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		78.97 1	84.56 1	72.45 1	81.70 2	86.20 2	77.82 4	59.97 5	88.89 1	65.96 1	86.00 5	84.02 1	70.03 1	76.19 4	76.17 5	79.22 19	94.46 1

DVP-MVS		77.54 2	84.41 2	69.54 6	79.93 3	86.08 3	77.20 9	60.31 3	88.62 2	62.54 2	86.67 3	83.77 2	58.04 33	75.84 7	75.69 8	79.21 20	94.17 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
SF-MVS		76.41 3	80.45 6	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 11	59.82 5	86.26 4	77.90 8	61.11 16	71.81 27	70.75 34	79.63 12	88.22 23

MSP-MVS		76.38 4	82.99 3	68.68 7	71.93 18	78.65 23	77.61 6	55.44 18	88.04 3	60.25 4	92.24 1	77.08 11	69.84 2	75.48 8	75.69 8	76.99 58	93.75 3
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS++		75.99 5	81.32 5	69.77 5	71.86 20	85.13 4	77.62 5	59.87 7	82.69 10	61.55 3	83.05 9	79.63 6	69.78 3	76.01 5	75.89 6	77.92 40	86.86 35

DPE-MVS		75.74 6	82.82 4	67.49 11	77.07 7	82.01 8	77.05 10	57.70 11	86.55 5	55.44 16	90.50 2	82.52 3	60.33 20	72.99 15	72.98 16	77.33 49	92.19 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		74.63 7	78.53 11	70.07 3	76.10 9	82.56 7	79.30 2	59.89 6	80.49 13	57.75 11	66.98 26	76.16 14	65.95 5	79.35 1	78.47 1	81.45 5	85.71 44

APDe-MVS		74.59 8	80.23 7	68.01 10	76.51 8	80.20 15	77.39 7	58.18 9	85.31 6	56.84 13	84.89 6	76.08 15	60.66 18	71.85 26	71.76 21	78.47 29	91.49 9

MCST-MVS		74.06 9	77.71 14	69.79 4	78.95 4	81.99 9	76.33 11	62.16 2	75.89 19	52.96 24	64.37 31	73.30 22	65.66 6	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 10	78.60 10	68.35 9	73.32 13	81.97 10	76.19 12	59.29 8	80.12 14	56.70 14	67.09 25	76.48 12	64.26 8	75.88 6	75.75 7	80.32 8	92.93 5

SMA-MVS		73.31 11	79.53 8	66.05 13	71.25 21	80.13 16	74.99 13	56.09 14	84.14 7	54.48 18	73.74 16	80.23 4	61.43 13	74.96 9	74.09 12	78.08 37	89.42 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
CSCG		72.98 12	76.86 16	68.46 8	78.23 6	81.74 11	77.26 8	60.00 4	75.61 22	59.06 6	62.72 33	77.42 10	56.63 45	74.24 11	77.18 4	79.56 13	89.13 17

HPM-MVS++		72.44 13	78.73 9	65.11 14	71.88 19	77.31 33	71.98 21	55.67 16	83.11 9	53.59 22	75.90 12	78.49 7	61.00 17	73.99 12	73.31 15	76.55 62	88.97 18

APD-MVS		71.86 14	77.91 13	64.80 16	70.39 25	75.69 43	74.02 15	56.14 13	83.59 8	52.92 25	84.67 7	73.46 21	59.30 26	69.47 43	69.66 43	76.02 69	88.84 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 15	77.27 15	64.77 17	69.64 27	79.26 17	73.53 16	54.73 24	79.32 16	54.23 19	74.81 13	74.61 19	59.40 25	73.00 14	72.17 19	77.10 57	87.72 27

NCCC		71.36 16	75.44 18	66.60 12	72.46 16	79.18 19	74.16 14	57.83 10	76.93 17	54.19 20	63.47 32	71.08 26	61.30 15	73.56 13	73.70 13	79.69 11	90.19 10

train_agg		70.74 17	76.53 17	63.98 19	70.33 26	75.16 47	72.33 20	55.78 15	75.74 20	50.41 33	80.08 11	73.15 23	57.75 37	71.96 25	70.94 31	77.25 53	88.69 21

TSAR-MVS + MP.		70.28 18	75.09 19	64.66 18	69.34 29	64.61 131	72.60 19	56.29 12	80.73 12	58.36 9	84.56 8	75.22 17	55.37 52	69.11 49	69.45 44	75.97 71	81.97 77
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepPCF-MVS	62.48 1	70.07 19	78.36 12	60.39 41	62.38 59	76.96 36	65.54 57	52.23 32	87.46 4	49.07 34	74.05 15	76.19 13	59.01 28	72.79 19	71.61 23	74.13 111	89.49 12

SteuartSystems-ACMMP		69.78 20	74.76 20	63.98 19	73.45 12	78.56 24	73.13 18	55.24 21	70.68 32	48.93 36	70.43 21	69.10 28	54.00 58	72.78 21	72.98 16	79.14 21	88.74 20
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 21	72.84 22	63.98 19	68.87 33	75.09 48	71.87 22	51.22 36	73.50 26	58.17 10	68.05 24	68.67 29	57.79 36	70.49 36	69.23 46	75.98 70	84.84 55

SD-MVS		68.30 22	72.58 24	63.31 24	69.24 30	67.85 104	70.81 27	53.65 29	79.64 15	58.52 8	74.31 14	75.37 16	53.52 64	65.63 74	63.56 109	74.13 111	81.73 82
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
DELS-MVS		67.36 23	70.34 37	63.89 22	69.12 31	81.55 12	70.82 26	55.02 22	53.38 75	48.83 37	56.45 47	59.35 56	60.05 23	74.93 10	74.78 10	79.51 14	91.95 7
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MP-MVS		67.34 24	73.08 21	60.64 38	66.20 38	76.62 38	69.22 33	50.92 38	70.07 33	48.81 38	69.66 22	70.12 27	53.68 61	68.41 54	69.13 48	74.98 90	87.53 29
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	60.65 2	67.33 25	71.52 31	62.44 27	59.79 79	74.84 50	68.89 34	55.56 17	73.91 25	53.50 23	55.00 53	65.63 34	60.08 22	71.99 24	71.33 27	76.85 59	87.94 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		67.22 26	72.08 26	61.56 33	66.76 36	73.58 59	71.41 23	52.98 30	69.92 35	43.85 60	70.58 20	58.75 58	56.76 43	72.90 17	71.88 20	77.57 45	86.94 34

CANet		67.21 27	71.83 28	61.83 29	64.51 44	79.25 18	66.72 50	48.73 55	68.49 40	50.63 32	61.40 37	66.47 32	61.44 12	69.31 47	69.90 39	78.94 25	88.00 24

CDPH-MVS		67.03 28	71.64 29	61.65 32	69.10 32	76.84 37	71.35 25	55.42 19	67.02 43	42.83 65	65.27 30	64.60 38	53.16 67	69.70 42	71.40 25	78.02 39	86.67 36

