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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SED-MVS		95.53 1	95.79 1	95.23 2	97.60 10	98.92 1	95.99 5	92.05 9	97.14 1	94.19 3	94.71 7	93.25 2	95.08 1	94.32 11	92.59 15	96.49 19	99.58 3

ME-MVS		95.35 2	95.21 5	95.51 1	97.58 12	98.09 13	95.37 12	93.61 1	96.66 5	95.96 2	97.24 1	90.86 10	93.58 6	92.95 26	91.63 32	96.96 8	99.03 16

DPE-MVS		95.10 3	95.53 2	94.60 6	97.77 8	98.64 5	96.60 4	92.45 7	96.34 7	91.41 8	96.70 3	92.26 6	93.56 7	93.68 18	91.73 30	95.79 49	99.37 7
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		95.06 4	95.37 4	94.70 4	97.59 11	98.89 2	95.37 12	92.04 10	96.85 3	94.00 4	92.81 15	93.02 3	92.93 8	94.22 14	92.15 21	96.30 27	99.61 2
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
DVP-MVS++		95.03 5	95.03 6	95.03 3	97.91 6	98.84 3	95.80 6	91.88 12	96.65 6	93.15 5	93.79 9	90.11 13	95.03 2	94.20 16	92.39 16	96.44 23	99.22 10

MSP-MVS		95.00 6	95.47 3	94.45 7	96.78 20	98.11 11	95.72 8	90.91 16	96.68 4	91.57 7	96.98 2	89.47 16	94.76 3	95.24 3	92.15 21	96.98 7	99.64 1
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
CNVR-MVS		94.53 7	94.85 8	94.15 9	98.03 4	98.59 7	95.56 9	92.91 4	94.86 14	88.46 16	91.32 22	90.83 11	94.03 5	95.20 4	94.16 6	95.89 40	99.01 18

SF-MVS		94.40 8	94.15 14	94.70 4	98.25 3	98.24 9	96.86 3	93.46 3	94.87 13	90.26 11	95.96 4	88.42 19	92.76 11	92.29 32	90.84 44	96.62 14	98.44 28

APDe-MVS		94.31 9	94.30 11	94.33 8	97.57 13	98.06 14	95.79 7	91.98 11	95.50 10	92.19 6	95.25 5	87.97 22	92.93 8	93.01 24	91.02 42	95.52 54	99.29 8
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MCST-MVS		94.10 10	94.77 9	93.31 11	98.31 2	98.34 8	95.43 10	92.54 6	94.41 18	83.05 34	91.38 20	90.97 9	92.24 15	95.05 7	94.02 7	98.31 1	99.20 11

HPM-MVS++		94.04 11	94.96 7	92.96 13	97.93 5	97.71 20	94.65 17	91.01 15	95.91 8	87.43 18	93.52 12	92.63 5	92.29 14	94.22 14	92.34 18	94.47 80	98.37 29

NCCC		93.59 12	94.00 16	93.10 12	97.90 7	97.93 16	95.40 11	92.39 8	94.47 16	84.94 24	91.21 23	89.32 17	92.53 12	93.90 17	92.98 12	95.44 56	98.22 32

SMA-MVS		93.47 13	94.29 12	92.52 15	97.72 9	97.77 19	94.46 20	90.19 19	94.96 12	87.15 19	90.15 27	90.99 8	91.49 18	94.31 12	93.33 10	94.10 87	98.53 26
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
APD-MVS		93.47 13	93.44 19	93.50 10	97.06 16	97.09 28	95.27 15	91.47 13	95.71 9	89.57 13	93.66 10	86.28 28	92.81 10	92.06 35	90.70 45	94.83 77	98.60 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		93.36 15	94.33 10	92.22 17	94.68 45	97.89 18	94.56 18	90.89 17	94.80 15	90.04 12	93.53 11	90.14 12	89.78 25	92.74 28	92.17 19	93.35 131	99.07 15
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
TSAR-MVS + MP.		93.07 16	93.53 18	92.53 14	94.23 48	97.54 22	94.75 16	89.87 20	95.26 11	89.20 15	93.16 13	88.19 21	92.15 16	91.79 40	89.65 71	94.99 72	99.16 13
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DPM-MVS		92.86 17	93.19 21	92.47 16	95.78 36	97.40 23	97.39 1	92.56 5	92.88 26	81.84 41	81.31 41	92.95 4	91.21 19	96.54 1	97.33 1	96.01 36	93.94 132

MGCNet		92.61 18	94.18 13	90.77 25	95.62 39	98.60 6	93.09 27	83.78 47	94.44 17	85.52 23	87.49 33	89.90 14	90.25 22	95.14 5	94.49 5	96.37 26	99.19 12

SteuartSystems-ACMMP		92.31 19	93.31 20	91.15 23	96.88 18	97.36 24	93.95 24	89.44 22	92.62 27	83.20 31	94.34 8	85.55 30	88.95 32	93.07 23	91.90 26	94.51 79	98.30 30
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		92.16 20	92.91 24	91.28 22	96.95 17	97.36 24	93.66 25	89.23 24	93.33 21	83.71 29	90.53 24	86.84 25	90.39 21	93.30 22	91.56 33	93.74 100	97.43 49

HFP-MVS		92.02 21	92.13 26	91.89 20	97.16 15	96.46 40	93.57 26	87.60 27	93.79 20	88.17 17	93.15 14	83.94 40	91.19 20	90.81 50	89.83 65	93.66 106	96.94 65

train_agg		91.99 22	93.71 17	89.98 29	96.42 28	97.03 30	94.31 22	89.05 25	93.33 21	77.75 50	95.06 6	88.27 20	88.38 39	92.02 37	91.41 36	94.00 91	98.84 21

DeepC-MVS_fast	86.59 2	91.69 23	91.39 29	92.05 19	97.43 14	96.92 33	94.05 23	90.23 18	93.31 24	83.19 32	77.91 47	84.23 36	92.42 13	94.62 9	94.83 3	95.00 71	97.88 39
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		91.29 24	93.11 23	89.18 34	87.81 92	96.21 46	92.51 35	83.83 46	94.24 19	83.77 28	91.87 19	89.62 15	90.07 23	90.40 55	90.31 51	97.09 6	99.10 14

ACMMPR		91.15 25	91.44 28	90.81 24	96.61 22	96.25 44	93.09 27	87.08 30	93.32 23	84.78 25	92.08 18	82.10 45	89.71 26	90.24 56	89.82 66	93.61 111	96.30 87

DeepPCF-MVS	86.71 1	91.00 26	94.05 15	87.43 45	95.58 40	98.17 10	86.22 92	88.59 26	97.01 2	76.77 60	85.11 37	88.90 18	87.29 47	95.02 8	94.69 4	90.15 216	99.48 6

TSAR-MVS + ACMM		90.98 27	93.18 22	88.42 39	95.69 37	96.73 35	94.52 19	86.97 33	92.99 25	76.32 65	92.31 17	86.64 26	84.40 76	92.97 25	92.02 23	92.62 157	98.59 24

MP-MVS		90.81 28	91.45 27	90.06 28	96.59 23	96.33 43	92.46 36	87.19 29	90.27 41	82.54 37	91.38 20	84.88 33	88.27 40	90.58 53	89.30 76	93.30 133	97.44 47
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		90.57 29	90.68 31	90.44 26	96.13 30	95.90 52	92.77 33	86.86 34	92.12 31	84.19 26	89.18 30	82.37 43	89.43 29	89.65 70	88.43 92	93.27 134	97.13 57

MSLP-MVS++		90.33 30	88.82 40	92.10 18	96.52 26	95.93 48	94.35 21	86.26 35	88.37 56	89.24 14	75.94 55	82.60 42	89.71 26	89.45 75	92.17 19	96.51 18	97.24 54

CANet		89.98 31	90.42 35	89.47 33	94.13 49	98.05 15	91.76 41	83.27 50	90.87 38	81.90 40	72.32 63	84.82 34	88.42 37	94.52 10	93.78 9	97.34 4	98.58 25

PGM-MVS		89.97 32	90.64 33	89.18 34	96.53 25	95.90 52	93.06 29	82.48 58	90.04 43	80.37 43	92.75 16	80.96 50	88.93 33	89.88 65	89.08 81	93.69 104	95.86 96

PHI-MVS		89.88 33	92.75 25	86.52 54	94.97 42	97.57 21	89.99 52	84.56 42	92.52 29	69.72 117	90.35 26	87.11 24	84.89 66	91.82 39	92.37 17	95.02 70	97.51 45

