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 65	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 17

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 74	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 23

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 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 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
DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 134
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 27	86.42 14	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 35	89.67 18	86.84 73
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 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 25
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
MM		80.20 7	80.28 8	79.99 2	82.19 85	60.01 49	86.19 17	83.93 55	73.19 1	77.08 38	91.21 18	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 75
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 24	90.64 22	58.63 25	87.24 55	79.00 14	90.37 14	85.26 146

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 40	85.03 37	66.96 5	77.58 33	90.06 41	59.47 21	89.13 22	78.67 17	89.73 16	87.03 67

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 30	90.98 19	54.26 59	90.06 14	78.42 23	89.02 23	87.69 40
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 63	85.33 29	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 55	88.67 26	88.12 27

SF-MVS		78.82 13	79.22 12	77.60 47	82.88 78	57.83 86	84.99 32	88.13 2	61.86 78	79.16 21	90.75 21	57.96 26	87.09 64	77.08 31	90.18 15	87.87 33

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 19	63.32 46	75.08 54	90.47 29	53.96 64	88.68 27	76.48 34	89.63 20	87.16 64

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 55	85.16 32	62.88 56	78.10 28	91.26 17	52.51 84	88.39 30	79.34 9	90.52 13	86.78 76

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 67	89.38 54	55.30 48	89.18 21	74.19 56	87.34 46	86.38 89

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 81	90.25 36	57.68 29	89.96 15	74.62 53	89.03 22	87.89 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_0304		78.45 18	78.28 19	78.98 26	80.73 110	57.91 85	84.68 36	81.64 111	68.35 2	75.77 44	90.38 30	53.98 62	90.26 13	81.30 3	87.68 42	88.77 11

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 135	79.37 20	89.76 50	59.84 16	87.62 52	76.69 32	86.74 55	87.68 41
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 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 42	91.51 11	52.47 86	86.78 71	80.66 4	89.64 19	87.80 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 39	73.60 83	90.60 23	54.85 54	86.72 72	77.20 29	88.06 36	85.74 122
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 22	85.21 31	63.56 43	74.29 73	90.03 43	52.56 83	88.53 29	74.79 52	88.34 29	86.63 83

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 99	79.05 22	90.30 34	55.54 47	88.32 32	73.48 63	87.03 48	84.83 161
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 78	90.50 27	53.20 76	88.35 31	74.02 58	87.05 47	86.13 104

lecture		77.75 25	77.84 25	77.50 49	82.75 80	57.62 89	85.92 21	86.20 17	60.53 101	78.99 23	91.45 12	51.51 104	87.78 47	75.65 42	87.55 43	87.10 66

ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 58	73.55 84	90.56 25	49.80 124	88.24 33	74.02 58	87.03 48	86.32 97

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 107	79.89 18	89.38 54	54.97 52	85.58 106	76.12 38	84.94 66	86.33 95
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 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 60	73.30 86	90.58 24	49.90 121	88.21 34	73.78 60	87.03 48	86.29 101

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 180	74.91 59	88.19 70	59.15 23	87.68 51	73.67 61	87.45 45	86.57 84

HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 95	89.97 46	50.90 114	87.48 53	75.30 46	86.85 53	87.33 59
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 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 96	88.88 62	53.72 69	89.06 23	68.27 95	88.04 37	87.42 51
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 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 110	90.01 45	47.95 147	88.01 40	71.55 81	86.74 55	86.37 91

CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 67	72.68 103	90.50 27	48.18 145	87.34 54	73.59 62	85.71 62	84.76 165

CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 95	75.27 49	84.83 154	60.76 15	86.56 77	67.86 102	87.87 41	86.06 106

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 115	87.69 49	72.46 69	84.53 70	85.46 132

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 115	87.69 49	72.46 69	84.53 70	85.46 132

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 229	80.97 137	65.13 15	75.77 44	90.88 20	48.63 140	86.66 74	77.23 28	88.17 33	84.81 162

PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 73	83.74 65	61.71 79	72.45 109	90.34 33	48.48 143	88.13 37	72.32 71	86.85 53	85.78 116

balanced_conf03		76.58 39	76.55 38	76.68 62	81.73 91	52.90 179	80.94 94	85.70 24	61.12 90	74.90 60	87.17 96	56.46 39	88.14 36	72.87 66	88.03 38	89.00 8

mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 101	62.90 55	71.77 114	90.26 35	46.61 172	86.55 79	71.71 79	85.66 63	84.97 157

CANet		76.46 41	75.93 45	78.06 39	81.29 100	57.53 91	82.35 75	83.31 81	67.78 3	70.09 130	86.34 122	54.92 53	88.90 25	72.68 68	84.55 69	87.76 39

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 93	59.65 126	77.31 34	91.43 13	49.62 126	87.24 55	71.99 75	83.75 81	85.14 148

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 195	74.05 76	88.98 59	53.34 75	87.92 43	69.23 93	88.42 28	87.59 46

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 126	55.86 203	74.93 57	88.81 63	53.70 70	84.68 129	75.24 48	88.33 30	83.65 205

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 85	64.69 22	74.21 74	87.40 88	49.48 127	86.17 89	68.04 100	87.55 43	87.42 51

CS-MVS		76.25 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 86	87.27 94	55.06 50	86.30 88	71.78 78	84.58 68	89.25 5

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 235	51.83 206	79.67 114	85.08 34	65.02 19	75.84 43	88.58 68	59.42 22	85.08 117	72.75 67	83.93 78	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 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 102	59.99 119	75.10 53	90.35 32	47.66 152	86.52 80	71.64 80	82.99 86	84.47 171

ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 140	89.74 51	45.43 186	87.16 61	72.01 74	82.87 91	85.14 148
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 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 201	73.41 85	86.58 114	50.94 113	88.54 28	70.79 85	89.71 17	87.79 38

EC-MVSNet		75.84 51	75.87 47	75.74 80	78.86 153	52.65 186	83.73 56	86.08 18	63.47 45	72.77 102	87.25 95	53.13 77	87.93 42	71.97 76	85.57 64	86.66 81

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 183	89.24 56	42.03 222	89.38 19	64.07 135	86.50 59	89.69 3

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 176	78.62 129	85.13 33	59.65 126	71.53 118	87.47 86	56.92 35	88.17 35	72.18 73	86.63 58	88.80 10

SPE-MVS-test		75.62 54	75.31 54	76.56 67	80.63 114	55.13 137	83.88 54	85.22 30	62.05 74	71.49 119	86.03 132	53.83 66	86.36 86	67.74 103	86.91 52	88.19 25

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 189	72.46 107	86.76 104	56.89 36	87.86 45	66.36 116	88.91 25	83.64 206

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 239	64.69 22	74.21 74	87.40 88	49.48 127	86.17 89	68.04 100	83.88 79	85.85 113

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 171	55.37 134	77.30 169	73.95 270	61.40 83	79.46 19	90.14 37	57.07 34	81.15 207	80.00 5	79.31 136	88.51 16

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 152	73.71 82	90.14 37	45.62 179	85.99 96	69.64 89	82.85 92	85.78 116

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 181	58.58 149	74.32 72	84.51 166	55.94 44	87.22 58	67.11 110	84.48 73	85.52 128

casdiffmvs		74.80 60	74.89 60	74.53 109	75.59 248	50.37 227	78.17 142	85.06 36	62.80 61	74.40 70	87.86 79	57.88 27	83.61 149	69.46 92	82.79 93	89.59 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
DELS-MVS		74.76 61	74.46 64	75.65 83	77.84 190	52.25 196	75.59 212	84.17 50	63.76 40	73.15 91	82.79 198	59.58 20	86.80 70	67.24 109	86.04 61	87.89 31
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 62	74.25 68	76.19 71	80.81 109	59.01 72	82.60 72	83.64 67	63.74 41	72.52 106	87.49 85	47.18 163	85.88 99	69.47 91	80.78 110	83.66 204
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
sasdasda		74.67 63	74.98 58	73.71 135	78.94 151	50.56 224	80.23 101	83.87 61	60.30 111	77.15 36	86.56 115	59.65 17	82.00 187	66.01 120	82.12 97	88.58 14

canonicalmvs		74.67 63	74.98 58	73.71 135	78.94 151	50.56 224	80.23 101	83.87 61	60.30 111	77.15 36	86.56 115	59.65 17	82.00 187	66.01 120	82.12 97	88.58 14

baseline		74.61 65	74.70 61	74.34 113	75.70 244	49.99 235	77.54 161	84.63 43	62.73 62	73.98 77	87.79 82	57.67 30	83.82 145	69.49 90	82.74 94	89.20 7

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 119	58.07 158	73.14 92	90.07 39	44.74 194	85.84 100	68.20 96	81.76 104	84.03 183

dcpmvs_2		74.55 67	75.23 55	72.48 171	82.34 83	53.34 169	77.87 150	81.46 115	57.80 168	75.49 46	86.81 103	62.22 13	77.75 275	71.09 84	82.02 100	86.34 93

ETV-MVS		74.46 68	73.84 73	76.33 70	79.27 141	55.24 136	79.22 120	85.00 39	64.97 21	72.65 104	79.46 279	53.65 73	87.87 44	67.45 108	82.91 89	85.89 112

HQP_MVS		74.31 69	73.73 74	76.06 72	81.41 97	56.31 108	84.22 46	84.01 53	64.52 27	69.27 149	86.10 129	45.26 190	87.21 59	68.16 98	80.58 115	84.65 166

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 122	75.33 254	52.89 181	78.24 138	77.32 212	61.65 80	78.13 27	88.90 61	52.82 80	81.54 197	78.46 22	78.67 152	87.60 45

HPM-MVS_fast		74.30 70	73.46 77	76.80 59	84.45 60	59.04 71	83.65 58	81.05 134	60.15 116	70.43 126	89.84 48	41.09 241	85.59 105	67.61 106	82.90 90	85.77 119

MVS_111021_HR		74.02 72	73.46 77	75.69 81	83.01 76	60.63 40	77.29 170	78.40 192	61.18 88	70.58 125	85.97 134	54.18 61	84.00 142	67.52 107	82.98 88	82.45 237

MG-MVS		73.96 73	73.89 72	74.16 119	85.65 42	49.69 242	81.59 88	81.29 125	61.45 82	71.05 122	88.11 71	51.77 99	87.73 48	61.05 169	83.09 84	85.05 153

alignmvs		73.86 74	73.99 70	73.45 149	78.20 175	50.50 226	78.57 131	82.43 99	59.40 133	76.57 40	86.71 108	56.42 41	81.23 206	65.84 123	81.79 103	88.62 12

MSLP-MVS++		73.77 75	73.47 76	74.66 101	83.02 75	59.29 63	82.30 80	81.88 106	59.34 135	71.59 117	86.83 102	45.94 177	83.65 148	65.09 128	85.22 65	81.06 266

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 215	74.09 288	51.86 205	77.77 155	75.60 235	61.18 88	78.67 25	88.98 59	55.88 45	77.73 276	78.69 16	78.68 151	83.50 209

HQP-MVS		73.45 77	72.80 84	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 177	85.54 148	45.46 184	86.93 67	67.04 111	80.35 119	84.32 173

BP-MVS1		73.41 78	72.25 91	76.88 57	76.68 228	53.70 159	79.15 121	81.07 133	60.66 98	71.81 113	87.39 90	40.93 242	87.24 55	71.23 83	81.29 109	89.71 2

CLD-MVS		73.33 79	72.68 86	75.29 91	78.82 155	53.33 170	78.23 139	84.79 42	61.30 86	70.41 127	81.04 245	52.41 87	87.12 62	64.61 134	82.49 96	85.41 138
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 80	72.54 88	75.62 84	77.87 188	53.64 161	79.62 116	79.61 157	61.63 81	72.02 112	82.61 203	56.44 40	85.97 97	63.99 138	79.07 144	87.25 61

fmvsm_l_conf0.5_n_3		73.23 81	73.13 80	73.55 145	74.40 277	55.13 137	78.97 123	74.96 254	56.64 183	74.76 65	88.75 66	55.02 51	78.77 260	76.33 36	78.31 160	86.74 77

UA-Net		73.13 82	72.93 82	73.76 130	83.58 67	51.66 207	78.75 125	77.66 203	67.75 4	72.61 105	89.42 52	49.82 123	83.29 156	53.61 232	83.14 83	86.32 97

