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

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.
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

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

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

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

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

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
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 39	85.43 27

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

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

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

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

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

AdaColmap		67.89 42	68.85 52	66.77 25	73.73 17	74.30 38	75.28 36	53.58 31	70.24 41	57.59 30	51.19 84	59.19 77	60.74 24	75.33 38	73.72 41	79.69 46	77.96 61

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.
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

TSAR-MVS + ACMM		72.56 24	79.07 13	64.96 36	73.24 20	73.16 44	78.50 22	48.80 60	79.34 14	55.32 36	85.04 6	81.49 9	58.57 34	75.06 40	73.75 40	75.35 104	85.61 25

CDPH-MVS		71.47 27	75.82 27	66.41 27	72.97 21	77.15 20	78.14 25	54.71 23	69.88 44	53.07 52	70.98 19	74.83 31	56.95 45	76.22 29	76.57 20	82.62 15	85.09 30

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

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

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

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

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

MAR-MVS		68.04 41	70.74 44	64.90 38	71.68 27	76.33 26	74.63 40	50.48 50	63.81 52	55.52 35	54.88 70	69.90 47	57.39 40	75.42 37	74.79 33	79.71 43	80.03 51
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
mPP-MVS					71.67 28							74.36 35

CLD-MVS		67.02 46	71.57 39	61.71 48	71.01 29	74.81 33	71.62 48	38.91 168	71.86 36	60.70 17	64.97 36	67.88 54	51.88 95	76.77 28	74.98 32	76.11 96	69.75 121
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 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

ACMM	60.30 7	67.58 44	68.82 53	66.13 29	70.59 31	72.01 49	76.54 30	54.26 27	65.64 50	54.78 42	50.35 86	61.72 68	58.74 33	75.79 34	75.03 29	81.88 26	81.17 48
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
XVS					70.49 32	76.96 21	74.36 41			54.48 44		74.47 32				82.24 19

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

ACMMP		71.57 26	75.84 26	66.59 26	70.30 34	76.85 24	78.46 23	53.95 29	73.52 31	55.56 34	70.13 22	71.36 43	58.55 35	77.00 24	76.23 23	82.71 13	85.81 23
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
abl_6				64.36 41	70.08 35	77.45 17	72.88 46	50.15 51	71.31 38	54.77 43	62.79 42	77.99 22	56.80 46			81.50 34	83.91 35

MVS_111021_HR		67.62 43	70.39 46	64.39 40	69.77 36	70.45 54	71.44 50	51.72 42	60.77 59	55.06 38	62.14 47	66.40 57	58.13 37	76.13 30	74.79 33	80.19 41	82.04 45

MSLP-MVS++		68.17 40	70.72 45	65.19 34	69.41 37	70.64 52	74.99 37	45.76 69	70.20 42	60.17 19	56.42 63	73.01 38	61.14 20	72.80 48	70.54 52	79.70 44	81.42 47

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

ACMP	61.42 5	68.72 39	71.37 40	65.64 33	69.06 39	74.45 37	75.88 34	53.30 32	68.10 46	55.74 33	61.53 50	62.29 65	56.97 44	74.70 41	74.23 37	82.88 11	84.31 31
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
DeepC-MVS_fast	65.08 3	72.00 25	76.11 25	67.21 24	68.93 40	77.46 16	76.54 30	54.35 26	74.92 27	58.64 26	65.18 35	74.04 37	62.62 11	77.92 17	77.02 18	82.16 24	86.21 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		69.26 35	72.98 35	64.92 37	68.82 41	71.44 50	75.94 33	49.64 53	70.13 43	53.48 51	65.33 34	72.95 39	60.21 28	75.33 38	72.90 45	81.41 35	82.78 41

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

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

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

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

DeepC-MVS	66.32 2	73.85 19	78.10 19	68.90 18	67.92 45	79.31 7	78.16 24	59.28 1	78.24 18	61.13 14	67.36 32	76.10 27	63.40 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
DeepPCF-MVS	66.49 1	74.25 16	80.97 6	66.41 27	67.75 47	78.87 9	75.61 35	54.16 28	84.86 2	58.22 28	77.94 12	81.01 11	62.52 12	78.34 10	77.38 13	80.16 42	88.40 7

UA-Net		58.50 87	64.68 67	51.30 119	66.97 48	67.13 74	53.68 160	45.65 72	49.51 90	31.58 147	62.91 41	68.47 50	35.85 169	68.20 82	67.28 77	74.03 113	69.24 131

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

3Dnovator	60.86 6	66.99 47	70.32 47	63.11 45	66.63 50	74.52 34	71.56 49	45.76 69	67.37 48	55.00 39	54.31 74	68.19 52	58.49 36	73.97 44	73.63 42	81.22 36	80.23 50

EPNet		65.14 52	69.54 50	60.00 52	66.61 51	67.67 67	67.53 56	55.32 21	62.67 56	46.22 72	67.74 29	65.93 58	48.07 111	72.17 50	72.12 46	76.28 88	78.47 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OpenMVS	57.13 9	62.81 56	65.75 60	59.39 54	66.47 52	69.52 56	64.26 101	43.07 123	61.34 58	50.19 59	47.29 120	64.41 59	54.60 56	70.18 64	68.62 67	77.73 58	78.89 55

ACMH	52.42 13	58.24 98	59.56 111	56.70 89	66.34 53	69.59 55	66.71 72	49.12 57	46.08 127	28.90 160	42.67 169	41.20 188	52.60 85	71.39 54	70.28 54	76.51 83	75.72 81
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		58.46 89	58.97 118	57.85 66	66.27 54	66.23 82	67.72 54	42.33 139	53.43 72	43.68 97	43.39 155	45.35 156	49.75 101	68.66 73	67.77 72	77.38 64	67.96 135

QAPM		65.27 50	69.49 51	60.35 50	65.43 55	72.20 48	65.69 90	47.23 64	63.46 53	49.14 61	53.56 75	71.04 44	57.01 43	72.60 49	71.41 49	77.62 60	82.14 44

CPTT-MVS		68.76 38	73.01 33	63.81 44	65.42 56	73.66 42	76.39 32	52.08 38	72.61 34	50.33 58	60.73 51	72.65 41	59.43 32	73.32 46	72.12 46	79.19 50	85.99 21

MS-PatchMatch		58.19 100	60.20 91	55.85 94	65.17 57	64.16 101	64.82 95	41.48 150	50.95 82	42.17 106	45.38 140	56.42 87	48.08 110	68.30 78	66.70 84	73.39 121	69.46 129

PCF-MVS	59.98 8	67.32 45	71.04 43	62.97 46	64.77 58	74.49 35	74.78 39	49.54 54	67.44 47	54.39 47	58.35 58	72.81 40	55.79 53	71.54 53	69.24 60	78.57 52	83.41 38
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
EG-PatchMatch MVS		56.98 107	58.24 124	55.50 96	64.66 59	68.62 59	61.48 110	43.63 106	38.44 199	41.44 108	38.05 191	46.18 153	43.95 128	71.71 52	70.61 51	77.87 57	74.08 107

