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.

SMA-MVS		97.53 7	97.93 7	97.07 10	99.21 1	99.02 8	98.08 19	96.25 11	96.36 12	93.57 15	96.56 14	99.27 5	96.78 16	97.91 4	97.43 3	98.51 26	98.94 12
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
APDe-MVS		97.79 5	97.96 6	97.60 2	99.20 2	99.10 6	98.88 2	96.68 2	96.81 7	94.64 6	97.84 3	98.02 11	97.24 3	97.74 8	97.02 14	98.97 5	99.16 6
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
DVP-MVS++		98.07 1	98.46 1	97.62 1	99.08 3	99.29 2	98.84 3	96.63 4	97.89 1	95.35 3	97.83 4	99.48 3	96.98 9	97.99 2	97.14 11	98.82 11	99.60 1

HPM-MVS++		97.22 11	97.40 12	97.01 11	99.08 3	98.55 24	98.19 14	96.48 7	96.02 19	93.28 20	96.26 17	98.71 8	96.76 17	97.30 16	96.25 38	98.30 54	98.68 16

ACMMP_NAP		96.93 16	97.27 15	96.53 23	99.06 5	98.95 9	98.24 13	96.06 15	95.66 21	90.96 32	95.63 24	97.71 16	96.53 20	97.66 10	96.68 20	98.30 54	98.61 21

DVP-MVS		97.93 3	98.23 3	97.58 3	99.05 6	99.31 1	98.64 6	96.62 5	97.56 2	95.08 5	96.61 13	99.64 1	97.32 1	97.91 4	97.31 6	98.77 15	99.26 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
PGM-MVS		96.16 24	96.33 28	95.95 26	99.04 7	98.63 19	98.32 12	92.76 42	93.42 48	90.49 37	96.30 16	95.31 41	96.71 18	96.46 40	96.02 47	98.38 45	98.19 43

APD-MVS		97.12 13	97.05 18	97.19 7	99.04 7	98.63 19	98.45 8	96.54 6	94.81 36	93.50 16	96.10 19	97.40 22	96.81 13	97.05 22	96.82 19	98.80 12	98.56 23
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		96.75 19	96.67 24	96.85 16	99.03 9	98.44 34	98.15 16	96.28 10	96.32 13	92.39 25	92.16 35	97.55 20	96.68 19	97.32 14	96.65 22	98.55 25	98.26 39

CNVR-MVS		97.30 10	97.41 11	97.18 8	99.02 10	98.60 21	98.15 16	96.24 13	96.12 17	94.10 11	95.54 25	97.99 12	96.99 7	97.97 3	97.17 9	98.57 24	98.50 30

MSP-MVS		97.70 6	98.09 5	97.24 6	99.00 11	99.17 5	98.76 5	96.41 9	96.91 5	93.88 14	97.72 5	99.04 7	96.93 11	97.29 17	97.31 6	98.45 37	99.23 4
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
ACMMPR		96.92 17	96.96 19	96.87 15	98.99 12	98.78 11	98.38 10	95.52 24	96.57 10	92.81 24	96.06 20	95.90 36	97.07 5	96.60 37	96.34 35	98.46 34	98.42 34

HFP-MVS		97.11 14	97.19 16	97.00 12	98.97 13	98.73 12	98.37 11	95.69 21	96.60 9	93.28 20	96.87 8	96.64 29	97.27 2	96.64 35	96.33 36	98.44 38	98.56 23

SteuartSystems-ACMMP		97.10 15	97.49 10	96.65 18	98.97 13	98.95 9	98.43 9	95.96 17	95.12 28	91.46 28	96.85 9	97.60 18	96.37 24	97.76 6	97.16 10	98.68 18	98.97 11
Skip Steuart: Steuart Systems R&D Blog.
SF-MVS		97.20 12	97.29 14	97.10 9	98.95 15	98.51 29	97.51 29	96.48 7	96.17 16	94.64 6	97.32 6	97.57 19	96.23 26	96.78 29	96.15 42	98.79 14	98.55 28

SED-MVS		97.98 2	98.36 2	97.54 4	98.94 16	99.29 2	98.81 4	96.64 3	97.14 3	95.16 4	97.96 2	99.61 2	96.92 12	98.00 1	97.24 8	98.75 17	99.25 3

X-MVS		96.07 26	96.33 28	95.77 29	98.94 16	98.66 14	97.94 23	95.41 30	95.12 28	88.03 54	93.00 33	96.06 32	95.85 29	96.65 34	96.35 32	98.47 32	98.48 31

SR-MVS					98.93 18			96.00 16				97.75 15

MP-MVS		96.56 21	96.72 23	96.37 24	98.93 18	98.48 30	98.04 20	95.55 23	94.32 40	90.95 34	95.88 22	97.02 26	96.29 25	96.77 30	96.01 48	98.47 32	98.56 23
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MCST-MVS		96.83 18	97.06 17	96.57 19	98.88 20	98.47 32	98.02 21	96.16 14	95.58 23	90.96 32	95.78 23	97.84 14	96.46 22	97.00 25	96.17 40	98.94 7	98.55 28

CP-MVS		96.68 20	96.59 26	96.77 17	98.85 21	98.58 22	98.18 15	95.51 26	95.34 25	92.94 23	95.21 28	96.25 31	96.79 15	96.44 42	95.77 50	98.35 46	98.56 23

DPE-MVS		97.83 4	98.13 4	97.48 5	98.83 22	99.19 4	98.99 1	96.70 1	96.05 18	94.39 9	98.30 1	99.47 4	97.02 6	97.75 7	97.02 14	98.98 3	99.10 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
mPP-MVS					98.76 23							95.49 39

CSCG		95.68 30	95.46 35	95.93 27	98.71 24	99.07 7	97.13 35	93.55 37	95.48 24	93.35 19	90.61 45	93.82 46	95.16 37	94.60 82	95.57 54	97.70 107	99.08 10

DeepC-MVS_fast	93.32 1	96.48 22	96.42 27	96.56 20	98.70 25	98.31 38	97.97 22	95.76 20	96.31 14	92.01 27	91.43 40	95.42 40	96.46 22	97.65 11	97.69 1	98.49 31	98.12 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		95.02 37	93.71 50	96.54 22	98.51 26	97.76 58	96.69 40	95.94 19	93.72 47	93.50 16	89.01 53	90.53 66	96.49 21	94.51 85	93.76 84	98.07 78	96.69 97

train_agg		96.15 25	96.64 25	95.58 33	98.44 27	98.03 48	98.14 18	95.40 31	93.90 46	87.72 59	96.26 17	98.10 10	95.75 31	96.25 47	95.45 56	98.01 85	98.47 32

CDPH-MVS		94.80 41	95.50 33	93.98 46	98.34 28	98.06 47	97.41 30	93.23 39	92.81 53	82.98 99	92.51 34	94.82 42	93.53 59	96.08 50	96.30 37	98.42 40	97.94 55

TPM-MVS					98.33 29	97.85 54	97.06 36			89.97 40	93.26 31	97.16 25	93.12 65			97.79 97	95.95 125
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
MSLP-MVS++		96.05 27	95.63 31	96.55 21	98.33 29	98.17 44	96.94 37	94.61 34	94.70 38	94.37 10	89.20 52	95.96 35	96.81 13	95.57 58	97.33 5	98.24 63	98.47 32

