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 3	86.20 3	77.82 4	59.97 6	88.89 1	65.96 1	86.00 6	84.02 1	70.03 1	76.19 4	76.17 5	79.22 20	94.46 1

DVP-MVS		77.54 2	84.41 2	69.54 6	79.93 4	86.08 4	77.20 8	60.31 4	88.62 2	62.54 2	86.67 3	83.77 2	58.04 33	75.84 6	75.69 7	79.21 21	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 5	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 10	59.82 4	86.26 4	77.90 7	61.11 15	71.81 26	70.75 34	79.63 12	88.22 25

MSP-MVS		76.38 4	82.99 3	68.68 7	71.93 18	78.65 23	77.61 5	55.44 18	88.04 3	60.25 3	92.24 1	77.08 9	69.84 2	75.48 7	75.69 7	76.99 54	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
DPE-MVS		75.74 5	82.82 4	67.49 11	77.07 7	82.01 7	77.05 9	57.70 11	86.55 5	55.44 15	90.50 2	82.52 3	60.33 20	72.99 14	72.98 15	77.33 47	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 6	78.53 10	70.07 4	76.10 9	82.56 6	79.30 3	59.89 7	80.49 13	57.75 10	66.98 25	76.16 12	65.95 3	79.35 1	78.47 1	81.45 5	85.71 46

APDe-MVS		74.59 7	80.23 6	68.01 10	76.51 8	80.20 15	77.39 6	58.18 9	85.31 6	56.84 12	84.89 7	76.08 13	60.66 18	71.85 25	71.76 20	78.47 29	91.49 9

MCST-MVS		74.06 8	77.71 13	69.79 5	78.95 5	81.99 8	76.33 10	62.16 3	75.89 21	52.96 25	64.37 31	73.30 21	65.66 4	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 9	78.60 9	68.35 9	73.32 13	81.97 9	76.19 11	59.29 8	80.12 14	56.70 13	67.09 24	76.48 10	64.26 6	75.88 5	75.75 6	80.32 7	92.93 5

SMA-MVS		73.31 10	79.53 7	66.05 13	71.25 20	80.13 16	74.99 12	56.09 14	84.14 7	54.48 18	73.74 16	80.23 4	61.43 12	74.96 8	74.09 11	78.08 36	89.42 15
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
xxxxxxxxxxxxxcwj		73.17 11	74.44 20	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 10	59.82 4	86.26 4	35.82 146	61.11 15	71.81 26	70.75 34	79.63 12	88.22 25

CSCG		72.98 12	76.86 15	68.46 8	78.23 6	81.74 10	77.26 7	60.00 5	75.61 24	59.06 6	62.72 33	77.42 8	56.63 46	74.24 10	77.18 4	79.56 14	89.13 19

HPM-MVS++		72.44 13	78.73 8	65.11 14	71.88 19	77.31 32	71.98 20	55.67 16	83.11 9	53.59 22	75.90 12	78.49 6	61.00 17	73.99 11	73.31 14	76.55 57	88.97 20

APD-MVS		71.86 14	77.91 12	64.80 16	70.39 25	75.69 43	74.02 14	56.14 13	83.59 8	52.92 26	84.67 8	73.46 20	59.30 27	69.47 41	69.66 42	76.02 63	88.84 21
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 15	77.27 14	64.77 17	69.64 27	79.26 17	73.53 15	54.73 24	79.32 16	54.23 19	74.81 13	74.61 17	59.40 26	73.00 13	72.17 18	77.10 53	87.72 30

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

train_agg		70.74 17	76.53 16	63.98 19	70.33 26	75.16 46	72.33 19	55.78 15	75.74 22	50.41 34	80.08 11	73.15 22	57.75 38	71.96 24	70.94 31	77.25 51	88.69 23

TSAR-MVS + MP.		70.28 18	75.09 18	64.66 18	69.34 29	64.61 128	72.60 18	56.29 12	80.73 12	58.36 8	84.56 9	75.22 15	55.37 53	69.11 48	69.45 43	75.97 65	81.97 76
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 11	60.39 43	62.38 59	76.96 35	65.54 56	52.23 33	87.46 4	49.07 35	74.05 15	76.19 11	59.01 29	72.79 18	71.61 22	74.13 108	89.49 14

SteuartSystems-ACMMP		69.78 20	74.76 19	63.98 19	73.45 12	78.56 24	73.13 17	55.24 21	70.68 34	48.93 37	70.43 20	69.10 28	54.00 58	72.78 20	72.98 15	79.14 22	88.74 22
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 21	72.84 22	63.98 19	68.87 33	75.09 47	71.87 21	51.22 38	73.50 28	58.17 9	68.05 23	68.67 29	57.79 37	70.49 35	69.23 45	75.98 64	84.84 54

SD-MVS		68.30 22	72.58 24	63.31 25	69.24 30	67.85 101	70.81 26	53.65 29	79.64 15	58.52 7	74.31 14	75.37 14	53.52 64	65.63 73	63.56 105	74.13 108	81.73 80
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
zzz-MVS		67.78 23	72.46 25	62.33 30	66.09 39	74.21 52	70.05 28	51.54 36	77.27 17	54.61 17	60.30 41	71.51 25	56.73 44	69.19 46	68.63 54	74.96 88	86.11 43

DELS-MVS		67.36 24	70.34 39	63.89 22	69.12 31	81.55 11	70.82 25	55.02 22	53.38 74	48.83 38	56.45 47	59.35 56	60.05 24	74.93 9	74.78 9	79.51 15	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 25	73.08 21	60.64 40	66.20 38	76.62 37	69.22 32	50.92 40	70.07 35	48.81 39	69.66 21	70.12 27	53.68 61	68.41 53	69.13 47	74.98 87	87.53 32
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 26	71.52 32	62.44 28	59.79 79	74.84 49	68.89 33	55.56 17	73.91 27	53.50 23	55.00 52	65.63 34	60.08 23	71.99 23	71.33 26	76.85 55	87.94 29
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 27	72.08 27	61.56 35	66.76 36	73.58 59	71.41 22	52.98 31	69.92 37	43.85 59	70.58 19	58.75 58	56.76 43	72.90 16	71.88 19	77.57 43	86.94 36

CANet		67.21 28	71.83 29	61.83 31	64.51 45	79.25 18	66.72 48	48.73 56	68.49 42	50.63 33	61.40 37	66.47 32	61.44 11	69.31 45	69.90 38	78.94 25	88.00 27

CDPH-MVS		67.03 29	71.64 30	61.65 34	69.10 32	76.84 36	71.35 24	55.42 19	67.02 45	42.83 64	65.27 29	64.60 38	53.16 67	69.70 40	71.40 24	78.02 38	86.67 38

MAR-MVS		66.85 30	69.81 40	63.39 24	73.56 11	80.51 14	69.87 29	51.51 37	67.78 44	46.44 44	51.09 66	61.60 51	60.38 19	72.67 21	73.61 13	78.59 26	81.44 84
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 31	70.69 37	62.36 29	62.76 53	73.21 62	67.96 36	52.31 32	72.26 31	51.03 28	56.50 46	64.26 39	63.37 8	71.64 28	70.85 32	76.70 56	86.10 44
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 32	72.21 26	60.44 42	61.23 67	70.00 83	64.26 61	47.79 68	72.98 29	56.32 14	71.35 18	72.33 23	55.68 52	65.49 74	66.66 69	77.35 45	86.62 39

