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++		81.67 1	82.40 1	79.47 9	87.24 14	59.15 59	88.18 1	87.15 3	65.04 14	84.26 5	91.86 6	67.01 1	90.84 3	79.48 4	91.38 2	88.42 9

SED-MVS		81.56 2	82.30 2	79.32 12	87.77 4	58.90 68	87.82 7	86.78 10	64.18 30	85.97 1	91.84 8	66.87 3	90.83 5	78.63 15	90.87 5	88.23 14

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 17	86.83 8	65.51 10	83.81 10	90.51 21	63.71 12	89.23 18	81.51 1	88.44 27	88.09 19
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
DVP-MVS		80.84 4	81.64 3	78.42 32	87.75 7	59.07 63	87.85 5	85.03 34	64.26 27	83.82 8	92.00 3	64.82 8	90.75 8	78.66 13	90.61 11	85.45 109
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
DPE-MVS		80.56 5	80.98 5	79.29 14	87.27 13	60.56 41	85.71 25	86.42 14	63.28 42	83.27 13	91.83 10	64.96 7	90.47 10	76.41 24	89.67 18	86.84 57
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
SMA-MVS		80.28 6	80.39 7	79.95 3	86.60 23	61.95 19	86.33 13	85.75 21	62.49 60	82.20 15	92.28 1	56.53 33	89.70 15	79.85 3	91.48 1	88.19 16
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
APDe-MVS		80.16 7	80.59 6	78.86 27	86.64 21	60.02 45	88.12 3	86.42 14	62.94 49	82.40 14	92.12 2	59.64 18	89.76 14	78.70 11	88.32 31	86.79 59

HPM-MVS++		79.88 8	80.14 8	79.10 20	88.17 1	64.80 1	86.59 12	83.70 60	65.37 11	78.78 22	90.64 17	58.63 24	87.24 49	79.00 10	90.37 14	85.26 119

CNVR-MVS		79.84 9	79.97 9	79.45 10	87.90 2	62.17 17	84.37 34	85.03 34	66.96 3	77.58 27	90.06 34	59.47 20	89.13 20	78.67 12	89.73 16	87.03 51

SteuartSystems-ACMMP		79.48 10	79.31 10	79.98 2	83.01 72	62.18 16	87.60 9	85.83 19	66.69 7	78.03 26	90.98 14	54.26 50	90.06 12	78.42 17	89.02 23	87.69 31
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 11	79.00 12	79.13 18	84.92 56	60.32 44	83.03 55	85.33 27	62.86 52	80.17 17	90.03 36	61.76 14	88.95 22	74.21 33	88.67 26	88.12 18

SF-MVS		78.82 12	79.22 11	77.60 42	82.88 74	57.83 79	84.99 30	88.13 2	61.86 73	79.16 20	90.75 16	57.96 25	87.09 58	77.08 21	90.18 15	87.87 24

ZNCC-MVS		78.82 12	78.67 15	79.30 13	86.43 28	62.05 18	86.62 11	86.01 18	63.32 41	75.08 38	90.47 24	53.96 54	88.68 25	76.48 23	89.63 20	87.16 49

ACMMP_NAP		78.77 14	78.78 13	78.74 28	85.44 45	61.04 31	83.84 47	85.16 30	62.88 51	78.10 24	91.26 13	52.51 68	88.39 28	79.34 6	90.52 13	86.78 60

NCCC		78.58 15	78.31 17	79.39 11	87.51 12	62.61 13	85.20 29	84.42 42	66.73 6	74.67 47	89.38 47	55.30 41	89.18 19	74.19 34	87.34 41	86.38 67

DeepC-MVS	69.38 2	78.56 16	78.14 20	79.83 6	83.60 63	61.62 23	84.17 40	86.85 6	63.23 44	73.84 56	90.25 30	57.68 27	89.96 13	74.62 31	89.03 22	87.89 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + MP.		78.44 17	78.28 18	78.90 25	84.96 52	61.41 26	84.03 43	83.82 58	59.34 115	79.37 19	89.76 43	59.84 16	87.62 45	76.69 22	86.74 50	87.68 32
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MP-MVS-pluss		78.35 18	78.46 16	78.03 38	84.96 52	59.52 52	82.93 57	85.39 26	62.15 65	76.41 32	91.51 11	52.47 70	86.78 65	80.66 2	89.64 19	87.80 28
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 18	78.26 19	78.64 29	86.54 25	63.47 4	86.02 19	83.55 64	63.89 35	73.60 58	90.60 18	54.85 46	86.72 66	77.20 20	88.06 35	85.74 98
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 20	77.85 22	78.99 24	86.05 38	61.82 22	85.84 20	85.21 29	63.56 39	74.29 51	90.03 36	52.56 67	88.53 27	74.79 30	88.34 29	86.63 63

APD-MVS		78.02 21	78.04 21	77.98 39	86.44 27	60.81 38	85.52 26	84.36 43	60.61 87	79.05 21	90.30 28	55.54 40	88.32 30	73.48 41	87.03 43	84.83 130
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 22	77.65 23	79.10 20	86.71 19	62.81 8	86.29 14	84.32 44	62.82 53	73.96 54	90.50 22	53.20 63	88.35 29	74.02 36	87.05 42	86.13 81

ACMMPR		77.71 23	77.23 26	79.16 16	86.75 18	62.93 7	86.29 14	84.24 45	62.82 53	73.55 59	90.56 20	49.80 97	88.24 31	74.02 36	87.03 43	86.32 75

SD-MVS		77.70 24	77.62 24	77.93 40	84.47 59	61.88 21	84.55 32	83.87 56	60.37 94	79.89 18	89.38 47	54.97 44	85.58 95	76.12 25	84.94 60	86.33 73
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
region2R		77.67 25	77.18 27	79.15 17	86.76 17	62.95 6	86.29 14	84.16 47	62.81 55	73.30 61	90.58 19	49.90 95	88.21 32	73.78 38	87.03 43	86.29 78

MCST-MVS		77.48 26	77.45 25	77.54 43	86.67 20	58.36 75	83.22 53	86.93 5	56.91 153	74.91 42	88.19 59	59.15 22	87.68 44	73.67 39	87.45 40	86.57 64

HPM-MVS		77.28 27	76.85 28	78.54 30	85.00 51	60.81 38	82.91 58	85.08 31	62.57 58	73.09 67	89.97 39	50.90 91	87.48 47	75.30 26	86.85 48	87.33 47
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DeepC-MVS_fast	68.24 3	77.25 28	76.63 31	79.12 19	86.15 34	60.86 36	84.71 31	84.85 38	61.98 72	73.06 68	88.88 53	53.72 57	89.06 21	68.27 66	88.04 36	87.42 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS		77.17 29	76.56 32	79.00 22	86.32 29	62.62 11	85.83 21	83.92 51	64.55 21	72.17 80	90.01 38	47.95 117	88.01 36	71.55 53	86.74 50	86.37 69

CP-MVS		77.12 30	76.68 30	78.43 31	86.05 38	63.18 5	87.55 10	83.45 67	62.44 62	72.68 73	90.50 22	48.18 115	87.34 48	73.59 40	85.71 56	84.76 134

CSCG		76.92 31	76.75 29	77.41 44	83.96 62	59.60 50	82.95 56	86.50 13	60.78 85	75.27 36	84.83 121	60.76 15	86.56 71	67.86 72	87.87 39	86.06 83

MTAPA		76.90 32	76.42 33	78.35 33	86.08 37	63.57 2	74.92 196	80.97 121	65.13 13	75.77 34	90.88 15	48.63 110	86.66 68	77.23 19	88.17 33	84.81 131

PGM-MVS		76.77 33	76.06 36	78.88 26	86.14 35	62.73 9	82.55 65	83.74 59	61.71 74	72.45 79	90.34 27	48.48 113	88.13 33	72.32 46	86.85 48	85.78 92

mPP-MVS		76.54 34	75.93 38	78.34 34	86.47 26	63.50 3	85.74 24	82.28 89	62.90 50	71.77 83	90.26 29	46.61 140	86.55 72	71.71 51	85.66 57	84.97 127

CANet		76.46 35	75.93 38	78.06 37	81.29 92	57.53 83	82.35 67	83.31 73	67.78 1	70.09 97	86.34 93	54.92 45	88.90 23	72.68 45	84.55 63	87.76 30

CDPH-MVS		76.31 36	75.67 42	78.22 35	85.35 48	59.14 61	81.31 85	84.02 48	56.32 165	74.05 52	88.98 52	53.34 62	87.92 39	69.23 64	88.42 28	87.59 36

train_agg		76.27 37	76.15 35	76.64 53	85.58 43	61.59 24	81.62 80	81.26 113	55.86 173	74.93 40	88.81 54	53.70 58	84.68 116	75.24 28	88.33 30	83.65 170

CS-MVS		76.25 38	75.98 37	77.06 48	80.15 114	55.63 116	84.51 33	83.90 53	63.24 43	73.30 61	87.27 74	55.06 43	86.30 81	71.78 50	84.58 62	89.25 4

casdiffmvs_mvg		76.14 39	76.30 34	75.66 69	76.46 206	51.83 174	79.67 107	85.08 31	65.02 17	75.84 33	88.58 58	59.42 21	85.08 106	72.75 44	83.93 70	90.08 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
SR-MVS		76.13 40	75.70 41	77.40 46	85.87 40	61.20 29	85.52 26	82.19 90	59.99 103	75.10 37	90.35 26	47.66 121	86.52 73	71.64 52	82.99 76	84.47 140

ACMMP		76.02 41	75.33 44	78.07 36	85.20 49	61.91 20	85.49 28	84.44 41	63.04 47	69.80 107	89.74 44	45.43 153	87.16 53	72.01 48	82.87 81	85.14 120
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
PHI-MVS		75.87 42	75.36 43	77.41 44	80.62 105	55.91 111	84.28 37	85.78 20	56.08 171	73.41 60	86.58 88	50.94 90	88.54 26	70.79 56	89.71 17	87.79 29

EC-MVSNet		75.84 43	75.87 40	75.74 67	78.86 140	52.65 156	83.73 48	86.08 17	63.47 40	72.77 72	87.25 75	53.13 64	87.93 38	71.97 49	85.57 58	86.66 62

3Dnovator+	66.72 4	75.84 43	74.57 51	79.66 8	82.40 76	59.92 47	85.83 21	86.32 16	66.92 5	67.80 145	89.24 49	42.03 184	89.38 17	64.07 104	86.50 53	89.69 2

CS-MVS-test		75.62 45	75.31 45	76.56 55	80.63 104	55.13 124	83.88 46	85.22 28	62.05 69	71.49 87	86.03 100	53.83 56	86.36 79	67.74 73	86.91 47	88.19 16

DPM-MVS		75.47 46	75.00 47	76.88 49	81.38 91	59.16 58	79.94 100	85.71 22	56.59 161	72.46 77	86.76 79	56.89 31	87.86 41	66.36 85	88.91 25	83.64 171

APD-MVS_3200maxsize		74.96 47	74.39 53	76.67 52	82.20 78	58.24 76	83.67 49	83.29 74	58.41 129	73.71 57	90.14 31	45.62 146	85.99 85	69.64 60	82.85 82	85.78 92

