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 6	94.16 1	86.57 1	90.85 5	87.07 1	86.18 1	86.36 7	85.08 12	88.67 20	98.21 3

DVP-MVS++		87.98 3	89.76 5	85.89 2	92.57 6	94.57 3	88.34 6	76.61 7	92.40 6	83.40 3	89.26 10	85.57 5	86.04 2	86.24 11	84.89 15	88.39 30	95.42 20

MSP-MVS		87.87 4	90.57 3	84.73 5	89.38 27	91.60 17	88.24 8	74.15 12	93.55 3	82.28 4	94.99 1	83.21 12	85.96 3	87.67 4	84.67 18	88.32 31	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
TPM-MVS					94.34 2	93.91 5	89.34 3			75.49 18	82.52 20	83.34 10	83.53 4			89.62 7	90.78 72

MCST-MVS		85.75 9	86.99 13	84.31 6	94.07 3	92.80 8	88.15 9	79.10 2	85.66 21	70.72 29	76.50 33	80.45 22	82.17 5	88.35 2	87.49 3	91.63 2	97.65 4

CNVR-MVS		85.96 8	87.58 11	84.06 8	92.58 5	92.40 11	87.62 11	77.77 4	88.44 14	75.93 16	79.49 26	81.97 18	81.65 6	87.04 6	86.58 4	88.79 17	97.18 7

DPE-MVS		87.60 5	90.44 4	84.29 7	92.09 9	93.44 6	88.69 4	75.11 9	93.06 5	80.80 6	94.23 2	86.70 3	81.44 7	84.84 18	83.52 27	87.64 48	97.28 5
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
APDe-MVS		86.37 7	88.41 8	84.00 9	91.43 15	91.83 15	88.34 6	74.67 10	91.19 7	81.76 5	91.13 4	81.94 19	80.07 8	83.38 27	82.58 34	87.69 46	96.78 10

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 11	79.75 9	83.52 26	82.72 32	88.75 19	95.37 23

ET-MVSNet_ETH3D		71.38 78	74.70 69	67.51 98	51.61 208	88.06 49	77.29 65	60.95 104	63.61 83	48.36 108	66.60 47	60.67 85	79.55 10	73.56 127	80.58 62	87.30 59	89.80 85

DeepC-MVS_fast	75.41 2	81.69 24	82.10 32	81.20 17	91.04 17	87.81 52	83.42 27	74.04 13	83.77 25	71.09 27	66.88 46	72.44 38	79.48 11	85.08 15	84.97 14	88.12 38	93.78 40
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DVP-MVS		88.07 2	90.73 2	84.97 4	91.98 10	95.01 2	87.86 10	76.88 5	93.90 2	85.15 2	90.11 7	86.90 2	79.46 12	86.26 10	84.67 18	88.50 27	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
DPM-MVS		85.41 11	86.72 16	83.89 10	91.66 13	91.92 14	90.49 2	78.09 3	86.90 17	73.95 20	74.52 35	82.01 17	79.29 13	90.24 1	90.65 1	89.86 6	90.78 72

APD-MVS		84.83 13	87.00 12	82.30 13	89.61 25	89.21 35	86.51 15	73.64 16	90.98 8	77.99 12	89.89 8	80.04 24	79.18 14	82.00 48	81.37 49	86.88 70	95.49 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		84.16 16	85.46 21	82.64 11	92.34 8	90.57 23	86.57 14	76.51 8	86.85 18	72.91 23	77.20 32	78.69 26	79.09 15	84.64 20	84.88 16	88.44 28	95.41 21

HPM-MVS++		85.64 10	88.43 7	82.39 12	92.65 4	90.24 26	85.83 17	74.21 11	90.68 9	75.63 17	86.77 13	84.15 8	78.68 16	86.33 8	85.26 9	87.32 57	95.60 17

AdaColmap		76.23 52	73.55 73	79.35 24	89.38 27	85.00 81	79.99 51	73.04 20	76.60 50	71.17 26	55.18 80	57.99 101	77.87 17	76.82 93	76.82 94	84.67 129	86.45 115

TSAR-MVS + MP.		84.39 14	86.58 17	81.83 14	88.09 38	86.47 67	85.63 19	73.62 17	90.13 11	79.24 9	89.67 9	82.99 13	77.72 18	81.22 53	80.92 58	86.68 74	94.66 28
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
3Dnovator	70.49 5	78.42 38	76.77 57	80.35 20	91.43 15	90.27 25	81.84 36	70.79 26	72.10 58	71.95 24	50.02 100	67.86 57	77.47 19	82.89 32	84.24 20	88.61 23	89.99 83

SMA-MVS		85.24 12	88.27 9	81.72 15	91.74 12	90.71 20	86.71 13	73.16 19	90.56 10	74.33 19	83.07 18	85.88 4	77.16 20	86.28 9	85.58 6	87.23 61	95.77 13
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
CANet		80.90 27	82.93 28	78.53 29	86.83 44	92.26 12	81.19 42	66.95 47	81.60 34	69.90 32	66.93 45	74.80 32	76.79 21	84.68 19	84.77 17	89.50 10	95.50 18

TSAR-MVS + GP.		82.27 23	85.98 19	77.94 31	80.72 70	88.25 45	81.12 43	67.71 43	87.10 16	73.31 21	85.23 15	83.68 9	76.64 22	80.43 61	81.47 48	88.15 37	95.66 16

MVS_0304		79.43 32	82.20 30	76.20 40	84.22 52	91.79 16	81.82 37	63.81 69	76.83 49	61.71 57	66.37 48	75.52 31	76.38 23	85.54 14	85.03 13	89.28 12	94.32 32

3Dnovator+	70.16 6	77.87 41	77.29 53	78.55 28	89.25 29	88.32 44	80.09 49	67.95 42	74.89 56	71.83 25	52.05 93	70.68 48	76.27 24	82.27 42	82.04 36	85.92 90	90.77 74

ACMMP_NAP		83.54 17	86.37 18	80.25 21	89.57 26	90.10 28	85.27 21	71.66 23	87.38 15	73.08 22	84.23 17	80.16 23	75.31 25	84.85 17	83.64 24	86.57 75	94.21 35

MVS_Test		75.22 58	76.69 58	73.51 56	79.30 77	88.82 38	80.06 50	58.74 114	69.77 65	57.50 76	59.78 70	61.35 80	75.31 25	82.07 45	83.60 26	90.13 5	91.41 64

HFP-MVS		82.48 22	84.12 24	80.56 19	90.15 19	87.55 53	84.28 24	69.67 32	85.22 22	77.95 13	84.69 16	75.94 30	75.04 27	81.85 49	81.17 53	86.30 82	92.40 55

MAR-MVS		77.19 47	78.37 48	75.81 44	89.87 22	90.58 22	79.33 54	65.56 58	77.62 47	58.33 71	59.24 71	67.98 55	74.83 28	82.37 40	83.12 29	86.95 68	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
DeepPCF-MVS	76.94 1	83.08 19	87.77 10	77.60 33	90.11 20	90.96 19	78.48 55	72.63 22	93.10 4	65.84 41	80.67 24	81.55 20	74.80 29	85.94 13	85.39 8	83.75 144	96.77 11

