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.

HSP-MVS		87.45 2	90.22 2	84.22 6	90.00 17	91.80 2	90.59 3	75.80 3	89.93 3	78.35 14	92.54 1	89.18 2	80.89 1	87.99 10	86.29 24	89.70 34	93.85 7

APDe-MVS		88.00 1	90.50 1	85.08 1	90.95 4	91.58 3	92.03 1	75.53 7	91.15 1	80.10 8	92.27 2	88.34 5	80.80 2	88.00 9	86.99 13	91.09 4	95.16 1

TSAR-MVS + MP.		86.88 5	89.23 4	84.14 7	89.78 20	88.67 25	90.59 3	73.46 20	88.99 5	80.52 7	91.26 3	88.65 3	79.91 4	86.96 24	86.22 25	90.59 12	93.83 8

TSAR-MVS + ACMM		85.10 18	88.81 9	80.77 28	89.55 22	88.53 27	88.59 21	72.55 23	87.39 10	71.90 36	90.95 4	87.55 7	74.57 28	87.08 21	86.54 20	87.47 70	93.67 11

APD-MVS		86.84 6	88.91 8	84.41 3	90.66 7	90.10 6	90.78 2	75.64 4	87.38 11	78.72 12	90.68 5	86.82 9	80.15 3	87.13 19	86.45 22	90.51 14	93.83 8
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		86.96 4	89.45 3	84.05 9	90.13 14	89.23 16	89.77 11	74.59 8	89.17 4	80.70 5	89.93 6	89.67 1	78.47 7	87.57 14	86.79 16	90.67 11	93.76 10

train_agg		84.86 19	87.21 16	82.11 21	90.59 9	85.47 48	89.81 9	73.55 19	83.95 26	73.30 31	89.84 7	87.23 8	75.61 26	86.47 28	85.46 32	89.78 30	92.06 26

HPM-MVS++		87.09 3	88.92 7	84.95 2	92.61 1	87.91 33	90.23 8	76.06 2	88.85 6	81.20 4	87.33 8	87.93 6	79.47 5	88.59 4	88.23 4	90.15 27	93.60 14

SteuartSystems-ACMMP		85.99 10	88.31 10	83.27 15	90.73 6	89.84 8	90.27 7	74.31 9	84.56 24	75.88 23	87.32 9	85.04 17	77.31 18	89.01 3	88.46 2	91.14 3	93.96 6
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_Plus		86.52 7	89.01 5	83.62 11	90.28 13	90.09 7	90.32 6	74.05 14	88.32 8	79.74 9	87.04 10	85.59 16	76.97 23	89.35 1	88.44 3	90.35 23	94.27 5

HFP-MVS		86.15 9	87.95 12	84.06 8	90.80 5	89.20 17	89.62 13	74.26 10	87.52 9	80.63 6	86.82 11	84.19 22	78.22 9	87.58 13	87.19 11	90.81 6	93.13 18

ACMMPR		85.52 12	87.53 14	83.17 16	90.13 14	89.27 14	89.30 14	73.97 15	86.89 14	77.14 19	86.09 12	83.18 25	77.74 14	87.42 15	87.20 10	90.77 7	92.63 19

TSAR-MVS + COLMAP		78.34 50	81.64 37	74.48 60	80.13 71	85.01 53	81.73 51	65.93 64	84.75 22	61.68 70	85.79 13	66.27 84	71.39 50	82.91 54	80.78 61	86.01 124	85.98 63

MP-MVS		85.50 13	87.40 15	83.28 14	90.65 8	89.51 13	89.16 17	74.11 13	83.70 27	78.06 16	85.54 14	84.89 20	77.31 18	87.40 16	87.14 12	90.41 21	93.65 13

TSAR-MVS + GP.		83.69 24	86.58 20	80.32 29	85.14 47	86.96 38	84.91 43	70.25 34	84.71 23	73.91 29	85.16 15	85.63 15	77.92 12	85.44 34	85.71 30	89.77 31	92.45 21

DeepPCF-MVS	79.04 1	85.30 15	88.93 6	81.06 25	88.77 28	90.48 4	85.46 39	73.08 21	90.97 2	73.77 30	84.81 16	85.95 13	77.43 17	88.22 6	87.73 6	87.85 65	94.34 3

PGM-MVS		84.42 22	86.29 22	82.23 20	90.04 16	88.82 21	89.23 16	71.74 28	82.82 30	74.61 26	84.41 17	82.09 27	77.03 22	87.13 19	86.73 18	90.73 9	92.06 26

ACMMP		83.42 25	85.27 26	81.26 24	88.47 29	88.49 28	88.31 24	72.09 25	83.42 28	72.77 34	82.65 18	78.22 41	75.18 27	86.24 31	85.76 29	90.74 8	92.13 25
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		82.64 27	85.03 28	79.86 32	89.41 24	88.31 29	88.32 23	71.84 27	80.11 38	67.47 54	82.09 19	81.44 33	71.85 46	85.89 33	86.15 26	90.24 25	91.25 32

CP-MVS		84.74 21	86.43 21	82.77 18	89.48 23	88.13 32	88.64 19	73.93 16	84.92 19	76.77 20	81.94 20	83.50 23	77.29 20	86.92 25	86.49 21	90.49 15	93.14 17

PHI-MVS		82.36 29	85.89 24	78.24 42	86.40 40	89.52 12	85.52 37	69.52 41	82.38 33	65.67 59	81.35 21	82.36 26	73.07 38	87.31 18	86.76 17	89.24 41	91.56 29

CNVR-MVS		86.36 8	88.19 11	84.23 5	91.33 3	89.84 8	90.34 5	75.56 5	87.36 12	78.97 11	81.19 22	86.76 10	78.74 6	89.30 2	88.58 1	90.45 20	94.33 4

CSCG		85.28 16	87.68 13	82.49 19	89.95 18	91.99 1	88.82 18	71.20 30	86.41 16	79.63 10	79.26 23	88.36 4	73.94 33	86.64 26	86.67 19	91.40 2	94.41 2

HQP-MVS		81.19 34	83.27 31	78.76 39	87.40 33	85.45 49	86.95 28	70.47 33	81.31 35	66.91 57	79.24 24	76.63 45	71.67 49	84.43 41	83.78 42	89.19 43	92.05 28

MCST-MVS		85.13 17	86.62 18	83.39 12	90.55 10	89.82 10	89.29 15	73.89 17	84.38 25	76.03 22	79.01 25	85.90 14	78.47 7	87.81 11	86.11 27	92.11 1	93.29 16

MPTG		85.71 11	86.88 17	84.34 4	90.54 11	87.11 37	89.77 11	74.17 12	88.54 7	83.08 2	78.60 26	86.10 12	78.11 10	87.80 12	87.46 9	90.35 23	92.56 20

X-MVS		83.23 26	85.20 27	80.92 27	89.71 21	88.68 22	88.21 25	73.60 18	82.57 31	71.81 39	77.07 27	81.92 29	71.72 48	86.98 23	86.86 14	90.47 16	92.36 23

EPNet		79.08 47	80.62 42	77.28 46	88.90 27	83.17 65	83.65 47	72.41 24	74.41 51	67.15 56	76.78 28	74.37 53	64.43 99	83.70 47	83.69 43	87.15 76	88.19 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
NCCC		85.34 14	86.59 19	83.88 10	91.48 2	88.88 19	89.79 10	75.54 6	86.67 15	77.94 17	76.55 29	84.99 18	78.07 11	88.04 7	87.68 7	90.46 19	93.31 15