TSAR-MVS + GP.		74.90 48	74.15 55	77.17 47	82.00 80	58.77 71	81.80 77	78.57 159	58.58 126	74.32 50	84.51 131	55.94 38	87.22 50	67.11 80	84.48 65	85.52 105

casdiffmvs		74.80 49	74.89 49	74.53 96	75.59 218	50.37 192	78.17 126	85.06 33	62.80 56	74.40 49	87.86 66	57.88 26	83.61 136	69.46 63	82.79 83	89.59 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
DELS-MVS		74.76 50	74.46 52	75.65 70	77.84 171	52.25 166	75.59 180	84.17 46	63.76 36	73.15 64	82.79 162	59.58 19	86.80 64	67.24 79	86.04 55	87.89 22
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
OPM-MVS		74.73 51	74.25 54	76.19 59	80.81 100	59.01 66	82.60 64	83.64 61	63.74 37	72.52 76	87.49 69	47.18 131	85.88 88	69.47 62	80.78 97	83.66 169
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
canonicalmvs		74.67 52	74.98 48	73.71 116	78.94 139	50.56 190	80.23 94	83.87 56	60.30 98	77.15 29	86.56 89	59.65 17	82.00 172	66.01 89	82.12 87	88.58 8

baseline		74.61 53	74.70 50	74.34 100	75.70 214	49.99 200	77.54 139	84.63 40	62.73 57	73.98 53	87.79 68	57.67 28	83.82 132	69.49 61	82.74 84	89.20 5

SR-MVS-dyc-post		74.57 54	73.90 57	76.58 54	83.49 65	59.87 48	84.29 35	81.36 106	58.07 135	73.14 65	90.07 32	44.74 160	85.84 89	68.20 67	81.76 92	84.03 150

dcpmvs_2		74.55 55	75.23 46	72.48 148	82.34 77	53.34 145	77.87 130	81.46 102	57.80 142	75.49 35	86.81 78	62.22 13	77.75 246	71.09 55	82.02 89	86.34 71

ETV-MVS		74.46 56	73.84 59	76.33 58	79.27 130	55.24 123	79.22 113	85.00 36	64.97 19	72.65 74	79.46 234	53.65 61	87.87 40	67.45 78	82.91 79	85.89 89

HQP_MVS		74.31 57	73.73 60	76.06 60	81.41 89	56.31 100	84.22 38	84.01 49	64.52 23	69.27 115	86.10 97	45.26 157	87.21 51	68.16 69	80.58 101	84.65 135

HPM-MVS_fast		74.30 58	73.46 63	76.80 50	84.45 60	59.04 65	83.65 50	81.05 118	60.15 100	70.43 93	89.84 41	41.09 199	85.59 94	67.61 76	82.90 80	85.77 95

MVS_111021_HR		74.02 59	73.46 63	75.69 68	83.01 72	60.63 40	77.29 147	78.40 170	61.18 80	70.58 92	85.97 102	54.18 52	84.00 129	67.52 77	82.98 78	82.45 195

MG-MVS		73.96 60	73.89 58	74.16 103	85.65 42	49.69 205	81.59 82	81.29 112	61.45 76	71.05 89	88.11 60	51.77 79	87.73 43	61.05 133	83.09 74	85.05 124

alignmvs		73.86 61	73.99 56	73.45 128	78.20 159	50.50 191	78.57 121	82.43 87	59.40 113	76.57 30	86.71 83	56.42 35	81.23 188	65.84 91	81.79 91	88.62 6

MSLP-MVS++		73.77 62	73.47 62	74.66 89	83.02 71	59.29 57	82.30 72	81.88 94	59.34 115	71.59 86	86.83 77	45.94 144	83.65 135	65.09 98	85.22 59	81.06 220

HQP-MVS		73.45 63	72.80 67	75.40 74	80.66 101	54.94 125	82.31 69	83.90 53	62.10 66	67.85 140	85.54 114	45.46 151	86.93 60	67.04 81	80.35 105	84.32 142

CLD-MVS		73.33 64	72.68 68	75.29 78	78.82 142	53.33 146	78.23 125	84.79 39	61.30 79	70.41 94	81.04 202	52.41 71	87.12 56	64.61 103	82.49 86	85.41 113
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+		73.31 65	72.54 69	75.62 71	77.87 170	53.64 137	79.62 109	79.61 139	61.63 75	72.02 82	82.61 167	56.44 34	85.97 86	63.99 107	79.07 125	87.25 48

UA-Net		73.13 66	72.93 66	73.76 112	83.58 64	51.66 175	78.75 116	77.66 181	67.75 2	72.61 75	89.42 45	49.82 96	83.29 141	53.61 186	83.14 73	86.32 75

EPNet		73.09 67	72.16 71	75.90 63	75.95 212	56.28 102	83.05 54	72.39 250	66.53 8	65.27 194	87.00 76	50.40 93	85.47 100	62.48 120	86.32 54	85.94 85
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
nrg030		72.96 68	73.01 65	72.84 141	75.41 221	50.24 193	80.02 98	82.89 83	58.36 131	74.44 48	86.73 81	58.90 23	80.83 198	65.84 91	74.46 168	87.44 40

CPTT-MVS		72.78 69	72.08 73	74.87 84	84.88 57	61.41 26	84.15 41	77.86 177	55.27 188	67.51 151	88.08 62	41.93 186	81.85 174	69.04 65	80.01 109	81.35 213

LPG-MVS_test		72.74 70	71.74 75	75.76 65	80.22 109	57.51 84	82.55 65	83.40 69	61.32 77	66.67 167	87.33 72	39.15 212	86.59 69	67.70 74	77.30 146	83.19 181

h-mvs33		72.71 71	71.49 78	76.40 56	81.99 81	59.58 51	76.92 156	76.74 197	60.40 91	74.81 43	85.95 104	45.54 149	85.76 91	70.41 58	70.61 220	83.86 158

PAPM_NR		72.63 72	71.80 74	75.13 81	81.72 84	53.42 144	79.91 102	83.28 75	59.14 117	66.31 174	85.90 105	51.86 78	86.06 82	57.45 154	80.62 99	85.91 87

VDD-MVS		72.50 73	72.09 72	73.75 114	81.58 85	49.69 205	77.76 134	77.63 182	63.21 45	73.21 63	89.02 51	42.14 183	83.32 140	61.72 127	82.50 85	88.25 13

3Dnovator	64.47 5	72.49 74	71.39 81	75.79 64	77.70 174	58.99 67	80.66 92	83.15 78	62.24 64	65.46 190	86.59 87	42.38 182	85.52 96	59.59 145	84.72 61	82.85 189

MVS_Test		72.45 75	72.46 70	72.42 152	74.88 226	48.50 220	76.28 167	83.14 79	59.40 113	72.46 77	84.68 123	55.66 39	81.12 189	65.98 90	79.66 113	87.63 34

EI-MVSNet-Vis-set		72.42 76	71.59 76	74.91 82	78.47 151	54.02 133	77.05 152	79.33 145	65.03 16	71.68 85	79.35 237	52.75 66	84.89 112	66.46 84	74.23 171	85.83 91

ACMP	63.53 6	72.30 77	71.20 86	75.59 73	80.28 107	57.54 82	82.74 61	82.84 84	60.58 88	65.24 198	86.18 95	39.25 210	86.03 84	66.95 83	76.79 153	83.22 179
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 78	71.21 85	75.31 76	78.50 149	55.93 110	81.63 79	82.12 91	56.24 168	70.02 101	85.68 110	47.05 133	84.34 122	65.27 97	74.41 170	85.67 99

Vis-MVSNet		72.18 79	71.37 82	74.61 92	81.29 92	55.41 121	80.90 88	78.28 172	60.73 86	69.23 118	88.09 61	44.36 165	82.65 160	57.68 152	81.75 94	85.77 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
API-MVS		72.17 80	71.41 80	74.45 98	81.95 82	57.22 87	84.03 43	80.38 130	59.89 107	68.40 127	82.33 174	49.64 98	87.83 42	51.87 200	84.16 69	78.30 252

EPP-MVSNet		72.16 81	71.31 84	74.71 86	78.68 146	49.70 203	82.10 74	81.65 98	60.40 91	65.94 179	85.84 106	51.74 80	86.37 78	55.93 163	79.55 116	88.07 21

DP-MVS Recon		72.15 82	70.73 93	76.40 56	86.57 24	57.99 78	81.15 87	82.96 80	57.03 150	66.78 163	85.56 111	44.50 163	88.11 34	51.77 202	80.23 108	83.10 184

EI-MVSNet-UG-set		71.92 83	71.06 89	74.52 97	77.98 168	53.56 139	76.62 160	79.16 146	64.40 25	71.18 88	78.95 242	52.19 74	84.66 118	65.47 95	73.57 179	85.32 116

VDDNet		71.81 84	71.33 83	73.26 135	82.80 75	47.60 232	78.74 117	75.27 214	59.59 112	72.94 69	89.40 46	41.51 194	83.91 130	58.75 149	82.99 76	88.26 12

EIA-MVS		71.78 85	70.60 94	75.30 77	79.85 118	53.54 140	77.27 148	83.26 76	57.92 139	66.49 169	79.39 235	52.07 76	86.69 67	60.05 139	79.14 124	85.66 100

LFMVS		71.78 85	71.59 76	72.32 153	83.40 67	46.38 241	79.75 105	71.08 257	64.18 30	72.80 71	88.64 57	42.58 179	83.72 133	57.41 155	84.49 64	86.86 56

test_fmvsm_n_1920		71.73 87	71.14 87	73.50 125	72.52 263	56.53 99	75.60 179	76.16 201	48.11 270	77.22 28	85.56 111	53.10 65	77.43 250	74.86 29	77.14 148	86.55 65

PAPR		71.72 88	70.82 92	74.41 99	81.20 96	51.17 177	79.55 110	83.33 72	55.81 177	66.93 162	84.61 127	50.95 89	86.06 82	55.79 166	79.20 122	86.00 84

IS-MVSNet		71.57 89	71.00 90	73.27 134	78.86 140	45.63 252	80.22 95	78.69 156	64.14 33	66.46 170	87.36 71	49.30 101	85.60 93	50.26 213	83.71 72	88.59 7

MAR-MVS		71.51 90	70.15 102	75.60 72	81.84 83	59.39 54	81.38 84	82.90 82	54.90 201	68.08 136	78.70 243	47.73 119	85.51 97	51.68 204	84.17 68	81.88 205
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
MVSFormer		71.50 91	70.38 99	74.88 83	78.76 143	57.15 92	82.79 59	78.48 163	51.26 241	69.49 110	83.22 156	43.99 168	83.24 142	66.06 87	79.37 117	84.23 145

PVSNet_Blended_VisFu		71.45 92	70.39 98	74.65 90	82.01 79	58.82 70	79.93 101	80.35 131	55.09 193	65.82 185	82.16 180	49.17 104	82.64 161	60.34 137	78.62 133	82.50 194

OMC-MVS		71.40 93	70.60 94	73.78 110	76.60 202	53.15 149	79.74 106	79.78 135	58.37 130	68.75 122	86.45 91	45.43 153	80.60 202	62.58 118	77.73 139	87.58 37

mvsmamba		71.15 94	69.54 110	75.99 61	77.61 182	53.46 142	81.95 76	75.11 220	57.73 143	66.95 161	85.96 103	37.14 235	87.56 46	67.94 71	75.49 164	86.97 52

UniMVSNet_NR-MVSNet		71.11 95	71.00 90	71.44 166	79.20 132	44.13 264	76.02 175	82.60 86	66.48 9	68.20 130	84.60 128	56.82 32	82.82 156	54.62 177	70.43 222	87.36 46

hse-mvs2		71.04 96	69.86 105	74.60 93	79.58 122	57.12 94	73.96 212	75.25 215	60.40 91	74.81 43	81.95 185	45.54 149	82.90 149	70.41 58	66.83 268	83.77 163