ACMMP		95.54 31	95.49 34	95.61 32	98.27 31	98.53 26	97.16 34	94.86 32	94.88 34	89.34 43	95.36 27	91.74 55	95.50 35	95.51 59	94.16 75	98.50 29	98.22 41
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
3Dnovator+	90.56 5	95.06 36	94.56 45	95.65 31	98.11 32	98.15 45	97.19 33	91.59 51	95.11 30	93.23 22	81.99 102	94.71 43	95.43 36	96.48 39	96.88 18	98.35 46	98.63 18

3Dnovator	90.28 7	94.70 42	94.34 48	95.11 35	98.06 33	98.21 42	96.89 38	91.03 57	94.72 37	91.45 29	82.87 93	93.10 50	94.61 42	96.24 48	97.08 13	98.63 21	98.16 44

PLC	90.69 4	94.32 46	92.99 58	95.87 28	97.91 34	96.49 93	95.95 51	94.12 35	94.94 32	94.09 12	85.90 71	90.77 63	95.58 33	94.52 84	93.32 98	97.55 116	95.00 147
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EPNet		93.92 50	94.40 46	93.36 54	97.89 35	96.55 91	96.08 47	92.14 45	91.65 65	89.16 45	94.07 30	90.17 70	87.78 125	95.24 64	94.97 64	97.09 135	98.15 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CPTT-MVS		95.54 31	95.07 37	96.10 25	97.88 36	97.98 50	97.92 24	94.86 32	94.56 39	92.16 26	91.01 41	95.71 37	96.97 10	94.56 83	93.50 91	96.81 156	98.14 46

QAPM		94.13 49	94.33 49	93.90 47	97.82 37	98.37 37	96.47 42	90.89 58	92.73 56	85.63 82	85.35 75	93.87 45	94.17 49	95.71 57	95.90 49	98.40 42	98.42 34

DeepC-MVS	92.10 3	95.22 34	94.77 41	95.75 30	97.77 38	98.54 25	97.63 28	95.96 17	95.07 31	88.85 48	85.35 75	91.85 54	95.82 30	96.88 28	97.10 12	98.44 38	98.63 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OpenMVS	88.18 11	92.51 62	91.61 79	93.55 53	97.74 39	98.02 49	95.66 53	90.46 61	89.14 102	86.50 70	75.80 136	90.38 69	92.69 70	94.99 67	95.30 58	98.27 58	97.63 66

TSAR-MVS + ACMM		96.19 23	97.39 13	94.78 37	97.70 40	98.41 35	97.72 27	95.49 27	96.47 11	86.66 69	96.35 15	97.85 13	93.99 51	97.19 20	96.37 31	97.12 133	99.13 7

MAR-MVS		92.71 61	92.63 62	92.79 67	97.70 40	97.15 77	93.75 90	87.98 97	90.71 71	85.76 80	86.28 68	86.38 79	94.35 47	94.95 68	95.49 55	97.22 126	97.44 74
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
PHI-MVS		95.86 28	96.93 22	94.61 40	97.60 42	98.65 18	96.49 41	93.13 40	94.07 43	87.91 58	97.12 7	97.17 24	93.90 54	96.46 40	96.93 17	98.64 20	98.10 50

DPM-MVS		95.07 35	94.84 40	95.34 34	97.44 43	97.49 67	97.76 26	95.52 24	94.88 34	88.92 47	87.25 60	96.44 30	94.41 44	95.78 55	96.11 44	97.99 87	95.95 125

SD-MVS		97.35 8	97.73 8	96.90 14	97.35 44	98.66 14	97.85 25	96.25 11	96.86 6	94.54 8	96.75 11	99.13 6	96.99 7	96.94 26	96.58 23	98.39 44	99.20 5
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
MVS_111021_HR		94.84 39	95.91 30	93.60 52	97.35 44	98.46 33	95.08 61	91.19 54	94.18 42	85.97 74	95.38 26	92.56 52	93.61 58	96.61 36	96.25 38	98.40 42	97.92 57

TSAR-MVS + MP.		97.31 9	97.64 9	96.92 13	97.28 46	98.56 23	98.61 7	95.48 28	96.72 8	94.03 13	96.73 12	98.29 9	97.15 4	97.61 12	96.42 26	98.96 6	99.13 7
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CANet		94.85 38	94.92 39	94.78 37	97.25 47	98.52 28	97.20 32	91.81 48	93.25 50	91.06 31	86.29 67	94.46 44	92.99 66	97.02 24	96.68 20	98.34 48	98.20 42

OMC-MVS		94.49 45	94.36 47	94.64 39	97.17 48	97.73 60	95.49 55	92.25 44	96.18 15	90.34 38	88.51 55	92.88 51	94.90 41	94.92 70	94.17 74	97.69 109	96.15 117

MVS_111021_LR		94.84 39	95.57 32	94.00 44	97.11 49	97.72 62	94.88 65	91.16 55	95.24 27	88.74 49	96.03 21	91.52 59	94.33 48	95.96 52	95.01 63	97.79 97	97.49 73

CNLPA		93.69 53	92.50 64	95.06 36	97.11 49	97.36 69	93.88 87	93.30 38	95.64 22	93.44 18	80.32 111	90.73 64	94.99 40	93.58 102	93.33 96	97.67 111	96.57 102

LS3D		91.97 68	90.98 88	93.12 60	97.03 51	97.09 80	95.33 60	95.59 22	92.47 57	79.26 119	81.60 105	82.77 100	94.39 46	94.28 87	94.23 73	97.14 132	94.45 153

TAPA-MVS	90.35 6	93.69 53	93.52 51	93.90 47	96.89 52	97.62 64	96.15 45	91.67 50	94.94 32	85.97 74	87.72 59	91.96 53	94.40 45	93.76 100	93.06 108	98.30 54	95.58 135
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
DELS-MVS		93.71 52	93.47 52	94.00 44	96.82 53	98.39 36	96.80 39	91.07 56	89.51 99	89.94 41	83.80 85	89.29 72	90.95 89	97.32 14	97.65 2	98.42 40	98.32 37
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
EPNet_dtu		88.32 118	90.61 90	85.64 147	96.79 54	92.27 176	92.03 123	90.31 62	89.05 103	65.44 191	89.43 50	85.90 84	74.22 200	92.76 116	92.09 126	95.02 189	92.76 173
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSDG		90.42 95	88.25 115	92.94 65	96.67 55	94.41 120	93.96 82	92.91 41	89.59 97	86.26 72	76.74 129	80.92 116	90.43 97	92.60 121	92.08 127	97.44 121	91.41 179

CS-MVS-test		94.63 43	95.28 36	93.88 49	96.56 56	98.67 13	93.41 99	89.31 80	94.27 41	89.64 42	90.84 43	91.64 57	95.58 33	97.04 23	96.17 40	98.77 15	98.32 37

DeepPCF-MVS	92.65 2	95.50 33	96.96 19	93.79 51	96.44 57	98.21 42	93.51 97	94.08 36	96.94 4	89.29 44	93.08 32	96.77 28	93.82 55	97.68 9	97.40 4	95.59 179	98.65 17

PCF-MVS	90.19 8	92.98 57	92.07 72	94.04 43	96.39 58	97.87 51	96.03 48	95.47 29	87.16 117	85.09 92	84.81 79	93.21 49	93.46 61	91.98 134	91.98 130	97.78 99	97.51 72
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_0304		94.30 47	94.68 43	93.86 50	96.33 59	98.48 30	97.41 30	91.20 53	92.75 54	86.96 66	86.03 70	93.81 47	92.64 71	96.89 27	96.54 25	98.61 22	98.24 40

