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.

WR-MVS_H		88.99 35	93.28 6	83.99 53	91.92 11	89.13 40	91.95 46	83.23 1	90.14 30	71.92 125	95.85 4	98.01 10	71.83 97	95.82 9	93.19 22	93.07 59	90.83 47

DTE-MVSNet		88.99 35	92.77 12	84.59 43	93.31 2	88.10 49	90.96 53	83.09 2	91.38 14	76.21 96	96.03 2	98.04 8	70.78 106	95.65 14	92.32 32	93.18 56	87.84 73

PS-CasMVS		89.07 32	93.23 7	84.21 50	92.44 8	88.23 48	90.54 62	82.95 3	90.50 26	75.31 103	95.80 6	98.37 6	71.16 100	96.30 5	93.32 21	92.88 61	90.11 50

CP-MVSNet		88.71 41	92.63 15	84.13 51	92.39 9	88.09 50	90.47 66	82.86 4	88.79 42	75.16 104	94.87 9	97.68 13	71.05 102	96.16 6	93.18 23	92.85 62	89.64 54

PEN-MVS		88.86 39	92.92 9	84.11 52	92.92 4	88.05 51	90.83 55	82.67 5	91.04 18	74.83 105	95.97 3	98.47 3	70.38 107	95.70 13	92.43 30	93.05 60	88.78 64

UniMVSNet_ETH3D		85.39 62	91.12 43	78.71 99	90.48 37	83.72 79	81.76 140	82.41 6	93.84 6	64.43 160	95.41 7	98.76 1	63.72 142	93.63 33	89.74 57	89.47 108	82.74 114

SR-MVS					91.82 13			80.80 7				95.53 50

CP-MVS		91.09 5	92.33 25	89.65 2	92.16 10	90.41 27	96.46 10	80.38 8	88.26 45	89.17 10	87.00 98	96.34 30	83.95 10	95.77 11	94.72 7	95.81 17	93.78 10

DeepC-MVS	83.59 4	90.37 12	92.56 18	87.82 14	91.26 27	92.33 3	94.72 30	80.04 9	90.01 32	84.61 42	93.33 23	94.22 79	80.59 27	92.90 43	92.52 28	95.69 21	92.57 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
WR-MVS		89.79 23	93.66 5	85.27 37	91.32 23	88.27 46	93.49 38	79.86 10	92.75 9	75.37 102	96.86 1	98.38 5	75.10 71	95.93 8	94.07 14	96.46 5	89.39 56

MP-MVS		90.84 6	91.95 34	89.55 3	92.92 4	90.90 19	96.56 6	79.60 11	86.83 59	88.75 12	89.00 74	94.38 78	84.01 9	94.94 24	94.34 10	95.45 24	93.24 23
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
HFP-MVS		90.32 13	92.37 22	87.94 13	91.46 21	90.91 18	95.69 17	79.49 12	89.94 34	83.50 50	89.06 73	94.44 76	81.68 22	94.17 30	94.19 13	95.81 17	93.87 7

TSAR-MVS + ACMM		89.14 29	92.11 32	85.67 31	89.27 47	90.61 24	90.98 52	79.48 13	88.86 40	79.80 79	93.01 28	93.53 88	83.17 15	92.75 45	92.45 29	91.32 82	93.59 13

ACMMPR		91.30 4	92.88 11	89.46 4	91.92 11	91.61 5	96.60 5	79.46 14	90.08 31	88.53 13	89.54 66	95.57 48	84.25 7	95.24 20	94.27 12	95.97 11	93.85 8

CPTT-MVS		89.63 25	90.52 47	88.59 6	90.95 31	90.74 21	95.71 16	79.13 15	87.70 50	85.68 38	80.05 142	95.74 46	84.77 6	94.28 29	92.68 26	95.28 26	92.45 31

PGM-MVS		90.42 11	91.58 37	89.05 5	91.77 14	91.06 13	96.51 7	78.94 16	85.41 73	87.67 18	87.02 97	95.26 57	83.62 12	95.01 23	93.94 15	95.79 19	93.40 20

X-MVS		89.36 28	90.73 45	87.77 16	91.50 20	91.23 8	96.76 4	78.88 17	87.29 54	87.14 25	78.98 147	94.53 72	76.47 57	95.25 19	94.28 11	95.85 14	93.55 16

LTVRE_ROB	86.82 1	91.55 3	94.43 3	88.19 10	83.19 112	86.35 65	93.60 37	78.79 18	95.48 3	91.79 2	93.08 27	97.21 20	86.34 3	97.06 2	96.27 3	95.46 23	95.56 3
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
DU-MVS		84.88 68	88.27 64	80.92 79	88.30 57	83.59 81	87.06 102	78.35 19	80.64 119	70.49 133	92.67 33	96.91 21	68.13 117	91.79 51	89.29 64	93.20 55	83.02 108

NR-MVSNet		82.89 89	87.43 72	77.59 108	83.91 102	83.59 81	87.10 101	78.35 19	80.64 119	68.85 142	92.67 33	96.50 24	54.19 182	87.19 108	88.68 67	93.16 58	82.75 113

TDRefinement		93.16 1	95.57 1	90.36 1	88.79 52	93.57 1	97.27 1	78.23 21	95.55 1	93.00 1	93.98 18	96.01 38	87.53 1	97.69 1	96.81 1	97.33 1	95.34 4

HPM-MVS++		88.74 40	89.54 52	87.80 15	92.58 6	85.69 69	95.10 26	78.01 22	87.08 56	87.66 19	87.89 86	92.07 107	80.28 30	90.97 69	91.41 43	93.17 57	91.69 37

SteuartSystems-ACMMP		90.00 17	91.73 35	87.97 12	91.21 29	90.29 28	96.51 7	78.00 23	86.33 62	85.32 40	88.23 83	94.67 70	82.08 20	95.13 22	93.88 16	94.72 35	93.59 13
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		89.14 29	91.25 42	86.67 24	91.73 15	91.02 15	95.50 20	77.74 24	84.04 85	79.47 82	91.48 46	94.85 67	81.14 25	92.94 41	92.20 35	94.47 38	92.24 32
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
LGP-MVS_train		90.56 9	92.38 21	88.43 9	90.88 32	91.15 11	95.35 21	77.65 25	86.26 65	87.23 23	90.45 56	97.35 17	83.20 14	95.44 16	93.41 20	96.28 8	92.63 27

ACMMP		90.63 8	92.40 20	88.56 8	91.24 28	91.60 6	96.49 9	77.53 26	87.89 48	86.87 30	87.24 95	96.46 25	82.87 16	95.59 15	94.50 8	96.35 6	93.51 18
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
MSP-MVS		88.51 42	91.36 40	85.19 39	90.63 36	92.01 4	95.29 22	77.52 27	90.48 27	80.21 76	90.21 58	96.08 34	76.38 59	88.30 97	91.42 41	91.12 89	91.01 44
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
UA-Net		89.02 33	91.44 39	86.20 28	94.88 1	89.84 34	94.76 29	77.45 28	85.41 73	74.79 106	88.83 78	88.90 139	78.67 40	96.06 7	95.45 4	96.66 3	95.58 2

Fast-Effi-MVS+-dtu		76.92 138	77.18 157	76.62 114	79.55 142	79.17 122	84.80 120	77.40 29	64.46 193	68.75 144	70.81 200	86.57 150	63.36 147	81.74 156	81.76 135	85.86 154	75.78 159

