High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SED-MVS		97.92 1	98.27 2	97.52 1	98.88 11	99.60 1	98.80 4	95.08 7	98.57 2	95.63 2	96.98 9	99.73 1	97.67 1	97.26 10	95.86 22	99.04 15	99.89 5

MSP-MVS		97.74 2	98.32 1	97.06 7	98.66 14	99.35 7	98.66 7	94.75 13	98.22 5	93.60 6	97.99 1	98.58 8	97.41 4	98.24 2	95.95 18	99.27 4	99.91 1
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS++		97.71 3	98.01 6	97.37 2	98.98 5	99.58 3	98.79 5	95.06 8	98.24 4	94.66 3	96.35 15	99.20 4	97.63 2	97.20 12	95.68 23	99.08 13	99.84 7

DPE-MVS		97.69 4	98.16 3	97.14 5	99.01 4	99.52 5	99.12 2	95.38 2	98.00 8	93.31 9	97.71 2	99.61 3	96.94 5	96.99 16	95.45 27	99.09 12	99.81 9
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		97.61 5	97.87 7	97.30 3	98.94 10	99.60 1	98.21 12	95.11 4	98.39 3	95.83 1	94.40 28	99.70 2	96.79 6	97.16 13	95.95 18	98.92 25	99.90 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
CNVR-MVS		97.60 6	98.08 4	97.03 8	99.14 1	99.55 4	98.67 6	95.32 3	97.91 9	92.55 11	97.11 6	97.23 13	97.49 3	98.16 3	97.05 5	99.04 15	99.55 19

APDe-MVS		97.31 7	97.51 12	97.08 6	98.95 9	99.29 12	98.58 9	95.11 4	97.69 14	94.16 4	96.91 10	96.81 17	96.57 9	96.71 20	95.39 29	99.08 13	99.79 10

SF-MVS		97.17 8	97.18 15	97.17 4	99.11 2	99.20 14	99.05 3	95.55 1	97.39 17	93.56 7	97.48 4	96.71 19	96.75 7	95.73 32	94.40 44	98.98 20	99.33 24

NCCC		97.01 9	97.74 8	96.16 11	99.02 3	99.35 7	98.63 8	95.04 9	97.84 11	88.95 24	96.83 12	97.02 16	96.39 14	97.44 7	96.51 9	98.90 27	99.16 40

SMA-MVS		96.96 10	97.65 11	96.15 12	98.98 5	99.31 11	97.91 17	94.68 15	97.52 15	90.59 18	94.54 27	99.20 4	96.54 11	97.29 9	96.48 10	98.22 62	99.19 36
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MCST-MVS		96.93 11	98.07 5	95.61 18	98.98 5	99.44 6	98.04 13	95.04 9	98.10 6	86.55 30	97.65 3	97.56 11	95.60 22	97.67 6	96.45 11	99.43 1	99.61 18

HPM-MVS++		96.91 12	97.70 9	96.00 13	98.97 8	99.16 16	97.82 19	94.81 12	98.04 7	89.61 21	96.56 14	98.60 7	96.39 14	97.09 14	95.22 31	98.39 56	99.22 32

SD-MVS		96.87 13	97.69 10	95.92 14	96.38 46	99.25 13	97.76 20	94.75 13	97.72 12	92.46 13	95.94 16	99.09 6	96.48 13	96.01 29	96.08 16	97.68 93	99.73 13
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
APD-MVS		96.79 14	96.99 18	96.56 9	98.76 13	98.87 25	98.42 10	94.93 11	97.70 13	91.83 14	95.52 19	95.94 24	96.63 8	95.94 30	95.47 26	98.80 33	99.47 22
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		96.50 15	97.08 16	95.82 16	96.12 50	98.97 22	98.00 14	94.13 20	97.89 10	91.49 15	95.11 24	97.52 12	96.26 18	96.27 27	94.07 54	98.91 26	99.74 12
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SteuartSystems-ACMMP		96.20 16	97.22 14	95.01 22	98.40 21	99.11 17	97.93 16	93.62 23	96.28 29	87.45 27	97.05 8	96.00 23	94.23 30	96.83 19	95.97 17	98.40 55	99.27 29
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.09 17	96.41 23	95.72 17	98.58 16	98.84 26	97.95 15	93.08 27	96.96 22	90.24 19	96.60 13	94.40 30	96.52 12	95.13 42	94.33 45	97.93 83	98.59 65

ACMMP_NAP		95.81 18	96.50 22	95.01 22	98.79 12	99.17 15	97.52 25	94.20 19	96.19 30	85.71 34	93.80 31	96.20 22	95.89 19	96.62 22	94.98 37	97.93 83	98.52 68

train_agg		95.72 19	97.37 13	93.80 28	97.82 30	98.92 23	97.84 18	93.50 24	96.86 24	81.35 53	97.10 7	97.71 9	94.19 31	96.02 28	95.37 30	98.07 70	99.64 16

ACMMPR		95.59 20	95.89 25	95.25 20	98.41 20	98.74 28	97.69 23	92.73 31	96.88 23	88.95 24	95.33 21	92.91 37	95.79 20	94.73 52	94.33 45	97.92 85	98.32 77

DeepC-MVS_fast	91.53 1	95.57 21	95.67 28	95.45 19	98.57 17	99.00 21	97.76 20	94.41 17	97.06 19	86.84 29	86.39 44	92.27 42	96.38 16	97.89 5	98.06 3	98.73 38	99.01 48
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		95.49 22	94.84 33	96.25 10	98.64 15	98.63 31	98.35 11	92.37 33	95.04 48	92.62 10	87.12 43	93.79 31	96.55 10	93.53 72	96.78 6	98.98 20	98.99 49

CP-MVS		95.43 23	95.67 28	95.14 21	98.24 26	98.60 32	97.45 26	92.80 29	95.98 33	89.21 23	95.22 22	93.60 32	95.43 23	94.37 59	93.22 73	97.68 93	98.72 56

DPM-MVS		95.36 24	95.84 26	94.82 24	96.70 42	98.49 42	99.27 1	95.09 6	96.71 25	83.87 42	86.34 46	96.44 21	95.06 25	98.35 1	98.82 1	98.89 28	95.69 131

MP-MVS		95.24 25	95.96 24	94.40 26	98.32 23	98.38 47	97.12 28	92.87 28	95.17 46	85.50 35	95.68 17	94.91 28	94.58 27	95.11 43	93.76 59	98.05 73	98.68 58
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
TSAR-MVS + ACMM		94.99 26	97.02 17	92.61 38	97.19 36	98.71 30	97.74 22	93.21 26	96.97 21	79.27 69	94.09 29	97.14 14	90.84 64	96.64 21	95.94 20	97.42 108	99.67 15

X-MVS		94.70 27	95.71 27	93.52 32	98.38 22	98.56 34	96.99 29	92.62 32	95.58 37	81.00 60	94.57 26	93.49 33	94.16 33	94.82 48	94.29 48	97.99 79	98.68 58

PGM-MVS		94.64 28	95.49 30	93.66 30	98.55 18	98.51 40	97.63 24	87.77 46	94.45 52	84.92 38	97.23 5	91.90 44	95.22 24	94.56 55	93.80 58	97.87 89	97.97 88

