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
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

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 47	97.28 5
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
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

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 45	96.78 10

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

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

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 56	95.60 18

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

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 60	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
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 68	95.49 20
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
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 72	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
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	44.71 147	79.75 8	83.52 26	82.72 32	88.75 19	95.37 24

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	85.48 106	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
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

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 73	94.21 39

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 64	95.30 26

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	83.75 143	96.77 11

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 59	95.40 23

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 41	94.34 34
Skip Steuart: Steuart Systems R&D Blog.
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 78	92.40 57

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

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
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 88	93.19 50

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	83.93 141	93.75 44

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 92	94.06 40
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
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

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 85	91.43 64

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 71	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
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
CP-MVS		79.44 33	81.51 36	77.02 39	86.95 44	85.96 73	82.00 35	68.44 43	81.82 35	67.39 40	77.43 30	73.68 35	71.62 53	79.56 70	79.58 69	85.73 96	92.51 56

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

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 84	94.24 37

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 82	90.76 76

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 60	92.84 53

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 41	92.09 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		78.57 39	77.33 53	80.02 23	88.39 35	84.79 80	84.62 23	66.17 57	75.96 54	78.40 11	61.59 63	71.47 46	73.54 41	78.43 79	78.88 75	88.97 14	90.18 82

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

HQP-MVS		78.26 41	80.91 38	75.17 51	85.67 51	84.33 86	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 78	93.18 51

X-MVS		78.16 42	80.55 39	75.38 49	87.99 41	86.27 69	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	85.66 103	91.20 68

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 89	90.77 75

canonicalmvs		77.65 44	79.59 43	75.39 48	81.52 65	89.83 33	81.32 42	60.74 106	80.05 42	66.72 41	68.43 43	65.09 65	74.72 33	78.87 75	82.73 31	87.32 56	92.16 58

ACMMP		77.61 45	79.59 43	75.30 50	85.87 50	85.58 74	81.42 40	67.38 49	79.38 45	62.61 51	78.53 27	65.79 64	68.80 75	78.56 78	78.50 80	85.75 93	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
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

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

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
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 66	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
MVSTER		76.92 50	79.92 41	73.42 60	74.98 115	82.97 95	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

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

ETV-MVS		76.25 53	80.22 40	71.63 71	78.23 85	87.95 51	72.75 94	60.27 110	77.50 51	57.73 73	71.53 38	66.60 61	73.16 42	80.99 58	81.23 51	87.63 48	95.73 15

AdaColmap		76.23 54	73.55 74	79.35 26	89.38 28	85.00 79	79.99 51	73.04 21	76.60 53	71.17 28	55.18 81	57.99 101	77.87 17	76.82 92	76.82 93	84.67 128	86.45 116

DROMVSNet		76.05 55	78.87 45	72.77 64	78.87 82	86.63 62	77.50 65	57.04 134	75.34 55	61.68 58	64.20 54	69.56 53	73.96 36	82.12 44	80.65 61	87.57 49	93.57 46

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 89	77.65 87	84.75 126	94.01 41
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CPTT-MVS		75.43 57	77.13 56	73.44 59	81.43 66	82.55 99	80.96 46	64.35 67	77.95 49	61.39 59	69.20 42	70.94 48	69.38 71	73.89 122	73.32 137	83.14 153	92.06 60

MVS_Test		75.22 58	76.69 60	73.51 58	79.30 78	88.82 38	80.06 50	58.74 113	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

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

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 51	89.20 92

diffmvs		74.32 61	75.42 67	73.04 62	75.60 111	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 82	79.87 66	87.80 43	94.23 38

MVS_111021_LR		74.26 62	75.95 64	72.27 67	79.43 77	85.04 78	72.71 95	65.27 64	70.92 64	63.58 49	69.32 41	60.31 88	69.43 69	77.01 90	77.15 90	83.22 150	91.93 62

OMC-MVS		74.03 63	75.82 65	71.95 69	79.56 75	80.98 113	75.35 80	63.21 76	84.48 26	61.83 56	61.54 64	66.89 59	69.41 70	76.60 93	74.07 127	82.34 164	86.15 120

CS-MVS-test		73.97 64	76.86 57	70.60 77	75.53 112	83.16 93	77.50 65	57.04 134	71.34 62	53.25 89	63.44 57	64.85 68	73.96 36	82.12 44	78.80 76	86.30 78	94.34 34

DI_MVS_plusplus_trai		73.94 65	74.85 69	72.88 63	76.57 103	86.80 60	80.41 48	61.47 97	62.35 88	59.44 69	47.91 106	68.12 55	72.24 48	82.84 34	81.50 46	87.15 65	94.42 33

CS-MVS		73.80 66	77.47 51	69.53 83	74.86 117	85.07 77	69.93 121	56.91 137	72.12 59	54.28 87	64.82 53	66.85 60	74.88 29	79.25 72	79.64 68	86.30 78	94.52 31

EIA-MVS		73.48 67	76.05 63	70.47 78	78.12 86	87.21 57	71.78 101	60.63 107	69.66 69	55.56 81	64.86 52	60.69 84	69.53 67	77.35 88	78.59 77	87.22 62	94.01 41

TSAR-MVS + COLMAP		73.09 68	76.86 57	68.71 90	74.97 116	82.49 100	74.51 89	61.83 93	83.16 29	49.31 106	82.22 22	51.62 129	68.94 74	78.76 77	75.52 111	82.67 158	84.23 136

baseline		72.89 69	74.46 71	71.07 72	75.99 107	87.50 55	74.57 84	60.49 108	70.72 65	57.60 74	60.63 68	60.97 83	70.79 58	75.27 106	76.33 99	86.94 67	89.79 86