ACMM	80.67 7	90.67 7	92.46 19	88.57 7	91.35 22	89.93 32	96.34 11	77.36 30	90.17 29	86.88 29	87.32 93	96.63 23	83.32 13	95.79 10	94.49 9	96.19 9	92.91 26
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet		79.36 124	79.44 144	79.27 98	89.51 46	77.20 139	88.35 87	77.35 31	68.27 177	74.29 110	76.31 168	79.22 177	59.63 156	85.02 130	85.45 100	86.49 145	84.61 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SMA-MVS		90.13 15	92.26 27	87.64 17	91.68 16	90.44 26	95.22 24	77.34 32	90.79 23	87.80 16	90.42 57	92.05 109	79.05 35	93.89 32	93.59 18	94.77 32	94.62 5
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
DPE-MVS		89.81 22	92.34 24	86.86 23	89.69 44	91.00 16	95.53 18	76.91 33	88.18 46	83.43 53	93.48 21	95.19 58	81.07 26	92.75 45	92.07 36	94.55 36	93.74 11
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TranMVSNet+NR-MVSNet		85.23 65	89.38 53	80.39 90	88.78 53	83.77 78	87.40 96	76.75 34	85.47 71	68.99 141	95.18 8	97.55 16	67.13 124	91.61 56	89.13 65	93.26 54	82.95 111

gg-mvs-nofinetune		72.68 168	75.21 174	69.73 160	81.48 130	69.04 182	70.48 200	76.67 35	86.92 58	67.80 152	88.06 85	64.67 205	42.12 209	77.60 175	73.65 180	79.81 179	66.57 187

ACMMP_NAP		89.86 19	91.96 33	87.42 19	91.00 30	90.08 30	96.00 15	76.61 36	89.28 35	87.73 17	90.04 59	91.80 113	78.71 38	94.36 28	93.82 17	94.48 37	94.32 6

UniMVSNet (Re)		84.95 67	88.53 58	80.78 81	87.82 63	84.21 75	88.03 88	76.50 37	81.18 114	69.29 139	92.63 35	96.83 22	69.07 114	91.23 62	89.60 60	93.97 43	84.00 100

UniMVSNet_NR-MVSNet		84.62 71	88.00 67	80.68 85	88.18 59	83.83 77	87.06 102	76.47 38	81.46 110	70.49 133	93.24 24	95.56 49	68.13 117	90.43 73	88.47 68	93.78 45	83.02 108

APDe-MVS		89.85 20	92.91 10	86.29 26	90.47 38	91.34 7	96.04 14	76.41 39	91.11 17	78.50 89	93.44 22	95.82 42	81.55 23	93.16 37	91.90 38	94.77 32	93.58 15
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
ACMP	80.00 8	90.12 16	92.30 26	87.58 18	90.83 34	91.10 12	94.96 28	76.06 40	87.47 52	85.33 39	88.91 77	97.65 14	82.13 19	95.31 17	93.44 19	96.14 10	92.22 33
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DVP-MVS++		90.50 10	94.18 4	86.21 27	92.52 7	90.29 28	95.29 22	76.02 41	94.24 5	82.82 55	95.84 5	97.56 15	76.82 55	93.13 38	91.20 44	93.78 45	97.01 1

SED-MVS		88.96 37	92.37 22	84.99 40	88.64 54	89.65 37	95.11 25	75.98 42	90.73 24	80.15 77	94.21 15	94.51 75	76.59 56	92.94 41	91.17 45	93.46 50	93.37 22

OPM-MVS		89.82 21	92.24 29	86.99 22	90.86 33	89.35 38	95.07 27	75.91 43	91.16 16	86.87 30	91.07 52	97.29 18	79.13 34	93.32 35	91.99 37	94.12 40	91.49 40
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
PMVS	79.51 9	90.23 14	92.67 14	87.39 20	90.16 39	88.75 42	93.64 36	75.78 44	90.00 33	83.70 47	92.97 29	92.22 104	86.13 4	97.01 3	96.79 2	94.94 28	90.96 45
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DVP-MVS		89.40 27	92.69 13	85.56 34	89.01 50	89.85 33	93.72 35	75.42 45	92.28 11	80.49 72	94.36 13	94.87 66	81.46 24	92.49 49	91.42 41	93.27 53	93.54 17
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
3Dnovator+	83.71 3	88.13 44	90.00 50	85.94 29	86.82 71	91.06 13	94.26 33	75.39 46	88.85 41	85.76 37	85.74 110	86.92 148	78.02 45	93.03 40	92.21 34	95.39 25	92.21 34

SD-MVS		89.91 18	92.23 30	87.19 21	91.31 24	89.79 35	94.31 32	75.34 47	89.26 37	81.79 67	92.68 32	95.08 63	83.88 11	93.10 39	92.69 25	96.54 4	93.02 24
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
Baseline_NR-MVSNet		82.79 91	86.51 76	78.44 103	88.30 57	75.62 153	87.81 90	74.97 48	81.53 107	66.84 155	94.71 12	96.46 25	66.90 125	91.79 51	83.37 124	85.83 155	82.09 119

NCCC		86.74 52	87.97 68	85.31 36	90.64 35	87.25 58	93.27 39	74.59 49	86.50 60	83.72 46	75.92 175	92.39 101	77.08 53	91.72 53	90.68 48	92.57 67	91.30 42

DeepC-MVS_fast	81.78 5	87.38 49	89.64 51	84.75 41	89.89 42	90.70 22	92.74 43	74.45 50	86.02 66	82.16 64	86.05 107	91.99 111	75.84 65	91.16 63	90.44 49	93.41 51	91.09 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		86.29 57	89.10 55	83.01 58	85.71 82	89.79 35	87.04 104	74.39 51	85.17 75	78.92 86	77.59 158	93.57 86	82.60 17	93.23 36	91.88 39	89.42 109	92.46 30

ACMH+	79.05 11	89.62 26	93.08 8	85.58 32	88.58 55	89.26 39	92.18 45	74.23 52	93.55 8	82.66 58	92.32 37	98.35 7	80.29 29	95.28 18	92.34 31	95.52 22	90.43 48

train_agg		86.67 53	87.73 69	85.43 35	91.51 19	82.72 89	94.47 31	74.22 53	81.71 103	81.54 70	89.20 72	92.87 95	78.33 43	90.12 79	88.47 68	92.51 69	89.04 60

DeepPCF-MVS	81.61 6	87.95 47	90.29 49	85.22 38	87.48 65	90.01 31	93.79 34	73.54 54	88.93 39	83.89 45	89.40 68	90.84 124	80.26 31	90.62 72	90.19 53	92.36 70	92.03 35

CNVR-MVS		86.93 51	88.98 56	84.54 44	90.11 40	87.41 57	93.23 40	73.47 55	86.31 63	82.25 61	82.96 130	92.15 105	76.04 62	91.69 54	90.69 47	92.17 73	91.64 39

TSAR-MVS + MP.		89.67 24	92.25 28	86.65 25	91.53 18	90.98 17	96.15 13	73.30 56	87.88 49	81.83 66	92.92 30	95.15 61	82.23 18	93.58 34	92.25 33	94.87 29	93.01 25
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
test2506		75.32 152	76.87 161	73.50 137	84.55 91	80.37 111	79.63 159	73.23 57	82.64 93	55.41 184	76.87 165	45.42 229	59.61 157	90.35 76	86.46 86	88.58 123	75.98 157