EPNet		73.09 83	72.16 92	75.90 74	75.95 241	56.28 110	83.05 62	72.39 287	66.53 10	65.27 233	87.00 98	50.40 118	85.47 111	62.48 157	86.32 60	85.94 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmconf_n		73.01 84	72.59 87	74.27 116	71.28 339	55.88 120	78.21 141	75.56 237	54.31 249	74.86 61	87.80 81	54.72 55	80.23 231	78.07 25	78.48 156	86.70 78

nrg030		72.96 85	73.01 81	72.84 162	75.41 252	50.24 228	80.02 105	82.89 95	58.36 154	74.44 69	86.73 106	58.90 24	80.83 217	65.84 123	74.46 208	87.44 50

test_fmvsmconf0.1_n		72.81 86	72.33 90	74.24 117	69.89 361	55.81 121	78.22 140	75.40 242	54.17 251	75.00 56	88.03 77	53.82 67	80.23 231	78.08 24	78.34 159	86.69 79

CPTT-MVS		72.78 87	72.08 94	74.87 97	84.88 57	61.41 26	84.15 49	77.86 199	55.27 221	67.51 189	88.08 73	41.93 225	81.85 190	69.04 94	80.01 124	81.35 258

LPG-MVS_test		72.74 88	71.74 97	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 207	87.33 92	39.15 260	86.59 75	67.70 104	77.30 177	83.19 217

h-mvs33		72.71 89	71.49 101	76.40 68	81.99 88	59.58 57	76.92 180	76.74 221	60.40 104	74.81 62	85.95 135	45.54 182	85.76 102	70.41 87	70.61 270	83.86 193

fmvsm_s_conf0.5_n_5		72.69 90	72.80 84	72.37 176	74.11 287	53.21 172	78.12 143	73.31 277	53.98 254	76.81 39	88.05 74	53.38 74	77.37 283	76.64 33	80.78 110	86.53 86

GDP-MVS		72.64 91	71.28 108	76.70 60	77.72 194	54.22 151	79.57 117	84.45 44	55.30 220	71.38 120	86.97 99	39.94 248	87.00 66	67.02 113	79.20 140	88.89 9

PAPM_NR		72.63 92	71.80 96	75.13 92	81.72 92	53.42 168	79.91 109	83.28 83	59.14 137	66.31 214	85.90 136	51.86 97	86.06 93	57.45 199	80.62 113	85.91 111

fmvsm_s_conf0.5_n_6		72.59 93	72.87 83	71.73 189	75.14 259	51.96 203	76.28 194	77.12 215	57.63 169	73.85 80	86.91 100	51.54 103	77.87 272	77.18 30	80.18 123	85.37 140

VDD-MVS		72.50 94	72.09 93	73.75 132	81.58 93	49.69 242	77.76 156	77.63 204	63.21 50	73.21 89	89.02 58	42.14 221	83.32 155	61.72 164	82.50 95	88.25 22

3Dnovator	64.47 5	72.49 95	71.39 104	75.79 77	77.70 195	58.99 73	80.66 99	83.15 89	62.24 69	65.46 229	86.59 113	42.38 220	85.52 107	59.59 183	84.72 67	82.85 227

MGCFI-Net		72.45 96	73.34 79	69.81 240	77.77 192	43.21 319	75.84 209	81.18 130	59.59 131	75.45 47	86.64 109	57.74 28	77.94 269	63.92 139	81.90 102	88.30 20

MVS_Test		72.45 96	72.46 89	72.42 175	74.88 261	48.50 262	76.28 194	83.14 90	59.40 133	72.46 107	84.68 157	55.66 46	81.12 208	65.98 122	79.66 128	87.63 43

EI-MVSNet-Vis-set		72.42 98	71.59 98	74.91 95	78.47 164	54.02 153	77.05 176	79.33 163	65.03 18	71.68 116	79.35 283	52.75 81	84.89 124	66.46 115	74.23 212	85.83 115

ACMP	63.53 6	72.30 99	71.20 110	75.59 86	80.28 117	57.54 90	82.74 69	82.84 96	60.58 100	65.24 237	86.18 126	39.25 258	86.03 95	66.95 114	76.79 185	83.22 215
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
PS-MVSNAJss		72.24 100	71.21 109	75.31 89	78.50 162	55.93 118	81.63 85	82.12 103	56.24 198	70.02 134	85.68 144	47.05 165	84.34 135	65.27 127	74.41 211	85.67 123

Vis-MVSNet		72.18 101	71.37 105	74.61 104	81.29 100	55.41 132	80.90 95	78.28 194	60.73 96	69.23 152	88.09 72	44.36 200	82.65 175	57.68 197	81.75 106	85.77 119
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_fmvsmconf0.01_n		72.17 102	71.50 100	74.16 119	67.96 379	55.58 129	78.06 146	74.67 257	54.19 250	74.54 68	88.23 69	50.35 120	80.24 230	78.07 25	77.46 173	86.65 82

API-MVS		72.17 102	71.41 103	74.45 111	81.95 89	57.22 95	84.03 51	80.38 147	59.89 124	68.40 163	82.33 214	49.64 125	87.83 46	51.87 246	84.16 77	78.30 308

EPP-MVSNet		72.16 104	71.31 107	74.71 98	78.68 159	49.70 240	82.10 81	81.65 110	60.40 104	65.94 219	85.84 138	51.74 100	86.37 85	55.93 208	79.55 131	88.07 30

DP-MVS Recon		72.15 105	70.73 118	76.40 68	86.57 24	57.99 84	81.15 93	82.96 91	57.03 177	66.78 202	85.56 145	44.50 198	88.11 38	51.77 248	80.23 122	83.10 222

fmvsm_s_conf0.5_n_4		72.04 106	71.85 95	72.58 167	73.74 291	52.49 192	76.69 185	72.42 286	56.42 193	75.32 48	87.04 97	52.13 93	78.01 268	79.29 12	73.65 222	87.26 60

EI-MVSNet-UG-set		71.92 107	71.06 113	74.52 110	77.98 186	53.56 164	76.62 186	79.16 164	64.40 29	71.18 121	78.95 288	52.19 91	84.66 131	65.47 126	73.57 225	85.32 142

VDDNet		71.81 108	71.33 106	73.26 156	82.80 79	47.60 275	78.74 126	75.27 244	59.59 131	72.94 98	89.40 53	41.51 234	83.91 143	58.75 192	82.99 86	88.26 21

EIA-MVS		71.78 109	70.60 120	75.30 90	79.85 128	53.54 165	77.27 171	83.26 84	57.92 164	66.49 209	79.39 281	52.07 94	86.69 73	60.05 177	79.14 143	85.66 124

LFMVS		71.78 109	71.59 98	72.32 177	83.40 71	46.38 284	79.75 112	71.08 296	64.18 34	72.80 101	88.64 67	42.58 217	83.72 146	57.41 200	84.49 72	86.86 72

test_fmvsm_n_1920		71.73 111	71.14 111	73.50 146	72.52 313	56.53 107	75.60 211	76.16 225	48.11 333	77.22 35	85.56 145	53.10 78	77.43 280	74.86 50	77.14 179	86.55 85

PAPR		71.72 112	70.82 116	74.41 112	81.20 104	51.17 210	79.55 118	83.33 80	55.81 206	66.93 201	84.61 161	50.95 112	86.06 93	55.79 211	79.20 140	86.00 107

IS-MVSNet		71.57 113	71.00 114	73.27 155	78.86 153	45.63 295	80.22 103	78.69 176	64.14 37	66.46 210	87.36 91	49.30 131	85.60 104	50.26 259	83.71 82	88.59 13

MAR-MVS		71.51 114	70.15 131	75.60 85	81.84 90	59.39 60	81.38 90	82.90 93	54.90 238	68.08 173	78.70 289	47.73 150	85.51 108	51.68 250	84.17 76	81.88 248
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		71.50 115	70.38 125	74.88 96	78.76 156	57.15 100	82.79 67	78.48 185	51.26 291	69.49 143	83.22 193	43.99 204	83.24 157	66.06 118	79.37 132	84.23 177

RRT-MVS		71.46 116	70.70 119	73.74 133	77.76 193	49.30 249	76.60 187	80.45 145	61.25 87	68.17 168	84.78 156	44.64 196	84.90 123	64.79 130	77.88 166	87.03 67

PVSNet_Blended_VisFu		71.45 117	70.39 124	74.65 102	82.01 86	58.82 76	79.93 108	80.35 148	55.09 226	65.82 225	82.16 222	49.17 134	82.64 176	60.34 175	78.62 154	82.50 236

OMC-MVS		71.40 118	70.60 120	73.78 128	76.60 231	53.15 173	79.74 113	79.78 153	58.37 153	68.75 157	86.45 120	45.43 186	80.60 221	62.58 155	77.73 167	87.58 47

KinetiMVS		71.26 119	70.16 130	74.57 107	74.59 271	52.77 185	75.91 206	81.20 129	60.72 97	69.10 155	85.71 143	41.67 229	83.53 151	63.91 141	78.62 154	87.42 51

UniMVSNet_NR-MVSNet		71.11 120	71.00 114	71.44 200	79.20 143	44.13 308	76.02 204	82.60 98	66.48 11	68.20 166	84.60 163	56.82 37	82.82 171	54.62 222	70.43 272	87.36 58

hse-mvs2		71.04 121	69.86 134	74.60 105	79.58 133	57.12 102	73.96 247	75.25 245	60.40 104	74.81 62	81.95 227	45.54 182	82.90 164	70.41 87	66.83 325	83.77 198

GeoE		71.01 122	70.15 131	73.60 143	79.57 134	52.17 197	78.93 124	78.12 196	58.02 160	67.76 186	83.87 178	52.36 88	82.72 173	56.90 202	75.79 196	85.92 110

fmvsm_l_conf0.5_n		70.99 123	70.82 116	71.48 197	71.45 332	54.40 147	77.18 173	70.46 302	48.67 324	75.17 51	86.86 101	53.77 68	76.86 295	76.33 36	77.51 172	83.17 221

PCF-MVS	61.88 8	70.95 124	69.49 141	75.35 88	77.63 199	55.71 123	76.04 203	81.81 108	50.30 302	69.66 141	85.40 151	52.51 84	84.89 124	51.82 247	80.24 121	85.45 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mamba_0404		70.84 125	69.41 144	75.12 93	79.20 143	53.86 155	77.89 149	80.00 152	53.88 256	69.40 146	84.61 161	43.21 210	86.56 77	58.80 191	77.68 169	84.95 158

test_fmvsmvis_n_1920		70.84 125	70.38 125	72.22 179	71.16 340	55.39 133	75.86 207	72.21 289	49.03 319	73.28 88	86.17 127	51.83 98	77.29 285	75.80 39	78.05 163	83.98 186

114514_t		70.83 127	69.56 139	74.64 103	86.21 31	54.63 144	82.34 76	81.81 108	48.22 331	63.01 273	85.83 139	40.92 243	87.10 63	57.91 196	79.79 125	82.18 242

FIs		70.82 128	71.43 102	68.98 255	78.33 172	38.14 366	76.96 178	83.59 69	61.02 91	67.33 191	86.73 106	55.07 49	81.64 193	54.61 224	79.22 139	87.14 65

ACMM	61.98 7	70.80 129	69.73 136	74.02 121	80.59 116	58.59 79	82.68 70	82.02 105	55.46 216	67.18 196	84.39 168	38.51 266	83.17 159	60.65 173	76.10 192	80.30 280
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
diffmvs		70.69 130	70.43 123	71.46 198	69.45 367	48.95 256	72.93 268	78.46 187	57.27 173	71.69 115	83.97 177	51.48 105	77.92 271	70.70 86	77.95 165	87.53 48
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)		70.63 131	70.20 128	71.89 183	78.55 161	45.29 298	75.94 205	82.92 92	63.68 42	68.16 169	83.59 184	53.89 65	83.49 153	53.97 228	71.12 265	86.89 71

xiu_mvs_v2_base		70.52 132	69.75 135	72.84 162	81.21 103	55.63 126	75.11 222	78.92 169	54.92 237	69.96 137	79.68 274	47.00 169	82.09 186	61.60 166	79.37 132	80.81 271

PS-MVSNAJ		70.51 133	69.70 137	72.93 160	81.52 94	55.79 122	74.92 229	79.00 167	55.04 232	69.88 138	78.66 291	47.05 165	82.19 184	61.61 165	79.58 129	80.83 270

fmvsm_l_conf0.5_n_a		70.50 134	70.27 127	71.18 210	71.30 338	54.09 152	76.89 181	69.87 306	47.90 337	74.37 71	86.49 118	53.07 79	76.69 300	75.41 45	77.11 180	82.76 228

v2v482		70.50 134	69.45 143	73.66 138	72.62 310	50.03 234	77.58 158	80.51 144	59.90 120	69.52 142	82.14 223	47.53 156	84.88 126	65.07 129	70.17 280	86.09 105

v1144		70.42 136	69.31 145	73.76 130	73.22 298	50.64 221	77.83 153	81.43 116	58.58 149	69.40 146	81.16 242	47.53 156	85.29 116	64.01 137	70.64 268	85.34 141

