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 1	97.60 9	98.92 1	95.99 5	92.05 8	97.14 1	94.19 1	94.71 7	93.25 1	95.08 1	94.32 11	92.59 15	96.49 18	99.58 3

DPE-MVS		95.10 2	95.53 2	94.60 5	97.77 7	98.64 3	96.60 4	92.45 6	96.34 5	91.41 5	96.70 2	92.26 5	93.56 4	93.68 17	91.73 29	95.79 37	99.37 7

DVP-MVS		95.06 3	95.37 4	94.70 2	97.59 10	98.89 2	95.37 11	92.04 9	96.85 3	94.00 2	92.81 14	93.02 2	92.93 5	94.22 14	92.15 20	96.30 24	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
MSP-MVS		95.00 4	95.47 3	94.45 6	96.78 18	98.11 9	95.72 7	90.91 14	96.68 4	91.57 4	96.98 1	89.47 13	94.76 2	95.24 3	92.15 20	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 5	94.85 6	94.15 8	98.03 4	98.59 4	95.56 8	92.91 3	94.86 13	88.46 15	91.32 21	90.83 10	94.03 3	95.20 4	94.16 5	95.89 33	99.01 15

SF-MVS		94.40 6	94.15 11	94.70 2	98.25 2	98.24 6	96.86 2	93.46 1	94.87 11	90.26 9	95.96 3	88.42 16	92.76 8	92.29 30	90.84 40	96.62 13	98.44 26

APDe-MVS		94.31 7	94.30 9	94.33 7	97.57 11	98.06 11	95.79 6	91.98 10	95.50 8	92.19 3	95.25 5	87.97 19	92.93 5	93.01 23	91.02 38	95.52 39	99.29 8

MCST-MVS		94.10 8	94.77 7	93.31 10	98.31 1	98.34 5	95.43 9	92.54 5	94.41 17	83.05 31	91.38 19	90.97 9	92.24 13	95.05 6	94.02 6	98.31 1	99.20 10

HPM-MVS++		94.04 9	94.96 5	92.96 12	97.93 5	97.71 17	94.65 14	91.01 13	95.91 6	87.43 17	93.52 11	92.63 4	92.29 12	94.22 14	92.34 17	94.47 58	98.37 28

NCCC		93.59 10	94.00 13	93.10 11	97.90 6	97.93 13	95.40 10	92.39 7	94.47 16	84.94 22	91.21 22	89.32 14	92.53 10	93.90 16	92.98 12	95.44 41	98.22 31

SMA-MVS		93.47 11	94.29 10	92.52 14	97.72 8	97.77 16	94.46 17	90.19 17	94.96 10	87.15 18	90.15 25	90.99 8	91.49 16	94.31 12	93.33 10	94.10 63	98.53 24
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 11	93.44 16	93.50 9	97.06 14	97.09 27	95.27 12	91.47 11	95.71 7	89.57 12	93.66 9	86.28 25	92.81 7	92.06 34	90.70 42	94.83 55	98.60 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		93.36 13	94.33 8	92.22 16	94.68 44	97.89 15	94.56 15	90.89 15	94.80 14	90.04 11	93.53 10	90.14 11	89.78 23	92.74 25	92.17 18	93.35 101	99.07 13
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 14	93.53 15	92.53 13	94.23 47	97.54 21	94.75 13	89.87 18	95.26 9	89.20 14	93.16 12	88.19 18	92.15 14	91.79 38	89.65 56	94.99 51	99.16 11
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 15	93.19 18	92.47 15	95.78 35	97.40 22	97.39 1	92.56 4	92.88 26	81.84 39	81.31 40	92.95 3	91.21 17	96.54 1	97.33 1	96.01 30	93.94 107

SteuartSystems-ACMMP		92.31 16	93.31 17	91.15 23	96.88 16	97.36 23	93.95 21	89.44 20	92.62 27	83.20 28	94.34 8	85.55 27	88.95 30	93.07 22	91.90 25	94.51 57	98.30 29
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_NAP		92.16 17	92.91 21	91.28 22	96.95 15	97.36 23	93.66 22	89.23 22	93.33 21	83.71 26	90.53 23	86.84 22	90.39 19	93.30 21	91.56 31	93.74 73	97.43 45

HFP-MVS		92.02 18	92.13 23	91.89 20	97.16 13	96.46 40	93.57 23	87.60 26	93.79 19	88.17 16	93.15 13	83.94 38	91.19 18	90.81 48	89.83 51	93.66 77	96.94 61

train_agg		91.99 19	93.71 14	89.98 28	96.42 27	97.03 29	94.31 19	89.05 23	93.33 21	77.75 46	95.06 6	88.27 17	88.38 36	92.02 35	91.41 33	94.00 66	98.84 18

xxxxxxxxxxxxxcwj		91.86 20	89.43 37	94.70 2	98.25 2	98.24 6	96.86 2	93.46 1	94.87 11	90.26 9	95.96 3	55.37 143	92.76 8	92.29 30	90.84 40	96.62 13	98.44 26

DeepC-MVS_fast	86.59 2	91.69 21	91.39 26	92.05 19	97.43 12	96.92 32	94.05 20	90.23 16	93.31 24	83.19 29	77.91 47	84.23 34	92.42 11	94.62 9	94.83 3	95.00 50	97.88 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		91.59 22	91.12 27	92.13 17	96.76 19	96.68 35	93.39 24	88.00 25	93.63 20	90.76 8	83.97 36	85.33 29	89.89 22	91.60 40	89.65 56	94.00 66	96.97 59

TSAR-MVS + GP.		91.29 23	93.11 20	89.18 34	87.81 85	96.21 46	92.51 33	83.83 46	94.24 18	83.77 25	91.87 18	89.62 12	90.07 20	90.40 52	90.31 46	97.09 6	99.10 12

ACMMPR		91.15 24	91.44 25	90.81 24	96.61 21	96.25 44	93.09 25	87.08 29	93.32 23	84.78 23	92.08 17	82.10 44	89.71 24	90.24 53	89.82 52	93.61 82	96.30 73

DeepPCF-MVS	86.71 1	91.00 25	94.05 12	87.43 45	95.58 38	98.17 8	86.22 74	88.59 24	97.01 2	76.77 52	85.11 34	88.90 15	87.29 42	95.02 7	94.69 4	90.15 172	99.48 6

TSAR-MVS + ACMM		90.98 26	93.18 19	88.42 39	95.69 36	96.73 34	94.52 16	86.97 32	92.99 25	76.32 53	92.31 16	86.64 23	84.40 68	92.97 24	92.02 22	92.62 124	98.59 22

MP-MVS		90.81 27	91.45 24	90.06 27	96.59 22	96.33 43	92.46 34	87.19 28	90.27 40	82.54 35	91.38 19	84.88 31	88.27 37	90.58 50	89.30 62	93.30 103	97.44 43
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		90.57 28	90.68 29	90.44 25	96.13 29	95.90 51	92.77 30	86.86 33	92.12 31	84.19 24	89.18 28	82.37 42	89.43 28	89.65 62	88.43 69	93.27 104	97.13 53

