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		97.92 1	98.27 2	97.52 1	98.88 12	99.60 1	98.80 5	95.08 8	98.57 2	95.63 2	96.98 10	99.73 1	97.67 1	97.26 10	95.86 22	99.04 15	99.89 5

MSP-MVS		97.74 2	98.32 1	97.06 8	98.66 15	99.35 7	98.66 8	94.75 14	98.22 5	93.60 6	97.99 1	98.58 8	97.41 4	98.24 2	95.95 18	99.27 4	99.91 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
DVP-MVS++		97.71 3	98.01 6	97.37 2	98.98 6	99.58 3	98.79 6	95.06 9	98.24 4	94.66 3	96.35 16	99.20 4	97.63 2	97.20 12	95.68 23	99.08 13	99.84 7

DPE-MVS		97.69 4	98.16 3	97.14 6	99.01 5	99.52 5	99.12 2	95.38 3	98.00 8	93.31 10	97.71 2	99.61 3	96.94 5	96.99 16	95.45 27	99.09 12	99.81 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		97.61 5	97.87 7	97.30 3	98.94 11	99.60 1	98.21 13	95.11 5	98.39 3	95.83 1	94.40 29	99.70 2	96.79 6	97.16 13	95.95 18	98.92 26	99.90 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
CNVR-MVS		97.60 6	98.08 4	97.03 9	99.14 1	99.55 4	98.67 7	95.32 4	97.91 9	92.55 13	97.11 7	97.23 13	97.49 3	98.16 3	97.05 5	99.04 15	99.55 19

APDe-MVS		97.31 7	97.51 12	97.08 7	98.95 10	99.29 12	98.58 10	95.11 5	97.69 15	94.16 4	96.91 11	96.81 17	96.57 10	96.71 20	95.39 29	99.08 13	99.79 10

SF-MVS		97.17 8	97.18 15	97.17 4	99.11 2	99.20 14	99.05 3	95.55 1	97.39 18	93.56 7	97.48 4	96.71 19	96.75 7	95.73 32	94.40 45	98.98 20	99.33 26

NCCC		97.01 9	97.74 8	96.16 12	99.02 4	99.35 7	98.63 9	95.04 10	97.84 12	88.95 26	96.83 13	97.02 16	96.39 15	97.44 7	96.51 9	98.90 28	99.16 42

SMA-MVS		96.96 10	97.65 11	96.15 13	98.98 6	99.31 11	97.91 18	94.68 16	97.52 16	90.59 20	94.54 28	99.20 4	96.54 12	97.29 9	96.48 10	98.22 63	99.19 38
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
MCST-MVS		96.93 11	98.07 5	95.61 20	98.98 6	99.44 6	98.04 14	95.04 10	98.10 6	86.55 33	97.65 3	97.56 11	95.60 24	97.67 6	96.45 11	99.43 1	99.61 18

HPM-MVS++		96.91 12	97.70 9	96.00 15	98.97 9	99.16 17	97.82 21	94.81 13	98.04 7	89.61 23	96.56 15	98.60 7	96.39 15	97.09 14	95.22 31	98.39 56	99.22 36

SD-MVS		96.87 13	97.69 10	95.92 16	96.38 49	99.25 13	97.76 22	94.75 14	97.72 13	92.46 15	95.94 17	99.09 6	96.48 14	96.01 28	96.08 16	97.68 94	99.73 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
APD-MVS		96.79 14	96.99 18	96.56 10	98.76 14	98.87 26	98.42 11	94.93 12	97.70 14	91.83 16	95.52 20	95.94 24	96.63 9	95.94 29	95.47 26	98.80 34	99.47 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		96.50 15	97.08 16	95.82 18	96.12 53	98.97 23	98.00 15	94.13 21	97.89 10	91.49 17	95.11 25	97.52 12	96.26 19	96.27 26	94.07 56	98.91 27	99.74 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		96.20 16	97.22 14	95.01 24	98.40 23	99.11 18	97.93 17	93.62 25	96.28 30	87.45 29	97.05 9	96.00 23	94.23 33	96.83 19	95.97 17	98.40 55	99.27 32
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.09 17	96.41 23	95.72 19	98.58 18	98.84 27	97.95 16	93.08 29	96.96 23	90.24 21	96.60 14	94.40 32	96.52 13	95.13 44	94.33 47	97.93 84	98.59 67

zzz-MVS		95.87 18	95.63 30	96.15 13	98.60 17	98.83 28	97.89 19	93.65 24	96.24 31	93.08 11	91.13 36	95.46 29	95.72 23	95.64 34	93.67 63	97.97 81	98.46 74

ACMMP_NAP		95.81 19	96.50 22	95.01 24	98.79 13	99.17 16	97.52 27	94.20 20	96.19 32	85.71 37	93.80 32	96.20 22	95.89 20	96.62 22	94.98 37	97.93 84	98.52 70

train_agg		95.72 20	97.37 13	93.80 30	97.82 32	98.92 24	97.84 20	93.50 26	96.86 25	81.35 54	97.10 8	97.71 9	94.19 34	96.02 27	95.37 30	98.07 71	99.64 16

ACMMPR		95.59 21	95.89 25	95.25 22	98.41 22	98.74 30	97.69 25	92.73 33	96.88 24	88.95 26	95.33 22	92.91 39	95.79 21	94.73 54	94.33 47	97.92 86	98.32 80

DeepC-MVS_fast	91.53 1	95.57 22	95.67 28	95.45 21	98.57 19	99.00 22	97.76 22	94.41 18	97.06 21	86.84 32	86.39 47	92.27 44	96.38 17	97.89 5	98.06 3	98.73 40	99.01 50
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		95.49 23	94.84 34	96.25 11	98.64 16	98.63 34	98.35 12	92.37 35	95.04 49	92.62 12	87.12 46	93.79 33	96.55 11	93.53 72	96.78 6	98.98 20	98.99 51

CP-MVS		95.43 24	95.67 28	95.14 23	98.24 28	98.60 35	97.45 28	92.80 31	95.98 35	89.21 25	95.22 23	93.60 34	95.43 25	94.37 61	93.22 74	97.68 94	98.72 58

DPM-MVS		95.36 25	95.84 26	94.82 26	96.70 45	98.49 45	99.27 1	95.09 7	96.71 26	83.87 45	86.34 49	96.44 21	95.06 27	98.35 1	98.82 1	98.89 29	95.69 133

MP-MVS		95.24 26	95.96 24	94.40 28	98.32 25	98.38 50	97.12 30	92.87 30	95.17 47	85.50 38	95.68 18	94.91 30	94.58 29	95.11 45	93.76 60	98.05 74	98.68 60
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + ACMM		94.99 27	97.02 17	92.61 41	97.19 38	98.71 32	97.74 24	93.21 28	96.97 22	79.27 68	94.09 30	97.14 14	90.84 67	96.64 21	95.94 20	97.42 109	99.67 15

X-MVS		94.70 28	95.71 27	93.52 34	98.38 24	98.56 37	96.99 31	92.62 34	95.58 39	81.00 60	94.57 27	93.49 35	94.16 38	94.82 50	94.29 50	97.99 80	98.68 60

PGM-MVS		94.64 29	95.49 31	93.66 32	98.55 20	98.51 43	97.63 26	87.77 49	94.45 54	84.92 41	97.23 6	91.90 46	95.22 26	94.56 57	93.80 59	97.87 90	97.97 90