CS-MVS		94.53 44	94.73 42	94.31 42	96.30 60	98.53 26	94.98 62	89.24 82	93.37 49	90.24 39	88.96 54	89.76 71	96.09 28	97.48 13	96.42 26	98.99 2	98.59 22

OPM-MVS		91.08 81	89.34 101	93.11 61	96.18 61	96.13 102	96.39 43	92.39 43	82.97 156	81.74 102	82.55 99	80.20 119	93.97 53	94.62 80	93.23 99	98.00 86	95.73 131
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PVSNet_BlendedMVS		92.80 58	92.44 66	93.23 55	96.02 62	97.83 56	93.74 91	90.58 59	91.86 62	90.69 35	85.87 73	82.04 109	90.01 99	96.39 43	95.26 59	98.34 48	97.81 62

PVSNet_Blended		92.80 58	92.44 66	93.23 55	96.02 62	97.83 56	93.74 91	90.58 59	91.86 62	90.69 35	85.87 73	82.04 109	90.01 99	96.39 43	95.26 59	98.34 48	97.81 62

XVS					95.68 64	98.66 14	94.96 63			88.03 54		96.06 32				98.46 34

X-MVStestdata					95.68 64	98.66 14	94.96 63			88.03 54		96.06 32				98.46 34

HQP-MVS		92.39 64	92.49 65	92.29 74	95.65 66	95.94 105	95.64 54	92.12 46	92.46 58	79.65 117	91.97 37	82.68 101	92.92 69	93.47 107	92.77 113	97.74 103	98.12 48

HyFIR lowres test		87.87 120	86.42 137	89.57 105	95.56 67	96.99 83	92.37 113	84.15 139	86.64 122	77.17 126	57.65 207	83.97 91	91.08 87	92.09 132	92.44 118	97.09 135	95.16 144

ACMM	88.76 10	91.70 76	90.43 91	93.19 57	95.56 67	95.14 111	93.35 101	91.48 52	92.26 59	87.12 64	84.02 83	79.34 122	93.99 51	94.07 93	92.68 114	97.62 115	95.50 136
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
COLMAP_ROB	84.39 15	87.61 122	86.03 142	89.46 106	95.54 69	94.48 117	91.77 127	90.14 66	87.16 117	75.50 131	73.41 151	76.86 141	87.33 132	90.05 166	89.76 176	96.48 160	90.46 188
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LGP-MVS_train		91.83 72	92.04 73	91.58 81	95.46 70	96.18 101	95.97 50	89.85 68	90.45 78	77.76 122	91.92 38	80.07 120	92.34 75	94.27 88	93.47 92	98.11 75	97.90 60

CHOSEN 1792x2688		88.57 115	87.82 122	89.44 107	95.46 70	96.89 86	93.74 91	85.87 121	89.63 96	77.42 125	61.38 201	83.31 95	88.80 120	93.44 108	93.16 104	95.37 184	96.95 91

PVSNet_Blended_VisFu		91.92 70	92.39 68	91.36 89	95.45 72	97.85 54	92.25 116	89.54 76	88.53 109	87.47 61	79.82 113	90.53 66	85.47 151	96.31 46	95.16 62	97.99 87	98.56 23

PatchMatch-RL		90.30 96	88.93 108	91.89 77	95.41 73	95.68 107	90.94 129	88.67 88	89.80 94	86.95 67	85.90 71	72.51 150	92.46 72	93.56 104	92.18 123	96.93 148	92.89 172

TSAR-MVS + COLMAP		92.39 64	92.31 69	92.47 70	95.35 74	96.46 95	96.13 46	92.04 47	95.33 26	80.11 115	94.95 29	77.35 138	94.05 50	94.49 86	93.08 106	97.15 130	94.53 151

test2506		90.93 85	89.20 104	92.95 64	94.97 75	98.30 39	94.53 67	90.25 64	89.91 91	88.39 53	83.23 89	64.17 193	90.69 92	96.75 32	96.10 45	98.87 8	95.97 124

ECVR-MVS		90.77 89	89.27 102	92.52 69	94.97 75	98.30 39	94.53 67	90.25 64	89.91 91	85.80 79	73.64 146	74.31 147	90.69 92	96.75 32	96.10 45	98.87 8	95.91 128

test1111		90.47 94	89.10 106	92.07 76	94.92 77	98.30 39	94.17 80	90.30 63	89.56 98	83.92 95	73.25 153	73.66 148	90.26 98	96.77 30	96.14 43	98.87 8	96.04 121

ACMP	89.13 9	92.03 67	91.70 78	92.41 72	94.92 77	96.44 97	93.95 83	89.96 67	91.81 64	85.48 87	90.97 42	79.12 123	92.42 73	93.28 113	92.55 117	97.76 101	97.74 65
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UA-Net		90.81 86	92.58 63	88.74 114	94.87 79	97.44 68	92.61 110	88.22 93	82.35 160	78.93 120	85.20 77	95.61 38	79.56 186	96.52 38	96.57 24	98.23 64	94.37 154

IB-MVS	85.10 14	87.98 119	87.97 120	87.99 123	94.55 80	96.86 87	84.52 195	88.21 94	86.48 127	88.54 52	74.41 144	77.74 135	74.10 202	89.65 172	92.85 112	98.06 80	97.80 64
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
CANet_DTU		90.74 91	92.93 60	88.19 120	94.36 81	96.61 89	94.34 73	84.66 132	90.66 72	68.75 170	90.41 46	86.89 77	89.78 101	95.46 60	94.87 65	97.25 125	95.62 133

sasdasda		93.08 55	93.09 55	93.07 62	94.24 82	97.86 52	95.45 57	87.86 103	94.00 44	87.47 61	88.32 56	82.37 105	95.13 38	93.96 98	96.41 29	98.27 58	98.73 13

canonicalmvs		93.08 55	93.09 55	93.07 62	94.24 82	97.86 52	95.45 57	87.86 103	94.00 44	87.47 61	88.32 56	82.37 105	95.13 38	93.96 98	96.41 29	98.27 58	98.73 13

UGNet		91.52 77	93.41 53	89.32 108	94.13 84	97.15 77	91.83 126	89.01 83	90.62 74	85.86 78	86.83 61	91.73 56	77.40 191	94.68 79	94.43 70	97.71 105	98.40 36
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
thres600view7		89.28 112	87.47 131	91.39 86	94.12 85	97.25 72	93.94 85	89.74 71	85.62 134	80.63 113	75.24 140	69.33 165	91.66 82	94.92 70	93.23 99	98.27 58	96.72 96

IS_MVSNet		91.87 71	93.35 54	90.14 102	94.09 86	97.73 60	93.09 104	88.12 95	88.71 106	79.98 116	84.49 80	90.63 65	87.49 130	97.07 21	96.96 16	98.07 78	97.88 61

TSAR-MVS + GP.		95.86 28	96.95 21	94.60 41	94.07 87	98.11 46	96.30 44	91.76 49	95.67 20	91.07 30	96.82 10	97.69 17	95.71 32	95.96 52	95.75 51	98.68 18	98.63 18

thres400		89.40 108	87.58 128	91.53 83	94.06 88	97.21 75	94.19 79	89.83 69	85.69 131	81.08 109	75.50 138	69.76 163	91.80 78	94.79 77	93.51 88	98.20 67	96.60 100