MSLP-MVS++		90.33 29	88.82 41	92.10 18	96.52 25	95.93 47	94.35 18	86.26 34	88.37 53	89.24 13	75.94 52	82.60 41	89.71 24	89.45 65	92.17 18	96.51 17	97.24 50

CANet		89.98 30	90.42 33	89.47 33	94.13 48	98.05 12	91.76 39	83.27 49	90.87 37	81.90 38	72.32 58	84.82 32	88.42 34	94.52 10	93.78 8	97.34 4	98.58 23

PGM-MVS		89.97 31	90.64 31	89.18 34	96.53 24	95.90 51	93.06 26	82.48 57	90.04 42	80.37 41	92.75 15	80.96 49	88.93 31	89.88 58	89.08 64	93.69 76	95.86 76

PHI-MVS		89.88 32	92.75 22	86.52 55	94.97 41	97.57 20	89.99 50	84.56 42	92.52 29	69.72 86	90.35 24	87.11 21	84.89 60	91.82 37	92.37 16	95.02 49	97.51 41

CSCG		89.81 33	89.69 34	89.96 29	96.55 23	97.90 14	92.89 28	87.06 30	88.74 51	86.17 19	78.24 46	86.53 24	84.75 63	87.82 85	90.59 43	92.32 129	98.01 34

X-MVS		89.73 34	90.65 30	88.66 37	96.44 26	95.93 47	92.26 36	86.98 31	90.73 38	76.32 53	89.56 27	82.05 45	86.51 49	89.98 56	89.60 58	93.43 96	96.72 68

EPNet		89.30 35	90.89 28	87.44 44	95.67 37	96.81 33	91.13 42	83.12 51	91.14 34	76.31 57	87.60 30	80.40 52	84.45 66	92.13 33	91.12 37	93.96 68	97.01 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	84.14 3	88.80 36	88.03 45	89.71 31	94.83 42	96.56 36	92.57 32	89.38 21	89.25 48	79.59 43	70.02 67	77.05 65	88.24 38	92.44 28	92.79 13	93.65 80	98.10 32
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 37	90.43 32	86.81 51	96.04 31	96.53 39	92.95 27	85.95 36	90.36 39	67.93 91	85.80 33	80.69 50	83.82 71	90.81 48	91.85 28	94.18 61	96.99 58

3Dnovator+	81.14 5	88.59 38	87.49 47	89.88 30	95.83 34	96.45 42	91.94 38	82.41 58	87.09 59	85.94 21	62.80 95	85.37 28	89.46 26	91.51 41	91.89 27	93.72 74	97.30 48

ACMMP		88.48 39	88.71 42	88.22 41	94.61 45	95.53 56	90.64 46	85.60 38	90.97 35	78.62 45	89.88 26	74.20 78	86.29 50	88.16 82	86.37 88	93.57 83	95.86 76
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 40	85.75 61	91.62 21	96.25 28	95.35 61	90.71 44	91.08 12	90.22 41	86.17 19	74.33 54	73.67 81	92.00 15	86.31 101	85.82 96	93.52 86	94.53 94

MVS_0304		88.43 41	89.46 36	87.21 46	91.85 60	97.60 18	92.62 31	81.10 64	87.16 58	73.80 63	72.19 60	83.36 40	87.03 44	94.64 8	93.67 9	96.88 9	97.64 40

3Dnovator	80.58 8	88.20 42	86.53 53	90.15 26	96.86 17	96.46 40	91.97 37	83.06 52	85.16 64	83.66 27	62.28 98	82.15 43	88.98 29	90.99 46	92.65 14	96.38 23	96.03 74

CPTT-MVS		88.17 43	87.84 46	88.55 38	93.33 50	93.75 76	92.33 35	84.75 41	89.87 44	81.72 40	83.93 37	81.12 48	88.45 33	85.42 110	84.07 114	90.72 164	96.72 68

MVS_111021_HR		87.82 44	88.84 40	86.62 53	94.42 46	97.36 23	88.21 59	83.26 50	83.42 67	72.52 73	82.63 38	76.93 66	84.95 59	91.93 36	91.15 36	96.39 22	98.49 25

DELS-MVS		87.75 45	86.92 51	88.71 36	94.69 43	97.34 26	92.78 29	84.50 43	77.87 90	81.94 37	67.17 75	75.49 73	82.84 77	95.38 2	95.93 2	95.55 38	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 57	88.11 83	90.05 112	89.28 53	77.05 84	91.37 32	79.97 42	76.70 50	85.25 30	84.89 60	93.53 18	91.41 33	96.73 11	95.55 83

MVS_111021_LR		87.58 47	88.67 43	86.31 56	92.58 54	95.89 53	86.20 75	82.49 56	89.08 50	77.47 49	86.20 32	74.22 77	85.49 55	90.03 55	88.52 67	93.66 77	96.74 67

QAPM		87.06 48	86.46 54	87.75 42	96.63 20	97.09 27	91.71 40	82.62 55	80.58 80	71.28 79	66.04 82	84.24 33	87.01 45	89.93 57	89.91 50	97.26 5	97.44 43

PVSNet_BlendedMVS		86.98 49	87.05 49	86.90 48	93.03 51	96.98 30	86.57 71	81.82 60	89.78 45	82.78 33	71.54 61	66.07 109	80.73 88	93.46 19	91.97 23	96.45 20	99.53 4

PVSNet_Blended		86.98 49	87.05 49	86.90 48	93.03 51	96.98 30	86.57 71	81.82 60	89.78 45	82.78 33	71.54 61	66.07 109	80.73 88	93.46 19	91.97 23	96.45 20	99.53 4

ETV-MVS		86.94 51	89.49 35	83.95 67	87.28 91	95.61 55	83.58 100	76.37 89	92.59 28	73.20 65	80.35 42	76.42 69	87.38 41	92.20 32	90.45 45	95.90 32	98.83 19

CS-MVS		86.48 52	89.02 39	83.52 71	87.37 90	95.52 57	84.21 94	75.09 97	87.63 55	71.30 78	80.94 41	78.24 55	87.23 43	92.72 26	90.05 48	95.95 31	98.03 33

OMC-MVS		86.38 53	86.21 58	86.57 54	92.30 56	94.35 70	87.60 63	83.51 48	92.32 30	77.37 50	72.27 59	77.83 58	86.59 48	87.62 87	85.95 93	92.08 133	93.11 120

HQP-MVS		86.17 54	87.35 48	84.80 63	91.41 63	92.37 93	91.05 43	84.35 45	88.52 52	64.21 96	87.05 31	68.91 99	84.80 62	89.12 68	88.16 73	92.96 115	97.31 47

canonicalmvs		85.93 55	86.26 57	85.54 58	88.94 74	95.44 58	89.56 51	76.01 91	87.83 54	77.70 47	76.43 51	68.66 101	87.80 40	87.02 90	91.51 32	93.25 105	96.95 60