ECVR-MVS		79.31 126	84.20 116	73.60 135	84.55 91	80.37 111	79.63 159	73.23 57	82.64 93	55.98 181	87.50 89	86.85 149	59.61 157	90.35 76	86.46 86	88.58 123	75.26 164

test1111		79.67 118	84.40 110	74.16 133	85.29 84	79.56 120	81.16 144	73.13 59	84.65 80	56.08 180	88.38 82	86.14 152	60.49 153	89.78 82	85.59 98	88.79 117	76.68 154

TSAR-MVS + GP.		85.32 64	87.41 73	82.89 62	90.07 41	85.69 69	89.07 81	72.99 60	82.45 96	74.52 109	85.09 115	87.67 145	79.24 33	91.11 64	90.41 50	91.45 79	89.45 55

CDPH-MVS		86.66 54	88.52 59	84.48 45	89.61 45	88.27 46	92.86 42	72.69 61	80.55 121	82.71 56	86.92 99	93.32 90	75.55 67	91.00 68	89.85 56	93.47 49	89.71 53

COLMAP_ROB	85.66 2	91.85 2	95.01 2	88.16 11	88.98 51	92.86 2	95.51 19	72.17 62	94.95 4	91.27 3	94.11 17	97.77 11	84.22 8	96.49 4	95.27 5	96.79 2	93.60 12
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EC-MVSNet		83.70 77	84.77 106	82.46 66	87.47 66	82.79 88	85.50 112	72.00 63	69.81 168	77.66 93	85.02 117	89.63 131	78.14 44	90.40 74	87.56 75	94.00 41	88.16 69

SixPastTwentyTwo		89.14 29	92.19 31	85.58 32	84.62 89	82.56 92	90.53 63	71.93 64	91.95 12	85.89 35	94.22 14	97.25 19	85.42 5	95.73 12	91.71 40	95.08 27	91.89 36

SF-MVS		87.85 48	90.95 44	84.22 49	88.17 60	87.90 53	90.80 56	71.80 65	89.28 35	82.70 57	89.90 61	95.37 55	77.91 47	91.69 54	90.04 54	93.95 44	92.47 29

LS3D		89.02 33	91.69 36	85.91 30	89.72 43	90.81 20	92.56 44	71.69 66	90.83 22	87.24 22	89.71 64	92.07 107	78.37 42	94.43 27	92.59 27	95.86 13	91.35 41

HQP-MVS		85.02 66	86.41 79	83.40 54	89.19 48	86.59 63	91.28 50	71.60 67	82.79 92	83.48 51	78.65 151	93.54 87	72.55 89	86.49 113	85.89 96	92.28 72	90.95 46

PVSNet_Blended_VisFu		83.00 88	84.16 117	81.65 74	82.17 126	86.01 66	88.03 88	71.23 68	76.05 143	79.54 81	83.88 126	83.44 161	77.49 51	87.38 103	84.93 105	91.41 80	87.40 77

AdaColmap		84.15 73	85.14 97	83.00 59	89.08 49	87.14 60	90.56 61	70.90 69	82.40 97	80.41 73	73.82 186	84.69 159	75.19 70	91.58 57	89.90 55	91.87 76	86.48 80

MVS_0304		84.73 70	86.19 81	83.02 57	88.32 56	86.71 62	91.55 48	70.87 70	73.79 150	82.88 54	85.13 114	93.35 89	72.55 89	88.62 91	87.69 74	91.93 75	88.05 72

CDS-MVSNet		73.07 165	77.02 158	68.46 168	81.62 129	72.89 168	79.56 161	70.78 71	69.56 170	52.52 194	77.37 161	81.12 172	42.60 207	84.20 137	83.93 114	83.65 168	70.07 180
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TSAR-MVS + COLMAP		85.51 60	88.36 62	82.19 67	86.05 79	87.69 54	90.50 65	70.60 72	86.40 61	82.33 59	89.69 65	92.52 99	74.01 81	87.53 102	86.84 83	89.63 104	87.80 74

PHI-MVS		86.37 56	88.14 65	84.30 47	86.65 73	87.56 55	90.76 57	70.16 73	82.55 95	89.65 7	84.89 118	92.40 100	75.97 63	90.88 70	89.70 58	92.58 65	89.03 61

IS_MVSNet		81.72 101	85.01 98	77.90 105	86.19 76	82.64 91	85.56 111	70.02 74	80.11 124	63.52 162	87.28 94	81.18 171	67.26 122	91.08 67	89.33 63	94.82 31	83.42 105

Vis-MVSNet (Re-imp)		76.15 145	80.84 139	70.68 153	83.66 107	74.80 161	81.66 142	69.59 75	80.48 122	46.94 207	87.44 91	80.63 173	53.14 187	86.87 109	84.56 110	89.12 111	71.12 175

MAR-MVS		81.98 99	82.92 130	80.88 80	85.18 86	85.85 67	89.13 80	69.52 76	71.21 164	82.25 61	71.28 196	88.89 140	69.69 109	88.71 89	86.96 79	89.52 106	87.57 75
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
UGNet		79.62 120	85.91 87	72.28 144	73.52 182	83.91 76	86.64 106	69.51 77	79.85 126	62.57 166	85.82 109	89.63 131	53.18 186	88.39 96	87.35 77	88.28 128	86.43 81
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
Vis-MVSNet		83.32 84	88.12 66	77.71 106	77.91 160	83.44 83	90.58 59	69.49 78	81.11 115	67.10 154	89.85 62	91.48 118	71.71 98	91.34 59	89.37 62	89.48 107	90.26 49
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
RPMNet		67.02 189	63.99 204	70.56 155	71.55 190	67.63 185	75.81 180	69.44 79	59.93 209	63.24 163	64.32 213	47.51 228	59.68 155	70.37 202	69.64 193	83.64 169	68.49 185

IterMVS-LS		79.79 116	82.56 132	76.56 116	81.83 128	77.85 132	79.90 155	69.42 80	78.93 132	71.21 129	90.47 55	85.20 158	70.86 105	80.54 165	80.57 143	86.15 148	84.36 94
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MCST-MVS		84.79 69	86.48 77	82.83 63	87.30 67	87.03 61	90.46 67	69.33 81	83.14 89	82.21 63	81.69 138	92.14 106	75.09 72	87.27 105	84.78 107	92.58 65	89.30 57

PCF-MVS	76.59 14	84.11 74	85.27 94	82.76 64	86.12 78	88.30 45	91.24 51	69.10 82	82.36 98	84.45 43	77.56 159	90.40 129	72.91 88	85.88 118	83.88 115	92.72 64	88.53 65
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CANet		82.84 90	84.60 108	80.78 81	87.30 67	85.20 72	90.23 69	69.00 83	72.16 160	78.73 88	84.49 123	90.70 127	69.54 112	87.65 101	86.17 90	89.87 101	85.84 85

DPM-MVS		81.42 104	82.11 134	80.62 86	87.54 64	85.30 71	90.18 71	68.96 84	81.00 117	79.15 84	70.45 202	83.29 163	67.67 121	82.81 146	83.46 119	90.19 95	88.48 66

SCA		68.54 184	67.52 195	69.73 160	67.79 201	75.04 156	76.96 173	68.94 85	66.41 182	67.86 151	74.03 184	60.96 208	65.55 134	68.99 205	65.67 199	71.30 197	61.54 204

pmmvs6		80.46 111	88.34 63	71.26 148	81.96 127	77.51 134	77.54 168	68.83 86	93.72 7	55.92 182	93.94 19	98.03 9	55.94 172	89.21 87	85.61 97	87.36 137	80.38 132