CANet_DTU		72.84 70	76.63 61	68.43 94	76.81 100	86.62 64	75.54 77	54.71 162	72.06 61	43.54 128	67.11 45	58.46 96	72.40 47	81.13 57	80.82 60	87.57 49	90.21 81

OPM-MVS		72.74 71	70.93 90	74.85 54	85.30 52	84.34 85	82.82 33	69.79 33	49.96 138	55.39 83	54.09 89	60.14 89	70.04 63	80.38 63	79.43 70	85.74 95	88.20 104
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
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	30.27 193	50.74 132	66.14 85	86.22 12	85.41 7	91.90 1	96.75 12

CostFormer		72.18 73	73.90 73	70.18 80	79.47 76	86.19 72	76.94 70	48.62 181	66.07 79	60.40 67	54.14 88	65.82 63	67.98 77	75.84 101	76.41 98	87.67 46	92.83 54

ACMP	68.86 7	72.15 74	72.25 79	72.03 68	80.96 68	80.87 115	77.93 60	64.13 69	69.29 70	60.79 65	64.04 55	53.54 124	63.91 95	73.74 125	75.27 112	84.45 133	88.98 94
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		72.02 75	73.18 77	70.67 76	82.13 62	80.26 121	79.58 52	63.04 78	70.09 66	51.98 92	65.06 51	55.62 112	62.49 105	75.97 100	76.32 100	84.80 125	88.93 95

PVSNet_Blended_VisFu		71.76 76	73.54 75	69.69 82	79.01 80	87.16 58	72.05 98	61.80 94	56.46 111	59.66 68	53.88 90	62.48 73	59.08 128	81.17 55	78.90 74	86.53 75	94.74 29

baseline1		71.47 77	72.02 80	70.82 74	80.56 73	84.51 82	76.61 71	66.93 51	56.22 113	48.66 107	55.40 80	60.43 86	62.55 104	83.35 29	80.99 55	89.60 7	83.28 144

TAPA-MVS	67.10 9	71.45 78	73.47 76	69.10 88	77.04 98	80.78 116	73.81 92	62.10 89	80.80 39	51.28 95	60.91 66	63.80 72	67.98 77	74.59 112	72.42 149	82.37 163	80.97 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
ET-MVSNet_ETH3D		71.38 79	74.70 70	67.51 100	51.61 208	88.06 49	77.29 68	60.95 105	63.61 85	48.36 109	66.60 48	60.67 85	79.55 10	73.56 126	80.58 62	87.30 58	89.80 85

CNLPA		71.37 80	70.27 94	72.66 66	80.79 71	81.33 109	71.07 114	65.75 59	82.36 32	64.80 46	42.46 135	56.49 106	72.70 46	73.00 133	70.52 168	80.84 177	85.76 125

baseline2		71.22 81	73.01 78	69.13 87	75.76 109	86.34 68	71.23 109	62.78 86	62.62 86	52.85 90	57.32 75	54.31 118	63.27 100	79.74 68	79.31 71	88.89 15	91.43 64

Effi-MVS+		70.42 82	71.23 87	69.47 84	78.04 87	85.24 76	75.57 76	58.88 112	59.56 98	48.47 108	52.73 93	54.94 115	69.69 65	78.34 81	77.06 91	86.18 82	90.73 77

ACMM	66.70 10	70.42 82	68.49 104	72.67 65	82.85 56	77.76 144	77.70 63	64.76 66	64.61 84	60.74 66	49.29 101	53.97 121	65.86 86	74.97 108	75.57 109	84.13 140	83.29 143
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet3		70.41 84	71.89 83	68.68 91	70.89 138	79.42 128	75.63 74	60.97 102	65.32 80	51.06 96	47.37 111	62.05 75	64.90 90	82.49 36	82.27 36	88.64 23	84.34 135

PMMVS		70.37 85	75.06 68	64.90 114	71.46 132	81.88 101	64.10 153	55.64 149	71.31 63	46.69 113	70.69 40	58.56 93	69.53 67	79.03 74	75.63 107	81.96 168	88.32 103

MS-PatchMatch		70.34 86	69.00 100	71.91 70	85.20 53	85.35 75	77.84 62	61.77 95	58.01 105	55.40 82	41.26 142	58.34 98	61.69 109	81.70 51	78.29 81	89.56 8	80.02 161

GBi-Net		69.21 87	70.40 92	67.81 97	69.49 143	78.65 133	74.54 85	60.97 102	65.32 80	51.06 96	47.37 111	62.05 75	63.43 97	77.49 84	78.22 82	87.37 53	83.73 138

test1		69.21 87	70.40 92	67.81 97	69.49 143	78.65 133	74.54 85	60.97 102	65.32 80	51.06 96	47.37 111	62.05 75	63.43 97	77.49 84	78.22 82	87.37 53	83.73 138

DCV-MVSNet		69.13 89	69.07 99	69.21 86	77.65 91	77.52 146	74.68 83	57.85 123	54.92 123	55.34 84	55.74 78	55.56 113	66.35 84	75.05 107	76.56 96	83.35 147	88.13 105

IB-MVS	64.48 11	69.02 90	68.97 101	69.09 89	81.75 64	89.01 37	64.50 151	64.91 65	56.65 109	62.59 52	47.89 107	45.23 145	51.99 155	69.18 170	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
GeoE		68.96 91	69.32 97	68.54 92	76.61 102	83.12 94	71.78 101	56.87 138	60.21 96	54.86 85	45.95 124	54.79 117	64.27 93	74.59 112	75.54 110	86.84 70	91.01 71

FC-MVSNet-train		68.83 92	68.29 106	69.47 84	78.35 84	79.94 122	64.72 150	66.38 54	54.96 122	54.51 86	56.75 76	47.91 139	66.91 83	75.57 105	75.75 105	85.92 89	87.12 110

