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.

ESAPD		78.19 1	83.74 1	71.72 1	79.01 1	81.38 1	83.23 2	58.63 2	83.92 5	62.44 12	87.06 2	85.82 1	64.54 3	79.39 5	77.99 8	82.44 17	90.61 1

APDe-MVS		77.58 2	82.93 2	71.35 2	77.86 2	80.55 2	83.38 1	57.61 7	85.57 1	61.11 15	86.10 4	82.98 4	64.76 2	78.29 12	76.78 19	83.40 6	90.20 2

SMA-MVS		77.32 3	82.51 3	71.26 3	75.43 9	80.19 4	82.22 3	58.26 4	84.83 3	64.36 3	78.19 11	83.46 2	63.61 6	81.00 1	80.28 1	83.66 4	89.62 3

HSP-MVS		76.78 4	82.44 4	70.19 8	75.26 11	80.22 3	80.59 8	57.85 6	84.79 4	60.84 16	88.54 1	83.43 3	66.24 1	78.21 15	76.47 21	80.34 39	85.43 27

ACMMP_Plus		76.15 5	81.17 5	70.30 6	74.09 15	79.47 6	81.59 6	57.09 10	81.38 7	63.89 6	79.02 9	80.48 13	62.24 14	80.05 4	79.12 4	82.94 10	88.64 5

HPM-MVS++		76.01 6	80.47 8	70.81 4	76.60 4	74.96 31	80.18 12	58.36 3	81.96 6	63.50 7	78.80 10	82.53 7	64.40 4	78.74 8	78.84 5	81.81 27	87.46 13

APD-MVS		75.80 7	80.90 7	69.86 12	75.42 10	78.48 12	81.43 7	57.44 8	80.45 11	59.32 22	85.28 5	80.82 12	63.96 5	76.89 25	76.08 24	81.58 33	88.30 8
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CNVR-MVS		75.62 8	79.91 10	70.61 5	75.76 6	78.82 10	81.66 5	57.12 9	79.77 13	63.04 8	70.69 20	81.15 10	62.99 8	80.23 3	79.54 3	83.11 7	89.16 4

SteuartSystems-ACMMP		75.23 9	79.60 11	70.13 9	76.81 3	78.92 8	81.74 4	57.99 5	75.30 25	59.83 21	75.69 14	78.45 19	60.48 25	80.58 2	79.77 2	83.94 3	88.52 6
Skip Steuart: Steuart Systems R&D Blog.
TSAR-MVS + MP.		75.22 10	80.06 9	69.56 13	74.61 13	72.74 45	80.59 8	55.70 20	80.80 9	62.65 10	86.25 3	82.92 5	62.07 16	76.89 25	75.66 27	81.77 29	85.19 29

HFP-MVS		74.87 11	78.86 16	70.21 7	73.99 16	77.91 14	80.36 11	56.63 12	78.41 16	64.27 4	74.54 16	77.75 23	62.96 9	78.70 9	77.82 10	83.02 8	86.91 16

CSCG		74.68 12	79.22 12	69.40 14	75.69 8	80.01 5	79.12 19	52.83 36	79.34 14	63.99 5	70.49 21	82.02 8	60.35 27	77.48 22	77.22 16	84.38 1	87.97 11

SD-MVS		74.43 13	78.87 14	69.26 16	74.39 14	73.70 41	79.06 20	55.24 22	81.04 8	62.71 9	80.18 8	82.61 6	61.70 18	75.43 36	73.92 39	82.44 17	85.22 28

MP-MVS		74.31 14	78.87 14	68.99 17	73.49 18	78.56 11	79.25 18	56.51 13	75.33 23	60.69 18	75.30 15	79.12 18	61.81 17	77.78 19	77.93 9	82.18 23	88.06 10
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		74.27 15	77.83 21	70.13 9	75.70 7	77.41 18	80.51 10	57.09 10	78.25 17	62.28 13	65.54 33	78.26 20	62.18 15	79.13 6	78.51 6	83.01 9	87.68 12

zzz-MVS		74.25 16	77.97 20	69.91 11	73.43 19	74.06 39	79.69 14	56.44 14	80.74 10	64.98 2	68.72 26	79.98 15	62.92 10	78.24 14	77.77 12	81.99 25	86.30 18

DeepPCF-MVS	66.49 1	74.25 16	80.97 6	66.41 27	67.75 47	78.87 9	75.61 35	54.16 28	84.86 2	58.22 28	77.94 12	81.01 11	62.52 12	78.34 10	77.38 13	80.16 42	88.40 7

train_agg		73.89 18	78.25 18	68.80 19	75.25 12	72.27 47	79.75 13	56.05 17	74.87 28	58.97 23	81.83 7	79.76 16	61.05 22	77.39 23	76.01 25	81.71 30	85.61 25

DeepC-MVS	66.32 2	73.85 19	78.10 19	68.90 18	67.92 45	79.31 7	78.16 24	59.28 1	78.24 18	61.13 14	67.36 32	76.10 27	63.40 7	79.11 7	78.41 7	83.52 5	88.16 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		73.79 20	78.41 17	68.40 20	72.35 23	77.79 15	79.32 16	56.38 15	77.67 20	58.30 27	74.16 17	76.66 24	61.40 19	78.32 11	77.80 11	82.68 14	86.51 17

MCST-MVS		73.67 21	77.39 22	69.33 15	76.26 5	78.19 13	78.77 21	54.54 25	75.33 23	59.99 20	67.96 28	79.23 17	62.43 13	78.00 16	75.71 26	84.02 2	87.30 14

PGM-MVS		72.89 22	77.13 23	67.94 21	72.47 22	77.25 19	79.27 17	54.63 24	73.71 30	57.95 29	72.38 18	75.33 29	60.75 23	78.25 13	77.36 15	82.57 16	85.62 24

CP-MVS		72.63 23	76.95 24	67.59 22	70.67 30	75.53 29	77.95 26	56.01 18	75.65 22	58.82 24	69.16 25	76.48 25	60.46 26	77.66 20	77.20 17	81.65 31	86.97 15

TSAR-MVS + ACMM		72.56 24	79.07 13	64.96 36	73.24 20	73.16 44	78.50 22	48.80 60	79.34 14	55.32 36	85.04 6	81.49 9	58.57 34	75.06 40	73.75 40	75.35 107	85.61 25

DeepC-MVS_fast	65.08 3	72.00 25	76.11 25	67.21 24	68.93 40	77.46 16	76.54 30	54.35 26	74.92 27	58.64 26	65.18 35	74.04 37	62.62 11	77.92 17	77.02 18	82.16 24	86.21 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMP		71.57 26	75.84 26	66.59 26	70.30 34	76.85 24	78.46 23	53.95 29	73.52 31	55.56 34	70.13 22	71.36 43	58.55 35	77.00 24	76.23 23	82.71 13	85.81 23
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		71.47 27	75.82 27	66.41 27	72.97 21	77.15 20	78.14 25	54.71 23	69.88 44	53.07 52	70.98 19	74.83 31	56.95 45	76.22 29	76.57 20	82.62 15	85.09 30

X-MVS		71.18 28	75.66 28	65.96 31	71.71 25	76.96 21	77.26 28	55.88 19	72.75 33	54.48 44	64.39 38	74.47 32	54.19 57	77.84 18	77.37 14	82.21 21	85.85 22

HQP-MVS		70.88 29	75.02 29	66.05 30	71.69 26	74.47 36	77.51 27	53.17 33	72.89 32	54.88 40	70.03 23	70.48 45	57.26 41	76.02 31	75.01 31	81.78 28	86.21 19

TSAR-MVS + GP.		69.71 30	73.92 32	64.80 39	68.27 43	70.56 53	71.90 47	50.75 46	71.38 37	57.46 31	68.68 27	75.42 28	60.10 29	73.47 45	73.99 38	80.32 40	83.97 34

