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		77.34 3	82.65 3	71.13 3	75.33 10	80.39 3	82.14 3	58.49 3	84.51 4	63.89 5	78.09 11	83.76 2	63.31 7	81.19 1	80.62 1	83.60 4	90.03 3

HSP-MVS		76.78 4	82.44 4	70.19 8	75.26 11	80.22 4	80.59 8	57.85 6	84.79 3	60.84 16	88.54 1	83.43 3	66.24 1	78.21 15	76.47 21	80.34 38	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 5	79.02 9	80.48 13	62.24 14	80.05 4	79.12 4	82.94 10	88.64 5

DeepPCF-MVS	66.49 1	74.25 16	80.97 6	66.41 27	67.75 46	78.87 9	75.61 34	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 41	88.40 7

APD-MVS		75.80 7	80.90 7	69.86 12	75.42 9	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
HPM-MVS++		76.01 6	80.47 8	70.81 4	76.60 4	74.96 31	80.18 12	58.36 4	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

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

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.
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 4	70.49 21	82.02 8	60.35 27	77.48 22	77.22 16	84.38 1	87.97 11

TSAR-MVS + ACMM		72.56 24	79.07 13	64.96 36	73.24 20	73.16 44	78.50 22	48.80 59	79.34 14	55.32 36	85.04 6	81.49 9	58.57 33	75.06 39	73.75 40	75.35 103	85.61 25

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.
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 3	74.54 16	77.75 23	62.96 9	78.70 9	77.82 10	83.02 8	86.91 16

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

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 44	79.31 7	78.16 24	59.28 1	78.24 18	61.13 14	67.36 32	76.10 27	63.40 6	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
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

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

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

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 34	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 42	58.55 34	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 43	53.07 51	70.98 19	74.83 31	56.95 44	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 37	74.47 32	54.19 56	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 44	57.26 40	76.02 31	75.01 31	81.78 28	86.21 19

PHI-MVS		69.27 34	74.84 30	62.76 46	66.83 48	74.83 32	73.88 42	49.32 55	70.61 40	50.93 55	69.62 24	74.84 30	57.25 41	75.53 35	74.32 36	78.35 55	84.17 33

MVS_0304		69.49 32	73.96 31	64.28 41	67.92 44	76.13 27	74.90 37	47.60 61	63.29 53	54.09 48	67.44 31	76.35 26	59.53 30	75.81 33	75.03 29	81.62 32	83.70 37

TSAR-MVS + GP.		69.71 30	73.92 32	64.80 38	68.27 42	70.56 52	71.90 46	50.75 46	71.38 37	57.46 31	68.68 27	75.42 28	60.10 28	73.47 44	73.99 38	80.32 39	83.97 34

CANet		68.77 36	73.01 33	63.83 42	68.30 41	75.19 30	73.73 43	47.90 60	63.86 50	54.84 41	67.51 30	74.36 35	57.62 37	74.22 42	73.57 43	80.56 37	82.36 41

CPTT-MVS		68.76 37	73.01 33	63.81 43	65.42 55	73.66 42	76.39 32	52.08 38	72.61 34	50.33 57	60.73 50	72.65 40	59.43 31	73.32 45	72.12 45	79.19 49	85.99 21

3Dnovator+	62.63 4	69.51 31	72.62 35	65.88 32	68.21 43	76.47 25	73.50 44	52.74 37	70.85 39	58.65 25	55.97 64	69.95 45	61.11 21	76.80 27	75.09 28	81.09 36	83.23 40

canonicalmvs		65.62 48	72.06 36	58.11 57	63.94 65	71.05 50	64.49 98	43.18 117	74.08 29	47.35 64	64.17 38	71.97 41	51.17 97	71.87 50	70.74 49	78.51 53	80.56 48

LGP-MVS_train		68.87 35	72.03 37	65.18 35	69.33 38	74.03 40	76.67 29	53.88 30	68.46 44	52.05 54	63.21 39	63.89 59	56.31 46	75.99 32	74.43 35	82.83 12	84.18 32

CLD-MVS		67.02 45	71.57 38	61.71 47	71.01 29	74.81 33	71.62 47	38.91 167	71.86 36	60.70 17	64.97 35	67.88 53	51.88 94	76.77 28	74.98 32	76.11 95	69.75 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
ACMP	61.42 5	68.72 38	71.37 39	65.64 33	69.06 39	74.45 37	75.88 33	53.30 32	68.10 45	55.74 33	61.53 49	62.29 64	56.97 43	74.70 40	74.23 37	82.88 11	84.31 31
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
OMC-MVS		65.16 50	71.35 40	57.94 61	52.95 160	68.82 57	69.00 51	38.28 174	79.89 12	55.20 37	62.76 42	68.31 50	56.14 49	71.30 54	68.70 64	76.06 97	79.67 51

OPM-MVS		69.33 33	71.05 41	67.32 23	72.34 24	75.70 28	79.57 15	56.34 16	55.21 64	53.81 49	59.51 53	68.96 47	59.67 29	77.61 21	76.44 22	82.19 22	83.88 36

PCF-MVS	59.98 8	67.32 44	71.04 42	62.97 45	64.77 57	74.49 35	74.78 38	49.54 53	67.44 46	54.39 47	58.35 57	72.81 39	55.79 52	71.54 52	69.24 59	78.57 51	83.41 38
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		68.04 40	70.74 43	64.90 37	71.68 27	76.33 26	74.63 39	50.48 50	63.81 51	55.52 35	54.88 69	69.90 46	57.39 39	75.42 37	74.79 33	79.71 42	80.03 50
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
MSLP-MVS++		68.17 39	70.72 44	65.19 34	69.41 37	70.64 51	74.99 36	45.76 67	70.20 42	60.17 19	56.42 62	73.01 38	61.14 20	72.80 47	70.54 51	79.70 43	81.42 46

MVS_111021_HR		67.62 42	70.39 45	64.39 39	69.77 36	70.45 53	71.44 49	51.72 42	60.77 58	55.06 38	62.14 46	66.40 56	58.13 36	76.13 30	74.79 33	80.19 40	82.04 44

3Dnovator	60.86 6	66.99 46	70.32 46	63.11 44	66.63 49	74.52 34	71.56 48	45.76 67	67.37 47	55.00 39	54.31 73	68.19 51	58.49 35	73.97 43	73.63 42	81.22 35	80.23 49

DELS-MVS		65.87 47	70.30 47	60.71 48	64.05 64	72.68 46	70.90 50	45.43 71	57.49 61	49.05 61	64.43 36	68.66 48	55.11 54	74.31 41	73.02 44	79.70 43	81.51 45
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
TSAR-MVS + COLMAP		62.65 57	69.90 48	54.19 100	46.31 193	66.73 76	65.49 91	41.36 150	76.57 21	46.31 70	76.80 13	56.68 84	53.27 69	69.50 66	66.65 85	72.40 142	76.36 77

