25 265	77.67 246	88.96 227

miper_lstm_enhance		74.11 244	73.11 245	77.13 274	80.11 316	59.62 266	72.23 335	86.92 228	66.76 236	70.40 269	82.92 287	56.93 198	82.92 317	69.06 185	72.63 307	88.87 230

EG-PatchMatch MVS		74.04 245	71.82 254	80.71 214	84.92 238	67.42 133	85.86 195	88.08 203	66.04 247	64.22 327	83.85 274	35.10 343	92.56 184	57.44 284	80.83 209	82.16 334

pmmvs4		74.03 246	71.91 253	80.39 219	81.96 293	68.32 114	81.45 273	82.14 290	59.32 312	69.87 280	85.13 256	52.40 228	88.13 280	60.21 258	74.74 289	84.73 309

MS-PatchMatch		73.83 247	72.67 247	77.30 271	83.87 254	66.02 157	81.82 266	84.66 254	61.37 298	68.61 290	82.82 290	47.29 281	88.21 278	59.27 264	84.32 166	77.68 349

test_cas_vis1_n_1920		73.76 248	73.74 239	73.81 302	75.90 340	59.77 264	80.51 283	82.40 288	58.30 320	81.62 98	85.69 241	44.35 304	76.41 346	76.29 114	78.61 235	85.23 301

sss		73.60 249	73.64 240	73.51 304	82.80 278	55.01 320	76.12 319	81.69 295	62.47 289	74.68 230	85.85 239	57.32 194	78.11 336	60.86 254	80.93 207	87.39 260

RPMNet		73.51 250	70.49 267	82.58 173	81.32 305	65.19 179	75.92 321	92.27 76	57.60 326	72.73 248	76.45 341	52.30 229	95.43 63	48.14 335	77.71 244	87.11 270

SixPastTwentyTwo		73.37 251	71.26 261	79.70 234	85.08 236	57.89 282	85.57 199	83.56 272	71.03 170	65.66 317	85.88 237	42.10 319	92.57 183	59.11 267	63.34 342	88.65 238

CR-MVSNet		73.37 251	71.27 260	79.67 236	81.32 305	65.19 179	75.92 321	80.30 309	59.92 307	72.73 248	81.19 304	52.50 226	86.69 290	59.84 260	77.71 244	87.11 270

MSDG		73.36 253	70.99 262	80.49 218	84.51 243	65.80 165	80.71 280	86.13 239	65.70 251	65.46 318	83.74 278	44.60 302	90.91 238	51.13 316	76.89 253	84.74 308

tpm2		73.26 254	71.46 256	78.63 249	83.34 263	56.71 300	80.65 281	80.40 308	56.63 331	73.55 239	82.02 301	51.80 241	91.24 228	56.35 295	78.42 240	87.95 247

RPSCF		73.23 255	71.46 256	78.54 252	82.50 285	59.85 263	82.18 264	82.84 285	58.96 315	71.15 265	89.41 143	45.48 300	84.77 305	58.82 272	71.83 313	91.02 151

PatchmatchNet		73.12 256	71.33 259	78.49 254	83.18 268	60.85 251	79.63 293	78.57 321	64.13 268	71.73 259	79.81 321	51.20 246	85.97 296	57.40 285	76.36 265	88.66 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
COLMAP_ROB	66.92 17	73.01 257	70.41 269	80.81 212	87.13 206	65.63 168	88.30 123	84.19 264	62.96 281	63.80 331	87.69 186	38.04 335	92.56 184	46.66 340	74.91 287	84.24 314
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 258	72.58 248	74.25 299	84.28 245	50.85 348	86.41 179	83.45 276	44.56 354	73.23 243	87.54 192	49.38 266	85.70 297	65.90 213	78.44 239	86.19 286

test-LLR		72.94 259	72.43 249	74.48 296	81.35 303	58.04 278	78.38 305	77.46 327	66.66 238	69.95 278	79.00 325	48.06 278	79.24 330	66.13 209	84.83 158	86.15 287

test_0402		72.79 260	70.44 268	79.84 231	88.13 169	65.99 159	85.93 192	84.29 261	65.57 253	67.40 301	85.49 247	46.92 284	92.61 182	35.88 361	74.38 292	80.94 340