ETV-MVS		93.80 51	94.57 44	92.91 66	93.98 89	97.50 66	93.62 94	88.70 87	91.95 61	87.57 60	90.21 47	90.79 62	94.56 43	97.20 19	96.35 32	99.02 1	97.98 52

ACMH	85.51 13	87.31 125	86.59 135	88.14 121	93.96 90	94.51 116	89.00 165	87.99 96	81.58 163	70.15 160	78.41 120	71.78 155	90.60 95	91.30 143	91.99 129	97.17 129	96.58 101
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MS-PatchMatch		87.63 121	87.61 126	87.65 128	93.95 91	94.09 126	92.60 111	81.52 172	86.64 122	76.41 129	73.46 150	85.94 83	85.01 155	92.23 130	90.00 170	96.43 163	90.93 185

thres200		89.49 107	87.72 123	91.55 82	93.95 91	97.25 72	94.34 73	89.74 71	85.66 132	81.18 106	76.12 135	70.19 162	91.80 78	94.92 70	93.51 88	98.27 58	96.40 107

CLD-MVS		92.50 63	91.96 74	93.13 59	93.93 93	96.24 99	95.69 52	88.77 86	92.92 51	89.01 46	88.19 58	81.74 112	93.13 64	93.63 101	93.08 106	98.23 64	97.91 59
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
thres100view900		89.36 109	87.61 126	91.39 86	93.90 94	96.86 87	94.35 72	89.66 75	85.87 129	81.15 107	76.46 131	70.38 159	91.17 85	94.09 92	93.43 94	98.13 72	96.16 116

tfpn200view9		89.55 106	87.86 121	91.53 83	93.90 94	97.26 71	94.31 75	89.74 71	85.87 129	81.15 107	76.46 131	70.38 159	91.76 80	94.92 70	93.51 88	98.28 57	96.61 99

EIA-MVS		92.72 60	92.96 59	92.44 71	93.86 96	97.76 58	93.13 103	88.65 89	89.78 95	86.68 68	86.69 64	87.57 73	93.74 56	96.07 51	95.32 57	98.58 23	97.53 71

CHOSEN 280x420		90.77 89	92.14 71	89.17 110	93.86 96	92.81 164	93.16 102	80.22 180	90.21 83	84.67 94	89.89 49	91.38 60	90.57 96	94.94 69	92.11 125	92.52 200	93.65 164

FC-MVSNet-train		90.55 92	90.19 94	90.97 92	93.78 98	95.16 110	92.11 121	88.85 84	87.64 114	83.38 98	84.36 82	78.41 129	89.53 103	94.69 78	93.15 105	98.15 70	97.92 57

FA-MVS(training)		90.79 88	91.33 82	90.17 100	93.76 99	97.22 74	92.74 108	77.79 190	90.60 76	88.03 54	78.80 117	87.41 74	91.00 88	95.40 62	93.43 94	97.70 107	96.46 104

Vis-MVSNet (Re-imp)		90.54 93	92.76 61	87.94 124	93.73 100	96.94 85	92.17 119	87.91 98	88.77 105	76.12 130	83.68 86	90.80 61	79.49 187	96.34 45	96.35 32	98.21 66	96.46 104

baseline1		90.81 86	90.29 92	91.42 85	93.67 101	95.86 106	93.94 85	89.69 74	89.29 101	82.85 100	82.91 92	80.30 118	89.60 102	95.05 66	94.79 67	98.80 12	93.82 162

EPP-MVSNet		92.13 66	93.06 57	91.05 91	93.66 102	97.30 70	92.18 117	87.90 99	90.24 82	83.63 96	86.14 69	90.52 68	90.76 91	94.82 75	94.38 71	98.18 69	97.98 52

EC-MVSNet		94.19 48	95.05 38	93.18 58	93.56 103	97.65 63	95.34 59	86.37 117	92.05 60	88.71 50	89.91 48	93.32 48	96.14 27	97.29 17	96.42 26	98.98 3	98.70 15

ACMH+	85.75 12	87.19 127	86.02 143	88.56 116	93.42 104	94.41 120	89.91 149	87.66 108	83.45 153	72.25 147	76.42 133	71.99 154	90.78 90	89.86 167	90.94 144	97.32 122	95.11 146

casdiffmvs_mvg		91.94 69	91.25 84	92.75 68	93.41 105	97.19 76	95.48 56	89.77 70	89.86 93	86.41 71	81.02 109	82.23 108	92.93 67	95.44 61	95.61 53	98.51 26	97.40 76
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		91.81 73	92.19 70	91.37 88	93.24 106	96.95 84	94.43 69	86.25 118	91.45 68	83.45 97	86.31 66	85.15 87	92.93 67	93.99 94	94.71 68	97.92 91	96.77 95

MVSTER		91.73 74	91.61 79	91.86 78	93.18 107	94.56 114	94.37 71	87.90 99	90.16 86	88.69 51	89.23 51	81.28 114	88.92 118	95.75 56	93.95 81	98.12 73	96.37 108

Anonymous202405211			88.00 118		93.16 108	96.38 98	93.58 95	89.34 79	87.92 113		65.04 190	83.03 97	92.07 76	92.67 118	93.33 96	96.96 143	97.63 66

casdiffmvs		91.72 75	91.16 86	92.38 73	93.16 108	97.15 77	93.95 83	89.49 77	91.58 67	86.03 73	80.75 110	80.95 115	93.16 63	95.25 63	95.22 61	98.50 29	97.23 82
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tttt0517		91.01 84	91.71 77	90.19 99	92.98 110	97.07 81	91.96 125	87.63 109	90.61 75	81.42 104	86.76 63	82.26 107	89.23 110	94.86 74	93.03 110	97.90 92	97.36 77

Effi-MVS+		89.79 103	89.83 99	89.74 104	92.98 110	96.45 96	93.48 98	84.24 137	87.62 115	76.45 128	81.76 103	77.56 137	93.48 60	94.61 81	93.59 87	97.82 96	97.22 84

RPSCF		89.68 104	89.24 103	90.20 98	92.97 112	92.93 160	92.30 114	87.69 106	90.44 79	85.12 91	91.68 39	85.84 85	90.69 92	87.34 189	86.07 191	92.46 201	90.37 189

TDRefinement		84.97 153	83.39 168	86.81 136	92.97 112	94.12 125	92.18 117	87.77 105	82.78 157	71.31 152	68.43 171	68.07 171	81.10 182	89.70 171	89.03 183	95.55 181	91.62 177

thisisatest0530		91.04 83	91.74 76	90.21 97	92.93 114	97.00 82	92.06 122	87.63 109	90.74 70	81.51 103	86.81 62	82.48 102	89.23 110	94.81 76	93.03 110	97.90 92	97.33 79

DCV-MVSNet		91.24 79	91.26 83	91.22 90	92.84 115	93.44 142	93.82 88	86.75 114	91.33 69	85.61 83	84.00 84	85.46 86	91.27 83	92.91 115	93.62 86	97.02 139	98.05 51

baseline		91.19 80	91.89 75	90.38 93	92.76 116	95.04 112	93.55 96	84.54 135	92.92 51	85.71 81	86.68 65	86.96 76	89.28 109	92.00 133	92.62 116	96.46 161	96.99 89

EPMVS		85.77 141	86.24 139	85.23 152	92.76 116	93.78 132	89.91 149	73.60 203	90.19 84	74.22 135	82.18 101	78.06 131	87.55 129	85.61 198	85.38 196	93.32 194	88.48 201

