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.

MCST-MVS		93.81 17	94.06 19	93.53 16	96.79 23	96.85 20	95.95 14	91.69 16	92.20 25	87.17 31	90.83 27	93.41 7	91.96 14	94.49 25	93.50 30	97.61 1	97.12 22

SF-MVS		94.61 8	94.96 10	94.20 9	96.75 24	97.07 12	95.82 18	92.60 7	93.98 12	91.09 8	95.89 6	92.54 12	91.93 15	94.40 27	93.56 29	97.04 2	97.27 17

DVP-MVS++		95.79 1	96.42 1	95.06 1	97.84 2	98.17 2	97.03 4	92.84 3	96.68 1	92.83 3	95.90 5	94.38 4	92.90 5	95.98 2	94.85 5	96.93 3	98.99 1

SED-MVS		95.61 2	96.36 2	94.73 3	96.84 19	98.15 3	97.08 3	92.92 2	95.64 3	91.84 4	95.98 4	95.33 1	92.83 7	96.00 1	94.94 3	96.90 4	98.45 3

DVP-MVS		95.56 3	96.26 3	94.73 3	96.93 16	98.19 1	96.62 7	92.81 5	96.15 2	91.73 5	95.01 7	95.31 2	93.41 1	95.95 3	94.77 8	96.90 4	98.46 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
DPE-MVS		95.53 4	96.13 4	94.82 2	96.81 22	98.05 4	97.42 1	93.09 1	94.31 9	91.49 6	97.12 1	95.03 3	93.27 3	95.55 6	94.58 12	96.86 6	98.25 4
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + GP.		92.71 27	93.91 21	91.30 34	91.96 73	96.00 40	93.43 41	87.94 40	92.53 21	86.27 39	93.57 15	91.94 19	91.44 24	93.29 44	92.89 44	96.78 7	97.15 21

EC-MVSNet		89.96 47	90.77 43	89.01 55	90.54 91	95.15 56	91.34 61	81.43 107	85.27 61	83.08 54	82.83 47	87.22 49	90.97 29	94.79 18	93.38 34	96.73 8	96.71 32

ETV-MVS		89.22 52	89.76 49	88.60 61	91.60 77	94.61 67	89.48 81	83.46 85	85.20 63	81.58 63	82.75 48	82.59 66	88.80 40	94.57 23	93.28 38	96.68 9	95.31 56

APDe-MVS		95.23 5	95.69 6	94.70 5	97.12 10	97.81 6	97.19 2	92.83 4	95.06 6	90.98 9	96.47 2	92.77 10	93.38 2	95.34 9	94.21 16	96.68 9	98.17 5

MVS_0304		90.88 39	91.35 37	90.34 40	93.91 51	96.79 23	94.49 34	86.54 48	86.57 55	82.85 56	81.68 55	89.70 33	87.57 55	94.64 21	93.93 21	96.67 11	96.15 42

3Dnovator	85.17 5	90.48 41	89.90 48	91.16 36	94.88 43	95.74 47	93.82 37	85.36 55	89.28 43	87.81 27	74.34 95	87.40 48	88.56 44	93.07 47	93.74 26	96.53 12	95.71 48

TSAR-MVS + MP.		94.48 11	94.97 9	93.90 12	95.53 37	97.01 15	96.69 6	90.71 23	94.24 10	90.92 10	94.97 8	92.19 15	93.03 4	94.83 16	93.60 27	96.51 13	97.97 9
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CS-MVS		90.34 42	90.58 44	90.07 43	93.11 60	95.82 45	90.57 65	83.62 76	87.07 53	85.35 41	82.98 46	83.47 61	91.37 26	94.94 13	93.37 36	96.37 14	96.41 37

MSP-MVS		95.12 6	95.83 5	94.30 6	96.82 21	97.94 5	96.98 5	92.37 11	95.40 4	90.59 12	96.16 3	93.71 6	92.70 8	94.80 17	94.77 8	96.37 14	97.99 8
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
QAPM		89.49 50	89.58 51	89.38 52	94.73 45	95.94 41	92.35 49	85.00 58	85.69 60	80.03 74	76.97 79	87.81 46	87.87 50	92.18 63	92.10 53	96.33 16	96.40 39

MVS_111021_HR		90.56 40	91.29 38	89.70 48	94.71 46	95.63 48	91.81 57	86.38 49	87.53 51	81.29 65	87.96 32	85.43 53	87.69 52	93.90 34	92.93 42	96.33 16	95.69 49

test1111		84.86 91	84.21 97	85.61 83	91.75 76	95.14 57	88.63 99	84.57 62	81.88 85	71.21 113	65.66 145	68.51 140	81.19 96	93.74 39	92.68 48	96.31 18	91.86 125

CANet		91.33 37	91.46 35	91.18 35	95.01 40	96.71 24	93.77 38	87.39 45	87.72 50	87.26 30	81.77 53	89.73 32	87.32 59	94.43 26	93.86 22	96.31 18	96.02 44

APD-MVS		94.37 12	94.47 16	94.26 7	97.18 8	96.99 16	96.53 8	92.68 6	92.45 23	89.96 16	94.53 11	91.63 21	92.89 6	94.58 22	93.82 23	96.31 18	97.26 18
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test2506		85.20 86	84.11 98	86.47 76	91.84 74	95.28 52	89.18 84	84.49 63	82.59 75	75.34 95	74.66 93	58.07 188	81.68 92	93.76 36	92.71 46	96.28 21	91.71 128

ECVR-MVS		85.25 85	84.47 94	86.16 78	91.84 74	95.28 52	89.18 84	84.49 63	82.59 75	73.49 104	66.12 138	69.28 136	81.68 92	93.76 36	92.71 46	96.28 21	91.58 135

SteuartSystems-ACMMP		94.06 14	94.65 12	93.38 18	96.97 15	97.36 9	96.12 10	91.78 14	92.05 27	87.34 29	94.42 12	90.87 25	91.87 18	95.47 8	94.59 11	96.21 23	97.77 11
Skip Steuart: Steuart Systems R&D Blog.
PHI-MVS		92.05 31	93.74 22	90.08 42	94.96 41	97.06 13	93.11 45	87.71 43	90.71 36	80.78 70	92.40 22	91.03 23	87.68 53	94.32 28	94.48 13	96.21 23	96.16 41

CNVR-MVS		94.37 12	94.65 12	94.04 10	97.29 6	97.11 11	96.00 11	92.43 10	93.45 15	89.85 18	90.92 25	93.04 9	92.59 10	95.77 5	94.82 6	96.11 25	97.42 16

baseline1		84.54 94	84.43 95	84.67 90	90.62 88	91.16 111	88.63 99	83.75 74	79.78 105	71.16 114	75.14 89	74.10 111	77.84 138	91.56 68	90.67 73	96.04 26	88.58 157

MSLP-MVS++		92.02 33	91.40 36	92.75 23	96.01 32	95.88 43	93.73 40	89.00 33	89.89 42	90.31 14	81.28 58	88.85 39	91.45 22	92.88 51	94.24 15	96.00 27	96.76 29

NCCC		93.69 19	93.66 23	93.72 15	97.37 5	96.66 29	95.93 17	92.50 9	93.40 18	88.35 24	87.36 34	92.33 14	92.18 13	94.89 15	94.09 18	96.00 27	96.91 26