MAR-MVS		85.65 56	86.30 56	84.88 62	95.51 40	95.89 53	86.50 73	76.71 85	89.23 49	68.59 88	70.93 65	74.49 75	88.55 32	89.40 66	90.30 47	93.42 97	93.88 111
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 57	84.41 66	86.81 51	91.51 62	96.23 45	90.27 47	89.81 19	77.87 90	70.67 82	69.20 69	77.86 56	85.55 54	85.92 106	86.38 87	93.03 112	97.43 45
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		85.43 58	84.24 69	86.83 50	87.69 87	93.16 84	90.01 49	82.72 54	87.17 57	79.28 44	71.43 64	65.81 111	86.02 51	87.33 89	86.96 81	95.25 45	97.83 37
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 59	83.42 73	87.03 47	96.12 30	96.55 38	89.36 52	81.59 62	79.19 86	75.20 59	55.84 124	79.04 54	84.45 66	88.47 76	89.35 61	95.48 40	95.48 84

TSAR-MVS + COLMAP		84.93 60	85.79 60	83.92 68	90.90 65	93.57 80	89.25 54	82.00 59	91.29 33	61.66 104	88.25 29	59.46 131	86.71 47	89.79 59	87.09 79	93.01 113	91.09 139

TAPA-MVS	80.99 7	84.83 61	84.42 65	85.31 59	91.89 59	93.73 78	88.53 58	82.80 53	89.99 43	69.78 85	71.53 63	75.03 74	85.47 56	86.26 102	84.54 109	93.39 99	89.90 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	81.02 6	84.81 62	81.81 90	88.31 40	93.77 49	90.35 107	88.80 56	84.47 44	86.76 60	82.17 36	66.56 78	71.01 92	88.41 35	85.48 108	84.28 112	92.26 131	88.21 161
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EIA-MVS		84.75 63	86.43 55	82.79 74	86.88 94	95.36 60	82.84 105	76.39 88	87.61 56	71.03 80	74.33 54	71.12 91	85.16 57	89.69 61	88.70 66	94.40 59	98.23 30

CNLPA		84.72 64	82.14 85	87.73 43	92.85 53	93.83 75	84.70 90	85.07 39	90.90 36	83.16 30	56.28 120	71.53 88	88.14 39	84.19 114	84.00 118	92.48 126	94.26 101

MVS_Test		84.60 65	85.13 64	83.99 66	88.17 81	95.27 62	88.21 59	73.15 112	84.31 66	70.55 83	68.67 73	68.78 100	86.99 46	91.71 39	91.90 25	96.84 10	95.27 88

casdiffmvs		83.84 66	82.65 81	85.22 60	87.25 92	94.62 68	86.01 78	79.62 67	79.48 83	77.59 48	61.92 101	64.34 115	85.57 53	90.55 51	90.51 44	95.26 43	97.14 52

baseline		83.83 67	84.38 67	83.18 73	86.65 96	94.59 69	85.79 81	73.78 109	85.83 62	72.94 66	69.28 68	70.80 94	83.45 74	86.80 93	87.59 75	96.47 19	95.77 80

diffmvs		83.69 68	83.17 77	84.31 64	85.45 109	93.92 71	86.89 66	78.62 70	82.71 73	75.95 58	66.78 77	63.90 118	83.84 70	87.90 84	89.16 63	95.10 48	97.82 38

CANet_DTU		83.33 69	86.59 52	79.53 94	88.88 75	94.87 65	86.63 70	68.85 143	85.45 63	50.54 149	77.86 48	69.94 97	85.62 52	92.63 27	90.88 39	96.63 12	94.46 95

DI_MVS_plusplus_trai		83.32 70	82.53 83	84.25 65	86.26 103	93.66 79	90.23 48	77.16 83	77.05 97	74.06 62	53.74 133	74.33 76	83.61 73	91.40 43	89.82 52	94.17 62	97.73 39

baseline1		82.63 71	82.02 86	83.34 72	88.30 80	91.89 97	88.03 62	80.86 65	75.05 104	65.96 92	64.27 89	72.20 86	80.01 92	91.32 44	89.56 59	96.90 8	89.85 149

PVSNet_Blended_VisFu		82.55 72	83.70 72	81.21 83	89.66 69	95.15 64	82.41 106	77.36 82	72.53 118	73.64 64	61.15 104	77.19 64	70.35 146	91.31 45	89.72 55	93.84 70	98.85 17

ET-MVSNet_ETH3D		82.37 73	85.68 62	78.51 103	62.90 205	94.66 66	87.06 65	73.57 110	83.13 69	61.52 106	78.37 45	76.01 71	89.99 21	84.14 115	89.03 65	96.03 29	94.42 96

PMMVS		82.26 74	85.48 63	78.51 103	85.92 106	91.92 96	78.30 135	70.77 129	86.30 61	61.11 108	82.46 39	70.88 93	84.70 64	88.05 83	84.78 105	90.24 171	93.98 105

ACMP	79.58 9	82.23 75	81.82 89	82.71 75	88.15 82	90.95 105	85.23 86	78.52 72	81.70 75	72.52 73	78.41 44	60.63 126	80.48 90	82.88 125	83.44 122	91.37 150	94.70 92
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CHOSEN 280x420		82.15 76	85.87 59	77.80 108	86.54 99	93.42 82	81.74 108	59.96 184	78.99 88	63.99 97	74.50 53	83.95 37	80.99 83	89.53 64	85.01 101	93.56 85	95.71 82

LGP-MVS_train		82.12 77	82.57 82	81.59 80	89.26 73	90.23 110	88.76 57	78.05 73	81.26 77	61.64 105	79.52 43	62.11 121	79.59 94	85.20 111	84.68 107	92.27 130	95.02 90

FMVSNet3		81.93 78	81.98 87	81.88 79	79.49 145	87.02 127	88.15 61	72.57 115	83.02 70	72.63 70	56.55 116	73.48 82	82.34 80	91.49 42	91.20 35	96.07 25	91.13 138

thisisatest0530		81.67 79	84.27 68	78.63 99	85.53 107	93.88 74	81.77 107	73.84 106	81.35 76	63.85 99	68.79 71	77.64 60	73.02 127	88.73 74	85.73 97	93.76 72	93.80 115

tttt0517		81.51 80	84.12 71	78.47 105	85.33 111	93.74 77	81.42 111	73.84 106	81.21 78	63.59 100	68.73 72	77.46 63	73.02 127	88.47 76	85.73 97	93.63 81	93.49 119

OPM-MVS		81.34 81	78.18 106	85.02 61	91.27 64	91.78 98	90.66 45	83.62 47	62.39 147	65.91 93	63.35 93	64.33 116	85.03 58	87.77 86	85.88 95	93.66 77	91.75 135

baseline2		81.21 82	83.36 76	78.70 98	83.22 124	92.71 86	80.32 116	74.25 105	80.39 81	63.94 98	68.89 70	68.44 102	74.67 113	89.61 63	86.68 85	95.83 36	96.81 66

IS_MVSNet		80.92 83	84.14 70	77.16 111	87.43 88	93.90 73	80.44 112	74.64 99	75.05 104	61.10 109	65.59 84	76.89 67	67.39 154	90.88 47	90.05 48	91.95 137	96.62 71