TranMVSNet+NR-MVSNet		70.36 137	70.10 133	71.17 211	78.64 160	42.97 322	76.53 189	81.16 132	66.95 6	68.53 161	85.42 150	51.61 102	83.07 160	52.32 240	69.70 292	87.46 49

v8		70.33 138	69.28 146	73.49 147	73.15 300	50.22 229	78.62 129	80.78 140	60.79 94	66.45 211	82.11 225	49.35 130	84.98 120	63.58 147	68.71 308	85.28 144

Fast-Effi-MVS+		70.28 139	69.12 150	73.73 134	78.50 162	51.50 208	75.01 225	79.46 161	56.16 200	68.59 158	79.55 277	53.97 63	84.05 138	53.34 234	77.53 171	85.65 125

X-MVStestdata		70.21 140	67.28 195	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 110	6.49 451	47.95 147	88.01 40	71.55 81	86.74 55	86.37 91

v10		70.21 140	69.02 151	73.81 127	73.51 294	50.92 216	78.74 126	81.39 117	60.05 118	66.39 212	81.83 230	47.58 154	85.41 114	62.80 154	68.86 307	85.09 152

Elysia		70.19 142	68.29 170	75.88 75	74.15 284	54.33 149	78.26 135	83.21 85	55.04 232	67.28 192	83.59 184	30.16 358	86.11 91	63.67 145	79.26 137	87.20 62

StellarMVS		70.19 142	68.29 170	75.88 75	74.15 284	54.33 149	78.26 135	83.21 85	55.04 232	67.28 192	83.59 184	30.16 358	86.11 91	63.67 145	79.26 137	87.20 62

QAPM		70.05 144	68.81 156	73.78 128	76.54 233	53.43 167	83.23 60	83.48 71	52.89 268	65.90 221	86.29 123	41.55 233	86.49 82	51.01 253	78.40 158	81.42 252

DU-MVS		70.01 145	69.53 140	71.44 200	78.05 183	44.13 308	75.01 225	81.51 114	64.37 30	68.20 166	84.52 164	49.12 137	82.82 171	54.62 222	70.43 272	87.37 56

AdaColmap		69.99 146	68.66 160	73.97 124	84.94 54	57.83 86	82.63 71	78.71 175	56.28 197	64.34 252	84.14 171	41.57 231	87.06 65	46.45 291	78.88 145	77.02 329

v1192		69.97 147	68.68 159	73.85 125	73.19 299	50.94 214	77.68 157	81.36 119	57.51 171	68.95 156	80.85 252	45.28 189	85.33 115	62.97 153	70.37 274	85.27 145

Anonymous20240529		69.91 148	69.02 151	72.56 168	80.19 122	47.65 273	77.56 160	80.99 136	55.45 217	69.88 138	86.76 104	39.24 259	82.18 185	54.04 227	77.10 181	87.85 34

patch_mono-2		69.85 149	71.09 112	66.16 291	79.11 148	54.80 143	71.97 284	74.31 262	53.50 262	70.90 123	84.17 170	57.63 31	63.31 382	66.17 117	82.02 100	80.38 279

fmvsm_s_conf0.5_n_2		69.82 150	69.27 147	71.46 198	72.00 324	51.08 211	73.30 261	67.79 325	55.06 231	75.24 50	87.51 84	44.02 203	77.00 291	75.67 41	72.86 240	86.31 100

FA-MVS(test-final)		69.82 150	68.48 163	73.84 126	78.44 165	50.04 233	75.58 214	78.99 168	58.16 156	67.59 187	82.14 223	42.66 215	85.63 103	56.60 203	76.19 191	85.84 114

FC-MVSNet-test		69.80 152	70.58 122	67.46 271	77.61 204	34.73 399	76.05 202	83.19 88	60.84 93	65.88 223	86.46 119	54.52 58	80.76 220	52.52 239	78.12 162	86.91 70

v144192		69.71 153	68.51 162	73.33 154	73.10 301	50.13 231	77.54 161	80.64 141	56.65 182	68.57 160	80.55 255	46.87 170	84.96 122	62.98 152	69.66 293	84.89 160

test_yl		69.69 154	69.13 148	71.36 204	78.37 169	45.74 291	74.71 233	80.20 149	57.91 165	70.01 135	83.83 179	42.44 218	82.87 167	54.97 218	79.72 126	85.48 130

DCV-MVSNet		69.69 154	69.13 148	71.36 204	78.37 169	45.74 291	74.71 233	80.20 149	57.91 165	70.01 135	83.83 179	42.44 218	82.87 167	54.97 218	79.72 126	85.48 130

VNet		69.68 156	70.19 129	68.16 265	79.73 130	41.63 336	70.53 305	77.38 209	60.37 107	70.69 124	86.63 111	51.08 110	77.09 288	53.61 232	81.69 108	85.75 121

jason		69.65 157	68.39 169	73.43 151	78.27 174	56.88 104	77.12 174	73.71 273	46.53 355	69.34 148	83.22 193	43.37 208	79.18 244	64.77 131	79.20 140	84.23 177
jason: jason.
fmvsm_s_conf0.1_n_2		69.64 158	69.01 153	71.52 196	71.66 329	51.04 212	73.39 260	67.14 331	55.02 235	75.11 52	87.64 83	42.94 214	77.01 290	75.55 43	72.63 246	86.52 87

Effi-MVS+-dtu		69.64 158	67.53 185	75.95 73	76.10 239	62.29 15	80.20 104	76.06 229	59.83 125	65.26 236	77.09 321	41.56 232	84.02 141	60.60 174	71.09 266	81.53 251

fmvsm_s_conf0.5_n		69.58 160	68.84 155	71.79 187	72.31 320	52.90 179	77.90 148	62.43 374	49.97 307	72.85 100	85.90 136	52.21 90	76.49 303	75.75 40	70.26 279	85.97 108

lupinMVS		69.57 161	68.28 172	73.44 150	78.76 156	57.15 100	76.57 188	73.29 279	46.19 358	69.49 143	82.18 219	43.99 204	79.23 243	64.66 132	79.37 132	83.93 188

fmvsm_s_conf0.5_n_7		69.54 162	69.67 138	69.15 254	73.47 296	51.41 209	70.35 309	73.34 276	57.05 176	68.41 162	85.83 139	49.86 122	72.84 324	71.86 77	76.83 184	83.19 217

fmvsm_s_conf0.5_n_a		69.54 162	68.74 158	71.93 182	72.47 315	53.82 157	78.25 137	62.26 376	49.78 309	73.12 94	86.21 125	52.66 82	76.79 297	75.02 49	68.88 305	85.18 147

NR-MVSNet		69.54 162	68.85 154	71.59 195	78.05 183	43.81 313	74.20 243	80.86 139	65.18 14	62.76 277	84.52 164	52.35 89	83.59 150	50.96 255	70.78 267	87.37 56

MVS_111021_LR		69.50 165	68.78 157	71.65 193	78.38 167	59.33 61	74.82 231	70.11 304	58.08 157	67.83 182	84.68 157	41.96 223	76.34 307	65.62 125	77.54 170	79.30 299

v1921920		69.47 166	68.17 173	73.36 153	73.06 302	50.10 232	77.39 164	80.56 142	56.58 190	68.59 158	80.37 257	44.72 195	84.98 120	62.47 158	69.82 288	85.00 154

test_djsdf		69.45 167	67.74 178	74.58 106	74.57 273	54.92 141	82.79 67	78.48 185	51.26 291	65.41 230	83.49 189	38.37 268	83.24 157	66.06 118	69.25 300	85.56 127

fmvsm_s_conf0.1_n		69.41 168	68.60 161	71.83 185	71.07 341	52.88 182	77.85 152	62.44 373	49.58 312	72.97 97	86.22 124	51.68 101	76.48 304	75.53 44	70.10 282	86.14 103

fmvsm_s_conf0.1_n_a		69.32 169	68.44 167	71.96 180	70.91 343	53.78 158	78.12 143	62.30 375	49.35 315	73.20 90	86.55 117	51.99 95	76.79 297	74.83 51	68.68 310	85.32 142

Anonymous20231211		69.28 170	68.47 165	71.73 189	80.28 117	47.18 279	79.98 106	82.37 100	54.61 242	67.24 194	84.01 175	39.43 255	82.41 182	55.45 216	72.83 241	85.62 126

EI-MVSNet		69.27 171	68.44 167	71.73 189	74.47 274	49.39 247	75.20 220	78.45 188	59.60 128	69.16 153	76.51 333	51.29 106	82.50 179	59.86 182	71.45 262	83.30 212

v1240		69.24 172	67.91 177	73.25 157	73.02 304	49.82 236	77.21 172	80.54 143	56.43 192	68.34 165	80.51 256	43.33 209	84.99 118	62.03 162	69.77 291	84.95 158

IterMVS-LS		69.22 173	68.48 163	71.43 202	74.44 276	49.40 246	76.23 196	77.55 205	59.60 128	65.85 224	81.59 237	51.28 107	81.58 196	59.87 181	69.90 287	83.30 212
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
icg_test_0403		69.09 174	68.14 174	71.95 181	77.06 219	49.73 238	74.51 237	78.60 179	52.70 270	66.69 205	82.58 204	46.43 173	83.38 154	59.20 188	75.46 202	82.74 229

VPA-MVSNet		69.02 175	69.47 142	67.69 269	77.42 209	41.00 343	74.04 245	79.68 155	60.06 117	69.26 151	84.81 155	51.06 111	77.58 278	54.44 225	74.43 210	84.48 170

v7n		69.01 176	67.36 192	73.98 123	72.51 314	52.65 186	78.54 133	81.30 124	60.26 113	62.67 279	81.62 234	43.61 206	84.49 132	57.01 201	68.70 309	84.79 163

OpenMVS	61.03 9	68.85 177	67.56 182	72.70 166	74.26 282	53.99 154	81.21 92	81.34 123	52.70 270	62.75 278	85.55 147	38.86 264	84.14 137	48.41 275	83.01 85	79.97 286

XVG-OURS-SEG-HR		68.81 178	67.47 188	72.82 164	74.40 277	56.87 105	70.59 304	79.04 166	54.77 240	66.99 199	86.01 133	39.57 254	78.21 265	62.54 156	73.33 232	83.37 211

BH-RMVSNet		68.81 178	67.42 189	72.97 159	80.11 125	52.53 190	74.26 242	76.29 224	58.48 151	68.38 164	84.20 169	42.59 216	83.83 144	46.53 290	75.91 194	82.56 231

UGNet		68.81 178	67.39 190	73.06 158	78.33 172	54.47 145	79.77 111	75.40 242	60.45 103	63.22 266	84.40 167	32.71 335	80.91 216	51.71 249	80.56 117	83.81 194
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		68.76 181	67.37 191	72.90 161	74.32 280	57.22 95	70.09 313	78.81 172	55.24 222	67.79 184	85.81 142	36.54 291	78.28 264	62.04 161	75.74 197	83.19 217

V42		68.65 182	67.35 193	72.56 168	68.93 373	50.18 230	72.90 269	79.47 160	56.92 179	69.45 145	80.26 261	46.29 175	82.99 161	64.07 135	67.82 316	84.53 168

PVSNet_Blended		68.59 183	67.72 179	71.19 209	77.03 222	50.57 222	72.51 276	81.52 112	51.91 280	64.22 258	77.77 312	49.13 135	82.87 167	55.82 209	79.58 129	80.14 284

xiu_mvs_v1_base_debu		68.58 184	67.28 195	72.48 171	78.19 176	57.19 97	75.28 217	75.09 250	51.61 282	70.04 131	81.41 239	32.79 331	79.02 253	63.81 142	77.31 174	81.22 261

xiu_mvs_v1_base		68.58 184	67.28 195	72.48 171	78.19 176	57.19 97	75.28 217	75.09 250	51.61 282	70.04 131	81.41 239	32.79 331	79.02 253	63.81 142	77.31 174	81.22 261

xiu_mvs_v1_base_debi		68.58 184	67.28 195	72.48 171	78.19 176	57.19 97	75.28 217	75.09 250	51.61 282	70.04 131	81.41 239	32.79 331	79.02 253	63.81 142	77.31 174	81.22 261

PVSNet_BlendedMVS		68.56 187	67.72 179	71.07 214	77.03 222	50.57 222	74.50 238	81.52 112	53.66 261	64.22 258	79.72 273	49.13 135	82.87 167	55.82 209	73.92 216	79.77 294

