High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 59	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 10	96.68 2	94.95 11

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 66	93.57 7	94.06 10	77.24 53	93.10 1	95.72 8	82.99 1	97.44 7	89.07 15	96.63 4	94.88 15

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 59	93.49 9	92.73 64	77.33 50	92.12 9	95.78 4	80.98 9	97.40 9	89.08 13	96.41 12	93.33 92
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 41	78.35 13	96.77 24	89.59 9	94.22 60	94.67 28
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 90	92.29 7	95.66 10	81.67 6	97.38 11	87.44 34	96.34 15	93.95 60
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MM		89.16 6	89.23 7	88.97 4	90.79 95	73.65 10	92.66 23	91.17 125	86.57 1	87.39 42	94.97 18	71.70 53	97.68 1	92.19 1	95.63 28	95.57 1

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 94	91.06 16	96.03 1	76.84 14	97.03 17	89.09 12	95.65 27	94.47 38
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 115	92.29 7	95.97 2	74.28 29	97.24 13	88.58 22	96.91 1	94.87 17
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 59	80.26 11	87.78 34	94.27 36	75.89 19	96.81 23	87.45 33	96.44 9	93.05 107

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 62	93.00 46	80.90 7	88.06 29	94.06 46	76.43 16	96.84 21	88.48 25	95.99 18	94.34 44

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 72	72.96 25	93.73 5	93.67 20	80.19 12	88.10 28	94.80 19	73.76 33	97.11 15	87.51 32	95.82 21	94.90 14
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 64	71.95 49	92.40 24	94.74 2	75.71 92	89.16 19	95.10 16	75.65 21	96.19 46	87.07 35	96.01 17	94.79 22

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 53	92.24 71	69.03 103	89.57 90	93.39 30	77.53 46	89.79 18	94.12 43	78.98 12	96.58 35	85.66 41	95.72 24	94.58 33

SD-MVS		88.06 14	88.50 14	86.71 54	92.60 69	72.71 29	91.81 41	93.19 35	77.87 36	90.32 17	94.00 50	74.83 23	93.78 141	87.63 31	94.27 59	93.65 77
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
NCCC		88.06 14	88.01 18	88.24 11	94.41 22	73.62 11	91.22 54	92.83 60	81.50 5	85.79 56	93.47 64	73.02 40	97.00 18	84.90 47	94.94 40	94.10 52

ACMMP_NAP		88.05 16	88.08 17	87.94 19	93.70 41	73.05 22	90.86 57	93.59 23	76.27 84	88.14 27	95.09 17	71.06 63	96.67 29	87.67 30	96.37 14	94.09 53

TSAR-MVS + MP.		88.02 17	88.11 16	87.72 30	93.68 43	72.13 46	91.41 50	92.35 82	74.62 119	88.90 22	93.85 56	75.75 20	96.00 54	87.80 29	94.63 48	95.04 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ZNCC-MVS		87.94 18	87.85 19	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 66	85.24 61	94.32 34	71.76 51	96.93 19	85.53 44	95.79 22	94.32 45

MP-MVS		87.71 19	87.64 21	87.93 21	94.36 26	73.88 6	92.71 22	92.65 70	77.57 42	83.84 91	94.40 32	72.24 45	96.28 43	85.65 42	95.30 35	93.62 80
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MVS_0304		87.69 20	87.55 24	88.12 13	89.45 129	71.76 51	91.47 49	89.54 173	82.14 3	86.65 50	94.28 35	68.28 95	97.46 6	90.81 2	95.31 34	95.15 7

MP-MVS-pluss		87.67 21	87.72 20	87.54 36	93.64 44	72.04 48	89.80 81	93.50 25	75.17 105	86.34 52	95.29 15	70.86 65	96.00 54	88.78 20	96.04 16	94.58 33
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		87.58 22	87.47 26	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 68	84.91 66	94.44 30	70.78 66	96.61 32	84.53 55	94.89 42	93.66 73

reproduce-ours		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 108	88.96 20	95.54 12	71.20 61	96.54 36	86.28 38	93.49 65	93.06 105

our_new_method		87.47 23	87.61 22	87.07 45	93.27 50	71.60 53	91.56 46	93.19 35	74.98 108	88.96 20	95.54 12	71.20 61	96.54 36	86.28 38	93.49 65	93.06 105

ACMMPR		87.44 25	87.23 30	88.08 15	94.64 13	73.59 12	93.04 12	93.20 34	76.78 68	84.66 73	94.52 23	68.81 90	96.65 30	84.53 55	94.90 41	94.00 57

APD-MVS		87.44 25	87.52 25	87.19 42	94.24 32	72.39 39	91.86 40	92.83 60	73.01 159	88.58 24	94.52 23	73.36 34	96.49 38	84.26 58	95.01 37	92.70 117
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
GST-MVS		87.42 27	87.26 28	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 64	84.68 70	93.99 52	70.67 68	96.82 22	84.18 62	95.01 37	93.90 63

region2R		87.42 27	87.20 31	88.09 14	94.63 14	73.55 13	93.03 14	93.12 40	76.73 71	84.45 78	94.52 23	69.09 84	96.70 27	84.37 57	94.83 45	94.03 56

MCST-MVS		87.37 29	87.25 29	87.73 28	94.53 17	72.46 38	89.82 79	93.82 16	73.07 157	84.86 69	92.89 78	76.22 17	96.33 41	84.89 49	95.13 36	94.40 41

reproduce_model		87.28 30	87.39 27	86.95 48	93.10 56	71.24 63	91.60 42	93.19 35	74.69 116	88.80 23	95.61 11	70.29 72	96.44 39	86.20 40	93.08 69	93.16 100

MTAPA		87.23 31	87.00 32	87.90 22	94.18 35	74.25 5	86.58 194	92.02 93	79.45 19	85.88 54	94.80 19	68.07 96	96.21 45	86.69 37	95.34 32	93.23 95

XVS		87.18 32	86.91 37	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 21	83.67 95	94.17 40	67.45 103	96.60 33	83.06 70	94.50 51	94.07 54

HPM-MVS		87.11 33	86.98 34	87.50 38	93.88 39	72.16 45	92.19 33	93.33 31	76.07 87	83.81 92	93.95 55	69.77 78	96.01 53	85.15 45	94.66 47	94.32 45
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
CP-MVS		87.11 33	86.92 36	87.68 34	94.20 34	73.86 7	93.98 3	92.82 63	76.62 74	83.68 94	94.46 27	67.93 98	95.95 57	84.20 61	94.39 55	93.23 95

DeepC-MVS	79.81 2	87.08 35	86.88 38	87.69 33	91.16 84	72.32 43	90.31 71	93.94 14	77.12 58	82.82 106	94.23 39	72.13 47	97.09 16	84.83 50	95.37 31	93.65 77
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.65 3	86.91 36	86.62 40	87.76 27	93.52 46	72.37 41	91.26 51	93.04 41	76.62 74	84.22 82	93.36 67	71.44 57	96.76 25	80.82 95	95.33 33	94.16 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
balanced_conf03		86.78 37	86.99 33	86.15 63	91.24 83	67.61 145	90.51 62	92.90 56	77.26 52	87.44 41	91.63 106	71.27 60	96.06 49	85.62 43	95.01 37	94.78 23

SR-MVS		86.73 38	86.67 39	86.91 49	94.11 37	72.11 47	92.37 28	92.56 75	74.50 120	86.84 49	94.65 22	67.31 105	95.77 59	84.80 51	92.85 72	92.84 115

CS-MVS		86.69 39	86.95 35	85.90 71	90.76 96	67.57 147	92.83 17	93.30 32	79.67 17	84.57 77	92.27 90	71.47 56	95.02 93	84.24 60	93.46 67	95.13 8

PGM-MVS		86.68 40	86.27 44	87.90 22	94.22 33	73.38 18	90.22 73	93.04 41	75.53 96	83.86 90	94.42 31	67.87 100	96.64 31	82.70 80	94.57 50	93.66 73

mPP-MVS		86.67 41	86.32 43	87.72 30	94.41 22	73.55 13	92.74 20	92.22 88	76.87 65	82.81 107	94.25 38	66.44 113	96.24 44	82.88 75	94.28 58	93.38 89

CANet		86.45 42	86.10 49	87.51 37	90.09 107	70.94 70	89.70 85	92.59 74	81.78 4	81.32 122	91.43 114	70.34 70	97.23 14	84.26 58	93.36 68	94.37 42

train_agg		86.43 43	86.20 45	87.13 44	93.26 52	72.96 25	88.75 120	91.89 101	68.69 247	85.00 64	93.10 71	74.43 26	95.41 73	84.97 46	95.71 25	93.02 109

PHI-MVS		86.43 43	86.17 47	87.24 41	90.88 92	70.96 68	92.27 32	94.07 9	72.45 164	85.22 62	91.90 97	69.47 80	96.42 40	83.28 69	95.94 19	94.35 43

CSCG		86.41 45	86.19 46	87.07 45	92.91 61	72.48 37	90.81 58	93.56 24	73.95 132	83.16 101	91.07 126	75.94 18	95.19 82	79.94 104	94.38 56	93.55 84

SPE-MVS-test		86.29 46	86.48 41	85.71 73	91.02 88	67.21 160	92.36 29	93.78 18	78.97 28	83.51 98	91.20 121	70.65 69	95.15 84	81.96 84	94.89 42	94.77 24

EC-MVSNet		86.01 47	86.38 42	84.91 96	89.31 138	66.27 173	92.32 30	93.63 21	79.37 20	84.17 84	91.88 98	69.04 88	95.43 70	83.93 64	93.77 63	93.01 110

MVSMamba_PlusPlus		85.99 48	85.96 52	86.05 66	91.09 85	67.64 144	89.63 88	92.65 70	72.89 162	84.64 74	91.71 102	71.85 49	96.03 50	84.77 52	94.45 54	94.49 37

casdiffmvs_mvg		85.99 48	86.09 50	85.70 74	87.65 209	67.22 159	88.69 124	93.04 41	79.64 18	85.33 60	92.54 87	73.30 35	94.50 112	83.49 66	91.14 95	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
APD-MVS_3200maxsize		85.97 50	85.88 53	86.22 60	92.69 66	69.53 92	91.93 37	92.99 49	73.54 144	85.94 53	94.51 26	65.80 123	95.61 62	83.04 72	92.51 76	93.53 86

test_fmvsmconf_n		85.92 51	86.04 51	85.57 76	85.03 263	69.51 93	89.62 89	90.58 140	73.42 148	87.75 36	94.02 48	72.85 41	93.24 166	90.37 3	90.75 99	93.96 58

sasdasda		85.91 52	85.87 54	86.04 67	89.84 117	69.44 98	90.45 68	93.00 46	76.70 72	88.01 31	91.23 118	73.28 36	93.91 135	81.50 87	88.80 128	94.77 24

canonicalmvs		85.91 52	85.87 54	86.04 67	89.84 117	69.44 98	90.45 68	93.00 46	76.70 72	88.01 31	91.23 118	73.28 36	93.91 135	81.50 87	88.80 128	94.77 24

ACMMP		85.89 54	85.39 61	87.38 39	93.59 45	72.63 33	92.74 20	93.18 39	76.78 68	80.73 131	93.82 57	64.33 133	96.29 42	82.67 81	90.69 100	93.23 95
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
SR-MVS-dyc-post		85.77 55	85.61 58	86.23 59	93.06 58	70.63 76	91.88 38	92.27 84	73.53 145	85.69 57	94.45 28	65.00 131	95.56 63	82.75 76	91.87 84	92.50 126

CDPH-MVS		85.76 56	85.29 66	87.17 43	93.49 47	71.08 64	88.58 128	92.42 80	68.32 254	84.61 75	93.48 62	72.32 44	96.15 48	79.00 107	95.43 30	94.28 47

TSAR-MVS + GP.		85.71 57	85.33 63	86.84 50	91.34 81	72.50 36	89.07 110	87.28 237	76.41 77	85.80 55	90.22 145	74.15 31	95.37 78	81.82 85	91.88 83	92.65 121

dcpmvs_2		85.63 58	86.15 48	84.06 133	91.71 78	64.94 202	86.47 197	91.87 103	73.63 140	86.60 51	93.02 76	76.57 15	91.87 224	83.36 67	92.15 80	95.35 3

test_fmvsmconf0.1_n		85.61 59	85.65 57	85.50 77	82.99 310	69.39 100	89.65 86	90.29 153	73.31 151	87.77 35	94.15 42	71.72 52	93.23 167	90.31 4	90.67 101	93.89 64

alignmvs		85.48 60	85.32 64	85.96 70	89.51 126	69.47 95	89.74 83	92.47 76	76.17 85	87.73 38	91.46 113	70.32 71	93.78 141	81.51 86	88.95 125	94.63 32