ACMM	78.09 10	80.91 84	78.39 104	83.86 69	89.61 72	87.71 124	85.16 87	80.67 66	79.04 87	74.18 61	63.82 92	60.84 125	82.59 78	84.33 113	83.59 121	90.96 158	89.39 154
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPP-MVSNet		80.82 85	82.79 79	78.52 101	86.31 102	92.37 93	79.83 119	74.51 100	73.79 113	64.46 95	67.01 76	80.63 51	74.33 116	85.63 107	84.35 111	91.68 143	95.79 79

CostFormer		80.72 86	81.81 90	79.44 96	86.50 100	91.65 99	84.31 92	59.84 185	80.86 79	72.69 68	62.46 97	73.74 79	79.93 93	82.58 129	84.50 110	93.37 100	96.90 64

GBi-Net		80.72 86	80.49 93	81.00 86	78.18 149	86.19 141	86.73 67	72.57 115	83.02 70	72.63 70	56.55 116	73.48 82	80.99 83	86.57 95	86.83 82	94.89 52	90.77 141

test1		80.72 86	80.49 93	81.00 86	78.18 149	86.19 141	86.73 67	72.57 115	83.02 70	72.63 70	56.55 116	73.48 82	80.99 83	86.57 95	86.83 82	94.89 52	90.77 141

UGNet		80.71 89	83.09 78	77.93 107	87.02 93	92.71 86	80.28 117	76.53 86	73.83 112	71.35 77	70.07 66	73.71 80	58.93 174	87.39 88	86.97 80	93.48 93	96.94 61
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
CHOSEN 1792x2688		80.23 90	79.16 101	81.48 81	91.97 57	96.56 36	86.18 76	75.40 95	76.17 100	61.32 107	37.43 192	61.08 124	76.52 106	92.35 29	91.64 30	97.46 3	98.86 16

thres100view900		79.83 91	77.79 110	82.21 76	88.42 77	93.54 81	87.07 64	81.11 63	70.15 125	61.01 110	56.65 114	51.22 147	81.78 81	89.77 60	85.95 93	93.84 70	97.26 49

Effi-MVS+		79.80 92	80.04 95	79.52 95	85.53 107	93.31 83	85.28 84	70.68 131	74.15 108	58.79 119	62.03 100	60.51 127	83.37 75	88.41 78	86.09 92	93.49 92	95.80 78

DCV-MVSNet		79.76 93	79.17 100	80.44 91	84.65 115	84.51 165	84.20 95	72.36 120	75.17 103	70.81 81	66.21 81	66.56 106	80.99 83	82.89 124	84.56 108	89.65 177	94.30 100

FC-MVSNet-train		79.54 94	78.20 105	81.09 85	86.55 98	88.63 120	79.96 118	78.53 71	70.90 123	68.24 89	65.87 83	56.45 141	80.29 91	86.20 104	84.08 113	92.97 114	95.31 87

test-LLR		79.52 95	83.42 73	74.97 120	81.79 129	91.26 100	76.17 156	70.57 132	77.71 92	52.14 136	66.26 79	77.47 61	73.10 123	87.02 90	87.16 77	96.05 27	97.02 55

FMVSNet2		79.24 96	78.14 107	80.53 90	78.18 149	86.19 141	86.73 67	71.91 121	72.97 115	70.48 84	50.63 142	66.56 106	80.99 83	90.10 54	89.77 54	94.89 52	90.77 141

TESTMET0.1,1		79.15 97	83.42 73	74.18 126	79.81 143	91.26 100	76.17 156	67.83 156	77.71 92	52.14 136	66.26 79	77.47 61	73.10 123	87.02 90	87.16 77	96.05 27	97.02 55

tfpn200view9		79.05 98	77.21 112	81.18 84	88.42 77	92.55 91	85.12 88	77.94 75	70.15 125	61.01 110	56.65 114	51.22 147	81.11 82	88.23 79	84.80 104	93.50 91	96.90 64

PatchMatch-RL		78.75 99	76.47 119	81.41 82	88.53 76	91.10 102	78.09 136	77.51 81	77.33 94	71.98 75	64.38 88	48.10 159	82.55 79	84.06 116	82.35 131	89.78 174	87.97 163

LS3D		78.72 100	75.79 123	82.15 77	91.91 58	89.39 117	83.66 98	85.88 37	76.81 98	59.22 118	57.67 111	58.53 135	83.72 72	82.07 134	81.63 142	88.50 185	84.39 174

thres200		78.69 101	76.71 115	80.99 88	88.35 79	92.56 89	86.03 77	77.94 75	66.27 132	60.66 112	56.08 121	51.11 149	79.45 95	88.23 79	85.54 100	93.52 86	97.20 51

Anonymous20231211		78.61 102	75.57 126	82.15 77	84.43 118	90.26 108	84.08 96	77.68 78	71.09 121	72.90 67	39.24 185	66.21 108	84.23 69	82.15 132	84.04 115	89.61 178	96.03 74

IB-MVS	74.10 12	78.52 103	78.51 103	78.52 101	90.15 67	95.39 59	71.95 176	77.53 80	74.95 106	77.25 51	58.93 108	55.92 142	58.37 176	79.01 159	87.89 74	95.88 34	97.47 42
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		78.49 104	81.96 88	74.45 125	92.57 55	88.74 119	82.98 101	78.83 69	83.28 68	44.64 180	77.40 49	67.73 103	53.98 185	85.44 109	84.91 102	93.71 75	86.22 169
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres400		78.39 105	76.39 120	80.73 89	88.02 84	92.94 85	84.77 89	78.88 68	65.20 140	59.70 116	55.20 127	50.85 150	79.45 95	88.81 71	84.81 103	93.57 83	96.91 63

UA-Net		78.30 106	80.92 92	75.25 119	87.42 89	92.48 92	79.54 122	75.49 94	60.47 151	60.52 113	68.44 74	84.08 36	57.54 178	88.54 75	88.45 68	90.96 158	83.97 176

Vis-MVSNet (Re-imp)		78.28 107	82.68 80	73.16 137	86.64 97	92.68 88	78.07 137	74.48 101	74.05 109	53.47 129	64.22 90	76.52 68	54.28 181	88.96 70	88.29 71	92.03 135	94.00 104

MSDG		78.11 108	73.17 139	83.86 69	91.78 61	86.83 129	85.25 85	86.02 35	72.84 116	69.69 87	51.43 139	54.00 145	77.61 99	81.95 137	82.27 133	92.83 120	82.91 181

HyFIR lowres test		78.08 109	76.81 113	79.56 93	90.77 66	94.64 67	82.97 102	69.85 137	69.81 127	59.53 117	33.52 197	64.66 112	78.97 97	88.77 73	88.38 70	95.27 42	97.86 36

test-mter		77.90 110	82.44 84	72.60 142	78.52 147	90.24 109	73.85 169	65.31 170	76.37 99	51.29 140	65.58 85	75.94 72	71.36 137	85.98 105	86.26 89	95.26 43	96.71 70

thres600view7		77.66 111	75.67 124	79.98 92	87.71 86	92.56 89	83.79 97	77.94 75	64.41 142	58.69 120	54.32 132	50.54 151	78.23 98	88.23 79	83.06 125	93.52 86	96.55 72