CS-MVS-test		90.29 43	90.96 39	89.51 51	93.18 59	95.87 44	89.18 84	83.72 75	88.32 48	84.82 46	84.89 42	85.23 54	90.25 33	94.04 29	92.66 49	95.94 29	95.69 49

ACMMP		92.03 32	92.16 31	91.87 33	95.88 34	96.55 31	94.47 35	89.49 32	91.71 30	85.26 42	91.52 24	84.48 57	90.21 34	92.82 52	91.63 57	95.92 30	96.42 36
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
SMA-MVS		94.70 7	95.35 7	93.93 11	97.57 3	97.57 8	95.98 12	91.91 13	94.50 7	90.35 13	93.46 17	92.72 11	91.89 17	95.89 4	95.22 1	95.88 31	98.10 6
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
ACMMPR		93.72 18	93.94 20	93.48 17	97.07 11	96.93 17	95.78 20	90.66 25	93.88 13	89.24 20	93.53 16	89.08 38	92.24 12	93.89 35	93.50 30	95.88 31	96.73 30

HFP-MVS		94.02 15	94.22 18	93.78 13	97.25 7	96.85 20	95.81 19	90.94 22	94.12 11	90.29 15	94.09 14	89.98 31	92.52 11	93.94 33	93.49 32	95.87 33	97.10 23

PVSNet_BlendedMVS		88.19 63	88.00 63	88.42 63	92.71 69	94.82 64	89.08 89	83.81 72	84.91 66	86.38 37	79.14 66	78.11 93	82.66 85	93.05 48	91.10 61	95.86 34	94.86 62

PVSNet_Blended		88.19 63	88.00 63	88.42 63	92.71 69	94.82 64	89.08 89	83.81 72	84.91 66	86.38 37	79.14 66	78.11 93	82.66 85	93.05 48	91.10 61	95.86 34	94.86 62

DeepC-MVS_fast	88.76 1	93.10 22	93.02 29	93.19 21	97.13 9	96.51 33	95.35 25	91.19 19	93.14 20	88.14 25	85.26 40	89.49 35	91.45 22	95.17 10	95.07 2	95.85 36	96.48 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		92.76 25	93.03 28	92.45 27	97.03 13	96.67 28	95.73 22	87.92 41	90.15 41	86.53 35	92.97 20	88.33 44	91.69 20	93.62 41	93.03 40	95.83 37	96.41 37

SD-MVS		94.53 10	95.22 8	93.73 14	95.69 36	97.03 14	95.77 21	91.95 12	94.41 8	91.35 7	94.97 8	93.34 8	91.80 19	94.72 20	93.99 20	95.82 38	98.07 7
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
DeepC-MVS	87.86 3	92.26 30	91.86 33	92.73 24	96.18 29	96.87 19	95.19 27	91.76 15	92.17 26	86.58 34	81.79 52	85.85 51	90.88 30	94.57 23	94.61 10	95.80 39	97.18 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS					93.11 60	96.70 25	91.91 53			83.95 49		88.82 40				95.79 40

X-MVStestdata					93.11 60	96.70 25	91.91 53			83.95 49		88.82 40				95.79 40

DELS-MVS		89.71 48	89.68 50	89.74 46	93.75 53	96.22 36	93.76 39	85.84 51	82.53 77	85.05 44	78.96 69	84.24 58	84.25 77	94.91 14	94.91 4	95.78 42	96.02 44
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
X-MVS		92.36 29	92.75 30	91.90 32	96.89 17	96.70 25	95.25 26	90.48 28	91.50 32	83.95 49	88.20 31	88.82 40	89.11 38	93.75 38	93.43 33	95.75 43	96.83 28

casdiffmvs		87.45 70	87.15 72	87.79 70	90.15 102	94.22 71	89.96 72	83.93 71	85.08 64	80.91 67	75.81 85	77.88 96	86.08 68	91.86 66	90.86 67	95.74 44	94.37 71
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CP-MVS		93.25 21	93.26 26	93.24 20	96.84 19	96.51 33	95.52 23	90.61 26	92.37 24	88.88 21	90.91 26	89.52 34	91.91 16	93.64 40	92.78 45	95.69 45	97.09 24

MVSTER		86.03 78	86.12 80	85.93 80	88.62 114	89.93 129	89.33 83	79.91 126	81.87 86	81.35 64	81.07 59	74.91 107	80.66 104	92.13 64	90.10 83	95.68 46	92.80 102

3Dnovator+	86.06 4	91.60 35	90.86 42	92.47 26	96.00 33	96.50 35	94.70 32	87.83 42	90.49 38	89.92 17	74.68 92	89.35 36	90.66 31	94.02 31	94.14 17	95.67 47	96.85 27

OpenMVS	82.53 11	87.71 67	86.84 74	88.73 58	94.42 47	95.06 59	91.02 64	83.49 82	82.50 79	82.24 61	67.62 133	85.48 52	85.56 71	91.19 74	91.30 60	95.67 47	94.75 64

EIA-MVS		87.94 66	88.05 62	87.81 68	91.46 78	95.00 61	88.67 96	82.81 91	82.53 77	80.81 69	80.04 62	80.20 77	87.48 56	92.58 55	91.61 58	95.63 49	94.36 72

ET-MVSNet_ETH3D		84.65 92	85.58 87	83.56 109	74.99 212	92.62 100	90.29 69	80.38 114	82.16 82	73.01 109	83.41 44	71.10 128	87.05 62	87.77 125	90.17 82	95.62 50	91.82 126

CDPH-MVS		91.14 38	92.01 32	90.11 41	96.18 29	96.18 37	94.89 30	88.80 37	88.76 46	77.88 86	89.18 30	87.71 47	87.29 60	93.13 46	93.31 37	95.62 50	95.84 46

MP-MVS		93.35 20	93.59 24	93.08 22	97.39 4	96.82 22	95.38 24	90.71 23	90.82 35	88.07 26	92.83 21	90.29 29	91.32 27	94.03 30	93.19 39	95.61 52	97.16 20
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
FMVSNet3		84.44 97	84.64 93	84.21 98	84.32 163	90.13 124	89.85 74	80.37 115	81.17 91	75.50 91	69.63 118	79.69 83	79.62 124	89.72 102	90.52 77	95.59 53	91.58 135

HPM-MVS++		94.60 9	94.91 11	94.24 8	97.86 1	96.53 32	96.14 9	92.51 8	93.87 14	90.76 11	93.45 18	93.84 5	92.62 9	95.11 12	94.08 19	95.58 54	97.48 14

TPM-MVS					96.31 27	96.02 38	94.89 30			86.52 36	87.18 36	92.17 16	86.76 64			95.56 55	93.85 82

casdiffmvs_mvg		87.97 65	87.63 70	88.37 65	90.55 90	94.42 68	91.82 56	84.69 60	84.05 69	82.08 62	76.57 80	79.00 87	85.49 72	92.35 57	92.29 52	95.55 56	94.70 66
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CLD-MVS		88.66 55	88.52 56	88.82 57	91.37 80	94.22 71	92.82 48	82.08 102	88.27 49	85.14 43	81.86 51	78.53 91	85.93 70	91.17 75	90.61 74	95.55 56	95.00 58
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMMP_NAP		93.94 16	94.49 15	93.30 19	97.03 13	97.31 10	95.96 13	91.30 18	93.41 17	88.55 23	93.00 19	90.33 28	91.43 25	95.53 7	94.41 14	95.53 58	97.47 15