TSAR-MVS + GP.		94.59 30	96.60 21	92.25 42	90.25 92	98.17 56	96.22 37	86.53 55	97.49 17	87.26 30	95.21 24	97.06 15	94.07 40	94.34 63	94.20 52	99.18 5	99.71 14

xxxxxxxxxxxxxcwj		94.57 31	92.34 50	97.17 4	99.11 2	99.20 14	99.05 3	95.55 1	97.39 18	93.56 7	97.48 4	62.85 151	96.75 7	95.73 32	94.40 45	98.98 20	99.33 26

PHI-MVS		94.49 32	96.72 20	91.88 44	97.06 40	98.88 25	94.99 48	89.13 43	96.15 33	79.70 64	96.91 11	95.78 26	91.87 57	94.65 55	95.68 23	98.53 49	98.98 53

AdaColmap		94.28 33	92.94 45	95.84 17	98.32 25	98.33 52	96.06 39	94.62 17	96.29 29	91.22 18	89.89 40	85.50 76	96.38 17	91.85 101	90.89 89	98.44 51	97.81 93

DeepPCF-MVS	91.00 2	94.15 34	96.87 19	90.97 52	96.82 43	99.33 10	89.40 101	92.76 32	98.76 1	82.36 50	88.74 41	95.49 28	90.58 74	98.13 4	97.80 4	93.88 189	99.88 6

CPTT-MVS		94.11 35	93.99 39	94.25 29	96.58 46	97.66 63	97.31 29	91.94 36	94.84 50	88.72 28	92.51 33	93.04 38	95.78 22	91.51 104	89.97 105	95.15 178	98.37 77

EPNet		93.69 36	95.34 32	91.76 45	96.98 42	98.47 47	95.40 45	86.79 52	95.47 41	82.84 48	95.66 19	89.17 52	90.47 75	95.25 43	94.69 40	98.10 68	98.68 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP		93.32 37	93.59 42	93.00 39	97.03 41	98.24 53	95.27 46	91.66 39	95.20 45	83.25 46	95.39 21	85.52 74	92.80 48	92.60 91	90.21 101	98.01 77	97.99 88
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
CANet		93.23 38	93.72 41	92.65 40	95.48 56	99.09 20	96.55 35	86.74 53	95.28 44	85.22 39	77.30 75	91.25 48	92.60 50	97.06 15	96.63 7	99.31 2	99.45 23

CDPH-MVS		93.22 39	95.08 33	91.04 51	97.57 35	98.49 45	96.74 33	89.35 42	95.19 46	73.57 100	90.26 38	91.59 47	90.68 71	95.09 47	96.15 14	98.31 61	98.81 56

CSCG		93.16 40	92.65 47	93.76 31	98.32 25	99.09 20	96.12 38	89.91 41	93.15 64	89.64 22	83.62 56	88.91 55	92.40 52	91.09 109	93.70 61	96.14 161	98.99 51

MVS_111021_LR		93.05 41	94.53 36	91.32 49	96.43 48	98.38 50	92.81 62	87.20 51	95.94 37	81.45 53	94.75 26	86.08 69	92.12 55	94.83 49	93.34 67	97.89 89	98.42 76

3Dnovator+	86.26 7	92.90 42	92.45 49	93.42 35	97.25 37	98.45 49	95.82 40	85.71 61	93.83 58	89.55 24	72.31 105	92.28 43	94.01 41	95.10 46	95.92 21	98.17 64	99.23 35

MVS_111021_HR		92.73 43	94.83 35	90.28 57	96.27 50	99.10 19	92.77 63	86.15 58	93.41 62	77.11 88	93.82 31	87.39 61	90.61 72	95.60 37	95.15 33	98.79 35	99.32 28

PLC	89.12 3	92.67 44	90.84 61	94.81 27	97.69 33	96.10 91	95.42 44	91.70 37	95.82 38	92.52 14	81.24 60	86.01 70	94.36 31	92.44 95	90.27 98	97.19 118	93.99 159
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator	85.78 8	92.53 45	91.96 53	93.20 37	97.99 29	98.47 47	95.78 41	85.94 59	93.07 66	86.40 34	73.43 97	89.00 54	94.08 39	94.74 53	96.44 12	99.01 19	98.57 68

DeepC-MVS	88.77 4	92.39 46	91.74 55	93.14 38	96.21 51	98.55 40	96.30 36	93.84 22	93.06 67	81.09 58	74.69 90	85.20 79	93.48 44	95.41 40	96.13 15	97.92 86	99.18 39
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OMC-MVS		92.05 47	91.88 54	92.25 42	96.51 47	97.94 58	93.18 59	88.97 45	96.53 27	84.47 43	80.79 63	87.85 57	93.25 46	92.48 94	91.81 82	97.12 119	95.73 132

MVSTER		91.91 48	93.43 44	90.14 58	89.81 98	92.32 131	94.53 51	81.32 88	96.00 34	84.77 42	85.41 53	92.39 42	91.32 59	96.41 23	94.01 57	99.11 8	97.45 102

MVS_0304		91.90 49	92.93 46	90.69 56	93.66 64	98.78 29	96.73 34	85.43 65	93.13 65	78.11 81	77.02 78	89.09 53	91.10 63	96.98 17	96.54 8	99.11 8	98.96 54

QAPM		91.68 50	91.97 52	91.34 48	97.86 31	98.72 31	95.60 43	85.72 60	90.86 80	77.14 87	76.06 79	90.35 49	92.69 49	94.10 66	94.60 41	99.04 15	99.09 44

CNLPA		91.53 51	89.74 73	93.63 33	96.75 44	97.63 65	91.16 83	91.70 37	96.38 28	90.82 19	69.66 116	85.52 74	93.76 42	90.44 115	91.14 88	97.55 103	97.40 103

ETV-MVS		91.51 52	94.06 38	88.54 69	89.39 103	97.52 66	89.48 98	80.88 91	97.09 20	79.41 66	87.87 42	86.18 68	92.95 47	95.94 29	94.33 47	99.13 7	99.52 21

DROMVSNet		91.25 53	93.45 43	88.68 67	88.90 109	96.18 90	91.66 71	76.70 123	95.57 40	82.00 51	84.18 54	89.28 51	94.17 36	95.64 34	94.19 53	98.68 42	99.14 43

DELS-MVS		91.09 54	90.56 69	91.71 46	95.82 54	98.59 36	95.74 42	86.68 54	85.86 107	85.12 40	72.71 100	81.36 87	88.06 94	97.31 8	98.27 2	98.86 32	99.82 8
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
TAPA-MVS	87.40 6	90.98 55	90.71 63	91.30 50	96.14 52	97.66 63	94.80 49	89.00 44	94.74 53	77.42 86	80.22 64	86.70 64	92.27 53	91.65 103	90.17 103	98.15 67	93.83 163
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_BlendedMVS		90.74 56	90.66 65	90.82 54	94.75 59	98.54 41	91.30 80	86.53 55	95.43 42	85.75 35	78.66 70	70.67 124	87.60 95	96.37 24	95.08 35	98.98 20	99.90 2

PVSNet_Blended		90.74 56	90.66 65	90.82 54	94.75 59	98.54 41	91.30 80	86.53 55	95.43 42	85.75 35	78.66 70	70.67 124	87.60 95	96.37 24	95.08 35	98.98 20	99.90 2

