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 5	95.08 8	98.57 2	95.63 2	96.98 10	99.73 1	97.67 1	97.26 10	95.86 22	99.04 14	99.89 5

MSP-MVS		97.74 2	98.32 1	97.06 7	98.66 14	99.35 6	98.66 7	94.75 13	98.22 4	93.60 5	97.99 1	98.58 7	97.41 3	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
DPE-MVS		97.69 3	98.16 3	97.14 5	99.01 5	99.52 4	99.12 2	95.38 3	98.00 7	93.31 9	97.71 2	99.61 3	96.94 4	96.99 15	95.45 26	99.09 12	99.81 8
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		97.61 4	97.87 6	97.30 2	98.94 10	99.60 1	98.21 12	95.11 5	98.39 3	95.83 1	94.40 28	99.70 2	96.79 5	97.16 12	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 5	98.08 4	97.03 8	99.14 1	99.55 3	98.67 6	95.32 4	97.91 8	92.55 12	97.11 7	97.23 12	97.49 2	98.16 3	97.05 5	99.04 14	99.55 18

APDe-MVS		97.31 6	97.51 11	97.08 6	98.95 9	99.29 11	98.58 9	95.11 5	97.69 14	94.16 3	96.91 11	96.81 16	96.57 9	96.71 19	95.39 28	99.08 13	99.79 9

SF-MVS		97.17 7	97.18 14	97.17 3	99.11 2	99.20 13	99.05 3	95.55 1	97.39 17	93.56 6	97.48 4	96.71 18	96.75 6	95.73 31	94.40 44	98.98 19	99.33 25

NCCC		97.01 8	97.74 7	96.16 11	99.02 4	99.35 6	98.63 8	95.04 9	97.84 11	88.95 25	96.83 13	97.02 15	96.39 14	97.44 7	96.51 9	98.90 27	99.16 40

SMA-MVS		96.96 9	97.65 10	96.15 12	98.98 6	99.31 10	97.91 17	94.68 15	97.52 15	90.59 19	94.54 27	99.20 4	96.54 11	97.29 9	96.48 10	98.22 57	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 10	98.07 5	95.61 19	98.98 6	99.44 5	98.04 13	95.04 9	98.10 5	86.55 32	97.65 3	97.56 10	95.60 23	97.67 6	96.45 11	99.43 1	99.61 17

HPM-MVS++		96.91 11	97.70 8	96.00 14	98.97 8	99.16 16	97.82 20	94.81 12	98.04 6	89.61 22	96.56 15	98.60 6	96.39 14	97.09 13	95.22 30	98.39 51	99.22 34

SD-MVS		96.87 12	97.69 9	95.92 15	96.38 48	99.25 12	97.76 21	94.75 13	97.72 12	92.46 14	95.94 16	99.09 5	96.48 13	96.01 28	96.08 16	97.68 89	99.73 12
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 13	96.99 17	96.56 9	98.76 13	98.87 25	98.42 10	94.93 11	97.70 13	91.83 15	95.52 19	95.94 23	96.63 8	95.94 29	95.47 25	98.80 33	99.47 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		96.50 14	97.08 15	95.82 17	96.12 52	98.97 22	98.00 14	94.13 20	97.89 9	91.49 16	95.11 24	97.52 11	96.26 18	96.27 26	94.07 54	98.91 26	99.74 11
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 15	97.22 13	95.01 23	98.40 22	99.11 17	97.93 16	93.62 24	96.28 29	87.45 28	97.05 9	96.00 22	94.23 32	96.83 18	95.97 17	98.40 50	99.27 31
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.09 16	96.41 22	95.72 18	98.58 17	98.84 26	97.95 15	93.08 28	96.96 22	90.24 20	96.60 14	94.40 31	96.52 12	95.13 42	94.33 46	97.93 79	98.59 65

zzz-MVS		95.87 17	95.63 29	96.15 12	98.60 16	98.83 27	97.89 18	93.65 23	96.24 30	93.08 10	91.13 35	95.46 28	95.72 22	95.64 33	93.67 61	97.97 76	98.46 72

ACMMP_NAP		95.81 18	96.50 21	95.01 23	98.79 12	99.17 15	97.52 26	94.20 19	96.19 31	85.71 36	93.80 31	96.20 21	95.89 19	96.62 21	94.98 36	97.93 79	98.52 68

train_agg		95.72 19	97.37 12	93.80 29	97.82 31	98.92 23	97.84 19	93.50 25	96.86 24	81.35 52	97.10 8	97.71 8	94.19 33	96.02 27	95.37 29	98.07 66	99.64 15

ACMMPR		95.59 20	95.89 24	95.25 21	98.41 21	98.74 29	97.69 24	92.73 32	96.88 23	88.95 25	95.33 21	92.91 38	95.79 20	94.73 52	94.33 46	97.92 81	98.32 78

DeepC-MVS_fast	91.53 1	95.57 21	95.67 27	95.45 20	98.57 18	99.00 21	97.76 21	94.41 17	97.06 20	86.84 31	86.39 46	92.27 43	96.38 16	97.89 5	98.06 3	98.73 39	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 33	98.35 11	92.37 34	95.04 48	92.62 11	87.12 45	93.79 32	96.55 10	93.53 67	96.78 6	98.98 19	98.99 49

CP-MVS		95.43 23	95.67 27	95.14 22	98.24 27	98.60 34	97.45 27	92.80 30	95.98 35	89.21 24	95.22 22	93.60 33	95.43 24	94.37 58	93.22 68	97.68 89	98.72 56

DPM-MVS		95.36 24	95.84 25	94.82 25	96.70 44	98.49 44	99.27 1	95.09 7	96.71 25	83.87 44	86.34 48	96.44 20	95.06 26	98.35 1	98.82 1	98.89 28	95.69 132

MP-MVS		95.24 25	95.96 23	94.40 27	98.32 24	98.38 49	97.12 29	92.87 29	95.17 46	85.50 37	95.68 17	94.91 29	94.58 29	95.11 43	93.76 58	98.05 69	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 16	92.61 40	97.19 37	98.71 31	97.74 23	93.21 27	96.97 21	79.27 66	94.09 29	97.14 13	90.84 65	96.64 20	95.94 20	97.42 105	99.67 14

X-MVS		94.70 27	95.71 26	93.52 33	98.38 23	98.56 36	96.99 30	92.62 33	95.58 39	81.00 58	94.57 26	93.49 34	94.16 35	94.82 48	94.29 49	97.99 75	98.68 58

PGM-MVS		94.64 28	95.49 30	93.66 31	98.55 19	98.51 42	97.63 25	87.77 48	94.45 52	84.92 40	97.23 6	91.90 45	95.22 25	94.56 55	93.80 57	97.87 85	97.97 88

TSAR-MVS + GP.		94.59 29	96.60 20	92.25 41	90.25 88	98.17 55	96.22 36	86.53 54	97.49 16	87.26 29	95.21 23	97.06 14	94.07 37	94.34 60	94.20 51	99.18 5	99.71 13

xxxxxxxxxxxxxcwj		94.57 30	92.34 48	97.17 3	99.11 2	99.20 13	99.05 3	95.55 1	97.39 17	93.56 6	97.48 4	62.85 148	96.75 6	95.73 31	94.40 44	98.98 19	99.33 25

