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.

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 13	94.08 20	95.58 54	97.48 15

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 6	96.93 3	98.99 1

SMA-MVS		94.70 7	95.35 7	93.93 11	97.57 3	97.57 9	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
MP-MVS		93.35 21	93.59 25	93.08 22	97.39 4	96.82 23	95.38 24	90.71 23	90.82 36	88.07 27	92.83 21	90.29 30	91.32 27	94.03 30	93.19 41	95.61 52	97.16 21
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		93.69 19	93.66 24	93.72 15	97.37 5	96.66 29	95.93 17	92.50 9	93.40 18	88.35 25	87.36 35	92.33 14	92.18 13	94.89 16	94.09 19	96.00 27	96.91 29

CNVR-MVS		94.37 12	94.65 12	94.04 10	97.29 6	97.11 12	96.00 11	92.43 10	93.45 15	89.85 18	90.92 26	93.04 9	92.59 10	95.77 5	94.82 7	96.11 25	97.42 17

HFP-MVS		94.02 15	94.22 19	93.78 13	97.25 7	96.85 21	95.81 19	90.94 22	94.12 11	90.29 15	94.09 14	89.98 32	92.52 11	93.94 33	93.49 33	95.87 33	97.10 24

APD-MVS		94.37 12	94.47 16	94.26 7	97.18 8	96.99 17	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 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	88.76 1	93.10 23	93.02 30	93.19 21	97.13 9	96.51 33	95.35 25	91.19 19	93.14 20	88.14 26	85.26 41	89.49 35	91.45 22	95.17 11	95.07 2	95.85 36	96.48 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
APDe-MVS		95.23 5	95.69 6	94.70 5	97.12 10	97.81 7	97.19 2	92.83 4	95.06 6	90.98 9	96.47 2	92.77 10	93.38 2	95.34 10	94.21 17	96.68 9	98.17 5
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMMPR		93.72 18	93.94 21	93.48 17	97.07 11	96.93 18	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 31	95.88 31	96.73 33

mPP-MVS					97.06 12							88.08 45

ACMMP_NAP		93.94 16	94.49 15	93.30 19	97.03 13	97.31 11	95.96 13	91.30 18	93.41 17	88.55 24	93.00 19	90.33 29	91.43 25	95.53 8	94.41 15	95.53 58	97.47 16

PGM-MVS		92.76 26	93.03 29	92.45 27	97.03 13	96.67 28	95.73 22	87.92 42	90.15 44	86.53 36	92.97 20	88.33 44	91.69 20	93.62 41	93.03 42	95.83 37	96.41 40

SteuartSystems-ACMMP		94.06 14	94.65 12	93.38 18	96.97 15	97.36 10	96.12 10	91.78 14	92.05 28	87.34 30	94.42 12	90.87 26	91.87 18	95.47 9	94.59 12	96.21 23	97.77 11
Skip Steuart: Steuart Systems R&D Blog.
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 9	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
X-MVS		92.36 30	92.75 31	91.90 33	96.89 17	96.70 25	95.25 26	90.48 28	91.50 33	83.95 50	88.20 32	88.82 40	89.11 38	93.75 38	93.43 34	95.75 43	96.83 31

train_agg		92.87 25	93.53 26	92.09 30	96.88 18	95.38 52	95.94 15	90.59 27	90.65 38	83.65 53	94.31 13	91.87 20	90.30 32	93.38 43	92.42 52	95.17 78	96.73 33

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 4	96.90 4	98.45 3

CP-MVS		93.25 22	93.26 27	93.24 20	96.84 19	96.51 33	95.52 23	90.61 26	92.37 24	88.88 22	90.91 27	89.52 34	91.91 16	93.64 40	92.78 47	95.69 45	97.09 25

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 18	94.77 9	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
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 7	94.58 13	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
MCST-MVS		93.81 17	94.06 20	93.53 16	96.79 23	96.85 21	95.95 14	91.69 16	92.20 26	87.17 32	90.83 28	93.41 7	91.96 14	94.49 25	93.50 31	97.61 1	97.12 23

SF-MVS		94.61 8	94.96 10	94.20 9	96.75 24	97.07 13	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 30	97.04 2	97.27 18

SR-MVS					96.58 25			90.99 21				92.40 13

EPNet		89.60 49	89.91 48	89.24 54	96.45 26	93.61 83	92.95 47	88.03 39	85.74 61	83.36 54	87.29 36	83.05 64	80.98 103	92.22 60	91.85 57	93.69 144	95.58 55
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TPM-MVS					96.31 27	96.02 38	94.89 31			86.52 37	87.18 37	92.17 16	86.76 65			95.56 55	93.85 86
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
CSCG		92.76 26	93.16 28	92.29 29	96.30 28	97.74 8	94.67 34	88.98 35	92.46 22	89.73 19	86.67 38	92.15 18	88.69 44	92.26 59	92.92 45	95.40 63	97.89 10

CDPH-MVS		91.14 39	92.01 33	90.11 41	96.18 29	96.18 37	94.89 31	88.80 37	88.76 49	77.88 89	89.18 31	87.71 47	87.29 61	93.13 46	93.31 38	95.62 50	95.84 48

DeepC-MVS	87.86 3	92.26 31	91.86 34	92.73 24	96.18 29	96.87 20	95.19 28	91.76 15	92.17 27	86.58 35	81.79 55	85.85 51	90.88 30	94.57 23	94.61 11	95.80 39	97.18 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
AdaColmap		90.29 43	88.38 60	92.53 25	96.10 31	95.19 57	92.98 46	91.40 17	89.08 48	88.65 23	78.35 74	81.44 71	91.30 28	90.81 90	90.21 84	94.72 99	93.59 93

MSLP-MVS++		92.02 34	91.40 37	92.75 23	96.01 32	95.88 44	93.73 40	89.00 33	89.89 45	90.31 14	81.28 60	88.85 39	91.45 22	92.88 51	94.24 16	96.00 27	96.76 32

3Dnovator+	86.06 4	91.60 36	90.86 42	92.47 26	96.00 33	96.50 35	94.70 33	87.83 43	90.49 39	89.92 17	74.68 96	89.35 36	90.66 31	94.02 31	94.14 18	95.67 47	96.85 30

MVS_0304		93.46 20	94.44 17	92.32 28	95.88 34	97.84 6	95.25 26	87.99 40	92.23 25	89.16 21	91.23 25	91.51 22	88.98 39	95.64 6	95.04 3	96.67 11	97.57 14

TSAR-MVS + ACMM		92.97 24	94.51 14	91.16 37	95.88 34	96.59 30	95.09 29	90.45 29	93.42 16	83.01 56	94.68 10	90.74 27	88.74 43	94.75 20	93.78 24	93.82 139	97.63 12

ACMMP		92.03 33	92.16 32	91.87 34	95.88 34	96.55 31	94.47 35	89.49 32	91.71 31	85.26 43	91.52 24	84.48 57	90.21 34	92.82 52	91.63 59	95.92 30	96.42 39
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
SD-MVS		94.53 10	95.22 8	93.73 14	95.69 37	97.03 15	95.77 21	91.95 12	94.41 8	91.35 7	94.97 8	93.34 8	91.80 19	94.72 21	93.99 21	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
DPM-MVS		91.72 35	91.48 35	92.00 31	95.53 38	95.75 47	95.94 15	91.07 20	91.20 34	85.58 41	81.63 58	90.74 27	88.40 47	93.40 42	93.75 25	95.45 62	93.85 86