GeoE		89.29 111	88.68 110	89.99 103	92.75 118	96.03 104	93.07 106	83.79 144	86.98 119	81.34 105	74.72 141	78.92 124	91.22 84	93.31 111	93.21 102	97.78 99	97.60 70

diffmvs		91.37 78	91.09 87	91.70 80	92.71 119	96.47 94	94.03 81	88.78 85	92.74 55	85.43 89	83.63 87	80.37 117	91.76 80	93.39 109	93.78 83	97.50 118	97.23 82
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DI_MVS_plusplus_trai		91.05 82	90.15 95	92.11 75	92.67 120	96.61 89	96.03 48	88.44 91	90.25 81	85.92 76	73.73 145	84.89 89	91.92 77	94.17 91	94.07 79	97.68 110	97.31 80

Anonymous20231211		89.82 102	88.18 116	91.74 79	92.52 121	96.09 103	93.38 100	89.30 81	88.95 104	85.90 77	64.55 195	84.39 90	92.41 74	92.24 129	93.06 108	96.93 148	97.95 54

tpmrst		83.72 171	83.45 165	84.03 168	92.21 122	91.66 188	88.74 168	73.58 204	88.14 111	72.67 144	77.37 125	72.11 153	86.34 141	82.94 206	82.05 205	90.63 210	89.86 193

CostFormer		86.78 130	86.05 141	87.62 130	92.15 123	93.20 151	91.55 128	75.83 195	88.11 112	85.29 90	81.76 103	76.22 143	87.80 124	84.45 201	85.21 197	93.12 195	93.42 167

Vis-MVSNet		89.36 109	91.49 81	86.88 135	92.10 124	97.60 65	92.16 120	85.89 120	84.21 145	75.20 132	82.58 97	87.13 75	77.40 191	95.90 54	95.63 52	98.51 26	97.36 77
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
IterMVS-LS		88.60 114	88.45 111	88.78 113	92.02 125	92.44 174	92.00 124	83.57 148	86.52 125	78.90 121	78.61 119	81.34 113	89.12 113	90.68 155	93.18 103	97.10 134	96.35 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchmatchNet		85.70 142	86.65 134	84.60 159	91.79 126	93.40 143	89.27 158	73.62 202	90.19 84	72.63 145	82.74 96	81.93 111	87.64 127	84.99 199	84.29 201	92.64 199	89.00 196
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm cat1		84.13 164	81.99 184	86.63 139	91.74 127	91.50 191	90.68 131	75.69 196	86.12 128	85.44 88	72.39 156	70.72 157	85.16 153	80.89 210	81.56 206	91.07 208	90.71 186

USDC		86.73 131	85.96 145	87.63 129	91.64 128	93.97 128	92.76 107	84.58 134	88.19 110	70.67 157	80.10 112	67.86 172	89.43 104	91.81 135	89.77 175	96.69 158	90.05 192

SCA		86.25 133	87.52 129	84.77 156	91.59 129	93.90 129	89.11 162	73.25 207	90.38 80	72.84 143	83.26 88	83.79 93	88.49 122	86.07 196	85.56 194	93.33 193	89.67 194

gg-mvs-nofinetune		81.83 190	83.58 163	79.80 197	91.57 130	96.54 92	93.79 89	68.80 214	62.71 218	43.01 223	55.28 210	85.06 88	83.65 165	96.13 49	94.86 66	97.98 90	94.46 152

Fast-Effi-MVS+		88.56 116	87.99 119	89.22 109	91.56 131	95.21 109	92.29 115	82.69 155	86.82 120	77.73 123	76.24 134	73.39 149	93.36 62	94.22 90	93.64 85	97.65 112	96.43 106

CMPMVS	61.19 17	79.86 197	77.46 205	82.66 186	91.54 132	91.82 186	83.25 198	81.57 171	70.51 210	68.64 171	59.89 206	66.77 178	79.63 185	84.00 204	84.30 200	91.34 206	84.89 209
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ADS-MVSNet		84.08 165	84.95 153	83.05 181	91.53 133	91.75 187	88.16 172	70.70 211	89.96 90	69.51 165	78.83 116	76.97 140	86.29 142	84.08 203	84.60 199	92.13 204	88.48 201

test-LLR		86.88 128	88.28 113	85.24 151	91.22 134	92.07 180	87.41 178	83.62 146	84.58 138	69.33 166	83.00 90	82.79 98	84.24 159	92.26 127	89.81 173	95.64 177	93.44 165

test0.0.03 1		85.58 144	87.69 125	83.11 178	91.22 134	92.54 171	85.60 194	83.62 146	85.66 132	67.84 177	82.79 95	79.70 121	73.51 204	91.15 147	90.79 146	96.88 152	91.23 182

baseline2		88.97 113	89.50 100	88.36 117	91.14 136	95.30 108	90.13 143	85.17 129	87.24 116	80.80 111	84.46 81	78.44 128	85.60 148	93.54 105	91.87 131	97.31 123	95.66 132

Effi-MVS+-dtu		87.51 123	88.13 117	86.77 137	91.10 137	94.90 113	90.91 130	82.67 156	83.47 152	71.55 149	81.11 108	77.04 139	89.41 105	92.65 120	91.68 137	95.00 190	96.09 119

RPMNet		84.82 155	85.90 146	83.56 173	91.10 137	92.10 178	88.73 169	71.11 210	84.75 136	68.79 169	73.56 147	77.62 136	85.33 152	90.08 165	89.43 179	96.32 164	93.77 163

CR-MVSNet		85.48 146	86.29 138	84.53 161	91.08 139	92.10 178	89.18 160	73.30 205	84.75 136	71.08 154	73.12 155	77.91 133	86.27 143	91.48 139	90.75 149	96.27 165	93.94 159

TinyColmap		84.04 166	82.01 183	86.42 141	90.87 140	91.84 185	88.89 167	84.07 141	82.11 162	69.89 162	71.08 160	60.81 206	89.04 114	90.52 157	89.19 181	95.76 171	88.50 200

tpm		83.16 177	83.64 162	82.60 187	90.75 141	91.05 194	88.49 170	73.99 200	82.36 159	67.08 183	78.10 121	68.79 166	84.17 161	85.95 197	85.96 192	91.09 207	93.23 169

dps		85.00 152	83.21 172	87.08 133	90.73 142	92.55 170	89.34 157	75.29 197	84.94 135	87.01 65	79.27 115	67.69 173	87.27 133	84.22 202	83.56 202	92.83 198	90.25 190

MDTV_nov1_ep13		86.64 132	87.50 130	85.65 146	90.73 142	93.69 136	89.96 147	78.03 189	89.48 100	76.85 127	84.92 78	82.42 104	86.14 145	86.85 193	86.15 190	92.17 202	88.97 197

CDS-MVSNet		88.34 117	88.71 109	87.90 125	90.70 144	94.54 115	92.38 112	86.02 119	80.37 169	79.42 118	79.30 114	83.43 94	82.04 174	93.39 109	94.01 80	96.86 154	95.93 127
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IterMVS-SCA-FT		85.44 148	86.71 133	83.97 169	90.59 145	90.84 197	89.73 153	78.34 186	84.07 149	66.40 186	77.27 127	78.66 126	83.06 167	91.20 144	90.10 168	95.72 174	94.78 148