Effi-MVS+-dtu		60.34 65	62.32 74	58.03 61	64.31 60	67.44 70	65.99 86	42.26 140	49.55 88	42.00 107	48.92 98	59.79 75	56.27 48	68.07 88	67.03 78	77.35 65	75.45 84

FC-MVSNet-train		58.40 95	63.15 72	52.85 111	64.29 61	61.84 121	55.98 141	46.47 65	53.06 75	34.96 134	61.95 49	56.37 89	39.49 147	68.67 72	68.36 68	75.92 101	71.81 110

Effi-MVS+		63.28 54	65.96 59	60.17 51	64.26 62	68.06 62	68.78 53	45.71 71	54.08 69	46.64 68	55.92 66	63.13 63	55.94 51	70.38 62	71.43 48	79.68 47	78.70 56

LS3D		60.20 66	61.70 75	58.45 57	64.18 63	67.77 64	67.19 58	48.84 59	61.67 57	41.27 110	45.89 134	51.81 117	54.18 58	68.78 70	66.50 91	75.03 105	69.48 127

ACMH+	53.71 12	59.26 69	60.28 88	58.06 59	64.17 64	68.46 60	67.51 57	50.93 45	52.46 79	35.83 131	40.83 181	45.12 159	52.32 91	69.88 65	69.00 63	77.59 62	76.21 79

DELS-MVS		65.87 48	70.30 48	60.71 49	64.05 65	72.68 46	70.90 51	45.43 73	57.49 62	49.05 62	64.43 37	68.66 49	55.11 55	74.31 42	73.02 44	79.70 44	81.51 46
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
canonicalmvs		65.62 49	72.06 37	58.11 58	63.94 66	71.05 51	64.49 99	43.18 118	74.08 29	47.35 65	64.17 39	71.97 42	51.17 98	71.87 51	70.74 50	78.51 54	80.56 49

Anonymous20240521		56.75 112	60.48 86	52.38 115	63.71 67	63.79 103	58.12 124	47.59 63	53.88 70	33.16 138	45.79 136	57.11 85	39.99 146	68.59 74	66.26 93	71.74 153	67.97 134

tpmp4_e23		56.84 111	57.14 131	56.49 92	62.45 68	62.05 119	67.57 55	41.56 148	54.17 68	48.57 64	49.18 90	46.54 149	50.44 100	61.93 166	58.82 180	68.34 173	67.28 140

gg-mvs-nofinetune		49.07 169	52.56 168	45.00 170	61.99 69	59.78 145	53.55 163	41.63 144	31.62 216	12.08 213	29.56 209	53.28 98	29.57 189	66.27 131	64.49 136	71.19 157	62.92 175

IS_MVSNet		57.95 102	64.26 69	50.60 121	61.62 70	65.25 95	57.18 130	45.42 74	50.79 83	26.49 174	57.81 60	60.05 74	34.51 173	71.24 57	70.20 56	78.36 55	74.44 100

NR-MVSNet		55.35 121	59.46 113	50.56 122	61.33 71	62.97 115	57.91 127	51.80 40	48.62 105	20.59 192	51.99 81	44.73 167	34.10 176	68.58 75	68.64 66	77.66 59	70.67 118

MVS_111021_LR		63.05 55	66.43 56	59.10 55	61.33 71	63.77 104	65.87 88	43.58 107	60.20 60	53.70 50	62.09 48	62.38 64	55.84 52	70.24 63	68.08 69	74.30 109	78.28 60

EPP-MVSNet		59.39 68	65.45 62	52.32 116	60.96 73	67.70 66	58.42 123	44.75 80	49.71 87	27.23 172	59.03 55	62.20 66	43.34 133	70.71 59	69.13 61	79.25 49	79.63 53

TransMVSNet (Re)		51.92 148	55.38 141	47.88 154	60.95 74	59.90 143	53.95 157	45.14 76	39.47 190	24.85 180	43.87 150	46.51 150	29.15 190	67.55 103	65.23 125	73.26 125	65.16 164

gm-plane-assit		44.74 195	45.95 202	43.33 181	60.88 75	46.79 209	36.97 218	32.24 210	24.15 227	11.79 214	29.26 212	32.97 214	46.64 118	65.09 148	62.95 154	71.45 155	60.42 186

DI_MVS_plusplus_trai		61.88 60	65.17 64	58.06 59	60.05 76	65.26 94	66.03 85	44.22 85	55.75 64	46.73 67	54.64 72	68.12 53	54.13 59	69.13 68	66.66 85	77.18 66	76.61 71

PVSNet_Blended_VisFu		63.65 53	66.92 54	59.83 53	60.03 77	73.44 43	66.33 81	48.95 58	52.20 80	50.81 57	56.07 64	60.25 73	53.56 62	73.23 47	70.01 57	79.30 48	83.24 39

UniMVSNet_NR-MVSNet		56.94 109	61.14 77	52.05 118	60.02 78	65.21 96	57.44 128	52.93 35	49.37 91	24.31 183	54.62 73	50.54 124	39.04 149	68.69 71	68.84 64	78.53 53	70.72 114

IterMVS-LS		58.30 97	61.39 76	54.71 99	59.92 79	58.40 161	59.42 120	43.64 104	48.71 101	40.25 114	57.53 61	58.55 79	52.15 93	65.42 146	65.34 122	72.85 128	75.77 80
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS_Test		62.40 59	66.23 58	57.94 62	59.77 80	64.77 99	66.50 80	41.76 143	57.26 63	49.33 60	62.68 44	67.47 56	53.50 65	68.57 76	66.25 94	76.77 73	76.58 73

TranMVSNet+NR-MVSNet		55.87 115	60.14 96	50.88 120	59.46 81	63.82 102	57.93 126	52.98 34	48.94 96	20.52 193	52.87 77	47.33 140	36.81 166	69.12 69	69.03 62	77.56 63	69.89 120

Fast-Effi-MVS+		60.36 64	63.35 71	56.87 84	58.70 82	65.86 90	65.08 94	37.11 181	53.00 77	45.36 81	52.12 80	56.07 91	56.27 48	71.28 56	69.42 59	78.71 51	75.69 82

IB-MVS	54.11 11	58.36 96	60.70 79	55.62 95	58.67 83	68.02 63	61.56 108	43.15 119	46.09 126	44.06 96	44.24 147	50.99 122	48.71 106	66.70 123	70.33 53	77.60 61	78.50 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
Fast-Effi-MVS+-dtu		56.30 114	59.29 115	52.82 112	58.64 84	64.89 97	65.56 91	32.89 206	45.80 135	35.04 133	45.89 134	54.14 95	49.41 102	67.16 116	66.45 92	75.37 103	70.69 116

