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		88.94 1	90.98 1	86.56 1	92.53 7	95.09 1	88.55 5	76.83 7	94.16 1	86.57 1	90.85 6	87.07 1	86.18 1	86.36 7	85.08 12	88.67 21	98.21 3

DVP-MVS		88.07 2	90.73 2	84.97 5	91.98 10	95.01 2	87.86 10	76.88 6	93.90 2	85.15 2	90.11 8	86.90 2	79.46 12	86.26 10	84.67 18	88.50 28	98.25 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
MSP-MVS		87.87 4	90.57 3	84.73 6	89.38 28	91.60 18	88.24 8	74.15 13	93.55 3	82.28 4	94.99 1	83.21 11	85.96 3	87.67 4	84.67 18	88.32 32	98.29 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		87.60 5	90.44 4	84.29 8	92.09 9	93.44 6	88.69 4	75.11 10	93.06 5	80.80 6	94.23 2	86.70 3	81.44 6	84.84 18	83.52 27	87.64 46	97.28 5
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++.		87.98 3	89.76 5	85.89 2	92.57 6	94.57 3	88.34 6	76.61 8	92.40 6	83.40 3	89.26 11	85.57 5	86.04 2	86.24 11	84.89 15	88.39 31	95.42 21

SF-MVS		87.30 6	88.71 6	85.64 3	94.57 1	94.55 4	91.01 1	79.94 1	89.15 12	79.85 7	92.37 3	83.29 10	79.75 8	83.52 26	82.72 32	88.75 19	95.37 24

HPM-MVS++		85.64 10	88.43 7	82.39 13	92.65 4	90.24 27	85.83 17	74.21 12	90.68 9	75.63 19	86.77 14	84.15 8	78.68 16	86.33 8	85.26 9	87.32 55	95.60 18

APDe-MVS		86.37 7	88.41 8	84.00 10	91.43 15	91.83 16	88.34 6	74.67 11	91.19 7	81.76 5	91.13 5	81.94 18	80.07 7	83.38 28	82.58 35	87.69 44	96.78 10

SMA-MVS		85.24 12	88.27 9	81.72 16	91.74 12	90.71 21	86.71 13	73.16 20	90.56 10	74.33 20	83.07 19	85.88 4	77.16 20	86.28 9	85.58 6	87.23 59	95.77 14
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
DeepPCF-MVS	76.94 1	83.08 20	87.77 10	77.60 36	90.11 20	90.96 20	78.48 55	72.63 23	93.10 4	65.84 43	80.67 24	81.55 19	74.80 31	85.94 13	85.39 8	83.75 142	96.77 11

CNVR-MVS		85.96 8	87.58 11	84.06 9	92.58 5	92.40 11	87.62 11	77.77 5	88.44 15	75.93 18	79.49 26	81.97 17	81.65 5	87.04 6	86.58 4	88.79 17	97.18 7

APD-MVS		84.83 13	87.00 12	82.30 14	89.61 26	89.21 35	86.51 15	73.64 17	90.98 8	77.99 13	89.89 9	80.04 24	79.18 14	82.00 48	81.37 48	86.88 67	95.49 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MCST-MVS		85.75 9	86.99 13	84.31 7	94.07 3	92.80 8	88.15 9	79.10 3	85.66 24	70.72 31	76.50 33	80.45 21	82.17 4	88.35 2	87.49 3	91.63 2	97.65 4

SD-MVS		84.31 16	86.96 14	81.22 17	88.98 32	88.68 39	85.65 18	73.85 16	89.09 14	79.63 9	87.34 13	84.84 6	73.71 38	82.66 35	81.60 45	85.48 105	94.51 32
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
train_agg		83.35 19	86.93 15	79.17 28	89.70 24	88.41 42	85.60 20	72.89 22	86.31 22	66.58 42	90.48 7	82.24 15	73.06 44	83.10 31	82.64 34	87.21 63	95.30 26

DPM-MVS		85.41 11	86.72 16	83.89 11	91.66 13	91.92 15	90.49 3	78.09 4	86.90 19	73.95 21	74.52 35	82.01 16	79.29 13	90.24 1	90.65 1	89.86 6	90.78 74

TSAR-MVS + MP.		84.39 14	86.58 17	81.83 15	88.09 40	86.47 66	85.63 19	73.62 18	90.13 11	79.24 10	89.67 10	82.99 12	77.72 18	81.22 54	80.92 58	86.68 71	94.66 30
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP_NAP		83.54 18	86.37 18	80.25 22	89.57 27	90.10 29	85.27 21	71.66 24	87.38 16	73.08 23	84.23 18	80.16 22	75.31 26	84.85 17	83.64 24	86.57 72	94.21 39

TSAR-MVS + GP.		82.27 24	85.98 19	77.94 34	80.72 72	88.25 45	81.12 44	67.71 46	87.10 17	73.31 22	85.23 16	83.68 9	76.64 22	80.43 62	81.47 47	88.15 38	95.66 17

TSAR-MVS + ACMM		81.59 27	85.84 20	76.63 40	89.82 23	86.53 65	86.32 16	66.72 53	85.96 23	65.43 44	88.98 12	82.29 14	67.57 81	82.06 47	81.33 49	83.93 140	93.75 44

NCCC		84.16 17	85.46 21	82.64 12	92.34 8	90.57 24	86.57 14	76.51 9	86.85 21	72.91 24	77.20 32	78.69 27	79.09 15	84.64 20	84.88 16	88.44 29	95.41 22

SteuartSystems-ACMMP		82.51 22	85.35 22	79.20 27	90.25 18	89.39 34	84.79 22	70.95 26	82.86 30	68.32 39	86.44 15	77.19 28	73.07 43	83.63 25	83.64 24	87.82 40	94.34 34
Skip Steuart: Steuart Systems R&D Blog.
CSCG		82.90 21	84.52 23	81.02 19	91.85 11	93.43 7	87.14 12	74.01 15	81.96 34	76.14 16	70.84 39	82.49 13	69.71 64	82.32 41	85.18 11	87.26 58	95.40 23

HFP-MVS		82.48 23	84.12 24	80.56 20	90.15 19	87.55 54	84.28 24	69.67 35	85.22 25	77.95 14	84.69 17	75.94 31	75.04 28	81.85 49	81.17 52	86.30 77	92.40 57

PHI-MVS		79.43 34	84.06 25	74.04 57	86.15 49	91.57 19	80.85 47	68.90 41	82.22 33	51.81 94	78.10 28	74.28 34	70.39 61	84.01 24	84.00 22	86.14 83	94.24 37

MP-MVS		80.94 28	83.49 26	77.96 33	88.48 33	88.16 46	82.82 33	69.34 37	80.79 40	69.67 35	82.35 21	77.13 29	71.60 54	80.97 59	80.96 57	85.87 91	94.06 40
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
xxxxxxxxxxxxxcwj		84.33 15	83.20 27	85.64 3	94.57 1	94.55 4	91.01 1	79.94 1	89.15 12	79.85 7	92.37 3	44.71 146	79.75 8	83.52 26	82.72 32	88.75 19	95.37 24

zzz-MVS		81.65 26	83.10 28	79.97 24	88.14 39	87.62 53	83.96 27	69.90 32	86.92 18	77.67 15	72.47 37	78.74 26	74.13 35	81.59 52	81.15 53	86.01 87	93.19 50