DeepC-MVS	74.46 3	80.30 29	81.05 35	79.42 23	87.42 40	88.50 41	83.23 28	73.27 18	82.78 28	71.01 28	62.86 58	69.93 51	74.80 29	84.30 21	84.20 21	86.79 73	94.77 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
canonicalmvs		77.65 42	79.59 41	75.39 45	81.52 63	89.83 32	81.32 41	60.74 105	80.05 39	66.72 39	68.43 41	65.09 63	74.72 31	78.87 74	82.73 31	87.32 57	92.16 56

QAPM		77.50 44	77.43 51	77.59 34	91.52 14	92.00 13	81.41 40	70.63 27	66.22 74	58.05 72	54.70 81	71.79 44	74.49 32	82.46 37	82.04 36	89.46 11	92.79 53

EC-MVSNet		76.05 53	78.87 43	72.77 62	78.87 83	86.63 63	77.50 63	57.04 135	75.34 52	61.68 58	64.20 53	69.56 52	73.96 33	82.12 44	80.65 61	87.57 50	93.57 43

SD-MVS		84.31 15	86.96 14	81.22 16	88.98 31	88.68 39	85.65 18	73.85 15	89.09 13	79.63 8	87.34 12	84.84 6	73.71 34	82.66 35	81.60 46	85.48 107	94.51 29
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
casdiffmvs		75.20 59	75.69 65	74.63 54	79.26 79	89.07 36	78.47 56	63.59 72	67.05 71	63.79 47	55.72 78	60.32 87	73.58 35	82.16 43	81.78 41	89.08 14	93.72 42
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CLD-MVS		77.36 46	77.29 53	77.45 35	82.21 59	88.11 47	81.92 35	68.96 37	77.97 45	69.62 34	62.08 59	59.44 92	73.57 36	81.75 50	81.27 51	88.41 29	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
MSLP-MVS++		78.57 37	77.33 52	80.02 22	88.39 34	84.79 82	84.62 23	66.17 54	75.96 51	78.40 10	61.59 61	71.47 45	73.54 37	78.43 78	78.88 76	88.97 15	90.18 82

ETV-MVS		76.25 51	80.22 38	71.63 71	78.23 87	87.95 51	72.75 93	60.27 110	77.50 48	57.73 73	71.53 36	66.60 59	73.16 38	80.99 57	81.23 52	87.63 49	95.73 14

SteuartSystems-ACMMP		82.51 21	85.35 22	79.20 25	90.25 18	89.39 33	84.79 22	70.95 25	82.86 27	68.32 37	86.44 14	77.19 27	73.07 39	83.63 25	83.64 24	87.82 42	94.34 31
Skip Steuart: Steuart Systems R&D Blog.
train_agg		83.35 18	86.93 15	79.17 26	89.70 24	88.41 42	85.60 20	72.89 21	86.31 19	66.58 40	90.48 6	82.24 16	73.06 40	83.10 31	82.64 33	87.21 65	95.30 24

casdiffmvs_mvg		75.57 56	76.04 62	75.02 49	80.48 72	89.31 34	80.79 47	64.04 67	66.95 72	63.87 46	57.52 73	61.33 82	72.90 41	82.01 47	81.99 39	88.03 39	93.16 48
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CS-MVS		75.84 54	78.61 44	72.61 65	79.03 80	86.74 61	74.43 89	60.27 110	74.15 57	62.78 51	66.26 49	64.25 68	72.81 42	83.36 28	81.69 45	86.32 80	93.85 39

DELS-MVS		79.49 30	79.84 40	79.08 27	88.26 37	92.49 9	84.12 26	70.63 27	65.27 81	69.60 35	61.29 63	66.50 60	72.75 43	88.07 3	88.03 2	89.13 13	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
CNLPA		71.37 79	70.27 95	72.66 64	80.79 69	81.33 110	71.07 114	65.75 56	82.36 29	64.80 44	42.46 136	56.49 106	72.70 44	73.00 134	70.52 168	80.84 177	85.76 125

CANet_DTU		72.84 69	76.63 59	68.43 92	76.81 102	86.62 65	75.54 74	54.71 160	72.06 59	43.54 128	67.11 44	58.46 96	72.40 45	81.13 56	80.82 60	87.57 50	90.21 81

DI_MVS_plusplus_trai		73.94 65	74.85 68	72.88 61	76.57 105	86.80 60	80.41 48	61.47 96	62.35 87	59.44 69	47.91 107	68.12 54	72.24 46	82.84 34	81.50 47	87.15 67	94.42 30

ACMMPR		80.62 28	82.98 27	77.87 32	88.41 33	87.05 58	83.02 29	69.18 35	83.91 24	68.35 36	82.89 19	73.64 35	72.16 47	80.78 59	81.13 54	86.10 87	91.43 62

MVS_111021_HR		77.42 45	78.40 47	76.28 39	86.95 42	90.68 21	77.41 64	70.56 30	66.21 75	62.48 54	66.17 50	63.98 69	72.08 48	82.87 33	83.15 28	88.24 34	95.71 15

diffmvs		74.32 62	75.42 66	73.04 60	75.60 113	87.27 55	78.20 57	62.96 78	68.66 70	61.89 55	59.79 69	59.84 90	71.80 49	78.30 81	79.87 66	87.80 44	94.23 34
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
OpenMVS	67.62 8	74.92 61	73.91 71	76.09 42	90.10 21	90.38 24	78.01 59	66.35 52	66.09 76	62.80 50	46.33 124	64.55 67	71.77 50	79.92 65	80.88 59	87.52 52	89.20 92

CP-MVS		79.44 31	81.51 34	77.02 36	86.95 42	85.96 76	82.00 34	68.44 40	81.82 32	67.39 38	77.43 30	73.68 34	71.62 51	79.56 70	79.58 70	85.73 97	92.51 54

MP-MVS		80.94 26	83.49 26	77.96 30	88.48 32	88.16 46	82.82 32	69.34 34	80.79 37	69.67 33	82.35 21	77.13 28	71.60 52	80.97 58	80.96 57	85.87 93	94.06 36
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CS-MVS-test		75.09 60	77.84 49	71.87 70	79.27 78	86.92 59	70.53 119	60.36 108	75.13 53	63.13 49	67.92 42	65.08 64	71.43 53	78.15 82	78.51 80	86.53 77	93.16 48

PGM-MVS		79.42 34	81.84 33	76.60 38	88.38 35	86.69 62	82.97 31	65.75 56	80.39 38	64.94 43	81.95 23	72.11 43	71.41 54	80.45 60	80.55 63	86.18 84	90.76 75

MVSTER		76.92 48	79.92 39	73.42 58	74.98 116	82.97 96	78.15 58	63.41 73	78.02 44	64.41 45	67.54 43	72.80 37	71.05 55	83.29 30	83.73 23	88.53 26	91.12 67

HQP-MVS		78.26 39	80.91 36	75.17 48	85.67 49	84.33 88	83.01 30	69.38 33	79.88 40	55.83 78	79.85 25	64.90 66	70.81 56	82.46 37	81.78 41	86.30 82	93.18 47

baseline		72.89 68	74.46 70	71.07 72	75.99 109	87.50 54	74.57 81	60.49 107	70.72 62	57.60 74	60.63 66	60.97 83	70.79 57	75.27 107	76.33 100	86.94 69	89.79 86