EPNet		65.14 51	69.54 49	60.00 51	66.61 50	67.67 66	67.53 55	55.32 21	62.67 55	46.22 71	67.74 29	65.93 57	48.07 110	72.17 49	72.12 45	76.28 87	78.47 57
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
QAPM		65.27 49	69.49 50	60.35 49	65.43 54	72.20 48	65.69 89	47.23 62	63.46 52	49.14 60	53.56 74	71.04 43	57.01 42	72.60 48	71.41 48	77.62 59	82.14 43

AdaColmap		67.89 41	68.85 51	66.77 25	73.73 17	74.30 38	75.28 35	53.58 31	70.24 41	57.59 30	51.19 83	59.19 76	60.74 24	75.33 38	73.72 41	79.69 45	77.96 60

ACMM	60.30 7	67.58 43	68.82 52	66.13 29	70.59 31	72.01 49	76.54 30	54.26 27	65.64 49	54.78 42	50.35 85	61.72 67	58.74 32	75.79 34	75.03 29	81.88 26	81.17 47
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PVSNet_Blended_VisFu		63.65 52	66.92 53	59.83 52	60.03 75	73.44 43	66.33 80	48.95 57	52.20 78	50.81 56	56.07 63	60.25 72	53.56 61	73.23 46	70.01 56	79.30 47	83.24 39

TAPA-MVS	54.74 10	60.85 62	66.61 54	54.12 101	47.38 189	65.33 91	65.35 92	36.51 185	75.16 26	48.82 62	54.70 70	63.51 60	53.31 68	68.36 75	64.97 127	73.37 121	74.27 105
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_111021_LR		63.05 54	66.43 55	59.10 54	61.33 69	63.77 102	65.87 87	43.58 106	60.20 59	53.70 50	62.09 47	62.38 63	55.84 51	70.24 62	68.08 68	74.30 108	78.28 59

CNLPA		62.78 56	66.31 56	58.65 55	58.47 83	68.41 60	65.98 86	41.22 152	78.02 19	56.04 32	46.65 122	59.50 75	57.50 38	69.67 65	65.27 122	72.70 136	76.67 69

MVS_Test		62.40 58	66.23 57	57.94 61	59.77 78	64.77 98	66.50 79	41.76 142	57.26 62	49.33 59	62.68 43	67.47 55	53.50 64	68.57 74	66.25 92	76.77 72	76.58 72

Effi-MVS+		63.28 53	65.96 58	60.17 50	64.26 61	68.06 61	68.78 52	45.71 69	54.08 68	46.64 67	55.92 65	63.13 62	55.94 50	70.38 61	71.43 47	79.68 46	78.70 55

OpenMVS	57.13 9	62.81 55	65.75 59	59.39 53	66.47 51	69.52 55	64.26 100	43.07 122	61.34 57	50.19 58	47.29 119	64.41 58	54.60 55	70.18 63	68.62 66	77.73 57	78.89 54

Vis-MVSNet		58.48 87	65.70 60	50.06 124	53.40 158	67.20 71	60.24 118	43.32 114	48.83 96	30.23 150	62.38 45	61.61 68	40.35 143	71.03 57	69.77 57	72.82 129	79.11 53
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
EPP-MVSNet		59.39 67	65.45 61	52.32 114	60.96 71	67.70 65	58.42 122	44.75 78	49.71 85	27.23 169	59.03 54	62.20 65	43.34 132	70.71 58	69.13 60	79.25 48	79.63 52

UGNet		57.03 105	65.25 62	47.44 156	46.54 192	66.73 76	56.30 135	43.28 115	50.06 83	32.99 137	62.57 44	63.26 61	33.31 177	68.25 77	67.58 73	72.20 146	78.29 58
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
DI_MVS_plusplus_trai		61.88 59	65.17 63	58.06 58	60.05 74	65.26 93	66.03 84	44.22 84	55.75 63	46.73 66	54.64 71	68.12 52	54.13 58	69.13 67	66.66 84	77.18 65	76.61 70

PVSNet_BlendedMVS		61.63 60	64.82 64	57.91 63	57.21 125	67.55 67	63.47 104	46.08 65	54.72 65	52.46 52	58.59 55	60.73 69	51.82 95	70.46 59	65.20 124	76.44 84	76.50 75

PVSNet_Blended		61.63 60	64.82 64	57.91 63	57.21 125	67.55 67	63.47 104	46.08 65	54.72 65	52.46 52	58.59 55	60.73 69	51.82 95	70.46 59	65.20 124	76.44 84	76.50 75

CANet_DTU		58.88 73	64.68 66	52.12 115	55.77 133	66.75 75	63.92 101	37.04 181	53.32 71	37.45 124	59.81 52	61.81 66	44.43 126	68.25 77	67.47 75	74.12 111	75.33 84

UA-Net		58.50 86	64.68 66	51.30 117	66.97 47	67.13 73	53.68 158	45.65 70	49.51 88	31.58 144	62.91 40	68.47 49	35.85 167	68.20 80	67.28 76	74.03 112	69.24 130

IS_MVSNet		57.95 101	64.26 68	50.60 119	61.62 68	65.25 94	57.18 128	45.42 72	50.79 81	26.49 171	57.81 59	60.05 73	34.51 171	71.24 56	70.20 55	78.36 54	74.44 99

diffmvs		59.53 66	64.04 69	54.26 99	55.09 140	59.86 142	64.80 95	39.55 166	58.39 60	46.21 72	60.48 51	67.82 54	49.27 102	63.53 151	63.32 147	70.64 158	74.89 87

Fast-Effi-MVS+		60.36 63	63.35 70	56.87 83	58.70 80	65.86 89	65.08 93	37.11 180	53.00 75	45.36 80	52.12 79	56.07 89	56.27 47	71.28 55	69.42 58	78.71 50	75.69 81

FC-MVSNet-train		58.40 94	63.15 71	52.85 110	64.29 60	61.84 119	55.98 139	46.47 63	53.06 73	34.96 133	61.95 48	56.37 87	39.49 145	68.67 71	68.36 67	75.92 100	71.81 109

PLC	52.09 14	59.21 69	62.47 72	55.41 96	53.24 159	64.84 97	64.47 99	40.41 160	65.92 48	44.53 93	46.19 130	55.69 90	55.33 53	68.24 79	65.30 121	74.50 106	71.09 112
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Effi-MVS+-dtu		60.34 64	62.32 73	58.03 60	64.31 59	67.44 69	65.99 85	42.26 139	49.55 86	42.00 106	48.92 97	59.79 74	56.27 47	68.07 86	67.03 77	77.35 64	75.45 83

LS3D		60.20 65	61.70 74	58.45 56	64.18 62	67.77 63	67.19 57	48.84 58	61.67 56	41.27 109	45.89 133	51.81 115	54.18 57	68.78 69	66.50 90	75.03 104	69.48 126

IterMVS-LS		58.30 96	61.39 75	54.71 98	59.92 77	58.40 159	59.42 119	43.64 103	48.71 99	40.25 113	57.53 60	58.55 78	52.15 92	65.42 144	65.34 120	72.85 127	75.77 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		56.94 108	61.14 76	52.05 116	60.02 76	65.21 95	57.44 126	52.93 35	49.37 89	24.31 180	54.62 72	50.54 122	39.04 147	68.69 70	68.84 63	78.53 52	70.72 113