ACMMPR		80.62 30	82.98 29	77.87 35	88.41 34	87.05 59	83.02 30	69.18 38	83.91 27	68.35 38	82.89 20	73.64 36	72.16 49	80.78 60	81.13 54	86.10 84	91.43 64

CANet		80.90 29	82.93 30	78.53 32	86.83 46	92.26 12	81.19 43	66.95 50	81.60 37	69.90 34	66.93 46	74.80 33	76.79 21	84.68 19	84.77 17	89.50 9	95.50 19

EPNet		79.28 38	82.25 31	75.83 46	88.31 37	90.14 28	79.43 53	68.07 44	81.76 36	61.26 60	77.26 31	70.08 51	70.06 62	82.43 39	82.00 39	87.82 40	92.09 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_0304		79.43 34	82.20 32	76.20 43	84.22 54	91.79 17	81.82 38	63.81 71	76.83 52	61.71 57	66.37 49	75.52 32	76.38 24	85.54 14	85.03 13	89.28 11	94.32 36

CDPH-MVS		79.39 37	82.13 33	76.19 44	89.22 31	88.34 43	84.20 25	71.00 25	79.67 44	56.97 77	77.77 29	72.24 43	68.50 76	81.33 53	82.74 30	87.23 59	92.84 53

DeepC-MVS_fast	75.41 2	81.69 25	82.10 34	81.20 18	91.04 17	87.81 52	83.42 28	74.04 14	83.77 28	71.09 29	66.88 47	72.44 39	79.48 11	85.08 15	84.97 14	88.12 39	93.78 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		79.42 36	81.84 35	76.60 41	88.38 36	86.69 61	82.97 32	65.75 59	80.39 41	64.94 45	81.95 23	72.11 44	71.41 55	80.45 61	80.55 63	86.18 81	90.76 76

CP-MVS		79.44 33	81.51 36	77.02 39	86.95 44	85.96 72	82.00 35	68.44 43	81.82 35	67.39 40	77.43 30	73.68 35	71.62 53	79.56 69	79.58 68	85.73 95	92.51 56

DeepC-MVS	74.46 3	80.30 31	81.05 37	79.42 25	87.42 42	88.50 41	83.23 29	73.27 19	82.78 31	71.01 30	62.86 60	69.93 52	74.80 31	84.30 21	84.20 21	86.79 70	94.77 28
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		78.26 41	80.91 38	75.17 51	85.67 51	84.33 85	83.01 31	69.38 36	79.88 43	55.83 78	79.85 25	64.90 67	70.81 57	82.46 37	81.78 41	86.30 77	93.18 51

X-MVS		78.16 42	80.55 39	75.38 49	87.99 41	86.27 68	81.05 45	68.98 39	78.33 46	61.07 62	75.25 34	72.27 40	67.52 82	80.03 64	80.52 64	85.66 102	91.20 68

ETV-MVS		76.25 53	80.22 40	71.63 71	78.23 84	87.95 51	72.75 94	60.27 109	77.50 51	57.73 73	71.53 38	66.60 61	73.16 42	80.99 58	81.23 51	87.63 47	95.73 15

MVSTER		76.92 50	79.92 41	73.42 60	74.98 114	82.97 94	78.15 58	63.41 75	78.02 47	64.41 47	67.54 44	72.80 38	71.05 56	83.29 30	83.73 23	88.53 27	91.12 69

DELS-MVS		79.49 32	79.84 42	79.08 29	88.26 38	92.49 9	84.12 26	70.63 28	65.27 83	69.60 37	61.29 65	66.50 62	72.75 45	88.07 3	88.03 2	89.13 12	97.22 6
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
canonicalmvs		77.65 44	79.59 43	75.39 48	81.52 65	89.83 33	81.32 42	60.74 105	80.05 42	66.72 41	68.43 43	65.09 65	74.72 33	78.87 74	82.73 31	87.32 55	92.16 58

ACMMP		77.61 45	79.59 43	75.30 50	85.87 50	85.58 73	81.42 40	67.38 49	79.38 45	62.61 51	78.53 27	65.79 64	68.80 75	78.56 77	78.50 79	85.75 92	90.80 73
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
DROMVSNet		76.05 55	78.87 45	72.77 64	78.87 82	86.63 62	77.50 65	57.04 133	75.34 55	61.68 58	64.20 54	69.56 53	73.96 36	82.12 44	80.65 61	87.57 48	93.57 46

PVSNet_BlendedMVS		76.84 51	78.47 46	74.95 52	82.37 59	89.90 31	75.45 78	65.45 62	74.99 56	70.66 32	63.07 58	58.27 99	67.60 79	84.24 22	81.70 43	88.18 36	97.10 8

PVSNet_Blended		76.84 51	78.47 46	74.95 52	82.37 59	89.90 31	75.45 78	65.45 62	74.99 56	70.66 32	63.07 58	58.27 99	67.60 79	84.24 22	81.70 43	88.18 36	97.10 8

MVS_111021_HR		77.42 47	78.40 48	76.28 42	86.95 44	90.68 22	77.41 67	70.56 31	66.21 77	62.48 53	66.17 50	63.98 70	72.08 50	82.87 33	83.15 28	88.24 35	95.71 16

MAR-MVS		77.19 49	78.37 49	75.81 47	89.87 22	90.58 23	79.33 54	65.56 61	77.62 50	58.33 71	59.24 73	67.98 56	74.83 30	82.37 40	83.12 29	86.95 65	87.67 108
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
GG-mvs-BLEND		54.54 181	77.58 50	27.67 210	0.03 224	90.09 30	77.20 69	0.02 221	66.83 75	0.05 225	59.90 70	73.33 37	0.04 220	78.40 79	79.30 71	88.65 22	95.20 27

CS-MVS		73.80 66	77.47 51	69.53 83	74.86 116	85.07 76	69.93 120	56.91 136	72.12 59	54.28 87	64.82 53	66.85 60	74.88 29	79.25 71	79.64 67	86.30 77	94.52 31

QAPM		77.50 46	77.43 52	77.59 37	91.52 14	92.00 14	81.41 41	70.63 28	66.22 76	58.05 72	54.70 82	71.79 45	74.49 34	82.46 37	82.04 37	89.46 10	92.79 55

MSLP-MVS++		78.57 39	77.33 53	80.02 23	88.39 35	84.79 79	84.62 23	66.17 57	75.96 54	78.40 11	61.59 63	71.47 46	73.54 41	78.43 78	78.88 74	88.97 14	90.18 82

3Dnovator+	70.16 6	77.87 43	77.29 54	78.55 31	89.25 30	88.32 44	80.09 49	67.95 45	74.89 58	71.83 27	52.05 94	70.68 49	76.27 25	82.27 42	82.04 37	85.92 88	90.77 75

CLD-MVS		77.36 48	77.29 54	77.45 38	82.21 61	88.11 47	81.92 36	68.96 40	77.97 48	69.62 36	62.08 61	59.44 92	73.57 40	81.75 50	81.27 50	88.41 30	90.39 79
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CPTT-MVS		75.43 57	77.13 56	73.44 59	81.43 66	82.55 98	80.96 46	64.35 67	77.95 49	61.39 59	69.20 42	70.94 48	69.38 71	73.89 121	73.32 136	83.14 152	92.06 60

