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 10	87.24 14	59.15 60	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 11

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 69	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 17	90.87 5	88.23 16

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 23	63.71 12	89.23 20	81.51 3	88.44 27	88.09 21
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 34	87.75 7	59.07 64	87.85 5	85.03 34	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 15	90.61 11	85.45 116
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 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 29	89.67 18	86.84 59
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 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 62	82.20 15	92.28 1	56.53 34	89.70 16	79.85 5	91.48 1	88.19 18
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
APDe-MVS		80.16 7	80.59 6	78.86 28	86.64 21	60.02 45	88.12 3	86.42 14	62.94 51	82.40 14	92.12 2	59.64 18	89.76 15	78.70 13	88.32 31	86.79 61
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
HPM-MVS++		79.88 8	80.14 8	79.10 21	88.17 1	64.80 1	86.59 12	83.70 61	65.37 13	78.78 22	90.64 19	58.63 24	87.24 51	79.00 12	90.37 14	85.26 127

CNVR-MVS		79.84 9	79.97 9	79.45 11	87.90 2	62.17 17	84.37 36	85.03 34	66.96 5	77.58 27	90.06 36	59.47 20	89.13 22	78.67 14	89.73 16	87.03 53

SteuartSystems-ACMMP		79.48 10	79.31 10	79.98 3	83.01 72	62.18 16	87.60 9	85.83 19	66.69 9	78.03 26	90.98 16	54.26 53	90.06 13	78.42 19	89.02 23	87.69 33
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 11	79.00 12	79.13 19	84.92 56	60.32 44	83.03 57	85.33 27	62.86 54	80.17 17	90.03 38	61.76 14	88.95 24	74.21 45	88.67 26	88.12 20

SF-MVS		78.82 12	79.22 11	77.60 44	82.88 74	57.83 80	84.99 32	88.13 2	61.86 75	79.16 20	90.75 18	57.96 25	87.09 60	77.08 26	90.18 15	87.87 26

ZNCC-MVS		78.82 12	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 40	90.47 26	53.96 57	88.68 27	76.48 28	89.63 20	87.16 51

ACMMP_NAP		78.77 14	78.78 13	78.74 29	85.44 45	61.04 31	83.84 49	85.16 30	62.88 53	78.10 24	91.26 13	52.51 75	88.39 30	79.34 8	90.52 13	86.78 62

MVS_0304		78.73 15	78.75 14	78.66 30	80.82 100	57.62 83	85.31 30	81.31 112	70.51 2	74.17 58	91.24 14	54.99 45	89.56 17	82.29 2	88.13 34	88.80 7

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 42	66.73 8	74.67 51	89.38 49	55.30 42	89.18 21	74.19 46	87.34 43	86.38 72

DeepC-MVS	69.38 2	78.56 17	78.14 21	79.83 7	83.60 63	61.62 23	84.17 42	86.85 6	63.23 46	73.84 63	90.25 32	57.68 27	89.96 14	74.62 43	89.03 22	87.89 24
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 18	78.28 19	78.90 26	84.96 52	61.41 26	84.03 45	83.82 59	59.34 117	79.37 19	89.76 45	59.84 16	87.62 47	76.69 27	86.74 52	87.68 34
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 19	78.46 17	78.03 40	84.96 52	59.52 53	82.93 59	85.39 26	62.15 67	76.41 33	91.51 11	52.47 77	86.78 67	80.66 4	89.64 19	87.80 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 19	78.26 20	78.64 31	86.54 25	63.47 4	86.02 20	83.55 65	63.89 37	73.60 65	90.60 20	54.85 48	86.72 68	77.20 25	88.06 37	85.74 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 21	77.85 23	78.99 25	86.05 38	61.82 22	85.84 21	85.21 29	63.56 41	74.29 57	90.03 38	52.56 74	88.53 29	74.79 42	88.34 29	86.63 68

APD-MVS		78.02 22	78.04 22	77.98 41	86.44 27	60.81 38	85.52 27	84.36 43	60.61 89	79.05 21	90.30 30	55.54 41	88.32 32	73.48 53	87.03 45	84.83 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 23	77.65 24	79.10 21	86.71 19	62.81 8	86.29 14	84.32 44	62.82 55	73.96 61	90.50 24	53.20 68	88.35 31	74.02 48	87.05 44	86.13 87

ACMMPR		77.71 24	77.23 27	79.16 17	86.75 18	62.93 7	86.29 14	84.24 45	62.82 55	73.55 66	90.56 22	49.80 109	88.24 33	74.02 48	87.03 45	86.32 80

SD-MVS		77.70 25	77.62 25	77.93 42	84.47 59	61.88 21	84.55 34	83.87 57	60.37 96	79.89 18	89.38 49	54.97 46	85.58 97	76.12 31	84.94 62	86.33 78
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 26	77.18 28	79.15 18	86.76 17	62.95 6	86.29 14	84.16 47	62.81 57	73.30 68	90.58 21	49.90 107	88.21 34	73.78 50	87.03 45	86.29 83

MCST-MVS		77.48 27	77.45 26	77.54 45	86.67 20	58.36 76	83.22 55	86.93 5	56.91 157	74.91 45	88.19 62	59.15 22	87.68 46	73.67 51	87.45 42	86.57 69

HPM-MVS		77.28 28	76.85 29	78.54 32	85.00 51	60.81 38	82.91 60	85.08 31	62.57 60	73.09 77	89.97 41	50.90 102	87.48 49	75.30 36	86.85 50	87.33 49
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 29	76.63 32	79.12 20	86.15 34	60.86 36	84.71 33	84.85 38	61.98 74	73.06 78	88.88 55	53.72 62	89.06 23	68.27 78	88.04 38	87.42 43
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 30	76.56 33	79.00 23	86.32 29	62.62 11	85.83 22	83.92 52	64.55 23	72.17 92	90.01 40	47.95 129	88.01 38	71.55 65	86.74 52	86.37 74

CP-MVS		77.12 31	76.68 31	78.43 33	86.05 38	63.18 5	87.55 10	83.45 68	62.44 64	72.68 85	90.50 24	48.18 127	87.34 50	73.59 52	85.71 58	84.76 143

CSCG		76.92 32	76.75 30	77.41 46	83.96 62	59.60 51	82.95 58	86.50 13	60.78 87	75.27 37	84.83 133	60.76 15	86.56 73	67.86 84	87.87 41	86.06 89

MTAPA		76.90 33	76.42 34	78.35 35	86.08 37	63.57 2	74.92 208	80.97 123	65.13 15	75.77 35	90.88 17	48.63 122	86.66 70	77.23 24	88.17 33	84.81 140

PGM-MVS		76.77 34	76.06 37	78.88 27	86.14 35	62.73 9	82.55 67	83.74 60	61.71 76	72.45 91	90.34 29	48.48 125	88.13 35	72.32 58	86.85 50	85.78 99

mPP-MVS		76.54 35	75.93 39	78.34 36	86.47 26	63.50 3	85.74 25	82.28 90	62.90 52	71.77 95	90.26 31	46.61 153	86.55 74	71.71 63	85.66 59	84.97 136

CANet		76.46 36	75.93 39	78.06 39	81.29 92	57.53 85	82.35 69	83.31 74	67.78 3	70.09 109	86.34 101	54.92 47	88.90 25	72.68 57	84.55 65	87.76 32

CDPH-MVS		76.31 37	75.67 43	78.22 37	85.35 48	59.14 62	81.31 87	84.02 48	56.32 170	74.05 59	88.98 54	53.34 67	87.92 41	69.23 76	88.42 28	87.59 38

train_agg		76.27 38	76.15 36	76.64 55	85.58 43	61.59 24	81.62 82	81.26 115	55.86 178	74.93 43	88.81 56	53.70 63	84.68 118	75.24 38	88.33 30	83.65 180

CS-MVS		76.25 39	75.98 38	77.06 50	80.15 115	55.63 120	84.51 35	83.90 54	63.24 45	73.30 68	87.27 79	55.06 44	86.30 83	71.78 62	84.58 64	89.25 4

casdiffmvs_mvg		76.14 40	76.30 35	75.66 71	76.46 211	51.83 186	79.67 109	85.08 31	65.02 19	75.84 34	88.58 60	59.42 21	85.08 108	72.75 56	83.93 72	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 41	75.70 42	77.40 48	85.87 40	61.20 29	85.52 27	82.19 91	59.99 105	75.10 39	90.35 28	47.66 134	86.52 75	71.64 64	82.99 78	84.47 149

ACMMP		76.02 42	75.33 45	78.07 38	85.20 49	61.91 20	85.49 29	84.44 41	63.04 49	69.80 119	89.74 46	45.43 166	87.16 55	72.01 60	82.87 83	85.14 129
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 43	75.36 44	77.41 46	80.62 106	55.91 113	84.28 39	85.78 20	56.08 176	73.41 67	86.58 94	50.94 101	88.54 28	70.79 68	89.71 17	87.79 31

EC-MVSNet		75.84 44	75.87 41	75.74 69	78.86 141	52.65 168	83.73 50	86.08 17	63.47 42	72.77 84	87.25 80	53.13 69	87.93 40	71.97 61	85.57 60	86.66 66

3Dnovator+	66.72 4	75.84 44	74.57 52	79.66 9	82.40 76	59.92 48	85.83 22	86.32 16	66.92 7	67.80 157	89.24 51	42.03 197	89.38 19	64.07 116	86.50 55	89.69 2

CS-MVS-test		75.62 46	75.31 46	76.56 57	80.63 105	55.13 130	83.88 48	85.22 28	62.05 71	71.49 99	86.03 111	53.83 59	86.36 81	67.74 85	86.91 49	88.19 18

DPM-MVS		75.47 47	75.00 48	76.88 51	81.38 91	59.16 59	79.94 102	85.71 22	56.59 165	72.46 89	86.76 85	56.89 32	87.86 43	66.36 97	88.91 25	83.64 181

APD-MVS_3200maxsize		74.96 48	74.39 54	76.67 54	82.20 78	58.24 77	83.67 51	83.29 75	58.41 131	73.71 64	90.14 33	45.62 159	85.99 87	69.64 72	82.85 84	85.78 99

TSAR-MVS + GP.		74.90 49	74.15 56	77.17 49	82.00 80	58.77 72	81.80 79	78.57 162	58.58 128	74.32 56	84.51 143	55.94 39	87.22 52	67.11 92	84.48 67	85.52 112

casdiffmvs		74.80 50	74.89 50	74.53 98	75.59 223	50.37 204	78.17 131	85.06 33	62.80 58	74.40 54	87.86 70	57.88 26	83.61 138	69.46 75	82.79 85	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 51	74.46 53	75.65 72	77.84 173	52.25 178	75.59 192	84.17 46	63.76 38	73.15 73	82.79 174	59.58 19	86.80 66	67.24 91	86.04 57	87.89 24
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 52	74.25 55	76.19 61	80.81 101	59.01 67	82.60 66	83.64 62	63.74 39	72.52 88	87.49 74	47.18 144	85.88 90	69.47 74	80.78 99	83.66 179
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
canonicalmvs		74.67 53	74.98 49	73.71 121	78.94 140	50.56 202	80.23 96	83.87 57	60.30 100	77.15 29	86.56 95	59.65 17	82.00 174	66.01 101	82.12 89	88.58 10

baseline		74.61 54	74.70 51	74.34 102	75.70 219	49.99 212	77.54 148	84.63 40	62.73 59	73.98 60	87.79 73	57.67 28	83.82 134	69.49 73	82.74 86	89.20 6

SR-MVS-dyc-post		74.57 55	73.90 58	76.58 56	83.49 65	59.87 49	84.29 37	81.36 107	58.07 137	73.14 74	90.07 34	44.74 173	85.84 91	68.20 79	81.76 94	84.03 159

dcpmvs_2		74.55 56	75.23 47	72.48 153	82.34 77	53.34 155	77.87 138	81.46 103	57.80 146	75.49 36	86.81 84	62.22 13	77.75 251	71.09 67	82.02 91	86.34 76