MAR-MVS		66.85 29	69.81 38	63.39 23	73.56 11	80.51 14	69.87 29	51.51 35	67.78 42	46.44 46	51.09 67	61.60 51	60.38 19	72.67 22	73.61 14	78.59 26	81.44 86
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
DeepC-MVS_fast	60.18 3	66.84 30	70.69 35	62.36 28	62.76 53	73.21 62	67.96 37	52.31 31	72.26 29	51.03 27	56.50 46	64.26 39	63.37 9	71.64 28	70.85 32	76.70 61	86.10 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		66.77 31	72.21 25	60.44 40	61.23 67	70.00 85	64.26 61	47.79 68	72.98 27	56.32 15	71.35 19	72.33 24	55.68 51	65.49 75	66.66 70	77.35 47	86.62 37

MVS_0304		66.31 32	71.61 30	60.14 43	62.59 57	78.98 21	67.13 46	45.75 95	64.35 48	45.23 54	60.69 39	67.67 31	61.73 11	71.09 31	71.03 29	78.41 33	87.44 30

ACMMPR		66.20 33	71.51 32	60.00 45	65.34 42	74.04 54	69.39 31	50.92 38	71.97 30	46.04 48	66.79 27	65.68 33	53.07 68	68.93 51	69.12 49	75.21 84	84.05 61

3Dnovator	58.39 4	65.97 34	66.85 52	64.94 15	73.72 10	79.03 20	67.73 40	54.25 25	61.52 51	52.79 26	42.27 94	60.73 54	62.01 10	71.29 29	71.75 22	79.12 22	81.34 89

TSAR-MVS + ACMM		65.95 35	72.83 23	57.93 55	69.35 28	65.85 123	73.36 17	39.84 149	76.00 18	48.69 39	82.54 10	75.03 18	49.38 96	65.33 77	63.42 111	66.94 171	81.67 83

canonicalmvs		65.55 36	70.75 34	59.49 49	62.11 62	78.26 28	66.52 51	43.82 119	71.54 31	47.84 41	61.30 38	61.68 49	58.48 31	67.56 61	69.67 42	78.16 36	85.25 51

QAPM		65.47 37	67.82 45	62.72 26	72.56 14	81.17 13	67.43 43	55.38 20	56.07 68	43.29 63	43.60 89	65.38 36	59.10 27	72.20 23	70.76 33	78.56 27	85.59 48

PGM-MVS		65.35 38	70.43 36	59.43 50	65.78 40	73.75 56	69.41 30	48.18 64	68.80 39	45.37 52	65.88 29	64.04 40	52.68 75	68.94 50	68.68 54	75.18 85	82.93 68

PHI-MVS		65.17 39	72.07 27	57.11 65	63.02 51	77.35 32	67.04 47	48.14 66	68.03 41	37.56 91	66.00 28	65.39 35	53.19 66	70.68 33	70.57 36	73.72 119	86.46 40

CLD-MVS		64.69 40	67.25 47	61.69 31	68.22 35	78.33 26	63.09 65	47.59 71	69.64 36	53.98 21	54.87 54	53.94 74	57.87 34	72.79 19	71.34 26	79.40 16	69.87 159
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		64.66 41	67.11 50	61.80 30	71.04 22	77.91 29	62.75 68	54.78 23	51.43 78	47.54 42	53.77 57	54.85 71	56.84 41	70.59 34	71.50 24	77.86 41	89.70 11

EPNet		64.39 42	70.93 33	56.77 67	60.58 74	75.77 40	59.28 89	50.58 42	69.93 34	40.73 80	68.59 23	61.60 51	53.72 59	68.65 52	68.07 56	75.75 76	83.87 63
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 43	69.48 39	58.39 52	62.21 61	71.81 77	67.27 44	49.51 49	69.40 38	45.76 50	60.41 40	64.96 37	51.84 77	67.33 65	67.57 63	73.78 118	84.89 53

EC-MVSNet		64.30 44	68.19 41	59.76 47	62.97 52	75.31 46	67.26 45	44.19 113	60.73 54	47.52 43	55.84 49	62.12 47	57.67 38	70.71 32	67.47 64	78.97 24	85.13 52

casdiffmvs_mvg		64.26 45	67.60 46	60.36 42	62.26 60	78.54 25	69.39 31	48.33 62	56.54 63	45.36 53	52.86 61	57.36 63	58.42 32	70.28 37	70.24 38	78.43 30	87.39 32
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		63.87 46	67.08 51	60.12 44	60.90 70	78.29 27	67.91 38	48.01 67	55.89 70	44.97 55	50.45 69	56.94 64	59.54 24	70.17 40	69.81 40	79.41 15	87.99 25
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		63.75 47	67.24 48	59.68 48	60.01 75	76.99 35	68.13 36	45.17 99	57.45 62	43.74 61	53.07 60	56.16 69	61.33 14	70.27 38	71.11 28	79.72 10	85.63 47

X-MVS		63.53 48	68.62 40	57.60 59	64.77 43	73.06 63	65.82 55	50.53 43	65.77 45	42.02 73	58.20 44	63.42 43	47.83 107	68.25 58	68.50 55	74.61 100	83.16 67

ACMMP		63.27 49	67.85 44	57.93 55	62.64 56	72.30 74	68.23 35	48.77 54	66.50 44	43.05 64	62.07 34	57.84 61	49.98 88	66.58 69	66.46 76	74.93 91	83.17 65
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
CS-MVS		63.16 50	68.01 43	57.49 60	57.39 95	72.73 68	63.38 64	45.16 100	59.37 56	46.49 45	58.93 43	57.68 62	56.31 46	71.12 30	70.37 37	76.23 68	85.88 42

ETV-MVS		62.88 51	68.18 42	56.70 68	58.47 87	74.89 49	60.26 81	43.96 116	58.27 61	42.37 71	61.47 36	56.56 65	57.80 35	68.00 59	68.74 52	77.34 48	89.33 16

AdaColmap		62.79 52	62.63 68	62.98 25	70.82 23	72.90 66	67.84 39	54.09 27	65.14 46	50.71 30	41.78 96	47.64 102	60.17 21	67.41 64	66.83 68	74.28 106	76.69 113

3Dnovator+	55.76 7	62.70 53	65.10 60	59.90 46	65.89 39	72.15 75	62.94 67	49.82 48	62.77 50	49.06 35	43.62 88	61.47 53	58.60 30	68.51 53	66.75 69	73.08 133	80.40 97

OpenMVS	55.62 8	62.57 54	63.76 65	61.19 35	72.13 17	78.84 22	64.42 59	50.51 44	56.44 65	45.67 51	36.88 125	56.51 66	56.66 44	68.28 57	68.96 50	77.73 43	80.44 96

PVSNet_BlendedMVS		62.53 55	66.37 54	58.05 53	58.17 88	75.70 41	61.30 74	48.67 58	58.67 57	50.93 28	55.43 51	49.39 91	53.01 70	69.46 44	66.55 73	76.24 66	89.39 14

PVSNet_Blended		62.53 55	66.37 54	58.05 53	58.17 88	75.70 41	61.30 74	48.67 58	58.67 57	50.93 28	55.43 51	49.39 91	53.01 70	69.46 44	66.55 73	76.24 66	89.39 14

MVSTER		62.51 57	67.22 49	57.02 66	55.05 115	69.23 93	63.02 66	46.88 82	61.11 53	43.95 59	59.20 42	58.86 57	56.80 42	69.13 48	70.98 30	76.41 64	82.04 74