GeoE		71.01 97	70.15 102	73.60 123	79.57 123	52.17 167	78.93 115	78.12 174	58.02 137	67.76 148	83.87 143	52.36 72	82.72 158	56.90 157	75.79 160	85.92 86

PCF-MVS	61.88 8	70.95 98	69.49 112	75.35 75	77.63 177	55.71 113	76.04 174	81.81 96	50.30 250	69.66 108	85.40 117	52.51 68	84.89 112	51.82 201	80.24 107	85.45 109
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
114514_t		70.83 99	69.56 109	74.64 91	86.21 31	54.63 130	82.34 68	81.81 96	48.22 268	63.01 225	85.83 107	40.92 200	87.10 57	57.91 151	79.79 110	82.18 198

FIs		70.82 100	71.43 79	68.98 215	78.33 156	38.14 311	76.96 154	83.59 63	61.02 81	67.33 153	86.73 81	55.07 42	81.64 177	54.61 179	79.22 121	87.14 50

ACMM	61.98 7	70.80 101	69.73 107	74.02 104	80.59 106	58.59 73	82.68 62	82.02 93	55.46 185	67.18 156	84.39 133	38.51 217	83.17 144	60.65 135	76.10 158	80.30 231
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 102	70.43 97	71.46 165	69.45 303	48.95 215	72.93 228	78.46 165	57.27 147	71.69 84	83.97 142	51.48 82	77.92 243	70.70 57	77.95 138	87.53 38
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UniMVSNet (Re)		70.63 103	70.20 100	71.89 155	78.55 148	45.29 255	75.94 176	82.92 81	63.68 38	68.16 133	83.59 149	53.89 55	83.49 139	53.97 182	71.12 215	86.89 55

xiu_mvs_v2_base		70.52 104	69.75 106	72.84 141	81.21 95	55.63 116	75.11 190	78.92 150	54.92 200	69.96 104	79.68 229	47.00 137	82.09 171	61.60 129	79.37 117	80.81 224

PS-MVSNAJ		70.51 105	69.70 108	72.93 139	81.52 86	55.79 112	74.92 196	79.00 148	55.04 198	69.88 105	78.66 244	47.05 133	82.19 169	61.61 128	79.58 114	80.83 223

v2v482		70.50 106	69.45 114	73.66 118	72.62 260	50.03 199	77.58 136	80.51 128	59.90 104	69.52 109	82.14 181	47.53 124	84.88 114	65.07 99	70.17 228	86.09 82

v1144		70.42 107	69.31 115	73.76 112	73.22 248	50.64 187	77.83 132	81.43 103	58.58 126	69.40 113	81.16 199	47.53 124	85.29 105	64.01 106	70.64 218	85.34 115

TranMVSNet+NR-MVSNet		70.36 108	70.10 104	71.17 176	78.64 147	42.97 276	76.53 162	81.16 117	66.95 4	68.53 126	85.42 116	51.61 81	83.07 145	52.32 194	69.70 240	87.46 39

v8		70.33 109	69.28 116	73.49 126	73.15 250	50.22 194	78.62 120	80.78 124	60.79 84	66.45 171	82.11 183	49.35 100	84.98 109	63.58 111	68.71 254	85.28 117

Fast-Effi-MVS+		70.28 110	69.12 119	73.73 115	78.50 149	51.50 176	75.01 193	79.46 143	56.16 170	68.59 123	79.55 232	53.97 53	84.05 125	53.34 188	77.53 142	85.65 101

X-MVStestdata		70.21 111	67.28 158	79.00 22	86.32 29	62.62 11	85.83 21	83.92 51	64.55 21	72.17 80	6.49 376	47.95 117	88.01 36	71.55 53	86.74 50	86.37 69

v10		70.21 111	69.02 120	73.81 109	73.51 247	50.92 182	78.74 117	81.39 104	60.05 102	66.39 172	81.83 188	47.58 123	85.41 103	62.80 117	68.86 253	85.09 123

QAPM		70.05 113	68.81 123	73.78 110	76.54 204	53.43 143	83.23 52	83.48 65	52.89 222	65.90 181	86.29 94	41.55 193	86.49 75	51.01 207	78.40 135	81.42 209

DU-MVS		70.01 114	69.53 111	71.44 166	78.05 165	44.13 264	75.01 193	81.51 101	64.37 26	68.20 130	84.52 129	49.12 107	82.82 156	54.62 177	70.43 222	87.37 44

AdaColmap		69.99 115	68.66 126	73.97 106	84.94 54	57.83 79	82.63 63	78.71 155	56.28 167	64.34 210	84.14 136	41.57 191	87.06 59	46.45 242	78.88 126	77.02 269

v1192		69.97 116	68.68 125	73.85 107	73.19 249	50.94 180	77.68 135	81.36 106	57.51 145	68.95 121	80.85 209	45.28 156	85.33 104	62.97 116	70.37 224	85.27 118

Anonymous20240529		69.91 117	69.02 120	72.56 146	80.19 112	47.65 230	77.56 138	80.99 120	55.45 186	69.88 105	86.76 79	39.24 211	82.18 170	54.04 181	77.10 149	87.85 25

patch_mono-2		69.85 118	71.09 88	66.16 246	79.11 136	54.80 129	71.97 244	74.31 231	53.50 217	70.90 90	84.17 135	57.63 29	63.31 317	66.17 86	82.02 89	80.38 230

FA-MVS(test-final)		69.82 119	68.48 128	73.84 108	78.44 152	50.04 198	75.58 182	78.99 149	58.16 133	67.59 149	82.14 181	42.66 177	85.63 92	56.60 158	76.19 157	85.84 90

iter_conf_final		69.82 119	68.02 137	75.23 79	79.38 127	52.91 153	80.11 97	73.96 237	54.99 199	68.04 137	83.59 149	29.05 304	87.16 53	65.41 96	77.62 140	85.63 102

FC-MVSNet-test		69.80 121	70.58 96	67.46 230	77.61 182	34.73 340	76.05 173	83.19 77	60.84 83	65.88 183	86.46 90	54.52 49	80.76 201	52.52 193	78.12 136	86.91 54

v144192		69.71 122	68.51 127	73.33 133	73.10 251	50.13 196	77.54 139	80.64 125	56.65 155	68.57 125	80.55 212	46.87 138	84.96 111	62.98 115	69.66 241	84.89 129

test_yl		69.69 123	69.13 117	71.36 170	78.37 154	45.74 248	74.71 200	80.20 132	57.91 140	70.01 102	83.83 144	42.44 180	82.87 152	54.97 173	79.72 111	85.48 107

DCV-MVSNet		69.69 123	69.13 117	71.36 170	78.37 154	45.74 248	74.71 200	80.20 132	57.91 140	70.01 102	83.83 144	42.44 180	82.87 152	54.97 173	79.72 111	85.48 107

VNet		69.68 125	70.19 101	68.16 225	79.73 120	41.63 289	70.53 263	77.38 187	60.37 94	70.69 91	86.63 85	51.08 87	77.09 255	53.61 186	81.69 96	85.75 97

jason		69.65 126	68.39 133	73.43 130	78.27 158	56.88 96	77.12 150	73.71 240	46.53 287	69.34 114	83.22 156	43.37 172	79.18 222	64.77 100	79.20 122	84.23 145
jason: jason.
Effi-MVS+-dtu		69.64 127	67.53 147	75.95 62	76.10 210	62.29 15	80.20 96	76.06 205	59.83 108	65.26 197	77.09 264	41.56 192	84.02 128	60.60 136	71.09 216	81.53 208

lupinMVS		69.57 128	68.28 134	73.44 129	78.76 143	57.15 92	76.57 161	73.29 244	46.19 290	69.49 110	82.18 177	43.99 168	79.23 221	64.66 101	79.37 117	83.93 153

NR-MVSNet		69.54 129	68.85 122	71.59 164	78.05 165	43.81 268	74.20 208	80.86 123	65.18 12	62.76 227	84.52 129	52.35 73	83.59 137	50.96 209	70.78 217	87.37 44

MVS_111021_LR		69.50 130	68.78 124	71.65 162	78.38 153	59.33 55	74.82 198	70.11 264	58.08 134	67.83 144	84.68 123	41.96 185	76.34 263	65.62 94	77.54 141	79.30 246

v1921920		69.47 131	68.17 135	73.36 132	73.06 252	50.10 197	77.39 142	80.56 126	56.58 162	68.59 123	80.37 214	44.72 161	84.98 109	62.47 121	69.82 236	85.00 125

test_djsdf		69.45 132	67.74 139	74.58 94	74.57 235	54.92 127	82.79 59	78.48 163	51.26 241	65.41 191	83.49 154	38.37 219	83.24 142	66.06 87	69.25 247	85.56 104

RRT_MVS		69.42 133	67.49 150	75.21 80	78.01 167	52.56 160	82.23 73	78.15 173	55.84 175	65.65 186	85.07 118	30.86 290	86.83 63	61.56 131	70.00 231	86.24 80

iter_conf05		69.40 134	67.62 143	74.73 85	77.84 171	51.13 178	79.28 112	73.71 240	54.62 203	68.17 132	83.59 149	28.68 309	87.16 53	65.74 93	76.95 150	85.91 87

Anonymous20231211		69.28 135	68.47 130	71.73 159	80.28 107	47.18 236	79.98 99	82.37 88	54.61 204	67.24 154	84.01 140	39.43 208	82.41 167	55.45 171	72.83 193	85.62 103

EI-MVSNet		69.27 136	68.44 132	71.73 159	74.47 236	49.39 210	75.20 188	78.45 166	59.60 109	69.16 119	76.51 274	51.29 83	82.50 164	59.86 144	71.45 213	83.30 176

v1240		69.24 137	67.91 138	73.25 136	73.02 254	49.82 201	77.21 149	80.54 127	56.43 164	68.34 129	80.51 213	43.33 173	84.99 107	62.03 125	69.77 239	84.95 128

IterMVS-LS		69.22 138	68.48 128	71.43 168	74.44 238	49.40 209	76.23 168	77.55 183	59.60 109	65.85 184	81.59 194	51.28 84	81.58 180	59.87 143	69.90 235	83.30 176
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet		69.02 139	69.47 113	67.69 228	77.42 186	41.00 293	74.04 210	79.68 137	60.06 101	69.26 117	84.81 122	51.06 88	77.58 248	54.44 180	74.43 169	84.48 139

v7n		69.01 140	67.36 155	73.98 105	72.51 264	52.65 156	78.54 123	81.30 111	60.26 99	62.67 229	81.62 191	43.61 170	84.49 119	57.01 156	68.70 255	84.79 132

OpenMVS	61.03 9	68.85 141	67.56 144	72.70 145	74.26 242	53.99 134	81.21 86	81.34 110	52.70 223	62.75 228	85.55 113	38.86 215	84.14 124	48.41 229	83.01 75	79.97 236

XVG-OURS-SEG-HR		68.81 142	67.47 151	72.82 143	74.40 239	56.87 97	70.59 262	79.04 147	54.77 202	66.99 159	86.01 101	39.57 207	78.21 239	62.54 119	73.33 185	83.37 175

BH-RMVSNet		68.81 142	67.42 152	72.97 138	80.11 115	52.53 161	74.26 207	76.29 200	58.48 128	68.38 128	84.20 134	42.59 178	83.83 131	46.53 241	75.91 159	82.56 190