TSAR-MVS + MP.		94.48 11	94.97 9	93.90 12	95.53 38	97.01 16	96.69 6	90.71 23	94.24 10	90.92 10	94.97 8	92.19 15	93.03 4	94.83 17	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
CPTT-MVS		91.39 37	90.95 40	91.91 32	95.06 40	95.24 56	95.02 30	88.98 35	91.02 35	86.71 34	84.89 43	88.58 43	91.60 21	90.82 89	89.67 100	94.08 126	96.45 38

CANet		91.33 38	91.46 36	91.18 36	95.01 41	96.71 24	93.77 38	87.39 46	87.72 53	87.26 31	81.77 56	89.73 33	87.32 60	94.43 26	93.86 22	96.31 18	96.02 46

PHI-MVS		92.05 32	93.74 23	90.08 42	94.96 42	97.06 14	93.11 45	87.71 44	90.71 37	80.78 71	92.40 22	91.03 24	87.68 55	94.32 28	94.48 14	96.21 23	96.16 44

MAR-MVS		88.39 61	88.44 59	88.33 67	94.90 43	95.06 61	90.51 68	83.59 79	85.27 63	79.07 81	77.13 79	82.89 65	87.70 53	92.19 62	92.32 53	94.23 123	94.20 80
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
3Dnovator	85.17 5	90.48 41	89.90 49	91.16 37	94.88 44	95.74 48	93.82 37	85.36 55	89.28 46	87.81 28	74.34 99	87.40 48	88.56 45	93.07 47	93.74 26	96.53 12	95.71 50

DeepPCF-MVS	88.51 2	92.64 29	94.42 18	90.56 40	94.84 45	96.92 19	91.31 63	89.61 31	95.16 5	84.55 48	89.91 30	91.45 23	90.15 35	95.12 12	94.81 8	92.90 159	97.58 13

QAPM		89.49 50	89.58 53	89.38 52	94.73 46	95.94 41	92.35 49	85.00 58	85.69 62	80.03 77	76.97 81	87.81 46	87.87 52	92.18 63	92.10 55	96.33 16	96.40 42

MVS_111021_HR		90.56 40	91.29 38	89.70 48	94.71 47	95.63 49	91.81 57	86.38 49	87.53 54	81.29 66	87.96 33	85.43 53	87.69 54	93.90 34	92.93 44	96.33 16	95.69 51

OpenMVS	82.53 11	87.71 69	86.84 77	88.73 58	94.42 48	95.06 61	91.02 66	83.49 82	82.50 82	82.24 62	67.62 137	85.48 52	85.56 73	91.19 74	91.30 62	95.67 47	94.75 66

PLC	83.76 9	88.61 58	86.83 78	90.70 39	94.22 49	92.63 101	91.50 60	87.19 47	89.16 47	86.87 33	75.51 91	80.87 73	89.98 36	90.01 101	89.20 112	94.41 118	90.45 152
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		85.96 82	84.37 100	87.81 69	94.13 50	93.27 89	90.26 73	89.00 33	84.91 68	72.84 114	71.74 112	72.47 127	87.45 58	89.53 109	89.09 114	93.20 155	89.60 155

EPNet_dtu		81.98 120	83.82 105	79.83 153	94.10 51	85.97 179	87.29 121	84.08 70	80.61 104	59.96 186	81.62 59	77.19 102	62.91 202	87.21 132	86.38 152	90.66 184	87.77 172
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		87.56 71	85.80 88	89.62 49	93.90 52	94.09 77	94.12 36	88.18 38	75.40 136	77.30 92	76.41 84	77.93 97	88.79 42	92.20 61	90.82 71	95.40 63	93.72 91
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DELS-MVS		89.71 48	89.68 52	89.74 46	93.75 53	96.22 36	93.76 39	85.84 51	82.53 80	85.05 45	78.96 71	84.24 58	84.25 80	94.91 15	94.91 5	95.78 42	96.02 46
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
CNLPA		88.40 59	87.00 75	90.03 44	93.73 54	94.28 72	89.56 82	85.81 52	91.87 29	87.55 29	69.53 126	81.49 70	89.23 37	89.45 110	88.59 122	94.31 122	93.82 88

HQP-MVS		89.13 54	89.58 53	88.60 62	93.53 55	93.67 81	93.29 43	87.58 45	88.53 50	75.50 94	87.60 34	80.32 76	87.07 62	90.66 96	89.95 92	94.62 105	96.35 43

OMC-MVS		90.23 45	90.40 45	90.03 44	93.45 56	95.29 53	91.89 55	86.34 50	93.25 19	84.94 46	81.72 57	86.65 50	88.90 40	91.69 67	90.27 83	94.65 103	93.95 84

ACMM	83.27 10	87.68 70	86.09 84	89.54 50	93.26 57	92.19 107	91.43 61	86.74 48	86.02 59	82.85 57	75.63 89	75.14 109	88.41 46	90.68 95	89.99 89	94.59 106	92.97 101
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_111021_LR		90.14 46	90.89 41	89.26 53	93.23 58	94.05 78	90.43 69	84.65 61	90.16 43	84.52 49	90.14 29	83.80 60	87.99 51	92.50 56	90.92 68	94.74 97	94.70 68

SPE-MVS-test		90.29 43	90.96 39	89.51 51	93.18 59	95.87 45	89.18 88	83.72 75	88.32 51	84.82 47	84.89 43	85.23 54	90.25 33	94.04 29	92.66 51	95.94 29	95.69 51

CS-MVS		90.34 42	90.58 44	90.07 43	93.11 60	95.82 46	90.57 67	83.62 76	87.07 56	85.35 42	82.98 47	83.47 61	91.37 26	94.94 14	93.37 37	96.37 14	96.41 40

XVS					93.11 60	96.70 25	91.91 53			83.95 50		88.82 40				95.79 40

X-MVStestdata					93.11 60	96.70 25	91.91 53			83.95 50		88.82 40				95.79 40

PCF-MVS	84.60 6	88.66 56	87.75 70	89.73 47	93.06 63	96.02 38	93.22 44	90.00 30	82.44 83	80.02 78	77.96 77	85.16 55	87.36 59	88.54 120	88.54 123	94.72 99	95.61 54
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
TAPA-MVS	84.37 7	88.91 55	88.93 56	88.89 56	93.00 64	94.85 65	92.00 52	84.84 59	91.68 32	80.05 76	79.77 66	84.56 56	88.17 50	90.11 100	89.00 118	95.30 70	92.57 116
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_Blended_VisFu		87.40 73	87.80 67	86.92 76	92.86 65	95.40 51	88.56 106	83.45 86	79.55 112	82.26 60	74.49 98	84.03 59	79.24 133	92.97 50	91.53 61	95.15 80	96.65 36

UA-Net		86.07 80	87.78 68	84.06 106	92.85 66	95.11 60	87.73 114	84.38 65	73.22 156	73.18 110	79.99 65	89.22 37	71.47 178	93.22 45	93.03 42	94.76 96	90.69 147

LGP-MVS_train		88.25 64	88.55 57	87.89 68	92.84 67	93.66 82	93.35 42	85.22 57	85.77 60	74.03 105	86.60 39	76.29 106	86.62 67	91.20 73	90.58 79	95.29 71	95.75 49