CSCG		89.81 34	89.69 36	89.96 30	96.55 24	97.90 17	92.89 31	87.06 31	88.74 53	86.17 20	78.24 46	86.53 27	84.75 70	87.82 100	90.59 47	92.32 162	98.01 35

X-MVS		89.73 35	90.65 32	88.66 37	96.44 27	95.93 48	92.26 38	86.98 32	90.73 39	76.32 65	89.56 29	82.05 46	86.51 52	89.98 63	89.60 72	93.43 126	96.72 75

EPNet		89.30 36	90.89 30	87.44 44	95.67 38	96.81 34	91.13 44	83.12 52	91.14 35	76.31 69	87.60 32	80.40 54	84.45 73	92.13 34	91.12 41	93.96 92	97.01 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	84.14 3	88.80 37	88.03 46	89.71 32	94.83 43	96.56 36	92.57 34	89.38 23	89.25 49	79.59 45	70.02 71	77.05 66	88.24 41	92.44 30	92.79 13	93.65 109	98.10 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		88.76 38	90.43 34	86.81 50	96.04 32	96.53 39	92.95 30	85.95 37	90.36 40	67.93 123	85.80 36	80.69 51	83.82 83	90.81 50	91.85 29	94.18 85	96.99 62

3Dnovator+	81.14 5	88.59 39	87.49 49	89.88 31	95.83 35	96.45 42	91.94 40	82.41 59	87.09 62	85.94 22	62.80 110	85.37 31	89.46 28	91.51 42	91.89 28	93.72 101	97.30 52

ACMMP		88.48 40	88.71 41	88.22 41	94.61 46	95.53 58	90.64 48	85.60 39	90.97 36	78.62 47	89.88 28	74.20 80	86.29 54	88.16 97	86.37 114	93.57 113	95.86 96
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
AdaColmap		88.46 41	85.75 65	91.62 21	96.25 29	95.35 63	90.71 46	91.08 14	90.22 42	86.17 20	74.33 59	73.67 83	92.00 17	86.31 125	85.82 123	93.52 116	94.53 119

3Dnovator	80.58 8	88.20 42	86.53 55	90.15 27	96.86 19	96.46 40	91.97 39	83.06 53	85.16 67	83.66 30	62.28 118	82.15 44	88.98 31	90.99 47	92.65 14	96.38 25	96.03 91

CPTT-MVS		88.17 43	87.84 47	88.55 38	93.33 51	93.75 100	92.33 37	84.75 41	89.87 45	81.72 42	83.93 38	81.12 49	88.45 36	85.42 135	84.07 144	90.72 206	96.72 75

MVS_111021_HR		87.82 44	88.84 39	86.62 52	94.42 47	97.36 24	88.21 63	83.26 51	83.42 70	72.52 97	82.63 39	76.93 67	84.95 65	91.93 38	91.15 40	96.39 24	98.49 27

DELS-MVS		87.75 45	86.92 53	88.71 36	94.69 44	97.34 27	92.78 32	84.50 43	77.87 100	81.94 39	67.17 79	75.49 75	82.84 95	95.38 2	95.93 2	95.55 53	99.27 9
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
MVSTER		87.68 46	89.12 38	86.01 56	88.11 90	90.05 144	89.28 56	77.05 108	91.37 32	79.97 44	76.70 51	85.25 32	84.89 66	93.53 19	91.41 36	96.73 12	95.55 103

MVS_111021_LR		87.58 47	88.67 42	86.31 55	92.58 55	95.89 54	86.20 93	82.49 57	89.08 51	77.47 56	86.20 35	74.22 79	85.49 59	90.03 61	88.52 90	93.66 106	96.74 73

QAPM		87.06 48	86.46 56	87.75 42	96.63 21	97.09 28	91.71 42	82.62 56	80.58 85	71.28 103	66.04 86	84.24 35	87.01 48	89.93 64	89.91 62	97.26 5	97.44 47

PVSNet_BlendedMVS		86.98 49	87.05 51	86.90 47	93.03 52	96.98 31	86.57 83	81.82 61	89.78 46	82.78 35	71.54 65	66.07 122	80.73 114	93.46 20	91.97 24	96.45 21	99.53 4

PVSNet_Blended		86.98 49	87.05 51	86.90 47	93.03 52	96.98 31	86.57 83	81.82 61	89.78 46	82.78 35	71.54 65	66.07 122	80.73 114	93.46 20	91.97 24	96.45 21	99.53 4

ETV-MVS		86.94 51	89.49 37	83.95 83	87.28 99	95.61 57	83.58 129	76.37 115	92.59 28	73.20 89	80.35 42	76.42 70	87.38 46	92.20 33	90.45 49	95.90 39	98.83 22

SPE-MVS-test		86.72 52	88.35 43	84.83 69	91.78 61	96.03 47	81.71 140	76.71 109	91.19 34	77.12 59	77.64 49	75.63 74	87.59 45	90.82 49	89.11 79	94.06 89	97.99 37

CS-MVS		86.70 53	87.61 48	85.65 57	91.33 65	95.64 56	84.73 116	76.64 111	88.68 54	77.78 49	74.87 56	72.86 87	89.09 30	92.89 27	90.18 55	94.31 84	98.16 33

EC-MVSNet		86.42 54	88.31 44	84.20 79	86.61 119	94.08 93	86.20 93	72.18 152	89.06 52	76.02 70	74.48 58	80.47 53	88.90 34	92.03 36	90.07 58	95.30 58	98.00 36

OMC-MVS		86.38 55	86.21 61	86.57 53	92.30 57	94.35 86	87.60 68	83.51 49	92.32 30	77.37 57	72.27 64	77.83 59	86.59 51	87.62 105	85.95 120	92.08 166	93.11 149

HQP-MVS		86.17 56	87.35 50	84.80 70	91.41 64	92.37 119	91.05 45	84.35 45	88.52 55	64.21 130	87.05 34	68.91 103	84.80 68	89.12 78	88.16 96	92.96 148	97.31 51

sasdasda		85.93 57	86.26 59	85.54 59	88.94 78	95.44 59	89.56 53	76.01 119	87.83 57	77.70 51	76.43 52	68.66 105	87.80 43	87.02 112	91.51 34	93.25 135	96.95 63

canonicalmvs		85.93 57	86.26 59	85.54 59	88.94 78	95.44 59	89.56 53	76.01 119	87.83 57	77.70 51	76.43 52	68.66 105	87.80 43	87.02 112	91.51 34	93.25 135	96.95 63

MAR-MVS		85.65 59	86.30 58	84.88 68	95.51 41	95.89 54	86.50 85	76.71 109	89.23 50	68.59 120	70.93 69	74.49 77	88.55 35	89.40 76	90.30 52	93.42 127	93.88 137
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
PCF-MVS	82.38 4	85.52 60	84.41 70	86.81 50	91.51 63	96.23 45	90.27 49	89.81 21	77.87 100	70.67 113	69.20 73	77.86 57	85.55 58	85.92 131	86.38 113	93.03 145	97.43 49
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		85.43 61	84.24 73	86.83 49	87.69 95	93.16 110	90.01 51	82.72 55	87.17 61	79.28 46	71.43 68	65.81 125	86.02 55	87.33 108	86.96 107	95.25 64	97.83 41
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OpenMVS	77.91 11	85.09 62	83.42 77	87.03 46	96.12 31	96.55 38	89.36 55	81.59 63	79.19 95	75.20 78	55.84 156	79.04 56	84.45 73	88.47 91	89.35 75	95.48 55	95.48 104

MGCFI-Net		85.07 63	85.99 62	83.99 81	88.81 81	95.23 68	89.06 58	75.74 122	87.40 60	70.72 112	75.99 54	68.44 113	86.51 52	86.83 116	91.24 38	93.11 142	96.78 71

TSAR-MVS + COLMAP		84.93 64	85.79 64	83.92 84	90.90 67	93.57 104	89.25 57	82.00 60	91.29 33	61.66 139	88.25 31	59.46 161	86.71 50	89.79 66	87.09 104	93.01 146	91.09 172

TAPA-MVS	80.99 7	84.83 65	84.42 69	85.31 62	91.89 60	93.73 102	88.53 62	82.80 54	89.99 44	69.78 116	71.53 67	75.03 76	85.47 60	86.26 126	84.54 138	93.39 129	89.90 182
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	81.02 6	84.81 66	81.81 101	88.31 40	93.77 50	90.35 137	88.80 60	84.47 44	86.76 63	82.17 38	66.56 82	71.01 95	88.41 38	85.48 133	84.28 141	92.26 164	88.21 202
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EIA-MVS		84.75 67	86.43 57	82.79 95	86.88 108	95.36 62	82.84 136	76.39 114	87.61 59	71.03 104	74.33 59	71.12 94	85.16 61	89.69 69	88.70 88	94.40 82	98.23 31