ACMH	78.40 12	88.94 38	92.62 16	84.65 42	86.45 74	87.16 59	91.47 49	68.79 87	95.49 2	89.74 6	93.55 20	98.50 2	77.96 46	94.14 31	89.57 61	93.49 47	89.94 52
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PLC	76.06 15	85.38 63	87.46 71	82.95 61	85.79 81	88.84 41	88.86 83	68.70 88	87.06 57	83.60 48	79.02 145	90.05 130	77.37 52	90.88 70	89.66 59	93.37 52	86.74 79
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CSCG		88.12 45	91.45 38	84.23 48	88.12 61	90.59 25	90.57 60	68.60 89	91.37 15	83.45 52	89.94 60	95.14 62	78.71 38	91.45 58	88.21 72	95.96 12	93.44 19

TinyColmap		83.79 76	86.12 82	81.07 78	83.42 109	81.44 101	85.42 114	68.55 90	88.71 43	89.46 8	87.60 88	92.72 96	70.34 108	89.29 86	81.94 133	89.20 110	81.12 127

EPP-MVSNet		82.76 92	86.47 78	78.45 102	86.00 80	84.47 74	85.39 115	68.42 91	84.17 82	62.97 164	89.26 71	76.84 187	72.13 94	92.56 48	90.40 51	95.76 20	87.56 76

Gipuma		86.47 55	89.25 54	83.23 55	83.88 103	78.78 126	85.35 116	68.42 91	92.69 10	89.03 11	91.94 39	96.32 32	81.80 21	94.45 26	86.86 82	90.91 90	83.69 102
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Effi-MVS+-dtu		82.04 98	83.39 127	80.48 89	85.48 83	86.57 64	88.40 86	68.28 93	69.04 175	73.13 118	76.26 170	91.11 123	74.74 75	88.40 95	87.76 73	92.84 63	84.57 93

ETV-MVS		79.01 129	77.98 151	80.22 91	86.69 72	79.73 118	88.80 84	68.27 94	63.22 198	71.56 127	70.25 204	73.63 197	73.66 84	90.30 78	86.77 84	92.33 71	81.95 121

v7n		87.11 50	90.46 48	83.19 56	85.22 85	83.69 80	90.03 73	68.20 95	91.01 19	86.71 33	94.80 10	98.46 4	77.69 48	91.10 65	85.98 93	91.30 83	88.19 68

Effi-MVS+		82.33 94	83.87 120	80.52 88	84.51 94	81.32 102	87.53 94	68.05 96	74.94 148	79.67 80	82.37 135	92.31 102	72.21 91	85.06 126	86.91 81	91.18 85	84.20 97

MVS_111021_HR		83.95 75	86.10 83	81.44 76	84.62 89	80.29 113	90.51 64	68.05 96	84.07 84	80.38 74	84.74 121	91.37 120	74.23 77	90.37 75	87.25 78	90.86 91	84.59 92

Anonymous202405211			84.68 107		83.92 101	79.45 121	79.03 163	67.79 98	82.01 101		88.77 80	92.58 98	55.93 173	86.68 111	84.26 112	88.92 115	78.98 144

CR-MVSNet		69.56 179	68.34 193	70.99 151	72.78 187	67.63 185	64.47 212	67.74 99	59.93 209	72.30 121	80.10 140	56.77 219	65.04 137	71.64 197	72.91 183	83.61 170	69.40 182

Patchmtry						56.88 208	64.47 212	67.74 99		72.30 121

FMVSNet1		78.20 134	84.83 104	70.46 156	78.62 152	79.03 123	77.90 167	67.53 101	83.02 90	55.10 186	87.19 96	93.18 92	55.65 175	85.57 119	83.39 121	87.98 130	82.40 117

CS-MVS-test		83.59 79	84.86 103	82.10 69	83.04 115	81.05 107	91.58 47	67.48 102	72.52 157	78.42 90	84.75 120	91.82 112	78.62 41	91.98 50	87.54 76	93.48 48	84.35 95

OMC-MVS		88.16 43	91.34 41	84.46 46	86.85 70	90.63 23	93.01 41	67.00 103	90.35 28	87.40 21	86.86 100	96.35 29	77.66 49	92.63 47	90.84 46	94.84 30	91.68 38

pmmvs-eth3d		79.64 119	82.06 135	76.83 112	80.05 138	72.64 169	87.47 95	66.59 104	80.83 118	73.50 114	89.32 70	93.20 91	67.78 119	80.78 163	81.64 137	85.58 158	76.01 156

CS-MVS		83.57 80	84.79 105	82.14 68	83.83 104	81.48 100	87.29 97	66.54 105	72.73 156	80.05 78	84.04 125	93.12 94	80.35 28	89.50 83	86.34 88	94.76 34	86.32 83

v10		83.17 87	85.22 96	80.78 81	83.26 111	82.99 87	88.66 85	66.49 106	79.24 130	83.60 48	91.46 47	95.47 51	74.12 78	82.60 149	80.66 142	88.53 125	84.11 99

RPSCF		88.05 46	92.61 17	82.73 65	84.24 96	88.40 44	90.04 72	66.29 107	91.46 13	82.29 60	88.93 76	96.01 38	79.38 32	95.15 21	94.90 6	94.15 39	93.40 20

v1192		83.61 78	85.23 95	81.72 73	84.05 98	82.15 95	89.54 76	66.20 108	81.38 112	86.76 32	91.79 43	96.03 36	74.88 74	81.81 155	80.92 141	88.91 116	82.50 116

USDC		81.39 106	83.07 128	79.43 96	81.48 130	78.95 125	82.62 135	66.17 109	87.45 53	90.73 4	82.40 134	93.65 85	66.57 127	83.63 141	77.97 158	89.00 114	77.45 153

DCV-MVSNet		80.04 114	85.67 91	73.48 138	82.91 117	81.11 106	80.44 149	66.06 110	85.01 76	62.53 167	78.84 148	94.43 77	58.51 163	88.66 90	85.91 94	90.41 93	85.73 86

v1240		83.57 80	84.94 101	81.97 70	84.05 98	81.27 103	89.46 78	66.06 110	81.31 113	87.50 20	91.88 42	95.46 52	76.25 60	81.16 160	80.51 145	88.52 126	82.98 110

v144192		83.43 83	84.97 100	81.63 75	83.43 108	81.23 104	89.42 79	66.04 112	81.45 111	86.40 34	91.46 47	95.70 47	75.76 66	82.14 151	80.23 148	88.74 118	82.57 115

v1921920		83.49 82	84.94 101	81.80 72	83.78 105	81.20 105	89.50 77	65.91 113	81.64 105	87.18 24	91.70 44	95.39 54	75.85 64	81.56 158	80.27 147	88.60 121	82.80 112

GeoE		81.92 100	83.87 120	79.66 94	84.64 88	79.87 115	89.75 74	65.90 114	76.12 142	75.87 99	84.62 122	92.23 103	71.96 96	86.83 110	83.60 118	89.83 102	83.81 101

test20.03		69.91 176	76.20 167	62.58 191	84.01 100	67.34 187	75.67 186	65.88 115	79.98 125	40.28 217	82.65 131	89.31 135	39.63 212	77.41 176	73.28 181	69.98 200	63.40 196

sasdasda		81.22 108	86.04 85	75.60 119	83.17 113	83.18 85	80.29 150	65.82 116	85.97 67	67.98 149	77.74 156	91.51 116	65.17 135	88.62 91	86.15 91	91.17 86	89.09 58

canonicalmvs		81.22 108	86.04 85	75.60 119	83.17 113	83.18 85	80.29 150	65.82 116	85.97 67	67.98 149	77.74 156	91.51 116	65.17 135	88.62 91	86.15 91	91.17 86	89.09 58