TSAR-MVS + COLMAP		88.40 59	89.09 55	87.60 72	92.72 68	93.92 80	92.21 50	85.57 54	91.73 30	73.72 106	91.75 23	73.22 125	87.64 56	91.49 69	89.71 99	93.73 142	91.82 130

PVSNet_BlendedMVS		88.19 65	88.00 65	88.42 64	92.71 69	94.82 66	89.08 93	83.81 72	84.91 68	86.38 38	79.14 68	78.11 95	82.66 89	93.05 48	91.10 63	95.86 34	94.86 64

PVSNet_Blended		88.19 65	88.00 65	88.42 64	92.71 69	94.82 66	89.08 93	83.81 72	84.91 68	86.38 38	79.14 68	78.11 95	82.66 89	93.05 48	91.10 63	95.86 34	94.86 64

ACMP	83.90 8	88.32 63	88.06 63	88.62 61	92.18 71	93.98 79	91.28 64	85.24 56	86.69 57	81.23 67	85.62 40	75.13 110	87.01 64	89.83 103	89.77 97	94.79 93	95.43 57
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSDG		83.87 104	81.02 126	87.19 75	92.17 72	89.80 137	89.15 91	85.72 53	80.61 104	79.24 80	66.66 140	68.75 143	82.69 88	87.95 127	87.44 133	94.19 124	85.92 184

TSAR-MVS + GP.		92.71 28	93.91 22	91.30 35	91.96 73	96.00 40	93.43 41	87.94 41	92.53 21	86.27 40	93.57 15	91.94 19	91.44 24	93.29 44	92.89 46	96.78 7	97.15 22

test2506		85.20 90	84.11 102	86.47 79	91.84 74	95.28 54	89.18 88	84.49 63	82.59 78	75.34 98	74.66 97	58.07 192	81.68 96	93.76 36	92.71 48	96.28 21	91.71 132

ECVR-MVS		85.25 89	84.47 98	86.16 81	91.84 74	95.28 54	89.18 88	84.49 63	82.59 78	73.49 108	66.12 142	69.28 140	81.68 96	93.76 36	92.71 48	96.28 21	91.58 139

test1111		84.86 95	84.21 101	85.61 87	91.75 76	95.14 59	88.63 103	84.57 62	81.88 89	71.21 117	65.66 149	68.51 144	81.19 100	93.74 39	92.68 50	96.31 18	91.86 129

ETV-MVS		89.22 53	89.76 50	88.60 62	91.60 77	94.61 69	89.48 84	83.46 85	85.20 65	81.58 64	82.75 49	82.59 66	88.80 41	94.57 23	93.28 39	96.68 9	95.31 58

EIA-MVS		87.94 68	88.05 64	87.81 69	91.46 78	95.00 63	88.67 100	82.81 92	82.53 80	80.81 70	80.04 64	80.20 77	87.48 57	92.58 55	91.61 60	95.63 49	94.36 74

sasdasda		89.36 51	89.92 46	88.70 59	91.38 79	95.92 42	91.81 57	82.61 100	90.37 40	82.73 58	82.09 51	79.28 86	88.30 48	91.17 75	93.59 28	95.36 65	97.04 26

canonicalmvs		89.36 51	89.92 46	88.70 59	91.38 79	95.92 42	91.81 57	82.61 100	90.37 40	82.73 58	82.09 51	79.28 86	88.30 48	91.17 75	93.59 28	95.36 65	97.04 26

CLD-MVS		88.66 56	88.52 58	88.82 57	91.37 81	94.22 73	92.82 48	82.08 105	88.27 52	85.14 44	81.86 54	78.53 93	85.93 71	91.17 75	90.61 77	95.55 56	95.00 60
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MGCFI-Net		88.38 62	89.72 51	86.83 77	91.21 82	95.59 50	91.14 65	82.37 103	90.25 42	75.33 99	81.89 53	79.13 88	85.69 72	90.98 86	93.23 40	95.23 75	96.94 28

CHOSEN 1792x2688		82.16 118	80.91 129	83.61 111	91.14 83	92.01 108	89.55 83	79.15 139	79.87 108	70.29 121	52.51 205	72.56 126	81.39 98	88.87 118	88.17 126	90.15 188	92.37 123

IB-MVS	79.09 12	82.60 115	82.19 114	83.07 117	91.08 84	93.55 84	80.90 184	81.35 112	76.56 128	80.87 69	64.81 157	69.97 136	68.87 185	85.64 159	90.06 88	95.36 65	94.74 67
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
IS_MVSNet		86.18 79	88.18 62	83.85 109	91.02 85	94.72 68	87.48 117	82.46 102	81.05 99	70.28 122	76.98 80	82.20 69	76.65 150	93.97 32	93.38 35	95.18 77	94.97 61

HyFIR lowres test		81.62 128	79.45 149	84.14 105	91.00 86	93.38 88	88.27 108	78.19 148	76.28 130	70.18 123	48.78 209	73.69 120	83.52 83	87.05 135	87.83 130	93.68 145	89.15 158

COLMAP_ROB	76.78 15	80.50 135	78.49 154	82.85 118	90.96 87	89.65 143	86.20 138	83.40 87	77.15 126	66.54 140	62.27 165	65.62 153	77.89 141	85.23 166	84.70 173	92.11 166	84.83 188
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CANet_DTU		85.43 87	87.72 71	82.76 120	90.95 88	93.01 94	89.99 74	75.46 172	82.67 77	64.91 153	83.14 46	80.09 78	80.68 107	92.03 65	91.03 65	94.57 108	92.08 124

FC-MVSNet-train		85.18 91	85.31 92	85.03 92	90.67 89	91.62 112	87.66 115	83.61 77	79.75 110	74.37 103	78.69 72	71.21 131	78.91 134	91.23 71	89.96 91	94.96 86	94.69 70

baseline1		84.54 98	84.43 99	84.67 94	90.62 90	91.16 115	88.63 103	83.75 74	79.78 109	71.16 118	75.14 93	74.10 115	77.84 142	91.56 68	90.67 76	96.04 26	88.58 161

thres600view7		82.53 117	81.02 126	84.28 101	90.61 91	93.05 92	88.57 105	82.67 96	74.12 147	68.56 133	65.09 154	62.13 172	80.40 113	91.15 78	89.02 117	94.88 89	92.59 114

casdiffmvs_mvg		87.97 67	87.63 72	88.37 66	90.55 92	94.42 70	91.82 56	84.69 60	84.05 72	82.08 63	76.57 83	79.00 89	85.49 74	92.35 57	92.29 54	95.55 56	94.70 68
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
EC-MVSNet		89.96 47	90.77 43	89.01 55	90.54 93	95.15 58	91.34 62	81.43 110	85.27 63	83.08 55	82.83 48	87.22 49	90.97 29	94.79 19	93.38 35	96.73 8	96.71 35

thres400		82.68 114	81.15 124	84.47 97	90.52 94	92.89 96	88.95 98	82.71 94	74.33 144	69.22 130	65.31 151	62.61 167	80.63 109	90.96 87	89.50 104	94.79 93	92.45 122