MVS_0304		72.48 261	70.89 264	77.24 272	82.20 290	59.68 265	84.11 238	83.49 274	67.10 233	66.87 306	80.59 312	35.00 344	87.40 286	59.07 269	79.58 224	84.63 310

tpmrst		72.39 262	72.13 252	73.18 308	80.54 312	49.91 352	79.91 292	79.08 319	63.11 278	71.69 260	79.95 318	55.32 203	82.77 318	65.66 216	73.89 296	86.87 274

PatchMatch-RL		72.38 263	70.90 263	76.80 277	88.60 155	67.38 135	79.53 294	76.17 337	62.75 286	69.36 285	82.00 302	45.51 298	84.89 304	53.62 305	80.58 213	78.12 348

CL-MVSNet_self_test		72.37 264	71.46 256	75.09 290	79.49 327	53.53 331	80.76 279	85.01 251	69.12 209	70.51 267	82.05 300	57.92 187	84.13 308	52.27 311	66.00 336	87.60 255

tpm		72.37 264	71.71 255	74.35 298	82.19 291	52.00 339	79.22 298	77.29 330	64.56 263	72.95 246	83.68 280	51.35 244	83.26 316	58.33 277	75.80 269	87.81 251

PVSNet	64.34 18	72.08 266	70.87 265	75.69 283	86.21 218	56.44 304	74.37 331	80.73 302	62.06 293	70.17 273	82.23 298	42.86 312	83.31 315	54.77 300	84.45 165	87.32 263

pmmvs5		71.55 267	70.20 272	75.61 284	77.83 333	56.39 305	81.74 268	80.89 299	57.76 324	67.46 299	84.49 264	49.26 269	85.32 301	57.08 288	75.29 282	85.11 305

test-mter		71.41 268	70.39 270	74.48 296	81.35 303	58.04 278	78.38 305	77.46 327	60.32 303	69.95 278	79.00 325	36.08 341	79.24 330	66.13 209	84.83 158	86.15 287

K. test v3		71.19 269	68.51 280	79.21 244	83.04 273	57.78 285	84.35 233	76.91 333	72.90 144	62.99 334	82.86 289	39.27 329	91.09 235	61.65 247	52.66 360	88.75 235

tpmvs		71.09 270	69.29 275	76.49 278	82.04 292	56.04 310	78.92 302	81.37 298	64.05 271	67.18 303	78.28 331	49.74 263	89.77 252	49.67 326	72.37 308	83.67 321

AllTest		70.96 271	68.09 286	79.58 238	85.15 233	63.62 208	84.58 224	79.83 313	62.31 290	60.32 341	86.73 210	32.02 348	88.96 269	50.28 321	71.57 315	86.15 287

test_fmvs1		70.93 272	70.52 266	72.16 312	73.71 350	55.05 319	80.82 276	78.77 320	51.21 348	78.58 137	84.41 266	31.20 351	76.94 342	75.88 120	80.12 221	84.47 312

test_fmvs1_n		70.86 273	70.24 271	72.73 309	72.51 358	55.28 317	81.27 275	79.71 315	51.49 347	78.73 131	84.87 260	27.54 355	77.02 341	76.06 117	79.97 222	85.88 294

Patchmtry		70.74 274	69.16 277	75.49 287	80.72 309	54.07 328	74.94 330	80.30 309	58.34 319	70.01 275	81.19 304	52.50 226	86.54 291	53.37 307	71.09 318	85.87 295

MIMVSNet		70.69 275	69.30 274	74.88 292	84.52 242	56.35 307	75.87 323	79.42 317	64.59 262	67.76 294	82.41 294	41.10 323	81.54 322	46.64 342	81.34 202	86.75 278

tpm cat1		70.57 276	68.31 282	77.35 270	82.41 288	57.95 281	78.08 309	80.22 311	52.04 343	68.54 291	77.66 336	52.00 236	87.84 283	51.77 312	72.07 312	86.25 284

OpenMVS_ROB	64.09 19	70.56 277	68.19 283	77.65 265	80.26 314	59.41 269	85.01 213	82.96 284	58.76 317	65.43 319	82.33 295	37.63 337	91.23 229	45.34 348	76.03 267	82.32 332

pmmvs-eth3d		70.50 278	67.83 290	78.52 253	77.37 336	66.18 155	81.82 266	81.51 296	58.90 316	63.90 330	80.42 314	42.69 313	86.28 294	58.56 274	65.30 338	83.11 327