CNLPA		84.72 68	82.14 94	87.73 43	92.85 54	93.83 99	84.70 117	85.07 40	90.90 37	83.16 33	56.28 152	71.53 91	88.14 42	84.19 141	84.00 149	92.48 159	94.26 126

MVS_Test		84.60 69	85.13 68	83.99 81	88.17 88	95.27 67	88.21 63	73.15 143	84.31 69	70.55 114	68.67 77	68.78 104	86.99 49	91.71 41	91.90 26	96.84 11	95.27 109

E2		84.16 70	82.96 83	85.55 58	87.24 102	94.92 71	86.60 82	79.90 71	78.46 99	78.56 48	65.58 89	64.57 128	84.77 69	90.00 62	90.43 50	96.22 29	96.77 72

casdiffmvs_mvg		83.97 71	82.62 87	85.54 59	87.71 93	94.38 84	88.93 59	80.11 68	77.34 104	77.57 55	63.01 108	65.95 124	84.96 64	90.69 52	90.23 54	93.95 93	96.74 73
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		83.84 72	82.65 86	85.22 64	87.25 101	94.62 80	86.01 99	79.62 75	79.48 92	77.59 54	61.92 121	64.34 130	85.57 57	90.55 54	90.51 48	95.26 62	97.14 56
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		83.83 73	84.38 71	83.18 94	86.65 115	94.59 82	85.79 103	73.78 140	85.83 65	72.94 90	69.28 72	70.80 97	83.45 89	86.80 117	87.59 100	96.47 20	95.77 100

diffmvs		83.69 74	83.17 81	84.31 76	85.45 133	93.92 94	86.89 73	78.62 84	82.71 76	75.95 71	66.78 81	63.90 133	83.84 82	87.90 99	89.16 77	95.10 67	97.82 42
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
viewcassd2359sk11		83.64 75	82.24 92	85.27 63	87.13 103	94.82 74	86.47 86	79.81 72	76.49 112	77.69 53	64.03 99	63.90 133	84.40 76	89.49 73	90.26 53	96.12 30	96.68 78

CANet_DTU		83.33 76	86.59 54	79.53 124	88.88 80	94.87 72	86.63 81	68.85 183	85.45 66	50.54 191	77.86 48	69.94 100	85.62 56	92.63 29	90.88 43	96.63 13	94.46 120

DI_MVS_pp		83.32 77	82.53 89	84.25 78	86.26 127	93.66 103	90.23 50	77.16 107	77.05 109	74.06 85	53.74 165	74.33 78	83.61 88	91.40 44	89.82 66	94.17 86	97.73 43

viewmanbaseed2359cas		83.27 78	82.21 93	84.51 74	87.27 100	94.83 73	86.41 87	79.61 76	77.03 110	73.99 86	63.66 102	63.85 135	84.06 79	88.94 81	90.63 46	95.72 51	96.56 80

viewdifsd2359ckpt09		83.21 79	81.85 99	84.79 71	86.60 120	94.61 81	86.12 96	79.22 79	76.41 113	76.76 61	64.54 93	62.66 144	85.00 63	89.79 66	88.69 89	95.03 69	96.29 88

diffmvs_AUTHOR		83.09 80	82.28 91	84.04 80	85.22 137	93.85 98	86.75 75	78.41 87	79.81 90	75.32 75	64.61 92	63.57 137	83.62 87	87.60 106	88.86 87	94.93 73	97.68 44

E3new		82.99 81	81.31 105	84.95 66	86.95 106	94.63 78	86.33 89	79.69 73	73.85 131	76.69 62	62.37 116	63.02 140	84.04 80	88.73 87	90.04 60	95.99 37	96.53 82

E3		82.98 82	81.31 105	84.92 67	86.95 106	94.63 78	86.32 90	79.69 73	73.86 130	76.54 63	62.35 117	63.05 139	84.01 81	88.73 87	90.03 61	95.99 37	96.52 83

viewdifsd2359ckpt13		82.93 83	81.75 104	84.30 77	87.00 105	94.76 75	85.59 105	79.57 77	76.32 115	74.15 82	62.74 112	62.67 142	84.44 75	89.49 73	90.15 56	95.06 68	96.13 90

baseline1		82.63 84	82.02 95	83.34 92	88.30 87	91.89 123	88.03 66	80.86 65	75.05 120	65.96 125	64.27 96	72.20 89	80.01 118	91.32 45	89.56 73	96.90 10	89.85 183

PVSNet_Blended_VisFu		82.55 85	83.70 76	81.21 108	89.66 71	95.15 70	82.41 137	77.36 106	72.53 141	73.64 87	61.15 125	77.19 65	70.35 178	91.31 46	89.72 69	93.84 96	98.85 20

ET-MVSNet_ETH3D		82.37 86	85.68 66	78.51 134	62.90 240	94.66 76	87.06 70	73.57 141	83.13 72	61.52 141	78.37 45	76.01 72	89.99 24	84.14 142	89.03 82	96.03 35	94.42 121

PMMVS		82.26 87	85.48 67	78.51 134	85.92 130	91.92 122	78.30 171	70.77 160	86.30 64	61.11 143	82.46 40	70.88 96	84.70 71	88.05 98	84.78 133	90.24 215	93.98 130

E5new		82.25 88	80.16 112	84.68 72	86.67 111	94.33 87	86.64 79	79.95 69	70.44 149	75.30 76	60.18 130	62.15 145	83.73 84	87.82 100	89.87 63	95.80 45	96.32 84

E5		82.25 88	80.16 112	84.68 72	86.67 111	94.33 87	86.64 79	79.95 69	70.44 149	75.30 76	60.18 130	62.15 145	83.73 84	87.82 100	89.87 63	95.80 45	96.32 84

ACMP	79.58 9	82.23 90	81.82 100	82.71 96	88.15 89	90.95 133	85.23 109	78.52 86	81.70 78	72.52 97	78.41 44	60.63 156	80.48 116	82.88 154	83.44 154	91.37 184	94.70 116
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
viewmambaseed2359dif		82.18 91	80.85 108	83.72 88	85.36 135	93.20 109	86.29 91	77.89 95	77.11 108	76.48 64	62.40 114	63.42 138	83.28 92	84.01 144	87.92 97	93.04 144	97.91 38

CHOSEN 280x420		82.15 92	85.87 63	77.80 139	86.54 122	93.42 106	81.74 139	59.96 228	78.99 97	63.99 131	74.50 57	83.95 39	80.99 109	89.53 72	85.01 128	93.56 115	95.71 102

LGP-MVS_train		82.12 93	82.57 88	81.59 102	89.26 75	90.23 140	88.76 61	78.05 88	81.26 81	61.64 140	79.52 43	62.11 149	79.59 122	85.20 136	84.68 135	92.27 163	95.02 113

E4		82.02 94	79.99 119	84.39 75	86.67 111	94.32 89	86.01 99	79.43 78	69.96 156	75.10 79	59.77 133	62.13 147	83.38 90	87.74 104	89.71 70	95.77 50	96.31 86

viewdifsd2359ckpt07		81.95 95	80.38 111	83.79 87	86.81 109	94.23 90	84.62 118	79.22 79	71.88 143	75.48 74	61.13 126	62.70 141	81.05 108	88.84 83	89.15 78	95.57 52	94.70 116

FMVSNet3		81.93 96	81.98 96	81.88 101	79.49 174	87.02 161	88.15 65	72.57 146	83.02 73	72.63 94	56.55 148	73.48 84	82.34 104	91.49 43	91.20 39	96.07 31	91.13 171

test2506		81.91 97	81.78 103	82.06 100	89.09 76	95.32 64	84.61 120	77.54 100	74.61 124	68.77 119	63.80 101	67.53 116	77.09 131	90.19 58	89.01 83	95.27 59	92.00 163

thisisatest0530		81.67 98	84.27 72	78.63 130	85.53 131	93.88 97	81.77 138	73.84 137	81.35 80	63.85 133	68.79 75	77.64 61	73.02 159	88.73 87	85.73 124	93.76 99	93.80 141

viewmacassd2359aftdt		81.60 99	79.90 121	83.58 91	86.67 111	94.36 85	86.02 98	79.17 81	70.40 151	71.64 101	58.95 137	62.12 148	82.55 101	87.08 111	90.14 57	95.41 57	96.24 89

tttt0517		81.51 100	84.12 75	78.47 136	85.33 136	93.74 101	81.42 143	73.84 137	81.21 82	63.59 134	68.73 76	77.46 64	73.02 159	88.47 91	85.73 124	93.63 110	93.49 145