IterMVS		85.25 150	86.49 136	83.80 170	90.42 146	90.77 200	90.02 145	78.04 188	84.10 147	66.27 187	77.28 126	78.41 129	83.01 168	90.88 149	89.72 177	95.04 188	94.24 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+-dtu		86.25 133	87.70 124	84.56 160	90.37 147	93.70 135	90.54 133	78.14 187	83.50 151	65.37 192	81.59 106	75.83 145	86.09 147	91.70 137	91.70 135	96.88 152	95.84 129

dmvs_re		87.31 125	86.10 140	88.74 114	89.84 148	94.28 123	92.66 109	89.41 78	82.61 158	74.69 133	74.69 142	69.47 164	87.78 125	92.38 125	93.23 99	98.03 82	96.02 123

FC-MVSNet-test		86.15 136	89.10 106	82.71 185	89.83 149	93.18 152	87.88 175	84.69 131	86.54 124	62.18 201	82.39 100	83.31 95	74.18 201	92.52 123	91.86 132	97.50 118	93.88 161

GA-MVS		85.08 151	85.65 149	84.42 162	89.77 150	94.25 124	89.26 159	84.62 133	81.19 166	62.25 200	75.72 137	68.44 169	84.14 162	93.57 103	91.68 137	96.49 159	94.71 150

PMMVS		89.88 101	91.19 85	88.35 118	89.73 151	91.97 184	90.62 132	81.92 167	90.57 77	80.58 114	92.16 35	86.85 78	91.17 85	92.31 126	91.35 141	96.11 167	93.11 171

tfpnnormal		83.80 170	81.26 192	86.77 137	89.60 152	93.26 150	89.72 154	87.60 111	72.78 203	70.44 158	60.53 204	61.15 205	85.55 149	92.72 117	91.44 139	97.71 105	96.92 92

CVMVSNet		83.83 169	85.53 150	81.85 192	89.60 152	90.92 195	87.81 176	83.21 152	80.11 172	60.16 205	76.47 130	78.57 127	76.79 193	89.76 168	90.13 163	93.51 192	92.75 174

testgi		81.94 189	84.09 160	79.43 198	89.53 154	90.83 198	82.49 201	81.75 170	80.59 167	59.46 207	82.82 94	65.75 182	67.97 206	90.10 164	89.52 178	95.39 183	89.03 195

UniMVSNet_ETH3D		84.57 156	81.40 190	88.28 119	89.34 155	94.38 122	90.33 135	86.50 116	74.74 201	77.52 124	59.90 205	62.04 201	88.78 121	88.82 182	92.65 115	97.22 126	97.24 81

LTVRE_ROB	81.71 16	82.44 187	81.84 185	83.13 177	89.01 156	92.99 157	88.90 166	82.32 162	66.26 214	54.02 215	74.68 143	59.62 212	88.87 119	90.71 154	92.02 128	95.68 176	96.62 98
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
TAMVS		84.94 154	84.95 153	84.93 155	88.82 157	93.18 152	88.44 171	81.28 174	77.16 188	73.76 139	75.43 139	76.57 142	82.04 174	90.59 156	90.79 146	95.22 186	90.94 184

EG-PatchMatch MVS		81.70 192	81.31 191	82.15 190	88.75 158	93.81 131	87.14 181	78.89 185	71.57 206	64.12 197	61.20 203	68.46 168	76.73 195	91.48 139	90.77 148	97.28 124	91.90 176

TransMVSNet (Re)		82.67 184	80.93 195	84.69 158	88.71 159	91.50 191	87.90 174	87.15 112	71.54 208	68.24 174	63.69 197	64.67 192	78.51 190	91.65 138	90.73 151	97.64 113	92.73 175

FMVSNet3		90.19 99	90.06 98	90.34 94	88.69 160	93.85 130	94.58 66	85.78 122	90.03 87	85.56 84	77.38 122	86.13 80	89.22 112	93.29 112	94.36 72	98.20 67	95.40 141

GBi-Net		90.21 97	90.11 96	90.32 95	88.66 161	93.65 138	94.25 76	85.78 122	90.03 87	85.56 84	77.38 122	86.13 80	89.38 106	93.97 95	94.16 75	98.31 51	95.47 137

test1		90.21 97	90.11 96	90.32 95	88.66 161	93.65 138	94.25 76	85.78 122	90.03 87	85.56 84	77.38 122	86.13 80	89.38 106	93.97 95	94.16 75	98.31 51	95.47 137

FMVSNet2		89.61 105	89.14 105	90.16 101	88.66 161	93.65 138	94.25 76	85.44 126	88.57 108	84.96 93	73.53 148	83.82 92	89.38 106	94.23 89	94.68 69	98.31 51	95.47 137

PatchT		83.86 168	85.51 151	81.94 191	88.41 164	91.56 190	78.79 209	71.57 209	84.08 148	71.08 154	70.62 161	76.13 144	86.27 143	91.48 139	90.75 149	95.52 182	93.94 159

UniMVSNet (Re)		86.22 135	85.46 152	87.11 132	88.34 165	94.42 119	89.65 155	87.10 113	84.39 142	74.61 134	70.41 165	68.10 170	85.10 154	91.17 146	91.79 133	97.84 95	97.94 55

NR-MVSNet		85.46 147	84.54 157	86.52 140	88.33 166	93.78 132	90.45 134	87.87 101	84.40 140	71.61 148	70.59 162	62.09 200	82.79 170	91.75 136	91.75 134	98.10 76	97.44 74

UniMVSNet_NR-MVSNet		86.80 129	85.86 147	87.89 126	88.17 167	94.07 127	90.15 141	88.51 90	84.20 146	73.45 140	72.38 157	70.30 161	88.95 116	90.25 160	92.21 122	98.12 73	97.62 68

thisisatest0515		85.70 142	87.00 132	84.19 165	88.16 168	93.67 137	84.20 197	84.14 140	83.39 154	72.91 142	76.79 128	74.75 146	78.82 189	92.57 122	91.26 142	96.94 145	96.56 103

pm-mvs1		84.55 157	83.46 164	85.82 143	88.16 168	93.39 144	89.05 164	85.36 128	74.03 202	72.43 146	65.08 189	71.11 156	82.30 173	93.48 106	91.70 135	97.64 113	95.43 140

gm-plane-assit		77.65 202	78.50 200	76.66 202	87.96 170	85.43 213	64.70 219	74.50 198	64.15 216	51.26 218	61.32 202	58.17 214	84.11 163	95.16 65	93.83 82	97.45 120	91.41 179

test-mter		86.09 139	88.38 112	83.43 175	87.89 171	92.61 168	86.89 183	77.11 193	84.30 143	68.62 172	82.57 98	82.45 103	84.34 158	92.40 124	90.11 167	95.74 172	94.21 157

pmmvs4		86.00 140	84.28 159	88.00 122	87.80 172	92.01 183	89.94 148	84.91 130	86.79 121	80.98 110	73.41 151	66.34 181	88.12 123	89.31 175	88.90 184	96.24 166	93.20 170

TESTMET0.1,1		86.11 138	88.28 113	83.59 172	87.80 172	92.07 180	87.41 178	77.12 192	84.58 138	69.33 166	83.00 90	82.79 98	84.24 159	92.26 127	89.81 173	95.64 177	93.44 165

DU-MVS		86.12 137	84.81 155	87.66 127	87.77 174	93.78 132	90.15 141	87.87 101	84.40 140	73.45 140	70.59 162	64.82 190	88.95 116	90.14 161	92.33 119	97.76 101	97.62 68