3Dnovator+	62.63 4	69.51 31	72.62 36	65.88 32	68.21 44	76.47 25	73.50 45	52.74 37	70.85 39	58.65 25	55.97 65	69.95 46	61.11 21	76.80 27	75.09 28	81.09 37	83.23 40

MVS_0304		69.49 32	73.96 31	64.28 42	67.92 45	76.13 27	74.90 38	47.60 62	63.29 54	54.09 48	67.44 31	76.35 26	59.53 31	75.81 33	75.03 29	81.62 32	83.70 37

OPM-MVS		69.33 33	71.05 42	67.32 23	72.34 24	75.70 28	79.57 15	56.34 16	55.21 65	53.81 49	59.51 54	68.96 48	59.67 30	77.61 21	76.44 22	82.19 22	83.88 36

PHI-MVS		69.27 34	74.84 30	62.76 47	66.83 49	74.83 32	73.88 43	49.32 56	70.61 40	50.93 56	69.62 24	74.84 30	57.25 42	75.53 35	74.32 36	78.35 56	84.17 33

casdiffmvs		69.26 35	72.98 35	64.92 37	68.82 41	71.44 50	75.94 33	49.64 53	70.13 43	53.48 51	65.33 34	72.95 39	60.21 28	75.33 38	72.90 45	81.41 35	82.78 41

LGP-MVS_train		68.87 36	72.03 38	65.18 35	69.33 38	74.03 40	76.67 29	53.88 30	68.46 45	52.05 55	63.21 40	63.89 60	56.31 47	75.99 32	74.43 35	82.83 12	84.18 32

CANet		68.77 37	73.01 33	63.83 43	68.30 42	75.19 30	73.73 44	47.90 61	63.86 51	54.84 41	67.51 30	74.36 35	57.62 38	74.22 43	73.57 43	80.56 38	82.36 42

CPTT-MVS		68.76 38	73.01 33	63.81 44	65.42 56	73.66 42	76.39 32	52.08 38	72.61 34	50.33 58	60.73 52	72.65 41	59.43 32	73.32 46	72.12 46	79.19 50	85.99 21

ACMP	61.42 5	68.72 39	71.37 40	65.64 33	69.06 39	74.45 37	75.88 34	53.30 32	68.10 46	55.74 33	61.53 50	62.29 66	56.97 44	74.70 41	74.23 37	82.88 11	84.31 31
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSLP-MVS++		68.17 40	70.72 45	65.19 34	69.41 37	70.64 52	74.99 37	45.76 69	70.20 42	60.17 19	56.42 63	73.01 38	61.14 20	72.80 48	70.54 52	79.70 44	81.42 47

MAR-MVS		68.04 41	70.74 44	64.90 38	71.68 27	76.33 26	74.63 40	50.48 50	63.81 52	55.52 35	54.88 71	69.90 47	57.39 40	75.42 37	74.79 33	79.71 43	80.03 51
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
AdaColmap		67.89 42	68.85 52	66.77 25	73.73 17	74.30 38	75.28 36	53.58 31	70.24 41	57.59 30	51.19 85	59.19 80	60.74 24	75.33 38	73.72 41	79.69 46	77.96 62

MVS_111021_HR		67.62 43	70.39 46	64.39 40	69.77 36	70.45 54	71.44 50	51.72 42	60.77 59	55.06 38	62.14 47	66.40 56	58.13 37	76.13 30	74.79 33	80.19 41	82.04 45

ACMM	60.30 7	67.58 44	68.82 53	66.13 29	70.59 31	72.01 49	76.54 30	54.26 27	65.64 50	54.78 42	50.35 87	61.72 70	58.74 33	75.79 34	75.03 29	81.88 26	81.17 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PCF-MVS	59.98 8	67.32 45	71.04 43	62.97 46	64.77 58	74.49 35	74.78 39	49.54 54	67.44 47	54.39 47	58.35 58	72.81 40	55.79 53	71.54 53	69.24 60	78.57 52	83.41 38
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		67.02 46	71.57 39	61.71 48	71.01 29	74.81 33	71.62 48	38.91 169	71.86 36	60.70 17	64.97 36	67.88 54	51.88 95	76.77 28	74.98 32	76.11 97	69.75 124
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
3Dnovator	60.86 6	66.99 47	70.32 47	63.11 45	66.63 50	74.52 34	71.56 49	45.76 69	67.37 48	55.00 39	54.31 75	68.19 52	58.49 36	73.97 44	73.63 42	81.22 36	80.23 50

DELS-MVS		65.87 48	70.30 48	60.71 49	64.05 65	72.68 46	70.90 51	45.43 73	57.49 61	49.05 62	64.43 37	68.66 49	55.11 55	74.31 42	73.02 44	79.70 44	81.51 46
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
canonicalmvs		65.62 49	72.06 37	58.11 58	63.94 66	71.05 51	64.49 99	43.18 119	74.08 29	47.35 65	64.17 39	71.97 42	51.17 98	71.87 51	70.74 50	78.51 54	80.56 49

QAPM		65.27 50	69.49 51	60.35 50	65.43 55	72.20 48	65.69 91	47.23 63	63.46 53	49.14 61	53.56 76	71.04 44	57.01 43	72.60 49	71.41 49	77.62 60	82.14 44

OMC-MVS		65.16 51	71.35 41	57.94 62	52.95 162	68.82 58	69.00 52	38.28 176	79.89 12	55.20 37	62.76 43	68.31 51	56.14 50	71.30 55	68.70 65	76.06 99	79.67 52

EPNet		65.14 52	69.54 50	60.00 52	66.61 51	67.67 67	67.53 56	55.32 21	62.67 56	46.22 73	67.74 29	65.93 57	48.07 111	72.17 50	72.12 46	76.28 88	78.47 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		63.65 53	66.92 54	59.83 53	60.03 79	73.44 43	66.33 82	48.95 58	52.20 80	50.81 57	56.07 64	60.25 76	53.56 62	73.23 47	70.01 57	79.30 48	83.24 39

Effi-MVS+		63.28 54	65.96 59	60.17 51	64.26 62	68.06 62	68.78 53	45.71 71	54.08 69	46.64 68	55.92 66	63.13 64	55.94 51	70.38 62	71.43 48	79.68 47	78.70 57

MVS_111021_LR		63.05 55	66.43 56	59.10 55	61.33 73	63.77 107	65.87 89	43.58 108	60.20 60	53.70 50	62.09 48	62.38 65	55.84 52	70.24 63	68.08 69	74.30 112	78.28 61

OpenMVS	57.13 9	62.81 56	65.75 61	59.39 54	66.47 52	69.52 56	64.26 101	43.07 124	61.34 58	50.19 59	47.29 121	64.41 59	54.60 56	70.18 64	68.62 67	77.73 58	78.89 56

CNLPA		62.78 57	66.31 57	58.65 56	58.47 88	68.41 61	65.98 88	41.22 155	78.02 19	56.04 32	46.65 124	59.50 79	57.50 39	69.67 66	65.27 127	72.70 141	76.67 71

TSAR-MVS + COLMAP		62.65 58	69.90 49	54.19 102	46.31 195	66.73 77	65.49 93	41.36 153	76.57 21	46.31 72	76.80 13	56.68 88	53.27 70	69.50 67	66.65 88	72.40 147	76.36 79

MVS_Test		62.40 59	66.23 58	57.94 62	59.77 83	64.77 102	66.50 81	41.76 145	57.26 62	49.33 60	62.68 44	67.47 55	53.50 65	68.57 77	66.25 96	76.77 73	76.58 74

DI_MVS_plusplus_trai		61.88 60	65.17 65	58.06 59	60.05 78	65.26 97	66.03 86	44.22 84	55.75 63	46.73 67	54.64 73	68.12 53	54.13 59	69.13 70	66.66 87	77.18 66	76.61 72