USDC		70.33 279	68.37 281	76.21 280	80.60 311	56.23 308	79.19 299	86.49 232	60.89 299	61.29 338	85.47 248	31.78 350	89.47 259	53.37 307	76.21 266	82.94 331

Patchmatch-RL test		70.24 280	67.78 292	77.61 266	77.43 335	59.57 268	71.16 338	70.33 350	62.94 282	68.65 289	72.77 350	50.62 252	85.49 299	69.58 180	66.58 333	87.77 252

CMPMVS	51.72 21	70.19 281	68.16 284	76.28 279	73.15 355	57.55 288	79.47 295	83.92 266	48.02 351	56.48 354	84.81 261	43.13 310	86.42 293	62.67 237	81.81 199	84.89 306
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		70.04 282	67.34 297	78.14 257	79.80 322	61.13 247	79.19 299	80.59 304	59.16 314	65.27 320	79.29 322	46.75 286	87.29 287	49.33 327	66.72 331	86.00 293

gg-mvs-nofinetune		69.95 283	67.96 287	75.94 281	83.07 271	54.51 325	77.23 316	70.29 351	63.11 278	70.32 270	62.33 359	43.62 308	88.69 273	53.88 304	87.76 123	84.62 311

TESTMET0.1,1		69.89 284	69.00 278	72.55 310	79.27 330	56.85 296	78.38 305	74.71 343	57.64 325	68.09 293	77.19 338	37.75 336	76.70 343	63.92 226	84.09 169	84.10 317

test_vis1_n		69.85 285	69.21 276	71.77 314	72.66 357	55.27 318	81.48 272	76.21 336	52.03 344	75.30 217	83.20 285	28.97 353	76.22 348	74.60 130	78.41 241	83.81 320

FMVSNet5		69.50 286	67.96 287	74.15 300	82.97 276	55.35 316	80.01 290	82.12 291	62.56 288	63.02 332	81.53 303	36.92 338	81.92 320	48.42 330	74.06 294	85.17 304

PMMVS		69.34 287	68.67 279	71.35 319	75.67 342	62.03 237	75.17 325	73.46 346	50.00 349	68.68 288	79.05 323	52.07 235	78.13 335	61.16 252	82.77 187	73.90 354

our_test_3		69.14 288	67.00 299	75.57 285	79.80 322	58.80 270	77.96 310	77.81 324	59.55 310	62.90 335	78.25 332	47.43 280	83.97 309	51.71 313	67.58 330	83.93 319

EPMVS		69.02 289	68.16 284	71.59 315	79.61 325	49.80 354	77.40 314	66.93 359	62.82 285	70.01 275	79.05 323	45.79 295	77.86 338	56.58 293	75.26 283	87.13 269

KD-MVS_self_test		68.81 290	67.59 295	72.46 311	74.29 348	45.45 360	77.93 311	87.00 226	63.12 277	63.99 329	78.99 327	42.32 315	84.77 305	56.55 294	64.09 341	87.16 268

Anonymous20240521		68.80 291	67.22 298	73.55 303	74.33 347	54.11 327	83.18 253	85.61 244	58.15 321	61.68 337	80.94 309	30.71 352	81.27 324	57.00 289	73.34 304	85.28 300

Anonymous20231206		68.60 292	67.80 291	71.02 321	80.23 315	50.75 349	78.30 308	80.47 306	56.79 330	66.11 316	82.63 293	46.35 289	78.95 332	43.62 351	75.70 270	83.36 324

MIMVSNet1		68.58 293	66.78 301	73.98 301	80.07 317	51.82 341	80.77 278	84.37 258	64.40 265	59.75 344	82.16 299	36.47 339	83.63 312	42.73 353	70.33 320	86.48 282

EU-MVSNet		68.53 294	67.61 294	71.31 320	78.51 332	47.01 358	84.47 226	84.27 262	42.27 357	66.44 314	84.79 262	40.44 326	83.76 310	58.76 273	68.54 329	83.17 325