GBi-Net		84.51 95	84.80 91	84.17 99	84.20 164	89.95 126	89.70 75	80.37 115	81.17 91	75.50 91	69.63 118	79.69 83	79.75 121	90.73 89	90.72 69	95.52 59	91.71 128

test1		84.51 95	84.80 91	84.17 99	84.20 164	89.95 126	89.70 75	80.37 115	81.17 91	75.50 91	69.63 118	79.69 83	79.75 121	90.73 89	90.72 69	95.52 59	91.71 128

FMVSNet2		83.87 100	83.73 102	84.05 103	84.20 164	89.95 126	89.70 75	80.21 120	79.17 112	74.89 96	65.91 139	77.49 97	79.75 121	90.87 86	91.00 65	95.52 59	91.71 128

DPM-MVS		91.72 34	91.48 34	92.00 30	95.53 37	95.75 46	95.94 15	91.07 20	91.20 33	85.58 40	81.63 56	90.74 26	88.40 46	93.40 42	93.75 25	95.45 62	93.85 82

OPM-MVS		87.56 69	85.80 85	89.62 49	93.90 52	94.09 74	94.12 36	88.18 38	75.40 132	77.30 89	76.41 81	77.93 95	88.79 41	92.20 61	90.82 68	95.40 63	93.72 87
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CSCG		92.76 25	93.16 27	92.29 28	96.30 28	97.74 7	94.67 33	88.98 35	92.46 22	89.73 19	86.67 37	92.15 18	88.69 43	92.26 59	92.92 43	95.40 63	97.89 10

canonicalmvs		89.36 51	89.92 46	88.70 59	91.38 79	95.92 42	91.81 57	82.61 99	90.37 39	82.73 58	82.09 50	79.28 86	88.30 47	91.17 75	93.59 28	95.36 65	97.04 25

IB-MVS	79.09 12	82.60 111	82.19 110	83.07 113	91.08 82	93.55 81	80.90 180	81.35 108	76.56 124	80.87 68	64.81 153	69.97 132	68.87 181	85.64 155	90.06 85	95.36 65	94.74 65
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
gg-mvs-nofinetune		75.64 186	77.26 165	73.76 190	87.92 119	92.20 103	87.32 116	64.67 208	51.92 214	35.35 219	46.44 207	77.05 100	71.97 171	92.64 54	91.02 64	95.34 67	89.53 152

Vis-MVSNet		84.38 99	86.68 78	81.70 127	87.65 124	94.89 62	88.14 105	80.90 111	74.48 138	68.23 130	77.53 76	80.72 74	69.98 178	92.68 53	91.90 54	95.33 68	94.58 69
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
TAPA-MVS	84.37 7	88.91 54	88.93 54	88.89 56	93.00 64	94.85 63	92.00 52	84.84 59	91.68 31	80.05 73	79.77 64	84.56 56	88.17 48	90.11 97	89.00 115	95.30 69	92.57 112
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_Test		86.93 72	87.24 71	86.56 75	90.10 103	93.47 82	90.31 68	80.12 121	83.55 71	78.12 82	79.58 65	79.80 81	85.45 73	90.17 96	90.59 75	95.29 70	93.53 90

LGP-MVS_train		88.25 62	88.55 55	87.89 67	92.84 67	93.66 79	93.35 42	85.22 57	85.77 58	74.03 101	86.60 38	76.29 102	86.62 66	91.20 73	90.58 76	95.29 70	95.75 47

FA-MVS(training)		85.65 82	85.79 86	85.48 85	90.44 96	93.47 82	88.66 98	73.11 175	83.34 72	82.26 59	71.79 107	78.39 92	83.14 82	91.00 82	89.47 103	95.28 72	93.06 95

UniMVSNet_NR-MVSNet		81.87 117	81.33 118	82.50 118	85.31 150	91.30 109	85.70 138	84.25 66	75.89 128	64.21 151	66.95 135	64.65 152	80.22 111	87.07 131	89.18 110	95.27 73	94.29 73

IS_MVSNet		86.18 76	88.18 60	83.85 105	91.02 83	94.72 66	87.48 113	82.46 100	81.05 95	70.28 118	76.98 78	82.20 69	76.65 146	93.97 32	93.38 34	95.18 74	94.97 59

train_agg		92.87 24	93.53 25	92.09 29	96.88 18	95.38 50	95.94 15	90.59 27	90.65 37	83.65 52	94.31 13	91.87 20	90.30 32	93.38 43	92.42 50	95.17 75	96.73 30

NR-MVSNet		80.25 133	79.98 136	80.56 142	85.20 152	90.94 113	85.65 140	83.58 80	75.74 129	61.36 175	65.30 148	56.75 195	72.38 170	88.46 119	88.80 117	95.16 76	93.87 81

PVSNet_Blended_VisFu		87.40 71	87.80 65	86.92 74	92.86 65	95.40 49	88.56 102	83.45 86	79.55 108	82.26 59	74.49 94	84.03 59	79.24 129	92.97 50	91.53 59	95.15 77	96.65 33

EPP-MVSNet		86.55 73	87.76 67	85.15 87	90.52 92	94.41 69	87.24 119	82.32 101	81.79 87	73.60 103	78.57 71	82.41 67	82.07 90	91.23 71	90.39 78	95.14 78	95.48 54

thisisatest0530		85.15 88	85.86 83	84.33 95	89.19 110	92.57 101	87.22 120	80.11 122	82.15 83	74.41 98	78.15 73	73.80 115	79.90 117	90.99 83	89.58 98	95.13 79	93.75 86

TranMVSNet+NR-MVSNet		80.52 130	79.84 139	81.33 133	84.92 159	90.39 118	85.53 143	84.22 68	74.27 141	60.68 180	64.93 152	59.96 176	77.48 140	86.75 139	89.28 106	95.12 80	93.29 91

tttt0517		85.11 89	85.81 84	84.30 96	89.24 108	92.68 97	87.12 124	80.11 122	81.98 84	74.31 100	78.08 74	73.57 117	79.90 117	91.01 81	89.58 98	95.11 81	93.77 85

UniMVSNet (Re)		81.22 125	81.08 121	81.39 131	85.35 149	91.76 107	84.93 147	82.88 90	76.13 127	65.02 148	64.94 151	63.09 159	75.17 154	87.71 126	89.04 113	94.97 82	94.88 61

FC-MVSNet-train		85.18 87	85.31 89	85.03 88	90.67 87	91.62 108	87.66 111	83.61 77	79.75 106	74.37 99	78.69 70	71.21 127	78.91 130	91.23 71	89.96 88	94.96 83	94.69 68

GeoE		84.62 93	83.98 100	85.35 86	89.34 107	92.83 94	88.34 103	78.95 136	79.29 110	77.16 90	68.10 130	74.56 108	83.40 80	89.31 109	89.23 108	94.92 84	94.57 70

tfpn200view9		82.86 107	81.46 115	84.48 92	90.30 100	93.09 88	89.05 91	82.71 93	75.14 133	69.56 121	65.72 142	63.13 157	80.38 110	91.15 77	89.51 100	94.91 85	92.50 116

thres600view7		82.53 113	81.02 122	84.28 97	90.61 89	93.05 89	88.57 101	82.67 95	74.12 143	68.56 129	65.09 150	62.13 168	80.40 109	91.15 77	89.02 114	94.88 86	92.59 110

thres200		82.77 109	81.25 119	84.54 91	90.38 97	93.05 89	89.13 88	82.67 95	74.40 139	69.53 123	65.69 144	63.03 160	80.63 105	91.15 77	89.42 104	94.88 86	92.04 122