CNLPA		62.78 57	66.31 57	58.65 56	58.47 85	68.41 61	65.98 87	41.22 153	78.02 19	56.04 32	46.65 123	59.50 76	57.50 39	69.67 66	65.27 124	72.70 137	76.67 70

tpm cat1		53.30 134	53.41 162	53.17 109	58.16 86	59.15 154	63.73 104	38.27 176	50.73 84	46.98 66	45.57 139	44.00 174	49.20 104	55.90 202	54.02 201	62.65 195	64.50 169

v1144		58.88 74	60.16 95	57.39 69	58.03 87	67.26 71	67.14 60	44.46 83	45.17 139	44.33 95	47.81 117	49.92 128	53.20 71	67.77 99	66.62 87	77.15 67	76.58 73

v7		59.19 71	60.62 80	57.53 67	57.96 88	67.19 73	67.09 61	44.28 84	46.84 117	45.45 79	48.19 112	51.06 119	53.62 61	67.84 94	66.59 88	76.79 70	76.60 72

v10		59.17 72	60.60 81	57.50 68	57.95 89	66.73 77	67.09 61	44.11 86	46.85 116	45.42 80	48.18 114	51.07 118	53.63 60	67.84 94	66.59 88	76.79 70	76.92 68

v1192		58.51 86	59.66 108	57.17 70	57.82 90	67.72 65	66.21 84	44.83 79	44.15 146	43.49 98	46.68 122	47.94 132	53.55 63	67.39 113	66.51 90	77.13 68	77.20 66

DWT-MVSNet_training		53.80 129	54.31 154	53.21 107	57.65 91	59.04 155	60.65 114	40.11 164	46.35 121	42.77 101	49.07 91	41.07 189	51.06 99	58.62 184	58.96 179	67.00 183	67.06 141

v144192		58.23 99	59.40 114	56.87 84	57.56 92	66.89 75	65.70 89	45.01 78	44.06 147	42.88 100	46.61 124	48.09 131	53.49 66	66.94 119	65.90 100	76.61 81	77.29 64

v1921920		57.89 103	59.02 117	56.58 91	57.55 93	66.66 80	64.72 98	44.70 81	43.55 150	42.73 102	46.17 132	46.93 146	53.51 64	66.78 122	65.75 104	76.29 87	77.28 65

v13		58.44 91	59.72 107	56.94 74	57.55 93	63.51 105	66.86 64	42.81 128	45.90 131	44.98 86	48.17 115	51.87 113	52.68 80	68.20 82	65.78 102	76.78 72	74.63 96

v12		58.44 91	59.74 106	56.92 78	57.54 95	63.50 106	66.84 67	42.77 129	45.96 129	44.95 87	48.31 108	51.94 112	52.67 81	68.14 85	65.75 104	76.75 74	74.55 98

v11		58.19 100	59.47 112	56.70 89	57.54 95	63.42 110	66.28 83	42.49 135	45.62 137	44.59 93	48.16 116	50.78 123	52.84 72	67.80 98	65.76 103	76.49 84	74.76 89

Vis-MVSNet (Re-imp)		50.37 159	57.73 128	41.80 191	57.53 97	54.35 180	45.70 197	45.24 75	49.80 86	13.43 211	58.23 59	56.42 87	20.11 211	62.96 156	63.36 148	68.76 172	58.96 192

CostFormer		56.57 113	59.13 116	53.60 103	57.52 98	61.12 131	66.94 63	35.95 188	53.44 71	44.68 89	55.87 67	54.44 94	48.21 109	60.37 174	58.33 183	68.27 175	70.33 119

V9		58.45 90	59.75 103	56.92 78	57.51 99	63.49 107	66.86 64	42.73 130	46.07 128	45.05 84	48.45 107	51.99 111	52.66 82	68.04 92	65.75 104	76.72 75	74.50 99

thres400		52.38 142	55.51 138	48.74 143	57.49 100	60.10 141	55.45 145	43.54 108	42.90 159	26.72 173	43.34 157	45.03 165	36.61 167	66.20 135	64.53 135	72.66 138	66.43 146

V14		58.44 91	59.75 103	56.90 81	57.48 101	63.46 108	66.85 66	42.68 131	46.16 125	45.03 85	48.57 105	52.04 110	52.65 83	67.93 93	65.72 107	76.69 76	74.40 101

v15		58.43 94	59.75 103	56.88 83	57.45 102	63.44 109	66.84 67	42.65 132	46.24 124	45.07 83	48.68 104	52.07 109	52.63 84	67.84 94	65.70 108	76.65 77	74.31 104

thres600view7		51.91 149	55.14 145	48.14 150	57.43 103	60.18 137	54.60 155	43.73 101	42.61 167	25.20 178	43.10 163	44.47 171	35.19 171	66.36 128	63.28 151	72.66 138	66.01 153

view600		51.96 147	55.13 146	48.27 149	57.41 104	60.05 142	54.74 154	43.64 104	42.57 168	25.88 176	43.11 162	44.48 170	35.34 170	66.27 131	63.61 145	72.61 141	65.80 155

thres200		52.39 141	55.37 143	48.90 141	57.39 105	60.18 137	55.60 143	43.73 101	42.93 158	27.41 170	43.35 156	45.09 160	36.61 167	66.36 128	63.92 143	72.66 138	65.78 156

v17		58.69 80	60.19 94	56.94 74	57.38 106	63.37 111	66.67 77	42.47 137	48.52 107	46.10 74	48.90 99	53.00 100	52.84 72	67.58 101	65.60 114	76.19 93	74.38 102

view800		51.55 151	54.89 148	47.66 157	57.37 107	59.77 146	53.62 161	43.72 103	42.22 170	24.94 179	42.80 167	43.81 176	33.94 177	66.09 136	64.38 137	72.39 144	65.14 165

v1141		58.56 83	60.05 100	56.81 87	57.36 108	66.18 84	66.80 69	43.11 120	45.87 133	44.60 91	48.71 102	51.83 115	52.38 88	67.46 108	65.64 112	76.63 78	74.66 93

divwei89l23v2f112		58.56 83	60.05 100	56.81 87	57.36 108	66.18 84	66.80 69	43.11 120	45.89 132	44.60 91	48.71 102	51.84 114	52.38 88	67.45 110	65.65 109	76.63 78	74.66 93

v1		58.56 83	60.06 99	56.83 86	57.36 108	66.19 83	66.80 69	43.10 122	45.87 133	44.68 89	48.73 101	51.83 115	52.38 88	67.45 110	65.65 109	76.63 78	74.66 93

v16		58.71 79	60.20 91	56.97 72	57.35 111	63.36 112	66.67 77	42.49 135	48.69 103	46.36 70	48.87 100	52.92 105	52.82 74	67.57 102	65.58 118	76.15 95	74.38 102

v8		58.88 74	60.57 82	56.92 78	57.35 111	65.69 91	66.69 76	42.64 133	47.89 110	45.77 76	49.04 92	52.98 101	52.77 78	67.51 107	65.57 119	76.26 89	75.30 86