PVSNet_BlendedMVS		61.63 61	64.82 66	57.91 64	57.21 129	67.55 68	63.47 105	46.08 67	54.72 66	52.46 53	58.59 56	60.73 72	51.82 96	70.46 60	65.20 129	76.44 85	76.50 77

PVSNet_Blended		61.63 61	64.82 66	57.91 64	57.21 129	67.55 68	63.47 105	46.08 67	54.72 66	52.46 53	58.59 56	60.73 72	51.82 96	70.46 60	65.20 129	76.44 85	76.50 77

diffmvs		61.60 63	65.90 60	56.58 91	59.78 82	65.35 94	66.56 80	42.79 130	55.46 64	46.47 69	61.43 51	65.52 58	51.16 99	68.04 94	66.17 99	72.71 137	79.31 54

TAPA-MVS	54.74 10	60.85 64	66.61 55	54.12 104	47.38 191	65.33 95	65.35 94	36.51 187	75.16 26	48.82 63	54.70 72	63.51 62	53.31 69	68.36 79	64.97 132	73.37 125	74.27 107
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
Fast-Effi-MVS+		60.36 65	63.35 72	56.87 84	58.70 85	65.86 91	65.08 95	37.11 182	53.00 76	45.36 81	52.12 81	56.07 93	56.27 48	71.28 56	69.42 59	78.71 51	75.69 84

Effi-MVS+-dtu		60.34 66	62.32 75	58.03 61	64.31 60	67.44 70	65.99 87	42.26 142	49.55 90	42.00 107	48.92 99	59.79 78	56.27 48	68.07 90	67.03 80	77.35 65	75.45 86

LS3D		60.20 67	61.70 76	58.45 57	64.18 63	67.77 64	67.19 58	48.84 59	61.67 57	41.27 110	45.89 135	51.81 119	54.18 58	68.78 72	66.50 94	75.03 108	69.48 130

Anonymous20240521		59.49 68	64.00 71	54.23 101	61.81 70	64.33 104	61.42 111	43.77 94	52.85 77	38.94 120	55.62 68	62.15 68	43.24 136	69.39 68	67.66 75	76.22 93	75.97 81

EPP-MVSNet		59.39 69	65.45 63	52.32 117	60.96 75	67.70 66	58.42 126	44.75 79	49.71 89	27.23 172	59.03 55	62.20 67	43.34 133	70.71 59	69.13 61	79.25 49	79.63 53

ACMH+	53.71 12	59.26 70	60.28 90	58.06 59	64.17 64	68.46 60	67.51 57	50.93 45	52.46 79	35.83 133	40.83 181	45.12 161	52.32 91	69.88 65	69.00 63	77.59 62	76.21 80

PLC	52.09 14	59.21 71	62.47 74	55.41 98	53.24 161	64.84 101	64.47 100	40.41 163	65.92 49	44.53 94	46.19 132	55.69 94	55.33 54	68.24 83	65.30 126	74.50 110	71.09 116
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7		59.19 72	60.62 82	57.53 67	57.96 91	67.19 73	67.09 61	44.28 83	46.84 119	45.45 79	48.19 113	51.06 121	53.62 61	67.84 97	66.59 91	76.79 70	76.60 73

v10		59.17 73	60.60 83	57.50 68	57.95 92	66.73 77	67.09 61	44.11 85	46.85 118	45.42 80	48.18 115	51.07 120	53.63 60	67.84 97	66.59 91	76.79 70	76.92 69

v6		58.89 74	60.54 86	56.96 73	57.34 116	66.13 87	66.71 72	42.84 126	47.85 113	45.80 75	49.04 93	52.95 104	52.79 75	67.53 107	65.59 118	76.26 89	74.73 91

CANet_DTU		58.88 75	64.68 68	52.12 118	55.77 137	66.75 76	63.92 102	37.04 183	53.32 72	37.45 127	59.81 53	61.81 69	44.43 127	68.25 81	67.47 78	74.12 115	75.33 87

v1144		58.88 75	60.16 97	57.39 69	58.03 90	67.26 71	67.14 60	44.46 82	45.17 141	44.33 95	47.81 118	49.92 130	53.20 71	67.77 102	66.62 90	77.15 67	76.58 74

v1neww		58.88 75	60.54 86	56.94 74	57.33 118	66.13 87	66.70 74	42.84 126	47.84 114	45.74 77	49.02 95	52.93 105	52.78 76	67.53 107	65.59 118	76.26 89	74.73 91

v7new		58.88 75	60.54 86	56.94 74	57.33 118	66.13 87	66.70 74	42.84 126	47.84 114	45.74 77	49.02 95	52.93 105	52.78 76	67.53 107	65.59 118	76.26 89	74.73 91

v8		58.88 75	60.57 85	56.92 78	57.35 114	65.69 93	66.69 76	42.64 135	47.89 112	45.77 76	49.04 93	52.98 103	52.77 78	67.51 110	65.57 122	76.26 89	75.30 88

v16		58.71 80	60.20 93	56.97 72	57.35 114	63.36 115	66.67 77	42.49 137	48.69 105	46.36 71	48.87 101	52.92 107	52.82 74	67.57 105	65.58 121	76.15 96	74.38 103

v17		58.69 81	60.19 96	56.94 74	57.38 109	63.37 114	66.67 77	42.47 139	48.52 109	46.10 74	48.90 100	53.00 102	52.84 72	67.58 104	65.60 117	76.19 94	74.38 103

v2v482		58.69 81	60.12 100	57.03 71	57.16 131	66.05 90	67.17 59	43.52 110	46.33 124	45.19 82	49.46 90	51.02 122	52.51 86	67.30 117	66.03 100	76.61 81	74.62 98

v18		58.68 83	60.20 93	56.90 81	57.26 127	63.28 116	66.58 79	42.42 140	48.86 99	46.37 70	49.01 97	53.05 101	52.74 79	67.40 115	65.52 123	76.02 101	74.28 106

v1141		58.56 84	60.05 102	56.81 87	57.36 111	66.18 85	66.80 69	43.11 121	45.87 135	44.60 91	48.71 103	51.83 117	52.38 88	67.46 111	65.64 115	76.63 78	74.66 94

divwei89l23v2f112		58.56 84	60.05 102	56.81 87	57.36 111	66.18 85	66.80 69	43.11 121	45.89 134	44.60 91	48.71 103	51.84 116	52.38 88	67.45 113	65.65 112	76.63 78	74.66 94

v1		58.56 84	60.06 101	56.83 86	57.36 111	66.19 84	66.80 69	43.10 123	45.87 135	44.68 89	48.73 102	51.83 117	52.38 88	67.45 113	65.65 112	76.63 78	74.66 94

v1192		58.51 87	59.66 110	57.17 70	57.82 93	67.72 65	66.21 85	44.83 78	44.15 148	43.49 98	46.68 123	47.94 134	53.55 63	67.39 116	66.51 93	77.13 68	77.20 67

UA-Net		58.50 88	64.68 68	51.30 120	66.97 48	67.13 74	53.68 162	45.65 72	49.51 92	31.58 147	62.91 41	68.47 50	35.85 171	68.20 84	67.28 79	74.03 116	69.24 134

Vis-MVSNet		58.48 89	65.70 62	50.06 127	53.40 160	67.20 72	60.24 122	43.32 116	48.83 100	30.23 153	62.38 46	61.61 71	40.35 147	71.03 58	69.77 58	72.82 133	79.11 55
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MSDG		58.46 90	58.97 120	57.85 66	66.27 54	66.23 83	67.72 54	42.33 141	53.43 71	43.68 97	43.39 155	45.35 158	49.75 102	68.66 75	67.77 72	77.38 64	67.96 137