PHI-MVS		94.49 31	96.72 19	91.88 43	97.06 39	98.88 24	94.99 47	89.13 42	96.15 32	79.70 62	96.91 11	95.78 25	91.87 55	94.65 53	95.68 23	98.53 44	98.98 51

AdaColmap		94.28 32	92.94 44	95.84 16	98.32 24	98.33 51	96.06 38	94.62 16	96.29 28	91.22 17	89.89 39	85.50 74	96.38 16	91.85 96	90.89 84	98.44 46	97.81 91

DeepPCF-MVS	91.00 2	94.15 33	96.87 18	90.97 51	96.82 42	99.33 9	89.40 97	92.76 31	98.76 1	82.36 48	88.74 40	95.49 27	90.58 72	98.13 4	97.80 4	93.88 185	99.88 6

CPTT-MVS		94.11 34	93.99 39	94.25 28	96.58 45	97.66 59	97.31 28	91.94 35	94.84 49	88.72 27	92.51 32	93.04 37	95.78 21	91.51 99	89.97 101	95.15 174	98.37 75

EPNet		93.69 35	95.34 31	91.76 44	96.98 41	98.47 46	95.40 44	86.79 51	95.47 40	82.84 46	95.66 18	89.17 51	90.47 73	95.25 41	94.69 40	98.10 63	98.68 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP		93.32 36	93.59 42	93.00 38	97.03 40	98.24 52	95.27 45	91.66 38	95.20 44	83.25 45	95.39 20	85.52 72	92.80 46	92.60 86	90.21 97	98.01 72	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 37	93.72 41	92.65 39	95.48 55	99.09 19	96.55 34	86.74 52	95.28 43	85.22 38	77.30 73	91.25 47	92.60 48	97.06 14	96.63 7	99.31 2	99.45 22

CDPH-MVS		93.22 38	95.08 32	91.04 50	97.57 34	98.49 44	96.74 32	89.35 41	95.19 45	73.57 95	90.26 37	91.59 46	90.68 69	95.09 45	96.15 14	98.31 56	98.81 54

CSCG		93.16 39	92.65 46	93.76 30	98.32 24	99.09 19	96.12 37	89.91 40	93.15 61	89.64 21	83.62 55	88.91 54	92.40 50	91.09 105	93.70 59	96.14 157	98.99 49

MVS_111021_LR		93.05 40	94.53 35	91.32 48	96.43 47	98.38 49	92.81 61	87.20 50	95.94 37	81.45 51	94.75 25	86.08 68	92.12 53	94.83 47	93.34 64	97.89 84	98.42 74

3Dnovator+	86.26 7	92.90 41	92.45 47	93.42 34	97.25 36	98.45 48	95.82 39	85.71 60	93.83 56	89.55 23	72.31 102	92.28 42	94.01 38	95.10 44	95.92 21	98.17 59	99.23 33

MVS_111021_HR		92.73 42	94.83 34	90.28 56	96.27 49	99.10 18	92.77 62	86.15 57	93.41 59	77.11 84	93.82 30	87.39 60	90.61 70	95.60 34	95.15 32	98.79 34	99.32 27

PLC	89.12 3	92.67 43	90.84 59	94.81 26	97.69 32	96.10 86	95.42 43	91.70 36	95.82 38	92.52 13	81.24 58	86.01 69	94.36 30	92.44 90	90.27 94	97.19 114	93.99 155
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator	85.78 8	92.53 44	91.96 50	93.20 36	97.99 28	98.47 46	95.78 40	85.94 58	93.07 63	86.40 33	73.43 94	89.00 53	94.08 36	94.74 51	96.44 12	99.01 18	98.57 66

DeepC-MVS	88.77 4	92.39 45	91.74 52	93.14 37	96.21 50	98.55 39	96.30 35	93.84 21	93.06 64	81.09 56	74.69 88	85.20 77	93.48 41	95.41 37	96.13 15	97.92 81	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 46	91.88 51	92.25 41	96.51 46	97.94 57	93.18 58	88.97 44	96.53 26	84.47 42	80.79 60	87.85 56	93.25 43	92.48 89	91.81 77	97.12 115	95.73 131

MVSTER		91.91 47	93.43 43	90.14 57	89.81 94	92.32 127	94.53 50	81.32 84	96.00 34	84.77 41	85.41 53	92.39 41	91.32 57	96.41 23	94.01 55	99.11 8	97.45 100

MVS_0304		91.90 48	92.93 45	90.69 55	93.66 63	98.78 28	96.73 33	85.43 64	93.13 62	78.11 78	77.02 76	89.09 52	91.10 61	96.98 16	96.54 8	99.11 8	98.96 52

CS-MVS		91.80 49	94.06 37	89.16 64	88.79 105	96.30 84	91.85 69	79.33 102	96.03 33	81.56 50	86.11 49	89.31 50	94.68 27	96.42 22	94.85 37	98.73 39	99.37 23

QAPM		91.68 50	91.97 49	91.34 47	97.86 30	98.72 30	95.60 42	85.72 59	90.86 77	77.14 83	76.06 77	90.35 48	92.69 47	94.10 61	94.60 41	99.04 14	99.09 41

CNLPA		91.53 51	89.74 71	93.63 32	96.75 43	97.63 61	91.16 81	91.70 36	96.38 27	90.82 18	69.66 113	85.52 72	93.76 39	90.44 111	91.14 83	97.55 98	97.40 101

ETV-MVS		91.51 52	94.06 37	88.54 67	89.39 99	97.52 62	89.48 93	80.88 87	97.09 19	79.41 64	87.87 41	86.18 67	92.95 45	95.94 29	94.33 46	99.13 7	99.52 20

DELS-MVS		91.09 53	90.56 67	91.71 45	95.82 53	98.59 35	95.74 41	86.68 53	85.86 105	85.12 39	72.71 97	81.36 85	88.06 93	97.31 8	98.27 2	98.86 31	99.82 7
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 54	90.71 61	91.30 49	96.14 51	97.66 59	94.80 48	89.00 43	94.74 51	77.42 82	80.22 61	86.70 63	92.27 51	91.65 98	90.17 99	98.15 62	93.83 159
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_BlendedMVS		90.74 55	90.66 63	90.82 53	94.75 58	98.54 40	91.30 78	86.53 54	95.43 41	85.75 34	78.66 68	70.67 121	87.60 94	96.37 24	95.08 34	98.98 19	99.90 2

PVSNet_Blended		90.74 55	90.66 63	90.82 53	94.75 58	98.54 40	91.30 78	86.53 54	95.43 41	85.75 34	78.66 68	70.67 121	87.60 94	96.37 24	95.08 34	98.98 19	99.90 2

CHOSEN 280x420		90.61 57	94.27 36	86.35 86	93.12 67	98.16 56	89.99 89	69.62 175	92.48 68	76.89 87	87.28 44	96.72 17	90.31 75	94.81 49	92.33 74	98.17 59	98.08 84