PatchT		68.46 295	67.85 289	70.29 324	80.70 310	43.93 367	72.47 334	74.88 340	60.15 305	70.55 266	76.57 340	49.94 260	81.59 321	50.58 317	74.83 288	85.34 299

test_fmvs2		68.35 296	67.48 296	70.98 322	69.50 361	51.95 340	80.05 289	76.38 335	49.33 350	74.65 231	84.38 267	23.30 361	75.40 353	74.51 131	75.17 285	85.60 296

test0.0.03 1		68.00 297	67.69 293	68.90 329	77.55 334	47.43 356	75.70 324	72.95 348	66.66 238	66.56 309	82.29 297	48.06 278	75.87 350	44.97 349	74.51 291	83.41 323

TDRefinement		67.49 298	64.34 307	76.92 275	73.47 353	61.07 248	84.86 217	82.98 283	59.77 308	58.30 348	85.13 256	26.06 356	87.89 282	47.92 337	60.59 349	81.81 336

test20.03		67.45 299	66.95 300	68.94 328	75.48 344	44.84 365	77.50 313	77.67 325	66.66 238	63.01 333	83.80 276	47.02 283	78.40 334	42.53 354	68.86 328	83.58 322

UnsupCasMVSNet_eth		67.33 300	65.99 303	71.37 317	73.48 352	51.47 345	75.16 326	85.19 248	65.20 255	60.78 340	80.93 311	42.35 314	77.20 340	57.12 287	53.69 359	85.44 298

TinyColmap		67.30 301	64.81 305	74.76 294	81.92 294	56.68 301	80.29 287	81.49 297	60.33 302	56.27 355	83.22 283	24.77 358	87.66 285	45.52 346	69.47 323	79.95 344

dp		66.80 302	65.43 304	70.90 323	79.74 324	48.82 355	75.12 328	74.77 341	59.61 309	64.08 328	77.23 337	42.89 311	80.72 326	48.86 329	66.58 333	83.16 326

MDA-MVSNet-bldmvs		66.68 303	63.66 311	75.75 282	79.28 329	60.56 256	73.92 332	78.35 322	64.43 264	50.13 361	79.87 320	44.02 306	83.67 311	46.10 344	56.86 352	83.03 329

testgi		66.67 304	66.53 302	67.08 336	75.62 343	41.69 371	75.93 320	76.50 334	66.11 245	65.20 323	86.59 220	35.72 342	74.71 355	43.71 350	73.38 303	84.84 307

CHOSEN 280x420		66.51 305	64.71 306	71.90 313	81.45 300	63.52 213	57.98 366	68.95 357	53.57 339	62.59 336	76.70 339	46.22 290	75.29 354	55.25 298	79.68 223	76.88 351

PM-MVS		66.41 306	64.14 308	73.20 307	73.92 349	56.45 303	78.97 301	64.96 364	63.88 275	64.72 324	80.24 315	19.84 364	83.44 314	66.24 208	64.52 340	79.71 345

JIA-IIPM		66.32 307	62.82 317	76.82 276	77.09 337	61.72 243	65.34 359	75.38 338	58.04 323	64.51 325	62.32 360	42.05 320	86.51 292	51.45 315	69.22 325	82.21 333

KD-MVS_2432*1600		66.22 308	63.89 309	73.21 305	75.47 345	53.42 333	70.76 341	84.35 259	64.10 269	66.52 311	78.52 329	34.55 345	84.98 302	50.40 319	50.33 363	81.23 338

miper_refine_blended		66.22 308	63.89 309	73.21 305	75.47 345	53.42 333	70.76 341	84.35 259	64.10 269	66.52 311	78.52 329	34.55 345	84.98 302	50.40 319	50.33 363	81.23 338

ADS-MVSNet2		66.20 310	63.33 312	74.82 293	79.92 318	58.75 271	67.55 352	75.19 339	53.37 340	65.25 321	75.86 343	42.32 315	80.53 327	41.57 355	68.91 326	85.18 302

YYNet1		65.03 311	62.91 315	71.38 316	75.85 341	56.60 302	69.12 349	74.66 344	57.28 328	54.12 357	77.87 334	45.85 294	74.48 356	49.95 324	61.52 346	83.05 328

