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.

v1.0		71.22 28	70.80 44	71.72 1	79.01 1	81.38 1	83.23 2	58.63 2	83.92 6	62.44 13	87.06 3	85.82 2	64.54 3	79.39 5	77.99 8	82.44 17	0.00 242

APDe-MVS		77.58 2	82.93 2	71.35 3	77.86 2	80.55 3	83.38 1	57.61 8	85.57 1	61.11 16	86.10 5	82.98 5	64.76 2	78.29 13	76.78 20	83.40 6	90.20 2

ESAPD		78.11 1	83.84 1	71.42 2	77.82 3	81.32 2	82.92 3	57.81 7	84.04 5	63.19 8	88.63 1	86.00 1	64.52 4	78.71 9	77.63 13	82.26 19	90.57 1

SteuartSystems-ACMMP		75.23 9	79.60 11	70.13 10	76.81 4	78.92 9	81.74 5	57.99 5	75.30 26	59.83 22	75.69 15	78.45 20	60.48 26	80.58 2	79.77 2	83.94 3	88.52 6
Skip Steuart: Steuart Systems R&D Blog.
HPM-MVS++		76.01 6	80.47 8	70.81 5	76.60 5	74.96 32	80.18 13	58.36 3	81.96 7	63.50 7	78.80 11	82.53 8	64.40 5	78.74 8	78.84 5	81.81 28	87.46 13

MCST-MVS		73.67 21	77.39 22	69.33 16	76.26 6	78.19 14	78.77 22	54.54 26	75.33 24	59.99 21	67.96 29	79.23 18	62.43 14	78.00 17	75.71 27	84.02 2	87.30 14

CNVR-MVS		75.62 8	79.91 10	70.61 6	75.76 7	78.82 11	81.66 6	57.12 10	79.77 14	63.04 9	70.69 21	81.15 11	62.99 9	80.23 3	79.54 3	83.11 7	89.16 4

NCCC		74.27 15	77.83 21	70.13 10	75.70 8	77.41 19	80.51 11	57.09 11	78.25 18	62.28 14	65.54 35	78.26 21	62.18 16	79.13 6	78.51 6	83.01 9	87.68 12

CSCG		74.68 12	79.22 12	69.40 15	75.69 9	80.01 6	79.12 20	52.83 37	79.34 15	63.99 5	70.49 22	82.02 9	60.35 28	77.48 23	77.22 17	84.38 1	87.97 11

SMA-MVS		77.32 3	82.51 3	71.26 4	75.43 10	80.19 5	82.22 4	58.26 4	84.83 3	64.36 3	78.19 12	83.46 3	63.61 7	81.00 1	80.28 1	83.66 4	89.62 3

APD-MVS		75.80 7	80.90 7	69.86 13	75.42 11	78.48 13	81.43 8	57.44 9	80.45 12	59.32 23	85.28 6	80.82 13	63.96 6	76.89 26	76.08 25	81.58 34	88.30 8
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HSP-MVS		76.78 4	82.44 4	70.19 9	75.26 12	80.22 4	80.59 9	57.85 6	84.79 4	60.84 17	88.54 2	83.43 4	66.24 1	78.21 16	76.47 22	80.34 39	85.43 27

train_agg		73.89 18	78.25 18	68.80 20	75.25 13	72.27 50	79.75 14	56.05 18	74.87 29	58.97 24	81.83 8	79.76 17	61.05 23	77.39 24	76.01 26	81.71 31	85.61 25

TSAR-MVS + MP.		75.22 10	80.06 9	69.56 14	74.61 14	72.74 48	80.59 9	55.70 21	80.80 10	62.65 11	86.25 4	82.92 6	62.07 17	76.89 26	75.66 28	81.77 30	85.19 29

SD-MVS		74.43 13	78.87 14	69.26 17	74.39 15	73.70 43	79.06 21	55.24 23	81.04 9	62.71 10	80.18 9	82.61 7	61.70 19	75.43 37	73.92 40	82.44 17	85.22 28

ACMMP_Plus		76.15 5	81.17 5	70.30 7	74.09 16	79.47 7	81.59 7	57.09 11	81.38 8	63.89 6	79.02 10	80.48 14	62.24 15	80.05 4	79.12 4	82.94 10	88.64 5

HFP-MVS		74.87 11	78.86 16	70.21 8	73.99 17	77.91 15	80.36 12	56.63 13	78.41 17	64.27 4	74.54 17	77.75 24	62.96 10	78.70 10	77.82 10	83.02 8	86.91 16

AdaColmap		67.89 43	68.85 54	66.77 26	73.73 18	74.30 40	75.28 37	53.58 32	70.24 42	57.59 31	51.19 87	59.19 82	60.74 25	75.33 39	73.72 42	79.69 48	77.96 63

MP-MVS		74.31 14	78.87 14	68.99 18	73.49 19	78.56 12	79.25 19	56.51 14	75.33 24	60.69 19	75.30 16	79.12 19	61.81 18	77.78 20	77.93 9	82.18 24	88.06 10
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		74.25 16	77.97 20	69.91 12	73.43 20	74.06 41	79.69 15	56.44 15	80.74 11	64.98 2	68.72 27	79.98 16	62.92 11	78.24 15	77.77 12	81.99 26	86.30 18

TSAR-MVS + ACMM		72.56 24	79.07 13	64.96 37	73.24 21	73.16 47	78.50 23	48.80 60	79.34 15	55.32 38	85.04 7	81.49 10	58.57 35	75.06 40	73.75 41	75.35 109	85.61 25

CDPH-MVS		71.47 27	75.82 27	66.41 28	72.97 22	77.15 21	78.14 26	54.71 24	69.88 44	53.07 54	70.98 20	74.83 32	56.95 47	76.22 30	76.57 21	82.62 15	85.09 30

PGM-MVS		72.89 22	77.13 23	67.94 22	72.47 23	77.25 20	79.27 18	54.63 25	73.71 31	57.95 30	72.38 19	75.33 30	60.75 24	78.25 14	77.36 16	82.57 16	85.62 24

ACMMPR		73.79 20	78.41 17	68.40 21	72.35 24	77.79 16	79.32 17	56.38 16	77.67 21	58.30 28	74.16 18	76.66 25	61.40 20	78.32 12	77.80 11	82.68 14	86.51 17

OPM-MVS		69.33 34	71.05 42	67.32 24	72.34 25	75.70 29	79.57 16	56.34 17	55.21 67	53.81 52	59.51 56	68.96 49	59.67 30	77.61 22	76.44 23	82.19 23	83.88 36

X-MVS		71.18 29	75.66 28	65.96 32	71.71 26	76.96 22	77.26 29	55.88 20	72.75 34	54.48 47	64.39 39	74.47 33	54.19 59	77.84 19	77.37 15	82.21 22	85.85 22

HQP-MVS		70.88 30	75.02 29	66.05 31	71.69 27	74.47 37	77.51 28	53.17 34	72.89 33	54.88 42	70.03 24	70.48 46	57.26 43	76.02 32	75.01 32	81.78 29	86.21 19

MAR-MVS		68.04 42	70.74 45	64.90 38	71.68 28	76.33 27	74.63 42	50.48 51	63.81 53	55.52 37	54.88 73	69.90 48	57.39 42	75.42 38	74.79 34	79.71 45	80.03 52
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
mPP-MVS					71.67 29							74.36 36

CLD-MVS		67.02 47	71.57 39	61.71 50	71.01 30	74.81 34	71.62 50	38.91 171	71.86 37	60.70 18	64.97 37	67.88 56	51.88 97	76.77 29	74.98 33	76.11 99	69.75 125
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CP-MVS		72.63 23	76.95 24	67.59 23	70.67 31	75.53 30	77.95 27	56.01 19	75.65 23	58.82 25	69.16 26	76.48 26	60.46 27	77.66 21	77.20 18	81.65 32	86.97 15