MAR-MVS		90.44 58	91.17 56	89.59 60	97.48 35	97.92 58	90.96 84	79.80 92	95.07 47	77.03 85	80.83 59	79.10 95	94.68 27	93.16 72	94.46 43	97.59 97	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 59	89.56 76	91.41 46	94.44 60	98.18 54	94.35 52	94.33 18	84.55 117	76.61 88	75.84 80	88.47 55	91.29 58	90.37 113	90.66 91	97.46 100	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 60	88.89 82	90.95 52	97.63 33	98.51 42	94.64 49	85.47 63	88.14 91	78.39 76	65.06 125	85.42 75	91.04 63	93.06 75	93.70 59	98.53 44	98.37 75

EIA-MVS		89.82 61	91.48 54	87.89 75	89.16 101	97.31 64	88.99 98	80.92 86	94.29 53	77.65 80	82.16 57	79.77 93	91.90 54	94.61 54	93.03 70	98.70 41	99.21 35

canonicalmvs		89.62 62	89.87 70	89.33 62	90.47 83	97.02 70	93.46 57	79.67 95	92.45 69	81.05 57	82.84 56	73.00 110	93.71 40	90.38 112	94.85 37	97.65 93	98.54 67

TSAR-MVS + COLMAP		89.59 63	89.64 73	89.53 61	93.32 66	96.51 77	95.03 46	88.53 45	95.98 35	69.10 111	91.81 33	64.53 144	93.40 42	93.53 67	91.35 82	97.77 86	93.75 162

HQP-MVS		89.57 64	90.57 66	88.41 69	92.77 68	94.71 102	94.24 53	87.97 46	93.44 58	68.18 114	91.75 34	71.54 120	89.90 79	92.31 93	91.43 80	97.39 106	98.80 55

CS-MVS-test		89.04 65	91.14 57	86.59 85	87.75 120	95.51 96	89.44 95	76.32 124	90.52 79	74.56 94	80.06 62	84.40 80	93.23 44	95.41 37	92.98 72	98.22 57	99.09 41

MVS_Test		89.02 66	90.20 68	87.64 77	89.83 93	97.05 69	92.30 64	77.59 115	92.89 65	75.01 92	77.36 72	76.10 104	92.27 51	95.30 40	95.42 27	98.83 32	97.30 104

CLD-MVS		88.99 67	88.07 85	90.07 58	89.61 96	94.94 99	93.82 56	85.70 61	92.73 67	82.73 47	79.97 63	69.59 124	90.44 74	90.32 114	89.93 103	98.10 63	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 76	89.44 98	96.74 75	91.62 72	77.92 112	93.79 57	78.76 71	77.55 71	78.46 98	89.38 85	92.26 94	92.52 73	99.10 10	98.23 79

PMMVS		88.56 69	91.22 55	85.47 94	90.04 90	95.60 94	86.62 121	78.49 107	93.86 55	70.62 106	90.00 38	80.08 91	91.64 56	92.36 91	89.80 107	95.40 169	96.84 113

baseline1		88.16 70	88.15 84	88.17 73	90.02 91	94.79 101	91.85 69	83.89 67	87.37 97	75.67 91	73.75 92	79.89 92	88.44 92	94.41 56	93.33 66	99.18 5	93.55 164

thisisatest0530		87.99 71	90.76 60	84.75 97	88.36 110	96.82 72	87.65 110	79.67 95	91.77 71	70.93 102	79.94 64	87.65 58	84.21 111	92.98 78	89.07 118	97.66 92	97.13 107

tttt0517		87.93 72	90.71 61	84.68 98	88.33 111	96.76 74	87.42 113	79.67 95	91.74 72	70.83 103	79.91 65	87.61 59	84.21 111	92.88 83	89.07 118	97.62 95	97.03 109

CANet_DTU		87.91 73	91.57 53	83.64 106	90.96 77	97.12 67	91.90 68	75.97 127	92.83 66	53.16 169	86.02 50	79.02 96	90.80 66	95.40 39	94.15 52	99.03 17	96.47 124

diffmvs		87.86 74	87.40 91	88.39 70	88.57 108	96.10 86	91.24 80	83.15 71	90.62 78	79.13 68	72.45 100	67.71 130	90.07 78	92.58 87	93.31 67	98.17 59	99.03 46

IS_MVSNet		87.83 75	90.66 63	84.53 99	90.08 89	96.79 73	88.16 104	79.89 91	85.44 107	72.20 97	75.50 84	87.14 61	80.21 139	95.53 35	95.22 30	96.65 131	99.02 47

EPP-MVSNet		87.72 76	89.74 71	85.37 95	89.11 102	95.57 95	86.31 122	79.44 98	85.83 106	75.73 90	77.23 74	90.05 49	84.78 108	91.22 103	90.25 95	96.83 122	98.04 85

ET-MVSNet_ETH3D		87.63 77	91.08 58	83.59 107	67.96 209	96.30 84	92.06 66	78.47 108	91.95 70	69.87 108	87.57 43	84.14 82	94.34 31	88.58 127	92.10 75	98.88 29	96.93 110

DI_MVS_plusplus_trai		87.63 77	87.13 93	88.22 72	88.61 107	95.92 90	94.09 55	81.41 83	87.00 100	78.38 77	59.70 145	80.52 89	89.08 87	94.37 58	93.34 64	97.73 87	99.05 44

casdiffmvs		87.59 79	86.69 97	88.64 66	89.06 103	96.32 83	90.18 87	83.21 70	87.74 95	80.20 61	67.99 117	68.34 128	90.79 67	93.83 63	94.08 53	98.41 49	98.50 70

PVSNet_Blended_VisFu		87.44 80	88.72 83	85.95 90	92.02 72	97.26 65	86.88 119	82.66 78	83.86 123	79.16 67	66.96 120	84.91 78	77.26 156	94.97 46	93.48 62	97.73 87	99.64 15

FMVSNet3		87.19 81	87.32 92	87.04 83	82.82 148	90.21 142	92.88 60	76.53 121	91.69 73	81.31 53	64.81 128	80.64 86	89.79 83	94.80 50	94.76 39	98.88 29	94.32 151

LS3D		87.19 81	85.48 104	89.18 63	94.96 57	95.47 97	92.02 67	93.36 26	88.69 89	67.01 115	70.56 109	72.10 115	92.47 49	89.96 117	89.93 103	95.25 171	91.68 173

ACMP	85.16 9	87.15 83	87.04 94	87.27 81	90.80 79	94.45 105	89.41 96	83.09 75	89.15 86	76.98 86	86.35 47	65.80 137	86.94 97	88.45 128	87.52 137	96.42 146	97.56 98
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		87.04 84	89.59 75	84.07 101	90.94 78	95.95 89	86.02 124	81.65 82	85.94 104	78.54 75	78.00 70	85.40 76	69.62 176	91.83 97	91.53 79	97.63 94	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 85	87.65 88	86.12 89	91.77 75	93.84 111	93.04 59	82.77 77	88.04 92	65.33 120	87.69 42	67.09 134	86.79 98	90.20 115	88.99 121	97.05 117	97.71 92