Effi-MVS+		85.33 84	85.08 90	85.63 82	89.69 105	93.42 84	89.90 73	80.31 119	79.32 109	72.48 112	73.52 101	74.03 112	86.55 67	90.99 83	89.98 87	94.83 88	94.27 77

dmvs_re		81.08 127	79.92 137	82.44 120	86.66 134	87.70 162	87.91 108	83.30 89	72.86 155	65.29 147	65.76 141	63.43 156	76.69 145	88.93 113	89.50 101	94.80 89	91.23 140

thres400		82.68 110	81.15 120	84.47 93	90.52 92	92.89 93	88.95 94	82.71 93	74.33 140	69.22 126	65.31 147	62.61 163	80.63 105	90.96 85	89.50 101	94.79 90	92.45 118

ACMP	83.90 8	88.32 61	88.06 61	88.62 60	92.18 71	93.98 76	91.28 63	85.24 56	86.69 54	81.23 66	85.62 39	75.13 106	87.01 63	89.83 100	89.77 94	94.79 90	95.43 55
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DU-MVS		81.20 126	80.30 130	82.25 122	84.98 157	90.94 113	85.70 138	83.58 80	75.74 129	64.21 151	65.30 148	59.60 181	80.22 111	86.89 134	89.31 105	94.77 92	94.29 73

UA-Net		86.07 77	87.78 66	84.06 102	92.85 66	95.11 58	87.73 110	84.38 65	73.22 152	73.18 106	79.99 63	89.22 37	71.47 174	93.22 45	93.03 40	94.76 93	90.69 143

MVS_111021_LR		90.14 46	90.89 41	89.26 53	93.23 58	94.05 75	90.43 67	84.65 61	90.16 40	84.52 48	90.14 28	83.80 60	87.99 49	92.50 56	90.92 66	94.74 94	94.70 66

thres100view900		82.55 112	81.01 124	84.34 94	90.30 100	92.27 102	89.04 92	82.77 92	75.14 133	69.56 121	65.72 142	63.13 157	79.62 124	89.97 99	89.26 107	94.73 95	91.61 134

AdaColmap		90.29 43	88.38 58	92.53 25	96.10 31	95.19 55	92.98 46	91.40 17	89.08 45	88.65 22	78.35 72	81.44 71	91.30 28	90.81 88	90.21 81	94.72 96	93.59 89

PCF-MVS	84.60 6	88.66 55	87.75 68	89.73 47	93.06 63	96.02 38	93.22 44	90.00 30	82.44 80	80.02 75	77.96 75	85.16 55	87.36 58	88.54 117	88.54 120	94.72 96	95.61 52
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DI_MVS_plusplus_trai		86.41 75	85.54 88	87.42 72	89.24 108	93.13 87	92.16 51	82.65 97	82.30 81	80.75 71	68.30 129	80.41 75	85.01 74	90.56 94	90.07 84	94.70 98	94.01 79

Fast-Effi-MVS+		83.77 102	82.98 105	84.69 89	87.98 118	91.87 106	88.10 106	77.70 150	78.10 118	73.04 108	69.13 124	68.51 140	86.66 65	90.49 95	89.85 92	94.67 99	92.88 99

WR-MVS_H		75.84 184	76.93 170	74.57 189	82.86 183	89.50 142	78.34 193	79.36 133	66.90 181	52.51 198	60.20 175	59.71 178	59.73 200	83.61 178	85.77 158	94.65 100	92.84 100

OMC-MVS		90.23 45	90.40 45	90.03 44	93.45 56	95.29 51	91.89 55	86.34 50	93.25 19	84.94 45	81.72 54	86.65 50	88.90 39	91.69 67	90.27 80	94.65 100	93.95 80

HQP-MVS		89.13 53	89.58 51	88.60 61	93.53 55	93.67 78	93.29 43	87.58 44	88.53 47	75.50 91	87.60 33	80.32 76	87.07 61	90.66 93	89.95 89	94.62 102	96.35 40

PEN-MVS		76.02 181	76.07 177	75.95 179	83.17 177	87.97 160	79.65 184	80.07 125	66.57 183	51.45 201	60.94 169	55.47 200	66.81 190	82.72 183	86.80 139	94.59 103	92.03 123

ACMM	83.27 10	87.68 68	86.09 81	89.54 50	93.26 57	92.19 104	91.43 60	86.74 47	86.02 57	82.85 56	75.63 86	75.14 105	88.41 45	90.68 92	89.99 86	94.59 103	92.97 97
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		85.43 83	87.72 69	82.76 116	90.95 86	93.01 91	89.99 71	75.46 167	82.67 74	64.91 149	83.14 45	80.09 78	80.68 103	92.03 65	91.03 63	94.57 105	92.08 120

CP-MVSNet		76.36 178	76.41 174	76.32 176	82.73 186	88.64 155	79.39 187	79.62 128	67.21 179	53.70 195	60.72 171	55.22 201	67.91 186	83.52 179	86.34 149	94.55 106	93.19 92

EG-PatchMatch MVS		76.40 177	75.47 186	77.48 166	85.86 142	90.22 122	82.45 166	73.96 173	59.64 206	59.60 184	52.75 199	62.20 167	68.44 183	88.23 121	87.50 128	94.55 106	87.78 167

tfpnnormal		77.46 165	74.86 190	80.49 143	86.34 138	88.92 153	84.33 154	81.26 109	61.39 201	61.70 172	51.99 201	53.66 207	74.84 157	88.63 116	87.38 131	94.50 108	92.08 120

baseline2		82.80 108	82.86 107	82.73 117	87.68 123	90.50 117	84.92 148	78.93 137	78.07 119	73.06 107	75.08 90	69.77 133	77.31 141	88.90 114	86.94 137	94.50 108	90.74 142

Vis-MVSNet (Re-imp)		83.65 103	86.81 76	79.96 147	90.46 95	92.71 95	84.84 149	82.00 103	80.93 97	62.44 164	76.29 82	82.32 68	65.54 194	92.29 58	91.66 56	94.49 110	91.47 137

PS-CasMVS		75.90 183	75.86 182	75.96 178	82.59 187	88.46 158	79.23 190	79.56 130	66.00 186	52.77 197	59.48 179	54.35 205	67.14 189	83.37 180	86.23 150	94.47 111	93.10 94

Baseline_NR-MVSNet		79.84 137	78.37 154	81.55 130	84.98 157	86.66 171	85.06 145	83.49 82	75.57 131	63.31 158	58.22 187	60.97 171	78.00 136	86.89 134	87.13 133	94.47 111	93.15 93

FMVSNet1		81.64 122	80.61 127	82.84 115	82.36 189	89.20 147	88.67 96	79.58 129	70.79 164	72.63 111	58.95 183	72.26 124	79.34 127	90.73 89	90.72 69	94.47 111	91.62 133

DTE-MVSNet		75.14 188	75.44 187	74.80 186	83.18 176	87.19 168	78.25 195	80.11 122	66.05 185	48.31 206	60.88 170	54.67 202	64.54 195	82.57 185	86.17 151	94.43 114	90.53 147