MVS_0304		66.31 33	71.61 31	60.14 44	62.59 57	78.98 21	67.13 44	45.75 92	64.35 51	45.23 52	60.69 39	67.67 31	61.73 10	71.09 31	71.03 29	78.41 32	87.44 33

ACMMPR		66.20 34	71.51 33	60.00 46	65.34 43	74.04 54	69.39 31	50.92 40	71.97 32	46.04 47	66.79 26	65.68 33	53.07 68	68.93 50	69.12 48	75.21 81	84.05 60

3Dnovator	58.39 4	65.97 35	66.85 51	64.94 15	73.72 10	79.03 20	67.73 39	54.25 25	61.52 54	52.79 27	42.27 92	60.73 54	62.01 9	71.29 29	71.75 21	79.12 23	81.34 87

TSAR-MVS + ACMM		65.95 36	72.83 23	57.93 56	69.35 28	65.85 120	73.36 16	39.84 145	76.00 20	48.69 40	82.54 10	75.03 16	49.38 95	65.33 76	63.42 107	66.94 167	81.67 81

canonicalmvs		65.55 37	70.75 36	59.49 49	62.11 61	78.26 27	66.52 49	43.82 113	71.54 33	47.84 42	61.30 38	61.68 49	58.48 32	67.56 60	69.67 41	78.16 35	85.25 51

QAPM		65.47 38	67.82 45	62.72 27	72.56 14	81.17 13	67.43 42	55.38 20	56.07 67	43.29 62	43.60 87	65.38 36	59.10 28	72.20 22	70.76 33	78.56 27	85.59 49

CS-MVS		65.45 39	70.81 35	59.20 51	59.77 81	75.98 38	64.01 62	42.11 134	65.23 48	46.15 46	59.84 42	62.01 47	63.88 7	71.27 30	71.06 28	79.29 18	90.29 10

PGM-MVS		65.35 40	70.43 38	59.43 50	65.78 41	73.75 56	69.41 30	48.18 64	68.80 41	45.37 51	65.88 28	64.04 40	52.68 74	68.94 49	68.68 53	75.18 82	82.93 67

PHI-MVS		65.17 41	72.07 28	57.11 65	63.02 52	77.35 31	67.04 45	48.14 66	68.03 43	37.56 92	66.00 27	65.39 35	53.19 66	70.68 32	70.57 37	73.72 115	86.46 42

CLD-MVS		64.69 42	67.25 46	61.69 33	68.22 35	78.33 25	63.09 65	47.59 71	69.64 38	53.98 21	54.87 53	53.94 72	57.87 34	72.79 18	71.34 25	79.40 17	69.87 155
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 43	67.11 49	61.80 32	71.04 21	77.91 28	62.75 68	54.78 23	51.43 77	47.54 43	53.77 56	54.85 69	56.84 41	70.59 33	71.50 23	77.86 39	89.70 13

EPNet		64.39 44	70.93 34	56.77 67	60.58 74	75.77 40	59.28 89	50.58 44	69.93 36	40.73 79	68.59 22	61.60 51	53.72 59	68.65 51	68.07 56	75.75 72	83.87 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 45	69.48 41	58.39 53	62.21 60	71.81 74	67.27 43	49.51 50	69.40 40	45.76 49	60.41 40	64.96 37	51.84 76	67.33 64	67.57 62	73.78 114	84.89 52

casdiffmvs		63.87 46	67.08 50	60.12 45	60.90 70	78.29 26	67.91 37	48.01 67	55.89 69	44.97 53	50.45 68	56.94 62	59.54 25	70.17 38	69.81 39	79.41 16	87.99 28

MVS_Test		63.75 47	67.24 47	59.68 48	60.01 75	76.99 34	68.13 35	45.17 95	57.45 62	43.74 60	53.07 59	56.16 67	61.33 13	70.27 36	71.11 27	79.72 10	85.63 48

X-MVS		63.53 48	68.62 42	57.60 60	64.77 44	73.06 63	65.82 54	50.53 45	65.77 47	42.02 72	58.20 44	63.42 43	47.83 107	68.25 57	68.50 55	74.61 98	83.16 66

ACMMP		63.27 49	67.85 44	57.93 56	62.64 56	72.30 71	68.23 34	48.77 55	66.50 46	43.05 63	62.07 34	57.84 61	49.98 87	66.58 69	66.46 74	74.93 89	83.17 64
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
ETV-MVS		62.88 50	68.18 43	56.70 68	58.47 88	74.89 48	60.26 81	43.96 110	58.27 61	42.37 70	61.47 36	56.56 63	57.80 35	68.00 58	68.74 51	77.34 46	89.33 18

AdaColmap		62.79 51	62.63 67	62.98 26	70.82 22	72.90 66	67.84 38	54.09 27	65.14 49	50.71 31	41.78 94	47.64 101	60.17 22	67.41 63	66.83 67	74.28 103	76.69 112

3Dnovator+	55.76 7	62.70 52	65.10 58	59.90 47	65.89 40	72.15 72	62.94 67	49.82 49	62.77 53	49.06 36	43.62 86	61.47 53	58.60 31	68.51 52	66.75 68	73.08 129	80.40 96

OpenMVS	55.62 8	62.57 53	63.76 64	61.19 37	72.13 17	78.84 22	64.42 59	50.51 46	56.44 64	45.67 50	36.88 122	56.51 64	56.66 45	68.28 56	68.96 49	77.73 41	80.44 95

PVSNet_BlendedMVS		62.53 54	66.37 53	58.05 54	58.17 89	75.70 41	61.30 74	48.67 59	58.67 58	50.93 29	55.43 50	49.39 90	53.01 69	69.46 42	66.55 71	76.24 61	89.39 16

PVSNet_Blended		62.53 54	66.37 53	58.05 54	58.17 89	75.70 41	61.30 74	48.67 59	58.67 58	50.93 29	55.43 50	49.39 90	53.01 69	69.46 42	66.55 71	76.24 61	89.39 16

MVSTER		62.51 56	67.22 48	57.02 66	55.05 111	69.23 92	63.02 66	46.88 81	61.11 56	43.95 58	59.20 43	58.86 57	56.80 42	69.13 47	70.98 30	76.41 59	82.04 73

CHOSEN 1792x2688		62.48 57	64.06 63	60.64 40	72.50 15	84.18 5	62.43 69	53.77 28	47.90 91	39.85 83	25.15 184	44.76 114	53.72 59	77.29 3	77.61 2	81.60 4	91.53 8

CostFormer		62.45 58	65.68 56	58.67 52	63.29 49	77.65 29	67.62 40	38.42 155	54.04 72	46.00 48	48.27 76	57.89 60	56.97 40	67.03 67	67.79 61	79.74 9	87.09 35