MVSTER		57.19 104	61.11 77	52.62 112	50.82 177	58.79 155	61.55 108	37.86 177	48.81 97	41.31 108	57.43 61	52.10 106	48.60 106	68.19 82	66.75 82	75.56 101	75.68 82

IB-MVS	54.11 11	58.36 95	60.70 78	55.62 94	58.67 81	68.02 62	61.56 107	43.15 118	46.09 124	44.06 95	44.24 145	50.99 120	48.71 105	66.70 121	70.33 52	77.60 60	78.50 56
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
v7		59.19 70	60.62 79	57.53 66	57.96 86	67.19 72	67.09 60	44.28 83	46.84 115	45.45 78	48.19 111	51.06 117	53.62 60	67.84 92	66.59 87	76.79 69	76.60 71

v10		59.17 71	60.60 80	57.50 67	57.95 87	66.73 76	67.09 60	44.11 85	46.85 114	45.42 79	48.18 113	51.07 116	53.63 59	67.84 92	66.59 87	76.79 69	76.92 67

v8		58.88 73	60.57 81	56.92 77	57.35 110	65.69 90	66.69 75	42.64 132	47.89 108	45.77 75	49.04 91	52.98 99	52.77 77	67.51 105	65.57 117	76.26 88	75.30 85

v1neww		58.88 73	60.54 82	56.94 73	57.33 114	66.13 85	66.70 73	42.84 124	47.84 110	45.74 76	49.02 93	52.93 101	52.78 75	67.53 102	65.59 113	76.26 88	74.73 89

v7new		58.88 73	60.54 82	56.94 73	57.33 114	66.13 85	66.70 73	42.84 124	47.84 110	45.74 76	49.02 93	52.93 101	52.78 75	67.53 102	65.59 113	76.26 88	74.73 89

v6		58.89 72	60.54 82	56.96 72	57.34 112	66.13 85	66.71 71	42.84 124	47.85 109	45.80 74	49.04 91	52.95 100	52.79 74	67.53 102	65.59 113	76.26 88	74.73 89

UniMVSNet (Re)		55.15 122	60.39 85	49.03 134	55.31 135	64.59 99	55.77 140	50.63 47	48.66 102	20.95 188	51.47 82	50.40 123	34.41 173	67.81 95	67.89 70	77.11 68	71.88 108

ACMH+	53.71 12	59.26 68	60.28 86	58.06 58	64.17 63	68.46 59	67.51 56	50.93 45	52.46 77	35.83 130	40.83 180	45.12 158	52.32 90	69.88 64	69.00 62	77.59 61	76.21 78

GBi-Net		55.20 120	60.25 87	49.31 128	52.42 162	61.44 123	57.03 129	44.04 88	49.18 91	30.47 146	48.28 108	58.19 79	38.22 150	68.05 87	66.96 78	73.69 116	69.65 121

test1		55.20 120	60.25 87	49.31 128	52.42 162	61.44 123	57.03 129	44.04 88	49.18 91	30.47 146	48.28 108	58.19 79	38.22 150	68.05 87	66.96 78	73.69 116	69.65 121

MS-PatchMatch		58.19 99	60.20 89	55.85 93	65.17 56	64.16 100	64.82 94	41.48 149	50.95 80	42.17 105	45.38 138	56.42 85	48.08 109	68.30 76	66.70 83	73.39 120	69.46 128

v18		58.68 81	60.20 89	56.90 80	57.26 123	63.28 111	66.58 78	42.42 137	48.86 95	46.37 68	49.01 95	53.05 97	52.74 78	67.40 110	65.52 118	76.02 99	74.28 104

v16		58.71 78	60.20 89	56.97 71	57.35 110	63.36 110	66.67 76	42.49 134	48.69 101	46.36 69	48.87 99	52.92 103	52.82 73	67.57 100	65.58 116	76.15 94	74.38 101

v17		58.69 79	60.19 92	56.94 73	57.38 105	63.37 109	66.67 76	42.47 136	48.52 105	46.10 73	48.90 98	53.00 98	52.84 71	67.58 99	65.60 112	76.19 92	74.38 101

v1144		58.88 73	60.16 93	57.39 68	58.03 85	67.26 70	67.14 59	44.46 81	45.17 137	44.33 94	47.81 116	49.92 126	53.20 70	67.77 97	66.62 86	77.15 66	76.58 72

V42		56.97 107	60.14 94	53.28 105	48.16 184	62.78 116	66.30 81	37.93 176	47.44 112	42.68 102	48.19 111	52.59 105	51.90 93	67.46 106	65.94 97	72.72 130	76.55 74

TranMVSNet+NR-MVSNet		55.87 113	60.14 94	50.88 118	59.46 79	63.82 101	57.93 124	52.98 34	48.94 94	20.52 190	52.87 76	47.33 138	36.81 164	69.12 68	69.03 61	77.56 62	69.89 119

v2v482		58.69 79	60.12 96	57.03 70	57.16 127	66.05 88	67.17 58	43.52 108	46.33 120	45.19 81	49.46 88	51.02 118	52.51 85	67.30 112	66.03 95	76.61 80	74.62 96

v1		58.56 82	60.06 97	56.83 85	57.36 107	66.19 82	66.80 68	43.10 121	45.87 131	44.68 88	48.73 100	51.83 113	52.38 87	67.45 108	65.65 107	76.63 77	74.66 92

v1141		58.56 82	60.05 98	56.81 86	57.36 107	66.18 83	66.80 68	43.11 119	45.87 131	44.60 90	48.71 101	51.83 113	52.38 87	67.46 106	65.64 110	76.63 77	74.66 92

divwei89l23v2f112		58.56 82	60.05 98	56.81 86	57.36 107	66.18 83	66.80 68	43.11 119	45.89 130	44.60 90	48.71 101	51.84 112	52.38 87	67.45 108	65.65 107	76.63 77	74.66 92

FMVSNet2		55.04 123	59.95 100	49.31 128	52.42 162	61.44 123	57.03 129	44.08 87	49.55 86	30.40 149	46.89 120	58.84 77	38.22 150	67.07 116	66.21 93	73.69 116	69.65 121

v15		58.43 93	59.75 101	56.88 82	57.45 101	63.44 107	66.84 66	42.65 131	46.24 122	45.07 82	48.68 103	52.07 107	52.63 83	67.84 92	65.70 106	76.65 76	74.31 103

V14		58.44 90	59.75 101	56.90 80	57.48 100	63.46 106	66.85 65	42.68 130	46.16 123	45.03 84	48.57 104	52.04 108	52.65 82	67.93 91	65.72 105	76.69 75	74.40 100

V9		58.45 89	59.75 101	56.92 77	57.51 98	63.49 105	66.86 63	42.73 129	46.07 126	45.05 83	48.45 106	51.99 109	52.66 81	68.04 90	65.75 102	76.72 74	74.50 98

v12		58.44 90	59.74 104	56.92 77	57.54 94	63.50 104	66.84 66	42.77 128	45.96 127	44.95 86	48.31 107	51.94 110	52.67 80	68.14 83	65.75 102	76.75 73	74.55 97

v13		58.44 90	59.72 105	56.94 73	57.55 92	63.51 103	66.86 63	42.81 127	45.90 129	44.98 85	48.17 114	51.87 111	52.68 79	68.20 80	65.78 100	76.78 71	74.63 95

v1192		58.51 85	59.66 106	57.17 69	57.82 88	67.72 64	66.21 83	44.83 77	44.15 144	43.49 97	46.68 121	47.94 130	53.55 62	67.39 111	66.51 89	77.13 67	77.20 65