PatchMatch-RL		86.75 86	85.43 105	88.29 71	94.06 61	96.37 82	86.82 120	82.94 76	88.94 87	79.59 63	79.83 66	59.17 156	89.46 84	91.12 104	88.81 125	96.88 121	93.78 160

baseline2		86.51 87	89.35 79	83.19 109	85.70 133	94.88 100	85.75 129	77.13 117	89.87 83	70.65 105	79.03 67	79.14 94	81.51 132	93.70 64	90.22 96	98.38 52	98.60 64

thres100view900		86.48 88	85.08 107	88.12 74	90.54 80	96.90 71	92.39 63	84.82 65	84.16 121	71.65 98	70.86 106	60.49 151	91.23 60	93.65 65	90.19 98	98.10 63	99.32 27

ACMM	84.23 10	86.40 89	84.64 110	88.46 68	91.90 73	91.93 133	88.11 105	85.59 62	88.61 90	79.13 68	75.31 85	66.25 135	89.86 82	89.88 118	87.64 134	96.16 156	92.86 169
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		86.16 90	86.00 100	86.35 86	81.81 154	89.52 151	91.40 74	76.53 121	91.69 73	81.31 53	64.81 128	80.64 86	88.72 88	90.54 108	90.72 87	98.34 53	94.08 152

test1		86.16 90	86.00 100	86.35 86	81.81 154	89.52 151	91.40 74	76.53 121	91.69 73	81.31 53	64.81 128	80.64 86	88.72 88	90.54 108	90.72 87	98.34 53	94.08 152

tfpn200view9		86.07 92	84.76 109	87.61 78	90.54 80	96.39 79	91.35 77	83.15 71	84.16 121	71.65 98	70.86 106	60.49 151	90.91 64	92.89 80	89.34 110	98.05 69	99.17 38

DCV-MVSNet		85.90 93	85.88 102	85.93 91	87.86 116	88.37 168	89.45 94	77.46 116	87.33 98	77.51 81	76.06 77	75.76 106	88.48 91	87.40 136	88.89 124	94.80 180	97.37 102

Vis-MVSNet (Re-imp)		85.89 94	89.62 74	81.55 120	89.85 92	96.08 88	87.55 111	79.80 92	84.80 114	66.55 117	73.70 93	86.71 62	68.25 183	94.40 57	94.53 42	97.32 109	97.09 108

MSDG		85.81 95	82.29 133	89.93 59	95.52 54	92.61 122	91.51 73	91.46 39	85.12 111	78.56 73	63.25 134	69.01 126	85.31 105	88.45 128	88.23 128	97.21 113	89.33 184

thres200		85.80 96	84.38 111	87.46 79	90.51 82	96.39 79	91.64 71	83.15 71	81.59 130	71.54 100	70.24 110	60.41 153	89.88 80	92.89 80	89.85 106	98.06 67	99.26 32

OPM-MVS		85.69 97	82.79 125	89.06 65	93.42 64	94.21 109	94.21 54	87.61 49	72.68 155	70.79 104	71.09 104	67.27 133	90.74 68	91.29 102	89.05 120	97.61 96	93.94 157

thres400		85.59 98	84.08 114	87.36 80	90.45 84	96.60 76	90.95 85	83.67 69	80.99 133	71.17 101	69.08 115	60.25 154	89.88 80	93.14 73	89.34 110	98.02 71	99.17 38

CostFormer		85.47 99	86.98 95	83.71 104	88.70 106	94.02 110	88.07 106	62.72 192	89.78 84	78.68 72	72.69 98	78.37 99	87.35 96	85.96 149	89.32 114	96.73 128	98.72 56

thres600view7		85.14 100	83.58 120	86.96 84	90.37 87	96.39 79	90.33 86	83.15 71	80.46 134	70.60 107	67.96 118	60.04 155	89.22 86	92.89 80	88.28 127	98.06 67	99.08 43

test-LLR		85.11 101	89.49 77	80.00 129	85.32 138	94.49 103	82.27 159	74.18 137	87.83 93	56.70 147	75.55 82	86.26 64	82.75 124	93.06 75	90.60 92	98.77 36	98.65 62

FMVSNet2		84.89 102	84.02 116	85.91 92	81.81 154	89.52 151	91.40 74	75.79 128	84.45 118	79.39 65	58.75 148	74.35 108	88.72 88	93.51 69	93.46 63	98.34 53	94.08 152

FC-MVSNet-train		84.88 103	84.08 114	85.82 93	89.21 100	91.74 134	85.87 125	81.20 85	81.71 129	74.66 93	73.38 95	64.99 141	86.60 99	90.75 106	88.08 129	97.36 107	97.90 89

EPNet_dtu		84.87 104	89.01 80	80.05 128	95.25 56	92.88 120	88.84 100	84.11 66	91.69 73	49.28 185	85.69 51	78.95 97	65.39 188	92.22 95	91.66 78	97.43 104	89.95 180
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Effi-MVS+		84.80 105	85.71 103	83.73 103	87.94 115	95.76 91	90.08 88	73.45 144	85.12 111	62.66 130	72.39 101	64.97 142	90.59 71	92.95 79	90.69 90	97.67 91	98.12 81

UA-Net		84.69 106	87.64 89	81.25 122	90.38 86	95.67 92	87.33 114	79.41 99	72.07 159	66.48 118	75.09 86	92.48 40	66.88 184	94.03 62	94.25 50	97.01 120	89.88 181

TESTMET0.1,1		84.62 107	89.49 77	78.94 138	82.18 151	94.49 103	82.27 159	70.94 165	87.83 93	56.70 147	75.55 82	86.26 64	82.75 124	93.06 75	90.60 92	98.77 36	98.65 62

CHOSEN 1792x2688		84.59 108	84.30 113	84.93 96	93.71 62	98.23 53	89.91 90	77.96 111	84.81 113	65.93 119	45.19 193	71.76 119	83.13 122	95.46 36	95.13 33	98.94 24	99.53 19

Anonymous20231211		84.23 109	81.71 138	87.17 82	87.38 124	93.59 114	88.95 99	82.14 80	83.82 124	78.56 73	48.09 186	73.89 109	91.25 59	86.38 143	88.06 131	94.74 181	98.14 80

MDTV_nov1_ep13		84.17 110	88.03 86	79.66 131	86.00 131	94.41 106	85.05 131	66.01 187	90.36 80	64.34 126	77.13 75	84.56 79	82.71 126	87.12 140	88.92 122	93.84 187	93.69 163

test-mter		84.06 111	89.00 81	78.29 143	81.92 152	94.23 108	81.07 169	70.38 169	87.12 99	56.10 156	74.75 87	85.80 70	81.81 131	92.52 88	90.10 100	98.43 47	98.49 71

IB-MVS	79.58 12	83.83 112	84.81 108	82.68 112	91.85 74	97.35 63	75.75 188	82.57 79	86.55 102	84.01 43	70.90 105	65.43 139	63.18 194	84.19 163	89.92 105	98.74 38	99.31 29
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		83.71 113	86.76 96	80.16 127	89.72 95	95.64 93	84.68 132	59.73 197	89.61 85	62.67 129	72.65 99	81.80 84	86.22 101	86.23 145	88.03 132	97.96 77	93.35 165