FPMVS		81.56 102	84.04 119	78.66 100	82.92 116	75.96 149	86.48 108	65.66 118	84.67 79	71.47 128	77.78 155	83.22 164	77.57 50	91.24 61	90.21 52	87.84 131	85.21 89

v1144		83.22 85	85.01 98	81.14 77	83.76 106	81.60 99	88.95 82	65.58 119	81.89 102	85.80 36	91.68 45	95.84 41	74.04 80	82.12 152	80.56 144	88.70 120	81.41 125

MGCFI-Net		79.42 122	85.64 92	72.15 145	82.80 120	82.09 96	76.92 174	65.46 120	86.31 63	57.48 175	78.15 153	91.38 119	59.10 160	88.23 99	84.47 111	91.14 88	88.88 62

FMVSNet2		74.43 157	79.70 142	68.27 169	76.76 166	77.36 136	75.77 182	65.36 121	72.28 158	52.97 192	81.92 136	85.61 155	52.73 191	80.66 164	79.73 151	86.04 150	80.37 133

MVS_111021_LR		83.20 86	85.33 93	80.73 84	82.88 118	78.23 130	89.61 75	65.23 122	82.08 100	81.19 71	85.31 112	92.04 110	75.22 69	89.50 83	85.90 95	90.24 94	84.23 96

pm-mvs1		78.21 133	85.68 90	69.50 163	80.38 136	75.73 151	76.25 178	65.04 123	87.59 51	54.47 188	93.16 26	95.99 40	54.20 181	86.37 114	82.98 128	86.64 142	77.96 151

FC-MVSNet-test		75.91 148	83.59 125	66.95 177	76.63 174	69.07 181	85.33 117	64.97 124	84.87 78	41.95 213	93.17 25	87.04 147	47.78 202	91.09 66	85.56 99	85.06 162	74.34 165

pmmvs4		75.92 147	77.48 156	74.10 134	78.21 156	70.94 173	84.06 124	64.78 125	75.13 147	68.47 147	84.12 124	83.32 162	64.74 139	75.93 185	79.14 156	84.31 165	73.77 169

GBi-Net		73.17 162	77.64 153	67.95 172	76.76 166	77.36 136	75.77 182	64.57 126	62.99 200	51.83 198	76.05 171	77.76 183	52.73 191	85.57 119	83.39 121	86.04 150	80.37 133

test1		73.17 162	77.64 153	67.95 172	76.76 166	77.36 136	75.77 182	64.57 126	62.99 200	51.83 198	76.05 171	77.76 183	52.73 191	85.57 119	83.39 121	86.04 150	80.37 133

FMVSNet3		71.40 174	75.20 175	66.97 176	75.00 180	76.59 143	74.29 189	64.57 126	62.99 200	51.83 198	76.05 171	77.76 183	51.49 196	76.58 181	77.03 167	84.62 164	79.43 143

casdiffmvs_mvg		81.50 103	85.70 89	76.60 115	82.68 121	80.54 110	83.50 127	64.49 129	83.40 86	72.53 119	92.15 38	95.40 53	65.84 132	84.69 133	81.89 134	90.59 92	81.86 123
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v8		82.20 96	84.56 109	79.45 95	82.42 123	81.65 98	87.26 98	64.27 130	79.36 129	81.70 68	91.04 53	95.75 45	73.30 87	82.82 145	79.18 155	87.74 133	82.09 119

TAPA-MVS	78.00 13	85.88 58	88.37 61	82.96 60	84.69 87	88.62 43	90.62 58	64.22 131	89.15 38	88.05 14	78.83 149	93.71 83	76.20 61	90.11 80	88.22 71	94.00 41	89.97 51
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPNet_dtu		71.90 171	73.03 183	70.59 154	78.28 154	61.64 201	82.44 136	64.12 132	63.26 197	69.74 136	71.47 194	82.41 167	51.89 195	78.83 172	78.01 157	77.07 185	75.60 161
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
FC-MVSNet-train		79.20 127	86.29 80	70.94 152	84.06 97	77.67 133	85.68 110	64.11 133	82.90 91	52.22 197	92.57 36	93.69 84	49.52 199	88.30 97	86.93 80	90.03 97	81.95 121

ET-MVSNet_ETH3D		74.71 156	74.19 177	75.31 123	79.22 147	75.29 155	82.70 134	64.05 134	65.45 188	70.96 132	77.15 163	57.70 217	65.89 131	84.40 136	81.65 136	89.03 112	77.67 152

EIA-MVS		78.57 131	77.90 152	79.35 97	87.24 69	80.71 108	86.16 109	64.03 135	62.63 203	73.49 115	73.60 187	76.12 191	73.83 82	88.49 94	84.93 105	91.36 81	78.78 146

EG-PatchMatch MVS		84.35 72	87.55 70	80.62 86	86.38 75	82.24 94	86.75 105	64.02 136	84.24 81	78.17 92	89.38 69	95.03 65	78.78 37	89.95 81	86.33 89	89.59 105	85.65 87

TransMVSNet (Re)		79.05 128	86.66 75	70.18 158	83.32 110	75.99 148	77.54 168	63.98 137	90.68 25	55.84 183	94.80 10	96.06 35	53.73 185	86.27 115	83.22 125	86.65 141	79.61 142

v2v482		82.20 96	84.26 113	79.81 93	82.67 122	80.18 114	87.67 92	63.96 138	81.69 104	84.73 41	91.27 50	96.33 31	72.05 95	81.94 154	79.56 152	87.79 132	78.84 145

Anonymous20231211		79.37 123	85.78 88	71.89 146	82.87 119	79.66 119	78.77 165	63.93 139	83.36 87	59.39 171	90.54 54	94.66 71	56.46 170	87.38 103	84.12 113	89.92 99	80.74 129

DI_MVS_plusplus_trai		77.64 135	79.64 143	75.31 123	79.87 140	76.89 142	81.55 143	63.64 140	76.21 141	72.03 124	85.59 111	82.97 165	66.63 126	79.27 171	77.78 160	88.14 129	78.76 147

CNLPA		85.50 61	88.58 57	81.91 71	84.55 91	87.52 56	90.89 54	63.56 141	88.18 46	84.06 44	83.85 127	91.34 121	76.46 58	91.27 60	89.00 66	91.96 74	88.88 62

Fast-Effi-MVS+		81.42 104	83.82 122	78.62 101	82.24 125	80.62 109	87.72 91	63.51 142	73.01 152	74.75 107	83.80 128	92.70 97	73.44 86	88.15 100	85.26 101	90.05 96	83.17 106

PatchMatch-RL		76.05 146	76.64 162	75.36 122	77.84 162	69.87 179	81.09 146	63.43 143	71.66 162	68.34 148	71.70 192	81.76 170	74.98 73	84.83 132	83.44 120	86.45 146	73.22 172

tfpnnormal		77.16 137	84.26 113	68.88 166	81.02 133	75.02 157	76.52 177	63.30 144	87.29 54	52.40 195	91.24 51	93.97 80	54.85 179	85.46 122	81.08 139	85.18 161	75.76 160