WR-MVS		68.47 188	68.47 165	68.44 262	80.20 121	39.84 350	73.75 255	76.07 228	64.68 24	68.11 171	83.63 183	50.39 119	79.14 249	49.78 260	69.66 293	86.34 93

mvsmamba		68.47 188	66.56 209	74.21 118	79.60 132	52.95 177	74.94 228	75.48 240	52.09 279	60.10 310	83.27 192	36.54 291	84.70 128	59.32 187	77.69 168	84.99 156

AUN-MVS		68.45 190	66.41 216	74.57 107	79.53 135	57.08 103	73.93 250	75.23 246	54.44 247	66.69 205	81.85 229	37.10 286	82.89 165	62.07 160	66.84 324	83.75 199

c3_l		68.33 191	67.56 182	70.62 224	70.87 344	46.21 287	74.47 239	78.80 173	56.22 199	66.19 215	78.53 296	51.88 96	81.40 200	62.08 159	69.04 303	84.25 176

BH-untuned		68.27 192	67.29 194	71.21 208	79.74 129	53.22 171	76.06 201	77.46 208	57.19 174	66.10 216	81.61 235	45.37 188	83.50 152	45.42 306	76.68 187	76.91 333

jajsoiax		68.25 193	66.45 212	73.66 138	75.62 246	55.49 131	80.82 96	78.51 184	52.33 276	64.33 253	84.11 172	28.28 376	81.81 192	63.48 148	70.62 269	83.67 202

LuminaMVS		68.24 194	66.82 207	72.51 170	73.46 297	53.60 163	76.23 196	78.88 170	52.78 269	68.08 173	80.13 263	32.70 336	81.41 199	63.16 151	75.97 193	82.53 233

v148		68.24 194	67.19 202	71.40 203	70.43 351	47.77 272	75.76 210	77.03 216	58.91 141	67.36 190	80.10 265	48.60 142	81.89 189	60.01 178	66.52 328	84.53 168

CANet_DTU		68.18 196	67.71 181	69.59 243	74.83 264	46.24 286	78.66 128	76.85 218	59.60 128	63.45 264	82.09 226	35.25 300	77.41 281	59.88 180	78.76 149	85.14 148

mvs_tets		68.18 196	66.36 218	73.63 141	75.61 247	55.35 135	80.77 97	78.56 182	52.48 275	64.27 255	84.10 173	27.45 384	81.84 191	63.45 149	70.56 271	83.69 201

guyue		68.10 198	67.23 201	70.71 223	73.67 293	49.27 250	73.65 257	76.04 230	55.62 213	67.84 181	82.26 217	41.24 239	78.91 259	61.01 170	73.72 220	83.94 187

SDMVSNet		68.03 199	68.10 176	67.84 267	77.13 216	48.72 260	65.32 354	79.10 165	58.02 160	65.08 240	82.55 207	47.83 149	73.40 321	63.92 139	73.92 216	81.41 253

miper_ehance_all_eth		68.03 199	67.24 199	70.40 228	70.54 348	46.21 287	73.98 246	78.68 177	55.07 229	66.05 217	77.80 309	52.16 92	81.31 203	61.53 168	69.32 297	83.67 202

mvs_anonymous		68.03 199	67.51 186	69.59 243	72.08 322	44.57 305	71.99 283	75.23 246	51.67 281	67.06 198	82.57 206	54.68 56	77.94 269	56.56 204	75.71 198	86.26 102

ET-MVSNet_ETH3D		67.96 202	65.72 230	74.68 100	76.67 229	55.62 128	75.11 222	74.74 255	52.91 267	60.03 312	80.12 264	33.68 320	82.64 176	61.86 163	76.34 189	85.78 116

thisisatest0530		67.92 203	65.78 229	74.33 114	76.29 236	51.03 213	76.89 181	74.25 264	53.67 260	65.59 227	81.76 232	35.15 301	85.50 109	55.94 207	72.47 247	86.47 88

PAPM		67.92 203	66.69 208	71.63 194	78.09 181	49.02 253	77.09 175	81.24 128	51.04 294	60.91 304	83.98 176	47.71 151	84.99 118	40.81 342	79.32 135	80.90 269

AstraMVS		67.86 205	66.83 206	70.93 217	73.50 295	49.34 248	73.28 264	74.01 268	55.45 217	68.10 172	83.28 191	38.93 263	79.14 249	63.22 150	71.74 257	84.30 175

tttt0517		67.83 206	65.66 231	74.33 114	76.69 227	50.82 218	77.86 151	73.99 269	54.54 245	64.64 250	82.53 210	35.06 302	85.50 109	55.71 212	69.91 286	86.67 80

tt0805		67.77 207	67.24 199	69.34 248	74.87 262	40.08 347	77.36 165	81.37 118	55.31 219	66.33 213	84.65 159	37.35 280	82.55 178	55.65 214	72.28 252	85.39 139

ECVR-MVS		67.72 208	67.51 186	68.35 263	79.46 136	36.29 389	74.79 232	66.93 333	58.72 144	67.19 195	88.05 74	36.10 293	81.38 201	52.07 243	84.25 74	87.39 54

eth_miper_zixun_eth		67.63 209	66.28 222	71.67 192	71.60 330	48.33 264	73.68 256	77.88 198	55.80 207	65.91 220	78.62 294	47.35 162	82.88 166	59.45 184	66.25 329	83.81 194

UniMVSNet_ETH3D		67.60 210	67.07 204	69.18 252	77.39 210	42.29 327	74.18 244	75.59 236	60.37 107	66.77 203	86.06 131	37.64 276	78.93 258	52.16 242	73.49 227	86.32 97

VPNet		67.52 211	68.11 175	65.74 301	79.18 145	36.80 381	72.17 281	72.83 283	62.04 75	67.79 184	85.83 139	48.88 139	76.60 302	51.30 251	72.97 239	83.81 194

cl22		67.47 212	66.45 212	70.54 226	69.85 362	46.49 283	73.85 253	77.35 210	55.07 229	65.51 228	77.92 305	47.64 153	81.10 209	61.58 167	69.32 297	84.01 185

Fast-Effi-MVS+-dtu		67.37 213	65.33 237	73.48 148	72.94 305	57.78 88	77.47 163	76.88 217	57.60 170	61.97 291	76.85 325	39.31 256	80.49 225	54.72 221	70.28 278	82.17 244

MVS		67.37 213	66.33 219	70.51 227	75.46 250	50.94 214	73.95 248	81.85 107	41.57 395	62.54 283	78.57 295	47.98 146	85.47 111	52.97 237	82.05 99	75.14 349

test1111		67.21 215	67.14 203	67.42 272	79.24 142	34.76 398	73.89 252	65.65 342	58.71 146	66.96 200	87.95 78	36.09 294	80.53 222	52.03 244	83.79 80	86.97 69

GBi-Net		67.21 215	66.55 210	69.19 249	77.63 199	43.33 316	77.31 166	77.83 200	56.62 186	65.04 242	82.70 199	41.85 226	80.33 227	47.18 285	72.76 242	83.92 189

test1		67.21 215	66.55 210	69.19 249	77.63 199	43.33 316	77.31 166	77.83 200	56.62 186	65.04 242	82.70 199	41.85 226	80.33 227	47.18 285	72.76 242	83.92 189

cl____		67.18 218	66.26 223	69.94 235	70.20 354	45.74 291	73.30 261	76.83 219	55.10 224	65.27 233	79.57 276	47.39 160	80.53 222	59.41 186	69.22 301	83.53 208

DIV-MVS_self_test		67.18 218	66.26 223	69.94 235	70.20 354	45.74 291	73.29 263	76.83 219	55.10 224	65.27 233	79.58 275	47.38 161	80.53 222	59.43 185	69.22 301	83.54 207

MVSTER		67.16 220	65.58 233	71.88 184	70.37 353	49.70 240	70.25 311	78.45 188	51.52 285	69.16 153	80.37 257	38.45 267	82.50 179	60.19 176	71.46 261	83.44 210

miper_enhance_ethall		67.11 221	66.09 225	70.17 232	69.21 370	45.98 289	72.85 270	78.41 191	51.38 288	65.65 226	75.98 343	51.17 109	81.25 204	60.82 172	69.32 297	83.29 214

Baseline_NR-MVSNet		67.05 222	67.56 182	65.50 305	75.65 245	37.70 372	75.42 215	74.65 258	59.90 120	68.14 170	83.15 196	49.12 137	77.20 286	52.23 241	69.78 289	81.60 250

WR-MVS_H		67.02 223	66.92 205	67.33 275	77.95 187	37.75 370	77.57 159	82.11 104	62.03 76	62.65 280	82.48 211	50.57 117	79.46 239	42.91 328	64.01 346	84.79 163

anonymousdsp		67.00 224	64.82 242	73.57 144	70.09 357	56.13 113	76.35 192	77.35 210	48.43 329	64.99 245	80.84 253	33.01 328	80.34 226	64.66 132	67.64 318	84.23 177

FMVSNet2		66.93 225	66.31 221	68.79 258	77.63 199	42.98 321	76.11 199	77.47 206	56.62 186	65.22 239	82.17 221	41.85 226	80.18 233	47.05 288	72.72 245	83.20 216

BH-w/o		66.85 226	65.83 228	69.90 238	79.29 138	52.46 193	74.66 235	76.65 222	54.51 246	64.85 247	78.12 299	45.59 181	82.95 163	43.26 324	75.54 200	74.27 363

Anonymous202405211		66.84 227	65.99 226	69.40 247	80.19 122	42.21 329	71.11 298	71.31 295	58.80 143	67.90 175	86.39 121	29.83 363	79.65 236	49.60 266	78.78 148	86.33 95

CDS-MVSNet		66.80 228	65.37 235	71.10 213	78.98 150	53.13 175	73.27 265	71.07 297	52.15 278	64.72 248	80.23 262	43.56 207	77.10 287	45.48 304	78.88 145	83.05 223
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		66.78 229	65.27 238	71.33 207	79.16 147	53.67 160	73.84 254	69.59 310	52.32 277	65.28 232	81.72 233	44.49 199	77.40 282	42.32 332	78.66 153	82.92 224

FMVSNet1		66.70 230	65.87 227	69.19 249	77.49 207	43.33 316	77.31 166	77.83 200	56.45 191	64.60 251	82.70 199	38.08 274	80.33 227	46.08 294	72.31 251	83.92 189

ab-mvs		66.65 231	66.42 215	67.37 273	76.17 238	41.73 333	70.41 308	76.14 227	53.99 253	65.98 218	83.51 188	49.48 127	76.24 308	48.60 273	73.46 229	84.14 181

PEN-MVS		66.60 232	66.45 212	67.04 276	77.11 218	36.56 383	77.03 177	80.42 146	62.95 53	62.51 285	84.03 174	46.69 171	79.07 251	44.22 310	63.08 356	85.51 129

TAPA-MVS	59.36 10	66.60 232	65.20 239	70.81 219	76.63 230	48.75 258	76.52 190	80.04 151	50.64 299	65.24 237	84.93 153	39.15 260	78.54 261	36.77 369	76.88 183	85.14 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
TR-MVS		66.59 234	65.07 240	71.17 211	79.18 145	49.63 244	73.48 258	75.20 248	52.95 266	67.90 175	80.33 260	39.81 252	83.68 147	43.20 325	73.56 226	80.20 282

CP-MVSNet		66.49 235	66.41 216	66.72 278	77.67 197	36.33 386	76.83 184	79.52 159	62.45 66	62.54 283	83.47 190	46.32 174	78.37 262	45.47 305	63.43 353	85.45 134

PS-CasMVS		66.42 236	66.32 220	66.70 280	77.60 205	36.30 388	76.94 179	79.61 157	62.36 68	62.43 288	83.66 182	45.69 178	78.37 262	45.35 307	63.26 354	85.42 137

VortexMVS		66.41 237	65.50 234	69.16 253	73.75 289	48.14 266	73.41 259	78.28 194	53.73 258	64.98 246	78.33 297	40.62 244	79.07 251	58.88 190	67.50 319	80.26 281

FMVSNet3		66.32 238	65.61 232	68.46 261	76.48 234	42.34 326	74.98 227	77.15 214	55.83 205	65.04 242	81.16 242	39.91 249	80.14 234	47.18 285	72.76 242	82.90 226

ACMH+	57.40 11	66.12 239	64.06 247	72.30 178	77.79 191	52.83 183	80.39 100	78.03 197	57.30 172	57.47 343	82.55 207	27.68 382	84.17 136	45.54 301	69.78 289	79.90 288

cascas		65.98 240	63.42 259	73.64 140	77.26 214	52.58 189	72.26 280	77.21 213	48.56 325	61.21 301	74.60 358	32.57 342	85.82 101	50.38 258	76.75 186	82.52 235