HyFIR lowres test		83.43 114	82.94 123	84.01 102	93.41 65	97.10 68	87.21 115	74.04 139	80.15 136	64.98 121	41.09 201	76.61 103	86.51 100	93.31 70	93.01 71	97.91 83	99.30 30

PatchmatchNet		83.28 115	87.57 90	78.29 143	87.46 122	94.95 98	83.36 141	59.43 200	90.20 82	58.10 142	74.29 90	86.20 66	84.13 113	85.27 155	87.39 138	97.25 112	94.67 149
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
SCA		83.26 116	87.76 87	78.00 148	87.45 123	92.20 128	82.63 155	58.42 202	90.30 81	58.23 140	75.74 81	87.75 57	83.97 116	86.10 148	87.64 134	97.30 110	94.62 150

GeoE		83.17 117	82.86 124	83.53 108	87.24 125	93.78 112	87.94 107	72.75 149	82.19 127	69.76 109	60.54 142	65.95 136	86.01 102	89.41 122	89.72 108	97.47 99	98.43 73

CDS-MVSNet		83.13 118	83.73 119	82.43 118	84.52 143	92.92 119	88.26 103	77.67 114	72.08 158	69.08 112	66.96 120	74.66 107	78.61 145	90.70 107	91.96 76	96.46 145	96.86 112
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		82.91 119	81.86 135	84.13 100	88.25 112	88.32 169	87.67 109	80.86 88	84.78 115	76.57 89	85.56 52	76.00 105	84.61 109	78.20 199	76.52 202	86.81 208	83.63 201

Vis-MVSNet		82.88 120	86.04 99	79.20 136	87.77 119	96.42 78	86.10 123	76.70 119	74.82 149	61.38 132	70.70 108	77.91 100	64.83 190	93.22 71	93.19 69	98.43 47	96.01 128
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dps		82.63 121	82.64 128	82.62 114	87.81 118	92.81 121	84.39 133	61.96 193	86.43 103	81.63 49	69.72 112	67.60 132	84.42 110	82.51 177	83.90 176	95.52 165	95.50 139

IterMVS-LS		82.62 122	82.75 127	82.48 115	87.09 126	87.48 183	87.19 116	72.85 147	79.09 137	66.63 116	65.22 123	72.14 114	84.06 115	88.33 131	91.39 81	97.03 119	95.60 138
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		82.61 123	82.51 130	82.72 110	85.49 136	93.06 117	87.17 117	71.39 161	84.18 119	64.59 123	63.03 135	58.89 157	90.22 76	91.39 100	90.83 85	97.44 101	96.21 126

DROMVSNet		82.61 123	82.51 130	82.72 110	85.49 136	93.06 117	87.17 117	71.39 161	84.18 119	64.59 123	63.03 135	58.89 157	90.22 76	91.39 100	90.83 85	97.44 101	96.21 126

tpm cat1		82.39 125	82.32 132	82.47 116	88.13 113	92.42 126	87.43 112	62.79 191	85.30 108	78.05 79	60.14 143	72.10 115	83.20 121	82.26 180	85.67 155	95.23 172	98.35 77

MS-PatchMatch		82.16 126	82.18 134	82.12 119	91.65 76	93.50 115	89.51 92	71.95 155	81.48 131	64.45 125	59.58 147	77.54 101	77.23 157	89.88 118	85.62 156	97.94 78	87.68 188

tpmrst		81.71 127	83.87 118	79.20 136	89.01 104	93.67 113	84.22 134	60.14 195	87.45 96	59.49 136	64.97 126	71.86 118	85.30 106	84.72 159	86.30 146	97.04 118	98.09 83

RPMNet		81.47 128	86.24 98	75.90 166	86.72 127	92.12 130	82.82 153	55.76 208	85.21 109	53.73 167	63.45 132	83.16 83	80.13 140	92.34 92	89.52 109	96.23 154	97.90 89

CR-MVSNet		81.44 129	85.29 106	76.94 157	86.53 128	92.12 130	83.86 135	58.37 203	85.21 109	56.28 151	59.60 146	80.39 90	80.50 137	92.77 84	89.32 114	96.12 158	97.59 96

Effi-MVS+-dtu		81.18 130	82.77 126	79.33 134	84.70 142	92.54 124	85.81 126	71.55 159	78.84 138	57.06 146	71.98 103	63.77 146	85.09 107	88.94 124	87.62 136	91.79 200	95.68 133

test0.0.03 1		80.99 131	84.37 112	77.05 155	85.32 138	89.79 147	78.43 179	74.18 137	84.78 115	57.98 145	76.06 77	72.88 111	69.14 180	88.02 133	87.70 133	97.27 111	91.37 174

Fast-Effi-MVS+-dtu		80.57 132	83.44 121	77.22 153	83.98 146	91.52 136	85.78 128	64.54 190	80.38 135	50.28 181	74.06 91	62.89 147	82.00 130	89.10 123	88.91 123	96.75 126	97.21 106

FMVSNet5		80.56 133	82.53 129	78.26 145	73.80 204	81.52 202	82.26 161	68.36 180	88.85 88	64.21 127	69.09 114	84.38 81	83.49 120	87.13 139	86.76 143	97.44 101	79.95 204

ADS-MVSNet		80.25 134	82.96 122	77.08 154	87.86 116	92.60 123	81.82 166	56.19 207	86.95 101	56.16 154	68.19 116	72.42 113	83.70 119	82.05 181	85.45 161	96.75 126	93.08 168

FMVSNet1		80.18 135	78.07 149	82.65 113	78.55 178	87.57 182	88.41 102	73.93 140	70.16 164	73.57 95	49.80 176	64.45 145	85.35 104	90.54 108	90.72 87	96.10 159	93.21 166

USDC		80.10 136	79.33 145	81.00 124	86.36 129	91.71 135	88.74 101	75.77 129	81.90 128	54.90 161	67.67 119	52.05 170	83.94 117	88.44 130	86.25 147	96.31 149	87.28 192

COLMAP_ROB	75.69 15	79.47 137	76.90 156	82.46 117	92.20 69	90.53 138	85.30 130	83.69 68	78.27 141	61.47 131	58.26 150	62.75 149	78.28 148	82.41 178	82.13 189	93.83 189	83.98 200
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test_part1		79.37 138	75.64 161	83.71 104	86.18 130	87.74 176	87.84 108	75.69 131	66.33 181	78.93 70	45.92 191	64.85 143	82.44 127	83.08 175	85.69 154	91.17 201	95.90 130

pmmvs4		79.32 139	77.78 151	81.11 123	80.18 163	88.96 163	83.39 139	76.07 125	81.27 132	69.35 110	58.66 149	51.19 173	82.01 129	87.16 138	84.39 173	95.66 163	92.82 170

PatchT		79.28 140	83.88 117	73.93 175	85.54 135	90.95 137	66.14 205	56.53 206	83.21 125	56.28 151	56.50 153	76.80 102	80.50 137	92.77 84	89.32 114	98.57 43	97.59 96