3Dnovator+	77.84 4	85.48 60	84.47 76	88.51 7	91.08 86	73.49 16	93.18 11	93.78 18	80.79 8	76.66 204	93.37 66	60.40 197	96.75 26	77.20 126	93.73 64	95.29 5

MSLP-MVS++		85.43 62	85.76 56	84.45 109	91.93 75	70.24 79	90.71 59	92.86 58	77.46 48	84.22 82	92.81 82	67.16 107	92.94 186	80.36 99	94.35 57	90.16 204

DELS-MVS		85.41 63	85.30 65	85.77 72	88.49 169	67.93 137	85.52 227	93.44 27	78.70 29	83.63 97	89.03 173	74.57 24	95.71 61	80.26 101	94.04 61	93.66 73
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
HPM-MVS_fast		85.35 64	84.95 70	86.57 56	93.69 42	70.58 78	92.15 35	91.62 111	73.89 135	82.67 109	94.09 44	62.60 152	95.54 65	80.93 93	92.93 71	93.57 82

test_fmvsm_n_1920		85.29 65	85.34 62	85.13 87	86.12 241	69.93 86	88.65 126	90.78 136	69.97 215	88.27 26	93.98 53	71.39 58	91.54 236	88.49 24	90.45 103	93.91 61

MVS_111021_HR		85.14 66	84.75 71	86.32 58	91.65 79	72.70 30	85.98 210	90.33 150	76.11 86	82.08 112	91.61 108	71.36 59	94.17 124	81.02 92	92.58 75	92.08 143

casdiffmvs		85.11 67	85.14 67	85.01 90	87.20 223	65.77 185	87.75 157	92.83 60	77.84 37	84.36 81	92.38 89	72.15 46	93.93 134	81.27 91	90.48 102	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
UA-Net		85.08 68	84.96 69	85.45 78	92.07 73	68.07 134	89.78 82	90.86 135	82.48 2	84.60 76	93.20 70	69.35 81	95.22 81	71.39 184	90.88 98	93.07 104

MGCFI-Net		85.06 69	85.51 59	83.70 148	89.42 130	63.01 244	89.43 93	92.62 73	76.43 76	87.53 39	91.34 116	72.82 42	93.42 161	81.28 90	88.74 131	94.66 31

DPM-MVS		84.93 70	84.29 77	86.84 50	90.20 105	73.04 23	87.12 175	93.04 41	69.80 219	82.85 105	91.22 120	73.06 39	96.02 52	76.72 134	94.63 48	91.46 160

baseline		84.93 70	84.98 68	84.80 100	87.30 221	65.39 193	87.30 171	92.88 57	77.62 40	84.04 87	92.26 91	71.81 50	93.96 128	81.31 89	90.30 105	95.03 10

ETV-MVS		84.90 72	84.67 72	85.59 75	89.39 133	68.66 120	88.74 122	92.64 72	79.97 15	84.10 85	85.71 262	69.32 82	95.38 75	80.82 95	91.37 92	92.72 116

test_fmvsmconf0.01_n		84.73 73	84.52 75	85.34 80	80.25 351	69.03 103	89.47 91	89.65 170	73.24 155	86.98 47	94.27 36	66.62 109	93.23 167	90.26 5	89.95 113	93.78 70

fmvsm_l_conf0.5_n		84.47 74	84.54 73	84.27 119	85.42 253	68.81 109	88.49 130	87.26 238	68.08 256	88.03 30	93.49 61	72.04 48	91.77 226	88.90 18	89.14 124	92.24 137

BP-MVS1		84.32 75	83.71 82	86.17 61	87.84 199	67.85 138	89.38 98	89.64 171	77.73 38	83.98 88	92.12 94	56.89 221	95.43 70	84.03 63	91.75 87	95.24 6

EI-MVSNet-Vis-set		84.19 76	83.81 80	85.31 81	88.18 180	67.85 138	87.66 159	89.73 168	80.05 14	82.95 102	89.59 158	70.74 67	94.82 101	80.66 98	84.72 182	93.28 94

fmvsm_l_conf0.5_n_a		84.13 77	84.16 78	84.06 133	85.38 254	68.40 125	88.34 137	86.85 248	67.48 263	87.48 40	93.40 65	70.89 64	91.61 230	88.38 26	89.22 122	92.16 141

test_fmvsmvis_n_1920		84.02 78	83.87 79	84.49 108	84.12 279	69.37 101	88.15 145	87.96 221	70.01 213	83.95 89	93.23 69	68.80 91	91.51 239	88.61 21	89.96 112	92.57 122

nrg030		83.88 79	83.53 84	84.96 92	86.77 231	69.28 102	90.46 67	92.67 67	74.79 114	82.95 102	91.33 117	72.70 43	93.09 180	80.79 97	79.28 260	92.50 126

EI-MVSNet-UG-set		83.81 80	83.38 87	85.09 88	87.87 197	67.53 148	87.44 167	89.66 169	79.74 16	82.23 111	89.41 167	70.24 73	94.74 104	79.95 103	83.92 196	92.99 112

fmvsm_s_conf0.1_n_2		83.80 81	83.79 81	83.83 146	85.62 249	64.94 202	87.03 178	86.62 252	74.32 124	87.97 33	94.33 33	60.67 189	92.60 194	89.72 6	87.79 142	93.96 58

fmvsm_s_conf0.5_n		83.80 81	83.71 82	84.07 131	86.69 233	67.31 154	89.46 92	83.07 304	71.09 189	86.96 48	93.70 59	69.02 89	91.47 241	88.79 19	84.62 184	93.44 88

CPTT-MVS		83.73 83	83.33 89	84.92 95	93.28 49	70.86 72	92.09 36	90.38 146	68.75 246	79.57 143	92.83 80	60.60 193	93.04 184	80.92 94	91.56 90	90.86 177

EPNet		83.72 84	82.92 96	86.14 65	84.22 277	69.48 94	91.05 56	85.27 269	81.30 6	76.83 199	91.65 104	66.09 118	95.56 63	76.00 140	93.85 62	93.38 89
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
patch_mono-2		83.65 85	84.54 73	80.99 227	90.06 112	65.83 182	84.21 256	88.74 207	71.60 179	85.01 63	92.44 88	74.51 25	83.50 346	82.15 83	92.15 80	93.64 79

HQP_MVS		83.64 86	83.14 90	85.14 85	90.08 108	68.71 116	91.25 52	92.44 77	79.12 23	78.92 152	91.00 131	60.42 195	95.38 75	78.71 111	86.32 163	91.33 161

fmvsm_s_conf0.5_n_a		83.63 87	83.41 86	84.28 117	86.14 240	68.12 132	89.43 93	82.87 309	70.27 208	87.27 44	93.80 58	69.09 84	91.58 232	88.21 27	83.65 204	93.14 102

Effi-MVS+		83.62 88	83.08 91	85.24 83	88.38 175	67.45 149	88.89 115	89.15 189	75.50 97	82.27 110	88.28 194	69.61 79	94.45 114	77.81 120	87.84 141	93.84 67

fmvsm_s_conf0.1_n		83.56 89	83.38 87	84.10 125	84.86 265	67.28 155	89.40 97	83.01 305	70.67 197	87.08 45	93.96 54	68.38 93	91.45 242	88.56 23	84.50 185	93.56 83

GDP-MVS		83.52 90	82.64 100	86.16 62	88.14 183	68.45 124	89.13 108	92.69 65	72.82 163	83.71 93	91.86 100	55.69 226	95.35 79	80.03 102	89.74 116	94.69 27

OPM-MVS		83.50 91	82.95 95	85.14 85	88.79 159	70.95 69	89.13 108	91.52 114	77.55 45	80.96 129	91.75 101	60.71 187	94.50 112	79.67 106	86.51 161	89.97 220
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
Vis-MVSNet		83.46 92	82.80 98	85.43 79	90.25 104	68.74 114	90.30 72	90.13 157	76.33 83	80.87 130	92.89 78	61.00 184	94.20 122	72.45 178	90.97 96	93.35 91
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MG-MVS		83.41 93	83.45 85	83.28 160	92.74 65	62.28 256	88.17 143	89.50 175	75.22 101	81.49 121	92.74 86	66.75 108	95.11 87	72.85 172	91.58 89	92.45 129

EPP-MVSNet		83.40 94	83.02 93	84.57 104	90.13 106	64.47 213	92.32 30	90.73 137	74.45 123	79.35 146	91.10 124	69.05 87	95.12 85	72.78 173	87.22 150	94.13 51

3Dnovator	76.31 5	83.38 95	82.31 105	86.59 55	87.94 194	72.94 28	90.64 60	92.14 92	77.21 55	75.47 229	92.83 80	58.56 204	94.72 105	73.24 169	92.71 74	92.13 142

fmvsm_s_conf0.1_n_a		83.32 96	82.99 94	84.28 117	83.79 287	68.07 134	89.34 100	82.85 310	69.80 219	87.36 43	94.06 46	68.34 94	91.56 234	87.95 28	83.46 209	93.21 98

EIA-MVS		83.31 97	82.80 98	84.82 98	89.59 122	65.59 188	88.21 141	92.68 66	74.66 118	78.96 150	86.42 249	69.06 86	95.26 80	75.54 146	90.09 109	93.62 80

h-mvs33		83.15 98	82.19 106	86.02 69	90.56 98	70.85 73	88.15 145	89.16 188	76.02 88	84.67 71	91.39 115	61.54 170	95.50 66	82.71 78	75.48 308	91.72 150

MVS_Test		83.15 98	83.06 92	83.41 157	86.86 227	63.21 240	86.11 208	92.00 95	74.31 125	82.87 104	89.44 166	70.03 74	93.21 169	77.39 125	88.50 136	93.81 68

IS-MVSNet		83.15 98	82.81 97	84.18 123	89.94 115	63.30 238	91.59 43	88.46 213	79.04 25	79.49 144	92.16 92	65.10 128	94.28 117	67.71 220	91.86 86	94.95 11

DP-MVS Recon		83.11 101	82.09 109	86.15 63	94.44 19	70.92 71	88.79 118	92.20 89	70.53 202	79.17 148	91.03 129	64.12 135	96.03 50	68.39 217	90.14 108	91.50 156

PAPM_NR		83.02 102	82.41 102	84.82 98	92.47 70	66.37 171	87.93 152	91.80 106	73.82 136	77.32 187	90.66 136	67.90 99	94.90 97	70.37 194	89.48 119	93.19 99

VDD-MVS		83.01 103	82.36 104	84.96 92	91.02 88	66.40 170	88.91 114	88.11 216	77.57 42	84.39 80	93.29 68	52.19 258	93.91 135	77.05 129	88.70 132	94.57 35

MVSFormer		82.85 104	82.05 110	85.24 83	87.35 215	70.21 80	90.50 64	90.38 146	68.55 249	81.32 122	89.47 161	61.68 167	93.46 158	78.98 108	90.26 106	92.05 144

OMC-MVS		82.69 105	81.97 113	84.85 97	88.75 161	67.42 150	87.98 148	90.87 134	74.92 110	79.72 141	91.65 104	62.19 162	93.96 128	75.26 150	86.42 162	93.16 100

PVSNet_Blended_VisFu		82.62 106	81.83 115	84.96 92	90.80 94	69.76 90	88.74 122	91.70 110	69.39 227	78.96 150	88.46 189	65.47 125	94.87 100	74.42 155	88.57 133	90.24 202

MVS_111021_LR		82.61 107	82.11 107	84.11 124	88.82 156	71.58 55	85.15 230	86.16 260	74.69 116	80.47 133	91.04 127	62.29 159	90.55 264	80.33 100	90.08 110	90.20 203

HQP-MVS		82.61 107	82.02 111	84.37 111	89.33 135	66.98 163	89.17 103	92.19 90	76.41 77	77.23 190	90.23 144	60.17 198	95.11 87	77.47 123	85.99 171	91.03 171

RRT-MVS		82.60 109	82.10 108	84.10 125	87.98 193	62.94 249	87.45 166	91.27 121	77.42 49	79.85 139	90.28 141	56.62 223	94.70 107	79.87 105	88.15 140	94.67 28

CLD-MVS		82.31 110	81.65 116	84.29 116	88.47 170	67.73 142	85.81 218	92.35 82	75.78 91	78.33 166	86.58 244	64.01 136	94.35 115	76.05 139	87.48 147	90.79 178
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VNet		82.21 111	82.41 102	81.62 208	90.82 93	60.93 271	84.47 247	89.78 165	76.36 82	84.07 86	91.88 98	64.71 132	90.26 266	70.68 191	88.89 126	93.66 73

diffmvs		82.10 112	81.88 114	82.76 190	83.00 308	63.78 226	83.68 264	89.76 166	72.94 160	82.02 113	89.85 150	65.96 122	90.79 260	82.38 82	87.30 149	93.71 72
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
LPG-MVS_test		82.08 113	81.27 119	84.50 106	89.23 142	68.76 112	90.22 73	91.94 99	75.37 99	76.64 205	91.51 110	54.29 239	94.91 95	78.44 113	83.78 197	89.83 225