CS-MVS-test		73.97 64	76.86 57	70.60 77	75.53 111	83.16 92	77.50 65	57.04 133	71.34 62	53.25 89	63.44 57	64.85 68	73.96 36	82.12 44	78.80 75	86.30 77	94.34 34

TSAR-MVS + COLMAP		73.09 68	76.86 57	68.71 90	74.97 115	82.49 99	74.51 89	61.83 92	83.16 29	49.31 106	82.22 22	51.62 129	68.94 74	78.76 76	75.52 110	82.67 157	84.23 135

3Dnovator	70.49 5	78.42 40	76.77 59	80.35 21	91.43 15	90.27 26	81.84 37	70.79 27	72.10 60	71.95 25	50.02 100	67.86 58	77.47 19	82.89 32	84.24 20	88.61 24	89.99 83

MVS_Test		75.22 58	76.69 60	73.51 58	79.30 78	88.82 38	80.06 50	58.74 112	69.77 68	57.50 76	59.78 72	61.35 81	75.31 26	82.07 46	83.60 26	90.13 5	91.41 66

CANet_DTU		72.84 70	76.63 61	68.43 94	76.81 99	86.62 64	75.54 77	54.71 161	72.06 61	43.54 127	67.11 45	58.46 96	72.40 47	81.13 57	80.82 60	87.57 48	90.21 81

PCF-MVS	70.85 4	75.73 56	76.55 62	74.78 55	83.67 55	88.04 50	81.47 39	70.62 30	69.24 72	57.52 75	60.59 69	69.18 54	70.65 59	77.11 88	77.65 86	84.75 125	94.01 41
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EIA-MVS		73.48 67	76.05 63	70.47 78	78.12 85	87.21 57	71.78 100	60.63 106	69.66 69	55.56 81	64.86 52	60.69 84	69.53 67	77.35 87	78.59 76	87.22 61	94.01 41

MVS_111021_LR		74.26 62	75.95 64	72.27 67	79.43 77	85.04 77	72.71 95	65.27 64	70.92 64	63.58 49	69.32 41	60.31 88	69.43 69	77.01 89	77.15 89	83.22 149	91.93 62

OMC-MVS		74.03 63	75.82 65	71.95 69	79.56 75	80.98 112	75.35 80	63.21 76	84.48 26	61.83 56	61.54 64	66.89 59	69.41 70	76.60 92	74.07 126	82.34 163	86.15 119

casdiffmvs		75.20 59	75.69 66	74.63 56	79.26 79	89.07 36	78.47 56	63.59 74	67.05 74	63.79 48	55.72 79	60.32 87	73.58 39	82.16 43	81.78 41	89.08 13	93.72 45

diffmvs		74.32 61	75.42 67	73.04 62	75.60 110	87.27 56	78.20 57	62.96 80	68.66 73	61.89 55	59.79 71	59.84 90	71.80 51	78.30 81	79.87 66	87.80 42	94.23 38

PMMVS		70.37 85	75.06 68	64.90 113	71.46 131	81.88 100	64.10 152	55.64 148	71.31 63	46.69 113	70.69 40	58.56 93	69.53 67	79.03 73	75.63 106	81.96 167	88.32 103

DI_MVS_plusplus_trai		73.94 65	74.85 69	72.88 63	76.57 102	86.80 60	80.41 48	61.47 96	62.35 88	59.44 69	47.91 106	68.12 55	72.24 48	82.84 34	81.50 46	87.15 64	94.42 33

ET-MVSNet_ETH3D		71.38 79	74.70 70	67.51 100	51.61 207	88.06 49	77.29 68	60.95 104	63.61 85	48.36 109	66.60 48	60.67 85	79.55 10	73.56 125	80.58 62	87.30 57	89.80 85

baseline		72.89 69	74.46 71	71.07 72	75.99 106	87.50 55	74.57 84	60.49 107	70.72 65	57.60 74	60.63 68	60.97 83	70.79 58	75.27 105	76.33 98	86.94 66	89.79 86

OpenMVS	67.62 8	74.92 60	73.91 72	76.09 45	90.10 21	90.38 25	78.01 59	66.35 55	66.09 78	62.80 50	46.33 123	64.55 69	71.77 52	79.92 66	80.88 59	87.52 50	89.20 92

CostFormer		72.18 73	73.90 73	70.18 80	79.47 76	86.19 71	76.94 70	48.62 180	66.07 79	60.40 67	54.14 88	65.82 63	67.98 77	75.84 100	76.41 97	87.67 45	92.83 54

AdaColmap		76.23 54	73.55 74	79.35 26	89.38 28	85.00 78	79.99 51	73.04 21	76.60 53	71.17 28	55.18 81	57.99 101	77.87 17	76.82 91	76.82 92	84.67 127	86.45 116

PVSNet_Blended_VisFu		71.76 76	73.54 75	69.69 82	79.01 80	87.16 58	72.05 97	61.80 93	56.46 110	59.66 68	53.88 90	62.48 73	59.08 128	81.17 55	78.90 73	86.53 74	94.74 29

TAPA-MVS	67.10 9	71.45 78	73.47 76	69.10 88	77.04 97	80.78 115	73.81 92	62.10 88	80.80 39	51.28 95	60.91 66	63.80 72	67.98 77	74.59 111	72.42 148	82.37 162	80.97 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LGP-MVS_train		72.02 75	73.18 77	70.67 76	82.13 62	80.26 120	79.58 52	63.04 78	70.09 66	51.98 92	65.06 51	55.62 112	62.49 105	75.97 99	76.32 99	84.80 124	88.93 95

baseline2		71.22 81	73.01 78	69.13 87	75.76 108	86.34 67	71.23 108	62.78 86	62.62 86	52.85 90	57.32 75	54.31 118	63.27 100	79.74 67	79.31 70	88.89 15	91.43 64

ACMP	68.86 7	72.15 74	72.25 79	72.03 68	80.96 68	80.87 114	77.93 60	64.13 69	69.29 70	60.79 65	64.04 55	53.54 124	63.91 95	73.74 124	75.27 111	84.45 132	88.98 94
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
baseline1		71.47 77	72.02 80	70.82 74	80.56 73	84.51 81	76.61 71	66.93 51	56.22 112	48.66 107	55.40 80	60.43 86	62.55 104	83.35 29	80.99 55	89.60 7	83.28 143

CHOSEN 1792x2688		72.55 72	71.98 81	73.22 61	86.57 47	92.41 10	75.63 74	66.77 52	62.08 89	52.32 91	30.27 192	50.74 132	66.14 85	86.22 12	85.41 7	91.90 1	96.75 12

IS_MVSNet		67.29 105	71.98 81	61.82 140	76.92 98	84.32 86	65.90 148	58.22 115	55.75 116	39.22 150	54.51 85	62.47 74	45.99 175	78.83 75	78.52 78	84.70 126	89.47 89

FMVSNet3		70.41 84	71.89 83	68.68 91	70.89 137	79.42 127	75.63 74	60.97 101	65.32 80	51.06 96	47.37 111	62.05 75	64.90 90	82.49 36	82.27 36	88.64 23	84.34 134

UGNet		67.57 102	71.69 84	62.76 133	69.88 140	82.58 97	66.43 145	58.64 113	54.71 125	51.87 93	61.74 62	62.01 78	45.46 177	74.78 110	74.99 112	84.24 135	91.02 70
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
test-LLR		68.23 97	71.61 85	64.28 120	71.37 132	81.32 109	63.98 155	61.03 99	58.62 101	42.96 132	52.74 91	61.65 79	57.74 137	75.64 102	78.09 84	88.61 24	93.21 48