PCF-MVS	55.99 6	62.31 59	66.60 52	57.32 63	59.12 87	73.68 58	67.53 41	48.71 57	61.35 55	42.83 64	51.33 65	63.48 42	53.48 65	65.64 72	64.87 88	72.22 134	85.83 45
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 60	66.05 55	57.92 58	57.08 94	75.60 45	66.90 46	47.06 79	55.45 71	43.37 61	53.45 58	55.60 68	57.21 39	66.57 70	68.00 58	75.89 69	87.70 31

DI_MVS_plusplus_trai		61.86 61	65.26 57	57.90 59	57.93 92	74.51 51	66.30 51	46.49 87	49.96 81	41.62 75	42.69 90	61.77 48	58.74 30	70.25 37	69.32 44	76.31 60	88.30 24

MSLP-MVS++		61.81 62	62.19 72	61.37 36	68.33 34	63.08 142	70.75 27	38.89 151	63.96 52	57.51 11	48.59 74	61.66 50	53.67 62	62.04 115	59.92 150	79.03 24	76.08 115

OPM-MVS		61.59 63	62.30 71	60.76 39	66.53 37	73.35 61	71.41 22	54.18 26	40.82 120	41.57 76	45.70 82	54.84 70	54.43 57	69.92 39	69.19 46	76.45 58	82.25 70

MS-PatchMatch		61.41 64	61.88 75	60.85 38	70.57 24	75.98 38	66.29 52	46.91 80	50.56 79	48.28 41	36.30 125	51.64 76	50.95 82	72.89 17	70.65 36	82.13 3	75.17 121

EIA-MVS		60.56 65	64.29 62	56.20 74	59.14 86	72.68 68	59.55 87	43.56 117	51.78 76	41.01 78	55.47 49	51.93 75	55.87 48	65.01 80	66.57 70	78.06 37	86.60 41

CS-MVS-test		59.84 66	64.47 60	54.44 80	56.78 96	71.68 75	59.73 85	38.46 153	49.87 82	38.00 88	52.31 60	56.19 65	57.80 35	67.25 65	67.08 64	75.88 70	86.93 37

ACMP	56.21 5	59.78 67	61.81 77	57.41 62	61.15 68	68.88 94	65.98 53	48.85 54	58.56 60	44.19 56	48.89 72	46.31 107	48.56 100	63.61 97	64.49 97	75.75 72	81.91 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 68	62.59 68	56.31 72	61.94 62	68.15 100	66.90 46	48.15 65	59.75 57	38.47 87	50.38 69	48.34 98	46.87 112	65.39 75	64.93 87	75.51 77	81.21 89

Effi-MVS+		59.63 69	61.78 78	57.12 64	61.56 64	71.63 76	63.61 63	47.59 71	47.18 92	37.79 89	45.29 83	49.93 86	56.27 47	67.45 61	67.06 65	75.91 66	83.93 61

CPTT-MVS		59.54 70	64.47 60	53.79 85	54.99 113	67.63 104	65.48 57	44.59 102	64.81 50	37.74 90	51.55 63	59.90 55	49.77 91	61.83 117	61.26 135	70.18 148	84.31 59

baseline2		59.20 71	61.72 79	56.27 73	59.61 82	74.12 53	58.65 92	49.42 51	48.10 89	40.12 82	49.10 71	44.15 116	51.24 79	66.65 68	67.88 60	78.56 27	82.06 72

GeoE		58.97 72	60.94 80	56.67 69	61.27 66	72.71 67	61.35 73	45.69 93	49.19 86	41.22 77	39.55 109	49.58 89	52.79 73	64.79 82	65.89 77	77.73 41	84.87 53

baseline		58.65 73	61.99 73	54.75 79	54.70 115	71.85 73	60.20 82	43.91 111	55.99 68	40.13 81	53.50 57	50.91 82	55.76 49	61.29 125	61.73 127	73.83 112	78.68 105

PVSNet_Blended_VisFu		58.56 74	62.33 70	54.16 82	56.90 95	73.92 55	57.72 95	46.16 90	44.23 100	42.73 67	46.26 79	51.06 81	46.28 115	67.99 59	65.38 82	75.18 82	87.44 33

ACMM	53.73 9	57.91 75	58.27 94	57.49 61	63.10 50	66.45 114	65.65 55	49.02 53	53.69 73	42.67 68	36.41 124	46.07 110	50.38 85	64.74 84	64.63 93	74.14 107	75.91 116
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 76	63.63 65	51.15 100	52.18 122	70.20 82	58.14 94	37.32 162	56.49 63	31.06 123	57.38 45	50.05 84	53.67 62	64.98 81	65.04 86	74.57 99	81.29 88

ET-MVSNet_ETH3D		57.84 77	61.91 74	53.09 87	32.91 203	74.53 50	63.51 64	46.80 83	46.52 95	36.14 97	56.00 48	46.20 108	64.41 5	60.75 133	66.99 66	74.79 90	82.35 68

tpm cat1		57.41 78	58.26 95	56.42 71	60.80 72	72.56 69	64.35 60	38.43 154	49.18 87	46.36 45	36.69 123	43.50 119	54.47 55	61.39 123	62.64 115	74.11 110	81.81 78

IB-MVS	53.15 10	57.33 79	59.02 86	55.37 76	60.83 71	77.11 33	54.51 120	50.10 48	43.22 104	42.82 66	40.50 100	37.61 138	44.67 127	59.27 147	69.81 39	79.29 18	85.59 49
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 80	59.08 85	55.06 77	59.91 77	70.65 80	60.71 77	35.38 173	47.91 90	42.58 69	39.78 104	45.45 112	54.44 56	62.19 112	62.82 112	77.37 44	84.73 55

baseline1		57.21 81	60.53 82	53.33 86	62.50 58	69.86 85	57.33 99	50.59 43	43.39 103	30.00 129	48.60 73	51.09 80	42.36 139	69.38 44	68.03 57	77.20 52	73.39 128

HyFIR lowres test		57.12 82	59.11 84	54.80 78	61.55 65	77.55 30	59.02 90	45.00 97	41.84 117	33.93 109	22.44 191	49.16 93	51.02 81	68.39 54	68.71 52	78.26 34	85.70 47

MVS_111021_LR		57.06 83	60.60 81	52.93 88	56.25 100	65.14 126	55.16 118	41.21 137	52.32 75	44.89 54	53.92 55	49.27 92	52.16 75	61.46 121	60.54 143	67.92 160	81.53 83

DCV-MVSNet		56.80 84	58.96 87	54.28 81	59.96 76	66.74 112	60.37 80	44.87 99	41.01 119	36.81 95	47.57 77	47.87 100	48.23 103	64.41 87	65.17 84	75.45 78	79.95 98

test_part1		56.55 85	56.50 110	56.60 70	61.77 63	69.59 89	66.45 50	43.70 115	38.22 131	44.13 57	29.53 169	49.96 85	47.92 105	63.09 102	64.59 94	75.61 75	80.54 94

Anonymous20231211		56.40 86	57.00 105	55.70 75	59.78 80	72.49 70	61.29 76	46.83 82	40.50 121	40.46 80	22.12 193	49.73 87	51.07 80	64.39 88	65.30 83	74.74 92	84.44 58