FIs		82.07 114	82.42 101	81.04 226	88.80 158	58.34 298	88.26 140	93.49 26	76.93 63	78.47 163	91.04 127	69.92 76	92.34 207	69.87 201	84.97 179	92.44 130

PS-MVSNAJss		82.07 114	81.31 118	84.34 114	86.51 236	67.27 156	89.27 101	91.51 115	71.75 174	79.37 145	90.22 145	63.15 146	94.27 118	77.69 121	82.36 223	91.49 157

API-MVS		81.99 116	81.23 120	84.26 121	90.94 90	70.18 85	91.10 55	89.32 180	71.51 181	78.66 157	88.28 194	65.26 126	95.10 90	64.74 247	91.23 94	87.51 289

UniMVSNet_NR-MVSNet		81.88 117	81.54 117	82.92 179	88.46 171	63.46 234	87.13 174	92.37 81	80.19 12	78.38 164	89.14 169	71.66 55	93.05 182	70.05 197	76.46 291	92.25 135

MAR-MVS		81.84 118	80.70 128	85.27 82	91.32 82	71.53 56	89.82 79	90.92 131	69.77 221	78.50 161	86.21 253	62.36 158	94.52 111	65.36 241	92.05 82	89.77 228
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
LFMVS		81.82 119	81.23 120	83.57 152	91.89 76	63.43 236	89.84 78	81.85 321	77.04 61	83.21 99	93.10 71	52.26 257	93.43 160	71.98 179	89.95 113	93.85 65

hse-mvs2		81.72 120	80.94 126	84.07 131	88.72 162	67.68 143	85.87 214	87.26 238	76.02 88	84.67 71	88.22 197	61.54 170	93.48 156	82.71 78	73.44 336	91.06 169

GeoE		81.71 121	81.01 125	83.80 147	89.51 126	64.45 214	88.97 112	88.73 208	71.27 185	78.63 158	89.76 152	66.32 115	93.20 172	69.89 200	86.02 170	93.74 71

xiu_mvs_v2_base		81.69 122	81.05 123	83.60 150	89.15 145	68.03 136	84.46 249	90.02 159	70.67 197	81.30 125	86.53 247	63.17 145	94.19 123	75.60 145	88.54 134	88.57 268

PS-MVSNAJ		81.69 122	81.02 124	83.70 148	89.51 126	68.21 131	84.28 255	90.09 158	70.79 194	81.26 126	85.62 267	63.15 146	94.29 116	75.62 144	88.87 127	88.59 267

PAPR		81.66 124	80.89 127	83.99 141	90.27 103	64.00 221	86.76 190	91.77 109	68.84 245	77.13 197	89.50 159	67.63 101	94.88 99	67.55 222	88.52 135	93.09 103

UniMVSNet (Re)		81.60 125	81.11 122	83.09 170	88.38 175	64.41 215	87.60 160	93.02 45	78.42 32	78.56 160	88.16 198	69.78 77	93.26 165	69.58 204	76.49 290	91.60 151

FC-MVSNet-test		81.52 126	82.02 111	80.03 247	88.42 174	55.97 337	87.95 150	93.42 29	77.10 59	77.38 185	90.98 133	69.96 75	91.79 225	68.46 216	84.50 185	92.33 131

VDDNet		81.52 126	80.67 129	84.05 136	90.44 101	64.13 220	89.73 84	85.91 263	71.11 188	83.18 100	93.48 62	50.54 284	93.49 155	73.40 166	88.25 138	94.54 36

ACMP	74.13 6	81.51 128	80.57 130	84.36 112	89.42 130	68.69 119	89.97 77	91.50 118	74.46 122	75.04 251	90.41 140	53.82 244	94.54 109	77.56 122	82.91 215	89.86 224
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
jason		81.39 129	80.29 137	84.70 102	86.63 235	69.90 88	85.95 211	86.77 249	63.24 314	81.07 128	89.47 161	61.08 183	92.15 213	78.33 116	90.07 111	92.05 144
jason: jason.
lupinMVS		81.39 129	80.27 138	84.76 101	87.35 215	70.21 80	85.55 223	86.41 254	62.85 321	81.32 122	88.61 184	61.68 167	92.24 211	78.41 115	90.26 106	91.83 147

test_yl		81.17 131	80.47 133	83.24 163	89.13 146	63.62 227	86.21 205	89.95 162	72.43 167	81.78 118	89.61 156	57.50 214	93.58 149	70.75 189	86.90 154	92.52 124

DCV-MVSNet		81.17 131	80.47 133	83.24 163	89.13 146	63.62 227	86.21 205	89.95 162	72.43 167	81.78 118	89.61 156	57.50 214	93.58 149	70.75 189	86.90 154	92.52 124

DU-MVS		81.12 133	80.52 132	82.90 180	87.80 201	63.46 234	87.02 179	91.87 103	79.01 26	78.38 164	89.07 171	65.02 129	93.05 182	70.05 197	76.46 291	92.20 138

PVSNet_Blended		80.98 134	80.34 135	82.90 180	88.85 153	65.40 191	84.43 251	92.00 95	67.62 260	78.11 171	85.05 281	66.02 120	94.27 118	71.52 181	89.50 118	89.01 249

FA-MVS(test-final)		80.96 135	79.91 143	84.10 125	88.30 178	65.01 200	84.55 246	90.01 160	73.25 154	79.61 142	87.57 211	58.35 206	94.72 105	71.29 185	86.25 165	92.56 123

QAPM		80.88 136	79.50 152	85.03 89	88.01 192	68.97 107	91.59 43	92.00 95	66.63 275	75.15 247	92.16 92	57.70 211	95.45 68	63.52 253	88.76 130	90.66 184

TranMVSNet+NR-MVSNet		80.84 137	80.31 136	82.42 195	87.85 198	62.33 254	87.74 158	91.33 120	80.55 9	77.99 175	89.86 149	65.23 127	92.62 192	67.05 229	75.24 318	92.30 133

UGNet		80.83 138	79.59 150	84.54 105	88.04 189	68.09 133	89.42 95	88.16 215	76.95 62	76.22 215	89.46 163	49.30 299	93.94 131	68.48 215	90.31 104	91.60 151
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Fast-Effi-MVS+		80.81 139	79.92 142	83.47 153	88.85 153	64.51 210	85.53 225	89.39 178	70.79 194	78.49 162	85.06 280	67.54 102	93.58 149	67.03 230	86.58 159	92.32 132

XVG-OURS-SEG-HR		80.81 139	79.76 146	83.96 143	85.60 250	68.78 111	83.54 270	90.50 143	70.66 200	76.71 203	91.66 103	60.69 188	91.26 247	76.94 130	81.58 231	91.83 147

xiu_mvs_v1_base_debu		80.80 141	79.72 147	84.03 138	87.35 215	70.19 82	85.56 220	88.77 203	69.06 239	81.83 114	88.16 198	50.91 278	92.85 188	78.29 117	87.56 144	89.06 244

xiu_mvs_v1_base		80.80 141	79.72 147	84.03 138	87.35 215	70.19 82	85.56 220	88.77 203	69.06 239	81.83 114	88.16 198	50.91 278	92.85 188	78.29 117	87.56 144	89.06 244

xiu_mvs_v1_base_debi		80.80 141	79.72 147	84.03 138	87.35 215	70.19 82	85.56 220	88.77 203	69.06 239	81.83 114	88.16 198	50.91 278	92.85 188	78.29 117	87.56 144	89.06 244

ACMM	73.20 8	80.78 144	79.84 145	83.58 151	89.31 138	68.37 126	89.99 76	91.60 112	70.28 207	77.25 188	89.66 154	53.37 249	93.53 154	74.24 158	82.85 216	88.85 257
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
114514_t		80.68 145	79.51 151	84.20 122	94.09 38	67.27 156	89.64 87	91.11 128	58.75 358	74.08 265	90.72 135	58.10 207	95.04 92	69.70 202	89.42 120	90.30 200

CANet_DTU		80.61 146	79.87 144	82.83 182	85.60 250	63.17 243	87.36 168	88.65 209	76.37 81	75.88 222	88.44 190	53.51 247	93.07 181	73.30 167	89.74 116	92.25 135

VPA-MVSNet		80.60 147	80.55 131	80.76 233	88.07 188	60.80 274	86.86 184	91.58 113	75.67 95	80.24 135	89.45 165	63.34 140	90.25 267	70.51 193	79.22 261	91.23 164

mvsmamba		80.60 147	79.38 154	84.27 119	89.74 120	67.24 158	87.47 164	86.95 244	70.02 212	75.38 235	88.93 174	51.24 275	92.56 196	75.47 148	89.22 122	93.00 111

PVSNet_BlendedMVS		80.60 147	80.02 140	82.36 197	88.85 153	65.40 191	86.16 207	92.00 95	69.34 229	78.11 171	86.09 257	66.02 120	94.27 118	71.52 181	82.06 226	87.39 291

AdaColmap		80.58 150	79.42 153	84.06 133	93.09 57	68.91 108	89.36 99	88.97 198	69.27 230	75.70 225	89.69 153	57.20 218	95.77 59	63.06 258	88.41 137	87.50 290

EI-MVSNet		80.52 151	79.98 141	82.12 198	84.28 275	63.19 242	86.41 198	88.95 199	74.18 129	78.69 155	87.54 214	66.62 109	92.43 201	72.57 176	80.57 244	90.74 182

XVG-OURS		80.41 152	79.23 160	83.97 142	85.64 248	69.02 105	83.03 281	90.39 145	71.09 189	77.63 181	91.49 112	54.62 238	91.35 245	75.71 142	83.47 208	91.54 154

SDMVSNet		80.38 153	80.18 139	80.99 227	89.03 151	64.94 202	80.45 313	89.40 177	75.19 103	76.61 207	89.98 147	60.61 192	87.69 310	76.83 132	83.55 206	90.33 198

PCF-MVS	73.52 7	80.38 153	78.84 168	85.01 90	87.71 206	68.99 106	83.65 265	91.46 119	63.00 318	77.77 179	90.28 141	66.10 117	95.09 91	61.40 277	88.22 139	90.94 175
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
X-MVStestdata		80.37 155	77.83 191	88.00 17	94.42 20	73.33 19	92.78 18	92.99 49	79.14 21	83.67 95	12.47 421	67.45 103	96.60 33	83.06 70	94.50 51	94.07 54

test_djsdf		80.30 156	79.32 157	83.27 161	83.98 283	65.37 194	90.50 64	90.38 146	68.55 249	76.19 216	88.70 180	56.44 224	93.46 158	78.98 108	80.14 250	90.97 174

v2v482		80.23 157	79.29 158	83.05 173	83.62 291	64.14 219	87.04 177	89.97 161	73.61 141	78.18 170	87.22 222	61.10 182	93.82 139	76.11 137	76.78 288	91.18 165

NR-MVSNet		80.23 157	79.38 154	82.78 188	87.80 201	63.34 237	86.31 202	91.09 129	79.01 26	72.17 289	89.07 171	67.20 106	92.81 191	66.08 236	75.65 304	92.20 138

Anonymous20240529		80.19 159	78.89 167	84.10 125	90.60 97	64.75 207	88.95 113	90.90 132	65.97 283	80.59 132	91.17 123	49.97 289	93.73 147	69.16 208	82.70 220	93.81 68

IterMVS-LS		80.06 160	79.38 154	82.11 199	85.89 244	63.20 241	86.79 187	89.34 179	74.19 128	75.45 232	86.72 234	66.62 109	92.39 203	72.58 175	76.86 285	90.75 181
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		80.03 161	78.57 172	84.42 110	85.13 261	68.74 114	88.77 119	88.10 217	74.99 107	74.97 252	83.49 313	57.27 217	93.36 162	73.53 163	80.88 238	91.18 165

v1144		80.03 161	79.03 164	83.01 175	83.78 288	64.51 210	87.11 176	90.57 142	71.96 173	78.08 173	86.20 254	61.41 174	93.94 131	74.93 151	77.23 279	90.60 187

v8		79.97 163	79.02 165	82.80 185	84.09 280	64.50 212	87.96 149	90.29 153	74.13 131	75.24 244	86.81 231	62.88 151	93.89 138	74.39 156	75.40 313	90.00 216