E6new		81.43 101	79.49 124	83.69 89	86.62 117	94.23 90	84.87 113	77.93 93	69.39 161	74.14 83	59.07 135	61.48 150	82.80 97	87.23 109	89.01 83	95.80 45	96.01 93

E6		81.43 101	79.49 124	83.69 89	86.62 117	94.23 90	84.87 113	77.93 93	69.39 161	74.14 83	59.07 135	61.48 150	82.80 97	87.23 109	89.01 83	95.80 45	96.01 93

FA-MVS(training)		81.41 103	81.98 96	80.76 117	87.58 96	94.59 82	83.09 131	61.18 225	79.80 91	74.74 80	58.46 140	69.76 101	82.12 105	88.90 82	87.00 105	95.83 43	95.33 106

OPM-MVS		81.34 104	78.18 133	85.02 65	91.27 66	91.78 124	90.66 47	83.62 48	62.39 181	65.91 126	63.35 106	64.33 131	85.03 62	87.77 103	85.88 122	93.66 106	91.75 167
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline2		81.21 105	83.36 80	78.70 128	83.22 152	92.71 112	80.32 151	74.25 136	80.39 87	63.94 132	68.89 74	68.44 113	74.67 145	89.61 71	86.68 111	95.83 43	96.81 70

IS_MVSNet		80.92 106	84.14 74	77.16 143	87.43 97	93.90 96	80.44 147	74.64 130	75.05 120	61.10 144	65.59 88	76.89 68	67.39 194	90.88 48	90.05 59	91.95 170	96.62 79

ACMM	78.09 10	80.91 107	78.39 130	83.86 85	89.61 74	87.71 158	85.16 110	80.67 67	79.04 96	74.18 81	63.82 100	60.84 155	82.59 100	84.33 139	83.59 152	90.96 200	89.39 188
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPP-MVSNet		80.82 108	82.79 84	78.52 132	86.31 126	92.37 119	79.83 154	74.51 131	73.79 133	64.46 129	67.01 80	80.63 52	74.33 148	85.63 132	84.35 140	91.68 177	95.79 99

CostFormer		80.72 109	81.81 101	79.44 126	86.50 123	91.65 125	84.31 122	59.84 229	80.86 83	72.69 92	62.46 113	73.74 81	79.93 119	82.58 158	84.50 139	93.37 130	96.90 68

GBi-Net		80.72 109	80.49 109	81.00 113	78.18 178	86.19 175	86.73 76	72.57 146	83.02 73	72.63 94	56.55 148	73.48 84	80.99 109	86.57 119	86.83 108	94.89 74	90.77 175

test1		80.72 109	80.49 109	81.00 113	78.18 178	86.19 175	86.73 76	72.57 146	83.02 73	72.63 94	56.55 148	73.48 84	80.99 109	86.57 119	86.83 108	94.89 74	90.77 175

UGNet		80.71 112	83.09 82	77.93 138	87.02 104	92.71 112	80.28 152	76.53 112	73.83 132	71.35 102	70.07 70	73.71 82	58.93 215	87.39 107	86.97 106	93.48 123	96.94 65
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
0.3-1-1-0.015		80.56 113	80.14 114	81.04 112	76.03 196	91.03 132	86.78 74	76.11 117	80.64 84	70.88 105	62.86 109	68.56 107	82.79 99	80.60 178	84.02 148	93.67 105	93.30 147

0.4-1-1-0.2		80.56 113	80.09 115	81.10 110	76.02 197	91.04 130	86.90 72	76.23 116	80.57 86	70.87 109	62.39 115	68.47 111	82.82 96	80.69 177	84.19 142	93.72 101	93.32 146

0.4-1-1-0.1		80.27 115	79.82 123	80.80 116	75.96 199	90.95 133	86.41 87	75.70 123	80.30 88	70.74 111	61.89 122	68.45 112	82.38 103	80.37 182	83.53 153	93.60 112	93.22 148

CHOSEN 1792x2688		80.23 116	79.16 127	81.48 104	91.97 58	96.56 36	86.18 95	75.40 126	76.17 116	61.32 142	37.43 233	61.08 154	76.52 137	92.35 31	91.64 31	97.46 3	98.86 19

casdiffseed414692147		79.95 117	77.08 142	83.29 93	86.42 125	93.30 108	85.12 111	77.72 96	69.61 159	73.32 88	52.68 171	56.35 174	83.20 93	84.96 137	87.92 97	93.95 93	94.75 114

thres100view900		79.83 118	77.79 137	82.21 97	88.42 84	93.54 105	87.07 69	81.11 64	70.15 152	61.01 145	56.65 146	51.22 182	81.78 106	89.77 68	85.95 120	93.84 96	97.26 53

Effi-MVS+		79.80 119	80.04 116	79.52 125	85.53 131	93.31 107	85.28 107	70.68 162	74.15 126	58.79 154	62.03 120	60.51 157	83.37 91	88.41 93	86.09 119	93.49 122	95.80 98

ECVR-MVS		79.76 120	78.27 131	81.50 103	89.09 76	95.32 64	84.61 120	77.54 100	74.61 124	65.38 127	50.22 178	56.31 175	77.09 131	90.19 58	89.01 83	95.27 59	92.25 157

DCV-MVSNet		79.76 120	79.17 126	80.44 120	84.65 141	84.51 199	84.20 123	72.36 151	75.17 119	70.81 110	66.21 85	66.56 119	80.99 109	82.89 153	84.56 137	89.65 221	94.30 125

FC-MVSNet-train		79.54 122	78.20 132	81.09 111	86.55 121	88.63 153	79.96 153	78.53 85	70.90 147	68.24 121	65.87 87	56.45 173	80.29 117	86.20 129	84.08 143	92.97 147	95.31 108

test-LLR		79.52 123	83.42 77	74.97 152	81.79 157	91.26 126	76.17 192	70.57 163	77.71 102	52.14 176	66.26 83	77.47 62	73.10 155	87.02 112	87.16 102	96.05 33	97.02 59

FMVSNet2		79.24 124	78.14 134	80.53 119	78.18 178	86.19 175	86.73 76	71.91 153	72.97 136	70.48 115	50.63 176	66.56 119	80.99 109	90.10 60	89.77 68	94.89 74	90.77 175

TESTMET0.1,1		79.15 125	83.42 77	74.18 158	79.81 172	91.26 126	76.17 192	67.83 196	77.71 102	52.14 176	66.26 83	77.47 62	73.10 155	87.02 112	87.16 102	96.05 33	97.02 59

tfpn200view9		79.05 126	77.21 141	81.18 109	88.42 84	92.55 117	85.12 111	77.94 90	70.15 152	61.01 145	56.65 146	51.22 182	81.11 107	88.23 94	84.80 132	93.50 121	96.90 68

test1111		78.99 127	77.77 138	80.42 121	88.64 82	95.31 66	83.39 130	77.67 98	72.76 139	61.91 137	49.58 181	55.59 177	75.67 142	90.23 57	89.09 80	95.23 65	91.83 166

viewdifsd2359ckpt11		78.83 128	76.68 146	81.33 106	84.03 149	90.13 142	80.89 145	77.43 104	70.01 154	75.72 72	60.97 127	59.03 165	79.67 120	79.81 187	81.58 176	90.25 213	95.22 110

viewmsd2359difaftdt		78.82 129	76.68 146	81.33 106	84.03 149	90.13 142	80.89 145	77.43 104	70.00 155	75.68 73	60.97 127	59.01 166	79.67 120	79.82 186	81.58 176	90.25 213	95.22 110

PatchMatch-RL		78.75 130	76.47 151	81.41 105	88.53 83	91.10 128	78.09 172	77.51 103	77.33 105	71.98 99	64.38 95	48.10 200	82.55 101	84.06 143	82.35 163	89.78 218	87.97 204

LS3D		78.72 131	75.79 157	82.15 98	91.91 59	89.39 149	83.66 127	85.88 38	76.81 111	59.22 153	57.67 143	58.53 167	83.72 86	82.07 163	81.63 174	88.50 229	84.39 216

thres200		78.69 132	76.71 145	80.99 115	88.35 86	92.56 115	86.03 97	77.94 90	66.27 166	60.66 147	56.08 153	51.11 184	79.45 123	88.23 94	85.54 127	93.52 116	97.20 55

Anonymous20231211		78.61 133	75.57 160	82.15 98	84.43 145	90.26 138	84.08 125	77.68 97	71.09 145	72.90 91	39.24 227	66.21 121	84.23 78	82.15 161	84.04 145	89.61 222	96.03 91