TESTMET0.1,1		67.38 104	71.61 85	62.45 136	66.05 165	81.32 109	63.98 155	55.36 153	58.62 101	42.96 132	52.74 91	61.65 79	57.74 137	75.64 102	78.09 84	88.61 24	93.21 48

Effi-MVS+		70.42 82	71.23 87	69.47 84	78.04 86	85.24 75	75.57 76	58.88 111	59.56 98	48.47 108	52.73 93	54.94 115	69.69 65	78.34 80	77.06 90	86.18 81	90.73 77

EPP-MVSNet		67.58 101	71.10 88	63.48 126	75.71 109	83.35 91	66.85 141	57.83 123	53.02 129	41.15 141	55.82 77	67.89 57	56.01 143	74.40 114	72.92 144	83.33 147	90.30 80

thisisatest0530		68.38 96	70.98 89	65.35 109	72.61 125	84.42 82	68.21 131	57.98 118	59.77 97	50.80 99	54.63 83	58.48 95	57.92 134	76.99 90	77.47 87	84.60 128	85.07 128

OPM-MVS		72.74 71	70.93 90	74.85 54	85.30 52	84.34 84	82.82 33	69.79 33	49.96 137	55.39 83	54.09 89	60.14 89	70.04 63	80.38 63	79.43 69	85.74 94	88.20 104
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tttt0517		67.99 99	70.61 91	64.94 112	71.94 130	83.96 88	67.62 135	57.98 118	59.30 99	49.90 104	54.50 86	57.98 102	57.92 134	76.48 93	77.47 87	84.24 135	84.58 131

GBi-Net		69.21 87	70.40 92	67.81 97	69.49 142	78.65 132	74.54 85	60.97 101	65.32 80	51.06 96	47.37 111	62.05 75	63.43 97	77.49 83	78.22 81	87.37 52	83.73 137

test1		69.21 87	70.40 92	67.81 97	69.49 142	78.65 132	74.54 85	60.97 101	65.32 80	51.06 96	47.37 111	62.05 75	63.43 97	77.49 83	78.22 81	87.37 52	83.73 137

CNLPA		71.37 80	70.27 94	72.66 66	80.79 71	81.33 108	71.07 113	65.75 59	82.36 32	64.80 46	42.46 135	56.49 106	72.70 46	73.00 132	70.52 167	80.84 176	85.76 124

EPNet_dtu		66.17 111	70.13 95	61.54 142	81.04 67	77.39 147	68.87 128	62.50 87	69.78 67	33.51 178	63.77 56	56.22 107	37.65 191	72.20 140	72.18 151	85.69 98	79.38 162
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet		65.53 116	69.83 96	60.52 146	70.80 138	84.59 80	66.37 147	55.47 152	48.40 144	40.62 145	57.67 74	58.43 97	45.37 178	77.49 83	76.24 100	84.47 131	85.99 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
GeoE		68.96 91	69.32 97	68.54 92	76.61 101	83.12 93	71.78 100	56.87 137	60.21 96	54.86 85	45.95 124	54.79 117	64.27 93	74.59 111	75.54 109	86.84 69	91.01 71

test-mter		64.06 127	69.24 98	58.01 161	59.07 194	77.40 146	59.13 177	48.11 183	55.64 117	39.18 151	51.56 96	58.54 94	55.38 146	73.52 126	76.00 102	87.22 61	92.05 61

DCV-MVSNet		69.13 89	69.07 99	69.21 86	77.65 90	77.52 145	74.68 83	57.85 122	54.92 122	55.34 84	55.74 78	55.56 113	66.35 84	75.05 106	76.56 95	83.35 146	88.13 105

MS-PatchMatch		70.34 86	69.00 100	71.91 70	85.20 53	85.35 74	77.84 62	61.77 94	58.01 104	55.40 82	41.26 142	58.34 98	61.69 109	81.70 51	78.29 80	89.56 8	80.02 160

IB-MVS	64.48 11	69.02 90	68.97 101	69.09 89	81.75 64	89.01 37	64.50 150	64.91 65	56.65 108	62.59 52	47.89 107	45.23 144	51.99 154	69.18 169	81.88 40	88.77 18	92.93 52
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
Vis-MVSNet (Re-imp)		62.25 141	68.74 102	54.68 178	73.70 119	78.74 131	56.51 183	57.49 127	55.22 119	26.86 191	54.56 84	61.35 81	31.06 193	73.10 129	74.90 113	82.49 160	83.31 141

FMVSNet2		68.06 98	68.57 103	67.45 101	69.49 142	78.65 132	74.54 85	60.23 110	56.29 111	49.64 105	42.13 138	57.08 104	63.43 97	81.15 56	80.99 55	87.37 52	83.73 137

ACMM	66.70 10	70.42 82	68.49 104	72.67 65	82.85 56	77.76 143	77.70 63	64.76 66	64.61 84	60.74 66	49.29 101	53.97 121	65.86 86	74.97 107	75.57 108	84.13 139	83.29 142
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FC-MVSNet-train		68.83 92	68.29 105	69.47 84	78.35 83	79.94 121	64.72 149	66.38 54	54.96 121	54.51 86	56.75 76	47.91 138	66.91 83	75.57 104	75.75 104	85.92 88	87.12 110

HyFIR lowres test		68.39 95	68.28 106	68.52 93	80.85 69	88.11 47	71.08 112	58.09 117	54.87 124	47.80 112	27.55 198	55.80 110	64.97 89	79.11 72	79.14 72	88.31 33	93.35 47

UA-Net		64.62 121	68.23 107	60.42 147	77.53 92	81.38 107	60.08 174	57.47 128	47.01 148	44.75 122	60.68 67	71.32 47	41.84 185	73.27 127	72.25 150	80.83 177	71.68 187

tpmrst		67.15 106	68.12 108	66.03 106	76.21 104	80.98 112	71.27 107	45.05 191	60.69 94	50.63 100	46.95 119	54.15 120	65.30 87	71.80 146	71.77 152	87.72 43	90.48 78

ECVR-MVS		66.72 108	67.80 109	65.45 108	77.42 94	86.63 62	69.69 122	62.98 79	55.29 118	39.47 147	40.12 150	47.11 139	55.70 144	79.96 65	80.00 65	87.47 51	85.49 127

PatchmatchNet		65.43 117	67.71 110	62.78 132	73.49 122	82.83 95	66.42 146	45.40 190	60.40 95	45.27 118	49.22 102	57.60 103	60.01 120	70.61 155	71.38 159	86.08 85	81.91 154
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Fast-Effi-MVS+		67.59 100	67.56 111	67.62 99	73.67 120	81.14 111	71.12 111	54.79 160	58.88 100	50.61 101	46.70 121	47.05 140	69.12 73	76.06 98	76.44 96	86.43 75	86.65 114

EPMVS		66.21 110	67.49 112	64.73 114	75.81 107	84.20 87	68.94 127	44.37 195	61.55 90	48.07 111	49.21 103	54.87 116	62.88 101	71.82 145	71.40 158	88.28 34	79.37 163