ACMM	60.30 7	67.58 45	68.82 55	66.13 30	70.59 32	72.01 52	76.54 31	54.26 28	65.64 51	54.78 45	50.35 89	61.72 72	58.74 34	75.79 35	75.03 30	81.88 27	81.17 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					70.49 33	76.96 22	74.36 43			54.48 47		74.47 33				82.24 20

X-MVStestdata					70.49 33	76.96 22	74.36 43			54.48 47		74.47 33				82.24 20

ACMMP		71.57 26	75.84 26	66.59 27	70.30 35	76.85 25	78.46 24	53.95 30	73.52 32	55.56 36	70.13 23	71.36 44	58.55 36	77.00 25	76.23 24	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
abl_6				64.36 41	70.08 36	77.45 18	72.88 48	50.15 52	71.31 39	54.77 46	62.79 43	77.99 23	56.80 48			81.50 35	83.91 35

MVS_111021_HR		67.62 44	70.39 47	64.39 40	69.77 37	70.45 56	71.44 52	51.72 43	60.77 60	55.06 40	62.14 49	66.40 58	58.13 38	76.13 31	74.79 34	80.19 41	82.04 45

MSLP-MVS++		68.17 41	70.72 46	65.19 35	69.41 38	70.64 54	74.99 38	45.76 71	70.20 43	60.17 20	56.42 65	73.01 39	61.14 21	72.80 48	70.54 52	79.70 46	81.42 47

LGP-MVS_train		68.87 36	72.03 38	65.18 36	69.33 39	74.03 42	76.67 30	53.88 31	68.46 46	52.05 57	63.21 41	63.89 62	56.31 49	75.99 33	74.43 36	82.83 12	84.18 32

ACMP	61.42 5	68.72 39	71.37 40	65.64 34	69.06 40	74.45 38	75.88 34	53.30 33	68.10 47	55.74 35	61.53 52	62.29 68	56.97 46	74.70 41	74.23 38	82.88 11	84.31 31
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DeepC-MVS_fast	65.08 3	72.00 25	76.11 25	67.21 25	68.93 41	77.46 17	76.54 31	54.35 27	74.92 28	58.64 27	65.18 36	74.04 38	62.62 12	77.92 18	77.02 19	82.16 25	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
casdiffmvs1		68.43 40	72.04 37	64.22 43	68.59 42	74.31 39	75.76 35	46.07 70	69.19 45	56.14 33	65.82 34	72.62 42	59.03 33	71.40 54	68.78 66	79.74 44	82.19 43

CANet		68.77 37	73.01 33	63.83 44	68.30 43	75.19 31	73.73 46	47.90 62	63.86 52	54.84 43	67.51 31	74.36 36	57.62 40	74.22 43	73.57 44	80.56 38	82.36 41

TSAR-MVS + GP.		69.71 31	73.92 32	64.80 39	68.27 44	70.56 55	71.90 49	50.75 47	71.38 38	57.46 32	68.68 28	75.42 29	60.10 29	73.47 45	73.99 39	80.32 40	83.97 34

3Dnovator+	62.63 4	69.51 32	72.62 35	65.88 33	68.21 45	76.47 26	73.50 47	52.74 38	70.85 40	58.65 26	55.97 67	69.95 47	61.11 22	76.80 28	75.09 29	81.09 37	83.23 40

MVS_0304		69.49 33	73.96 31	64.28 42	67.92 46	76.13 28	74.90 39	47.60 63	63.29 55	54.09 51	67.44 32	76.35 27	59.53 31	75.81 34	75.03 30	81.62 33	83.70 37

DeepC-MVS	66.32 2	73.85 19	78.10 19	68.90 19	67.92 46	79.31 8	78.16 25	59.28 1	78.24 19	61.13 15	67.36 33	76.10 28	63.40 8	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
DeepPCF-MVS	66.49 1	74.25 16	80.97 6	66.41 28	67.75 48	78.87 10	75.61 36	54.16 29	84.86 2	58.22 29	77.94 13	81.01 12	62.52 13	78.34 11	77.38 14	80.16 42	88.40 7

casdiffmvs		66.78 49	69.32 53	63.81 45	67.28 49	73.52 45	74.65 41	48.35 61	60.14 62	54.81 44	62.56 47	68.80 50	57.75 39	70.88 60	68.99 64	80.03 43	80.36 50

UA-Net		58.50 90	64.68 70	51.30 122	66.97 50	67.13 76	53.68 164	45.65 74	49.51 94	31.58 149	62.91 42	68.47 52	35.85 173	68.20 86	67.28 81	74.03 118	69.24 135

PHI-MVS		69.27 35	74.84 30	62.76 49	66.83 51	74.83 33	73.88 45	49.32 56	70.61 41	50.93 58	69.62 25	74.84 31	57.25 44	75.53 36	74.32 37	78.35 58	84.17 33

3Dnovator	60.86 6	66.99 48	70.32 48	63.11 47	66.63 52	74.52 35	71.56 51	45.76 71	67.37 49	55.00 41	54.31 77	68.19 54	58.49 37	73.97 44	73.63 43	81.22 36	80.23 51

EPNet		65.14 54	69.54 51	60.00 54	66.61 53	67.67 69	67.53 58	55.32 22	62.67 57	46.22 75	67.74 30	65.93 59	48.07 113	72.17 50	72.12 46	76.28 90	78.47 60
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OpenMVS	57.13 9	62.81 58	65.75 63	59.39 56	66.47 54	69.52 58	64.26 103	43.07 126	61.34 59	50.19 61	47.29 123	64.41 61	54.60 58	70.18 66	68.62 69	77.73 60	78.89 57

ACMH	52.42 13	58.24 101	59.56 115	56.70 91	66.34 55	69.59 57	66.71 74	49.12 57	46.08 131	28.90 162	42.67 171	41.20 192	52.60 87	71.39 55	70.28 54	76.51 85	75.72 84
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		58.46 92	58.97 122	57.85 68	66.27 56	66.23 85	67.72 56	42.33 143	53.43 73	43.68 99	43.39 157	45.35 160	49.75 104	68.66 77	67.77 74	77.38 66	67.96 138

QAPM		65.27 52	69.49 52	60.35 52	65.43 57	72.20 51	65.69 93	47.23 64	63.46 54	49.14 63	53.56 78	71.04 45	57.01 45	72.60 49	71.41 49	77.62 62	82.14 44

CPTT-MVS		68.76 38	73.01 33	63.81 45	65.42 58	73.66 44	76.39 33	52.08 39	72.61 35	50.33 60	60.73 54	72.65 41	59.43 32	73.32 46	72.12 46	79.19 52	85.99 21

MS-PatchMatch		58.19 103	60.20 95	55.85 97	65.17 59	64.16 107	64.82 98	41.48 154	50.95 85	42.17 108	45.38 142	56.42 91	48.08 112	68.30 82	66.70 88	73.39 126	69.46 133

PCF-MVS	59.98 8	67.32 46	71.04 43	62.97 48	64.77 60	74.49 36	74.78 40	49.54 54	67.44 48	54.39 50	58.35 60	72.81 40	55.79 55	71.54 53	69.24 60	78.57 54	83.41 38
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EG-PatchMatch MVS		56.98 111	58.24 128	55.50 99	64.66 61	68.62 61	61.48 112	43.63 109	38.44 203	41.44 110	38.05 193	46.18 157	43.95 130	71.71 52	70.61 51	77.87 59	74.08 110

Effi-MVS+-dtu		60.34 68	62.32 77	58.03 63	64.31 62	67.44 72	65.99 89	42.26 144	49.55 92	42.00 109	48.92 101	59.79 80	56.27 50	68.07 92	67.03 82	77.35 67	75.45 87

FC-MVSNet-train		58.40 98	63.15 75	52.85 115	64.29 63	61.84 126	55.98 145	46.47 66	53.06 76	34.96 138	61.95 51	56.37 93	39.49 151	68.67 76	68.36 70	75.92 104	71.81 114