MVS_111021_LR		78.13 51	79.85 49	76.13 51	81.12 64	81.50 72	80.28 57	65.25 66	76.09 47	71.32 44	76.49 30	72.87 58	72.21 42	82.79 56	81.29 56	86.59 111	87.91 52

LGP-MVS_train		79.83 37	81.22 40	78.22 43	86.28 41	85.36 51	86.76 29	69.59 39	77.34 43	65.14 61	75.68 31	70.79 64	71.37 51	84.60 39	84.01 40	90.18 26	90.74 36

DeepC-MVS_fast	78.24 3	84.27 23	85.50 25	82.85 17	90.46 12	89.24 15	87.83 26	74.24 11	84.88 20	76.23 21	75.26 32	81.05 35	77.62 15	88.02 8	87.62 8	90.69 10	92.41 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS	78.47 2	84.81 20	86.03 23	83.37 13	89.29 25	90.38 5	88.61 20	76.50 1	86.25 17	77.22 18	75.12 33	80.28 37	77.59 16	88.39 5	88.17 5	91.02 5	93.66 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		81.62 33	83.41 30	79.53 34	87.06 35	88.59 26	85.47 38	67.96 51	76.59 46	74.05 27	74.69 34	81.98 28	72.98 39	86.14 32	85.47 31	89.68 35	90.42 40

MVS_0304		81.73 32	83.86 29	79.26 35	86.22 42	89.18 18	86.41 31	67.15 55	75.28 48	70.75 46	74.59 35	83.49 24	74.42 30	87.05 22	86.34 23	90.58 13	91.08 34

ACMP	73.23 7	79.79 38	80.53 43	78.94 37	85.61 45	85.68 46	85.61 36	69.59 39	77.33 44	71.00 45	74.45 36	69.16 73	71.88 44	83.15 52	83.37 45	89.92 29	90.57 39
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVS_111021_HR		80.13 36	81.46 38	78.58 40	85.77 44	85.17 52	83.45 49	69.28 42	74.08 54	70.31 47	74.31 37	75.26 51	73.13 37	86.46 29	85.15 35	89.53 37	89.81 43

CPTT-MVS		81.77 31	83.10 32	80.21 30	85.93 43	86.45 43	87.72 27	70.98 31	82.54 32	71.53 42	74.23 38	81.49 32	76.31 25	82.85 55	81.87 51	88.79 50	92.26 24

abl_6				79.05 36	87.27 34	88.85 20	83.62 48	68.25 47	81.68 34	72.94 33	73.79 39	84.45 21	72.55 41			89.66 36	90.64 37

CLD-MVS		79.35 43	81.23 39	77.16 47	85.01 50	86.92 39	85.87 34	60.89 112	80.07 40	75.35 25	72.96 40	73.21 57	68.43 64	85.41 36	84.63 38	87.41 71	85.44 72
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
canonicalmvs		79.16 45	82.37 36	75.41 53	82.33 60	86.38 44	80.80 55	63.18 79	82.90 29	67.34 55	72.79 41	76.07 47	69.62 57	83.46 51	84.41 39	89.20 42	90.60 38

UA-Net		74.47 62	77.80 54	70.59 81	85.33 46	85.40 50	73.54 134	65.98 63	60.65 104	56.00 105	72.11 42	79.15 38	54.63 160	83.13 53	82.25 49	88.04 59	81.92 120

OMC-MVS		80.26 35	82.59 35	77.54 45	83.04 55	85.54 47	83.25 50	65.05 68	87.32 13	72.42 35	72.04 43	78.97 39	73.30 36	83.86 44	81.60 54	88.15 56	88.83 48

UGNet		72.78 69	77.67 55	67.07 130	71.65 160	83.24 63	75.20 109	63.62 76	64.93 76	56.72 99	71.82 44	73.30 55	49.02 175	81.02 72	80.70 68	86.22 115	88.67 49
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
DELS-MVS		79.15 46	81.07 41	76.91 48	83.54 54	87.31 35	84.45 44	64.92 69	69.98 59	69.34 48	71.62 45	76.26 46	69.84 56	86.57 27	85.90 28	89.39 39	89.88 42
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
CANet_DTU		73.29 66	76.96 63	69.00 105	77.04 99	82.06 70	79.49 63	56.30 163	67.85 64	53.29 119	71.12 46	70.37 68	61.81 115	81.59 61	80.96 59	86.09 118	84.73 84

MVS_Test		75.37 59	77.13 62	73.31 64	79.07 76	81.32 74	79.98 58	60.12 129	69.72 62	64.11 65	70.53 47	73.22 56	68.90 60	80.14 86	79.48 84	87.67 67	85.50 70

FC-MVSNet-train		72.60 71	75.07 69	69.71 100	81.10 65	78.79 106	73.74 132	65.23 67	66.10 69	53.34 118	70.36 48	63.40 92	56.92 143	81.44 63	80.96 59	87.93 61	84.46 86

Vis-MVSNet		72.77 70	77.20 61	67.59 120	74.19 137	84.01 57	76.61 105	61.69 108	60.62 105	50.61 135	70.25 49	71.31 63	55.57 155	83.85 45	82.28 48	86.90 90	88.08 51
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PMMVS		65.06 152	69.17 112	60.26 170	55.25 212	63.43 195	66.71 169	43.01 210	62.41 90	50.64 134	69.44 50	67.04 82	63.29 104	74.36 152	73.54 159	82.68 158	73.99 178

diffmvs		73.13 67	75.65 67	70.19 93	74.07 138	77.17 127	78.24 92	57.45 156	72.44 57	64.02 66	69.05 51	75.92 48	64.86 97	75.18 148	75.27 150	82.47 159	84.53 85

MVSTER		72.06 72	74.24 70	69.51 101	70.39 168	75.97 148	76.91 102	57.36 158	64.64 79	61.39 72	68.86 52	63.76 90	63.46 102	81.44 63	79.70 77	87.56 69	85.31 74

TAPA-MVS	71.42 9	77.69 52	80.05 48	74.94 56	80.68 67	84.52 55	81.36 52	63.14 80	84.77 21	64.82 63	68.72 53	75.91 49	71.86 45	81.62 60	79.55 82	87.80 66	85.24 75
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EPP-MVSNet		74.00 64	77.41 59	70.02 96	80.53 69	83.91 58	74.99 115	62.68 93	65.06 75	49.77 141	68.68 54	72.09 60	63.06 105	82.49 57	80.73 62	89.12 45	88.91 47

MSLP-MVS++		82.09 30	82.66 34	81.42 23	87.03 36	87.22 36	85.82 35	70.04 35	80.30 37	78.66 13	68.67 55	81.04 36	77.81 13	85.19 37	84.88 37	89.19 43	91.31 31

PVSNet_BlendedMVS		76.21 55	77.52 57	74.69 58	79.46 73	83.79 59	77.50 98	64.34 73	69.88 60	71.88 37	68.54 56	70.42 66	67.05 66	83.48 49	79.63 78	87.89 63	86.87 59

PVSNet_Blended		76.21 55	77.52 57	74.69 58	79.46 73	83.79 59	77.50 98	64.34 73	69.88 60	71.88 37	68.54 56	70.42 66	67.05 66	83.48 49	79.63 78	87.89 63	86.87 59