MS-PatchMatch		77.47 112	76.48 118	78.63 99	89.89 68	90.42 106	85.42 83	69.53 139	70.79 124	60.43 114	50.05 144	70.62 96	70.66 143	86.71 94	82.54 128	95.86 35	84.23 175

Fast-Effi-MVS+		77.37 113	76.68 116	78.17 106	82.84 126	89.94 113	81.47 110	68.01 152	72.99 114	60.26 115	55.07 128	53.20 146	82.99 76	86.47 100	86.12 91	93.46 94	92.98 123

Vis-MVSNet		77.24 114	79.99 98	74.02 127	84.62 116	93.92 71	80.33 115	72.55 118	62.58 146	55.25 127	64.45 87	69.49 98	57.00 179	88.78 72	88.21 72	94.36 60	92.54 126
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MDTV_nov1_ep13		77.20 115	80.04 95	73.90 129	82.22 127	90.14 111	79.25 126	61.52 180	78.63 89	56.98 121	65.52 86	72.80 85	73.05 125	80.93 145	83.20 123	90.36 168	89.05 157

EPMVS		77.16 116	79.08 102	74.92 121	86.73 95	91.98 95	78.62 131	55.44 193	79.43 84	56.59 123	61.24 103	70.73 95	76.97 103	80.59 148	81.43 149	95.15 47	88.17 162

tpm cat1		76.93 117	76.19 122	77.79 109	85.08 114	88.58 121	82.96 103	59.33 186	75.72 102	72.64 69	51.25 140	64.41 114	75.74 110	77.90 168	80.10 165	90.97 157	95.35 85

PatchmatchNet		76.85 118	80.03 97	73.15 138	84.08 120	91.04 104	77.76 141	55.85 192	79.43 84	52.74 134	62.08 99	76.02 70	74.56 114	79.92 153	81.41 150	93.92 69	90.29 146
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 119	76.33 121	77.35 110	84.07 121	84.11 167	81.54 109	68.52 145	66.17 133	61.74 103	57.84 110	64.31 117	74.88 112	83.48 121	86.21 90	93.34 102	92.16 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		76.57 120	76.78 114	76.32 114	80.94 136	89.75 114	82.94 104	72.64 114	59.01 157	62.95 102	58.60 109	62.67 120	66.91 156	86.26 102	87.20 76	91.57 145	93.97 106
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
SCA		76.41 121	79.90 99	72.35 146	84.26 119	85.24 156	75.57 163	54.56 195	79.95 82	52.72 135	64.22 90	77.84 57	73.73 120	80.48 149	81.37 151	93.25 105	90.20 147

tpmrst		76.27 122	77.65 111	74.66 123	86.13 105	89.53 116	79.31 125	54.91 194	77.19 96	56.27 124	55.87 123	64.58 113	77.25 100	80.85 146	80.21 162	94.07 64	95.32 86

dps		75.76 123	75.02 128	76.63 113	84.51 117	88.12 122	77.51 142	58.33 188	75.91 101	71.98 75	57.37 112	57.85 136	76.81 105	77.89 169	78.40 174	90.63 165	89.63 151

CR-MVSNet		74.84 124	77.91 108	71.26 159	81.77 131	85.52 152	78.32 133	54.14 197	74.05 109	51.09 143	50.00 145	71.38 90	70.77 141	86.48 98	84.03 116	91.46 149	93.92 108

Effi-MVS+-dtu		74.57 125	74.60 132	74.53 124	81.38 133	86.74 131	80.39 114	67.70 157	67.36 131	53.06 130	59.86 106	57.50 137	75.84 109	80.19 151	78.62 172	88.79 184	91.95 134

test_part1		74.38 126	70.16 152	79.31 97	83.30 123	84.45 166	84.31 92	71.43 126	55.24 169	74.88 60	38.77 187	59.61 130	75.29 111	78.96 160	81.53 144	86.63 193	92.55 125

RPSCF		74.27 127	73.24 138	75.48 118	81.01 135	80.18 189	76.24 155	72.37 119	74.84 107	68.24 89	72.47 57	67.39 104	73.89 117	71.05 193	69.38 200	81.14 205	77.37 193

FMVSNet1		74.26 128	71.95 144	76.95 112	74.28 181	83.94 169	83.61 99	69.99 135	57.08 162	65.08 94	42.39 174	57.41 138	76.98 102	86.57 95	86.83 82	91.77 142	89.42 152

GA-MVS		73.62 129	74.52 133	72.58 143	79.93 141	89.29 118	78.02 138	71.67 124	60.79 150	42.68 184	54.41 131	49.07 155	70.07 147	89.39 67	86.55 86	93.13 110	92.12 131

Fast-Effi-MVS+-dtu		73.56 130	75.32 127	71.50 155	80.35 138	86.83 129	79.72 120	58.07 189	67.64 130	44.83 177	60.28 105	54.07 144	73.59 122	81.90 139	82.30 132	92.46 127	94.18 102

tpm		73.50 131	74.85 129	71.93 149	83.19 125	86.84 128	78.61 132	55.91 191	65.64 135	48.90 156	56.30 119	61.09 123	72.31 129	79.10 158	80.61 161	92.68 122	94.35 99

RPMNet		73.46 132	77.85 109	68.34 169	81.71 132	85.52 152	73.83 170	50.54 204	74.05 109	46.10 171	53.03 136	71.91 87	66.31 158	83.55 119	82.18 135	91.55 147	94.71 91

USDC		73.43 133	72.31 142	74.73 122	80.86 137	86.21 139	80.42 113	71.83 123	71.69 120	46.94 164	59.60 107	42.58 180	76.47 107	82.66 128	81.22 154	91.88 139	82.24 187

pmmvs4		73.38 134	71.53 147	75.55 117	75.95 167	85.24 156	77.25 146	71.59 125	71.03 122	63.10 101	49.09 150	44.22 170	73.73 120	82.04 135	80.18 163	91.68 143	88.89 159

UniMVSNet_NR-MVSNet		73.11 135	72.59 140	73.71 132	76.90 158	86.58 135	77.01 147	75.82 92	65.59 136	48.82 157	50.97 141	48.42 157	71.61 133	79.19 157	83.03 126	92.11 132	94.37 97

FMVSNet5		72.83 136	73.89 136	71.59 153	67.42 199	76.28 197	75.88 160	63.74 174	77.27 95	54.59 128	53.32 134	71.48 89	73.85 118	81.95 137	81.69 140	94.06 65	75.20 197

PatchT		72.66 137	76.58 117	68.09 171	79.02 146	86.09 145	59.81 198	51.78 202	72.00 119	51.09 143	46.84 154	66.70 105	70.77 141	86.48 98	84.03 116	96.07 25	93.92 108