Effi-MVS+		63.28 56	65.96 61	60.17 53	64.26 64	68.06 64	68.78 55	45.71 73	54.08 71	46.64 70	55.92 68	63.13 66	55.94 53	70.38 64	71.43 48	79.68 49	78.70 58

LS3D		60.20 69	61.70 78	58.45 59	64.18 65	67.77 66	67.19 60	48.84 59	61.67 58	41.27 112	45.89 137	51.81 121	54.18 60	68.78 74	66.50 96	75.03 110	69.48 131

ACMH+	53.71 12	59.26 72	60.28 92	58.06 61	64.17 66	68.46 62	67.51 59	50.93 46	52.46 81	35.83 135	40.83 183	45.12 163	52.32 93	69.88 67	69.00 63	77.59 64	76.21 81

DELS-MVS		65.87 50	70.30 49	60.71 51	64.05 67	72.68 49	70.90 53	45.43 75	57.49 63	49.05 64	64.43 38	68.66 51	55.11 57	74.31 42	73.02 45	79.70 46	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 51	72.06 36	58.11 60	63.94 68	71.05 53	64.49 101	43.18 121	74.08 30	47.35 67	64.17 40	71.97 43	51.17 100	71.87 51	70.74 50	78.51 56	80.56 49

Anonymous202405211			60.60 85		63.44 69	66.71 82	61.00 117	47.23 64	50.62 88		36.85 196	60.63 77	43.03 139	69.17 71	67.72 76	75.41 107	72.54 112

tpmp4_e23		56.84 115	57.14 135	56.49 95	62.45 70	62.05 124	67.57 57	41.56 152	54.17 70	48.57 66	49.18 93	46.54 153	50.44 103	61.93 170	58.82 184	68.34 177	67.28 143

gg-mvs-nofinetune		49.07 172	52.56 172	45.00 173	61.99 71	59.78 149	53.55 167	41.63 148	31.62 220	12.08 215	29.56 213	53.28 102	29.57 193	66.27 136	64.49 141	71.19 162	62.92 178

Anonymous20240521		59.49 70	64.00 73	54.23 103	61.81 72	64.33 106	61.42 113	43.77 96	52.85 79	38.94 122	55.62 70	62.15 70	43.24 138	69.39 70	67.66 77	76.22 95	75.97 82

Anonymous20231211		57.71 107	60.79 82	54.13 105	61.68 73	65.81 94	60.81 118	43.70 106	51.97 83	39.67 118	34.82 201	63.59 63	43.31 136	68.55 80	66.63 91	75.59 105	74.13 109

IS_MVSNet		57.95 105	64.26 72	50.60 124	61.62 74	65.25 100	57.18 134	45.42 76	50.79 86	26.49 176	57.81 62	60.05 79	34.51 177	71.24 58	70.20 56	78.36 57	74.44 102

NR-MVSNet		55.35 124	59.46 117	50.56 125	61.33 75	62.97 120	57.91 131	51.80 41	48.62 109	20.59 194	51.99 84	44.73 171	34.10 180	68.58 78	68.64 68	77.66 61	70.67 122

MVS_111021_LR		63.05 57	66.43 58	59.10 57	61.33 75	63.77 109	65.87 91	43.58 110	60.20 61	53.70 53	62.09 50	62.38 67	55.84 54	70.24 65	68.08 71	74.30 114	78.28 62

EPP-MVSNet		59.39 71	65.45 65	52.32 119	60.96 77	67.70 68	58.42 128	44.75 81	49.71 91	27.23 174	59.03 57	62.20 69	43.34 135	70.71 61	69.13 61	79.25 51	79.63 54

TransMVSNet (Re)		51.92 151	55.38 145	47.88 157	60.95 78	59.90 148	53.95 161	45.14 78	39.47 194	24.85 182	43.87 152	46.51 154	29.15 194	67.55 108	65.23 130	73.26 130	65.16 167

gm-plane-assit		44.74 198	45.95 206	43.33 183	60.88 79	46.79 212	36.97 221	32.24 213	24.15 230	11.79 216	29.26 216	32.97 218	46.64 120	65.09 153	62.95 158	71.45 160	60.42 188

DI_MVS_plusplus_trai		61.88 62	65.17 67	58.06 61	60.05 80	65.26 99	66.03 88	44.22 86	55.75 65	46.73 69	54.64 75	68.12 55	54.13 61	69.13 72	66.66 89	77.18 68	76.61 73

PVSNet_Blended_VisFu		63.65 55	66.92 56	59.83 55	60.03 81	73.44 46	66.33 84	48.95 58	52.20 82	50.81 59	56.07 66	60.25 78	53.56 64	73.23 47	70.01 57	79.30 50	83.24 39

UniMVSNet_NR-MVSNet		56.94 113	61.14 80	52.05 121	60.02 82	65.21 101	57.44 132	52.93 36	49.37 95	24.31 185	54.62 76	50.54 128	39.04 153	68.69 75	68.84 65	78.53 55	70.72 118

IterMVS-LS		58.30 100	61.39 79	54.71 102	59.92 83	58.40 165	59.42 125	43.64 107	48.71 105	40.25 116	57.53 63	58.55 84	52.15 95	65.42 151	65.34 127	72.85 133	75.77 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs		61.60 65	65.90 62	56.58 93	59.78 84	65.35 96	66.56 82	42.79 132	55.46 66	46.47 71	61.43 53	65.52 60	51.16 101	68.04 96	66.17 101	72.71 139	79.31 55

MVS_Test		62.40 61	66.23 60	57.94 64	59.77 85	64.77 104	66.50 83	41.76 147	57.26 64	49.33 62	62.68 45	67.47 57	53.50 67	68.57 79	66.25 98	76.77 75	76.58 75

TranMVSNet+NR-MVSNet		55.87 118	60.14 100	50.88 123	59.46 86	63.82 108	57.93 130	52.98 35	48.94 100	20.52 195	52.87 80	47.33 144	36.81 170	69.12 73	69.03 62	77.56 65	69.89 124

Fast-Effi-MVS+		60.36 67	63.35 74	56.87 86	58.70 87	65.86 93	65.08 97	37.11 184	53.00 78	45.36 83	52.12 83	56.07 95	56.27 50	71.28 57	69.42 59	78.71 53	75.69 85

IB-MVS	54.11 11	58.36 99	60.70 83	55.62 98	58.67 88	68.02 65	61.56 110	43.15 122	46.09 130	44.06 98	44.24 149	50.99 126	48.71 108	66.70 128	70.33 53	77.60 63	78.50 59
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
Fast-Effi-MVS+-dtu		56.30 117	59.29 119	52.82 116	58.64 89	64.89 102	65.56 94	32.89 209	45.80 139	35.04 137	45.89 137	54.14 99	49.41 105	67.16 121	66.45 97	75.37 108	70.69 120

CNLPA		62.78 59	66.31 59	58.65 58	58.47 90	68.41 63	65.98 90	41.22 157	78.02 20	56.04 34	46.65 126	59.50 81	57.50 41	69.67 68	65.27 129	72.70 143	76.67 72

tpm cat1		53.30 137	53.41 166	53.17 113	58.16 91	59.15 158	63.73 106	38.27 179	50.73 87	46.98 68	45.57 141	44.00 178	49.20 106	55.90 205	54.02 204	62.65 198	64.50 172

v1144		58.88 77	60.16 99	57.39 71	58.03 92	67.26 73	67.14 62	44.46 84	45.17 143	44.33 97	47.81 120	49.92 132	53.20 73	67.77 104	66.62 92	77.15 69	76.58 75

v7		59.19 74	60.62 84	57.53 69	57.96 93	67.19 75	67.09 63	44.28 85	46.84 121	45.45 81	48.19 115	51.06 123	53.62 63	67.84 99	66.59 93	76.79 72	76.60 74

v10		59.17 75	60.60 85	57.50 70	57.95 94	66.73 79	67.09 63	44.11 87	46.85 120	45.42 82	48.18 117	51.07 122	53.63 62	67.84 99	66.59 93	76.79 72	76.92 70

v1192		58.51 89	59.66 112	57.17 72	57.82 95	67.72 67	66.21 87	44.83 80	44.15 150	43.49 100	46.68 125	47.94 136	53.55 65	67.39 118	66.51 95	77.13 70	77.20 68