PCF-MVS	73.28 6	79.42 42	80.41 45	78.26 41	84.88 53	88.17 30	86.08 32	69.85 36	75.23 50	68.43 49	68.03 58	78.38 40	71.76 47	81.26 69	80.65 70	88.56 53	91.18 33
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OPM-MVS		79.68 41	79.28 50	80.15 31	87.99 31	86.77 40	88.52 22	72.72 22	64.55 80	67.65 53	67.87 59	74.33 54	74.31 31	86.37 30	85.25 34	89.73 33	89.81 43

RPSCF		67.64 133	71.25 83	63.43 158	61.86 195	70.73 170	67.26 164	50.86 184	74.20 53	58.91 79	67.49 60	69.33 71	64.10 100	71.41 169	68.45 185	77.61 178	77.17 156

EPNet_dtu		68.08 123	71.00 84	64.67 149	79.64 72	68.62 178	75.05 114	63.30 78	66.36 67	45.27 162	67.40 61	66.84 83	43.64 185	75.37 146	74.98 154	81.15 164	77.44 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Vis-MVSNet (Re-imp)		67.83 128	73.52 72	61.19 165	78.37 80	76.72 139	66.80 168	62.96 82	65.50 73	34.17 190	67.19 62	69.68 70	39.20 193	79.39 97	79.44 85	85.68 136	76.73 162

IS_MVSNet		73.33 65	77.34 60	68.65 109	81.29 62	83.47 61	74.45 118	63.58 77	65.75 72	48.49 144	67.11 63	70.61 65	54.63 160	84.51 40	83.58 44	89.48 38	86.34 62

PVSNet_Blended_VisFu		76.57 54	77.90 53	75.02 55	80.56 68	86.58 42	79.24 64	66.18 59	64.81 77	68.18 51	65.61 64	71.45 61	67.05 66	84.16 42	81.80 52	88.90 47	90.92 35

FC-MVSNet-test		56.90 190	65.20 160	47.21 202	66.98 180	63.20 197	49.11 209	58.60 151	59.38 114	11.50 222	65.60 65	56.68 119	24.66 213	71.17 172	71.36 169	72.38 198	69.02 189

QAPM		78.47 49	80.22 47	76.43 50	85.03 49	86.75 41	80.62 56	66.00 62	73.77 55	65.35 60	65.54 66	78.02 42	72.69 40	83.71 46	83.36 46	88.87 49	90.41 41

IterMVS-LS		71.69 74	72.82 77	70.37 90	77.54 94	76.34 145	75.13 113	60.46 119	61.53 99	57.57 88	64.89 67	67.33 81	66.04 91	77.09 133	77.37 113	85.48 139	85.18 76
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
3Dnovator+	75.73 4	82.40 28	82.76 33	81.97 22	88.02 30	89.67 11	86.60 30	71.48 29	81.28 36	78.18 15	64.78 68	77.96 43	77.13 21	87.32 17	86.83 15	90.41 21	91.48 30

MAR-MVS		79.21 44	80.32 46	77.92 44	87.46 32	88.15 31	83.95 46	67.48 54	74.28 52	68.25 50	64.70 69	77.04 44	72.17 43	85.42 35	85.00 36	88.22 54	87.62 55
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
Effi-MVS+		75.28 60	76.20 65	74.20 61	81.15 63	83.24 63	81.11 53	63.13 81	66.37 66	60.27 75	64.30 70	68.88 77	70.93 54	81.56 62	81.69 53	88.61 51	87.35 56

CostFormer		68.92 113	69.58 101	68.15 112	75.98 107	76.17 147	78.22 93	51.86 179	65.80 71	61.56 71	63.57 71	62.83 93	61.85 113	70.40 183	68.67 181	79.42 172	79.62 141

AdaColmap		79.74 40	78.62 51	81.05 26	89.23 26	86.06 45	84.95 42	71.96 26	79.39 41	75.51 24	63.16 72	68.84 78	76.51 24	83.55 48	82.85 47	88.13 57	86.46 61

ACMM	72.26 8	78.86 48	78.13 52	79.71 33	86.89 37	83.40 62	86.02 33	70.50 32	75.28 48	71.49 43	63.01 73	69.26 72	73.57 35	84.11 43	83.98 41	89.76 32	87.84 53
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
3Dnovator	73.76 5	79.75 39	80.52 44	78.84 38	84.94 52	87.35 34	84.43 45	65.54 65	78.29 42	73.97 28	63.00 74	75.62 50	74.07 32	85.00 38	85.34 33	90.11 28	89.04 46

DI_MVS_plusplus_trai		75.13 61	76.12 66	73.96 62	78.18 81	81.55 71	80.97 54	62.54 97	68.59 63	65.13 62	61.43 75	74.81 52	69.32 59	81.01 73	79.59 80	87.64 68	85.89 64

USDC		67.36 137	67.90 135	66.74 137	71.72 158	75.23 153	71.58 149	60.28 123	67.45 65	50.54 136	60.93 76	45.20 195	62.08 109	76.56 139	74.50 155	84.25 150	75.38 170

IterMVS		66.36 143	68.30 131	64.10 151	69.48 175	74.61 158	73.41 137	50.79 185	57.30 129	48.28 146	60.64 77	59.92 101	60.85 122	74.14 153	72.66 163	81.80 161	78.82 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		70.59 81	72.19 79	68.72 107	77.72 92	80.72 82	73.81 130	69.65 38	61.99 93	43.23 169	60.54 78	57.50 108	58.57 129	79.56 94	81.07 58	89.34 40	83.97 89

EPMVS		60.00 183	61.97 183	57.71 181	68.46 178	63.17 198	64.54 179	48.23 196	63.30 85	44.72 165	60.19 79	56.05 130	50.85 172	65.27 197	62.02 203	69.44 205	63.81 198

GA-MVS		68.14 120	69.17 112	66.93 133	73.77 143	78.50 110	74.45 118	58.28 153	55.11 157	48.44 145	60.08 80	53.99 144	61.50 116	78.43 106	77.57 107	85.13 142	80.54 130

MDTV_nov1_ep13		64.37 156	65.24 158	63.37 159	68.94 177	70.81 169	72.40 144	50.29 188	60.10 108	53.91 115	60.07 81	59.15 104	57.21 139	69.43 187	67.30 188	77.47 179	69.78 187

Fast-Effi-MVS+		73.11 68	73.66 71	72.48 66	77.72 92	80.88 81	78.55 84	58.83 148	65.19 74	60.36 74	59.98 82	62.42 95	71.22 52	81.66 59	80.61 72	88.20 55	84.88 83

tpm		62.41 168	63.15 173	61.55 164	72.24 154	63.79 194	71.31 150	46.12 201	57.82 122	55.33 107	59.90 83	54.74 138	53.63 164	67.24 193	64.29 196	70.65 203	74.25 177

PatchmatchNet		64.21 158	64.65 165	63.69 155	71.29 166	68.66 177	69.63 155	51.70 181	63.04 87	53.77 116	59.83 84	58.34 106	60.23 125	68.54 190	66.06 193	75.56 187	68.08 191
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CDS-MVSNet		67.65 132	69.83 98	65.09 144	75.39 110	76.55 140	74.42 121	63.75 75	53.55 167	49.37 143	59.41 85	62.45 94	44.44 183	79.71 90	79.82 76	83.17 157	77.36 155
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
CVMVSNet		62.55 165	65.89 152	58.64 178	66.95 181	69.15 175	66.49 172	56.29 164	52.46 172	32.70 191	59.27 86	58.21 107	50.09 173	71.77 168	71.39 168	79.31 173	78.99 146