UGNet		68.81 142	67.39 153	73.06 137	78.33 156	54.47 131	79.77 104	75.40 213	60.45 90	63.22 222	84.40 132	32.71 279	80.91 197	51.71 203	80.56 103	83.81 159
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
XVG-OURS		68.76 145	67.37 154	72.90 140	74.32 241	57.22 87	70.09 269	78.81 152	55.24 189	67.79 146	85.81 109	36.54 242	78.28 238	62.04 124	75.74 161	83.19 181

V42		68.65 146	67.35 156	72.56 146	68.93 308	50.18 195	72.90 229	79.47 142	56.92 152	69.45 112	80.26 218	46.29 142	82.99 146	64.07 104	67.82 261	84.53 137

PVSNet_Blended		68.59 147	67.72 140	71.19 175	77.03 194	50.57 188	72.51 236	81.52 99	51.91 230	64.22 215	77.77 260	49.13 105	82.87 152	55.82 164	79.58 114	80.14 234

xiu_mvs_v1_base_debu		68.58 148	67.28 158	72.48 148	78.19 160	57.19 89	75.28 185	75.09 221	51.61 232	70.04 98	81.41 196	32.79 275	79.02 229	63.81 108	77.31 143	81.22 215

xiu_mvs_v1_base		68.58 148	67.28 158	72.48 148	78.19 160	57.19 89	75.28 185	75.09 221	51.61 232	70.04 98	81.41 196	32.79 275	79.02 229	63.81 108	77.31 143	81.22 215

xiu_mvs_v1_base_debi		68.58 148	67.28 158	72.48 148	78.19 160	57.19 89	75.28 185	75.09 221	51.61 232	70.04 98	81.41 196	32.79 275	79.02 229	63.81 108	77.31 143	81.22 215

PVSNet_BlendedMVS		68.56 151	67.72 140	71.07 179	77.03 194	50.57 188	74.50 204	81.52 99	53.66 216	64.22 215	79.72 228	49.13 105	82.87 152	55.82 164	73.92 174	79.77 241

WR-MVS		68.47 152	68.47 130	68.44 222	80.20 111	39.84 297	73.75 220	76.07 204	64.68 20	68.11 135	83.63 148	50.39 94	79.14 227	49.78 214	69.66 241	86.34 71

AUN-MVS		68.45 153	66.41 175	74.57 95	79.53 124	57.08 95	73.93 215	75.23 216	54.44 209	66.69 166	81.85 187	37.10 237	82.89 150	62.07 123	66.84 267	83.75 164

c3_l		68.33 154	67.56 144	70.62 186	70.87 283	46.21 244	74.47 205	78.80 153	56.22 169	66.19 175	78.53 249	51.88 77	81.40 182	62.08 122	69.04 250	84.25 144

BH-untuned		68.27 155	67.29 157	71.21 174	79.74 119	53.22 148	76.06 172	77.46 186	57.19 148	66.10 176	81.61 192	45.37 155	83.50 138	45.42 256	76.68 155	76.91 273

jajsoiax		68.25 156	66.45 171	73.66 118	75.62 216	55.49 120	80.82 89	78.51 162	52.33 227	64.33 211	84.11 137	28.28 311	81.81 176	63.48 112	70.62 219	83.67 167

v148		68.24 157	67.19 164	71.40 169	70.43 288	47.77 229	75.76 178	77.03 192	58.91 119	67.36 152	80.10 221	48.60 112	81.89 173	60.01 140	66.52 271	84.53 137

CANet_DTU		68.18 158	67.71 142	69.59 205	74.83 228	46.24 243	78.66 119	76.85 194	59.60 109	63.45 221	82.09 184	35.25 249	77.41 251	59.88 142	78.76 130	85.14 120

mvs_tets		68.18 158	66.36 177	73.63 121	75.61 217	55.35 122	80.77 90	78.56 160	52.48 226	64.27 213	84.10 138	27.45 317	81.84 175	63.45 113	70.56 221	83.69 166

miper_ehance_all_eth		68.03 160	67.24 162	70.40 190	70.54 286	46.21 244	73.98 211	78.68 157	55.07 196	66.05 177	77.80 258	52.16 75	81.31 185	61.53 132	69.32 244	83.67 167

mvs_anonymous		68.03 160	67.51 148	69.59 205	72.08 269	44.57 262	71.99 243	75.23 216	51.67 231	67.06 158	82.57 168	54.68 47	77.94 242	56.56 159	75.71 162	86.26 79

ET-MVSNet_ETH3D		67.96 162	65.72 189	74.68 88	76.67 200	55.62 118	75.11 190	74.74 225	52.91 221	60.03 255	80.12 220	33.68 265	82.64 161	61.86 126	76.34 156	85.78 92

thisisatest0530		67.92 163	65.78 188	74.33 101	76.29 207	51.03 179	76.89 157	74.25 233	53.67 215	65.59 188	81.76 189	35.15 250	85.50 98	55.94 162	72.47 198	86.47 66

PAPM		67.92 163	66.69 168	71.63 163	78.09 163	49.02 213	77.09 151	81.24 115	51.04 244	60.91 249	83.98 141	47.71 120	84.99 107	40.81 289	79.32 120	80.90 222

tttt0517		67.83 165	65.66 190	74.33 101	76.69 199	50.82 184	77.86 131	73.99 236	54.54 207	64.64 208	82.53 170	35.06 251	85.50 98	55.71 167	69.91 234	86.67 61

tt0805		67.77 166	67.24 162	69.34 210	74.87 227	40.08 295	77.36 143	81.37 105	55.31 187	66.33 173	84.65 125	37.35 230	82.55 163	55.65 169	72.28 204	85.39 114

ECVR-MVS		67.72 167	67.51 148	68.35 223	79.46 125	36.29 334	74.79 199	66.93 286	58.72 122	67.19 155	88.05 63	36.10 243	81.38 183	52.07 197	84.25 66	87.39 42

eth_miper_zixun_eth		67.63 168	66.28 181	71.67 161	71.60 275	48.33 222	73.68 221	77.88 176	55.80 178	65.91 180	78.62 247	47.35 130	82.88 151	59.45 146	66.25 272	83.81 159

UniMVSNet_ETH3D		67.60 169	67.07 166	69.18 214	77.39 187	42.29 280	74.18 209	75.59 210	60.37 94	66.77 164	86.06 99	37.64 226	78.93 234	52.16 196	73.49 181	86.32 75

VPNet		67.52 170	68.11 136	65.74 255	79.18 133	36.80 326	72.17 241	72.83 247	62.04 70	67.79 146	85.83 107	48.88 109	76.60 260	51.30 205	72.97 192	83.81 159

cl22		67.47 171	66.45 171	70.54 188	69.85 299	46.49 240	73.85 218	77.35 188	55.07 196	65.51 189	77.92 254	47.64 122	81.10 190	61.58 130	69.32 244	84.01 152

Fast-Effi-MVS+-dtu		67.37 172	65.33 195	73.48 127	72.94 255	57.78 81	77.47 141	76.88 193	57.60 144	61.97 240	76.85 268	39.31 209	80.49 206	54.72 176	70.28 227	82.17 200

MVS		67.37 172	66.33 178	70.51 189	75.46 220	50.94 180	73.95 213	81.85 95	41.57 325	62.54 233	78.57 248	47.98 116	85.47 100	52.97 191	82.05 88	75.14 285

test1111		67.21 174	67.14 165	67.42 231	79.24 131	34.76 339	73.89 217	65.65 293	58.71 124	66.96 160	87.95 65	36.09 244	80.53 203	52.03 198	83.79 71	86.97 52

GBi-Net		67.21 174	66.55 169	69.19 211	77.63 177	43.33 271	77.31 144	77.83 178	56.62 158	65.04 201	82.70 163	41.85 187	80.33 208	47.18 236	72.76 194	83.92 154

test1		67.21 174	66.55 169	69.19 211	77.63 177	43.33 271	77.31 144	77.83 178	56.62 158	65.04 201	82.70 163	41.85 187	80.33 208	47.18 236	72.76 194	83.92 154

cl____		67.18 177	66.26 182	69.94 197	70.20 291	45.74 248	73.30 223	76.83 195	55.10 191	65.27 194	79.57 231	47.39 128	80.53 203	59.41 148	69.22 248	83.53 173

DIV-MVS_self_test		67.18 177	66.26 182	69.94 197	70.20 291	45.74 248	73.29 224	76.83 195	55.10 191	65.27 194	79.58 230	47.38 129	80.53 203	59.43 147	69.22 248	83.54 172

MVSTER		67.16 179	65.58 192	71.88 156	70.37 290	49.70 203	70.25 268	78.45 166	51.52 235	69.16 119	80.37 214	38.45 218	82.50 164	60.19 138	71.46 212	83.44 174

miper_enhance_ethall		67.11 180	66.09 184	70.17 194	69.21 305	45.98 246	72.85 230	78.41 169	51.38 238	65.65 186	75.98 282	51.17 86	81.25 186	60.82 134	69.32 244	83.29 178

Baseline_NR-MVSNet		67.05 181	67.56 144	65.50 257	75.65 215	37.70 317	75.42 183	74.65 227	59.90 104	68.14 134	83.15 159	49.12 107	77.20 253	52.23 195	69.78 237	81.60 207

WR-MVS_H		67.02 182	66.92 167	67.33 234	77.95 169	37.75 315	77.57 137	82.11 92	62.03 71	62.65 230	82.48 171	50.57 92	79.46 217	42.91 276	64.01 287	84.79 132

anonymousdsp		67.00 183	64.82 200	73.57 124	70.09 294	56.13 105	76.35 165	77.35 188	48.43 266	64.99 204	80.84 210	33.01 272	80.34 207	64.66 101	67.64 263	84.23 145

FMVSNet2		66.93 184	66.31 180	68.79 218	77.63 177	42.98 275	76.11 170	77.47 184	56.62 158	65.22 200	82.17 179	41.85 187	80.18 211	47.05 239	72.72 197	83.20 180

BH-w/o		66.85 185	65.83 187	69.90 200	79.29 128	52.46 163	74.66 202	76.65 198	54.51 208	64.85 205	78.12 250	45.59 148	82.95 148	43.26 272	75.54 163	74.27 298

Anonymous202405211		66.84 186	65.99 185	69.40 209	80.19 112	42.21 281	71.11 257	71.31 256	58.80 121	67.90 138	86.39 92	29.83 299	79.65 214	49.60 220	78.78 129	86.33 73

CDS-MVSNet		66.80 187	65.37 193	71.10 178	78.98 138	53.13 151	73.27 225	71.07 258	52.15 229	64.72 206	80.23 219	43.56 171	77.10 254	45.48 254	78.88 126	83.05 185
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 188	65.27 196	71.33 173	79.16 135	53.67 136	73.84 219	69.59 268	52.32 228	65.28 193	81.72 190	44.49 164	77.40 252	42.32 280	78.66 132	82.92 186

FMVSNet1		66.70 189	65.87 186	69.19 211	77.49 185	43.33 271	77.31 144	77.83 178	56.45 163	64.60 209	82.70 163	38.08 224	80.33 208	46.08 245	72.31 203	83.92 154

ab-mvs		66.65 190	66.42 174	67.37 232	76.17 209	41.73 286	70.41 266	76.14 203	53.99 211	65.98 178	83.51 153	49.48 99	76.24 264	48.60 227	73.46 183	84.14 148

PEN-MVS		66.60 191	66.45 171	67.04 235	77.11 192	36.56 328	77.03 153	80.42 129	62.95 48	62.51 235	84.03 139	46.69 139	79.07 228	44.22 260	63.08 297	85.51 106