PMMVS		55.74 87	62.68 66	47.64 129	44.34 172	65.58 124	47.22 155	37.96 158	56.43 65	34.11 107	61.51 35	47.41 102	54.55 54	65.88 71	62.49 119	67.67 162	79.48 99

Fast-Effi-MVS+		55.73 88	58.26 95	52.76 89	54.33 116	68.19 98	57.05 100	34.66 175	46.92 93	38.96 85	40.53 98	41.55 128	55.69 50	65.31 77	65.99 75	75.90 67	79.34 100

DROMVSNet		55.73 88	58.26 95	52.76 89	54.33 116	68.19 98	57.05 100	34.66 175	46.92 93	38.96 85	40.53 98	41.55 128	55.69 50	65.31 77	65.99 75	75.90 67	79.34 100

FC-MVSNet-train		55.68 90	57.00 105	54.13 83	63.37 47	66.16 116	46.77 158	52.14 34	42.36 111	37.67 91	48.50 75	41.42 131	51.28 78	61.58 120	63.22 109	73.56 117	75.76 118

FMVSNet3		55.66 91	59.68 83	50.96 102	50.59 136	66.49 113	57.57 96	46.61 84	49.30 83	28.77 134	39.61 105	51.42 77	43.85 132	68.29 55	68.80 50	78.35 33	73.86 123

OMC-MVS		55.48 92	61.85 76	48.04 128	41.55 179	60.32 159	56.80 105	31.78 196	75.67 23	42.30 71	51.52 64	54.15 71	49.91 89	60.28 138	57.59 157	65.91 170	73.42 126

tpm		54.94 93	57.86 101	51.54 98	59.48 84	67.04 108	58.34 93	34.60 178	41.93 116	34.41 104	42.40 91	47.14 103	49.07 98	61.46 121	61.67 131	73.31 124	83.39 63

GBi-Net		54.66 94	58.42 92	50.26 110	49.36 145	65.81 121	56.80 105	46.61 84	49.30 83	28.77 134	39.61 105	51.42 77	42.71 135	64.25 90	65.54 79	77.32 48	73.03 131

test1		54.66 94	58.42 92	50.26 110	49.36 145	65.81 121	56.80 105	46.61 84	49.30 83	28.77 134	39.61 105	51.42 77	42.71 135	64.25 90	65.54 79	77.32 48	73.03 131

test-LLR		54.62 96	58.66 90	49.89 116	51.68 128	65.89 118	47.88 149	46.35 88	42.51 108	29.84 130	41.41 95	48.87 94	45.20 120	62.91 106	64.43 98	78.43 30	84.62 56

TSAR-MVS + COLMAP		54.37 97	62.43 69	44.98 143	34.33 199	58.94 166	54.11 123	34.15 187	74.06 26	34.57 103	71.63 17	42.03 127	47.88 106	61.26 126	57.33 160	64.83 173	71.74 141

EPMVS		54.07 98	56.06 112	51.75 97	56.74 98	70.80 78	55.32 116	34.20 184	46.46 96	36.59 96	40.38 102	42.55 122	49.77 91	61.25 127	60.90 139	77.86 39	70.08 152

v2v482		54.00 99	55.12 118	52.69 92	51.73 127	69.42 91	60.65 78	45.09 96	34.56 153	33.73 112	35.29 128	35.36 149	49.92 88	64.05 94	65.16 85	75.00 86	81.98 75

CNLPA		54.00 99	57.08 104	50.40 109	49.83 142	61.75 150	53.47 126	37.27 163	74.55 25	44.85 55	33.58 140	45.42 113	52.94 72	58.89 149	53.66 179	64.06 176	71.68 142

FMVSNet2		53.94 101	57.29 102	50.03 113	49.36 145	65.81 121	56.80 105	45.95 91	43.13 105	28.04 138	35.68 126	48.18 99	42.71 135	67.23 66	67.95 59	77.32 48	73.03 131

v8		53.77 102	54.82 122	52.54 93	52.12 123	66.95 111	60.56 79	43.23 123	37.17 142	35.35 99	34.96 131	37.50 140	49.51 94	63.67 96	64.59 94	74.48 100	78.91 104

GA-MVS		53.77 102	56.41 111	50.70 104	51.63 130	69.96 84	57.55 97	44.39 103	34.31 154	27.15 140	40.99 97	36.40 144	47.65 109	67.45 61	67.16 63	75.83 71	78.60 106

Effi-MVS+-dtu		53.63 104	54.85 121	52.20 95	59.32 85	61.33 153	56.42 111	40.24 143	43.84 102	34.22 106	39.49 110	46.18 109	53.00 71	58.72 153	57.49 159	69.99 151	76.91 111

thisisatest0530		53.61 105	57.22 103	49.40 121	51.30 132	68.22 97	52.72 134	43.34 121	42.72 107	35.31 100	43.57 88	44.14 117	44.37 130	63.00 104	64.86 89	69.34 154	74.00 122

v1144		53.47 106	54.65 123	52.10 96	51.93 125	69.81 86	59.32 88	44.77 101	33.21 160	32.52 115	33.55 141	34.34 157	49.29 96	64.58 85	64.81 91	74.74 92	82.27 69

v10		53.44 107	54.40 124	52.31 94	52.08 124	66.99 109	59.68 86	43.41 118	35.90 148	34.30 105	33.98 138	35.56 147	50.10 86	64.39 88	64.67 92	74.32 101	79.30 102

PatchmatchNet		53.37 108	55.62 116	50.75 103	55.93 107	70.54 81	51.39 139	36.41 166	44.85 98	37.26 93	39.40 112	42.54 123	47.83 107	60.29 137	60.88 141	75.69 74	70.87 146
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		53.36 109	55.65 115	50.68 106	55.34 109	59.04 164	55.00 119	39.98 144	38.72 129	33.22 113	44.52 85	47.05 104	49.63 93	61.82 118	61.77 126	70.92 143	76.61 114
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 110	58.66 90	47.04 132	44.94 166	65.89 118	47.88 149	35.95 169	42.51 108	29.84 130	41.41 95	48.87 94	45.20 120	62.91 106	64.43 98	78.43 30	84.62 56

tttt0517		53.05 111	56.73 109	48.76 124	50.35 138	67.51 105	51.96 138	43.34 121	42.00 115	33.88 110	43.19 89	43.49 120	44.37 130	62.58 111	64.86 89	68.67 156	73.46 125

MDTV_nov1_ep13		52.99 112	55.59 117	49.95 115	54.08 118	70.69 79	56.47 110	38.42 155	42.78 106	30.19 128	39.56 108	43.31 121	45.78 117	60.07 142	62.11 123	74.74 92	70.62 147