EPP-MVSNet		86.55 76	87.76 69	85.15 91	90.52 94	94.41 71	87.24 123	82.32 104	81.79 91	73.60 107	78.57 73	82.41 67	82.07 94	91.23 71	90.39 81	95.14 81	95.48 56

ACMH	78.52 14	81.86 122	80.45 133	83.51 115	90.51 96	91.22 114	85.62 145	84.23 67	70.29 173	62.21 169	69.04 130	64.05 158	84.48 79	87.57 130	88.45 125	94.01 130	92.54 118
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Vis-MVSNet (Re-imp)		83.65 107	86.81 79	79.96 151	90.46 97	92.71 98	84.84 153	82.00 106	80.93 101	62.44 168	76.29 85	82.32 68	65.54 198	92.29 58	91.66 58	94.49 113	91.47 141

FA-MVS(training)		85.65 85	85.79 89	85.48 89	90.44 98	93.47 85	88.66 102	73.11 180	83.34 75	82.26 60	71.79 111	78.39 94	83.14 86	91.00 83	89.47 106	95.28 73	93.06 99

thres200		82.77 113	81.25 123	84.54 95	90.38 99	93.05 92	89.13 92	82.67 96	74.40 143	69.53 127	65.69 148	63.03 164	80.63 109	91.15 78	89.42 107	94.88 89	92.04 126

MS-PatchMatch		81.79 124	81.44 120	82.19 128	90.35 100	89.29 149	88.08 111	75.36 173	77.60 124	69.00 131	64.37 160	78.87 92	77.14 148	88.03 126	85.70 163	93.19 156	86.24 181

PatchMatch-RL		83.34 109	81.36 121	85.65 85	90.33 101	89.52 145	84.36 157	81.82 107	80.87 103	79.29 79	74.04 100	62.85 166	86.05 70	88.40 123	87.04 140	92.04 167	86.77 177

thres100view900		82.55 116	81.01 128	84.34 98	90.30 102	92.27 105	89.04 96	82.77 93	75.14 137	69.56 125	65.72 146	63.13 161	79.62 128	89.97 102	89.26 110	94.73 98	91.61 138

tfpn200view9		82.86 111	81.46 119	84.48 96	90.30 102	93.09 91	89.05 95	82.71 94	75.14 137	69.56 125	65.72 146	63.13 161	80.38 114	91.15 78	89.51 103	94.91 88	92.50 120

casdiffmvs		87.45 72	87.15 74	87.79 71	90.15 104	94.22 73	89.96 75	83.93 71	85.08 66	80.91 68	75.81 88	77.88 98	86.08 69	91.86 66	90.86 70	95.74 44	94.37 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
MVS_Test		86.93 75	87.24 73	86.56 78	90.10 105	93.47 85	90.31 70	80.12 125	83.55 74	78.12 85	79.58 67	79.80 81	85.45 75	90.17 99	90.59 78	95.29 71	93.53 94

viewmanbaseed2359cas		87.17 74	86.90 76	87.48 73	90.08 106	94.14 75	90.30 71	83.19 90	84.17 71	80.68 73	76.78 82	77.43 100	85.43 76	90.78 91	90.92 68	95.21 76	94.10 81

ACMH+	79.08 13	81.84 123	80.06 138	83.91 108	89.92 107	90.62 119	86.21 137	83.48 84	73.88 149	65.75 145	66.38 141	65.30 154	84.63 78	85.90 156	87.25 136	93.45 150	91.13 145

Effi-MVS+		85.33 88	85.08 93	85.63 86	89.69 108	93.42 87	89.90 76	80.31 123	79.32 113	72.48 116	73.52 105	74.03 116	86.55 68	90.99 84	89.98 90	94.83 91	94.27 79

Anonymous202405211			82.75 112		89.58 109	92.97 95	89.04 96	84.13 69	78.72 118		57.18 192	76.64 105	83.13 87	89.55 108	89.92 93	93.38 152	94.28 78

GeoE		84.62 97	83.98 104	85.35 90	89.34 110	92.83 97	88.34 107	78.95 140	79.29 114	77.16 93	68.10 134	74.56 112	83.40 84	89.31 112	89.23 111	94.92 87	94.57 72

tttt0517		85.11 93	85.81 87	84.30 100	89.24 111	92.68 100	87.12 128	80.11 126	81.98 88	74.31 104	78.08 76	73.57 121	79.90 121	91.01 82	89.58 101	95.11 84	93.77 89

DI_MVS_pp		86.41 78	85.54 91	87.42 74	89.24 111	93.13 90	92.16 51	82.65 98	82.30 84	80.75 72	68.30 133	80.41 75	85.01 77	90.56 97	90.07 87	94.70 101	94.01 82

thisisatest0530		85.15 92	85.86 86	84.33 99	89.19 113	92.57 104	87.22 124	80.11 126	82.15 87	74.41 102	78.15 75	73.80 119	79.90 121	90.99 84	89.58 101	95.13 82	93.75 90

DCV-MVSNet		85.88 84	86.17 82	85.54 88	89.10 114	89.85 135	89.34 86	80.70 116	83.04 76	78.08 87	76.19 86	79.00 89	82.42 92	89.67 106	90.30 82	93.63 147	95.12 59

UGNet		85.90 83	88.23 61	83.18 116	88.96 115	94.10 76	87.52 116	83.60 78	81.66 92	77.90 88	80.76 62	83.19 63	66.70 195	91.13 81	90.71 75	94.39 119	96.06 45
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
Anonymous20231211		84.42 102	83.02 108	86.05 83	88.85 116	92.70 99	88.92 99	83.40 87	79.99 107	78.31 84	55.83 196	78.92 91	83.33 85	89.06 114	89.76 98	93.50 149	94.90 62

MVSTER		86.03 81	86.12 83	85.93 84	88.62 117	89.93 133	89.33 87	79.91 130	81.87 90	81.35 65	81.07 61	74.91 111	80.66 108	92.13 64	90.10 86	95.68 46	92.80 106

TDRefinement		79.05 153	77.05 172	81.39 135	88.45 118	89.00 156	86.92 129	82.65 98	74.21 146	64.41 154	59.17 184	59.16 188	74.52 164	85.23 166	85.09 168	91.37 176	87.51 173

viewmambaseed2359dif		85.52 86	85.01 94	86.12 82	88.39 119	91.96 109	89.39 85	81.43 110	82.16 85	80.47 74	75.52 90	76.85 104	83.66 81	87.03 136	87.60 131	93.37 153	93.98 83

IterMVS-LS		83.28 110	82.95 110	83.65 110	88.39 119	88.63 160	86.80 132	78.64 145	76.56 128	73.43 109	72.52 110	75.35 108	80.81 105	86.43 151	88.51 124	93.84 138	92.66 111
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs		86.52 77	86.76 80	86.23 80	88.31 121	92.63 101	89.58 81	81.61 109	86.14 58	80.26 75	79.00 70	77.27 101	83.58 82	88.94 115	89.06 115	94.05 128	94.29 75
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Fast-Effi-MVS+		83.77 106	82.98 109	84.69 93	87.98 122	91.87 110	88.10 110	77.70 154	78.10 122	73.04 112	69.13 128	68.51 144	86.66 66	90.49 98	89.85 95	94.67 102	92.88 103

gg-mvs-nofinetune		75.64 190	77.26 169	73.76 194	87.92 123	92.20 106	87.32 120	64.67 213	51.92 218	35.35 223	46.44 212	77.05 103	71.97 175	92.64 54	91.02 66	95.34 68	89.53 156