ACMH	78.51 14	79.27 141	78.08 148	80.65 125	89.52 97	90.40 139	80.45 171	79.77 94	69.54 169	54.85 162	64.83 127	56.16 164	83.94 117	84.58 161	86.01 151	95.41 168	95.03 146
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		79.23 142	78.95 147	79.56 132	81.89 153	92.52 125	82.97 148	73.70 141	67.27 175	64.97 122	61.66 141	65.06 140	78.61 145	87.12 140	88.07 130	95.23 172	90.95 176

ACMH+	79.09 13	79.12 143	77.22 155	81.35 121	88.50 109	90.36 140	82.14 163	79.38 101	72.78 154	58.59 137	62.31 140	56.44 163	84.10 114	82.03 182	84.05 174	95.40 169	92.55 171

UniMVSNet_NR-MVSNet		78.89 144	78.04 150	79.88 130	79.40 169	89.70 148	82.92 150	80.17 89	76.37 147	58.56 138	57.10 152	54.92 166	81.44 133	83.51 168	87.12 140	96.76 125	97.60 94

tpm		78.87 145	81.33 141	76.00 164	85.57 134	90.19 143	82.81 154	59.66 198	78.35 140	51.40 176	66.30 122	67.92 129	80.94 135	83.28 171	85.73 152	95.65 164	97.56 98

GA-MVS		78.86 146	80.42 142	77.05 155	83.27 147	92.17 129	83.24 143	75.73 130	73.75 151	46.27 195	62.43 138	57.12 160	76.94 159	93.14 73	89.34 110	96.83 122	95.00 147

IterMVS		78.85 147	81.36 139	75.93 165	84.27 145	85.74 189	83.83 137	66.35 185	76.82 142	50.48 179	63.48 131	68.82 127	73.99 164	89.68 120	89.34 110	96.63 134	95.67 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		78.71 148	81.34 140	75.64 170	84.31 144	85.67 190	83.51 138	66.14 186	76.67 143	50.38 180	63.45 132	69.02 125	73.23 166	89.66 121	89.22 117	96.24 153	95.67 134

UniMVSNet (Re)		78.00 149	77.52 152	78.57 141	79.66 168	90.36 140	82.09 164	77.86 113	76.38 146	60.26 133	54.63 159	52.07 169	75.31 162	84.97 158	86.10 149	96.22 155	98.11 82

DU-MVS		77.98 150	76.71 157	79.46 133	78.68 175	89.26 157	82.92 150	79.06 104	76.52 144	58.56 138	54.89 157	48.35 187	81.44 133	83.16 173	87.21 139	96.08 160	97.60 94

FC-MVSNet-test		77.95 151	81.85 136	73.39 180	82.31 149	88.99 162	79.33 175	74.24 136	78.75 139	47.40 193	70.22 111	72.09 117	60.78 200	86.66 142	85.62 156	96.30 150	90.61 177

NR-MVSNet		77.21 152	76.41 158	78.14 147	80.18 163	89.26 157	83.38 140	79.06 104	76.52 144	56.59 149	54.89 157	45.32 197	72.89 168	85.39 154	86.12 148	96.71 129	97.36 103

thisisatest0515		77.13 153	79.36 144	74.52 172	79.79 167	89.65 149	73.54 193	73.69 142	74.10 150	58.14 141	62.79 137	60.57 150	66.49 186	88.08 132	85.16 166	95.49 167	95.15 144

gg-mvs-nofinetune		77.08 154	79.79 143	73.92 176	85.95 132	97.23 66	92.18 65	52.65 211	46.19 214	27.79 218	38.27 205	85.63 71	85.67 103	96.95 17	95.62 24	99.30 3	98.67 61

TranMVSNet+NR-MVSNet		77.02 155	75.76 160	78.49 142	78.46 181	88.24 170	83.03 147	79.97 90	73.49 153	54.73 163	54.00 162	48.74 182	78.15 150	82.36 179	86.90 142	96.59 136	96.55 118

CVMVSNet		76.86 156	79.09 146	74.26 173	85.29 140	89.44 154	79.91 174	78.47 108	68.94 172	44.45 200	62.35 139	69.70 123	64.50 191	85.82 150	87.03 141	92.94 195	90.33 178

Baseline_NR-MVSNet		76.71 157	74.56 168	79.23 135	78.68 175	84.15 198	82.45 157	78.87 106	75.83 148	60.05 134	47.92 187	50.18 179	79.06 144	83.16 173	83.86 177	96.26 151	96.80 114

v2v482		76.25 158	74.78 165	77.96 149	78.50 180	89.14 160	83.05 146	76.02 126	68.78 173	54.11 164	51.36 168	48.59 184	79.49 142	83.53 167	85.60 159	96.59 136	96.49 123

V42		76.21 159	75.04 164	77.58 150	78.68 175	89.33 156	82.93 149	74.64 134	69.84 166	56.13 155	50.42 173	50.93 174	76.30 161	83.32 169	84.89 170	96.83 122	96.54 119

v8		75.89 160	74.74 166	77.23 152	79.09 171	88.00 173	83.19 144	71.08 164	70.03 165	56.29 150	50.50 171	50.88 175	77.06 158	83.32 169	84.99 168	96.68 130	95.49 140

TinyColmap		75.75 161	73.19 179	78.74 140	84.82 141	87.69 178	81.59 167	74.62 135	71.81 160	54.01 165	55.79 156	44.42 202	82.89 123	84.61 160	83.76 178	94.50 182	84.22 199

MIMVSNet		75.71 162	77.26 153	73.90 177	70.93 205	88.71 166	79.98 173	57.67 205	73.58 152	58.08 144	53.93 163	58.56 159	79.41 143	90.04 116	89.97 101	97.34 108	86.04 193

UniMVSNet_ETH3D		75.63 163	71.59 188	80.35 126	81.03 158	89.90 146	83.25 142	76.58 120	60.08 197	64.19 128	42.89 200	45.01 198	82.14 128	80.20 192	86.75 144	94.90 177	96.29 125

pm-mvs1		75.61 164	74.19 170	77.26 151	80.16 165	88.79 164	81.49 168	75.49 133	59.49 199	58.09 143	48.32 184	55.53 165	72.35 169	88.61 126	85.48 160	95.99 161	93.12 167

v10		75.57 165	74.67 167	76.62 160	78.73 174	87.46 184	83.14 145	69.41 176	69.27 170	53.44 168	49.73 177	49.21 181	78.44 147	86.17 147	85.18 165	96.53 141	95.65 137

v1144		75.54 166	74.55 169	76.69 158	78.33 184	88.77 165	82.89 152	72.76 148	67.18 177	51.73 173	49.34 179	48.37 185	78.10 151	86.22 146	85.24 163	96.35 148	96.74 115