PLC	64.00 12	68.54 93	66.66 117	70.74 75	80.28 74	74.88 165	72.64 96	63.70 73	69.26 71	55.71 79	47.24 114	55.31 114	70.42 60	72.05 145	70.67 166	81.66 171	77.19 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous20231211		68.44 94	66.37 120	70.86 73	77.58 92	83.49 91	75.15 81	61.89 92	52.54 131	58.50 70	28.89 195	56.78 105	69.29 72	74.96 110	76.61 94	82.73 156	91.36 67

HyFIR lowres test		68.39 95	68.28 107	68.52 93	80.85 69	88.11 47	71.08 113	58.09 118	54.87 125	47.80 112	27.55 199	55.80 110	64.97 89	79.11 73	79.14 73	88.31 33	93.35 47

thisisatest0530		68.38 96	70.98 89	65.35 110	72.61 126	84.42 83	68.21 132	57.98 119	59.77 97	50.80 99	54.63 83	58.48 95	57.92 135	76.99 91	77.47 88	84.60 129	85.07 129

test-LLR		68.23 97	71.61 85	64.28 121	71.37 133	81.32 110	63.98 156	61.03 100	58.62 101	42.96 133	52.74 91	61.65 79	57.74 138	75.64 103	78.09 85	88.61 24	93.21 48

FMVSNet2		68.06 98	68.57 103	67.45 101	69.49 143	78.65 133	74.54 85	60.23 111	56.29 112	49.64 105	42.13 138	57.08 104	63.43 97	81.15 56	80.99 55	87.37 53	83.73 138

tttt0517		67.99 99	70.61 91	64.94 113	71.94 131	83.96 89	67.62 136	57.98 119	59.30 99	49.90 104	54.50 86	57.98 102	57.92 135	76.48 94	77.47 88	84.24 136	84.58 132

ECVR-MVS1		67.93 100	68.49 104	67.28 103	78.64 83	86.40 67	72.22 97	62.75 87	58.05 104	44.06 126	40.92 146	48.20 137	58.05 134	79.82 67	79.70 67	87.96 40	86.32 119

Fast-Effi-MVS+		67.59 101	67.56 112	67.62 99	73.67 121	81.14 112	71.12 112	54.79 161	58.88 100	50.61 101	46.70 121	47.05 141	69.12 73	76.06 99	76.44 97	86.43 76	86.65 114

EPP-MVSNet		67.58 102	71.10 88	63.48 127	75.71 110	83.35 92	66.85 142	57.83 124	53.02 130	41.15 142	55.82 77	67.89 57	56.01 144	74.40 115	72.92 145	83.33 148	90.30 80

UGNet		67.57 103	71.69 84	62.76 134	69.88 141	82.58 98	66.43 146	58.64 114	54.71 126	51.87 93	61.74 62	62.01 78	45.46 178	74.78 111	74.99 113	84.24 136	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
tpm cat1		67.47 104	67.05 115	67.98 96	76.63 101	81.51 107	74.49 90	47.65 186	61.18 91	61.12 61	42.51 134	53.02 127	64.74 92	70.11 164	71.50 155	83.22 150	89.49 88

TESTMET0.1,1		67.38 105	71.61 85	62.45 137	66.05 166	81.32 110	63.98 156	55.36 154	58.62 101	42.96 133	52.74 91	61.65 79	57.74 138	75.64 103	78.09 85	88.61 24	93.21 48

IS_MVSNet		67.29 106	71.98 81	61.82 141	76.92 99	84.32 87	65.90 149	58.22 116	55.75 117	39.22 151	54.51 85	62.47 74	45.99 176	78.83 76	78.52 79	84.70 127	89.47 89

tpmrst		67.15 107	68.12 109	66.03 107	76.21 105	80.98 113	71.27 108	45.05 192	60.69 94	50.63 100	46.95 119	54.15 120	65.30 87	71.80 147	71.77 153	87.72 44	90.48 78

thres100view900		67.14 108	66.09 123	68.38 95	77.70 89	83.84 90	74.52 88	66.33 56	49.16 142	43.40 130	43.24 127	41.34 153	62.59 103	79.31 71	75.92 104	85.73 96	89.81 84

ECVR-MVS		66.72 109	67.80 110	65.45 109	77.42 95	86.63 62	69.69 123	62.98 79	55.29 119	39.47 148	40.12 151	47.11 140	55.70 145	79.96 65	80.00 65	87.47 52	85.49 128

test_part1		66.32 110	63.35 136	69.77 81	77.40 96	78.35 137	77.85 61	56.25 142	44.52 161	62.15 54	33.05 184	53.91 122	62.38 107	72.19 142	74.65 117	82.59 159	86.81 112

EPMVS		66.21 111	67.49 113	64.73 115	75.81 108	84.20 88	68.94 128	44.37 196	61.55 90	48.07 111	49.21 103	54.87 116	62.88 101	71.82 146	71.40 159	88.28 34	79.37 164

EPNet_dtu		66.17 112	70.13 95	61.54 143	81.04 67	77.39 148	68.87 129	62.50 88	69.78 67	33.51 179	63.77 56	56.22 107	37.65 192	72.20 141	72.18 152	85.69 99	79.38 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-LS		66.08 113	66.56 119	65.51 108	73.67 121	74.88 165	70.89 116	53.55 168	50.42 136	48.32 110	50.59 98	55.66 111	61.83 108	73.93 121	74.42 123	84.82 124	86.01 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		65.90 114	64.96 127	67.00 104	77.70 89	81.58 105	71.71 104	62.94 83	49.16 142	43.40 130	43.24 127	41.34 153	61.42 111	76.24 96	74.63 119	84.84 121	88.52 101

thres200		65.58 115	64.74 129	66.56 105	77.52 94	81.61 103	73.44 93	62.95 81	46.23 154	42.45 137	42.76 129	41.18 155	58.12 132	76.24 96	75.59 108	84.89 119	89.58 87