CHOSEN 1792x2688		62.48 58	64.06 64	60.64 38	72.50 15	84.18 5	62.43 69	53.77 28	47.90 92	39.85 84	25.15 188	44.76 116	53.72 59	77.29 3	77.61 2	81.60 4	91.53 8

CostFormer		62.45 59	65.68 58	58.67 51	63.29 48	77.65 30	67.62 41	38.42 159	54.04 73	46.00 49	48.27 77	57.89 60	56.97 40	67.03 67	67.79 62	79.74 9	87.09 33

PCF-MVS	55.99 6	62.31 60	66.60 53	57.32 63	59.12 86	73.68 58	67.53 42	48.71 56	61.35 52	42.83 65	51.33 66	63.48 42	53.48 65	65.64 73	64.87 93	72.22 138	85.83 43
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 61	66.05 56	57.92 57	57.08 96	75.60 45	66.90 48	47.06 80	55.45 72	43.37 62	53.45 59	55.60 70	57.21 39	66.57 70	68.00 58	75.89 74	87.70 28
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DI_MVS_plusplus_trai		61.86 62	65.26 59	57.90 58	57.93 92	74.51 52	66.30 52	46.49 88	49.96 82	41.62 76	42.69 92	61.77 48	58.74 29	70.25 39	69.32 45	76.31 65	88.30 22

MSLP-MVS++		61.81 63	62.19 73	61.37 34	68.33 34	63.08 145	70.75 28	38.89 155	63.96 49	57.51 12	48.59 75	61.66 50	53.67 62	62.04 119	59.92 154	79.03 23	76.08 116

CS-MVS-test		61.68 64	65.97 57	56.67 69	57.77 93	72.59 71	57.63 96	45.54 97	58.53 60	47.11 44	59.45 41	56.34 67	55.15 53	64.52 87	65.03 91	76.80 60	85.34 50

OPM-MVS		61.59 65	62.30 72	60.76 37	66.53 37	73.35 61	71.41 23	54.18 26	40.82 123	41.57 77	45.70 83	54.84 72	54.43 57	69.92 41	69.19 47	76.45 63	82.25 71
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MS-PatchMatch		61.41 66	61.88 76	60.85 36	70.57 24	75.98 39	66.29 53	46.91 81	50.56 80	48.28 40	36.30 128	51.64 78	50.95 83	72.89 18	70.65 35	82.13 3	75.17 124

EIA-MVS		60.56 67	64.29 63	56.20 74	59.14 85	72.68 70	59.55 87	43.56 122	51.78 77	41.01 79	55.47 50	51.93 77	55.87 48	65.01 81	66.57 72	78.06 38	86.60 39

ACMP	56.21 5	59.78 68	61.81 78	57.41 62	61.15 68	68.88 95	65.98 54	48.85 53	58.56 59	44.19 58	48.89 73	46.31 108	48.56 101	63.61 102	64.49 101	75.75 76	81.91 78
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 69	62.59 69	56.31 72	61.94 63	68.15 101	66.90 48	48.15 65	59.75 55	38.47 87	50.38 70	48.34 99	46.87 112	65.39 76	64.93 92	75.51 80	81.21 91

Effi-MVS+		59.63 70	61.78 79	57.12 64	61.56 64	71.63 78	63.61 62	47.59 71	47.18 93	37.79 88	45.29 84	49.93 87	56.27 47	67.45 62	67.06 66	75.91 72	83.93 62

CPTT-MVS		59.54 71	64.47 62	53.79 84	54.99 117	67.63 107	65.48 58	44.59 107	64.81 47	37.74 89	51.55 64	59.90 55	49.77 92	61.83 121	61.26 139	70.18 152	84.31 60

baseline2		59.20 72	61.72 80	56.27 73	59.61 81	74.12 53	58.65 92	49.42 50	48.10 90	40.12 83	49.10 72	44.15 118	51.24 80	66.65 68	67.88 61	78.56 27	82.06 73

GeoE		58.97 73	60.94 81	56.67 69	61.27 66	72.71 69	61.35 73	45.69 96	49.19 86	41.22 78	39.55 112	49.58 90	52.79 74	64.79 83	65.89 80	77.73 43	84.87 54

baseline		58.65 74	61.99 74	54.75 79	54.70 119	71.85 76	60.20 82	43.91 117	55.99 69	40.13 82	53.50 58	50.91 84	55.76 49	61.29 129	61.73 131	73.83 115	78.68 105

PVSNet_Blended_VisFu		58.56 75	62.33 71	54.16 81	56.90 97	73.92 55	57.72 95	46.16 93	44.23 101	42.73 68	46.26 80	51.06 83	46.28 115	67.99 60	65.38 86	75.18 85	87.44 30

ACMM	53.73 9	57.91 76	58.27 96	57.49 60	63.10 49	66.45 117	65.65 56	49.02 52	53.69 74	42.67 69	36.41 127	46.07 111	50.38 86	64.74 85	64.63 98	74.14 110	75.91 117
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 77	63.63 66	51.15 100	52.18 126	70.20 84	58.14 94	37.32 166	56.49 64	31.06 123	57.38 45	50.05 86	53.67 62	64.98 82	65.04 90	74.57 101	81.29 90

ET-MVSNet_ETH3D		57.84 78	61.91 75	53.09 87	32.91 207	74.53 51	63.51 63	46.80 84	46.52 95	36.14 97	56.00 48	46.20 109	64.41 7	60.75 137	66.99 67	74.79 92	82.35 69

tpm cat1		57.41 79	58.26 97	56.42 71	60.80 72	72.56 72	64.35 60	38.43 158	49.18 87	46.36 47	36.69 126	43.50 122	54.47 55	61.39 127	62.64 119	74.11 113	81.81 79

IB-MVS	53.15 10	57.33 80	59.02 88	55.37 76	60.83 71	77.11 34	54.51 121	50.10 47	43.22 107	42.82 67	40.50 102	37.61 143	44.67 127	59.27 151	69.81 40	79.29 18	85.59 48
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
tpmrst		57.23 81	59.08 87	55.06 77	59.91 77	70.65 82	60.71 77	35.38 177	47.91 91	42.58 70	39.78 107	45.45 113	54.44 56	62.19 116	62.82 116	77.37 46	84.73 56

baseline1		57.21 82	60.53 83	53.33 86	62.50 58	69.86 87	57.33 100	50.59 41	43.39 106	30.00 129	48.60 74	51.09 82	42.36 140	69.38 46	68.03 57	77.20 54	73.39 132

FA-MVS(training)		57.15 83	60.42 84	53.34 85	58.15 90	72.77 67	59.79 85	38.68 156	49.01 88	36.56 96	40.79 100	45.44 114	53.04 69	65.23 80	67.93 60	73.82 116	81.80 81

HyFIR lowres test		57.12 84	59.11 86	54.80 78	61.55 65	77.55 31	59.02 90	45.00 102	41.84 120	33.93 109	22.44 195	49.16 94	51.02 82	68.39 55	68.71 53	78.26 35	85.70 46

MVS_111021_LR		57.06 85	60.60 82	52.93 88	56.25 102	65.14 129	55.16 119	41.21 141	52.32 76	44.89 56	53.92 56	49.27 93	52.16 76	61.46 125	60.54 147	67.92 164	81.53 85