FE-MVS		65.91 241	63.33 261	73.63 141	77.36 211	51.95 204	72.62 273	75.81 231	53.70 259	65.31 231	78.96 287	28.81 372	86.39 84	43.93 315	73.48 228	82.55 232

thisisatest0515		65.83 242	63.50 257	72.82 164	73.75 289	49.50 245	71.32 292	73.12 282	49.39 314	63.82 260	76.50 335	34.95 304	84.84 127	53.20 236	75.49 201	84.13 182

DP-MVS		65.68 243	63.66 255	71.75 188	84.93 55	56.87 105	80.74 98	73.16 280	53.06 265	59.09 326	82.35 213	36.79 290	85.94 98	32.82 393	69.96 285	72.45 377

HyFIR lowres test		65.67 244	63.01 266	73.67 137	79.97 127	55.65 125	69.07 323	75.52 238	42.68 389	63.53 263	77.95 303	40.43 246	81.64 193	46.01 295	71.91 255	83.73 200

DTE-MVSNet		65.58 245	65.34 236	66.31 287	76.06 240	34.79 396	76.43 191	79.38 162	62.55 64	61.66 296	83.83 179	45.60 180	79.15 248	41.64 340	60.88 371	85.00 154

GA-MVS		65.53 246	63.70 254	71.02 216	70.87 344	48.10 267	70.48 306	74.40 260	56.69 181	64.70 249	76.77 326	33.66 321	81.10 209	55.42 217	70.32 277	83.87 192

CNLPA		65.43 247	64.02 248	69.68 241	78.73 158	58.07 83	77.82 154	70.71 300	51.49 286	61.57 298	83.58 187	38.23 272	70.82 338	43.90 316	70.10 282	80.16 283

MVP-Stereo		65.41 248	63.80 252	70.22 229	77.62 203	55.53 130	76.30 193	78.53 183	50.59 300	56.47 353	78.65 292	39.84 251	82.68 174	44.10 314	72.12 254	72.44 378
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IB-MVS	56.42 12	65.40 249	62.73 270	73.40 152	74.89 260	52.78 184	73.09 267	75.13 249	55.69 209	58.48 335	73.73 366	32.86 330	86.32 87	50.63 256	70.11 281	81.10 265
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		65.33 250	64.61 243	67.50 270	79.46 136	34.19 404	74.43 241	51.92 414	58.72 144	66.75 204	88.05 74	25.99 396	80.92 215	51.94 245	84.25 74	87.39 54

pm-mvs1		65.24 251	64.97 241	66.04 295	72.38 317	39.40 356	72.62 273	75.63 234	55.53 214	62.35 290	83.18 195	47.45 158	76.47 305	49.06 270	66.54 327	82.24 241

ACMH	55.70 15	65.20 252	63.57 256	70.07 233	78.07 182	52.01 202	79.48 119	79.69 154	55.75 208	56.59 350	80.98 247	27.12 387	80.94 213	42.90 329	71.58 260	77.25 327
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	56.13 14	65.09 253	63.21 264	70.72 222	81.04 106	54.87 142	78.57 131	77.47 206	48.51 327	55.71 358	81.89 228	33.71 319	79.71 235	41.66 338	70.37 274	77.58 320
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 1792x2688		65.08 254	62.84 268	71.82 186	81.49 96	56.26 111	66.32 342	74.20 266	40.53 401	63.16 269	78.65 292	41.30 235	77.80 274	45.80 297	74.09 213	81.40 255

TransMVSNet (Re)		64.72 255	64.33 245	65.87 300	75.22 255	38.56 362	74.66 235	75.08 253	58.90 142	61.79 294	82.63 202	51.18 108	78.07 267	43.63 321	55.87 394	80.99 268

EG-PatchMatch MVS		64.71 256	62.87 267	70.22 229	77.68 196	53.48 166	77.99 147	78.82 171	53.37 263	56.03 357	77.41 317	24.75 404	84.04 139	46.37 292	73.42 231	73.14 369

LS3D		64.71 256	62.50 272	71.34 206	79.72 131	55.71 123	79.82 110	74.72 256	48.50 328	56.62 349	84.62 160	33.59 322	82.34 183	29.65 414	75.23 204	75.97 339

ICG_test_0404		64.63 258	64.22 246	65.88 299	77.06 219	49.73 238	64.40 362	78.60 179	52.70 270	53.16 387	82.58 204	34.82 305	65.16 376	59.20 188	75.46 202	82.74 229

1314		64.61 259	63.21 264	68.80 257	71.87 327	47.46 276	73.95 248	78.39 193	42.88 388	59.97 313	76.60 332	38.11 273	79.39 241	54.84 220	72.32 250	79.55 295

HY-MVS	56.14 13	64.55 260	63.89 249	66.55 283	74.73 267	41.02 340	69.96 314	74.43 259	49.29 316	61.66 296	80.92 249	47.43 159	76.68 301	44.91 309	71.69 258	81.94 246

testing91		64.46 261	63.80 252	66.47 284	78.43 166	40.06 348	67.63 333	69.59 310	59.06 138	63.18 268	78.05 301	34.05 313	76.99 292	48.30 276	75.87 195	82.37 239

sd_testset		64.46 261	64.45 244	64.51 315	77.13 216	42.25 328	62.67 374	72.11 290	58.02 160	65.08 240	82.55 207	41.22 240	69.88 346	47.32 283	73.92 216	81.41 253

XVG-ACMP-BASELINE		64.36 263	62.23 276	70.74 221	72.35 318	52.45 194	70.80 302	78.45 188	53.84 257	59.87 315	81.10 244	16.24 423	79.32 242	55.64 215	71.76 256	80.47 275

MonoMVSNet		64.15 264	63.31 262	66.69 281	70.51 349	44.12 310	74.47 239	74.21 265	57.81 167	63.03 271	76.62 329	38.33 269	77.31 284	54.22 226	60.59 376	78.64 306

testing99		64.05 265	63.29 263	66.34 286	78.17 179	39.76 352	67.33 338	68.00 324	58.60 148	63.03 271	78.10 300	32.57 342	76.94 294	48.22 277	75.58 199	82.34 240

CostFormer		64.04 266	62.51 271	68.61 260	71.88 326	45.77 290	71.30 293	70.60 301	47.55 342	64.31 254	76.61 331	41.63 230	79.62 238	49.74 262	69.00 304	80.42 277

1112_ss		64.00 267	63.36 260	65.93 297	79.28 140	42.58 325	71.35 291	72.36 288	46.41 356	60.55 307	77.89 307	46.27 176	73.28 322	46.18 293	69.97 284	81.92 247

baseline1		63.81 268	63.87 251	63.62 322	76.29 236	36.36 384	71.78 288	67.29 329	56.05 202	64.23 257	82.95 197	47.11 164	74.41 317	47.30 284	61.85 365	80.10 285

pmmvs6		63.69 269	62.82 269	66.27 289	70.63 346	39.27 357	73.13 266	75.47 241	52.69 273	59.75 319	82.30 215	39.71 253	77.03 289	47.40 282	64.35 345	82.53 233

Vis-MVSNet (Re-imp)		63.69 269	63.88 250	63.14 327	74.75 266	31.04 421	71.16 296	63.64 362	56.32 195	59.80 317	84.99 152	44.51 197	75.46 312	39.12 354	80.62 113	82.92 224

baseline2		63.42 271	61.26 290	69.89 239	72.55 312	47.62 274	71.54 289	68.38 321	50.11 304	54.82 369	75.55 348	43.06 212	80.96 212	48.13 278	67.16 323	81.11 264

thres400		63.31 272	62.18 277	66.72 278	76.85 225	39.62 353	71.96 285	69.44 313	56.63 184	62.61 281	79.83 268	37.18 282	79.17 245	31.84 399	73.25 234	81.36 256

thres600view7		63.30 273	62.27 275	66.41 285	77.18 215	38.87 359	72.35 278	69.11 317	56.98 178	62.37 289	80.96 248	37.01 288	79.00 256	31.43 406	73.05 238	81.36 256

thres100view900		63.28 274	62.41 273	65.89 298	77.31 213	38.66 361	72.65 271	69.11 317	57.07 175	62.45 286	81.03 246	37.01 288	79.17 245	31.84 399	73.25 234	79.83 291

test_0402		63.25 275	61.01 295	69.96 234	80.00 126	54.37 148	76.86 183	72.02 291	54.58 244	58.71 329	80.79 254	35.00 303	84.36 134	26.41 426	64.71 340	71.15 396

tfpn200view9		63.18 276	62.18 277	66.21 290	76.85 225	39.62 353	71.96 285	69.44 313	56.63 184	62.61 281	79.83 268	37.18 282	79.17 245	31.84 399	73.25 234	79.83 291

LTVRE_ROB	55.42 16	63.15 277	61.23 291	68.92 256	76.57 232	47.80 270	59.92 390	76.39 223	54.35 248	58.67 331	82.46 212	29.44 367	81.49 198	42.12 333	71.14 264	77.46 321
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
SD_0403		63.07 278	63.49 258	61.82 335	75.16 258	31.14 420	71.89 287	73.47 274	53.34 264	58.22 337	81.81 231	45.17 192	73.86 320	37.43 363	74.87 206	80.45 276

F-COLMAP		63.05 279	60.87 298	69.58 245	76.99 224	53.63 162	78.12 143	76.16 225	47.97 336	52.41 390	81.61 235	27.87 379	78.11 266	40.07 345	66.66 326	77.00 330

testing11		62.81 280	61.90 280	65.54 303	78.38 167	40.76 345	67.59 335	66.78 335	55.48 215	60.13 309	77.11 320	31.67 349	76.79 297	45.53 302	74.45 209	79.06 301

IterMVS		62.79 281	61.27 289	67.35 274	69.37 368	52.04 201	71.17 295	68.24 323	52.63 274	59.82 316	76.91 324	37.32 281	72.36 326	52.80 238	63.19 355	77.66 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
reproduce_monomvs		62.56 282	61.20 292	66.62 282	70.62 347	44.30 307	70.13 312	73.13 281	54.78 239	61.13 302	76.37 336	25.63 399	75.63 311	58.75 192	60.29 377	79.93 287

IterMVS-SCA-FT		62.49 283	61.52 284	65.40 307	71.99 325	50.80 219	71.15 297	69.63 309	45.71 364	60.61 306	77.93 304	37.45 278	65.99 372	55.67 213	63.50 352	79.42 297

tfpnnormal		62.47 284	61.63 283	64.99 312	74.81 265	39.01 358	71.22 294	73.72 272	55.22 223	60.21 308	80.09 266	41.26 238	76.98 293	30.02 412	68.09 314	78.97 304

MS-PatchMatch		62.42 285	61.46 285	65.31 309	75.21 256	52.10 198	72.05 282	74.05 267	46.41 356	57.42 345	74.36 359	34.35 311	77.57 279	45.62 300	73.67 221	66.26 415

Test_1112_low_res		62.32 286	61.77 281	64.00 320	79.08 149	39.53 355	68.17 329	70.17 303	43.25 384	59.03 327	79.90 267	44.08 201	71.24 336	43.79 318	68.42 311	81.25 260

D2MVS		62.30 287	60.29 301	68.34 264	66.46 391	48.42 263	65.70 346	73.42 275	47.71 340	58.16 338	75.02 354	30.51 353	77.71 277	53.96 229	71.68 259	78.90 305

testing222		62.29 288	61.31 288	65.25 310	77.87 188	38.53 363	68.34 327	66.31 339	56.37 194	63.15 270	77.58 315	28.47 374	76.18 310	37.04 367	76.65 188	81.05 267

thres200		62.20 289	61.16 293	65.34 308	75.38 253	39.99 349	69.60 318	69.29 315	55.64 212	61.87 293	76.99 322	37.07 287	78.96 257	31.28 407	73.28 233	77.06 328

tpm2		62.07 290	60.10 302	67.99 266	72.79 307	43.86 312	71.05 300	66.85 334	43.14 386	62.77 276	75.39 352	38.32 270	80.80 218	41.69 337	68.88 305	79.32 298

testing3-2		62.06 291	62.36 274	61.17 343	79.29 138	30.31 423	64.09 367	63.49 363	63.50 44	62.84 274	82.22 218	32.35 346	69.02 350	40.01 348	73.43 230	84.17 180

miper_lstm_enhance		62.03 292	60.88 297	65.49 306	66.71 388	46.25 285	56.29 409	75.70 233	50.68 297	61.27 300	75.48 350	40.21 247	68.03 356	56.31 206	65.25 336	82.18 242

EPNet_dtu		61.90 293	61.97 279	61.68 336	72.89 306	39.78 351	75.85 208	65.62 343	55.09 226	54.56 373	79.36 282	37.59 277	67.02 365	39.80 350	76.95 182	78.25 309
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LCM-MVSNet-Re		61.88 294	61.35 287	63.46 323	74.58 272	31.48 419	61.42 381	58.14 392	58.71 146	53.02 388	79.55 277	43.07 211	76.80 296	45.69 298	77.96 164	82.11 245