TAPA-MVS	59.36 10	66.60 191	65.20 197	70.81 182	76.63 201	48.75 217	76.52 163	80.04 134	50.64 248	65.24 198	84.93 120	39.15 212	78.54 235	36.77 309	76.88 152	85.14 120
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 193	65.07 198	71.17 176	79.18 133	49.63 207	73.48 222	75.20 218	52.95 220	67.90 138	80.33 217	39.81 205	83.68 134	43.20 273	73.56 180	80.20 232

CP-MVSNet		66.49 194	66.41 175	66.72 237	77.67 176	36.33 331	76.83 159	79.52 141	62.45 61	62.54 233	83.47 155	46.32 141	78.37 236	45.47 255	63.43 294	85.45 109

PS-CasMVS		66.42 195	66.32 179	66.70 239	77.60 184	36.30 333	76.94 155	79.61 139	62.36 63	62.43 237	83.66 147	45.69 145	78.37 236	45.35 257	63.26 295	85.42 112

FMVSNet3		66.32 196	65.61 191	68.46 221	76.48 205	42.34 279	74.98 195	77.15 191	55.83 176	65.04 201	81.16 199	39.91 203	80.14 212	47.18 236	72.76 194	82.90 188

ACMH+	57.40 11	66.12 197	64.06 203	72.30 154	77.79 173	52.83 154	80.39 93	78.03 175	57.30 146	57.47 282	82.55 169	27.68 315	84.17 123	45.54 252	69.78 237	79.90 237

cascas		65.98 198	63.42 213	73.64 120	77.26 190	52.58 159	72.26 240	77.21 190	48.56 263	61.21 248	74.60 294	32.57 283	85.82 90	50.38 212	76.75 154	82.52 193

FE-MVS		65.91 199	63.33 215	73.63 121	77.36 188	51.95 173	72.62 233	75.81 206	53.70 214	65.31 192	78.96 241	28.81 308	86.39 77	43.93 265	73.48 182	82.55 191

thisisatest0515		65.83 200	63.50 212	72.82 143	73.75 245	49.50 208	71.32 251	73.12 246	49.39 256	63.82 217	76.50 276	34.95 253	84.84 115	53.20 190	75.49 164	84.13 149

DP-MVS		65.68 201	63.66 210	71.75 158	84.93 55	56.87 97	80.74 91	73.16 245	53.06 219	59.09 269	82.35 173	36.79 241	85.94 87	32.82 331	69.96 233	72.45 313

HyFIR lowres test		65.67 202	63.01 219	73.67 117	79.97 117	55.65 115	69.07 277	75.52 211	42.68 319	63.53 220	77.95 252	40.43 201	81.64 177	46.01 246	71.91 207	83.73 165

DTE-MVSNet		65.58 203	65.34 194	66.31 242	76.06 211	34.79 337	76.43 164	79.38 144	62.55 59	61.66 244	83.83 144	45.60 147	79.15 226	41.64 288	60.88 311	85.00 125

GA-MVS		65.53 204	63.70 209	71.02 180	70.87 283	48.10 224	70.48 264	74.40 229	56.69 154	64.70 207	76.77 269	33.66 266	81.10 190	55.42 172	70.32 226	83.87 157

CNLPA		65.43 205	64.02 204	69.68 203	78.73 145	58.07 77	77.82 133	70.71 261	51.49 236	61.57 246	83.58 152	38.23 222	70.82 285	43.90 266	70.10 230	80.16 233

MVP-Stereo		65.41 206	63.80 208	70.22 191	77.62 181	55.53 119	76.30 166	78.53 161	50.59 249	56.47 288	78.65 245	39.84 204	82.68 159	44.10 264	72.12 206	72.44 314
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 207	62.73 223	73.40 131	74.89 225	52.78 155	73.09 227	75.13 219	55.69 180	58.48 276	73.73 300	32.86 274	86.32 80	50.63 210	70.11 229	81.10 219
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
test2506		65.33 208	64.61 201	67.50 229	79.46 125	34.19 344	74.43 206	51.92 347	58.72 122	66.75 165	88.05 63	25.99 327	80.92 196	51.94 199	84.25 66	87.39 42

pm-mvs1		65.24 209	64.97 199	66.04 250	72.38 265	39.40 302	72.62 233	75.63 209	55.53 184	62.35 239	83.18 158	47.45 126	76.47 261	49.06 224	66.54 270	82.24 197

ACMH	55.70 15	65.20 210	63.57 211	70.07 195	78.07 164	52.01 172	79.48 111	79.69 136	55.75 179	56.59 287	80.98 204	27.12 319	80.94 194	42.90 277	71.58 211	77.25 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 211	63.21 217	70.72 185	81.04 98	54.87 128	78.57 121	77.47 184	48.51 264	55.71 291	81.89 186	33.71 264	79.71 213	41.66 286	70.37 224	77.58 261
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 212	62.84 221	71.82 157	81.49 88	56.26 103	66.32 289	74.20 234	40.53 330	63.16 224	78.65 245	41.30 195	77.80 245	45.80 248	74.09 172	81.40 210

bld_raw_dy_0_64		64.87 213	63.22 216	69.83 202	74.79 230	53.32 147	78.15 127	62.02 317	51.20 243	60.17 253	83.12 160	24.15 336	74.20 274	63.08 114	72.33 201	81.96 202

TransMVSNet (Re)		64.72 214	64.33 202	65.87 254	75.22 223	38.56 308	74.66 202	75.08 224	58.90 120	61.79 243	82.63 166	51.18 85	78.07 241	43.63 269	55.87 331	80.99 221

EG-PatchMatch MVS		64.71 215	62.87 220	70.22 191	77.68 175	53.48 141	77.99 129	78.82 151	53.37 218	56.03 290	77.41 263	24.75 334	84.04 126	46.37 243	73.42 184	73.14 305

LS3D		64.71 215	62.50 225	71.34 172	79.72 121	55.71 113	79.82 103	74.72 226	48.50 265	56.62 286	84.62 126	33.59 267	82.34 168	29.65 350	75.23 166	75.97 277

1314		64.61 217	63.21 217	68.80 217	71.87 273	47.46 233	73.95 213	78.39 171	42.88 318	59.97 256	76.60 273	38.11 223	79.39 219	54.84 175	72.32 202	79.55 242

HY-MVS	56.14 13	64.55 218	63.89 205	66.55 240	74.73 232	41.02 291	69.96 270	74.43 228	49.29 257	61.66 244	80.92 206	47.43 127	76.68 259	44.91 259	71.69 209	81.94 203

XVG-ACMP-BASELINE		64.36 219	62.23 228	70.74 184	72.35 266	52.45 164	70.80 261	78.45 166	53.84 213	59.87 258	81.10 201	16.24 351	79.32 220	55.64 170	71.76 208	80.47 227

CostFormer		64.04 220	62.51 224	68.61 220	71.88 272	45.77 247	71.30 252	70.60 262	47.55 277	64.31 212	76.61 272	41.63 190	79.62 216	49.74 216	69.00 251	80.42 228

1112_ss		64.00 221	63.36 214	65.93 252	79.28 129	42.58 278	71.35 250	72.36 251	46.41 288	60.55 251	77.89 256	46.27 143	73.28 275	46.18 244	69.97 232	81.92 204

baseline1		63.81 222	63.87 207	63.62 269	76.29 207	36.36 329	71.78 247	67.29 283	56.05 172	64.23 214	82.95 161	47.11 132	74.41 271	47.30 235	61.85 305	80.10 235

pmmvs6		63.69 223	62.82 222	66.27 244	70.63 285	39.27 303	73.13 226	75.47 212	52.69 224	59.75 262	82.30 175	39.71 206	77.03 256	47.40 234	64.35 286	82.53 192

Vis-MVSNet (Re-imp)		63.69 223	63.88 206	63.14 274	74.75 231	31.04 357	71.16 255	63.64 306	56.32 165	59.80 260	84.99 119	44.51 162	75.46 266	39.12 298	80.62 99	82.92 186

baseline2		63.42 225	61.26 239	69.89 201	72.55 262	47.62 231	71.54 248	68.38 278	50.11 251	54.82 302	75.55 286	43.06 175	80.96 193	48.13 230	67.16 266	81.11 218

thres400		63.31 226	62.18 229	66.72 237	76.85 197	39.62 299	71.96 245	69.44 270	56.63 156	62.61 231	79.83 224	37.18 232	79.17 223	31.84 335	73.25 187	81.36 211

thres600view7		63.30 227	62.27 227	66.41 241	77.18 191	38.87 305	72.35 238	69.11 274	56.98 151	62.37 238	80.96 205	37.01 239	79.00 232	31.43 342	73.05 191	81.36 211

thres100view900		63.28 228	62.41 226	65.89 253	77.31 189	38.66 307	72.65 231	69.11 274	57.07 149	62.45 236	81.03 203	37.01 239	79.17 223	31.84 335	73.25 187	79.83 239

test_0402		63.25 229	61.01 242	69.96 196	80.00 116	54.37 132	76.86 158	72.02 252	54.58 206	58.71 272	80.79 211	35.00 252	84.36 121	26.41 359	64.71 283	71.15 329

tfpn200view9		63.18 230	62.18 229	66.21 245	76.85 197	39.62 299	71.96 245	69.44 270	56.63 156	62.61 231	79.83 224	37.18 232	79.17 223	31.84 335	73.25 187	79.83 239

LTVRE_ROB	55.42 16	63.15 231	61.23 240	68.92 216	76.57 203	47.80 227	59.92 324	76.39 199	54.35 210	58.67 273	82.46 172	29.44 302	81.49 181	42.12 282	71.14 214	77.46 262
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
F-COLMAP		63.05 232	60.87 245	69.58 207	76.99 196	53.63 138	78.12 128	76.16 201	47.97 273	52.41 322	81.61 192	27.87 313	78.11 240	40.07 292	66.66 269	77.00 270

IterMVS		62.79 233	61.27 238	67.35 233	69.37 304	52.04 171	71.17 254	68.24 279	52.63 225	59.82 259	76.91 267	37.32 231	72.36 278	52.80 192	63.19 296	77.66 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		62.49 234	61.52 235	65.40 259	71.99 271	50.80 185	71.15 256	69.63 267	45.71 296	60.61 250	77.93 253	37.45 228	65.99 310	55.67 168	63.50 293	79.42 244

tfpnnormal		62.47 235	61.63 234	64.99 263	74.81 229	39.01 304	71.22 253	73.72 239	55.22 190	60.21 252	80.09 222	41.26 198	76.98 257	30.02 348	68.09 259	78.97 249

MS-PatchMatch		62.42 236	61.46 236	65.31 261	75.21 224	52.10 168	72.05 242	74.05 235	46.41 288	57.42 283	74.36 295	34.35 259	77.57 249	45.62 251	73.67 176	66.26 345

Test_1112_low_res		62.32 237	61.77 232	64.00 268	79.08 137	39.53 301	68.17 279	70.17 263	43.25 314	59.03 270	79.90 223	44.08 166	71.24 284	43.79 268	68.42 257	81.25 214

