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	0.00 246

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 90	59.19 83	60.74 25	75.33 39	73.72 42	79.69 48	77.96 64

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	75.35 110	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 68	53.81 52	59.51 57	68.96 50	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 74	69.90 48	57.39 42	75.42 38	74.79 34	79.71 45	80.03 53
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	38.91 175	71.86 37	60.70 18	64.97 37	67.88 57	51.88 98	76.77 29	74.98 33	76.11 99	69.75 129
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 92	61.72 73	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 44	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 61	55.06 40	62.14 50	66.40 59	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 66	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 42	63.89 63	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 53	62.29 69	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 68	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 63	54.81 44	62.56 48	68.80 51	57.75 39	70.88 60	68.99 64	80.03 43	80.36 50

UA-Net		58.50 91	64.68 71	51.30 125	66.97 50	67.13 76	53.68 168	45.65 74	49.51 95	31.58 153	62.91 43	68.47 53	35.85 177	68.20 87	67.28 83	74.03 120	69.24 139

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 78	68.19 55	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 59	55.32 22	62.67 58	46.22 76	67.74 30	65.93 60	48.07 116	72.17 50	72.12 46	76.28 90	78.47 61
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OpenMVS	57.13 9	62.81 59	65.75 64	59.39 56	66.47 54	69.52 58	64.26 104	43.07 126	61.34 60	50.19 61	47.29 126	64.41 62	54.60 58	70.18 66	68.62 69	77.73 60	78.89 58

ACMH	52.42 13	58.24 102	59.56 118	56.70 92	66.34 55	69.59 57	66.71 75	49.12 57	46.08 134	28.90 166	42.67 175	41.20 196	52.60 88	71.39 55	70.28 54	76.51 85	75.72 85
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		58.46 93	58.97 125	57.85 68	66.27 56	66.23 86	67.72 57	42.33 144	53.43 74	43.68 100	43.39 161	45.35 163	49.75 107	68.66 77	67.77 76	77.38 66	67.96 142

QAPM		65.27 52	69.49 52	60.35 52	65.43 57	72.20 51	65.69 94	47.23 64	63.46 54	49.14 63	53.56 79	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 55	72.65 41	59.43 32	73.32 46	72.12 46	79.19 52	85.99 21

MS-PatchMatch		58.19 104	60.20 96	55.85 98	65.17 59	64.16 108	64.82 99	41.48 155	50.95 86	42.17 109	45.38 146	56.42 92	48.08 115	68.30 83	66.70 92	73.39 128	69.46 137

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 61	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		56.98 112	58.24 131	55.50 100	64.66 61	68.62 61	61.48 113	43.63 109	38.44 207	41.44 111	38.05 197	46.18 159	43.95 134	71.71 52	70.61 51	77.87 59	74.08 112

Effi-MVS+-dtu		60.34 69	62.32 78	58.03 63	64.31 62	67.44 72	65.99 90	42.26 145	49.55 93	42.00 110	48.92 104	59.79 81	56.27 50	68.07 93	67.03 84	77.35 67	75.45 88

FC-MVSNet-train		58.40 99	63.15 76	52.85 118	64.29 63	61.84 127	55.98 149	46.47 66	53.06 77	34.96 142	61.95 52	56.37 94	39.49 155	68.67 76	68.36 70	75.92 104	71.81 118

Effi-MVS+		63.28 57	65.96 62	60.17 53	64.26 64	68.06 64	68.78 55	45.71 73	54.08 72	46.64 71	55.92 69	63.13 67	55.94 53	70.38 64	71.43 48	79.68 49	78.70 59

LS3D		60.20 70	61.70 79	58.45 59	64.18 65	67.77 66	67.19 61	48.84 59	61.67 59	41.27 113	45.89 141	51.81 122	54.18 60	68.78 74	66.50 100	75.03 112	69.48 135

ACMH+	53.71 12	59.26 73	60.28 93	58.06 61	64.17 66	68.46 62	67.51 60	50.93 46	52.46 82	35.83 139	40.83 187	45.12 167	52.32 94	69.88 67	69.00 63	77.59 64	76.21 82

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 64	49.05 64	64.43 38	68.66 52	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	64.49 102	43.18 121	74.08 30	47.35 67	64.17 40	71.97 43	51.17 101	71.87 51	70.74 50	78.51 56	80.56 49

Anonymous202405211			60.60 86		63.44 69	66.71 82	61.00 118	47.23 64	50.62 89		36.85 200	60.63 78	43.03 143	69.17 71	67.72 78	75.41 107	72.54 116

tpmp4_e23		56.84 116	57.14 138	56.49 96	62.45 70	62.05 125	67.57 58	41.56 153	54.17 71	48.57 66	49.18 96	46.54 154	50.44 104	61.93 174	58.82 188	68.34 181	67.28 147

gg-mvs-nofinetune		49.07 176	52.56 176	45.00 177	61.99 71	59.78 152	53.55 171	41.63 149	31.62 224	12.08 219	29.56 217	53.28 103	29.57 197	66.27 139	64.49 145	71.19 166	62.92 182

Anonymous20240521		59.49 71	64.00 74	54.23 104	61.81 72	64.33 107	61.42 114	43.77 96	52.85 80	38.94 125	55.62 71	62.15 71	43.24 142	69.39 70	67.66 79	76.22 95	75.97 83

Anonymous20231211		57.71 108	60.79 83	54.13 106	61.68 73	65.81 95	60.81 119	43.70 106	51.97 84	39.67 121	34.82 205	63.59 64	43.31 140	68.55 81	66.63 95	75.59 105	74.13 111

IS_MVSNet		57.95 106	64.26 73	50.60 127	61.62 74	65.25 101	57.18 137	45.42 76	50.79 87	26.49 180	57.81 63	60.05 80	34.51 181	71.24 58	70.20 56	78.36 57	74.44 103

NR-MVSNet		55.35 127	59.46 120	50.56 128	61.33 75	62.97 121	57.91 134	51.80 41	48.62 110	20.59 198	51.99 87	44.73 175	34.10 184	68.58 79	68.64 68	77.66 61	70.67 126

MVS_111021_LR		63.05 58	66.43 59	59.10 57	61.33 75	63.77 110	65.87 92	43.58 110	60.20 62	53.70 53	62.09 51	62.38 68	55.84 54	70.24 65	68.08 71	74.30 116	78.28 63