TDRefinement		75.54 166	73.22 177	78.25 146	87.65 121	89.65 149	85.81 126	79.28 103	71.14 162	56.06 157	52.17 166	51.96 171	68.74 182	81.60 183	80.58 191	91.94 198	85.45 194

pmmvs5		75.46 168	75.12 163	75.87 167	79.39 170	89.44 154	78.12 181	72.27 153	65.98 183	51.54 174	55.83 155	46.23 192	76.80 160	88.77 125	85.73 152	97.07 116	93.84 158

tfpnnormal		75.27 169	72.12 185	78.94 138	82.30 150	88.52 167	82.41 158	79.41 99	58.03 200	55.59 159	43.83 199	44.71 199	77.35 154	87.70 135	85.45 161	96.60 135	96.61 117

anonymousdsp		75.14 170	77.25 154	72.69 183	76.68 194	89.26 157	75.26 190	68.44 179	65.53 186	46.65 194	58.16 151	56.67 162	73.96 165	87.84 134	86.05 150	95.13 175	97.22 105

v148		74.98 171	73.52 175	76.69 158	78.84 173	89.02 161	78.78 177	76.82 118	67.22 176	59.61 135	49.18 180	47.94 189	70.57 175	80.76 187	83.99 175	95.52 165	96.52 121

v1192		74.96 172	73.92 171	76.17 161	77.76 187	88.19 172	82.54 156	71.94 156	66.84 178	50.07 183	48.10 185	46.14 193	78.28 148	86.30 144	85.23 164	96.41 147	96.67 116

v144192		74.76 173	73.64 172	76.06 163	77.58 188	88.23 171	81.87 165	71.63 158	66.03 182	51.08 177	48.63 183	46.77 191	77.59 153	84.53 162	84.76 171	96.64 133	96.54 119

v1921920		74.60 174	73.56 174	75.81 168	77.43 190	87.94 174	82.18 162	71.33 163	66.48 180	49.23 187	47.84 188	45.56 195	78.03 152	85.70 152	84.92 169	96.65 131	96.50 122

v1240		74.04 175	73.04 181	75.20 171	77.19 192	87.69 178	80.93 170	70.72 168	65.08 187	48.47 188	47.31 189	44.71 199	77.33 155	85.50 153	85.07 167	96.59 136	95.94 129

testgi		73.22 176	75.84 159	70.16 194	81.67 157	85.50 193	71.45 195	70.81 166	69.56 168	44.74 199	74.52 89	49.25 180	58.45 201	84.10 165	83.37 182	93.86 186	84.56 198

CP-MVSNet		73.19 177	72.37 183	74.15 174	77.54 189	86.77 187	76.34 184	72.05 154	65.66 185	51.47 175	50.49 172	43.66 203	70.90 171	80.93 186	83.40 181	96.59 136	95.66 136

WR-MVS		72.93 178	73.57 173	72.19 186	78.14 185	87.71 177	76.21 186	73.02 146	67.78 174	50.09 182	50.35 174	50.53 177	61.27 199	80.42 190	83.10 185	94.43 183	95.11 145

TransMVSNet (Re)		72.90 179	70.51 192	75.69 169	80.88 159	85.26 195	79.25 176	78.43 110	56.13 206	52.81 170	46.81 190	48.20 188	66.77 185	85.18 157	83.70 179	95.98 162	88.28 187

WR-MVS_H		72.69 180	72.80 182	72.56 185	77.94 186	87.83 175	75.26 190	71.53 160	64.75 188	52.19 172	49.83 175	48.62 183	61.96 197	81.12 185	82.44 187	96.50 142	95.00 147

SixPastTwentyTwo		72.65 181	73.22 177	71.98 189	78.40 182	87.64 180	70.09 198	70.37 170	66.49 179	47.60 191	65.09 124	45.94 194	73.09 167	78.94 194	78.66 197	92.33 196	89.82 182

LTVRE_ROB	71.82 16	72.62 182	71.77 186	73.62 178	80.74 160	87.59 181	80.42 172	70.37 170	49.73 210	37.12 212	59.76 144	42.52 208	80.92 136	83.20 172	85.61 158	92.13 197	93.95 156
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		72.37 183	71.47 190	73.43 179	77.32 191	86.43 188	75.99 187	71.94 156	63.37 191	49.24 186	49.07 181	42.42 209	69.60 177	80.59 189	83.18 184	96.48 144	95.23 142

MVS-HIRNet		72.32 184	73.45 176	71.00 192	80.58 161	89.97 144	68.51 202	55.28 209	70.89 163	52.27 171	39.09 203	57.11 161	75.02 163	85.76 151	86.33 145	94.36 184	85.00 196

PEN-MVS		72.24 185	71.30 191	73.33 181	77.08 193	85.57 191	76.75 182	72.52 151	63.89 190	48.12 189	50.79 169	43.09 206	69.03 181	78.54 196	83.46 180	96.50 142	93.76 161

v7n		72.11 186	71.66 187	72.63 184	75.26 199	86.85 185	76.74 183	68.77 178	62.70 194	49.40 184	45.92 191	43.51 204	70.63 174	84.16 164	83.21 183	94.99 176	95.25 141

EG-PatchMatch MVS		71.81 187	71.54 189	72.12 187	80.53 162	89.94 145	78.51 178	66.56 184	57.38 202	47.46 192	44.28 198	52.22 168	63.10 195	85.22 156	84.42 172	96.56 140	87.35 191

CMPMVS	54.54 17	71.74 188	67.94 197	76.16 162	90.41 85	93.25 116	78.32 180	75.60 132	59.81 198	53.95 166	44.64 196	51.22 172	70.70 172	74.59 205	75.88 203	88.01 205	76.23 207
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		71.65 189	73.08 180	69.97 195	75.22 200	86.81 186	73.98 192	59.61 199	69.75 167	48.01 190	54.21 161	53.06 167	69.19 179	78.50 197	80.43 192	93.84 187	88.79 185

pmnet_mix02		71.64 190	72.36 184	70.81 193	78.39 183	85.57 191	68.64 200	73.65 143	72.13 156	45.07 198	56.01 154	50.61 176	65.34 189	76.21 202	76.60 201	93.75 190	89.35 183

gm-plane-assit		71.33 191	75.18 162	66.83 198	79.06 172	75.57 209	48.05 216	60.33 194	48.28 211	34.67 216	44.34 197	67.70 131	79.78 141	97.25 11	96.21 13	99.10 10	96.92 111

DTE-MVSNet		71.19 192	70.45 193	72.06 188	76.61 195	84.59 197	75.61 189	72.32 152	63.12 193	45.70 197	50.72 170	43.02 207	65.89 187	77.53 201	82.23 188	96.26 151	91.93 172

pmmvs6		70.29 193	67.90 198	73.07 182	76.17 196	85.31 194	76.29 185	70.75 167	47.39 213	55.33 160	37.15 209	50.49 178	69.55 178	82.96 176	80.85 190	90.34 204	91.18 175

PM-MVS		70.17 194	69.42 195	71.04 191	70.82 206	81.26 204	71.25 196	67.80 181	69.16 171	51.04 178	53.15 165	34.93 213	72.19 170	80.30 191	76.95 200	93.16 194	90.21 179

pmmvs-eth3d		69.59 195	67.57 200	71.95 190	70.04 207	80.05 205	71.48 194	70.00 174	62.57 195	55.99 158	44.92 194	35.73 212	70.64 173	81.56 184	79.69 193	93.55 191	88.43 186