DU-MVS		55.41 118	59.59 107	50.54 121	54.60 147	62.97 113	57.44 126	51.80 40	48.62 103	24.31 180	51.99 80	47.00 143	39.04 147	68.11 84	67.75 72	76.03 98	70.72 113

FMVSNet3		54.78 124	59.58 108	49.17 131	52.37 165	61.31 127	56.72 133	44.04 88	49.18 91	30.47 146	48.28 108	58.19 79	38.09 153	65.48 142	65.20 124	73.31 122	69.45 129

ACMH	52.42 13	58.24 97	59.56 109	56.70 88	66.34 52	69.59 54	66.71 71	49.12 56	46.08 125	28.90 157	42.67 168	41.20 187	52.60 84	71.39 53	70.28 53	76.51 82	75.72 80
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v11		58.19 99	59.47 110	56.70 88	57.54 94	63.42 108	66.28 82	42.49 134	45.62 135	44.59 92	48.16 115	50.78 121	52.84 71	67.80 96	65.76 101	76.49 83	74.76 88

NR-MVSNet		55.35 119	59.46 111	50.56 120	61.33 69	62.97 113	57.91 125	51.80 40	48.62 103	20.59 189	51.99 80	44.73 166	34.10 174	68.58 73	68.64 65	77.66 58	70.67 117

v144192		58.23 98	59.40 112	56.87 83	57.56 91	66.89 74	65.70 88	45.01 76	44.06 145	42.88 99	46.61 123	48.09 129	53.49 65	66.94 117	65.90 98	76.61 80	77.29 63

Fast-Effi-MVS+-dtu		56.30 112	59.29 113	52.82 111	58.64 82	64.89 96	65.56 90	32.89 205	45.80 133	35.04 132	45.89 133	54.14 93	49.41 101	67.16 114	66.45 91	75.37 102	70.69 115

CostFormer		56.57 111	59.13 114	53.60 102	57.52 97	61.12 129	66.94 62	35.95 187	53.44 69	44.68 88	55.87 66	54.44 92	48.21 108	60.37 172	58.33 182	68.27 174	70.33 118

v1921920		57.89 102	59.02 115	56.58 90	57.55 92	66.66 79	64.72 97	44.70 79	43.55 148	42.73 101	46.17 131	46.93 144	53.51 63	66.78 120	65.75 102	76.29 86	77.28 64

MSDG		58.46 88	58.97 116	57.85 65	66.27 53	66.23 81	67.72 53	42.33 138	53.43 70	43.68 96	43.39 154	45.35 155	49.75 100	68.66 72	67.77 71	77.38 63	67.96 133

FMVSNet1		54.08 126	58.68 117	48.71 142	50.90 176	61.35 126	56.73 132	43.94 92	45.91 128	29.32 156	42.72 167	56.26 88	37.70 154	68.05 87	66.96 78	73.69 116	69.50 125

v1240		57.55 103	58.63 118	56.29 92	57.30 120	66.48 80	63.77 102	44.56 80	42.77 164	42.48 103	45.64 136	46.28 150	53.46 66	66.32 128	65.80 99	76.16 93	77.13 66

HyFIR lowres test		56.87 109	58.60 119	54.84 97	56.62 130	69.27 56	64.77 96	42.21 140	45.66 134	37.50 123	33.08 199	57.47 83	53.33 67	65.46 143	67.94 69	74.60 105	71.35 111

GA-MVS		55.67 115	58.33 120	52.58 113	55.23 138	63.09 112	61.08 111	40.15 162	42.95 155	37.02 126	52.61 77	47.68 133	47.51 112	65.92 136	65.35 119	74.49 107	70.68 116

EPNet_dtu		52.05 141	58.26 121	44.81 169	54.10 154	50.09 195	52.01 166	40.82 157	53.03 74	27.41 167	54.90 68	57.96 82	26.72 197	62.97 153	62.70 156	67.78 176	66.19 150
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		56.98 106	58.24 122	55.50 95	64.66 58	68.62 58	61.48 109	43.63 105	38.44 198	41.44 107	38.05 190	46.18 152	43.95 127	71.71 51	70.61 50	77.87 56	74.08 106

CHOSEN 1792x2688		55.85 114	58.01 123	53.33 104	57.26 123	62.82 115	63.29 106	41.55 148	46.65 117	38.34 118	34.55 197	53.50 94	52.43 86	67.10 115	67.56 74	67.13 179	73.92 107

Baseline_NR-MVSNet		53.50 130	57.89 124	48.37 145	54.60 147	59.25 151	56.10 136	51.84 39	49.32 90	17.92 200	45.38 138	47.68 133	36.93 163	68.11 84	65.95 96	72.84 128	69.57 124

anonymousdsp		52.84 134	57.78 125	47.06 157	40.24 212	58.95 154	53.70 157	33.54 200	36.51 205	32.69 139	43.88 147	45.40 154	47.97 111	67.17 113	70.28 53	74.22 109	82.29 42

Vis-MVSNet (Re-imp)		50.37 157	57.73 126	41.80 188	57.53 96	54.35 179	45.70 195	45.24 73	49.80 84	13.43 208	58.23 58	56.42 85	20.11 209	62.96 154	63.36 146	68.76 171	58.96 191

v148		55.58 117	57.61 127	53.20 107	54.59 149	61.86 118	61.18 110	38.70 172	44.30 143	42.25 104	47.53 117	50.24 125	48.73 104	65.15 145	62.61 157	73.79 114	71.61 110

v7n		55.67 115	57.46 128	53.59 103	56.06 131	65.29 92	61.06 112	43.26 116	40.17 184	37.99 120	40.79 181	45.27 157	47.09 114	67.67 98	66.21 93	76.08 96	76.82 68

tpmp4_e23		56.84 110	57.14 129	56.49 91	62.45 66	62.05 117	67.57 54	41.56 147	54.17 67	48.57 63	49.18 89	46.54 147	50.44 99	61.93 164	58.82 179	68.34 172	67.28 138

IterMVS		53.45 131	57.12 130	49.17 131	49.23 181	60.93 130	59.05 121	34.63 191	44.53 139	33.22 136	51.09 84	51.01 119	48.38 107	62.43 157	60.79 168	70.54 160	69.05 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		52.42 138	57.06 131	47.02 158	53.92 156	58.30 161	55.50 142	46.47 63	42.52 167	29.38 155	49.50 87	52.85 104	28.49 192	66.70 121	66.89 81	68.34 172	62.63 177
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v52		53.60 128	56.74 132	49.93 125	45.54 196	61.64 121	60.65 113	36.99 182	38.75 194	36.32 128	39.64 185	47.13 140	47.05 115	66.89 118	65.65 107	73.04 125	77.48 61