RPSCF		83.46 108	83.36 107	83.59 112	87.75 124	87.35 170	84.82 154	79.46 135	83.84 73	78.12 85	82.69 50	79.87 79	82.60 91	82.47 190	81.13 193	88.78 195	86.13 182

Effi-MVS+-dtu		82.05 119	81.76 116	82.38 125	87.72 125	90.56 120	86.90 131	78.05 150	73.85 150	66.85 139	71.29 114	71.90 129	82.00 95	86.64 146	85.48 165	92.76 161	92.58 115

CostFormer		80.94 132	80.21 135	81.79 130	87.69 126	88.58 161	87.47 118	70.66 189	80.02 106	77.88 89	73.03 106	71.40 130	78.24 138	79.96 199	79.63 195	88.82 194	88.84 159

baseline2		82.80 112	82.86 111	82.73 121	87.68 127	90.50 121	84.92 152	78.93 141	78.07 123	73.06 111	75.08 94	69.77 137	77.31 145	88.90 117	86.94 141	94.50 111	90.74 146

Vis-MVSNet		84.38 103	86.68 81	81.70 131	87.65 128	94.89 64	88.14 109	80.90 115	74.48 142	68.23 134	77.53 78	80.72 74	69.98 182	92.68 53	91.90 56	95.33 69	94.58 71
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test-LLR		79.47 148	79.84 143	79.03 157	87.47 129	82.40 204	81.24 181	78.05 150	73.72 151	62.69 165	73.76 102	74.42 113	73.49 169	84.61 175	82.99 185	91.25 178	87.01 175

test0.0.03 1		76.03 184	78.51 153	73.12 198	87.47 129	85.13 190	76.32 202	78.05 150	73.19 158	50.98 208	70.64 116	69.28 140	55.53 206	85.33 164	84.38 177	90.39 186	81.63 200

tpmrst		76.55 177	75.99 184	77.20 171	87.32 131	83.05 197	82.86 167	65.62 208	78.61 120	67.22 138	69.19 127	65.71 152	75.87 154	76.75 209	75.33 208	84.31 213	83.28 194

baseline		84.89 94	86.06 85	83.52 114	87.25 132	89.67 142	87.76 113	75.68 171	84.92 67	78.40 83	80.10 63	80.98 72	80.20 117	86.69 145	87.05 139	91.86 170	92.99 100

CDS-MVSNet		81.63 127	82.09 115	81.09 140	87.21 133	90.28 124	87.46 119	80.33 122	69.06 177	70.66 119	71.30 113	73.87 117	67.99 188	89.58 107	89.87 94	92.87 160	90.69 147
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpm cat1		77.78 166	75.28 192	80.70 143	87.14 134	85.84 181	85.81 141	70.40 190	77.44 125	78.80 82	63.72 161	64.01 159	76.55 151	75.60 211	75.21 209	85.51 211	85.12 186

tpm		76.30 183	76.05 183	76.59 177	86.97 135	83.01 198	83.83 161	67.06 204	71.83 162	63.87 159	69.56 125	62.88 165	73.41 171	79.79 200	78.59 199	84.41 212	86.68 178

EPMVS		77.53 168	78.07 161	76.90 175	86.89 136	84.91 191	82.18 176	66.64 206	81.00 100	64.11 157	72.75 109	69.68 138	74.42 166	79.36 202	78.13 201	87.14 203	80.68 205

PatchmatchNet		78.67 158	78.85 152	78.46 165	86.85 137	86.03 178	83.77 162	68.11 201	80.88 102	66.19 142	72.90 108	73.40 123	78.06 139	79.25 203	77.71 203	87.75 200	81.75 199
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dmvs_re		81.08 131	79.92 141	82.44 124	86.66 138	87.70 166	87.91 112	83.30 89	72.86 159	65.29 151	65.76 145	63.43 160	76.69 149	88.93 116	89.50 104	94.80 92	91.23 144

SCA		79.51 147	80.15 137	78.75 160	86.58 139	87.70 166	83.07 166	68.53 198	81.31 94	66.40 141	73.83 101	75.38 107	79.30 132	80.49 197	79.39 198	88.63 197	82.96 196

USDC		80.69 133	79.89 142	81.62 133	86.48 140	89.11 154	86.53 134	78.86 142	81.15 98	63.48 161	72.98 107	59.12 190	81.16 101	87.10 133	85.01 169	93.23 154	84.77 189

Fast-Effi-MVS+-dtu		79.95 139	80.69 130	79.08 156	86.36 141	89.14 153	85.85 140	72.28 183	72.85 160	59.32 189	70.43 120	68.42 146	77.57 143	86.14 153	86.44 151	93.11 157	91.39 142

tfpnnormal		77.46 169	74.86 194	80.49 147	86.34 142	88.92 157	84.33 158	81.26 113	61.39 205	61.70 176	51.99 206	53.66 211	74.84 161	88.63 119	87.38 135	94.50 111	92.08 124

dps		78.02 163	75.94 185	80.44 148	86.06 143	86.62 176	82.58 168	69.98 193	75.14 137	77.76 91	69.08 129	59.93 181	78.47 136	79.47 201	77.96 202	87.78 199	83.40 193

IterMVS-SCA-FT		79.41 149	80.20 136	78.49 164	85.88 144	86.26 177	83.95 160	71.94 184	73.55 154	61.94 172	70.48 119	70.50 133	75.23 156	85.81 158	84.61 175	91.99 169	90.18 153

LTVRE_ROB	74.41 16	75.78 189	74.72 195	77.02 174	85.88 144	89.22 150	82.44 171	77.17 157	50.57 219	45.45 214	65.44 150	52.29 213	81.25 99	85.50 162	87.42 134	89.94 190	92.62 112
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
EG-PatchMatch MVS		76.40 181	75.47 190	77.48 170	85.86 146	90.22 126	82.45 170	73.96 178	59.64 210	59.60 188	52.75 204	62.20 171	68.44 187	88.23 124	87.50 132	94.55 109	87.78 171

CR-MVSNet		78.71 157	78.86 151	78.55 163	85.85 147	85.15 188	82.30 173	68.23 199	74.71 140	65.37 148	64.39 159	69.59 139	77.18 146	85.10 171	84.87 170	92.34 165	88.21 165

GA-MVS		79.52 146	79.71 146	79.30 155	85.68 148	90.36 123	84.55 155	78.44 146	70.47 172	57.87 194	68.52 132	61.38 173	76.21 152	89.40 111	87.89 127	93.04 158	89.96 154

UniMVSNet_ETH3D		79.24 151	76.47 177	82.48 123	85.66 149	90.97 116	86.08 139	81.63 108	64.48 197	68.94 132	54.47 198	57.65 194	78.83 135	85.20 169	88.91 119	93.72 143	93.60 92

TransMVSNet (Re)		76.57 176	75.16 193	78.22 167	85.60 150	87.24 171	82.46 169	81.23 114	59.80 209	59.05 192	57.07 193	59.14 189	66.60 196	88.09 125	86.82 142	94.37 120	87.95 170

RPMNet		77.07 171	77.63 167	76.42 178	85.56 151	85.15 188	81.37 178	65.27 210	74.71 140	60.29 185	63.71 162	66.59 150	73.64 168	82.71 188	82.12 190	92.38 164	88.39 163