PLC	68.99 11	75.68 58	75.31 68	76.12 52	82.94 56	81.26 75	79.94 59	66.10 60	77.15 45	66.86 58	59.13 87	68.53 79	73.73 34	80.38 80	79.04 86	87.13 80	81.68 122
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TranMVSNet+NR-MVSNet		69.25 110	70.81 86	67.43 121	77.23 98	79.46 98	73.48 136	69.66 37	60.43 106	39.56 180	58.82 88	53.48 150	55.74 153	79.59 92	81.21 57	88.89 48	82.70 110

DU-MVS		69.63 99	70.91 85	68.13 113	75.99 105	79.54 96	73.81 130	69.20 43	61.20 101	43.23 169	58.52 89	53.50 148	58.57 129	79.22 98	80.45 73	87.97 60	83.97 89

NR-MVSNet		68.79 115	70.56 87	66.71 138	77.48 95	79.54 96	73.52 135	69.20 43	61.20 101	39.76 179	58.52 89	50.11 179	51.37 171	80.26 84	80.71 67	88.97 46	83.59 96

Effi-MVS+-dtu		71.82 73	71.86 81	71.78 67	78.77 77	80.47 90	78.55 84	61.67 109	60.68 103	55.49 106	58.48 91	65.48 86	68.85 61	76.92 134	75.55 148	87.35 72	85.46 71

GBi-Net		70.78 78	73.37 74	67.76 114	72.95 148	78.00 114	75.15 110	62.72 88	64.13 81	51.44 127	58.37 92	69.02 74	57.59 135	81.33 66	80.72 63	86.70 105	82.02 114

test1		70.78 78	73.37 74	67.76 114	72.95 148	78.00 114	75.15 110	62.72 88	64.13 81	51.44 127	58.37 92	69.02 74	57.59 135	81.33 66	80.72 63	86.70 105	82.02 114

FMVSNet3		70.49 82	72.90 76	67.67 118	72.88 151	77.98 117	74.96 116	62.72 88	64.13 81	51.44 127	58.37 92	69.02 74	57.43 138	79.43 96	79.57 81	86.59 111	81.81 121

UniMVSNet (Re)		69.53 104	71.90 80	66.76 136	76.42 102	80.93 78	72.59 142	68.03 50	61.75 97	41.68 177	58.34 95	57.23 116	53.27 167	79.53 95	80.62 71	88.57 52	84.90 82

tpmrst		62.00 172	62.35 182	61.58 163	71.62 161	64.14 191	69.07 158	48.22 197	62.21 92	53.93 114	58.26 96	55.30 133	55.81 152	63.22 200	62.62 201	70.85 202	70.70 185

DWT-MVSNet_training		67.24 139	65.96 151	68.74 106	76.15 103	74.36 160	74.37 122	56.66 161	61.82 96	60.51 73	58.23 97	49.76 181	65.07 96	70.04 184	70.39 171	79.70 171	77.11 158

OpenMVS	70.44 10	76.15 57	76.82 64	75.37 54	85.01 50	84.79 54	78.99 69	62.07 103	71.27 58	67.88 52	57.91 98	72.36 59	70.15 55	82.23 58	81.41 55	88.12 58	87.78 54

LS3D		74.08 63	73.39 73	74.88 57	85.05 48	82.62 68	79.71 61	68.66 45	72.82 56	58.80 80	57.61 99	61.31 97	71.07 53	80.32 82	78.87 88	86.00 126	80.18 135

test-LLR		64.42 155	64.36 167	64.49 150	75.02 113	63.93 192	66.61 170	61.96 104	54.41 162	47.77 148	57.46 100	60.25 99	55.20 158	70.80 177	69.33 176	80.40 168	74.38 175

TESTMET0.1,1		61.10 179	64.36 167	57.29 182	57.53 205	63.93 192	66.61 170	36.22 216	54.41 162	47.77 148	57.46 100	60.25 99	55.20 158	70.80 177	69.33 176	80.40 168	74.38 175

test-mter		60.84 180	64.62 166	56.42 185	55.99 210	64.18 190	65.39 175	34.23 218	54.39 164	46.21 157	57.40 102	59.49 103	55.86 151	71.02 175	69.65 174	80.87 167	76.20 163

tpmp4_e23		68.32 119	67.08 143	69.76 99	77.86 85	75.22 154	78.37 89	56.17 165	66.06 70	64.27 64	57.15 103	54.89 137	63.40 103	70.97 176	68.29 186	78.46 176	77.00 160

SixPastTwentyTwo		61.84 175	62.45 180	61.12 166	69.20 176	72.20 165	62.03 188	57.40 157	46.54 195	38.03 185	57.14 104	41.72 200	58.12 133	69.67 185	71.58 167	81.94 160	78.30 149

PatchMatch-RL		67.78 129	66.65 148	69.10 104	73.01 147	72.69 164	68.49 160	61.85 106	62.93 89	60.20 76	56.83 105	50.42 177	69.52 58	75.62 145	74.46 156	81.51 162	73.62 180

CNLPA		77.20 53	77.54 56	76.80 49	82.63 57	84.31 56	79.77 60	64.64 70	85.17 18	73.18 32	56.37 106	69.81 69	74.53 29	81.12 71	78.69 89	86.04 123	87.29 58

Fast-Effi-MVS+-dtu		68.34 118	69.47 103	67.01 131	75.15 111	77.97 119	77.12 101	55.40 166	57.87 121	46.68 155	56.17 107	60.39 98	62.36 108	76.32 141	76.25 142	85.35 141	81.34 123

FMVSNet2		70.39 83	72.67 78	67.72 117	72.95 148	78.00 114	75.15 110	62.69 92	63.29 86	51.25 131	55.64 108	68.49 80	57.59 135	80.91 74	80.35 74	86.70 105	82.02 114

GG-mvs-BLEND		46.86 207	67.51 138	22.75 220	0.05 227	76.21 146	64.69 178	0.04 225	61.90 94	0.09 230	55.57 109	71.32 62	0.08 225	70.54 179	67.19 189	71.58 200	69.86 186

dps		64.00 159	62.99 174	65.18 143	73.29 145	72.07 166	68.98 159	53.07 172	57.74 125	58.41 82	55.55 110	47.74 188	60.89 121	69.53 186	67.14 190	76.44 184	71.19 184

v2v482		70.05 93	69.46 104	70.74 75	74.62 134	80.32 92	79.00 68	60.62 115	57.41 128	56.89 94	55.43 111	55.14 135	66.39 78	77.25 130	77.14 116	86.90 90	83.57 99

v18		70.10 91	69.52 102	70.77 74	74.66 133	77.06 130	78.84 72	58.84 147	60.01 109	59.23 77	55.06 112	57.47 109	66.34 81	77.50 125	76.75 126	86.71 104	82.77 108

v6		70.35 84	69.94 92	70.83 70	74.68 130	80.62 83	78.81 74	60.16 128	58.81 116	58.17 84	55.01 113	57.31 113	66.32 84	77.53 121	76.73 132	86.82 97	83.62 93

v1neww		70.34 85	69.93 93	70.82 71	74.68 130	80.61 84	78.80 75	60.17 125	58.74 118	58.10 85	55.00 114	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

v7new		70.34 85	69.93 93	70.82 71	74.68 130	80.61 84	78.80 75	60.17 125	58.74 118	58.10 85	55.00 114	57.28 114	66.33 82	77.53 121	76.74 128	86.82 97	83.61 94

v8		70.23 88	69.86 97	70.67 79	74.69 129	79.82 95	78.79 77	59.18 138	58.80 117	58.20 83	55.00 114	57.33 111	66.31 85	77.51 124	76.71 136	86.82 97	83.88 92

pmmvs4		67.89 126	67.39 141	68.48 110	71.60 162	73.57 162	74.45 118	60.98 111	64.65 78	57.97 87	54.95 117	51.73 170	61.88 112	73.78 155	75.11 152	83.99 153	77.91 151

v17		70.03 94	69.43 109	70.72 77	74.75 127	77.09 128	78.78 79	58.85 144	59.53 113	58.72 81	54.87 118	57.39 110	66.38 79	77.60 120	76.75 126	86.83 96	82.80 104

v16		70.07 92	69.46 104	70.79 73	74.74 128	77.08 129	78.79 77	58.86 142	59.75 110	59.15 78	54.87 118	57.33 111	66.38 79	77.61 119	76.77 121	86.81 102	82.79 106