V9		58.45 91	59.75 105	56.92 78	57.51 102	63.49 110	66.86 64	42.73 132	46.07 130	45.05 84	48.45 108	51.99 113	52.66 82	68.04 94	65.75 107	76.72 75	74.50 100

v13		58.44 92	59.72 109	56.94 74	57.55 96	63.51 108	66.86 64	42.81 129	45.90 133	44.98 86	48.17 116	51.87 115	52.68 80	68.20 84	65.78 105	76.78 72	74.63 97

v12		58.44 92	59.74 108	56.92 78	57.54 98	63.50 109	66.84 67	42.77 131	45.96 131	44.95 87	48.31 109	51.94 114	52.67 81	68.14 87	65.75 107	76.75 74	74.55 99

V14		58.44 92	59.75 105	56.90 81	57.48 104	63.46 111	66.85 66	42.68 133	46.16 127	45.03 85	48.57 106	52.04 112	52.65 83	67.93 96	65.72 110	76.69 76	74.40 102

v15		58.43 95	59.75 105	56.88 83	57.45 105	63.44 112	66.84 67	42.65 134	46.24 126	45.07 83	48.68 105	52.07 111	52.63 84	67.84 97	65.70 111	76.65 77	74.31 105

FC-MVSNet-train		58.40 96	63.15 73	52.85 113	64.29 61	61.84 124	55.98 143	46.47 65	53.06 74	34.96 136	61.95 49	56.37 91	39.49 149	68.67 74	68.36 68	75.92 102	71.81 113

IB-MVS	54.11 11	58.36 97	60.70 81	55.62 96	58.67 86	68.02 63	61.56 108	43.15 120	46.09 128	44.06 96	44.24 147	50.99 124	48.71 106	66.70 126	70.33 53	77.60 61	78.50 58
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
IterMVS-LS		58.30 98	61.39 77	54.71 100	59.92 81	58.40 163	59.42 123	43.64 105	48.71 103	40.25 114	57.53 61	58.55 82	52.15 93	65.42 149	65.34 125	72.85 131	75.77 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	52.42 13	58.24 99	59.56 113	56.70 89	66.34 53	69.59 55	66.71 72	49.12 57	46.08 129	28.90 160	42.67 169	41.20 190	52.60 85	71.39 54	70.28 54	76.51 83	75.72 83
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v144192		58.23 100	59.40 116	56.87 84	57.56 95	66.89 75	65.70 90	45.01 77	44.06 149	42.88 100	46.61 125	48.09 133	53.49 66	66.94 122	65.90 103	76.61 81	77.29 65

MS-PatchMatch		58.19 101	60.20 93	55.85 95	65.17 57	64.16 105	64.82 96	41.48 152	50.95 83	42.17 106	45.38 140	56.42 89	48.08 110	68.30 80	66.70 86	73.39 124	69.46 132

v11		58.19 101	59.47 114	56.70 89	57.54 98	63.42 113	66.28 84	42.49 137	45.62 139	44.59 93	48.16 117	50.78 125	52.84 72	67.80 101	65.76 106	76.49 84	74.76 90

IS_MVSNet		57.95 103	64.26 70	50.60 122	61.62 72	65.25 98	57.18 132	45.42 74	50.79 84	26.49 174	57.81 60	60.05 77	34.51 175	71.24 57	70.20 56	78.36 55	74.44 101

v1921920		57.89 104	59.02 119	56.58 91	57.55 96	66.66 81	64.72 98	44.70 80	43.55 152	42.73 102	46.17 133	46.93 148	53.51 64	66.78 125	65.75 107	76.29 87	77.28 66

Anonymous20231211		57.71 105	60.79 80	54.13 103	61.68 71	65.81 92	60.81 116	43.70 104	51.97 81	39.67 116	34.82 199	63.59 61	43.31 134	68.55 78	66.63 89	75.59 103	74.13 108

v1240		57.55 106	58.63 122	56.29 94	57.30 124	66.48 82	63.77 103	44.56 81	42.77 168	42.48 104	45.64 138	46.28 153	53.46 67	66.32 133	65.80 104	76.16 95	77.13 68

MVSTER		57.19 107	61.11 79	52.62 115	50.82 179	58.79 159	61.55 109	37.86 179	48.81 101	41.31 109	57.43 62	52.10 110	48.60 107	68.19 86	66.75 85	75.56 104	75.68 85

UGNet		57.03 108	65.25 64	47.44 159	46.54 194	66.73 77	56.30 139	43.28 117	50.06 87	32.99 140	62.57 45	63.26 63	33.31 181	68.25 81	67.58 76	72.20 151	78.29 60
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
EG-PatchMatch MVS		56.98 109	58.24 126	55.50 97	64.66 59	68.62 59	61.48 110	43.63 107	38.44 201	41.44 108	38.05 191	46.18 155	43.95 128	71.71 52	70.61 51	77.87 57	74.08 109

V42		56.97 110	60.14 98	53.28 108	48.16 186	62.78 121	66.30 83	37.93 178	47.44 116	42.68 103	48.19 113	52.59 109	51.90 94	67.46 111	65.94 102	72.72 134	76.55 76

UniMVSNet_NR-MVSNet		56.94 111	61.14 78	52.05 119	60.02 80	65.21 99	57.44 130	52.93 35	49.37 93	24.31 183	54.62 74	50.54 126	39.04 151	68.69 73	68.84 64	78.53 53	70.72 117

HyFIR lowres test		56.87 112	58.60 123	54.84 99	56.62 134	69.27 57	64.77 97	42.21 143	45.66 138	37.50 126	33.08 202	57.47 87	53.33 68	65.46 148	67.94 70	74.60 109	71.35 115

tpmp4_e23		56.84 113	57.14 133	56.49 93	62.45 68	62.05 122	67.57 55	41.56 150	54.17 68	48.57 64	49.18 91	46.54 151	50.44 101	61.93 168	58.82 182	68.34 175	67.28 142

CostFormer		56.57 114	59.13 118	53.60 105	57.52 101	61.12 134	66.94 63	35.95 189	53.44 70	44.68 89	55.87 67	54.44 96	48.21 109	60.37 176	58.33 184	68.27 177	70.33 122

Fast-Effi-MVS+-dtu		56.30 115	59.29 117	52.82 114	58.64 87	64.89 100	65.56 92	32.89 207	45.80 137	35.04 135	45.89 135	54.14 97	49.41 103	67.16 119	66.45 95	75.37 106	70.69 119

TranMVSNet+NR-MVSNet		55.87 116	60.14 98	50.88 121	59.46 84	63.82 106	57.93 128	52.98 34	48.94 98	20.52 193	52.87 78	47.33 142	36.81 168	69.12 71	69.03 62	77.56 63	69.89 123

CHOSEN 1792x2688		55.85 117	58.01 127	53.33 107	57.26 127	62.82 120	63.29 107	41.55 151	46.65 121	38.34 121	34.55 200	53.50 98	52.43 87	67.10 120	67.56 77	67.13 181	73.92 110

v7n		55.67 118	57.46 132	53.59 106	56.06 135	65.29 96	61.06 114	43.26 118	40.17 188	37.99 123	40.79 182	45.27 160	47.09 115	67.67 103	66.21 97	76.08 98	76.82 70

GA-MVS		55.67 118	58.33 124	52.58 116	55.23 142	63.09 117	61.08 113	40.15 165	42.95 159	37.02 129	52.61 79	47.68 137	47.51 113	65.92 141	65.35 124	74.49 111	70.68 120

v148		55.58 120	57.61 131	53.20 110	54.59 151	61.86 123	61.18 112	38.70 174	44.30 147	42.25 105	47.53 119	50.24 129	48.73 105	65.15 150	62.61 161	73.79 118	71.61 114

DU-MVS		55.41 121	59.59 111	50.54 124	54.60 149	62.97 118	57.44 130	51.80 40	48.62 107	24.31 183	51.99 82	47.00 147	39.04 151	68.11 88	67.75 73	76.03 100	70.72 117

NR-MVSNet		55.35 122	59.46 115	50.56 123	61.33 73	62.97 118	57.91 129	51.80 40	48.62 107	20.59 192	51.99 82	44.73 169	34.10 178	68.58 76	68.64 66	77.66 59	70.67 121