MDA-MVSNet_test_wron		65.03 311	62.92 314	71.37 317	75.93 339	56.73 298	69.09 350	74.73 342	57.28 328	54.03 358	77.89 333	45.88 293	74.39 357	49.89 325	61.55 345	82.99 330

Patchmatch-test		64.82 313	63.24 313	69.57 326	79.42 328	49.82 353	63.49 363	69.05 356	51.98 345	59.95 343	80.13 316	50.91 248	70.98 361	40.66 357	73.57 299	87.90 249

ADS-MVSNet		64.36 314	62.88 316	68.78 331	79.92 318	47.17 357	67.55 352	71.18 349	53.37 340	65.25 321	75.86 343	42.32 315	73.99 358	41.57 355	68.91 326	85.18 302

LF4IMVS		64.02 315	62.19 318	69.50 327	70.90 359	53.29 336	76.13 318	77.18 331	52.65 342	58.59 346	80.98 308	23.55 360	76.52 344	53.06 309	66.66 332	78.68 347

UnsupCasMVSNet_bld		63.70 316	61.53 321	70.21 325	73.69 351	51.39 346	72.82 333	81.89 292	55.63 335	57.81 350	71.80 352	38.67 331	78.61 333	49.26 328	52.21 361	80.63 341

test_fmvs3		63.36 317	61.82 319	67.98 333	62.51 368	46.96 359	77.37 315	74.03 345	45.24 353	67.50 298	78.79 328	12.16 372	72.98 360	72.77 151	66.02 335	83.99 318

mvsany_test1		62.30 318	61.26 322	65.41 338	69.52 360	54.86 321	66.86 354	49.78 375	46.65 352	68.50 292	83.21 284	49.15 270	66.28 367	56.93 290	60.77 347	75.11 353

new-patchmatchnet		61.73 319	61.73 320	61.70 342	72.74 356	24.50 381	69.16 348	78.03 323	61.40 296	56.72 353	75.53 346	38.42 332	76.48 345	45.95 345	57.67 351	84.13 316

PVSNet_0	57.27 20	61.67 320	59.27 323	68.85 330	79.61 325	57.44 290	68.01 351	73.44 347	55.93 334	58.54 347	70.41 355	44.58 303	77.55 339	47.01 339	35.91 369	71.55 357

test_vis1_rt		60.28 321	58.42 324	65.84 337	67.25 364	55.60 315	70.44 343	60.94 368	44.33 355	59.00 345	66.64 357	24.91 357	68.67 365	62.80 233	69.48 322	73.25 355

MVS-HIRNet		59.14 322	57.67 325	63.57 340	81.65 296	43.50 368	71.73 336	65.06 363	39.59 361	51.43 360	57.73 365	38.34 333	82.58 319	39.53 358	73.95 295	64.62 361

pmmvs3		57.79 323	54.26 327	68.37 332	64.02 367	56.72 299	75.12 328	65.17 362	40.20 359	52.93 359	69.86 356	20.36 363	75.48 352	45.45 347	55.25 358	72.90 356

DSMNet-mixed		57.77 324	56.90 326	60.38 344	67.70 363	35.61 374	69.18 347	53.97 373	32.30 369	57.49 351	79.88 319	40.39 327	68.57 366	38.78 359	72.37 308	76.97 350

LCM-MVSNet		54.25 325	49.68 334	67.97 334	53.73 376	45.28 363	66.85 355	80.78 301	35.96 365	39.45 366	62.23 361	8.70 376	78.06 337	48.24 334	51.20 362	80.57 342

mvsany_test3		53.99 326	51.45 330	61.61 343	55.51 372	44.74 366	63.52 362	45.41 379	43.69 356	58.11 349	76.45 341	17.99 365	63.76 370	54.77 300	47.59 365	76.34 352

FPMVS		53.68 327	51.64 329	59.81 345	65.08 366	51.03 347	69.48 346	69.58 354	41.46 358	40.67 364	72.32 351	16.46 368	70.00 364	24.24 371	65.42 337	58.40 366

APD_test1		53.31 328	49.93 333	63.42 341	65.68 365	50.13 351	71.59 337	66.90 360	34.43 366	40.58 365	71.56 353	8.65 377	76.27 347	34.64 363	55.36 357	63.86 362