MSDG		65.57 116	61.57 153	70.24 79	82.02 63	76.47 153	74.46 91	68.73 42	56.52 110	50.33 102	38.47 157	41.10 157	62.42 106	72.12 143	72.94 144	83.47 146	73.37 183

Vis-MVSNet		65.53 117	69.83 96	60.52 147	70.80 139	84.59 81	66.37 148	55.47 153	48.40 145	40.62 146	57.67 74	58.43 97	45.37 179	77.49 84	76.24 101	84.47 132	85.99 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PatchmatchNet		65.43 118	67.71 111	62.78 133	73.49 123	82.83 96	66.42 147	45.40 191	60.40 95	45.27 118	49.22 102	57.60 103	60.01 120	70.61 156	71.38 160	86.08 86	81.91 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		65.21 119	67.28 114	62.79 132	70.91 137	81.72 102	69.28 127	49.50 180	58.08 103	43.94 127	50.50 99	56.02 108	58.86 129	70.72 155	73.37 135	84.24 136	80.52 160

thres400		65.18 120	64.44 131	66.04 106	76.40 104	82.63 97	71.52 106	64.27 68	44.93 160	40.69 145	41.86 139	40.79 159	58.12 132	77.67 83	74.64 118	85.26 109	88.56 100

tpm		64.85 121	66.02 124	63.48 127	74.52 118	78.38 136	70.98 115	44.99 194	51.61 133	43.28 132	47.66 109	53.18 125	60.57 115	70.58 158	71.30 162	86.54 74	89.45 90

UA-Net		64.62 122	68.23 108	60.42 148	77.53 93	81.38 108	60.08 175	57.47 129	47.01 149	44.75 122	60.68 67	71.32 47	41.84 186	73.27 128	72.25 151	80.83 178	71.68 188

Effi-MVS+-dtu		64.58 123	64.08 132	65.16 111	73.04 125	75.17 164	70.68 118	56.23 143	54.12 128	44.71 123	47.42 110	51.10 130	63.82 96	68.08 173	66.32 184	82.47 162	86.38 117

GA-MVS		64.55 124	65.76 126	63.12 129	69.68 142	81.56 106	69.59 124	58.16 117	45.23 159	35.58 171	47.01 118	41.82 152	59.41 124	79.62 69	78.54 78	86.32 77	86.56 115

LS3D		64.54 125	62.14 149	67.34 102	80.85 69	75.79 159	69.99 119	65.87 58	60.77 93	44.35 124	42.43 136	45.95 144	65.01 88	69.88 165	68.69 175	77.97 192	71.43 190

CDS-MVSNet		64.22 126	65.89 125	62.28 139	70.05 140	80.59 117	69.91 122	57.98 119	43.53 165	46.58 114	48.22 105	50.76 131	46.45 173	75.68 102	76.08 102	82.70 157	86.34 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
dps		64.08 127	63.22 137	65.08 112	75.27 114	79.65 125	66.68 144	46.63 190	56.94 107	55.67 80	43.96 126	43.63 150	64.00 94	69.50 169	69.82 170	82.25 165	79.02 165

test-mter		64.06 128	69.24 98	58.01 162	59.07 195	77.40 147	59.13 178	48.11 184	55.64 118	39.18 152	51.56 96	58.54 94	55.38 147	73.52 127	76.00 103	87.22 62	92.05 61

SCA		63.90 129	66.67 116	60.66 146	73.75 119	71.78 180	59.87 176	43.66 197	61.13 92	45.03 120	51.64 95	59.45 91	57.92 135	70.96 153	70.80 164	83.71 144	80.92 159

thres600view7		63.77 130	63.14 138	64.51 117	75.49 113	81.61 103	69.59 124	62.95 81	43.96 164	38.90 153	41.09 143	40.24 164	55.25 148	76.24 96	71.54 154	84.89 119	87.30 109

v2v482		63.68 131	62.85 143	64.65 116	68.01 152	80.46 119	71.90 99	57.60 126	44.26 162	42.82 135	39.80 153	38.62 169	61.56 110	73.06 131	74.86 115	86.03 87	88.90 97

FMVSNet1		63.48 132	63.07 139	63.97 123	65.31 171	76.37 155	71.77 103	57.90 122	43.32 166	45.66 116	35.06 180	49.43 134	58.57 130	77.49 84	78.22 82	84.59 130	81.60 157

v8		63.44 133	62.58 145	64.43 118	68.28 151	78.07 139	71.82 100	54.85 159	46.70 152	45.20 119	39.40 154	40.91 158	60.54 116	72.85 135	74.39 124	85.92 89	85.76 125

pmmvs4		63.14 134	62.46 146	63.94 124	66.03 167	76.40 154	66.82 143	57.60 126	56.74 108	50.26 103	40.81 147	37.51 172	59.26 126	71.75 148	71.48 156	83.68 145	82.53 149

Fast-Effi-MVS+-dtu		63.05 135	64.72 130	61.11 144	71.21 136	76.81 152	70.72 117	43.13 201	52.51 132	35.34 172	46.55 122	46.36 142	61.40 112	71.57 150	71.44 157	84.84 121	87.79 107

v1144		63.00 136	62.39 147	63.70 126	67.72 155	80.27 120	71.23 109	56.40 139	42.51 167	40.81 144	38.12 161	37.73 170	60.42 118	74.46 114	74.55 121	85.64 104	89.12 93

v10		63.00 136	62.22 148	63.90 125	67.88 154	77.78 143	71.59 105	54.34 163	45.37 158	42.76 136	38.53 156	38.93 167	61.05 114	74.39 116	74.52 122	85.75 93	86.04 121