EPP-MVSNet		59.39 72	65.45 66	52.32 122	60.96 77	67.70 68	58.42 131	44.75 81	49.71 92	27.23 178	59.03 58	62.20 70	43.34 139	70.71 61	69.13 61	79.25 51	79.63 55

TransMVSNet (Re)		51.92 155	55.38 149	47.88 161	60.95 78	59.90 151	53.95 165	45.14 78	39.47 198	24.85 186	43.87 156	46.51 155	29.15 198	67.55 111	65.23 134	73.26 133	65.16 171

diffmvs1		63.32 56	68.34 56	57.46 71	60.91 79	66.62 84	67.89 56	42.99 127	62.75 57	47.35 67	63.95 41	69.65 49	52.69 82	68.66 77	66.71 91	72.95 136	80.05 52

gm-plane-assit		44.74 202	45.95 210	43.33 187	60.88 80	46.79 216	36.97 225	32.24 217	24.15 234	11.79 220	29.26 220	32.97 222	46.64 123	65.09 157	62.95 162	71.45 164	60.42 192

DI_MVS_plusplus_trai		61.88 63	65.17 68	58.06 61	60.05 81	65.26 100	66.03 89	44.22 86	55.75 66	46.73 70	54.64 76	68.12 56	54.13 61	69.13 72	66.66 93	77.18 68	76.61 74

PVSNet_Blended_VisFu		63.65 55	66.92 57	59.83 55	60.03 82	73.44 46	66.33 85	48.95 58	52.20 83	50.81 59	56.07 67	60.25 79	53.56 64	73.23 47	70.01 57	79.30 50	83.24 39

UniMVSNet_NR-MVSNet		56.94 114	61.14 81	52.05 124	60.02 83	65.21 102	57.44 135	52.93 36	49.37 96	24.31 189	54.62 77	50.54 129	39.04 157	68.69 75	68.84 65	78.53 55	70.72 122

IterMVS-LS		58.30 101	61.39 80	54.71 103	59.92 84	58.40 168	59.42 126	43.64 107	48.71 106	40.25 119	57.53 64	58.55 85	52.15 96	65.42 155	65.34 131	72.85 137	75.77 84
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
diffmvs		61.60 66	65.90 63	56.58 94	59.78 85	65.35 97	66.56 83	42.79 133	55.46 67	46.47 72	61.43 54	65.52 61	51.16 102	68.04 97	66.17 105	72.71 143	79.31 56

MVS_Test		62.40 62	66.23 61	57.94 64	59.77 86	64.77 105	66.50 84	41.76 148	57.26 65	49.33 62	62.68 46	67.47 58	53.50 67	68.57 80	66.25 102	76.77 75	76.58 76

TranMVSNet+NR-MVSNet		55.87 121	60.14 101	50.88 126	59.46 87	63.82 109	57.93 133	52.98 35	48.94 101	20.52 199	52.87 81	47.33 145	36.81 174	69.12 73	69.03 62	77.56 65	69.89 128

Fast-Effi-MVS+		60.36 68	63.35 75	56.87 87	58.70 88	65.86 94	65.08 98	37.11 188	53.00 79	45.36 84	52.12 86	56.07 96	56.27 50	71.28 57	69.42 59	78.71 53	75.69 86

IB-MVS	54.11 11	58.36 100	60.70 84	55.62 99	58.67 89	68.02 65	61.56 111	43.15 122	46.09 133	44.06 99	44.24 153	50.99 127	48.71 111	66.70 131	70.33 53	77.60 63	78.50 60
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		56.30 120	59.29 122	52.82 119	58.64 90	64.89 103	65.56 95	32.89 213	45.80 142	35.04 141	45.89 141	54.14 100	49.41 108	67.16 124	66.45 101	75.37 109	70.69 124

CNLPA		62.78 60	66.31 60	58.65 58	58.47 91	68.41 63	65.98 91	41.22 158	78.02 20	56.04 34	46.65 129	59.50 82	57.50 41	69.67 68	65.27 133	72.70 147	76.67 73

tpm cat1		53.30 141	53.41 170	53.17 115	58.16 92	59.15 161	63.73 107	38.27 183	50.73 88	46.98 69	45.57 145	44.00 182	49.20 109	55.90 209	54.02 208	62.65 202	64.50 176

v1144		58.88 78	60.16 100	57.39 72	58.03 93	67.26 73	67.14 63	44.46 84	45.17 146	44.33 98	47.81 123	49.92 133	53.20 73	67.77 105	66.62 96	77.15 69	76.58 76

v7		59.19 75	60.62 85	57.53 69	57.96 94	67.19 75	67.09 64	44.28 85	46.84 124	45.45 82	48.19 118	51.06 124	53.62 63	67.84 100	66.59 97	76.79 72	76.60 75

v10		59.17 76	60.60 86	57.50 70	57.95 95	66.73 79	67.09 64	44.11 87	46.85 123	45.42 83	48.18 120	51.07 123	53.63 62	67.84 100	66.59 97	76.79 72	76.92 71

v1192		58.51 90	59.66 114	57.17 73	57.82 96	67.72 67	66.21 88	44.83 80	44.15 153	43.49 101	46.68 128	47.94 137	53.55 65	67.39 121	66.51 99	77.13 70	77.20 69

DWT-MVSNet_training		53.80 136	54.31 162	53.21 112	57.65 97	59.04 162	60.65 120	40.11 171	46.35 128	42.77 104	49.07 97	41.07 197	51.06 103	58.62 192	58.96 187	67.00 190	67.06 148

v144192		58.23 103	59.40 121	56.87 87	57.56 98	66.89 77	65.70 93	45.01 79	44.06 154	42.88 103	46.61 130	48.09 136	53.49 68	66.94 127	65.90 109	76.61 83	77.29 67

v1921920		57.89 107	59.02 124	56.58 94	57.55 99	66.66 83	64.72 101	44.70 82	43.55 157	42.73 105	46.17 138	46.93 151	53.51 66	66.78 130	65.75 113	76.29 89	77.28 68

v13		58.44 95	59.72 112	56.94 77	57.55 99	63.51 111	66.86 67	42.81 132	45.90 138	44.98 89	48.17 121	51.87 118	52.68 83	68.20 87	65.78 111	76.78 74	74.63 99

v12		58.44 95	59.74 111	56.92 81	57.54 101	63.50 112	66.84 70	42.77 134	45.96 136	44.95 90	48.31 114	51.94 117	52.67 84	68.14 90	65.75 113	76.75 76	74.55 101

v11		58.19 104	59.47 119	56.70 92	57.54 101	63.42 116	66.28 87	42.49 140	45.62 144	44.59 96	48.16 122	50.78 128	52.84 74	67.80 104	65.76 112	76.49 86	74.76 92