OpenMVS	72.83 10	79.77 164	78.33 179	84.09 129	85.17 257	69.91 87	90.57 61	90.97 130	66.70 269	72.17 289	91.91 96	54.70 236	93.96 128	61.81 274	90.95 97	88.41 272

v10		79.74 165	78.67 169	82.97 178	84.06 281	64.95 201	87.88 155	90.62 139	73.11 156	75.11 248	86.56 245	61.46 173	94.05 127	73.68 161	75.55 306	89.90 222

ECVR-MVS		79.61 166	79.26 159	80.67 235	90.08 108	54.69 352	87.89 154	77.44 361	74.88 111	80.27 134	92.79 83	48.96 305	92.45 200	68.55 214	92.50 77	94.86 18

BH-RMVSNet		79.61 166	78.44 175	83.14 168	89.38 134	65.93 179	84.95 236	87.15 241	73.56 143	78.19 169	89.79 151	56.67 222	93.36 162	59.53 292	86.74 157	90.13 206

v1192		79.59 168	78.43 176	83.07 172	83.55 293	64.52 209	86.93 182	90.58 140	70.83 193	77.78 178	85.90 258	59.15 201	93.94 131	73.96 160	77.19 281	90.76 180

ab-mvs		79.51 169	78.97 166	81.14 223	88.46 171	60.91 272	83.84 261	89.24 185	70.36 204	79.03 149	88.87 177	63.23 144	90.21 268	65.12 243	82.57 221	92.28 134

WR-MVS		79.49 170	79.22 161	80.27 243	88.79 159	58.35 297	85.06 233	88.61 211	78.56 30	77.65 180	88.34 192	63.81 139	90.66 263	64.98 245	77.22 280	91.80 149

v144192		79.47 171	78.37 177	82.78 188	83.35 296	63.96 222	86.96 180	90.36 149	69.99 214	77.50 182	85.67 265	60.66 190	93.77 143	74.27 157	76.58 289	90.62 185

BH-untuned		79.47 171	78.60 171	82.05 200	89.19 144	65.91 180	86.07 209	88.52 212	72.18 169	75.42 233	87.69 208	61.15 181	93.54 153	60.38 284	86.83 156	86.70 310

test1111		79.43 173	79.18 162	80.15 245	89.99 113	53.31 365	87.33 170	77.05 365	75.04 106	80.23 136	92.77 85	48.97 304	92.33 208	68.87 211	92.40 79	94.81 21

mvs_anonymous		79.42 174	79.11 163	80.34 241	84.45 274	57.97 304	82.59 283	87.62 230	67.40 264	76.17 219	88.56 187	68.47 92	89.59 279	70.65 192	86.05 169	93.47 87

thisisatest0530		79.40 175	77.76 196	84.31 115	87.69 208	65.10 199	87.36 168	84.26 284	70.04 211	77.42 184	88.26 196	49.94 290	94.79 103	70.20 195	84.70 183	93.03 108

tttt0517		79.40 175	77.91 188	83.90 145	88.10 186	63.84 224	88.37 136	84.05 286	71.45 182	76.78 201	89.12 170	49.93 292	94.89 98	70.18 196	83.18 213	92.96 113

V42		79.38 177	78.24 181	82.83 182	81.10 343	65.50 190	85.55 223	89.82 164	71.57 180	78.21 168	86.12 256	60.66 190	93.18 175	75.64 143	75.46 310	89.81 227

jajsoiax		79.29 178	77.96 186	83.27 161	84.68 268	66.57 169	89.25 102	90.16 156	69.20 235	75.46 231	89.49 160	45.75 330	93.13 178	76.84 131	80.80 240	90.11 208

v1921920		79.22 179	78.03 185	82.80 185	83.30 298	63.94 223	86.80 186	90.33 150	69.91 217	77.48 183	85.53 268	58.44 205	93.75 145	73.60 162	76.85 286	90.71 183

AUN-MVS		79.21 180	77.60 201	84.05 136	88.71 163	67.61 145	85.84 216	87.26 238	69.08 238	77.23 190	88.14 202	53.20 251	93.47 157	75.50 147	73.45 335	91.06 169

TAPA-MVS	73.13 9	79.15 181	77.94 187	82.79 187	89.59 122	62.99 248	88.16 144	91.51 115	65.77 284	77.14 196	91.09 125	60.91 185	93.21 169	50.26 355	87.05 152	92.17 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_tets		79.13 182	77.77 195	83.22 165	84.70 267	66.37 171	89.17 103	90.19 155	69.38 228	75.40 234	89.46 163	44.17 340	93.15 176	76.78 133	80.70 242	90.14 205

UniMVSNet_ETH3D		79.10 183	78.24 181	81.70 207	86.85 228	60.24 283	87.28 172	88.79 202	74.25 127	76.84 198	90.53 139	49.48 295	91.56 234	67.98 218	82.15 224	93.29 93

CDS-MVSNet		79.07 184	77.70 198	83.17 167	87.60 210	68.23 130	84.40 253	86.20 259	67.49 262	76.36 212	86.54 246	61.54 170	90.79 260	61.86 273	87.33 148	90.49 192
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVSTER		79.01 185	77.88 190	82.38 196	83.07 305	64.80 206	84.08 260	88.95 199	69.01 242	78.69 155	87.17 225	54.70 236	92.43 201	74.69 152	80.57 244	89.89 223

v1240		78.99 186	77.78 194	82.64 191	83.21 300	63.54 231	86.62 193	90.30 152	69.74 224	77.33 186	85.68 264	57.04 219	93.76 144	73.13 170	76.92 283	90.62 185

Anonymous20231211		78.97 187	77.69 199	82.81 184	90.54 99	64.29 217	90.11 75	91.51 115	65.01 295	76.16 220	88.13 203	50.56 283	93.03 185	69.68 203	77.56 278	91.11 167

v7n		78.97 187	77.58 202	83.14 168	83.45 295	65.51 189	88.32 138	91.21 123	73.69 139	72.41 285	86.32 252	57.93 208	93.81 140	69.18 207	75.65 304	90.11 208

TAMVS		78.89 189	77.51 203	83.03 174	87.80 201	67.79 141	84.72 240	85.05 273	67.63 259	76.75 202	87.70 207	62.25 160	90.82 259	58.53 303	87.13 151	90.49 192

c3_l		78.75 190	77.91 188	81.26 219	82.89 312	61.56 265	84.09 259	89.13 191	69.97 215	75.56 227	84.29 295	66.36 114	92.09 215	73.47 165	75.48 308	90.12 207

tt0805		78.73 191	77.83 191	81.43 213	85.17 257	60.30 282	89.41 96	90.90 132	71.21 186	77.17 195	88.73 179	46.38 319	93.21 169	72.57 176	78.96 262	90.79 178

v148		78.72 192	77.80 193	81.47 212	82.73 315	61.96 260	86.30 203	88.08 218	73.26 153	76.18 217	85.47 270	62.46 156	92.36 205	71.92 180	73.82 332	90.09 210

VPNet		78.69 193	78.66 170	78.76 270	88.31 177	55.72 341	84.45 250	86.63 251	76.79 67	78.26 167	90.55 138	59.30 200	89.70 278	66.63 231	77.05 282	90.88 176

ET-MVSNet_ETH3D		78.63 194	76.63 224	84.64 103	86.73 232	69.47 95	85.01 234	84.61 277	69.54 225	66.51 351	86.59 242	50.16 287	91.75 227	76.26 136	84.24 193	92.69 119

anonymousdsp		78.60 195	77.15 209	82.98 177	80.51 349	67.08 161	87.24 173	89.53 174	65.66 286	75.16 246	87.19 224	52.52 252	92.25 210	77.17 127	79.34 259	89.61 232

miper_ehance_all_eth		78.59 196	77.76 196	81.08 225	82.66 317	61.56 265	83.65 265	89.15 189	68.87 244	75.55 228	83.79 306	66.49 112	92.03 216	73.25 168	76.39 293	89.64 231

WR-MVS_H		78.51 197	78.49 173	78.56 275	88.02 190	56.38 331	88.43 131	92.67 67	77.14 57	73.89 266	87.55 213	66.25 116	89.24 286	58.92 298	73.55 334	90.06 214

GBi-Net		78.40 198	77.40 204	81.40 215	87.60 210	63.01 244	88.39 133	89.28 181	71.63 176	75.34 237	87.28 218	54.80 232	91.11 250	62.72 260	79.57 254	90.09 210

test1		78.40 198	77.40 204	81.40 215	87.60 210	63.01 244	88.39 133	89.28 181	71.63 176	75.34 237	87.28 218	54.80 232	91.11 250	62.72 260	79.57 254	90.09 210

Vis-MVSNet (Re-imp)		78.36 200	78.45 174	78.07 286	88.64 165	51.78 375	86.70 191	79.63 346	74.14 130	75.11 248	90.83 134	61.29 178	89.75 276	58.10 308	91.60 88	92.69 119

Anonymous202405211		78.25 201	77.01 211	81.99 202	91.03 87	60.67 276	84.77 239	83.90 288	70.65 201	80.00 138	91.20 121	41.08 359	91.43 243	65.21 242	85.26 177	93.85 65

CP-MVSNet		78.22 202	78.34 178	77.84 288	87.83 200	54.54 354	87.94 151	91.17 125	77.65 39	73.48 271	88.49 188	62.24 161	88.43 301	62.19 268	74.07 327	90.55 189

BH-w/o		78.21 203	77.33 207	80.84 231	88.81 157	65.13 198	84.87 237	87.85 226	69.75 222	74.52 260	84.74 287	61.34 176	93.11 179	58.24 307	85.84 173	84.27 347

FMVSNet2		78.20 204	77.21 208	81.20 221	87.60 210	62.89 250	87.47 164	89.02 194	71.63 176	75.29 243	87.28 218	54.80 232	91.10 253	62.38 265	79.38 258	89.61 232

MVS		78.19 205	76.99 213	81.78 205	85.66 247	66.99 162	84.66 241	90.47 144	55.08 378	72.02 291	85.27 273	63.83 138	94.11 126	66.10 235	89.80 115	84.24 348

Baseline_NR-MVSNet		78.15 206	78.33 179	77.61 293	85.79 245	56.21 335	86.78 188	85.76 265	73.60 142	77.93 176	87.57 211	65.02 129	88.99 290	67.14 228	75.33 315	87.63 285

CNLPA		78.08 207	76.79 218	81.97 203	90.40 102	71.07 65	87.59 161	84.55 278	66.03 282	72.38 286	89.64 155	57.56 213	86.04 323	59.61 291	83.35 210	88.79 260

cl22		78.07 208	77.01 211	81.23 220	82.37 324	61.83 262	83.55 269	87.98 220	68.96 243	75.06 250	83.87 302	61.40 175	91.88 223	73.53 163	76.39 293	89.98 219

PLC	70.83 11	78.05 209	76.37 229	83.08 171	91.88 77	67.80 140	88.19 142	89.46 176	64.33 303	69.87 315	88.38 191	53.66 245	93.58 149	58.86 299	82.73 218	87.86 281
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Fast-Effi-MVS+-dtu		78.02 210	76.49 225	82.62 192	83.16 304	66.96 165	86.94 181	87.45 235	72.45 164	71.49 297	84.17 299	54.79 235	91.58 232	67.61 221	80.31 247	89.30 240

PS-CasMVS		78.01 211	78.09 184	77.77 290	87.71 206	54.39 356	88.02 147	91.22 122	77.50 47	73.26 273	88.64 183	60.73 186	88.41 302	61.88 272	73.88 331	90.53 190

HY-MVS	69.67 12	77.95 212	77.15 209	80.36 240	87.57 214	60.21 284	83.37 272	87.78 228	66.11 279	75.37 236	87.06 229	63.27 142	90.48 265	61.38 278	82.43 222	90.40 196

eth_miper_zixun_eth		77.92 213	76.69 222	81.61 210	83.00 308	61.98 259	83.15 275	89.20 187	69.52 226	74.86 254	84.35 294	61.76 166	92.56 196	71.50 183	72.89 340	90.28 201

FMVSNet3		77.88 214	76.85 216	80.97 229	86.84 229	62.36 253	86.52 196	88.77 203	71.13 187	75.34 237	86.66 240	54.07 242	91.10 253	62.72 260	79.57 254	89.45 236

miper_enhance_ethall		77.87 215	76.86 215	80.92 230	81.65 331	61.38 267	82.68 282	88.98 196	65.52 288	75.47 229	82.30 333	65.76 124	92.00 218	72.95 171	76.39 293	89.39 237

FE-MVS		77.78 216	75.68 235	84.08 130	88.09 187	66.00 177	83.13 276	87.79 227	68.42 253	78.01 174	85.23 275	45.50 333	95.12 85	59.11 296	85.83 174	91.11 167