PCF-MVS	70.85 4	75.73 55	76.55 60	74.78 53	83.67 53	88.04 50	81.47 38	70.62 29	69.24 69	57.52 75	60.59 67	69.18 53	70.65 58	77.11 90	77.65 88	84.75 127	94.01 37
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PLC	64.00 12	68.54 94	66.66 118	70.74 75	80.28 73	74.88 165	72.64 95	63.70 71	69.26 68	55.71 80	47.24 115	55.31 115	70.42 59	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
PHI-MVS		79.43 32	84.06 25	74.04 55	86.15 47	91.57 18	80.85 46	68.90 38	82.22 30	51.81 93	78.10 28	74.28 33	70.39 60	84.01 24	84.00 22	86.14 86	94.24 33

EPNet		79.28 36	82.25 29	75.83 43	88.31 36	90.14 27	79.43 53	68.07 41	81.76 33	61.26 60	77.26 31	70.08 50	70.06 61	82.43 39	82.00 38	87.82 42	92.09 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		72.74 70	70.93 90	74.85 52	85.30 50	84.34 87	82.82 32	69.79 31	49.96 138	55.39 84	54.09 88	60.14 89	70.04 62	80.38 62	79.43 71	85.74 96	88.20 104
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CSCG		82.90 20	84.52 23	81.02 18	91.85 11	93.43 7	87.14 12	74.01 14	81.96 31	76.14 14	70.84 37	82.49 14	69.71 63	82.32 41	85.18 11	87.26 60	95.40 22

Effi-MVS+		70.42 81	71.23 87	69.47 81	78.04 89	85.24 79	75.57 73	58.88 113	59.56 97	48.47 107	52.73 92	54.94 116	69.69 64	78.34 80	77.06 92	86.18 84	90.73 76

Anonymous202405211			66.35 122		78.00 90	84.41 86	74.85 79	63.18 75	51.00 134		31.37 190	53.73 124	69.67 65	76.28 96	76.84 93	83.21 153	90.85 70

EIA-MVS		73.48 66	76.05 61	70.47 77	78.12 88	87.21 56	71.78 101	60.63 106	69.66 66	55.56 82	64.86 52	60.69 84	69.53 66	77.35 89	78.59 77	87.22 63	94.01 37

PMMVS		70.37 84	75.06 67	64.90 113	71.46 132	81.88 102	64.10 153	55.64 147	71.31 60	46.69 112	70.69 38	58.56 93	69.53 66	79.03 73	75.63 108	81.96 168	88.32 103

MVS_111021_LR		74.26 63	75.95 63	72.27 66	79.43 76	85.04 80	72.71 94	65.27 61	70.92 61	63.58 48	69.32 39	60.31 88	69.43 68	77.01 91	77.15 91	83.22 151	91.93 60

OMC-MVS		74.03 64	75.82 64	71.95 68	79.56 74	80.98 114	75.35 77	63.21 74	84.48 23	61.83 56	61.54 62	66.89 58	69.41 69	76.60 94	74.07 127	82.34 164	86.15 119

CPTT-MVS		75.43 57	77.13 55	73.44 57	81.43 64	82.55 100	80.96 45	64.35 64	77.95 46	61.39 59	69.20 40	70.94 47	69.38 70	73.89 123	73.32 137	83.14 154	92.06 58

Anonymous20231211		68.44 95	66.37 121	70.86 73	77.58 94	83.49 93	75.15 78	61.89 91	52.54 131	58.50 70	28.89 195	56.78 105	69.29 71	74.96 111	76.61 95	82.73 157	91.36 65

Fast-Effi-MVS+		67.59 102	67.56 113	67.62 97	73.67 121	81.14 113	71.12 112	54.79 159	58.88 99	50.61 100	46.70 122	47.05 142	69.12 72	76.06 100	76.44 98	86.43 79	86.65 113

TSAR-MVS + COLMAP		73.09 67	76.86 56	68.71 87	74.97 117	82.49 101	74.51 86	61.83 92	83.16 26	49.31 105	82.22 22	51.62 130	68.94 73	78.76 76	75.52 112	82.67 159	84.23 136

ACMMP		77.61 43	79.59 41	75.30 47	85.87 48	85.58 77	81.42 39	67.38 46	79.38 42	62.61 52	78.53 27	65.79 62	68.80 74	78.56 77	78.50 81	85.75 94	90.80 71
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
CDPH-MVS		79.39 35	82.13 31	76.19 41	89.22 30	88.34 43	84.20 25	71.00 24	79.67 41	56.97 77	77.77 29	72.24 42	68.50 75	81.33 52	82.74 30	87.23 61	92.84 51

CostFormer		72.18 72	73.90 72	70.18 79	79.47 75	86.19 74	76.94 67	48.62 180	66.07 77	60.40 67	54.14 87	65.82 61	67.98 76	75.84 102	76.41 99	87.67 47	92.83 52

TAPA-MVS	67.10 9	71.45 77	73.47 75	69.10 85	77.04 100	80.78 117	73.81 90	62.10 88	80.80 36	51.28 94	60.91 64	63.80 71	67.98 76	74.59 113	72.42 149	82.37 163	80.97 158
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FA-MVS(training)		70.24 86	71.77 83	68.45 91	77.52 96	86.03 75	73.33 92	49.12 179	63.55 84	55.77 79	48.91 104	56.26 107	67.78 78	77.60 84	79.62 69	87.19 66	90.40 78

PVSNet_BlendedMVS		76.84 49	78.47 45	74.95 50	82.37 57	89.90 30	75.45 75	65.45 59	74.99 54	70.66 30	63.07 56	58.27 99	67.60 79	84.24 22	81.70 43	88.18 35	97.10 8

PVSNet_Blended		76.84 49	78.47 45	74.95 50	82.37 57	89.90 30	75.45 75	65.45 59	74.99 54	70.66 30	63.07 56	58.27 99	67.60 79	84.24 22	81.70 43	88.18 35	97.10 8

TSAR-MVS + ACMM		81.59 25	85.84 20	76.63 37	89.82 23	86.53 66	86.32 16	66.72 50	85.96 20	65.43 42	88.98 11	82.29 15	67.57 81	82.06 46	81.33 50	83.93 142	93.75 41

X-MVS		78.16 40	80.55 37	75.38 46	87.99 39	86.27 71	81.05 44	68.98 36	78.33 43	61.07 62	75.25 34	72.27 39	67.52 82	80.03 63	80.52 64	85.66 104	91.20 66

FC-MVSNet-train		68.83 93	68.29 107	69.47 81	78.35 86	79.94 123	64.72 150	66.38 51	54.96 122	54.51 87	56.75 75	47.91 140	66.91 83	75.57 106	75.75 106	85.92 90	87.12 110

DCV-MVSNet		69.13 90	69.07 100	69.21 83	77.65 93	77.52 146	74.68 80	57.85 124	54.92 123	55.34 85	55.74 77	55.56 114	66.35 84	75.05 108	76.56 97	83.35 148	88.13 105