Vis-MVSNet (Re-imp)		50.37 166	57.73 135	41.80 197	57.53 103	54.35 188	45.70 205	45.24 77	49.80 91	13.43 217	58.23 62	56.42 92	20.11 218	62.96 164	63.36 157	68.76 180	58.96 198

CostFormer		56.57 118	59.13 123	53.60 108	57.52 104	61.12 137	66.94 66	35.95 195	53.44 73	44.68 92	55.87 70	54.44 99	48.21 114	60.37 182	58.33 190	68.27 183	70.33 127

V9		58.45 94	59.75 108	56.92 81	57.51 105	63.49 113	66.86 67	42.73 135	46.07 135	45.05 87	48.45 113	51.99 116	52.66 85	68.04 97	65.75 113	76.72 77	74.50 102

thres400		52.38 149	55.51 146	48.74 150	57.49 106	60.10 149	55.45 153	43.54 111	42.90 166	26.72 179	43.34 163	45.03 173	36.61 175	66.20 143	64.53 144	72.66 148	66.43 153

V14		58.44 95	59.75 108	56.90 84	57.48 107	63.46 114	66.85 69	42.68 136	46.16 132	45.03 88	48.57 111	52.04 115	52.65 86	67.93 99	65.72 116	76.69 78	74.40 104

v15		58.43 98	59.75 108	56.88 86	57.45 108	63.44 115	66.84 70	42.65 137	46.24 131	45.07 86	48.68 110	52.07 114	52.63 87	67.84 100	65.70 117	76.65 79	74.31 107

thres600view7		51.91 156	55.14 153	48.14 157	57.43 109	60.18 145	54.60 163	43.73 103	42.61 174	25.20 184	43.10 169	44.47 179	35.19 179	66.36 136	63.28 159	72.66 148	66.01 160

view600		51.96 154	55.13 154	48.27 156	57.41 110	60.05 150	54.74 162	43.64 107	42.57 175	25.88 182	43.11 168	44.48 178	35.34 178	66.27 139	63.61 154	72.61 151	65.80 162

thres200		52.39 148	55.37 151	48.90 148	57.39 111	60.18 145	55.60 151	43.73 103	42.93 165	27.41 176	43.35 162	45.09 168	36.61 175	66.36 136	63.92 152	72.66 148	65.78 163

v17		58.69 84	60.19 99	56.94 77	57.38 112	63.37 117	66.67 80	42.47 142	48.52 112	46.10 77	48.90 105	53.00 105	52.84 74	67.58 109	65.60 123	76.19 96	74.38 105

view800		51.55 158	54.89 156	47.66 164	57.37 113	59.77 153	53.62 169	43.72 105	42.22 177	24.94 185	42.80 173	43.81 184	33.94 185	66.09 145	64.38 146	72.39 154	65.14 172

v1141		58.56 87	60.05 105	56.81 90	57.36 114	66.18 88	66.80 72	43.11 123	45.87 140	44.60 94	48.71 108	51.83 120	52.38 91	67.46 116	65.64 121	76.63 80	74.66 96

divwei89l23v2f112		58.56 87	60.05 105	56.81 90	57.36 114	66.18 88	66.80 72	43.11 123	45.89 139	44.60 94	48.71 108	51.84 119	52.38 91	67.45 118	65.65 118	76.63 80	74.66 96

v1		58.56 87	60.06 104	56.83 89	57.36 114	66.19 87	66.80 72	43.10 125	45.87 140	44.68 92	48.73 107	51.83 120	52.38 91	67.45 118	65.65 118	76.63 80	74.66 96

v16		58.71 83	60.20 96	56.97 75	57.35 117	63.36 118	66.67 80	42.49 140	48.69 108	46.36 74	48.87 106	52.92 110	52.82 76	67.57 110	65.58 127	76.15 98	74.38 105

v8		58.88 78	60.57 88	56.92 81	57.35 117	65.69 96	66.69 79	42.64 138	47.89 116	45.77 79	49.04 98	52.98 106	52.77 80	67.51 115	65.57 128	76.26 91	75.30 90

tfpn		50.58 163	53.65 168	47.00 168	57.34 119	59.31 157	52.41 174	43.76 100	41.81 182	23.86 191	42.49 176	37.80 212	32.63 190	65.68 152	64.02 150	71.99 160	64.41 177

v6		58.89 77	60.54 89	56.96 76	57.34 119	66.13 90	66.71 75	42.84 129	47.85 118	45.80 78	49.04 98	52.95 107	52.79 77	67.53 112	65.59 124	76.26 91	74.73 93

v1neww		58.88 78	60.54 89	56.94 77	57.33 121	66.13 90	66.70 77	42.84 129	47.84 119	45.74 80	49.02 100	52.93 108	52.78 78	67.53 112	65.59 124	76.26 91	74.73 93

v7new		58.88 78	60.54 89	56.94 77	57.33 121	66.13 90	66.70 77	42.84 129	47.84 119	45.74 80	49.02 100	52.93 108	52.78 78	67.53 112	65.59 124	76.26 91	74.73 93

tfpn111		52.44 146	55.38 149	49.01 144	57.31 123	60.24 142	55.42 154	43.77 96	42.85 167	27.51 172	42.03 181	45.06 169	37.32 166	66.38 133	64.54 140	72.71 143	66.54 150

conf200view11		52.51 145	55.51 146	49.01 144	57.31 123	60.24 142	55.42 154	43.77 96	42.85 167	27.51 172	43.00 170	45.06 169	37.32 166	66.38 133	64.54 140	72.71 143	66.54 150

thres100view900		52.04 151	54.81 158	48.80 149	57.31 123	59.33 156	55.30 159	42.92 128	42.85 167	27.81 170	43.00 170	45.06 169	36.99 172	64.74 158	63.51 155	72.47 152	65.21 170

tfpn200view9		52.53 144	55.51 146	49.06 142	57.31 123	60.24 142	55.42 154	43.77 96	42.85 167	27.81 170	43.00 170	45.06 169	37.32 166	66.38 133	64.54 140	72.71 143	66.54 150

conf0.01		52.02 152	54.62 159	49.00 146	57.30 127	60.17 147	55.42 154	43.76 100	42.85 167	27.49 174	43.12 167	39.71 205	37.32 166	66.26 141	64.54 140	72.72 140	65.66 165

v1240		57.55 109	58.63 127	56.29 97	57.30 127	66.48 85	63.77 106	44.56 83	42.77 173	42.48 107	45.64 144	46.28 157	53.46 69	66.32 138	65.80 110	76.16 97	77.13 70

conf0.002		51.76 157	54.13 164	49.00 146	57.28 129	60.15 148	55.42 154	43.75 102	42.85 167	27.49 174	43.13 166	37.12 217	37.32 166	66.23 142	64.17 147	72.72 140	65.24 169

v18		58.68 86	60.20 96	56.90 84	57.26 130	63.28 119	66.58 82	42.42 143	48.86 102	46.37 73	49.01 102	53.05 104	52.74 81	67.40 120	65.52 129	76.02 103	74.28 108