CHOSEN 280x420		90.61 58	94.27 37	86.35 91	93.12 68	98.16 57	89.99 94	69.62 179	92.48 71	76.89 91	87.28 45	96.72 18	90.31 77	94.81 51	92.33 79	98.17 64	98.08 86

MAR-MVS		90.44 59	91.17 59	89.59 61	97.48 36	97.92 59	90.96 86	79.80 96	95.07 48	77.03 89	80.83 62	79.10 97	94.68 28	93.16 77	94.46 43	97.59 102	97.63 95
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	88.14 5	90.42 60	89.56 78	91.41 47	94.44 61	98.18 55	94.35 53	94.33 19	84.55 119	76.61 92	75.84 82	88.47 56	91.29 60	90.37 117	90.66 95	97.46 105	98.88 55
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CS-MVS		90.22 61	92.57 48	87.47 83	88.74 110	96.62 80	88.44 106	79.14 107	94.83 51	75.49 96	81.07 61	85.81 71	94.39 30	95.85 31	94.45 44	98.62 43	99.38 24

CS-MVS-test		90.11 62	92.10 51	87.78 79	88.28 117	94.21 113	91.66 71	76.70 123	93.75 60	77.56 84	83.50 57	85.05 80	94.17 36	95.64 34	93.28 73	98.31 61	99.26 33

OpenMVS	83.41 11	89.84 63	88.89 84	90.95 53	97.63 34	98.51 43	94.64 50	85.47 64	88.14 93	78.39 78	65.06 128	85.42 77	91.04 65	93.06 80	93.70 61	98.53 49	98.37 77

EIA-MVS		89.82 64	91.48 57	87.89 78	89.16 105	97.31 68	88.99 102	80.92 90	94.29 55	77.65 83	82.16 59	79.77 95	91.90 56	94.61 56	93.03 76	98.70 41	99.21 37

canonicalmvs		89.62 65	89.87 72	89.33 63	90.47 87	97.02 74	93.46 58	79.67 99	92.45 72	81.05 59	82.84 58	73.00 113	93.71 43	90.38 116	94.85 38	97.65 98	98.54 69

TSAR-MVS + COLMAP		89.59 66	89.64 75	89.53 62	93.32 67	96.51 82	95.03 47	88.53 46	95.98 35	69.10 116	91.81 34	64.53 147	93.40 45	93.53 72	91.35 87	97.77 91	93.75 166

HQP-MVS		89.57 67	90.57 68	88.41 71	92.77 69	94.71 106	94.24 54	87.97 47	93.44 61	68.18 119	91.75 35	71.54 123	89.90 80	92.31 98	91.43 85	97.39 110	98.80 57

MVS_Test		89.02 68	90.20 70	87.64 81	89.83 97	97.05 73	92.30 65	77.59 119	92.89 68	75.01 98	77.36 74	76.10 107	92.27 53	95.30 42	95.42 28	98.83 33	97.30 106

CLD-MVS		88.99 69	88.07 87	90.07 59	89.61 100	94.94 103	93.82 57	85.70 62	92.73 70	82.73 49	79.97 65	69.59 127	90.44 76	90.32 118	89.93 107	98.10 68	99.04 47
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline		88.91 70	89.94 71	87.70 80	89.44 102	96.74 79	91.62 74	77.92 116	93.79 59	78.76 73	77.55 73	78.46 100	89.38 86	92.26 99	92.52 78	99.10 10	98.23 81

PMMVS		88.56 71	91.22 58	85.47 99	90.04 94	95.60 99	86.62 125	78.49 111	93.86 57	70.62 111	90.00 39	80.08 93	91.64 58	92.36 96	89.80 111	95.40 173	96.84 115

test2506		88.38 72	88.02 89	88.80 66	91.55 78	97.78 60	90.87 88	83.36 72	84.51 120	83.06 47	74.13 93	76.93 104	85.39 105	94.34 63	93.33 69	98.60 44	95.10 148

baseline1		88.16 73	88.15 86	88.17 75	90.02 95	94.79 105	91.85 70	83.89 68	87.37 99	75.67 95	73.75 95	79.89 94	88.44 93	94.41 58	93.33 69	99.18 5	93.55 168

thisisatest0530		87.99 74	90.76 62	84.75 102	88.36 115	96.82 76	87.65 115	79.67 99	91.77 74	70.93 107	79.94 66	87.65 59	84.21 115	92.98 83	89.07 122	97.66 97	97.13 109

tttt0517		87.93 75	90.71 63	84.68 103	88.33 116	96.76 78	87.42 118	79.67 99	91.74 75	70.83 108	79.91 67	87.61 60	84.21 115	92.88 88	89.07 122	97.62 100	97.03 111

CANet_DTU		87.91 76	91.57 56	83.64 111	90.96 81	97.12 71	91.90 69	75.97 132	92.83 69	53.16 173	86.02 50	79.02 98	90.80 68	95.40 41	94.15 54	99.03 18	96.47 126

diffmvs		87.86 77	87.40 94	88.39 72	88.57 113	96.10 91	91.24 82	83.15 75	90.62 81	79.13 70	72.45 103	67.71 133	90.07 79	92.58 92	93.31 72	98.17 64	99.03 48

IS_MVSNet		87.83 78	90.66 65	84.53 104	90.08 93	96.79 77	88.16 109	79.89 95	85.44 109	72.20 102	75.50 86	87.14 62	80.21 143	95.53 38	95.22 31	96.65 135	99.02 49

EPP-MVSNet		87.72 79	89.74 73	85.37 100	89.11 106	95.57 100	86.31 126	79.44 102	85.83 108	75.73 94	77.23 76	90.05 50	84.78 111	91.22 107	90.25 99	96.83 126	98.04 87

ET-MVSNet_ETH3D		87.63 80	91.08 60	83.59 112	67.96 213	96.30 89	92.06 67	78.47 112	91.95 73	69.87 113	87.57 44	84.14 84	94.34 32	88.58 131	92.10 80	98.88 30	96.93 112

DI_MVS_plusplus_trai		87.63 80	87.13 96	88.22 74	88.61 112	95.92 95	94.09 56	81.41 87	87.00 102	78.38 79	59.70 147	80.52 91	89.08 88	94.37 61	93.34 67	97.73 92	99.05 46

casdiffmvs		87.59 82	86.69 100	88.64 68	89.06 107	96.32 88	90.18 91	83.21 74	87.74 97	80.20 63	67.99 120	68.34 131	90.79 69	93.83 68	94.08 55	98.41 54	98.50 72

PVSNet_Blended_VisFu		87.44 83	88.72 85	85.95 95	92.02 73	97.26 69	86.88 123	82.66 82	83.86 126	79.16 69	66.96 123	84.91 81	77.26 160	94.97 48	93.48 64	97.73 92	99.64 16

FMVSNet3		87.19 84	87.32 95	87.04 89	82.82 152	90.21 146	92.88 61	76.53 127	91.69 76	81.31 55	64.81 131	80.64 88	89.79 84	94.80 52	94.76 39	98.88 30	94.32 155

LS3D		87.19 84	85.48 107	89.18 64	94.96 58	95.47 101	92.02 68	93.36 27	88.69 91	67.01 120	70.56 112	72.10 118	92.47 51	89.96 121	89.93 107	95.25 175	91.68 177