TAMVS		59.58 184	62.81 177	55.81 187	66.03 185	65.64 189	63.86 182	48.74 192	49.95 182	37.07 187	54.77 120	58.54 105	44.44 183	72.29 162	71.79 165	74.70 191	66.66 193

v1		69.97 95	69.45 106	70.59 81	74.78 124	80.51 87	78.84 72	60.30 120	56.98 131	56.81 96	54.69 121	56.29 124	65.91 94	77.37 128	76.71 136	86.89 92	83.59 96

v1141		69.96 97	69.44 107	70.58 83	74.78 124	80.50 88	78.85 70	60.30 120	56.95 133	56.74 98	54.68 122	56.26 126	65.93 92	77.38 127	76.72 133	86.88 93	83.57 99

divwei89l23v2f112		69.97 95	69.44 107	70.58 83	74.78 124	80.50 88	78.85 70	60.30 120	56.97 132	56.75 97	54.67 123	56.27 125	65.92 93	77.37 128	76.72 133	86.88 93	83.58 98

v15		69.61 100	68.88 115	70.46 85	74.81 121	77.03 133	78.75 80	58.83 148	57.06 130	57.18 90	54.55 124	56.37 120	66.13 89	77.70 116	76.76 123	87.03 87	82.69 111

V14		69.59 101	68.86 116	70.45 87	74.83 120	77.04 131	78.70 81	58.83 148	56.95 133	57.08 92	54.41 125	56.34 121	66.15 86	77.77 115	76.76 123	87.08 85	82.74 109

ACMH	65.37 14	70.71 80	70.00 91	71.54 68	82.51 59	82.47 69	77.78 95	68.13 48	56.19 147	46.06 158	54.30 126	51.20 173	68.68 62	80.66 75	80.72 63	86.07 119	84.45 87
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
anonymousdsp		65.28 148	67.98 134	62.13 161	58.73 203	73.98 161	67.10 166	50.69 186	48.41 189	47.66 151	54.27 127	52.75 162	61.45 117	76.71 138	80.20 75	87.13 80	89.53 45

CR-MVSNet		64.83 153	65.54 156	64.01 154	70.64 167	69.41 173	65.97 173	52.74 174	57.81 123	52.65 122	54.27 127	56.31 122	60.92 119	72.20 165	73.09 161	81.12 165	75.69 167

V42		68.76 116	69.63 100	67.74 116	64.93 189	78.01 113	78.30 90	56.48 162	58.65 120	56.30 103	54.26 129	57.03 117	64.85 98	77.47 126	77.01 118	85.60 137	84.96 81

V9		69.58 102	68.83 117	70.46 85	74.85 119	77.04 131	78.65 82	58.85 144	56.83 136	57.12 91	54.26 129	56.31 122	66.14 88	77.83 114	76.76 123	87.13 80	82.79 106

pmmvs5		62.37 171	64.04 169	60.42 168	65.03 187	71.67 168	67.17 165	52.70 176	50.30 180	44.80 164	54.23 131	51.19 174	49.37 174	72.88 159	73.48 160	83.45 154	74.55 174

v7		70.33 87	69.87 95	70.88 69	74.79 122	81.04 77	79.22 65	60.57 116	57.70 127	56.65 101	54.23 131	55.29 134	66.95 69	78.28 108	77.47 109	87.12 83	85.05 79

v10		70.22 89	69.76 99	70.74 75	74.79 122	80.30 93	79.22 65	59.81 132	57.71 126	56.58 102	54.22 133	55.31 132	66.95 69	78.28 108	77.47 109	87.12 83	85.07 78

v12		69.54 103	68.79 119	70.41 88	74.88 116	77.03 133	78.54 87	58.85 144	56.71 137	56.87 95	54.13 134	56.23 127	66.15 86	77.89 112	76.74 128	87.17 75	82.80 104

v11		69.37 108	68.65 125	70.20 92	74.87 118	76.97 136	78.29 91	58.55 152	56.38 144	56.04 104	54.02 135	54.98 136	66.47 77	78.30 107	76.91 119	86.97 88	83.02 102

v13		69.52 105	68.76 121	70.41 88	74.88 116	77.02 135	78.52 88	58.86 142	56.61 142	56.91 93	54.00 136	56.17 128	66.11 90	77.93 111	76.74 128	87.21 74	82.83 103

WR-MVS		63.03 161	67.40 140	57.92 180	75.14 112	77.60 124	60.56 191	66.10 60	54.11 166	23.88 202	53.94 137	53.58 146	34.50 198	73.93 154	77.71 104	87.35 72	80.94 126

v1144		69.93 98	69.36 110	70.61 80	74.89 115	80.93 78	79.11 67	60.64 114	55.97 150	55.31 108	53.85 138	54.14 141	66.54 76	78.10 110	77.44 111	87.14 79	85.09 77

v148		67.85 127	67.53 137	68.23 111	73.25 146	77.57 125	74.26 126	57.36 158	55.70 152	57.45 89	53.53 139	55.42 131	61.96 111	75.23 147	73.92 157	85.08 143	81.32 124

PEN-MVS		62.96 162	65.77 154	59.70 173	73.98 141	75.45 150	63.39 184	67.61 53	52.49 171	25.49 201	53.39 140	49.12 184	40.85 191	71.94 167	77.26 115	86.86 95	80.72 128

CP-MVSNet		62.68 164	65.49 157	59.40 176	71.84 156	75.34 151	62.87 186	67.04 56	52.64 170	27.19 199	53.38 141	48.15 186	41.40 189	71.26 170	75.68 146	86.07 119	82.00 117

DTE-MVSNet		61.85 174	64.96 164	58.22 179	74.32 136	74.39 159	61.01 190	67.85 52	51.76 177	21.91 212	53.28 142	48.17 185	37.74 194	72.22 164	76.44 139	86.52 113	78.49 148

IB-MVS	66.94 12	71.21 77	71.66 82	70.68 78	79.18 75	82.83 67	72.61 141	61.77 107	59.66 111	63.44 68	53.26 143	59.65 102	59.16 128	76.78 137	82.11 50	87.90 62	87.33 57
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
tpm cat1		65.41 147	63.81 171	67.28 126	75.61 109	72.88 163	75.32 107	52.85 173	62.97 88	63.66 67	53.24 144	53.29 157	61.83 114	65.54 195	64.14 198	74.43 192	74.60 173