CHOSEN 1792x2688		55.85 122	58.01 132	53.33 110	57.26 130	62.82 123	63.29 110	41.55 154	46.65 126	38.34 126	34.55 206	53.50 101	52.43 90	67.10 125	67.56 81	67.13 187	73.92 114

PVSNet_BlendedMVS		61.63 64	64.82 69	57.91 66	57.21 132	67.55 70	63.47 108	46.08 68	54.72 69	52.46 55	58.59 59	60.73 75	51.82 99	70.46 62	65.20 135	76.44 87	76.50 79

PVSNet_Blended		61.63 64	64.82 69	57.91 66	57.21 132	67.55 70	63.47 108	46.08 68	54.72 69	52.46 55	58.59 59	60.73 75	51.82 99	70.46 62	65.20 135	76.44 87	76.50 79

v2v482		58.69 84	60.12 103	57.03 74	57.16 134	66.05 93	67.17 62	43.52 112	46.33 129	45.19 85	49.46 95	51.02 125	52.51 89	67.30 122	66.03 106	76.61 83	74.62 100

conf0.05thres1000		50.64 162	53.84 165	46.92 169	57.02 135	59.29 158	52.29 175	43.80 95	39.84 197	23.81 192	39.26 194	43.14 187	32.52 191	65.74 149	64.04 148	72.05 159	65.53 166

tfpnnormal		50.16 168	52.19 181	47.78 163	56.86 136	58.37 169	54.15 164	44.01 93	38.35 209	25.94 181	36.10 201	37.89 211	34.50 182	65.93 146	63.42 156	71.26 165	65.28 168

HyFIR lowres test		56.87 115	58.60 128	54.84 102	56.62 137	69.27 59	64.77 100	42.21 146	45.66 143	37.50 132	33.08 208	57.47 90	53.33 70	65.46 154	67.94 72	74.60 113	71.35 120

v7n		55.67 123	57.46 137	53.59 109	56.06 138	65.29 99	61.06 117	43.26 120	40.17 194	37.99 129	40.79 188	45.27 166	47.09 120	67.67 107	66.21 103	76.08 100	76.82 72

dps		50.42 165	51.20 192	49.51 136	55.88 139	56.07 179	53.73 166	38.89 176	43.66 155	40.36 118	45.66 143	37.63 214	45.23 129	59.05 185	56.18 193	62.94 201	60.16 193

CANet_DTU		58.88 78	64.68 71	52.12 123	55.77 140	66.75 78	63.92 105	37.04 189	53.32 75	37.45 133	59.81 56	61.81 72	44.43 133	68.25 84	67.47 82	74.12 119	75.33 89

WR-MVS		48.78 178	55.06 155	41.45 199	55.50 141	60.40 141	43.77 214	49.99 53	41.92 179	8.10 232	45.24 149	45.56 161	17.47 220	61.57 176	64.60 139	73.85 121	66.14 159

UniMVSNet (Re)		55.15 130	60.39 92	49.03 143	55.31 142	64.59 106	55.77 150	50.63 48	48.66 109	20.95 197	51.47 89	50.40 130	34.41 183	67.81 103	67.89 73	77.11 71	71.88 117

test-LLR		49.28 172	50.29 196	48.10 158	55.26 143	47.16 211	49.52 181	43.48 115	39.22 199	31.98 149	43.65 159	47.93 138	41.29 150	56.80 200	55.36 199	67.08 188	61.94 186

test0.0.03 1		43.15 207	46.95 209	38.72 208	55.26 143	50.56 200	42.48 217	43.48 115	38.16 211	15.11 213	35.07 204	44.69 176	16.47 223	55.95 208	54.34 207	59.54 208	49.87 222

GA-MVS		55.67 123	58.33 129	52.58 121	55.23 145	63.09 120	61.08 116	40.15 170	42.95 164	37.02 135	52.61 83	47.68 140	47.51 118	65.92 147	65.35 130	74.49 115	70.68 125

thresconf0.02		48.17 183	51.22 191	44.60 180	55.14 146	55.73 181	48.95 185	41.35 157	43.43 161	21.23 195	42.03 181	37.25 216	31.19 193	62.33 170	60.61 179	69.76 173	57.17 203

tfpnview11		47.58 189	51.57 184	42.92 190	54.94 147	55.30 183	46.21 199	41.58 152	42.10 178	18.54 204	42.25 178	41.54 193	27.12 204	62.29 171	61.12 172	69.15 175	56.40 207

tfpn_n400		47.56 190	51.56 185	42.90 191	54.91 148	55.28 184	46.21 199	41.59 150	41.51 185	18.54 204	42.25 178	41.54 193	27.12 204	62.41 168	61.02 174	69.05 176	56.90 205

tfpnconf		47.56 190	51.56 185	42.90 191	54.91 148	55.28 184	46.21 199	41.59 150	41.51 185	18.54 204	42.25 178	41.54 193	27.12 204	62.41 168	61.02 174	69.05 176	56.90 205

DTE-MVSNet		48.03 186	53.28 172	41.91 196	54.64 150	57.50 175	44.63 212	51.66 44	41.02 189	7.97 233	46.26 135	40.90 198	20.24 217	60.45 181	62.89 163	72.33 156	63.97 178

pmmvs4		54.66 133	56.07 144	53.00 116	54.63 151	57.08 177	60.43 124	44.10 88	51.69 85	40.55 116	46.55 133	44.79 174	45.95 127	62.54 166	63.66 153	72.36 155	66.20 157

DU-MVS		55.41 126	59.59 115	50.54 129	54.60 152	62.97 121	57.44 135	51.80 41	48.62 110	24.31 189	51.99 87	47.00 150	39.04 157	68.11 91	67.75 77	76.03 102	70.72 122