CHOSEN 1792x2688		72.55 71	71.98 80	73.22 59	86.57 45	92.41 10	75.63 71	66.77 49	62.08 88	52.32 90	30.27 193	50.74 133	66.14 85	86.22 12	85.41 7	91.90 1	96.75 12

ACMM	66.70 10	70.42 81	68.49 105	72.67 63	82.85 54	77.76 144	77.70 62	64.76 63	64.61 82	60.74 66	49.29 101	53.97 123	65.86 86	74.97 109	75.57 110	84.13 141	83.29 143
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tpmrst		67.15 108	68.12 110	66.03 106	76.21 107	80.98 114	71.27 108	45.05 191	60.69 93	50.63 99	46.95 120	54.15 122	65.30 87	71.80 147	71.77 153	87.72 45	90.48 77

LS3D		64.54 125	62.14 149	67.34 101	80.85 67	75.79 159	69.99 120	65.87 55	60.77 92	44.35 124	42.43 137	45.95 145	65.01 88	69.88 165	68.69 175	77.97 192	71.43 190

HyFIR lowres test		68.39 96	68.28 108	68.52 90	80.85 67	88.11 47	71.08 113	58.09 119	54.87 125	47.80 111	27.55 199	55.80 111	64.97 89	79.11 72	79.14 74	88.31 32	93.35 44

FMVSNet3		70.41 83	71.89 82	68.68 88	70.89 138	79.42 129	75.63 71	60.97 101	65.32 78	51.06 95	47.37 112	62.05 74	64.90 90	82.49 36	82.27 35	88.64 22	84.34 135

PatchMatch-RL		62.22 145	60.69 159	64.01 121	68.74 148	75.75 160	59.27 177	60.35 109	56.09 114	53.80 88	47.06 118	36.45 178	64.80 91	68.22 172	67.22 179	77.10 194	74.02 178

tpm cat1		67.47 105	67.05 116	67.98 94	76.63 103	81.51 108	74.49 87	47.65 185	61.18 90	61.12 61	42.51 135	53.02 128	64.74 92	70.11 164	71.50 155	83.22 151	89.49 88

GeoE		68.96 92	69.32 98	68.54 89	76.61 104	83.12 95	71.78 101	56.87 137	60.21 95	54.86 86	45.95 125	54.79 119	64.27 93	74.59 113	75.54 111	86.84 72	91.01 69

dps		64.08 127	63.22 137	65.08 111	75.27 115	79.65 126	66.68 144	46.63 189	56.94 107	55.67 81	43.96 127	43.63 150	64.00 94	69.50 169	69.82 170	82.25 165	79.02 165

ACMP	68.86 7	72.15 73	72.25 78	72.03 67	80.96 66	80.87 116	77.93 60	64.13 66	69.29 67	60.79 65	64.04 54	53.54 125	63.91 95	73.74 126	75.27 113	84.45 134	88.98 94
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Effi-MVS+-dtu		64.58 123	64.08 133	65.16 110	73.04 125	75.17 164	70.68 118	56.23 141	54.12 128	44.71 123	47.42 111	51.10 131	63.82 96	68.08 173	66.32 184	82.47 162	86.38 116

GBi-Net		69.21 88	70.40 93	67.81 95	69.49 143	78.65 134	74.54 82	60.97 101	65.32 78	51.06 95	47.37 112	62.05 74	63.43 97	77.49 85	78.22 83	87.37 54	83.73 138

test1		69.21 88	70.40 93	67.81 95	69.49 143	78.65 134	74.54 82	60.97 101	65.32 78	51.06 95	47.37 112	62.05 74	63.43 97	77.49 85	78.22 83	87.37 54	83.73 138

FMVSNet2		68.06 99	68.57 104	67.45 100	69.49 143	78.65 134	74.54 82	60.23 112	56.29 112	49.64 104	42.13 139	57.08 104	63.43 97	81.15 55	80.99 55	87.37 54	83.73 138

baseline2		71.22 80	73.01 77	69.13 84	75.76 111	86.34 70	71.23 109	62.78 84	62.62 85	52.85 89	57.32 74	54.31 120	63.27 100	79.74 68	79.31 72	88.89 16	91.43 62

EPMVS		66.21 111	67.49 114	64.73 114	75.81 110	84.20 90	68.94 128	44.37 195	61.55 89	48.07 110	49.21 103	54.87 118	62.88 101	71.82 146	71.40 159	88.28 33	79.37 164

CHOSEN 280x420		62.23 144	66.57 119	57.17 169	59.88 192	68.92 189	61.20 172	42.28 202	54.17 127	39.57 147	47.78 109	64.97 65	62.68 102	73.85 124	69.52 173	77.43 193	86.75 112

thres100view900		67.14 109	66.09 124	68.38 93	77.70 91	83.84 92	74.52 85	66.33 53	49.16 142	43.40 130	43.24 128	41.34 153	62.59 103	79.31 71	75.92 105	85.73 97	89.81 84

baseline1		71.47 76	72.02 79	70.82 74	80.56 71	84.51 84	76.61 68	66.93 48	56.22 113	48.66 106	55.40 79	60.43 86	62.55 104	83.35 29	80.99 55	89.60 8	83.28 144

LGP-MVS_train		72.02 74	73.18 76	70.67 76	82.13 60	80.26 122	79.58 52	63.04 76	70.09 63	51.98 91	65.06 51	55.62 113	62.49 105	75.97 101	76.32 101	84.80 126	88.93 95

MSDG		65.57 116	61.57 153	70.24 78	82.02 61	76.47 153	74.46 88	68.73 39	56.52 110	50.33 101	38.47 158	41.10 157	62.42 106	72.12 143	72.94 144	83.47 147	73.37 183

IterMVS-LS		66.08 113	66.56 120	65.51 107	73.67 121	74.88 165	70.89 116	53.55 166	50.42 136	48.32 109	50.59 98	55.66 112	61.83 107	73.93 122	74.42 123	84.82 125	86.01 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MS-PatchMatch		70.34 85	69.00 101	71.91 69	85.20 51	85.35 78	77.84 61	61.77 94	58.01 105	55.40 83	41.26 143	58.34 98	61.69 108	81.70 51	78.29 82	89.56 9	80.02 161

v2v482		63.68 131	62.85 143	64.65 115	68.01 152	80.46 120	71.90 99	57.60 127	44.26 161	42.82 135	39.80 154	38.62 169	61.56 109	73.06 132	74.86 116	86.03 89	88.90 97

tfpn200view9		65.90 114	64.96 128	67.00 103	77.70 91	81.58 106	71.71 104	62.94 81	49.16 142	43.40 130	43.24 128	41.34 153	61.42 110	76.24 97	74.63 119	84.84 122	88.52 101

Fast-Effi-MVS+-dtu		63.05 135	64.72 131	61.11 143	71.21 136	76.81 152	70.72 117	43.13 200	52.51 132	35.34 172	46.55 123	46.36 143	61.40 111	71.57 150	71.44 157	84.84 122	87.79 107