TSAR-MVS + GP.		94.59 29	96.60 21	92.25 39	90.25 91	98.17 54	96.22 35	86.53 52	97.49 16	87.26 28	95.21 23	97.06 15	94.07 35	94.34 61	94.20 50	99.18 5	99.71 14

PHI-MVS		94.49 30	96.72 20	91.88 41	97.06 37	98.88 24	94.99 46	89.13 41	96.15 31	79.70 65	96.91 10	95.78 25	91.87 54	94.65 53	95.68 23	98.53 47	98.98 51

AdaColmap		94.28 31	92.94 45	95.84 15	98.32 23	98.33 49	96.06 37	94.62 16	96.29 28	91.22 16	89.89 37	85.50 73	96.38 16	91.85 101	90.89 88	98.44 51	97.81 91

DeepPCF-MVS	91.00 2	94.15 32	96.87 19	90.97 49	96.82 40	99.33 10	89.40 99	92.76 30	98.76 1	82.36 49	88.74 38	95.49 27	90.58 71	98.13 4	97.80 4	93.88 188	99.88 6

CPTT-MVS		94.11 33	93.99 38	94.25 27	96.58 43	97.66 62	97.31 27	91.94 34	94.84 49	88.72 26	92.51 32	93.04 36	95.78 21	91.51 104	89.97 105	95.15 177	98.37 74

EPNet		93.69 34	95.34 31	91.76 42	96.98 39	98.47 44	95.40 43	86.79 49	95.47 39	82.84 46	95.66 18	89.17 50	90.47 73	95.25 41	94.69 40	98.10 67	98.68 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP		93.32 35	93.59 41	93.00 36	97.03 38	98.24 50	95.27 44	91.66 37	95.20 44	83.25 44	95.39 20	85.52 71	92.80 45	92.60 91	90.21 101	98.01 76	97.99 86
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CANet		93.23 36	93.72 40	92.65 37	95.48 53	99.09 19	96.55 33	86.74 50	95.28 42	85.22 36	77.30 72	91.25 46	92.60 47	97.06 15	96.63 7	99.31 2	99.45 23

CDPH-MVS		93.22 37	95.08 32	91.04 48	97.57 33	98.49 42	96.74 31	89.35 40	95.19 45	73.57 98	90.26 35	91.59 45	90.68 68	95.09 45	96.15 14	98.31 61	98.81 54

CSCG		93.16 38	92.65 47	93.76 29	98.32 23	99.09 19	96.12 36	89.91 39	93.15 61	89.64 20	83.62 54	88.91 53	92.40 49	91.09 109	93.70 60	96.14 160	98.99 49

MVS_111021_LR		93.05 39	94.53 35	91.32 46	96.43 45	98.38 47	92.81 59	87.20 48	95.94 35	81.45 52	94.75 25	86.08 67	92.12 52	94.83 47	93.34 67	97.89 88	98.42 73

3Dnovator+	86.26 7	92.90 40	92.45 49	93.42 33	97.25 35	98.45 46	95.82 38	85.71 58	93.83 56	89.55 22	72.31 102	92.28 41	94.01 37	95.10 44	95.92 21	98.17 63	99.23 31

MVS_111021_HR		92.73 41	94.83 34	90.28 55	96.27 47	99.10 18	92.77 60	86.15 55	93.41 59	77.11 87	93.82 30	87.39 59	90.61 69	95.60 34	95.15 33	98.79 34	99.32 25

PLC	89.12 3	92.67 42	90.84 59	94.81 25	97.69 31	96.10 90	95.42 42	91.70 35	95.82 36	92.52 12	81.24 58	86.01 68	94.36 28	92.44 95	90.27 98	97.19 117	93.99 157
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator	85.78 8	92.53 43	91.96 51	93.20 34	97.99 27	98.47 44	95.78 39	85.94 56	93.07 63	86.40 31	73.43 94	89.00 52	94.08 34	94.74 51	96.44 12	99.01 19	98.57 66

DeepC-MVS	88.77 4	92.39 44	91.74 53	93.14 35	96.21 48	98.55 37	96.30 34	93.84 21	93.06 64	81.09 58	74.69 87	85.20 77	93.48 40	95.41 37	96.13 15	97.92 85	99.18 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OMC-MVS		92.05 45	91.88 52	92.25 39	96.51 44	97.94 56	93.18 56	88.97 43	96.53 26	84.47 40	80.79 60	87.85 55	93.25 43	92.48 94	91.81 81	97.12 118	95.73 130

MVSTER		91.91 46	93.43 44	90.14 56	89.81 98	92.32 130	94.53 49	81.32 88	96.00 32	84.77 39	85.41 51	92.39 40	91.32 56	96.41 23	94.01 56	99.11 8	97.45 100

MVS_0304		91.90 47	92.93 46	90.69 53	93.66 63	98.78 27	96.73 32	85.43 62	93.13 62	78.11 81	77.02 75	89.09 51	91.10 60	96.98 17	96.54 8	99.11 8	98.96 52

CS-MVS-test		91.76 48	93.47 42	89.76 59	94.64 58	98.22 52	88.13 108	81.58 85	97.02 20	82.47 48	85.49 50	85.41 75	93.28 42	95.33 39	93.61 62	98.45 50	99.22 32

QAPM		91.68 49	91.97 50	91.34 45	97.86 29	98.72 29	95.60 41	85.72 57	90.86 77	77.14 86	76.06 76	90.35 47	92.69 46	94.10 64	94.60 41	99.04 15	99.09 42

CS-MVS		91.55 50	92.49 48	90.45 54	94.00 61	97.91 58	91.17 80	81.40 87	95.22 43	83.51 43	82.37 56	82.29 83	94.07 35	96.36 26	94.03 55	98.56 45	99.22 32

CNLPA		91.53 51	89.74 71	93.63 31	96.75 41	97.63 64	91.16 81	91.70 35	96.38 27	90.82 17	69.66 113	85.52 71	93.76 38	90.44 115	91.14 87	97.55 102	97.40 101

ETV-MVS		91.51 52	94.06 37	88.54 69	89.39 104	97.52 65	89.48 96	80.88 91	97.09 18	79.41 67	87.87 39	86.18 66	92.95 44	95.94 30	94.33 45	99.13 7	99.52 21

DROMVSNet		91.25 53	93.45 43	88.68 67	88.90 110	96.18 89	91.66 69	76.70 122	95.57 38	82.00 50	84.18 52	89.28 49	94.17 32	95.64 33	94.19 51	98.68 40	99.14 41

DELS-MVS		91.09 54	90.56 67	91.71 43	95.82 51	98.59 33	95.74 40	86.68 51	85.86 105	85.12 37	72.71 97	81.36 86	88.06 93	97.31 8	98.27 2	98.86 31	99.82 8
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
TAPA-MVS	87.40 6	90.98 55	90.71 61	91.30 47	96.14 49	97.66 62	94.80 47	89.00 42	94.74 51	77.42 85	80.22 61	86.70 62	92.27 50	91.65 103	90.17 103	98.15 66	93.83 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_BlendedMVS		90.74 56	90.66 63	90.82 51	94.75 56	98.54 38	91.30 77	86.53 52	95.43 40	85.75 32	78.66 67	70.67 123	87.60 94	96.37 24	95.08 35	98.98 20	99.90 2