EPP-MVSNet		52.91 113	58.91 88	45.91 137	54.99 113	68.84 95	49.27 145	42.71 130	37.53 136	20.20 168	46.09 80	56.19 65	36.90 150	61.37 124	60.90 139	71.41 138	81.41 85

dps		52.84 114	52.92 135	52.74 91	59.89 78	69.49 90	54.47 121	37.38 161	42.49 110	39.53 84	35.33 127	32.71 162	51.83 77	60.45 134	61.12 136	73.33 123	68.86 161

v1192		52.69 115	53.86 127	51.31 99	51.22 133	69.76 87	57.37 98	44.39 103	32.21 163	31.39 122	32.41 148	32.44 165	49.19 97	64.25 90	64.17 100	74.31 102	81.81 78

V42		52.63 116	55.08 119	49.76 118	44.93 167	67.49 107	60.19 83	42.13 133	37.21 141	34.08 108	34.57 134	37.30 141	47.29 110	63.48 99	64.15 101	69.96 152	81.38 86

MSDG		52.58 117	51.40 148	53.95 84	65.48 42	64.31 136	61.44 72	44.02 108	44.17 101	32.92 114	30.40 160	31.81 169	46.35 114	62.13 113	62.55 117	73.49 119	64.41 169

Fast-Effi-MVS+-dtu		52.47 118	55.89 113	48.48 126	56.25 100	65.07 127	58.75 91	23.79 207	41.27 118	27.07 142	37.95 117	41.34 132	50.85 83	62.90 108	62.34 121	74.17 106	80.37 97

v144192		52.43 119	53.63 129	51.03 101	51.06 134	69.60 88	56.94 103	44.84 100	32.15 164	30.88 124	32.45 147	32.71 162	48.36 101	62.98 105	63.52 106	74.10 111	82.02 74

thres100view900		52.33 120	53.91 126	50.48 108	56.10 102	67.79 102	56.18 113	49.18 52	35.86 150	25.22 148	34.74 132	34.10 158	42.41 138	64.45 86	62.62 116	73.81 113	77.85 107

v1921920		51.95 121	53.19 131	50.51 107	50.82 135	69.14 93	55.45 115	44.34 107	31.53 168	30.53 126	31.96 150	31.67 170	48.31 102	63.12 101	63.28 108	73.59 116	81.60 82

v148		51.72 122	53.15 132	50.05 112	50.15 140	67.51 105	56.98 102	42.85 128	32.60 162	32.41 117	33.88 139	34.71 154	44.45 128	61.06 128	63.00 111	73.45 120	79.24 103

TAPA-MVS	47.92 11	51.66 123	57.88 100	44.40 146	36.46 194	58.42 169	53.82 125	30.83 197	69.51 39	34.97 102	46.90 78	49.67 88	46.99 111	58.00 156	54.64 175	63.33 182	68.00 163
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 124	57.98 99	44.01 150	55.96 106	66.16 116	47.65 151	42.84 129	39.82 124	19.09 176	44.97 84	50.28 83	27.20 182	63.43 100	63.84 102	71.33 140	77.33 109

v1240		51.42 125	52.66 137	49.97 114	50.31 139	68.70 96	54.05 124	43.85 112	30.78 172	30.22 127	31.43 153	31.03 177	47.98 104	62.62 110	63.16 110	73.40 121	80.93 91

pmmvs4		51.28 126	52.50 139	49.85 117	49.54 144	63.02 143	52.83 133	43.41 118	44.65 99	35.71 98	34.38 135	32.25 166	45.14 123	60.21 141	60.03 147	72.44 133	72.98 134

Vis-MVSNet		51.13 127	58.04 98	43.06 156	47.68 152	67.71 103	49.10 146	39.09 150	37.75 134	22.57 159	51.03 67	48.78 96	32.42 167	62.12 114	61.80 125	67.49 164	77.12 110
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 128	58.79 89	42.00 162	40.59 181	65.32 125	46.65 160	39.26 148	39.90 123	27.30 139	54.12 54	52.03 74	30.93 171	59.85 144	59.62 152	67.23 166	80.70 92
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		50.91 129	52.45 140	49.11 123	56.10 102	64.53 131	53.06 130	47.31 76	35.86 150	25.22 148	34.74 132	34.10 158	41.08 141	60.84 130	61.37 133	71.90 137	75.70 119

SCA		50.88 130	53.70 128	47.59 130	55.99 104	55.81 178	43.14 172	33.45 190	45.16 97	37.14 94	41.83 93	43.82 118	44.43 129	60.37 135	60.02 148	71.38 139	68.90 160

gg-mvs-nofinetune		50.82 131	55.83 114	44.97 144	60.63 73	75.69 43	53.40 127	34.48 180	20.05 207	6.93 202	18.27 199	52.70 73	33.57 158	70.50 34	72.93 17	80.84 6	80.68 93

thres200		50.76 132	52.52 138	48.70 125	55.98 105	64.60 129	55.29 117	47.34 74	33.91 157	24.36 151	34.33 136	33.90 160	37.27 148	60.84 130	62.41 120	71.99 135	77.63 108

thres400		50.39 133	52.22 141	48.26 127	55.02 112	66.32 115	52.97 131	48.33 63	32.68 161	22.94 157	33.21 143	33.38 161	37.27 148	62.74 109	61.38 132	73.04 130	75.81 117

EG-PatchMatch MVS		50.23 134	50.89 151	49.47 119	59.54 83	70.88 77	52.46 135	44.01 109	26.22 193	31.91 118	24.97 185	31.45 173	33.48 160	64.79 82	66.51 73	75.40 79	71.39 144

IterMVS		50.23 134	53.27 130	46.68 133	47.59 154	60.58 157	53.10 129	36.62 165	36.07 146	25.89 145	39.42 111	40.05 135	43.65 133	60.22 140	61.35 134	73.23 125	75.23 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 136	52.78 136	47.06 131	45.56 163	63.56 139	54.22 122	43.74 114	34.10 156	25.37 147	29.79 166	42.06 126	38.70 144	64.25 90	65.54 79	74.75 91	70.18 151

ACMH	47.82 13	50.10 137	49.60 157	50.69 105	63.36 48	66.99 109	56.83 104	52.13 35	31.06 171	17.74 181	28.22 173	26.24 193	45.17 122	60.88 129	63.80 103	68.91 155	70.00 154
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 138	56.99 107	41.52 165	52.79 120	57.06 172	41.44 177	43.13 124	56.13 66	19.24 175	52.11 61	48.38 97	22.14 189	58.19 155	58.38 155	70.35 146	68.71 162
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		49.59 139	49.75 156	49.40 121	55.88 108	59.86 161	56.31 112	45.33 94	48.57 88	28.32 137	31.54 152	36.81 143	46.27 116	57.17 161	55.88 170	64.29 175	58.42 187

UniMVSNet_NR-MVSNet		49.56 140	53.04 133	45.49 140	51.59 131	64.42 135	46.97 156	51.01 39	37.87 132	16.42 182	39.87 103	34.91 153	33.43 162	59.59 145	62.70 113	73.52 118	71.94 137