DCV-MVSNet		56.80 86	58.96 89	54.28 80	59.96 76	66.74 115	60.37 80	44.87 104	41.01 122	36.81 94	47.57 78	47.87 101	48.23 104	64.41 89	65.17 88	75.45 81	79.95 99

Anonymous20231211		56.40 87	57.00 108	55.70 75	59.78 80	72.49 73	61.29 76	46.83 83	40.50 125	40.46 81	22.12 197	49.73 88	51.07 81	64.39 90	65.30 87	74.74 94	84.44 59

PMMVS		55.74 88	62.68 67	47.64 131	44.34 176	65.58 127	47.22 159	37.96 162	56.43 66	34.11 107	61.51 35	47.41 103	54.55 54	65.88 72	62.49 123	67.67 166	79.48 100

Fast-Effi-MVS+		55.73 89	58.26 97	52.76 89	54.33 120	68.19 100	57.05 101	34.66 179	46.92 94	38.96 86	40.53 101	41.55 131	55.69 50	65.31 78	65.99 77	75.90 73	79.34 101

FC-MVSNet-train		55.68 90	57.00 108	54.13 82	63.37 46	66.16 119	46.77 162	52.14 33	42.36 114	37.67 90	48.50 76	41.42 133	51.28 79	61.58 124	63.22 113	73.56 121	75.76 120

FMVSNet3		55.66 91	59.68 85	50.96 102	50.59 140	66.49 116	57.57 97	46.61 85	49.30 83	28.77 134	39.61 108	51.42 79	43.85 132	68.29 56	68.80 51	78.35 34	73.86 127

OMC-MVS		55.48 92	61.85 77	48.04 130	41.55 183	60.32 163	56.80 105	31.78 199	75.67 21	42.30 72	51.52 65	54.15 73	49.91 90	60.28 142	57.59 161	65.91 174	73.42 130

tpm		54.94 93	57.86 102	51.54 98	59.48 83	67.04 111	58.34 93	34.60 181	41.93 119	34.41 104	42.40 93	47.14 104	49.07 99	61.46 125	61.67 135	73.31 128	83.39 64

GBi-Net		54.66 94	58.42 94	50.26 110	49.36 149	65.81 124	56.80 105	46.61 85	49.30 83	28.77 134	39.61 108	51.42 79	42.71 136	64.25 93	65.54 82	77.32 50	73.03 135

test1		54.66 94	58.42 94	50.26 110	49.36 149	65.81 124	56.80 105	46.61 85	49.30 83	28.77 134	39.61 108	51.42 79	42.71 136	64.25 93	65.54 82	77.32 50	73.03 135

test-LLR		54.62 96	58.66 92	49.89 116	51.68 132	65.89 121	47.88 153	46.35 89	42.51 111	29.84 130	41.41 97	48.87 95	45.20 120	62.91 110	64.43 102	78.43 30	84.62 57

dmvs_re		54.51 97	57.04 107	51.56 97	56.51 100	62.63 149	55.56 115	50.45 45	45.31 97	24.75 151	43.94 87	39.99 138	42.74 135	66.53 71	65.44 85	79.33 17	75.46 122

TSAR-MVS + COLMAP		54.37 98	62.43 70	44.98 146	34.33 203	58.94 170	54.11 126	34.15 190	74.06 24	34.57 103	71.63 18	42.03 130	47.88 106	61.26 130	57.33 164	64.83 177	71.74 145

EPMVS		54.07 99	56.06 114	51.75 96	56.74 99	70.80 80	55.32 117	34.20 187	46.46 96	36.59 95	40.38 104	42.55 125	49.77 92	61.25 131	60.90 143	77.86 41	70.08 156

v2v482		54.00 100	55.12 121	52.69 91	51.73 131	69.42 92	60.65 78	45.09 101	34.56 156	33.73 112	35.29 131	35.36 153	49.92 89	64.05 99	65.16 89	75.00 89	81.98 76

CNLPA		54.00 100	57.08 106	50.40 109	49.83 146	61.75 154	53.47 129	37.27 167	74.55 23	44.85 57	33.58 143	45.42 115	52.94 73	58.89 153	53.66 183	64.06 180	71.68 146

FMVSNet2		53.94 102	57.29 104	50.03 113	49.36 149	65.81 124	56.80 105	45.95 94	43.13 108	28.04 138	35.68 129	48.18 100	42.71 136	67.23 66	67.95 59	77.32 50	73.03 135

v8		53.77 103	54.82 126	52.54 92	52.12 127	66.95 114	60.56 79	43.23 128	37.17 145	35.35 99	34.96 134	37.50 145	49.51 95	63.67 101	64.59 99	74.48 103	78.91 104

GA-MVS		53.77 103	56.41 113	50.70 104	51.63 134	69.96 86	57.55 98	44.39 108	34.31 157	27.15 140	40.99 99	36.40 149	47.65 109	67.45 62	67.16 65	75.83 75	78.60 106

Effi-MVS+-dtu		53.63 105	54.85 125	52.20 94	59.32 84	61.33 157	56.42 111	40.24 147	43.84 103	34.22 106	39.49 113	46.18 110	53.00 72	58.72 157	57.49 163	69.99 155	76.91 111

thisisatest0530		53.61 106	57.22 105	49.40 121	51.30 136	68.22 99	52.72 137	43.34 126	42.72 110	35.31 100	43.57 90	44.14 119	44.37 130	63.00 108	64.86 94	69.34 158	74.00 126

v1144		53.47 107	54.65 127	52.10 95	51.93 129	69.81 88	59.32 88	44.77 106	33.21 163	32.52 115	33.55 144	34.34 161	49.29 97	64.58 86	64.81 96	74.74 94	82.27 70

v10		53.44 108	54.40 128	52.31 93	52.08 128	66.99 112	59.68 86	43.41 123	35.90 151	34.30 105	33.98 141	35.56 151	50.10 87	64.39 90	64.67 97	74.32 104	79.30 102

PatchmatchNet		53.37 109	55.62 119	50.75 103	55.93 109	70.54 83	51.39 142	36.41 170	44.85 99	37.26 92	39.40 115	42.54 126	47.83 107	60.29 141	60.88 145	75.69 78	70.87 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test2506		53.36 110	57.36 103	48.68 126	55.53 111	68.11 102	54.31 123	46.25 91	43.54 104	22.21 163	40.19 105	43.69 121	36.56 153	64.15 97	65.94 78	77.20 54	75.91 117

IterMVS-LS		53.36 110	55.65 118	50.68 106	55.34 113	59.04 168	55.00 120	39.98 148	38.72 133	33.22 113	44.52 86	47.05 105	49.63 94	61.82 122	61.77 130	70.92 147	76.61 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 112	58.66 92	47.04 134	44.94 170	65.89 121	47.88 153	35.95 173	42.51 111	29.84 130	41.41 97	48.87 95	45.20 120	62.91 110	64.43 102	78.43 30	84.62 57

tttt0517		53.05 113	56.73 112	48.76 124	50.35 142	67.51 108	51.96 141	43.34 126	42.00 118	33.88 110	43.19 91	43.49 123	44.37 130	62.58 115	64.86 94	68.67 160	73.46 129