PVSNet_Blended		90.74 56	90.66 63	90.82 51	94.75 56	98.54 38	91.30 77	86.53 52	95.43 40	85.75 32	78.66 67	70.67 123	87.60 94	96.37 24	95.08 35	98.98 20	99.90 2

CHOSEN 280x420		90.61 58	94.27 36	86.35 89	93.12 67	98.16 55	89.99 92	69.62 176	92.48 68	76.89 90	87.28 42	96.72 18	90.31 75	94.81 49	92.33 78	98.17 63	98.08 84

MAR-MVS		90.44 59	91.17 57	89.59 60	97.48 34	97.92 57	90.96 84	79.80 96	95.07 47	77.03 88	80.83 59	79.10 96	94.68 26	93.16 77	94.46 43	97.59 101	97.63 93
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PCF-MVS	88.14 5	90.42 60	89.56 76	91.41 44	94.44 59	98.18 53	94.35 50	94.33 18	84.55 118	76.61 91	75.84 79	88.47 54	91.29 57	90.37 117	90.66 94	97.46 104	98.88 53
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OpenMVS	83.41 11	89.84 61	88.89 82	90.95 50	97.63 32	98.51 40	94.64 48	85.47 61	88.14 91	78.39 78	65.06 127	85.42 74	91.04 62	93.06 80	93.70 60	98.53 47	98.37 74

EIA-MVS		89.82 62	91.48 55	87.89 78	89.16 106	97.31 67	88.99 100	80.92 90	94.29 53	77.65 83	82.16 57	79.77 94	91.90 53	94.61 54	93.03 75	98.70 39	99.21 35

canonicalmvs		89.62 63	89.87 70	89.33 62	90.47 86	97.02 73	93.46 55	79.67 99	92.45 69	81.05 59	82.84 55	73.00 112	93.71 39	90.38 116	94.85 38	97.65 97	98.54 67

TSAR-MVS + COLMAP		89.59 64	89.64 73	89.53 61	93.32 66	96.51 81	95.03 45	88.53 44	95.98 33	69.10 114	91.81 33	64.53 146	93.40 41	93.53 72	91.35 86	97.77 90	93.75 164

HQP-MVS		89.57 65	90.57 66	88.41 71	92.77 68	94.71 106	94.24 51	87.97 45	93.44 58	68.18 117	91.75 34	71.54 122	89.90 78	92.31 98	91.43 84	97.39 109	98.80 55

MVS_Test		89.02 66	90.20 68	87.64 80	89.83 97	97.05 72	92.30 63	77.59 118	92.89 65	75.01 96	77.36 71	76.10 106	92.27 50	95.30 40	95.42 28	98.83 32	97.30 105

CLD-MVS		88.99 67	88.07 85	90.07 57	89.61 100	94.94 103	93.82 54	85.70 59	92.73 67	82.73 47	79.97 62	69.59 127	90.44 74	90.32 118	89.93 107	98.10 67	99.04 45
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
baseline		88.91 68	89.94 69	87.70 79	89.44 103	96.74 79	91.62 71	77.92 115	93.79 57	78.76 73	77.55 70	78.46 99	89.38 84	92.26 99	92.52 77	99.10 10	98.23 78

PMMVS		88.56 69	91.22 56	85.47 97	90.04 93	95.60 99	86.62 123	78.49 110	93.86 55	70.62 109	90.00 36	80.08 92	91.64 55	92.36 96	89.80 111	95.40 172	96.84 114

test2506		88.38 70	88.02 87	88.80 66	91.55 77	97.78 59	90.87 86	83.36 70	84.51 119	83.06 45	74.13 90	76.93 103	85.39 104	94.34 61	93.33 69	98.60 41	95.10 146

baseline1		88.16 71	88.15 84	88.17 75	90.02 94	94.79 105	91.85 68	83.89 65	87.37 97	75.67 94	73.75 92	79.89 93	88.44 92	94.41 56	93.33 69	99.18 5	93.55 166

thisisatest0530		87.99 72	90.76 60	84.75 101	88.36 115	96.82 76	87.65 113	79.67 99	91.77 71	70.93 105	79.94 63	87.65 57	84.21 114	92.98 83	89.07 122	97.66 96	97.13 108

tttt0517		87.93 73	90.71 61	84.68 102	88.33 116	96.76 78	87.42 116	79.67 99	91.74 72	70.83 106	79.91 64	87.61 58	84.21 114	92.88 88	89.07 122	97.62 99	97.03 110

CANet_DTU		87.91 74	91.57 54	83.64 109	90.96 80	97.12 70	91.90 67	75.97 130	92.83 66	53.16 171	86.02 47	79.02 97	90.80 65	95.40 38	94.15 52	99.03 18	96.47 125

diffmvs		87.86 75	87.40 93	88.39 72	88.57 113	96.10 90	91.24 79	83.15 73	90.62 78	79.13 71	72.45 100	67.71 133	90.07 77	92.58 92	93.31 72	98.17 63	99.03 46
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IS_MVSNet		87.83 76	90.66 63	84.53 103	90.08 92	96.79 77	88.16 107	79.89 95	85.44 107	72.20 100	75.50 83	87.14 60	80.21 141	95.53 35	95.22 31	96.65 134	99.02 47

EPP-MVSNet		87.72 77	89.74 71	85.37 98	89.11 107	95.57 100	86.31 124	79.44 102	85.83 106	75.73 93	77.23 73	90.05 48	84.78 110	91.22 107	90.25 99	96.83 125	98.04 85

casdiffmvs_mvg		87.64 78	86.46 100	89.01 65	89.45 102	96.09 92	92.69 61	83.42 69	84.60 117	80.01 64	68.55 116	70.29 125	90.51 72	93.93 67	93.59 63	97.96 80	98.18 79
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
ET-MVSNet_ETH3D		87.63 79	91.08 58	83.59 110	67.96 211	96.30 88	92.06 65	78.47 111	91.95 70	69.87 111	87.57 41	84.14 81	94.34 29	88.58 131	92.10 79	98.88 29	96.93 111

DI_MVS_plusplus_trai		87.63 79	87.13 95	88.22 74	88.61 112	95.92 95	94.09 53	81.41 86	87.00 100	78.38 79	59.70 146	80.52 90	89.08 87	94.37 59	93.34 67	97.73 91	99.05 44

casdiffmvs		87.59 81	86.69 99	88.64 68	89.06 108	96.32 87	90.18 89	83.21 72	87.74 95	80.20 63	67.99 118	68.34 131	90.79 66	93.83 68	94.08 53	98.41 54	98.50 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet_Blended_VisFu		87.44 82	88.72 83	85.95 93	92.02 72	97.26 68	86.88 121	82.66 80	83.86 125	79.16 70	66.96 121	84.91 78	77.26 158	94.97 46	93.48 64	97.73 91	99.64 16

FMVSNet3		87.19 83	87.32 94	87.04 87	82.82 150	90.21 145	92.88 58	76.53 125	91.69 73	81.31 54	64.81 130	80.64 87	89.79 82	94.80 50	94.76 39	98.88 29	94.32 153

LS3D		87.19 83	85.48 107	89.18 63	94.96 55	95.47 101	92.02 66	93.36 25	88.69 89	67.01 118	70.56 109	72.10 117	92.47 48	89.96 121	89.93 107	95.25 174	91.68 175