IB-MVS	74.10 12	78.52 134	78.51 129	78.52 132	90.15 69	95.39 61	71.95 218	77.53 102	74.95 122	77.25 58	58.93 138	55.92 176	58.37 217	79.01 193	87.89 99	95.88 41	97.47 46
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
EPNet_dtu		78.49 135	81.96 98	74.45 157	92.57 56	88.74 152	82.98 132	78.83 83	83.28 71	44.64 222	77.40 50	67.73 115	53.98 226	85.44 134	84.91 129	93.71 103	86.22 211
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres400		78.39 136	76.39 152	80.73 118	88.02 91	92.94 111	84.77 115	78.88 82	65.20 174	59.70 151	55.20 159	50.85 185	79.45 123	88.81 84	84.81 131	93.57 113	96.91 67

UA-Net		78.30 137	80.92 107	75.25 151	87.42 98	92.48 118	79.54 157	75.49 125	60.47 185	60.52 148	68.44 78	84.08 38	57.54 219	88.54 90	88.45 91	90.96 200	83.97 218

Vis-MVSNet (Re-imp)		78.28 138	82.68 85	73.16 170	86.64 116	92.68 114	78.07 173	74.48 132	74.05 127	53.47 165	64.22 97	76.52 69	54.28 222	88.96 80	88.29 94	92.03 168	94.00 129

MSDG		78.11 139	73.17 174	83.86 85	91.78 61	86.83 163	85.25 108	86.02 36	72.84 138	69.69 118	51.43 173	54.00 179	77.61 127	81.95 166	82.27 165	92.83 153	82.91 223

HyFIR lowres test		78.08 140	76.81 143	79.56 123	90.77 68	94.64 77	82.97 133	69.85 173	69.81 158	59.53 152	33.52 239	64.66 126	78.97 125	88.77 86	88.38 93	95.27 59	97.86 40

GeoE		78.04 141	77.52 140	78.65 129	84.51 143	90.84 135	80.94 144	69.24 181	72.86 137	66.06 124	53.45 166	60.46 158	77.37 128	84.20 140	84.85 130	93.78 98	96.00 95

test-mter		77.90 142	82.44 90	72.60 176	78.52 176	90.24 139	73.85 211	65.31 212	76.37 114	51.29 182	65.58 89	75.94 73	71.36 169	85.98 130	86.26 115	95.26 62	96.71 77

thres600view7		77.66 143	75.67 158	79.98 122	87.71 93	92.56 115	83.79 126	77.94 90	64.41 176	58.69 155	54.32 164	50.54 187	78.23 126	88.23 94	83.06 157	93.52 116	96.55 81

MS-PatchMatch		77.47 144	76.48 150	78.63 130	89.89 70	90.42 136	85.42 106	69.53 177	70.79 148	60.43 149	50.05 179	70.62 99	70.66 175	86.71 118	82.54 160	95.86 42	84.23 217

Fast-Effi-MVS+		77.37 145	76.68 146	78.17 137	82.84 154	89.94 145	81.47 142	68.01 192	72.99 135	60.26 150	55.07 160	53.20 180	82.99 94	86.47 124	86.12 118	93.46 124	92.98 152

dmvs_re		77.25 146	75.86 155	78.86 127	81.08 163	89.36 150	84.15 124	80.73 66	73.02 134	55.58 161	58.33 141	48.97 196	75.32 143	83.92 147	86.25 116	96.29 28	91.20 170

Vis-MVSNet		77.24 147	79.99 119	74.02 160	84.62 142	93.92 94	80.33 150	72.55 149	62.58 180	55.25 163	64.45 94	69.49 102	57.00 220	88.78 85	88.21 95	94.36 83	92.54 154
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MDTV_nov1_ep13		77.20 148	80.04 116	73.90 162	82.22 155	90.14 141	79.25 161	61.52 223	78.63 98	56.98 156	65.52 91	72.80 88	73.05 157	80.93 174	83.20 155	90.36 210	89.05 191

EPMVS		77.16 149	79.08 128	74.92 153	86.73 110	91.98 121	78.62 166	55.44 237	79.43 93	56.59 158	61.24 124	70.73 98	76.97 134	80.59 179	81.43 182	95.15 66	88.17 203

tpm cat1		76.93 150	76.19 154	77.79 140	85.08 140	88.58 154	82.96 134	59.33 230	75.72 118	72.64 93	51.25 174	64.41 129	75.74 141	77.90 201	80.10 198	90.97 199	95.35 105

PatchmatchNet		76.85 151	80.03 118	73.15 171	84.08 147	91.04 130	77.76 177	55.85 236	79.43 93	52.74 170	62.08 119	76.02 71	74.56 146	79.92 185	81.41 183	93.92 95	90.29 180
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 152	76.33 153	77.35 142	84.07 148	84.11 200	81.54 141	68.52 185	66.17 167	61.74 138	57.84 142	64.31 132	74.88 144	83.48 150	86.21 117	93.34 132	92.16 159
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
blend_shiyan4		76.72 153	75.85 156	77.73 141	76.42 194	82.48 210	87.78 67	70.39 166	81.47 79	70.88 105	63.45 103	68.56 107	69.59 181	73.85 219	72.21 228	91.32 185	88.93 193

CDS-MVSNet		76.57 154	76.78 144	76.32 146	80.94 165	89.75 146	82.94 135	72.64 145	59.01 191	62.95 136	58.60 139	62.67 142	66.91 196	86.26 126	87.20 101	91.57 179	93.97 131
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SCA		76.41 155	79.90 121	72.35 180	84.26 146	85.24 190	75.57 199	54.56 239	79.95 89	52.72 171	64.22 97	77.84 58	73.73 152	80.48 180	81.37 184	93.25 135	90.20 181

tpmrst		76.27 156	77.65 139	74.66 155	86.13 129	89.53 148	79.31 160	54.91 238	77.19 107	56.27 159	55.87 155	64.58 127	77.25 129	80.85 175	80.21 195	94.07 88	95.32 107

dps		75.76 157	75.02 162	76.63 145	84.51 143	88.12 155	77.51 178	58.33 232	75.91 117	71.98 99	57.37 144	57.85 168	76.81 136	77.89 202	78.40 207	90.63 207	89.63 185

CR-MVSNet		74.84 158	77.91 135	71.26 194	81.77 159	85.52 186	78.32 169	54.14 241	74.05 127	51.09 185	50.00 180	71.38 93	70.77 173	86.48 122	84.03 146	91.46 183	93.92 134

Effi-MVS+-dtu		74.57 159	74.60 166	74.53 156	81.38 161	86.74 165	80.39 149	67.70 197	67.36 165	53.06 166	59.86 132	57.50 169	75.84 140	80.19 183	78.62 205	88.79 228	91.95 165

RPSCF		74.27 160	73.24 173	75.48 150	81.01 164	80.18 230	76.24 191	72.37 150	74.84 123	68.24 121	72.47 62	67.39 117	73.89 149	71.05 235	69.38 243	81.14 249	77.37 237

FMVSNet1		74.26 161	71.95 181	76.95 144	74.28 214	83.94 202	83.61 128	69.99 167	57.08 197	65.08 128	42.39 216	57.41 170	76.98 133	86.57 119	86.83 108	91.77 176	89.42 186

GA-MVS		73.62 162	74.52 167	72.58 177	79.93 170	89.29 151	78.02 174	71.67 156	60.79 184	42.68 226	54.41 163	49.07 195	70.07 179	89.39 77	86.55 112	93.13 141	92.12 160

Fast-Effi-MVS+-dtu		73.56 163	75.32 161	71.50 190	80.35 167	86.83 163	79.72 155	58.07 233	67.64 164	44.83 219	60.28 129	54.07 178	73.59 154	81.90 168	82.30 164	92.46 160	94.18 127

tpm		73.50 164	74.85 163	71.93 184	83.19 153	86.84 162	78.61 167	55.91 235	65.64 169	48.90 198	56.30 151	61.09 153	72.31 161	79.10 192	80.61 194	92.68 155	94.35 124

RPMNet		73.46 165	77.85 136	68.34 208	81.71 160	85.52 186	73.83 212	50.54 249	74.05 127	46.10 213	53.03 169	71.91 90	66.31 198	83.55 148	82.18 167	91.55 181	94.71 115