N_pmnet		52.79 329	53.26 328	51.40 353	78.99 331	7.68 384	69.52 345	3.89 384	51.63 346	57.01 352	74.98 347	40.83 325	65.96 368	37.78 360	64.67 339	80.56 343

test_f		52.09 330	50.82 331	55.90 349	53.82 375	42.31 370	59.42 365	58.31 371	36.45 364	56.12 356	70.96 354	12.18 371	57.79 372	53.51 306	56.57 354	67.60 358

EGC-MVSNET		52.07 331	47.05 335	67.14 335	83.51 260	60.71 253	80.50 284	67.75 358	0.07 379	0.43 380	75.85 345	24.26 359	81.54 322	28.82 365	62.25 343	59.16 364

new_pmnet		50.91 332	50.29 332	52.78 352	68.58 362	34.94 376	63.71 361	56.63 372	39.73 360	44.95 362	65.47 358	21.93 362	58.48 371	34.98 362	56.62 353	64.92 360

ANet_high		50.57 333	46.10 337	63.99 339	48.67 379	39.13 372	70.99 340	80.85 300	61.39 297	31.18 368	57.70 366	17.02 367	73.65 359	31.22 364	15.89 376	79.18 346

test_vis3_rt		49.26 334	47.02 336	56.00 348	54.30 373	45.27 364	66.76 356	48.08 376	36.83 363	44.38 363	53.20 368	7.17 379	64.07 369	56.77 292	55.66 355	58.65 365

testf1		45.72 335	41.96 338	57.00 346	56.90 370	45.32 361	66.14 357	59.26 369	26.19 370	30.89 369	60.96 363	4.14 380	70.64 362	26.39 369	46.73 367	55.04 367

APD_test2		45.72 335	41.96 338	57.00 346	56.90 370	45.32 361	66.14 357	59.26 369	26.19 370	30.89 369	60.96 363	4.14 380	70.64 362	26.39 369	46.73 367	55.04 367

Gipuma		45.18 337	41.86 340	55.16 351	77.03 338	51.52 344	32.50 372	80.52 305	32.46 368	27.12 371	35.02 372	9.52 375	75.50 351	22.31 372	60.21 350	38.45 371
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 338	40.28 341	55.82 350	40.82 381	42.54 369	65.12 360	63.99 365	34.43 366	24.48 372	57.12 367	3.92 382	76.17 349	17.10 374	55.52 356	48.75 369
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 339	38.86 342	46.69 354	53.84 374	16.45 382	48.61 369	49.92 374	37.49 362	31.67 367	60.97 362	8.14 378	56.42 373	28.42 366	30.72 371	67.19 359

E-PMN		31.77 340	30.64 343	35.15 357	52.87 377	27.67 378	57.09 367	47.86 377	24.64 372	16.40 377	33.05 373	11.23 373	54.90 374	14.46 376	18.15 374	22.87 373

test_method		31.52 341	29.28 345	38.23 356	27.03 383	6.50 385	20.94 374	62.21 367	4.05 377	22.35 375	52.50 369	13.33 369	47.58 376	27.04 368	34.04 370	60.62 363

EMVS		30.81 342	29.65 344	34.27 358	50.96 378	25.95 380	56.58 368	46.80 378	24.01 373	15.53 378	30.68 374	12.47 370	54.43 375	12.81 377	17.05 375	22.43 374

MVE	26.22 23	30.37 343	25.89 347	43.81 355	44.55 380	35.46 375	28.87 373	39.07 380	18.20 374	18.58 376	40.18 371	2.68 383	47.37 377	17.07 375	23.78 373	48.60 370
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 344	26.61 346	0.00 364	0.00 387	0.00 388	0.00 375	89.26 168	0.00 382	0.00 383	88.61 162	61.62 149	0.00 383	0.00 381	0.00 381	0.00 379

tmp_tt		18.61 345	21.40 348	10.23 361	4.82 384	10.11 383	34.70 371	30.74 382	1.48 378	23.91 374	26.07 375	28.42 354	13.41 380	27.12 367	15.35 377	7.17 375

wuyk23d		16.82 346	15.94 349	19.46 360	58.74 369	31.45 377	39.22 370	3.74 385	6.84 376	6.04 379	2.70 379	1.27 384	24.29 379	10.54 378	14.40 378	2.63 376