MSDG		61.81 295	59.23 307	69.55 246	72.64 309	52.63 188	70.45 307	75.81 231	51.38 288	53.70 380	76.11 338	29.52 365	81.08 211	37.70 361	65.79 333	74.93 354

SixPastTwentyTwo		61.65 296	58.80 313	70.20 231	75.80 242	47.22 278	75.59 212	69.68 308	54.61 242	54.11 377	79.26 284	27.07 388	82.96 162	43.27 323	49.79 415	80.41 278

CL-MVSNet_self_test		61.53 297	60.94 296	63.30 325	68.95 372	36.93 380	67.60 334	72.80 284	55.67 210	59.95 314	76.63 328	45.01 193	72.22 330	39.74 351	62.09 364	80.74 273

RPMNet		61.53 297	58.42 316	70.86 218	69.96 359	52.07 199	65.31 355	81.36 119	43.20 385	59.36 322	70.15 393	35.37 299	85.47 111	36.42 376	64.65 341	75.06 350

pmmvs4		61.48 299	59.39 306	67.76 268	71.57 331	53.86 155	71.42 290	65.34 345	44.20 375	59.46 321	77.92 305	35.90 295	74.71 315	43.87 317	64.87 339	74.71 359

OurMVSNet-221017-0		61.37 300	58.63 315	69.61 242	72.05 323	48.06 268	73.93 250	72.51 285	47.23 348	54.74 370	80.92 249	21.49 414	81.24 205	48.57 274	56.22 393	79.53 296

COLMAP_ROB	52.97 17	61.27 301	58.81 311	68.64 259	74.63 270	52.51 191	78.42 134	73.30 278	49.92 308	50.96 395	81.51 238	23.06 407	79.40 240	31.63 403	65.85 331	74.01 366
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XXY-MVS		60.68 302	61.67 282	57.70 369	70.43 351	38.45 364	64.19 364	66.47 336	48.05 335	63.22 266	80.86 251	49.28 132	60.47 391	45.25 308	67.28 322	74.19 364

myMVS_eth3d28		60.66 303	61.04 294	59.51 350	77.32 212	31.58 418	63.11 371	63.87 359	59.00 139	60.90 305	78.26 298	32.69 337	66.15 371	36.10 378	78.13 161	80.81 271

SSC-MVS3.2		60.57 304	61.39 286	58.12 365	74.29 281	32.63 413	59.52 391	65.53 344	59.90 120	62.45 286	79.75 272	41.96 223	63.90 381	39.47 352	69.65 295	77.84 317

WBMVS		60.54 305	60.61 299	60.34 347	78.00 185	35.95 391	64.55 361	64.89 348	49.63 310	63.39 265	78.70 289	33.85 318	67.65 359	42.10 334	70.35 276	77.43 322

SCA		60.49 306	58.38 317	66.80 277	74.14 286	48.06 268	63.35 370	63.23 366	49.13 318	59.33 325	72.10 376	37.45 278	74.27 318	44.17 311	62.57 359	78.05 312

K. test v3		60.47 307	57.11 326	70.56 225	73.74 291	48.22 265	75.10 224	62.55 371	58.27 155	53.62 383	76.31 337	27.81 380	81.59 195	47.42 281	39.18 430	81.88 248

mmtdpeth		60.40 308	59.12 309	64.27 318	69.59 364	48.99 254	70.67 303	70.06 305	54.96 236	62.78 275	73.26 370	27.00 389	67.66 358	58.44 195	45.29 422	76.16 338

UWE-MVS		60.18 309	59.78 303	61.39 341	77.67 197	33.92 407	69.04 324	63.82 360	48.56 325	64.27 255	77.64 314	27.20 386	70.40 343	33.56 390	76.24 190	79.83 291

OpenMVS_ROB	52.78 18	60.03 310	58.14 320	65.69 302	70.47 350	44.82 300	75.33 216	70.86 299	45.04 367	56.06 356	76.00 340	26.89 391	79.65 236	35.36 382	67.29 321	72.60 374

CR-MVSNet		59.91 311	57.90 323	65.96 296	69.96 359	52.07 199	65.31 355	63.15 367	42.48 390	59.36 322	74.84 355	35.83 296	70.75 339	45.50 303	64.65 341	75.06 350

PatchmatchNet		59.84 312	58.24 318	64.65 314	73.05 303	46.70 282	69.42 320	62.18 377	47.55 342	58.88 328	71.96 378	34.49 309	69.16 348	42.99 327	63.60 350	78.07 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
sc_t1		59.76 313	57.84 324	65.54 303	74.87 262	42.95 323	69.61 317	64.16 357	48.90 321	58.68 330	77.12 319	28.19 377	72.35 327	43.75 320	55.28 396	81.31 259

WTY-MVS		59.75 314	60.39 300	57.85 367	72.32 319	37.83 369	61.05 386	64.18 355	45.95 363	61.91 292	79.11 286	47.01 168	60.88 390	42.50 331	69.49 296	74.83 355

WB-MVSnew		59.66 315	59.69 304	59.56 349	75.19 257	35.78 393	69.34 321	64.28 354	46.88 352	61.76 295	75.79 344	40.61 245	65.20 375	32.16 395	71.21 263	77.70 318

CVMVSNet		59.63 316	59.14 308	61.08 345	74.47 274	38.84 360	75.20 220	68.74 319	31.15 421	58.24 336	76.51 333	32.39 344	68.58 352	49.77 261	65.84 332	75.81 341

UBG		59.62 317	59.53 305	59.89 348	78.12 180	35.92 392	64.11 366	60.81 384	49.45 313	61.34 299	75.55 348	33.05 326	67.39 363	38.68 356	74.62 207	76.35 337

ETVMVS		59.51 318	58.81 311	61.58 338	77.46 208	34.87 395	64.94 359	59.35 387	54.06 252	61.08 303	76.67 327	29.54 364	71.87 332	32.16 395	74.07 214	78.01 316

tpm cat1		59.25 319	56.95 329	66.15 292	72.19 321	46.96 280	68.09 330	65.76 341	40.03 405	57.81 341	70.56 388	38.32 270	74.51 316	38.26 359	61.50 368	77.00 330

test_vis1_n_1920		58.86 320	59.06 310	58.25 361	63.76 403	43.14 320	67.49 336	66.36 338	40.22 403	65.89 222	71.95 379	31.04 350	59.75 396	59.94 179	64.90 338	71.85 386

pmmvs-eth3d		58.81 321	56.31 338	66.30 288	67.61 381	52.42 195	72.30 279	64.76 350	43.55 381	54.94 368	74.19 361	28.95 369	72.60 325	43.31 322	57.21 388	73.88 367

tt0320		58.59 322	56.81 332	63.92 321	75.46 250	41.32 338	68.63 326	64.06 358	47.05 350	56.19 355	74.19 361	30.34 355	71.36 334	39.92 349	55.45 395	79.09 300

tpmvs		58.47 323	56.95 329	63.03 329	70.20 354	41.21 339	67.90 332	67.23 330	49.62 311	54.73 371	70.84 386	34.14 312	76.24 308	36.64 373	61.29 369	71.64 388

PVSNet	50.76 19	58.40 324	57.39 325	61.42 339	75.53 249	44.04 311	61.43 380	63.45 364	47.04 351	56.91 347	73.61 367	27.00 389	64.76 377	39.12 354	72.40 248	75.47 346

tt0320-xc		58.33 325	56.41 337	64.08 319	75.79 243	41.34 337	68.30 328	62.72 370	47.90 337	56.29 354	74.16 363	28.53 373	71.04 337	41.50 341	52.50 407	79.88 289

tpmrst		58.24 326	58.70 314	56.84 371	66.97 385	34.32 402	69.57 319	61.14 382	47.17 349	58.58 334	71.60 381	41.28 237	60.41 392	49.20 268	62.84 357	75.78 342

Patchmatch-RL test		58.16 327	55.49 344	66.15 292	67.92 380	48.89 257	60.66 388	51.07 418	47.86 339	59.36 322	62.71 423	34.02 315	72.27 329	56.41 205	59.40 380	77.30 324

test-LLR		58.15 328	58.13 321	58.22 362	68.57 374	44.80 301	65.46 351	57.92 393	50.08 305	55.44 361	69.82 395	32.62 339	57.44 408	49.66 264	73.62 223	72.41 379

ppachtmachnet_test		58.06 329	55.38 345	66.10 294	69.51 365	48.99 254	68.01 331	66.13 340	44.50 372	54.05 378	70.74 387	32.09 347	72.34 328	36.68 372	56.71 392	76.99 332

gg-mvs-nofinetune		57.86 330	56.43 336	62.18 333	72.62 310	35.35 394	66.57 339	56.33 402	50.65 298	57.64 342	57.10 429	30.65 352	76.36 306	37.38 364	78.88 145	74.82 356

CMPMVS	42.80 21	57.81 331	55.97 340	63.32 324	60.98 419	47.38 277	64.66 360	69.50 312	32.06 419	46.83 412	77.80 309	29.50 366	71.36 334	48.68 272	73.75 219	71.21 395
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		57.35 332	57.07 327	58.22 362	74.21 283	37.18 375	62.46 375	60.88 383	48.88 322	55.29 364	75.99 342	31.68 348	62.04 387	31.87 398	72.35 249	75.43 347

tpm		57.34 333	58.16 319	54.86 381	71.80 328	34.77 397	67.47 337	56.04 405	48.20 332	60.10 310	76.92 323	37.17 284	53.41 425	40.76 343	65.01 337	76.40 336

Patchmtry		57.16 334	56.47 335	59.23 353	69.17 371	34.58 400	62.98 372	63.15 367	44.53 371	56.83 348	74.84 355	35.83 296	68.71 351	40.03 346	60.91 370	74.39 362

AllTest		57.08 335	54.65 349	64.39 316	71.44 333	49.03 251	69.92 315	67.30 327	45.97 361	47.16 410	79.77 270	17.47 417	67.56 361	33.65 387	59.16 381	76.57 334

test_cas_vis1_n_1920		56.91 336	56.71 333	57.51 370	59.13 425	45.40 297	63.58 368	61.29 381	36.24 413	67.14 197	71.85 380	29.89 362	56.69 412	57.65 198	63.58 351	70.46 400

mamv4		56.85 337	58.00 322	53.43 391	72.46 316	54.47 145	57.56 404	54.74 406	38.81 409	57.42 345	79.45 280	47.57 155	38.70 444	60.88 171	53.07 404	67.11 414

dmvs_re		56.77 338	56.83 331	56.61 372	69.23 369	41.02 340	58.37 396	64.18 355	50.59 300	57.45 344	71.42 382	35.54 298	58.94 401	37.23 365	67.45 320	69.87 405

testing3		56.54 339	55.92 341	58.41 360	77.52 206	27.93 431	69.72 316	56.36 401	54.75 241	58.63 333	77.80 309	20.88 415	71.75 333	25.31 428	62.25 362	75.53 345

our_test_3		56.49 340	54.42 352	62.68 331	69.51 365	45.48 296	66.08 343	61.49 380	44.11 378	50.73 399	69.60 398	33.05 326	68.15 353	38.38 358	56.86 389	74.40 361

pmmvs5		56.47 341	55.68 343	58.86 357	61.41 415	36.71 382	66.37 341	62.75 369	40.38 402	53.70 380	76.62 329	34.56 307	67.05 364	40.02 347	65.27 335	72.83 372

test-mter		56.42 342	55.82 342	58.22 362	68.57 374	44.80 301	65.46 351	57.92 393	39.94 406	55.44 361	69.82 395	21.92 410	57.44 408	49.66 264	73.62 223	72.41 379

USDC		56.35 343	54.24 356	62.69 330	64.74 399	40.31 346	65.05 357	73.83 271	43.93 379	47.58 408	77.71 313	15.36 426	75.05 314	38.19 360	61.81 366	72.70 373

PatchMatch-RL		56.25 344	54.55 351	61.32 342	77.06 219	56.07 115	65.57 348	54.10 411	44.13 377	53.49 386	71.27 385	25.20 401	66.78 366	36.52 375	63.66 349	61.12 419

sss		56.17 345	56.57 334	54.96 380	66.93 386	36.32 387	57.94 399	61.69 379	41.67 393	58.64 332	75.32 353	38.72 265	56.25 415	42.04 335	66.19 330	72.31 382

Syy-MVS		56.00 346	56.23 339	55.32 378	74.69 268	26.44 437	65.52 349	57.49 396	50.97 295	56.52 351	72.18 374	39.89 250	68.09 354	24.20 429	64.59 343	71.44 392