MDTV_nov1_ep13		79.14 152	79.49 148	78.74 161	85.40 152	86.89 174	84.32 159	70.29 191	78.85 117	69.42 128	75.37 92	73.29 124	75.64 155	80.61 196	79.48 197	87.36 201	81.91 198

UniMVSNet (Re)		81.22 129	81.08 125	81.39 135	85.35 153	91.76 111	84.93 151	82.88 91	76.13 131	65.02 152	64.94 155	63.09 163	75.17 158	87.71 129	89.04 116	94.97 85	94.88 63

UniMVSNet_NR-MVSNet		81.87 121	81.33 122	82.50 122	85.31 154	91.30 113	85.70 142	84.25 66	75.89 132	64.21 155	66.95 139	64.65 156	80.22 115	87.07 134	89.18 113	95.27 74	94.29 75

IterMVS		78.79 156	79.71 146	77.71 168	85.26 155	85.91 180	84.54 156	69.84 195	73.38 155	61.25 180	70.53 118	70.35 134	74.43 165	85.21 168	83.80 180	90.95 182	88.77 160
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
NR-MVSNet		80.25 137	79.98 140	80.56 146	85.20 156	90.94 117	85.65 144	83.58 80	75.74 133	61.36 179	65.30 152	56.75 199	72.38 174	88.46 122	88.80 120	95.16 79	93.87 85

CMPMVS	56.49 17	73.84 198	71.73 204	76.31 181	85.20 156	85.67 183	75.80 203	73.23 179	62.26 202	65.40 147	53.40 203	59.70 183	71.77 177	80.25 198	79.56 196	86.45 207	81.28 201
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
TinyColmap		76.73 173	73.95 197	79.96 151	85.16 158	85.64 184	82.34 172	78.19 148	70.63 170	62.06 171	60.69 176	49.61 216	80.81 105	85.12 170	83.69 181	91.22 180	82.27 197

gm-plane-assit		70.29 203	70.65 205	69.88 203	85.03 159	78.50 214	58.41 221	65.47 209	50.39 220	40.88 219	49.60 208	50.11 215	75.14 159	91.43 70	89.78 96	94.32 121	84.73 190

FC-MVSNet-test		76.53 178	81.62 118	70.58 202	84.99 160	85.73 182	74.81 205	78.85 143	77.00 127	39.13 221	75.90 87	73.50 122	54.08 210	86.54 148	85.99 160	91.65 172	86.68 178

DU-MVS		81.20 130	80.30 134	82.25 126	84.98 161	90.94 117	85.70 142	83.58 80	75.74 133	64.21 155	65.30 152	59.60 185	80.22 115	86.89 138	89.31 108	94.77 95	94.29 75

Baseline_NR-MVSNet		79.84 141	78.37 158	81.55 134	84.98 161	86.66 175	85.06 149	83.49 82	75.57 135	63.31 162	58.22 191	60.97 175	78.00 140	86.89 138	87.13 137	94.47 114	93.15 97

TranMVSNet+NR-MVSNet		80.52 134	79.84 143	81.33 137	84.92 163	90.39 122	85.53 147	84.22 68	74.27 145	60.68 184	64.93 156	59.96 180	77.48 144	86.75 143	89.28 109	95.12 83	93.29 95

pm-mvs1		78.51 161	77.75 166	79.40 154	84.83 164	89.30 148	83.55 164	79.38 136	62.64 201	63.68 160	58.73 189	64.68 155	70.78 181	89.79 104	87.84 128	94.17 125	91.28 143

testgi		71.92 201	74.20 196	69.27 204	84.58 165	83.06 196	73.40 208	74.39 175	64.04 199	46.17 213	68.90 131	57.15 197	48.89 214	84.07 180	83.08 184	88.18 198	79.09 209

thisisatest0515		79.76 143	80.59 132	78.80 159	84.40 166	88.91 158	79.48 190	76.94 160	72.29 161	67.33 137	67.82 136	65.99 151	70.80 180	88.50 121	87.84 128	93.86 137	92.75 109

FMVSNet3		84.44 101	84.64 97	84.21 102	84.32 167	90.13 128	89.85 77	80.37 119	81.17 95	75.50 94	69.63 122	79.69 83	79.62 128	89.72 105	90.52 80	95.59 53	91.58 139

GBi-Net		84.51 99	84.80 95	84.17 103	84.20 168	89.95 130	89.70 78	80.37 119	81.17 95	75.50 94	69.63 122	79.69 83	79.75 125	90.73 92	90.72 72	95.52 59	91.71 132

test1		84.51 99	84.80 95	84.17 103	84.20 168	89.95 130	89.70 78	80.37 119	81.17 95	75.50 94	69.63 122	79.69 83	79.75 125	90.73 92	90.72 72	95.52 59	91.71 132

FMVSNet2		83.87 104	83.73 106	84.05 107	84.20 168	89.95 130	89.70 78	80.21 124	79.17 116	74.89 100	65.91 143	77.49 99	79.75 125	90.87 88	91.00 67	95.52 59	91.71 132

WR-MVS		76.63 175	78.02 163	75.02 188	84.14 171	89.76 139	78.34 197	80.64 117	69.56 174	52.32 203	61.26 169	61.24 174	60.66 203	84.45 177	87.07 138	93.99 131	92.77 107

v8		79.90 140	78.39 157	81.66 132	83.97 172	89.81 136	87.16 126	77.40 156	71.49 163	67.71 135	61.24 170	62.49 168	79.83 124	85.48 163	86.17 155	93.89 135	92.02 128

v2v482		79.84 141	78.07 161	81.90 129	83.75 173	90.21 127	87.17 125	79.85 131	70.65 169	65.93 144	61.93 167	60.07 179	80.82 104	85.25 165	86.71 144	93.88 136	91.70 136

v10		79.62 144	78.19 159	81.28 138	83.73 174	89.69 141	87.27 122	76.86 161	70.50 171	65.46 146	60.58 177	60.47 177	80.44 112	86.91 137	86.63 147	93.93 132	92.55 117

v1144		79.38 150	77.83 164	81.18 139	83.62 175	90.23 125	87.15 127	78.35 147	69.13 176	64.02 158	60.20 179	59.41 186	80.14 119	86.78 141	86.57 148	93.81 140	92.53 119

v148		78.59 159	76.84 175	80.62 145	83.61 176	89.16 152	83.65 163	79.24 138	69.38 175	69.34 129	59.88 181	60.41 178	75.19 157	83.81 181	84.63 174	92.70 162	90.63 149

SixPastTwentyTwo		76.02 185	75.72 187	76.36 179	83.38 177	87.54 168	75.50 204	76.22 165	65.50 194	57.05 195	70.64 116	53.97 210	74.54 163	80.96 195	82.12 190	91.44 174	89.35 157

CVMVSNet		76.70 174	78.46 155	74.64 192	83.34 178	84.48 192	81.83 177	74.58 174	68.88 178	51.23 207	69.77 121	70.05 135	67.49 191	84.27 178	83.81 179	89.38 192	87.96 169

v1192		78.94 154	77.33 168	80.82 142	83.25 179	89.90 134	86.91 130	77.72 153	68.63 180	62.61 167	59.17 184	57.53 195	80.62 111	86.89 138	86.47 150	93.79 141	92.75 109