ETV-MVS		74.46 57	73.84 60	76.33 60	79.27 131	55.24 129	79.22 115	85.00 36	64.97 21	72.65 86	79.46 248	53.65 66	87.87 42	67.45 90	82.91 81	85.89 96

HQP_MVS		74.31 58	73.73 61	76.06 62	81.41 89	56.31 102	84.22 40	84.01 49	64.52 25	69.27 127	86.10 108	45.26 170	87.21 53	68.16 81	80.58 103	84.65 144

HPM-MVS_fast		74.30 59	73.46 64	76.80 52	84.45 60	59.04 66	83.65 52	81.05 120	60.15 102	70.43 105	89.84 43	41.09 213	85.59 96	67.61 88	82.90 82	85.77 102

MVS_111021_HR		74.02 60	73.46 64	75.69 70	83.01 72	60.63 40	77.29 156	78.40 173	61.18 82	70.58 104	85.97 113	54.18 55	84.00 131	67.52 89	82.98 80	82.45 207

MG-MVS		73.96 61	73.89 59	74.16 107	85.65 42	49.69 217	81.59 84	81.29 114	61.45 78	71.05 101	88.11 63	51.77 89	87.73 45	61.05 146	83.09 76	85.05 133

alignmvs		73.86 62	73.99 57	73.45 133	78.20 160	50.50 203	78.57 123	82.43 88	59.40 115	76.57 31	86.71 89	56.42 36	81.23 190	65.84 103	81.79 93	88.62 8

MSLP-MVS++		73.77 63	73.47 63	74.66 91	83.02 71	59.29 58	82.30 74	81.88 95	59.34 117	71.59 98	86.83 83	45.94 157	83.65 137	65.09 110	85.22 61	81.06 234

HQP-MVS		73.45 64	72.80 68	75.40 76	80.66 102	54.94 131	82.31 71	83.90 54	62.10 68	67.85 152	85.54 126	45.46 164	86.93 62	67.04 93	80.35 107	84.32 151

CLD-MVS		73.33 65	72.68 69	75.29 80	78.82 143	53.33 156	78.23 128	84.79 39	61.30 81	70.41 106	81.04 216	52.41 78	87.12 58	64.61 115	82.49 88	85.41 120
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 66	72.54 71	75.62 73	77.87 171	53.64 147	79.62 111	79.61 141	61.63 77	72.02 94	82.61 179	56.44 35	85.97 88	63.99 119	79.07 127	87.25 50

UA-Net		73.13 67	72.93 67	73.76 117	83.58 64	51.66 187	78.75 118	77.66 184	67.75 4	72.61 87	89.42 47	49.82 108	83.29 143	53.61 199	83.14 75	86.32 80

EPNet		73.09 68	72.16 74	75.90 65	75.95 217	56.28 104	83.05 56	72.39 256	66.53 10	65.27 206	87.00 81	50.40 104	85.47 102	62.48 133	86.32 56	85.94 92
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 69	72.59 70	74.27 105	71.28 294	55.88 114	78.21 130	75.56 214	54.31 217	74.86 46	87.80 72	54.72 49	80.23 214	78.07 21	78.48 136	86.70 63

nrg030		72.96 70	73.01 66	72.84 146	75.41 226	50.24 205	80.02 100	82.89 84	58.36 133	74.44 53	86.73 87	58.90 23	80.83 200	65.84 103	74.46 177	87.44 42

test_fmvsmconf0.1_n		72.81 71	72.33 73	74.24 106	69.89 314	55.81 115	78.22 129	75.40 217	54.17 219	75.00 42	88.03 68	53.82 60	80.23 214	78.08 20	78.34 139	86.69 64

CPTT-MVS		72.78 72	72.08 76	74.87 86	84.88 57	61.41 26	84.15 43	77.86 180	55.27 193	67.51 163	88.08 65	41.93 199	81.85 176	69.04 77	80.01 111	81.35 227

LPG-MVS_test		72.74 73	71.74 78	75.76 67	80.22 110	57.51 86	82.55 67	83.40 70	61.32 79	66.67 179	87.33 77	39.15 229	86.59 71	67.70 86	77.30 153	83.19 191

h-mvs33		72.71 74	71.49 82	76.40 58	81.99 81	59.58 52	76.92 166	76.74 200	60.40 93	74.81 47	85.95 115	45.54 162	85.76 93	70.41 70	70.61 232	83.86 168

PAPM_NR		72.63 75	71.80 77	75.13 83	81.72 84	53.42 154	79.91 104	83.28 76	59.14 119	66.31 186	85.90 116	51.86 87	86.06 84	57.45 167	80.62 101	85.91 94

VDD-MVS		72.50 76	72.09 75	73.75 119	81.58 85	49.69 217	77.76 143	77.63 185	63.21 47	73.21 71	89.02 53	42.14 196	83.32 142	61.72 140	82.50 87	88.25 15

3Dnovator	64.47 5	72.49 77	71.39 85	75.79 66	77.70 176	58.99 68	80.66 94	83.15 79	62.24 66	65.46 202	86.59 93	42.38 195	85.52 98	59.59 158	84.72 63	82.85 200

MVS_Test		72.45 78	72.46 72	72.42 157	74.88 232	48.50 233	76.28 178	83.14 80	59.40 115	72.46 89	84.68 135	55.66 40	81.12 191	65.98 102	79.66 115	87.63 36

EI-MVSNet-Vis-set		72.42 79	71.59 79	74.91 84	78.47 152	54.02 141	77.05 162	79.33 147	65.03 18	71.68 97	79.35 251	52.75 72	84.89 114	66.46 96	74.23 180	85.83 98

ACMP	63.53 6	72.30 80	71.20 90	75.59 75	80.28 108	57.54 84	82.74 63	82.84 85	60.58 90	65.24 210	86.18 105	39.25 227	86.03 86	66.95 95	76.79 161	83.22 189
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 81	71.21 89	75.31 78	78.50 150	55.93 112	81.63 81	82.12 92	56.24 173	70.02 113	85.68 122	47.05 146	84.34 124	65.27 109	74.41 179	85.67 106

Vis-MVSNet		72.18 82	71.37 86	74.61 94	81.29 92	55.41 126	80.90 90	78.28 175	60.73 88	69.23 130	88.09 64	44.36 178	82.65 162	57.68 165	81.75 96	85.77 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 83	71.50 81	74.16 107	67.96 331	55.58 123	78.06 135	74.67 232	54.19 218	74.54 52	88.23 61	50.35 106	80.24 213	78.07 21	77.46 149	86.65 67

API-MVS		72.17 83	71.41 84	74.45 100	81.95 82	57.22 89	84.03 45	80.38 132	59.89 109	68.40 139	82.33 188	49.64 110	87.83 44	51.87 213	84.16 71	78.30 267

EPP-MVSNet		72.16 85	71.31 88	74.71 88	78.68 147	49.70 215	82.10 76	81.65 99	60.40 93	65.94 191	85.84 118	51.74 90	86.37 80	55.93 176	79.55 118	88.07 23

DP-MVS Recon		72.15 86	70.73 98	76.40 58	86.57 24	57.99 79	81.15 89	82.96 81	57.03 154	66.78 175	85.56 123	44.50 176	88.11 36	51.77 215	80.23 110	83.10 195

EI-MVSNet-UG-set		71.92 87	71.06 93	74.52 99	77.98 169	53.56 149	76.62 171	79.16 148	64.40 27	71.18 100	78.95 256	52.19 82	84.66 120	65.47 107	73.57 190	85.32 123

VDDNet		71.81 88	71.33 87	73.26 140	82.80 75	47.60 245	78.74 119	75.27 219	59.59 114	72.94 80	89.40 48	41.51 207	83.91 132	58.75 162	82.99 78	88.26 14

EIA-MVS		71.78 89	70.60 99	75.30 79	79.85 119	53.54 150	77.27 157	83.26 77	57.92 143	66.49 181	79.39 249	52.07 84	86.69 69	60.05 152	79.14 126	85.66 107

LFMVS		71.78 89	71.59 79	72.32 158	83.40 67	46.38 254	79.75 107	71.08 265	64.18 32	72.80 83	88.64 59	42.58 192	83.72 135	57.41 168	84.49 66	86.86 58

test_fmvsm_n_1920		71.73 91	71.14 91	73.50 130	72.52 271	56.53 101	75.60 191	76.16 204	48.11 288	77.22 28	85.56 123	53.10 70	77.43 255	74.86 40	77.14 155	86.55 70

PAPR		71.72 92	70.82 96	74.41 101	81.20 96	51.17 189	79.55 112	83.33 73	55.81 182	66.93 174	84.61 139	50.95 100	86.06 84	55.79 179	79.20 124	86.00 90

IS-MVSNet		71.57 93	71.00 94	73.27 139	78.86 141	45.63 265	80.22 97	78.69 159	64.14 35	66.46 182	87.36 76	49.30 113	85.60 95	50.26 226	83.71 74	88.59 9

MAR-MVS		71.51 94	70.15 109	75.60 74	81.84 83	59.39 55	81.38 86	82.90 83	54.90 206	68.08 148	78.70 257	47.73 132	85.51 99	51.68 217	84.17 70	81.88 217
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 95	70.38 104	74.88 85	78.76 144	57.15 94	82.79 61	78.48 166	51.26 250	69.49 122	83.22 168	43.99 181	83.24 144	66.06 99	79.37 119	84.23 154

PVSNet_Blended_VisFu		71.45 96	70.39 103	74.65 92	82.01 79	58.82 71	79.93 103	80.35 133	55.09 198	65.82 197	82.16 194	49.17 116	82.64 163	60.34 150	78.62 135	82.50 206

OMC-MVS		71.40 97	70.60 99	73.78 115	76.60 207	53.15 159	79.74 108	79.78 137	58.37 132	68.75 134	86.45 99	45.43 166	80.60 204	62.58 131	77.73 144	87.58 39

mvsmamba		71.15 98	69.54 117	75.99 63	77.61 184	53.46 152	81.95 78	75.11 225	57.73 147	66.95 173	85.96 114	37.14 252	87.56 48	67.94 83	75.49 173	86.97 54

UniMVSNet_NR-MVSNet		71.11 99	71.00 94	71.44 177	79.20 133	44.13 277	76.02 186	82.60 87	66.48 11	68.20 142	84.60 140	56.82 33	82.82 158	54.62 190	70.43 234	87.36 48

hse-mvs2		71.04 100	69.86 112	74.60 95	79.58 123	57.12 96	73.96 224	75.25 220	60.40 93	74.81 47	81.95 199	45.54 162	82.90 151	70.41 70	66.83 284	83.77 173

GeoE		71.01 101	70.15 109	73.60 128	79.57 124	52.17 179	78.93 117	78.12 177	58.02 139	67.76 160	83.87 155	52.36 79	82.72 160	56.90 170	75.79 169	85.92 93

fmvsm_l_conf0.5_n		70.99 102	70.82 96	71.48 175	71.45 287	54.40 138	77.18 159	70.46 271	48.67 280	75.17 38	86.86 82	53.77 61	76.86 264	76.33 30	77.51 148	83.17 194

PCF-MVS	61.88 8	70.95 103	69.49 119	75.35 77	77.63 179	55.71 117	76.04 185	81.81 97	50.30 262	69.66 120	85.40 129	52.51 75	84.89 114	51.82 214	80.24 109	85.45 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
test_fmvsmvis_n_1920		70.84 104	70.38 104	72.22 160	71.16 295	55.39 127	75.86 188	72.21 258	49.03 276	73.28 70	86.17 106	51.83 88	77.29 258	75.80 32	78.05 141	83.98 162

114514_t		70.83 105	69.56 116	74.64 93	86.21 31	54.63 136	82.34 70	81.81 97	48.22 286	63.01 240	85.83 119	40.92 214	87.10 59	57.91 164	79.79 112	82.18 210