DWT-MVSNet_training		53.80 132	54.31 158	53.21 111	57.65 96	59.04 159	60.65 119	40.11 168	46.35 125	42.77 103	49.07 94	41.07 193	51.06 102	58.62 188	58.96 183	67.00 186	67.06 144

v144192		58.23 102	59.40 118	56.87 86	57.56 97	66.89 77	65.70 92	45.01 79	44.06 151	42.88 102	46.61 127	48.09 135	53.49 68	66.94 124	65.90 105	76.61 83	77.29 66

v1921920		57.89 106	59.02 121	56.58 93	57.55 98	66.66 83	64.72 100	44.70 82	43.55 154	42.73 104	46.17 135	46.93 150	53.51 66	66.78 127	65.75 109	76.29 89	77.28 67

v13		58.44 94	59.72 111	56.94 76	57.55 98	63.51 110	66.86 66	42.81 131	45.90 135	44.98 88	48.17 118	51.87 117	52.68 82	68.20 86	65.78 107	76.78 74	74.63 98

v12		58.44 94	59.74 110	56.92 80	57.54 100	63.50 111	66.84 69	42.77 133	45.96 133	44.95 89	48.31 111	51.94 116	52.67 83	68.14 89	65.75 109	76.75 76	74.55 100

v11		58.19 103	59.47 116	56.70 91	57.54 100	63.42 115	66.28 86	42.49 139	45.62 141	44.59 95	48.16 119	50.78 127	52.84 74	67.80 103	65.76 108	76.49 86	74.76 91

Vis-MVSNet (Re-imp)		50.37 162	57.73 132	41.80 193	57.53 102	54.35 184	45.70 201	45.24 77	49.80 90	13.43 213	58.23 61	56.42 91	20.11 214	62.96 160	63.36 153	68.76 176	58.96 194

CostFormer		56.57 116	59.13 120	53.60 107	57.52 103	61.12 136	66.94 65	35.95 191	53.44 72	44.68 91	55.87 69	54.44 98	48.21 111	60.37 178	58.33 186	68.27 179	70.33 123

V9		58.45 93	59.75 107	56.92 80	57.51 104	63.49 112	66.86 66	42.73 134	46.07 132	45.05 86	48.45 110	51.99 115	52.66 84	68.04 96	65.75 109	76.72 77	74.50 101

thres400		52.38 145	55.51 142	48.74 146	57.49 105	60.10 146	55.45 149	43.54 111	42.90 163	26.72 175	43.34 159	45.03 169	36.61 171	66.20 140	64.53 140	72.66 144	66.43 149

V14		58.44 94	59.75 107	56.90 83	57.48 106	63.46 113	66.85 68	42.68 135	46.16 129	45.03 87	48.57 108	52.04 114	52.65 85	67.93 98	65.72 112	76.69 78	74.40 103

v15		58.43 97	59.75 107	56.88 85	57.45 107	63.44 114	66.84 69	42.65 136	46.24 128	45.07 85	48.68 107	52.07 113	52.63 86	67.84 99	65.70 113	76.65 79	74.31 106

thres600view7		51.91 152	55.14 149	48.14 153	57.43 108	60.18 142	54.60 159	43.73 103	42.61 171	25.20 180	43.10 165	44.47 175	35.19 175	66.36 133	63.28 155	72.66 144	66.01 156

view600		51.96 150	55.13 150	48.27 152	57.41 109	60.05 147	54.74 158	43.64 107	42.57 172	25.88 178	43.11 164	44.48 174	35.34 174	66.27 136	63.61 150	72.61 147	65.80 158

thres200		52.39 144	55.37 147	48.90 144	57.39 110	60.18 142	55.60 147	43.73 103	42.93 162	27.41 172	43.35 158	45.09 164	36.61 171	66.36 133	63.92 148	72.66 144	65.78 159

v17		58.69 83	60.19 98	56.94 76	57.38 111	63.37 116	66.67 79	42.47 141	48.52 111	46.10 76	48.90 102	53.00 104	52.84 74	67.58 106	65.60 119	76.19 96	74.38 104

view800		51.55 154	54.89 152	47.66 160	57.37 112	59.77 150	53.62 165	43.72 105	42.22 174	24.94 181	42.80 169	43.81 180	33.94 181	66.09 141	64.38 142	72.39 150	65.14 168

v1141		58.56 86	60.05 104	56.81 89	57.36 113	66.18 87	66.80 71	43.11 123	45.87 137	44.60 93	48.71 105	51.83 119	52.38 90	67.46 113	65.64 117	76.63 80	74.66 95

divwei89l23v2f112		58.56 86	60.05 104	56.81 89	57.36 113	66.18 87	66.80 71	43.11 123	45.89 136	44.60 93	48.71 105	51.84 118	52.38 90	67.45 115	65.65 114	76.63 80	74.66 95

v1		58.56 86	60.06 103	56.83 88	57.36 113	66.19 86	66.80 71	43.10 125	45.87 137	44.68 91	48.73 104	51.83 119	52.38 90	67.45 115	65.65 114	76.63 80	74.66 95

v16		58.71 82	60.20 95	56.97 74	57.35 116	63.36 117	66.67 79	42.49 139	48.69 107	46.36 73	48.87 103	52.92 109	52.82 76	67.57 107	65.58 123	76.15 98	74.38 104

v8		58.88 77	60.57 87	56.92 80	57.35 116	65.69 95	66.69 78	42.64 137	47.89 114	45.77 78	49.04 95	52.98 105	52.77 80	67.51 112	65.57 124	76.26 91	75.30 89

tfpn		50.58 159	53.65 164	47.00 164	57.34 118	59.31 154	52.41 170	43.76 100	41.81 178	23.86 187	42.49 172	37.80 208	32.63 186	65.68 148	64.02 146	71.99 156	64.41 173

v6		58.89 76	60.54 88	56.96 75	57.34 118	66.13 89	66.71 74	42.84 128	47.85 115	45.80 77	49.04 95	52.95 106	52.79 77	67.53 109	65.59 120	76.26 91	74.73 92

v1neww		58.88 77	60.54 88	56.94 76	57.33 120	66.13 89	66.70 76	42.84 128	47.84 116	45.74 79	49.02 97	52.93 107	52.78 78	67.53 109	65.59 120	76.26 91	74.73 92

v7new		58.88 77	60.54 88	56.94 76	57.33 120	66.13 89	66.70 76	42.84 128	47.84 116	45.74 79	49.02 97	52.93 107	52.78 78	67.53 109	65.59 120	76.26 91	74.73 92

tfpn111		52.44 142	55.38 145	49.01 140	57.31 122	60.24 139	55.42 150	43.77 96	42.85 164	27.51 168	42.03 177	45.06 165	37.32 162	66.38 130	64.54 136	72.71 139	66.54 146

conf200view11		52.51 141	55.51 142	49.01 140	57.31 122	60.24 139	55.42 150	43.77 96	42.85 164	27.51 168	43.00 166	45.06 165	37.32 162	66.38 130	64.54 136	72.71 139	66.54 146

thres100view900		52.04 147	54.81 154	48.80 145	57.31 122	59.33 153	55.30 155	42.92 127	42.85 164	27.81 166	43.00 166	45.06 165	36.99 168	64.74 154	63.51 151	72.47 148	65.21 166

tfpn200view9		52.53 140	55.51 142	49.06 138	57.31 122	60.24 139	55.42 150	43.77 96	42.85 164	27.81 166	43.00 166	45.06 165	37.32 162	66.38 130	64.54 136	72.71 139	66.54 146

conf0.01		52.02 148	54.62 155	49.00 142	57.30 126	60.17 144	55.42 150	43.76 100	42.85 164	27.49 170	43.12 163	39.71 201	37.32 162	66.26 138	64.54 136	72.72 136	65.66 161

v1240		57.55 108	58.63 124	56.29 96	57.30 126	66.48 84	63.77 105	44.56 83	42.77 170	42.48 106	45.64 140	46.28 155	53.46 69	66.32 135	65.80 106	76.16 97	77.13 69

conf0.002		51.76 153	54.13 160	49.00 142	57.28 128	60.15 145	55.42 150	43.75 102	42.85 164	27.49 170	43.13 162	37.12 213	37.32 162	66.23 139	64.17 143	72.72 136	65.24 165

v18		58.68 85	60.20 95	56.90 83	57.26 129	63.28 118	66.58 81	42.42 142	48.86 101	46.37 72	49.01 99	53.05 103	52.74 81	67.40 117	65.52 125	76.02 103	74.28 107