tfpn		50.58 156	53.65 160	47.00 161	57.34 113	59.31 150	52.41 166	43.76 98	41.81 174	23.86 185	42.49 170	37.80 204	32.63 182	65.68 143	64.02 141	71.99 150	64.41 170

v6		58.89 73	60.54 83	56.96 73	57.34 113	66.13 86	66.71 72	42.84 125	47.85 111	45.80 75	49.04 92	52.95 102	52.79 75	67.53 104	65.59 115	76.26 89	74.73 90

v1neww		58.88 74	60.54 83	56.94 74	57.33 115	66.13 86	66.70 74	42.84 125	47.84 112	45.74 77	49.02 94	52.93 103	52.78 76	67.53 104	65.59 115	76.26 89	74.73 90

v7new		58.88 74	60.54 83	56.94 74	57.33 115	66.13 86	66.70 74	42.84 125	47.84 112	45.74 77	49.02 94	52.93 103	52.78 76	67.53 104	65.59 115	76.26 89	74.73 90

tfpn111		52.44 139	55.38 141	49.01 137	57.31 117	60.24 134	55.42 146	43.77 95	42.85 160	27.51 166	42.03 175	45.06 161	37.32 158	66.38 125	64.54 131	72.71 134	66.54 143

conf200view11		52.51 138	55.51 138	49.01 137	57.31 117	60.24 134	55.42 146	43.77 95	42.85 160	27.51 166	43.00 164	45.06 161	37.32 158	66.38 125	64.54 131	72.71 134	66.54 143

thres100view900		52.04 144	54.81 150	48.80 142	57.31 117	59.33 149	55.30 151	42.92 124	42.85 160	27.81 164	43.00 164	45.06 161	36.99 164	64.74 149	63.51 146	72.47 142	65.21 163

tfpn200view9		52.53 137	55.51 138	49.06 135	57.31 117	60.24 134	55.42 146	43.77 95	42.85 160	27.81 164	43.00 164	45.06 161	37.32 158	66.38 125	64.54 131	72.71 134	66.54 143

conf0.01		52.02 145	54.62 151	49.00 139	57.30 121	60.17 139	55.42 146	43.76 98	42.85 160	27.49 168	43.12 161	39.71 197	37.32 158	66.26 133	64.54 131	72.72 131	65.66 158

v1240		57.55 104	58.63 120	56.29 93	57.30 121	66.48 81	63.77 103	44.56 82	42.77 166	42.48 104	45.64 138	46.28 151	53.46 67	66.32 130	65.80 101	76.16 94	77.13 67

conf0.002		51.76 150	54.13 156	49.00 139	57.28 123	60.15 140	55.42 146	43.75 100	42.85 160	27.49 168	43.13 160	37.12 209	37.32 158	66.23 134	64.17 138	72.72 131	65.24 162

v18		58.68 82	60.20 91	56.90 81	57.26 124	63.28 113	66.58 79	42.42 138	48.86 97	46.37 69	49.01 96	53.05 99	52.74 79	67.40 112	65.52 120	76.02 100	74.28 105

CHOSEN 1792x2688		55.85 116	58.01 125	53.33 105	57.26 124	62.82 117	63.29 107	41.55 149	46.65 119	38.34 119	34.55 198	53.50 96	52.43 87	67.10 117	67.56 75	67.13 180	73.92 108

PVSNet_BlendedMVS		61.63 61	64.82 65	57.91 64	57.21 126	67.55 68	63.47 105	46.08 67	54.72 66	52.46 53	58.59 56	60.73 70	51.82 96	70.46 60	65.20 126	76.44 85	76.50 76

PVSNet_Blended		61.63 61	64.82 65	57.91 64	57.21 126	67.55 68	63.47 105	46.08 67	54.72 66	52.46 53	58.59 56	60.73 70	51.82 96	70.46 60	65.20 126	76.44 85	76.50 76

v2v482		58.69 80	60.12 98	57.03 71	57.16 128	66.05 89	67.17 59	43.52 109	46.33 122	45.19 82	49.46 89	51.02 120	52.51 86	67.30 114	66.03 97	76.61 81	74.62 97

conf0.05thres1000		50.64 155	53.84 157	46.92 162	57.02 129	59.29 151	52.29 167	43.80 94	39.84 189	23.81 186	39.26 188	43.14 179	32.52 183	65.74 140	64.04 139	72.05 149	65.53 159

tfpnnormal		50.16 161	52.19 173	47.78 156	56.86 130	58.37 162	54.15 156	44.01 92	38.35 201	25.94 175	36.10 194	37.89 203	34.50 174	65.93 137	63.42 147	71.26 156	65.28 161

HyFIR lowres test		56.87 110	58.60 121	54.84 98	56.62 131	69.27 57	64.77 97	42.21 141	45.66 136	37.50 124	33.08 200	57.47 84	53.33 68	65.46 145	67.94 70	74.60 106	71.35 112

v7n		55.67 117	57.46 130	53.59 104	56.06 132	65.29 93	61.06 113	43.26 117	40.17 186	37.99 121	40.79 182	45.27 158	47.09 115	67.67 100	66.21 95	76.08 97	76.82 69

dps		50.42 158	51.20 184	49.51 129	55.88 133	56.07 171	53.73 158	38.89 169	43.66 148	40.36 113	45.66 137	37.63 206	45.23 124	59.05 177	56.18 186	62.94 194	60.16 187

CANet_DTU		58.88 74	64.68 67	52.12 117	55.77 134	66.75 76	63.92 102	37.04 182	53.32 73	37.45 125	59.81 53	61.81 67	44.43 127	68.25 79	67.47 76	74.12 112	75.33 85

WR-MVS		48.78 171	55.06 147	41.45 193	55.50 135	60.40 133	43.77 206	49.99 52	41.92 172	8.10 226	45.24 143	45.56 154	17.47 213	61.57 168	64.60 130	73.85 114	66.14 152

UniMVSNet (Re)		55.15 124	60.39 87	49.03 136	55.31 136	64.59 100	55.77 142	50.63 47	48.66 104	20.95 191	51.47 83	50.40 125	34.41 175	67.81 97	67.89 71	77.11 69	71.88 109

test-LLR		49.28 165	50.29 188	48.10 151	55.26 137	47.16 204	49.52 173	43.48 112	39.22 191	31.98 142	43.65 153	47.93 133	41.29 141	56.80 192	55.36 192	67.08 181	61.94 179

test0.0.03 1		43.15 200	46.95 201	38.72 202	55.26 137	50.56 193	42.48 209	43.48 112	38.16 203	15.11 207	35.07 197	44.69 168	16.47 216	55.95 201	54.34 200	59.54 201	49.87 216