PLC	83.76 9	88.61 57	86.83 75	90.70 38	94.22 48	92.63 98	91.50 59	87.19 46	89.16 44	86.87 32	75.51 87	80.87 73	89.98 36	90.01 98	89.20 109	94.41 115	90.45 148
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
UGNet		85.90 80	88.23 59	83.18 112	88.96 112	94.10 73	87.52 112	83.60 78	81.66 88	77.90 85	80.76 60	83.19 63	66.70 191	91.13 80	90.71 72	94.39 116	96.06 43
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
TransMVSNet (Re)		76.57 172	75.16 189	78.22 163	85.60 146	87.24 167	82.46 165	81.23 110	59.80 205	59.05 188	57.07 189	59.14 185	66.60 192	88.09 122	86.82 138	94.37 117	87.95 166

gm-plane-assit		70.29 199	70.65 201	69.88 199	85.03 155	78.50 210	58.41 217	65.47 204	50.39 216	40.88 215	49.60 203	50.11 211	75.14 155	91.43 70	89.78 93	94.32 118	84.73 186

CNLPA		88.40 58	87.00 73	90.03 44	93.73 54	94.28 70	89.56 79	85.81 52	91.87 28	87.55 28	69.53 122	81.49 70	89.23 37	89.45 107	88.59 119	94.31 119	93.82 84

MAR-MVS		88.39 60	88.44 57	88.33 66	94.90 42	95.06 59	90.51 66	83.59 79	85.27 61	79.07 78	77.13 77	82.89 65	87.70 51	92.19 62	92.32 51	94.23 120	94.20 78
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
MSDG		83.87 100	81.02 122	87.19 73	92.17 72	89.80 133	89.15 87	85.72 53	80.61 100	79.24 77	66.66 136	68.75 139	82.69 84	87.95 124	87.44 129	94.19 121	85.92 180

pm-mvs1		78.51 157	77.75 162	79.40 150	84.83 160	89.30 144	83.55 160	79.38 132	62.64 197	63.68 156	58.73 185	64.68 151	70.78 177	89.79 101	87.84 125	94.17 122	91.28 139

CPTT-MVS		91.39 36	90.95 40	91.91 31	95.06 39	95.24 54	95.02 29	88.98 35	91.02 34	86.71 33	84.89 42	88.58 43	91.60 21	90.82 87	89.67 97	94.08 123	96.45 35

v144192		78.81 151	77.22 166	80.67 140	82.95 180	89.79 134	86.40 131	77.42 151	68.26 178	63.13 159	59.50 178	58.13 187	80.08 116	85.93 151	86.08 153	94.06 124	92.83 101

diffmvs		86.52 74	86.76 77	86.23 77	88.31 117	92.63 98	89.58 78	81.61 106	86.14 56	80.26 72	79.00 68	77.27 98	83.58 78	88.94 112	89.06 112	94.05 125	94.29 73
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v1921920		78.57 156	76.99 169	80.41 145	82.93 181	89.63 140	86.38 132	77.14 154	68.31 177	61.80 171	58.89 184	56.79 194	80.19 114	86.50 146	86.05 155	94.02 126	92.76 104

ACMH	78.52 14	81.86 118	80.45 129	83.51 111	90.51 94	91.22 110	85.62 141	84.23 67	70.29 169	62.21 165	69.04 126	64.05 154	84.48 76	87.57 127	88.45 122	94.01 127	92.54 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
WR-MVS		76.63 171	78.02 159	75.02 184	84.14 167	89.76 135	78.34 193	80.64 113	69.56 170	52.32 199	61.26 165	61.24 170	60.66 199	84.45 173	87.07 134	93.99 128	92.77 103

v10		79.62 140	78.19 155	81.28 134	83.73 170	89.69 137	87.27 118	76.86 157	70.50 167	65.46 142	60.58 173	60.47 173	80.44 108	86.91 133	86.63 143	93.93 129	92.55 113

anonymousdsp		77.94 160	79.00 146	76.71 172	79.03 201	87.83 161	79.58 185	72.87 176	65.80 188	58.86 189	65.82 140	62.48 165	75.99 149	86.77 138	88.66 118	93.92 130	95.68 51

v1240		78.15 158	76.53 172	80.04 146	82.85 184	89.48 143	85.61 142	76.77 158	67.05 180	61.18 178	58.37 186	56.16 198	79.89 119	86.11 150	86.08 153	93.92 130	92.47 117

v8		79.90 136	78.39 153	81.66 128	83.97 168	89.81 132	87.16 122	77.40 152	71.49 159	67.71 131	61.24 166	62.49 164	79.83 120	85.48 159	86.17 151	93.89 132	92.02 124

v2v482		79.84 137	78.07 157	81.90 125	83.75 169	90.21 123	87.17 121	79.85 127	70.65 165	65.93 140	61.93 163	60.07 175	80.82 100	85.25 161	86.71 140	93.88 133	91.70 132

thisisatest0515		79.76 139	80.59 128	78.80 155	84.40 162	88.91 154	79.48 186	76.94 156	72.29 157	67.33 133	67.82 132	65.99 147	70.80 176	88.50 118	87.84 125	93.86 134	92.75 105

IterMVS-LS		83.28 106	82.95 106	83.65 106	88.39 116	88.63 156	86.80 128	78.64 141	76.56 124	73.43 105	72.52 106	75.35 104	80.81 101	86.43 147	88.51 121	93.84 135	92.66 107
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TSAR-MVS + ACMM		92.97 23	94.51 14	91.16 36	95.88 34	96.59 30	95.09 28	90.45 29	93.42 16	83.01 55	94.68 10	90.74 26	88.74 42	94.75 19	93.78 24	93.82 136	97.63 12

v1144		79.38 146	77.83 160	81.18 135	83.62 171	90.23 121	87.15 123	78.35 143	69.13 172	64.02 154	60.20 175	59.41 182	80.14 115	86.78 137	86.57 144	93.81 137	92.53 115

v1192		78.94 150	77.33 164	80.82 138	83.25 175	89.90 130	86.91 126	77.72 149	68.63 176	62.61 163	59.17 180	57.53 191	80.62 107	86.89 134	86.47 146	93.79 138	92.75 105

TSAR-MVS + COLMAP		88.40 58	89.09 53	87.60 71	92.72 68	93.92 77	92.21 50	85.57 54	91.73 29	73.72 102	91.75 23	73.22 121	87.64 54	91.49 69	89.71 96	93.73 139	91.82 126

UniMVSNet_ETH3D		79.24 147	76.47 173	82.48 119	85.66 145	90.97 112	86.08 135	81.63 105	64.48 193	68.94 128	54.47 194	57.65 190	78.83 131	85.20 165	88.91 116	93.72 140	93.60 88

EPNet		89.60 49	89.91 47	89.24 54	96.45 26	93.61 80	92.95 47	88.03 39	85.74 59	83.36 53	87.29 35	83.05 64	80.98 99	92.22 60	91.85 55	93.69 141	95.58 53
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HyFIR lowres test		81.62 124	79.45 145	84.14 101	91.00 84	93.38 85	88.27 104	78.19 144	76.28 126	70.18 119	48.78 204	73.69 116	83.52 79	87.05 132	87.83 127	93.68 142	89.15 154