ACMP	85.16 9	87.15 86	87.04 97	87.27 86	90.80 83	94.45 109	89.41 100	83.09 79	89.15 88	76.98 90	86.35 48	65.80 140	86.94 98	88.45 132	87.52 141	96.42 150	97.56 100
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		87.04 87	89.59 77	84.07 106	90.94 82	95.95 94	86.02 128	81.65 86	85.94 106	78.54 77	78.00 72	85.40 78	69.62 180	91.83 102	91.53 84	97.63 99	98.51 71
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
LGP-MVS_train		86.95 88	87.65 91	86.12 94	91.77 76	93.84 116	93.04 60	82.77 81	88.04 94	65.33 125	87.69 43	67.09 137	86.79 99	90.20 119	88.99 125	97.05 121	97.71 94

PatchMatch-RL		86.75 89	85.43 108	88.29 73	94.06 62	96.37 87	86.82 124	82.94 80	88.94 89	79.59 65	79.83 68	59.17 161	89.46 85	91.12 108	88.81 129	96.88 125	93.78 164

baseline2		86.51 90	89.35 81	83.19 114	85.70 138	94.88 104	85.75 133	77.13 121	89.87 85	70.65 110	79.03 69	79.14 96	81.51 136	93.70 69	90.22 100	98.38 57	98.60 66

thres100view900		86.48 91	85.08 110	88.12 76	90.54 84	96.90 75	92.39 64	84.82 66	84.16 124	71.65 103	70.86 109	60.49 156	91.23 62	93.65 70	90.19 102	98.10 68	99.32 28

ACMM	84.23 10	86.40 92	84.64 113	88.46 70	91.90 74	91.93 137	88.11 110	85.59 63	88.61 92	79.13 70	75.31 87	66.25 138	89.86 83	89.88 122	87.64 138	96.16 160	92.86 173
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		86.16 93	86.00 103	86.35 91	81.81 158	89.52 155	91.40 76	76.53 127	91.69 76	81.31 55	64.81 131	80.64 88	88.72 89	90.54 112	90.72 91	98.34 58	94.08 156

test1		86.16 93	86.00 103	86.35 91	81.81 158	89.52 155	91.40 76	76.53 127	91.69 76	81.31 55	64.81 131	80.64 88	88.72 89	90.54 112	90.72 91	98.34 58	94.08 156

tfpn200view9		86.07 95	84.76 112	87.61 82	90.54 84	96.39 84	91.35 79	83.15 75	84.16 124	71.65 103	70.86 109	60.49 156	90.91 66	92.89 85	89.34 114	98.05 74	99.17 40

DCV-MVSNet		85.90 96	85.88 105	85.93 96	87.86 122	88.37 172	89.45 99	77.46 120	87.33 100	77.51 85	76.06 79	75.76 109	88.48 92	87.40 140	88.89 128	94.80 184	97.37 104

Vis-MVSNet (Re-imp)		85.89 97	89.62 76	81.55 124	89.85 96	96.08 93	87.55 116	79.80 96	84.80 116	66.55 122	73.70 96	86.71 63	68.25 187	94.40 59	94.53 42	97.32 113	97.09 110

MSDG		85.81 98	82.29 137	89.93 60	95.52 55	92.61 126	91.51 75	91.46 40	85.12 113	78.56 75	63.25 137	69.01 129	85.31 108	88.45 132	88.23 132	97.21 117	89.33 188

thres200		85.80 99	84.38 114	87.46 84	90.51 86	96.39 84	91.64 73	83.15 75	81.59 134	71.54 105	70.24 113	60.41 158	89.88 81	92.89 85	89.85 110	98.06 72	99.26 33

ECVR-MVS		85.74 100	83.80 122	88.00 77	91.55 78	97.78 60	90.87 88	83.36 72	84.51 120	78.21 80	58.65 152	62.75 152	85.39 105	94.34 63	93.33 69	98.60 44	95.25 142

OPM-MVS		85.69 101	82.79 130	89.06 65	93.42 65	94.21 113	94.21 55	87.61 50	72.68 159	70.79 109	71.09 107	67.27 136	90.74 70	91.29 106	89.05 124	97.61 101	93.94 161
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
thres400		85.59 102	84.08 117	87.36 85	90.45 88	96.60 81	90.95 87	83.67 70	80.99 137	71.17 106	69.08 118	60.25 159	89.88 81	93.14 78	89.34 114	98.02 76	99.17 40

CostFormer		85.47 103	86.98 98	83.71 109	88.70 111	94.02 115	88.07 111	62.72 196	89.78 86	78.68 74	72.69 101	78.37 101	87.35 97	85.96 153	89.32 118	96.73 132	98.72 58

test1111		85.17 104	83.46 125	87.17 87	91.36 80	97.75 62	90.06 93	83.44 71	83.41 128	75.25 97	58.08 155	62.19 154	84.39 114	94.39 60	93.38 66	98.54 48	95.00 150

thres600view7		85.14 105	83.58 124	86.96 90	90.37 91	96.39 84	90.33 90	83.15 75	80.46 138	70.60 112	67.96 121	60.04 160	89.22 87	92.89 85	88.28 131	98.06 72	99.08 45

test-LLR		85.11 106	89.49 79	80.00 133	85.32 142	94.49 107	82.27 163	74.18 142	87.83 95	56.70 151	75.55 84	86.26 65	82.75 128	93.06 80	90.60 96	98.77 37	98.65 64

FMVSNet2		84.89 107	84.02 119	85.91 97	81.81 158	89.52 155	91.40 76	75.79 133	84.45 122	79.39 67	58.75 150	74.35 111	88.72 89	93.51 74	93.46 65	98.34 58	94.08 156

FC-MVSNet-train		84.88 108	84.08 117	85.82 98	89.21 104	91.74 138	85.87 129	81.20 89	81.71 133	74.66 99	73.38 98	64.99 144	86.60 100	90.75 110	88.08 133	97.36 111	97.90 91

EPNet_dtu		84.87 109	89.01 82	80.05 132	95.25 57	92.88 124	88.84 104	84.11 67	91.69 76	49.28 189	85.69 51	78.95 99	65.39 192	92.22 100	91.66 83	97.43 108	89.95 184
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+		84.80 110	85.71 106	83.73 108	87.94 121	95.76 96	90.08 92	73.45 149	85.12 113	62.66 134	72.39 104	64.97 145	90.59 73	92.95 84	90.69 94	97.67 96	98.12 83

UA-Net		84.69 111	87.64 92	81.25 126	90.38 90	95.67 97	87.33 119	79.41 103	72.07 163	66.48 123	75.09 88	92.48 41	66.88 188	94.03 67	94.25 51	97.01 124	89.88 185

TESTMET0.1,1		84.62 112	89.49 79	78.94 142	82.18 155	94.49 107	82.27 163	70.94 169	87.83 95	56.70 151	75.55 84	86.26 65	82.75 128	93.06 80	90.60 96	98.77 37	98.65 64

CHOSEN 1792x2688		84.59 113	84.30 116	84.93 101	93.71 63	98.23 54	89.91 95	77.96 115	84.81 115	65.93 124	45.19 197	71.76 122	83.13 126	95.46 39	95.13 34	98.94 25	99.53 20