MSDG		71.52 75	69.87 95	73.44 63	82.21 61	79.35 99	79.52 62	64.59 71	66.15 68	61.87 69	53.21 145	56.09 129	65.85 95	78.94 101	78.50 90	86.60 110	76.85 161

MS-PatchMatch		70.17 90	70.49 88	69.79 98	80.98 66	77.97 119	77.51 97	58.95 140	62.33 91	55.22 109	53.14 146	65.90 85	62.03 110	79.08 100	77.11 117	84.08 151	77.91 151

WR-MVS_H		61.83 176	65.87 153	57.12 183	71.72 158	76.87 137	61.45 189	66.19 58	51.97 175	22.92 209	53.13 147	52.30 167	33.80 199	71.03 174	75.00 153	86.65 109	80.78 127

thres400		67.95 125	68.62 126	67.17 127	77.90 82	78.59 109	74.27 125	62.72 88	56.34 146	45.77 160	53.00 148	53.35 155	56.46 146	80.21 85	78.43 91	85.91 130	80.43 133

thres200		67.98 124	68.55 127	67.30 125	77.89 84	78.86 104	74.18 127	62.75 86	56.35 145	46.48 156	52.98 149	53.54 147	56.46 146	80.41 76	77.97 99	86.05 121	79.78 140

v144192		69.34 109	68.68 124	70.12 94	74.06 139	80.54 86	78.08 94	60.54 117	54.99 160	54.13 112	52.92 150	52.80 161	66.73 74	77.13 132	76.72 133	87.15 76	85.63 65

view600		67.63 134	68.36 129	66.77 135	77.84 86	78.66 107	73.74 132	62.62 95	56.04 149	44.98 163	52.86 151	52.83 159	55.48 157	80.36 81	77.75 103	85.95 129	80.02 137

thres600view7		67.68 130	68.43 128	66.80 134	77.90 82	78.86 104	73.84 129	62.75 86	56.07 148	44.70 166	52.85 152	52.81 160	55.58 154	80.41 76	77.77 102	86.05 121	80.28 134

v1192		69.50 106	68.83 117	70.29 91	74.49 135	80.92 80	78.55 84	60.54 117	55.04 158	54.21 111	52.79 153	52.33 165	66.92 71	77.88 113	77.35 114	87.04 86	85.51 69

view800		67.35 138	68.22 132	66.35 139	77.83 87	78.62 108	72.97 140	62.58 96	55.71 151	44.13 167	52.69 154	52.24 168	54.58 162	80.27 83	78.19 96	86.01 124	79.79 139

conf200view11		68.11 121	68.72 122	67.39 123	77.83 87	78.93 101	74.28 123	62.81 84	56.64 139	46.70 154	52.65 155	53.47 151	56.59 144	80.41 76	78.43 91	86.11 116	80.53 131

thres100view900		67.60 135	68.02 133	67.12 129	77.83 87	77.75 121	73.90 128	62.52 98	56.64 139	46.82 152	52.65 155	53.47 151	55.92 150	78.77 103	77.62 106	85.72 135	79.23 144

tfpn200view9		68.11 121	68.72 122	67.40 122	77.83 87	78.93 101	74.28 123	62.81 84	56.64 139	46.82 152	52.65 155	53.47 151	56.59 144	80.41 76	78.43 91	86.11 116	80.52 132

LTVRE_ROB	59.44 16	61.82 177	62.64 178	60.87 167	72.83 152	77.19 126	64.37 180	58.97 139	33.56 215	28.00 198	52.59 158	42.21 199	63.93 101	74.52 150	76.28 140	77.15 181	82.13 113
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
tfpn		66.58 142	67.18 142	65.88 141	77.82 91	78.45 111	72.07 145	62.52 98	55.35 154	43.21 171	52.54 159	46.12 192	53.68 163	80.02 87	78.23 95	85.99 127	79.55 142

v1921920		69.03 112	68.32 130	69.86 97	74.03 140	80.37 91	77.55 96	60.25 124	54.62 161	53.59 117	52.36 160	51.50 172	66.75 73	77.17 131	76.69 138	86.96 89	85.56 66

ADS-MVSNet		55.94 192	58.01 192	53.54 196	62.48 192	58.48 204	59.12 196	46.20 200	59.65 112	42.88 174	52.34 161	53.31 156	46.31 180	62.00 204	60.02 208	64.23 214	60.24 206

PS-CasMVS		62.38 170	65.06 161	59.25 177	71.73 157	75.21 155	62.77 187	66.99 57	51.94 176	26.96 200	52.00 162	47.52 189	41.06 190	71.16 173	75.60 147	85.97 128	81.97 119

v1240		68.64 117	67.89 136	69.51 101	73.89 142	80.26 94	76.73 103	59.97 131	53.43 168	53.08 120	51.82 163	50.84 175	66.62 75	76.79 136	76.77 121	86.78 103	85.34 73

ACMH+	66.54 13	71.36 76	70.09 90	72.85 65	82.59 58	81.13 76	78.56 83	68.04 49	61.55 98	52.52 125	51.50 164	54.14 141	68.56 63	78.85 102	79.50 83	86.82 97	83.94 91

Baseline_NR-MVSNet		67.53 136	68.77 120	66.09 140	75.99 105	74.75 157	72.43 143	68.41 46	61.33 100	38.33 183	51.31 165	54.13 143	56.03 149	79.22 98	78.19 96	85.37 140	82.45 112

pm-mvs1		65.62 146	67.42 139	63.53 157	73.66 144	76.39 144	69.66 154	60.87 113	49.73 186	43.97 168	51.24 166	57.00 118	48.16 176	79.89 88	77.84 101	84.85 148	79.82 138

PatchT		61.97 173	64.04 169	59.55 175	60.49 197	67.40 181	56.54 198	48.65 193	56.69 138	52.65 122	51.10 167	52.14 169	60.92 119	72.20 165	73.09 161	78.03 177	75.69 167

RPMNet		61.71 178	62.88 175	60.34 169	69.51 174	69.41 173	63.48 183	49.23 189	57.81 123	45.64 161	50.51 168	50.12 178	53.13 168	68.17 192	68.49 184	81.07 166	75.62 169

EU-MVSNet		54.63 193	58.69 191	49.90 200	56.99 206	62.70 200	56.41 199	50.64 187	45.95 197	23.14 206	50.42 169	46.51 191	36.63 195	65.51 196	64.85 195	75.57 186	74.91 172

TransMVSNet (Re)		64.74 154	65.66 155	63.66 156	77.40 97	75.33 152	69.86 153	62.67 94	47.63 192	41.21 178	50.01 170	52.33 165	45.31 182	79.57 93	77.69 105	85.49 138	77.07 159

FMVSNet1		68.84 114	70.47 89	66.94 132	71.35 165	77.68 122	74.71 117	62.35 102	56.93 135	49.94 140	50.01 170	64.59 88	57.07 141	81.33 66	80.72 63	86.25 114	82.00 117

PM-MVS		60.48 181	60.94 188	59.94 171	58.85 202	66.83 184	64.27 181	51.39 182	55.03 159	48.03 147	50.00 172	40.79 202	58.26 132	69.20 188	67.13 191	78.84 175	77.60 153

test0.0.03 1		58.80 185	61.58 185	55.56 188	75.02 113	68.45 179	59.58 195	61.96 104	52.74 169	29.57 194	49.75 173	54.56 139	31.46 201	71.19 171	69.77 173	75.75 185	64.57 196