V4		53.60 128	56.73 133	49.93 125	45.54 196	61.64 121	60.65 113	36.99 182	38.74 196	36.33 127	39.64 185	47.12 141	47.05 115	66.89 118	65.64 110	73.04 125	77.48 61

pmmvs4		54.66 125	56.07 134	53.00 109	54.63 146	57.08 168	60.43 117	44.10 86	51.69 79	40.55 111	46.55 126	44.79 165	45.95 121	62.54 156	63.66 142	72.36 144	66.20 149

pm-mvs1		51.02 152	55.55 135	45.73 164	54.16 153	58.52 157	50.92 168	42.56 133	40.32 183	25.67 174	43.66 151	50.34 124	30.06 186	65.85 137	63.97 140	70.99 156	66.21 148

conf200view11		52.51 136	55.51 136	49.01 135	57.31 116	60.24 132	55.42 144	43.77 94	42.85 158	27.51 163	43.00 163	45.06 160	37.32 156	66.38 123	64.54 129	72.71 133	66.54 142

tfpn200view9		52.53 135	55.51 136	49.06 133	57.31 116	60.24 132	55.42 144	43.77 94	42.85 158	27.81 161	43.00 163	45.06 160	37.32 156	66.38 123	64.54 129	72.71 133	66.54 142

thres400		52.38 140	55.51 136	48.74 141	57.49 99	60.10 139	55.45 143	43.54 107	42.90 157	26.72 170	43.34 156	45.03 164	36.61 165	66.20 133	64.53 133	72.66 137	66.43 145

tfpn111		52.44 137	55.38 139	49.01 135	57.31 116	60.24 132	55.42 144	43.77 94	42.85 158	27.51 163	42.03 174	45.06 160	37.32 156	66.38 123	64.54 129	72.71 133	66.54 142

TransMVSNet (Re)		51.92 146	55.38 139	47.88 152	60.95 72	59.90 141	53.95 155	45.14 74	39.47 189	24.85 177	43.87 148	46.51 148	29.15 188	67.55 101	65.23 123	73.26 124	65.16 163

thres200		52.39 139	55.37 141	48.90 139	57.39 104	60.18 135	55.60 141	43.73 100	42.93 156	27.41 167	43.35 155	45.09 159	36.61 165	66.36 126	63.92 141	72.66 137	65.78 155

v748		52.93 133	55.29 142	50.19 123	51.90 169	61.31 127	56.54 134	40.05 164	39.12 192	34.82 135	39.93 184	43.83 174	43.66 128	64.26 149	63.32 147	74.15 110	75.28 86

thres600view7		51.91 147	55.14 143	48.14 148	57.43 102	60.18 135	54.60 153	43.73 100	42.61 165	25.20 175	43.10 162	44.47 170	35.19 169	66.36 126	63.28 149	72.66 137	66.01 152

view600		51.96 145	55.13 144	48.27 147	57.41 103	60.05 140	54.74 152	43.64 103	42.57 166	25.88 173	43.11 161	44.48 169	35.34 168	66.27 129	63.61 143	72.61 140	65.80 154

WR-MVS		48.78 169	55.06 145	41.45 190	55.50 134	60.40 131	43.77 204	49.99 52	41.92 170	8.10 224	45.24 141	45.56 153	17.47 212	61.57 166	64.60 128	73.85 113	66.14 151

view800		51.55 149	54.89 146	47.66 155	57.37 106	59.77 144	53.62 159	43.72 102	42.22 168	24.94 176	42.80 166	43.81 175	33.94 175	66.09 134	64.38 135	72.39 143	65.14 164

COLMAP_ROB	46.52 15	51.99 144	54.86 147	48.63 143	49.13 182	61.73 120	60.53 116	36.57 184	53.14 72	32.95 138	37.10 191	38.68 200	40.49 142	65.72 139	63.08 150	72.11 147	64.60 167
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
thres100view900		52.04 142	54.81 148	48.80 140	57.31 116	59.33 147	55.30 149	42.92 123	42.85 158	27.81 161	43.00 163	45.06 160	36.99 162	64.74 147	63.51 144	72.47 141	65.21 162

conf0.01		52.02 143	54.62 149	49.00 137	57.30 120	60.17 137	55.42 144	43.76 97	42.85 158	27.49 165	43.12 160	39.71 196	37.32 156	66.26 131	64.54 129	72.72 130	65.66 157

RPSCF		46.41 189	54.42 150	37.06 206	25.70 235	45.14 212	45.39 197	20.81 228	62.79 54	35.10 131	44.92 142	55.60 91	43.56 130	56.12 198	52.45 207	51.80 218	63.91 171

PEN-MVS		49.21 165	54.32 151	43.24 180	54.33 152	59.26 150	47.04 186	51.37 44	41.67 173	9.97 219	46.22 129	41.80 182	22.97 206	60.52 170	64.03 138	73.73 115	66.75 141

DWT-MVSNet_training		53.80 127	54.31 152	53.21 106	57.65 89	59.04 153	60.65 113	40.11 163	46.35 119	42.77 100	49.07 90	41.07 188	51.06 98	58.62 183	58.96 178	67.00 182	67.06 139

PMMVS		49.20 166	54.28 153	43.28 179	34.13 221	45.70 211	48.98 174	26.09 223	46.31 121	34.92 134	55.22 67	53.47 95	47.48 113	59.43 175	59.04 177	68.05 175	60.77 183

conf0.002		51.76 148	54.13 154	49.00 137	57.28 122	60.15 138	55.42 144	43.75 99	42.85 158	27.49 165	43.13 159	37.12 208	37.32 156	66.23 132	64.17 136	72.72 130	65.24 161

Anonymous20240521		47.88 178	53.90 155	40.86 193	57.64 90	58.16 162	41.40 211	44.44 82	40.12 185	12.39 209	43.81 149	46.47 149	18.52 211	59.84 173	61.88 159	70.74 157	67.01 140

conf0.05thres1000		50.64 153	53.84 156	46.92 160	57.02 128	59.29 149	52.29 165	43.80 93	39.84 188	23.81 183	39.26 187	43.14 178	32.52 181	65.74 138	64.04 137	72.05 148	65.53 158

USDC		51.11 151	53.71 157	48.08 150	44.76 199	55.99 171	53.01 163	40.90 154	52.49 76	36.14 129	44.67 143	33.66 212	43.27 133	63.23 152	61.10 164	70.39 161	64.82 165

WR-MVS_H		47.65 179	53.67 158	40.63 194	51.45 171	59.74 145	44.71 201	49.37 54	40.69 181	7.61 226	46.04 132	44.34 172	17.32 213	57.79 187	61.18 162	73.30 123	65.86 153

tfpn		50.58 154	53.65 159	47.00 159	57.34 112	59.31 148	52.41 164	43.76 97	41.81 172	23.86 182	42.49 169	37.80 203	32.63 180	65.68 141	64.02 139	71.99 149	64.41 169