ACMP	85.16 9	87.15 85	87.04 96	87.27 84	90.80 82	94.45 109	89.41 98	83.09 77	89.15 85	76.98 89	86.35 45	65.80 140	86.94 97	88.45 132	87.52 141	96.42 149	97.56 98
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		87.04 86	89.59 75	84.07 105	90.94 81	95.95 94	86.02 126	81.65 84	85.94 104	78.54 77	78.00 69	85.40 76	69.62 178	91.83 102	91.53 83	97.63 98	98.51 69
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
LGP-MVS_train		86.95 87	87.65 90	86.12 92	91.77 75	93.84 115	93.04 57	82.77 79	88.04 92	65.33 123	87.69 40	67.09 137	86.79 98	90.20 119	88.99 125	97.05 120	97.71 92

PatchMatch-RL		86.75 88	85.43 108	88.29 73	94.06 60	96.37 86	86.82 122	82.94 78	88.94 87	79.59 66	79.83 65	59.17 159	89.46 83	91.12 108	88.81 129	96.88 124	93.78 162

FA-MVS(training)		86.74 89	88.01 88	85.26 99	89.86 95	96.99 74	88.54 104	64.26 192	89.04 86	81.30 57	66.74 123	81.52 85	89.11 86	94.04 65	90.37 97	98.47 49	97.37 102

baseline2		86.51 90	89.35 79	83.19 112	85.70 136	94.88 104	85.75 131	77.13 120	89.87 82	70.65 108	79.03 66	79.14 95	81.51 134	93.70 69	90.22 100	98.38 57	98.60 64

thres100view900		86.48 91	85.08 110	88.12 76	90.54 83	96.90 75	92.39 62	84.82 63	84.16 123	71.65 101	70.86 106	60.49 154	91.23 59	93.65 70	90.19 102	98.10 67	99.32 25

ACMM	84.23 10	86.40 92	84.64 113	88.46 70	91.90 73	91.93 136	88.11 109	85.59 60	88.61 90	79.13 71	75.31 84	66.25 138	89.86 81	89.88 122	87.64 138	96.16 159	92.86 171
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		86.16 93	86.00 103	86.35 89	81.81 156	89.52 154	91.40 73	76.53 125	91.69 73	81.31 54	64.81 130	80.64 87	88.72 88	90.54 112	90.72 90	98.34 58	94.08 154

test1		86.16 93	86.00 103	86.35 89	81.81 156	89.52 154	91.40 73	76.53 125	91.69 73	81.31 54	64.81 130	80.64 87	88.72 88	90.54 112	90.72 90	98.34 58	94.08 154

tfpn200view9		86.07 95	84.76 112	87.61 81	90.54 83	96.39 83	91.35 76	83.15 73	84.16 123	71.65 101	70.86 106	60.49 154	90.91 63	92.89 85	89.34 114	98.05 73	99.17 38

DCV-MVSNet		85.90 96	85.88 105	85.93 94	87.86 121	88.37 171	89.45 97	77.46 119	87.33 98	77.51 84	76.06 76	75.76 108	88.48 91	87.40 140	88.89 128	94.80 183	97.37 102

Vis-MVSNet (Re-imp)		85.89 97	89.62 74	81.55 122	89.85 96	96.08 93	87.55 114	79.80 96	84.80 114	66.55 120	73.70 93	86.71 61	68.25 185	94.40 57	94.53 42	97.32 112	97.09 109

MSDG		85.81 98	82.29 137	89.93 58	95.52 52	92.61 125	91.51 72	91.46 38	85.12 111	78.56 75	63.25 136	69.01 129	85.31 107	88.45 132	88.23 132	97.21 116	89.33 186

thres200		85.80 99	84.38 114	87.46 82	90.51 85	96.39 83	91.64 70	83.15 73	81.59 133	71.54 103	70.24 110	60.41 156	89.88 79	92.89 85	89.85 110	98.06 71	99.26 30

ECVR-MVS		85.74 100	83.80 122	88.00 77	91.55 77	97.78 59	90.87 86	83.36 70	84.51 119	78.21 80	58.65 151	62.75 150	85.39 104	94.34 61	93.33 69	98.60 41	95.25 140

OPM-MVS		85.69 101	82.79 130	89.06 64	93.42 64	94.21 113	94.21 52	87.61 47	72.68 158	70.79 107	71.09 104	67.27 136	90.74 67	91.29 106	89.05 124	97.61 100	93.94 159
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
thres400		85.59 102	84.08 117	87.36 83	90.45 87	96.60 80	90.95 85	83.67 67	80.99 136	71.17 104	69.08 115	60.25 157	89.88 79	93.14 78	89.34 114	98.02 75	99.17 38

CostFormer		85.47 103	86.98 97	83.71 108	88.70 111	94.02 114	88.07 110	62.72 194	89.78 83	78.68 74	72.69 98	78.37 100	87.35 96	85.96 153	89.32 118	96.73 131	98.72 56

test1111		85.17 104	83.46 125	87.17 85	91.36 79	97.75 61	90.06 91	83.44 68	83.41 127	75.25 95	58.08 154	62.19 152	84.39 113	94.39 58	93.38 66	98.54 46	95.00 148

thres600view7		85.14 105	83.58 124	86.96 88	90.37 90	96.39 83	90.33 88	83.15 73	80.46 137	70.60 110	67.96 119	60.04 158	89.22 85	92.89 85	88.28 131	98.06 71	99.08 43

test-LLR		85.11 106	89.49 77	80.00 131	85.32 140	94.49 107	82.27 161	74.18 139	87.83 93	56.70 149	75.55 81	86.26 63	82.75 127	93.06 80	90.60 95	98.77 35	98.65 62

FMVSNet2		84.89 107	84.02 119	85.91 95	81.81 156	89.52 154	91.40 73	75.79 131	84.45 121	79.39 68	58.75 149	74.35 110	88.72 88	93.51 74	93.46 65	98.34 58	94.08 154

FC-MVSNet-train		84.88 108	84.08 117	85.82 96	89.21 105	91.74 137	85.87 127	81.20 89	81.71 132	74.66 97	73.38 95	64.99 144	86.60 99	90.75 110	88.08 133	97.36 110	97.90 89

EPNet_dtu		84.87 109	89.01 80	80.05 130	95.25 54	92.88 123	88.84 102	84.11 64	91.69 73	49.28 187	85.69 48	78.95 98	65.39 190	92.22 100	91.66 82	97.43 107	89.95 182
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+		84.80 110	85.71 106	83.73 107	87.94 120	95.76 96	90.08 90	73.45 146	85.12 111	62.66 132	72.39 101	64.97 145	90.59 70	92.95 84	90.69 93	97.67 95	98.12 81

UA-Net		84.69 111	87.64 91	81.25 124	90.38 89	95.67 97	87.33 117	79.41 103	72.07 162	66.48 121	75.09 85	92.48 39	66.88 186	94.03 66	94.25 49	97.01 123	89.88 183

TESTMET0.1,1		84.62 112	89.49 77	78.94 140	82.18 153	94.49 107	82.27 161	70.94 166	87.83 93	56.70 149	75.55 81	86.26 63	82.75 127	93.06 80	90.60 95	98.77 35	98.65 62