MDTV_nov1_ep13		52.99 114	55.59 120	49.95 115	54.08 121	70.69 81	56.47 110	38.42 159	42.78 109	30.19 128	39.56 111	43.31 124	45.78 117	60.07 146	62.11 127	74.74 94	70.62 151

EPP-MVSNet		52.91 115	58.91 90	45.91 139	54.99 117	68.84 96	49.27 148	42.71 135	37.53 139	20.20 171	46.09 81	56.19 68	36.90 151	61.37 128	60.90 143	71.41 142	81.41 87

dps		52.84 116	52.92 139	52.74 90	59.89 78	69.49 91	54.47 122	37.38 165	42.49 113	39.53 85	35.33 130	32.71 166	51.83 78	60.45 138	61.12 140	73.33 127	68.86 165

v1192		52.69 117	53.86 131	51.31 99	51.22 137	69.76 89	57.37 99	44.39 108	32.21 166	31.39 122	32.41 152	32.44 169	49.19 98	64.25 93	64.17 104	74.31 105	81.81 79

V42		52.63 118	55.08 122	49.76 118	44.93 171	67.49 110	60.19 83	42.13 138	37.21 144	34.08 108	34.57 137	37.30 146	47.29 110	63.48 104	64.15 105	69.96 156	81.38 88

MSDG		52.58 119	51.40 152	53.95 83	65.48 41	64.31 139	61.44 72	44.02 114	44.17 102	32.92 114	30.40 165	31.81 173	46.35 114	62.13 117	62.55 121	73.49 123	64.41 173

ECVR-MVS		52.52 120	55.88 116	48.60 127	55.53 111	68.11 102	54.31 123	46.25 91	43.54 104	21.75 165	32.76 149	39.83 141	36.56 153	64.15 97	65.94 78	77.20 54	76.81 112

Fast-Effi-MVS+-dtu		52.47 121	55.89 115	48.48 128	56.25 102	65.07 130	58.75 91	23.79 210	41.27 121	27.07 142	37.95 120	41.34 134	50.85 84	62.90 112	62.34 125	74.17 109	80.37 98

v144192		52.43 122	53.63 133	51.03 101	51.06 138	69.60 90	56.94 103	44.84 105	32.15 167	30.88 124	32.45 151	32.71 166	48.36 102	62.98 109	63.52 110	74.10 114	82.02 75

thres100view900		52.33 123	53.91 130	50.48 108	56.10 104	67.79 105	56.18 113	49.18 51	35.86 153	25.22 148	34.74 135	34.10 162	42.41 139	64.45 88	62.62 120	73.81 117	77.85 107

v1921920		51.95 124	53.19 135	50.51 107	50.82 139	69.14 94	55.45 116	44.34 112	31.53 171	30.53 126	31.96 154	31.67 174	48.31 103	63.12 106	63.28 112	73.59 120	81.60 84

v148		51.72 125	53.15 136	50.05 112	50.15 144	67.51 108	56.98 102	42.85 133	32.60 165	32.41 117	33.88 142	34.71 158	44.45 128	61.06 132	63.00 115	73.45 124	79.24 103

TAPA-MVS	47.92 11	51.66 126	57.88 101	44.40 149	36.46 198	58.42 173	53.82 128	30.83 200	69.51 37	34.97 102	46.90 79	49.67 89	46.99 111	58.00 160	54.64 179	63.33 186	68.00 167
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 127	57.98 100	44.01 153	55.96 108	66.16 119	47.65 155	42.84 134	39.82 128	19.09 179	44.97 85	50.28 85	27.20 186	63.43 105	63.84 106	71.33 144	77.33 109

v1240		51.42 128	52.66 141	49.97 114	50.31 143	68.70 97	54.05 127	43.85 118	30.78 175	30.22 127	31.43 158	31.03 181	47.98 105	62.62 114	63.16 114	73.40 125	80.93 93

pmmvs4		51.28 129	52.50 143	49.85 117	49.54 148	63.02 146	52.83 136	43.41 123	44.65 100	35.71 98	34.38 138	32.25 170	45.14 123	60.21 145	60.03 151	72.44 137	72.98 138

Vis-MVSNet		51.13 130	58.04 99	43.06 159	47.68 156	67.71 106	49.10 149	39.09 154	37.75 137	22.57 160	51.03 68	48.78 97	32.42 171	62.12 118	61.80 129	67.49 168	77.12 110
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 131	58.79 91	42.00 165	40.59 185	65.32 128	46.65 164	39.26 152	39.90 127	27.30 139	54.12 55	52.03 76	30.93 175	59.85 148	59.62 156	67.23 170	80.70 94
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
tfpn200view9		50.91 132	52.45 144	49.11 123	56.10 104	64.53 134	53.06 133	47.31 76	35.86 153	25.22 148	34.74 135	34.10 162	41.08 142	60.84 134	61.37 137	71.90 141	75.70 121

SCA		50.88 133	53.70 132	47.59 132	55.99 106	55.81 182	43.14 176	33.45 193	45.16 98	37.14 93	41.83 95	43.82 120	44.43 129	60.37 139	60.02 152	71.38 143	68.90 164

gg-mvs-nofinetune		50.82 134	55.83 117	44.97 147	60.63 73	75.69 43	53.40 130	34.48 183	20.05 210	6.93 206	18.27 203	52.70 75	33.57 161	70.50 35	72.93 18	80.84 6	80.68 95

thres200		50.76 135	52.52 142	48.70 125	55.98 107	64.60 132	55.29 118	47.34 74	33.91 160	24.36 152	34.33 139	33.90 164	37.27 149	60.84 134	62.41 124	71.99 139	77.63 108

test1111		50.62 136	54.98 124	45.55 142	53.84 123	68.48 98	48.99 150	47.25 77	40.60 124	15.64 187	31.51 157	38.32 142	33.01 168	64.34 92	66.62 71	74.55 102	74.95 125

thres400		50.39 137	52.22 145	48.26 129	55.02 116	66.32 118	52.97 134	48.33 62	32.68 164	22.94 158	33.21 146	33.38 165	37.27 149	62.74 113	61.38 136	73.04 134	75.81 119

EG-PatchMatch MVS		50.23 138	50.89 155	49.47 119	59.54 82	70.88 79	52.46 138	44.01 115	26.22 196	31.91 118	24.97 189	31.45 177	33.48 163	64.79 83	66.51 75	75.40 82	71.39 148

IterMVS		50.23 138	53.27 134	46.68 135	47.59 158	60.58 161	53.10 132	36.62 169	36.07 149	25.89 145	39.42 114	40.05 137	43.65 133	60.22 144	61.35 138	73.23 129	75.23 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 140	52.78 140	47.06 133	45.56 167	63.56 142	54.22 125	43.74 120	34.10 159	25.37 147	29.79 171	42.06 129	38.70 145	64.25 93	65.54 82	74.75 93	70.18 155

ACMH	47.82 13	50.10 141	49.60 161	50.69 105	63.36 47	66.99 112	56.83 104	52.13 34	31.06 174	17.74 184	28.22 177	26.24 197	45.17 122	60.88 133	63.80 107	68.91 159	70.00 158
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 142	56.99 110	41.52 168	52.79 124	57.06 176	41.44 181	43.13 129	56.13 67	19.24 178	52.11 62	48.38 98	22.14 193	58.19 159	58.38 159	70.35 150	68.71 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		49.59 143	49.75 160	49.40 121	55.88 110	59.86 165	56.31 112	45.33 98	48.57 89	28.32 137	31.54 156	36.81 148	46.27 116	57.17 165	55.88 174	64.29 179	58.42 191