Baseline_NR-MVSNet		85.28 149	83.42 167	87.46 131	87.77 174	90.80 199	89.90 151	87.69 106	83.93 150	74.16 136	64.72 193	66.43 180	87.48 131	90.14 161	90.83 145	97.73 104	97.11 87

SixPastTwentyTwo		83.12 179	83.44 166	82.74 184	87.71 176	93.11 156	82.30 202	82.33 161	79.24 177	64.33 195	78.77 118	62.75 196	84.11 163	88.11 184	87.89 186	95.70 175	94.21 157

TranMVSNet+NR-MVSNet		85.57 145	84.41 158	86.92 134	87.67 177	93.34 145	90.31 137	88.43 92	83.07 155	70.11 161	69.99 168	65.28 185	86.96 135	89.73 169	92.27 120	98.06 80	97.17 86

WR-MVS		83.14 178	83.38 169	82.87 183	87.55 178	93.29 147	86.36 188	84.21 138	80.05 173	66.41 185	66.91 177	66.92 177	75.66 198	88.96 180	90.56 154	97.05 137	96.96 90

v8		84.45 162	83.30 171	85.80 144	87.53 179	92.95 158	90.31 137	82.46 160	80.46 168	71.43 150	66.99 176	67.16 175	86.14 145	89.26 176	90.22 162	96.94 145	96.06 120

WR-MVS_H		82.86 183	82.66 177	83.10 179	87.44 180	93.33 146	85.71 193	83.20 153	77.36 187	68.20 175	66.37 180	65.23 186	76.05 197	89.35 173	90.13 163	97.99 87	96.89 93

v148		83.61 172	82.10 181	85.37 148	87.34 181	92.94 159	87.48 177	85.72 125	78.92 178	73.87 138	65.71 186	64.69 191	81.78 178	87.82 185	89.35 180	96.01 168	95.26 143

v10		84.18 163	83.17 173	85.37 148	87.34 181	92.68 166	90.32 136	81.33 173	79.93 176	69.23 168	66.33 181	65.74 183	87.03 134	90.84 150	90.38 157	96.97 141	96.29 113

v2v482		84.51 158	83.05 174	86.20 142	87.25 183	93.28 148	90.22 139	85.40 127	79.94 175	69.78 163	67.74 173	65.15 187	87.57 128	89.12 178	90.55 155	96.97 141	95.60 134

CP-MVSNet		83.11 180	82.15 180	84.23 164	87.20 184	92.70 165	86.42 187	83.53 149	77.83 185	67.67 178	66.89 179	60.53 208	82.47 171	89.23 177	90.65 153	98.08 77	97.20 85

v1144		84.03 167	82.88 175	85.37 148	87.17 185	93.15 155	90.18 140	83.31 151	78.83 179	67.85 176	65.99 183	64.99 188	86.79 137	90.75 152	90.33 159	96.90 150	96.15 117

V42		84.48 160	83.36 170	85.79 145	87.14 186	93.28 148	90.03 144	83.98 142	80.30 170	71.20 153	66.90 178	67.17 174	85.55 149	89.35 173	90.27 160	96.82 155	96.27 114

pmmvs5		83.37 175	82.68 176	84.18 166	87.13 187	93.18 152	86.74 184	82.08 165	76.48 192	67.28 181	71.26 159	62.70 197	84.71 156	90.77 151	90.12 166	97.15 130	94.24 155

FMVSNet1		87.33 124	86.00 144	88.89 111	87.13 187	92.83 163	93.08 105	84.46 136	81.35 165	82.20 101	66.33 181	77.96 132	88.96 115	93.97 95	94.16 75	97.54 117	95.38 142

PS-CasMVS		82.53 185	81.54 188	83.68 171	87.08 189	92.54 171	86.20 189	83.46 150	76.46 193	65.73 190	65.71 186	59.41 213	81.61 179	89.06 179	90.55 155	98.03 82	97.07 88

our_test_3					86.93 190	89.77 201	81.61 203

PEN-MVS		82.49 186	81.58 187	83.56 173	86.93 190	92.05 182	86.71 185	83.84 143	76.94 190	64.68 194	67.24 174	60.11 209	81.17 181	87.78 186	90.70 152	98.02 84	96.21 115

v1192		83.56 173	82.35 178	84.98 153	86.84 192	92.84 161	90.01 146	82.70 154	78.54 180	66.48 184	64.88 191	62.91 195	86.91 136	90.72 153	90.25 161	96.94 145	96.32 111

v144192		83.48 174	82.23 179	84.94 154	86.65 193	92.84 161	89.63 156	82.48 159	77.87 184	67.36 180	65.33 188	63.50 194	86.51 139	89.72 170	89.99 171	97.03 138	96.35 109

DTE-MVSNet		81.76 191	81.04 193	82.60 187	86.63 194	91.48 193	85.97 191	83.70 145	76.45 194	62.44 199	67.16 175	59.98 210	78.98 188	87.15 190	89.93 172	97.88 94	95.12 145

pmnet_mix02		80.14 196	80.21 197	80.06 195	86.61 195	89.66 202	80.40 206	82.20 164	82.29 161	61.35 202	71.52 158	66.67 179	76.75 194	82.55 207	80.18 210	93.05 196	88.62 198

v1921920		83.30 176	82.09 182	84.70 157	86.59 196	92.67 167	89.82 152	82.23 163	78.32 181	65.76 189	64.64 194	62.35 198	86.78 138	90.34 159	90.02 169	97.02 139	96.31 112

v1240		82.88 182	81.66 186	84.29 163	86.46 197	92.52 173	89.06 163	81.82 169	77.16 188	65.09 193	64.17 196	61.50 203	86.36 140	90.12 163	90.13 163	96.95 144	96.04 121

anonymousdsp		84.51 158	85.85 148	82.95 182	86.30 198	93.51 141	85.77 192	80.38 179	78.25 183	63.42 198	73.51 149	72.20 152	84.64 157	93.21 114	92.16 124	97.19 128	98.14 46

pmmvs6		80.90 193	78.77 199	83.38 176	85.84 199	91.61 189	86.01 190	82.54 158	64.17 215	70.43 159	54.14 214	67.06 176	80.73 183	90.50 158	89.17 182	94.74 191	94.75 149

MVS-HIRNet		78.16 200	77.57 204	78.83 199	85.83 200	87.76 207	76.67 210	70.22 212	75.82 198	67.39 179	55.61 209	70.52 158	81.96 176	86.67 194	85.06 198	90.93 209	81.58 212

test20.03		76.41 204	78.49 201	73.98 205	85.64 201	87.50 208	75.89 211	80.71 178	70.84 209	51.07 219	68.06 172	61.40 204	54.99 215	88.28 183	87.20 188	95.58 180	86.15 205

v7n		82.25 188	81.54 188	83.07 180	85.55 202	92.58 169	86.68 186	81.10 177	76.54 191	65.97 188	62.91 198	60.56 207	82.36 172	91.07 148	90.35 158	96.77 157	96.80 94

N_pmnet		77.55 203	76.68 206	78.56 200	85.43 203	87.30 210	78.84 208	81.88 168	78.30 182	60.61 203	61.46 200	62.15 199	74.03 203	82.04 208	80.69 209	90.59 211	84.81 210

Anonymous20231206		78.09 201	78.11 202	78.07 201	85.19 204	89.17 203	80.99 204	81.24 176	75.46 199	58.25 209	54.78 213	59.90 211	66.73 209	88.94 181	88.26 185	96.01 168	90.25 190