PEN-MVS		77.73 217	77.69 199	77.84 288	87.07 226	53.91 359	87.91 153	91.18 124	77.56 44	73.14 275	88.82 178	61.23 179	89.17 287	59.95 287	72.37 342	90.43 194

cl____		77.72 218	76.76 219	80.58 236	82.49 321	60.48 279	83.09 277	87.87 224	69.22 233	74.38 263	85.22 276	62.10 163	91.53 237	71.09 186	75.41 312	89.73 230

DIV-MVS_self_test		77.72 218	76.76 219	80.58 236	82.48 322	60.48 279	83.09 277	87.86 225	69.22 233	74.38 263	85.24 274	62.10 163	91.53 237	71.09 186	75.40 313	89.74 229

sd_testset		77.70 220	77.40 204	78.60 273	89.03 151	60.02 285	79.00 332	85.83 264	75.19 103	76.61 207	89.98 147	54.81 231	85.46 331	62.63 264	83.55 206	90.33 198

PAPM		77.68 221	76.40 228	81.51 211	87.29 222	61.85 261	83.78 262	89.59 172	64.74 297	71.23 298	88.70 180	62.59 153	93.66 148	52.66 340	87.03 153	89.01 249

CHOSEN 1792x2688		77.63 222	75.69 234	83.44 154	89.98 114	68.58 122	78.70 337	87.50 233	56.38 373	75.80 224	86.84 230	58.67 203	91.40 244	61.58 276	85.75 175	90.34 197

HyFIR lowres test		77.53 223	75.40 242	83.94 144	89.59 122	66.62 167	80.36 314	88.64 210	56.29 374	76.45 209	85.17 277	57.64 212	93.28 164	61.34 279	83.10 214	91.91 146

FMVSNet1		77.44 224	76.12 231	81.40 215	86.81 230	63.01 244	88.39 133	89.28 181	70.49 203	74.39 262	87.28 218	49.06 303	91.11 250	60.91 281	78.52 265	90.09 210

TR-MVS		77.44 224	76.18 230	81.20 221	88.24 179	63.24 239	84.61 244	86.40 255	67.55 261	77.81 177	86.48 248	54.10 241	93.15 176	57.75 311	82.72 219	87.20 296

1112_ss		77.40 226	76.43 227	80.32 242	89.11 150	60.41 281	83.65 265	87.72 229	62.13 331	73.05 276	86.72 234	62.58 154	89.97 272	62.11 271	80.80 240	90.59 188

thisisatest0515		77.33 227	75.38 243	83.18 166	85.27 256	63.80 225	82.11 288	83.27 298	65.06 293	75.91 221	83.84 304	49.54 294	94.27 118	67.24 226	86.19 166	91.48 158

test2506		77.30 228	76.49 225	79.74 253	90.08 108	52.02 369	87.86 156	63.10 407	74.88 111	80.16 137	92.79 83	38.29 373	92.35 206	68.74 213	92.50 77	94.86 18

pm-mvs1		77.25 229	76.68 223	78.93 268	84.22 277	58.62 295	86.41 198	88.36 214	71.37 183	73.31 272	88.01 204	61.22 180	89.15 288	64.24 251	73.01 339	89.03 248

LCM-MVSNet-Re		77.05 230	76.94 214	77.36 297	87.20 223	51.60 376	80.06 317	80.46 336	75.20 102	67.69 333	86.72 234	62.48 155	88.98 291	63.44 255	89.25 121	91.51 155

DTE-MVSNet		76.99 231	76.80 217	77.54 296	86.24 238	53.06 368	87.52 162	90.66 138	77.08 60	72.50 283	88.67 182	60.48 194	89.52 280	57.33 315	70.74 354	90.05 215

baseline1		76.98 232	76.75 221	77.66 291	88.13 184	55.66 342	85.12 231	81.89 319	73.04 158	76.79 200	88.90 175	62.43 157	87.78 309	63.30 257	71.18 352	89.55 234

LS3D		76.95 233	74.82 250	83.37 158	90.45 100	67.36 153	89.15 107	86.94 245	61.87 333	69.52 318	90.61 137	51.71 271	94.53 110	46.38 376	86.71 158	88.21 275

GA-MVS		76.87 234	75.17 247	81.97 203	82.75 314	62.58 251	81.44 297	86.35 257	72.16 171	74.74 255	82.89 324	46.20 324	92.02 217	68.85 212	81.09 236	91.30 163

mamv4		76.81 235	78.23 183	72.54 345	86.12 241	65.75 186	78.76 336	82.07 318	64.12 305	72.97 277	91.02 130	67.97 97	68.08 409	83.04 72	78.02 272	83.80 355

DP-MVS		76.78 236	74.57 252	83.42 155	93.29 48	69.46 97	88.55 129	83.70 290	63.98 310	70.20 306	88.89 176	54.01 243	94.80 102	46.66 373	81.88 229	86.01 322

cascas		76.72 237	74.64 251	82.99 176	85.78 246	65.88 181	82.33 285	89.21 186	60.85 339	72.74 279	81.02 344	47.28 312	93.75 145	67.48 223	85.02 178	89.34 239

testing91		76.54 238	75.66 237	79.18 265	88.43 173	55.89 338	81.08 300	83.00 306	73.76 138	75.34 237	84.29 295	46.20 324	90.07 270	64.33 249	84.50 185	91.58 153

1314		76.53 239	75.30 246	80.21 244	83.93 284	62.32 255	84.66 241	88.81 201	60.23 343	70.16 309	84.07 301	55.30 229	90.73 262	67.37 224	83.21 212	87.59 288

thres100view900		76.50 240	75.55 239	79.33 261	89.52 125	56.99 320	85.83 217	83.23 299	73.94 133	76.32 213	87.12 226	51.89 267	91.95 219	48.33 364	83.75 200	89.07 242

thres600view7		76.50 240	75.44 240	79.68 255	89.40 132	57.16 317	85.53 225	83.23 299	73.79 137	76.26 214	87.09 227	51.89 267	91.89 222	48.05 369	83.72 203	90.00 216

thres400		76.50 240	75.37 244	79.86 250	89.13 146	57.65 311	85.17 228	83.60 291	73.41 149	76.45 209	86.39 250	52.12 259	91.95 219	48.33 364	83.75 200	90.00 216

MonoMVSNet		76.49 243	75.80 232	78.58 274	81.55 334	58.45 296	86.36 201	86.22 258	74.87 113	74.73 256	83.73 308	51.79 270	88.73 296	70.78 188	72.15 345	88.55 269

tfpn200view9		76.42 244	75.37 244	79.55 260	89.13 146	57.65 311	85.17 228	83.60 291	73.41 149	76.45 209	86.39 250	52.12 259	91.95 219	48.33 364	83.75 200	89.07 242

Test_1112_low_res		76.40 245	75.44 240	79.27 262	89.28 140	58.09 300	81.69 292	87.07 242	59.53 350	72.48 284	86.67 239	61.30 177	89.33 283	60.81 283	80.15 249	90.41 195

F-COLMAP		76.38 246	74.33 258	82.50 194	89.28 140	66.95 166	88.41 132	89.03 193	64.05 308	66.83 343	88.61 184	46.78 316	92.89 187	57.48 312	78.55 264	87.67 284

LTVRE_ROB	69.57 13	76.25 247	74.54 254	81.41 214	88.60 166	64.38 216	79.24 327	89.12 192	70.76 196	69.79 317	87.86 205	49.09 302	93.20 172	56.21 325	80.16 248	86.65 311
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
MVP-Stereo		76.12 248	74.46 256	81.13 224	85.37 255	69.79 89	84.42 252	87.95 222	65.03 294	67.46 336	85.33 272	53.28 250	91.73 229	58.01 309	83.27 211	81.85 374
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
XVG-ACMP-BASELINE		76.11 249	74.27 259	81.62 208	83.20 301	64.67 208	83.60 268	89.75 167	69.75 222	71.85 292	87.09 227	32.78 386	92.11 214	69.99 199	80.43 246	88.09 277

testing99		76.09 250	75.12 248	79.00 266	88.16 181	55.50 344	80.79 304	81.40 325	73.30 152	75.17 245	84.27 297	44.48 338	90.02 271	64.28 250	84.22 194	91.48 158

ACMH+	68.96 14	76.01 251	74.01 260	82.03 201	88.60 166	65.31 195	88.86 116	87.55 231	70.25 209	67.75 332	87.47 216	41.27 357	93.19 174	58.37 305	75.94 301	87.60 286

ACMH	67.68 16	75.89 252	73.93 262	81.77 206	88.71 163	66.61 168	88.62 127	89.01 195	69.81 218	66.78 344	86.70 238	41.95 356	91.51 239	55.64 326	78.14 271	87.17 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	68.01 15	75.85 253	73.36 269	83.31 159	84.76 266	66.03 175	83.38 271	85.06 272	70.21 210	69.40 319	81.05 343	45.76 329	94.66 108	65.10 244	75.49 307	89.25 241
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
baseline2		75.70 254	73.83 265	81.30 218	83.26 299	61.79 263	82.57 284	80.65 332	66.81 266	66.88 342	83.42 314	57.86 210	92.19 212	63.47 254	79.57 254	89.91 221

WTY-MVS		75.65 255	75.68 235	75.57 313	86.40 237	56.82 322	77.92 349	82.40 314	65.10 292	76.18 217	87.72 206	63.13 149	80.90 361	60.31 285	81.96 227	89.00 251

thres200		75.55 256	74.47 255	78.82 269	87.78 204	57.85 307	83.07 279	83.51 294	72.44 166	75.84 223	84.42 290	52.08 262	91.75 227	47.41 371	83.64 205	86.86 306

test_vis1_n_1920		75.52 257	75.78 233	74.75 326	79.84 357	57.44 315	83.26 273	85.52 267	62.83 322	79.34 147	86.17 255	45.10 335	79.71 365	78.75 110	81.21 235	87.10 303

EPNet_dtu		75.46 258	74.86 249	77.23 300	82.57 319	54.60 353	86.89 183	83.09 303	71.64 175	66.25 353	85.86 260	55.99 225	88.04 306	54.92 329	86.55 160	89.05 247
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		75.43 259	73.87 264	80.11 246	82.69 316	64.85 205	81.57 294	83.47 295	69.16 236	70.49 303	84.15 300	51.95 265	88.15 304	69.23 206	72.14 346	87.34 293

XXY-MVS		75.41 260	75.56 238	74.96 322	83.59 292	57.82 308	80.59 310	83.87 289	66.54 276	74.93 253	88.31 193	63.24 143	80.09 364	62.16 269	76.85 286	86.97 304

reproduce_monomvs		75.40 261	74.38 257	78.46 280	83.92 285	57.80 309	83.78 262	86.94 245	73.47 147	72.25 288	84.47 289	38.74 369	89.27 285	75.32 149	70.53 355	88.31 273

TransMVSNet (Re)		75.39 262	74.56 253	77.86 287	85.50 252	57.10 319	86.78 188	86.09 262	72.17 170	71.53 296	87.34 217	63.01 150	89.31 284	56.84 320	61.83 381	87.17 297

CostFormer		75.24 263	73.90 263	79.27 262	82.65 318	58.27 299	80.80 303	82.73 312	61.57 334	75.33 241	83.13 319	55.52 227	91.07 256	64.98 245	78.34 270	88.45 270

testing11		75.14 264	74.01 260	78.53 277	88.16 181	56.38 331	80.74 307	80.42 337	70.67 197	72.69 282	83.72 309	43.61 344	89.86 273	62.29 267	83.76 199	89.36 238

D2MVS		74.82 265	73.21 270	79.64 257	79.81 358	62.56 252	80.34 315	87.35 236	64.37 302	68.86 324	82.66 328	46.37 320	90.10 269	67.91 219	81.24 234	86.25 315

pmmvs6		74.69 266	73.39 268	78.61 272	81.38 338	57.48 314	86.64 192	87.95 222	64.99 296	70.18 307	86.61 241	50.43 285	89.52 280	62.12 270	70.18 357	88.83 258

tfpnnormal		74.39 267	73.16 271	78.08 285	86.10 243	58.05 301	84.65 243	87.53 232	70.32 206	71.22 299	85.63 266	54.97 230	89.86 273	43.03 387	75.02 320	86.32 314

IterMVS		74.29 268	72.94 274	78.35 281	81.53 335	63.49 233	81.58 293	82.49 313	68.06 257	69.99 312	83.69 310	51.66 272	85.54 329	65.85 238	71.64 349	86.01 322
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
OurMVSNet-221017-0		74.26 269	72.42 280	79.80 252	83.76 289	59.59 290	85.92 213	86.64 250	66.39 277	66.96 341	87.58 210	39.46 365	91.60 231	65.76 239	69.27 360	88.22 274