FMVSNet5		55.86 347	54.93 347	58.66 359	71.05 342	36.35 385	64.18 365	62.48 372	46.76 354	50.66 400	74.73 357	25.80 397	64.04 379	33.11 391	65.57 334	75.59 344

RPSCF		55.80 348	54.22 357	60.53 346	65.13 398	42.91 324	64.30 363	57.62 395	36.84 412	58.05 340	82.28 216	28.01 378	56.24 416	37.14 366	58.61 383	82.44 238

mvs5depth		55.64 349	53.81 360	61.11 344	59.39 424	40.98 344	65.89 344	68.28 322	50.21 303	58.11 339	75.42 351	17.03 419	67.63 360	43.79 318	46.21 419	74.73 358

EU-MVSNet		55.61 350	54.41 353	59.19 355	65.41 397	33.42 409	72.44 277	71.91 292	28.81 423	51.27 393	73.87 365	24.76 403	69.08 349	43.04 326	58.20 384	75.06 350

Anonymous20240521		55.30 351	54.41 353	57.96 366	60.92 421	41.73 333	71.09 299	71.06 298	41.18 396	48.65 406	73.31 368	16.93 420	59.25 398	42.54 330	64.01 346	72.90 371

TESTMET0.1,1		55.28 352	54.90 348	56.42 373	66.56 389	43.67 314	65.46 351	56.27 403	39.18 408	53.83 379	67.44 407	24.21 405	55.46 419	48.04 279	73.11 237	70.13 403

KD-MVS_self_test		55.22 353	53.89 359	59.21 354	57.80 428	27.47 433	57.75 402	74.32 261	47.38 344	50.90 396	70.00 394	28.45 375	70.30 344	40.44 344	57.92 385	79.87 290

MIMVSNet1		55.17 354	54.31 355	57.77 368	70.03 358	32.01 416	65.68 347	64.81 349	49.19 317	46.75 413	76.00 340	25.53 400	64.04 379	28.65 417	62.13 363	77.26 326

Anonymous20231206		55.10 355	55.30 346	54.48 383	69.81 363	33.94 406	62.91 373	62.13 378	41.08 397	55.18 365	75.65 346	32.75 334	56.59 414	30.32 411	67.86 315	72.91 370

myMVS_eth3d		54.86 356	54.61 350	55.61 377	74.69 268	27.31 434	65.52 349	57.49 396	50.97 295	56.52 351	72.18 374	21.87 413	68.09 354	27.70 420	64.59 343	71.44 392

TinyColmap		54.14 357	51.72 369	61.40 340	66.84 387	41.97 330	66.52 340	68.51 320	44.81 368	42.69 424	75.77 345	11.66 433	72.94 323	31.96 397	56.77 391	69.27 409

EPMVS		53.96 358	53.69 361	54.79 382	66.12 394	31.96 417	62.34 377	49.05 422	44.42 374	55.54 359	71.33 384	30.22 357	56.70 411	41.65 339	62.54 360	75.71 343

PMMVS		53.96 358	53.26 364	56.04 374	62.60 410	50.92 216	61.17 384	56.09 404	32.81 418	53.51 385	66.84 412	34.04 314	59.93 395	44.14 313	68.18 313	57.27 427

test20.03		53.87 360	54.02 358	53.41 392	61.47 414	28.11 430	61.30 382	59.21 388	51.34 290	52.09 391	77.43 316	33.29 325	58.55 403	29.76 413	60.27 378	73.58 368

MDA-MVSNet-bldmvs		53.87 360	50.81 373	63.05 328	66.25 392	48.58 261	56.93 407	63.82 360	48.09 334	41.22 425	70.48 391	30.34 355	68.00 357	34.24 385	45.92 421	72.57 375

KD-MVS_2432*1600		53.45 362	51.50 371	59.30 351	62.82 407	37.14 376	55.33 410	71.79 293	47.34 346	55.09 366	70.52 389	21.91 411	70.45 341	35.72 380	42.97 425	70.31 401

miper_refine_blended		53.45 362	51.50 371	59.30 351	62.82 407	37.14 376	55.33 410	71.79 293	47.34 346	55.09 366	70.52 389	21.91 411	70.45 341	35.72 380	42.97 425	70.31 401

TDRefinement		53.44 364	50.72 374	61.60 337	64.31 402	46.96 280	70.89 301	65.27 347	41.78 391	44.61 419	77.98 302	11.52 435	66.36 369	28.57 418	51.59 409	71.49 391

test0.0.03 1		53.32 365	53.59 362	52.50 398	62.81 409	29.45 425	59.51 392	54.11 410	50.08 305	54.40 375	74.31 360	32.62 339	55.92 417	30.50 410	63.95 348	72.15 384

PatchT		53.17 366	53.44 363	52.33 399	68.29 378	25.34 441	58.21 397	54.41 409	44.46 373	54.56 373	69.05 401	33.32 324	60.94 389	36.93 368	61.76 367	70.73 399

UnsupCasMVSNet_eth		53.16 367	52.47 365	55.23 379	59.45 423	33.39 410	59.43 393	69.13 316	45.98 360	50.35 402	72.32 373	29.30 368	58.26 405	42.02 336	44.30 423	74.05 365

PM-MVS		52.33 368	50.19 377	58.75 358	62.10 412	45.14 299	65.75 345	40.38 440	43.60 380	53.52 384	72.65 371	9.16 441	65.87 373	50.41 257	54.18 401	65.24 417

UWE-MVS-28		52.25 369	52.35 367	51.93 402	66.99 384	22.79 445	63.48 369	48.31 426	46.78 353	52.73 389	76.11 338	27.78 381	57.82 407	20.58 435	68.41 312	75.17 348

testgi		51.90 370	52.37 366	50.51 405	60.39 422	23.55 444	58.42 395	58.15 391	49.03 319	51.83 392	79.21 285	22.39 408	55.59 418	29.24 416	62.64 358	72.40 381

dp		51.89 371	51.60 370	52.77 396	68.44 377	32.45 415	62.36 376	54.57 408	44.16 376	49.31 405	67.91 403	28.87 371	56.61 413	33.89 386	54.89 398	69.24 410

JIA-IIPM		51.56 372	47.68 386	63.21 326	64.61 400	50.73 220	47.71 429	58.77 390	42.90 387	48.46 407	51.72 433	24.97 402	70.24 345	36.06 379	53.89 402	68.64 411

test_fmvs1_n		51.37 373	50.35 376	54.42 385	52.85 432	37.71 371	61.16 385	51.93 413	28.15 425	63.81 261	69.73 397	13.72 427	53.95 423	51.16 252	60.65 374	71.59 389

ADS-MVSNet2		51.33 374	48.76 381	59.07 356	66.02 395	44.60 304	50.90 423	59.76 386	36.90 410	50.74 397	66.18 415	26.38 392	63.11 383	27.17 422	54.76 399	69.50 407

test_fmvs1		51.32 375	50.48 375	53.81 387	53.57 430	37.51 373	60.63 389	51.16 416	28.02 427	63.62 262	69.23 400	16.41 422	53.93 424	51.01 253	60.70 373	69.99 404

YYNet1		50.73 376	48.96 378	56.03 375	61.10 417	41.78 332	51.94 420	56.44 400	40.94 399	44.84 417	67.80 405	30.08 360	55.08 421	36.77 369	50.71 411	71.22 394

MDA-MVSNet_test_wron		50.71 377	48.95 379	56.00 376	61.17 416	41.84 331	51.90 421	56.45 399	40.96 398	44.79 418	67.84 404	30.04 361	55.07 422	36.71 371	50.69 412	71.11 397

dmvs_testset		50.16 378	51.90 368	44.94 413	66.49 390	11.78 453	61.01 387	51.50 415	51.17 293	50.30 403	67.44 407	39.28 257	60.29 393	22.38 432	57.49 387	62.76 418

UnsupCasMVSNet_bld		50.07 379	48.87 380	53.66 388	60.97 420	33.67 408	57.62 403	64.56 352	39.47 407	47.38 409	64.02 421	27.47 383	59.32 397	34.69 384	43.68 424	67.98 413

test_vis1_n		49.89 380	48.69 382	53.50 390	53.97 429	37.38 374	61.53 379	47.33 430	28.54 424	59.62 320	67.10 411	13.52 428	52.27 428	49.07 269	57.52 386	70.84 398

Patchmatch-test		49.08 381	48.28 383	51.50 403	64.40 401	30.85 422	45.68 433	48.46 425	35.60 414	46.10 416	72.10 376	34.47 310	46.37 436	27.08 424	60.65 374	77.27 325

test_fmvs2		48.69 382	47.49 387	52.29 400	48.63 439	33.06 412	57.76 401	48.05 428	25.71 431	59.76 318	69.60 398	11.57 434	52.23 429	49.45 267	56.86 389	71.58 390

ADS-MVSNet		48.48 383	47.77 384	50.63 404	66.02 395	29.92 424	50.90 423	50.87 420	36.90 410	50.74 397	66.18 415	26.38 392	52.47 427	27.17 422	54.76 399	69.50 407

CHOSEN 280x420		47.83 384	46.36 388	52.24 401	67.37 383	49.78 237	38.91 441	43.11 438	35.00 415	43.27 423	63.30 422	28.95 369	49.19 432	36.53 374	60.80 372	57.76 426

new-patchmatchnet		47.56 385	47.73 385	47.06 408	58.81 426	9.37 456	48.78 427	59.21 388	43.28 383	44.22 420	68.66 402	25.67 398	57.20 410	31.57 405	49.35 416	74.62 360

PVSNet_0	43.31 20	47.46 386	45.64 389	52.92 395	67.60 382	44.65 303	54.06 415	54.64 407	41.59 394	46.15 415	58.75 426	30.99 351	58.66 402	32.18 394	24.81 441	55.46 429

ttmdpeth		45.56 387	42.95 392	53.39 393	52.33 435	29.15 426	57.77 400	48.20 427	31.81 420	49.86 404	77.21 318	8.69 442	59.16 399	27.31 421	33.40 437	71.84 387

MVS-HIRNet		45.52 388	44.48 390	48.65 407	68.49 376	34.05 405	59.41 394	44.50 435	27.03 428	37.96 435	50.47 437	26.16 395	64.10 378	26.74 425	59.52 379	47.82 436

pmmvs3		44.92 389	41.95 396	53.86 386	52.58 434	43.55 315	62.11 378	46.90 432	26.05 430	40.63 426	60.19 425	11.08 438	57.91 406	31.83 402	46.15 420	60.11 420

test_fmvs3		44.30 390	42.55 393	49.55 406	42.83 444	27.15 436	53.03 417	44.93 434	22.03 439	53.69 382	64.94 418	4.21 449	49.63 431	47.47 280	49.82 414	71.88 385

WB-MVS		43.26 391	43.41 391	42.83 417	63.32 406	10.32 455	58.17 398	45.20 433	45.42 365	40.44 428	67.26 410	34.01 316	58.98 400	11.96 446	24.88 440	59.20 421

LF4IMVS		42.95 392	42.26 394	45.04 411	48.30 440	32.50 414	54.80 412	48.49 424	28.03 426	40.51 427	70.16 392	9.24 440	43.89 439	31.63 403	49.18 417	58.72 423

MVStest1		42.65 393	39.29 400	52.71 397	47.26 442	34.58 400	54.41 414	50.84 421	23.35 433	39.31 433	74.08 364	12.57 430	55.09 420	23.32 430	28.47 439	68.47 412

EGC-MVSNET		42.47 394	38.48 402	54.46 384	74.33 279	48.73 259	70.33 310	51.10 417	0.03 454	0.18 455	67.78 406	13.28 429	66.49 368	18.91 437	50.36 413	48.15 434

FPMVS		42.18 395	41.11 397	45.39 410	58.03 427	41.01 342	49.50 425	53.81 412	30.07 422	33.71 437	64.03 419	11.69 432	52.08 430	14.01 441	55.11 397	43.09 438

SSC-MVS		41.96 396	41.99 395	41.90 418	62.46 411	9.28 457	57.41 405	44.32 436	43.38 382	38.30 434	66.45 413	32.67 338	58.42 404	10.98 447	21.91 443	57.99 425

ANet_high		41.38 397	37.47 404	53.11 394	39.73 450	24.45 442	56.94 406	69.69 307	47.65 341	26.04 442	52.32 432	12.44 431	62.38 386	21.80 433	10.61 451	72.49 376

test_vis1_rt		41.35 398	39.45 399	47.03 409	46.65 443	37.86 368	47.76 428	38.65 441	23.10 435	44.21 421	51.22 435	11.20 437	44.08 438	39.27 353	53.02 405	59.14 422