UniMVSNet_NR-MVSNet		49.56 144	53.04 137	45.49 143	51.59 135	64.42 138	46.97 160	51.01 37	37.87 135	16.42 185	39.87 106	34.91 157	33.43 165	59.59 149	62.70 117	73.52 122	71.94 141

CDS-MVSNet		49.25 145	53.97 129	43.75 155	47.53 159	64.53 134	48.59 151	42.27 137	33.77 161	26.64 143	40.46 103	42.26 128	30.01 178	61.77 123	61.71 132	67.48 169	73.28 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 146	51.75 148	46.10 138	42.78 181	55.60 185	53.11 131	34.46 184	55.69 71	32.47 116	34.16 140	41.45 132	48.91 100	57.13 166	54.09 180	64.84 176	64.10 174
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 147	48.86 167	49.44 120	56.75 98	62.01 153	56.62 109	47.55 73	37.49 140	23.98 153	26.68 182	29.46 188	43.12 134	57.45 164	58.85 158	68.62 161	70.05 157

NR-MVSNet		48.84 148	51.76 147	45.44 144	57.66 94	60.64 159	47.39 156	47.63 69	37.26 141	13.31 190	37.31 122	29.64 187	33.53 162	63.52 103	62.09 128	73.10 132	71.89 144

CR-MVSNet		48.82 149	51.85 146	45.29 145	46.74 161	55.95 180	52.06 139	34.21 185	42.17 115	31.74 119	32.92 148	42.53 127	45.00 124	58.80 154	61.11 141	61.99 191	69.47 160

thres600view7		48.44 150	50.23 158	46.35 137	54.05 122	64.60 132	50.18 145	47.34 74	31.73 170	20.74 169	32.28 153	32.62 168	33.79 160	60.84 134	56.11 172	71.99 139	73.40 131

test-mter		48.31 151	55.04 123	40.45 172	34.12 204	59.02 169	41.77 180	28.05 204	38.43 134	22.67 159	39.35 116	44.40 117	41.88 141	60.30 140	61.68 134	74.20 107	82.12 72

PatchT		48.11 152	51.27 154	44.43 148	50.13 145	61.58 155	33.59 194	32.92 195	40.38 126	31.74 119	30.60 164	36.93 147	45.00 124	58.80 154	61.11 141	73.19 130	69.47 160

TranMVSNet+NR-MVSNet		48.06 153	51.36 153	44.21 151	50.38 141	62.09 152	47.28 157	50.88 40	36.11 148	13.25 191	37.51 121	31.60 176	30.70 176	59.34 150	62.53 122	72.81 135	70.31 153

TransMVSNet (Re)		47.46 154	48.94 166	45.74 141	57.96 91	64.29 140	48.26 152	48.47 61	26.33 195	19.33 176	29.45 174	31.28 180	25.31 190	63.05 107	62.70 117	75.10 88	65.47 171

DU-MVS		47.33 155	50.86 156	43.20 158	44.43 174	60.64 159	46.97 160	47.63 69	37.26 141	16.42 185	37.31 122	31.39 178	33.43 165	57.53 162	59.98 153	70.35 150	71.94 141

v7n		47.22 156	48.38 168	45.87 140	48.20 155	63.58 141	50.69 143	40.93 145	26.60 194	26.44 144	26.52 183	29.65 186	38.19 147	58.22 158	60.23 150	70.79 148	73.83 128

UA-Net		47.19 157	53.02 138	40.38 173	55.31 114	60.02 164	38.41 187	38.68 156	36.42 147	22.47 162	51.95 63	58.72 59	25.62 189	54.11 178	53.40 184	61.79 192	56.51 194

Baseline_NR-MVSNet		47.14 158	50.83 157	42.84 161	44.43 174	63.31 144	44.50 172	50.36 46	37.71 138	11.25 196	30.84 161	32.09 171	30.96 174	57.53 162	63.73 108	75.53 79	70.60 152

pmmvs5		47.02 159	50.02 159	43.51 157	43.48 179	62.65 148	47.24 158	37.78 164	30.59 176	24.80 150	35.26 132	30.43 182	34.36 158	59.05 152	60.28 149	73.40 125	71.92 143

UniMVSNet (Re)		46.89 160	51.65 150	41.34 170	45.60 166	62.71 147	44.05 173	47.10 79	37.24 143	13.55 189	36.90 124	34.54 160	26.76 187	57.56 161	59.90 155	70.98 146	72.69 139

thisisatest0515		46.88 161	49.57 162	43.74 156	45.33 169	60.46 162	46.19 166	41.06 144	30.34 177	29.73 132	32.50 150	31.63 175	35.43 156	58.75 156	61.71 132	64.70 178	71.59 147

tfpnnormal		46.61 162	46.82 175	46.37 136	52.70 125	62.31 150	50.39 144	47.17 78	25.74 198	21.80 164	23.13 193	24.15 205	33.45 164	60.28 142	60.77 146	72.70 136	71.39 148

pm-mvs1		46.14 163	49.34 165	42.41 162	48.93 152	62.22 151	44.98 170	42.68 136	27.66 188	20.76 168	29.88 170	34.96 156	26.41 188	60.03 147	60.42 148	70.70 149	70.20 154

IterMVS-SCA-FT		45.87 164	51.55 151	39.24 176	46.22 162	59.43 166	52.89 135	31.93 196	36.01 150	23.68 154	38.86 117	39.88 140	39.05 144	56.25 171	58.17 160	41.70 212	72.25 140

MIMVSNet		45.62 165	49.56 163	41.02 171	38.17 189	64.43 137	49.48 147	35.43 176	36.53 146	20.06 173	22.58 194	35.16 155	28.75 183	61.97 120	62.20 126	74.20 107	64.07 175

gm-plane-assit		45.41 166	48.03 170	42.34 163	56.49 101	40.48 210	24.54 214	34.15 190	14.44 216	6.59 207	17.82 204	35.32 154	49.82 91	72.93 16	74.11 11	82.47 2	81.12 92

ADS-MVSNet		45.39 167	46.42 176	44.19 152	48.74 154	57.52 174	43.91 174	31.93 196	35.89 152	27.11 141	30.12 166	32.06 172	45.30 118	53.13 184	55.19 176	68.15 163	61.07 183

GG-mvs-BLEND		44.87 168	64.59 61	21.86 209	0.01 226	73.70 57	55.99 114	0.01 222	50.70 79	0.01 227	49.18 71	63.61 41	0.01 222	63.83 100	64.50 100	75.13 87	86.62 37

pmmvs-eth3d		44.67 169	45.27 181	43.98 154	42.56 182	55.72 184	44.97 171	40.81 146	31.96 169	29.13 133	26.09 185	25.27 202	36.69 152	55.13 175	56.62 169	69.68 157	66.12 170