CHOSEN 1792x2688		55.85 119	58.01 129	53.33 109	57.26 129	62.82 122	63.29 109	41.55 153	46.65 123	38.34 123	34.55 202	53.50 100	52.43 89	67.10 122	67.56 79	67.13 183	73.92 111

PVSNet_BlendedMVS		61.63 63	64.82 68	57.91 66	57.21 131	67.55 70	63.47 107	46.08 68	54.72 68	52.46 55	58.59 58	60.73 74	51.82 98	70.46 62	65.20 131	76.44 87	76.50 78

PVSNet_Blended		61.63 63	64.82 68	57.91 66	57.21 131	67.55 70	63.47 107	46.08 68	54.72 68	52.46 55	58.59 58	60.73 74	51.82 98	70.46 62	65.20 131	76.44 87	76.50 78

v2v482		58.69 83	60.12 102	57.03 73	57.16 133	66.05 92	67.17 61	43.52 112	46.33 126	45.19 84	49.46 92	51.02 124	52.51 88	67.30 119	66.03 102	76.61 83	74.62 99

conf0.05thres1000		50.64 158	53.84 161	46.92 165	57.02 134	59.29 155	52.29 171	43.80 95	39.84 193	23.81 188	39.26 190	43.14 183	32.52 187	65.74 145	64.04 144	72.05 155	65.53 162

tfpnnormal		50.16 164	52.19 177	47.78 159	56.86 135	58.37 166	54.15 160	44.01 93	38.35 205	25.94 177	36.10 197	37.89 207	34.50 178	65.93 142	63.42 152	71.26 161	65.28 164

HyFIR lowres test		56.87 114	58.60 125	54.84 101	56.62 136	69.27 59	64.77 99	42.21 145	45.66 140	37.50 128	33.08 204	57.47 89	53.33 70	65.46 150	67.94 72	74.60 111	71.35 116

v7n		55.67 120	57.46 134	53.59 108	56.06 137	65.29 98	61.06 116	43.26 120	40.17 190	37.99 125	40.79 184	45.27 162	47.09 117	67.67 105	66.21 99	76.08 100	76.82 71

dps		50.42 161	51.20 188	49.51 132	55.88 138	56.07 175	53.73 162	38.89 172	43.66 152	40.36 115	45.66 139	37.63 210	45.23 126	59.05 181	56.18 189	62.94 197	60.16 189

CANet_DTU		58.88 77	64.68 70	52.12 120	55.77 139	66.75 78	63.92 104	37.04 185	53.32 74	37.45 129	59.81 55	61.81 71	44.43 129	68.25 83	67.47 80	74.12 117	75.33 88

WR-MVS		48.78 174	55.06 151	41.45 195	55.50 140	60.40 138	43.77 210	49.99 53	41.92 176	8.10 228	45.24 145	45.56 158	17.47 216	61.57 172	64.60 135	73.85 119	66.14 155

UniMVSNet (Re)		55.15 127	60.39 91	49.03 139	55.31 141	64.59 105	55.77 146	50.63 48	48.66 108	20.95 193	51.47 86	50.40 129	34.41 179	67.81 102	67.89 73	77.11 71	71.88 113

test-LLR		49.28 168	50.29 192	48.10 154	55.26 142	47.16 207	49.52 177	43.48 115	39.22 195	31.98 145	43.65 155	47.93 137	41.29 146	56.80 196	55.36 195	67.08 184	61.94 182

test0.0.03 1		43.15 203	46.95 205	38.72 204	55.26 142	50.56 196	42.48 213	43.48 115	38.16 207	15.11 209	35.07 200	44.69 172	16.47 219	55.95 204	54.34 203	59.54 204	49.87 218

GA-MVS		55.67 120	58.33 126	52.58 118	55.23 144	63.09 119	61.08 115	40.15 167	42.95 161	37.02 131	52.61 81	47.68 139	47.51 115	65.92 143	65.35 126	74.49 113	70.68 121

thresconf0.02		48.17 179	51.22 187	44.60 176	55.14 145	55.73 177	48.95 181	41.35 156	43.43 158	21.23 191	42.03 177	37.25 212	31.19 189	62.33 166	60.61 175	69.76 169	57.17 199

tfpnview11		47.58 185	51.57 180	42.92 186	54.94 146	55.30 179	46.21 195	41.58 151	42.10 175	18.54 200	42.25 174	41.54 189	27.12 200	62.29 167	61.12 168	69.15 171	56.40 203

tfpn_n400		47.56 186	51.56 181	42.90 187	54.91 147	55.28 180	46.21 195	41.59 149	41.51 181	18.54 200	42.25 174	41.54 189	27.12 200	62.41 164	61.02 170	69.05 172	56.90 201

tfpnconf		47.56 186	51.56 181	42.90 187	54.91 147	55.28 180	46.21 195	41.59 149	41.51 181	18.54 200	42.25 174	41.54 189	27.12 200	62.41 164	61.02 170	69.05 172	56.90 201

DTE-MVSNet		48.03 182	53.28 168	41.91 192	54.64 149	57.50 171	44.63 208	51.66 44	41.02 185	7.97 229	46.26 132	40.90 194	20.24 213	60.45 177	62.89 159	72.33 152	63.97 174

pmmvs4		54.66 130	56.07 140	53.00 114	54.63 150	57.08 173	60.43 123	44.10 88	51.69 84	40.55 114	46.55 130	44.79 170	45.95 124	62.54 162	63.66 149	72.36 151	66.20 153

DU-MVS		55.41 123	59.59 113	50.54 126	54.60 151	62.97 120	57.44 132	51.80 41	48.62 109	24.31 185	51.99 84	47.00 149	39.04 153	68.11 90	67.75 75	76.03 102	70.72 118

Baseline_NR-MVSNet		53.50 135	57.89 130	48.37 150	54.60 151	59.25 157	56.10 142	51.84 40	49.32 96	17.92 205	45.38 142	47.68 139	36.93 169	68.11 90	65.95 103	72.84 134	69.57 129

v148		55.58 122	57.61 133	53.20 112	54.59 153	61.86 125	61.18 114	38.70 176	44.30 149	42.25 107	47.53 121	50.24 131	48.73 107	65.15 152	62.61 163	73.79 120	71.61 115

tfpn1000		46.75 192	51.24 186	41.51 194	54.39 154	55.60 178	43.85 209	40.90 159	41.82 177	16.71 207	41.26 181	41.58 188	23.96 207	60.76 175	60.27 178	69.26 170	57.42 198

tfpn_ndepth		48.34 177	52.27 175	43.76 179	54.35 155	56.46 174	47.24 191	40.92 158	43.45 156	21.04 192	41.16 182	43.22 182	28.90 197	61.57 172	60.65 174	70.12 167	59.34 192

PEN-MVS		49.21 170	54.32 157	43.24 185	54.33 156	59.26 156	47.04 192	51.37 45	41.67 179	9.97 223	46.22 133	41.80 187	22.97 211	60.52 176	64.03 145	73.73 121	66.75 145

pm-mvs1		51.02 157	55.55 141	45.73 169	54.16 157	58.52 163	50.92 174	42.56 138	40.32 189	25.67 179	43.66 154	50.34 130	30.06 192	65.85 144	63.97 147	70.99 163	66.21 152