DTE-MVSNet		75.14 192	75.44 191	74.80 190	83.18 180	87.19 172	78.25 199	80.11 126	66.05 189	48.31 210	60.88 174	54.67 206	64.54 199	82.57 189	86.17 155	94.43 117	90.53 151

PEN-MVS		76.02 185	76.07 181	75.95 183	83.17 181	87.97 164	79.65 188	80.07 129	66.57 187	51.45 205	60.94 173	55.47 204	66.81 194	82.72 187	86.80 143	94.59 106	92.03 127

TAMVS		76.42 179	77.16 171	75.56 184	83.05 182	85.55 185	80.58 186	71.43 186	65.40 196	61.04 183	67.27 138	69.22 142	67.99 188	84.88 173	84.78 172	89.28 193	83.01 195

pmmvs4		79.99 138	78.08 160	82.22 127	83.04 183	87.16 173	84.95 150	78.80 144	78.64 119	74.53 101	64.61 158	59.41 186	79.45 130	84.13 179	84.54 176	92.53 163	88.08 167

v144192		78.81 155	77.22 170	80.67 144	82.95 184	89.79 138	86.40 135	77.42 155	68.26 182	63.13 163	59.50 182	58.13 191	80.08 120	85.93 155	86.08 157	94.06 127	92.83 105

v1921920		78.57 160	76.99 173	80.41 149	82.93 185	89.63 144	86.38 136	77.14 158	68.31 181	61.80 175	58.89 188	56.79 198	80.19 118	86.50 150	86.05 159	94.02 129	92.76 108

CHOSEN 280x420		80.28 136	81.66 117	78.67 162	82.92 186	79.24 213	85.36 148	66.79 205	78.11 121	70.32 120	75.03 95	79.87 79	81.09 102	89.07 113	83.16 183	85.54 210	87.17 174

WR-MVS_H		75.84 188	76.93 174	74.57 193	82.86 187	89.50 146	78.34 197	79.36 137	66.90 185	52.51 202	60.20 179	59.71 182	59.73 204	83.61 182	85.77 162	94.65 103	92.84 104

v1240		78.15 162	76.53 176	80.04 150	82.85 188	89.48 147	85.61 146	76.77 162	67.05 184	61.18 182	58.37 190	56.16 202	79.89 123	86.11 154	86.08 157	93.92 133	92.47 121

V42		79.59 145	78.43 156	80.94 141	82.79 189	89.71 140	86.66 133	76.73 163	71.38 164	67.42 136	61.01 172	62.30 170	78.39 137	85.56 161	86.48 149	93.65 146	92.60 113

CP-MVSNet		76.36 182	76.41 178	76.32 180	82.73 190	88.64 159	79.39 191	79.62 132	67.21 183	53.70 199	60.72 175	55.22 205	67.91 190	83.52 183	86.34 153	94.55 109	93.19 96

PS-CasMVS		75.90 187	75.86 186	75.96 182	82.59 191	88.46 162	79.23 194	79.56 134	66.00 190	52.77 201	59.48 183	54.35 209	67.14 193	83.37 184	86.23 154	94.47 114	93.10 98

test20.03		68.31 206	70.05 207	66.28 209	82.41 192	80.84 208	67.35 215	76.11 167	58.44 212	40.80 220	53.77 202	54.54 207	42.28 217	83.07 185	81.96 192	88.73 196	77.76 211

FMVSNet1		81.64 126	80.61 131	82.84 119	82.36 193	89.20 151	88.67 100	79.58 133	70.79 168	72.63 115	58.95 187	72.26 128	79.34 131	90.73 92	90.72 72	94.47 114	91.62 137

pmmvs6		74.83 193	72.89 200	77.09 172	82.11 194	87.50 169	80.88 185	76.97 159	52.79 217	61.91 174	46.66 211	60.49 176	69.28 184	86.74 144	85.46 166	91.39 175	90.56 150

pmmvs5		76.93 172	76.33 179	77.62 169	81.97 195	88.40 163	81.32 180	74.35 176	65.42 195	61.42 178	63.07 163	57.95 193	73.23 172	85.60 160	85.35 167	93.41 151	88.55 162

v7n		77.22 170	76.23 180	78.38 166	81.89 196	89.10 155	82.24 175	76.36 164	65.96 191	61.21 181	56.56 194	55.79 203	75.07 160	86.55 147	86.68 145	93.52 148	92.95 102

our_test_3					81.81 197	83.96 195	76.61 201

Anonymous20231206		70.80 202	70.59 206	71.04 201	81.60 198	82.49 203	74.64 206	75.87 169	64.17 198	49.27 209	44.85 215	53.59 212	54.68 209	83.07 185	82.34 189	90.17 187	83.65 192

ADS-MVSNet		74.53 195	75.69 188	73.17 197	81.57 199	80.71 209	79.27 193	63.03 215	79.27 115	59.94 187	67.86 135	68.32 148	71.08 179	77.33 207	76.83 205	84.12 215	79.53 206

pmnet_mix02		71.95 200	71.83 203	72.10 199	81.40 200	80.63 210	73.78 207	72.85 182	70.90 167	54.89 197	62.17 166	57.42 196	62.92 201	76.80 208	73.98 212	86.74 206	80.87 204

test-mter		77.79 165	80.02 139	75.18 187	81.18 201	82.85 199	80.52 187	62.03 217	73.62 153	62.16 170	73.55 104	73.83 118	73.81 167	84.67 174	83.34 182	91.37 176	88.31 164

TESTMET0.1,1		77.78 166	79.84 143	75.38 186	80.86 202	82.40 204	81.24 181	62.72 216	73.72 151	62.69 165	73.76 102	74.42 113	73.49 169	84.61 175	82.99 185	91.25 178	87.01 175

MDTV_nov1_ep13_2view		73.21 199	72.91 199	73.56 196	80.01 203	84.28 194	78.62 195	66.43 207	68.64 179	59.12 190	60.39 178	59.69 184	69.81 183	78.82 205	77.43 204	87.36 201	81.11 203

FPMVS		63.63 211	60.08 216	67.78 206	80.01 203	71.50 219	72.88 210	69.41 197	61.82 204	53.11 200	45.12 214	42.11 223	50.86 212	66.69 217	63.84 218	80.41 217	69.46 217

anonymousdsp		77.94 164	79.00 150	76.71 176	79.03 205	87.83 165	79.58 189	72.87 181	65.80 192	58.86 193	65.82 144	62.48 169	75.99 153	86.77 142	88.66 121	93.92 133	95.68 53

N_pmnet		66.85 207	66.63 208	67.11 208	78.73 206	74.66 217	70.53 212	71.07 187	66.46 188	46.54 212	51.68 207	51.91 214	55.48 207	74.68 212	72.38 213	80.29 218	74.65 214

PMMVS		81.65 125	84.05 103	78.86 158	78.56 207	82.63 201	83.10 165	67.22 203	81.39 93	70.11 124	84.91 42	79.74 82	82.12 93	87.31 131	85.70 163	92.03 168	86.67 180

PatchT		76.42 179	77.81 165	74.80 190	78.46 208	84.30 193	71.82 211	65.03 212	73.89 148	65.37 148	61.58 168	66.70 149	77.18 146	85.10 171	84.87 170	90.94 183	88.21 165