ACMH	59.42 14	61.59 150	59.22 169	64.36 119	78.92 82	78.26 138	67.65 135	67.48 45	39.81 179	30.98 186	38.25 160	34.59 189	61.37 112	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
v10		63.00 136	62.22 148	63.90 124	67.88 154	77.78 143	71.59 105	54.34 161	45.37 158	42.76 136	38.53 157	38.93 167	61.05 113	74.39 117	74.52 122	85.75 94	86.04 120

tpm		64.85 121	66.02 125	63.48 126	74.52 118	78.38 137	70.98 115	44.99 193	51.61 133	43.28 132	47.66 110	53.18 126	60.57 114	70.58 158	71.30 162	86.54 76	89.45 90

v8		63.44 133	62.58 145	64.43 117	68.28 151	78.07 139	71.82 100	54.85 157	46.70 152	45.20 119	39.40 155	40.91 158	60.54 115	72.85 136	74.39 124	85.92 90	85.76 125

v1192		62.25 142	61.64 152	62.96 129	66.88 160	79.72 125	69.96 121	55.77 145	41.58 171	39.42 149	37.05 167	35.96 183	60.50 116	74.30 120	74.09 126	85.24 111	88.76 98

v1144		63.00 136	62.39 147	63.70 125	67.72 155	80.27 121	71.23 109	56.40 138	42.51 166	40.81 144	38.12 162	37.73 170	60.42 117	74.46 115	74.55 121	85.64 105	89.12 93

gm-plane-assit		54.99 179	57.99 175	51.49 187	69.27 147	54.42 212	32.32 215	42.59 201	21.18 215	13.71 212	23.61 203	43.84 149	60.21 118	87.09 5	86.55 5	90.81 4	89.28 91

PatchmatchNet		65.43 118	67.71 112	62.78 132	73.49 123	82.83 97	66.42 147	45.40 190	60.40 94	45.27 117	49.22 102	57.60 103	60.01 119	70.61 156	71.38 160	86.08 88	81.91 155
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
ACMH+	60.36 13	61.16 151	58.38 171	64.42 118	77.37 99	74.35 170	68.45 130	62.81 83	45.86 156	38.48 155	35.71 176	37.35 173	59.81 120	67.24 175	69.80 172	79.58 185	78.32 167

v144192		62.05 146	61.46 154	62.73 135	66.59 164	79.87 124	69.30 126	55.88 143	41.50 173	39.41 150	37.23 165	36.45 178	59.62 121	72.69 139	73.51 132	85.61 106	88.93 95

v1921920		61.66 149	61.10 157	62.31 137	66.32 165	79.57 127	68.41 131	55.49 150	41.03 174	38.69 154	36.64 173	35.27 186	59.60 122	73.23 130	73.41 134	85.37 108	88.51 102

GA-MVS		64.55 124	65.76 127	63.12 128	69.68 142	81.56 107	69.59 124	58.16 118	45.23 159	35.58 171	47.01 119	41.82 152	59.41 123	79.62 69	78.54 78	86.32 80	86.56 114

ADS-MVSNet		58.40 169	59.16 170	57.52 166	65.80 170	74.57 169	60.26 173	40.17 209	50.51 135	38.01 158	40.11 153	44.72 147	59.36 124	64.91 182	66.55 182	81.53 172	72.72 186

pmmvs4		63.14 134	62.46 146	63.94 123	66.03 167	76.40 154	66.82 143	57.60 127	56.74 108	50.26 102	40.81 148	37.51 172	59.26 125	71.75 148	71.48 156	83.68 146	82.53 149

v1240		61.09 152	60.55 161	61.72 141	65.92 169	79.28 130	67.16 141	54.91 156	39.79 180	38.10 157	36.08 175	34.64 188	59.15 126	72.86 135	73.36 136	85.10 113	87.84 106

PVSNet_Blended_VisFu		71.76 75	73.54 74	69.69 80	79.01 81	87.16 57	72.05 98	61.80 93	56.46 111	59.66 68	53.88 89	62.48 72	59.08 127	81.17 54	78.90 75	86.53 77	94.74 27

MDTV_nov1_ep13		65.21 119	67.28 115	62.79 131	70.91 137	81.72 103	69.28 127	49.50 178	58.08 102	43.94 127	50.50 99	56.02 109	58.86 128	70.72 155	73.37 135	84.24 137	80.52 160

FMVSNet1		63.48 132	63.07 139	63.97 122	65.31 171	76.37 155	71.77 103	57.90 123	43.32 165	45.66 115	35.06 181	49.43 135	58.57 129	77.49 85	78.22 83	84.59 131	81.60 157

USDC		59.69 160	60.03 165	59.28 156	64.04 176	71.84 178	63.15 165	55.36 152	54.90 124	35.02 173	48.34 105	29.79 205	58.16 130	70.60 157	71.33 161	79.99 182	73.42 182

thres400		65.18 120	64.44 132	66.04 105	76.40 106	82.63 98	71.52 106	64.27 65	44.93 160	40.69 145	41.86 140	40.79 159	58.12 131	77.67 83	74.64 118	85.26 110	88.56 100

thres200		65.58 115	64.74 130	66.56 104	77.52 96	81.61 104	73.44 91	62.95 79	46.23 154	42.45 137	42.76 130	41.18 155	58.12 131	76.24 97	75.59 109	84.89 120	89.58 87

test2506		69.26 87	70.79 91	67.48 99	78.64 84	86.40 68	72.22 96	62.75 85	58.05 103	45.24 118	50.76 96	54.93 117	58.05 133	79.82 66	79.70 67	87.96 40	85.90 123

ECVR-MVS		67.93 101	68.49 105	67.28 102	78.64 84	86.40 68	72.22 96	62.75 85	58.05 103	44.06 126	40.92 147	48.20 138	58.05 133	79.82 66	79.70 67	87.96 40	86.32 118

thisisatest0530		68.38 97	70.98 89	65.35 109	72.61 126	84.42 85	68.21 132	57.98 120	59.77 96	50.80 98	54.63 82	58.48 95	57.92 135	76.99 92	77.47 89	84.60 130	85.07 129

tttt0517		67.99 100	70.61 92	64.94 112	71.94 131	83.96 91	67.62 136	57.98 120	59.30 98	49.90 103	54.50 85	57.98 102	57.92 135	76.48 95	77.47 89	84.24 137	84.58 132

SCA		63.90 129	66.67 117	60.66 145	73.75 119	71.78 180	59.87 176	43.66 196	61.13 91	45.03 120	51.64 94	59.45 91	57.92 135	70.96 153	70.80 164	83.71 145	80.92 159

test-LLR		68.23 98	71.61 85	64.28 120	71.37 133	81.32 111	63.98 156	61.03 99	58.62 100	42.96 133	52.74 90	61.65 78	57.74 138	75.64 104	78.09 86	88.61 23	93.21 45

TESTMET0.1,1		67.38 106	71.61 85	62.45 136	66.05 166	81.32 111	63.98 156	55.36 152	58.62 100	42.96 133	52.74 90	61.65 78	57.74 138	75.64 104	78.09 86	88.61 23	93.21 45

V42		62.86 138	62.97 140	62.74 134	60.84 189	78.99 132	71.46 107	57.13 134	46.85 150	44.28 125	38.87 156	40.73 161	57.63 140	72.60 140	74.14 125	85.09 115	88.63 99