CHOSEN 280x420		58.70 186	61.88 184	54.98 190	55.45 211	50.55 215	64.92 177	40.36 212	55.21 155	38.13 184	48.31 174	63.76 90	63.03 106	73.73 156	68.58 183	68.00 208	73.04 181

v7n		67.05 141	66.94 144	67.17 127	72.35 153	78.97 100	73.26 139	58.88 141	51.16 179	50.90 132	48.21 175	50.11 179	60.96 118	77.70 116	77.38 112	86.68 108	85.05 79

MDTV_nov1_ep13_2view		60.16 182	60.51 189	59.75 172	65.39 186	69.05 176	68.00 162	48.29 195	51.99 173	45.95 159	48.01 176	49.64 182	53.39 166	68.83 189	66.52 192	77.47 179	69.55 188

conf0.05thres1000		66.26 144	66.77 146	65.66 142	77.45 96	78.10 112	71.85 148	62.44 101	51.47 178	43.00 172	47.92 177	51.66 171	53.40 165	79.71 90	77.97 99	85.82 131	80.56 129

v748		65.12 151	65.24 158	64.98 146	69.77 171	76.45 141	69.47 156	57.06 160	49.93 184	50.70 133	47.87 178	49.50 183	57.14 140	73.64 157	75.18 151	85.75 134	84.14 88

V4		65.23 149	66.23 150	64.06 152	61.94 193	76.42 142	72.05 147	54.31 167	49.91 185	50.06 137	47.42 179	52.40 164	60.24 124	75.71 143	76.22 143	85.78 132	85.56 66

v52		65.23 149	66.24 149	64.06 152	61.94 193	76.42 142	72.06 146	54.30 168	49.94 183	50.04 138	47.41 180	52.42 163	60.23 125	75.71 143	76.22 143	85.78 132	85.56 66

TinyColmap		62.84 163	61.03 187	64.96 147	69.61 173	71.69 167	68.48 161	59.76 133	55.41 153	47.69 150	47.33 181	34.20 208	62.76 107	74.52 150	72.59 164	81.44 163	71.47 183

COLMAP_ROB	62.73 15	67.66 131	66.76 147	68.70 108	80.49 70	77.98 117	75.29 108	62.95 83	63.62 84	49.96 139	47.32 182	50.72 176	58.57 129	76.87 135	75.50 149	84.94 146	75.33 171
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs-eth3d		63.52 160	62.44 181	64.77 148	66.82 183	70.12 172	69.41 157	59.48 135	54.34 165	52.71 121	46.24 183	44.35 197	56.93 142	72.37 160	73.77 158	83.30 155	75.91 164

TDRefinement		66.09 145	65.03 163	67.31 124	69.73 172	76.75 138	75.33 106	64.55 72	60.28 107	49.72 142	45.63 184	42.83 198	60.46 123	75.75 142	75.95 145	84.08 151	78.04 150

testgi		54.39 195	57.86 193	50.35 199	71.59 163	67.24 182	54.95 201	53.25 171	43.36 199	23.78 203	44.64 185	47.87 187	24.96 210	70.45 180	68.66 182	73.60 195	62.78 201

EG-PatchMatch MVS		67.24 139	66.94 144	67.60 119	78.73 78	81.35 73	73.28 138	59.49 134	46.89 194	51.42 130	43.65 186	53.49 149	55.50 156	81.38 65	80.66 69	87.15 76	81.17 125

CMPMVS	47.78 17	62.49 167	62.52 179	62.46 160	70.01 170	70.66 171	62.97 185	51.84 180	51.98 174	56.71 100	42.87 187	53.62 145	57.80 134	72.23 163	70.37 172	75.45 189	75.91 164
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpnnormal		64.27 157	63.64 172	65.02 145	75.84 108	75.61 149	71.24 151	62.52 98	47.79 191	42.97 173	42.65 188	44.49 196	52.66 169	78.77 103	76.86 120	84.88 147	79.29 143

CHOSEN 1792x2688		69.20 111	69.26 111	69.13 103	76.86 100	78.93 101	77.27 100	60.12 129	61.86 95	54.42 110	42.54 189	61.61 96	66.91 72	78.55 105	78.14 98	79.23 174	83.23 101

MDA-MVSNet-bldmvs		53.37 198	53.01 200	53.79 195	43.67 220	67.95 180	59.69 194	57.92 154	43.69 198	32.41 192	41.47 190	27.89 218	52.38 170	56.97 214	65.99 194	76.68 182	67.13 192

testpf		47.41 203	48.47 209	46.18 203	66.30 184	50.67 214	48.15 210	42.60 211	37.10 210	28.75 196	40.97 191	39.01 205	30.82 202	52.95 217	53.74 216	60.46 215	64.87 195

FMVSNet5		57.24 188	60.02 190	53.99 193	56.45 207	62.74 199	65.27 176	47.03 198	55.14 156	39.55 181	40.88 192	53.42 154	41.83 186	72.35 161	71.10 170	73.79 194	64.50 197

N_pmnet		47.35 204	50.13 203	44.11 207	59.98 198	51.64 213	51.86 204	44.80 206	49.58 187	20.76 213	40.65 193	40.05 204	29.64 203	59.84 210	55.15 213	57.63 216	54.00 214

MIMVSNet		58.52 187	61.34 186	55.22 189	60.76 196	67.01 183	66.81 167	49.02 191	56.43 143	38.90 182	40.59 194	54.54 140	40.57 192	73.16 158	71.65 166	75.30 190	66.00 194

HyFIR lowres test		69.47 107	68.94 114	70.09 95	76.77 101	82.93 66	76.63 104	60.17 125	59.00 115	54.03 113	40.54 195	65.23 87	67.89 65	76.54 140	78.30 94	85.03 144	80.07 136

gm-plane-assit		57.00 189	57.62 195	56.28 186	76.10 104	62.43 202	47.62 211	46.57 199	33.84 214	23.24 205	37.52 196	40.19 203	59.61 127	79.81 89	77.55 108	84.55 149	72.03 182

pmmvs6		62.41 168	62.88 175	61.87 162	71.38 164	75.18 156	67.76 163	59.45 136	41.64 202	42.52 176	37.33 197	52.91 158	46.87 178	77.67 118	76.26 141	83.23 156	79.18 145

test20.03		53.93 196	56.28 196	51.19 198	72.19 155	65.83 187	53.20 203	61.08 110	42.74 200	22.08 210	37.07 198	45.76 194	24.29 214	70.44 181	69.04 178	74.31 193	63.05 200

gg-mvs-nofinetune		62.55 165	65.05 162	59.62 174	78.72 79	77.61 123	70.83 152	53.63 169	39.71 206	22.04 211	36.36 199	64.32 89	47.53 177	81.16 70	79.03 87	85.00 145	77.17 156

test2356		47.20 205	48.62 208	45.54 205	56.38 208	54.89 209	50.62 205	45.08 205	38.65 207	23.40 204	36.23 200	31.10 212	29.31 204	62.76 202	62.49 202	68.48 207	54.23 213

LP		53.62 197	53.43 197	53.83 194	58.51 204	62.59 201	57.31 197	46.04 202	47.86 190	42.69 175	36.08 201	36.86 206	46.53 179	64.38 198	64.25 197	71.92 199	62.00 203