USDC		73.43 166	72.31 177	74.73 154	80.86 166	86.21 173	80.42 148	71.83 155	71.69 144	46.94 206	59.60 134	42.58 221	76.47 138	82.66 157	81.22 187	91.88 172	82.24 229

pmmvs4		73.38 167	71.53 184	75.55 149	75.95 200	85.24 190	77.25 182	71.59 157	71.03 146	63.10 135	49.09 186	44.22 211	73.73 152	82.04 164	80.18 196	91.68 177	88.89 195

usedtu_dtu_shiyan1		73.19 168	73.51 172	72.82 172	67.62 232	88.00 157	78.54 168	74.77 128	69.96 156	51.51 181	46.24 192	52.09 181	69.99 180	86.25 128	84.58 136	94.46 81	87.44 206

UniMVSNet_NR-MVSNet		73.11 169	72.59 175	73.71 165	76.90 187	86.58 169	77.01 183	75.82 121	65.59 170	48.82 199	50.97 175	48.42 198	71.61 165	79.19 191	83.03 158	92.11 165	94.37 122

usedtu_blend_shiyan5		73.04 170	72.10 179	74.14 159	56.36 242	82.07 212	86.93 71	69.94 168	56.27 200	70.88 105	63.45 103	68.56 107	69.59 181	73.35 221	72.06 230	91.17 190	88.93 193

FMVSNet5		72.83 171	73.89 170	71.59 188	67.42 233	76.28 239	75.88 196	63.74 217	77.27 106	54.59 164	53.32 167	71.48 92	73.85 150	81.95 166	81.69 172	94.06 89	75.20 242

PatchT		72.66 172	76.58 149	68.09 210	79.02 175	86.09 179	59.81 241	51.78 247	72.00 142	51.09 185	46.84 190	66.70 118	70.77 173	86.48 122	84.03 146	96.07 31	93.92 134

ACMH	71.22 14	72.65 173	70.13 189	75.59 148	86.19 128	86.14 178	75.76 197	77.63 99	54.79 211	46.16 212	53.28 168	47.28 202	77.24 130	78.91 194	81.18 188	90.57 208	89.33 189
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		72.43 174	74.05 168	70.55 198	80.34 168	81.17 224	77.44 179	61.00 227	63.57 179	46.82 208	55.88 154	59.09 164	65.03 200	83.15 151	83.83 150	92.67 156	91.65 168
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH+	72.14 13	72.38 175	69.34 196	75.93 147	85.21 138	84.89 194	76.96 186	76.04 118	59.76 186	51.63 180	50.37 177	48.69 197	76.90 135	76.06 211	78.69 203	88.85 227	86.90 209

DU-MVS		72.19 176	71.35 185	73.17 169	75.95 200	86.02 180	77.01 183	74.42 133	65.39 172	48.82 199	49.10 184	42.81 219	71.61 165	78.67 195	83.10 156	91.22 188	94.37 122

IterMVS-SCA-FT		72.18 177	73.96 169	70.11 200	80.15 169	81.11 225	77.42 180	61.09 226	63.67 178	46.73 209	55.77 157	59.15 163	63.95 204	82.83 155	83.70 151	91.31 186	91.49 169

UniMVSNet (Re)		72.12 178	72.28 178	71.93 184	76.77 188	87.38 160	75.73 198	73.51 142	65.76 168	50.24 193	48.65 187	46.49 203	63.85 205	80.10 184	82.47 161	91.49 182	95.13 112

ADS-MVSNet		72.11 179	73.72 171	70.24 199	81.24 162	86.59 168	74.75 207	50.56 248	72.58 140	49.17 196	55.40 158	61.46 152	73.80 151	76.01 212	78.14 208	91.93 171	85.86 212

FE-MVSNET3		72.10 180	72.04 180	72.18 181	56.36 242	82.07 212	75.15 200	69.94 168	56.27 200	70.88 105	63.45 103	68.56 107	69.59 181	73.35 221	72.06 230	91.17 190	88.50 199

gg-mvs-nofinetune		72.10 180	74.79 164	68.97 203	83.31 151	95.22 69	85.66 104	48.77 250	35.68 250	22.17 259	30.49 242	77.73 60	76.37 139	94.30 13	93.03 11	97.55 2	97.05 58

TAMVS		72.06 182	71.76 183	72.41 179	76.68 189	88.12 155	74.82 204	68.09 190	53.52 216	56.91 157	52.94 170	56.93 172	66.91 196	81.37 171	82.44 162	91.07 196	86.99 208

v2v482		71.73 183	69.80 191	73.99 161	75.88 204	86.66 167	79.58 156	71.90 154	57.58 195	50.41 192	45.35 195	43.24 217	73.05 157	79.69 188	82.18 167	93.08 143	93.87 138

test0.0.03 1		71.70 184	74.68 165	68.23 209	81.79 157	83.81 203	68.64 222	70.57 163	68.81 163	43.47 223	62.77 111	60.09 160	51.77 234	82.48 159	81.67 173	93.16 139	83.13 221

V42		71.58 185	70.11 190	73.30 168	75.66 207	86.68 166	79.17 163	69.92 172	59.29 190	52.80 169	44.36 199	45.66 205	68.83 184	79.48 190	81.49 179	93.44 125	93.82 140

NR-MVSNet		71.47 186	71.11 186	71.90 186	77.73 183	86.02 180	76.88 187	74.42 133	65.39 172	46.09 214	49.10 184	39.87 234	64.27 203	81.40 170	82.24 166	91.99 169	93.75 142

v8		71.42 187	69.69 192	73.43 167	76.45 192	85.12 193	79.53 158	67.47 200	59.34 189	52.90 168	44.60 197	45.82 204	71.05 171	79.56 189	81.45 181	93.17 138	91.96 164

TranMVSNet+NR-MVSNet		71.12 188	70.24 188	72.15 182	76.01 198	84.80 196	76.55 189	75.65 124	61.99 182	45.29 217	48.42 188	43.07 218	67.55 192	78.28 198	82.83 159	91.85 173	92.29 155

v10		70.97 189	69.44 193	72.75 173	75.90 203	84.58 198	79.43 159	66.45 205	58.07 193	49.93 194	43.87 205	43.68 212	71.91 163	82.04 164	81.70 171	92.89 151	92.11 161

v1144		70.93 190	69.42 195	72.70 174	75.48 208	86.26 171	79.22 162	69.39 179	55.61 208	48.05 204	43.47 208	42.55 222	71.51 167	82.11 162	81.74 170	92.56 158	94.17 128

thisisatest0515		70.62 191	71.94 182	69.07 202	76.48 191	85.59 185	68.03 223	68.02 191	59.70 187	52.94 167	52.19 172	50.36 188	58.10 218	83.15 151	81.63 174	90.87 203	90.99 173

Baseline_NR-MVSNet		70.61 192	68.87 199	72.65 175	75.95 200	80.49 228	75.92 195	74.75 129	65.10 175	48.78 201	41.28 222	44.28 210	68.45 185	78.67 195	79.64 199	92.04 167	92.62 153

v148		70.34 193	68.46 202	72.54 178	76.04 195	86.38 170	74.83 203	72.73 144	55.88 207	55.26 162	43.32 211	43.49 213	64.52 202	76.93 209	80.11 197	91.85 173	93.11 149

v1192		70.32 194	68.77 200	72.12 183	74.76 210	85.62 184	78.73 164	68.53 184	55.08 210	46.34 211	42.39 216	40.67 229	71.90 164	82.27 160	81.53 178	92.43 161	93.86 139

v144192		70.10 195	68.55 201	71.90 186	74.55 211	85.67 183	77.81 175	68.22 189	54.65 212	46.91 207	42.76 214	41.27 226	70.95 172	80.48 180	81.11 192	92.96 148	93.90 136

pmmvs5		70.01 196	69.31 197	70.82 197	75.80 206	86.26 171	72.94 213	67.91 193	53.84 215	47.22 205	47.31 189	41.47 225	67.61 191	83.93 146	81.93 169	93.42 127	90.42 179

COLMAP_ROB	66.31 15	69.91 197	66.61 207	73.76 163	86.44 124	82.76 207	76.59 188	76.46 113	63.82 177	50.92 189	45.60 194	49.13 194	65.87 199	74.96 217	74.45 225	86.30 239	75.57 241
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		69.85 198	68.38 203	71.58 189	74.35 212	85.39 188	77.78 176	67.88 195	54.64 213	45.39 216	42.11 219	39.97 233	71.10 170	81.68 169	81.17 190	92.96 148	93.69 144

pm-mvs1		69.62 199	68.07 205	71.44 191	77.21 185	85.32 189	76.11 194	71.05 158	46.55 237	51.17 184	41.83 220	48.20 199	61.81 211	84.00 145	81.14 191	91.28 187	89.42 186