PVSNet_BlendedMVS		76.45 143	78.12 149	74.49 131	76.76 166	78.46 127	79.65 157	63.26 145	65.42 189	73.15 116	75.05 180	88.96 137	66.51 128	82.73 147	77.66 161	87.61 134	78.60 148

PVSNet_Blended		76.45 143	78.12 149	74.49 131	76.76 166	78.46 127	79.65 157	63.26 145	65.42 189	73.15 116	75.05 180	88.96 137	66.51 128	82.73 147	77.66 161	87.61 134	78.60 148

test-LLR		62.15 202	59.46 218	65.29 186	79.07 148	52.66 213	69.46 206	62.93 147	50.76 221	53.81 190	63.11 215	58.91 213	52.87 189	66.54 211	62.34 203	73.59 188	61.87 201

test0.0.03 1		61.79 204	65.33 200	57.65 201	79.07 148	64.09 196	68.51 209	62.93 147	61.59 206	33.71 221	61.58 217	71.58 201	33.43 217	70.95 200	68.68 195	68.26 205	58.82 208

casdiffmvs		79.93 115	84.11 118	75.05 125	81.41 132	78.99 124	82.95 132	62.90 149	81.53 107	68.60 146	91.94 39	96.03 36	65.84 132	82.89 144	77.07 166	88.59 122	80.34 136
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
tfpn200view9		72.01 170	75.40 172	68.06 171	77.97 158	76.44 144	77.04 172	62.67 150	66.81 180	50.82 202	67.30 209	75.67 193	52.46 194	85.06 126	82.64 129	87.41 136	73.86 168

thres600view7		74.34 158	78.43 148	69.56 162	80.47 134	76.28 146	78.65 166	62.56 151	77.39 136	52.53 193	74.03 184	76.78 188	55.90 174	85.06 126	85.19 102	87.25 138	74.29 166

thres200		72.41 169	76.00 169	68.21 170	78.28 154	76.28 146	74.94 188	62.56 151	72.14 161	51.35 201	69.59 207	76.51 189	54.89 177	85.06 126	80.51 145	87.25 138	71.92 174

HyFIR lowres test		73.29 161	74.14 178	72.30 143	73.08 184	78.33 129	83.12 129	62.41 153	63.81 195	62.13 168	76.67 167	78.50 180	71.09 101	74.13 189	77.47 164	81.98 176	70.10 179

baseline1		69.62 178	73.55 181	65.02 189	78.95 150	70.39 175	71.38 199	62.03 154	70.97 165	47.95 205	78.47 152	68.19 203	47.77 203	79.65 170	76.94 169	82.05 175	70.27 178

FA-MVS(training)		78.93 130	80.63 140	76.93 111	79.79 141	75.57 154	85.44 113	61.95 155	77.19 138	78.97 85	84.82 119	82.47 166	66.43 130	84.09 138	80.13 149	89.02 113	80.15 139

QAPM		80.43 112	84.34 111	75.86 117	79.40 144	82.06 97	79.86 156	61.94 156	83.28 88	74.73 108	81.74 137	85.44 156	70.97 103	84.99 131	84.71 109	88.29 127	88.14 70

dps		65.14 192	64.50 202	65.89 184	71.41 191	65.81 193	71.44 198	61.59 157	58.56 212	61.43 170	75.45 178	52.70 226	58.06 165	69.57 204	64.65 200	71.39 196	64.77 190

MVS_Test		76.72 140	79.40 145	73.60 135	78.85 151	74.99 158	79.91 154	61.56 158	69.67 169	72.44 120	85.98 108	90.78 125	63.50 145	78.30 173	75.74 174	85.33 159	80.31 137

WB-MVS		72.91 167	82.95 129	61.21 195	68.59 198	73.96 165	73.65 192	61.48 159	90.88 20	42.55 211	94.18 16	95.80 43	53.02 188	85.42 123	75.73 175	67.97 206	64.65 191

thisisatest0515		81.18 110	84.32 112	77.52 110	76.73 172	74.84 160	85.06 119	61.37 160	81.05 116	73.95 111	88.79 79	89.25 136	75.49 68	85.98 117	84.78 107	92.53 68	85.56 88

IterMVS-SCA-FT		77.23 136	79.18 146	74.96 129	76.67 173	79.85 116	75.58 187	61.34 161	73.10 151	73.79 113	86.23 104	79.61 176	79.00 36	80.28 167	75.50 176	83.41 172	79.70 141

thres400		73.13 164	76.99 160	68.62 167	79.46 143	74.93 159	77.23 170	61.23 162	75.54 144	52.31 196	72.20 191	77.10 186	54.89 177	82.92 143	82.62 130	86.57 144	73.66 171

3Dnovator	79.41 10	82.21 95	86.07 84	77.71 106	79.31 145	84.61 73	87.18 99	61.02 163	85.65 69	76.11 97	85.07 116	85.38 157	70.96 104	87.22 106	86.47 85	91.66 77	88.12 71

MSDG		81.39 106	84.23 115	78.09 104	82.40 124	82.47 93	85.31 118	60.91 164	79.73 127	80.26 75	86.30 103	88.27 143	69.67 110	87.20 107	84.98 104	89.97 98	80.67 130

CVMVSNet		75.65 150	77.62 155	73.35 141	71.95 188	69.89 178	83.04 131	60.84 165	69.12 173	68.76 143	79.92 143	78.93 179	73.64 85	81.02 161	81.01 140	81.86 177	83.43 104

thisisatest0530		75.54 151	75.95 170	75.05 125	75.08 179	73.56 166	82.15 138	60.31 166	69.17 172	69.32 138	79.02 145	58.78 216	72.17 92	83.88 139	83.08 126	91.30 83	84.20 97

tttt0517		75.86 149	76.23 166	75.42 121	75.55 178	74.06 164	82.73 133	60.31 166	69.24 171	70.24 135	79.18 144	58.79 215	72.17 92	84.49 135	83.08 126	91.54 78	84.80 90

MDA-MVSNet-bldmvs		76.51 141	82.87 131	69.09 165	50.71 223	74.72 162	84.05 125	60.27 168	81.62 106	71.16 130	88.21 84	91.58 114	69.62 111	92.78 44	77.48 163	78.75 184	73.69 170

PM-MVS		80.42 113	83.63 124	76.67 113	78.04 157	72.37 171	87.14 100	60.18 169	80.13 123	71.75 126	86.12 106	93.92 82	77.08 53	86.56 112	85.12 103	85.83 155	81.18 126

DELS-MVS		79.71 117	83.74 123	75.01 127	79.31 145	82.68 90	84.79 121	60.06 170	75.43 146	69.09 140	86.13 105	89.38 134	67.16 123	85.12 125	83.87 116	89.65 103	83.57 103
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
PatchmatchNet		64.81 194	63.74 205	66.06 183	69.21 196	58.62 205	73.16 194	60.01 171	65.92 184	66.19 158	76.27 169	59.09 212	60.45 154	66.58 210	61.47 208	67.33 207	58.24 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
dmvs_re		68.11 186	70.60 187	65.21 187	77.91 160	63.73 198	76.72 175	59.65 172	55.93 215	47.79 206	59.79 219	79.91 175	49.72 198	82.48 150	76.98 168	79.48 180	75.41 162

tpm cat1		64.79 195	62.74 209	67.17 175	74.61 181	65.91 192	76.18 179	59.32 173	64.88 192	66.41 157	71.21 197	53.56 225	59.17 159	61.53 217	58.16 211	67.33 207	63.95 193

thres100view900		69.86 177	72.97 184	66.24 179	77.97 158	72.49 170	73.29 193	59.12 174	66.81 180	50.82 202	67.30 209	75.67 193	50.54 197	78.24 174	79.40 153	85.71 157	70.88 176