CDS-MVSNet		49.25 141	53.97 125	43.75 152	47.53 155	64.53 131	48.59 147	42.27 132	33.77 158	26.64 143	40.46 101	42.26 125	30.01 174	61.77 119	61.71 128	67.48 165	73.28 130
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 142	51.75 144	46.10 136	42.78 177	55.60 181	53.11 128	34.46 181	55.69 70	32.47 116	34.16 137	41.45 130	48.91 99	57.13 162	54.09 176	64.84 172	64.10 170
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 143	48.86 163	49.44 120	56.75 97	62.01 149	56.62 109	47.55 73	37.49 137	23.98 152	26.68 178	29.46 184	43.12 134	57.45 160	58.85 154	68.62 157	70.05 153

NR-MVSNet		48.84 144	51.76 143	45.44 141	57.66 93	60.64 155	47.39 152	47.63 69	37.26 138	13.31 186	37.31 119	29.64 183	33.53 159	63.52 98	62.09 124	73.10 128	71.89 140

CR-MVSNet		48.82 145	51.85 142	45.29 142	46.74 157	55.95 176	52.06 136	34.21 182	42.17 112	31.74 119	32.92 145	42.53 124	45.00 124	58.80 150	61.11 137	61.99 187	69.47 156

thres600view7		48.44 146	50.23 154	46.35 135	54.05 119	64.60 129	50.18 142	47.34 74	31.73 167	20.74 166	32.28 149	32.62 164	33.79 157	60.84 130	56.11 168	71.99 135	73.40 127

test-mter		48.31 147	55.04 120	40.45 169	34.12 200	59.02 165	41.77 176	28.05 201	38.43 130	22.67 158	39.35 113	44.40 115	41.88 140	60.30 136	61.68 130	74.20 104	82.12 71

PatchT		48.11 148	51.27 150	44.43 145	50.13 141	61.58 151	33.59 190	32.92 192	40.38 122	31.74 119	30.60 159	36.93 142	45.00 124	58.80 150	61.11 137	73.19 126	69.47 156

TranMVSNet+NR-MVSNet		48.06 149	51.36 149	44.21 148	50.38 137	62.09 148	47.28 153	50.88 42	36.11 145	13.25 187	37.51 118	31.60 172	30.70 172	59.34 146	62.53 118	72.81 131	70.31 149

TransMVSNet (Re)		47.46 150	48.94 162	45.74 139	57.96 91	64.29 137	48.26 148	48.47 62	26.33 192	19.33 173	29.45 170	31.28 176	25.31 186	63.05 103	62.70 113	75.10 85	65.47 167

DU-MVS		47.33 151	50.86 152	43.20 155	44.43 170	60.64 155	46.97 156	47.63 69	37.26 138	16.42 182	37.31 119	31.39 174	33.43 162	57.53 158	59.98 149	70.35 146	71.94 137

v7n		47.22 152	48.38 164	45.87 138	48.20 151	63.58 138	50.69 140	40.93 141	26.60 191	26.44 144	26.52 179	29.65 182	38.19 146	58.22 154	60.23 146	70.79 144	73.83 124

UA-Net		47.19 153	53.02 134	40.38 170	55.31 110	60.02 160	38.41 183	38.68 152	36.42 144	22.47 161	51.95 62	58.72 59	25.62 185	54.11 174	53.40 180	61.79 188	56.51 190

Baseline_NR-MVSNet		47.14 154	50.83 153	42.84 158	44.43 170	63.31 141	44.50 168	50.36 47	37.71 135	11.25 192	30.84 156	32.09 167	30.96 170	57.53 158	63.73 104	75.53 76	70.60 148

pmmvs5		47.02 155	50.02 155	43.51 154	43.48 175	62.65 145	47.24 154	37.78 160	30.59 173	24.80 150	35.26 129	30.43 178	34.36 155	59.05 148	60.28 145	73.40 121	71.92 139

UniMVSNet (Re)		46.89 156	51.65 146	41.34 167	45.60 162	62.71 144	44.05 169	47.10 78	37.24 140	13.55 185	36.90 121	34.54 156	26.76 183	57.56 157	59.90 151	70.98 142	72.69 135

thisisatest0515		46.88 157	49.57 158	43.74 153	45.33 165	60.46 158	46.19 162	41.06 140	30.34 174	29.73 132	32.50 146	31.63 171	35.43 153	58.75 152	61.71 128	64.70 174	71.59 143

tfpnnormal		46.61 158	46.82 171	46.37 134	52.70 121	62.31 146	50.39 141	47.17 77	25.74 195	21.80 162	23.13 189	24.15 201	33.45 161	60.28 138	60.77 142	72.70 132	71.39 144

pm-mvs1		46.14 159	49.34 161	42.41 159	48.93 148	62.22 147	44.98 166	42.68 131	27.66 185	20.76 165	29.88 165	34.96 152	26.41 184	60.03 143	60.42 144	70.70 145	70.20 150

IterMVS-SCA-FT		45.87 160	51.55 147	39.24 173	46.22 158	59.43 162	52.89 132	31.93 193	36.01 147	23.68 153	38.86 114	39.88 137	39.05 143	56.25 167	58.17 156	41.70 208	72.25 136

MIMVSNet		45.62 161	49.56 159	41.02 168	38.17 185	64.43 134	49.48 144	35.43 172	36.53 143	20.06 170	22.58 190	35.16 151	28.75 179	61.97 116	62.20 122	74.20 104	64.07 171

gm-plane-assit		45.41 162	48.03 166	42.34 160	56.49 99	40.48 206	24.54 210	34.15 187	14.44 213	6.59 203	17.82 200	35.32 150	49.82 90	72.93 15	74.11 10	82.47 2	81.12 90

ADS-MVSNet		45.39 163	46.42 172	44.19 149	48.74 150	57.52 170	43.91 170	31.93 193	35.89 149	27.11 141	30.12 161	32.06 168	45.30 118	53.13 180	55.19 172	68.15 159	61.07 179

GG-mvs-BLEND		44.87 164	64.59 59	21.86 206	0.01 222	73.70 57	55.99 114	0.01 219	50.70 78	0.01 223	49.18 70	63.61 41	0.01 218	63.83 95	64.50 96	75.13 84	86.62 39

pmmvs-eth3d		44.67 165	45.27 177	43.98 151	42.56 178	55.72 180	44.97 167	40.81 142	31.96 166	29.13 133	26.09 181	25.27 198	36.69 151	55.13 171	56.62 165	69.68 153	66.12 166

MDTV_nov1_ep13_2view		44.44 166	45.75 175	42.91 157	46.13 159	63.43 140	46.53 161	34.20 184	29.08 180	19.95 171	26.23 180	27.89 188	35.88 152	53.36 179	56.43 166	74.74 92	63.86 172

CMPMVS	33.64 16	44.39 167	46.41 173	42.03 161	44.21 173	56.50 174	46.73 159	26.48 206	34.20 155	35.14 101	24.22 186	34.64 155	40.52 142	56.50 166	56.07 169	59.12 192	62.74 175
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 168	51.67 145	35.72 183	51.82 126	55.24 182	34.57 189	41.63 135	39.10 127	8.84 199	45.93 81	46.63 106	14.45 199	54.09 175	57.03 162	63.00 183	63.65 173