SCA		74.22 270	72.33 281	79.91 249	84.05 282	62.17 257	79.96 320	79.29 349	66.30 278	72.38 286	80.13 354	51.95 265	88.60 299	59.25 294	77.67 277	88.96 253

mmtdpeth		74.16 271	73.01 273	77.60 295	83.72 290	61.13 268	85.10 232	85.10 271	72.06 172	77.21 194	80.33 352	43.84 342	85.75 325	77.14 128	52.61 399	85.91 325

miper_lstm_enhance		74.11 272	73.11 272	77.13 301	80.11 353	59.62 289	72.23 377	86.92 247	66.76 268	70.40 304	82.92 323	56.93 220	82.92 350	69.06 209	72.63 341	88.87 256

testing222		74.04 273	72.66 277	78.19 283	87.89 196	55.36 345	81.06 301	79.20 350	71.30 184	74.65 258	83.57 312	39.11 368	88.67 298	51.43 347	85.75 175	90.53 190

EG-PatchMatch MVS		74.04 273	71.82 285	80.71 234	84.92 264	67.42 150	85.86 215	88.08 218	66.04 281	64.22 365	83.85 303	35.10 382	92.56 196	57.44 313	80.83 239	82.16 373

pmmvs4		74.03 275	71.91 284	80.39 239	81.96 327	68.32 127	81.45 296	82.14 316	59.32 351	69.87 315	85.13 278	52.40 255	88.13 305	60.21 286	74.74 323	84.73 344

MS-PatchMatch		73.83 276	72.67 276	77.30 299	83.87 286	66.02 176	81.82 289	84.66 276	61.37 337	68.61 327	82.82 326	47.29 311	88.21 303	59.27 293	84.32 192	77.68 389

test_cas_vis1_n_1920		73.76 277	73.74 266	73.81 334	75.90 378	59.77 287	80.51 311	82.40 314	58.30 360	81.62 120	85.69 263	44.35 339	76.41 383	76.29 135	78.61 263	85.23 335

sss		73.60 278	73.64 267	73.51 336	82.80 313	55.01 350	76.12 356	81.69 322	62.47 327	74.68 257	85.85 261	57.32 216	78.11 372	60.86 282	80.93 237	87.39 291

RPMNet		73.51 279	70.49 301	82.58 193	81.32 341	65.19 196	75.92 358	92.27 84	57.60 366	72.73 280	76.45 381	52.30 256	95.43 70	48.14 368	77.71 275	87.11 301

WBMVS		73.43 280	72.81 275	75.28 319	87.91 195	50.99 382	78.59 340	81.31 327	65.51 290	74.47 261	84.83 284	46.39 318	86.68 316	58.41 304	77.86 273	88.17 276

SixPastTwentyTwo		73.37 281	71.26 294	79.70 254	85.08 262	57.89 306	85.57 219	83.56 293	71.03 191	65.66 355	85.88 259	42.10 354	92.57 195	59.11 296	63.34 379	88.65 266

CR-MVSNet		73.37 281	71.27 293	79.67 256	81.32 341	65.19 196	75.92 358	80.30 339	59.92 346	72.73 280	81.19 341	52.50 253	86.69 315	59.84 288	77.71 275	87.11 301

MSDG		73.36 283	70.99 296	80.49 238	84.51 273	65.80 183	80.71 308	86.13 261	65.70 285	65.46 356	83.74 307	44.60 336	90.91 258	51.13 348	76.89 284	84.74 343

tpm2		73.26 284	71.46 289	78.63 271	83.34 297	56.71 325	80.65 309	80.40 338	56.63 372	73.55 270	82.02 338	51.80 269	91.24 248	56.35 324	78.42 268	87.95 278

RPSCF		73.23 285	71.46 289	78.54 276	82.50 320	59.85 286	82.18 287	82.84 311	58.96 355	71.15 300	89.41 167	45.48 334	84.77 338	58.82 300	71.83 348	91.02 173

PatchmatchNet		73.12 286	71.33 292	78.49 279	83.18 302	60.85 273	79.63 322	78.57 353	64.13 304	71.73 293	79.81 359	51.20 276	85.97 324	57.40 314	76.36 298	88.66 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
UBG		73.08 287	72.27 282	75.51 315	88.02 190	51.29 380	78.35 344	77.38 362	65.52 288	73.87 267	82.36 331	45.55 331	86.48 319	55.02 328	84.39 191	88.75 262

COLMAP_ROB	66.92 17	73.01 288	70.41 303	80.81 232	87.13 225	65.63 187	88.30 139	84.19 285	62.96 319	63.80 369	87.69 208	38.04 374	92.56 196	46.66 373	74.91 321	84.24 348
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CVMVSNet		72.99 289	72.58 278	74.25 330	84.28 275	50.85 383	86.41 198	83.45 296	44.56 398	73.23 274	87.54 214	49.38 297	85.70 326	65.90 237	78.44 267	86.19 317

test-LLR		72.94 290	72.43 279	74.48 327	81.35 339	58.04 302	78.38 341	77.46 359	66.66 270	69.95 313	79.00 365	48.06 308	79.24 366	66.13 233	84.83 180	86.15 318

test_0402		72.79 291	70.44 302	79.84 251	88.13 184	65.99 178	85.93 212	84.29 282	65.57 287	67.40 338	85.49 269	46.92 315	92.61 193	35.88 401	74.38 326	80.94 379

tpmrst		72.39 292	72.13 283	73.18 340	80.54 348	49.91 387	79.91 321	79.08 351	63.11 316	71.69 294	79.95 356	55.32 228	82.77 351	65.66 240	73.89 330	86.87 305

PatchMatch-RL		72.38 293	70.90 297	76.80 304	88.60 166	67.38 152	79.53 323	76.17 371	62.75 324	69.36 320	82.00 339	45.51 332	84.89 337	53.62 335	80.58 243	78.12 388

CL-MVSNet_self_test		72.37 294	71.46 289	75.09 321	79.49 364	53.53 361	80.76 306	85.01 274	69.12 237	70.51 302	82.05 337	57.92 209	84.13 341	52.27 342	66.00 373	87.60 286

tpm		72.37 294	71.71 286	74.35 329	82.19 325	52.00 370	79.22 328	77.29 363	64.56 299	72.95 278	83.68 311	51.35 273	83.26 349	58.33 306	75.80 302	87.81 282

ETVMVS		72.25 296	71.05 295	75.84 309	87.77 205	51.91 372	79.39 325	74.98 374	69.26 231	73.71 268	82.95 322	40.82 361	86.14 322	46.17 377	84.43 190	89.47 235

UWE-MVS		72.13 297	71.49 288	74.03 332	86.66 234	47.70 391	81.40 298	76.89 367	63.60 313	75.59 226	84.22 298	39.94 364	85.62 328	48.98 361	86.13 168	88.77 261

PVSNet	64.34 18	72.08 298	70.87 298	75.69 311	86.21 239	56.44 329	74.37 371	80.73 331	62.06 332	70.17 308	82.23 335	42.86 348	83.31 348	54.77 330	84.45 189	87.32 294

WB-MVSnew		71.96 299	71.65 287	72.89 341	84.67 271	51.88 373	82.29 286	77.57 358	62.31 328	73.67 269	83.00 321	53.49 248	81.10 360	45.75 380	82.13 225	85.70 328

pmmvs5		71.55 300	70.20 306	75.61 312	77.83 371	56.39 330	81.74 291	80.89 328	57.76 364	67.46 336	84.49 288	49.26 300	85.32 333	57.08 317	75.29 316	85.11 339

test-mter		71.41 301	70.39 304	74.48 327	81.35 339	58.04 302	78.38 341	77.46 359	60.32 342	69.95 313	79.00 365	36.08 380	79.24 366	66.13 233	84.83 180	86.15 318

K. test v3		71.19 302	68.51 314	79.21 264	83.04 307	57.78 310	84.35 254	76.91 366	72.90 161	62.99 372	82.86 325	39.27 366	91.09 255	61.65 275	52.66 398	88.75 262

dmvs_re		71.14 303	70.58 299	72.80 342	81.96 327	59.68 288	75.60 362	79.34 348	68.55 249	69.27 322	80.72 349	49.42 296	76.54 380	52.56 341	77.79 274	82.19 372

tpmvs		71.09 304	69.29 309	76.49 305	82.04 326	56.04 336	78.92 334	81.37 326	64.05 308	67.18 340	78.28 371	49.74 293	89.77 275	49.67 358	72.37 342	83.67 356

AllTest		70.96 305	68.09 320	79.58 258	85.15 259	63.62 227	84.58 245	79.83 343	62.31 328	60.32 381	86.73 232	32.02 387	88.96 293	50.28 353	71.57 350	86.15 318

test_fmvs1		70.93 306	70.52 300	72.16 347	73.71 389	55.05 349	80.82 302	78.77 352	51.21 390	78.58 159	84.41 291	31.20 391	76.94 378	75.88 141	80.12 251	84.47 346

test_fmvs1_n		70.86 307	70.24 305	72.73 343	72.51 400	55.28 347	81.27 299	79.71 345	51.49 389	78.73 154	84.87 283	27.54 396	77.02 377	76.06 138	79.97 252	85.88 326

Patchmtry		70.74 308	69.16 311	75.49 316	80.72 345	54.07 358	74.94 369	80.30 339	58.34 359	70.01 310	81.19 341	52.50 253	86.54 317	53.37 337	71.09 353	85.87 327

MIMVSNet		70.69 309	69.30 308	74.88 323	84.52 272	56.35 333	75.87 360	79.42 347	64.59 298	67.76 331	82.41 330	41.10 358	81.54 357	46.64 375	81.34 232	86.75 309

tpm cat1		70.57 310	68.31 316	77.35 298	82.41 323	57.95 305	78.08 346	80.22 341	52.04 385	68.54 328	77.66 376	52.00 264	87.84 308	51.77 343	72.07 347	86.25 315

OpenMVS_ROB	64.09 19	70.56 311	68.19 317	77.65 292	80.26 350	59.41 292	85.01 234	82.96 308	58.76 357	65.43 357	82.33 332	37.63 376	91.23 249	45.34 383	76.03 300	82.32 370

pmmvs-eth3d		70.50 312	67.83 325	78.52 278	77.37 374	66.18 174	81.82 289	81.51 323	58.90 356	63.90 368	80.42 351	42.69 349	86.28 321	58.56 302	65.30 375	83.11 362

USDC		70.33 313	68.37 315	76.21 307	80.60 347	56.23 334	79.19 329	86.49 253	60.89 338	61.29 377	85.47 270	31.78 389	89.47 282	53.37 337	76.21 299	82.94 366

Patchmatch-RL test		70.24 314	67.78 327	77.61 293	77.43 373	59.57 291	71.16 381	70.33 388	62.94 320	68.65 326	72.77 393	50.62 282	85.49 330	69.58 204	66.58 370	87.77 283

CMPMVS	51.72 21	70.19 315	68.16 318	76.28 306	73.15 396	57.55 313	79.47 324	83.92 287	48.02 394	56.48 394	84.81 285	43.13 346	86.42 320	62.67 263	81.81 230	84.89 341
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ppachtmachnet_test		70.04 316	67.34 334	78.14 284	79.80 359	61.13 268	79.19 329	80.59 333	59.16 353	65.27 358	79.29 362	46.75 317	87.29 312	49.33 359	66.72 368	86.00 324

gg-mvs-nofinetune		69.95 317	67.96 321	75.94 308	83.07 305	54.51 355	77.23 353	70.29 389	63.11 316	70.32 305	62.33 402	43.62 343	88.69 297	53.88 334	87.76 143	84.62 345

TESTMET0.1,1		69.89 318	69.00 312	72.55 344	79.27 367	56.85 321	78.38 341	74.71 378	57.64 365	68.09 330	77.19 378	37.75 375	76.70 379	63.92 252	84.09 195	84.10 351

test_vis1_n		69.85 319	69.21 310	71.77 349	72.66 399	55.27 348	81.48 295	76.21 370	52.03 386	75.30 242	83.20 318	28.97 394	76.22 385	74.60 153	78.41 269	83.81 354

FMVSNet5		69.50 320	67.96 321	74.15 331	82.97 311	55.35 346	80.01 319	82.12 317	62.56 326	63.02 370	81.53 340	36.92 377	81.92 355	48.42 363	74.06 328	85.17 338

mvs5depth		69.45 321	67.45 333	75.46 317	73.93 387	55.83 339	79.19 329	83.23 299	66.89 265	71.63 295	83.32 315	33.69 385	85.09 334	59.81 289	55.34 395	85.46 331