FIs		70.82 106	71.43 83	68.98 227	78.33 157	38.14 326	76.96 164	83.59 64	61.02 83	67.33 165	86.73 87	55.07 43	81.64 179	54.61 192	79.22 123	87.14 52

ACMM	61.98 7	70.80 107	69.73 114	74.02 109	80.59 107	58.59 74	82.68 64	82.02 94	55.46 190	67.18 168	84.39 145	38.51 234	83.17 146	60.65 148	76.10 167	80.30 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 108	70.43 102	71.46 176	69.45 319	48.95 227	72.93 240	78.46 168	57.27 151	71.69 96	83.97 154	51.48 93	77.92 248	70.70 69	77.95 143	87.53 40
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)		70.63 109	70.20 107	71.89 163	78.55 149	45.29 268	75.94 187	82.92 82	63.68 40	68.16 145	83.59 161	53.89 58	83.49 141	53.97 195	71.12 227	86.89 57

xiu_mvs_v2_base		70.52 110	69.75 113	72.84 146	81.21 95	55.63 120	75.11 202	78.92 153	54.92 205	69.96 116	79.68 243	47.00 150	82.09 173	61.60 142	79.37 119	80.81 239

PS-MVSNAJ		70.51 111	69.70 115	72.93 144	81.52 86	55.79 116	74.92 208	79.00 151	55.04 203	69.88 117	78.66 258	47.05 146	82.19 171	61.61 141	79.58 116	80.83 238

fmvsm_l_conf0.5_n_a		70.50 112	70.27 106	71.18 187	71.30 293	54.09 140	76.89 167	69.87 274	47.90 292	74.37 55	86.49 97	53.07 71	76.69 268	75.41 35	77.11 156	82.76 201

v2v482		70.50 112	69.45 121	73.66 123	72.62 268	50.03 211	77.58 145	80.51 130	59.90 106	69.52 121	82.14 195	47.53 137	84.88 116	65.07 111	70.17 241	86.09 88

v1144		70.42 114	69.31 122	73.76 117	73.22 256	50.64 199	77.83 141	81.43 104	58.58 128	69.40 125	81.16 213	47.53 137	85.29 107	64.01 118	70.64 230	85.34 122

TranMVSNet+NR-MVSNet		70.36 115	70.10 111	71.17 188	78.64 148	42.97 289	76.53 173	81.16 119	66.95 6	68.53 138	85.42 128	51.61 92	83.07 147	52.32 207	69.70 254	87.46 41

v8		70.33 116	69.28 123	73.49 131	73.15 258	50.22 206	78.62 122	80.78 126	60.79 86	66.45 183	82.11 197	49.35 112	84.98 111	63.58 124	68.71 269	85.28 125

Fast-Effi-MVS+		70.28 117	69.12 126	73.73 120	78.50 150	51.50 188	75.01 205	79.46 145	56.16 175	68.59 135	79.55 246	53.97 56	84.05 127	53.34 201	77.53 147	85.65 108

X-MVStestdata		70.21 118	67.28 170	79.00 23	86.32 29	62.62 11	85.83 22	83.92 52	64.55 23	72.17 92	6.49 398	47.95 129	88.01 38	71.55 65	86.74 52	86.37 74

v10		70.21 118	69.02 127	73.81 114	73.51 255	50.92 194	78.74 119	81.39 105	60.05 104	66.39 184	81.83 202	47.58 136	85.41 105	62.80 130	68.86 268	85.09 132

QAPM		70.05 120	68.81 131	73.78 115	76.54 209	53.43 153	83.23 54	83.48 66	52.89 231	65.90 193	86.29 102	41.55 206	86.49 77	51.01 220	78.40 138	81.42 221

DU-MVS		70.01 121	69.53 118	71.44 177	78.05 166	44.13 277	75.01 205	81.51 102	64.37 28	68.20 142	84.52 141	49.12 119	82.82 158	54.62 190	70.43 234	87.37 46

AdaColmap		69.99 122	68.66 135	73.97 111	84.94 54	57.83 80	82.63 65	78.71 158	56.28 172	64.34 224	84.14 148	41.57 204	87.06 61	46.45 256	78.88 128	77.02 285

v1192		69.97 123	68.68 134	73.85 112	73.19 257	50.94 192	77.68 144	81.36 107	57.51 149	68.95 133	80.85 223	45.28 169	85.33 106	62.97 129	70.37 236	85.27 126

Anonymous20240529		69.91 124	69.02 127	72.56 151	80.19 113	47.65 243	77.56 147	80.99 122	55.45 191	69.88 117	86.76 85	39.24 228	82.18 172	54.04 194	77.10 157	87.85 27

patch_mono-2		69.85 125	71.09 92	66.16 259	79.11 137	54.80 135	71.97 256	74.31 237	53.50 226	70.90 102	84.17 147	57.63 29	63.31 335	66.17 98	82.02 91	80.38 245

FA-MVS(test-final)		69.82 126	68.48 138	73.84 113	78.44 153	50.04 210	75.58 194	78.99 152	58.16 135	67.59 161	82.14 195	42.66 190	85.63 94	56.60 171	76.19 166	85.84 97

iter_conf_final		69.82 126	68.02 149	75.23 81	79.38 128	52.91 163	80.11 99	73.96 243	54.99 204	68.04 149	83.59 161	29.05 324	87.16 55	65.41 108	77.62 145	85.63 109

FC-MVSNet-test		69.80 128	70.58 101	67.46 243	77.61 184	34.73 356	76.05 184	83.19 78	60.84 85	65.88 195	86.46 98	54.52 52	80.76 203	52.52 206	78.12 140	86.91 56

v144192		69.71 129	68.51 137	73.33 138	73.10 259	50.13 208	77.54 148	80.64 127	56.65 159	68.57 137	80.55 226	46.87 151	84.96 113	62.98 128	69.66 255	84.89 138

test_yl		69.69 130	69.13 124	71.36 181	78.37 155	45.74 261	74.71 212	80.20 134	57.91 144	70.01 114	83.83 156	42.44 193	82.87 154	54.97 186	79.72 113	85.48 114

DCV-MVSNet		69.69 130	69.13 124	71.36 181	78.37 155	45.74 261	74.71 212	80.20 134	57.91 144	70.01 114	83.83 156	42.44 193	82.87 154	54.97 186	79.72 113	85.48 114

VNet		69.68 132	70.19 108	68.16 237	79.73 121	41.63 302	70.53 275	77.38 190	60.37 96	70.69 103	86.63 91	51.08 98	77.09 261	53.61 199	81.69 98	85.75 104

jason		69.65 133	68.39 144	73.43 135	78.27 159	56.88 98	77.12 160	73.71 246	46.53 307	69.34 126	83.22 168	43.37 185	79.18 227	64.77 112	79.20 124	84.23 154
jason: jason.
Effi-MVS+-dtu		69.64 134	67.53 159	75.95 64	76.10 215	62.29 15	80.20 98	76.06 208	59.83 110	65.26 209	77.09 280	41.56 205	84.02 130	60.60 149	71.09 228	81.53 220

fmvsm_s_conf0.5_n		69.58 135	68.84 130	71.79 167	72.31 277	52.90 164	77.90 137	62.43 328	49.97 266	72.85 82	85.90 116	52.21 81	76.49 271	75.75 33	70.26 240	85.97 91

lupinMVS		69.57 136	68.28 145	73.44 134	78.76 144	57.15 94	76.57 172	73.29 250	46.19 310	69.49 122	82.18 191	43.99 181	79.23 226	64.66 113	79.37 119	83.93 163

fmvsm_s_conf0.5_n_a		69.54 137	68.74 133	71.93 162	72.47 273	53.82 144	78.25 127	62.26 330	49.78 268	73.12 76	86.21 104	52.66 73	76.79 266	75.02 39	68.88 266	85.18 128

NR-MVSNet		69.54 137	68.85 129	71.59 174	78.05 166	43.81 281	74.20 220	80.86 125	65.18 14	62.76 242	84.52 141	52.35 80	83.59 139	50.96 222	70.78 229	87.37 46

MVS_111021_LR		69.50 139	68.78 132	71.65 172	78.38 154	59.33 56	74.82 210	70.11 273	58.08 136	67.83 156	84.68 135	41.96 198	76.34 275	65.62 106	77.54 146	79.30 261

v1921920		69.47 140	68.17 146	73.36 137	73.06 260	50.10 209	77.39 151	80.56 128	56.58 166	68.59 135	80.37 228	44.72 174	84.98 111	62.47 134	69.82 250	85.00 134

test_djsdf		69.45 141	67.74 151	74.58 96	74.57 243	54.92 133	82.79 61	78.48 166	51.26 250	65.41 203	83.49 166	38.37 236	83.24 144	66.06 99	69.25 261	85.56 111

RRT_MVS		69.42 142	67.49 162	75.21 82	78.01 168	52.56 172	82.23 75	78.15 176	55.84 180	65.65 198	85.07 130	30.86 310	86.83 65	61.56 144	70.00 245	86.24 85

fmvsm_s_conf0.1_n		69.41 143	68.60 136	71.83 165	71.07 296	52.88 165	77.85 140	62.44 327	49.58 270	72.97 79	86.22 103	51.68 91	76.48 272	75.53 34	70.10 243	86.14 86

iter_conf05		69.40 144	67.62 155	74.73 87	77.84 173	51.13 190	79.28 114	73.71 246	54.62 209	68.17 144	83.59 161	28.68 329	87.16 55	65.74 105	76.95 158	85.91 94

fmvsm_s_conf0.1_n_a		69.32 145	68.44 142	71.96 161	70.91 298	53.78 145	78.12 133	62.30 329	49.35 272	73.20 72	86.55 96	51.99 85	76.79 266	74.83 41	68.68 271	85.32 123

Anonymous20231211		69.28 146	68.47 140	71.73 169	80.28 108	47.18 249	79.98 101	82.37 89	54.61 210	67.24 166	84.01 152	39.43 224	82.41 169	55.45 184	72.83 204	85.62 110

EI-MVSNet		69.27 147	68.44 142	71.73 169	74.47 244	49.39 222	75.20 200	78.45 169	59.60 111	69.16 131	76.51 290	51.29 94	82.50 166	59.86 157	71.45 224	83.30 186

v1240		69.24 148	67.91 150	73.25 141	73.02 262	49.82 213	77.21 158	80.54 129	56.43 168	68.34 141	80.51 227	43.33 186	84.99 109	62.03 138	69.77 253	84.95 137

IterMVS-LS		69.22 149	68.48 138	71.43 179	74.44 246	49.40 221	76.23 179	77.55 186	59.60 111	65.85 196	81.59 208	51.28 95	81.58 182	59.87 156	69.90 249	83.30 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VPA-MVSNet		69.02 150	69.47 120	67.69 241	77.42 189	41.00 307	74.04 222	79.68 139	60.06 103	69.26 129	84.81 134	51.06 99	77.58 253	54.44 193	74.43 178	84.48 148

v7n		69.01 151	67.36 167	73.98 110	72.51 272	52.65 168	78.54 125	81.30 113	60.26 101	62.67 244	81.62 205	43.61 183	84.49 121	57.01 169	68.70 270	84.79 141

OpenMVS	61.03 9	68.85 152	67.56 156	72.70 150	74.26 250	53.99 142	81.21 88	81.34 111	52.70 232	62.75 243	85.55 125	38.86 232	84.14 126	48.41 242	83.01 77	79.97 251

XVG-OURS-SEG-HR		68.81 153	67.47 163	72.82 148	74.40 247	56.87 99	70.59 274	79.04 150	54.77 207	66.99 171	86.01 112	39.57 223	78.21 244	62.54 132	73.33 196	83.37 185

BH-RMVSNet		68.81 153	67.42 164	72.97 143	80.11 116	52.53 173	74.26 219	76.29 203	58.48 130	68.38 140	84.20 146	42.59 191	83.83 133	46.53 255	75.91 168	82.56 202