MDTV_nov1_ep13_2view		44.44 170	45.75 179	42.91 160	46.13 163	63.43 143	46.53 165	34.20 187	29.08 183	19.95 174	26.23 184	27.89 192	35.88 155	53.36 183	56.43 170	74.74 94	63.86 176

CMPMVS	33.64 16	44.39 171	46.41 177	42.03 164	44.21 177	56.50 178	46.73 163	26.48 209	34.20 158	35.14 101	24.22 190	34.64 159	40.52 143	56.50 170	56.07 173	59.12 196	62.74 179
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 172	51.67 149	35.72 186	51.82 130	55.24 186	34.57 193	41.63 139	39.10 131	8.84 203	45.93 82	46.63 107	14.45 203	54.09 179	57.03 166	63.00 187	63.65 177

TAMVS		44.27 173	49.35 164	38.35 180	44.74 172	61.04 158	39.07 185	31.82 198	29.95 179	18.34 182	33.55 144	39.94 139	30.01 178	56.85 168	57.58 162	66.13 173	66.54 168

MVS-HIRNet		43.98 174	43.63 185	44.39 150	47.66 157	59.31 167	32.66 200	33.88 192	30.15 178	33.75 111	16.82 209	28.39 191	45.25 119	53.92 182	55.00 178	73.16 131	61.80 180

UniMVSNet_ETH3D		43.97 175	46.01 178	41.59 166	38.31 188	56.20 179	49.69 146	38.18 161	28.18 184	19.88 175	27.82 179	30.20 183	33.41 167	54.18 177	56.30 171	70.05 154	69.17 162

RPMNet		43.70 176	48.17 169	38.48 179	45.52 168	55.95 180	37.66 188	26.63 208	42.17 115	25.47 146	29.59 173	37.61 143	33.87 159	50.85 189	52.02 188	61.75 193	69.00 163

PatchMatch-RL		43.37 177	44.93 182	41.56 167	37.94 190	51.70 188	40.02 183	35.75 174	39.04 132	30.71 125	35.14 133	27.43 194	46.58 113	51.99 185	50.55 192	58.38 198	58.64 189

FMVSNet5		43.29 178	47.07 173	38.87 177	30.46 209	50.99 190	45.87 167	37.19 168	42.17 115	19.32 177	26.77 181	40.51 135	30.26 177	56.82 169	55.81 175	70.10 153	56.46 195

test0.0.03 1		43.07 179	46.95 174	38.54 178	51.68 132	58.77 171	35.28 189	46.35 89	32.05 168	12.44 192	28.53 176	35.52 152	14.40 204	57.12 167	56.93 167	71.11 145	59.69 185

anonymousdsp		43.03 180	47.19 172	38.18 181	36.00 200	56.92 177	38.44 186	34.56 182	24.22 200	22.53 161	29.69 172	29.92 184	35.21 157	53.96 181	58.98 157	62.32 190	76.66 114

USDC		42.80 181	45.57 180	39.58 174	34.55 202	51.13 189	42.61 177	36.21 171	39.59 129	23.65 155	33.13 147	20.87 211	37.86 148	55.35 174	57.16 165	62.61 188	61.75 181

pmnet_mix02		42.41 182	43.24 187	41.44 169	45.80 165	57.46 175	42.19 178	41.57 140	29.38 181	23.39 156	26.08 186	23.96 206	27.31 185	51.50 186	53.76 182	68.36 162	60.58 184

CHOSEN 280x420		42.39 183	47.40 171	36.54 184	33.56 205	39.66 213	40.67 182	26.88 207	34.66 155	18.03 183	30.09 167	45.59 112	44.82 126	54.46 176	54.00 181	55.28 205	73.32 133

pmmvs6		41.90 184	44.01 184	39.43 175	44.45 173	58.77 171	41.92 179	39.22 153	21.74 203	19.08 180	17.40 207	31.33 179	24.28 192	55.94 172	56.67 168	67.60 167	66.24 169

Anonymous20231206		40.63 185	43.29 186	37.53 182	48.88 153	55.81 182	34.99 190	44.98 103	28.16 185	10.16 200	17.26 208	27.50 193	18.28 197	54.00 180	55.07 177	67.85 165	65.23 172

CVMVSNet		38.91 186	44.49 183	32.40 195	34.57 201	47.20 201	34.81 191	34.20 187	31.45 172	8.95 202	38.86 117	36.38 150	24.30 191	47.77 193	46.94 204	57.59 200	62.85 178

COLMAP_ROB	34.79 15	38.65 187	40.72 190	36.23 185	36.41 199	49.22 197	45.51 169	27.60 206	37.81 136	20.54 170	23.37 192	24.25 204	28.11 184	51.02 188	48.55 195	59.22 195	50.82 205
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 188	41.72 189	34.15 188	40.56 186	50.07 193	33.17 197	44.35 111	27.64 190	5.54 213	30.84 161	26.67 195	14.99 201	45.64 196	52.38 187	66.29 172	58.83 188

WR-MVS		37.61 189	42.15 188	32.31 197	43.64 178	51.85 187	29.39 206	43.35 125	27.65 189	4.40 215	29.90 169	29.80 185	10.46 208	46.73 195	51.98 189	62.60 189	57.16 192

TinyColmap		37.18 190	37.37 203	36.95 183	31.17 208	45.21 204	39.71 184	34.65 180	29.83 180	20.20 171	18.54 202	13.72 219	38.27 146	50.33 190	51.57 190	57.71 199	52.42 202

CP-MVSNet		37.09 191	40.62 191	32.99 190	37.56 192	48.25 198	32.75 198	43.05 130	27.88 187	5.93 209	31.27 159	25.82 200	15.09 199	43.37 203	48.82 193	63.54 184	58.90 186

DTE-MVSNet		36.91 192	40.44 192	32.79 193	40.74 184	47.55 200	30.71 204	44.39 108	27.03 192	4.32 216	30.88 160	25.99 198	12.73 206	45.58 197	50.80 191	63.86 181	55.23 198

PS-CasMVS		36.84 193	40.23 195	32.89 191	37.44 193	48.09 199	32.68 199	42.97 132	27.36 191	5.89 210	30.08 168	25.48 201	14.96 202	43.28 204	48.71 194	63.39 185	58.63 190

WR-MVS_H		36.29 194	40.35 194	31.55 199	37.80 191	49.94 195	30.57 205	41.11 143	26.90 193	4.14 217	30.72 163	28.85 189	10.45 209	42.47 205	47.99 199	65.24 175	55.54 196

SixPastTwentyTwo		36.11 195	37.80 199	34.13 189	37.13 196	46.72 202	34.58 192	34.96 178	21.20 206	11.66 193	29.15 175	19.88 212	29.77 180	44.93 198	48.34 196	56.67 202	54.41 200

test20.03		36.00 196	38.92 196	32.60 194	45.92 164	50.99 190	28.05 210	43.69 121	21.62 204	6.03 208	17.61 206	25.91 199	8.34 215	51.26 187	52.60 186	63.58 182	52.46 201

TDRefinement		35.76 197	38.23 197	32.88 192	19.09 218	46.04 203	43.29 175	29.49 201	33.49 162	19.04 181	22.29 196	17.82 214	29.69 182	48.60 192	47.24 202	56.65 203	52.12 203