ACMH	71.22 14	72.65 138	70.13 153	75.59 116	86.19 104	86.14 144	75.76 161	77.63 79	54.79 171	46.16 170	53.28 135	47.28 161	77.24 101	78.91 161	81.18 155	90.57 166	89.33 155
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		72.43 139	74.05 134	70.55 163	80.34 139	81.17 183	77.44 143	61.00 183	63.57 145	46.82 166	55.88 122	59.09 134	65.03 160	83.15 122	83.83 119	92.67 123	91.65 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH+	72.14 13	72.38 140	69.34 160	75.93 115	85.21 112	84.89 160	76.96 150	76.04 90	59.76 152	51.63 139	50.37 143	48.69 156	76.90 104	76.06 177	78.69 170	88.85 183	86.90 167

DU-MVS		72.19 141	71.35 148	73.17 136	75.95 167	86.02 146	77.01 147	74.42 102	65.39 138	48.82 157	49.10 148	42.81 178	71.61 133	78.67 162	83.10 124	91.22 153	94.37 97

IterMVS-SCA-FT		72.18 142	73.96 135	70.11 165	80.15 140	81.11 184	77.42 144	61.09 182	63.67 144	46.73 167	55.77 125	59.15 133	63.95 163	82.83 126	83.70 120	91.31 151	91.49 137

UniMVSNet (Re)		72.12 143	72.28 143	71.93 149	76.77 159	87.38 126	75.73 162	73.51 111	65.76 134	50.24 151	48.65 151	46.49 162	63.85 164	80.10 152	82.47 129	91.49 148	95.13 89

ADS-MVSNet		72.11 144	73.72 137	70.24 164	81.24 134	86.59 134	74.75 166	50.56 203	72.58 117	49.17 154	55.40 126	61.46 122	73.80 119	76.01 178	78.14 175	91.93 138	85.86 170

gg-mvs-nofinetune		72.10 145	74.79 130	68.97 168	83.31 122	95.22 63	85.66 82	48.77 205	35.68 207	22.17 213	30.49 200	77.73 59	76.37 108	94.30 13	93.03 11	97.55 2	97.05 54

TAMVS		72.06 146	71.76 146	72.41 145	76.68 160	88.12 122	74.82 165	68.09 150	53.52 176	56.91 122	52.94 137	56.93 140	66.91 156	81.37 142	82.44 130	91.07 155	86.99 166

v2v482		71.73 147	69.80 155	73.99 128	75.88 171	86.66 133	79.58 121	71.90 122	57.58 161	50.41 150	45.35 158	43.24 176	73.05 125	79.69 154	82.18 135	93.08 111	93.87 112

test0.0.03 1		71.70 148	74.68 131	68.23 170	81.79 129	83.81 170	68.64 180	70.57 132	68.81 129	43.47 181	62.77 96	60.09 129	51.77 192	82.48 130	81.67 141	93.16 108	83.13 179

V42		71.58 149	70.11 154	73.30 135	75.66 174	86.68 132	79.17 128	69.92 136	59.29 156	52.80 133	44.36 162	45.66 164	68.83 148	79.48 156	81.49 146	93.44 95	93.82 114

NR-MVSNet		71.47 150	71.11 149	71.90 151	77.73 154	86.02 146	76.88 151	74.42 102	65.39 138	46.09 172	49.10 148	39.87 192	64.27 162	81.40 141	82.24 134	91.99 136	93.75 116

v8		71.42 151	69.69 156	73.43 134	76.45 163	85.12 159	79.53 123	67.47 160	59.34 155	52.90 132	44.60 160	45.82 163	71.05 139	79.56 155	81.45 148	93.17 107	91.96 133

TranMVSNet+NR-MVSNet		71.12 152	70.24 151	72.15 147	76.01 166	84.80 162	76.55 153	75.65 93	61.99 148	45.29 175	48.42 152	43.07 177	67.55 152	78.28 165	82.83 127	91.85 140	92.29 127

v10		70.97 153	69.44 157	72.75 139	75.90 170	84.58 164	79.43 124	66.45 165	58.07 159	49.93 152	43.87 168	43.68 171	71.91 131	82.04 135	81.70 139	92.89 118	92.11 132

v1144		70.93 154	69.42 159	72.70 140	75.48 175	86.26 137	79.22 127	69.39 141	55.61 167	48.05 162	43.47 169	42.55 181	71.51 135	82.11 133	81.74 138	92.56 125	94.17 103

thisisatest0515		70.62 155	71.94 145	69.07 167	76.48 162	85.59 151	68.03 181	68.02 151	59.70 153	52.94 131	52.19 138	50.36 152	58.10 177	83.15 122	81.63 142	90.87 161	90.99 140

Baseline_NR-MVSNet		70.61 156	68.87 163	72.65 141	75.95 167	80.49 187	75.92 159	74.75 98	65.10 141	48.78 159	41.28 180	44.28 169	68.45 149	78.67 162	79.64 166	92.04 134	92.62 124

v148		70.34 157	68.46 166	72.54 144	76.04 165	86.38 136	74.83 164	72.73 113	55.88 166	55.26 126	43.32 171	43.49 172	64.52 161	76.93 175	80.11 164	91.85 140	93.11 120

v1192		70.32 158	68.77 164	72.12 148	74.76 177	85.62 150	78.73 129	68.53 144	55.08 170	46.34 169	42.39 174	40.67 187	71.90 132	82.27 131	81.53 144	92.43 128	93.86 113

v144192		70.10 159	68.55 165	71.90 151	74.55 178	85.67 149	77.81 139	68.22 149	54.65 172	46.91 165	42.76 172	41.27 185	70.95 140	80.48 149	81.11 159	92.96 115	93.90 110

pmmvs5		70.01 160	69.31 161	70.82 162	75.80 173	86.26 137	72.94 171	67.91 153	53.84 175	47.22 163	47.31 153	41.47 184	67.61 151	83.93 118	81.93 137	93.42 97	90.42 145

COLMAP_ROB	66.31 15	69.91 161	66.61 171	73.76 130	86.44 101	82.76 174	76.59 152	76.46 87	63.82 143	50.92 147	45.60 157	49.13 154	65.87 159	74.96 183	74.45 190	86.30 195	75.57 196
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		69.85 162	68.38 167	71.58 154	74.35 179	85.39 154	77.78 140	67.88 155	54.64 173	45.39 174	42.11 177	39.97 191	71.10 138	81.68 140	81.17 157	92.96 115	93.69 118

pm-mvs1		69.62 163	68.07 169	71.44 156	77.21 156	85.32 155	76.11 158	71.05 127	46.55 196	51.17 142	41.83 178	48.20 158	61.81 170	84.00 117	81.14 158	91.28 152	89.42 152

UniMVSNet_ETH3D		69.49 164	65.86 173	73.72 131	76.51 161	85.88 148	78.65 130	70.52 134	48.08 193	55.71 125	37.64 189	40.56 188	71.38 136	75.05 182	81.49 146	89.57 180	92.29 127

tfpnnormal		69.29 165	65.58 174	73.62 133	79.87 142	84.82 161	76.97 149	75.12 96	45.29 197	49.03 155	35.57 195	37.20 200	68.02 150	82.70 127	81.24 153	92.69 121	92.20 129

v1240		69.28 166	67.82 170	71.00 161	74.09 183	85.13 158	76.54 154	67.28 162	53.17 177	44.70 178	41.55 179	39.38 193	70.51 145	81.29 143	81.18 155	92.88 119	93.02 122