PMMVS		69.34 322	68.67 313	71.35 354	75.67 380	62.03 258	75.17 364	73.46 381	50.00 391	68.68 325	79.05 363	52.07 263	78.13 371	61.16 280	82.77 217	73.90 395

our_test_3		69.14 323	67.00 336	75.57 313	79.80 359	58.80 293	77.96 347	77.81 356	59.55 349	62.90 373	78.25 372	47.43 310	83.97 342	51.71 344	67.58 367	83.93 353

EPMVS		69.02 324	68.16 318	71.59 350	79.61 362	49.80 389	77.40 351	66.93 399	62.82 323	70.01 310	79.05 363	45.79 328	77.86 374	56.58 322	75.26 317	87.13 300

KD-MVS_self_test		68.81 325	67.59 331	72.46 346	74.29 386	45.45 397	77.93 348	87.00 243	63.12 315	63.99 367	78.99 367	42.32 351	84.77 338	56.55 323	64.09 378	87.16 299

Anonymous20240521		68.80 326	67.22 335	73.55 335	74.33 385	54.11 357	83.18 274	85.61 266	58.15 361	61.68 376	80.94 346	30.71 392	81.27 359	57.00 318	73.34 338	85.28 334

Anonymous20231206		68.60 327	67.80 326	71.02 357	80.23 352	50.75 384	78.30 345	80.47 335	56.79 371	66.11 354	82.63 329	46.35 321	78.95 368	43.62 386	75.70 303	83.36 359

MIMVSNet1		68.58 328	66.78 338	73.98 333	80.07 354	51.82 374	80.77 305	84.37 279	64.40 301	59.75 384	82.16 336	36.47 378	83.63 345	42.73 388	70.33 356	86.48 313

testing3		68.56 329	67.67 329	71.22 356	87.33 220	42.87 406	83.06 280	71.54 386	70.36 204	69.08 323	84.38 292	30.33 393	85.69 327	37.50 399	75.45 311	85.09 340

EU-MVSNet		68.53 330	67.61 330	71.31 355	78.51 370	47.01 394	84.47 247	84.27 283	42.27 401	66.44 352	84.79 286	40.44 362	83.76 343	58.76 301	68.54 365	83.17 360

PatchT		68.46 331	67.85 323	70.29 360	80.70 346	43.93 404	72.47 376	74.88 375	60.15 344	70.55 301	76.57 380	49.94 290	81.59 356	50.58 349	74.83 322	85.34 333

test_fmvs2		68.35 332	67.48 332	70.98 358	69.50 403	51.95 371	80.05 318	76.38 369	49.33 392	74.65 258	84.38 292	23.30 405	75.40 393	74.51 154	75.17 319	85.60 329

Syy-MVS		68.05 333	67.85 323	68.67 369	84.68 268	40.97 412	78.62 338	73.08 383	66.65 273	66.74 345	79.46 360	52.11 261	82.30 353	32.89 404	76.38 296	82.75 367

test0.0.03 1		68.00 334	67.69 328	68.90 366	77.55 372	47.43 392	75.70 361	72.95 385	66.66 270	66.56 347	82.29 334	48.06 308	75.87 388	44.97 384	74.51 325	83.41 358

TDRefinement		67.49 335	64.34 345	76.92 302	73.47 393	61.07 270	84.86 238	82.98 307	59.77 347	58.30 388	85.13 278	26.06 397	87.89 307	47.92 370	60.59 386	81.81 375

test20.03		67.45 336	66.95 337	68.94 365	75.48 382	44.84 402	77.50 350	77.67 357	66.66 270	63.01 371	83.80 305	47.02 314	78.40 370	42.53 390	68.86 364	83.58 357

UnsupCasMVSNet_eth		67.33 337	65.99 341	71.37 352	73.48 392	51.47 378	75.16 365	85.19 270	65.20 291	60.78 379	80.93 348	42.35 350	77.20 376	57.12 316	53.69 397	85.44 332

TinyColmap		67.30 338	64.81 343	74.76 325	81.92 329	56.68 326	80.29 316	81.49 324	60.33 341	56.27 395	83.22 316	24.77 401	87.66 311	45.52 381	69.47 359	79.95 384

myMVS_eth3d		67.02 339	66.29 340	69.21 364	84.68 268	42.58 407	78.62 338	73.08 383	66.65 273	66.74 345	79.46 360	31.53 390	82.30 353	39.43 396	76.38 296	82.75 367

dp		66.80 340	65.43 342	70.90 359	79.74 361	48.82 390	75.12 367	74.77 376	59.61 348	64.08 366	77.23 377	42.89 347	80.72 362	48.86 362	66.58 370	83.16 361

MDA-MVSNet-bldmvs		66.68 341	63.66 350	75.75 310	79.28 366	60.56 278	73.92 373	78.35 354	64.43 300	50.13 403	79.87 358	44.02 341	83.67 344	46.10 378	56.86 389	83.03 364

testgi		66.67 342	66.53 339	67.08 376	75.62 381	41.69 411	75.93 357	76.50 368	66.11 279	65.20 361	86.59 242	35.72 381	74.71 395	43.71 385	73.38 337	84.84 342

CHOSEN 280x420		66.51 343	64.71 344	71.90 348	81.45 336	63.52 232	57.98 411	68.95 395	53.57 381	62.59 374	76.70 379	46.22 323	75.29 394	55.25 327	79.68 253	76.88 391

PM-MVS		66.41 344	64.14 346	73.20 339	73.92 388	56.45 328	78.97 333	64.96 405	63.88 312	64.72 362	80.24 353	19.84 409	83.44 347	66.24 232	64.52 377	79.71 385

JIA-IIPM		66.32 345	62.82 356	76.82 303	77.09 375	61.72 264	65.34 404	75.38 372	58.04 363	64.51 363	62.32 403	42.05 355	86.51 318	51.45 346	69.22 361	82.21 371

KD-MVS_2432*1600		66.22 346	63.89 348	73.21 337	75.47 383	53.42 363	70.76 384	84.35 280	64.10 306	66.52 349	78.52 369	34.55 383	84.98 335	50.40 351	50.33 402	81.23 377

miper_refine_blended		66.22 346	63.89 348	73.21 337	75.47 383	53.42 363	70.76 384	84.35 280	64.10 306	66.52 349	78.52 369	34.55 383	84.98 335	50.40 351	50.33 402	81.23 377

ADS-MVSNet2		66.20 348	63.33 351	74.82 324	79.92 355	58.75 294	67.55 396	75.19 373	53.37 382	65.25 359	75.86 384	42.32 351	80.53 363	41.57 391	68.91 362	85.18 336

YYNet1		65.03 349	62.91 354	71.38 351	75.85 379	56.60 327	69.12 392	74.66 379	57.28 369	54.12 397	77.87 374	45.85 327	74.48 396	49.95 356	61.52 383	83.05 363

MDA-MVSNet_test_wron		65.03 349	62.92 353	71.37 352	75.93 377	56.73 323	69.09 393	74.73 377	57.28 369	54.03 398	77.89 373	45.88 326	74.39 397	49.89 357	61.55 382	82.99 365

Patchmatch-test		64.82 351	63.24 352	69.57 362	79.42 365	49.82 388	63.49 408	69.05 394	51.98 387	59.95 383	80.13 354	50.91 278	70.98 402	40.66 393	73.57 333	87.90 280

ADS-MVSNet		64.36 352	62.88 355	68.78 368	79.92 355	47.17 393	67.55 396	71.18 387	53.37 382	65.25 359	75.86 384	42.32 351	73.99 398	41.57 391	68.91 362	85.18 336

LF4IMVS		64.02 353	62.19 357	69.50 363	70.90 401	53.29 366	76.13 355	77.18 364	52.65 384	58.59 386	80.98 345	23.55 404	76.52 381	53.06 339	66.66 369	78.68 387

UnsupCasMVSNet_bld		63.70 354	61.53 360	70.21 361	73.69 390	51.39 379	72.82 375	81.89 319	55.63 376	57.81 390	71.80 395	38.67 370	78.61 369	49.26 360	52.21 400	80.63 381

test_fmvs3		63.36 355	61.82 358	67.98 373	62.51 412	46.96 395	77.37 352	74.03 380	45.24 397	67.50 335	78.79 368	12.16 417	72.98 401	72.77 174	66.02 372	83.99 352

dmvs_testset		62.63 356	64.11 347	58.19 386	78.55 369	24.76 424	75.28 363	65.94 402	67.91 258	60.34 380	76.01 383	53.56 246	73.94 399	31.79 405	67.65 366	75.88 393

mvsany_test1		62.30 357	61.26 361	65.41 378	69.52 402	54.86 351	66.86 398	49.78 418	46.65 395	68.50 329	83.21 317	49.15 301	66.28 410	56.93 319	60.77 384	75.11 394

new-patchmatchnet		61.73 358	61.73 359	61.70 382	72.74 398	24.50 425	69.16 391	78.03 355	61.40 335	56.72 393	75.53 387	38.42 371	76.48 382	45.95 379	57.67 388	84.13 350

PVSNet_0	57.27 20	61.67 359	59.27 362	68.85 367	79.61 362	57.44 315	68.01 394	73.44 382	55.93 375	58.54 387	70.41 398	44.58 337	77.55 375	47.01 372	35.91 410	71.55 398

test_vis1_rt		60.28 360	58.42 363	65.84 377	67.25 406	55.60 343	70.44 386	60.94 410	44.33 399	59.00 385	66.64 400	24.91 400	68.67 407	62.80 259	69.48 358	73.25 396

ttmdpeth		59.91 361	57.10 365	68.34 371	67.13 407	46.65 396	74.64 370	67.41 398	48.30 393	62.52 375	85.04 282	20.40 407	75.93 387	42.55 389	45.90 408	82.44 369

MVS-HIRNet		59.14 362	57.67 364	63.57 380	81.65 331	43.50 405	71.73 378	65.06 404	39.59 405	51.43 400	57.73 408	38.34 372	82.58 352	39.53 394	73.95 329	64.62 404

pmmvs3		57.79 363	54.26 368	68.37 370	64.02 411	56.72 324	75.12 367	65.17 403	40.20 403	52.93 399	69.86 399	20.36 408	75.48 391	45.45 382	55.25 396	72.90 397

DSMNet-mixed		57.77 364	56.90 366	60.38 384	67.70 405	35.61 415	69.18 390	53.97 416	32.30 414	57.49 391	79.88 357	40.39 363	68.57 408	38.78 397	72.37 342	76.97 390

MVStest1		56.63 365	52.76 371	68.25 372	61.67 413	53.25 367	71.67 379	68.90 396	38.59 406	50.59 402	83.05 320	25.08 399	70.66 403	36.76 400	38.56 409	80.83 380

WB-MVS		54.94 366	54.72 367	55.60 392	73.50 391	20.90 426	74.27 372	61.19 409	59.16 353	50.61 401	74.15 389	47.19 313	75.78 389	17.31 417	35.07 411	70.12 399

LCM-MVSNet		54.25 367	49.68 377	67.97 374	53.73 421	45.28 400	66.85 399	80.78 330	35.96 410	39.45 411	62.23 404	8.70 421	78.06 373	48.24 367	51.20 401	80.57 382

mvsany_test3		53.99 368	51.45 373	61.61 383	55.51 417	44.74 403	63.52 407	45.41 422	43.69 400	58.11 389	76.45 381	17.99 410	63.76 413	54.77 330	47.59 404	76.34 392

SSC-MVS		53.88 369	53.59 369	54.75 394	72.87 397	19.59 427	73.84 374	60.53 411	57.58 367	49.18 405	73.45 392	46.34 322	75.47 392	16.20 420	32.28 413	69.20 400

FPMVS		53.68 370	51.64 372	59.81 385	65.08 409	51.03 381	69.48 389	69.58 392	41.46 402	40.67 409	72.32 394	16.46 413	70.00 406	24.24 413	65.42 374	58.40 409

APD_test1		53.31 371	49.93 376	63.42 381	65.68 408	50.13 386	71.59 380	66.90 400	34.43 411	40.58 410	71.56 396	8.65 422	76.27 384	34.64 403	55.36 394	63.86 405

N_pmnet		52.79 372	53.26 370	51.40 396	78.99 368	7.68 430	69.52 388	3.89 429	51.63 388	57.01 392	74.98 388	40.83 360	65.96 411	37.78 398	64.67 376	80.56 383

test_f		52.09 373	50.82 374	55.90 390	53.82 420	42.31 410	59.42 410	58.31 414	36.45 409	56.12 396	70.96 397	12.18 416	57.79 416	53.51 336	56.57 391	67.60 401