UGNet		68.81 153	67.39 165	73.06 142	78.33 157	54.47 137	79.77 106	75.40 217	60.45 92	63.22 236	84.40 144	32.71 298	80.91 199	51.71 216	80.56 105	83.81 169
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		68.76 156	67.37 166	72.90 145	74.32 249	57.22 89	70.09 281	78.81 155	55.24 194	67.79 158	85.81 121	36.54 259	78.28 243	62.04 137	75.74 170	83.19 191

V42		68.65 157	67.35 168	72.56 151	68.93 325	50.18 207	72.90 241	79.47 144	56.92 156	69.45 124	80.26 232	46.29 155	82.99 148	64.07 116	67.82 276	84.53 146

PVSNet_Blended		68.59 158	67.72 152	71.19 186	77.03 199	50.57 200	72.51 248	81.52 100	51.91 239	64.22 229	77.77 275	49.13 117	82.87 154	55.82 177	79.58 116	80.14 249

xiu_mvs_v1_base_debu		68.58 159	67.28 170	72.48 153	78.19 161	57.19 91	75.28 197	75.09 226	51.61 241	70.04 110	81.41 210	32.79 294	79.02 234	63.81 121	77.31 150	81.22 229

xiu_mvs_v1_base		68.58 159	67.28 170	72.48 153	78.19 161	57.19 91	75.28 197	75.09 226	51.61 241	70.04 110	81.41 210	32.79 294	79.02 234	63.81 121	77.31 150	81.22 229

xiu_mvs_v1_base_debi		68.58 159	67.28 170	72.48 153	78.19 161	57.19 91	75.28 197	75.09 226	51.61 241	70.04 110	81.41 210	32.79 294	79.02 234	63.81 121	77.31 150	81.22 229

PVSNet_BlendedMVS		68.56 162	67.72 152	71.07 191	77.03 199	50.57 200	74.50 216	81.52 100	53.66 225	64.22 229	79.72 242	49.13 117	82.87 154	55.82 177	73.92 183	79.77 256

WR-MVS		68.47 163	68.47 140	68.44 234	80.20 112	39.84 311	73.75 232	76.07 207	64.68 22	68.11 147	83.63 160	50.39 105	79.14 232	49.78 227	69.66 255	86.34 76

AUN-MVS		68.45 164	66.41 187	74.57 97	79.53 125	57.08 97	73.93 227	75.23 221	54.44 215	66.69 178	81.85 201	37.10 254	82.89 152	62.07 136	66.84 283	83.75 174

c3_l		68.33 165	67.56 156	70.62 198	70.87 299	46.21 257	74.47 217	78.80 156	56.22 174	66.19 187	78.53 263	51.88 86	81.40 184	62.08 135	69.04 264	84.25 153

BH-untuned		68.27 166	67.29 169	71.21 185	79.74 120	53.22 158	76.06 183	77.46 189	57.19 152	66.10 188	81.61 206	45.37 168	83.50 140	45.42 270	76.68 163	76.91 289

jajsoiax		68.25 167	66.45 183	73.66 123	75.62 221	55.49 125	80.82 91	78.51 165	52.33 236	64.33 225	84.11 149	28.28 332	81.81 178	63.48 125	70.62 231	83.67 177

v148		68.24 168	67.19 176	71.40 180	70.43 304	47.77 242	75.76 190	77.03 195	58.91 121	67.36 164	80.10 235	48.60 124	81.89 175	60.01 153	66.52 287	84.53 146

CANet_DTU		68.18 169	67.71 154	69.59 217	74.83 234	46.24 256	78.66 121	76.85 197	59.60 111	63.45 235	82.09 198	35.25 267	77.41 256	59.88 155	78.76 132	85.14 129

mvs_tets		68.18 169	66.36 189	73.63 126	75.61 222	55.35 128	80.77 92	78.56 163	52.48 235	64.27 227	84.10 150	27.45 338	81.84 177	63.45 126	70.56 233	83.69 176

SDMVSNet		68.03 171	68.10 148	67.84 239	77.13 195	48.72 231	65.32 314	79.10 149	58.02 139	65.08 213	82.55 181	47.83 131	73.40 288	63.92 120	73.92 183	81.41 222

miper_ehance_all_eth		68.03 171	67.24 174	70.40 202	70.54 302	46.21 257	73.98 223	78.68 160	55.07 201	66.05 189	77.80 272	52.16 83	81.31 187	61.53 145	69.32 258	83.67 177

mvs_anonymous		68.03 171	67.51 160	69.59 217	72.08 279	44.57 275	71.99 255	75.23 221	51.67 240	67.06 170	82.57 180	54.68 50	77.94 247	56.56 172	75.71 171	86.26 84

ET-MVSNet_ETH3D		67.96 174	65.72 201	74.68 90	76.67 205	55.62 122	75.11 202	74.74 230	52.91 230	60.03 271	80.12 234	33.68 284	82.64 163	61.86 139	76.34 165	85.78 99

thisisatest0530		67.92 175	65.78 200	74.33 103	76.29 212	51.03 191	76.89 167	74.25 239	53.67 224	65.59 200	81.76 203	35.15 268	85.50 100	55.94 175	72.47 209	86.47 71

PAPM		67.92 175	66.69 180	71.63 173	78.09 164	49.02 225	77.09 161	81.24 117	51.04 254	60.91 265	83.98 153	47.71 133	84.99 109	40.81 302	79.32 122	80.90 237

tttt0517		67.83 177	65.66 202	74.33 103	76.69 204	50.82 196	77.86 139	73.99 242	54.54 213	64.64 222	82.53 184	35.06 269	85.50 100	55.71 180	69.91 248	86.67 65

tt0805		67.77 178	67.24 174	69.34 222	74.87 233	40.08 309	77.36 152	81.37 106	55.31 192	66.33 185	84.65 137	37.35 247	82.55 165	55.65 182	72.28 215	85.39 121

ECVR-MVS		67.72 179	67.51 160	68.35 235	79.46 126	36.29 349	74.79 211	66.93 296	58.72 124	67.19 167	88.05 66	36.10 260	81.38 185	52.07 210	84.25 68	87.39 44

eth_miper_zixun_eth		67.63 180	66.28 193	71.67 171	71.60 285	48.33 235	73.68 233	77.88 179	55.80 183	65.91 192	78.62 261	47.35 143	82.88 153	59.45 159	66.25 288	83.81 169

UniMVSNet_ETH3D		67.60 181	67.07 178	69.18 226	77.39 190	42.29 293	74.18 221	75.59 213	60.37 96	66.77 176	86.06 110	37.64 243	78.93 239	52.16 209	73.49 192	86.32 80

VPNet		67.52 182	68.11 147	65.74 268	79.18 134	36.80 341	72.17 253	72.83 253	62.04 72	67.79 158	85.83 119	48.88 121	76.60 270	51.30 218	72.97 203	83.81 169

cl22		67.47 183	66.45 183	70.54 200	69.85 315	46.49 253	73.85 230	77.35 191	55.07 201	65.51 201	77.92 268	47.64 135	81.10 192	61.58 143	69.32 258	84.01 161

Fast-Effi-MVS+-dtu		67.37 184	65.33 207	73.48 132	72.94 263	57.78 82	77.47 150	76.88 196	57.60 148	61.97 255	76.85 284	39.31 225	80.49 208	54.72 189	70.28 239	82.17 212

MVS		67.37 184	66.33 190	70.51 201	75.46 225	50.94 192	73.95 225	81.85 96	41.57 347	62.54 248	78.57 262	47.98 128	85.47 102	52.97 204	82.05 90	75.14 302

test1111		67.21 186	67.14 177	67.42 244	79.24 132	34.76 355	73.89 229	65.65 304	58.71 126	66.96 172	87.95 69	36.09 261	80.53 205	52.03 211	83.79 73	86.97 54

GBi-Net		67.21 186	66.55 181	69.19 223	77.63 179	43.33 284	77.31 153	77.83 181	56.62 162	65.04 215	82.70 175	41.85 200	80.33 210	47.18 250	72.76 205	83.92 164

test1		67.21 186	66.55 181	69.19 223	77.63 179	43.33 284	77.31 153	77.83 181	56.62 162	65.04 215	82.70 175	41.85 200	80.33 210	47.18 250	72.76 205	83.92 164

cl____		67.18 189	66.26 194	69.94 209	70.20 307	45.74 261	73.30 235	76.83 198	55.10 196	65.27 206	79.57 245	47.39 141	80.53 205	59.41 161	69.22 262	83.53 183

DIV-MVS_self_test		67.18 189	66.26 194	69.94 209	70.20 307	45.74 261	73.29 236	76.83 198	55.10 196	65.27 206	79.58 244	47.38 142	80.53 205	59.43 160	69.22 262	83.54 182

MVSTER		67.16 191	65.58 204	71.88 164	70.37 306	49.70 215	70.25 280	78.45 169	51.52 244	69.16 131	80.37 228	38.45 235	82.50 166	60.19 151	71.46 223	83.44 184

miper_enhance_ethall		67.11 192	66.09 196	70.17 206	69.21 322	45.98 259	72.85 242	78.41 172	51.38 247	65.65 198	75.98 298	51.17 97	81.25 188	60.82 147	69.32 258	83.29 188

Baseline_NR-MVSNet		67.05 193	67.56 156	65.50 270	75.65 220	37.70 332	75.42 195	74.65 233	59.90 106	68.14 146	83.15 171	49.12 119	77.20 259	52.23 208	69.78 251	81.60 219

WR-MVS_H		67.02 194	66.92 179	67.33 247	77.95 170	37.75 330	77.57 146	82.11 93	62.03 73	62.65 245	82.48 185	50.57 103	79.46 222	42.91 290	64.01 305	84.79 141

anonymousdsp		67.00 195	64.82 212	73.57 129	70.09 310	56.13 107	76.35 176	77.35 191	48.43 284	64.99 218	80.84 224	33.01 291	80.34 209	64.66 113	67.64 278	84.23 154

FMVSNet2		66.93 196	66.31 192	68.79 230	77.63 179	42.98 288	76.11 181	77.47 187	56.62 162	65.22 212	82.17 193	41.85 200	80.18 216	47.05 253	72.72 208	83.20 190

BH-w/o		66.85 197	65.83 199	69.90 212	79.29 129	52.46 175	74.66 214	76.65 201	54.51 214	64.85 219	78.12 264	45.59 161	82.95 150	43.26 286	75.54 172	74.27 315

Anonymous202405211		66.84 198	65.99 197	69.40 221	80.19 113	42.21 295	71.11 269	71.31 264	58.80 123	67.90 150	86.39 100	29.83 319	79.65 219	49.60 233	78.78 131	86.33 78

CDS-MVSNet		66.80 199	65.37 205	71.10 190	78.98 139	53.13 161	73.27 237	71.07 266	52.15 238	64.72 220	80.23 233	43.56 184	77.10 260	45.48 268	78.88 128	83.05 196
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 200	65.27 208	71.33 184	79.16 136	53.67 146	73.84 231	69.59 278	52.32 237	65.28 205	81.72 204	44.49 177	77.40 257	42.32 294	78.66 134	82.92 197

FMVSNet1		66.70 201	65.87 198	69.19 223	77.49 188	43.33 284	77.31 153	77.83 181	56.45 167	64.60 223	82.70 175	38.08 241	80.33 210	46.08 259	72.31 214	83.92 164

ab-mvs		66.65 202	66.42 186	67.37 245	76.17 214	41.73 299	70.41 278	76.14 206	53.99 220	65.98 190	83.51 165	49.48 111	76.24 276	48.60 240	73.46 194	84.14 157

PEN-MVS		66.60 203	66.45 183	67.04 248	77.11 197	36.56 343	77.03 163	80.42 131	62.95 50	62.51 250	84.03 151	46.69 152	79.07 233	44.22 274	63.08 315	85.51 113