N_pmnet		68.54 196	67.83 199	69.38 196	75.77 197	81.90 201	66.21 204	72.53 150	65.91 184	46.09 196	44.67 195	45.48 196	63.82 193	74.66 204	77.39 199	91.87 199	84.77 197

Anonymous20231206		68.09 197	68.68 196	67.39 197	75.16 201	82.55 199	69.33 199	70.06 173	63.34 192	42.28 203	37.91 207	43.12 205	52.67 204	83.56 166	82.71 186	94.84 179	87.59 189

EU-MVSNet		68.07 198	70.25 194	65.52 199	74.68 203	81.30 203	68.53 201	70.31 172	62.40 196	37.43 211	54.62 160	48.36 186	51.34 205	78.32 198	79.27 194	90.84 202	87.47 190

GG-mvs-BLEND		65.67 199	93.78 40	32.89 212	0.47 222	99.35 6	96.92 31	0.22 221	93.28 60	0.51 223	84.07 54	92.50 39	0.62 220	93.59 66	93.86 56	98.59 42	99.79 9

test20.03		65.17 200	67.41 201	62.55 201	75.35 198	79.31 206	62.22 206	68.83 177	56.50 205	35.35 215	51.97 167	44.70 201	40.01 210	80.69 188	79.25 195	93.55 191	79.47 206

MDA-MVSNet-bldmvs		62.23 201	61.13 205	63.52 200	58.94 213	82.44 200	60.71 209	73.28 145	57.22 203	38.42 209	49.63 178	27.64 219	62.83 196	54.98 211	74.16 204	86.96 207	81.83 203

new_pmnet		61.60 202	62.68 203	60.35 204	63.02 210	74.93 210	60.97 208	58.86 201	64.21 189	35.38 214	39.51 202	39.89 210	57.37 202	72.78 206	72.56 206	86.49 209	74.85 209

new-patchmatchnet		60.74 203	59.78 207	61.87 202	69.52 208	76.67 208	57.99 212	65.78 188	52.63 208	38.47 208	38.08 206	32.92 216	48.88 207	68.50 207	69.87 207	90.56 203	79.75 205

pmmvs3		60.52 204	60.87 206	60.12 205	61.38 211	71.62 211	57.42 213	53.94 210	48.09 212	35.95 213	38.62 204	32.19 218	64.12 192	75.33 203	77.99 198	87.89 206	82.28 202

MIMVSNet1		60.51 205	61.43 204	59.44 206	48.75 216	77.21 207	60.98 207	66.84 183	52.09 209	38.74 207	29.29 212	39.40 211	48.08 208	77.60 200	78.87 196	93.22 193	75.56 208

test_method		60.40 206	66.30 202	53.52 208	37.48 220	64.10 215	55.56 214	42.45 216	71.79 161	41.87 204	33.74 210	46.80 190	61.71 198	79.18 193	73.33 205	82.01 211	95.17 143

FPMVS		56.54 207	52.82 209	60.87 203	74.90 202	67.58 214	67.69 203	65.38 189	57.86 201	41.51 205	37.83 208	34.19 214	41.21 209	55.88 210	53.09 212	74.55 214	63.31 212

PMVS	42.57 18	45.71 208	42.61 211	49.32 209	61.35 212	37.82 219	36.96 218	60.10 196	37.20 215	41.50 206	28.53 213	33.11 215	28.82 215	53.45 212	48.70 214	67.22 216	59.42 213
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		43.95 209	42.62 210	45.50 210	50.79 215	41.20 218	35.55 219	52.51 212	52.95 207	29.09 217	12.92 215	11.48 222	38.15 211	62.01 209	66.62 209	66.89 217	51.17 214
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		41.25 210	42.55 212	39.74 211	43.25 217	55.05 217	38.15 217	47.11 215	31.78 216	11.83 220	21.16 214	19.12 220	20.98 217	49.95 214	56.09 211	77.09 212	64.68 211

E-PMN		27.87 211	24.36 214	31.97 213	41.27 219	25.56 222	16.62 221	49.16 213	22.00 218	9.90 221	11.75 217	7.86 224	29.57 214	22.22 216	34.70 215	45.27 218	46.41 216

MVE	32.98 19	27.61 212	29.89 213	24.94 215	21.97 221	37.22 220	15.56 223	38.83 217	17.49 219	14.72 219	11.64 219	5.62 225	21.26 216	35.20 215	50.95 213	37.29 220	51.13 215
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		26.96 213	22.96 215	31.63 214	41.91 218	25.73 221	16.30 222	49.10 214	22.38 217	9.03 222	11.22 220	8.12 223	29.93 213	20.16 217	31.04 216	43.49 219	42.04 217

testmvs		5.16 214	8.14 216	1.69 216	0.36 223	1.65 223	3.02 224	0.66 219	7.17 220	0.50 224	12.58 216	0.69 226	4.67 218	5.42 218	5.65 217	0.92 221	23.86 219

test123		4.39 215	7.11 217	1.21 217	0.11 224	1.16 224	1.67 225	0.35 220	5.91 221	0.16 225	11.65 218	0.16 227	4.45 219	1.72 219	4.92 218	0.51 222	24.28 218

uanet_test		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

sosnet		0.00 216	0.00 218	0.00 218	0.00 225	0.00 225	0.00 226	0.00 222	0.00 222	0.00 226	0.00 221	0.00 228	0.00 221	0.00 220	0.00 219	0.00 223	0.00 220

RE-MVS-def										43.17 201

9.14												97.59 9

SR-MVS					98.52 20			93.70 22				96.63 19

Anonymous202405211			81.72 137		88.09 114	94.27 107	89.62 91	82.14 80	82.27 126		48.83 182	72.58 112	91.08 62	87.40 136	88.70 126	94.90 177	97.99 86

our_test_3					78.55 178	84.98 196	70.12 197

ambc			57.08 208		58.68 214	67.71 213	60.07 210		57.13 204	42.79 202	30.00 211	11.64 221	50.18 206	78.89 195	69.14 208	82.64 210	85.02 195

MTAPA										93.37 8		95.71 26

MTMP										93.84 4		94.86 30

Patchmatch-RL test							19.65 220

tmp_tt				57.89 207	79.94 166	59.29 216	52.84 215	36.65 218	94.77 50	68.22 113	72.96 96	65.62 138	33.65 212	66.20 208	58.02 210	76.06 213

XVS					92.16 70	98.56 36	91.04 82			81.00 58		93.49 34				98.00 73

X-MVStestdata					92.16 70	98.56 36	91.04 82			81.00 58		93.49 34				98.00 73

abl_6				93.25 35	97.12 38	98.71 31	94.40 51	87.81 47	97.86 10	87.19 30	91.07 36	95.80 24	94.18 34			98.78 35	99.36 24

mPP-MVS					97.95 29							92.24 44

NP-MVS									94.12 54

Patchmtry						92.08 132	83.86 135	58.37 203		56.28 151

DeepMVS_CX						70.68 212	59.61 211	67.36 182	72.12 157	38.41 210	53.88 164	32.44 217	55.15 203	50.88 213		74.35 215	68.42 210

High-resolution multi-view benchmark