EGC-MVSNET		52.07 374	47.05 378	67.14 375	83.51 294	60.71 275	80.50 312	67.75 397	0.07 424	0.43 425	75.85 386	24.26 402	81.54 357	28.82 407	62.25 380	59.16 407

new_pmnet		50.91 375	50.29 375	52.78 395	68.58 404	34.94 417	63.71 406	56.63 415	39.73 404	44.95 406	65.47 401	21.93 406	58.48 415	34.98 402	56.62 390	64.92 403

ANet_high		50.57 376	46.10 380	63.99 379	48.67 424	39.13 413	70.99 383	80.85 329	61.39 336	31.18 413	57.70 409	17.02 412	73.65 400	31.22 406	15.89 421	79.18 386

test_vis3_rt		49.26 377	47.02 379	56.00 389	54.30 418	45.27 401	66.76 400	48.08 419	36.83 408	44.38 407	53.20 412	7.17 424	64.07 412	56.77 321	55.66 392	58.65 408

testf1		45.72 378	41.96 382	57.00 387	56.90 415	45.32 398	66.14 401	59.26 412	26.19 415	30.89 414	60.96 406	4.14 425	70.64 404	26.39 411	46.73 406	55.04 410

APD_test2		45.72 378	41.96 382	57.00 387	56.90 415	45.32 398	66.14 401	59.26 412	26.19 415	30.89 414	60.96 406	4.14 425	70.64 404	26.39 411	46.73 406	55.04 410

dongtai		45.42 380	45.38 381	45.55 398	73.36 394	26.85 422	67.72 395	34.19 424	54.15 380	49.65 404	56.41 411	25.43 398	62.94 414	19.45 415	28.09 415	46.86 414

Gipuma		45.18 381	41.86 384	55.16 393	77.03 376	51.52 377	32.50 417	80.52 334	32.46 413	27.12 416	35.02 417	9.52 420	75.50 390	22.31 414	60.21 387	38.45 416
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMVS	37.38 22	44.16 382	40.28 386	55.82 391	40.82 426	42.54 409	65.12 405	63.99 406	34.43 411	24.48 417	57.12 410	3.92 427	76.17 386	17.10 418	55.52 393	48.75 412
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		40.82 383	38.86 387	46.69 397	53.84 419	16.45 428	48.61 414	49.92 417	37.49 407	31.67 412	60.97 405	8.14 423	56.42 417	28.42 408	30.72 414	67.19 402

kuosan		39.70 384	40.40 385	37.58 401	64.52 410	26.98 420	65.62 403	33.02 425	46.12 396	42.79 408	48.99 414	24.10 403	46.56 422	12.16 423	26.30 416	39.20 415

E-PMN		31.77 385	30.64 388	35.15 402	52.87 422	27.67 419	57.09 412	47.86 420	24.64 417	16.40 422	33.05 418	11.23 418	54.90 418	14.46 421	18.15 419	22.87 418

test_method		31.52 386	29.28 390	38.23 400	27.03 428	6.50 431	20.94 419	62.21 408	4.05 422	22.35 420	52.50 413	13.33 414	47.58 420	27.04 410	34.04 412	60.62 406

EMVS		30.81 387	29.65 389	34.27 403	50.96 423	25.95 423	56.58 413	46.80 421	24.01 418	15.53 423	30.68 419	12.47 415	54.43 419	12.81 422	17.05 420	22.43 419

MVE	26.22 23	30.37 388	25.89 392	43.81 399	44.55 425	35.46 416	28.87 418	39.07 423	18.20 419	18.58 421	40.18 416	2.68 428	47.37 421	17.07 419	23.78 418	48.60 413
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
cdsmvs_eth3d_5k		19.96 389	26.61 391	0.00 409	0.00 432	0.00 434	0.00 420	89.26 184	0.00 427	0.00 428	88.61 184	61.62 169	0.00 428	0.00 427	0.00 426	0.00 424

tmp_tt		18.61 390	21.40 393	10.23 406	4.82 429	10.11 429	34.70 416	30.74 427	1.48 423	23.91 419	26.07 420	28.42 395	13.41 425	27.12 409	15.35 422	7.17 420

wuyk23d		16.82 391	15.94 394	19.46 405	58.74 414	31.45 418	39.22 415	3.74 430	6.84 421	6.04 424	2.70 424	1.27 429	24.29 424	10.54 424	14.40 423	2.63 421

ab-mvs-re		7.23 392	9.64 395	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	86.72 234	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

test123		6.12 393	8.11 396	0.14 407	0.06 431	0.09 432	71.05 382	0.03 432	0.04 426	0.25 427	1.30 426	0.05 430	0.03 427	0.21 426	0.01 425	0.29 422

testmvs		6.04 394	8.02 397	0.10 408	0.08 430	0.03 433	69.74 387	0.04 431	0.05 425	0.31 426	1.68 425	0.02 431	0.04 426	0.24 425	0.02 424	0.25 423

pcd_1.5k_mvsjas		5.26 395	7.02 398	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	63.15 146	0.00 428	0.00 427	0.00 426	0.00 424

mmdepth		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

monomultidepth		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

test_blank		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

uanet_test		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

DCPMVS		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

sosnet-low-res		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

sosnet		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

uncertanet		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

Regformer		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

uanet		0.00 396	0.00 399	0.00 409	0.00 432	0.00 434	0.00 420	0.00 433	0.00 427	0.00 428	0.00 427	0.00 432	0.00 428	0.00 427	0.00 426	0.00 424

WAC-MVS						42.58 407								39.46 395

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 121	91.30 15

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 7	96.44 9	94.41 39

PC_three_1452									68.21 255	92.02 12	94.00 50	82.09 5	95.98 56	84.58 54	96.68 2	94.95 11

No_MVS				89.16 1	94.34 27	75.53 2		92.99 49					97.53 2	89.67 7	96.44 9	94.41 39

test_one_0601					95.07 7	71.46 57		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

eth-test2					0.00 432

eth-test					0.00 432

ZD-MVS					94.38 25	72.22 44		92.67 67	70.98 192	87.75 36	94.07 45	74.01 32	96.70 27	84.66 53	94.84 44

RE-MVS-def			85.48 60		93.06 58	70.63 76	91.88 38	92.27 84	73.53 145	85.69 57	94.45 28	63.87 137		82.75 76	91.87 84	92.50 126

IU-MVS					95.30 2	71.25 59		92.95 55	66.81 266	92.39 6				88.94 17	96.63 4	94.85 20

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 48	82.45 3	96.87 20	83.77 65	96.48 8	94.88 15

test_241102_TWO								94.06 10	77.24 53	92.78 4	95.72 8	81.26 8	97.44 7	89.07 15	96.58 6	94.26 48

test_241102_ONE					95.30 2	70.98 66		94.06 10	77.17 56	93.10 1	95.39 14	82.99 1	97.27 12

9.14			88.26 15		92.84 63		91.52 48	94.75 1	73.93 134	88.57 25	94.67 21	75.57 22	95.79 58	86.77 36	95.76 23

save fliter					93.80 40	72.35 42	90.47 66	91.17 125	74.31 125

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 9	89.42 10	96.57 7	94.67 28

test_0728_SECOND				87.71 32	95.34 1	71.43 58	93.49 9	94.23 3					97.49 4	89.08 13	96.41 12	94.21 49

test0726					95.27 5	71.25 59	93.60 6	94.11 6	77.33 50	92.81 3	95.79 3	80.98 9

GSMVS																88.96 253

test_part2					95.06 8	72.65 32				91.80 13

sam_mvs1												51.32 274				88.96 253

sam_mvs												50.01 288

ambc				75.24 320	73.16 395	50.51 385	63.05 409	87.47 234		64.28 364	77.81 375	17.80 411	89.73 277	57.88 310	60.64 385	85.49 330

MTGPA								92.02 93

test_post1							78.90 335				5.43 423	48.81 307	85.44 332	59.25 294

test_post											5.46 422	50.36 286	84.24 340

patchmatchnet-post											74.00 390	51.12 277	88.60 299

GG-mvs-BLEND				75.38 318	81.59 333	55.80 340	79.32 326	69.63 391		67.19 339	73.67 391	43.24 345	88.90 295	50.41 350	84.50 185	81.45 376

MTMP							92.18 34	32.83 426

gm-plane-assit					81.40 337	53.83 360			62.72 325		80.94 346		92.39 203	63.40 256

test9_res														84.90 47	95.70 26	92.87 114

TEST9					93.26 52	72.96 25	88.75 120	91.89 101	68.44 252	85.00 64	93.10 71	74.36 28	95.41 73

test_8					93.13 54	72.57 35	88.68 125	91.84 105	68.69 247	84.87 68	93.10 71	74.43 26	95.16 83

agg_prior2														82.91 74	95.45 29	92.70 117

agg_prior					92.85 62	71.94 50		91.78 108		84.41 79			94.93 94

TestCases				79.58 258	85.15 259	63.62 227		79.83 343	62.31 328	60.32 381	86.73 232	32.02 387	88.96 293	50.28 353	71.57 350	86.15 318

test_prior4						72.60 34	89.01 111

test_prior2							88.85 117		75.41 98	84.91 66	93.54 60	74.28 29		83.31 68	95.86 20

test_prior				86.33 57	92.61 68	69.59 91		92.97 54					95.48 67			93.91 61

旧先验2							86.56 195		58.10 362	87.04 46			88.98 291	74.07 159

新几何2							86.29 204

新几何1				83.42 155	93.13 54	70.71 74		85.48 268	57.43 368	81.80 117	91.98 95	63.28 141	92.27 209	64.60 248	92.99 70	87.27 295

旧先验1					91.96 74	65.79 184		86.37 256			93.08 75	69.31 83			92.74 73	88.74 264

无先验							87.48 163	88.98 196	60.00 345				94.12 125	67.28 225		88.97 252

原ACMM2							86.86 184

原ACMM1				84.35 113	93.01 60	68.79 110		92.44 77	63.96 311	81.09 127	91.57 109	66.06 119	95.45 68	67.19 227	94.82 46	88.81 259

test222					91.50 80	68.26 129	84.16 257	83.20 302	54.63 379	79.74 140	91.63 106	58.97 202			91.42 91	86.77 308

testdata2													91.01 257	62.37 266

segment_acmp												73.08 38

testdata				79.97 248	90.90 91	64.21 218		84.71 275	59.27 352	85.40 59	92.91 77	62.02 165	89.08 289	68.95 210	91.37 92	86.63 312

testdata1							84.14 258		75.71 92

test12				86.80 52	92.63 67	70.70 75		91.79 107		82.71 108		71.67 54	96.16 47		94.50 51	93.54 85

plane_prior7					90.08 108	68.51 123

plane_prior6					89.84 117	68.70 118						60.42 195

plane_prior5								92.44 77					95.38 75	78.71 111	86.32 163	91.33 161

plane_prior4											91.00 131

plane_prior3						68.60 121			78.44 31	78.92 152

plane_prior2							91.25 52		79.12 23

plane_prior1					89.90 116

plane_prior						68.71 116	90.38 70		77.62 40						86.16 167

n2								0.00 433

nn								0.00 433

door-mid								69.98 390

lessismore_v0				78.97 267	81.01 344	57.15 318		65.99 401		61.16 378	82.82 326	39.12 367	91.34 246	59.67 290	46.92 405	88.43 271

LGP-MVS_train				84.50 106	89.23 142	68.76 112		91.94 99	75.37 99	76.64 205	91.51 110	54.29 239	94.91 95	78.44 113	83.78 197	89.83 225

test11								92.23 87

door								69.44 393

HQP5-MVS						66.98 163

HQP-NCC					89.33 135		89.17 103		76.41 77	77.23 190

ACMP_Plane					89.33 135		89.17 103		76.41 77	77.23 190

BP-MVS														77.47 123

HQP4-MVS										77.24 189			95.11 87			91.03 171

HQP3-MVS								92.19 90							85.99 171

HQP2-MVS												60.17 198

NP-MVS					89.62 121	68.32 127					90.24 143

MDTV_nov1_ep13_2view						37.79 414	75.16 365		55.10 377	66.53 348		49.34 298		53.98 333		87.94 279

MDTV_nov1_ep13			69.97 307		83.18 302	53.48 362	77.10 354	80.18 342	60.45 340	69.33 321	80.44 350	48.89 306	86.90 314	51.60 345	78.51 266

ACMMP++_ref															81.95 228

ACMMP++															81.25 233

Test By Simon												64.33 133

ITE_SJBPF				78.22 282	81.77 330	60.57 277		83.30 297	69.25 232	67.54 334	87.20 223	36.33 379	87.28 313	54.34 332	74.62 324	86.80 307

DeepMVS_CX				27.40 404	40.17 427	26.90 421		24.59 428	17.44 420	23.95 418	48.61 415	9.77 419	26.48 423	18.06 416	24.47 417	28.83 417

High-resolution multi-view benchmark