MDTV_nov1_ep13		65.21 118	67.28 113	62.79 131	70.91 136	81.72 101	69.28 126	49.50 179	58.08 103	43.94 126	50.50 99	56.02 108	58.86 129	70.72 154	73.37 134	84.24 135	80.52 159

tpm cat1		67.47 103	67.05 114	67.98 96	76.63 100	81.51 106	74.49 90	47.65 185	61.18 91	61.12 61	42.51 134	53.02 127	64.74 92	70.11 163	71.50 154	83.22 149	89.49 88

SCA		63.90 128	66.67 115	60.66 145	73.75 118	71.78 179	59.87 175	43.66 196	61.13 92	45.03 120	51.64 95	59.45 91	57.92 134	70.96 152	70.80 163	83.71 143	80.92 158

PLC	64.00 12	68.54 93	66.66 116	70.74 75	80.28 74	74.88 164	72.64 96	63.70 73	69.26 71	55.71 79	47.24 114	55.31 114	70.42 60	72.05 144	70.67 165	81.66 170	77.19 168
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CHOSEN 280x420		62.23 143	66.57 117	57.17 169	59.88 191	68.92 188	61.20 171	42.28 202	54.17 126	39.57 146	47.78 108	64.97 66	62.68 102	73.85 122	69.52 172	77.43 192	86.75 113

IterMVS-LS		66.08 112	66.56 118	65.51 107	73.67 120	74.88 164	70.89 115	53.55 167	50.42 135	48.32 110	50.59 98	55.66 111	61.83 108	73.93 120	74.42 122	84.82 123	86.01 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Anonymous20231211		68.44 94	66.37 119	70.86 73	77.58 91	83.49 90	75.15 81	61.89 91	52.54 130	58.50 70	28.89 194	56.78 105	69.29 72	74.96 109	76.61 93	82.73 155	91.36 67

Anonymous202405211			66.35 120		78.00 87	84.41 83	74.85 82	63.18 77	51.00 133		31.37 189	53.73 123	69.67 66	76.28 94	76.84 91	83.21 151	90.85 72

gg-mvs-nofinetune		62.34 138	66.19 121	57.86 163	76.15 105	88.61 40	71.18 110	41.24 208	25.74 211	13.16 213	22.91 205	63.97 71	54.52 149	85.06 16	85.25 10	90.92 3	91.78 63

thres100view900		67.14 107	66.09 122	68.38 95	77.70 88	83.84 89	74.52 88	66.33 56	49.16 141	43.40 129	43.24 127	41.34 152	62.59 103	79.31 70	75.92 103	85.73 95	89.81 84

tpm		64.85 120	66.02 123	63.48 126	74.52 117	78.38 135	70.98 114	44.99 193	51.61 132	43.28 131	47.66 109	53.18 125	60.57 115	70.58 157	71.30 161	86.54 73	89.45 90

CDS-MVSNet		64.22 125	65.89 124	62.28 138	70.05 139	80.59 116	69.91 121	57.98 118	43.53 164	46.58 114	48.22 105	50.76 131	46.45 172	75.68 101	76.08 101	82.70 156	86.34 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
GA-MVS		64.55 123	65.76 125	63.12 128	69.68 141	81.56 105	69.59 123	58.16 116	45.23 158	35.58 170	47.01 118	41.82 151	59.41 124	79.62 68	78.54 77	86.32 76	86.56 115

tfpn200view9		65.90 113	64.96 126	67.00 103	77.70 88	81.58 104	71.71 103	62.94 83	49.16 141	43.40 129	43.24 127	41.34 152	61.42 111	76.24 95	74.63 118	84.84 120	88.52 101

CR-MVSNet		62.31 139	64.75 127	59.47 153	68.63 148	71.29 182	67.53 136	43.18 198	55.83 114	41.40 138	41.04 144	55.85 109	57.29 140	72.76 135	73.27 138	78.77 188	83.23 144

thres200		65.58 114	64.74 128	66.56 104	77.52 93	81.61 102	73.44 93	62.95 81	46.23 153	42.45 136	42.76 129	41.18 154	58.12 132	76.24 95	75.59 107	84.89 118	89.58 87

Fast-Effi-MVS+-dtu		63.05 134	64.72 129	61.11 143	71.21 135	76.81 151	70.72 116	43.13 200	52.51 131	35.34 171	46.55 122	46.36 141	61.40 112	71.57 149	71.44 156	84.84 120	87.79 107

thres400		65.18 119	64.44 130	66.04 105	76.40 103	82.63 96	71.52 105	64.27 68	44.93 159	40.69 144	41.86 139	40.79 158	58.12 132	77.67 82	74.64 117	85.26 108	88.56 100

Effi-MVS+-dtu		64.58 122	64.08 131	65.16 110	73.04 124	75.17 163	70.68 117	56.23 142	54.12 127	44.71 123	47.42 110	51.10 130	63.82 96	68.08 172	66.32 183	82.47 161	86.38 117

PatchT		60.46 155	63.85 132	56.51 172	65.95 167	75.68 160	47.34 197	41.39 205	53.89 128	41.40 138	37.84 161	50.30 133	57.29 140	72.76 135	73.27 138	85.67 99	83.23 144

IterMVS		61.87 147	63.55 133	59.90 149	67.29 158	72.20 176	67.34 139	48.56 181	47.48 147	37.86 159	47.07 116	48.27 135	54.08 150	72.12 142	73.71 129	84.30 134	83.99 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		62.30 140	63.51 134	60.89 144	69.48 145	77.83 141	64.07 153	63.94 70	50.03 136	31.17 183	44.82 125	41.12 155	51.37 157	71.02 151	74.81 115	85.30 107	84.95 129

test_part1		66.32 109	63.35 135	69.77 81	77.40 95	78.35 136	77.85 61	56.25 141	44.52 160	62.15 54	33.05 183	53.91 122	62.38 107	72.19 141	74.65 116	82.59 158	86.81 112

dps		64.08 126	63.22 136	65.08 111	75.27 113	79.65 124	66.68 143	46.63 189	56.94 106	55.67 80	43.96 126	43.63 149	64.00 94	69.50 168	69.82 169	82.25 164	79.02 164

thres600view7		63.77 129	63.14 137	64.51 116	75.49 112	81.61 102	69.59 123	62.95 81	43.96 163	38.90 152	41.09 143	40.24 163	55.25 147	76.24 95	71.54 153	84.89 118	87.30 109

FMVSNet1		63.48 131	63.07 138	63.97 122	65.31 170	76.37 154	71.77 102	57.90 121	43.32 165	45.66 116	35.06 179	49.43 134	58.57 130	77.49 83	78.22 81	84.59 129	81.60 156

IterMVS-SCA-FT		60.21 157	62.97 139	57.00 170	66.64 162	71.84 177	67.53 136	46.93 188	47.56 146	36.77 164	46.85 120	48.21 136	52.51 153	70.36 160	72.40 149	71.63 206	83.53 140

V42		62.86 137	62.97 139	62.74 134	60.84 188	78.99 130	71.46 106	57.13 132	46.85 149	44.28 125	38.87 154	40.73 160	57.63 139	72.60 138	74.14 124	85.09 113	88.63 99

UniMVSNet (Re)		60.62 154	62.93 141	57.92 162	67.64 155	77.90 140	61.75 168	61.24 98	49.83 138	29.80 187	42.57 132	40.62 161	43.36 181	70.49 159	73.27 138	83.76 141	85.81 123

v2v482		63.68 130	62.85 142	64.65 115	68.01 151	80.46 118	71.90 98	57.60 125	44.26 161	42.82 134	39.80 152	38.62 168	61.56 110	73.06 130	74.86 114	86.03 86	88.90 97