Baseline_NR-MVSNet		53.50 139	57.89 133	48.37 154	54.60 152	59.25 160	56.10 145	51.84 40	49.32 97	17.92 209	45.38 146	47.68 140	36.93 173	68.11 91	65.95 107	72.84 138	69.57 133

v148		55.58 125	57.61 136	53.20 113	54.59 154	61.86 126	61.18 115	38.70 180	44.30 152	42.25 108	47.53 124	50.24 132	48.73 110	65.15 156	62.61 167	73.79 122	71.61 119

tfpn1000		46.75 196	51.24 190	41.51 198	54.39 155	55.60 182	43.85 213	40.90 160	41.82 181	16.71 211	41.26 185	41.58 192	23.96 211	60.76 179	60.27 182	69.26 174	57.42 202

tfpn_ndepth		48.34 181	52.27 179	43.76 183	54.35 156	56.46 178	47.24 195	40.92 159	43.45 159	21.04 196	41.16 186	43.22 186	28.90 201	61.57 176	60.65 178	70.12 171	59.34 196

PEN-MVS		49.21 174	54.32 161	43.24 189	54.33 157	59.26 159	47.04 196	51.37 45	41.67 183	9.97 227	46.22 136	41.80 191	22.97 215	60.52 180	64.03 149	73.73 123	66.75 149

pm-mvs1		51.02 161	55.55 145	45.73 173	54.16 158	58.52 166	50.92 178	42.56 139	40.32 193	25.67 183	43.66 158	50.34 131	30.06 196	65.85 148	63.97 151	70.99 167	66.21 156

EPNet_dtu		52.05 150	58.26 130	44.81 178	54.10 159	50.09 203	52.01 176	40.82 163	53.03 78	27.41 176	54.90 73	57.96 89	26.72 207	62.97 163	62.70 166	67.78 185	66.19 158
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		48.82 177	51.27 189	45.96 172	54.10 159	47.35 210	56.05 146	30.23 218	46.70 125	43.21 102	52.54 84	47.55 143	37.28 171	54.11 214	50.50 219	54.90 220	60.12 194

CDS-MVSNet		52.42 147	57.06 140	47.02 167	53.92 161	58.30 170	55.50 152	46.47 66	42.52 176	29.38 164	49.50 94	52.85 111	28.49 202	66.70 131	66.89 88	68.34 181	62.63 185
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test20.03		40.38 216	44.20 216	35.92 215	53.73 162	49.05 204	38.54 223	43.49 114	32.55 221	9.54 228	27.88 222	39.12 207	12.24 234	56.28 205	54.69 204	57.96 213	49.83 223

Vis-MVSNet		58.48 92	65.70 65	50.06 133	53.40 163	67.20 74	60.24 125	43.32 118	48.83 103	30.23 159	62.38 49	61.61 74	40.35 153	71.03 59	69.77 58	72.82 139	79.11 57
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PLC	52.09 14	59.21 74	62.47 77	55.41 101	53.24 164	64.84 104	64.47 103	40.41 168	65.92 50	44.53 97	46.19 137	55.69 97	55.33 56	68.24 86	65.30 132	74.50 114	71.09 121
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
thisisatest0530		56.68 117	59.68 113	53.19 114	52.97 165	60.96 139	59.41 127	40.51 165	48.26 113	41.06 114	52.67 82	46.30 156	49.78 105	67.66 108	67.83 74	75.39 108	74.07 113

OMC-MVS		65.16 53	71.35 41	57.94 64	52.95 166	68.82 60	69.00 54	38.28 182	79.89 13	55.20 39	62.76 45	68.31 54	56.14 52	71.30 56	68.70 67	76.06 101	79.67 54

tpmrst		48.08 184	49.88 200	45.98 171	52.71 167	48.11 208	53.62 169	33.70 206	48.70 107	39.74 120	48.96 103	46.23 158	40.29 154	50.14 223	49.28 221	55.80 217	57.71 201

tttt0517		56.53 119	59.59 115	52.95 117	52.66 168	60.99 138	59.21 129	40.51 165	47.89 116	40.40 117	52.50 85	46.04 160	49.78 105	67.75 106	67.83 74	75.15 111	74.17 110

GBi-Net		55.20 128	60.25 94	49.31 137	52.42 169	61.44 131	57.03 138	44.04 90	49.18 98	30.47 155	48.28 115	58.19 86	38.22 160	68.05 94	66.96 85	73.69 124	69.65 130

test1		55.20 128	60.25 94	49.31 137	52.42 169	61.44 131	57.03 138	44.04 90	49.18 98	30.47 155	48.28 115	58.19 86	38.22 160	68.05 94	66.96 85	73.69 124	69.65 130

FMVSNet2		55.04 131	59.95 107	49.31 137	52.42 169	61.44 131	57.03 138	44.08 89	49.55 93	30.40 158	46.89 127	58.84 84	38.22 160	67.07 126	66.21 103	73.69 124	69.65 130

FMVSNet3		54.78 132	59.58 117	49.17 140	52.37 172	61.31 135	56.72 142	44.04 90	49.18 98	30.47 155	48.28 115	58.19 86	38.09 163	65.48 153	65.20 135	73.31 131	69.45 138

testgi		38.71 218	43.64 217	32.95 220	52.30 173	48.63 207	35.59 229	35.05 198	31.58 225	9.03 231	30.29 213	40.75 200	11.19 239	55.30 210	53.47 213	54.53 222	45.48 226

Anonymous20231206		42.28 208	45.89 211	38.07 210	51.96 174	48.98 205	43.66 215	38.81 179	38.74 205	14.32 216	26.74 223	40.90 198	20.94 216	56.64 203	54.67 205	58.71 209	54.59 209

pmmvs6		48.35 180	51.64 183	44.51 181	51.92 175	57.94 173	49.44 183	42.17 147	34.45 217	24.62 188	28.87 221	46.90 152	29.07 200	64.60 159	63.08 160	69.83 172	65.68 164

v748		52.93 142	55.29 152	50.19 132	51.90 176	61.31 135	56.54 143	40.05 172	39.12 201	34.82 144	39.93 191	43.83 183	43.66 135	64.26 160	63.32 158	74.15 118	75.28 91

PatchmatchNet		49.92 170	51.29 188	48.32 155	51.83 177	51.86 197	53.38 172	37.63 187	47.90 115	40.83 115	48.54 112	45.30 164	45.19 130	56.86 199	53.99 210	61.08 206	54.57 210
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
WR-MVS_H		47.65 187	53.67 167	40.63 202	51.45 178	59.74 154	44.71 211	49.37 55	40.69 191	7.61 234	46.04 139	44.34 181	17.32 221	57.79 196	61.18 171	73.30 132	65.86 161