CHOSEN 1792x2688		84.59 113	84.30 116	84.93 100	93.71 62	98.23 51	89.91 93	77.96 114	84.81 113	65.93 122	45.19 195	71.76 121	83.13 125	95.46 36	95.13 34	98.94 24	99.53 20

Anonymous20231211		84.23 114	81.71 142	87.17 85	87.38 128	93.59 118	88.95 101	82.14 82	83.82 126	78.56 75	48.09 189	73.89 111	91.25 58	86.38 147	88.06 135	94.74 184	98.14 80

MDTV_nov1_ep13		84.17 115	88.03 86	79.66 133	86.00 134	94.41 110	85.05 133	66.01 188	90.36 79	64.34 128	77.13 74	84.56 79	82.71 129	87.12 144	88.92 126	93.84 190	93.69 165

test-mter		84.06 116	89.00 81	78.29 145	81.92 154	94.23 112	81.07 171	70.38 170	87.12 99	56.10 158	74.75 86	85.80 69	81.81 133	92.52 93	90.10 104	98.43 52	98.49 71

IB-MVS	79.58 12	83.83 117	84.81 111	82.68 114	91.85 74	97.35 66	75.75 190	82.57 81	86.55 102	84.01 41	70.90 105	65.43 142	63.18 196	84.19 167	89.92 109	98.74 37	99.31 27
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
EPMVS		83.71 118	86.76 98	80.16 129	89.72 99	95.64 98	84.68 134	59.73 199	89.61 84	62.67 131	72.65 99	81.80 84	86.22 101	86.23 149	88.03 136	97.96 80	93.35 167

HyFIR lowres test		83.43 119	82.94 128	84.01 106	93.41 65	97.10 71	87.21 118	74.04 141	80.15 139	64.98 124	41.09 203	76.61 105	86.51 100	93.31 75	93.01 76	97.91 87	99.30 28

PatchmatchNet		83.28 120	87.57 92	78.29 145	87.46 126	94.95 102	83.36 143	59.43 202	90.20 81	58.10 144	74.29 89	86.20 65	84.13 116	85.27 159	87.39 142	97.25 115	94.67 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		83.26 121	87.76 89	78.00 150	87.45 127	92.20 131	82.63 157	58.42 204	90.30 80	58.23 142	75.74 80	87.75 56	83.97 119	86.10 152	87.64 138	97.30 113	94.62 152

GeoE		83.17 122	82.86 129	83.53 111	87.24 129	93.78 116	87.94 111	72.75 151	82.19 130	69.76 112	60.54 143	65.95 139	86.01 102	89.41 126	89.72 112	97.47 103	98.43 72

CDS-MVSNet		83.13 123	83.73 123	82.43 120	84.52 145	92.92 122	88.26 106	77.67 117	72.08 161	69.08 115	66.96 121	74.66 109	78.61 147	90.70 111	91.96 80	96.46 148	96.86 113
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		82.91 124	81.86 139	84.13 104	88.25 117	88.32 172	87.67 112	80.86 92	84.78 115	76.57 92	85.56 49	76.00 107	84.61 111	78.20 202	76.52 205	86.81 210	83.63 203

Vis-MVSNet		82.88 125	86.04 102	79.20 138	87.77 124	96.42 82	86.10 125	76.70 122	74.82 152	61.38 134	70.70 108	77.91 101	64.83 192	93.22 76	93.19 74	98.43 52	96.01 128
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dps		82.63 126	82.64 133	82.62 116	87.81 123	92.81 124	84.39 135	61.96 195	86.43 103	81.63 51	69.72 112	67.60 135	84.42 112	82.51 180	83.90 179	95.52 168	95.50 138

IterMVS-LS		82.62 127	82.75 132	82.48 117	87.09 130	87.48 185	87.19 119	72.85 149	79.09 140	66.63 119	65.22 125	72.14 116	84.06 118	88.33 135	91.39 85	97.03 122	95.60 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		82.61 128	82.51 135	82.72 113	85.49 139	93.06 121	87.17 120	71.39 163	84.18 122	64.59 126	63.03 137	58.89 160	90.22 76	91.39 105	90.83 89	97.44 105	96.21 127

tpm cat1		82.39 129	82.32 136	82.47 118	88.13 118	92.42 129	87.43 115	62.79 193	85.30 108	78.05 82	60.14 144	72.10 117	83.20 124	82.26 183	85.67 158	95.23 175	98.35 76

MS-PatchMatch		82.16 130	82.18 138	82.12 121	91.65 76	93.50 119	89.51 95	71.95 157	81.48 134	64.45 127	59.58 148	77.54 102	77.23 159	89.88 122	85.62 159	97.94 82	87.68 190

tpmrst		81.71 131	83.87 121	79.20 138	89.01 109	93.67 117	84.22 136	60.14 197	87.45 96	59.49 138	64.97 128	71.86 120	85.30 108	84.72 163	86.30 150	97.04 121	98.09 83

RPMNet		81.47 132	86.24 101	75.90 168	86.72 131	92.12 133	82.82 155	55.76 210	85.21 109	53.73 169	63.45 134	83.16 82	80.13 142	92.34 97	89.52 113	96.23 157	97.90 89

CR-MVSNet		81.44 133	85.29 109	76.94 159	86.53 132	92.12 133	83.86 137	58.37 205	85.21 109	56.28 153	59.60 147	80.39 91	80.50 139	92.77 89	89.32 118	96.12 161	97.59 96

Effi-MVS+-dtu		81.18 134	82.77 131	79.33 136	84.70 144	92.54 127	85.81 128	71.55 161	78.84 141	57.06 148	71.98 103	63.77 148	85.09 109	88.94 128	87.62 140	91.79 203	95.68 132

test0.0.03 1		80.99 135	84.37 115	77.05 157	85.32 140	89.79 150	78.43 181	74.18 139	84.78 115	57.98 147	76.06 76	72.88 113	69.14 182	88.02 137	87.70 137	97.27 114	91.37 176

Fast-Effi-MVS+-dtu		80.57 136	83.44 126	77.22 155	83.98 148	91.52 139	85.78 130	64.54 191	80.38 138	50.28 183	74.06 91	62.89 149	82.00 132	89.10 127	88.91 127	96.75 129	97.21 107

FMVSNet5		80.56 137	82.53 134	78.26 147	73.80 206	81.52 204	82.26 163	68.36 181	88.85 88	64.21 129	69.09 114	84.38 80	83.49 123	87.13 143	86.76 147	97.44 105	79.95 206

ADS-MVSNet		80.25 138	82.96 127	77.08 156	87.86 121	92.60 126	81.82 168	56.19 209	86.95 101	56.16 156	68.19 117	72.42 115	83.70 122	82.05 184	85.45 164	96.75 129	93.08 170

FMVSNet1		80.18 139	78.07 153	82.65 115	78.55 180	87.57 184	88.41 105	73.93 142	70.16 167	73.57 98	49.80 179	64.45 147	85.35 106	90.54 112	90.72 90	96.10 162	93.21 168

USDC		80.10 140	79.33 149	81.00 126	86.36 133	91.71 138	88.74 103	75.77 132	81.90 131	54.90 163	67.67 120	52.05 172	83.94 120	88.44 134	86.25 151	96.31 152	87.28 194