GBi-Net		55.20 123	60.25 91	49.31 131	52.42 164	61.44 128	57.03 133	44.04 88	49.18 95	30.47 149	48.28 110	58.19 83	38.22 154	68.05 91	66.96 81	73.69 120	69.65 125

test1		55.20 123	60.25 91	49.31 131	52.42 164	61.44 128	57.03 133	44.04 88	49.18 95	30.47 149	48.28 110	58.19 83	38.22 154	68.05 91	66.96 81	73.69 120	69.65 125

UniMVSNet (Re)		55.15 125	60.39 89	49.03 137	55.31 139	64.59 103	55.77 144	50.63 47	48.66 106	20.95 191	51.47 84	50.40 127	34.41 177	67.81 100	67.89 71	77.11 69	71.88 112

FMVSNet2		55.04 126	59.95 104	49.31 131	52.42 164	61.44 128	57.03 133	44.08 87	49.55 90	30.40 152	46.89 122	58.84 81	38.22 154	67.07 121	66.21 97	73.69 120	69.65 125

FMVSNet3		54.78 127	59.58 112	49.17 134	52.37 167	61.31 132	56.72 137	44.04 88	49.18 95	30.47 149	48.28 110	58.19 83	38.09 157	65.48 147	65.20 129	73.31 126	69.45 133

pmmvs4		54.66 128	56.07 138	53.00 112	54.63 148	57.08 171	60.43 121	44.10 86	51.69 82	40.55 112	46.55 128	44.79 168	45.95 122	62.54 160	63.66 147	72.36 149	66.20 152

FMVSNet1		54.08 129	58.68 121	48.71 145	50.90 178	61.35 131	56.73 136	43.94 92	45.91 132	29.32 159	42.72 168	56.26 92	37.70 158	68.05 91	66.96 81	73.69 120	69.50 129

DWT-MVSNet_training		53.80 130	54.31 156	53.21 109	57.65 94	59.04 157	60.65 117	40.11 166	46.35 123	42.77 101	49.07 92	41.07 191	51.06 100	58.62 186	58.96 181	67.00 184	67.06 143

v52		53.60 131	56.74 136	49.93 128	45.54 198	61.64 126	60.65 117	36.99 184	38.75 197	36.32 131	39.64 186	47.13 144	47.05 116	66.89 123	65.65 112	73.04 129	77.48 63

V4		53.60 131	56.73 137	49.93 128	45.54 198	61.64 126	60.65 117	36.99 184	38.74 199	36.33 130	39.64 186	47.12 145	47.05 116	66.89 123	65.64 115	73.04 129	77.48 63

Baseline_NR-MVSNet		53.50 133	57.89 128	48.37 148	54.60 149	59.25 155	56.10 140	51.84 39	49.32 94	17.92 203	45.38 140	47.68 137	36.93 167	68.11 88	65.95 101	72.84 132	69.57 128

IterMVS		53.45 134	57.12 134	49.17 134	49.23 183	60.93 135	59.05 125	34.63 193	44.53 143	33.22 139	51.09 86	51.01 123	48.38 108	62.43 161	60.79 171	70.54 163	69.05 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tpm cat1		53.30 135	53.41 164	53.17 111	58.16 89	59.15 156	63.73 104	38.27 177	50.73 85	46.98 66	45.57 139	44.00 176	49.20 104	55.90 203	54.02 202	62.65 196	64.50 171

v748		52.93 136	55.29 146	50.19 126	51.90 171	61.31 132	56.54 138	40.05 167	39.12 195	34.82 138	39.93 185	43.83 177	43.66 129	64.26 154	63.32 152	74.15 114	75.28 89

anonymousdsp		52.84 137	57.78 129	47.06 160	40.24 214	58.95 158	53.70 161	33.54 202	36.51 208	32.69 142	43.88 149	45.40 157	47.97 112	67.17 118	70.28 54	74.22 113	82.29 43

tfpn200view9		52.53 138	55.51 140	49.06 136	57.31 120	60.24 137	55.42 148	43.77 94	42.85 162	27.81 164	43.00 164	45.06 163	37.32 160	66.38 128	64.54 134	72.71 137	66.54 145

conf200view11		52.51 139	55.51 140	49.01 138	57.31 120	60.24 137	55.42 148	43.77 94	42.85 162	27.51 166	43.00 164	45.06 163	37.32 160	66.38 128	64.54 134	72.71 137	66.54 145

tfpn111		52.44 140	55.38 143	49.01 138	57.31 120	60.24 137	55.42 148	43.77 94	42.85 162	27.51 166	42.03 175	45.06 163	37.32 160	66.38 128	64.54 134	72.71 137	66.54 145

CDS-MVSNet		52.42 141	57.06 135	47.02 161	53.92 158	58.30 165	55.50 146	46.47 65	42.52 171	29.38 158	49.50 89	52.85 108	28.49 196	66.70 126	66.89 84	68.34 175	62.63 180
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
thres200		52.39 142	55.37 145	48.90 142	57.39 108	60.18 140	55.60 145	43.73 101	42.93 160	27.41 170	43.35 156	45.09 162	36.61 169	66.36 131	63.92 146	72.66 142	65.78 158

thres400		52.38 143	55.51 140	48.74 144	57.49 103	60.10 144	55.45 147	43.54 109	42.90 161	26.72 173	43.34 157	45.03 167	36.61 169	66.20 138	64.53 138	72.66 142	66.43 148

EPNet_dtu		52.05 144	58.26 125	44.81 172	54.10 156	50.09 197	52.01 170	40.82 160	53.03 75	27.41 170	54.90 70	57.96 86	26.72 201	62.97 157	62.70 160	67.78 179	66.19 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres100view900		52.04 145	54.81 152	48.80 143	57.31 120	59.33 151	55.30 153	42.92 125	42.85 162	27.81 164	43.00 164	45.06 163	36.99 166	64.74 152	63.51 149	72.47 146	65.21 165

conf0.01		52.02 146	54.62 153	49.00 140	57.30 124	60.17 142	55.42 148	43.76 98	42.85 162	27.49 168	43.12 161	39.71 199	37.32 160	66.26 136	64.54 134	72.72 134	65.66 160

COLMAP_ROB	46.52 15	51.99 147	54.86 151	48.63 146	49.13 184	61.73 125	60.53 120	36.57 186	53.14 73	32.95 141	37.10 192	38.68 203	40.49 146	65.72 144	63.08 154	72.11 152	64.60 170
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
view600		51.96 148	55.13 148	48.27 150	57.41 107	60.05 145	54.74 156	43.64 105	42.57 170	25.88 176	43.11 162	44.48 172	35.34 172	66.27 134	63.61 148	72.61 145	65.80 157

TransMVSNet (Re)		51.92 149	55.38 143	47.88 155	60.95 76	59.90 146	53.95 159	45.14 76	39.47 192	24.85 180	43.87 150	46.51 152	29.15 192	67.55 106	65.23 128	73.26 128	65.16 166

thres600view7		51.91 150	55.14 147	48.14 151	57.43 106	60.18 140	54.60 157	43.73 101	42.61 169	25.20 178	43.10 163	44.47 173	35.19 173	66.36 131	63.28 153	72.66 142	66.01 155

conf0.002		51.76 151	54.13 158	49.00 140	57.28 126	60.15 143	55.42 148	43.75 100	42.85 162	27.49 168	43.13 160	37.12 211	37.32 160	66.23 137	64.17 141	72.72 134	65.24 164

view800		51.55 152	54.89 150	47.66 158	57.37 110	59.77 148	53.62 163	43.72 103	42.22 172	24.94 179	42.80 167	43.81 178	33.94 179	66.09 139	64.38 140	72.39 148	65.14 167

pmmvs-eth3d		51.33 153	52.25 174	50.26 125	50.82 179	54.65 181	56.03 142	43.45 115	43.51 153	37.20 128	39.20 189	39.04 202	42.28 139	61.85 169	62.78 158	71.78 156	64.72 169