TAPA-MVS	59.36 10	66.60 203	65.20 209	70.81 194	76.63 206	48.75 229	76.52 174	80.04 136	50.64 259	65.24 210	84.93 132	39.15 229	78.54 240	36.77 324	76.88 160	85.14 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 205	65.07 210	71.17 188	79.18 134	49.63 219	73.48 234	75.20 223	52.95 229	67.90 150	80.33 231	39.81 221	83.68 136	43.20 287	73.56 191	80.20 247

CP-MVSNet		66.49 206	66.41 187	66.72 250	77.67 178	36.33 346	76.83 170	79.52 143	62.45 63	62.54 248	83.47 167	46.32 154	78.37 241	45.47 269	63.43 312	85.45 116

PS-CasMVS		66.42 207	66.32 191	66.70 252	77.60 186	36.30 348	76.94 165	79.61 141	62.36 65	62.43 252	83.66 159	45.69 158	78.37 241	45.35 271	63.26 313	85.42 119

FMVSNet3		66.32 208	65.61 203	68.46 233	76.48 210	42.34 292	74.98 207	77.15 194	55.83 181	65.04 215	81.16 213	39.91 218	80.14 217	47.18 250	72.76 205	82.90 199

ACMH+	57.40 11	66.12 209	64.06 216	72.30 159	77.79 175	52.83 166	80.39 95	78.03 178	57.30 150	57.47 299	82.55 181	27.68 336	84.17 125	45.54 266	69.78 251	79.90 252

cascas		65.98 210	63.42 226	73.64 125	77.26 193	52.58 171	72.26 252	77.21 193	48.56 281	61.21 264	74.60 311	32.57 303	85.82 92	50.38 225	76.75 162	82.52 205

FE-MVS		65.91 211	63.33 228	73.63 126	77.36 191	51.95 185	72.62 245	75.81 209	53.70 223	65.31 204	78.96 255	28.81 328	86.39 79	43.93 279	73.48 193	82.55 203

thisisatest0515		65.83 212	63.50 225	72.82 148	73.75 253	49.50 220	71.32 263	73.12 252	49.39 271	63.82 231	76.50 292	34.95 271	84.84 117	53.20 203	75.49 173	84.13 158

DP-MVS		65.68 213	63.66 223	71.75 168	84.93 55	56.87 99	80.74 93	73.16 251	53.06 228	59.09 285	82.35 187	36.79 258	85.94 89	32.82 346	69.96 247	72.45 329

HyFIR lowres test		65.67 214	63.01 232	73.67 122	79.97 118	55.65 119	69.07 290	75.52 215	42.68 341	63.53 234	77.95 266	40.43 216	81.64 179	46.01 260	71.91 218	83.73 175

DTE-MVSNet		65.58 215	65.34 206	66.31 255	76.06 216	34.79 353	76.43 175	79.38 146	62.55 61	61.66 260	83.83 156	45.60 160	79.15 231	41.64 301	60.88 330	85.00 134

GA-MVS		65.53 216	63.70 222	71.02 192	70.87 299	48.10 237	70.48 276	74.40 235	56.69 158	64.70 221	76.77 285	33.66 285	81.10 192	55.42 185	70.32 238	83.87 167

CNLPA		65.43 217	64.02 217	69.68 215	78.73 146	58.07 78	77.82 142	70.71 269	51.49 245	61.57 262	83.58 164	38.23 239	70.82 300	43.90 280	70.10 243	80.16 248

MVP-Stereo		65.41 218	63.80 221	70.22 203	77.62 183	55.53 124	76.30 177	78.53 164	50.59 260	56.47 308	78.65 259	39.84 220	82.68 161	44.10 278	72.12 217	72.44 330
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 219	62.73 236	73.40 136	74.89 231	52.78 167	73.09 239	75.13 224	55.69 185	58.48 293	73.73 316	32.86 293	86.32 82	50.63 223	70.11 242	81.10 233
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
test2506		65.33 220	64.61 213	67.50 242	79.46 126	34.19 360	74.43 218	51.92 366	58.72 124	66.75 177	88.05 66	25.99 348	80.92 198	51.94 212	84.25 68	87.39 44

pm-mvs1		65.24 221	64.97 211	66.04 263	72.38 274	39.40 316	72.62 245	75.63 212	55.53 189	62.35 254	83.18 170	47.45 139	76.47 273	49.06 237	66.54 286	82.24 209

ACMH	55.70 15	65.20 222	63.57 224	70.07 207	78.07 165	52.01 184	79.48 113	79.69 138	55.75 184	56.59 305	80.98 218	27.12 340	80.94 196	42.90 291	71.58 222	77.25 283
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 223	63.21 230	70.72 197	81.04 98	54.87 134	78.57 123	77.47 187	48.51 282	55.71 311	81.89 200	33.71 283	79.71 218	41.66 299	70.37 236	77.58 277
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 224	62.84 234	71.82 166	81.49 88	56.26 105	66.32 303	74.20 240	40.53 352	63.16 238	78.65 259	41.30 208	77.80 250	45.80 262	74.09 181	81.40 224

bld_raw_dy_0_64		64.87 225	63.22 229	69.83 214	74.79 236	53.32 157	78.15 132	62.02 333	51.20 252	60.17 269	83.12 172	24.15 357	74.20 287	63.08 127	72.33 212	81.96 214

TransMVSNet (Re)		64.72 226	64.33 215	65.87 267	75.22 228	38.56 322	74.66 214	75.08 229	58.90 122	61.79 258	82.63 178	51.18 96	78.07 246	43.63 283	55.87 351	80.99 236

EG-PatchMatch MVS		64.71 227	62.87 233	70.22 203	77.68 177	53.48 151	77.99 136	78.82 154	53.37 227	56.03 310	77.41 279	24.75 355	84.04 128	46.37 257	73.42 195	73.14 321

LS3D		64.71 227	62.50 238	71.34 183	79.72 122	55.71 117	79.82 105	74.72 231	48.50 283	56.62 304	84.62 138	33.59 286	82.34 170	29.65 366	75.23 175	75.97 293

1314		64.61 229	63.21 230	68.80 229	71.87 283	47.46 246	73.95 225	78.39 174	42.88 340	59.97 272	76.60 289	38.11 240	79.39 224	54.84 188	72.32 213	79.55 257

HY-MVS	56.14 13	64.55 230	63.89 218	66.55 253	74.73 238	41.02 304	69.96 282	74.43 234	49.29 273	61.66 260	80.92 220	47.43 140	76.68 269	44.91 273	71.69 220	81.94 215

sd_testset		64.46 231	64.45 214	64.51 280	77.13 195	42.25 294	62.67 327	72.11 259	58.02 139	65.08 213	82.55 181	41.22 212	69.88 307	47.32 248	73.92 183	81.41 222

XVG-ACMP-BASELINE		64.36 232	62.23 241	70.74 196	72.35 275	52.45 176	70.80 273	78.45 169	53.84 222	59.87 274	81.10 215	16.24 373	79.32 225	55.64 183	71.76 219	80.47 242

CostFormer		64.04 233	62.51 237	68.61 232	71.88 282	45.77 260	71.30 264	70.60 270	47.55 296	64.31 226	76.61 288	41.63 203	79.62 221	49.74 229	69.00 265	80.42 243

1112_ss		64.00 234	63.36 227	65.93 265	79.28 130	42.58 291	71.35 262	72.36 257	46.41 308	60.55 267	77.89 270	46.27 156	73.28 289	46.18 258	69.97 246	81.92 216

baseline1		63.81 235	63.87 220	63.62 284	76.29 212	36.36 344	71.78 259	67.29 293	56.05 177	64.23 228	82.95 173	47.11 145	74.41 284	47.30 249	61.85 324	80.10 250

pmmvs6		63.69 236	62.82 235	66.27 257	70.63 301	39.27 317	73.13 238	75.47 216	52.69 233	59.75 278	82.30 189	39.71 222	77.03 262	47.40 247	64.35 304	82.53 204

Vis-MVSNet (Re-imp)		63.69 236	63.88 219	63.14 289	74.75 237	31.04 373	71.16 267	63.64 318	56.32 170	59.80 276	84.99 131	44.51 175	75.46 279	39.12 311	80.62 101	82.92 197

baseline2		63.42 238	61.26 253	69.89 213	72.55 270	47.62 244	71.54 260	68.38 288	50.11 263	54.82 322	75.55 303	43.06 188	80.96 195	48.13 243	67.16 282	81.11 232

thres400		63.31 239	62.18 242	66.72 250	76.85 202	39.62 313	71.96 257	69.44 280	56.63 160	62.61 246	79.83 238	37.18 249	79.17 228	31.84 351	73.25 198	81.36 225

thres600view7		63.30 240	62.27 240	66.41 254	77.18 194	38.87 319	72.35 250	69.11 284	56.98 155	62.37 253	80.96 219	37.01 256	79.00 237	31.43 358	73.05 202	81.36 225

thres100view900		63.28 241	62.41 239	65.89 266	77.31 192	38.66 321	72.65 243	69.11 284	57.07 153	62.45 251	81.03 217	37.01 256	79.17 228	31.84 351	73.25 198	79.83 254

test_0402		63.25 242	61.01 256	69.96 208	80.00 117	54.37 139	76.86 169	72.02 260	54.58 212	58.71 288	80.79 225	35.00 270	84.36 123	26.41 377	64.71 299	71.15 347

tfpn200view9		63.18 243	62.18 242	66.21 258	76.85 202	39.62 313	71.96 257	69.44 280	56.63 160	62.61 246	79.83 238	37.18 249	79.17 228	31.84 351	73.25 198	79.83 254

LTVRE_ROB	55.42 16	63.15 244	61.23 254	68.92 228	76.57 208	47.80 240	59.92 343	76.39 202	54.35 216	58.67 289	82.46 186	29.44 322	81.49 183	42.12 295	71.14 226	77.46 278
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		63.05 245	60.87 259	69.58 219	76.99 201	53.63 148	78.12 133	76.16 204	47.97 291	52.41 341	81.61 206	27.87 334	78.11 245	40.07 305	66.66 285	77.00 286

IterMVS		62.79 246	61.27 252	67.35 246	69.37 320	52.04 183	71.17 266	68.24 289	52.63 234	59.82 275	76.91 283	37.32 248	72.36 292	52.80 205	63.19 314	77.66 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		62.49 247	61.52 248	65.40 272	71.99 281	50.80 197	71.15 268	69.63 277	45.71 316	60.61 266	77.93 267	37.45 245	65.99 327	55.67 181	63.50 311	79.42 259

tfpnnormal		62.47 248	61.63 247	64.99 277	74.81 235	39.01 318	71.22 265	73.72 245	55.22 195	60.21 268	80.09 236	41.26 211	76.98 263	30.02 364	68.09 274	78.97 264

MS-PatchMatch		62.42 249	61.46 249	65.31 274	75.21 229	52.10 180	72.05 254	74.05 241	46.41 308	57.42 301	74.36 312	34.35 277	77.57 254	45.62 265	73.67 187	66.26 364

Test_1112_low_res		62.32 250	61.77 245	64.00 283	79.08 138	39.53 315	68.17 293	70.17 272	43.25 336	59.03 286	79.90 237	44.08 179	71.24 299	43.79 282	68.42 272	81.25 228

D2MVS		62.30 251	60.29 261	68.34 236	66.46 342	48.42 234	65.70 306	73.42 248	47.71 294	58.16 295	75.02 307	30.51 312	77.71 252	53.96 196	71.68 221	78.90 265

testing222		62.29 252	61.31 251	65.25 275	77.87 171	38.53 323	68.34 292	66.31 301	56.37 169	63.15 239	77.58 277	28.47 330	76.18 278	37.04 322	76.65 164	81.05 235

thres200		62.20 253	61.16 255	65.34 273	75.38 227	39.99 310	69.60 285	69.29 282	55.64 188	61.87 257	76.99 281	37.07 255	78.96 238	31.28 359	73.28 197	77.06 284

tpm2		62.07 254	60.10 262	67.99 238	72.79 265	43.86 280	71.05 271	66.85 297	43.14 338	62.77 241	75.39 305	38.32 237	80.80 201	41.69 298	68.88 266	79.32 260

miper_lstm_enhance		62.03 255	60.88 258	65.49 271	66.71 339	46.25 255	56.29 359	75.70 211	50.68 257	61.27 263	75.48 304	40.21 217	68.03 316	56.31 174	65.25 295	82.18 210