D2MVS		62.30 238	60.29 247	68.34 224	66.46 323	48.42 221	65.70 292	73.42 242	47.71 275	58.16 278	75.02 290	30.51 292	77.71 247	53.96 183	71.68 210	78.90 250

thres200		62.20 239	61.16 241	65.34 260	75.38 222	39.99 296	69.60 272	69.29 272	55.64 183	61.87 242	76.99 265	37.07 238	78.96 233	31.28 343	73.28 186	77.06 268

tpm2		62.07 240	60.10 248	67.99 226	72.79 257	43.86 267	71.05 259	66.85 287	43.14 316	62.77 226	75.39 288	38.32 220	80.80 199	41.69 285	68.88 252	79.32 245

miper_lstm_enhance		62.03 241	60.88 244	65.49 258	66.71 321	46.25 242	56.29 337	75.70 208	50.68 246	61.27 247	75.48 287	40.21 202	68.03 299	56.31 161	65.25 279	82.18 198

EPNet_dtu		61.90 242	61.97 231	61.68 283	72.89 256	39.78 298	75.85 177	65.62 294	55.09 193	54.56 306	79.36 236	37.59 227	67.02 304	39.80 295	76.95 150	78.25 253
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 243	61.35 237	63.46 270	74.58 234	31.48 356	61.42 316	58.14 329	58.71 124	53.02 321	79.55 232	43.07 174	76.80 258	45.69 249	77.96 137	82.11 201

MSDG		61.81 244	59.23 250	69.55 208	72.64 259	52.63 158	70.45 265	75.81 206	51.38 238	53.70 313	76.11 278	29.52 300	81.08 192	37.70 304	65.79 276	74.93 290

SixPastTwentyTwo		61.65 245	58.80 254	70.20 193	75.80 213	47.22 235	75.59 180	69.68 266	54.61 204	54.11 310	79.26 238	27.07 320	82.96 147	43.27 271	49.79 348	80.41 229

CL-MVSNet_self_test		61.53 246	60.94 243	63.30 272	68.95 307	36.93 325	67.60 283	72.80 248	55.67 181	59.95 257	76.63 270	45.01 159	72.22 281	39.74 296	62.09 304	80.74 225

RPMNet		61.53 246	58.42 257	70.86 181	69.96 297	52.07 169	65.31 298	81.36 106	43.20 315	59.36 265	70.15 323	35.37 248	85.47 100	36.42 316	64.65 284	75.06 286

pmmvs4		61.48 248	59.39 249	67.76 227	71.57 276	53.86 135	71.42 249	65.34 296	44.20 306	59.46 264	77.92 254	35.90 245	74.71 269	43.87 267	64.87 282	74.71 294

OurMVSNet-221017-0		61.37 249	58.63 256	69.61 204	72.05 270	48.06 225	73.93 215	72.51 249	47.23 283	54.74 303	80.92 206	21.49 345	81.24 187	48.57 228	56.22 330	79.53 243

COLMAP_ROB	52.97 17	61.27 250	58.81 253	68.64 219	74.63 233	52.51 162	78.42 124	73.30 243	49.92 254	50.96 327	81.51 195	23.06 338	79.40 218	31.63 339	65.85 274	74.01 301
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 251	61.67 233	57.70 306	70.43 288	38.45 309	64.19 304	66.47 288	48.05 272	63.22 222	80.86 208	49.28 102	60.47 326	45.25 258	67.28 265	74.19 299

SCA		60.49 252	58.38 258	66.80 236	74.14 244	48.06 225	63.35 307	63.23 309	49.13 259	59.33 268	72.10 307	37.45 228	74.27 272	44.17 261	62.57 300	78.05 256

K. test v3		60.47 253	57.11 266	70.56 187	73.74 246	48.22 223	75.10 192	62.55 313	58.27 132	53.62 316	76.31 277	27.81 314	81.59 179	47.42 233	39.18 361	81.88 205

OpenMVS_ROB	52.78 18	60.03 254	58.14 261	65.69 256	70.47 287	44.82 257	75.33 184	70.86 260	45.04 298	56.06 289	76.00 279	26.89 322	79.65 214	35.36 321	67.29 264	72.60 310

CR-MVSNet		59.91 255	57.90 263	65.96 251	69.96 297	52.07 169	65.31 298	63.15 310	42.48 320	59.36 265	74.84 291	35.83 246	70.75 286	45.50 253	64.65 284	75.06 286

PatchmatchNet		59.84 256	58.24 259	64.65 265	73.05 253	46.70 239	69.42 274	62.18 315	47.55 277	58.88 271	71.96 309	34.49 257	69.16 294	42.99 275	63.60 291	78.07 255
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WTY-MVS		59.75 257	60.39 246	57.85 304	72.32 267	37.83 314	61.05 321	64.18 304	45.95 295	61.91 241	79.11 240	47.01 136	60.88 325	42.50 279	69.49 243	74.83 291

CVMVSNet		59.63 258	59.14 251	61.08 288	74.47 236	38.84 306	75.20 188	68.74 276	31.15 347	58.24 277	76.51 274	32.39 284	68.58 297	49.77 215	65.84 275	75.81 279

tpm cat1		59.25 259	56.95 269	66.15 247	72.19 268	46.96 237	68.09 280	65.76 292	40.03 334	57.81 280	70.56 318	38.32 220	74.51 270	38.26 302	61.50 308	77.00 270

test_vis1_n_1920		58.86 260	59.06 252	58.25 299	63.76 335	43.14 274	67.49 284	66.36 290	40.22 332	65.89 182	71.95 310	31.04 288	59.75 330	59.94 141	64.90 281	71.85 322

pmmvs-eth3d		58.81 261	56.31 275	66.30 243	67.61 315	52.42 165	72.30 239	64.76 300	43.55 312	54.94 301	74.19 297	28.95 305	72.60 277	43.31 270	57.21 325	73.88 302

MVS_0304		58.51 262	57.36 265	61.96 282	70.04 295	41.83 284	69.40 275	65.46 295	50.73 245	53.30 320	74.06 298	22.65 339	70.18 292	42.16 281	68.44 256	73.86 303

tpmvs		58.47 263	56.95 269	63.03 276	70.20 291	41.21 290	67.90 282	67.23 284	49.62 255	54.73 304	70.84 316	34.14 260	76.24 264	36.64 313	61.29 309	71.64 323

PVSNet	50.76 19	58.40 264	57.39 264	61.42 285	75.53 219	44.04 266	61.43 315	63.45 307	47.04 285	56.91 284	73.61 301	27.00 321	64.76 313	39.12 298	72.40 199	75.47 283

tpmrst		58.24 265	58.70 255	56.84 308	66.97 318	34.32 342	69.57 273	61.14 321	47.17 284	58.58 275	71.60 312	41.28 197	60.41 327	49.20 222	62.84 298	75.78 280

Patchmatch-RL test		58.16 266	55.49 279	66.15 247	67.92 314	48.89 216	60.66 322	51.07 350	47.86 274	59.36 265	62.71 350	34.02 262	72.27 280	56.41 160	59.40 318	77.30 264

test-LLR		58.15 267	58.13 262	58.22 300	68.57 309	44.80 258	65.46 295	57.92 330	50.08 252	55.44 294	69.82 325	32.62 280	57.44 337	49.66 218	73.62 177	72.41 315

ppachtmachnet_test		58.06 268	55.38 280	66.10 249	69.51 301	48.99 214	68.01 281	66.13 291	44.50 303	54.05 311	70.74 317	32.09 286	72.34 279	36.68 312	56.71 329	76.99 272

gg-mvs-nofinetune		57.86 269	56.43 274	62.18 280	72.62 260	35.35 336	66.57 286	56.33 336	50.65 247	57.64 281	57.10 356	30.65 291	76.36 262	37.38 306	78.88 126	74.82 292

CMPMVS	42.80 21	57.81 270	55.97 276	63.32 271	60.98 348	47.38 234	64.66 302	69.50 269	32.06 346	46.83 342	77.80 258	29.50 301	71.36 283	48.68 226	73.75 175	71.21 328
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 271	57.07 267	58.22 300	74.21 243	37.18 320	62.46 310	60.88 322	48.88 261	55.29 297	75.99 281	31.68 287	62.04 322	31.87 334	72.35 200	75.43 284

tpm		57.34 272	58.16 260	54.86 315	71.80 274	34.77 338	67.47 285	56.04 339	48.20 269	60.10 254	76.92 266	37.17 234	53.41 353	40.76 290	65.01 280	76.40 276

Patchmtry		57.16 273	56.47 273	59.23 292	69.17 306	34.58 341	62.98 308	63.15 310	44.53 302	56.83 285	74.84 291	35.83 246	68.71 296	40.03 293	60.91 310	74.39 297

AllTest		57.08 274	54.65 284	64.39 266	71.44 277	49.03 211	69.92 271	67.30 281	45.97 293	47.16 340	79.77 226	17.47 347	67.56 301	33.65 326	59.16 319	76.57 274

test_cas_vis1_n_1920		56.91 275	56.71 271	57.51 307	59.13 353	45.40 254	63.58 306	61.29 320	36.24 341	67.14 157	71.85 311	29.89 298	56.69 341	57.65 153	63.58 292	70.46 333

our_test_3		56.49 276	54.42 286	62.68 278	69.51 301	45.48 253	66.08 290	61.49 319	44.11 309	50.73 331	69.60 328	33.05 271	68.15 298	38.38 301	56.86 326	74.40 296

pmmvs5		56.47 277	55.68 278	58.86 296	61.41 345	36.71 327	66.37 288	62.75 312	40.38 331	53.70 313	76.62 271	34.56 255	67.05 303	40.02 294	65.27 278	72.83 308

test-mter		56.42 278	55.82 277	58.22 300	68.57 309	44.80 258	65.46 295	57.92 330	39.94 335	55.44 294	69.82 325	21.92 342	57.44 337	49.66 218	73.62 177	72.41 315

USDC		56.35 279	54.24 290	62.69 277	64.74 331	40.31 294	65.05 300	73.83 238	43.93 310	47.58 338	77.71 261	15.36 353	75.05 268	38.19 303	61.81 306	72.70 309

PatchMatch-RL		56.25 280	54.55 285	61.32 287	77.06 193	56.07 107	65.57 294	54.10 344	44.13 308	53.49 319	71.27 315	25.20 331	66.78 305	36.52 315	63.66 290	61.12 348

sss		56.17 281	56.57 272	54.96 314	66.93 319	36.32 332	57.94 330	61.69 318	41.67 323	58.64 274	75.32 289	38.72 216	56.25 344	42.04 283	66.19 273	72.31 318

FMVSNet5		55.86 282	54.93 282	58.66 298	71.05 282	36.35 330	64.18 305	62.48 314	46.76 286	50.66 332	74.73 293	25.80 328	64.04 315	33.11 329	65.57 277	75.59 282

RPSCF		55.80 283	54.22 291	60.53 289	65.13 330	42.91 277	64.30 303	57.62 332	36.84 340	58.05 279	82.28 176	28.01 312	56.24 345	37.14 307	58.61 321	82.44 196

EU-MVSNet		55.61 284	54.41 287	59.19 294	65.41 329	33.42 348	72.44 237	71.91 253	28.81 349	51.27 325	73.87 299	24.76 333	69.08 295	43.04 274	58.20 322	75.06 286

Anonymous20240521		55.30 285	54.41 287	57.96 303	60.92 350	41.73 286	71.09 258	71.06 259	41.18 326	48.65 336	73.31 302	16.93 349	59.25 332	42.54 278	64.01 287	72.90 307

TESTMET0.1,1		55.28 286	54.90 283	56.42 309	66.56 322	43.67 269	65.46 295	56.27 337	39.18 337	53.83 312	67.44 337	24.21 335	55.46 348	48.04 231	73.11 190	70.13 336