RPMNet		58.63 167	62.80 143	53.76 183	67.59 156	71.29 182	54.60 186	38.13 210	55.83 114	35.70 169	41.58 141	53.04 126	47.89 166	66.10 176	67.38 176	78.65 190	84.40 133

v8		63.44 132	62.58 144	64.43 117	68.28 150	78.07 138	71.82 99	54.85 158	46.70 151	45.20 119	39.40 153	40.91 157	60.54 116	72.85 134	74.39 123	85.92 88	85.76 124

pmmvs4		63.14 133	62.46 145	63.94 123	66.03 166	76.40 153	66.82 142	57.60 125	56.74 107	50.26 103	40.81 146	37.51 171	59.26 126	71.75 147	71.48 155	83.68 144	82.53 148

v1144		63.00 135	62.39 146	63.70 125	67.72 154	80.27 119	71.23 108	56.40 138	42.51 166	40.81 143	38.12 160	37.73 169	60.42 118	74.46 113	74.55 120	85.64 103	89.12 93

v10		63.00 135	62.22 147	63.90 124	67.88 153	77.78 142	71.59 104	54.34 162	45.37 157	42.76 135	38.53 155	38.93 166	61.05 114	74.39 115	74.52 121	85.75 92	86.04 120

LS3D		64.54 124	62.14 148	67.34 102	80.85 69	75.79 158	69.99 118	65.87 58	60.77 93	44.35 124	42.43 136	45.95 143	65.01 88	69.88 164	68.69 174	77.97 191	71.43 189

NR-MVSNet		61.08 152	62.09 149	59.90 149	71.96 129	75.87 156	63.60 159	61.96 89	49.31 139	27.95 188	42.76 129	33.85 192	48.82 164	74.35 116	74.05 127	85.13 110	84.45 132

DU-MVS		60.87 153	61.82 150	59.76 151	66.69 160	75.87 156	64.07 153	61.96 89	49.31 139	31.17 183	42.76 129	36.95 174	51.37 157	69.67 166	73.20 141	83.30 148	84.95 129

v1192		62.25 141	61.64 151	62.96 129	66.88 159	79.72 123	69.96 119	55.77 146	41.58 171	39.42 148	37.05 165	35.96 182	60.50 117	74.30 118	74.09 125	85.24 109	88.76 98

MSDG		65.57 115	61.57 152	70.24 79	82.02 63	76.47 152	74.46 91	68.73 42	56.52 109	50.33 102	38.47 156	41.10 156	62.42 106	72.12 142	72.94 143	83.47 145	73.37 182

v144192		62.05 145	61.46 153	62.73 135	66.59 163	79.87 122	69.30 125	55.88 144	41.50 173	39.41 149	37.23 163	36.45 177	59.62 122	72.69 137	73.51 131	85.61 104	88.93 95

TranMVSNet+NR-MVSNet		60.38 156	61.30 154	59.30 155	68.34 149	75.57 162	63.38 162	63.78 72	46.74 150	27.73 189	42.56 133	36.84 175	47.66 167	70.36 160	74.59 119	84.91 117	82.46 149

v148		62.00 146	61.19 155	62.96 129	67.46 157	79.49 126	67.87 132	57.66 124	42.30 167	45.02 121	38.20 159	38.89 167	54.77 148	69.83 165	72.60 147	84.96 114	87.01 111

v1921920		61.66 148	61.10 156	62.31 137	66.32 164	79.57 125	68.41 130	55.49 151	41.03 174	38.69 153	36.64 171	35.27 185	59.60 123	73.23 128	73.41 133	85.37 106	88.51 102

TAMVS		58.86 164	60.91 157	56.47 173	62.38 184	77.57 144	58.97 178	52.98 170	38.76 183	36.17 166	42.26 137	47.94 137	46.45 172	70.23 162	70.79 164	81.86 168	78.82 165

PatchMatch-RL		62.22 144	60.69 158	64.01 121	68.74 147	75.75 159	59.27 176	60.35 108	56.09 113	53.80 88	47.06 117	36.45 177	64.80 91	68.22 171	67.22 178	77.10 193	74.02 177

thisisatest0515		59.37 161	60.68 159	57.84 164	64.39 174	75.65 161	58.56 179	53.86 165	41.55 172	42.12 137	40.40 148	39.59 164	47.09 170	71.69 148	73.79 128	81.02 175	82.08 153

v1240		61.09 151	60.55 160	61.72 141	65.92 168	79.28 128	67.16 140	54.91 157	39.79 180	38.10 156	36.08 173	34.64 187	59.15 127	72.86 133	73.36 135	85.10 111	87.84 106

Baseline_NR-MVSNet		59.47 160	60.28 161	58.54 160	66.69 160	73.90 170	61.63 169	62.90 84	49.15 143	26.87 190	35.18 178	37.62 170	48.20 165	69.67 166	73.61 130	84.92 115	82.82 147

pmmvs5		59.72 158	60.24 162	59.11 157	62.77 182	77.33 148	63.17 163	54.00 164	40.21 178	37.23 160	40.41 147	35.99 181	51.75 155	72.55 139	72.74 146	85.72 97	82.45 150

FMVSNet5		58.86 164	60.24 162	57.25 168	52.66 206	66.25 194	63.77 158	52.86 172	57.85 105	37.92 158	36.12 172	52.22 128	51.37 157	70.88 153	71.43 157	84.92 115	66.91 198

USDC		59.69 159	60.03 164	59.28 156	64.04 175	71.84 177	63.15 164	55.36 153	54.90 123	35.02 172	48.34 104	29.79 204	58.16 131	70.60 156	71.33 160	79.99 181	73.42 181

test0.0.03 1		57.35 173	59.89 165	54.38 181	71.37 132	73.45 172	52.71 189	61.03 99	46.11 154	26.33 192	41.73 140	44.08 147	29.72 195	71.43 150	70.90 162	85.10 111	71.56 188

MIMVSNet		57.78 171	59.71 166	55.53 175	54.79 202	77.10 149	63.89 157	45.02 192	46.59 152	36.79 163	28.36 196	40.77 159	45.84 176	74.97 107	76.58 94	86.87 68	73.60 180

pm-mvs1		59.21 162	59.58 167	58.77 159	67.97 152	77.07 150	64.12 151	57.20 130	34.73 196	36.86 161	35.34 176	40.54 162	43.34 182	74.32 117	73.30 137	83.13 153	81.77 155

ACMH	59.42 14	61.59 149	59.22 168	64.36 119	78.92 81	78.26 137	67.65 134	67.48 48	39.81 179	30.98 185	38.25 158	34.59 188	61.37 113	70.55 158	73.47 132	79.74 183	79.59 161
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ADS-MVSNet		58.40 168	59.16 169	57.52 166	65.80 169	74.57 168	60.26 172	40.17 209	50.51 134	38.01 157	40.11 151	44.72 145	59.36 125	64.91 181	66.55 181	81.53 171	72.72 185

ACMH+	60.36 13	61.16 150	58.38 170	64.42 118	77.37 96	74.35 169	68.45 129	62.81 85	45.86 155	38.48 154	35.71 174	37.35 172	59.81 121	67.24 174	69.80 171	79.58 184	78.32 166