EPNet_dtu		52.05 146	58.26 127	44.81 174	54.10 158	50.09 199	52.01 172	40.82 162	53.03 77	27.41 172	54.90 72	57.96 88	26.72 203	62.97 159	62.70 162	67.78 181	66.19 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		48.82 173	51.27 185	45.96 168	54.10 158	47.35 206	56.05 143	30.23 214	46.70 122	43.21 101	52.54 82	47.55 142	37.28 167	54.11 210	50.50 215	54.90 216	60.12 190

CDS-MVSNet		52.42 143	57.06 137	47.02 163	53.92 160	58.30 167	55.50 148	46.47 66	42.52 173	29.38 160	49.50 91	52.85 110	28.49 198	66.70 128	66.89 86	68.34 177	62.63 181
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test20.03		40.38 212	44.20 212	35.92 211	53.73 161	49.05 200	38.54 219	43.49 114	32.55 217	9.54 224	27.88 218	39.12 203	12.24 230	56.28 201	54.69 200	57.96 209	49.83 219

Vis-MVSNet		58.48 91	65.70 64	50.06 129	53.40 162	67.20 74	60.24 124	43.32 118	48.83 102	30.23 155	62.38 48	61.61 73	40.35 149	71.03 59	69.77 58	72.82 135	79.11 56
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PLC	52.09 14	59.21 73	62.47 76	55.41 100	53.24 163	64.84 103	64.47 102	40.41 165	65.92 50	44.53 96	46.19 134	55.69 96	55.33 56	68.24 85	65.30 128	74.50 112	71.09 117
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OMC-MVS		65.16 53	71.35 41	57.94 64	52.95 164	68.82 60	69.00 54	38.28 178	79.89 13	55.20 39	62.76 44	68.31 53	56.14 52	71.30 56	68.70 67	76.06 101	79.67 53

tpmrst		48.08 180	49.88 196	45.98 167	52.71 165	48.11 204	53.62 165	33.70 202	48.70 106	39.74 117	48.96 100	46.23 156	40.29 150	50.14 219	49.28 217	55.80 213	57.71 197

GBi-Net		55.20 125	60.25 93	49.31 133	52.42 166	61.44 130	57.03 135	44.04 90	49.18 97	30.47 151	48.28 112	58.19 85	38.22 156	68.05 93	66.96 83	73.69 122	69.65 126

test1		55.20 125	60.25 93	49.31 133	52.42 166	61.44 130	57.03 135	44.04 90	49.18 97	30.47 151	48.28 112	58.19 85	38.22 156	68.05 93	66.96 83	73.69 122	69.65 126

FMVSNet2		55.04 128	59.95 106	49.31 133	52.42 166	61.44 130	57.03 135	44.08 89	49.55 92	30.40 154	46.89 124	58.84 83	38.22 156	67.07 123	66.21 99	73.69 122	69.65 126

FMVSNet3		54.78 129	59.58 114	49.17 136	52.37 169	61.31 134	56.72 139	44.04 90	49.18 97	30.47 151	48.28 112	58.19 85	38.09 159	65.48 149	65.20 131	73.31 128	69.45 134

testgi		38.71 214	43.64 213	32.95 216	52.30 170	48.63 203	35.59 225	35.05 194	31.58 221	9.03 227	30.29 209	40.75 196	11.19 235	55.30 206	53.47 209	54.53 218	45.48 222

Anonymous20231206		42.28 204	45.89 207	38.07 206	51.96 171	48.98 201	43.66 211	38.81 175	38.74 201	14.32 212	26.74 219	40.90 194	20.94 212	56.64 199	54.67 201	58.71 205	54.59 205

pmmvs6		48.35 176	51.64 179	44.51 177	51.92 172	57.94 169	49.44 179	42.17 146	34.45 213	24.62 184	28.87 217	46.90 151	29.07 196	64.60 155	63.08 156	69.83 168	65.68 160

v748		52.93 138	55.29 148	50.19 128	51.90 173	61.31 134	56.54 140	40.05 169	39.12 197	34.82 140	39.93 187	43.83 179	43.66 131	64.26 156	63.32 154	74.15 116	75.28 90

PatchmatchNet		49.92 166	51.29 184	48.32 151	51.83 174	51.86 193	53.38 168	37.63 183	47.90 113	40.83 113	48.54 109	45.30 161	45.19 127	56.86 195	53.99 206	61.08 202	54.57 206
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		47.65 183	53.67 163	40.63 198	51.45 175	59.74 151	44.71 207	49.37 55	40.69 187	7.61 230	46.04 136	44.34 177	17.32 217	57.79 192	61.18 167	73.30 129	65.86 157

LTVRE_ROB	44.17 16	47.06 191	50.15 195	43.44 182	51.39 176	58.42 164	42.90 212	43.51 113	22.27 234	14.85 211	41.94 180	34.57 215	45.43 125	62.28 168	62.77 161	62.56 199	68.83 137
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
CP-MVSNet		48.37 175	53.53 165	42.34 190	51.35 177	58.01 168	46.56 193	50.54 49	41.62 180	10.61 219	46.53 131	40.68 197	23.18 209	58.71 186	61.83 165	71.81 157	67.36 142

PS-CasMVS		48.18 178	53.25 169	42.27 191	51.26 178	57.94 169	46.51 194	50.52 50	41.30 183	10.56 221	45.35 144	40.34 199	23.04 210	58.66 187	61.79 166	71.74 159	67.38 141

our_test_3					51.15 179	57.31 172	55.12 156

FMVSNet1		54.08 131	58.68 123	48.71 147	50.90 180	61.35 133	56.73 138	43.94 94	45.91 134	29.32 161	42.72 170	56.26 94	37.70 160	68.05 93	66.96 83	73.69 122	69.50 130

pmmvs-eth3d		51.33 155	52.25 176	50.26 127	50.82 181	54.65 183	56.03 144	43.45 117	43.51 155	37.20 130	39.20 191	39.04 204	42.28 141	61.85 171	62.78 160	71.78 158	64.72 170

MVSTER		57.19 109	61.11 81	52.62 117	50.82 181	58.79 161	61.55 111	37.86 181	48.81 103	41.31 111	57.43 64	52.10 112	48.60 109	68.19 88	66.75 87	75.56 106	75.68 86

ambc			45.54 210		50.66 183	52.63 191	40.99 216		38.36 204	24.67 183	22.62 226	13.94 238	29.14 195	65.71 147	58.06 187	58.60 207	67.43 140

MDTV_nov1_ep13		50.32 163	52.43 174	47.86 158	49.87 184	54.70 182	58.10 129	34.29 197	45.59 142	37.71 126	47.44 122	47.42 143	41.86 143	58.07 191	55.21 197	65.34 191	58.56 195

IterMVS		53.45 136	57.12 136	49.17 136	49.23 185	60.93 137	59.05 127	34.63 195	44.53 145	33.22 141	51.09 88	51.01 125	48.38 110	62.43 163	60.79 173	70.54 165	69.05 136
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
COLMAP_ROB	46.52 15	51.99 149	54.86 153	48.63 148	49.13 186	61.73 127	60.53 122	36.57 188	53.14 75	32.95 143	37.10 194	38.68 205	40.49 148	65.72 146	63.08 156	72.11 154	64.60 171
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CR-MVSNet		50.47 160	52.61 171	47.98 156	49.03 187	52.94 188	48.27 183	38.86 173	44.41 146	39.59 119	44.34 148	44.65 173	46.63 121	58.97 183	60.31 176	65.48 189	62.66 179

V42		56.97 112	60.14 100	53.28 110	48.16 188	62.78 123	66.30 85	37.93 180	47.44 118	42.68 105	48.19 115	52.59 111	51.90 96	67.46 113	65.94 104	72.72 136	76.55 77