ab-mvs-re		7.23 347	9.64 350	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	86.72 212	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

test123		6.12 348	8.11 351	0.14 362	0.06 386	0.09 386	71.05 339	0.03 387	0.04 381	0.25 382	1.30 381	0.05 385	0.03 382	0.21 380	0.01 380	0.29 377

testmvs		6.04 349	8.02 352	0.10 363	0.08 385	0.03 387	69.74 344	0.04 386	0.05 380	0.31 381	1.68 380	0.02 386	0.04 381	0.24 379	0.02 379	0.25 378

pcd_1.5k_mvsjas		5.26 350	7.02 353	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	63.15 126	0.00 383	0.00 381	0.00 381	0.00 379

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 388	0.00 375	0.00 388	0.00 382	0.00 383	0.00 382	0.00 387	0.00 383	0.00 381	0.00 381	0.00 379

FOURS1					95.00 10	72.39 38	95.06 1	93.84 15	74.49 109	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 45					97.53 1	89.67 1	96.44 9	94.41 30

PC_three_1452									68.21 226	92.02 12	94.00 40	82.09 5	95.98 49	84.58 36	96.68 2	94.95 8

No_MVS				89.16 1	94.34 27	75.53 2		92.99 45					97.53 1	89.67 1	96.44 9	94.41 30

test_one_0601					95.07 7	71.46 53		94.14 5	78.27 33	92.05 11	95.74 6	80.83 11

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					94.38 25	72.22 43		92.67 61	70.98 171	87.75 29	94.07 37	74.01 32	96.70 25	84.66 35	94.84 41

RE-MVS-def			85.48 49		93.06 55	70.63 71	91.88 37	92.27 76	73.53 132	85.69 39	94.45 24	63.87 117		82.75 56	91.87 77	92.50 104

IU-MVS					95.30 2	71.25 55		92.95 51	66.81 234	92.39 6				88.94 11	96.63 4	94.85 17

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 39	82.45 3	96.87 18	83.77 46	96.48 8	94.88 12

test_241102_TWO								94.06 10	77.24 48	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 39

test_241102_ONE					95.30 2	70.98 61		94.06 10	77.17 51	93.10 1	95.39 11	82.99 1	97.27 10

9.14			88.26 14		92.84 60		91.52 44	94.75 1	73.93 121	88.57 22	94.67 17	75.57 22	95.79 51	86.77 23	95.76 23

save fliter					93.80 40	72.35 41	90.47 62	91.17 116	74.31 112

test_0728_THIRD									78.38 31	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 22

test_0728_SECOND				87.71 30	95.34 1	71.43 54	93.49 9	94.23 3					97.49 3	89.08 7	96.41 12	94.21 40

test0726					95.27 5	71.25 55	93.60 6	94.11 6	77.33 46	92.81 3	95.79 3	80.98 9

GSMVS																88.96 227

test_part2					95.06 8	72.65 31				91.80 13

sam_mvs1												51.32 245				88.96 227

sam_mvs												50.01 258

ambc				75.24 289	73.16 354	50.51 350	63.05 364	87.47 218		64.28 326	77.81 335	17.80 366	89.73 254	57.88 281	60.64 348	85.49 297

MTGPA								92.02 85

test_post1							78.90 303				5.43 378	48.81 277	85.44 300	59.25 265

test_post											5.46 377	50.36 256	84.24 307

patchmatchnet-post											74.00 348	51.12 247	88.60 274

GG-mvs-BLEND				75.38 288	81.59 298	55.80 312	79.32 296	69.63 353		67.19 302	73.67 349	43.24 309	88.90 271	50.41 318	84.50 162	81.45 337