V42		79.59 141	78.43 152	80.94 137	82.79 185	89.71 136	86.66 129	76.73 159	71.38 160	67.42 132	61.01 168	62.30 166	78.39 133	85.56 157	86.48 145	93.65 143	92.60 109

DCV-MVSNet		85.88 81	86.17 79	85.54 84	89.10 111	89.85 131	89.34 82	80.70 112	83.04 73	78.08 84	76.19 83	79.00 87	82.42 88	89.67 103	90.30 79	93.63 144	95.12 57

v7n		77.22 166	76.23 176	78.38 162	81.89 192	89.10 151	82.24 171	76.36 160	65.96 187	61.21 177	56.56 190	55.79 199	75.07 156	86.55 143	86.68 141	93.52 145	92.95 98

Anonymous20231211		84.42 98	83.02 104	86.05 79	88.85 113	92.70 96	88.92 95	83.40 87	79.99 103	78.31 81	55.83 192	78.92 89	83.33 81	89.06 111	89.76 95	93.50 146	94.90 60

ACMH+	79.08 13	81.84 119	80.06 134	83.91 104	89.92 104	90.62 115	86.21 133	83.48 84	73.88 145	65.75 141	66.38 137	65.30 150	84.63 75	85.90 152	87.25 132	93.45 147	91.13 141

pmmvs5		76.93 168	76.33 175	77.62 165	81.97 191	88.40 159	81.32 176	74.35 171	65.42 191	61.42 174	63.07 159	57.95 189	73.23 168	85.60 156	85.35 163	93.41 148	88.55 158

Anonymous202405211			82.75 108		89.58 106	92.97 92	89.04 92	84.13 69	78.72 114		57.18 188	76.64 101	83.13 83	89.55 105	89.92 90	93.38 149	94.28 76

USDC		80.69 129	79.89 138	81.62 129	86.48 136	89.11 150	86.53 130	78.86 138	81.15 94	63.48 157	72.98 103	59.12 186	81.16 97	87.10 130	85.01 165	93.23 150	84.77 185

LS3D		85.96 79	84.37 96	87.81 68	94.13 49	93.27 86	90.26 70	89.00 33	84.91 66	72.84 110	71.74 108	72.47 123	87.45 57	89.53 106	89.09 111	93.20 151	89.60 151

MS-PatchMatch		81.79 120	81.44 116	82.19 124	90.35 98	89.29 145	88.08 107	75.36 168	77.60 120	69.00 127	64.37 156	78.87 90	77.14 144	88.03 123	85.70 159	93.19 152	86.24 177

Fast-Effi-MVS+-dtu		79.95 135	80.69 126	79.08 152	86.36 137	89.14 149	85.85 136	72.28 178	72.85 156	59.32 185	70.43 116	68.42 142	77.57 139	86.14 149	86.44 147	93.11 153	91.39 138

GA-MVS		79.52 142	79.71 142	79.30 151	85.68 144	90.36 119	84.55 151	78.44 142	70.47 168	57.87 190	68.52 128	61.38 169	76.21 148	89.40 108	87.89 124	93.04 154	89.96 150

DeepPCF-MVS	88.51 2	92.64 28	94.42 17	90.56 39	94.84 44	96.92 18	91.31 62	89.61 31	95.16 5	84.55 47	89.91 29	91.45 22	90.15 35	95.12 11	94.81 7	92.90 155	97.58 13

CDS-MVSNet		81.63 123	82.09 111	81.09 136	87.21 129	90.28 120	87.46 115	80.33 118	69.06 173	70.66 115	71.30 109	73.87 113	67.99 184	89.58 104	89.87 91	92.87 156	90.69 143
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Effi-MVS+-dtu		82.05 115	81.76 112	82.38 121	87.72 121	90.56 116	86.90 127	78.05 146	73.85 146	66.85 135	71.29 110	71.90 125	82.00 91	86.64 142	85.48 161	92.76 157	92.58 111

v148		78.59 155	76.84 171	80.62 141	83.61 172	89.16 148	83.65 159	79.24 134	69.38 171	69.34 125	59.88 177	60.41 174	75.19 153	83.81 177	84.63 170	92.70 158	90.63 145

pmmvs4		79.99 134	78.08 156	82.22 123	83.04 179	87.16 169	84.95 146	78.80 140	78.64 115	74.53 97	64.61 154	59.41 182	79.45 126	84.13 175	84.54 172	92.53 159	88.08 163

RPMNet		77.07 167	77.63 163	76.42 174	85.56 147	85.15 184	81.37 174	65.27 205	74.71 136	60.29 181	63.71 158	66.59 146	73.64 164	82.71 184	82.12 186	92.38 160	88.39 159

CR-MVSNet		78.71 153	78.86 147	78.55 159	85.85 143	85.15 184	82.30 169	68.23 194	74.71 136	65.37 144	64.39 155	69.59 135	77.18 142	85.10 167	84.87 166	92.34 161	88.21 161

COLMAP_ROB	76.78 15	80.50 131	78.49 150	82.85 114	90.96 85	89.65 139	86.20 134	83.40 87	77.15 122	66.54 136	62.27 161	65.62 149	77.89 137	85.23 162	84.70 169	92.11 162	84.83 184
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchMatch-RL		83.34 105	81.36 117	85.65 81	90.33 99	89.52 141	84.36 153	81.82 104	80.87 99	79.29 76	74.04 96	62.85 162	86.05 69	88.40 120	87.04 136	92.04 163	86.77 173

PMMVS		81.65 121	84.05 99	78.86 154	78.56 203	82.63 197	83.10 161	67.22 198	81.39 89	70.11 120	84.91 41	79.74 82	82.12 89	87.31 128	85.70 159	92.03 164	86.67 176

IterMVS-SCA-FT		79.41 145	80.20 132	78.49 160	85.88 140	86.26 173	83.95 156	71.94 179	73.55 150	61.94 168	70.48 115	70.50 129	75.23 152	85.81 154	84.61 171	91.99 165	90.18 149

baseline		84.89 90	86.06 82	83.52 110	87.25 128	89.67 138	87.76 109	75.68 166	84.92 65	78.40 80	80.10 61	80.98 72	80.20 113	86.69 141	87.05 135	91.86 166	92.99 96

MIMVSNet		74.69 190	75.60 185	73.62 191	76.02 210	85.31 183	81.21 179	67.43 197	71.02 162	59.07 187	54.48 193	64.07 153	66.14 193	86.52 145	86.64 142	91.83 167	81.17 198

FC-MVSNet-test		76.53 174	81.62 114	70.58 198	84.99 156	85.73 178	74.81 201	78.85 139	77.00 123	39.13 217	75.90 84	73.50 118	54.08 206	86.54 144	85.99 156	91.65 168	86.68 174

pmmvs-eth3d		74.32 192	71.96 198	77.08 169	77.33 206	82.71 196	78.41 192	76.02 164	66.65 182	65.98 139	54.23 196	49.02 214	73.14 169	82.37 187	82.69 183	91.61 169	86.05 179

SixPastTwentyTwo		76.02 181	75.72 183	76.36 175	83.38 173	87.54 164	75.50 200	76.22 161	65.50 190	57.05 191	70.64 112	53.97 206	74.54 159	80.96 191	82.12 186	91.44 170	89.35 153

pmmvs6		74.83 189	72.89 196	77.09 168	82.11 190	87.50 165	80.88 181	76.97 155	52.79 213	61.91 170	46.66 206	60.49 172	69.28 180	86.74 140	85.46 162	91.39 171	90.56 146

test-mter		77.79 161	80.02 135	75.18 183	81.18 197	82.85 195	80.52 183	62.03 212	73.62 149	62.16 166	73.55 100	73.83 114	73.81 163	84.67 170	83.34 178	91.37 172	88.31 160