GA-MVS		55.67 117	58.33 122	52.58 114	55.23 139	63.09 114	61.08 112	40.15 163	42.95 157	37.02 127	52.61 78	47.68 135	47.51 113	65.92 138	65.35 121	74.49 108	70.68 117

thresconf0.02		48.17 176	51.22 183	44.60 173	55.14 140	55.73 173	48.95 177	41.35 152	43.43 154	21.23 189	42.03 175	37.25 208	31.19 185	62.33 162	60.61 171	69.76 165	57.17 197

diffmvs		59.53 67	64.04 70	54.26 100	55.09 141	59.86 144	64.80 96	39.55 167	58.39 61	46.21 73	60.48 52	67.82 55	49.27 103	63.53 153	63.32 149	70.64 159	74.89 88

tfpnview11		47.58 182	51.57 176	42.92 184	54.94 142	55.30 175	46.21 191	41.58 147	42.10 171	18.54 198	42.25 172	41.54 185	27.12 196	62.29 163	61.12 164	69.15 167	56.40 201

tfpn_n400		47.56 183	51.56 177	42.90 185	54.91 143	55.28 176	46.21 191	41.59 145	41.51 177	18.54 198	42.25 172	41.54 185	27.12 196	62.41 160	61.02 166	69.05 168	56.90 199

tfpnconf		47.56 183	51.56 177	42.90 185	54.91 143	55.28 176	46.21 191	41.59 145	41.51 177	18.54 198	42.25 172	41.54 185	27.12 196	62.41 160	61.02 166	69.05 168	56.90 199

Anonymous20231211		42.68 201	41.57 213	43.96 176	54.71 145	52.32 188	41.42 212	45.09 77	24.45 226	31.78 146	14.58 230	23.43 229	23.08 206	56.20 198	58.74 181	67.68 178	61.31 182

DTE-MVSNet		48.03 179	53.28 164	41.91 190	54.64 146	57.50 167	44.63 204	51.66 43	41.02 181	7.97 227	46.26 129	40.90 190	20.24 210	60.45 173	62.89 155	72.33 146	63.97 171

pmmvs4		54.66 127	56.07 136	53.00 110	54.63 147	57.08 169	60.43 118	44.10 87	51.69 81	40.55 112	46.55 127	44.79 166	45.95 122	62.54 158	63.66 144	72.36 145	66.20 150

DU-MVS		55.41 120	59.59 109	50.54 123	54.60 148	62.97 115	57.44 128	51.80 40	48.62 105	24.31 183	51.99 81	47.00 145	39.04 149	68.11 86	67.75 73	76.03 99	70.72 114

Baseline_NR-MVSNet		53.50 132	57.89 126	48.37 147	54.60 148	59.25 153	56.10 138	51.84 39	49.32 92	17.92 203	45.38 140	47.68 135	36.93 165	68.11 86	65.95 98	72.84 129	69.57 125

v148		55.58 119	57.61 129	53.20 108	54.59 150	61.86 120	61.18 111	38.70 173	44.30 145	42.25 105	47.53 118	50.24 127	48.73 105	65.15 147	62.61 159	73.79 115	71.61 111

tfpn1000		46.75 189	51.24 182	41.51 192	54.39 151	55.60 174	43.85 205	40.90 155	41.82 173	16.71 205	41.26 179	41.58 184	23.96 203	60.76 171	60.27 174	69.26 166	57.42 196

tfpn_ndepth		48.34 174	52.27 171	43.76 177	54.35 152	56.46 170	47.24 187	40.92 154	43.45 152	21.04 190	41.16 180	43.22 178	28.90 193	61.57 168	60.65 170	70.12 163	59.34 190

PEN-MVS		49.21 167	54.32 153	43.24 183	54.33 153	59.26 152	47.04 188	51.37 44	41.67 175	9.97 221	46.22 130	41.80 183	22.97 208	60.52 172	64.03 140	73.73 116	66.75 142

pm-mvs1		51.02 154	55.55 137	45.73 166	54.16 154	58.52 159	50.92 170	42.56 134	40.32 185	25.67 177	43.66 152	50.34 126	30.06 188	65.85 139	63.97 142	70.99 158	66.21 149

EPNet_dtu		52.05 143	58.26 123	44.81 171	54.10 155	50.09 196	52.01 168	40.82 158	53.03 76	27.41 170	54.90 69	57.96 83	26.72 199	62.97 155	62.70 158	67.78 177	66.19 151
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		48.82 170	51.27 181	45.96 165	54.10 155	47.35 203	56.05 139	30.23 211	46.70 118	43.21 99	52.54 79	47.55 138	37.28 163	54.11 207	50.50 212	54.90 213	60.12 188

CDS-MVSNet		52.42 140	57.06 133	47.02 160	53.92 157	58.30 163	55.50 144	46.47 65	42.52 169	29.38 158	49.50 88	52.85 106	28.49 194	66.70 123	66.89 82	68.34 173	62.63 178
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test20.03		40.38 210	44.20 208	35.92 209	53.73 158	49.05 197	38.54 216	43.49 111	32.55 213	9.54 222	27.88 214	39.12 199	12.24 227	56.28 197	54.69 197	57.96 206	49.83 217

Vis-MVSNet		58.48 88	65.70 61	50.06 126	53.40 159	67.20 72	60.24 119	43.32 115	48.83 98	30.23 153	62.38 46	61.61 69	40.35 144	71.03 58	69.77 58	72.82 130	79.11 54
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PLC	52.09 14	59.21 70	62.47 73	55.41 97	53.24 160	64.84 98	64.47 100	40.41 161	65.92 49	44.53 94	46.19 131	55.69 92	55.33 54	68.24 81	65.30 123	74.50 107	71.09 113
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
OMC-MVS		65.16 51	71.35 41	57.94 62	52.95 161	68.82 58	69.00 52	38.28 175	79.89 12	55.20 37	62.76 43	68.31 51	56.14 50	71.30 55	68.70 65	76.06 98	79.67 52

tpmrst		48.08 177	49.88 192	45.98 164	52.71 162	48.11 201	53.62 161	33.70 199	48.70 102	39.74 115	48.96 97	46.23 152	40.29 145	50.14 216	49.28 214	55.80 210	57.71 195

GBi-Net		55.20 122	60.25 89	49.31 130	52.42 163	61.44 125	57.03 131	44.04 89	49.18 93	30.47 149	48.28 109	58.19 80	38.22 152	68.05 89	66.96 79	73.69 117	69.65 122

test1		55.20 122	60.25 89	49.31 130	52.42 163	61.44 125	57.03 131	44.04 89	49.18 93	30.47 149	48.28 109	58.19 80	38.22 152	68.05 89	66.96 79	73.69 117	69.65 122

FMVSNet2		55.04 125	59.95 102	49.31 130	52.42 163	61.44 125	57.03 131	44.08 88	49.55 88	30.40 152	46.89 121	58.84 78	38.22 152	67.07 118	66.21 95	73.69 117	69.65 122