MDTV_nov1_ep13_2view		47.62 184	49.72 197	45.18 172	48.05 189	53.70 186	54.90 157	33.80 201	39.90 192	29.79 158	38.85 192	41.89 186	39.17 152	58.99 182	55.55 194	65.34 191	59.17 193

EPMVS		44.66 199	47.86 203	40.92 197	47.97 190	44.70 217	47.58 188	33.27 205	48.11 112	29.58 159	49.65 90	44.38 176	34.65 176	51.71 214	47.90 221	52.49 221	48.57 220

RPMNet		46.41 193	48.72 199	43.72 180	47.77 191	52.94 188	46.02 200	33.92 199	44.41 146	31.82 148	36.89 195	37.42 211	37.41 161	53.88 211	54.02 204	65.37 190	61.47 184

testpf		34.85 219	36.16 225	33.31 215	47.49 192	35.56 231	36.85 222	32.31 212	23.08 231	15.63 208	29.39 214	29.48 222	19.62 215	41.38 232	41.07 231	47.95 228	53.18 207

TAPA-MVS	54.74 10	60.85 66	66.61 57	54.12 106	47.38 193	65.33 97	65.35 96	36.51 189	75.16 27	48.82 65	54.70 74	63.51 64	53.31 71	68.36 81	64.97 134	73.37 127	74.27 108
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SixPastTwentyTwo		47.55 188	50.25 194	44.41 178	47.30 194	54.31 185	47.81 186	40.36 166	33.76 214	19.93 197	43.75 153	32.77 219	42.07 142	59.82 179	60.94 172	68.98 174	66.37 151

TAMVS		44.02 201	49.18 198	37.99 207	47.03 195	45.97 214	45.04 204	28.47 219	39.11 198	20.23 196	43.22 161	48.52 133	28.49 198	58.15 190	57.95 188	58.71 205	51.36 211

UGNet		57.03 110	65.25 66	47.44 161	46.54 196	66.73 79	56.30 141	43.28 119	50.06 89	32.99 142	62.57 46	63.26 65	33.31 183	68.25 83	67.58 78	72.20 153	78.29 61
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
TSAR-MVS + COLMAP		62.65 60	69.90 50	54.19 104	46.31 197	66.73 79	65.49 95	41.36 155	76.57 22	46.31 74	76.80 14	56.68 90	53.27 72	69.50 69	66.65 90	72.40 149	76.36 80

PatchMatch-RL		50.11 165	51.56 181	48.43 149	46.23 198	51.94 192	50.21 176	38.62 177	46.62 124	37.51 127	42.43 173	39.38 202	52.24 94	60.98 174	59.56 180	65.76 188	60.01 191

CMPMVS	37.70 17	49.24 169	52.71 170	45.19 171	45.97 199	51.23 195	47.44 189	29.31 216	43.04 160	44.69 90	34.45 203	48.35 134	43.64 132	62.59 161	59.82 179	60.08 203	69.48 131
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v52		53.60 133	56.74 138	49.93 130	45.54 200	61.64 128	60.65 119	36.99 186	38.75 199	36.32 133	39.64 188	47.13 146	47.05 118	66.89 125	65.65 114	73.04 131	77.48 64

V4		53.60 133	56.73 139	49.93 130	45.54 200	61.64 128	60.65 119	36.99 186	38.74 201	36.33 132	39.64 188	47.12 147	47.05 118	66.89 125	65.64 117	73.04 131	77.48 64

pmmvs5		47.07 190	51.02 190	42.46 189	45.18 202	51.47 194	48.23 185	33.09 208	38.17 206	28.62 164	46.60 128	43.48 181	30.74 190	58.28 189	58.63 185	68.92 175	60.48 187

USDC		51.11 156	53.71 162	48.08 155	44.76 203	55.99 176	53.01 169	40.90 159	52.49 80	36.14 134	44.67 147	33.66 217	43.27 137	63.23 158	61.10 169	70.39 166	64.82 169

FC-MVSNet-test		39.65 213	48.35 201	29.49 220	44.43 204	39.28 224	30.23 231	40.44 164	43.59 153	3.12 240	53.00 79	42.03 185	10.02 237	55.09 207	54.77 199	48.66 226	50.71 213

ADS-MVSNet		40.67 210	43.38 214	37.50 208	44.36 205	39.79 223	42.09 215	32.67 211	44.34 148	28.87 163	40.76 185	40.37 198	30.22 191	48.34 229	45.87 227	46.81 230	44.21 224

new-patchmatchnet		33.24 222	37.20 221	28.62 223	44.32 206	38.26 228	29.68 234	36.05 190	31.97 219	6.33 232	26.59 220	27.33 224	11.12 236	50.08 220	41.05 232	44.23 231	45.15 223

1111		31.35 224	33.52 229	28.83 221	44.28 207	32.44 232	31.71 229	33.25 206	27.87 224	10.92 217	22.18 227	24.05 229	15.89 221	49.03 227	44.09 228	36.94 235	34.96 230

.test1245		22.44 232	22.23 234	22.67 230	44.28 207	32.44 232	31.71 229	33.25 206	27.87 224	10.92 217	22.18 227	24.05 229	15.89 221	49.03 227	0.01 239	0.00 243	0.06 240

MVS-HIRNet		42.24 205	41.15 218	43.51 181	44.06 209	40.74 220	35.77 224	35.35 192	35.38 211	38.34 123	25.63 221	38.55 206	43.48 134	50.77 216	47.03 225	64.07 193	49.98 216

PatchT		48.08 180	51.03 189	44.64 175	42.96 210	50.12 198	40.36 217	35.09 193	43.17 159	39.59 119	42.00 179	39.96 200	46.63 121	58.97 183	60.31 176	63.21 196	62.66 179

CVMVSNet		46.38 195	52.01 178	39.81 200	42.40 211	50.26 197	46.15 198	37.68 182	40.03 191	15.09 210	46.56 129	47.56 141	33.72 182	56.50 200	55.65 193	63.80 195	67.53 139

TinyColmap		47.08 189	47.56 204	46.52 166	42.35 212	53.44 187	51.77 173	40.70 163	43.44 157	31.92 147	29.78 212	23.72 232	45.04 128	61.99 169	59.54 181	67.35 182	61.03 185

MIMVSNet		43.79 202	48.53 200	38.27 205	41.46 213	48.97 202	50.81 175	32.88 210	44.55 144	22.07 189	32.05 205	47.15 145	24.76 206	58.73 185	56.09 191	57.63 210	52.14 209

LP		40.79 209	41.99 216	39.38 201	40.98 214	46.49 213	42.14 214	33.66 203	35.37 212	29.89 157	29.30 215	27.81 223	32.74 184	52.55 212	52.19 212	56.87 211	50.23 215

N_pmnet		32.67 223	36.85 222	27.79 224	40.55 215	32.13 234	35.80 223	26.79 225	37.24 209	9.10 225	32.02 206	30.94 220	16.30 220	47.22 230	41.21 230	38.21 233	37.21 229

anonymousdsp		52.84 139	57.78 131	47.06 162	40.24 216	58.95 160	53.70 163	33.54 204	36.51 210	32.69 144	43.88 151	45.40 159	47.97 114	67.17 120	70.28 54	74.22 115	82.29 42

EU-MVSNet		40.63 211	45.65 209	34.78 214	39.11 217	46.94 210	40.02 218	34.03 198	33.50 215	10.37 222	35.57 199	37.80 208	23.65 208	51.90 213	50.21 216	61.49 201	63.62 177