TAMVS		44.27 169	49.35 160	38.35 177	44.74 168	61.04 154	39.07 181	31.82 195	29.95 176	18.34 179	33.55 141	39.94 136	30.01 174	56.85 164	57.58 158	66.13 169	66.54 164

MVS-HIRNet		43.98 170	43.63 181	44.39 147	47.66 153	59.31 163	32.66 196	33.88 189	30.15 175	33.75 111	16.82 205	28.39 187	45.25 119	53.92 178	55.00 174	73.16 127	61.80 176

UniMVSNet_ETH3D		43.97 171	46.01 174	41.59 163	38.31 184	56.20 175	49.69 143	38.18 157	28.18 181	19.88 172	27.82 175	30.20 179	33.41 164	54.18 173	56.30 167	70.05 150	69.17 158

RPMNet		43.70 172	48.17 165	38.48 176	45.52 164	55.95 176	37.66 184	26.63 205	42.17 112	25.47 146	29.59 168	37.61 138	33.87 156	50.85 185	52.02 184	61.75 189	69.00 159

PatchMatch-RL		43.37 173	44.93 178	41.56 164	37.94 186	51.70 184	40.02 179	35.75 170	39.04 128	30.71 125	35.14 130	27.43 190	46.58 113	51.99 181	50.55 188	58.38 194	58.64 185

FMVSNet5		43.29 174	47.07 169	38.87 174	30.46 205	50.99 186	45.87 163	37.19 164	42.17 112	19.32 174	26.77 177	40.51 133	30.26 173	56.82 165	55.81 171	70.10 149	56.46 191

test0.0.03 1		43.07 175	46.95 170	38.54 175	51.68 128	58.77 167	35.28 185	46.35 88	32.05 165	12.44 188	28.53 172	35.52 148	14.40 200	57.12 163	56.93 163	71.11 141	59.69 181

anonymousdsp		43.03 176	47.19 168	38.18 178	36.00 196	56.92 173	38.44 182	34.56 179	24.22 197	22.53 160	29.69 167	29.92 180	35.21 154	53.96 177	58.98 153	62.32 186	76.66 113

USDC		42.80 177	45.57 176	39.58 171	34.55 198	51.13 185	42.61 173	36.21 167	39.59 125	23.65 154	33.13 144	20.87 207	37.86 147	55.35 170	57.16 161	62.61 184	61.75 177

pmnet_mix02		42.41 178	43.24 183	41.44 166	45.80 161	57.46 171	42.19 174	41.57 136	29.38 178	23.39 155	26.08 182	23.96 202	27.31 181	51.50 182	53.76 178	68.36 158	60.58 180

CHOSEN 280x420		42.39 179	47.40 167	36.54 181	33.56 201	39.66 209	40.67 178	26.88 204	34.66 152	18.03 180	30.09 162	45.59 111	44.82 126	54.46 172	54.00 177	55.28 201	73.32 129

pmmvs6		41.90 180	44.01 180	39.43 172	44.45 169	58.77 167	41.92 175	39.22 149	21.74 200	19.08 177	17.40 203	31.33 175	24.28 188	55.94 168	56.67 164	67.60 163	66.24 165

Anonymous20231206		40.63 181	43.29 182	37.53 179	48.88 149	55.81 178	34.99 186	44.98 98	28.16 182	10.16 196	17.26 204	27.50 189	18.28 193	54.00 176	55.07 173	67.85 161	65.23 168

CVMVSNet		38.91 182	44.49 179	32.40 192	34.57 197	47.20 197	34.81 187	34.20 184	31.45 169	8.95 198	38.86 114	36.38 145	24.30 187	47.77 189	46.94 200	57.59 196	62.85 174

COLMAP_ROB	34.79 15	38.65 183	40.72 186	36.23 182	36.41 195	49.22 193	45.51 165	27.60 203	37.81 133	20.54 167	23.37 188	24.25 200	28.11 180	51.02 184	48.55 191	59.22 191	50.82 201
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 184	41.72 185	34.15 185	40.56 182	50.07 189	33.17 193	44.35 106	27.64 187	5.54 209	30.84 156	26.67 191	14.99 197	45.64 192	52.38 183	66.29 168	58.83 184

WR-MVS		37.61 185	42.15 184	32.31 194	43.64 174	51.85 183	29.39 202	43.35 120	27.65 186	4.40 211	29.90 164	29.80 181	10.46 204	46.73 191	51.98 185	62.60 185	57.16 188

TinyColmap		37.18 186	37.37 199	36.95 180	31.17 204	45.21 200	39.71 180	34.65 177	29.83 177	20.20 168	18.54 198	13.72 215	38.27 145	50.33 186	51.57 186	57.71 195	52.42 198

CP-MVSNet		37.09 187	40.62 187	32.99 187	37.56 188	48.25 194	32.75 194	43.05 125	27.88 184	5.93 205	31.27 154	25.82 196	15.09 195	43.37 199	48.82 189	63.54 180	58.90 182

DTE-MVSNet		36.91 188	40.44 188	32.79 190	40.74 180	47.55 196	30.71 200	44.39 103	27.03 189	4.32 212	30.88 155	25.99 194	12.73 202	45.58 193	50.80 187	63.86 177	55.23 194

PS-CasMVS		36.84 189	40.23 191	32.89 188	37.44 189	48.09 195	32.68 195	42.97 127	27.36 188	5.89 206	30.08 163	25.48 197	14.96 198	43.28 200	48.71 190	63.39 181	58.63 186

WR-MVS_H		36.29 190	40.35 190	31.55 196	37.80 187	49.94 191	30.57 201	41.11 139	26.90 190	4.14 213	30.72 158	28.85 185	10.45 205	42.47 201	47.99 195	65.24 171	55.54 192

SixPastTwentyTwo		36.11 191	37.80 195	34.13 186	37.13 192	46.72 198	34.58 188	34.96 174	21.20 203	11.66 189	29.15 171	19.88 208	29.77 176	44.93 194	48.34 192	56.67 198	54.41 196

test20.03		36.00 192	38.92 192	32.60 191	45.92 160	50.99 186	28.05 206	43.69 116	21.62 201	6.03 204	17.61 202	25.91 195	8.34 211	51.26 183	52.60 182	63.58 178	52.46 197

TDRefinement		35.76 193	38.23 193	32.88 189	19.09 214	46.04 199	43.29 171	29.49 198	33.49 159	19.04 178	22.29 192	17.82 210	29.69 178	48.60 188	47.24 198	56.65 199	52.12 199

LTVRE_ROB	32.83 17	35.10 194	37.46 196	32.35 193	43.12 176	49.99 190	28.52 204	33.23 191	12.73 214	8.18 200	27.71 176	21.34 205	32.64 166	46.92 190	48.11 193	48.41 205	55.45 193
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		34.96 195	38.17 194	31.22 197	22.78 209	40.82 205	33.56 191	23.61 208	29.16 179	21.43 164	28.00 174	21.43 204	31.90 168	44.33 197	42.12 203	54.07 203	61.34 178

testgi		34.51 196	37.42 197	31.12 198	47.37 156	50.34 188	24.38 211	41.21 137	20.32 205	5.64 208	20.56 194	26.55 192	8.06 212	49.28 187	52.65 181	60.05 190	42.23 207