PatchT		66.25 191	66.76 197	65.67 185	55.87 218	60.75 202	70.17 201	59.00 175	59.80 211	72.30 121	78.68 150	54.12 224	65.04 137	71.64 197	72.91 183	71.63 194	69.40 182

IB-MVS	71.28 17	75.21 153	77.00 159	73.12 142	76.76 166	77.45 135	83.05 130	58.92 176	63.01 199	64.31 161	59.99 218	87.57 146	68.64 115	86.26 116	82.34 131	87.05 140	82.36 118
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
MVS-HIRNet		59.74 205	58.74 221	60.92 196	57.74 217	45.81 221	56.02 221	58.69 177	55.69 216	65.17 159	70.86 199	71.66 199	56.75 168	61.11 218	53.74 217	71.17 198	52.28 216

GA-MVS		75.01 155	76.39 164	73.39 139	78.37 153	75.66 152	80.03 152	58.40 178	70.51 166	75.85 100	83.24 129	76.14 190	63.75 141	77.28 177	76.62 170	83.97 167	75.30 163

OpenMVS	75.38 16	78.44 132	81.39 138	74.99 128	80.46 135	79.85 116	79.99 153	58.31 179	77.34 137	73.85 112	77.19 162	82.33 169	68.60 116	84.67 134	81.95 132	88.72 119	86.40 82

CANet_DTU		75.04 154	78.45 147	71.07 149	77.27 163	77.96 131	83.88 126	58.00 180	64.11 194	68.67 145	75.65 177	88.37 142	53.92 184	82.05 153	81.11 138	84.67 163	79.88 140

diffmvs		76.74 139	81.61 137	71.06 150	75.64 177	74.45 163	80.68 148	57.57 181	77.48 135	67.62 153	88.95 75	93.94 81	61.98 150	79.74 168	76.18 171	82.85 173	80.50 131
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
v148		79.33 125	82.32 133	75.84 118	80.14 137	75.74 150	81.98 139	57.06 182	81.51 109	79.36 83	89.42 67	96.42 27	71.32 99	81.54 159	75.29 177	85.20 160	76.32 155

Anonymous20231206		67.28 188	73.41 182	60.12 197	76.45 175	63.61 199	74.21 190	56.52 183	76.35 139	42.23 212	75.81 176	90.47 128	41.51 210	74.52 186	69.97 192	69.83 201	63.17 197

MS-PatchMatch		71.18 175	73.99 179	67.89 174	77.16 164	71.76 172	77.18 171	56.38 184	67.35 178	55.04 187	74.63 182	75.70 192	62.38 148	76.62 180	75.97 173	79.22 182	75.90 158

V42		79.59 121	83.59 125	74.93 130	69.61 195	77.05 141	86.59 107	55.84 185	78.42 134	77.29 94	89.84 63	95.08 63	74.12 78	83.05 142	80.11 150	86.12 149	81.59 124

CostFormer		66.81 190	66.94 196	66.67 178	72.79 186	68.25 184	79.55 162	55.57 186	65.52 187	62.77 165	76.98 164	60.09 211	56.73 169	65.69 213	62.35 202	72.59 191	69.71 181

baseline2		68.71 183	68.34 193	69.14 164	75.69 176	69.70 180	76.60 176	55.53 187	60.13 208	62.07 169	66.76 211	60.35 210	60.77 152	76.53 183	74.03 179	84.19 166	70.88 176

CHOSEN 1792x2688		68.80 182	71.09 185	66.13 181	69.11 197	68.89 183	78.98 164	54.68 188	61.63 205	56.69 177	71.56 193	78.39 181	67.69 120	72.13 196	72.01 186	69.63 202	73.02 173

new-patchmatchnet		62.59 201	73.79 180	49.53 214	76.98 165	53.57 211	53.46 223	54.64 189	85.43 72	28.81 222	91.94 39	96.41 28	25.28 220	76.80 178	53.66 218	57.99 216	58.69 209

CLD-MVS		82.75 93	87.22 74	77.54 109	88.01 62	85.76 68	90.23 69	54.52 190	82.28 99	82.11 65	88.48 81	95.27 56	63.95 140	89.41 85	88.29 70	86.45 146	81.01 128
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MDTV_nov1_ep13_2view		72.96 166	75.59 171	69.88 159	71.15 192	64.86 194	82.31 137	54.45 191	76.30 140	78.32 91	86.52 101	91.58 114	61.35 151	76.80 178	66.83 198	71.70 192	66.26 188

ADS-MVSNet		56.89 211	61.09 212	52.00 212	59.48 215	48.10 219	58.02 219	54.37 192	72.82 154	49.19 204	75.32 179	65.97 204	37.96 213	59.34 220	54.66 216	52.99 221	51.42 217

EU-MVSNet		76.48 142	80.53 141	71.75 147	67.62 202	70.30 176	81.74 141	54.06 193	75.47 145	71.01 131	80.10 140	93.17 93	73.67 83	83.73 140	77.85 159	82.40 174	83.07 107

MIMVSNet		63.02 196	69.02 191	56.01 203	68.20 199	59.26 204	70.01 203	53.79 194	71.56 163	41.26 216	71.38 195	82.38 168	36.38 214	71.43 199	67.32 197	66.45 209	59.83 207

MIMVSNet1		73.40 160	81.85 136	63.55 190	72.90 185	64.37 195	84.58 122	53.60 195	90.84 21	53.92 189	87.75 87	96.10 33	45.31 205	85.37 124	79.32 154	70.98 199	69.18 184

anonymousdsp		85.62 59	90.53 46	79.88 92	64.64 212	76.35 145	96.28 12	53.53 196	85.63 70	81.59 69	92.81 31	97.71 12	86.88 2	94.56 25	92.83 24	96.35 6	93.84 9

IterMVS		73.62 159	76.53 163	70.23 157	71.83 189	77.18 140	80.69 147	53.22 197	72.23 159	66.62 156	85.21 113	78.96 178	69.54 112	76.28 184	71.63 187	79.45 181	74.25 167
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPMVS		56.62 212	59.77 217	52.94 211	62.41 213	50.55 216	60.66 217	52.83 198	65.15 191	41.80 214	77.46 160	57.28 218	42.68 206	59.81 219	54.82 215	57.23 217	53.35 215

MVSTER		68.08 187	69.73 189	66.16 180	66.33 210	70.06 177	75.71 185	52.36 199	55.18 218	58.64 173	70.23 205	56.72 220	57.34 167	79.68 169	76.03 172	86.61 143	80.20 138

tpmrst		59.42 206	60.02 216	58.71 199	67.56 203	53.10 212	66.99 210	51.88 200	63.80 196	57.68 174	76.73 166	56.49 221	48.73 200	56.47 221	55.55 214	59.43 214	58.02 211

testgi		68.20 185	76.05 168	59.04 198	79.99 139	67.32 188	81.16 144	51.78 201	84.91 77	39.36 218	73.42 188	95.19 58	32.79 218	76.54 182	70.40 190	69.14 203	64.55 192

pmmvs5		68.91 181	74.35 176	62.56 192	67.45 204	66.78 189	71.70 196	51.47 202	67.17 179	56.25 179	82.41 133	88.59 141	47.21 204	73.21 195	74.23 178	81.30 178	68.03 186

gm-plane-assit		71.56 172	69.99 188	73.39 139	84.43 95	73.21 167	90.42 68	51.36 203	84.08 83	76.00 98	91.30 49	37.09 230	59.01 161	73.65 192	70.24 191	79.09 183	60.37 205