CVMVSNet		54.92 180	58.16 171	51.13 188	62.61 183	68.44 189	55.45 185	52.38 173	42.28 168	21.45 199	47.10 115	46.10 142	37.96 190	64.42 186	63.81 190	76.92 194	75.01 174

RPSCF		55.07 177	58.06 172	51.57 185	48.87 210	58.95 207	53.68 188	41.26 207	62.42 87	45.88 115	54.38 87	54.26 119	53.75 151	57.15 198	53.53 208	66.01 208	65.75 200

EG-PatchMatch MVS		58.73 166	58.03 173	59.55 152	72.32 126	80.49 117	63.44 161	55.55 150	32.49 200	38.31 155	28.87 195	37.22 173	42.84 183	74.30 118	75.70 105	84.84 120	77.14 169

gm-plane-assit		54.99 178	57.99 174	51.49 187	69.27 146	54.42 211	32.32 214	42.59 201	21.18 215	13.71 211	23.61 202	43.84 148	60.21 119	87.09 5	86.55 5	90.81 4	89.28 91

anonymousdsp		54.99 178	57.24 175	52.36 184	53.82 204	71.75 180	51.49 190	48.14 182	33.74 197	33.66 177	38.34 157	36.13 180	47.54 168	64.53 185	70.60 166	79.53 185	85.59 126

TransMVSNet (Re)		57.83 169	56.90 176	58.91 158	72.26 127	74.69 167	63.57 160	61.42 97	32.30 201	32.65 179	33.97 181	35.96 182	39.17 189	73.84 123	72.84 145	84.37 133	74.69 175

v7n		57.04 174	56.64 177	57.52 166	62.85 181	74.75 166	61.76 167	51.80 175	35.58 195	36.02 168	32.33 186	33.61 193	50.16 162	67.73 173	70.34 168	82.51 159	82.12 152

tfpnnormal		58.97 163	56.48 178	61.89 139	71.27 134	76.21 155	66.65 144	61.76 95	32.90 199	36.41 165	27.83 197	29.14 205	50.64 161	73.06 130	73.05 142	84.58 130	83.15 146

UniMVSNet_ETH3D		57.83 169	56.46 179	59.43 154	63.24 179	73.22 173	67.70 133	55.58 149	36.17 191	36.84 162	32.64 184	35.14 186	51.50 156	65.81 177	69.81 170	81.73 169	82.44 151

CMPMVS	43.63 17	57.67 172	55.43 180	60.28 148	72.01 128	79.00 129	62.77 165	53.23 169	41.77 170	45.42 117	30.74 191	39.03 165	53.01 152	64.81 183	64.65 189	75.26 198	68.03 196
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MDTV_nov1_ep13_2view		54.47 182	54.61 181	54.30 182	60.50 189	73.82 171	57.92 180	43.38 197	39.43 182	32.51 180	33.23 182	34.05 190	47.26 169	62.36 189	66.21 184	84.24 135	73.19 183

WR-MVS		51.02 190	54.56 182	46.90 197	63.84 176	69.23 187	44.78 204	56.38 139	38.19 184	14.19 209	37.38 162	36.82 176	22.39 205	60.14 193	66.20 185	79.81 182	73.95 179

FC-MVSNet-test		47.24 200	54.37 183	38.93 206	59.49 193	58.25 209	34.48 213	53.36 168	45.66 156	6.66 219	50.62 97	42.02 150	16.62 213	58.39 194	61.21 198	62.99 210	64.40 202

pmmvs-eth3d		55.20 175	53.95 184	56.65 171	57.34 200	67.77 190	57.54 181	53.74 166	40.93 175	41.09 142	31.19 190	29.10 206	49.07 163	65.54 178	67.28 177	81.14 173	75.81 170

pmmvs6		54.20 183	53.54 185	54.97 176	63.22 180	72.98 174	60.17 173	52.32 174	26.77 210	34.30 175	23.29 204	36.23 179	40.33 188	68.77 170	68.76 173	79.47 186	78.00 167

pmnet_mix02		53.92 184	53.30 186	54.65 180	61.89 185	71.33 181	54.54 187	54.17 163	40.38 176	34.65 173	34.76 180	30.68 203	40.44 187	60.97 191	63.71 191	82.19 165	71.24 190

MVS-HIRNet		53.86 185	53.02 187	54.85 177	60.30 190	72.36 175	44.63 205	42.20 203	39.45 181	43.47 128	21.66 208	34.00 191	55.47 145	65.42 179	67.16 179	83.02 154	71.08 191

PEN-MVS		51.04 189	52.94 188	48.82 191	61.45 187	66.00 195	48.68 194	57.20 130	36.87 186	15.36 207	36.98 166	32.72 194	28.77 199	57.63 197	66.37 182	81.44 172	74.00 178

COLMAP_ROB	51.17 15	55.13 176	52.90 189	57.73 165	73.47 123	67.21 192	62.13 166	55.82 145	47.83 145	34.39 174	31.60 188	34.24 189	44.90 179	63.88 188	62.52 196	75.67 196	63.02 205
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Anonymous20231206		52.23 188	52.80 190	51.56 186	64.70 173	69.41 186	51.01 191	58.60 114	36.63 188	22.44 198	21.80 207	31.42 199	30.52 194	66.79 175	67.83 175	82.10 166	75.73 171

WR-MVS_H		49.62 195	52.63 191	46.11 200	58.80 195	67.58 191	46.14 202	54.94 155	36.51 189	13.63 212	36.75 169	35.67 184	22.10 206	56.43 201	62.76 195	81.06 174	72.73 184

CP-MVSNet		50.57 191	52.60 192	48.21 194	58.77 196	65.82 196	48.17 195	56.29 140	37.41 185	16.59 204	37.14 164	31.95 196	29.21 196	56.60 200	63.71 191	80.22 179	75.56 172

PS-CasMVS		50.17 192	52.02 193	48.02 195	58.60 197	65.54 197	48.04 196	56.19 143	36.42 190	16.42 206	35.68 175	31.33 200	28.85 198	56.42 202	63.54 193	80.01 180	75.18 173

DTE-MVSNet		49.82 194	51.92 194	47.37 196	61.75 186	64.38 200	45.89 203	57.33 129	36.11 192	12.79 214	36.87 167	31.93 197	25.73 202	58.01 195	65.22 187	80.75 178	70.93 192

TDRefinement		52.70 186	51.02 195	54.66 179	57.41 199	65.06 198	61.47 170	54.94 155	44.03 162	33.93 176	30.13 193	27.57 207	46.17 174	61.86 190	62.48 197	74.01 202	66.06 199

testgi		48.51 198	50.53 196	46.16 199	64.78 171	67.15 193	41.54 207	54.81 159	29.12 206	17.03 203	32.07 187	31.98 195	20.15 209	65.26 180	67.00 180	78.67 189	61.10 209

PM-MVS		50.11 193	50.38 197	49.80 189	47.23 212	62.08 205	50.91 192	44.84 194	41.90 169	36.10 167	35.22 177	26.05 211	46.83 171	57.64 196	55.42 207	72.90 203	74.32 176

TinyColmap		52.66 187	50.09 198	55.65 174	59.72 192	64.02 202	57.15 182	52.96 171	40.28 177	32.51 180	32.42 185	20.97 215	56.65 142	63.95 187	65.15 188	74.91 199	63.87 203