Anonymous20231211		84.23 114	81.71 142	87.17 87	87.38 129	93.59 119	88.95 103	82.14 84	83.82 127	78.56 75	48.09 190	73.89 112	91.25 61	86.38 147	88.06 135	94.74 185	98.14 82

MDTV_nov1_ep13		84.17 115	88.03 88	79.66 135	86.00 136	94.41 110	85.05 135	66.01 191	90.36 82	64.34 130	77.13 77	84.56 82	82.71 130	87.12 144	88.92 126	93.84 191	93.69 167

test-mter		84.06 116	89.00 83	78.29 147	81.92 156	94.23 112	81.07 173	70.38 173	87.12 101	56.10 160	74.75 89	85.80 72	81.81 135	92.52 93	90.10 104	98.43 52	98.49 73

IB-MVS	79.58 12	83.83 117	84.81 111	82.68 116	91.85 75	97.35 67	75.75 192	82.57 83	86.55 104	84.01 44	70.90 108	65.43 142	63.18 198	84.19 167	89.92 109	98.74 39	99.31 30
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
EPMVS		83.71 118	86.76 99	80.16 131	89.72 99	95.64 98	84.68 136	59.73 201	89.61 87	62.67 133	72.65 102	81.80 86	86.22 102	86.23 149	88.03 136	97.96 82	93.35 169

HyFIR lowres test		83.43 119	82.94 128	84.01 107	93.41 66	97.10 72	87.21 120	74.04 144	80.15 140	64.98 126	41.09 205	76.61 106	86.51 101	93.31 75	93.01 77	97.91 88	99.30 31

PatchmatchNet		83.28 120	87.57 93	78.29 147	87.46 127	94.95 102	83.36 145	59.43 204	90.20 84	58.10 146	74.29 92	86.20 67	84.13 117	85.27 159	87.39 142	97.25 116	94.67 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		83.26 121	87.76 90	78.00 152	87.45 128	92.20 132	82.63 159	58.42 206	90.30 83	58.23 144	75.74 83	87.75 58	83.97 120	86.10 152	87.64 138	97.30 114	94.62 154

GeoE		83.17 122	82.86 129	83.53 113	87.24 130	93.78 117	87.94 112	72.75 154	82.19 131	69.76 114	60.54 144	65.95 139	86.01 103	89.41 126	89.72 112	97.47 104	98.43 75

CDS-MVSNet		83.13 123	83.73 123	82.43 122	84.52 147	92.92 123	88.26 108	77.67 118	72.08 162	69.08 117	66.96 123	74.66 110	78.61 149	90.70 111	91.96 81	96.46 149	96.86 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		82.91 124	81.86 139	84.13 105	88.25 118	88.32 173	87.67 114	80.86 92	84.78 117	76.57 93	85.56 52	76.00 108	84.61 112	78.20 203	76.52 206	86.81 212	83.63 205

Vis-MVSNet		82.88 125	86.04 102	79.20 140	87.77 125	96.42 83	86.10 127	76.70 123	74.82 153	61.38 136	70.70 111	77.91 102	64.83 194	93.22 76	93.19 75	98.43 52	96.01 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dps		82.63 126	82.64 133	82.62 118	87.81 124	92.81 125	84.39 137	61.96 197	86.43 105	81.63 52	69.72 115	67.60 135	84.42 113	82.51 181	83.90 180	95.52 169	95.50 140

IterMVS-LS		82.62 127	82.75 132	82.48 119	87.09 131	87.48 187	87.19 121	72.85 152	79.09 141	66.63 121	65.22 126	72.14 117	84.06 119	88.33 135	91.39 86	97.03 123	95.60 139
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		82.61 128	82.51 135	82.72 115	85.49 141	93.06 122	87.17 122	71.39 166	84.18 123	64.59 128	63.03 138	58.89 162	90.22 78	91.39 105	90.83 90	97.44 106	96.21 128

tpm cat1		82.39 129	82.32 136	82.47 120	88.13 119	92.42 130	87.43 117	62.79 195	85.30 110	78.05 82	60.14 145	72.10 118	83.20 125	82.26 184	85.67 159	95.23 176	98.35 79

MS-PatchMatch		82.16 130	82.18 138	82.12 123	91.65 77	93.50 120	89.51 97	71.95 160	81.48 135	64.45 129	59.58 149	77.54 103	77.23 161	89.88 122	85.62 160	97.94 83	87.68 192

tpmrst		81.71 131	83.87 121	79.20 140	89.01 108	93.67 118	84.22 138	60.14 199	87.45 98	59.49 140	64.97 129	71.86 121	85.30 109	84.72 163	86.30 150	97.04 122	98.09 85

RPMNet		81.47 132	86.24 101	75.90 170	86.72 132	92.12 134	82.82 157	55.76 212	85.21 111	53.73 171	63.45 135	83.16 85	80.13 144	92.34 97	89.52 113	96.23 158	97.90 91

CR-MVSNet		81.44 133	85.29 109	76.94 161	86.53 133	92.12 134	83.86 139	58.37 207	85.21 111	56.28 155	59.60 148	80.39 92	80.50 141	92.77 89	89.32 118	96.12 162	97.59 98

Effi-MVS+-dtu		81.18 134	82.77 131	79.33 138	84.70 146	92.54 128	85.81 130	71.55 164	78.84 142	57.06 150	71.98 106	63.77 149	85.09 110	88.94 128	87.62 140	91.79 204	95.68 134

test0.0.03 1		80.99 135	84.37 115	77.05 159	85.32 142	89.79 151	78.43 183	74.18 142	84.78 117	57.98 149	76.06 79	72.88 114	69.14 184	88.02 137	87.70 137	97.27 115	91.37 178

Fast-Effi-MVS+-dtu		80.57 136	83.44 126	77.22 157	83.98 150	91.52 140	85.78 132	64.54 194	80.38 139	50.28 185	74.06 94	62.89 150	82.00 134	89.10 127	88.91 127	96.75 130	97.21 108

FMVSNet5		80.56 137	82.53 134	78.26 149	73.80 208	81.52 206	82.26 165	68.36 184	88.85 90	64.21 131	69.09 117	84.38 83	83.49 124	87.13 143	86.76 147	97.44 106	79.95 208

ADS-MVSNet		80.25 138	82.96 127	77.08 158	87.86 122	92.60 127	81.82 170	56.19 211	86.95 103	56.16 158	68.19 119	72.42 116	83.70 123	82.05 185	85.45 165	96.75 130	93.08 172

FMVSNet1		80.18 139	78.07 153	82.65 117	78.55 182	87.57 186	88.41 107	73.93 145	70.16 168	73.57 100	49.80 180	64.45 148	85.35 107	90.54 112	90.72 91	96.10 163	93.21 170

USDC		80.10 140	79.33 149	81.00 128	86.36 134	91.71 139	88.74 105	75.77 134	81.90 132	54.90 165	67.67 122	52.05 174	83.94 121	88.44 134	86.25 151	96.31 153	87.28 196

COLMAP_ROB	75.69 15	79.47 141	76.90 160	82.46 121	92.20 70	90.53 142	85.30 134	83.69 69	78.27 145	61.47 135	58.26 153	62.75 152	78.28 152	82.41 182	82.13 193	93.83 193	83.98 204
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_part1		79.37 142	75.64 165	83.71 109	86.18 135	87.74 180	87.84 113	75.69 136	66.33 185	78.93 72	45.92 195	64.85 146	82.44 131	83.08 179	85.69 158	91.17 205	95.90 131

pmmvs4		79.32 143	77.78 155	81.11 127	80.18 167	88.96 167	83.39 143	76.07 130	81.27 136	69.35 115	58.66 151	51.19 177	82.01 133	87.16 142	84.39 177	95.66 167	92.82 174