CP-MVSNet		48.37 170	53.53 160	42.34 185	51.35 173	58.01 163	46.56 187	50.54 48	41.62 174	10.61 215	46.53 127	40.68 192	23.18 203	58.71 181	61.83 160	71.81 150	67.36 137

tpm cat1		53.30 132	53.41 161	53.17 108	58.16 84	59.15 152	63.73 103	38.27 175	50.73 82	46.98 65	45.57 137	44.00 173	49.20 103	55.90 201	54.02 200	62.65 194	64.50 168

GG-mvs-BLEND		36.62 213	53.39 162	17.06 231	0.01 240	58.61 156	48.63 176	0.01 237	47.13 113	0.02 242	43.98 146	60.64 71	0.03 237	54.92 205	51.47 210	53.64 215	56.99 197

DTE-MVSNet		48.03 177	53.28 163	41.91 187	54.64 145	57.50 166	44.63 202	51.66 43	41.02 179	7.97 225	46.26 128	40.90 189	20.24 208	60.45 171	62.89 153	72.33 145	63.97 170

PS-CasMVS		48.18 173	53.25 164	42.27 186	51.26 174	57.94 164	46.51 188	50.52 49	41.30 177	10.56 217	45.35 140	40.34 194	23.04 205	58.66 182	61.79 161	71.74 152	67.38 136

CMPMVS	37.70 17	49.24 164	52.71 165	45.19 166	45.97 195	51.23 191	47.44 183	29.31 212	43.04 154	44.69 87	34.45 198	48.35 128	43.64 129	62.59 155	59.82 174	60.08 199	69.48 126
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CR-MVSNet		50.47 155	52.61 166	47.98 151	49.03 183	52.94 183	48.27 177	38.86 169	44.41 140	39.59 115	44.34 144	44.65 168	46.63 118	58.97 178	60.31 171	65.48 185	62.66 175

gg-mvs-nofinetune		49.07 167	52.56 167	45.00 168	61.99 67	59.78 143	53.55 161	41.63 143	31.62 215	12.08 211	29.56 208	53.28 96	29.57 187	66.27 129	64.49 134	71.19 155	62.92 174

TDRefinement		49.31 162	52.44 168	45.67 165	30.44 227	59.42 146	59.24 120	39.78 165	48.76 98	31.20 145	35.73 194	29.90 216	42.81 134	64.24 150	62.59 158	70.55 159	66.43 145

MDTV_nov1_ep13		50.32 158	52.43 169	47.86 153	49.87 180	54.70 177	58.10 123	34.29 193	45.59 136	37.71 121	47.44 118	47.42 137	41.86 137	58.07 186	55.21 193	65.34 187	58.56 192

tfpn_ndepth		48.34 172	52.27 170	43.76 174	54.35 151	56.46 169	47.24 185	40.92 153	43.45 150	21.04 187	41.16 179	43.22 177	28.90 191	61.57 166	60.65 169	70.12 162	59.34 189

pmmvs-eth3d		51.33 150	52.25 171	50.26 122	50.82 177	54.65 178	56.03 138	43.45 113	43.51 149	37.20 125	39.20 188	39.04 199	42.28 135	61.85 165	62.78 154	71.78 151	64.72 166

tfpnnormal		50.16 159	52.19 172	47.78 154	56.86 129	58.37 160	54.15 154	44.01 91	38.35 200	25.94 172	36.10 193	37.89 202	34.50 172	65.93 135	63.42 145	71.26 154	65.28 160

CVMVSNet		46.38 191	52.01 173	39.81 196	42.40 207	50.26 193	46.15 192	37.68 178	40.03 186	15.09 205	46.56 125	47.56 135	33.72 176	56.50 195	55.65 189	63.80 191	67.53 134

pmmvs6		48.35 171	51.64 174	44.51 172	51.92 168	57.94 164	49.44 173	42.17 141	34.45 208	24.62 179	28.87 212	46.90 145	29.07 190	64.60 148	63.08 150	69.83 163	65.68 156

tfpnview11		47.58 181	51.57 175	42.92 181	54.94 141	55.30 174	46.21 189	41.58 146	42.10 169	18.54 195	42.25 171	41.54 184	27.12 194	62.29 161	61.12 163	69.15 166	56.40 200

tfpn_n400		47.56 182	51.56 176	42.90 182	54.91 142	55.28 175	46.21 189	41.59 144	41.51 175	18.54 195	42.25 171	41.54 184	27.12 194	62.41 158	61.02 165	69.05 167	56.90 198

tfpnconf		47.56 182	51.56 176	42.90 182	54.91 142	55.28 175	46.21 189	41.59 144	41.51 175	18.54 195	42.25 171	41.54 184	27.12 194	62.41 158	61.02 165	69.05 167	56.90 198

PatchMatch-RL		50.11 160	51.56 176	48.43 144	46.23 194	51.94 188	50.21 170	38.62 173	46.62 118	37.51 122	42.43 170	39.38 197	52.24 91	60.98 168	59.56 175	65.76 184	60.01 188

PatchmatchNet		49.92 161	51.29 179	48.32 146	51.83 170	51.86 189	53.38 162	37.63 179	47.90 107	40.83 110	48.54 105	45.30 156	45.19 124	56.86 190	53.99 202	61.08 198	54.57 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
tpm		48.82 168	51.27 180	45.96 163	54.10 154	47.35 202	56.05 137	30.23 210	46.70 116	43.21 98	52.54 78	47.55 136	37.28 161	54.11 206	50.50 211	54.90 212	60.12 187

tfpn1000		46.75 188	51.24 181	41.51 189	54.39 150	55.60 173	43.85 203	40.90 154	41.82 171	16.71 202	41.26 178	41.58 183	23.96 201	60.76 169	60.27 173	69.26 165	57.42 195

thresconf0.02		48.17 174	51.22 182	44.60 171	55.14 139	55.73 172	48.95 175	41.35 151	43.43 152	21.23 186	42.03 174	37.25 207	31.19 183	62.33 160	60.61 170	69.76 164	57.17 196

dps		50.42 156	51.20 183	49.51 127	55.88 132	56.07 170	53.73 156	38.89 168	43.66 146	40.36 112	45.66 135	37.63 205	45.23 123	59.05 176	56.18 185	62.94 193	60.16 186

PatchT		48.08 175	51.03 184	44.64 170	42.96 206	50.12 194	40.36 213	35.09 189	43.17 153	39.59 115	42.00 176	39.96 195	46.63 118	58.97 178	60.31 171	63.21 192	62.66 175

pmmvs5		47.07 186	51.02 185	42.46 184	45.18 198	51.47 190	48.23 179	33.09 204	38.17 201	28.62 159	46.60 124	43.48 176	30.74 184	58.28 184	58.63 181	68.92 170	60.48 184

test-mter		45.30 193	50.37 186	39.38 198	33.65 223	46.99 205	47.59 181	18.59 231	38.75 194	28.00 160	43.28 157	46.82 146	41.50 139	57.28 189	55.78 188	66.93 183	63.70 172

test-LLR		49.28 163	50.29 187	48.10 149	55.26 136	47.16 203	49.52 171	43.48 111	39.22 190	31.98 140	43.65 152	47.93 131	41.29 140	56.80 191	55.36 191	67.08 180	61.94 178