FMVSNet3		54.78 126	59.58 110	49.17 133	52.37 166	61.31 129	56.72 135	44.04 89	49.18 93	30.47 149	48.28 109	58.19 80	38.09 155	65.48 144	65.20 126	73.31 123	69.45 130

testgi		38.71 212	43.64 209	32.95 214	52.30 167	48.63 200	35.59 222	35.05 191	31.58 217	9.03 225	30.29 205	40.75 192	11.19 232	55.30 203	53.47 206	54.53 215	45.48 220

Anonymous20231206		42.28 202	45.89 203	38.07 204	51.96 168	48.98 198	43.66 207	38.81 172	38.74 197	14.32 210	26.74 215	40.90 190	20.94 209	56.64 195	54.67 198	58.71 202	54.59 203

pmmvs6		48.35 173	51.64 175	44.51 174	51.92 169	57.94 165	49.44 175	42.17 142	34.45 209	24.62 182	28.87 213	46.90 147	29.07 192	64.60 150	63.08 152	69.83 164	65.68 157

v748		52.93 135	55.29 144	50.19 125	51.90 170	61.31 129	56.54 136	40.05 165	39.12 193	34.82 136	39.93 185	43.83 175	43.66 129	64.26 151	63.32 149	74.15 111	75.28 87

PatchmatchNet		49.92 163	51.29 180	48.32 148	51.83 171	51.86 190	53.38 164	37.63 180	47.90 109	40.83 111	48.54 106	45.30 157	45.19 125	56.86 191	53.99 203	61.08 199	54.57 204
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		47.65 180	53.67 159	40.63 196	51.45 172	59.74 147	44.71 203	49.37 55	40.69 183	7.61 228	46.04 133	44.34 173	17.32 214	57.79 188	61.18 163	73.30 124	65.86 154

LTVRE_ROB	44.17 16	47.06 188	50.15 191	43.44 180	51.39 173	58.42 160	42.90 208	43.51 110	22.27 231	14.85 209	41.94 178	34.57 211	45.43 123	62.28 164	62.77 157	62.56 196	68.83 133
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		48.37 172	53.53 161	42.34 188	51.35 174	58.01 164	46.56 189	50.54 48	41.62 176	10.61 217	46.53 128	40.68 193	23.18 205	58.71 182	61.83 161	71.81 151	67.36 139

PS-CasMVS		48.18 175	53.25 165	42.27 189	51.26 175	57.94 165	46.51 190	50.52 49	41.30 179	10.56 219	45.35 142	40.34 195	23.04 207	58.66 183	61.79 162	71.74 153	67.38 138

our_test_3					51.15 176	57.31 168	55.12 152

FMVSNet1		54.08 128	58.68 119	48.71 144	50.90 177	61.35 128	56.73 134	43.94 93	45.91 130	29.32 159	42.72 168	56.26 90	37.70 156	68.05 89	66.96 79	73.69 117	69.50 126

pmmvs-eth3d		51.33 152	52.25 172	50.26 124	50.82 178	54.65 179	56.03 140	43.45 114	43.51 151	37.20 126	39.20 189	39.04 200	42.28 136	61.85 167	62.78 156	71.78 152	64.72 167

MVSTER		57.19 105	61.11 78	52.62 113	50.82 178	58.79 157	61.55 109	37.86 178	48.81 99	41.31 109	57.43 62	52.10 108	48.60 107	68.19 84	66.75 83	75.56 102	75.68 83

ambc			45.54 206		50.66 180	52.63 187	40.99 213		38.36 200	24.67 181	22.62 222	13.94 235	29.14 191	65.71 142	58.06 184	58.60 204	67.43 137

MDTV_nov1_ep13		50.32 160	52.43 170	47.86 155	49.87 181	54.70 178	58.10 125	34.29 194	45.59 138	37.71 122	47.44 119	47.42 139	41.86 138	58.07 187	55.21 194	65.34 188	58.56 193

IterMVS		53.45 133	57.12 132	49.17 133	49.23 182	60.93 132	59.05 122	34.63 192	44.53 141	33.22 137	51.09 85	51.01 121	48.38 108	62.43 159	60.79 169	70.54 161	69.05 132
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
COLMAP_ROB	46.52 15	51.99 146	54.86 149	48.63 145	49.13 183	61.73 122	60.53 117	36.57 185	53.14 74	32.95 140	37.10 192	38.68 201	40.49 143	65.72 141	63.08 152	72.11 148	64.60 168
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CR-MVSNet		50.47 157	52.61 167	47.98 153	49.03 184	52.94 184	48.27 179	38.86 170	44.41 142	39.59 116	44.34 146	44.65 169	46.63 119	58.97 179	60.31 172	65.48 186	62.66 176

V42		56.97 108	60.14 96	53.28 106	48.16 185	62.78 118	66.30 82	37.93 177	47.44 114	42.68 103	48.19 112	52.59 107	51.90 94	67.46 108	65.94 99	72.72 131	76.55 75

MDTV_nov1_ep13_2view		47.62 181	49.72 193	45.18 169	48.05 186	53.70 182	54.90 153	33.80 198	39.90 188	29.79 156	38.85 190	41.89 182	39.17 148	58.99 178	55.55 191	65.34 188	59.17 191

EPMVS		44.66 196	47.86 199	40.92 195	47.97 187	44.70 214	47.58 184	33.27 202	48.11 108	29.58 157	49.65 87	44.38 172	34.65 172	51.71 211	47.90 218	52.49 218	48.57 218

RPMNet		46.41 190	48.72 195	43.72 178	47.77 188	52.94 184	46.02 196	33.92 196	44.41 142	31.82 145	36.89 193	37.42 207	37.41 157	53.88 208	54.02 201	65.37 187	61.47 181

testpf		34.85 217	36.16 222	33.31 213	47.49 189	35.56 228	36.85 219	32.31 209	23.08 228	15.63 206	29.39 210	29.48 218	19.62 212	41.38 229	41.07 228	47.95 225	53.18 205

TAPA-MVS	54.74 10	60.85 63	66.61 55	54.12 102	47.38 190	65.33 92	65.35 93	36.51 186	75.16 26	48.82 63	54.70 71	63.51 61	53.31 69	68.36 77	64.97 129	73.37 122	74.27 106
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SixPastTwentyTwo		47.55 185	50.25 190	44.41 175	47.30 191	54.31 181	47.81 182	40.36 162	33.76 210	19.93 195	43.75 151	32.77 215	42.07 137	59.82 175	60.94 168	68.98 170	66.37 148

TAMVS		44.02 198	49.18 194	37.99 205	47.03 192	45.97 211	45.04 200	28.47 216	39.11 194	20.23 194	43.22 159	48.52 129	28.49 194	58.15 186	57.95 185	58.71 202	51.36 209