USDC		51.11 154	53.71 160	48.08 153	44.76 201	55.99 174	53.01 167	40.90 157	52.49 78	36.14 132	44.67 145	33.66 215	43.27 135	63.23 156	61.10 167	70.39 164	64.82 168

pm-mvs1		51.02 155	55.55 139	45.73 167	54.16 155	58.52 161	50.92 172	42.56 136	40.32 187	25.67 177	43.66 152	50.34 128	30.06 190	65.85 142	63.97 145	70.99 161	66.21 151

conf0.05thres1000		50.64 156	53.84 159	46.92 163	57.02 132	59.29 153	52.29 169	43.80 93	39.84 191	23.81 186	39.26 188	43.14 181	32.52 185	65.74 143	64.04 142	72.05 153	65.53 161

tfpn		50.58 157	53.65 162	47.00 162	57.34 116	59.31 152	52.41 168	43.76 98	41.81 176	23.86 185	42.49 170	37.80 206	32.63 184	65.68 146	64.02 144	71.99 154	64.41 172

CR-MVSNet		50.47 158	52.61 169	47.98 154	49.03 185	52.94 186	48.27 181	38.86 171	44.41 144	39.59 117	44.34 146	44.65 171	46.63 119	58.97 181	60.31 174	65.48 187	62.66 178

dps		50.42 159	51.20 186	49.51 130	55.88 136	56.07 173	53.73 160	38.89 170	43.66 150	40.36 113	45.66 137	37.63 208	45.23 124	59.05 179	56.18 187	62.94 195	60.16 188

Vis-MVSNet (Re-imp)		50.37 160	57.73 130	41.80 191	57.53 100	54.35 182	45.70 199	45.24 75	49.80 88	13.43 211	58.23 59	56.42 89	20.11 212	62.96 158	63.36 151	68.76 174	58.96 193

MDTV_nov1_ep13		50.32 161	52.43 172	47.86 156	49.87 182	54.70 180	58.10 127	34.29 195	45.59 140	37.71 124	47.44 120	47.42 141	41.86 141	58.07 189	55.21 195	65.34 189	58.56 194

tfpnnormal		50.16 162	52.19 175	47.78 157	56.86 133	58.37 164	54.15 158	44.01 91	38.35 203	25.94 175	36.10 195	37.89 205	34.50 176	65.93 140	63.42 150	71.26 159	65.28 163

PatchMatch-RL		50.11 163	51.56 179	48.43 147	46.23 196	51.94 190	50.21 174	38.62 175	46.62 122	37.51 125	42.43 171	39.38 200	52.24 92	60.98 172	59.56 178	65.76 186	60.01 190

PatchmatchNet		49.92 164	51.29 182	48.32 149	51.83 172	51.86 191	53.38 166	37.63 181	47.90 111	40.83 111	48.54 107	45.30 159	45.19 125	56.86 193	53.99 204	61.08 200	54.57 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
TDRefinement		49.31 165	52.44 171	45.67 168	30.44 229	59.42 150	59.24 124	39.78 168	48.76 102	31.20 148	35.73 196	29.90 219	42.81 138	64.24 155	62.59 162	70.55 162	66.43 148

test-LLR		49.28 166	50.29 190	48.10 152	55.26 140	47.16 205	49.52 175	43.48 113	39.22 193	31.98 143	43.65 153	47.93 135	41.29 144	56.80 194	55.36 193	67.08 182	61.94 181

CMPMVS	37.70 17	49.24 167	52.71 168	45.19 169	45.97 197	51.23 193	47.44 187	29.31 214	43.04 158	44.69 88	34.45 201	48.35 132	43.64 130	62.59 159	59.82 177	60.08 201	69.48 130
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PEN-MVS		49.21 168	54.32 155	43.24 183	54.33 154	59.26 154	47.04 190	51.37 44	41.67 177	9.97 221	46.22 131	41.80 185	22.97 209	60.52 174	64.03 143	73.73 119	66.75 144

PMMVS		49.20 169	54.28 157	43.28 182	34.13 223	45.70 213	48.98 178	26.09 225	46.31 125	34.92 137	55.22 69	53.47 99	47.48 114	59.43 178	59.04 180	68.05 178	60.77 185

gg-mvs-nofinetune		49.07 170	52.56 170	45.00 171	61.99 69	59.78 147	53.55 165	41.63 146	31.62 218	12.08 213	29.56 211	53.28 100	29.57 191	66.27 134	64.49 139	71.19 160	62.92 177

tpm		48.82 171	51.27 183	45.96 166	54.10 156	47.35 204	56.05 141	30.23 212	46.70 120	43.21 99	52.54 80	47.55 140	37.28 165	54.11 208	50.50 213	54.90 214	60.12 189

WR-MVS		48.78 172	55.06 149	41.45 193	55.50 138	60.40 136	43.77 208	49.99 52	41.92 174	8.10 226	45.24 143	45.56 156	17.47 214	61.57 170	64.60 133	73.85 117	66.14 154

CP-MVSNet		48.37 173	53.53 163	42.34 188	51.35 175	58.01 166	46.56 191	50.54 48	41.62 178	10.61 217	46.53 129	40.68 195	23.18 207	58.71 184	61.83 163	71.81 155	67.36 141

pmmvs6		48.35 174	51.64 177	44.51 175	51.92 170	57.94 167	49.44 177	42.17 144	34.45 211	24.62 182	28.87 215	46.90 149	29.07 194	64.60 153	63.08 154	69.83 166	65.68 159

tfpn_ndepth		48.34 175	52.27 173	43.76 177	54.35 153	56.46 172	47.24 189	40.92 156	43.45 154	21.04 190	41.16 180	43.22 180	28.90 195	61.57 170	60.65 172	70.12 165	59.34 191

PS-CasMVS		48.18 176	53.25 167	42.27 189	51.26 176	57.94 167	46.51 192	50.52 49	41.30 181	10.56 219	45.35 142	40.34 197	23.04 208	58.66 185	61.79 164	71.74 157	67.38 140

thresconf0.02		48.17 177	51.22 185	44.60 174	55.14 143	55.73 175	48.95 179	41.35 154	43.43 156	21.23 189	42.03 175	37.25 210	31.19 187	62.33 164	60.61 173	69.76 167	57.17 198

PatchT		48.08 178	51.03 187	44.64 173	42.96 208	50.12 196	40.36 215	35.09 191	43.17 157	39.59 117	42.00 177	39.96 198	46.63 119	58.97 181	60.31 174	63.21 194	62.66 178

tpmrst		48.08 178	49.88 194	45.98 165	52.71 163	48.11 202	53.62 163	33.70 200	48.70 104	39.74 115	48.96 98	46.23 154	40.29 148	50.14 217	49.28 215	55.80 211	57.71 196

DTE-MVSNet		48.03 180	53.28 166	41.91 190	54.64 147	57.50 169	44.63 206	51.66 43	41.02 183	7.97 227	46.26 130	40.90 192	20.24 211	60.45 175	62.89 157	72.33 150	63.97 173

WR-MVS_H		47.65 181	53.67 161	40.63 196	51.45 173	59.74 149	44.71 205	49.37 55	40.69 185	7.61 228	46.04 134	44.34 175	17.32 215	57.79 190	61.18 165	73.30 127	65.86 156