TESTMET0.1,1		46.09 192	50.29 187	41.18 191	36.91 217	47.16 203	49.52 171	20.32 229	39.22 190	31.98 140	43.65 152	47.93 131	41.29 140	56.80 191	55.36 191	67.08 180	61.94 178

SixPastTwentyTwo		47.55 184	50.25 189	44.41 173	47.30 190	54.31 180	47.81 180	40.36 161	33.76 209	19.93 192	43.75 150	32.77 214	42.07 136	59.82 174	60.94 167	68.98 169	66.37 147

LTVRE_ROB	44.17 16	47.06 187	50.15 190	43.44 177	51.39 172	58.42 158	42.90 206	43.51 109	22.27 230	14.85 206	41.94 177	34.57 210	45.43 122	62.28 162	62.77 155	62.56 195	68.83 132
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
tpmrst		48.08 175	49.88 191	45.98 162	52.71 161	48.11 200	53.62 159	33.70 198	48.70 100	39.74 114	48.96 96	46.23 151	40.29 144	50.14 215	49.28 213	55.80 209	57.71 194

MDTV_nov1_ep13_2view		47.62 180	49.72 192	45.18 167	48.05 185	53.70 181	54.90 151	33.80 197	39.90 187	29.79 153	38.85 189	41.89 181	39.17 146	58.99 177	55.55 190	65.34 187	59.17 190

TAMVS		44.02 197	49.18 193	37.99 204	47.03 191	45.97 210	45.04 198	28.47 215	39.11 193	20.23 191	43.22 158	48.52 127	28.49 192	58.15 185	57.95 184	58.71 201	51.36 208

RPMNet		46.41 189	48.72 194	43.72 175	47.77 187	52.94 183	46.02 194	33.92 195	44.41 140	31.82 143	36.89 192	37.42 206	37.41 155	53.88 207	54.02 200	65.37 186	61.47 180

MIMVSNet		43.79 198	48.53 195	38.27 202	41.46 209	48.97 198	50.81 169	32.88 206	44.55 138	22.07 184	32.05 200	47.15 139	24.76 200	58.73 180	56.09 187	57.63 206	52.14 206

FC-MVSNet-test		39.65 210	48.35 196	29.49 217	44.43 200	39.28 220	30.23 227	40.44 159	43.59 147	3.12 237	53.00 75	42.03 180	10.02 233	55.09 203	54.77 195	48.66 222	50.71 210

PM-MVS		44.55 196	48.13 197	40.37 195	32.85 225	46.82 207	46.11 193	29.28 213	40.48 182	29.99 151	39.98 183	34.39 211	41.80 138	56.08 199	53.88 204	62.19 196	65.31 159

EPMVS		44.66 195	47.86 198	40.92 192	47.97 186	44.70 213	47.58 182	33.27 201	48.11 106	29.58 154	49.65 86	44.38 171	34.65 170	51.71 210	47.90 217	52.49 217	48.57 217

TinyColmap		47.08 185	47.56 199	46.52 161	42.35 208	53.44 182	51.77 167	40.70 158	43.44 151	31.92 142	29.78 207	23.72 227	45.04 125	61.99 163	59.54 176	67.35 178	61.03 182

test0.0.03 1		43.15 199	46.95 200	38.72 201	55.26 136	50.56 192	42.48 207	43.48 111	38.16 202	15.11 204	35.07 196	44.69 167	16.47 215	55.95 200	54.34 199	59.54 200	49.87 215

gm-plane-assit		44.74 194	45.95 201	43.33 178	60.88 73	46.79 208	36.97 217	32.24 209	24.15 226	11.79 212	29.26 211	32.97 213	46.64 117	65.09 146	62.95 152	71.45 153	60.42 185

Anonymous20231206		42.28 200	45.89 202	38.07 203	51.96 167	48.98 197	43.66 205	38.81 171	38.74 196	14.32 207	26.74 214	40.90 189	20.94 207	56.64 194	54.67 197	58.71 201	54.59 202

FMVSNet5		40.96 203	45.81 203	35.29 210	34.30 220	44.55 214	47.28 184	28.84 214	40.76 180	21.62 185	29.85 206	42.44 179	24.77 199	57.53 188	55.00 194	54.93 211	50.56 211

EU-MVSNet		40.63 208	45.65 204	34.78 211	39.11 213	46.94 206	40.02 214	34.03 194	33.50 210	10.37 218	35.57 195	37.80 203	23.65 202	51.90 209	50.21 212	61.49 197	63.62 173

ambc			45.54 205		50.66 179	52.63 186	40.99 212		38.36 199	24.67 178	22.62 221	13.94 234	29.14 189	65.71 140	58.06 183	58.60 203	67.43 135

CHOSEN 280x420		40.80 204	45.05 206	35.84 209	32.95 224	29.57 231	44.98 199	23.71 226	37.54 203	18.42 198	31.36 203	47.07 142	46.41 120	56.71 193	54.65 198	48.55 223	58.47 193

test20.03		40.38 209	44.20 207	35.92 208	53.73 157	49.05 196	38.54 215	43.49 110	32.55 212	9.54 220	27.88 213	39.12 198	12.24 226	56.28 196	54.69 196	57.96 205	49.83 216

testgi		38.71 211	43.64 208	32.95 213	52.30 166	48.63 199	35.59 221	35.05 190	31.58 216	9.03 223	30.29 204	40.75 191	11.19 231	55.30 202	53.47 205	54.53 214	45.48 219

ADS-MVSNet		40.67 207	43.38 209	37.50 205	44.36 201	39.79 219	42.09 209	32.67 207	44.34 142	28.87 158	40.76 182	40.37 193	30.22 185	48.34 225	45.87 223	46.81 226	44.21 221

MDA-MVSNet-bldmvs		41.36 202	43.15 210	39.27 200	28.74 229	52.68 185	44.95 200	40.84 156	32.89 211	18.13 199	31.61 202	22.09 229	38.97 149	50.45 214	56.11 186	64.01 190	56.23 201

LP		40.79 205	41.99 211	39.38 198	40.98 210	46.49 209	42.14 208	33.66 199	35.37 207	29.89 152	29.30 210	27.81 218	32.74 178	52.55 208	52.19 208	56.87 207	50.23 212

Anonymous20231211		40.75 206	41.57 212	39.80 197	54.71 144	52.32 187	41.42 210	45.09 75	24.45 225	6.80 227	14.58 229	23.43 228	23.08 204	56.20 197	58.74 180	67.68 177	61.31 181

MIMVSNet1		35.51 214	41.41 213	28.63 219	27.53 231	43.36 215	38.09 216	33.82 196	32.01 213	6.77 228	21.63 224	35.43 209	11.97 228	55.05 204	53.99 202	53.59 216	48.36 218

MVS-HIRNet		42.24 201	41.15 214	43.51 176	44.06 205	40.74 216	35.77 220	35.35 188	35.38 206	38.34 118	25.63 216	38.55 201	43.48 131	50.77 212	47.03 221	64.07 189	49.98 213