UGNet		57.03 106	65.25 63	47.44 158	46.54 193	66.73 77	56.30 137	43.28 116	50.06 85	32.99 139	62.57 45	63.26 62	33.31 179	68.25 79	67.58 74	72.20 147	78.29 59
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 58	69.90 49	54.19 101	46.31 194	66.73 77	65.49 92	41.36 151	76.57 21	46.31 71	76.80 13	56.68 86	53.27 70	69.50 67	66.65 86	72.40 143	76.36 78

PatchMatch-RL		50.11 162	51.56 177	48.43 146	46.23 195	51.94 189	50.21 172	38.62 174	46.62 120	37.51 123	42.43 171	39.38 198	52.24 92	60.98 170	59.56 176	65.76 185	60.01 189

CMPMVS	37.70 17	49.24 166	52.71 166	45.19 168	45.97 196	51.23 192	47.44 185	29.31 213	43.04 156	44.69 88	34.45 199	48.35 130	43.64 130	62.59 157	59.82 175	60.08 200	69.48 127
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v52		53.60 130	56.74 134	49.93 127	45.54 197	61.64 123	60.65 114	36.99 183	38.75 195	36.32 129	39.64 186	47.13 142	47.05 116	66.89 120	65.65 109	73.04 126	77.48 62

V4		53.60 130	56.73 135	49.93 127	45.54 197	61.64 123	60.65 114	36.99 183	38.74 197	36.33 128	39.64 186	47.12 143	47.05 116	66.89 120	65.64 112	73.04 126	77.48 62

pmmvs5		47.07 187	51.02 186	42.46 187	45.18 199	51.47 191	48.23 181	33.09 205	38.17 202	28.62 162	46.60 125	43.48 177	30.74 186	58.28 185	58.63 182	68.92 171	60.48 185

USDC		51.11 153	53.71 158	48.08 152	44.76 200	55.99 172	53.01 165	40.90 155	52.49 78	36.14 130	44.67 145	33.66 213	43.27 134	63.23 154	61.10 165	70.39 162	64.82 166

FC-MVSNet-test		39.65 211	48.35 197	29.49 218	44.43 201	39.28 221	30.23 228	40.44 160	43.59 149	3.12 238	53.00 76	42.03 181	10.02 234	55.09 204	54.77 196	48.66 223	50.71 211

ADS-MVSNet		40.67 208	43.38 210	37.50 206	44.36 202	39.79 220	42.09 211	32.67 208	44.34 144	28.87 161	40.76 183	40.37 194	30.22 187	48.34 226	45.87 224	46.81 227	44.21 222

new-patchmatchnet		33.24 220	37.20 218	28.62 221	44.32 203	38.26 225	29.68 231	36.05 187	31.97 215	6.33 230	26.59 216	27.33 220	11.12 233	50.08 217	41.05 229	44.23 228	45.15 221

1111		31.35 222	33.52 226	28.83 219	44.28 204	32.44 229	31.71 226	33.25 203	27.87 220	10.92 215	22.18 223	24.05 225	15.89 218	49.03 224	44.09 225	36.94 232	34.96 228

.test1245		22.44 230	22.23 231	22.67 228	44.28 204	32.44 229	31.71 226	33.25 203	27.87 220	10.92 215	22.18 223	24.05 225	15.89 218	49.03 224	0.01 236	0.00 240	0.06 238

MVS-HIRNet		42.24 203	41.15 215	43.51 179	44.06 206	40.74 217	35.77 221	35.35 189	35.38 207	38.34 119	25.63 217	38.55 202	43.48 132	50.77 213	47.03 222	64.07 190	49.98 214

PatchT		48.08 177	51.03 185	44.64 172	42.96 207	50.12 195	40.36 214	35.09 190	43.17 155	39.59 116	42.00 177	39.96 196	46.63 119	58.97 179	60.31 172	63.21 193	62.66 176

CVMVSNet		46.38 192	52.01 174	39.81 198	42.40 208	50.26 194	46.15 194	37.68 179	40.03 187	15.09 208	46.56 126	47.56 137	33.72 178	56.50 196	55.65 190	63.80 192	67.53 136

TinyColmap		47.08 186	47.56 200	46.52 163	42.35 209	53.44 183	51.77 169	40.70 159	43.44 153	31.92 144	29.78 208	23.72 228	45.04 126	61.99 165	59.54 177	67.35 179	61.03 183

MIMVSNet		43.79 199	48.53 196	38.27 203	41.46 210	48.97 199	50.81 171	32.88 207	44.55 140	22.07 187	32.05 201	47.15 141	24.76 202	58.73 181	56.09 188	57.63 207	52.14 207

LP		40.79 207	41.99 212	39.38 199	40.98 211	46.49 210	42.14 210	33.66 200	35.37 208	29.89 155	29.30 211	27.81 219	32.74 180	52.55 209	52.19 209	56.87 208	50.23 213

N_pmnet		32.67 221	36.85 219	27.79 222	40.55 212	32.13 231	35.80 220	26.79 222	37.24 205	9.10 223	32.02 202	30.94 216	16.30 217	47.22 227	41.21 227	38.21 230	37.21 227

anonymousdsp		52.84 136	57.78 127	47.06 159	40.24 213	58.95 156	53.70 159	33.54 201	36.51 206	32.69 141	43.88 149	45.40 155	47.97 112	67.17 115	70.28 54	74.22 110	82.29 43

EU-MVSNet		40.63 209	45.65 205	34.78 212	39.11 214	46.94 207	40.02 215	34.03 195	33.50 211	10.37 220	35.57 196	37.80 204	23.65 204	51.90 210	50.21 213	61.49 198	63.62 174

FPMVS		38.36 213	40.41 216	35.97 208	38.92 215	39.85 219	45.50 198	25.79 225	41.13 180	18.70 197	30.10 206	24.56 223	31.86 184	49.42 221	46.80 223	55.04 211	51.03 210

testus		31.33 223	36.31 221	25.52 226	37.55 216	38.40 222	25.87 232	23.58 228	26.46 223	5.97 231	24.15 219	24.92 222	12.44 226	49.14 223	48.21 217	47.73 226	42.86 223

test2356		33.40 219	36.53 220	29.76 217	37.51 217	38.39 223	34.68 223	27.35 218	27.88 219	10.61 217	25.54 218	24.44 224	17.15 215	49.99 218	48.32 216	51.24 220	41.16 226

TESTMET0.1,1		46.09 193	50.29 188	41.18 194	36.91 218	47.16 204	49.52 173	20.32 230	39.22 191	31.98 142	43.65 153	47.93 133	41.29 141	56.80 192	55.36 192	67.08 181	61.94 179

testmv		30.97 224	34.42 224	26.95 223	36.49 219	37.38 226	29.80 229	27.28 219	22.34 229	4.72 232	20.63 227	20.64 231	13.22 224	49.86 220	47.74 219	50.20 221	42.36 224

test1235678		30.97 224	34.42 224	26.95 223	36.49 219	37.38 226	29.79 230	27.28 219	22.33 230	4.72 232	20.62 228	20.64 231	13.22 224	49.87 219	47.74 219	50.20 221	42.36 224