COLMAP_ROB	75.69 15	79.47 141	76.90 160	82.46 119	92.20 69	90.53 141	85.30 132	83.69 66	78.27 144	61.47 133	58.26 152	62.75 150	78.28 150	82.41 181	82.13 192	93.83 192	83.98 202
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs4		79.32 142	77.78 155	81.11 125	80.18 165	88.96 166	83.39 141	76.07 128	81.27 135	69.35 113	58.66 150	51.19 175	82.01 131	87.16 142	84.39 176	95.66 166	92.82 172

PatchT		79.28 143	83.88 120	73.93 177	85.54 138	90.95 140	66.14 207	56.53 208	83.21 128	56.28 153	56.50 156	76.80 104	80.50 139	92.77 89	89.32 118	98.57 44	97.59 96

ACMH	78.51 14	79.27 144	78.08 152	80.65 127	89.52 101	90.40 142	80.45 173	79.77 98	69.54 172	54.85 164	64.83 129	56.16 166	83.94 120	84.58 165	86.01 155	95.41 171	95.03 147
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		79.23 145	78.95 151	79.56 134	81.89 155	92.52 128	82.97 150	73.70 143	67.27 178	64.97 125	61.66 142	65.06 143	78.61 147	87.12 144	88.07 134	95.23 175	90.95 178

ACMH+	79.09 13	79.12 146	77.22 159	81.35 123	88.50 114	90.36 143	82.14 165	79.38 105	72.78 157	58.59 139	62.31 141	56.44 165	84.10 117	82.03 185	84.05 177	95.40 172	92.55 173

UniMVSNet_NR-MVSNet		78.89 147	78.04 154	79.88 132	79.40 171	89.70 151	82.92 152	80.17 93	76.37 150	58.56 140	57.10 155	54.92 168	81.44 135	83.51 172	87.12 144	96.76 128	97.60 94

tpm		78.87 148	81.33 145	76.00 166	85.57 137	90.19 146	82.81 156	59.66 200	78.35 143	51.40 178	66.30 124	67.92 132	80.94 137	83.28 175	85.73 156	95.65 167	97.56 98

GA-MVS		78.86 149	80.42 146	77.05 157	83.27 149	92.17 132	83.24 145	75.73 133	73.75 154	46.27 197	62.43 139	57.12 162	76.94 161	93.14 78	89.34 114	96.83 125	95.00 148

IterMVS		78.85 150	81.36 143	75.93 167	84.27 147	85.74 191	83.83 139	66.35 186	76.82 145	50.48 181	63.48 133	68.82 130	73.99 166	89.68 124	89.34 114	96.63 137	95.67 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		78.71 151	81.34 144	75.64 172	84.31 146	85.67 192	83.51 140	66.14 187	76.67 146	50.38 182	63.45 134	69.02 128	73.23 168	89.66 125	89.22 121	96.24 156	95.67 133

UniMVSNet (Re)		78.00 152	77.52 156	78.57 143	79.66 170	90.36 143	82.09 166	77.86 116	76.38 149	60.26 135	54.63 162	52.07 171	75.31 164	84.97 162	86.10 153	96.22 158	98.11 82

DU-MVS		77.98 153	76.71 161	79.46 135	78.68 177	89.26 160	82.92 152	79.06 107	76.52 147	58.56 140	54.89 160	48.35 189	81.44 135	83.16 177	87.21 143	96.08 163	97.60 94

FC-MVSNet-test		77.95 154	81.85 140	73.39 182	82.31 151	88.99 165	79.33 177	74.24 138	78.75 142	47.40 195	70.22 111	72.09 119	60.78 202	86.66 146	85.62 159	96.30 153	90.61 179

NR-MVSNet		77.21 155	76.41 162	78.14 149	80.18 165	89.26 160	83.38 142	79.06 107	76.52 147	56.59 151	54.89 160	45.32 199	72.89 170	85.39 158	86.12 152	96.71 132	97.36 104

thisisatest0515		77.13 156	79.36 148	74.52 174	79.79 169	89.65 152	73.54 195	73.69 144	74.10 153	58.14 143	62.79 138	60.57 153	66.49 188	88.08 136	85.16 169	95.49 170	95.15 144

gg-mvs-nofinetune		77.08 157	79.79 147	73.92 178	85.95 135	97.23 69	92.18 64	52.65 213	46.19 216	27.79 220	38.27 207	85.63 70	85.67 103	96.95 18	95.62 25	99.30 3	98.67 61

TranMVSNet+NR-MVSNet		77.02 158	75.76 164	78.49 144	78.46 183	88.24 173	83.03 149	79.97 94	73.49 156	54.73 165	54.00 165	48.74 184	78.15 152	82.36 182	86.90 146	96.59 139	96.55 119

CVMVSNet		76.86 159	79.09 150	74.26 175	85.29 142	89.44 157	79.91 176	78.47 111	68.94 175	44.45 202	62.35 140	69.70 126	64.50 193	85.82 154	87.03 145	92.94 198	90.33 180

Baseline_NR-MVSNet		76.71 160	74.56 171	79.23 137	78.68 177	84.15 200	82.45 159	78.87 109	75.83 151	60.05 136	47.92 190	50.18 181	79.06 146	83.16 177	83.86 180	96.26 154	96.80 115

v2v482		76.25 161	74.78 168	77.96 151	78.50 182	89.14 163	83.05 148	76.02 129	68.78 176	54.11 166	51.36 171	48.59 186	79.49 144	83.53 171	85.60 162	96.59 139	96.49 124

V42		76.21 162	75.04 167	77.58 152	78.68 177	89.33 159	82.93 151	74.64 136	69.84 169	56.13 157	50.42 176	50.93 176	76.30 163	83.32 173	84.89 173	96.83 125	96.54 120

v8		75.89 163	74.74 169	77.23 154	79.09 173	88.00 176	83.19 146	71.08 165	70.03 168	56.29 152	50.50 174	50.88 177	77.06 160	83.32 173	84.99 171	96.68 133	95.49 139

TinyColmap		75.75 164	73.19 182	78.74 142	84.82 143	87.69 180	81.59 169	74.62 137	71.81 163	54.01 167	55.79 159	44.42 204	82.89 126	84.61 164	83.76 181	94.50 185	84.22 201

MIMVSNet		75.71 165	77.26 157	73.90 179	70.93 207	88.71 169	79.98 175	57.67 207	73.58 155	58.08 146	53.93 166	58.56 161	79.41 145	90.04 120	89.97 105	97.34 111	86.04 195

UniMVSNet_ETH3D		75.63 166	71.59 191	80.35 128	81.03 160	89.90 149	83.25 144	76.58 124	60.08 199	64.19 130	42.89 202	45.01 200	82.14 130	80.20 195	86.75 148	94.90 180	96.29 126

pm-mvs1		75.61 167	74.19 173	77.26 153	80.16 167	88.79 167	81.49 170	75.49 135	59.49 201	58.09 145	48.32 187	55.53 167	72.35 171	88.61 130	85.48 163	95.99 164	93.12 169

v10		75.57 168	74.67 170	76.62 162	78.73 176	87.46 186	83.14 147	69.41 177	69.27 173	53.44 170	49.73 180	49.21 183	78.44 149	86.17 151	85.18 168	96.53 144	95.65 136

v1144		75.54 169	74.55 172	76.69 160	78.33 186	88.77 168	82.89 154	72.76 150	67.18 180	51.73 175	49.34 182	48.37 187	78.10 153	86.22 150	85.24 166	96.35 151	96.74 116