LTVRE_ROB	44.17 16	47.06 195	50.15 199	43.44 186	51.39 179	58.42 167	42.90 216	43.51 113	22.27 238	14.85 215	41.94 184	34.57 219	45.43 128	62.28 172	62.77 165	62.56 203	68.83 141
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		48.37 179	53.53 169	42.34 194	51.35 180	58.01 172	46.56 197	50.54 49	41.62 184	10.61 223	46.53 134	40.68 201	23.18 213	58.71 190	61.83 169	71.81 161	67.36 146

PS-CasMVS		48.18 182	53.25 173	42.27 195	51.26 181	57.94 173	46.51 198	50.52 50	41.30 187	10.56 225	45.35 148	40.34 203	23.04 214	58.66 191	61.79 170	71.74 163	67.38 145

our_test_3					51.15 182	57.31 176	55.12 160

FMVSNet1		54.08 134	58.68 126	48.71 151	50.90 183	61.35 134	56.73 141	43.94 94	45.91 137	29.32 165	42.72 174	56.26 95	37.70 164	68.05 94	66.96 85	73.69 124	69.50 134

pmmvs-eth3d		51.33 159	52.25 180	50.26 131	50.82 184	54.65 187	56.03 147	43.45 117	43.51 158	37.20 134	39.20 195	39.04 208	42.28 145	61.85 175	62.78 164	71.78 162	64.72 174

MVSTER		57.19 110	61.11 82	52.62 120	50.82 184	58.79 164	61.55 112	37.86 185	48.81 104	41.31 112	57.43 65	52.10 113	48.60 112	68.19 89	66.75 90	75.56 106	75.68 87

ambc			45.54 214		50.66 186	52.63 195	40.99 220		38.36 208	24.67 187	22.62 230	13.94 242	29.14 199	65.71 151	58.06 191	58.60 211	67.43 144

thisisatest0515		53.85 135	56.84 141	50.37 130	50.25 187	58.17 171	55.99 148	39.90 173	41.88 180	38.16 128	45.91 140	45.30 164	44.58 132	66.15 144	66.89 88	73.36 130	73.57 115

MDTV_nov1_ep13		50.32 167	52.43 178	47.86 162	49.87 188	54.70 186	58.10 132	34.29 201	45.59 145	37.71 130	47.44 125	47.42 144	41.86 147	58.07 195	55.21 201	65.34 195	58.56 199

IterMVS		53.45 140	57.12 139	49.17 140	49.23 189	60.93 140	59.05 130	34.63 199	44.53 148	33.22 145	51.09 91	51.01 126	48.38 113	62.43 167	60.79 177	70.54 169	69.05 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
COLMAP_ROB	46.52 15	51.99 153	54.86 157	48.63 152	49.13 190	61.73 128	60.53 123	36.57 192	53.14 76	32.95 147	37.10 198	38.68 209	40.49 152	65.72 150	63.08 160	72.11 158	64.60 175
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
CR-MVSNet		50.47 164	52.61 175	47.98 160	49.03 191	52.94 192	48.27 187	38.86 177	44.41 149	39.59 122	44.34 152	44.65 177	46.63 124	58.97 187	60.31 180	65.48 193	62.66 183

V42		56.97 113	60.14 101	53.28 111	48.16 192	62.78 124	66.30 86	37.93 184	47.44 121	42.68 106	48.19 118	52.59 112	51.90 97	67.46 116	65.94 108	72.72 140	76.55 78

MDTV_nov1_ep13_2view		47.62 188	49.72 201	45.18 176	48.05 193	53.70 190	54.90 161	33.80 205	39.90 196	29.79 162	38.85 196	41.89 190	39.17 156	58.99 186	55.55 198	65.34 195	59.17 197

EPMVS		44.66 203	47.86 207	40.92 201	47.97 194	44.70 221	47.58 192	33.27 209	48.11 114	29.58 163	49.65 93	44.38 180	34.65 180	51.71 218	47.90 225	52.49 225	48.57 224

RPMNet		46.41 197	48.72 203	43.72 184	47.77 195	52.94 192	46.02 204	33.92 203	44.41 149	31.82 152	36.89 199	37.42 215	37.41 165	53.88 215	54.02 208	65.37 194	61.47 188

testpf		34.85 223	36.16 229	33.31 219	47.49 196	35.56 235	36.85 226	32.31 216	23.08 235	15.63 212	29.39 218	29.48 226	19.62 219	41.38 236	41.07 235	47.95 232	53.18 211

TAPA-MVS	54.74 10	60.85 67	66.61 58	54.12 107	47.38 197	65.33 98	65.35 97	36.51 193	75.16 27	48.82 65	54.70 75	63.51 65	53.31 71	68.36 82	64.97 138	73.37 129	74.27 109
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
SixPastTwentyTwo		47.55 192	50.25 198	44.41 182	47.30 198	54.31 189	47.81 190	40.36 169	33.76 218	19.93 201	43.75 157	32.77 223	42.07 146	59.82 183	60.94 176	68.98 178	66.37 155

TAMVS		44.02 205	49.18 202	37.99 211	47.03 199	45.97 218	45.04 208	28.47 223	39.11 202	20.23 200	43.22 165	48.52 134	28.49 202	58.15 194	57.95 192	58.71 209	51.36 215

UGNet		57.03 111	65.25 67	47.44 165	46.54 200	66.73 79	56.30 144	43.28 119	50.06 90	32.99 146	62.57 47	63.26 66	33.31 187	68.25 84	67.58 80	72.20 157	78.29 62
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 61	69.90 50	54.19 105	46.31 201	66.73 79	65.49 96	41.36 156	76.57 22	46.31 75	76.80 14	56.68 91	53.27 72	69.50 69	66.65 94	72.40 153	76.36 81

PatchMatch-RL		50.11 169	51.56 185	48.43 153	46.23 202	51.94 196	50.21 180	38.62 181	46.62 127	37.51 131	42.43 177	39.38 206	52.24 95	60.98 178	59.56 184	65.76 192	60.01 195

CMPMVS	37.70 17	49.24 173	52.71 174	45.19 175	45.97 203	51.23 199	47.44 193	29.31 220	43.04 163	44.69 91	34.45 207	48.35 135	43.64 136	62.59 165	59.82 183	60.08 207	69.48 135
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v52		53.60 137	56.74 142	49.93 134	45.54 204	61.64 129	60.65 120	36.99 190	38.75 203	36.32 137	39.64 192	47.13 147	47.05 121	66.89 128	65.65 118	73.04 134	77.48 65