FPMVS		38.36 212	40.41 215	35.97 207	38.92 214	39.85 218	45.50 196	25.79 224	41.13 178	18.70 194	30.10 205	24.56 222	31.86 182	49.42 220	46.80 222	55.04 210	51.03 209

pmmvs3		35.10 215	38.47 216	31.17 215	26.37 234	40.47 217	34.51 223	18.09 232	24.75 224	16.88 201	23.05 220	26.69 220	32.69 179	50.73 213	51.60 209	58.46 204	51.98 207

new-patchmatchnet		33.24 219	37.20 217	28.62 220	44.32 202	38.26 224	29.68 230	36.05 186	31.97 214	6.33 229	26.59 215	27.33 219	11.12 232	50.08 216	41.05 228	44.23 227	45.15 220

N_pmnet		32.67 220	36.85 218	27.79 221	40.55 211	32.13 230	35.80 219	26.79 221	37.24 204	9.10 221	32.02 201	30.94 215	16.30 216	47.22 226	41.21 226	38.21 229	37.21 226

test2356		33.40 218	36.53 219	29.76 216	37.51 216	38.39 222	34.68 222	27.35 217	27.88 218	10.61 215	25.54 217	24.44 223	17.15 214	49.99 217	48.32 215	51.24 219	41.16 225

testus		31.33 222	36.31 220	25.52 225	37.55 215	38.40 221	25.87 231	23.58 227	26.46 222	5.97 230	24.15 218	24.92 221	12.44 225	49.14 222	48.21 216	47.73 225	42.86 222

testpf		34.85 216	36.16 221	33.31 212	47.49 188	35.56 227	36.85 218	32.31 208	23.08 227	15.63 203	29.39 209	29.48 217	19.62 210	41.38 228	41.07 227	47.95 224	53.18 204

PMVS	27.84 18	33.81 217	35.28 222	32.09 214	34.13 221	24.81 234	32.51 224	26.48 222	26.41 223	19.37 193	23.76 219	24.02 226	25.18 198	50.78 211	47.24 220	54.89 213	49.95 214
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testmv		30.97 223	34.42 223	26.95 222	36.49 218	37.38 225	29.80 228	27.28 218	22.34 228	4.72 231	20.63 226	20.64 230	13.22 223	49.86 219	47.74 218	50.20 220	42.36 223

test1235678		30.97 223	34.42 223	26.95 222	36.49 218	37.38 225	29.79 229	27.28 218	22.33 229	4.72 231	20.62 227	20.64 230	13.22 223	49.87 218	47.74 218	50.20 220	42.36 223

1111		31.35 221	33.52 225	28.83 218	44.28 203	32.44 228	31.71 225	33.25 202	27.87 219	10.92 213	22.18 222	24.05 224	15.89 217	49.03 223	44.09 224	36.94 231	34.96 227

no-one		29.19 225	31.89 226	26.05 224	30.96 226	38.33 223	21.54 232	29.86 211	15.84 234	3.56 234	11.28 233	13.03 235	14.44 222	38.96 229	52.83 206	55.96 208	52.92 205

test12356		23.91 227	28.47 227	18.60 228	26.80 233	28.30 232	20.92 233	19.76 230	19.89 231	2.88 239	18.48 228	16.57 233	4.05 234	42.34 227	41.93 225	37.21 230	31.75 228

new_pmnet		23.19 228	28.17 228	17.37 229	17.03 236	24.92 233	19.66 235	16.16 234	27.05 221	4.42 233	20.77 225	19.20 232	12.19 227	37.71 230	36.38 229	34.77 232	31.17 229

Gipuma		25.87 226	26.91 229	24.66 226	28.98 228	20.17 235	20.46 234	34.62 192	29.55 217	9.10 221	4.91 237	5.31 239	15.76 219	49.37 221	49.10 214	39.03 228	29.95 230
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		22.44 229	22.23 230	22.67 227	44.28 203	32.44 228	31.71 225	33.25 202	27.87 219	10.92 213	22.18 222	24.05 224	15.89 217	49.03 223	0.01 235	0.00 239	0.06 237

PMMVS2		15.84 230	19.68 231	11.35 233	15.74 237	16.95 236	13.31 236	17.64 233	16.08 233	0.36 241	13.12 230	11.47 236	1.69 236	28.82 231	27.24 231	19.38 235	24.09 232

MVE	12.28 19	13.53 233	15.72 232	10.96 234	7.39 238	15.71 237	6.05 239	23.73 225	10.29 238	3.01 238	5.77 236	3.41 240	11.91 229	20.11 232	29.79 230	13.67 236	24.98 231
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		15.09 231	13.19 233	17.30 230	27.80 230	12.62 238	7.81 238	27.54 216	14.62 236	3.19 235	6.89 234	2.52 242	15.09 220	15.93 233	20.22 232	22.38 233	19.53 233

EMVS		14.49 232	12.45 234	16.87 232	27.02 232	12.56 239	8.13 237	27.19 220	15.05 235	3.14 236	6.69 235	2.67 241	15.08 221	14.60 235	18.05 233	20.67 234	17.56 235

testmvs		0.01 234	0.02 235	0.00 236	0.00 241	0.00 242	0.01 243	0.00 238	0.01 239	0.00 243	0.03 239	0.00 243	0.01 238	0.01 237	0.01 235	0.00 239	0.06 237

test123		0.01 234	0.02 235	0.00 236	0.00 241	0.00 242	0.00 244	0.00 238	0.01 239	0.00 243	0.04 238	0.00 243	0.01 238	0.00 238	0.01 235	0.00 239	0.07 236

sosnet-low-res		0.00 236	0.00 237	0.00 236	0.00 241	0.00 242	0.00 244	0.00 238	0.00 241	0.00 243	0.00 240	0.00 243	0.00 240	0.00 238	0.00 238	0.00 239	0.00 239

sosnet		0.00 236	0.00 237	0.00 236	0.00 241	0.00 242	0.00 244	0.00 238	0.00 241	0.00 243	0.00 240	0.00 243	0.00 240	0.00 238	0.00 238	0.00 239	0.00 239

our_test_3					51.15 175	57.31 167	55.12 150

MTAPA										65.14 1		80.20 14

MTMP										62.63 11		78.04 21

Patchmatch-RL test							1.04 242

tmp_tt				5.40 235	3.97 239	2.35 241	3.26 241	0.44 236	17.56 232	12.09 210	11.48 232	7.14 237	1.98 235	15.68 234	15.49 234	10.69 237

XVS					70.49 32	76.96 21	74.36 40			54.48 44		74.47 32				82.24 19

X-MVStestdata					70.49 32	76.96 21	74.36 40			54.48 44		74.47 32				82.24 19

abl_6				64.36 40	70.08 35	77.45 17	72.88 45	50.15 51	71.31 38	54.77 43	62.79 41	77.99 22	56.80 45			81.50 34	83.91 35

mPP-MVS					71.67 28							74.36 35

NP-MVS									72.00 35

Patchmtry						47.61 201	48.27 177	38.86 169		39.59 115

DeepMVS_CX						6.95 240	5.98 240	2.25 235	11.73 237	2.07 240	11.85 231	5.43 238	11.75 230	11.40 236		8.10 238	18.38 234

High-resolution multi-view benchmark