EPNet_dtu		61.90 256	61.97 244	61.68 297	72.89 264	39.78 312	75.85 189	65.62 305	55.09 198	54.56 326	79.36 250	37.59 244	67.02 321	39.80 308	76.95 158	78.25 268
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 257	61.35 250	63.46 285	74.58 242	31.48 372	61.42 334	58.14 345	58.71 126	53.02 340	79.55 246	43.07 187	76.80 265	45.69 263	77.96 142	82.11 213

MSDG		61.81 258	59.23 265	69.55 220	72.64 267	52.63 170	70.45 277	75.81 209	51.38 247	53.70 333	76.11 294	29.52 320	81.08 194	37.70 317	65.79 292	74.93 307

SixPastTwentyTwo		61.65 259	58.80 269	70.20 205	75.80 218	47.22 248	75.59 192	69.68 276	54.61 210	54.11 330	79.26 252	27.07 341	82.96 149	43.27 285	49.79 368	80.41 244

CL-MVSNet_self_test		61.53 260	60.94 257	63.30 287	68.95 324	36.93 340	67.60 297	72.80 254	55.67 186	59.95 273	76.63 286	45.01 172	72.22 295	39.74 309	62.09 323	80.74 240

RPMNet		61.53 260	58.42 272	70.86 193	69.96 312	52.07 181	65.31 315	81.36 107	43.20 337	59.36 281	70.15 342	35.37 266	85.47 102	36.42 331	64.65 300	75.06 303

pmmvs4		61.48 262	59.39 264	67.76 240	71.57 286	53.86 143	71.42 261	65.34 306	44.20 327	59.46 280	77.92 268	35.90 262	74.71 282	43.87 281	64.87 298	74.71 311

OurMVSNet-221017-0		61.37 263	58.63 271	69.61 216	72.05 280	48.06 238	73.93 227	72.51 255	47.23 302	54.74 323	80.92 220	21.49 366	81.24 189	48.57 241	56.22 350	79.53 258

COLMAP_ROB	52.97 17	61.27 264	58.81 268	68.64 231	74.63 241	52.51 174	78.42 126	73.30 249	49.92 267	50.96 346	81.51 209	23.06 359	79.40 223	31.63 355	65.85 290	74.01 318
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 265	61.67 246	57.70 322	70.43 304	38.45 324	64.19 321	66.47 298	48.05 290	63.22 236	80.86 222	49.28 114	60.47 344	45.25 272	67.28 281	74.19 316

SCA		60.49 266	58.38 273	66.80 249	74.14 252	48.06 238	63.35 324	63.23 321	49.13 275	59.33 284	72.10 325	37.45 245	74.27 285	44.17 275	62.57 318	78.05 271

K. test v3		60.47 267	57.11 280	70.56 199	73.74 254	48.22 236	75.10 204	62.55 325	58.27 134	53.62 336	76.31 293	27.81 335	81.59 181	47.42 246	39.18 381	81.88 217

OpenMVS_ROB	52.78 18	60.03 268	58.14 276	65.69 269	70.47 303	44.82 270	75.33 196	70.86 268	45.04 319	56.06 309	76.00 295	26.89 343	79.65 219	35.36 336	67.29 280	72.60 326

CR-MVSNet		59.91 269	57.90 278	65.96 264	69.96 312	52.07 181	65.31 315	63.15 322	42.48 342	59.36 281	74.84 308	35.83 263	70.75 301	45.50 267	64.65 300	75.06 303

PatchmatchNet		59.84 270	58.24 274	64.65 279	73.05 261	46.70 252	69.42 287	62.18 331	47.55 296	58.88 287	71.96 327	34.49 275	69.16 309	42.99 289	63.60 309	78.07 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WTY-MVS		59.75 271	60.39 260	57.85 320	72.32 276	37.83 329	61.05 339	64.18 315	45.95 315	61.91 256	79.11 254	47.01 149	60.88 343	42.50 293	69.49 257	74.83 308

WB-MVSnew		59.66 272	59.69 263	59.56 304	75.19 230	35.78 351	69.34 288	64.28 314	46.88 305	61.76 259	75.79 299	40.61 215	65.20 330	32.16 348	71.21 225	77.70 275

CVMVSNet		59.63 273	59.14 266	61.08 302	74.47 244	38.84 320	75.20 200	68.74 286	31.15 369	58.24 294	76.51 290	32.39 304	68.58 312	49.77 228	65.84 291	75.81 295

tpm cat1		59.25 274	56.95 283	66.15 260	72.19 278	46.96 250	68.09 294	65.76 303	40.03 356	57.81 297	70.56 337	38.32 237	74.51 283	38.26 315	61.50 327	77.00 286

test_vis1_n_1920		58.86 275	59.06 267	58.25 315	63.76 354	43.14 287	67.49 298	66.36 300	40.22 354	65.89 194	71.95 328	31.04 308	59.75 349	59.94 154	64.90 297	71.85 338

pmmvs-eth3d		58.81 276	56.31 290	66.30 256	67.61 333	52.42 177	72.30 251	64.76 310	43.55 333	54.94 321	74.19 314	28.95 325	72.60 291	43.31 284	57.21 345	73.88 319

tpmvs		58.47 277	56.95 283	63.03 291	70.20 307	41.21 303	67.90 296	67.23 294	49.62 269	54.73 324	70.84 335	34.14 278	76.24 276	36.64 328	61.29 328	71.64 339

PVSNet	50.76 19	58.40 278	57.39 279	61.42 299	75.53 224	44.04 279	61.43 333	63.45 319	47.04 304	56.91 302	73.61 317	27.00 342	64.76 331	39.12 311	72.40 210	75.47 300

tpmrst		58.24 279	58.70 270	56.84 324	66.97 336	34.32 358	69.57 286	61.14 337	47.17 303	58.58 292	71.60 330	41.28 210	60.41 345	49.20 235	62.84 316	75.78 296

Patchmatch-RL test		58.16 280	55.49 296	66.15 260	67.92 332	48.89 228	60.66 341	51.07 370	47.86 293	59.36 281	62.71 372	34.02 280	72.27 294	56.41 173	59.40 337	77.30 280

test-LLR		58.15 281	58.13 277	58.22 316	68.57 326	44.80 271	65.46 311	57.92 346	50.08 264	55.44 314	69.82 344	32.62 300	57.44 359	49.66 231	73.62 188	72.41 331

ppachtmachnet_test		58.06 282	55.38 297	66.10 262	69.51 317	48.99 226	68.01 295	66.13 302	44.50 324	54.05 331	70.74 336	32.09 306	72.34 293	36.68 327	56.71 349	76.99 288

gg-mvs-nofinetune		57.86 283	56.43 289	62.18 295	72.62 268	35.35 352	66.57 300	56.33 355	50.65 258	57.64 298	57.10 378	30.65 311	76.36 274	37.38 319	78.88 128	74.82 309

CMPMVS	42.80 21	57.81 284	55.97 292	63.32 286	60.98 369	47.38 247	64.66 319	69.50 279	32.06 368	46.83 362	77.80 272	29.50 321	71.36 298	48.68 239	73.75 186	71.21 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 285	57.07 281	58.22 316	74.21 251	37.18 335	62.46 328	60.88 338	48.88 278	55.29 317	75.99 297	31.68 307	62.04 340	31.87 350	72.35 211	75.43 301

tpm		57.34 286	58.16 275	54.86 334	71.80 284	34.77 354	67.47 299	56.04 358	48.20 287	60.10 270	76.92 282	37.17 251	53.41 375	40.76 303	65.01 296	76.40 292

Patchmtry		57.16 287	56.47 288	59.23 307	69.17 323	34.58 357	62.98 325	63.15 322	44.53 323	56.83 303	74.84 308	35.83 263	68.71 311	40.03 306	60.91 329	74.39 314

AllTest		57.08 288	54.65 301	64.39 281	71.44 288	49.03 223	69.92 283	67.30 291	45.97 313	47.16 360	79.77 240	17.47 369	67.56 318	33.65 341	59.16 338	76.57 290

test_cas_vis1_n_1920		56.91 289	56.71 286	57.51 323	59.13 374	45.40 267	63.58 323	61.29 336	36.24 363	67.14 169	71.85 329	29.89 318	56.69 363	57.65 166	63.58 310	70.46 351

dmvs_re		56.77 290	56.83 285	56.61 325	69.23 321	41.02 304	58.37 348	64.18 315	50.59 260	57.45 300	71.42 331	35.54 265	58.94 353	37.23 320	67.45 279	69.87 356

testing3		56.54 291	55.92 293	58.41 314	77.52 187	27.93 381	69.72 284	56.36 354	54.75 208	58.63 291	77.80 272	20.88 367	71.75 297	25.31 379	62.25 321	75.53 299

our_test_3		56.49 292	54.42 304	62.68 293	69.51 317	45.48 266	66.08 304	61.49 335	44.11 330	50.73 350	69.60 347	33.05 290	68.15 313	38.38 314	56.86 346	74.40 313

pmmvs5		56.47 293	55.68 295	58.86 311	61.41 366	36.71 342	66.37 302	62.75 324	40.38 353	53.70 333	76.62 287	34.56 273	67.05 320	40.02 307	65.27 294	72.83 324

test-mter		56.42 294	55.82 294	58.22 316	68.57 326	44.80 271	65.46 311	57.92 346	39.94 357	55.44 314	69.82 344	21.92 362	57.44 359	49.66 231	73.62 188	72.41 331

USDC		56.35 295	54.24 308	62.69 292	64.74 350	40.31 308	65.05 317	73.83 244	43.93 331	47.58 358	77.71 276	15.36 375	75.05 281	38.19 316	61.81 325	72.70 325

PatchMatch-RL		56.25 296	54.55 303	61.32 301	77.06 198	56.07 109	65.57 308	54.10 363	44.13 329	53.49 339	71.27 334	25.20 352	66.78 322	36.52 330	63.66 308	61.12 368

sss		56.17 297	56.57 287	54.96 333	66.93 337	36.32 347	57.94 351	61.69 334	41.67 345	58.64 290	75.32 306	38.72 233	56.25 366	42.04 296	66.19 289	72.31 334

Syy-MVS		56.00 298	56.23 291	55.32 331	74.69 239	26.44 387	65.52 309	57.49 349	50.97 255	56.52 306	72.18 323	39.89 219	68.09 314	24.20 380	64.59 302	71.44 343

FMVSNet5		55.86 299	54.93 299	58.66 313	71.05 297	36.35 345	64.18 322	62.48 326	46.76 306	50.66 351	74.73 310	25.80 349	64.04 333	33.11 344	65.57 293	75.59 298

RPSCF		55.80 300	54.22 309	60.53 303	65.13 349	42.91 290	64.30 320	57.62 348	36.84 362	58.05 296	82.28 190	28.01 333	56.24 367	37.14 321	58.61 340	82.44 208

EU-MVSNet		55.61 301	54.41 305	59.19 309	65.41 348	33.42 364	72.44 249	71.91 261	28.81 371	51.27 344	73.87 315	24.76 354	69.08 310	43.04 288	58.20 341	75.06 303

Anonymous20240521		55.30 302	54.41 305	57.96 319	60.92 371	41.73 299	71.09 270	71.06 267	41.18 348	48.65 356	73.31 318	16.93 371	59.25 351	42.54 292	64.01 305	72.90 323

TESTMET0.1,1		55.28 303	54.90 300	56.42 326	66.56 340	43.67 282	65.46 311	56.27 356	39.18 359	53.83 332	67.44 356	24.21 356	55.46 370	48.04 244	73.11 201	70.13 354