KD-MVS_self_test		55.22 287	53.89 293	59.21 293	57.80 356	27.47 366	57.75 332	74.32 230	47.38 279	50.90 328	70.00 324	28.45 310	70.30 290	40.44 291	57.92 323	79.87 238

MIMVSNet1		55.17 288	54.31 289	57.77 305	70.03 296	32.01 354	65.68 293	64.81 299	49.19 258	46.75 343	76.00 279	25.53 330	64.04 315	28.65 353	62.13 303	77.26 266

Anonymous20231206		55.10 289	55.30 281	54.48 317	69.81 300	33.94 346	62.91 309	62.13 316	41.08 327	55.18 298	75.65 284	32.75 278	56.59 343	30.32 347	67.86 260	72.91 306

TinyColmap		54.14 290	51.72 300	61.40 286	66.84 320	41.97 282	66.52 287	68.51 277	44.81 299	42.69 354	75.77 283	11.66 359	72.94 276	31.96 333	56.77 328	69.27 341

EPMVS		53.96 291	53.69 294	54.79 316	66.12 326	31.96 355	62.34 312	49.05 353	44.42 305	55.54 292	71.33 314	30.22 295	56.70 340	41.65 287	62.54 301	75.71 281

PMMVS		53.96 291	53.26 297	56.04 310	62.60 341	50.92 182	61.17 319	56.09 338	32.81 345	53.51 318	66.84 340	34.04 261	59.93 329	44.14 263	68.18 258	57.27 354

test20.03		53.87 293	54.02 292	53.41 325	61.47 344	28.11 364	61.30 317	59.21 325	51.34 240	52.09 323	77.43 262	33.29 270	58.55 334	29.76 349	60.27 316	73.58 304

MDA-MVSNet-bldmvs		53.87 293	50.81 304	63.05 275	66.25 324	48.58 219	56.93 335	63.82 305	48.09 271	41.22 355	70.48 321	30.34 294	68.00 300	34.24 324	45.92 353	72.57 311

KD-MVS_2432*1600		53.45 295	51.50 302	59.30 290	62.82 338	37.14 321	55.33 338	71.79 254	47.34 281	55.09 299	70.52 319	21.91 343	70.45 288	35.72 319	42.97 356	70.31 334

miper_refine_blended		53.45 295	51.50 302	59.30 290	62.82 338	37.14 321	55.33 338	71.79 254	47.34 281	55.09 299	70.52 319	21.91 343	70.45 288	35.72 319	42.97 356	70.31 334

TDRefinement		53.44 297	50.72 305	61.60 284	64.31 334	46.96 237	70.89 260	65.27 298	41.78 321	44.61 349	77.98 251	11.52 361	66.36 308	28.57 354	51.59 342	71.49 326

test0.0.03 1		53.32 298	53.59 295	52.50 329	62.81 340	29.45 360	59.51 325	54.11 343	50.08 252	54.40 308	74.31 296	32.62 280	55.92 346	30.50 346	63.95 289	72.15 320

PatchT		53.17 299	53.44 296	52.33 330	68.29 313	25.34 371	58.21 329	54.41 342	44.46 304	54.56 306	69.05 331	33.32 269	60.94 324	36.93 308	61.76 307	70.73 332

UnsupCasMVSNet_eth		53.16 300	52.47 298	55.23 313	59.45 352	33.39 349	59.43 326	69.13 273	45.98 292	50.35 334	72.32 306	29.30 303	58.26 335	42.02 284	44.30 354	74.05 300

PM-MVS		52.33 301	50.19 308	58.75 297	62.10 342	45.14 256	65.75 291	40.38 367	43.60 311	53.52 317	72.65 304	9.16 367	65.87 311	50.41 211	54.18 336	65.24 347

testgi		51.90 302	52.37 299	50.51 335	60.39 351	23.55 374	58.42 328	58.15 328	49.03 260	51.83 324	79.21 239	22.39 340	55.59 347	29.24 352	62.64 299	72.40 317

dp		51.89 303	51.60 301	52.77 328	68.44 312	32.45 353	62.36 311	54.57 341	44.16 307	49.31 335	67.91 333	28.87 307	56.61 342	33.89 325	54.89 333	69.24 342

JIA-IIPM		51.56 304	47.68 317	63.21 273	64.61 332	50.73 186	47.71 354	58.77 327	42.90 317	48.46 337	51.72 360	24.97 332	70.24 291	36.06 318	53.89 337	68.64 343

test_fmvs1_n		51.37 305	50.35 307	54.42 319	52.85 359	37.71 316	61.16 320	51.93 346	28.15 351	63.81 218	69.73 327	13.72 354	53.95 351	51.16 206	60.65 314	71.59 324

ADS-MVSNet2		51.33 306	48.76 312	59.07 295	66.02 327	44.60 261	50.90 348	59.76 324	36.90 338	50.74 329	66.18 342	26.38 323	63.11 318	27.17 355	54.76 334	69.50 339

test_fmvs1		51.32 307	50.48 306	53.81 321	53.57 358	37.51 318	60.63 323	51.16 348	28.02 353	63.62 219	69.23 330	16.41 350	53.93 352	51.01 207	60.70 313	69.99 337

YYNet1		50.73 308	48.96 309	56.03 311	61.10 347	41.78 285	51.94 346	56.44 335	40.94 329	44.84 347	67.80 335	30.08 296	55.08 349	36.77 309	50.71 344	71.22 327

MDA-MVSNet_test_wron		50.71 309	48.95 310	56.00 312	61.17 346	41.84 283	51.90 347	56.45 334	40.96 328	44.79 348	67.84 334	30.04 297	55.07 350	36.71 311	50.69 345	71.11 330

UnsupCasMVSNet_bld		50.07 310	48.87 311	53.66 322	60.97 349	33.67 347	57.62 333	64.56 302	39.47 336	47.38 339	64.02 348	27.47 316	59.32 331	34.69 323	43.68 355	67.98 344

test_vis1_n		49.89 311	48.69 313	53.50 324	53.97 357	37.38 319	61.53 314	47.33 359	28.54 350	59.62 263	67.10 339	13.52 355	52.27 356	49.07 223	57.52 324	70.84 331

Patchmatch-test		49.08 312	48.28 314	51.50 333	64.40 333	30.85 358	45.68 358	48.46 356	35.60 342	46.10 346	72.10 307	34.47 258	46.37 363	27.08 357	60.65 314	77.27 265

test_fmvs2		48.69 313	47.49 318	52.29 331	48.63 365	33.06 351	57.76 331	48.05 357	25.71 357	59.76 261	69.60 328	11.57 360	52.23 357	49.45 221	56.86 326	71.58 325

ADS-MVSNet		48.48 314	47.77 315	50.63 334	66.02 327	29.92 359	50.90 348	50.87 352	36.90 338	50.74 329	66.18 342	26.38 323	52.47 355	27.17 355	54.76 334	69.50 339

CHOSEN 280x420		47.83 315	46.36 319	52.24 332	67.37 317	49.78 202	38.91 366	43.11 365	35.00 343	43.27 353	63.30 349	28.95 305	49.19 360	36.53 314	60.80 312	57.76 353

new-patchmatchnet		47.56 316	47.73 316	47.06 338	58.81 354	9.37 381	48.78 352	59.21 325	43.28 313	44.22 350	68.66 332	25.67 329	57.20 339	31.57 341	49.35 349	74.62 295

PVSNet_0	43.31 20	47.46 317	45.64 320	52.92 327	67.60 316	44.65 260	54.06 342	54.64 340	41.59 324	46.15 345	58.75 353	30.99 289	58.66 333	32.18 332	24.81 369	55.46 356

MVS-HIRNet		45.52 318	44.48 321	48.65 337	68.49 311	34.05 345	59.41 327	44.50 363	27.03 354	37.96 361	50.47 364	26.16 326	64.10 314	26.74 358	59.52 317	47.82 363

pmmvs3		44.92 319	41.95 324	53.86 320	52.58 361	43.55 270	62.11 313	46.90 361	26.05 356	40.63 356	60.19 352	11.08 364	57.91 336	31.83 338	46.15 352	60.11 349

test_fmvs3		44.30 320	42.55 322	49.55 336	42.83 369	27.15 367	53.03 344	44.93 362	22.03 364	53.69 315	64.94 345	4.21 374	49.63 359	47.47 232	49.82 347	71.88 321

LF4IMVS		42.95 321	42.26 323	45.04 341	48.30 366	32.50 352	54.80 340	48.49 355	28.03 352	40.51 357	70.16 322	9.24 366	43.89 366	31.63 339	49.18 350	58.72 351

EGC-MVSNET		42.47 322	38.48 329	54.46 318	74.33 240	48.73 218	70.33 267	51.10 349	0.03 379	0.18 380	67.78 336	13.28 356	66.49 307	18.91 365	50.36 346	48.15 361

FPMVS		42.18 323	41.11 325	45.39 340	58.03 355	41.01 292	49.50 350	53.81 345	30.07 348	33.71 362	64.03 346	11.69 358	52.08 358	14.01 369	55.11 332	43.09 365

ANet_high		41.38 324	37.47 331	53.11 326	39.73 375	24.45 372	56.94 334	69.69 265	47.65 276	26.04 367	52.32 359	12.44 357	62.38 321	21.80 362	10.61 376	72.49 312

test_vis1_rt		41.35 325	39.45 327	47.03 339	46.65 368	37.86 313	47.76 353	38.65 368	23.10 360	44.21 351	51.22 362	11.20 363	44.08 365	39.27 297	53.02 339	59.14 350

LCM-MVSNet		40.30 326	35.88 332	53.57 323	42.24 370	29.15 361	45.21 360	60.53 323	22.23 363	28.02 365	50.98 363	3.72 376	61.78 323	31.22 344	38.76 362	69.78 338

mvsany_test1		39.38 327	38.16 330	43.02 345	49.05 363	34.28 343	44.16 362	25.94 378	22.74 362	46.57 344	62.21 351	23.85 337	41.16 370	33.01 330	35.91 364	53.63 357

N_pmnet		39.35 328	40.28 326	36.54 351	63.76 335	1.62 385	49.37 351	0.76 385	34.62 344	43.61 352	66.38 341	26.25 325	42.57 367	26.02 360	51.77 341	65.44 346

DSMNet-mixed		39.30 329	38.72 328	41.03 346	51.22 362	19.66 377	45.53 359	31.35 374	15.83 371	39.80 359	67.42 338	22.19 341	45.13 364	22.43 361	52.69 340	58.31 352

APD_test1		37.39 330	34.94 333	44.72 343	48.88 364	33.19 350	52.95 345	44.00 364	19.49 365	27.28 366	58.59 354	3.18 378	52.84 354	18.92 364	41.17 359	48.14 362