MDA-MVSNet-bldmvs		34.31 197	34.11 203	34.54 184	24.73 207	49.66 192	33.42 192	43.03 126	21.59 202	11.10 193	19.81 196	12.68 216	31.41 169	35.59 206	48.05 194	63.56 179	51.39 200

N_pmnet		34.09 198	35.74 201	32.17 195	37.25 191	43.17 203	32.26 198	35.57 171	26.22 193	10.60 195	20.44 195	19.38 209	20.20 191	44.59 196	47.00 199	57.13 197	49.35 204

RPSCF		33.61 199	40.43 189	25.65 202	16.00 216	32.41 211	31.73 199	13.33 215	50.13 80	23.12 156	31.56 151	40.09 134	32.73 165	41.14 205	37.05 206	36.99 211	50.63 202

EU-MVSNet		33.00 200	36.49 200	28.92 199	33.10 202	42.86 204	29.32 203	35.99 168	22.94 198	5.83 207	25.29 183	24.43 199	15.21 194	41.22 204	41.65 205	54.08 202	57.01 189

pmmvs3		31.22 201	33.62 204	28.43 200	22.82 208	40.26 208	26.40 207	22.05 210	16.89 211	10.99 194	14.72 207	16.26 211	29.70 177	44.82 195	47.39 197	58.61 193	54.98 195

FC-MVSNet-test		30.97 202	37.38 198	23.49 205	37.42 190	33.68 210	19.43 213	39.27 147	31.37 170	1.67 219	38.56 116	28.85 185	6.06 215	41.40 202	43.80 202	37.10 210	44.03 206

new-patchmatchnet		30.47 203	32.80 206	27.75 201	36.81 193	43.98 201	24.85 209	39.29 146	20.52 204	4.06 214	15.94 206	16.05 212	9.57 206	41.32 203	42.05 204	51.94 204	49.74 203

MIMVSNet1		29.60 204	33.37 205	25.20 204	19.52 212	43.94 202	26.29 208	37.92 159	19.95 208	3.79 215	12.64 211	21.99 203	7.70 213	43.83 198	46.32 201	55.97 200	44.92 205

FPMVS		26.87 205	28.19 207	25.32 203	27.09 206	29.49 212	32.28 197	17.79 212	28.09 183	11.33 190	19.38 197	14.69 213	20.88 190	35.11 207	32.82 208	42.56 207	37.75 208

PMVS	18.18 18	21.95 206	22.85 208	20.90 207	21.92 210	14.78 214	19.95 212	17.31 213	15.69 212	11.32 191	13.70 208	13.91 214	15.02 196	34.92 208	31.72 209	39.85 209	35.20 209
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 207	22.71 209	14.89 209	10.93 218	24.08 213	14.22 214	13.94 214	18.68 209	2.93 216	12.84 210	11.27 217	11.94 203	30.57 210	30.58 210	35.38 212	30.93 210

Gipuma		17.16 208	17.83 210	16.36 208	18.76 215	12.15 217	11.97 215	27.78 202	17.94 210	4.86 210	2.53 218	2.73 222	8.90 209	34.32 209	36.09 207	25.92 213	19.06 213
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 209	17.14 211	10.16 212	1.69 221	6.92 220	11.25 216	5.74 216	22.41 199	8.11 201	10.40 212	20.91 206	13.73 201	22.17 211	13.98 213	20.44 214	23.18 211

PMMVS2		12.25 210	14.17 212	10.00 213	11.39 217	14.35 215	8.21 217	19.29 211	9.31 215	0.19 222	7.38 214	6.19 220	1.10 217	19.26 212	21.13 212	19.85 215	21.56 212

E-PMN		10.66 211	8.30 214	13.42 210	19.91 211	7.87 218	4.30 220	29.47 199	8.37 218	1.70 218	3.67 215	1.29 225	9.12 208	8.98 216	13.59 214	16.03 216	14.30 216

EMVS		10.15 212	7.67 215	13.05 211	19.22 213	7.77 219	4.48 218	29.34 200	8.65 217	1.67 219	3.55 216	1.36 224	9.15 207	8.15 217	11.79 216	14.44 217	12.43 217

MVE	10.35 19	9.76 213	11.08 213	8.22 214	4.43 219	13.04 216	3.36 221	23.57 209	5.74 219	1.76 217	3.09 217	1.75 223	6.78 214	12.78 214	23.04 211	9.44 218	18.09 214
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 214	0.01 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.01 220	0.00 224	0.02 219	0.00 226	0.00 220	0.01 218	0.01 217	0.00 221	0.03 218

test123		0.01 214	0.01 216	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.01 220	0.00 224	0.02 219	0.00 226	0.01 218	0.00 219	0.01 217	0.00 221	0.03 218

uanet_test		0.00 216	0.00 218	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 222	0.00 224	0.00 221	0.00 226	0.00 220	0.00 219	0.00 219	0.00 221	0.00 220

sosnet-low-res		0.00 216	0.00 218	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 222	0.00 224	0.00 221	0.00 226	0.00 220	0.00 219	0.00 219	0.00 221	0.00 220

sosnet		0.00 216	0.00 218	0.00 216	0.00 223	0.00 223	0.00 224	0.00 220	0.00 222	0.00 224	0.00 221	0.00 226	0.00 220	0.00 219	0.00 219	0.00 221	0.00 220

RE-MVS-def										21.59 163

9.14												80.07 5

SR-MVS					63.74 46			48.51 61				73.80 18

Anonymous202405211			56.81 108		60.91 69	73.48 60	59.82 84	48.68 58	39.26 126		24.00 187	46.77 105	50.73 84	65.28 79	65.72 78	75.37 80	83.17 64

our_test_3					49.68 143	61.50 152	45.84 164

ambc			35.52 202		38.36 183	40.40 207	28.38 205		25.20 196	14.87 184	13.22 209	7.54 219	19.34 192	55.63 169	47.79 196	47.91 206	58.89 183

MTAPA										54.82 16		71.98 24

MTMP										50.64 32		68.31 30

Patchmatch-RL test							0.69 223

tmp_tt				4.41 215	2.56 220	1.81 222	2.61 222	0.27 218	20.12 206	9.81 197	17.69 201	9.04 218	1.96 216	12.88 213	12.11 215	9.23 219

XVS					62.70 54	73.06 63	61.80 70			42.02 72		63.42 43				74.68 96

X-MVStestdata					62.70 54	73.06 63	61.80 70			42.02 72		63.42 43				74.68 96

abl_6				63.79 23	70.80 23	81.22 12	65.26 58	53.25 30	77.02 18	53.02 24	65.14 30	73.74 19	60.30 21			80.13 8	90.27 11

mPP-MVS					63.08 51							62.34 46

NP-MVS									72.62 30

Patchmtry						64.49 133	52.06 136	34.21 182		31.74 119

DeepMVS_CX						5.87 221	4.32 219	1.74 217	9.04 216	1.30 221	7.97 213	3.16 221	8.56 210	9.74 215		6.30 220	14.51 215

High-resolution multi-view benchmark