TDRefinement		79.05 149	77.05 168	81.39 131	88.45 115	89.00 152	86.92 125	82.65 97	74.21 142	64.41 150	59.17 180	59.16 184	74.52 160	85.23 162	85.09 164	91.37 172	87.51 169

test-LLR		79.47 144	79.84 139	79.03 153	87.47 125	82.40 200	81.24 177	78.05 146	73.72 147	62.69 161	73.76 98	74.42 109	73.49 165	84.61 171	82.99 181	91.25 174	87.01 171

TESTMET0.1,1		77.78 162	79.84 139	75.38 182	80.86 198	82.40 200	81.24 177	62.72 211	73.72 147	62.69 161	73.76 98	74.42 109	73.49 165	84.61 171	82.99 181	91.25 174	87.01 171

TinyColmap		76.73 169	73.95 193	79.96 147	85.16 154	85.64 180	82.34 168	78.19 144	70.63 166	62.06 167	60.69 172	49.61 212	80.81 101	85.12 166	83.69 177	91.22 176	82.27 193

FMVSNet5		75.50 187	76.07 177	74.83 185	76.16 208	81.19 203	81.34 175	70.21 187	73.20 153	61.59 173	58.97 182	68.33 143	68.50 182	85.87 153	85.85 157	91.18 177	79.11 204

IterMVS		78.79 152	79.71 142	77.71 164	85.26 151	85.91 176	84.54 152	69.84 190	73.38 151	61.25 176	70.53 114	70.35 130	74.43 161	85.21 164	83.80 176	90.95 178	88.77 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchT		76.42 175	77.81 161	74.80 186	78.46 204	84.30 189	71.82 207	65.03 207	73.89 144	65.37 144	61.58 164	66.70 145	77.18 142	85.10 167	84.87 166	90.94 179	88.21 161

EPNet_dtu		81.98 116	83.82 101	79.83 149	94.10 50	85.97 175	87.29 117	84.08 70	80.61 100	59.96 182	81.62 57	77.19 99	62.91 198	87.21 129	86.38 148	90.66 180	87.77 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PM-MVS		74.17 193	73.10 194	75.41 181	76.07 209	82.53 198	77.56 196	71.69 180	71.04 161	61.92 169	61.23 167	47.30 215	74.82 158	81.78 189	79.80 190	90.42 181	88.05 164

test0.0.03 1		76.03 180	78.51 149	73.12 194	87.47 125	85.13 186	76.32 198	78.05 146	73.19 154	50.98 204	70.64 112	69.28 136	55.53 202	85.33 160	84.38 173	90.39 182	81.63 196

Anonymous20231206		70.80 198	70.59 202	71.04 197	81.60 194	82.49 199	74.64 202	75.87 165	64.17 194	49.27 205	44.85 210	53.59 208	54.68 205	83.07 181	82.34 185	90.17 183	83.65 188

CHOSEN 1792x2688		82.16 114	80.91 125	83.61 107	91.14 81	92.01 105	89.55 80	79.15 135	79.87 104	70.29 117	52.51 200	72.56 122	81.39 94	88.87 115	88.17 123	90.15 184	92.37 119

MIMVSNet1		65.00 205	66.24 206	63.55 207	58.41 219	80.01 207	69.00 210	74.03 172	55.81 211	41.88 214	36.81 215	49.48 213	47.89 211	81.32 190	82.40 184	90.08 185	77.88 206

LTVRE_ROB	74.41 16	75.78 185	74.72 191	77.02 170	85.88 140	89.22 146	82.44 167	77.17 153	50.57 215	45.45 210	65.44 146	52.29 209	81.25 95	85.50 158	87.42 130	89.94 186	92.62 108
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
GG-mvs-BLEND		57.56 210	82.61 109	28.34 217	0.22 226	90.10 125	79.37 188	0.14 223	79.56 107	0.40 227	71.25 111	83.40 62	0.30 224	86.27 148	83.87 174	89.59 187	83.83 187

CVMVSNet		76.70 170	78.46 151	74.64 188	83.34 174	84.48 188	81.83 173	74.58 169	68.88 174	51.23 203	69.77 117	70.05 131	67.49 187	84.27 174	83.81 175	89.38 188	87.96 165

TAMVS		76.42 175	77.16 167	75.56 180	83.05 178	85.55 181	80.58 182	71.43 181	65.40 192	61.04 179	67.27 134	69.22 138	67.99 184	84.88 169	84.78 168	89.28 189	83.01 191

CostFormer		80.94 128	80.21 131	81.79 126	87.69 122	88.58 157	87.47 114	70.66 184	80.02 102	77.88 86	73.03 102	71.40 126	78.24 134	79.96 195	79.63 191	88.82 190	88.84 155

RPSCF		83.46 104	83.36 103	83.59 108	87.75 120	87.35 166	84.82 150	79.46 131	83.84 70	78.12 82	82.69 49	79.87 79	82.60 87	82.47 186	81.13 189	88.78 191	86.13 178

test20.03		68.31 202	70.05 203	66.28 205	82.41 188	80.84 204	67.35 211	76.11 163	58.44 208	40.80 216	53.77 197	54.54 203	42.28 213	83.07 181	81.96 188	88.73 192	77.76 207

SCA		79.51 143	80.15 133	78.75 156	86.58 135	87.70 162	83.07 162	68.53 193	81.31 90	66.40 137	73.83 97	75.38 103	79.30 128	80.49 193	79.39 194	88.63 193	82.96 192

testgi		71.92 197	74.20 192	69.27 200	84.58 161	83.06 192	73.40 204	74.39 170	64.04 195	46.17 209	68.90 127	57.15 193	48.89 210	84.07 176	83.08 180	88.18 194	79.09 205

dps		78.02 159	75.94 181	80.44 144	86.06 139	86.62 172	82.58 164	69.98 188	75.14 133	77.76 88	69.08 125	59.93 177	78.47 132	79.47 197	77.96 198	87.78 195	83.40 189

PatchmatchNet		78.67 154	78.85 148	78.46 161	86.85 133	86.03 174	83.77 158	68.11 196	80.88 98	66.19 138	72.90 104	73.40 119	78.06 135	79.25 199	77.71 199	87.75 196	81.75 195
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13_2view		73.21 195	72.91 195	73.56 192	80.01 199	84.28 190	78.62 191	66.43 202	68.64 175	59.12 186	60.39 174	59.69 180	69.81 179	78.82 201	77.43 200	87.36 197	81.11 199

MDTV_nov1_ep13		79.14 148	79.49 144	78.74 157	85.40 148	86.89 170	84.32 155	70.29 186	78.85 113	69.42 124	75.37 88	73.29 120	75.64 151	80.61 192	79.48 193	87.36 197	81.91 194

EPMVS		77.53 164	78.07 157	76.90 171	86.89 132	84.91 187	82.18 172	66.64 201	81.00 96	64.11 153	72.75 105	69.68 134	74.42 162	79.36 198	78.13 197	87.14 199	80.68 201