UniMVSNet_ETH3D		69.49 200	65.86 214	73.72 164	76.51 190	85.88 182	78.65 165	70.52 165	48.08 234	55.71 160	37.64 230	40.56 230	71.38 168	75.05 216	81.49 179	89.57 224	92.29 155

tfpnnormal		69.29 201	65.58 215	73.62 166	79.87 171	84.82 195	76.97 185	75.12 127	45.29 238	49.03 197	35.57 237	37.20 242	68.02 189	82.70 156	81.24 186	92.69 154	92.20 158

v1240		69.28 202	67.82 206	71.00 196	74.09 216	85.13 192	76.54 190	67.28 202	53.17 218	44.70 220	41.55 221	39.38 235	70.51 177	81.29 172	81.18 188	92.88 152	93.02 151

CVMVSNet		68.95 203	70.79 187	66.79 217	79.69 173	83.75 204	72.05 217	70.90 159	56.20 203	36.30 240	54.94 162	59.22 162	54.03 225	78.33 197	78.65 204	87.77 235	84.44 215

MIMVSNet		68.66 204	69.43 194	67.76 211	64.92 237	84.68 197	74.16 208	54.10 243	60.85 183	51.27 183	39.47 226	49.48 189	67.48 193	84.86 138	85.57 126	94.63 78	81.10 230

TDRefinement		67.82 205	64.91 221	71.22 195	82.08 156	81.45 219	77.42 180	73.79 139	59.62 188	48.35 203	42.35 218	42.40 223	60.87 213	74.69 218	74.64 224	84.83 243	79.20 234

wanda-best-256-512		67.61 206	66.46 210	68.94 204	56.36 242	82.07 212	75.15 200	69.94 168	56.27 200	52.66 172	43.54 206	49.41 190	68.38 186	73.35 221	72.06 230	91.17 190	88.53 197

FE-blended-shiyan7		67.61 206	66.46 210	68.94 204	56.36 242	82.07 212	75.15 200	69.94 168	56.28 199	52.66 172	43.54 206	49.41 190	68.38 186	73.35 221	72.06 230	91.17 190	88.53 197

anonymousdsp		67.61 206	68.94 198	66.04 218	71.44 228	83.97 201	66.45 227	63.53 219	50.54 227	42.42 227	49.39 182	45.63 206	62.84 208	77.99 200	81.34 185	89.59 223	93.75 142

blended_shiyan8		67.43 209	66.30 213	68.74 206	56.30 247	82.00 216	74.80 205	69.62 175	55.94 204	52.60 174	43.24 212	49.40 192	68.00 190	73.19 227	71.88 235	91.09 195	88.46 201

blended_shiyan6		67.43 209	66.32 212	68.72 207	56.29 248	81.99 217	74.78 206	69.62 175	55.89 206	52.56 175	43.36 209	49.38 193	68.03 188	73.20 226	71.89 234	91.07 196	88.50 199

TinyColmap		67.16 211	63.51 228	71.42 192	77.94 181	79.54 234	72.80 214	69.78 174	56.58 198	45.52 215	44.53 198	33.53 248	74.45 147	76.91 210	77.06 215	88.03 234	76.41 238

FC-MVSNet-test		67.04 212	72.47 176	60.70 235	76.92 186	81.41 220	61.52 238	69.45 178	65.58 171	26.74 254	61.79 123	60.40 159	41.17 244	77.60 205	77.78 211	88.41 230	82.70 225

gbinet_0.2-2-1-0.02		67.03 213	66.53 209	67.63 212	54.59 250	81.34 223	73.95 209	69.35 180	53.34 217	51.93 178	42.82 213	50.76 186	64.78 201	73.34 225	72.07 229	91.16 194	92.01 162

TransMVSNet (Re)		66.87 214	64.30 223	69.88 201	78.32 177	81.35 222	73.88 210	74.34 135	43.19 243	45.20 218	40.12 224	42.37 224	55.97 221	80.85 175	79.15 200	91.56 180	83.06 222

CMPMVS	50.59 17	66.74 215	62.72 232	71.42 192	85.40 134	89.72 147	72.69 215	70.72 161	51.24 223	51.75 179	38.91 228	44.40 208	63.74 206	70.84 237	71.52 237	84.19 244	72.45 246
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		66.43 216	65.51 216	67.51 213	71.63 227	83.10 205	70.89 221	65.02 213	50.13 230	44.68 221	39.59 225	38.77 236	62.57 209	77.59 206	78.91 201	90.29 212	90.44 178

EG-PatchMatch MVS		66.23 217	65.20 218	67.43 214	77.74 182	86.20 174	72.51 216	63.68 218	43.95 241	43.44 224	36.22 236	45.43 207	54.04 224	81.00 173	80.95 193	93.15 140	82.67 226

WR-MVS		64.98 218	66.59 208	63.09 228	74.34 213	82.68 208	64.98 233	69.17 182	54.42 214	36.18 241	44.32 200	44.35 209	44.65 237	73.60 220	77.83 210	89.21 226	88.96 192

gm-plane-assit		64.86 219	68.15 204	61.02 234	76.44 193	68.29 249	41.60 256	53.37 244	34.68 252	26.19 256	33.22 240	57.09 171	71.97 162	95.12 6	93.97 8	96.54 17	94.66 118

CP-MVSNet		64.84 220	64.97 219	64.69 223	72.09 223	81.04 226	66.66 226	67.53 199	52.45 220	37.40 235	44.00 204	38.37 238	53.54 228	72.26 231	76.93 216	90.94 202	89.75 184

MDTV_nov1_ep13_2view		64.72 221	64.94 220	64.46 224	71.14 229	81.94 218	67.53 224	54.54 240	55.92 205	43.29 225	44.02 203	43.27 216	59.87 214	71.85 233	74.77 223	90.36 210	82.82 224

MVS-HIRNet		64.63 222	64.03 227	65.33 220	75.01 209	82.84 206	58.54 245	52.10 246	55.42 209	49.29 195	29.83 245	43.48 214	66.97 195	78.28 198	78.81 202	90.07 217	79.52 233

pmnet_mix02		64.58 223	64.11 226	65.12 221	74.16 215	80.17 231	63.24 236	67.91 193	57.87 194	41.69 228	45.86 193	40.99 228	53.97 227	69.92 240	71.67 236	89.77 219	82.29 228

LTVRE_ROB	63.07 16	64.49 224	63.16 231	66.04 218	77.47 184	82.64 209	70.98 220	65.02 213	34.01 253	29.61 250	49.12 183	35.58 246	70.57 176	75.10 215	78.45 206	82.60 247	87.24 207
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
PEN-MVS		64.35 225	64.29 224	64.42 225	72.67 219	79.83 232	66.97 225	68.24 188	51.21 224	35.29 243	44.09 201	38.51 237	52.36 231	71.06 234	77.65 212	90.99 198	87.68 205

pmmvs6		64.24 226	61.77 236	67.12 215	72.39 222	81.39 221	71.33 219	65.95 211	36.05 249	48.48 202	30.55 241	43.45 215	58.75 216	77.88 203	76.36 219	85.83 240	86.70 210

pmmvs-eth3d		64.24 226	61.96 234	66.90 216	66.35 234	76.04 241	66.09 229	66.31 207	52.59 219	50.94 188	37.61 231	32.79 250	62.43 210	75.78 213	75.48 221	89.27 225	83.39 220

PS-CasMVS		64.22 228	64.19 225	64.25 226	71.86 225	80.67 227	66.42 228	67.43 201	50.64 226	36.48 238	42.60 215	37.46 241	52.56 230	71.98 232	76.69 218	90.76 204	89.29 190

WR-MVS_H		64.14 229	65.36 217	62.71 230	72.47 221	82.33 211	65.13 230	66.99 203	51.81 222	36.47 239	43.33 210	42.77 220	43.99 239	72.41 230	75.99 220	91.20 189	88.86 196

SixPastTwentyTwo		63.75 230	63.42 229	64.13 227	72.91 218	80.34 229	61.29 239	63.90 216	49.58 231	40.42 230	54.99 161	37.13 243	60.90 212	68.46 241	70.80 238	85.37 242	82.65 227

PM-MVS		63.52 231	62.51 233	64.70 222	64.79 239	76.08 240	65.07 231	62.08 221	58.13 192	46.56 210	44.98 196	31.31 252	62.89 207	72.58 229	69.93 242	86.81 237	84.55 214