MDTV_nov1_ep13		64.96 193	64.77 201	65.18 188	67.08 205	62.46 200	75.80 181	51.10 204	62.27 204	69.74 136	74.12 183	62.65 206	55.64 176	68.19 207	62.16 206	71.70 192	61.57 203

CMPMVS	55.74 18	71.56 172	76.26 165	66.08 182	68.11 200	63.91 197	63.17 214	50.52 205	68.79 176	75.49 101	70.78 201	85.67 154	63.54 144	81.58 157	77.20 165	75.63 186	85.86 84
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
baseline		69.33 180	75.37 173	62.28 193	66.54 208	66.67 190	73.95 191	48.07 206	66.10 183	59.26 172	82.45 132	86.30 151	54.44 180	74.42 188	73.25 182	71.42 195	78.43 150

tpm		62.79 198	63.25 206	62.26 194	70.09 194	53.78 210	71.65 197	47.31 207	65.72 186	76.70 95	80.62 139	56.40 222	48.11 201	64.20 215	58.54 209	59.70 213	63.47 195

pmnet_mix02		62.60 200	70.81 186	53.02 210	66.56 207	50.44 217	62.81 215	46.84 208	79.13 131	43.76 210	87.45 90	90.75 126	39.85 211	70.48 201	57.09 212	58.27 215	60.32 206

TAMVS		63.02 196	69.30 190	55.70 205	70.12 193	56.89 207	69.63 204	45.13 209	70.23 167	38.00 219	77.79 154	75.15 195	42.60 207	74.48 187	72.81 185	68.70 204	57.75 212

E-PMN		59.07 208	62.79 208	54.72 206	67.01 206	47.81 220	60.44 218	43.40 210	72.95 153	44.63 209	70.42 203	73.17 198	58.73 162	80.97 162	51.98 219	54.14 219	42.26 221

EMVS		58.97 209	62.63 210	54.70 207	66.26 211	48.71 218	61.74 216	42.71 211	72.80 155	46.00 208	73.01 190	71.66 199	57.91 166	80.41 166	50.68 221	53.55 220	41.11 222

FMVSNet5		56.37 213	60.14 215	51.98 213	60.83 214	59.58 203	66.85 211	42.37 212	52.68 220	41.33 215	47.09 222	54.68 223	35.28 215	73.88 190	70.77 189	65.24 210	62.26 200

MVE	41.12 19	51.80 217	60.92 213	41.16 216	35.21 226	34.14 226	48.45 226	41.39 213	69.11 174	19.53 225	63.33 214	73.80 196	63.56 143	67.19 208	61.51 207	38.85 223	57.38 213
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
N_pmnet		54.95 215	65.90 198	42.18 215	66.37 209	43.86 223	57.92 220	39.79 214	79.54 128	17.24 227	86.31 102	87.91 144	25.44 219	64.68 214	51.76 220	46.33 222	47.23 219

CHOSEN 280x420		56.32 214	58.85 220	53.36 209	51.63 220	39.91 224	69.12 208	38.61 215	56.29 214	36.79 220	48.84 221	62.59 207	63.39 146	73.61 193	67.66 196	60.61 211	63.07 198

PMMVS		61.98 203	65.61 199	57.74 200	45.03 224	51.76 215	69.54 205	35.05 216	55.49 217	55.32 185	68.23 208	78.39 181	58.09 164	70.21 203	71.56 188	83.42 171	63.66 194

new_pmnet		52.29 216	63.16 207	39.61 217	58.89 216	44.70 222	48.78 225	34.73 217	65.88 185	17.85 226	73.42 188	80.00 174	23.06 221	67.00 209	62.28 205	54.36 218	48.81 218

PMMVS2		48.13 218	64.06 203	29.55 218	44.06 225	36.69 225	51.95 224	29.97 218	74.75 149	8.90 229	76.02 174	91.24 122	7.53 223	73.78 191	55.91 213	34.87 224	40.01 223

pmmvs3		62.72 199	68.71 192	55.74 204	50.74 222	57.10 206	70.05 202	28.82 219	61.57 207	57.39 176	71.19 198	85.73 153	53.96 183	73.36 194	69.43 194	73.47 190	62.55 199

TESTMET0.1,1		57.21 210	59.46 218	54.60 208	50.95 221	52.66 213	69.46 206	26.91 220	50.76 221	53.81 190	63.11 215	58.91 213	52.87 189	66.54 211	62.34 203	73.59 188	61.87 201

test-mter		59.39 207	61.59 211	56.82 202	53.21 219	54.82 209	73.12 195	26.57 221	53.19 219	56.31 178	64.71 212	60.47 209	56.36 171	68.69 206	64.27 201	75.38 187	65.00 189

DeepMVS_CX						17.78 227	20.40 228	6.69 222	31.41 224	9.80 228	38.61 223	34.88 231	33.78 216	28.41 224		23.59 226	45.77 220

test_method		22.69 220	26.99 222	17.67 220	2.13 228	4.31 229	27.50 227	4.53 223	37.94 223	24.52 224	36.20 224	51.40 227	15.26 222	29.86 223	17.09 223	32.07 225	12.16 224

tmp_tt				13.54 221	16.73 227	6.42 228	8.49 229	2.36 224	28.69 225	27.44 223	18.40 225	13.51 232	3.70 224	33.23 222	36.26 222	22.54 227

testmvs		0.93 222	1.37 224	0.41 223	0.36 230	0.36 231	0.62 231	0.39 225	1.48 226	0.18 232	2.41 226	1.31 234	0.41 226	1.25 226	1.08 225	0.48 228	1.68 225

test123		1.06 221	1.41 223	0.64 222	0.39 229	0.48 230	0.52 232	0.25 226	1.11 227	1.37 231	2.01 227	1.98 233	0.87 225	1.43 225	1.27 224	0.46 229	1.62 226

GG-mvs-BLEND		41.63 219	60.36 214	19.78 219	0.14 231	66.04 191	55.66 222	0.17 227	57.64 213	2.42 230	51.82 220	69.42 202	0.28 227	64.11 216	58.29 210	60.02 212	55.18 214

uanet_test		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet-low-res		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet		0.00 223	0.00 225	0.00 224	0.00 232	0.00 232	0.00 233	0.00 228	0.00 228	0.00 233	0.00 228	0.00 235	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

TPM-MVS					86.18 77	83.43 84	87.57 93			78.77 87	69.75 206	84.63 160	62.24 149			89.88 100	88.48 66
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										87.10 28

9.14												89.43 133

our_test_3					73.27 183	70.91 174	83.26 128

ambc			88.38 60		91.62 17	87.97 52	84.48 123		88.64 44	87.93 15	87.38 92	94.82 69	74.53 76	89.14 88	83.86 117	85.94 153	86.84 78

MTAPA										89.37 9		94.85 67

MTMP										90.54 5		95.16 60

Patchmatch-RL test							4.13 230

XVS					91.28 25	91.23 8	96.89 2			87.14 25		94.53 72				95.84 15

X-MVStestdata					91.28 25	91.23 8	96.89 2			87.14 25		94.53 72				95.84 15

mPP-MVS					93.05 3							95.77 44

NP-MVS									78.65 133

High-resolution multi-view benchmark