CR-MVSNet		62.31 140	64.75 129	59.47 153	68.63 149	71.29 183	67.53 137	43.18 198	55.83 115	41.40 139	41.04 145	55.85 110	57.29 141	72.76 137	73.27 139	78.77 189	83.23 145

PatchT		60.46 156	63.85 134	56.51 172	65.95 168	75.68 161	47.34 198	41.39 205	53.89 129	41.40 139	37.84 163	50.30 134	57.29 141	72.76 137	73.27 139	85.67 101	83.23 145

TinyColmap		52.66 188	50.09 199	55.65 174	59.72 193	64.02 203	57.15 183	52.96 170	40.28 177	32.51 181	32.42 186	20.97 216	56.65 143	63.95 188	65.15 189	74.91 200	63.87 204

EPP-MVSNet		67.58 103	71.10 88	63.48 126	75.71 112	83.35 94	66.85 142	57.83 125	53.02 130	41.15 142	55.82 76	67.89 56	56.01 144	74.40 116	72.92 145	83.33 149	90.30 80

test1111		66.72 110	67.80 111	65.45 108	77.42 98	86.63 63	69.69 123	62.98 77	55.29 119	39.47 148	40.12 152	47.11 141	55.70 145	79.96 64	80.00 65	87.47 53	85.49 128

MVS-HIRNet		53.86 186	53.02 188	54.85 177	60.30 191	72.36 176	44.63 206	42.20 203	39.45 181	43.47 129	21.66 209	34.00 192	55.47 146	65.42 180	67.16 180	83.02 156	71.08 192

test-mter		64.06 128	69.24 99	58.01 161	59.07 195	77.40 147	59.13 178	48.11 183	55.64 118	39.18 152	51.56 95	58.54 94	55.38 147	73.52 128	76.00 104	87.22 63	92.05 59

thres600view7		63.77 130	63.14 138	64.51 116	75.49 114	81.61 104	69.59 124	62.95 79	43.96 163	38.90 153	41.09 144	40.24 164	55.25 148	76.24 97	71.54 154	84.89 120	87.30 109

v148		62.00 147	61.19 156	62.96 129	67.46 158	79.49 128	67.87 133	57.66 126	42.30 167	45.02 121	38.20 161	38.89 168	54.77 149	69.83 166	72.60 148	84.96 116	87.01 111

gg-mvs-nofinetune		62.34 139	66.19 123	57.86 163	76.15 108	88.61 40	71.18 111	41.24 208	25.74 211	13.16 214	22.91 206	63.97 70	54.52 150	85.06 16	85.25 10	90.92 3	91.78 61

IterMVS		61.87 148	63.55 135	59.90 149	67.29 159	72.20 177	67.34 140	48.56 181	47.48 148	37.86 160	47.07 117	48.27 136	54.08 151	72.12 143	73.71 130	84.30 136	83.99 137
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
RPSCF		55.07 178	58.06 173	51.57 185	48.87 211	58.95 208	53.68 189	41.26 207	62.42 86	45.88 114	54.38 86	54.26 121	53.75 152	57.15 199	53.53 209	66.01 209	65.75 201

CMPMVS	43.63 17	57.67 173	55.43 181	60.28 148	72.01 129	79.00 131	62.77 166	53.23 168	41.77 170	45.42 116	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
IterMVS-SCA-FT		60.21 158	62.97 140	57.00 170	66.64 163	71.84 178	67.53 137	46.93 188	47.56 147	36.77 165	46.85 121	48.21 137	52.51 154	70.36 161	72.40 150	71.63 207	83.53 141

IB-MVS	64.48 11	69.02 91	68.97 102	69.09 86	81.75 62	89.01 37	64.50 151	64.91 62	56.65 109	62.59 53	47.89 108	45.23 146	51.99 155	69.18 170	81.88 40	88.77 18	92.93 50
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
pmmvs5		59.72 159	60.24 163	59.11 157	62.77 183	77.33 149	63.17 164	54.00 163	40.21 178	37.23 161	40.41 149	35.99 182	51.75 156	72.55 141	72.74 147	85.72 99	82.45 151

UniMVSNet_ETH3D		57.83 170	56.46 180	59.43 154	63.24 180	73.22 174	67.70 134	55.58 148	36.17 191	36.84 163	32.64 185	35.14 187	51.50 157	65.81 178	69.81 171	81.73 170	82.44 152

UniMVSNet_NR-MVSNet		62.30 141	63.51 136	60.89 144	69.48 146	77.83 142	64.07 154	63.94 68	50.03 137	31.17 184	44.82 126	41.12 156	51.37 158	71.02 152	74.81 117	85.30 109	84.95 130

DU-MVS		60.87 154	61.82 151	59.76 151	66.69 161	75.87 157	64.07 154	61.96 89	49.31 140	31.17 184	42.76 130	36.95 175	51.37 158	69.67 167	73.20 142	83.30 150	84.95 130

FMVSNet5		58.86 165	60.24 163	57.25 168	52.66 207	66.25 195	63.77 159	52.86 171	57.85 106	37.92 159	36.12 174	52.22 129	51.37 158	70.88 154	71.43 158	84.92 117	66.91 199

LTVRE_ROB	47.26 16	49.41 197	49.91 200	48.82 191	64.76 173	69.79 186	49.05 194	47.12 187	20.36 217	16.52 206	36.65 172	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
tfpnnormal		58.97 164	56.48 179	61.89 139	71.27 135	76.21 156	66.65 145	61.76 95	32.90 199	36.41 166	27.83 198	29.14 206	50.64 162	73.06 132	73.05 143	84.58 132	83.15 147

v7n		57.04 175	56.64 178	57.52 166	62.85 182	74.75 167	61.76 168	51.80 174	35.58 195	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	56.65 171	57.34 201	67.77 191	57.54 182	53.74 165	40.93 175	41.09 143	31.19 191	29.10 207	49.07 164	65.54 179	67.28 178	81.14 174	75.81 171

NR-MVSNet		61.08 153	62.09 150	59.90 149	71.96 130	75.87 157	63.60 160	61.96 89	49.31 140	27.95 189	42.76 130	33.85 193	48.82 165	74.35 118	74.05 128	85.13 112	84.45 133

Baseline_NR-MVSNet		59.47 161	60.28 162	58.54 160	66.69 161	73.90 171	61.63 170	62.90 82	49.15 144	26.87 191	35.18 180	37.62 171	48.20 166	69.67 167	73.61 131	84.92 117	82.82 148

RPMNet		58.63 168	62.80 144	53.76 183	67.59 157	71.29 183	54.60 187	38.13 210	55.83 115	35.70 170	41.58 142	53.04 127	47.89 167	66.10 177	67.38 177	78.65 191	84.40 134

TranMVSNet+NR-MVSNet		60.38 157	61.30 155	59.30 155	68.34 150	75.57 163	63.38 163	63.78 70	46.74 151	27.73 190	42.56 134	36.84 176	47.66 168	70.36 161	74.59 120	84.91 119	82.46 150

anonymousdsp		54.99 179	57.24 176	52.36 184	53.82 205	71.75 181	51.49 191	48.14 182	33.74 197	33.66 178	38.34 159	36.13 181	47.54 169	64.53 186	70.60 167	79.53 186	85.59 127