LCM-MVSNet		40.30 399	35.88 405	53.57 389	42.24 445	29.15 426	45.21 435	60.53 385	22.23 438	28.02 440	50.98 436	3.72 451	61.78 388	31.22 408	38.76 431	69.78 406

mvsany_test1		39.38 400	38.16 403	43.02 416	49.05 437	34.28 403	44.16 437	25.94 451	22.74 437	46.57 414	62.21 424	23.85 406	41.16 443	33.01 392	35.91 433	53.63 430

N_pmnet		39.35 401	40.28 398	36.54 424	63.76 403	1.62 461	49.37 426	0.76 460	34.62 416	43.61 422	66.38 414	26.25 394	42.57 440	26.02 427	51.77 408	65.44 416

DSMNet-mixed		39.30 402	38.72 401	41.03 419	51.22 436	19.66 448	45.53 434	31.35 447	15.83 446	39.80 430	67.42 409	22.19 409	45.13 437	22.43 431	52.69 406	58.31 424

APD_test1		37.39 403	34.94 406	44.72 414	48.88 438	33.19 411	52.95 418	44.00 437	19.49 440	27.28 441	58.59 427	3.18 453	52.84 426	18.92 436	41.17 428	48.14 435

PMVS	28.69 22	36.22 404	33.29 409	45.02 412	36.82 452	35.98 390	54.68 413	48.74 423	26.31 429	21.02 445	51.61 434	2.88 454	60.10 394	9.99 450	47.58 418	38.99 443
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		34.77 405	31.91 410	43.33 415	62.05 413	37.87 367	20.39 446	67.03 332	23.23 434	18.41 447	25.84 447	4.24 448	62.73 384	14.71 440	51.32 410	29.38 445
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
dongtai		34.52 406	34.94 406	33.26 427	61.06 418	16.00 452	52.79 419	23.78 453	40.71 400	39.33 432	48.65 441	16.91 421	48.34 433	12.18 445	19.05 445	35.44 444

new_pmnet		34.13 407	34.29 408	33.64 426	52.63 433	18.23 450	44.43 436	33.90 446	22.81 436	30.89 439	53.18 431	10.48 439	35.72 448	20.77 434	39.51 429	46.98 437

mvsany_test3		32.62 408	30.57 413	38.77 422	36.16 453	24.20 443	38.10 442	20.63 455	19.14 441	40.36 429	57.43 428	5.06 446	36.63 447	29.59 415	28.66 438	55.49 428

test_vis3_rt		32.09 409	30.20 414	37.76 423	35.36 454	27.48 432	40.60 440	28.29 450	16.69 444	32.52 438	40.53 443	1.96 455	37.40 446	33.64 389	42.21 427	48.39 433

test_f		31.86 410	31.05 411	34.28 425	32.33 456	21.86 446	32.34 443	30.46 448	16.02 445	39.78 431	55.45 430	4.80 447	32.36 450	30.61 409	37.66 432	48.64 432

testf1		31.46 411	28.89 415	39.16 420	41.99 447	28.78 428	46.45 431	37.56 442	14.28 447	21.10 443	48.96 438	1.48 457	47.11 434	13.63 442	34.56 434	41.60 439

APD_test2		31.46 411	28.89 415	39.16 420	41.99 447	28.78 428	46.45 431	37.56 442	14.28 447	21.10 443	48.96 438	1.48 457	47.11 434	13.63 442	34.56 434	41.60 439

kuosan		29.62 413	30.82 412	26.02 432	52.99 431	16.22 451	51.09 422	22.71 454	33.91 417	33.99 436	40.85 442	15.89 424	33.11 449	7.59 453	18.37 446	28.72 446

PMMVS2		27.40 414	25.91 417	31.87 429	39.46 451	6.57 458	31.17 444	28.52 449	23.96 432	20.45 446	48.94 440	4.20 450	37.94 445	16.51 438	19.97 444	51.09 431

E-PMN		23.77 415	22.73 419	26.90 430	42.02 446	20.67 447	42.66 438	35.70 444	17.43 442	10.28 452	25.05 448	6.42 444	42.39 441	10.28 449	14.71 448	17.63 447

EMVS		22.97 416	21.84 420	26.36 431	40.20 449	19.53 449	41.95 439	34.64 445	17.09 443	9.73 453	22.83 449	7.29 443	42.22 442	9.18 451	13.66 449	17.32 448

MVE	17.77 23	21.41 417	17.77 422	32.34 428	34.34 455	25.44 440	16.11 447	24.11 452	11.19 449	13.22 449	31.92 445	1.58 456	30.95 451	10.47 448	17.03 447	40.62 442
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		19.68 418	18.10 421	24.41 433	13.68 458	3.11 460	12.06 449	42.37 439	2.00 452	11.97 450	36.38 444	5.77 445	29.35 452	15.06 439	23.65 442	40.76 441

cdsmvs_eth3d_5k		17.50 419	23.34 418	0.00 439	0.00 462	0.00 463	0.00 450	78.63 178	0.00 457	0.00 458	82.18 219	49.25 133	0.00 456	0.00 457	0.00 454	0.00 454

wuyk23d		13.32 420	12.52 423	15.71 434	47.54 441	26.27 438	31.06 445	1.98 459	4.93 451	5.18 454	1.94 454	0.45 459	18.54 453	6.81 454	12.83 450	2.33 451

tmp_tt		9.43 421	11.14 424	4.30 436	2.38 459	4.40 459	13.62 448	16.08 457	0.39 453	15.89 448	13.06 450	15.80 425	5.54 455	12.63 444	10.46 452	2.95 450

ab-mvs-re		6.49 422	8.65 425	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	77.89 307	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

test123		4.73 423	6.30 426	0.02 437	0.01 460	0.01 462	56.36 408	0.00 461	0.01 455	0.04 456	0.21 456	0.01 460	0.00 456	0.03 456	0.00 454	0.04 452

testmvs		4.52 424	6.03 427	0.01 438	0.01 460	0.00 463	53.86 416	0.00 461	0.01 455	0.04 456	0.27 455	0.00 461	0.00 456	0.04 455	0.00 454	0.03 453

pcd_1.5k_mvsjas		3.92 425	5.23 428	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	47.05 165	0.00 456	0.00 457	0.00 454	0.00 454

mmdepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

monomultidepth		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

test_blank		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

uanet_test		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

DCPMVS		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

sosnet-low-res		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

sosnet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

uncertanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

Regformer		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

uanet		0.00 426	0.00 429	0.00 439	0.00 462	0.00 463	0.00 450	0.00 461	0.00 457	0.00 458	0.00 457	0.00 461	0.00 456	0.00 457	0.00 454	0.00 454

WAC-MVS						27.31 434								27.77 419

FOURS1					86.12 36	60.82 37	88.18 1	83.61 68	60.87 92	81.50 16

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 34

PC_three_1452									55.09 226	84.46 4	89.84 48	66.68 5	89.41 18	74.24 54	91.38 2	88.42 17

No_MVS				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 34

test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

eth-test2					0.00 462

eth-test					0.00 462

ZD-MVS					86.64 21	60.38 45		82.70 97	57.95 163	78.10 28	90.06 41	56.12 43	88.84 26	74.05 57	87.00 51

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 119	58.07 158	73.14 92	90.07 39	43.06 212		68.20 96	81.76 104	84.03 183

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 255	84.64 3				79.07 13	90.87 5	88.37 19

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 47	67.01 1	90.33 12	73.16 64	91.15 4	88.23 23

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 42

test_241102_ONE					87.77 4	58.90 74		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 120	78.57 26	90.36 31	57.51 32	86.86 69	77.39 27	89.52 21

save fliter					86.17 33	61.30 28	83.98 53	79.66 156	59.00 139

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 28

test_0728_SECOND				79.19 16	87.82 3	59.11 68	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 44

test0726					87.75 7	59.07 69	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

GSMVS																78.05 312

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 306				78.05 312

sam_mvs												33.43 323

ambc				65.13 311	63.72 405	37.07 378	47.66 430	78.78 174		54.37 376	71.42 382	11.24 436	80.94 213	45.64 299	53.85 403	77.38 323

MTGPA								80.97 137

test_post1							68.67 325				3.64 452	32.39 344	69.49 347	44.17 311

test_post											3.55 453	33.90 317	66.52 367

patchmatchnet-post											64.03 419	34.50 308	74.27 318

GG-mvs-BLEND				62.34 332	71.36 337	37.04 379	69.20 322	57.33 398		54.73 371	65.48 417	30.37 354	77.82 273	34.82 383	74.93 205	72.17 383

MTMP							86.03 19	17.08 456

gm-plane-assit					71.40 336	41.72 335			48.85 323		73.31 368		82.48 181	48.90 271

test9_res														75.28 47	88.31 32	83.81 194

TEST9					85.58 43	61.59 24	81.62 86	81.26 126	55.65 211	74.93 57	88.81 63	53.70 70	84.68 129

test_8					85.40 46	60.96 34	81.54 89	81.18 130	55.86 203	74.81 62	88.80 65	53.70 70	84.45 133

agg_prior2														73.09 65	87.93 40	84.33 172

agg_prior					85.04 50	59.96 50		81.04 135		74.68 66			84.04 139

TestCases				64.39 316	71.44 333	49.03 251		67.30 327	45.97 361	47.16 410	79.77 270	17.47 417	67.56 361	33.65 387	59.16 381	76.57 334

test_prior4						62.51 14	82.08 82

test_prior2							81.75 84		60.37 107	75.01 55	89.06 57	56.22 42		72.19 72	88.96 24

test_prior				76.69 61	84.20 61	57.27 94		84.88 40					86.43 83			86.38 89

旧先验2							76.08 200		45.32 366	76.55 41			65.56 374	58.75 192

新几何2							76.12 198

新几何1				70.76 220	85.66 41	61.13 30		66.43 337	44.68 370	70.29 128	86.64 109	41.29 236	75.23 313	49.72 263	81.75 106	75.93 340

旧先验1					83.04 74	53.15 173		67.52 326			87.85 80	44.08 201			80.76 112	78.03 315

无先验							79.66 115	74.30 263	48.40 330				80.78 219	53.62 231		79.03 303

原ACMM2							79.02 122

原ACMM1				74.69 99	85.39 47	59.40 59		83.42 74	51.47 287	70.27 129	86.61 112	48.61 141	86.51 81	53.85 230	87.96 39	78.16 310

test222					83.14 72	58.68 78	72.57 275	63.45 364	41.78 391	67.56 188	86.12 128	37.13 285			78.73 150	74.98 353

testdata2													72.18 331	46.95 289

segment_acmp												54.23 60

testdata				64.66 313	81.52 94	52.93 178		65.29 346	46.09 359	73.88 79	87.46 87	38.08 274	66.26 370	53.31 235	78.48 156	74.78 357

testdata1							72.65 271		60.50 102

test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 99		54.61 57	88.05 39		88.12 34	86.81 74

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 190

plane_prior5								84.01 53					87.21 59	68.16 98	80.58 115	84.65 166

plane_prior4											86.10 129

plane_prior3						56.09 114			63.92 38	69.27 149

plane_prior2							84.22 46		64.52 27

plane_prior1					81.27 102

plane_prior						56.31 108	83.58 59		63.19 51						80.48 118

n2								0.00 461

nn								0.00 461

door-mid								47.19 431

lessismore_v0				69.91 237	71.42 335	47.80 270		50.90 419		50.39 401	75.56 347	27.43 385	81.33 202	45.91 296	34.10 436	80.59 274

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 207	87.33 92	39.15 260	86.59 75	67.70 104	77.30 177	83.19 217

test11								83.47 72

door								47.60 429

HQP5-MVS						54.94 139

HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 177

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 177

BP-MVS														67.04 111

HQP4-MVS										67.85 177			86.93 67			84.32 173

HQP3-MVS								83.90 58							80.35 119

HQP2-MVS												45.46 184

NP-MVS					80.98 107	56.05 116					85.54 148

MDTV_nov1_ep13_2view						25.89 439	61.22 383		40.10 404	51.10 394		32.97 329		38.49 357		78.61 307

MDTV_nov1_ep13			57.00 328		72.73 308	38.26 365	65.02 358	64.73 351	44.74 369	55.46 360	72.48 372	32.61 341	70.47 340	37.47 362	67.75 317

ACMMP++_ref															74.07 214

ACMMP++															72.16 253

Test By Simon												48.33 144

ITE_SJBPF				62.09 334	66.16 393	44.55 306		64.32 353	47.36 345	55.31 363	80.34 259	19.27 416	62.68 385	36.29 377	62.39 361	79.04 302

DeepMVS_CX				12.03 435	17.97 457	10.91 454		10.60 458	7.46 450	11.07 451	28.36 446	3.28 452	11.29 454	8.01 452	9.74 453	13.89 449

High-resolution multi-view benchmark