V42		62.86 138	62.97 140	62.74 135	60.84 189	78.99 131	71.46 107	57.13 133	46.85 150	44.28 125	38.87 155	40.73 161	57.63 140	72.60 139	74.14 125	85.09 114	88.63 99

gg-mvs-nofinetune		62.34 139	66.19 122	57.86 164	76.15 106	88.61 40	71.18 111	41.24 209	25.74 212	13.16 214	22.91 206	63.97 71	54.52 150	85.06 16	85.25 10	90.92 3	91.78 63

CR-MVSNet		62.31 140	64.75 128	59.47 154	68.63 149	71.29 183	67.53 137	43.18 199	55.83 115	41.40 139	41.04 144	55.85 109	57.29 141	72.76 136	73.27 139	78.77 189	83.23 145

UniMVSNet_NR-MVSNet		62.30 141	63.51 135	60.89 145	69.48 146	77.83 142	64.07 154	63.94 70	50.03 137	31.17 184	44.82 125	41.12 156	51.37 158	71.02 152	74.81 116	85.30 108	84.95 130

v1192		62.25 142	61.64 152	62.96 130	66.88 160	79.72 124	69.96 120	55.77 147	41.58 172	39.42 149	37.05 166	35.96 183	60.50 117	74.30 119	74.09 126	85.24 110	88.76 98

Vis-MVSNet (Re-imp)		62.25 142	68.74 102	54.68 179	73.70 120	78.74 132	56.51 184	57.49 128	55.22 120	26.86 192	54.56 84	61.35 81	31.06 194	73.10 130	74.90 114	82.49 161	83.31 142

CHOSEN 280x420		62.23 144	66.57 118	57.17 170	59.88 192	68.92 189	61.20 172	42.28 203	54.17 127	39.57 147	47.78 108	64.97 66	62.68 102	73.85 123	69.52 173	77.43 193	86.75 113

PatchMatch-RL		62.22 145	60.69 159	64.01 122	68.74 148	75.75 160	59.27 177	60.35 109	56.09 114	53.80 88	47.06 117	36.45 178	64.80 91	68.22 172	67.22 179	77.10 194	74.02 178

v144192		62.05 146	61.46 154	62.73 136	66.59 164	79.87 123	69.30 126	55.88 145	41.50 174	39.41 150	37.23 164	36.45 178	59.62 122	72.69 138	73.51 132	85.61 105	88.93 95

v148		62.00 147	61.19 156	62.96 130	67.46 158	79.49 127	67.87 133	57.66 125	42.30 168	45.02 121	38.20 160	38.89 168	54.77 149	69.83 166	72.60 148	84.96 115	87.01 111

IterMVS		61.87 148	63.55 134	59.90 150	67.29 159	72.20 177	67.34 140	48.56 182	47.48 148	37.86 160	47.07 116	48.27 135	54.08 151	72.12 143	73.71 130	84.30 135	83.99 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		61.66 149	61.10 157	62.31 138	66.32 165	79.57 126	68.41 131	55.49 152	41.03 175	38.69 154	36.64 172	35.27 186	59.60 123	73.23 129	73.41 134	85.37 107	88.51 102

ACMH	59.42 14	61.59 150	59.22 169	64.36 120	78.92 81	78.26 138	67.65 135	67.48 48	39.81 180	30.98 186	38.25 159	34.59 189	61.37 113	70.55 159	73.47 133	79.74 184	79.59 162
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	60.36 13	61.16 151	58.38 171	64.42 119	77.37 97	74.35 170	68.45 130	62.81 85	45.86 156	38.48 155	35.71 175	37.35 173	59.81 121	67.24 175	69.80 172	79.58 185	78.32 167

v1240		61.09 152	60.55 161	61.72 142	65.92 169	79.28 129	67.16 141	54.91 158	39.79 181	38.10 157	36.08 174	34.64 188	59.15 127	72.86 134	73.36 136	85.10 112	87.84 106

NR-MVSNet		61.08 153	62.09 150	59.90 150	71.96 130	75.87 157	63.60 160	61.96 90	49.31 140	27.95 189	42.76 129	33.85 193	48.82 165	74.35 117	74.05 128	85.13 111	84.45 133

DU-MVS		60.87 154	61.82 151	59.76 152	66.69 161	75.87 157	64.07 154	61.96 90	49.31 140	31.17 184	42.76 129	36.95 175	51.37 158	69.67 167	73.20 142	83.30 149	84.95 130

UniMVSNet (Re)		60.62 155	62.93 142	57.92 163	67.64 156	77.90 141	61.75 169	61.24 99	49.83 139	29.80 188	42.57 132	40.62 162	43.36 182	70.49 160	73.27 139	83.76 142	85.81 124

PatchT		60.46 156	63.85 133	56.51 173	65.95 168	75.68 161	47.34 198	41.39 206	53.89 129	41.40 139	37.84 162	50.30 133	57.29 141	72.76 136	73.27 139	85.67 100	83.23 145

TranMVSNet+NR-MVSNet		60.38 157	61.30 155	59.30 156	68.34 150	75.57 163	63.38 163	63.78 72	46.74 151	27.73 190	42.56 133	36.84 176	47.66 168	70.36 161	74.59 120	84.91 118	82.46 150

IterMVS-SCA-FT		60.21 158	62.97 140	57.00 171	66.64 163	71.84 178	67.53 137	46.93 189	47.56 147	36.77 165	46.85 120	48.21 136	52.51 154	70.36 161	72.40 150	71.63 207	83.53 141

pmmvs5		59.72 159	60.24 163	59.11 158	62.77 183	77.33 149	63.17 164	54.00 165	40.21 179	37.23 161	40.41 148	35.99 182	51.75 156	72.55 140	72.74 147	85.72 98	82.45 151

USDC		59.69 160	60.03 165	59.28 157	64.04 176	71.84 178	63.15 165	55.36 154	54.90 124	35.02 173	48.34 104	29.79 205	58.16 131	70.60 157	71.33 161	79.99 182	73.42 182