MDTV_nov1_ep13_2view		54.47 183	54.61 182	54.30 182	60.50 190	73.82 172	57.92 181	43.38 197	39.43 182	32.51 181	33.23 184	34.05 191	47.26 170	62.36 190	66.21 185	84.24 137	73.19 184

thisisatest0515		59.37 162	60.68 160	57.84 164	64.39 175	75.65 162	58.56 180	53.86 164	41.55 172	42.12 138	40.40 150	39.59 165	47.09 171	71.69 149	73.79 129	81.02 176	82.08 154

PM-MVS		50.11 194	50.38 198	49.80 189	47.23 213	62.08 206	50.91 193	44.84 194	41.90 169	36.10 168	35.22 179	26.05 212	46.83 172	57.64 197	55.42 208	72.90 204	74.32 177

CDS-MVSNet		64.22 126	65.89 126	62.28 138	70.05 140	80.59 118	69.91 122	57.98 120	43.53 164	46.58 113	48.22 106	50.76 132	46.45 173	75.68 103	76.08 103	82.70 158	86.34 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
TAMVS		58.86 165	60.91 158	56.47 173	62.38 185	77.57 145	58.97 179	52.98 169	38.76 183	36.17 167	42.26 138	47.94 139	46.45 173	70.23 163	70.79 165	81.86 169	78.82 166

TDRefinement		52.70 187	51.02 196	54.66 179	57.41 200	65.06 199	61.47 171	54.94 154	44.03 162	33.93 177	30.13 194	27.57 208	46.17 175	61.86 191	62.48 198	74.01 203	66.06 200

IS_MVSNet		67.29 107	71.98 80	61.82 140	76.92 101	84.32 89	65.90 149	58.22 117	55.75 117	39.22 151	54.51 84	62.47 73	45.99 176	78.83 75	78.52 79	84.70 128	89.47 89

MIMVSNet		57.78 172	59.71 167	55.53 175	54.79 203	77.10 150	63.89 158	45.02 192	46.59 153	36.79 164	28.36 197	40.77 160	45.84 177	74.97 109	76.58 96	86.87 71	73.60 181

UGNet		67.57 104	71.69 84	62.76 133	69.88 141	82.58 99	66.43 146	58.64 115	54.71 126	51.87 92	61.74 60	62.01 77	45.46 178	74.78 112	74.99 114	84.24 137	91.02 68
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
Vis-MVSNet		65.53 117	69.83 97	60.52 146	70.80 139	84.59 83	66.37 148	55.47 151	48.40 145	40.62 146	57.67 72	58.43 97	45.37 179	77.49 85	76.24 102	84.47 133	85.99 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
COLMAP_ROB	51.17 15	55.13 177	52.90 190	57.73 165	73.47 124	67.21 193	62.13 167	55.82 144	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
SixPastTwentyTwo		49.11 198	49.22 201	48.99 190	58.54 199	64.14 202	47.18 199	47.75 184	31.15 203	24.42 195	41.01 146	26.55 210	44.04 181	54.76 206	58.70 203	71.99 206	68.21 195

UniMVSNet (Re)		60.62 155	62.93 142	57.92 162	67.64 156	77.90 141	61.75 169	61.24 98	49.83 139	29.80 188	42.57 133	40.62 162	43.36 182	70.49 160	73.27 139	83.76 143	85.81 124

pm-mvs1		59.21 163	59.58 168	58.77 159	67.97 153	77.07 151	64.12 152	57.20 132	34.73 196	36.86 162	35.34 178	40.54 163	43.34 183	74.32 119	73.30 138	83.13 155	81.77 156

EG-PatchMatch MVS		58.73 167	58.03 174	59.55 152	72.32 127	80.49 119	63.44 162	55.55 149	32.49 200	38.31 156	28.87 196	37.22 174	42.84 184	74.30 120	75.70 107	84.84 122	77.14 170

MDA-MVSNet-bldmvs		44.15 204	42.27 209	46.34 198	38.34 215	62.31 205	46.28 201	55.74 146	29.83 204	20.98 201	27.11 200	16.45 221	41.98 185	41.11 213	57.47 204	74.72 201	61.65 209

UA-Net		64.62 122	68.23 109	60.42 147	77.53 95	81.38 109	60.08 175	57.47 130	47.01 149	44.75 122	60.68 65	71.32 46	41.84 186	73.27 129	72.25 151	80.83 178	71.68 188

pmmvs3		41.86 206	42.29 208	41.36 203	39.80 214	52.66 213	38.93 212	35.85 214	23.40 214	20.22 202	19.30 210	20.84 217	40.56 187	55.98 204	58.79 202	72.80 205	65.03 202

pmnet_mix02		53.92 185	53.30 187	54.65 180	61.89 186	71.33 182	54.54 188	54.17 162	40.38 176	34.65 174	34.76 182	30.68 204	40.44 188	60.97 192	63.71 192	82.19 166	71.24 191

pmmvs6		54.20 184	53.54 186	54.97 176	63.22 181	72.98 175	60.17 174	52.32 173	26.77 210	34.30 176	23.29 205	36.23 180	40.33 189	68.77 171	68.76 174	79.47 187	78.00 168

TransMVSNet (Re)		57.83 170	56.90 177	58.91 158	72.26 128	74.69 168	63.57 161	61.42 97	32.30 201	32.65 180	33.97 183	35.96 183	39.17 190	73.84 125	72.84 146	84.37 135	74.69 176

CVMVSNet		54.92 181	58.16 172	51.13 188	62.61 184	68.44 190	55.45 186	52.38 172	42.28 168	21.45 200	47.10 116	46.10 144	37.96 191	64.42 187	63.81 191	76.92 195	75.01 175

EPNet_dtu		66.17 112	70.13 96	61.54 142	81.04 65	77.39 148	68.87 129	62.50 87	69.78 64	33.51 179	63.77 55	56.22 108	37.65 192	72.20 142	72.18 152	85.69 100	79.38 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
N_pmnet		47.67 200	47.00 204	48.45 193	54.72 204	62.78 204	46.95 200	51.25 175	36.01 193	26.09 194	26.59 201	25.93 213	35.50 193	55.67 205	59.01 201	76.22 196	63.04 205

Vis-MVSNet (Re-imp)		62.25 142	68.74 103	54.68 178	73.70 120	78.74 133	56.51 184	57.49 129	55.22 120	26.86 192	54.56 83	61.35 80	31.06 194	73.10 131	74.90 115	82.49 161	83.31 142

Anonymous20231206		52.23 189	52.80 191	51.56 186	64.70 174	69.41 187	51.01 192	58.60 116	36.63 188	22.44 199	21.80 208	31.42 200	30.52 195	66.79 176	67.83 176	82.10 167	75.73 172

test0.0.03 1		57.35 174	59.89 166	54.38 181	71.37 133	73.45 173	52.71 190	61.03 99	46.11 155	26.33 193	41.73 141	44.08 148	29.72 196	71.43 151	70.90 163	85.10 113	71.56 189