CVMVSNet		68.95 167	70.79 150	66.79 177	79.69 144	83.75 171	72.05 175	70.90 128	56.20 164	36.30 196	54.94 130	59.22 132	54.03 184	78.33 164	78.65 171	87.77 190	84.44 173

MIMVSNet		68.66 168	69.43 158	67.76 172	64.92 202	84.68 163	74.16 167	54.10 199	60.85 149	51.27 141	39.47 184	49.48 153	67.48 153	84.86 112	85.57 99	94.63 56	81.10 188

TDRefinement		67.82 169	64.91 180	71.22 160	82.08 128	81.45 179	77.42 144	73.79 108	59.62 154	48.35 161	42.35 176	42.40 182	60.87 172	74.69 184	74.64 189	84.83 199	79.20 191

anonymousdsp		67.61 170	68.94 162	66.04 178	71.44 195	83.97 168	66.45 185	63.53 176	50.54 186	42.42 185	49.39 146	45.63 165	62.84 167	77.99 167	81.34 152	89.59 179	93.75 116

TinyColmap		67.16 171	63.51 187	71.42 157	77.94 152	79.54 193	72.80 172	69.78 138	56.58 163	45.52 173	44.53 161	33.53 205	74.45 115	76.91 176	77.06 181	88.03 189	76.41 194

FC-MVSNet-test		67.04 172	72.47 141	60.70 195	76.92 157	81.41 180	61.52 195	69.45 140	65.58 137	26.74 209	61.79 102	60.40 128	41.17 200	77.60 171	77.78 177	88.41 186	82.70 183

TransMVSNet (Re)		66.87 173	64.30 182	69.88 166	78.32 148	81.35 182	73.88 168	74.34 104	43.19 201	45.20 176	40.12 182	42.37 183	55.97 180	80.85 146	79.15 167	91.56 146	83.06 180

CMPMVS	50.59 17	66.74 174	62.72 191	71.42 157	85.40 110	89.72 115	72.69 173	70.72 130	51.24 182	51.75 138	38.91 186	44.40 167	63.74 165	70.84 194	71.52 194	84.19 200	72.45 201
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		66.43 175	65.51 175	67.51 173	71.63 194	83.10 172	70.89 179	65.02 171	50.13 189	44.68 179	39.59 183	38.77 194	62.57 168	77.59 172	78.91 168	90.29 170	90.44 144

EG-PatchMatch MVS		66.23 176	65.20 177	67.43 174	77.74 153	86.20 140	72.51 174	63.68 175	43.95 199	43.44 182	36.22 194	45.43 166	54.04 183	81.00 144	80.95 160	93.15 109	82.67 184

WR-MVS		64.98 177	66.59 172	63.09 188	74.34 180	82.68 175	64.98 191	69.17 142	54.42 174	36.18 197	44.32 163	44.35 168	44.65 195	73.60 185	77.83 176	89.21 182	88.96 158

gm-plane-assit		64.86 178	68.15 168	61.02 194	76.44 164	68.29 206	41.60 211	53.37 200	34.68 209	26.19 211	33.22 198	57.09 139	71.97 130	95.12 5	93.97 7	96.54 16	94.66 93

CP-MVSNet		64.84 179	64.97 178	64.69 183	72.09 190	81.04 185	66.66 184	67.53 159	52.45 179	37.40 192	44.00 167	38.37 196	53.54 187	72.26 189	76.93 182	90.94 160	89.75 150

MDTV_nov1_ep13_2view		64.72 180	64.94 179	64.46 184	71.14 196	81.94 178	67.53 182	54.54 196	55.92 165	43.29 183	44.02 166	43.27 175	59.87 173	71.85 191	74.77 188	90.36 168	82.82 182

MVS-HIRNet		64.63 181	64.03 186	65.33 180	75.01 176	82.84 173	58.54 202	52.10 201	55.42 168	49.29 153	29.83 203	43.48 173	66.97 155	78.28 165	78.81 169	90.07 173	79.52 190

pmnet_mix02		64.58 182	64.11 185	65.12 181	74.16 182	80.17 190	63.24 193	67.91 153	57.87 160	41.69 186	45.86 156	40.99 186	53.97 186	69.92 197	71.67 193	89.77 175	82.29 186

LTVRE_ROB	63.07 16	64.49 183	63.16 190	66.04 178	77.47 155	82.64 176	70.98 178	65.02 171	34.01 210	29.61 205	49.12 147	35.58 204	70.57 144	75.10 181	78.45 173	82.60 203	87.24 165
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		64.35 184	64.29 183	64.42 185	72.67 186	79.83 191	66.97 183	68.24 148	51.21 183	35.29 199	44.09 164	38.51 195	52.36 190	71.06 192	77.65 178	90.99 156	87.68 164

pmmvs6		64.24 185	61.77 195	67.12 175	72.39 189	81.39 181	71.33 177	65.95 169	36.05 206	48.48 160	30.55 199	43.45 174	58.75 175	77.88 170	76.36 185	85.83 196	86.70 168

pmmvs-eth3d		64.24 185	61.96 193	66.90 176	66.35 200	76.04 199	66.09 187	66.31 166	52.59 178	50.94 146	37.61 190	32.79 207	62.43 169	75.78 179	75.48 187	89.27 181	83.39 178

PS-CasMVS		64.22 187	64.19 184	64.25 186	71.86 192	80.67 186	66.42 186	67.43 161	50.64 185	36.48 194	42.60 173	37.46 199	52.56 189	71.98 190	76.69 184	90.76 162	89.29 156

WR-MVS_H		64.14 188	65.36 176	62.71 190	72.47 188	82.33 177	65.13 188	66.99 163	51.81 181	36.47 195	43.33 170	42.77 179	43.99 196	72.41 188	75.99 186	91.20 154	88.86 160

SixPastTwentyTwo		63.75 189	63.42 188	64.13 187	72.91 185	80.34 188	61.29 196	63.90 173	49.58 190	40.42 188	54.99 129	37.13 201	60.90 171	68.46 198	70.80 195	85.37 198	82.65 185

PM-MVS		63.52 190	62.51 192	64.70 182	64.79 204	76.08 198	65.07 189	62.08 178	58.13 158	46.56 168	44.98 159	31.31 208	62.89 166	72.58 187	69.93 199	86.81 192	84.55 172

DTE-MVSNet		63.26 191	63.41 189	63.08 189	72.59 187	78.56 194	65.03 190	68.28 147	50.53 187	32.38 202	44.03 165	37.79 198	49.48 193	70.83 195	76.73 183	90.73 163	85.42 171

testgi		63.11 192	64.88 181	61.05 193	75.83 172	78.51 195	60.42 197	66.20 167	48.77 191	34.56 200	56.96 113	40.35 189	40.95 201	77.46 173	77.22 180	88.37 188	74.86 199