Baseline_NR-MVSNet		59.47 161	60.28 162	58.54 161	66.69 161	73.90 171	61.63 170	62.90 84	49.15 144	26.87 191	35.18 179	37.62 171	48.20 166	69.67 167	73.61 131	84.92 116	82.82 148

thisisatest0515		59.37 162	60.68 160	57.84 165	64.39 175	75.65 162	58.56 180	53.86 166	41.55 173	42.12 138	40.40 149	39.59 165	47.09 171	71.69 149	73.79 129	81.02 176	82.08 154

pm-mvs1		59.21 163	59.58 168	58.77 160	67.97 153	77.07 151	64.12 152	57.20 131	34.73 197	36.86 162	35.34 177	40.54 163	43.34 183	74.32 118	73.30 138	83.13 154	81.77 156

tfpnnormal		58.97 164	56.48 179	61.89 140	71.27 135	76.21 156	66.65 145	61.76 96	32.90 200	36.41 166	27.83 198	29.14 206	50.64 162	73.06 131	73.05 143	84.58 131	83.15 147

FMVSNet5		58.86 165	60.24 163	57.25 169	52.66 207	66.25 195	63.77 159	52.86 173	57.85 106	37.92 159	36.12 173	52.22 128	51.37 158	70.88 154	71.43 158	84.92 116	66.91 199

TAMVS		58.86 165	60.91 158	56.47 174	62.38 185	77.57 145	58.97 179	52.98 171	38.76 184	36.17 167	42.26 137	47.94 138	46.45 173	70.23 163	70.79 165	81.86 169	78.82 166

EG-PatchMatch MVS		58.73 167	58.03 174	59.55 153	72.32 127	80.49 118	63.44 162	55.55 151	32.49 201	38.31 156	28.87 196	37.22 174	42.84 184	74.30 119	75.70 106	84.84 121	77.14 170

RPMNet		58.63 168	62.80 144	53.76 184	67.59 157	71.29 183	54.60 187	38.13 211	55.83 115	35.70 170	41.58 141	53.04 126	47.89 167	66.10 177	67.38 177	78.65 191	84.40 134

ADS-MVSNet		58.40 169	59.16 170	57.52 167	65.80 170	74.57 169	60.26 173	40.17 210	50.51 135	38.01 158	40.11 152	44.72 146	59.36 125	64.91 182	66.55 182	81.53 172	72.72 186

UniMVSNet_ETH3D		57.83 170	56.46 180	59.43 155	63.24 180	73.22 174	67.70 134	55.58 150	36.17 192	36.84 163	32.64 185	35.14 187	51.50 157	65.81 178	69.81 171	81.73 170	82.44 152

TransMVSNet (Re)		57.83 170	56.90 177	58.91 159	72.26 128	74.69 168	63.57 161	61.42 98	32.30 202	32.65 180	33.97 182	35.96 183	39.17 190	73.84 124	72.84 146	84.37 134	74.69 176

MIMVSNet		57.78 172	59.71 167	55.53 176	54.79 203	77.10 150	63.89 158	45.02 193	46.59 153	36.79 164	28.36 197	40.77 160	45.84 177	74.97 108	76.58 95	86.87 69	73.60 181

CMPMVS	43.63 17	57.67 173	55.43 181	60.28 149	72.01 129	79.00 130	62.77 166	53.23 170	41.77 171	45.42 117	30.74 192	39.03 166	53.01 153	64.81 184	64.65 190	75.26 199	68.03 197
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 1		57.35 174	59.89 166	54.38 182	71.37 133	73.45 173	52.71 190	61.03 100	46.11 155	26.33 193	41.73 140	44.08 148	29.72 196	71.43 151	70.90 163	85.10 112	71.56 189

v7n		57.04 175	56.64 178	57.52 167	62.85 182	74.75 167	61.76 168	51.80 176	35.58 196	36.02 169	32.33 187	33.61 194	50.16 163	67.73 174	70.34 169	82.51 160	82.12 153

pmmvs-eth3d		55.20 176	53.95 185	56.65 172	57.34 201	67.77 191	57.54 182	53.74 167	40.93 176	41.09 143	31.19 191	29.10 207	49.07 164	65.54 179	67.28 178	81.14 174	75.81 171

COLMAP_ROB	51.17 15	55.13 177	52.90 190	57.73 166	73.47 124	67.21 193	62.13 167	55.82 146	47.83 146	34.39 175	31.60 189	34.24 190	44.90 180	63.88 189	62.52 197	75.67 197	63.02 206
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		55.07 178	58.06 173	51.57 186	48.87 211	58.95 208	53.68 189	41.26 208	62.42 87	45.88 115	54.38 87	54.26 119	53.75 152	57.15 199	53.53 209	66.01 209	65.75 201

gm-plane-assit		54.99 179	57.99 175	51.49 188	69.27 147	54.42 212	32.32 215	42.59 202	21.18 216	13.71 212	23.61 203	43.84 149	60.21 119	87.09 5	86.55 5	90.81 4	89.28 91

anonymousdsp		54.99 179	57.24 176	52.36 185	53.82 205	71.75 181	51.49 191	48.14 183	33.74 198	33.66 178	38.34 158	36.13 181	47.54 169	64.53 186	70.60 167	79.53 186	85.59 127

CVMVSNet		54.92 181	58.16 172	51.13 189	62.61 184	68.44 190	55.45 186	52.38 174	42.28 169	21.45 200	47.10 115	46.10 143	37.96 191	64.42 187	63.81 191	76.92 195	75.01 175

GG-mvs-BLEND		54.54 182	77.58 50	27.67 211	0.03 225	90.09 30	77.20 69	0.02 222	66.83 75	0.05 226	59.90 70	73.33 37	0.04 221	78.40 80	79.30 72	88.65 22	95.20 27