MDTV_nov1_ep13_2view		47.62 182	49.72 195	45.18 170	48.05 187	53.70 184	54.90 155	33.80 199	39.90 190	29.79 156	38.85 190	41.89 184	39.17 150	58.99 180	55.55 192	65.34 189	59.17 192

tfpnview11		47.58 183	51.57 178	42.92 184	54.94 144	55.30 177	46.21 193	41.58 149	42.10 173	18.54 198	42.25 172	41.54 187	27.12 198	62.29 165	61.12 166	69.15 169	56.40 202

tfpn_n400		47.56 184	51.56 179	42.90 185	54.91 145	55.28 178	46.21 193	41.59 147	41.51 179	18.54 198	42.25 172	41.54 187	27.12 198	62.41 162	61.02 168	69.05 170	56.90 200

tfpnconf		47.56 184	51.56 179	42.90 185	54.91 145	55.28 178	46.21 193	41.59 147	41.51 179	18.54 198	42.25 172	41.54 187	27.12 198	62.41 162	61.02 168	69.05 170	56.90 200

SixPastTwentyTwo		47.55 186	50.25 192	44.41 176	47.30 192	54.31 183	47.81 184	40.36 164	33.76 212	19.93 195	43.75 151	32.77 217	42.07 140	59.82 177	60.94 170	68.98 172	66.37 150

TinyColmap		47.08 187	47.56 202	46.52 164	42.35 210	53.44 185	51.77 171	40.70 161	43.44 155	31.92 145	29.78 210	23.72 230	45.04 126	61.99 167	59.54 179	67.35 180	61.03 184

pmmvs5		47.07 188	51.02 188	42.46 187	45.18 200	51.47 192	48.23 183	33.09 206	38.17 204	28.62 162	46.60 126	43.48 179	30.74 188	58.28 187	58.63 183	68.92 173	60.48 186

LTVRE_ROB	44.17 16	47.06 189	50.15 193	43.44 180	51.39 174	58.42 162	42.90 210	43.51 111	22.27 232	14.85 209	41.94 178	34.57 213	45.43 123	62.28 166	62.77 159	62.56 197	68.83 136
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
tfpn1000		46.75 190	51.24 184	41.51 192	54.39 152	55.60 176	43.85 207	40.90 157	41.82 175	16.71 205	41.26 179	41.58 186	23.96 205	60.76 173	60.27 176	69.26 168	57.42 197

RPMNet		46.41 191	48.72 197	43.72 178	47.77 189	52.94 186	46.02 198	33.92 197	44.41 144	31.82 146	36.89 193	37.42 209	37.41 159	53.88 209	54.02 202	65.37 188	61.47 183

RPSCF		46.41 191	54.42 154	37.06 207	25.70 237	45.14 214	45.39 201	20.81 230	62.79 55	35.10 134	44.92 144	55.60 95	43.56 131	56.12 200	52.45 209	51.80 220	63.91 174

CVMVSNet		46.38 193	52.01 176	39.81 198	42.40 209	50.26 195	46.15 196	37.68 180	40.03 189	15.09 208	46.56 127	47.56 139	33.72 180	56.50 198	55.65 191	63.80 193	67.53 138

TESTMET0.1,1		46.09 194	50.29 190	41.18 194	36.91 219	47.16 205	49.52 175	20.32 231	39.22 193	31.98 143	43.65 153	47.93 135	41.29 144	56.80 194	55.36 193	67.08 182	61.94 181

test-mter		45.30 195	50.37 189	39.38 199	33.65 225	46.99 207	47.59 185	18.59 233	38.75 197	28.00 163	43.28 158	46.82 150	41.50 143	57.28 192	55.78 190	66.93 185	63.70 175

gm-plane-assit		44.74 196	45.95 204	43.33 181	60.88 77	46.79 210	36.97 219	32.24 211	24.15 228	11.79 214	29.26 214	32.97 216	46.64 118	65.09 151	62.95 156	71.45 158	60.42 187

EPMVS		44.66 197	47.86 201	40.92 195	47.97 188	44.70 215	47.58 186	33.27 203	48.11 110	29.58 157	49.65 88	44.38 174	34.65 174	51.71 212	47.90 219	52.49 219	48.57 219

PM-MVS		44.55 198	48.13 200	40.37 197	32.85 227	46.82 209	46.11 197	29.28 215	40.48 186	29.99 154	39.98 184	34.39 214	41.80 142	56.08 201	53.88 206	62.19 198	65.31 162

TAMVS		44.02 199	49.18 196	37.99 205	47.03 193	45.97 212	45.04 202	28.47 217	39.11 196	20.23 194	43.22 159	48.52 131	28.49 196	58.15 188	57.95 186	58.71 203	51.36 210

MIMVSNet		43.79 200	48.53 198	38.27 203	41.46 211	48.97 200	50.81 173	32.88 208	44.55 142	22.07 187	32.05 203	47.15 143	24.76 204	58.73 183	56.09 189	57.63 208	52.14 208

test0.0.03 1		43.15 201	46.95 203	38.72 202	55.26 140	50.56 194	42.48 211	43.48 113	38.16 205	15.11 207	35.07 198	44.69 170	16.47 217	55.95 202	54.34 201	59.54 202	49.87 217

Anonymous20231206		42.28 202	45.89 205	38.07 204	51.96 169	48.98 199	43.66 209	38.81 173	38.74 199	14.32 210	26.74 217	40.90 192	20.94 210	56.64 197	54.67 199	58.71 203	54.59 204

MVS-HIRNet		42.24 203	41.15 216	43.51 179	44.06 207	40.74 218	35.77 222	35.35 190	35.38 209	38.34 121	25.63 219	38.55 204	43.48 132	50.77 214	47.03 223	64.07 191	49.98 215

MDA-MVSNet-bldmvs		41.36 204	43.15 213	39.27 201	28.74 231	52.68 188	44.95 204	40.84 159	32.89 214	18.13 202	31.61 205	22.09 231	38.97 153	50.45 216	56.11 188	64.01 192	56.23 203

FMVSNet5		40.96 205	45.81 206	35.29 211	34.30 222	44.55 216	47.28 188	28.84 216	40.76 184	21.62 188	29.85 209	42.44 182	24.77 203	57.53 191	55.00 196	54.93 213	50.56 213

CHOSEN 280x420		40.80 206	45.05 209	35.84 210	32.95 226	29.57 233	44.98 203	23.71 228	37.54 206	18.42 201	31.36 206	47.07 146	46.41 121	56.71 196	54.65 200	48.55 225	58.47 195

LP		40.79 207	41.99 214	39.38 199	40.98 212	46.49 211	42.14 212	33.66 201	35.37 210	29.89 155	29.30 213	27.81 221	32.74 182	52.55 210	52.19 210	56.87 209	50.23 214

ADS-MVSNet		40.67 208	43.38 212	37.50 206	44.36 203	39.79 221	42.09 213	32.67 209	44.34 146	28.87 161	40.76 183	40.37 196	30.22 189	48.34 227	45.87 225	46.81 228	44.21 223

EU-MVSNet		40.63 209	45.65 207	34.78 212	39.11 215	46.94 208	40.02 216	34.03 196	33.50 213	10.37 220	35.57 197	37.80 206	23.65 206	51.90 211	50.21 214	61.49 199	63.62 176

test20.03		40.38 210	44.20 210	35.92 209	53.73 159	49.05 198	38.54 217	43.49 112	32.55 215	9.54 222	27.88 216	39.12 201	12.24 228	56.28 199	54.69 198	57.96 207	49.83 218

FC-MVSNet-test		39.65 211	48.35 199	29.49 218	44.43 202	39.28 222	30.23 229	40.44 162	43.59 151	3.12 238	53.00 77	42.03 183	10.02 235	55.09 205	54.77 197	48.66 224	50.71 212

testgi		38.71 212	43.64 211	32.95 214	52.30 168	48.63 201	35.59 223	35.05 192	31.58 219	9.03 225	30.29 207	40.75 194	11.19 233	55.30 204	53.47 207	54.53 216	45.48 221

FPMVS		38.36 213	40.41 217	35.97 208	38.92 216	39.85 220	45.50 200	25.79 226	41.13 182	18.70 197	30.10 208	24.56 225	31.86 186	49.42 222	46.80 224	55.04 212	51.03 211