PMVS	28.69 22	36.22 331	33.29 335	45.02 342	36.82 377	35.98 335	54.68 341	48.74 354	26.31 355	21.02 370	51.61 361	2.88 379	60.10 328	9.99 375	47.58 351	38.99 370
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 332	31.91 336	43.33 344	62.05 343	37.87 312	20.39 371	67.03 285	23.23 359	18.41 372	25.84 372	4.24 373	62.73 319	14.71 368	51.32 343	29.38 371
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		34.13 333	34.29 334	33.64 353	52.63 360	18.23 379	44.43 361	33.90 373	22.81 361	30.89 364	53.18 358	10.48 365	35.72 374	20.77 363	39.51 360	46.98 364

mvsany_test3		32.62 334	30.57 338	38.77 349	36.16 378	24.20 373	38.10 367	20.63 380	19.14 366	40.36 358	57.43 355	5.06 371	36.63 373	29.59 351	28.66 368	55.49 355

test_vis3_rt		32.09 335	30.20 339	37.76 350	35.36 379	27.48 365	40.60 365	28.29 377	16.69 369	32.52 363	40.53 368	1.96 380	37.40 372	33.64 328	42.21 358	48.39 360

test_f		31.86 336	31.05 337	34.28 352	32.33 381	21.86 375	32.34 368	30.46 375	16.02 370	39.78 360	55.45 357	4.80 372	32.36 375	30.61 345	37.66 363	48.64 359

testf1		31.46 337	28.89 340	39.16 347	41.99 372	28.78 362	46.45 356	37.56 369	14.28 372	21.10 368	48.96 365	1.48 382	47.11 361	13.63 370	34.56 365	41.60 366

APD_test2		31.46 337	28.89 340	39.16 347	41.99 372	28.78 362	46.45 356	37.56 369	14.28 372	21.10 368	48.96 365	1.48 382	47.11 361	13.63 370	34.56 365	41.60 366

PMMVS2		27.40 339	25.91 342	31.87 355	39.46 376	6.57 382	31.17 369	28.52 376	23.96 358	20.45 371	48.94 367	4.20 375	37.94 371	16.51 366	19.97 371	51.09 358

E-PMN		23.77 340	22.73 344	26.90 356	42.02 371	20.67 376	42.66 363	35.70 371	17.43 367	10.28 377	25.05 373	6.42 369	42.39 368	10.28 374	14.71 373	17.63 372

EMVS		22.97 341	21.84 345	26.36 357	40.20 374	19.53 378	41.95 364	34.64 372	17.09 368	9.73 378	22.83 374	7.29 368	42.22 369	9.18 376	13.66 374	17.32 373

MVE	17.77 23	21.41 342	17.77 347	32.34 354	34.34 380	25.44 370	16.11 372	24.11 379	11.19 374	13.22 374	31.92 370	1.58 381	30.95 376	10.47 373	17.03 372	40.62 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 343	18.10 346	24.41 358	13.68 383	3.11 384	12.06 374	42.37 366	2.00 377	11.97 375	36.38 369	5.77 370	29.35 377	15.06 367	23.65 370	40.76 368

cdsmvs_eth3d_5k		17.50 344	23.34 343	0.00 364	0.00 387	0.00 387	0.00 375	78.63 158	0.00 382	0.00 383	82.18 177	49.25 103	0.00 381	0.00 381	0.00 379	0.00 379

wuyk23d		13.32 345	12.52 348	15.71 359	47.54 367	26.27 368	31.06 370	1.98 384	4.93 376	5.18 379	1.94 379	0.45 384	18.54 378	6.81 378	12.83 375	2.33 376

tmp_tt		9.43 346	11.14 349	4.30 361	2.38 384	4.40 383	13.62 373	16.08 382	0.39 378	15.89 373	13.06 375	15.80 352	5.54 380	12.63 372	10.46 377	2.95 375

ab-mvs-re		6.49 347	8.65 350	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	77.89 256	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

test123		4.73 348	6.30 351	0.02 362	0.01 385	0.01 386	56.36 336	0.00 386	0.01 380	0.04 381	0.21 381	0.01 385	0.00 381	0.03 380	0.00 379	0.04 377

testmvs		4.52 349	6.03 352	0.01 363	0.01 385	0.00 387	53.86 343	0.00 386	0.01 380	0.04 381	0.27 380	0.00 386	0.00 381	0.04 379	0.00 379	0.03 378

pcd_1.5k_mvsjas		3.92 350	5.23 353	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	47.05 133	0.00 381	0.00 381	0.00 379	0.00 379

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

FOURS1					86.12 36	60.82 37	88.18 1	83.61 62	60.87 82	81.50 16

MSC_two_6792asdad				79.95 3	87.24 14	61.04 31		85.62 23					90.96 1	79.31 7	90.65 8	87.85 25

PC_three_1452									55.09 193	84.46 4	89.84 41	66.68 5	89.41 16	74.24 32	91.38 2	88.42 9

No_MVS				79.95 3	87.24 14	61.04 31		85.62 23					90.96 1	79.31 7	90.65 8	87.85 25

test_one_0601					87.58 9	59.30 56		86.84 7	65.01 18	83.80 11	91.86 6	64.03 11

eth-test2					0.00 387

eth-test					0.00 387

ZD-MVS					86.64 21	60.38 43		82.70 85	57.95 138	78.10 24	90.06 34	56.12 37	88.84 24	74.05 35	87.00 46

RE-MVS-def			73.71 61		83.49 65	59.87 48	84.29 35	81.36 106	58.07 135	73.14 65	90.07 32	43.06 175		68.20 67	81.76 92	84.03 150

IU-MVS					87.77 4	59.15 59		85.53 25	53.93 212	84.64 3				79.07 9	90.87 5	88.37 11

OPU-MVS				79.83 6	87.54 11	60.93 35	87.82 7				89.89 40	67.01 1	90.33 11	73.16 42	91.15 4	88.23 14

test_241102_TWO								86.73 12	64.18 30	84.26 5	91.84 8	65.19 6	90.83 5	78.63 15	90.70 7	87.65 33

test_241102_ONE					87.77 4	58.90 68		86.78 10	64.20 29	85.97 1	91.34 12	66.87 3	90.78 7

9.14			78.75 14		83.10 69		84.15 41	88.26 1	59.90 104	78.57 23	90.36 25	57.51 30	86.86 62	77.39 18	89.52 21

save fliter					86.17 33	61.30 28	83.98 45	79.66 138	59.00 118

test_0728_THIRD									65.04 14	83.82 8	92.00 3	64.69 10	90.75 8	79.48 4	90.63 10	88.09 19

test_0728_SECOND				79.19 15	87.82 3	59.11 62	87.85 5	87.15 3					90.84 3	78.66 13	90.61 11	87.62 35

test0726					87.75 7	59.07 63	87.86 4	86.83 8	64.26 27	84.19 7	91.92 5	64.82 8

GSMVS																78.05 256

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 254				78.05 256

sam_mvs												33.43 268

ambc				65.13 262	63.72 337	37.07 323	47.66 355	78.78 154		54.37 309	71.42 313	11.24 362	80.94 194	45.64 250	53.85 338	77.38 263

MTGPA								80.97 121

test_post1							68.67 278				3.64 377	32.39 284	69.49 293	44.17 261

test_post											3.55 378	33.90 263	66.52 306

patchmatchnet-post											64.03 346	34.50 256	74.27 272

GG-mvs-BLEND				62.34 279	71.36 281	37.04 324	69.20 276	57.33 333		54.73 304	65.48 344	30.37 293	77.82 244	34.82 322	74.93 167	72.17 319

MTMP							86.03 18	17.08 381

gm-plane-assit					71.40 280	41.72 288			48.85 262		73.31 302		82.48 166	48.90 225

test9_res														75.28 27	88.31 32	83.81 159

TEST9					85.58 43	61.59 24	81.62 80	81.26 113	55.65 182	74.93 40	88.81 54	53.70 58	84.68 116

test_8					85.40 46	60.96 34	81.54 83	81.18 116	55.86 173	74.81 43	88.80 56	53.70 58	84.45 120

agg_prior2														73.09 43	87.93 38	84.33 141

agg_prior					85.04 50	59.96 46		81.04 119		74.68 46			84.04 126

TestCases				64.39 266	71.44 277	49.03 211		67.30 281	45.97 293	47.16 340	79.77 226	17.47 347	67.56 301	33.65 326	59.16 319	76.57 274

test_prior4						62.51 14	82.08 75

test_prior2							81.75 78		60.37 94	75.01 39	89.06 50	56.22 36		72.19 47	88.96 24

test_prior				76.69 51	84.20 61	57.27 86		84.88 37					86.43 76			86.38 67

旧先验2							76.08 171		45.32 297	76.55 31			65.56 312	58.75 149

新几何2							76.12 169

新几何1				70.76 183	85.66 41	61.13 30		66.43 289	44.68 301	70.29 95	86.64 84	41.29 196	75.23 267	49.72 217	81.75 94	75.93 278

旧先验1					83.04 70	53.15 149		67.52 280			87.85 67	44.08 166			80.76 98	78.03 259

无先验							79.66 108	74.30 232	48.40 267				80.78 200	53.62 185		79.03 248

原ACMM2							79.02 114

原ACMM1				74.69 87	85.39 47	59.40 53		83.42 68	51.47 237	70.27 96	86.61 86	48.61 111	86.51 74	53.85 184	87.96 37	78.16 254

test222					83.14 68	58.68 72	72.57 235	63.45 307	41.78 321	67.56 150	86.12 96	37.13 236			78.73 131	74.98 289

testdata2													72.18 282	46.95 240

segment_acmp												54.23 51

testdata				64.66 264	81.52 86	52.93 152		65.29 297	46.09 291	73.88 55	87.46 70	38.08 224	66.26 309	53.31 189	78.48 134	74.78 293

testdata1							72.65 231		60.50 89

test12				77.76 41	84.52 58	58.41 74		83.36 71		72.93 70		54.61 48	88.05 35		88.12 34	86.81 58

plane_prior7					81.41 89	55.96 109

plane_prior6					81.20 96	56.24 104						45.26 157

plane_prior5								84.01 49					87.21 51	68.16 69	80.58 101	84.65 135

plane_prior4											86.10 97

plane_prior3						56.09 106			63.92 34	69.27 115

plane_prior2							84.22 38		64.52 23

plane_prior1					81.27 94

plane_prior						56.31 100	83.58 51		63.19 46						80.48 104

n2								0.00 386

nn								0.00 386

door-mid								47.19 360

lessismore_v0				69.91 199	71.42 279	47.80 227		50.90 351		50.39 333	75.56 285	27.43 318	81.33 184	45.91 247	34.10 367	80.59 226

LGP-MVS_train				75.76 65	80.22 109	57.51 84		83.40 69	61.32 77	66.67 167	87.33 72	39.15 212	86.59 69	67.70 74	77.30 146	83.19 181

test11								83.47 66

door								47.60 358

HQP5-MVS						54.94 125

HQP-NCC					80.66 101		82.31 69		62.10 66	67.85 140

ACMP_Plane					80.66 101		82.31 69		62.10 66	67.85 140

BP-MVS														67.04 81

HQP4-MVS										67.85 140			86.93 60			84.32 142

HQP3-MVS								83.90 53							80.35 105

HQP2-MVS												45.46 151

NP-MVS					80.98 99	56.05 108					85.54 114

MDTV_nov1_ep13_2view						25.89 369	61.22 318		40.10 333	51.10 326		32.97 273		38.49 300		78.61 251

MDTV_nov1_ep13			57.00 268		72.73 258	38.26 310	65.02 301	64.73 301	44.74 300	55.46 293	72.48 305	32.61 282	70.47 287	37.47 305	67.75 262

ACMMP++_ref															74.07 173

ACMMP++															72.16 205

Test By Simon												48.33 114

ITE_SJBPF				62.09 281	66.16 325	44.55 263		64.32 303	47.36 280	55.31 296	80.34 216	19.27 346	62.68 320	36.29 317	62.39 302	79.04 247

DeepMVS_CX				12.03 360	17.97 382	10.91 380		10.60 383	7.46 375	11.07 376	28.36 371	3.28 377	11.29 379	8.01 377	9.74 378	13.89 374

High-resolution multi-view benchmark