PatchT		79.28 144	83.88 120	73.93 179	85.54 140	90.95 141	66.14 209	56.53 210	83.21 129	56.28 155	56.50 157	76.80 105	80.50 141	92.77 89	89.32 118	98.57 47	97.59 98

ACMH	78.51 14	79.27 145	78.08 152	80.65 129	89.52 101	90.40 143	80.45 175	79.77 98	69.54 173	54.85 166	64.83 130	56.16 168	83.94 121	84.58 165	86.01 155	95.41 172	95.03 149
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		79.23 146	78.95 151	79.56 136	81.89 157	92.52 129	82.97 152	73.70 146	67.27 179	64.97 127	61.66 143	65.06 143	78.61 149	87.12 144	88.07 134	95.23 176	90.95 180

ACMH+	79.09 13	79.12 147	77.22 159	81.35 125	88.50 114	90.36 144	82.14 167	79.38 105	72.78 158	58.59 141	62.31 142	56.44 167	84.10 118	82.03 186	84.05 178	95.40 173	92.55 175

UniMVSNet_NR-MVSNet		78.89 148	78.04 154	79.88 134	79.40 173	89.70 152	82.92 154	80.17 93	76.37 151	58.56 142	57.10 156	54.92 170	81.44 137	83.51 172	87.12 144	96.76 129	97.60 96

tpm		78.87 149	81.33 145	76.00 168	85.57 139	90.19 147	82.81 158	59.66 202	78.35 144	51.40 180	66.30 125	67.92 132	80.94 139	83.28 175	85.73 156	95.65 168	97.56 100

GA-MVS		78.86 150	80.42 146	77.05 159	83.27 151	92.17 133	83.24 147	75.73 135	73.75 155	46.27 199	62.43 140	57.12 164	76.94 163	93.14 78	89.34 114	96.83 126	95.00 150

IterMVS		78.85 151	81.36 143	75.93 169	84.27 149	85.74 193	83.83 141	66.35 189	76.82 146	50.48 183	63.48 134	68.82 130	73.99 168	89.68 124	89.34 114	96.63 138	95.67 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		78.71 152	81.34 144	75.64 174	84.31 148	85.67 194	83.51 142	66.14 190	76.67 147	50.38 184	63.45 135	69.02 128	73.23 170	89.66 125	89.22 121	96.24 157	95.67 135

UniMVSNet (Re)		78.00 153	77.52 156	78.57 145	79.66 172	90.36 144	82.09 168	77.86 117	76.38 150	60.26 137	54.63 163	52.07 173	75.31 166	84.97 162	86.10 153	96.22 159	98.11 84

DU-MVS		77.98 154	76.71 161	79.46 137	78.68 179	89.26 161	82.92 154	79.06 108	76.52 148	58.56 142	54.89 161	48.35 191	81.44 137	83.16 177	87.21 143	96.08 164	97.60 96

FC-MVSNet-test		77.95 155	81.85 140	73.39 184	82.31 153	88.99 166	79.33 179	74.24 141	78.75 143	47.40 197	70.22 114	72.09 120	60.78 204	86.66 146	85.62 160	96.30 154	90.61 181

NR-MVSNet		77.21 156	76.41 162	78.14 151	80.18 167	89.26 161	83.38 144	79.06 108	76.52 148	56.59 153	54.89 161	45.32 201	72.89 172	85.39 158	86.12 152	96.71 133	97.36 105

thisisatest0515		77.13 157	79.36 148	74.52 176	79.79 171	89.65 153	73.54 197	73.69 147	74.10 154	58.14 145	62.79 139	60.57 155	66.49 190	88.08 136	85.16 170	95.49 171	95.15 146

gg-mvs-nofinetune		77.08 158	79.79 147	73.92 180	85.95 137	97.23 70	92.18 66	52.65 215	46.19 218	27.79 222	38.27 209	85.63 73	85.67 104	96.95 18	95.62 25	99.30 3	98.67 63

TranMVSNet+NR-MVSNet		77.02 159	75.76 164	78.49 146	78.46 185	88.24 174	83.03 151	79.97 94	73.49 157	54.73 167	54.00 166	48.74 186	78.15 154	82.36 183	86.90 146	96.59 140	96.55 120

CVMVSNet		76.86 160	79.09 150	74.26 177	85.29 144	89.44 158	79.91 178	78.47 112	68.94 176	44.45 204	62.35 141	69.70 126	64.50 195	85.82 154	87.03 145	92.94 199	90.33 182

Baseline_NR-MVSNet		76.71 161	74.56 172	79.23 139	78.68 179	84.15 202	82.45 161	78.87 110	75.83 152	60.05 138	47.92 191	50.18 183	79.06 148	83.16 177	83.86 181	96.26 155	96.80 116

v2v482		76.25 162	74.78 169	77.96 153	78.50 184	89.14 164	83.05 150	76.02 131	68.78 177	54.11 168	51.36 172	48.59 188	79.49 146	83.53 171	85.60 163	96.59 140	96.49 125

V42		76.21 163	75.04 168	77.58 154	78.68 179	89.33 160	82.93 153	74.64 139	69.84 170	56.13 159	50.42 177	50.93 178	76.30 165	83.32 173	84.89 174	96.83 126	96.54 121

v8		75.89 164	74.74 170	77.23 156	79.09 175	88.00 177	83.19 148	71.08 168	70.03 169	56.29 154	50.50 175	50.88 179	77.06 162	83.32 173	84.99 172	96.68 134	95.49 141

TinyColmap		75.75 165	73.19 183	78.74 144	84.82 145	87.69 182	81.59 171	74.62 140	71.81 164	54.01 169	55.79 160	44.42 206	82.89 127	84.61 164	83.76 182	94.50 186	84.22 203

MIMVSNet		75.71 166	77.26 157	73.90 181	70.93 209	88.71 170	79.98 177	57.67 209	73.58 156	58.08 148	53.93 167	58.56 163	79.41 147	90.04 120	89.97 105	97.34 112	86.04 197

UniMVSNet_ETH3D		75.63 167	71.59 192	80.35 130	81.03 162	89.90 150	83.25 146	76.58 126	60.08 201	64.19 132	42.89 204	45.01 202	82.14 132	80.20 196	86.75 148	94.90 181	96.29 127

pm-mvs1		75.61 168	74.19 174	77.26 155	80.16 169	88.79 168	81.49 172	75.49 138	59.49 203	58.09 147	48.32 188	55.53 169	72.35 173	88.61 130	85.48 164	95.99 165	93.12 171

v10		75.57 169	74.67 171	76.62 164	78.73 178	87.46 188	83.14 149	69.41 180	69.27 174	53.44 172	49.73 181	49.21 185	78.44 151	86.17 151	85.18 169	96.53 145	95.65 138

v1144		75.54 170	74.55 173	76.69 162	78.33 188	88.77 169	82.89 156	72.76 153	67.18 181	51.73 177	49.34 183	48.37 189	78.10 155	86.22 150	85.24 167	96.35 152	96.74 117