GG-mvs-BLEND		36.62 214	53.39 165	17.06 232	0.01 242	58.61 160	48.63 180	0.01 239	47.13 117	0.02 243	43.98 148	60.64 74	0.03 239	54.92 207	51.47 212	53.64 217	56.99 199

MIMVSNet1		35.51 215	41.41 215	28.63 220	27.53 233	43.36 217	38.09 218	33.82 198	32.01 216	6.77 229	21.63 227	35.43 212	11.97 230	55.05 206	53.99 204	53.59 218	48.36 220

pmmvs3		35.10 216	38.47 218	31.17 216	26.37 236	40.47 219	34.51 225	18.09 234	24.75 227	16.88 204	23.05 223	26.69 223	32.69 183	50.73 215	51.60 211	58.46 206	51.98 209

testpf		34.85 217	36.16 223	33.31 213	47.49 190	35.56 229	36.85 220	32.31 210	23.08 229	15.63 206	29.39 212	29.48 220	19.62 213	41.38 230	41.07 229	47.95 226	53.18 206

PMVS	27.84 18	33.81 218	35.28 224	32.09 215	34.13 223	24.81 236	32.51 226	26.48 224	26.41 226	19.37 196	23.76 222	24.02 229	25.18 202	50.78 213	47.24 222	54.89 215	49.95 216
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2356		33.40 219	36.53 221	29.76 217	37.51 218	38.39 224	34.68 224	27.35 219	27.88 221	10.61 217	25.54 220	24.44 226	17.15 216	49.99 219	48.32 217	51.24 221	41.16 227

new-patchmatchnet		33.24 220	37.20 219	28.62 221	44.32 204	38.26 226	29.68 232	36.05 188	31.97 217	6.33 230	26.59 218	27.33 222	11.12 234	50.08 218	41.05 230	44.23 229	45.15 222

N_pmnet		32.67 221	36.85 220	27.79 222	40.55 213	32.13 232	35.80 221	26.79 223	37.24 207	9.10 223	32.02 204	30.94 218	16.30 218	47.22 228	41.21 228	38.21 231	37.21 228

1111		31.35 222	33.52 227	28.83 219	44.28 205	32.44 230	31.71 227	33.25 204	27.87 222	10.92 215	22.18 225	24.05 227	15.89 219	49.03 225	44.09 226	36.94 233	34.96 229

testus		31.33 223	36.31 222	25.52 226	37.55 217	38.40 223	25.87 233	23.58 229	26.46 225	5.97 231	24.15 221	24.92 224	12.44 227	49.14 224	48.21 218	47.73 227	42.86 224

testmv		30.97 224	34.42 225	26.95 223	36.49 220	37.38 227	29.80 230	27.28 220	22.34 230	4.72 232	20.63 229	20.64 232	13.22 225	49.86 221	47.74 220	50.20 222	42.36 225

test1235678		30.97 224	34.42 225	26.95 223	36.49 220	37.38 227	29.79 231	27.28 220	22.33 231	4.72 232	20.62 230	20.64 232	13.22 225	49.87 220	47.74 220	50.20 222	42.36 225

no-one		29.19 226	31.89 228	26.05 225	30.96 228	38.33 225	21.54 234	29.86 213	15.84 236	3.56 235	11.28 235	13.03 237	14.44 224	38.96 231	52.83 208	55.96 210	52.92 207

Gipuma		25.87 227	26.91 231	24.66 227	28.98 230	20.17 237	20.46 236	34.62 194	29.55 220	9.10 223	4.91 239	5.31 241	15.76 221	49.37 223	49.10 216	39.03 230	29.95 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test12356		23.91 228	28.47 229	18.60 229	26.80 235	28.30 234	20.92 235	19.76 232	19.89 233	2.88 240	18.48 231	16.57 235	4.05 236	42.34 229	41.93 227	37.21 232	31.75 230

new_pmnet		23.19 229	28.17 230	17.37 230	17.03 238	24.92 235	19.66 237	16.16 236	27.05 224	4.42 234	20.77 228	19.20 234	12.19 229	37.71 232	36.38 231	34.77 234	31.17 231

.test1245		22.44 230	22.23 232	22.67 228	44.28 205	32.44 230	31.71 227	33.25 204	27.87 222	10.92 215	22.18 225	24.05 227	15.89 219	49.03 225	0.01 237	0.00 241	0.06 239

PMMVS2		15.84 231	19.68 233	11.35 234	15.74 239	16.95 238	13.31 238	17.64 235	16.08 235	0.36 242	13.12 232	11.47 238	1.69 238	28.82 233	27.24 233	19.38 237	24.09 234

E-PMN		15.09 232	13.19 235	17.30 231	27.80 232	12.62 240	7.81 240	27.54 218	14.62 238	3.19 236	6.89 236	2.52 244	15.09 222	15.93 235	20.22 234	22.38 235	19.53 235

EMVS		14.49 233	12.45 236	16.87 233	27.02 234	12.56 241	8.13 239	27.19 222	15.05 237	3.14 237	6.69 237	2.67 243	15.08 223	14.60 237	18.05 235	20.67 236	17.56 237

MVE	12.28 19	13.53 234	15.72 234	10.96 235	7.39 240	15.71 239	6.05 241	23.73 227	10.29 240	3.01 239	5.77 238	3.41 242	11.91 231	20.11 234	29.79 232	13.67 238	24.98 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 235	0.02 237	0.00 237	0.00 243	0.00 244	0.01 245	0.00 240	0.01 241	0.00 244	0.03 241	0.00 245	0.01 240	0.01 239	0.01 237	0.00 241	0.06 239

test123		0.01 235	0.02 237	0.00 237	0.00 243	0.00 244	0.00 246	0.00 240	0.01 241	0.00 244	0.04 240	0.00 245	0.01 240	0.00 240	0.01 237	0.00 241	0.07 238

sosnet-low-res		0.00 237	0.00 239	0.00 237	0.00 243	0.00 244	0.00 246	0.00 240	0.00 243	0.00 244	0.00 242	0.00 245	0.00 242	0.00 240	0.00 240	0.00 241	0.00 241

sosnet		0.00 237	0.00 239	0.00 237	0.00 243	0.00 244	0.00 246	0.00 240	0.00 243	0.00 244	0.00 242	0.00 245	0.00 242	0.00 240	0.00 240	0.00 241	0.00 241

Anonymous202405211			60.60 83		63.44 67	66.71 80	61.00 115	47.23 63	50.62 86		36.85 194	60.63 75	43.03 137	69.17 69	67.72 74	75.41 105	72.54 111

our_test_3					51.15 177	57.31 170	55.12 154

ambc			45.54 208		50.66 181	52.63 189	40.99 214		38.36 202	24.67 181	22.62 224	13.94 236	29.14 193	65.71 145	58.06 185	58.60 205	67.43 139

MTAPA										65.14 1		80.20 14

MTMP										62.63 11		78.04 21

Patchmatch-RL test							1.04 244

tmp_tt				5.40 236	3.97 241	2.35 243	3.26 243	0.44 238	17.56 234	12.09 212	11.48 234	7.14 239	1.98 237	15.68 236	15.49 236	10.69 239

XVS					70.49 32	76.96 21	74.36 41			54.48 44		74.47 32				82.24 19

X-MVStestdata					70.49 32	76.96 21	74.36 41			54.48 44		74.47 32				82.24 19

abl_6				64.36 41	70.08 35	77.45 17	72.88 46	50.15 51	71.31 38	54.77 43	62.79 42	77.99 22	56.80 46			81.50 34	83.91 35

mPP-MVS					71.67 28							74.36 35

NP-MVS									72.00 35

Patchmtry						47.61 203	48.27 181	38.86 171		39.59 117

DeepMVS_CX						6.95 242	5.98 242	2.25 237	11.73 239	2.07 241	11.85 233	5.43 240	11.75 232	11.40 238		8.10 240	18.38 236

High-resolution multi-view benchmark