V4		53.60 137	56.73 143	49.93 134	45.54 204	61.64 129	60.65 120	36.99 190	38.74 205	36.33 136	39.64 192	47.12 148	47.05 121	66.89 128	65.64 121	73.04 134	77.48 65

pmmvs5		47.07 194	51.02 194	42.46 193	45.18 206	51.47 198	48.23 189	33.09 212	38.17 210	28.62 168	46.60 131	43.48 185	30.74 194	58.28 193	58.63 189	68.92 179	60.48 191

USDC		51.11 160	53.71 166	48.08 159	44.76 207	55.99 180	53.01 173	40.90 160	52.49 81	36.14 138	44.67 151	33.66 221	43.27 141	63.23 162	61.10 173	70.39 170	64.82 173

FC-MVSNet-test		39.65 217	48.35 205	29.49 224	44.43 208	39.28 228	30.23 235	40.44 167	43.59 156	3.12 244	53.00 80	42.03 189	10.02 241	55.09 211	54.77 203	48.66 230	50.71 217

ADS-MVSNet		40.67 214	43.38 218	37.50 212	44.36 209	39.79 227	42.09 219	32.67 215	44.34 151	28.87 167	40.76 189	40.37 202	30.22 195	48.34 233	45.87 231	46.81 234	44.21 228

new-patchmatchnet		33.24 226	37.20 225	28.62 227	44.32 210	38.26 232	29.68 238	36.05 194	31.97 223	6.33 236	26.59 224	27.33 228	11.12 240	50.08 224	41.05 236	44.23 235	45.15 227

1111		31.35 228	33.52 233	28.83 225	44.28 211	32.44 236	31.71 233	33.25 210	27.87 228	10.92 221	22.18 231	24.05 233	15.89 225	49.03 231	44.09 232	36.94 239	34.96 234

.test1245		22.44 236	22.23 238	22.67 234	44.28 211	32.44 236	31.71 233	33.25 210	27.87 228	10.92 221	22.18 231	24.05 233	15.89 225	49.03 231	0.01 243	0.00 247	0.06 244

MVS-HIRNet		42.24 209	41.15 222	43.51 185	44.06 213	40.74 224	35.77 228	35.35 196	35.38 215	38.34 126	25.63 225	38.55 210	43.48 138	50.77 220	47.03 229	64.07 197	49.98 220

PatchT		48.08 184	51.03 193	44.64 179	42.96 214	50.12 202	40.36 221	35.09 197	43.17 162	39.59 122	42.00 183	39.96 204	46.63 124	58.97 187	60.31 180	63.21 200	62.66 183

CVMVSNet		46.38 199	52.01 182	39.81 204	42.40 215	50.26 201	46.15 202	37.68 186	40.03 195	15.09 214	46.56 132	47.56 142	33.72 186	56.50 204	55.65 197	63.80 199	67.53 143

TinyColmap		47.08 193	47.56 208	46.52 170	42.35 216	53.44 191	51.77 177	40.70 164	43.44 160	31.92 151	29.78 216	23.72 236	45.04 131	61.99 173	59.54 185	67.35 186	61.03 189

MIMVSNet		43.79 206	48.53 204	38.27 209	41.46 217	48.97 206	50.81 179	32.88 214	44.55 147	22.07 193	32.05 209	47.15 146	24.76 210	58.73 189	56.09 195	57.63 214	52.14 213

LP		40.79 213	41.99 220	39.38 205	40.98 218	46.49 217	42.14 218	33.66 207	35.37 216	29.89 161	29.30 219	27.81 227	32.74 188	52.55 216	52.19 216	56.87 215	50.23 219

N_pmnet		32.67 227	36.85 226	27.79 228	40.55 219	32.13 238	35.80 227	26.79 229	37.24 213	9.10 229	32.02 210	30.94 224	16.30 224	47.22 234	41.21 234	38.21 237	37.21 233

anonymousdsp		52.84 143	57.78 134	47.06 166	40.24 220	58.95 163	53.70 167	33.54 208	36.51 214	32.69 148	43.88 155	45.40 162	47.97 117	67.17 123	70.28 54	74.22 117	82.29 42

EU-MVSNet		40.63 215	45.65 213	34.78 218	39.11 221	46.94 214	40.02 222	34.03 202	33.50 219	10.37 226	35.57 203	37.80 212	23.65 212	51.90 217	50.21 220	61.49 205	63.62 181

FPMVS		38.36 219	40.41 223	35.97 214	38.92 222	39.85 226	45.50 206	25.79 232	41.13 188	18.70 203	30.10 214	24.56 231	31.86 192	49.42 228	46.80 230	55.04 218	51.03 216

testus		31.33 229	36.31 228	25.52 232	37.55 223	38.40 229	25.87 239	23.58 235	26.46 231	5.97 237	24.15 227	24.92 230	12.44 233	49.14 230	48.21 224	47.73 233	42.86 229

test2356		33.40 225	36.53 227	29.76 223	37.51 224	38.39 230	34.68 230	27.35 225	27.88 227	10.61 223	25.54 226	24.44 232	17.15 222	49.99 225	48.32 223	51.24 227	41.16 232

TESTMET0.1,1		46.09 200	50.29 196	41.18 200	36.91 225	47.16 211	49.52 181	20.32 237	39.22 199	31.98 149	43.65 159	47.93 138	41.29 150	56.80 200	55.36 199	67.08 188	61.94 186

testmv		30.97 230	34.42 231	26.95 229	36.49 226	37.38 233	29.80 236	27.28 226	22.34 236	4.72 238	20.63 235	20.64 238	13.22 231	49.86 227	47.74 226	50.20 228	42.36 230

test1235678		30.97 230	34.42 231	26.95 229	36.49 226	37.38 233	29.79 237	27.28 226	22.33 237	4.72 238	20.62 236	20.64 238	13.22 231	49.87 226	47.74 226	50.20 228	42.36 230

FMVSNet5		40.96 211	45.81 212	35.29 217	34.30 228	44.55 222	47.28 194	28.84 222	40.76 190	21.62 194	29.85 215	42.44 188	24.77 209	57.53 197	55.00 202	54.93 219	50.56 218