CP-MVSNet		50.57 192	52.60 193	48.21 194	58.77 197	65.82 197	48.17 196	56.29 140	37.41 185	16.59 205	37.14 166	31.95 197	29.21 197	56.60 201	63.71 192	80.22 180	75.56 173

ambc			42.30 207		50.36 209	49.51 214	35.47 213		32.04 202	23.53 196	17.36 212	8.95 223	29.06 198	64.88 183	56.26 205	61.29 212	67.12 198

PS-CasMVS		50.17 193	52.02 194	48.02 195	58.60 198	65.54 198	48.04 197	56.19 142	36.42 190	16.42 207	35.68 177	31.33 201	28.85 199	56.42 203	63.54 194	80.01 181	75.18 174

PEN-MVS		51.04 190	52.94 189	48.82 191	61.45 188	66.00 196	48.68 195	57.20 132	36.87 186	15.36 208	36.98 168	32.72 195	28.77 200	57.63 198	66.37 183	81.44 173	74.00 179

FPMVS		39.11 208	36.39 210	42.28 202	55.97 202	45.94 215	46.23 202	41.57 204	35.73 194	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	30.47 209	27.55 220	45.31 216	29.29 216	30.92 215	29.00 207	9.88 219	18.77 211	17.64 220	26.77 202	44.07 209	45.98 211	58.41 214	47.87 212

DTE-MVSNet		49.82 195	51.92 195	47.37 196	61.75 187	64.38 201	45.89 204	57.33 131	36.11 192	12.79 215	36.87 169	31.93 198	25.73 203	58.01 196	65.22 188	80.75 179	70.93 193

EU-MVSNet		44.84 203	47.85 203	41.32 205	49.26 210	56.59 211	43.07 207	47.64 186	33.03 198	13.82 211	36.78 170	30.99 202	24.37 204	53.80 207	55.57 207	69.78 208	68.21 195

test_method		28.15 211	34.48 212	20.76 212	6.76 224	21.18 220	21.03 218	18.41 218	36.77 187	17.52 203	15.67 216	31.63 199	24.05 205	41.03 214	26.69 216	36.82 218	68.38 194

WR-MVS		51.02 191	54.56 183	46.90 197	63.84 177	69.23 188	44.78 205	56.38 139	38.19 184	14.19 210	37.38 164	36.82 177	22.39 206	60.14 194	66.20 186	79.81 183	73.95 180

WR-MVS_H		49.62 196	52.63 192	46.11 200	58.80 196	67.58 192	46.14 203	54.94 154	36.51 189	13.63 213	36.75 171	35.67 185	22.10 207	56.43 202	62.76 196	81.06 175	72.73 185

DeepMVS_CX						19.81 222	17.01 220	10.02 219	23.61 213	5.85 221	17.21 213	8.03 224	21.13 208	22.60 217		21.42 222	30.01 216

PMVS	27.44 18	32.08 210	29.07 213	35.60 208	48.33 212	24.79 218	26.97 217	41.34 206	20.45 216	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)
testgi		48.51 199	50.53 197	46.16 199	64.78 172	67.15 194	41.54 208	54.81 158	29.12 206	17.03 204	32.07 188	31.98 196	20.15 210	65.26 181	67.00 181	78.67 190	61.10 210

new-patchmatchnet		42.21 205	42.97 206	41.33 204	53.05 206	59.89 207	39.38 210	49.61 177	28.26 208	12.10 216	22.17 207	21.54 215	19.22 211	50.96 208	56.04 206	74.61 202	61.92 208

MIMVSNet1		40.84 207	43.46 205	37.79 207	32.14 216	58.92 209	39.24 211	50.83 176	27.00 209	11.29 217	16.76 215	26.53 211	17.75 212	57.14 200	61.12 200	75.46 198	56.78 211

test20.03		47.23 202	48.69 202	45.53 201	63.28 179	64.39 200	41.01 209	56.93 136	29.16 205	15.21 209	23.90 202	30.76 203	17.51 213	64.63 185	65.26 187	79.21 188	62.71 207

FC-MVSNet-test		47.24 201	54.37 184	38.93 206	59.49 194	58.25 210	34.48 214	53.36 167	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

Gipuma		24.91 212	24.61 214	25.26 211	31.47 217	21.59 219	18.06 219	37.53 211	25.43 212	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
EMVS		14.40 215	10.71 218	18.70 214	28.15 219	12.09 224	7.06 223	36.89 212	11.00 219	3.56 224	4.95 219	2.27 227	13.91 216	10.13 220	16.06 219	22.63 221	18.51 220

E-PMN		15.08 214	11.65 217	19.08 213	28.73 218	12.31 223	6.95 224	36.87 213	10.71 220	3.63 223	5.13 218	2.22 228	13.81 217	11.34 219	18.50 218	24.49 220	21.32 219

MVE	15.98 19	14.37 216	16.36 216	12.04 217	7.72 223	20.24 221	5.90 225	29.05 216	8.28 221	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)
tmp_tt				16.09 216	13.07 222	8.12 225	13.61 222	2.08 220	55.09 121	30.10 187	40.26 151	22.83 214	5.35 219	29.91 215	25.25 217	32.33 219

PMMVS2		20.45 213	22.31 215	18.27 215	20.52 221	26.73 217	14.85 221	28.43 217	13.69 218	0.79 225	10.35 217	9.10 222	3.83 220	27.64 216	32.87 214	41.17 216	35.81 215

GG-mvs-BLEND		54.54 182	77.58 50	27.67 210	0.03 225	90.09 29	77.20 66	0.02 221	66.83 73	0.05 226	59.90 68	73.33 36	0.04 221	78.40 79	79.30 73	88.65 21	95.20 25

test123		0.05 217	0.08 219	0.01 218	0.00 226	0.01 226	0.01 228	0.00 223	0.05 222	0.00 227	0.16 222	0.00 230	0.04 221	0.02 222	0.05 220	0.00 225	0.26 221

testmvs		0.05 217	0.08 219	0.01 218	0.00 226	0.01 226	0.03 227	0.01 222	0.05 222	0.00 227	0.14 223	0.01 229	0.03 223	0.05 221	0.05 220	0.01 224	0.24 222

uanet_test		0.00 219	0.00 221	0.00 220	0.00 226	0.00 228	0.00 229	0.00 223	0.00 224	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	0.00 220	0.00 226	0.00 228	0.00 229	0.00 223	0.00 224	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	0.00 220	0.00 226	0.00 228	0.00 229	0.00 223	0.00 224	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 46			67.54 44				80.78 21

our_test_3					63.32 178	71.07 185	55.90 185

MTAPA										78.32 11		79.42 25

MTMP										76.04 15		76.65 29

Patchmatch-RL test							2.17 226

XVS					82.43 55	86.27 71	75.70 69			61.07 62		72.27 39				85.67 101

X-MVStestdata					82.43 55	86.27 71	75.70 69			61.07 62		72.27 39				85.67 101

mPP-MVS					86.96 41							70.61 49

NP-MVS									81.60 34

Patchmtry						78.06 140	67.53 137	43.18 198		41.40 139

High-resolution multi-view benchmark