GG-mvs-BLEND		62.08 193	88.31 44	31.46 207	0.16 218	98.10 10	91.57 41	0.09 215	85.07 65	0.21 219	73.90 56	83.74 39	0.19 216	88.98 69	89.39 60	96.58 15	99.02 14

Anonymous20231206		62.05 194	61.83 194	62.30 192	72.09 190	77.84 196	63.10 194	67.62 158	50.20 188	36.68 193	29.59 204	37.05 202	43.90 197	77.33 174	77.31 179	90.41 167	83.49 177

N_pmnet		60.52 195	58.83 198	62.50 191	68.97 198	75.61 200	59.72 200	66.47 164	51.90 180	41.26 187	35.42 196	35.63 203	52.25 191	67.07 201	70.08 198	86.35 194	76.10 195

EU-MVSNet		58.73 196	60.92 196	56.17 198	66.17 201	72.39 203	58.85 201	61.24 181	48.47 192	27.91 207	46.70 155	40.06 190	39.07 202	68.27 199	70.34 197	83.77 201	80.23 189

test20.03		57.93 197	59.22 197	56.44 197	71.84 193	73.78 202	53.55 205	65.96 168	43.02 202	28.46 206	37.50 191	38.17 197	30.41 206	75.25 180	74.42 191	88.41 186	72.37 202

MDA-MVSNet-bldmvs		54.99 198	52.66 201	57.71 196	52.74 210	74.87 201	55.61 203	68.41 146	43.65 200	32.54 201	37.93 188	22.11 214	54.11 182	48.85 207	67.34 201	82.85 202	73.88 200

new-patchmatchnet		53.91 199	52.69 200	55.33 200	64.83 203	70.90 204	52.24 206	61.75 179	41.09 203	30.82 203	29.90 202	28.22 210	36.69 203	61.52 202	65.08 202	85.64 197	72.14 203

MIMVSNet1		52.76 200	53.95 199	51.38 202	41.96 213	70.79 205	53.56 204	63.03 177	39.36 204	27.83 208	22.73 208	33.07 206	34.47 205	70.49 196	72.69 192	87.41 191	68.51 204

pmmvs3		52.59 201	52.43 202	52.78 201	54.53 209	64.49 208	50.07 207	46.89 208	35.31 208	30.19 204	27.27 206	26.96 212	53.02 188	67.28 200	70.54 196	81.96 204	75.20 197

new_pmnet		50.32 202	51.36 203	49.11 203	49.19 211	64.89 207	48.66 209	47.99 207	47.55 194	26.27 210	29.51 205	28.66 209	44.89 194	61.12 203	62.74 204	77.66 206	65.03 205

FPMVS		50.25 203	45.67 205	55.58 199	70.48 197	60.12 209	59.78 199	59.33 186	46.66 195	37.94 190	30.22 201	27.51 211	35.94 204	50.98 206	47.90 206	70.02 208	56.31 206

PMVS	36.83 18	40.62 204	36.39 206	45.56 204	58.40 206	33.20 213	32.62 213	56.02 190	28.25 211	37.92 191	22.29 209	26.15 213	25.29 208	48.49 208	43.82 209	63.13 211	52.53 209
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		35.20 205	33.96 207	36.65 206	43.30 212	32.51 214	26.96 215	48.31 206	38.87 205	20.08 214	8.08 211	7.41 218	26.44 207	53.60 204	58.43 205	54.81 212	38.79 211
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		32.52 206	33.92 208	30.88 208	34.15 216	44.70 212	27.79 214	39.69 212	22.21 212	4.31 218	15.73 210	14.13 216	12.45 213	40.11 209	47.00 207	66.88 209	53.54 207

E-PMN		21.42 207	17.56 210	25.94 209	36.25 215	19.02 217	11.56 216	43.72 210	15.25 214	6.99 216	8.04 212	4.53 220	21.77 210	16.13 212	26.16 211	35.34 214	33.77 212

MVE	25.07 19	21.25 208	23.51 209	18.62 211	15.07 217	29.77 216	10.67 218	34.60 213	12.51 215	9.46 215	7.84 213	3.82 221	14.38 212	27.45 211	42.42 210	27.56 216	40.74 210
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		20.61 209	16.32 211	25.62 210	36.41 214	18.93 218	11.51 217	43.75 209	15.65 213	6.53 217	7.56 214	4.68 219	22.03 209	14.56 213	23.10 212	33.51 215	29.77 213

testmvs		0.76 210	1.23 212	0.21 212	0.05 219	0.21 219	0.38 220	0.09 215	0.94 216	0.05 220	2.13 216	0.08 222	0.60 215	0.82 214	0.77 213	0.11 217	3.62 215

test123		0.67 211	1.11 213	0.16 213	0.01 220	0.14 220	0.20 221	0.04 217	0.77 217	0.02 221	2.15 215	0.02 223	0.61 214	0.23 215	0.72 214	0.07 218	3.76 214

uanet_test		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

sosnet-low-res		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

sosnet		0.00 212	0.00 214	0.00 214	0.00 221	0.00 221	0.00 222	0.00 218	0.00 218	0.00 222	0.00 217	0.00 224	0.00 217	0.00 216	0.00 215	0.00 219	0.00 216

RE-MVS-def										39.41 189

9.14												91.16 7

SR-MVS					96.04 31			87.51 27				87.60 20

Anonymous202405211			75.59 125		85.13 113	91.06 103	84.62 91	77.96 74	69.47 128		40.79 181	63.84 119	84.57 65	83.55 119	84.69 106	89.69 176	95.75 81

our_test_3					73.80 184	79.57 192	64.47 192

ambc			50.35 204		55.61 208	59.93 210	48.73 208		44.08 198	35.81 198	24.01 207	10.64 217	41.57 199	72.83 186	63.35 203	74.99 207	77.61 192

MTAPA										91.14 6		85.84 26

MTMP										90.95 7		84.13 35

Patchmatch-RL test							8.17 219

tmp_tt				39.78 205	56.31 207	31.71 215	35.84 212	15.08 214	82.57 74	50.83 148	63.07 94	47.51 160	15.28 211	52.23 205	44.24 208	65.35 210

XVS					89.65 70	95.93 47	85.97 79			76.32 53		82.05 45				93.51 89

X-MVStestdata					89.65 70	95.93 47	85.97 79			76.32 53		82.05 45				93.51 89

abl_6				89.54 32	95.55 39	97.59 19	89.01 55	85.00 40	94.67 15	83.04 32	84.70 35	91.47 6	89.46 26			95.20 46	98.63 20

mPP-MVS					95.90 33							80.22 53

NP-MVS									89.55 47

Patchmtry						87.41 125	78.32 133	54.14 197		51.09 143

DeepMVS_CX						48.96 211	43.77 210	40.58 211	50.93 184	24.67 212	36.95 193	20.18 215	41.60 198	38.92 210		52.37 213	53.31 208

High-resolution multi-view benchmark