PMMVS		49.20 175	54.28 163	43.28 188	34.13 229	45.70 219	48.98 184	26.09 231	46.31 130	34.92 143	55.22 72	53.47 102	47.48 119	59.43 184	59.04 186	68.05 184	60.77 190

PMVS	27.84 18	33.81 224	35.28 230	32.09 221	34.13 229	24.81 242	32.51 232	26.48 230	26.41 232	19.37 202	23.76 228	24.02 235	25.18 208	50.78 219	47.24 228	54.89 221	49.95 221
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test-mter		45.30 201	50.37 195	39.38 205	33.65 231	46.99 213	47.59 191	18.59 239	38.75 203	28.00 169	43.28 164	46.82 153	41.50 149	57.28 198	55.78 196	66.93 191	63.70 180

CHOSEN 280x420		40.80 212	45.05 215	35.84 216	32.95 232	29.57 239	44.98 209	23.71 234	37.54 212	18.42 207	31.36 212	47.07 149	46.41 126	56.71 202	54.65 206	48.55 231	58.47 200

PM-MVS		44.55 204	48.13 206	40.37 203	32.85 233	46.82 215	46.11 203	29.28 221	40.48 192	29.99 160	39.98 190	34.39 220	41.80 148	56.08 207	53.88 212	62.19 204	65.31 167

no-one		29.19 232	31.89 234	26.05 231	30.96 234	38.33 231	21.54 240	29.86 219	15.84 242	3.56 241	11.28 241	13.03 243	14.44 230	38.96 237	52.83 214	55.96 216	52.92 212

TDRefinement		49.31 171	52.44 177	45.67 174	30.44 235	59.42 155	59.24 128	39.78 174	48.76 105	31.20 154	35.73 202	29.90 225	42.81 144	64.24 161	62.59 168	70.55 168	66.43 153

Gipuma		25.87 233	26.91 237	24.66 233	28.98 236	20.17 243	20.46 242	34.62 200	29.55 226	9.10 229	4.91 245	5.31 247	15.76 227	49.37 229	49.10 222	39.03 236	29.95 237
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MDA-MVSNet-bldmvs		41.36 210	43.15 219	39.27 207	28.74 237	52.68 194	44.95 210	40.84 162	32.89 220	18.13 208	31.61 211	22.09 237	38.97 159	50.45 222	56.11 194	64.01 198	56.23 208

E-PMN		15.09 238	13.19 241	17.30 237	27.80 238	12.62 246	7.81 246	27.54 224	14.62 244	3.19 242	6.89 242	2.52 250	15.09 228	15.93 241	20.22 240	22.38 241	19.53 240

MIMVSNet1		35.51 221	41.41 221	28.63 226	27.53 239	43.36 223	38.09 224	33.82 204	32.01 222	6.77 235	21.63 233	35.43 218	11.97 236	55.05 212	53.99 210	53.59 224	48.36 225

EMVS		14.49 239	12.45 242	16.87 239	27.02 240	12.56 247	8.13 245	27.19 228	15.05 243	3.14 243	6.69 243	2.67 249	15.08 229	14.60 243	18.05 241	20.67 242	17.56 242

test12356		23.91 234	28.47 235	18.60 235	26.80 241	28.30 240	20.92 241	19.76 238	19.89 239	2.88 246	18.48 237	16.57 241	4.05 242	42.34 235	41.93 233	37.21 238	31.75 235

pmmvs3		35.10 222	38.47 224	31.17 222	26.37 242	40.47 225	34.51 231	18.09 240	24.75 233	16.88 210	23.05 229	26.69 229	32.69 189	50.73 221	51.60 217	58.46 212	51.98 214

RPSCF		46.41 197	54.42 160	37.06 213	25.70 243	45.14 220	45.39 207	20.81 236	62.79 56	35.10 140	44.92 150	55.60 98	43.56 137	56.12 206	52.45 215	51.80 226	63.91 179

new_pmnet		23.19 235	28.17 236	17.37 236	17.03 244	24.92 241	19.66 243	16.16 242	27.05 230	4.42 240	20.77 234	19.20 240	12.19 235	37.71 238	36.38 237	34.77 240	31.17 236

PMMVS2		15.84 237	19.68 239	11.35 240	15.74 245	16.95 244	13.31 244	17.64 241	16.08 241	0.36 248	13.12 238	11.47 244	1.69 244	28.82 239	27.24 239	19.38 243	24.09 239

MVE	12.28 19	13.53 240	15.72 240	10.96 241	7.39 246	15.71 245	6.05 247	23.73 233	10.29 246	3.01 245	5.77 244	3.41 248	11.91 237	20.11 240	29.79 238	13.67 244	24.98 238
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				5.40 242	3.97 247	2.35 249	3.26 249	0.44 244	17.56 240	12.09 218	11.48 240	7.14 245	1.98 243	15.68 242	15.49 242	10.69 245

GG-mvs-BLEND		36.62 220	53.39 171	17.06 238	0.01 248	58.61 165	48.63 186	0.01 245	47.13 122	0.02 249	43.98 154	60.64 77	0.03 245	54.92 213	51.47 218	53.64 223	56.99 204

sosnet-low-res		0.00 243	0.00 245	0.00 243	0.00 249	0.00 250	0.00 252	0.00 246	0.00 249	0.00 250	0.00 248	0.00 251	0.00 248	0.00 246	0.00 246	0.00 247	0.00 246

sosnet		0.00 243	0.00 245	0.00 243	0.00 249	0.00 250	0.00 252	0.00 246	0.00 249	0.00 250	0.00 248	0.00 251	0.00 248	0.00 246	0.00 246	0.00 247	0.00 246

testmvs		0.01 241	0.02 243	0.00 243	0.00 249	0.00 250	0.01 251	0.00 246	0.01 247	0.00 250	0.03 247	0.00 251	0.01 246	0.01 245	0.01 243	0.00 247	0.06 244

test123		0.01 241	0.02 243	0.00 243	0.00 249	0.00 250	0.00 252	0.00 246	0.01 247	0.00 250	0.04 246	0.00 251	0.01 246	0.00 246	0.01 243	0.00 247	0.07 243

MTAPA										65.14 1		80.20 15

MTMP										62.63 12		78.04 22

Patchmatch-RL test							1.04 250

NP-MVS									72.00 36

Patchmtry						47.61 209	48.27 187	38.86 177		39.59 122

DeepMVS_CX						6.95 248	5.98 248	2.25 243	11.73 245	2.07 247	11.85 239	5.43 246	11.75 238	11.40 244		8.10 246	18.38 241

High-resolution multi-view benchmark