LTVRE_ROB	32.83 17	35.10 198	37.46 200	32.35 196	43.12 180	49.99 194	28.52 208	33.23 194	12.73 217	8.18 204	27.71 180	21.34 209	32.64 170	46.92 194	48.11 197	48.41 209	55.45 197
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
PM-MVS		34.96 199	38.17 198	31.22 200	22.78 213	40.82 209	33.56 195	23.61 211	29.16 182	21.43 167	28.00 178	21.43 208	31.90 172	44.33 201	42.12 207	54.07 207	61.34 182

testgi		34.51 200	37.42 201	31.12 201	47.37 160	50.34 192	24.38 215	41.21 141	20.32 208	5.64 212	20.56 198	26.55 196	8.06 216	49.28 191	52.65 185	60.05 194	42.23 211

MDA-MVSNet-bldmvs		34.31 201	34.11 207	34.54 187	24.73 211	49.66 196	33.42 196	43.03 131	21.59 205	11.10 197	19.81 200	12.68 220	31.41 173	35.59 210	48.05 198	63.56 183	51.39 204

N_pmnet		34.09 202	35.74 205	32.17 198	37.25 195	43.17 207	32.26 202	35.57 175	26.22 196	10.60 199	20.44 199	19.38 213	20.20 195	44.59 200	47.00 203	57.13 201	49.35 208

RPSCF		33.61 203	40.43 193	25.65 205	16.00 220	32.41 215	31.73 203	13.33 218	50.13 81	23.12 157	31.56 155	40.09 136	32.73 169	41.14 209	37.05 210	36.99 215	50.63 206

EU-MVSNet		33.00 204	36.49 204	28.92 202	33.10 206	42.86 208	29.32 207	35.99 172	22.94 201	5.83 211	25.29 187	24.43 203	15.21 198	41.22 208	41.65 209	54.08 206	57.01 193

pmmvs3		31.22 205	33.62 208	28.43 203	22.82 212	40.26 212	26.40 211	22.05 213	16.89 214	10.99 198	14.72 211	16.26 215	29.70 181	44.82 199	47.39 201	58.61 197	54.98 199

FC-MVSNet-test		30.97 206	37.38 202	23.49 208	37.42 194	33.68 214	19.43 217	39.27 151	31.37 173	1.67 223	38.56 119	28.85 189	6.06 219	41.40 206	43.80 206	37.10 214	44.03 210

new-patchmatchnet		30.47 207	32.80 210	27.75 204	36.81 197	43.98 205	24.85 213	39.29 150	20.52 207	4.06 218	15.94 210	16.05 216	9.57 210	41.32 207	42.05 208	51.94 208	49.74 207

MIMVSNet1		29.60 208	33.37 209	25.20 207	19.52 216	43.94 206	26.29 212	37.92 163	19.95 211	3.79 219	12.64 215	21.99 207	7.70 217	43.83 202	46.32 205	55.97 204	44.92 209

FPMVS		26.87 209	28.19 211	25.32 206	27.09 210	29.49 216	32.28 201	17.79 215	28.09 186	11.33 194	19.38 201	14.69 217	20.88 194	35.11 211	32.82 212	42.56 211	37.75 212

PMVS	18.18 18	21.95 210	22.85 212	20.90 210	21.92 214	14.78 218	19.95 216	17.31 216	15.69 215	11.32 195	13.70 212	13.91 218	15.02 200	34.92 212	31.72 213	39.85 213	35.20 213
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 211	22.71 213	14.89 212	10.93 222	24.08 217	14.22 218	13.94 217	18.68 212	2.93 220	12.84 214	11.27 221	11.94 207	30.57 214	30.58 214	35.38 216	30.93 214

Gipuma		17.16 212	17.83 214	16.36 211	18.76 219	12.15 221	11.97 219	27.78 205	17.94 213	4.86 214	2.53 222	2.73 226	8.90 213	34.32 213	36.09 211	25.92 217	19.06 217
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 213	17.14 215	10.16 215	1.69 225	6.92 224	11.25 220	5.74 219	22.41 202	8.11 205	10.40 216	20.91 210	13.73 205	22.17 215	13.98 217	20.44 218	23.18 215

PMMVS2		12.25 214	14.17 216	10.00 216	11.39 221	14.35 219	8.21 221	19.29 214	9.31 218	0.19 226	7.38 218	6.19 224	1.10 221	19.26 216	21.13 216	19.85 219	21.56 216

E-PMN		10.66 215	8.30 218	13.42 213	19.91 215	7.87 222	4.30 224	29.47 202	8.37 221	1.70 222	3.67 219	1.29 229	9.12 212	8.98 220	13.59 218	16.03 220	14.30 220

EMVS		10.15 216	7.67 219	13.05 214	19.22 217	7.77 223	4.48 222	29.34 203	8.65 220	1.67 223	3.55 220	1.36 228	9.15 211	8.15 221	11.79 220	14.44 221	12.43 221

MVE	10.35 19	9.76 217	11.08 217	8.22 217	4.43 223	13.04 220	3.36 225	23.57 212	5.74 222	1.76 221	3.09 221	1.75 227	6.78 218	12.78 218	23.04 215	9.44 222	18.09 218
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 218	0.01 220	0.00 219	0.00 227	0.00 227	0.00 228	0.00 223	0.01 223	0.00 228	0.02 223	0.00 230	0.00 224	0.01 222	0.01 221	0.00 225	0.03 222

test123		0.01 218	0.01 220	0.00 219	0.00 227	0.00 227	0.00 228	0.00 223	0.01 223	0.00 228	0.02 223	0.00 230	0.01 222	0.00 223	0.01 221	0.00 225	0.03 222

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

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

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

TPM-MVS					78.45 5	83.50 6	78.26 3			58.88 7	72.62 17	77.54 9	69.42 4			80.40 7	85.71 44

RE-MVS-def										21.59 166

9.14												80.07 5

SR-MVS					63.74 45			48.51 60				73.80 20

Anonymous202405211			56.81 111		60.91 69	73.48 60	59.82 84	48.68 57	39.26 130		24.00 191	46.77 106	50.73 85	65.28 79	65.72 81	75.37 83	83.17 65

our_test_3					49.68 147	61.50 156	45.84 168

ambc			35.52 206		38.36 187	40.40 211	28.38 209		25.20 199	14.87 188	13.22 213	7.54 223	19.34 196	55.63 173	47.79 200	47.91 210	58.89 187

MTAPA										54.82 17		71.98 25

MTMP										50.64 31		68.31 30

Patchmatch-RL test							0.69 227

tmp_tt				4.41 218	2.56 224	1.81 226	2.61 226	0.27 221	20.12 209	9.81 201	17.69 205	9.04 222	1.96 220	12.88 217	12.11 219	9.23 223

XVS					62.70 54	73.06 63	61.80 70			42.02 73		63.42 43				74.68 98

X-MVStestdata					62.70 54	73.06 63	61.80 70			42.02 73		63.42 43				74.68 98

mPP-MVS					63.08 50							62.34 46

NP-MVS									72.62 28

Patchmtry						64.49 136	52.06 139	34.21 185		31.74 119

DeepMVS_CX						5.87 225	4.32 223	1.74 220	9.04 219	1.30 225	7.97 217	3.16 225	8.56 214	9.74 219		6.30 224	14.51 219

High-resolution multi-view benchmark