MDTV_nov1_ep13_2view		80.43 194	80.94 194	79.84 196	84.82 205	90.87 196	84.23 196	73.80 201	80.28 171	64.33 195	70.05 167	68.77 167	79.67 184	84.83 200	83.50 203	92.17 202	88.25 203

FPMVS		69.87 210	67.10 213	73.10 207	84.09 206	78.35 218	79.40 207	76.41 194	71.92 204	57.71 210	54.06 215	50.04 219	56.72 213	71.19 216	68.70 216	84.25 216	75.43 216

EU-MVSNet		78.43 199	80.25 196	76.30 203	83.81 207	87.27 211	80.99 204	79.52 182	76.01 195	54.12 214	70.44 164	64.87 189	67.40 208	86.23 195	85.54 195	91.95 205	91.41 179

FMVSNet5		84.47 161	84.72 156	84.18 166	83.30 208	88.43 205	88.09 173	79.42 183	84.25 144	74.14 137	73.15 154	78.74 125	83.65 165	91.19 145	91.19 143	96.46 161	86.07 206

WB-MVS		60.76 213	66.86 214	53.64 213	82.24 209	72.70 219	48.70 225	82.04 166	63.91 217	12.91 228	64.77 192	49.00 222	22.74 223	75.95 214	75.36 214	73.22 222	66.33 220

MIMVSNet		82.97 181	84.00 161	81.77 193	82.23 210	92.25 177	87.40 180	72.73 208	81.48 164	69.55 164	68.79 170	72.42 151	81.82 177	92.23 130	92.25 121	96.89 151	88.61 199

PM-MVS		80.29 195	79.30 198	81.45 194	81.91 211	88.23 206	82.61 200	79.01 184	79.99 174	67.15 182	69.07 169	51.39 218	82.92 169	87.55 188	85.59 193	95.08 187	93.28 168

pmmvs-eth3d		79.78 198	77.58 203	82.34 189	81.57 212	87.46 209	82.92 199	81.28 174	75.33 200	71.34 151	61.88 199	52.41 217	81.59 180	87.56 187	86.90 189	95.36 185	91.48 178

new-patchmatchnet		72.32 207	71.09 210	73.74 206	81.17 213	84.86 214	72.21 216	77.48 191	68.32 212	54.89 213	55.10 211	49.31 221	63.68 212	79.30 212	76.46 213	93.03 197	84.32 211

ET-MVSNet_ETH3D		89.93 100	90.84 89	88.87 112	79.60 214	96.19 100	94.43 69	86.56 115	90.63 73	80.75 112	90.71 44	77.78 134	93.73 57	91.36 142	93.45 93	98.15 70	95.77 130

PMVS	56.77 18	61.27 212	58.64 216	64.35 211	75.66 215	54.60 223	53.62 222	74.23 199	53.69 220	58.37 208	44.27 219	49.38 220	44.16 219	69.51 218	65.35 218	80.07 218	73.66 217
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		72.29 208	73.25 208	71.16 210	75.35 216	81.38 215	73.72 215	69.27 213	75.97 196	49.84 220	56.27 208	56.12 216	69.08 205	81.73 209	80.86 208	89.72 214	80.44 214

ambc			67.96 212		73.69 217	79.79 217	73.82 214		71.61 205	59.80 206	46.00 217	20.79 227	66.15 210	86.92 192	80.11 211	89.13 215	90.50 187

pmmvs3		71.13 209	71.06 211	71.21 209	73.54 218	80.19 216	71.69 217	64.86 216	62.04 219	52.10 216	54.92 212	48.00 223	75.03 199	83.75 205	83.24 204	90.04 213	85.27 207

MDA-MVSNet-bldmvs		73.81 205	72.56 209	75.28 204	72.52 219	88.87 204	74.95 213	82.67 156	71.57 206	55.02 212	65.96 184	42.84 225	76.11 196	70.61 217	81.47 207	90.38 212	86.59 204

tmp_tt				50.24 216	68.55 220	46.86 225	48.90 224	18.28 223	86.51 126	68.32 173	70.19 166	65.33 184	26.69 222	74.37 215	66.80 217	70.72 223

Gipuma		58.52 214	56.17 217	61.27 212	67.14 221	58.06 222	52.16 223	68.40 215	69.00 211	45.02 222	22.79 221	20.57 228	55.11 214	76.27 213	79.33 212	79.80 219	67.16 219
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		73.19 206	73.70 207	72.60 208	65.42 222	86.69 212	75.56 212	79.65 181	67.87 213	55.30 211	45.24 218	56.41 215	63.79 211	86.98 191	87.66 187	95.85 170	85.04 208

PMMVS2		53.68 216	55.72 218	51.30 214	58.84 223	67.02 221	54.23 221	60.97 219	47.50 221	19.42 225	34.81 220	31.97 226	30.88 221	65.84 219	69.99 215	83.47 217	72.92 218

EMVS		39.04 219	34.32 221	44.54 218	58.25 224	39.35 227	27.61 227	62.55 218	35.99 222	16.40 227	20.04 224	14.77 229	44.80 217	33.12 223	44.10 222	57.61 225	52.89 223

E-PMN		40.00 217	35.74 220	44.98 217	57.69 225	39.15 228	28.05 226	62.70 217	35.52 223	17.78 226	20.90 222	14.36 230	44.47 218	35.89 222	47.86 221	59.15 224	56.47 222

MVE	39.81 19	39.52 218	41.58 219	37.11 219	33.93 226	49.06 224	26.45 228	54.22 220	29.46 224	24.15 224	20.77 223	10.60 231	34.42 220	51.12 221	65.27 219	49.49 226	64.81 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		58.10 215	64.61 215	50.51 215	28.26 227	41.71 226	61.28 220	32.07 222	75.92 197	52.04 217	47.94 216	61.83 202	51.80 216	79.83 211	63.95 220	77.60 220	81.05 213

testmvs		4.35 220	6.54 222	1.79 221	0.60 228	1.82 229	3.06 230	0.95 224	7.22 225	0.88 230	12.38 225	1.25 232	3.87 225	6.09 224	5.58 223	1.40 227	11.42 225

GG-mvs-BLEND		62.84 211	90.21 93	30.91 220	0.57 229	94.45 118	86.99 182	0.34 226	88.71 106	0.98 229	81.55 107	91.58 58	0.86 226	92.66 119	91.43 140	95.73 173	91.11 183

test123		3.48 221	5.31 223	1.34 222	0.20 230	1.52 230	2.17 231	0.58 225	6.13 226	0.31 231	9.85 226	0.31 233	3.90 224	2.65 225	5.28 224	0.87 228	11.46 224

uanet_test		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet-low-res		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

sosnet		0.00 222	0.00 224	0.00 223	0.00 231	0.00 231	0.00 232	0.00 227	0.00 227	0.00 232	0.00 227	0.00 234	0.00 227	0.00 226	0.00 225	0.00 229	0.00 226

RE-MVS-def										60.19 204

9.14												97.28 23

MTAPA										95.36 2		97.46 21

MTMP										95.70 1		96.90 27

Patchmatch-RL test							18.47 229

NP-MVS									91.63 66

Patchmtry						92.39 175	89.18 160	73.30 205		71.08 154

DeepMVS_CX						71.82 220	68.37 218	48.05 221	77.38 186	46.88 221	65.77 185	47.03 224	67.48 207	64.27 220		76.89 221	76.72 215

High-resolution multi-view benchmark