FPMVS		51.87 199	50.00 204	54.07 192	66.83 182	57.25 205	60.25 193	50.91 183	50.25 181	34.36 189	36.04 202	32.02 210	41.49 188	58.98 212	56.07 212	70.56 204	59.36 207

new-patchmatchnet		46.97 206	49.47 205	44.05 208	62.82 191	56.55 206	45.35 212	52.01 178	42.47 201	17.04 218	35.73 203	35.21 207	21.84 219	61.27 205	54.83 214	65.26 213	60.26 204

testus		45.61 209	49.06 207	41.59 210	56.13 209	55.28 208	43.51 213	39.64 214	37.74 208	18.23 215	35.52 204	31.28 211	24.69 212	62.46 203	62.90 200	67.33 209	58.26 209

Anonymous20231206		56.36 191	57.80 194	54.67 191	70.08 169	66.39 186	60.46 192	57.54 155	49.50 188	29.30 195	33.86 205	46.64 190	35.18 197	70.44 181	68.88 180	75.47 188	68.88 190

MVS-HIRNet		54.41 194	52.10 201	57.11 184	58.99 201	56.10 207	49.68 208	49.10 190	46.18 196	52.15 126	33.18 206	46.11 193	56.10 148	63.19 201	59.70 209	76.64 183	60.25 205

pmmvs3		47.65 202	49.08 206	45.99 204	44.61 217	54.79 210	50.04 206	31.95 221	33.91 213	29.90 193	30.37 207	33.53 209	46.31 180	63.50 199	63.67 199	73.14 197	63.77 199

new_pmnet		38.40 213	42.64 214	33.44 215	37.54 223	45.00 219	36.60 219	32.72 220	40.27 204	12.72 221	29.89 208	28.90 216	24.78 211	53.17 216	52.90 217	56.31 217	48.34 215

ambc			53.42 198		64.99 188	63.36 196	49.96 207		47.07 193	37.12 186	28.97 209	16.36 224	41.82 187	75.10 149	67.34 187	71.55 201	75.72 166

PMVS	39.38 18	46.06 208	43.30 213	49.28 201	62.93 190	38.75 221	41.88 214	53.50 170	33.33 216	35.46 188	28.90 210	31.01 213	33.04 200	58.61 213	54.63 215	68.86 206	57.88 210
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
1111		43.08 210	44.02 212	41.98 209	59.22 199	49.27 217	41.48 215	45.63 203	35.01 211	23.06 207	28.60 211	30.15 214	27.22 205	60.42 208	57.97 210	55.27 219	46.74 216

.test1245		30.81 217	29.14 219	32.77 216	59.22 199	49.27 217	41.48 215	45.63 203	35.01 211	23.06 207	28.60 211	30.15 214	27.22 205	60.42 208	0.10 223	0.01 227	0.43 225

MIMVSNet1		49.27 201	53.25 199	44.62 206	44.61 217	61.52 203	53.61 202	52.18 177	41.62 203	18.68 214	28.14 213	41.58 201	25.50 208	68.46 191	69.04 178	73.15 196	62.37 202

testmv		42.58 211	44.36 210	40.49 211	54.63 213	52.76 211	41.21 217	44.37 207	28.83 218	12.87 219	27.16 214	25.03 219	23.01 215	60.83 206	61.13 204	66.88 210	54.81 211

test1235678		42.57 212	44.36 210	40.49 211	54.63 213	52.75 212	41.21 217	44.37 207	28.82 219	12.87 219	27.15 215	25.01 220	23.01 215	60.83 206	61.13 204	66.88 210	54.81 211

tmp_tt				14.50 223	14.68 225	7.17 228	10.46 228	2.21 224	37.73 209	28.71 197	25.26 216	16.98 222	4.37 224	31.49 220	29.77 220	26.56 224

test12356		35.10 216	38.50 215	31.13 217	44.14 219	43.70 220	32.27 220	34.42 217	26.51 221	9.47 223	25.22 217	20.34 221	10.86 222	53.47 215	56.15 211	55.59 218	44.11 217

DeepMVS_CX						18.74 227	18.55 224	8.02 223	26.96 220	7.33 224	23.81 218	13.05 226	25.99 207	25.17 222		22.45 226	36.25 221

Anonymous20231211		51.46 200	50.59 202	52.46 197	67.30 179	66.70 185	55.00 200	59.22 137	29.96 217	17.62 217	19.11 219	28.74 217	35.72 196	66.42 194	69.52 175	79.92 170	73.71 179

PMMVS2		25.60 218	29.75 218	20.76 221	28.00 224	30.93 223	23.10 223	29.18 222	23.14 222	1.46 229	18.23 220	16.54 223	5.08 223	40.22 219	41.40 219	37.76 221	37.79 220

no-one		36.35 215	37.59 216	34.91 214	46.13 215	49.89 216	27.99 222	43.56 209	20.91 223	7.03 225	14.64 221	15.50 225	18.92 220	42.95 218	60.20 207	65.84 212	59.03 208

E-PMN		21.77 219	18.24 221	25.89 218	40.22 221	19.58 225	12.46 226	39.87 213	18.68 225	6.71 226	9.57 222	4.31 230	22.36 218	19.89 223	27.28 221	33.73 222	28.34 222

EMVS		20.98 220	17.15 222	25.44 219	39.51 222	19.37 226	12.66 225	39.59 215	19.10 224	6.62 227	9.27 223	4.40 229	22.43 217	17.99 224	24.40 222	31.81 223	25.53 223

MVE	19.12 19	20.47 221	23.27 220	17.20 222	12.66 226	25.41 224	10.52 227	34.14 219	14.79 226	6.53 228	8.79 224	4.68 228	16.64 221	29.49 221	41.63 218	22.73 225	38.11 219
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		36.38 214	35.80 217	37.07 213	45.76 216	33.90 222	29.81 221	48.47 194	39.91 205	18.02 216	8.00 225	8.14 227	25.14 209	59.29 211	61.02 206	55.19 220	40.31 218
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test123		0.09 222	0.14 224	0.02 224	0.00 229	0.02 229	0.02 231	0.01 226	0.09 228	0.00 232	0.30 226	0.00 232	0.08 225	0.03 226	0.09 225	0.01 227	0.45 224

testmvs		0.09 222	0.15 223	0.02 224	0.01 228	0.02 229	0.05 230	0.01 226	0.11 227	0.01 231	0.26 227	0.01 231	0.06 227	0.10 225	0.10 223	0.01 227	0.43 225

ESAPD		0.00 224	0.00 225	0.00 226	0.00 229	0.00 231	0.00 232	0.00 228	0.00 229	0.00 232	0.00 228	0.00 232	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet-low-res		0.00 224	0.00 225	0.00 226	0.00 229	0.00 231	0.00 232	0.00 228	0.00 229	0.00 232	0.00 228	0.00 232	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

sosnet		0.00 224	0.00 225	0.00 226	0.00 229	0.00 231	0.00 232	0.00 228	0.00 229	0.00 232	0.00 228	0.00 232	0.00 228	0.00 227	0.00 226	0.00 230	0.00 227

MTAPA										83.48 1		86.45 11

MTMP										82.66 3		84.91 19

Patchmatch-RL test							2.85 229

XVS					86.63 38	88.68 22	85.00 40			71.81 39		81.92 29				90.47 16

X-MVStestdata					86.63 38	88.68 22	85.00 40			71.81 39		81.92 29				90.47 16

mPP-MVS					89.90 19							81.29 34

NP-MVS									80.10 39

Patchmtry						65.80 188	65.97 173	52.74 174		52.65 122

High-resolution multi-view benchmark