TDRefinement		75.54 169	73.22 180	78.25 148	87.65 125	89.65 152	85.81 128	79.28 106	71.14 165	56.06 159	52.17 169	51.96 173	68.74 184	81.60 186	80.58 194	91.94 201	85.45 196

pmmvs5		75.46 171	75.12 166	75.87 169	79.39 172	89.44 157	78.12 183	72.27 155	65.98 185	51.54 176	55.83 158	46.23 194	76.80 162	88.77 129	85.73 156	97.07 119	93.84 160

tfpnnormal		75.27 172	72.12 188	78.94 140	82.30 152	88.52 170	82.41 160	79.41 103	58.03 202	55.59 161	43.83 201	44.71 201	77.35 156	87.70 139	85.45 164	96.60 138	96.61 118

anonymousdsp		75.14 173	77.25 158	72.69 185	76.68 196	89.26 160	75.26 192	68.44 180	65.53 188	46.65 196	58.16 153	56.67 164	73.96 167	87.84 138	86.05 154	95.13 178	97.22 106

v148		74.98 174	73.52 178	76.69 160	78.84 175	89.02 164	78.78 179	76.82 121	67.22 179	59.61 137	49.18 183	47.94 191	70.57 177	80.76 190	83.99 178	95.52 168	96.52 122

v1192		74.96 175	73.92 174	76.17 163	77.76 189	88.19 175	82.54 158	71.94 158	66.84 181	50.07 185	48.10 188	46.14 195	78.28 150	86.30 148	85.23 167	96.41 150	96.67 117

v144192		74.76 176	73.64 175	76.06 165	77.58 190	88.23 174	81.87 167	71.63 160	66.03 184	51.08 179	48.63 186	46.77 193	77.59 155	84.53 166	84.76 174	96.64 136	96.54 120

v1921920		74.60 177	73.56 177	75.81 170	77.43 192	87.94 177	82.18 164	71.33 164	66.48 183	49.23 189	47.84 191	45.56 197	78.03 154	85.70 156	84.92 172	96.65 134	96.50 123

v1240		74.04 178	73.04 184	75.20 173	77.19 194	87.69 180	80.93 172	70.72 169	65.08 189	48.47 190	47.31 192	44.71 201	77.33 157	85.50 157	85.07 170	96.59 139	95.94 129

testgi		73.22 179	75.84 163	70.16 196	81.67 159	85.50 195	71.45 197	70.81 167	69.56 171	44.74 201	74.52 88	49.25 182	58.45 203	84.10 169	83.37 185	93.86 189	84.56 200

CP-MVSNet		73.19 180	72.37 186	74.15 176	77.54 191	86.77 189	76.34 186	72.05 156	65.66 187	51.47 177	50.49 175	43.66 205	70.90 173	80.93 189	83.40 184	96.59 139	95.66 135

WR-MVS		72.93 181	73.57 176	72.19 188	78.14 187	87.71 179	76.21 188	73.02 148	67.78 177	50.09 184	50.35 177	50.53 179	61.27 201	80.42 193	83.10 188	94.43 186	95.11 145

TransMVSNet (Re)		72.90 182	70.51 195	75.69 171	80.88 161	85.26 197	79.25 178	78.43 113	56.13 208	52.81 172	46.81 193	48.20 190	66.77 187	85.18 161	83.70 182	95.98 165	88.28 189

WR-MVS_H		72.69 183	72.80 185	72.56 187	77.94 188	87.83 178	75.26 192	71.53 162	64.75 190	52.19 174	49.83 178	48.62 185	61.96 199	81.12 188	82.44 190	96.50 145	95.00 148

SixPastTwentyTwo		72.65 184	73.22 180	71.98 191	78.40 184	87.64 182	70.09 200	70.37 171	66.49 182	47.60 193	65.09 126	45.94 196	73.09 169	78.94 197	78.66 200	92.33 199	89.82 184

LTVRE_ROB	71.82 16	72.62 185	71.77 189	73.62 180	80.74 162	87.59 183	80.42 174	70.37 171	49.73 212	37.12 214	59.76 145	42.52 210	80.92 138	83.20 176	85.61 161	92.13 200	93.95 158
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
PS-CasMVS		72.37 186	71.47 193	73.43 181	77.32 193	86.43 190	75.99 189	71.94 158	63.37 193	49.24 188	49.07 184	42.42 211	69.60 179	80.59 192	83.18 187	96.48 147	95.23 142

MVS-HIRNet		72.32 187	73.45 179	71.00 194	80.58 163	89.97 147	68.51 204	55.28 211	70.89 166	52.27 173	39.09 205	57.11 163	75.02 165	85.76 155	86.33 149	94.36 187	85.00 198

PEN-MVS		72.24 188	71.30 194	73.33 183	77.08 195	85.57 193	76.75 184	72.52 153	63.89 192	48.12 191	50.79 172	43.09 208	69.03 183	78.54 199	83.46 183	96.50 145	93.76 163

v7n		72.11 189	71.66 190	72.63 186	75.26 201	86.85 187	76.74 185	68.77 179	62.70 196	49.40 186	45.92 194	43.51 206	70.63 176	84.16 168	83.21 186	94.99 179	95.25 140

EG-PatchMatch MVS		71.81 190	71.54 192	72.12 189	80.53 164	89.94 148	78.51 180	66.56 185	57.38 204	47.46 194	44.28 200	52.22 170	63.10 197	85.22 160	84.42 175	96.56 143	87.35 193

CMPMVS	54.54 17	71.74 191	67.94 200	76.16 164	90.41 88	93.25 120	78.32 182	75.60 134	59.81 200	53.95 168	44.64 198	51.22 174	70.70 174	74.59 208	75.88 206	88.01 207	76.23 209
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		71.65 192	73.08 183	69.97 197	75.22 202	86.81 188	73.98 194	59.61 201	69.75 170	48.01 192	54.21 164	53.06 169	69.19 181	78.50 200	80.43 195	93.84 190	88.79 187

pmnet_mix02		71.64 193	72.36 187	70.81 195	78.39 185	85.57 193	68.64 202	73.65 145	72.13 159	45.07 200	56.01 157	50.61 178	65.34 191	76.21 205	76.60 204	93.75 193	89.35 185

gm-plane-assit		71.33 194	75.18 165	66.83 200	79.06 174	75.57 211	48.05 218	60.33 196	48.28 213	34.67 218	44.34 199	67.70 134	79.78 143	97.25 11	96.21 13	99.10 10	96.92 112

DTE-MVSNet		71.19 195	70.45 196	72.06 190	76.61 197	84.59 199	75.61 191	72.32 154	63.12 195	45.70 199	50.72 173	43.02 209	65.89 189	77.53 204	82.23 191	96.26 154	91.93 174

pmmvs6		70.29 196	67.90 201	73.07 184	76.17 198	85.31 196	76.29 187	70.75 168	47.39 215	55.33 162	37.15 211	50.49 180	69.55 180	82.96 179	80.85 193	90.34 206	91.18 177