EU-MVSNet		69.98 200	72.30 197	67.28 203	75.67 211	79.39 208	73.12 205	69.94 189	63.59 196	42.80 213	62.93 160	56.71 196	55.07 204	79.13 200	78.55 196	87.06 200	85.82 181

new-patchmatchnet		63.80 206	63.31 208	64.37 206	76.49 207	75.99 211	63.73 214	70.99 183	57.27 209	43.08 212	45.86 208	43.80 216	45.13 212	73.20 209	70.68 212	86.80 201	76.34 209

pmnet_mix02		71.95 196	71.83 199	72.10 195	81.40 196	80.63 206	73.78 203	72.85 177	70.90 163	54.89 193	62.17 162	57.42 192	62.92 197	76.80 204	73.98 208	86.74 202	80.87 200

CMPMVS	56.49 17	73.84 194	71.73 200	76.31 177	85.20 152	85.67 179	75.80 199	73.23 174	62.26 198	65.40 143	53.40 198	59.70 179	71.77 173	80.25 194	79.56 192	86.45 203	81.28 197
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDA-MVSNet-bldmvs		66.22 204	64.49 207	68.24 201	61.67 216	82.11 202	70.07 209	76.16 162	59.14 207	47.94 207	54.35 195	35.82 222	67.33 188	64.94 214	75.68 203	86.30 204	79.36 203

MVS-HIRNet		68.83 201	66.39 205	71.68 196	77.58 205	75.52 212	66.45 212	65.05 206	62.16 199	62.84 160	44.76 211	56.60 197	71.96 172	78.04 202	75.06 206	86.18 205	72.56 211

CHOSEN 280x420		80.28 132	81.66 113	78.67 158	82.92 182	79.24 209	85.36 144	66.79 200	78.11 117	70.32 116	75.03 91	79.87 79	81.09 98	89.07 110	83.16 179	85.54 206	87.17 170

tpm cat1		77.78 162	75.28 188	80.70 139	87.14 130	85.84 177	85.81 137	70.40 185	77.44 121	78.80 79	63.72 157	64.01 155	76.55 147	75.60 207	75.21 205	85.51 207	85.12 182

tpm		76.30 179	76.05 179	76.59 173	86.97 131	83.01 194	83.83 157	67.06 199	71.83 158	63.87 155	69.56 121	62.88 161	73.41 167	79.79 196	78.59 195	84.41 208	86.68 174

tpmrst		76.55 173	75.99 180	77.20 167	87.32 127	83.05 193	82.86 163	65.62 203	78.61 116	67.22 134	69.19 123	65.71 148	75.87 150	76.75 205	75.33 204	84.31 209	83.28 190

pmmvs3		61.89 208	61.74 210	62.06 208	64.30 215	70.83 216	64.22 213	52.14 216	48.78 217	44.47 211	41.67 213	41.70 220	63.03 196	76.06 206	76.02 202	84.18 210	77.14 208

ADS-MVSNet		74.53 191	75.69 184	73.17 193	81.57 195	80.71 205	79.27 189	63.03 210	79.27 111	59.94 183	67.86 131	68.32 144	71.08 175	77.33 203	76.83 201	84.12 211	79.53 202

ambc			61.92 209		70.98 214	73.54 214	63.64 215		60.06 203	52.23 200	38.44 214	19.17 225	57.12 201	82.33 188	75.03 207	83.21 212	84.89 183

FPMVS		63.63 207	60.08 212	67.78 202	80.01 199	71.50 215	72.88 206	69.41 192	61.82 200	53.11 196	45.12 209	42.11 219	50.86 208	66.69 212	63.84 213	80.41 213	69.46 213

N_pmnet		66.85 203	66.63 204	67.11 204	78.73 202	74.66 213	70.53 208	71.07 182	66.46 184	46.54 208	51.68 202	51.91 210	55.48 203	74.68 208	72.38 209	80.29 214	74.65 210

new_pmnet		59.28 209	61.47 211	56.73 210	61.66 217	68.29 217	59.57 216	54.91 213	60.83 202	34.38 220	44.66 212	43.65 217	49.90 209	71.66 210	71.56 211	79.94 215	69.67 212

PMVS	50.48 18	55.81 211	51.93 213	60.33 209	72.90 213	49.34 219	48.78 218	69.51 191	43.49 218	54.25 194	36.26 216	41.04 221	39.71 215	65.07 213	60.70 214	76.85 216	67.58 214
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		49.17 212	47.05 215	51.65 211	59.67 218	48.39 220	41.98 221	63.47 209	55.64 212	33.33 221	14.90 219	13.78 226	41.34 214	69.31 211	72.30 210	70.11 217	55.00 218
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		41.68 214	44.74 216	38.10 212	46.97 222	52.32 218	40.63 222	48.08 217	35.51 219	7.36 226	26.86 218	24.64 224	16.72 220	55.24 217	59.03 215	68.85 218	59.59 217

test_method		41.78 213	48.10 214	34.42 215	10.74 225	19.78 226	44.64 220	17.73 220	59.83 204	38.67 218	35.82 217	54.41 204	34.94 216	62.87 215	43.13 218	59.81 219	60.82 216

DeepMVS_CX						48.31 221	48.03 219	26.08 219	56.42 210	25.77 222	47.51 205	31.31 223	51.30 207	48.49 218		53.61 220	61.52 215

tmp_tt				32.73 216	43.96 223	21.15 225	26.71 223	8.99 221	65.67 189	51.39 202	56.01 191	42.64 218	11.76 221	56.60 216	50.81 217	53.55 221

E-PMN		31.40 215	26.80 218	36.78 213	51.39 221	29.96 223	20.20 224	54.17 214	25.93 221	12.75 224	14.73 220	8.58 228	34.10 218	27.36 220	37.83 219	48.07 222	43.18 220

EMVS		30.49 217	25.44 219	36.39 214	51.47 220	29.89 224	20.17 225	54.00 215	26.49 220	12.02 225	13.94 222	8.84 227	34.37 217	25.04 221	34.37 220	46.29 223	39.53 221

MVE	30.17 19	30.88 216	33.52 217	27.80 218	23.78 224	39.16 222	18.69 226	46.90 218	21.88 222	15.39 223	14.37 221	7.31 229	24.41 219	41.63 219	56.22 216	37.64 224	54.07 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		1.03 218	1.63 220	0.34 219	0.09 227	0.35 227	0.61 228	0.16 222	1.49 223	0.10 228	3.15 223	0.15 230	0.86 223	1.32 222	1.18 221	0.20 225	3.76 223

test123		0.87 219	1.40 221	0.25 220	0.03 228	0.25 228	0.35 229	0.08 224	1.21 224	0.05 229	2.84 224	0.03 231	0.89 222	0.43 223	1.16 222	0.13 226	3.87 222

uanet_test		0.00 220	0.00 222	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	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	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	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	0.00 221	0.00 229	0.00 229	0.00 230	0.00 225	0.00 225	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										56.08 192

9.14												92.16 17

SR-MVS					96.58 25			90.99 21				92.40 13

our_test_3					81.81 193	83.96 191	76.61 197

MTAPA										92.97 2		91.03 23

MTMP										93.14 1		90.21 30

Patchmatch-RL test							8.55 227

mPP-MVS					97.06 12							88.08 45

NP-MVS									87.47 52

Patchmtry						85.54 182	82.30 169	68.23 194		65.37 144

High-resolution multi-view benchmark