FPMVS		38.36 215	40.41 219	35.97 210	38.92 218	39.85 222	45.50 202	25.79 228	41.13 184	18.70 199	30.10 210	24.56 227	31.86 188	49.42 224	46.80 226	55.04 214	51.03 212

testus		31.33 225	36.31 224	25.52 228	37.55 219	38.40 225	25.87 235	23.58 231	26.46 227	5.97 233	24.15 223	24.92 226	12.44 229	49.14 226	48.21 220	47.73 229	42.86 225

test2356		33.40 221	36.53 223	29.76 219	37.51 220	38.39 226	34.68 226	27.35 221	27.88 223	10.61 219	25.54 222	24.44 228	17.15 218	49.99 221	48.32 219	51.24 223	41.16 228

TESTMET0.1,1		46.09 196	50.29 192	41.18 196	36.91 221	47.16 207	49.52 177	20.32 233	39.22 195	31.98 145	43.65 155	47.93 137	41.29 146	56.80 196	55.36 195	67.08 184	61.94 182

testmv		30.97 226	34.42 227	26.95 225	36.49 222	37.38 229	29.80 232	27.28 222	22.34 232	4.72 234	20.63 231	20.64 234	13.22 227	49.86 223	47.74 222	50.20 224	42.36 226

test1235678		30.97 226	34.42 227	26.95 225	36.49 222	37.38 229	29.79 233	27.28 222	22.33 233	4.72 234	20.62 232	20.64 234	13.22 227	49.87 222	47.74 222	50.20 224	42.36 226

FMVSNet5		40.96 207	45.81 208	35.29 213	34.30 224	44.55 218	47.28 190	28.84 218	40.76 186	21.62 190	29.85 211	42.44 184	24.77 205	57.53 193	55.00 198	54.93 215	50.56 214

PMMVS		49.20 171	54.28 159	43.28 184	34.13 225	45.70 215	48.98 180	26.09 227	46.31 127	34.92 139	55.22 71	53.47 101	47.48 116	59.43 180	59.04 182	68.05 180	60.77 186

PMVS	27.84 18	33.81 220	35.28 226	32.09 217	34.13 225	24.81 238	32.51 228	26.48 226	26.41 228	19.37 198	23.76 224	24.02 231	25.18 204	50.78 215	47.24 224	54.89 217	49.95 217
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test-mter		45.30 197	50.37 191	39.38 201	33.65 227	46.99 209	47.59 187	18.59 235	38.75 199	28.00 165	43.28 160	46.82 152	41.50 145	57.28 194	55.78 192	66.93 187	63.70 176

CHOSEN 280x420		40.80 208	45.05 211	35.84 212	32.95 228	29.57 235	44.98 205	23.71 230	37.54 208	18.42 203	31.36 208	47.07 148	46.41 123	56.71 198	54.65 202	48.55 227	58.47 196

PM-MVS		44.55 200	48.13 202	40.37 199	32.85 229	46.82 211	46.11 199	29.28 217	40.48 188	29.99 156	39.98 186	34.39 216	41.80 144	56.08 203	53.88 208	62.19 200	65.31 163

no-one		29.19 228	31.89 230	26.05 227	30.96 230	38.33 227	21.54 236	29.86 215	15.84 238	3.56 237	11.28 237	13.03 239	14.44 226	38.96 233	52.83 210	55.96 212	52.92 208

TDRefinement		49.31 167	52.44 173	45.67 170	30.44 231	59.42 152	59.24 126	39.78 170	48.76 104	31.20 150	35.73 198	29.90 221	42.81 140	64.24 157	62.59 164	70.55 164	66.43 149

Gipuma		25.87 229	26.91 233	24.66 229	28.98 232	20.17 239	20.46 238	34.62 196	29.55 222	9.10 225	4.91 241	5.31 243	15.76 223	49.37 225	49.10 218	39.03 232	29.95 233
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		41.36 206	43.15 215	39.27 203	28.74 233	52.68 190	44.95 206	40.84 161	32.89 216	18.13 204	31.61 207	22.09 233	38.97 155	50.45 218	56.11 190	64.01 194	56.23 204

E-PMN		15.09 234	13.19 237	17.30 233	27.80 234	12.62 242	7.81 242	27.54 220	14.62 240	3.19 238	6.89 238	2.52 246	15.09 224	15.93 237	20.22 236	22.38 237	19.53 236

MIMVSNet1		35.51 217	41.41 217	28.63 222	27.53 235	43.36 219	38.09 220	33.82 200	32.01 218	6.77 231	21.63 229	35.43 214	11.97 232	55.05 208	53.99 206	53.59 220	48.36 221

EMVS		14.49 235	12.45 238	16.87 235	27.02 236	12.56 243	8.13 241	27.19 224	15.05 239	3.14 239	6.69 239	2.67 245	15.08 225	14.60 239	18.05 237	20.67 238	17.56 238

test12356		23.91 230	28.47 231	18.60 231	26.80 237	28.30 236	20.92 237	19.76 234	19.89 235	2.88 242	18.48 233	16.57 237	4.05 238	42.34 231	41.93 229	37.21 234	31.75 231

pmmvs3		35.10 218	38.47 220	31.17 218	26.37 238	40.47 221	34.51 227	18.09 236	24.75 229	16.88 206	23.05 225	26.69 225	32.69 185	50.73 217	51.60 213	58.46 208	51.98 210

RPSCF		46.41 193	54.42 156	37.06 209	25.70 239	45.14 216	45.39 203	20.81 232	62.79 56	35.10 136	44.92 146	55.60 97	43.56 133	56.12 202	52.45 211	51.80 222	63.91 175

new_pmnet		23.19 231	28.17 232	17.37 232	17.03 240	24.92 237	19.66 239	16.16 238	27.05 226	4.42 236	20.77 230	19.20 236	12.19 231	37.71 234	36.38 233	34.77 236	31.17 232

PMMVS2		15.84 233	19.68 235	11.35 236	15.74 241	16.95 240	13.31 240	17.64 237	16.08 237	0.36 244	13.12 234	11.47 240	1.69 240	28.82 235	27.24 235	19.38 239	24.09 235

MVE	12.28 19	13.53 236	15.72 236	10.96 237	7.39 242	15.71 241	6.05 243	23.73 229	10.29 242	3.01 241	5.77 240	3.41 244	11.91 233	20.11 236	29.79 234	13.67 240	24.98 234
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				5.40 238	3.97 243	2.35 245	3.26 245	0.44 240	17.56 236	12.09 214	11.48 236	7.14 241	1.98 239	15.68 238	15.49 238	10.69 241

GG-mvs-BLEND		36.62 216	53.39 167	17.06 234	0.01 244	58.61 162	48.63 182	0.01 241	47.13 119	0.02 245	43.98 150	60.64 76	0.03 241	54.92 209	51.47 214	53.64 219	56.99 200

sosnet-low-res		0.00 239	0.00 241	0.00 239	0.00 245	0.00 246	0.00 248	0.00 242	0.00 245	0.00 246	0.00 244	0.00 247	0.00 244	0.00 242	0.00 242	0.00 243	0.00 242

sosnet		0.00 239	0.00 241	0.00 239	0.00 245	0.00 246	0.00 248	0.00 242	0.00 245	0.00 246	0.00 244	0.00 247	0.00 244	0.00 242	0.00 242	0.00 243	0.00 242

testmvs		0.01 237	0.02 239	0.00 239	0.00 245	0.00 246	0.01 247	0.00 242	0.01 243	0.00 246	0.03 243	0.00 247	0.01 242	0.01 241	0.01 239	0.00 243	0.06 240

test123		0.01 237	0.02 239	0.00 239	0.00 245	0.00 246	0.00 248	0.00 242	0.01 243	0.00 246	0.04 242	0.00 247	0.01 242	0.00 242	0.01 239	0.00 243	0.07 239

MTAPA										65.14 1		80.20 15

MTMP										62.63 12		78.04 22

Patchmatch-RL test							1.04 246

NP-MVS									72.00 36

Patchmtry						47.61 205	48.27 183	38.86 173		39.59 119

DeepMVS_CX						6.95 244	5.98 244	2.25 239	11.73 241	2.07 243	11.85 235	5.43 242	11.75 234	11.40 240		8.10 242	18.38 237

High-resolution multi-view benchmark