TDRefinement		75.54 170	73.22 181	78.25 150	87.65 126	89.65 153	85.81 130	79.28 106	71.14 166	56.06 161	52.17 170	51.96 175	68.74 186	81.60 187	80.58 195	91.94 202	85.45 198

pmmvs5		75.46 172	75.12 167	75.87 171	79.39 174	89.44 158	78.12 185	72.27 158	65.98 187	51.54 178	55.83 159	46.23 196	76.80 164	88.77 129	85.73 156	97.07 120	93.84 162

tfpnnormal		75.27 173	72.12 189	78.94 142	82.30 154	88.52 171	82.41 162	79.41 103	58.03 204	55.59 163	43.83 203	44.71 203	77.35 158	87.70 139	85.45 165	96.60 139	96.61 119

anonymousdsp		75.14 174	77.25 158	72.69 187	76.68 198	89.26 161	75.26 194	68.44 183	65.53 190	46.65 198	58.16 154	56.67 166	73.96 169	87.84 138	86.05 154	95.13 179	97.22 107

v148		74.98 175	73.52 179	76.69 162	78.84 177	89.02 165	78.78 181	76.82 122	67.22 180	59.61 139	49.18 184	47.94 193	70.57 179	80.76 191	83.99 179	95.52 169	96.52 123

v1192		74.96 176	73.92 175	76.17 165	77.76 191	88.19 176	82.54 160	71.94 161	66.84 182	50.07 187	48.10 189	46.14 197	78.28 152	86.30 148	85.23 168	96.41 151	96.67 118

v144192		74.76 177	73.64 176	76.06 167	77.58 192	88.23 175	81.87 169	71.63 163	66.03 186	51.08 181	48.63 187	46.77 195	77.59 157	84.53 166	84.76 175	96.64 137	96.54 121

v1921920		74.60 178	73.56 178	75.81 172	77.43 194	87.94 178	82.18 166	71.33 167	66.48 184	49.23 191	47.84 192	45.56 199	78.03 156	85.70 156	84.92 173	96.65 135	96.50 124

v1240		74.04 179	73.04 185	75.20 175	77.19 196	87.69 182	80.93 174	70.72 172	65.08 191	48.47 192	47.31 193	44.71 203	77.33 159	85.50 157	85.07 171	96.59 140	95.94 130

testgi		73.22 180	75.84 163	70.16 198	81.67 161	85.50 197	71.45 199	70.81 170	69.56 172	44.74 203	74.52 91	49.25 184	58.45 205	84.10 169	83.37 186	93.86 190	84.56 202

CP-MVSNet		73.19 181	72.37 187	74.15 178	77.54 193	86.77 191	76.34 188	72.05 159	65.66 189	51.47 179	50.49 176	43.66 207	70.90 175	80.93 190	83.40 185	96.59 140	95.66 137

WR-MVS		72.93 182	73.57 177	72.19 190	78.14 189	87.71 181	76.21 190	73.02 151	67.78 178	50.09 186	50.35 178	50.53 181	61.27 203	80.42 194	83.10 189	94.43 187	95.11 147

TransMVSNet (Re)		72.90 183	70.51 196	75.69 173	80.88 163	85.26 199	79.25 180	78.43 114	56.13 210	52.81 174	46.81 194	48.20 192	66.77 189	85.18 161	83.70 183	95.98 166	88.28 191

WR-MVS_H		72.69 184	72.80 186	72.56 189	77.94 190	87.83 179	75.26 194	71.53 165	64.75 192	52.19 176	49.83 179	48.62 187	61.96 201	81.12 189	82.44 191	96.50 146	95.00 150

SixPastTwentyTwo		72.65 185	73.22 181	71.98 193	78.40 186	87.64 184	70.09 202	70.37 174	66.49 183	47.60 195	65.09 127	45.94 198	73.09 171	78.94 198	78.66 201	92.33 200	89.82 186

LTVRE_ROB	71.82 16	72.62 186	71.77 190	73.62 182	80.74 164	87.59 185	80.42 176	70.37 174	49.73 214	37.12 216	59.76 146	42.52 212	80.92 140	83.20 176	85.61 162	92.13 201	93.95 160
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
PS-CasMVS		72.37 187	71.47 194	73.43 183	77.32 195	86.43 192	75.99 191	71.94 161	63.37 195	49.24 190	49.07 185	42.42 213	69.60 181	80.59 193	83.18 188	96.48 148	95.23 144

MVS-HIRNet		72.32 188	73.45 180	71.00 196	80.58 165	89.97 148	68.51 206	55.28 213	70.89 167	52.27 175	39.09 207	57.11 165	75.02 167	85.76 155	86.33 149	94.36 188	85.00 200

PEN-MVS		72.24 189	71.30 195	73.33 185	77.08 197	85.57 195	76.75 186	72.52 156	63.89 194	48.12 193	50.79 173	43.09 210	69.03 185	78.54 200	83.46 184	96.50 146	93.76 165

v7n		72.11 190	71.66 191	72.63 188	75.26 203	86.85 189	76.74 187	68.77 182	62.70 198	49.40 188	45.92 195	43.51 208	70.63 178	84.16 168	83.21 187	94.99 180	95.25 142

EG-PatchMatch MVS		71.81 191	71.54 193	72.12 191	80.53 166	89.94 149	78.51 182	66.56 188	57.38 206	47.46 196	44.28 202	52.22 172	63.10 199	85.22 160	84.42 176	96.56 144	87.35 195

CMPMVS	54.54 17	71.74 192	67.94 201	76.16 166	90.41 89	93.25 121	78.32 184	75.60 137	59.81 202	53.95 170	44.64 200	51.22 176	70.70 176	74.59 209	75.88 207	88.01 209	76.23 211
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		71.65 193	73.08 184	69.97 199	75.22 204	86.81 190	73.98 196	59.61 203	69.75 171	48.01 194	54.21 165	53.06 171	69.19 183	78.50 201	80.43 196	93.84 191	88.79 189

pmnet_mix02		71.64 194	72.36 188	70.81 197	78.39 187	85.57 195	68.64 204	73.65 148	72.13 160	45.07 202	56.01 158	50.61 180	65.34 193	76.21 206	76.60 205	93.75 194	89.35 187

gm-plane-assit		71.33 195	75.18 166	66.83 202	79.06 176	75.57 213	48.05 220	60.33 198	48.28 215	34.67 220	44.34 201	67.70 134	79.78 145	97.25 11	96.21 13	99.10 10	96.92 113

DTE-MVSNet		71.19 196	70.45 197	72.06 192	76.61 199	84.59 201	75.61 193	72.32 157	63.12 197	45.70 201	50.72 174	43.02 211	65.89 191	77.53 205	82.23 192	96.26 155	91.93 176

pmmvs6		70.29 197	67.90 202	73.07 186	76.17 200	85.31 198	76.29 189	70.75 171	47.39 217	55.33 164	37.15 213	50.49 182	69.55 182	82.96 180	80.85 194	90.34 208	91.18 179

PM-MVS		70.17 198	69.42 199	71.04 195	70.82 210	81.26 208	71.25 200	67.80 185	69.16 175	51.04 182	53.15 169	34.93 217	72.19 174	80.30 195	76.95 204	93.16 198	90.21 183

pmmvs-eth3d		69.59 199	67.57 204	71.95 194	70.04 211	80.05 209	71.48 198	70.00 178	62.57 199	55.99 162	44.92 198	35.73 216	70.64 177	81.56 188	79.69 197	93.55 195	88.43 190