KD-MVS_self_test		55.22 304	53.89 311	59.21 308	57.80 377	27.47 383	57.75 353	74.32 236	47.38 298	50.90 347	70.00 343	28.45 331	70.30 305	40.44 304	57.92 342	79.87 253

MIMVSNet1		55.17 305	54.31 307	57.77 321	70.03 311	32.01 370	65.68 307	64.81 309	49.19 274	46.75 363	76.00 295	25.53 351	64.04 333	28.65 369	62.13 322	77.26 282

Anonymous20231206		55.10 306	55.30 298	54.48 336	69.81 316	33.94 362	62.91 326	62.13 332	41.08 349	55.18 318	75.65 301	32.75 297	56.59 365	30.32 363	67.86 275	72.91 322

myMVS_eth3d		54.86 307	54.61 302	55.61 330	74.69 239	27.31 384	65.52 309	57.49 349	50.97 255	56.52 306	72.18 323	21.87 365	68.09 314	27.70 372	64.59 302	71.44 343

TinyColmap		54.14 308	51.72 319	61.40 300	66.84 338	41.97 296	66.52 301	68.51 287	44.81 320	42.69 374	75.77 300	11.66 381	72.94 290	31.96 349	56.77 348	69.27 360

EPMVS		53.96 309	53.69 312	54.79 335	66.12 345	31.96 371	62.34 330	49.05 373	44.42 326	55.54 312	71.33 333	30.22 315	56.70 362	41.65 300	62.54 319	75.71 297

PMMVS		53.96 309	53.26 315	56.04 327	62.60 361	50.92 194	61.17 337	56.09 357	32.81 367	53.51 338	66.84 361	34.04 279	59.93 348	44.14 277	68.18 273	57.27 376

test20.03		53.87 311	54.02 310	53.41 344	61.47 365	28.11 380	61.30 335	59.21 341	51.34 249	52.09 342	77.43 278	33.29 289	58.55 355	29.76 365	60.27 335	73.58 320

MDA-MVSNet-bldmvs		53.87 311	50.81 323	63.05 290	66.25 343	48.58 232	56.93 357	63.82 317	48.09 289	41.22 375	70.48 340	30.34 314	68.00 317	34.24 339	45.92 373	72.57 327

KD-MVS_2432*1600		53.45 313	51.50 321	59.30 305	62.82 358	37.14 336	55.33 360	71.79 262	47.34 300	55.09 319	70.52 338	21.91 363	70.45 303	35.72 334	42.97 376	70.31 352

miper_refine_blended		53.45 313	51.50 321	59.30 305	62.82 358	37.14 336	55.33 360	71.79 262	47.34 300	55.09 319	70.52 338	21.91 363	70.45 303	35.72 334	42.97 376	70.31 352

TDRefinement		53.44 315	50.72 324	61.60 298	64.31 353	46.96 250	70.89 272	65.27 308	41.78 343	44.61 369	77.98 265	11.52 383	66.36 325	28.57 370	51.59 362	71.49 342

test0.0.03 1		53.32 316	53.59 313	52.50 348	62.81 360	29.45 376	59.51 344	54.11 362	50.08 264	54.40 328	74.31 313	32.62 300	55.92 368	30.50 362	63.95 307	72.15 336

PatchT		53.17 317	53.44 314	52.33 349	68.29 330	25.34 391	58.21 349	54.41 361	44.46 325	54.56 326	69.05 350	33.32 288	60.94 342	36.93 323	61.76 326	70.73 350

UnsupCasMVSNet_eth		53.16 318	52.47 316	55.23 332	59.45 373	33.39 365	59.43 345	69.13 283	45.98 312	50.35 353	72.32 322	29.30 323	58.26 357	42.02 297	44.30 374	74.05 317

PM-MVS		52.33 319	50.19 327	58.75 312	62.10 363	45.14 269	65.75 305	40.38 389	43.60 332	53.52 337	72.65 320	9.16 389	65.87 328	50.41 224	54.18 356	65.24 366

testgi		51.90 320	52.37 317	50.51 354	60.39 372	23.55 394	58.42 347	58.15 344	49.03 276	51.83 343	79.21 253	22.39 360	55.59 369	29.24 368	62.64 317	72.40 333

dp		51.89 321	51.60 320	52.77 347	68.44 329	32.45 369	62.36 329	54.57 360	44.16 328	49.31 355	67.91 352	28.87 327	56.61 364	33.89 340	54.89 353	69.24 361

JIA-IIPM		51.56 322	47.68 336	63.21 288	64.61 351	50.73 198	47.71 376	58.77 343	42.90 339	48.46 357	51.72 382	24.97 353	70.24 306	36.06 333	53.89 357	68.64 362

test_fmvs1_n		51.37 323	50.35 326	54.42 338	52.85 380	37.71 331	61.16 338	51.93 365	28.15 373	63.81 232	69.73 346	13.72 376	53.95 373	51.16 219	60.65 333	71.59 340

ADS-MVSNet2		51.33 324	48.76 331	59.07 310	66.02 346	44.60 274	50.90 370	59.76 340	36.90 360	50.74 348	66.18 364	26.38 344	63.11 336	27.17 373	54.76 354	69.50 358

test_fmvs1		51.32 325	50.48 325	53.81 340	53.57 379	37.51 333	60.63 342	51.16 368	28.02 375	63.62 233	69.23 349	16.41 372	53.93 374	51.01 220	60.70 332	69.99 355

YYNet1		50.73 326	48.96 328	56.03 328	61.10 368	41.78 298	51.94 368	56.44 353	40.94 351	44.84 367	67.80 354	30.08 316	55.08 371	36.77 324	50.71 364	71.22 345

MDA-MVSNet_test_wron		50.71 327	48.95 329	56.00 329	61.17 367	41.84 297	51.90 369	56.45 352	40.96 350	44.79 368	67.84 353	30.04 317	55.07 372	36.71 326	50.69 365	71.11 348

dmvs_testset		50.16 328	51.90 318	44.94 362	66.49 341	11.78 400	61.01 340	51.50 367	51.17 253	50.30 354	67.44 356	39.28 226	60.29 346	22.38 382	57.49 344	62.76 367

UnsupCasMVSNet_bld		50.07 329	48.87 330	53.66 341	60.97 370	33.67 363	57.62 354	64.56 312	39.47 358	47.38 359	64.02 370	27.47 337	59.32 350	34.69 338	43.68 375	67.98 363

test_vis1_n		49.89 330	48.69 332	53.50 343	53.97 378	37.38 334	61.53 332	47.33 379	28.54 372	59.62 279	67.10 360	13.52 377	52.27 378	49.07 236	57.52 343	70.84 349

Patchmatch-test		49.08 331	48.28 333	51.50 352	64.40 352	30.85 374	45.68 380	48.46 376	35.60 364	46.10 366	72.10 325	34.47 276	46.37 385	27.08 375	60.65 333	77.27 281

test_fmvs2		48.69 332	47.49 337	52.29 350	48.63 386	33.06 367	57.76 352	48.05 377	25.71 379	59.76 277	69.60 347	11.57 382	52.23 379	49.45 234	56.86 346	71.58 341

ADS-MVSNet		48.48 333	47.77 334	50.63 353	66.02 346	29.92 375	50.90 370	50.87 372	36.90 360	50.74 348	66.18 364	26.38 344	52.47 377	27.17 373	54.76 354	69.50 358

CHOSEN 280x420		47.83 334	46.36 338	52.24 351	67.37 335	49.78 214	38.91 388	43.11 387	35.00 365	43.27 373	63.30 371	28.95 325	49.19 382	36.53 329	60.80 331	57.76 375

new-patchmatchnet		47.56 335	47.73 335	47.06 357	58.81 375	9.37 403	48.78 374	59.21 341	43.28 335	44.22 370	68.66 351	25.67 350	57.20 361	31.57 357	49.35 369	74.62 312

PVSNet_0	43.31 20	47.46 336	45.64 339	52.92 346	67.60 334	44.65 273	54.06 364	54.64 359	41.59 346	46.15 365	58.75 375	30.99 309	58.66 354	32.18 347	24.81 390	55.46 378

MVS-HIRNet		45.52 337	44.48 340	48.65 356	68.49 328	34.05 361	59.41 346	44.50 384	27.03 376	37.96 383	50.47 386	26.16 347	64.10 332	26.74 376	59.52 336	47.82 385

pmmvs3		44.92 338	41.95 345	53.86 339	52.58 382	43.55 283	62.11 331	46.90 381	26.05 378	40.63 376	60.19 374	11.08 386	57.91 358	31.83 354	46.15 372	60.11 369

test_fmvs3		44.30 339	42.55 342	49.55 355	42.83 390	27.15 386	53.03 366	44.93 383	22.03 386	53.69 335	64.94 367	4.21 396	49.63 381	47.47 245	49.82 367	71.88 337

WB-MVS		43.26 340	43.41 341	42.83 366	63.32 357	10.32 402	58.17 350	45.20 382	45.42 317	40.44 378	67.26 359	34.01 281	58.98 352	11.96 394	24.88 389	59.20 370

LF4IMVS		42.95 341	42.26 343	45.04 360	48.30 387	32.50 368	54.80 362	48.49 375	28.03 374	40.51 377	70.16 341	9.24 388	43.89 388	31.63 355	49.18 370	58.72 372

EGC-MVSNET		42.47 342	38.48 350	54.46 337	74.33 248	48.73 230	70.33 279	51.10 369	0.03 401	0.18 402	67.78 355	13.28 378	66.49 324	18.91 386	50.36 366	48.15 383

FPMVS		42.18 343	41.11 346	45.39 359	58.03 376	41.01 306	49.50 372	53.81 364	30.07 370	33.71 384	64.03 368	11.69 380	52.08 380	14.01 390	55.11 352	43.09 387

SSC-MVS		41.96 344	41.99 344	41.90 367	62.46 362	9.28 404	57.41 355	44.32 385	43.38 334	38.30 382	66.45 362	32.67 299	58.42 356	10.98 395	21.91 392	57.99 374

ANet_high		41.38 345	37.47 352	53.11 345	39.73 396	24.45 392	56.94 356	69.69 275	47.65 295	26.04 389	52.32 381	12.44 379	62.38 339	21.80 383	10.61 398	72.49 328

test_vis1_rt		41.35 346	39.45 348	47.03 358	46.65 389	37.86 328	47.76 375	38.65 390	23.10 382	44.21 371	51.22 384	11.20 385	44.08 387	39.27 310	53.02 359	59.14 371

LCM-MVSNet		40.30 347	35.88 353	53.57 342	42.24 391	29.15 377	45.21 382	60.53 339	22.23 385	28.02 387	50.98 385	3.72 398	61.78 341	31.22 360	38.76 382	69.78 357

mvsany_test1		39.38 348	38.16 351	43.02 365	49.05 384	34.28 359	44.16 384	25.94 400	22.74 384	46.57 364	62.21 373	23.85 358	41.16 392	33.01 345	35.91 384	53.63 379

N_pmnet		39.35 349	40.28 347	36.54 373	63.76 354	1.62 408	49.37 373	0.76 407	34.62 366	43.61 372	66.38 363	26.25 346	42.57 389	26.02 378	51.77 361	65.44 365

DSMNet-mixed		39.30 350	38.72 349	41.03 368	51.22 383	19.66 397	45.53 381	31.35 396	15.83 393	39.80 380	67.42 358	22.19 361	45.13 386	22.43 381	52.69 360	58.31 373

APD_test1		37.39 351	34.94 354	44.72 363	48.88 385	33.19 366	52.95 367	44.00 386	19.49 387	27.28 388	58.59 376	3.18 400	52.84 376	18.92 385	41.17 379	48.14 384