MVS-HIRNet		68.83 205	66.39 209	71.68 200	77.58 209	75.52 216	66.45 216	65.05 211	62.16 203	62.84 164	44.76 216	56.60 201	71.96 176	78.04 206	75.06 210	86.18 209	72.56 215

pmmvs-eth3d		74.32 196	71.96 202	77.08 173	77.33 210	82.71 200	78.41 196	76.02 168	66.65 186	65.98 143	54.23 200	49.02 218	73.14 173	82.37 191	82.69 187	91.61 173	86.05 183

new-patchmatchnet		63.80 210	63.31 212	64.37 210	76.49 211	75.99 215	63.73 218	70.99 188	57.27 213	43.08 216	45.86 213	43.80 220	45.13 216	73.20 213	70.68 216	86.80 205	76.34 213

FMVSNet5		75.50 191	76.07 181	74.83 189	76.16 212	81.19 207	81.34 179	70.21 192	73.20 157	61.59 177	58.97 186	68.33 147	68.50 186	85.87 157	85.85 161	91.18 181	79.11 208

PM-MVS		74.17 197	73.10 198	75.41 185	76.07 213	82.53 202	77.56 200	71.69 185	71.04 165	61.92 173	61.23 171	47.30 219	74.82 162	81.78 193	79.80 194	90.42 185	88.05 168

MIMVSNet		74.69 194	75.60 189	73.62 195	76.02 214	85.31 187	81.21 183	67.43 202	71.02 166	59.07 191	54.48 197	64.07 157	66.14 197	86.52 149	86.64 146	91.83 171	81.17 202

EU-MVSNet		69.98 204	72.30 201	67.28 207	75.67 215	79.39 212	73.12 209	69.94 194	63.59 200	42.80 217	62.93 164	56.71 200	55.07 208	79.13 204	78.55 200	87.06 204	85.82 185

WB-MVS		52.27 216	57.26 217	46.45 216	75.64 216	65.62 222	40.45 227	75.80 170	47.10 222	9.11 230	53.83 201	38.98 226	14.47 225	69.44 215	68.29 217	63.24 223	57.56 222

ET-MVSNet_ETH3D		84.65 96	85.58 90	83.56 113	74.99 217	92.62 103	90.29 72	80.38 118	82.16 85	73.01 113	83.41 45	71.10 132	87.05 63	87.77 128	90.17 85	95.62 50	91.82 130

PMVS	50.48 18	55.81 215	51.93 218	60.33 213	72.90 218	49.34 224	48.78 222	69.51 196	43.49 223	54.25 198	36.26 221	41.04 225	39.71 219	65.07 218	60.70 219	76.85 220	67.58 218
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
ambc			61.92 213		70.98 219	73.54 218	63.64 219		60.06 207	52.23 204	38.44 219	19.17 230	57.12 205	82.33 192	75.03 211	83.21 216	84.89 187

pmmvs3		61.89 212	61.74 214	62.06 212	64.30 220	70.83 220	64.22 217	52.14 221	48.78 221	44.47 215	41.67 218	41.70 224	63.03 200	76.06 210	76.02 206	84.18 214	77.14 212

MDA-MVSNet-bldmvs		66.22 208	64.49 211	68.24 205	61.67 221	82.11 206	70.07 213	76.16 166	59.14 211	47.94 211	54.35 199	35.82 227	67.33 192	64.94 219	75.68 207	86.30 208	79.36 207

new_pmnet		59.28 213	61.47 215	56.73 214	61.66 222	68.29 221	59.57 220	54.91 218	60.83 206	34.38 224	44.66 217	43.65 221	49.90 213	71.66 214	71.56 215	79.94 219	69.67 216

Gipuma		49.17 217	47.05 220	51.65 215	59.67 223	48.39 225	41.98 225	63.47 214	55.64 216	33.33 225	14.90 224	13.78 231	41.34 218	69.31 216	72.30 214	70.11 221	55.00 223
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MIMVSNet1		65.00 209	66.24 210	63.55 211	58.41 224	80.01 211	69.00 214	74.03 177	55.81 215	41.88 218	36.81 220	49.48 217	47.89 215	81.32 194	82.40 188	90.08 189	77.88 210

EMVS		30.49 222	25.44 224	36.39 219	51.47 225	29.89 229	20.17 230	54.00 220	26.49 225	12.02 229	13.94 227	8.84 232	34.37 221	25.04 226	34.37 225	46.29 228	39.53 226

E-PMN		31.40 220	26.80 223	36.78 218	51.39 226	29.96 228	20.20 229	54.17 219	25.93 226	12.75 228	14.73 225	8.58 233	34.10 222	27.36 225	37.83 224	48.07 227	43.18 225

PMMVS2		41.68 219	44.74 221	38.10 217	46.97 227	52.32 223	40.63 226	48.08 222	35.51 224	7.36 231	26.86 223	24.64 229	16.72 224	55.24 222	59.03 220	68.85 222	59.59 221

tmp_tt				32.73 221	43.96 228	21.15 230	26.71 228	8.99 226	65.67 193	51.39 206	56.01 195	42.64 222	11.76 226	56.60 221	50.81 222	53.55 226

MVE	30.17 19	30.88 221	33.52 222	27.80 223	23.78 229	39.16 227	18.69 231	46.90 223	21.88 227	15.39 227	14.37 226	7.31 234	24.41 223	41.63 224	56.22 221	37.64 229	54.07 224
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		41.78 218	48.10 219	34.42 220	10.74 230	19.78 231	44.64 224	17.73 225	59.83 208	38.67 222	35.82 222	54.41 208	34.94 220	62.87 220	43.13 223	59.81 224	60.82 220

GG-mvs-BLEND		57.56 214	82.61 113	28.34 222	0.22 231	90.10 129	79.37 192	0.14 228	79.56 111	0.40 232	71.25 115	83.40 62	0.30 229	86.27 152	83.87 178	89.59 191	83.83 191

testmvs		1.03 223	1.63 225	0.34 224	0.09 232	0.35 232	0.61 233	0.16 227	1.49 228	0.10 233	3.15 228	0.15 235	0.86 228	1.32 227	1.18 226	0.20 230	3.76 228

test123		0.87 224	1.40 226	0.25 225	0.03 233	0.25 233	0.35 234	0.08 229	1.21 229	0.05 234	2.84 229	0.03 236	0.89 227	0.43 228	1.16 227	0.13 231	3.87 227

uanet_test		0.00 225	0.00 227	0.00 226	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

sosnet-low-res		0.00 225	0.00 227	0.00 226	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

sosnet		0.00 225	0.00 227	0.00 226	0.00 234	0.00 234	0.00 235	0.00 230	0.00 230	0.00 235	0.00 230	0.00 237	0.00 230	0.00 229	0.00 228	0.00 232	0.00 229

RE-MVS-def										56.08 196

9.14												92.16 17

MTAPA										92.97 2		91.03 24

MTMP										93.14 1		90.21 31

Patchmatch-RL test							8.55 232

NP-MVS									87.47 55

Patchmtry						85.54 186	82.30 173	68.23 199		65.37 148

DeepMVS_CX						48.31 226	48.03 223	26.08 224	56.42 214	25.77 226	47.51 210	31.31 228	51.30 211	48.49 223		53.61 225	61.52 219

High-resolution multi-view benchmark