LTVRE_ROB	47.26 16	49.41 196	49.91 199	48.82 191	64.76 172	69.79 185	49.05 193	47.12 187	20.36 217	16.52 205	36.65 170	26.96 208	50.76 160	60.47 192	63.16 194	64.73 209	72.00 186
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
SixPastTwentyTwo		49.11 197	49.22 200	48.99 190	58.54 198	64.14 201	47.18 198	47.75 184	31.15 203	24.42 194	41.01 145	26.55 209	44.04 180	54.76 205	58.70 202	71.99 205	68.21 194

test20.03		47.23 201	48.69 201	45.53 201	63.28 178	64.39 199	41.01 208	56.93 135	29.16 205	15.21 208	23.90 201	30.76 202	17.51 212	64.63 184	65.26 186	79.21 187	62.71 206

EU-MVSNet		44.84 202	47.85 202	41.32 205	49.26 209	56.59 210	43.07 206	47.64 186	33.03 198	13.82 210	36.78 168	30.99 201	24.37 203	53.80 206	55.57 206	69.78 207	68.21 194

N_pmnet		47.67 199	47.00 203	48.45 193	54.72 203	62.78 203	46.95 199	51.25 176	36.01 193	26.09 193	26.59 200	25.93 212	35.50 192	55.67 204	59.01 200	76.22 195	63.04 204

MIMVSNet1		40.84 206	43.46 204	37.79 207	32.14 215	58.92 208	39.24 210	50.83 177	27.00 209	11.29 216	16.76 214	26.53 210	17.75 211	57.14 199	61.12 199	75.46 197	56.78 210

new-patchmatchnet		42.21 204	42.97 205	41.33 204	53.05 205	59.89 206	39.38 209	49.61 178	28.26 208	12.10 215	22.17 206	21.54 214	19.22 210	50.96 207	56.04 205	74.61 201	61.92 207

ambc			42.30 206		50.36 208	49.51 213	35.47 212		32.04 202	23.53 195	17.36 211	8.95 222	29.06 197	64.88 182	56.26 204	61.29 211	67.12 197

pmmvs3		41.86 205	42.29 207	41.36 203	39.80 213	52.66 212	38.93 211	35.85 214	23.40 214	20.22 201	19.30 209	20.84 216	40.56 186	55.98 203	58.79 201	72.80 204	65.03 201

MDA-MVSNet-bldmvs		44.15 203	42.27 208	46.34 198	38.34 214	62.31 204	46.28 200	55.74 147	29.83 204	20.98 200	27.11 199	16.45 220	41.98 184	41.11 212	57.47 203	74.72 200	61.65 208

FPMVS		39.11 207	36.39 209	42.28 202	55.97 201	45.94 214	46.23 201	41.57 204	35.73 194	22.61 196	23.46 203	19.82 217	28.32 200	43.57 209	40.67 211	58.96 212	45.54 212

new_pmnet		33.19 208	35.52 210	30.47 209	27.55 219	45.31 215	29.29 215	30.92 215	29.00 207	9.88 218	18.77 210	17.64 219	26.77 201	44.07 208	45.98 210	58.41 213	47.87 211

test_method		28.15 210	34.48 211	20.76 212	6.76 223	21.18 219	21.03 217	18.41 218	36.77 187	17.52 202	15.67 215	31.63 198	24.05 204	41.03 213	26.69 215	36.82 217	68.38 193

PMVS	27.44 18	32.08 209	29.07 212	35.60 208	48.33 211	24.79 217	26.97 216	41.34 206	20.45 216	22.50 197	17.11 213	18.64 218	20.44 208	41.99 211	38.06 212	54.02 214	42.44 213
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		24.91 211	24.61 213	25.26 211	31.47 216	21.59 218	18.06 218	37.53 211	25.43 212	10.03 217	4.18 220	4.25 224	14.85 214	43.20 210	47.03 209	39.62 216	26.55 217
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		20.45 212	22.31 214	18.27 215	20.52 220	26.73 216	14.85 220	28.43 217	13.69 218	0.79 224	10.35 216	9.10 221	3.83 219	27.64 215	32.87 213	41.17 215	35.81 214

MVE	15.98 19	14.37 215	16.36 215	12.04 217	7.72 222	20.24 220	5.90 224	29.05 216	8.28 221	3.92 221	4.72 219	2.42 225	9.57 217	18.89 217	31.46 214	16.07 222	28.53 216
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		15.08 213	11.65 216	19.08 213	28.73 217	12.31 222	6.95 223	36.87 213	10.71 220	3.63 222	5.13 217	2.22 227	13.81 216	11.34 218	18.50 217	24.49 219	21.32 218

EMVS		14.40 214	10.71 217	18.70 214	28.15 218	12.09 223	7.06 222	36.89 212	11.00 219	3.56 223	4.95 218	2.27 226	13.91 215	10.13 219	16.06 218	22.63 220	18.51 219

testmvs		0.05 216	0.08 218	0.01 218	0.00 225	0.01 225	0.03 226	0.01 222	0.05 222	0.00 226	0.14 222	0.01 228	0.03 222	0.05 220	0.05 219	0.01 223	0.24 221

test123		0.05 216	0.08 218	0.01 218	0.00 225	0.01 225	0.01 227	0.00 223	0.05 222	0.00 226	0.16 221	0.00 229	0.04 220	0.02 221	0.05 219	0.00 224	0.26 220

uanet_test		0.00 218	0.00 220	0.00 220	0.00 225	0.00 227	0.00 228	0.00 223	0.00 224	0.00 226	0.00 223	0.00 229	0.00 223	0.00 222	0.00 221	0.00 224	0.00 222

sosnet-low-res		0.00 218	0.00 220	0.00 220	0.00 225	0.00 227	0.00 228	0.00 223	0.00 224	0.00 226	0.00 223	0.00 229	0.00 223	0.00 222	0.00 221	0.00 224	0.00 222

sosnet		0.00 218	0.00 220	0.00 220	0.00 225	0.00 227	0.00 228	0.00 223	0.00 224	0.00 226	0.00 223	0.00 229	0.00 223	0.00 222	0.00 221	0.00 224	0.00 222

RE-MVS-def										31.47 182

9.14												84.47 7

SR-MVS					86.33 48			67.54 47				80.78 20

our_test_3					63.32 177	71.07 184	55.90 184

MTAPA										78.32 12		79.42 25

MTMP										76.04 17		76.65 30

Patchmatch-RL test							2.17 225

tmp_tt				16.09 216	13.07 221	8.12 224	13.61 221	2.08 220	55.09 120	30.10 186	40.26 149	22.83 213	5.35 218	29.91 214	25.25 216	32.33 218

XVS					82.43 57	86.27 68	75.70 72			61.07 62		72.27 40				85.67 99

X-MVStestdata					82.43 57	86.27 68	75.70 72			61.07 62		72.27 40				85.67 99

abl_6				79.06 30	89.68 25	92.14 13	77.70 63	69.68 34	86.87 20	71.88 26	74.29 36	80.06 23	76.56 23			88.84 16	95.82 13

mPP-MVS					86.96 43							70.61 50

NP-MVS									81.60 37

Patchmtry						78.06 139	67.53 136	43.18 198		41.40 138

DeepMVS_CX						19.81 221	17.01 219	10.02 219	23.61 213	5.85 220	17.21 212	8.03 223	21.13 207	22.60 216		21.42 221	30.01 215

High-resolution multi-view benchmark