PMVS	28.69 22	36.22 352	33.29 356	45.02 361	36.82 398	35.98 350	54.68 363	48.74 374	26.31 377	21.02 392	51.61 383	2.88 401	60.10 347	9.99 398	47.58 371	38.99 392
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 353	31.91 357	43.33 364	62.05 364	37.87 327	20.39 393	67.03 295	23.23 381	18.41 394	25.84 394	4.24 395	62.73 337	14.71 389	51.32 363	29.38 393
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		34.13 354	34.29 355	33.64 375	52.63 381	18.23 399	44.43 383	33.90 395	22.81 383	30.89 386	53.18 380	10.48 387	35.72 396	20.77 384	39.51 380	46.98 386

mvsany_test3		32.62 355	30.57 359	38.77 371	36.16 399	24.20 393	38.10 389	20.63 402	19.14 388	40.36 379	57.43 377	5.06 393	36.63 395	29.59 367	28.66 388	55.49 377

test_vis3_rt		32.09 356	30.20 360	37.76 372	35.36 400	27.48 382	40.60 387	28.29 399	16.69 391	32.52 385	40.53 390	1.96 402	37.40 394	33.64 343	42.21 378	48.39 382

test_f		31.86 357	31.05 358	34.28 374	32.33 402	21.86 395	32.34 390	30.46 397	16.02 392	39.78 381	55.45 379	4.80 394	32.36 397	30.61 361	37.66 383	48.64 381

testf1		31.46 358	28.89 361	39.16 369	41.99 393	28.78 378	46.45 378	37.56 391	14.28 394	21.10 390	48.96 387	1.48 404	47.11 383	13.63 391	34.56 385	41.60 388

APD_test2		31.46 358	28.89 361	39.16 369	41.99 393	28.78 378	46.45 378	37.56 391	14.28 394	21.10 390	48.96 387	1.48 404	47.11 383	13.63 391	34.56 385	41.60 388

PMMVS2		27.40 360	25.91 363	31.87 377	39.46 397	6.57 405	31.17 391	28.52 398	23.96 380	20.45 393	48.94 389	4.20 397	37.94 393	16.51 387	19.97 393	51.09 380

E-PMN		23.77 361	22.73 365	26.90 378	42.02 392	20.67 396	42.66 385	35.70 393	17.43 389	10.28 399	25.05 395	6.42 391	42.39 390	10.28 397	14.71 395	17.63 394

EMVS		22.97 362	21.84 366	26.36 379	40.20 395	19.53 398	41.95 386	34.64 394	17.09 390	9.73 400	22.83 396	7.29 390	42.22 391	9.18 399	13.66 396	17.32 395

MVE	17.77 23	21.41 363	17.77 368	32.34 376	34.34 401	25.44 390	16.11 394	24.11 401	11.19 396	13.22 396	31.92 392	1.58 403	30.95 398	10.47 396	17.03 394	40.62 391
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 364	18.10 367	24.41 380	13.68 404	3.11 407	12.06 396	42.37 388	2.00 399	11.97 397	36.38 391	5.77 392	29.35 399	15.06 388	23.65 391	40.76 390

cdsmvs_eth3d_5k		17.50 365	23.34 364	0.00 386	0.00 408	0.00 410	0.00 397	78.63 161	0.00 404	0.00 405	82.18 191	49.25 115	0.00 403	0.00 404	0.00 401	0.00 401

wuyk23d		13.32 366	12.52 369	15.71 381	47.54 388	26.27 388	31.06 392	1.98 406	4.93 398	5.18 401	1.94 401	0.45 406	18.54 400	6.81 401	12.83 397	2.33 398

tmp_tt		9.43 367	11.14 370	4.30 383	2.38 405	4.40 406	13.62 395	16.08 404	0.39 400	15.89 395	13.06 397	15.80 374	5.54 402	12.63 393	10.46 399	2.95 397

ab-mvs-re		6.49 368	8.65 371	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	77.89 270	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

test123		4.73 369	6.30 372	0.02 384	0.01 406	0.01 409	56.36 358	0.00 408	0.01 402	0.04 403	0.21 403	0.01 407	0.00 403	0.03 403	0.00 401	0.04 399

testmvs		4.52 370	6.03 373	0.01 385	0.01 406	0.00 410	53.86 365	0.00 408	0.01 402	0.04 403	0.27 402	0.00 408	0.00 403	0.04 402	0.00 401	0.03 400

pcd_1.5k_mvsjas		3.92 371	5.23 374	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	47.05 146	0.00 403	0.00 404	0.00 401	0.00 401

test_blank		0.00 372	0.00 375	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

uanet_test		0.00 372	0.00 375	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

DCPMVS		0.00 372	0.00 375	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

sosnet-low-res		0.00 372	0.00 375	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

sosnet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

uncertanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

Regformer		0.00 372	0.00 375	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

uanet		0.00 372	0.00 375	0.00 386	0.00 408	0.00 410	0.00 397	0.00 408	0.00 404	0.00 405	0.00 404	0.00 408	0.00 403	0.00 404	0.00 401	0.00 401

MM				79.99 2		60.01 46	86.19 17	83.93 51	73.19 1	77.08 30	91.21 15	57.23 31	90.73 10	83.35 1	88.12 35	89.22 5

WAC-MVS						27.31 384								27.77 371

FOURS1					86.12 36	60.82 37	88.18 1	83.61 63	60.87 84	81.50 16

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 27

PC_three_1452									55.09 198	84.46 4	89.84 43	66.68 5	89.41 18	74.24 44	91.38 2	88.42 11

No_MVS				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 27

test_one_0601					87.58 9	59.30 57		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 408

eth-test					0.00 408

ZD-MVS					86.64 21	60.38 43		82.70 86	57.95 142	78.10 24	90.06 36	56.12 38	88.84 26	74.05 47	87.00 48

RE-MVS-def			73.71 62		83.49 65	59.87 49	84.29 37	81.36 107	58.07 137	73.14 74	90.07 34	43.06 188		68.20 79	81.76 94	84.03 159

IU-MVS					87.77 4	59.15 60		85.53 25	53.93 221	84.64 3				79.07 11	90.87 5	88.37 13

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 42	67.01 1	90.33 12	73.16 54	91.15 4	88.23 16

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 17	90.70 7	87.65 35

test_241102_ONE					87.77 4	58.90 69		86.78 10	64.20 31	85.97 1	91.34 12	66.87 3	90.78 7

9.14			78.75 14		83.10 69		84.15 43	88.26 1	59.90 106	78.57 23	90.36 27	57.51 30	86.86 64	77.39 23	89.52 21

save fliter					86.17 33	61.30 28	83.98 47	79.66 140	59.00 120

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 21

test_0728_SECOND				79.19 16	87.82 3	59.11 63	87.85 5	87.15 3					90.84 3	78.66 15	90.61 11	87.62 37

test0726					87.75 7	59.07 64	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

GSMVS																78.05 271

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 272				78.05 271

sam_mvs												33.43 287

ambc				65.13 276	63.72 356	37.07 338	47.66 377	78.78 157		54.37 329	71.42 331	11.24 384	80.94 196	45.64 264	53.85 358	77.38 279

MTGPA								80.97 123

test_post1							68.67 291				3.64 399	32.39 304	69.49 308	44.17 275

test_post											3.55 400	33.90 282	66.52 323

patchmatchnet-post											64.03 368	34.50 274	74.27 285

GG-mvs-BLEND				62.34 294	71.36 292	37.04 339	69.20 289	57.33 351		54.73 324	65.48 366	30.37 313	77.82 249	34.82 337	74.93 176	72.17 335

MTMP							86.03 19	17.08 403

gm-plane-assit					71.40 291	41.72 301			48.85 279		73.31 318		82.48 168	48.90 238

test9_res														75.28 37	88.31 32	83.81 169

TEST9					85.58 43	61.59 24	81.62 82	81.26 115	55.65 187	74.93 43	88.81 56	53.70 63	84.68 118

test_8					85.40 46	60.96 34	81.54 85	81.18 118	55.86 178	74.81 47	88.80 58	53.70 63	84.45 122

agg_prior2														73.09 55	87.93 40	84.33 150

agg_prior					85.04 50	59.96 47		81.04 121		74.68 50			84.04 128

TestCases				64.39 281	71.44 288	49.03 223		67.30 291	45.97 313	47.16 360	79.77 240	17.47 369	67.56 318	33.65 341	59.16 338	76.57 290

test_prior4						62.51 14	82.08 77

test_prior2							81.75 80		60.37 96	75.01 41	89.06 52	56.22 37		72.19 59	88.96 24

test_prior				76.69 53	84.20 61	57.27 88		84.88 37					86.43 78			86.38 72

旧先验2							76.08 182		45.32 318	76.55 32			65.56 329	58.75 162

新几何2							76.12 180

新几何1				70.76 195	85.66 41	61.13 30		66.43 299	44.68 322	70.29 107	86.64 90	41.29 209	75.23 280	49.72 230	81.75 96	75.93 294

旧先验1					83.04 70	53.15 159		67.52 290			87.85 71	44.08 179			80.76 100	78.03 274

无先验							79.66 110	74.30 238	48.40 285				80.78 202	53.62 198		79.03 263

原ACMM2							79.02 116

原ACMM1				74.69 89	85.39 47	59.40 54		83.42 69	51.47 246	70.27 108	86.61 92	48.61 123	86.51 76	53.85 197	87.96 39	78.16 269

test222					83.14 68	58.68 73	72.57 247	63.45 319	41.78 343	67.56 162	86.12 107	37.13 253			78.73 133	74.98 306

testdata2													72.18 296	46.95 254

segment_acmp												54.23 54

testdata				64.66 278	81.52 86	52.93 162		65.29 307	46.09 311	73.88 62	87.46 75	38.08 241	66.26 326	53.31 202	78.48 136	74.78 310

testdata1							72.65 243		60.50 91

test12				77.76 43	84.52 58	58.41 75		83.36 72		72.93 81		54.61 51	88.05 37		88.12 35	86.81 60

plane_prior7					81.41 89	55.96 111

plane_prior6					81.20 96	56.24 106						45.26 170

plane_prior5								84.01 49					87.21 53	68.16 81	80.58 103	84.65 144

plane_prior4											86.10 108

plane_prior3						56.09 108			63.92 36	69.27 127

plane_prior2							84.22 40		64.52 25

plane_prior1					81.27 94

plane_prior						56.31 102	83.58 53		63.19 48						80.48 106

n2								0.00 408

nn								0.00 408

door-mid								47.19 380

lessismore_v0				69.91 211	71.42 290	47.80 240		50.90 371		50.39 352	75.56 302	27.43 339	81.33 186	45.91 261	34.10 387	80.59 241

LGP-MVS_train				75.76 67	80.22 110	57.51 86		83.40 70	61.32 79	66.67 179	87.33 77	39.15 229	86.59 71	67.70 86	77.30 153	83.19 191

test11								83.47 67

door								47.60 378

HQP5-MVS						54.94 131

HQP-NCC					80.66 102		82.31 71		62.10 68	67.85 152

ACMP_Plane					80.66 102		82.31 71		62.10 68	67.85 152

BP-MVS														67.04 93

HQP4-MVS										67.85 152			86.93 62			84.32 151

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 164

NP-MVS					80.98 99	56.05 110					85.54 126

MDTV_nov1_ep13_2view						25.89 389	61.22 336		40.10 355	51.10 345		32.97 292		38.49 313		78.61 266

MDTV_nov1_ep13			57.00 282		72.73 266	38.26 325	65.02 318	64.73 311	44.74 321	55.46 313	72.48 321	32.61 302	70.47 302	37.47 318	67.75 277

ACMMP++_ref															74.07 182

ACMMP++															72.16 216

Test By Simon												48.33 126

ITE_SJBPF				62.09 296	66.16 344	44.55 276		64.32 313	47.36 299	55.31 316	80.34 230	19.27 368	62.68 338	36.29 332	62.39 320	79.04 262

DeepMVS_CX				12.03 382	17.97 403	10.91 401		10.60 405	7.46 397	11.07 398	28.36 393	3.28 399	11.29 401	8.01 400	9.74 400	13.89 396

High-resolution multi-view benchmark