PM-MVS		70.17 197	69.42 198	71.04 193	70.82 208	81.26 206	71.25 198	67.80 182	69.16 174	51.04 180	53.15 168	34.93 215	72.19 172	80.30 194	76.95 203	93.16 197	90.21 181

pmmvs-eth3d		69.59 198	67.57 203	71.95 192	70.04 209	80.05 207	71.48 196	70.00 175	62.57 197	55.99 160	44.92 196	35.73 214	70.64 175	81.56 187	79.69 196	93.55 194	88.43 188

N_pmnet		68.54 199	67.83 202	69.38 198	75.77 199	81.90 203	66.21 206	72.53 152	65.91 186	46.09 198	44.67 197	45.48 198	63.82 195	74.66 207	77.39 202	91.87 202	84.77 199

Anonymous20231206		68.09 200	68.68 199	67.39 199	75.16 203	82.55 201	69.33 201	70.06 174	63.34 194	42.28 205	37.91 209	43.12 207	52.67 206	83.56 170	82.71 189	94.84 182	87.59 191

EU-MVSNet		68.07 201	70.25 197	65.52 201	74.68 205	81.30 205	68.53 203	70.31 173	62.40 198	37.43 213	54.62 163	48.36 188	51.34 207	78.32 201	79.27 197	90.84 204	87.47 192

GG-mvs-BLEND		65.67 202	93.78 39	32.89 214	0.47 224	99.35 7	96.92 30	0.22 223	93.28 60	0.51 225	84.07 53	92.50 38	0.62 222	93.59 71	93.86 57	98.59 43	99.79 10

test20.03		65.17 203	67.41 204	62.55 203	75.35 200	79.31 208	62.22 208	68.83 178	56.50 207	35.35 217	51.97 170	44.70 203	40.01 212	80.69 191	79.25 198	93.55 194	79.47 208

MDA-MVSNet-bldmvs		62.23 204	61.13 208	63.52 202	58.94 215	82.44 202	60.71 211	73.28 147	57.22 205	38.42 211	49.63 181	27.64 221	62.83 198	54.98 214	74.16 207	86.96 209	81.83 205

new_pmnet		61.60 205	62.68 206	60.35 206	63.02 212	74.93 212	60.97 210	58.86 203	64.21 191	35.38 216	39.51 204	39.89 212	57.37 204	72.78 209	72.56 209	86.49 211	74.85 211

new-patchmatchnet		60.74 206	59.78 210	61.87 204	69.52 210	76.67 210	57.99 214	65.78 189	52.63 210	38.47 210	38.08 208	32.92 218	48.88 209	68.50 210	69.87 210	90.56 205	79.75 207

pmmvs3		60.52 207	60.87 209	60.12 207	61.38 213	71.62 213	57.42 215	53.94 212	48.09 214	35.95 215	38.62 206	32.19 220	64.12 194	75.33 206	77.99 201	87.89 208	82.28 204

MIMVSNet1		60.51 208	61.43 207	59.44 208	48.75 218	77.21 209	60.98 209	66.84 184	52.09 211	38.74 209	29.29 214	39.40 213	48.08 210	77.60 203	78.87 199	93.22 196	75.56 210

test_method		60.40 209	66.30 205	53.52 210	37.48 222	64.10 217	55.56 216	42.45 218	71.79 164	41.87 206	33.74 212	46.80 192	61.71 200	79.18 196	73.33 208	82.01 213	95.17 143

FPMVS		56.54 210	52.82 212	60.87 205	74.90 204	67.58 216	67.69 205	65.38 190	57.86 203	41.51 207	37.83 210	34.19 216	41.21 211	55.88 213	53.09 215	74.55 216	63.31 214

PMVS	42.57 18	45.71 211	42.61 214	49.32 211	61.35 214	37.82 221	36.96 220	60.10 198	37.20 217	41.50 208	28.53 215	33.11 217	28.82 217	53.45 215	48.70 217	67.22 218	59.42 215
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		43.95 212	42.62 213	45.50 212	50.79 217	41.20 220	35.55 221	52.51 214	52.95 209	29.09 219	12.92 217	11.48 224	38.15 213	62.01 212	66.62 212	66.89 219	51.17 216
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		41.25 213	42.55 215	39.74 213	43.25 219	55.05 219	38.15 219	47.11 217	31.78 218	11.83 222	21.16 216	19.12 222	20.98 219	49.95 217	56.09 214	77.09 214	64.68 213

E-PMN		27.87 214	24.36 217	31.97 215	41.27 221	25.56 224	16.62 223	49.16 215	22.00 220	9.90 223	11.75 219	7.86 226	29.57 216	22.22 219	34.70 218	45.27 220	46.41 218

MVE	32.98 19	27.61 215	29.89 216	24.94 217	21.97 223	37.22 222	15.56 225	38.83 219	17.49 221	14.72 221	11.64 221	5.62 227	21.26 218	35.20 218	50.95 216	37.29 222	51.13 217
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		26.96 216	22.96 218	31.63 216	41.91 220	25.73 223	16.30 224	49.10 216	22.38 219	9.03 224	11.22 222	8.12 225	29.93 215	20.16 220	31.04 219	43.49 221	42.04 219

testmvs		5.16 217	8.14 219	1.69 218	0.36 225	1.65 225	3.02 226	0.66 221	7.17 222	0.50 226	12.58 218	0.69 228	4.67 220	5.42 221	5.65 220	0.92 223	23.86 221

test123		4.39 218	7.11 220	1.21 219	0.11 226	1.16 226	1.67 227	0.35 222	5.91 223	0.16 227	11.65 220	0.16 229	4.45 221	1.72 222	4.92 221	0.51 224	24.28 220

uanet_test		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

sosnet-low-res		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

sosnet		0.00 219	0.00 221	0.00 220	0.00 227	0.00 227	0.00 228	0.00 224	0.00 224	0.00 228	0.00 223	0.00 230	0.00 223	0.00 223	0.00 222	0.00 225	0.00 222

RE-MVS-def										43.17 203

9.14												97.59 10

SR-MVS					98.52 19			93.70 22				96.63 20

Anonymous202405211			81.72 141		88.09 119	94.27 111	89.62 94	82.14 82	82.27 129		48.83 185	72.58 114	91.08 61	87.40 140	88.70 130	94.90 180	97.99 86

our_test_3					78.55 180	84.98 198	70.12 199

ambc			57.08 211		58.68 216	67.71 215	60.07 212		57.13 206	42.79 204	30.00 213	11.64 223	50.18 208	78.89 198	69.14 211	82.64 212	85.02 197

MTAPA										93.37 8		95.71 26

MTMP										93.84 5		94.86 29

Patchmatch-RL test							19.65 222

tmp_tt				57.89 209	79.94 168	59.29 218	52.84 217	36.65 220	94.77 50	68.22 116	72.96 96	65.62 141	33.65 214	66.20 211	58.02 213	76.06 215

XVS					92.16 70	98.56 34	91.04 82			81.00 60		93.49 33				98.00 77

X-MVStestdata					92.16 70	98.56 34	91.04 82			81.00 60		93.49 33				98.00 77

mPP-MVS					97.95 28							92.24 43

NP-MVS									94.12 54

Patchmtry						92.08 135	83.86 137	58.37 205		56.28 153

DeepMVS_CX						70.68 214	59.61 213	67.36 183	72.12 160	38.41 212	53.88 167	32.44 219	55.15 205	50.88 216		74.35 217	68.42 212

High-resolution multi-view benchmark