FMVSNet5		40.96 205	45.81 204	35.29 211	34.30 221	44.55 215	47.28 186	28.84 215	40.76 182	21.62 188	29.85 207	42.44 180	24.77 201	57.53 189	55.00 195	54.93 212	50.56 212

PMMVS		49.20 168	54.28 155	43.28 182	34.13 222	45.70 212	48.98 176	26.09 224	46.31 123	34.92 135	55.22 68	53.47 97	47.48 114	59.43 176	59.04 178	68.05 176	60.77 184

PMVS	27.84 18	33.81 218	35.28 223	32.09 215	34.13 222	24.81 235	32.51 225	26.48 223	26.41 224	19.37 196	23.76 220	24.02 227	25.18 200	50.78 212	47.24 221	54.89 214	49.95 215
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test-mter		45.30 194	50.37 187	39.38 199	33.65 224	46.99 206	47.59 183	18.59 232	38.75 195	28.00 163	43.28 158	46.82 148	41.50 140	57.28 190	55.78 189	66.93 184	63.70 173

CHOSEN 280x420		40.80 206	45.05 207	35.84 210	32.95 225	29.57 232	44.98 201	23.71 227	37.54 204	18.42 201	31.36 204	47.07 144	46.41 121	56.71 194	54.65 199	48.55 224	58.47 194

PM-MVS		44.55 197	48.13 198	40.37 197	32.85 226	46.82 208	46.11 195	29.28 214	40.48 184	29.99 154	39.98 184	34.39 212	41.80 139	56.08 200	53.88 205	62.19 197	65.31 160

no-one		29.19 226	31.89 227	26.05 225	30.96 227	38.33 224	21.54 233	29.86 212	15.84 235	3.56 235	11.28 234	13.03 236	14.44 223	38.96 230	52.83 207	55.96 209	52.92 206

TDRefinement		49.31 164	52.44 169	45.67 167	30.44 228	59.42 148	59.24 121	39.78 166	48.76 100	31.20 148	35.73 195	29.90 217	42.81 135	64.24 152	62.59 160	70.55 160	66.43 146

Gipuma		25.87 227	26.91 230	24.66 227	28.98 229	20.17 236	20.46 235	34.62 193	29.55 218	9.10 223	4.91 238	5.31 240	15.76 220	49.37 222	49.10 215	39.03 229	29.95 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		41.36 204	43.15 211	39.27 201	28.74 230	52.68 186	44.95 202	40.84 157	32.89 212	18.13 202	31.61 203	22.09 230	38.97 151	50.45 215	56.11 187	64.01 191	56.23 202

E-PMN		15.09 232	13.19 234	17.30 231	27.80 231	12.62 239	7.81 239	27.54 217	14.62 237	3.19 236	6.89 235	2.52 243	15.09 221	15.93 234	20.22 233	22.38 234	19.53 234

MIMVSNet1		35.51 215	41.41 214	28.63 220	27.53 232	43.36 216	38.09 217	33.82 197	32.01 214	6.77 229	21.63 225	35.43 210	11.97 229	55.05 205	53.99 203	53.59 217	48.36 219

EMVS		14.49 233	12.45 235	16.87 233	27.02 233	12.56 240	8.13 238	27.19 221	15.05 236	3.14 237	6.69 236	2.67 242	15.08 222	14.60 236	18.05 234	20.67 235	17.56 236

test12356		23.91 228	28.47 228	18.60 229	26.80 234	28.30 233	20.92 234	19.76 231	19.89 232	2.88 240	18.48 229	16.57 234	4.05 235	42.34 228	41.93 226	37.21 231	31.75 229

pmmvs3		35.10 216	38.47 217	31.17 216	26.37 235	40.47 218	34.51 224	18.09 233	24.75 225	16.88 204	23.05 221	26.69 221	32.69 181	50.73 214	51.60 210	58.46 205	51.98 208

RPSCF		46.41 190	54.42 152	37.06 207	25.70 236	45.14 213	45.39 199	20.81 229	62.79 55	35.10 132	44.92 144	55.60 93	43.56 131	56.12 199	52.45 208	51.80 219	63.91 172

new_pmnet		23.19 229	28.17 229	17.37 230	17.03 237	24.92 234	19.66 236	16.16 235	27.05 222	4.42 234	20.77 226	19.20 233	12.19 228	37.71 231	36.38 230	34.77 233	31.17 230

PMMVS2		15.84 231	19.68 232	11.35 234	15.74 238	16.95 237	13.31 237	17.64 234	16.08 234	0.36 242	13.12 231	11.47 237	1.69 237	28.82 232	27.24 232	19.38 236	24.09 233

MVE	12.28 19	13.53 234	15.72 233	10.96 235	7.39 239	15.71 238	6.05 240	23.73 226	10.29 239	3.01 239	5.77 237	3.41 241	11.91 230	20.11 233	29.79 231	13.67 237	24.98 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				5.40 236	3.97 240	2.35 242	3.26 242	0.44 237	17.56 233	12.09 212	11.48 233	7.14 238	1.98 236	15.68 235	15.49 235	10.69 238

GG-mvs-BLEND		36.62 214	53.39 163	17.06 232	0.01 241	58.61 158	48.63 178	0.01 238	47.13 115	0.02 243	43.98 148	60.64 72	0.03 238	54.92 206	51.47 211	53.64 216	56.99 198

sosnet-low-res		0.00 237	0.00 238	0.00 237	0.00 242	0.00 243	0.00 245	0.00 239	0.00 242	0.00 244	0.00 241	0.00 244	0.00 241	0.00 239	0.00 239	0.00 240	0.00 240

sosnet		0.00 237	0.00 238	0.00 237	0.00 242	0.00 243	0.00 245	0.00 239	0.00 242	0.00 244	0.00 241	0.00 244	0.00 241	0.00 239	0.00 239	0.00 240	0.00 240

testmvs		0.01 235	0.02 236	0.00 237	0.00 242	0.00 243	0.01 244	0.00 239	0.01 240	0.00 244	0.03 240	0.00 244	0.01 239	0.01 238	0.01 236	0.00 240	0.06 238

test123		0.01 235	0.02 236	0.00 237	0.00 242	0.00 243	0.00 245	0.00 239	0.01 240	0.00 244	0.04 239	0.00 244	0.01 239	0.00 239	0.01 236	0.00 240	0.07 237

MTAPA										65.14 1		80.20 14

MTMP										62.63 11		78.04 21

Patchmatch-RL test							1.04 243

NP-MVS									72.00 35

Patchmtry						47.61 202	48.27 179	38.86 170		39.59 116

DeepMVS_CX						6.95 241	5.98 241	2.25 236	11.73 238	2.07 241	11.85 232	5.43 239	11.75 231	11.40 237		8.10 239	18.38 235

High-resolution multi-view benchmark