MDTV_nov1_ep13_2view		54.47 183	54.61 182	54.30 183	60.50 190	73.82 172	57.92 181	43.38 198	39.43 183	32.51 181	33.23 183	34.05 191	47.26 170	62.36 190	66.21 185	84.24 136	73.19 184

pmmvs6		54.20 184	53.54 186	54.97 177	63.22 181	72.98 175	60.17 174	52.32 175	26.77 211	34.30 176	23.29 205	36.23 180	40.33 189	68.77 171	68.76 174	79.47 187	78.00 168

pmnet_mix02		53.92 185	53.30 187	54.65 181	61.89 186	71.33 182	54.54 188	54.17 164	40.38 177	34.65 174	34.76 181	30.68 204	40.44 188	60.97 192	63.71 192	82.19 166	71.24 191

MVS-HIRNet		53.86 186	53.02 188	54.85 178	60.30 191	72.36 176	44.63 206	42.20 204	39.45 182	43.47 129	21.66 209	34.00 192	55.47 146	65.42 180	67.16 180	83.02 155	71.08 192

TDRefinement		52.70 187	51.02 196	54.66 180	57.41 200	65.06 199	61.47 171	54.94 156	44.03 163	33.93 177	30.13 194	27.57 208	46.17 175	61.86 191	62.48 198	74.01 203	66.06 200

TinyColmap		52.66 188	50.09 199	55.65 175	59.72 193	64.02 203	57.15 183	52.96 172	40.28 178	32.51 181	32.42 186	20.97 216	56.65 143	63.95 188	65.15 189	74.91 200	63.87 204

Anonymous20231206		52.23 189	52.80 191	51.56 187	64.70 174	69.41 187	51.01 192	58.60 115	36.63 189	22.44 199	21.80 208	31.42 200	30.52 195	66.79 176	67.83 176	82.10 167	75.73 172

PEN-MVS		51.04 190	52.94 189	48.82 192	61.45 188	66.00 196	48.68 195	57.20 131	36.87 187	15.36 208	36.98 167	32.72 195	28.77 200	57.63 198	66.37 183	81.44 173	74.00 179

WR-MVS		51.02 191	54.56 183	46.90 198	63.84 177	69.23 188	44.78 205	56.38 140	38.19 185	14.19 210	37.38 163	36.82 177	22.39 206	60.14 194	66.20 186	79.81 183	73.95 180

CP-MVSNet		50.57 192	52.60 193	48.21 195	58.77 197	65.82 197	48.17 196	56.29 141	37.41 186	16.59 205	37.14 165	31.95 197	29.21 197	56.60 201	63.71 192	80.22 180	75.56 173

PS-CasMVS		50.17 193	52.02 194	48.02 196	58.60 198	65.54 198	48.04 197	56.19 144	36.42 191	16.42 207	35.68 176	31.33 201	28.85 199	56.42 203	63.54 194	80.01 181	75.18 174

PM-MVS		50.11 194	50.38 198	49.80 190	47.23 213	62.08 206	50.91 193	44.84 195	41.90 170	36.10 168	35.22 178	26.05 212	46.83 172	57.64 197	55.42 208	72.90 204	74.32 177

DTE-MVSNet		49.82 195	51.92 195	47.37 197	61.75 187	64.38 201	45.89 204	57.33 130	36.11 193	12.79 215	36.87 168	31.93 198	25.73 203	58.01 196	65.22 188	80.75 179	70.93 193

WR-MVS_H		49.62 196	52.63 192	46.11 201	58.80 196	67.58 192	46.14 203	54.94 156	36.51 190	13.63 213	36.75 170	35.67 185	22.10 207	56.43 202	62.76 196	81.06 175	72.73 185

LTVRE_ROB	47.26 16	49.41 197	49.91 200	48.82 192	64.76 173	69.79 186	49.05 194	47.12 188	20.36 218	16.52 206	36.65 171	26.96 209	50.76 161	60.47 193	63.16 195	64.73 210	72.00 187
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		49.11 198	49.22 201	48.99 191	58.54 199	64.14 202	47.18 199	47.75 185	31.15 204	24.42 195	41.01 145	26.55 210	44.04 181	54.76 206	58.70 203	71.99 206	68.21 195

testgi		48.51 199	50.53 197	46.16 200	64.78 172	67.15 194	41.54 208	54.81 160	29.12 207	17.03 204	32.07 188	31.98 196	20.15 210	65.26 181	67.00 181	78.67 190	61.10 210

N_pmnet		47.67 200	47.00 204	48.45 194	54.72 204	62.78 204	46.95 200	51.25 177	36.01 194	26.09 194	26.59 201	25.93 213	35.50 193	55.67 205	59.01 201	76.22 196	63.04 205

FC-MVSNet-test		47.24 201	54.37 184	38.93 207	59.49 194	58.25 210	34.48 214	53.36 169	45.66 157	6.66 220	50.62 97	42.02 151	16.62 214	58.39 195	61.21 199	62.99 211	64.40 203

test20.03		47.23 202	48.69 202	45.53 202	63.28 179	64.39 200	41.01 209	56.93 136	29.16 206	15.21 209	23.90 202	30.76 203	17.51 213	64.63 185	65.26 187	79.21 188	62.71 207

EU-MVSNet		44.84 203	47.85 203	41.32 206	49.26 210	56.59 211	43.07 207	47.64 187	33.03 199	13.82 211	36.78 169	30.99 202	24.37 204	53.80 207	55.57 207	69.78 208	68.21 195