MTMP							92.18 33	32.83 381

gm-plane-assit					81.40 301	53.83 330			62.72 287		80.94 309		92.39 190	63.40 230

test9_res														84.90 30	95.70 26	92.87 93

TEST9					93.26 50	72.96 24	88.75 106	91.89 93	68.44 223	85.00 46	93.10 55	74.36 28	95.41 65

test_8					93.13 52	72.57 34	88.68 111	91.84 97	68.69 219	84.87 50	93.10 55	74.43 26	95.16 74

agg_prior2														82.91 54	95.45 28	92.70 96

agg_prior					92.85 59	71.94 49		91.78 100		84.41 60			94.93 85

TestCases				79.58 238	85.15 233	63.62 208		79.83 313	62.31 290	60.32 341	86.73 210	32.02 348	88.96 269	50.28 321	71.57 315	86.15 287

test_prior4						72.60 33	89.01 96

test_prior2							88.85 102		75.41 91	84.91 48	93.54 47	74.28 29		83.31 49	95.86 20

test_prior				86.33 52	92.61 65	69.59 86		92.97 50					95.48 60			93.91 50

旧先验2							86.56 176		58.10 322	87.04 31			88.98 267	74.07 136

新几何2							86.29 184

新几何1				83.42 135	93.13 52	70.71 69		85.48 246	57.43 327	81.80 95	91.98 78	63.28 121	92.27 196	64.60 224	92.99 63	87.27 264

旧先验1					91.96 71	65.79 166		86.37 235			93.08 59	69.31 71			92.74 66	88.74 236

无先验							87.48 147	88.98 180	60.00 306				94.12 119	67.28 201		88.97 226

原ACMM2							86.86 165

原ACMM1				84.35 101	93.01 57	68.79 99		92.44 69	63.96 274	81.09 105	91.57 89	66.06 99	95.45 61	67.19 203	94.82 43	88.81 233

test222					91.50 77	68.26 116	84.16 236	83.20 281	54.63 338	79.74 117	91.63 87	58.97 180			91.42 83	86.77 277

testdata2													91.01 237	62.37 239

segment_acmp												73.08 37

testdata				79.97 228	90.90 86	64.21 199		84.71 253	59.27 313	85.40 41	92.91 61	62.02 145	89.08 265	68.95 186	91.37 84	86.63 281

testdata1							84.14 237		75.71 85

test12				86.80 47	92.63 64	70.70 70		91.79 99		82.71 86		71.67 47	96.16 42		94.50 48	93.54 71

plane_prior7					90.08 102	68.51 112

plane_prior6					89.84 111	68.70 107						60.42 173

plane_prior5								92.44 69					95.38 67	78.71 90	86.32 143	91.33 137

plane_prior4											91.00 108

plane_prior3						68.60 110			78.44 29	78.92 129

plane_prior2							91.25 48		79.12 21

plane_prior1					89.90 110

plane_prior						68.71 105	90.38 65		77.62 37						86.16 147

n2								0.00 388

nn								0.00 388

door-mid								69.98 352

lessismore_v0				78.97 245	81.01 308	57.15 293		65.99 361		61.16 339	82.82 290	39.12 330	91.34 226	59.67 261	46.92 366	88.43 242

LGP-MVS_train				84.50 94	89.23 133	68.76 101		91.94 91	75.37 92	76.64 184	91.51 90	54.29 214	94.91 86	78.44 92	83.78 171	89.83 200

test11								92.23 79

door								69.44 355

HQP5-MVS						66.98 143

HQP-NCC					89.33 126		89.17 89		76.41 70	77.23 170

ACMP_Plane					89.33 126		89.17 89		76.41 70	77.23 170

BP-MVS														77.47 102

HQP4-MVS										77.24 169			95.11 78			91.03 149

HQP3-MVS								92.19 82							85.99 150

HQP2-MVS												60.17 176

NP-MVS					89.62 113	68.32 114					90.24 120

MDTV_nov1_ep13_2view						37.79 373	75.16 326		55.10 336	66.53 310		49.34 267		53.98 303		87.94 248

MDTV_nov1_ep13			69.97 273		83.18 268	53.48 332	77.10 317	80.18 312	60.45 301	69.33 286	80.44 313	48.89 276	86.90 289	51.60 314	78.51 238

ACMMP++_ref															81.95 197

ACMMP++															81.25 203

Test By Simon												64.33 113

ITE_SJBPF				78.22 256	81.77 295	60.57 255		83.30 277	69.25 203	67.54 297	87.20 201	36.33 340	87.28 288	54.34 302	74.62 290	86.80 276

DeepMVS_CX				27.40 359	40.17 382	26.90 379		24.59 383	17.44 375	23.95 373	48.61 370	9.77 374	26.48 378	18.06 373	24.47 372	28.83 372

High-resolution multi-view benchmark