DTE-MVSNet		63.26 232	63.41 230	63.08 229	72.59 220	78.56 235	65.03 232	68.28 187	50.53 228	32.38 247	44.03 202	37.79 240	49.48 235	70.83 238	76.73 217	90.73 205	85.42 213

testgi		63.11 233	64.88 222	61.05 233	75.83 205	78.51 236	60.42 240	66.20 208	48.77 232	34.56 244	56.96 145	40.35 231	40.95 245	77.46 207	77.22 214	88.37 232	74.86 244

GG-mvs-BLEND		62.08 234	88.31 44	31.46 252	0.16 264	98.10 12	91.57 43	0.09 261	85.07 68	0.21 266	73.90 61	83.74 41	0.19 262	88.98 79	89.39 74	96.58 15	99.02 17

Anonymous20231206		62.05 235	61.83 235	62.30 232	72.09 223	77.84 237	63.10 237	67.62 198	50.20 229	36.68 237	29.59 246	37.05 244	43.90 240	77.33 208	77.31 213	90.41 209	83.49 219

N_pmnet		60.52 236	58.83 240	62.50 231	68.97 231	75.61 242	59.72 243	66.47 204	51.90 221	41.26 229	35.42 238	35.63 245	52.25 232	67.07 244	70.08 241	86.35 238	76.10 239

FE-MVSNET2		60.10 237	59.87 238	60.37 236	51.97 253	77.72 238	63.63 235	66.11 209	45.14 239	36.89 236	26.42 250	33.72 247	51.78 233	77.68 204	78.09 209	91.85 173	80.29 231

EU-MVSNet		58.73 238	60.92 237	56.17 239	66.17 236	72.39 245	58.85 244	61.24 224	48.47 233	27.91 252	46.70 191	40.06 232	39.07 247	68.27 242	70.34 240	83.77 245	80.23 232

test20.03		57.93 239	59.22 239	56.44 238	71.84 226	73.78 244	53.55 250	65.96 210	43.02 244	28.46 251	37.50 232	38.17 239	30.41 251	75.25 214	74.42 226	88.41 230	72.37 247

MDA-MVSNet-bldmvs		54.99 240	52.66 245	57.71 237	52.74 252	74.87 243	55.61 247	68.41 186	43.65 242	32.54 245	37.93 229	22.11 260	54.11 223	48.85 253	67.34 244	82.85 246	73.88 245

FE-MVSNET		54.54 241	55.85 241	53.01 242	44.64 255	70.42 248	54.91 248	64.61 215	39.64 246	23.66 258	26.69 249	32.48 251	41.99 241	71.03 236	74.94 222	88.27 233	75.74 240

new-patchmatchnet		53.91 242	52.69 244	55.33 241	64.83 238	70.90 246	52.24 251	61.75 222	41.09 245	30.82 248	29.90 244	28.22 255	36.69 248	61.52 247	65.08 245	85.64 241	72.14 248

MIMVSNet1		52.76 243	53.95 243	51.38 244	41.96 257	70.79 247	53.56 249	63.03 220	39.36 247	27.83 253	22.73 253	33.07 249	34.47 250	70.49 239	72.69 227	87.41 236	68.51 249

pmmvs3		52.59 244	52.43 246	52.78 243	54.53 251	64.49 252	50.07 252	46.89 253	35.31 251	30.19 249	27.27 248	26.96 257	53.02 229	67.28 243	70.54 239	81.96 248	75.20 242

new_pmnet		50.32 245	51.36 247	49.11 246	49.19 254	64.89 251	48.66 254	47.99 252	47.55 235	26.27 255	29.51 247	28.66 254	44.89 236	61.12 248	62.74 247	77.66 251	65.03 250

FPMVS		50.25 246	45.67 250	55.58 240	70.48 230	60.12 253	59.78 242	59.33 230	46.66 236	37.94 233	30.22 243	27.51 256	35.94 249	50.98 252	47.90 252	70.02 253	56.31 251

usedtu_dtu_shiyan2		50.22 247	49.72 249	50.80 245	33.02 261	67.71 250	57.83 246	52.96 245	27.83 256	39.36 232	20.55 255	29.77 253	40.68 246	61.53 246	62.06 248	80.93 250	78.57 235

test_method		47.92 248	55.39 242	39.21 249	19.90 262	49.24 256	39.29 257	34.65 258	57.37 196	32.54 245	25.11 251	41.02 227	44.31 238	66.58 245	57.57 250	64.59 256	90.82 174

PMVS	36.83 18	40.62 249	36.39 252	45.56 247	58.40 241	33.20 259	32.62 259	56.02 234	28.25 255	37.92 234	22.29 254	26.15 258	25.29 253	48.49 254	43.82 255	63.13 257	52.53 254
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
WB-MVS		39.74 250	42.23 251	36.84 250	66.24 235	50.82 255	26.18 262	66.39 206	31.14 254	4.85 264	37.06 234	24.28 259	7.95 259	54.48 249	54.23 251	49.46 260	43.61 255

Gipuma		35.20 251	33.96 253	36.65 251	43.30 256	32.51 260	26.96 261	48.31 251	38.87 248	20.08 260	8.08 257	7.41 264	26.44 252	53.60 250	58.43 249	54.81 258	38.79 257
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		32.52 252	33.92 254	30.88 253	34.15 260	44.70 258	27.79 260	39.69 257	22.21 257	4.31 265	15.73 256	14.13 262	12.45 258	40.11 255	47.00 253	66.88 254	53.54 252

E-PMN		21.42 253	17.56 256	25.94 254	36.25 259	19.02 263	11.56 263	43.72 255	15.25 259	6.99 262	8.04 258	4.53 266	21.77 255	16.13 258	26.16 257	35.34 261	33.77 258

MVE	25.07 19	21.25 254	23.51 255	18.62 256	15.07 263	29.77 262	10.67 265	34.60 259	12.51 260	9.46 261	7.84 259	3.82 267	14.38 257	27.45 257	42.42 256	27.56 263	40.74 256
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		20.61 255	16.32 257	25.62 255	36.41 258	18.93 264	11.51 264	43.75 254	15.65 258	6.53 263	7.56 260	4.68 265	22.03 254	14.56 259	23.10 258	33.51 262	29.77 259

testmvs		0.76 256	1.23 258	0.21 257	0.05 265	0.21 265	0.38 267	0.09 261	0.94 261	0.05 267	2.13 262	0.08 268	0.60 261	0.82 260	0.77 259	0.11 264	3.62 261

test123		0.67 257	1.11 259	0.16 258	0.01 266	0.14 266	0.20 268	0.04 263	0.77 262	0.02 268	2.15 261	0.02 269	0.61 260	0.23 261	0.72 260	0.07 265	3.76 260

uanet_test		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 270	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

sosnet-low-res		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 270	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

sosnet		0.00 258	0.00 260	0.00 259	0.00 267	0.00 267	0.00 269	0.00 264	0.00 263	0.00 269	0.00 263	0.00 270	0.00 263	0.00 262	0.00 261	0.00 266	0.00 262

TestfortrainingZip							95.37 12	93.61 1		96.88 1						96.96 8

TPM-MVS					98.35 1	98.66 4	96.92 2			83.78 27	90.39 25	94.36 1	94.48 4			96.58 15	93.94 132
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										39.41 231

9.14												91.16 7

SR-MVS					96.04 32			87.51 28				87.60 23

Anonymous202405211			75.59 159		85.13 139	91.06 129	84.62 118	77.96 89	69.47 160		40.79 223	63.84 136	84.57 72	83.55 148	84.69 134	89.69 220	95.75 101

our_test_3					73.80 217	79.57 233	64.47 234

ambc			50.35 248		55.61 249	59.93 254	48.73 253		44.08 240	35.81 242	24.01 252	10.64 263	41.57 243	72.83 228	63.35 246	74.99 252	77.61 236

MTAPA										91.14 9		85.84 29

MTMP										90.95 10		84.13 37

Patchmatch-RL test							8.17 266

tmp_tt				39.78 248	56.31 246	31.71 261	35.84 258	15.08 260	82.57 77	50.83 190	63.07 107	47.51 201	15.28 256	52.23 251	44.24 254	65.35 255

XVS					89.65 72	95.93 48	85.97 101			76.32 65		82.05 46				93.51 119

X-MVStestdata					89.65 72	95.93 48	85.97 101			76.32 65		82.05 46				93.51 119

mPP-MVS					95.90 34							80.22 55

NP-MVS									89.55 48

Patchmtry						87.41 159	78.32 169	54.14 241		51.09 185

DeepMVS_CX						48.96 257	43.77 255	40.58 256	50.93 225	24.67 257	36.95 235	20.18 261	41.60 242	38.92 256		52.37 259	53.31 253

High-resolution multi-view benchmark