MDA-MVSNet-bldmvs		44.15 204	42.27 209	46.34 199	38.34 215	62.31 205	46.28 201	55.74 148	29.83 205	20.98 201	27.11 200	16.45 221	41.98 185	41.11 213	57.47 204	74.72 201	61.65 209

new-patchmatchnet		42.21 205	42.97 206	41.33 205	53.05 206	59.89 207	39.38 210	49.61 179	28.26 209	12.10 216	22.17 207	21.54 215	19.22 211	50.96 208	56.04 206	74.61 202	61.92 208

pmmvs3		41.86 206	42.29 208	41.36 204	39.80 214	52.66 213	38.93 212	35.85 215	23.40 215	20.22 202	19.30 210	20.84 217	40.56 187	55.98 204	58.79 202	72.80 205	65.03 202

MIMVSNet1		40.84 207	43.46 205	37.79 208	32.14 216	58.92 209	39.24 211	50.83 178	27.00 210	11.29 217	16.76 215	26.53 211	17.75 212	57.14 200	61.12 200	75.46 198	56.78 211

FPMVS		39.11 208	36.39 210	42.28 203	55.97 202	45.94 215	46.23 202	41.57 205	35.73 195	22.61 197	23.46 204	19.82 218	28.32 201	43.57 210	40.67 212	58.96 213	45.54 213

new_pmnet		33.19 209	35.52 211	30.47 210	27.55 220	45.31 216	29.29 216	30.92 216	29.00 208	9.88 219	18.77 211	17.64 220	26.77 202	44.07 209	45.98 211	58.41 214	47.87 212

PMVS	27.44 18	32.08 210	29.07 213	35.60 209	48.33 212	24.79 218	26.97 217	41.34 207	20.45 217	22.50 198	17.11 214	18.64 219	20.44 209	41.99 212	38.06 213	54.02 215	42.44 214
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_method		28.15 211	34.48 212	20.76 213	6.76 224	21.18 220	21.03 218	18.41 219	36.77 188	17.52 203	15.67 216	31.63 199	24.05 205	41.03 214	26.69 216	36.82 218	68.38 194

Gipuma		24.91 212	24.61 214	25.26 212	31.47 217	21.59 219	18.06 219	37.53 212	25.43 213	10.03 218	4.18 221	4.25 225	14.85 215	43.20 211	47.03 210	39.62 217	26.55 218
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		20.45 213	22.31 215	18.27 216	20.52 221	26.73 217	14.85 221	28.43 218	13.69 219	0.79 225	10.35 217	9.10 222	3.83 220	27.64 216	32.87 214	41.17 216	35.81 215

E-PMN		15.08 214	11.65 217	19.08 214	28.73 218	12.31 223	6.95 224	36.87 214	10.71 221	3.63 223	5.13 218	2.22 228	13.81 217	11.34 219	18.50 218	24.49 220	21.32 219

EMVS		14.40 215	10.71 218	18.70 215	28.15 219	12.09 224	7.06 223	36.89 213	11.00 220	3.56 224	4.95 219	2.27 227	13.91 216	10.13 220	16.06 219	22.63 221	18.51 220

MVE	15.98 19	14.37 216	16.36 216	12.04 218	7.72 223	20.24 221	5.90 225	29.05 217	8.28 222	3.92 222	4.72 220	2.42 226	9.57 218	18.89 218	31.46 215	16.07 223	28.53 217
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.05 217	0.08 219	0.01 219	0.00 226	0.01 226	0.03 227	0.01 223	0.05 223	0.00 227	0.14 223	0.01 229	0.03 223	0.05 221	0.05 220	0.01 224	0.24 222

test123		0.05 217	0.08 219	0.01 219	0.00 226	0.01 226	0.01 228	0.00 224	0.05 223	0.00 227	0.16 222	0.00 230	0.04 221	0.02 222	0.05 220	0.00 225	0.26 221

uanet_test		0.00 219	0.00 221	0.00 221	0.00 226	0.00 228	0.00 229	0.00 224	0.00 225	0.00 227	0.00 224	0.00 230	0.00 224	0.00 223	0.00 222	0.00 225	0.00 223

sosnet-low-res		0.00 219	0.00 221	0.00 221	0.00 226	0.00 228	0.00 229	0.00 224	0.00 225	0.00 227	0.00 224	0.00 230	0.00 224	0.00 223	0.00 222	0.00 225	0.00 223

sosnet		0.00 219	0.00 221	0.00 221	0.00 226	0.00 228	0.00 229	0.00 224	0.00 225	0.00 227	0.00 224	0.00 230	0.00 224	0.00 223	0.00 222	0.00 225	0.00 223

RE-MVS-def										31.47 183

9.14												84.47 7

SR-MVS					86.33 48			67.54 47				80.78 20

Anonymous202405211			66.35 121		78.00 88	84.41 84	74.85 82	63.18 77	51.00 134		31.37 190	53.73 123	69.67 66	76.28 95	76.84 92	83.21 152	90.85 72

our_test_3					63.32 178	71.07 185	55.90 185

ambc			42.30 207		50.36 209	49.51 214	35.47 213		32.04 203	23.53 196	17.36 212	8.95 223	29.06 198	64.88 183	56.26 205	61.29 212	67.12 198

MTAPA										78.32 12		79.42 25

MTMP										76.04 17		76.65 30

Patchmatch-RL test							2.17 226

tmp_tt				16.09 217	13.07 222	8.12 225	13.61 222	2.08 221	55.09 121	30.10 187	40.26 150	22.83 214	5.35 219	29.91 215	25.25 217	32.33 219

XVS					82.43 57	86.27 69	75.70 72			61.07 62		72.27 40				85.67 100

X-MVStestdata					82.43 57	86.27 69	75.70 72			61.07 62		72.27 40				85.67 100

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						78.06 140	67.53 137	43.18 199		41.40 139

DeepMVS_CX						19.81 222	17.01 220	10.02 220	23.61 214	5.85 221	17.21 213	8.03 224	21.13 208	22.60 217		21.42 222	30.01 216

High-resolution multi-view benchmark