N_pmnet		68.54 200	67.83 203	69.38 200	75.77 201	81.90 205	66.21 208	72.53 155	65.91 188	46.09 200	44.67 199	45.48 200	63.82 197	74.66 208	77.39 203	91.87 203	84.77 201

Anonymous20231206		68.09 201	68.68 200	67.39 201	75.16 205	82.55 203	69.33 203	70.06 177	63.34 196	42.28 207	37.91 211	43.12 209	52.67 208	83.56 170	82.71 190	94.84 183	87.59 193

EU-MVSNet		68.07 202	70.25 198	65.52 203	74.68 207	81.30 207	68.53 205	70.31 176	62.40 200	37.43 215	54.62 164	48.36 190	51.34 209	78.32 202	79.27 198	90.84 206	87.47 194

GG-mvs-BLEND		65.67 203	93.78 40	32.89 216	0.47 226	99.35 7	96.92 32	0.22 225	93.28 63	0.51 227	84.07 55	92.50 40	0.62 224	93.59 71	93.86 58	98.59 46	99.79 10

test20.03		65.17 204	67.41 205	62.55 205	75.35 202	79.31 210	62.22 210	68.83 181	56.50 209	35.35 219	51.97 171	44.70 205	40.01 214	80.69 192	79.25 199	93.55 195	79.47 210

MDA-MVSNet-bldmvs		62.23 205	61.13 209	63.52 204	58.94 217	82.44 204	60.71 213	73.28 150	57.22 207	38.42 213	49.63 182	27.64 223	62.83 200	54.98 215	74.16 208	86.96 211	81.83 207

new_pmnet		61.60 206	62.68 207	60.35 208	63.02 214	74.93 214	60.97 212	58.86 205	64.21 193	35.38 218	39.51 206	39.89 214	57.37 206	72.78 210	72.56 210	86.49 213	74.85 213

new-patchmatchnet		60.74 207	59.78 211	61.87 206	69.52 212	76.67 212	57.99 216	65.78 192	52.63 212	38.47 212	38.08 210	32.92 220	48.88 211	68.50 211	69.87 211	90.56 207	79.75 209

pmmvs3		60.52 208	60.87 210	60.12 209	61.38 215	71.62 215	57.42 217	53.94 214	48.09 216	35.95 217	38.62 208	32.19 222	64.12 196	75.33 207	77.99 202	87.89 210	82.28 206

MIMVSNet1		60.51 209	61.43 208	59.44 210	48.75 220	77.21 211	60.98 211	66.84 187	52.09 213	38.74 211	29.29 216	39.40 215	48.08 212	77.60 204	78.87 200	93.22 197	75.56 212

test_method		60.40 210	66.30 206	53.52 212	37.48 224	64.10 219	55.56 218	42.45 220	71.79 165	41.87 208	33.74 214	46.80 194	61.71 202	79.18 197	73.33 209	82.01 215	95.17 145

FPMVS		56.54 211	52.82 213	60.87 207	74.90 206	67.58 218	67.69 207	65.38 193	57.86 205	41.51 209	37.83 212	34.19 218	41.21 213	55.88 214	53.09 216	74.55 218	63.31 216

PMVS	42.57 18	45.71 212	42.61 215	49.32 213	61.35 216	37.82 223	36.96 222	60.10 200	37.20 219	41.50 210	28.53 217	33.11 219	28.82 219	53.45 216	48.70 218	67.22 220	59.42 217
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		43.95 213	42.62 214	45.50 214	50.79 219	41.20 222	35.55 223	52.51 216	52.95 211	29.09 221	12.92 219	11.48 226	38.15 215	62.01 213	66.62 213	66.89 221	51.17 218
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		41.25 214	42.55 216	39.74 215	43.25 221	55.05 221	38.15 221	47.11 219	31.78 220	11.83 224	21.16 218	19.12 224	20.98 221	49.95 218	56.09 215	77.09 216	64.68 215

E-PMN		27.87 215	24.36 218	31.97 217	41.27 223	25.56 226	16.62 225	49.16 217	22.00 222	9.90 225	11.75 221	7.86 228	29.57 218	22.22 220	34.70 219	45.27 222	46.41 220

MVE	32.98 19	27.61 216	29.89 217	24.94 219	21.97 225	37.22 224	15.56 227	38.83 221	17.49 223	14.72 223	11.64 223	5.62 229	21.26 220	35.20 219	50.95 217	37.29 224	51.13 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		26.96 217	22.96 219	31.63 218	41.91 222	25.73 225	16.30 226	49.10 218	22.38 221	9.03 226	11.22 224	8.12 227	29.93 217	20.16 221	31.04 220	43.49 223	42.04 221

testmvs		5.16 218	8.14 220	1.69 220	0.36 227	1.65 227	3.02 228	0.66 223	7.17 224	0.50 228	12.58 220	0.69 230	4.67 222	5.42 222	5.65 221	0.92 225	23.86 223

test123		4.39 219	7.11 221	1.21 221	0.11 228	1.16 228	1.67 229	0.35 224	5.91 225	0.16 229	11.65 222	0.16 231	4.45 223	1.72 223	4.92 222	0.51 226	24.28 222

uanet_test		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet-low-res		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

sosnet		0.00 220	0.00 222	0.00 222	0.00 229	0.00 229	0.00 230	0.00 226	0.00 226	0.00 230	0.00 225	0.00 232	0.00 225	0.00 224	0.00 223	0.00 227	0.00 224

RE-MVS-def										43.17 205

9.14												97.59 10

SR-MVS					98.52 21			93.70 23				96.63 20

Anonymous202405211			81.72 141		88.09 120	94.27 111	89.62 96	82.14 84	82.27 130		48.83 186	72.58 115	91.08 64	87.40 140	88.70 130	94.90 181	97.99 88

our_test_3					78.55 182	84.98 200	70.12 201

ambc			57.08 212		58.68 218	67.71 217	60.07 214		57.13 208	42.79 206	30.00 215	11.64 225	50.18 210	78.89 199	69.14 212	82.64 214	85.02 199

MTAPA										93.37 9		95.71 27

MTMP										93.84 5		94.86 31

Patchmatch-RL test							19.65 224

tmp_tt				57.89 211	79.94 170	59.29 220	52.84 219	36.65 222	94.77 52	68.22 118	72.96 99	65.62 141	33.65 216	66.20 212	58.02 214	76.06 217

XVS					92.16 71	98.56 37	91.04 84			81.00 60		93.49 35				98.00 78

X-MVStestdata					92.16 71	98.56 37	91.04 84			81.00 60		93.49 35				98.00 78

abl_6				93.25 36	97.12 39	98.71 32	94.40 52	87.81 48	97.86 11	87.19 31	91.07 37	95.80 25	94.18 35			98.78 36	99.36 25

mPP-MVS					97.95 30							92.24 45

NP-MVS									94.12 56

Patchmtry						92.08 136	83.86 139	58.37 207		56.28 155

DeepMVS_CX						70.68 216	59.61 215	67.36 186	72.12 161	38.41 214	53.88 168	32.44 221	55.15 207	50.88 217		74.35 219	68.42 214

High-resolution multi-view benchmark