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.

MTAPA										83.48 1		86.45 13

zzz-MVS		85.71 13	86.88 19	84.34 6	90.54 13	87.11 39	89.77 13	74.17 14	88.54 9	83.08 2	78.60 28	86.10 14	78.11 11	87.80 14	87.46 11	90.35 25	92.56 22

MTMP										82.66 3		84.91 21

HPM-MVS++		87.09 5	88.92 9	84.95 3	92.61 1	87.91 35	90.23 10	76.06 3	88.85 8	81.20 4	87.33 9	87.93 8	79.47 6	88.59 6	88.23 5	90.15 29	93.60 16

SD-MVS		86.96 6	89.45 5	84.05 11	90.13 16	89.23 18	89.77 13	74.59 10	89.17 6	80.70 5	89.93 7	89.67 2	78.47 8	87.57 16	86.79 18	90.67 13	93.76 12

HFP-MVS		86.15 11	87.95 14	84.06 10	90.80 7	89.20 19	89.62 15	74.26 12	87.52 11	80.63 6	86.82 12	84.19 24	78.22 10	87.58 15	87.19 13	90.81 8	93.13 20

TSAR-MVS + MP.		86.88 7	89.23 6	84.14 9	89.78 22	88.67 27	90.59 5	73.46 22	88.99 7	80.52 7	91.26 4	88.65 5	79.91 5	86.96 26	86.22 27	90.59 14	93.83 10

ESAPD		88.46 1	91.07 1	85.41 1	91.73 3	92.08 1	91.91 2	76.73 1	90.14 4	80.33 8	92.75 1	90.44 1	80.73 3	88.97 5	87.63 9	91.01 6	95.48 2

APDe-MVS		88.00 2	90.50 2	85.08 2	90.95 6	91.58 4	92.03 1	75.53 8	91.15 1	80.10 9	92.27 3	88.34 7	80.80 2	88.00 11	86.99 15	91.09 4	95.16 3

SMA-MVS		87.48 3	90.13 4	84.39 5	91.76 2	90.70 5	90.63 4	75.36 9	90.51 3	79.89 10	85.65 15	88.82 4	77.90 14	90.00 1	89.77 1	90.82 7	95.49 1

ACMMP_Plus		86.52 9	89.01 7	83.62 13	90.28 15	90.09 9	90.32 8	74.05 16	88.32 10	79.74 11	87.04 11	85.59 18	76.97 25	89.35 2	88.44 4	90.35 25	94.27 7

CSCG		85.28 18	87.68 15	82.49 21	89.95 20	91.99 2	88.82 20	71.20 32	86.41 18	79.63 12	79.26 25	88.36 6	73.94 35	86.64 28	86.67 21	91.40 2	94.41 4

CNVR-MVS		86.36 10	88.19 13	84.23 7	91.33 5	89.84 10	90.34 7	75.56 6	87.36 14	78.97 13	81.19 24	86.76 12	78.74 7	89.30 3	88.58 2	90.45 22	94.33 6

APD-MVS		86.84 8	88.91 10	84.41 4	90.66 9	90.10 8	90.78 3	75.64 5	87.38 13	78.72 14	90.68 6	86.82 11	80.15 4	87.13 21	86.45 24	90.51 16	93.83 10
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MSLP-MVS++		82.09 32	82.66 36	81.42 25	87.03 38	87.22 38	85.82 37	70.04 37	80.30 39	78.66 15	68.67 57	81.04 38	77.81 15	85.19 39	84.88 39	89.19 45	91.31 33

HSP-MVS		87.45 4	90.22 3	84.22 8	90.00 19	91.80 3	90.59 5	75.80 4	89.93 5	78.35 16	92.54 2	89.18 3	80.89 1	87.99 12	86.29 26	89.70 36	93.85 9

3Dnovator+	75.73 4	82.40 30	82.76 35	81.97 24	88.02 32	89.67 13	86.60 32	71.48 31	81.28 38	78.18 17	64.78 70	77.96 45	77.13 23	87.32 19	86.83 17	90.41 23	91.48 32

MP-MVS		85.50 15	87.40 17	83.28 16	90.65 10	89.51 15	89.16 19	74.11 15	83.70 29	78.06 18	85.54 16	84.89 22	77.31 20	87.40 18	87.14 14	90.41 23	93.65 15
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
NCCC		85.34 16	86.59 21	83.88 12	91.48 4	88.88 21	89.79 12	75.54 7	86.67 17	77.94 19	76.55 31	84.99 20	78.07 12	88.04 9	87.68 8	90.46 21	93.31 17

DeepC-MVS	78.47 2	84.81 22	86.03 25	83.37 15	89.29 27	90.38 7	88.61 22	76.50 2	86.25 19	77.22 20	75.12 35	80.28 39	77.59 18	88.39 7	88.17 6	91.02 5	93.66 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ACMMPR		85.52 14	87.53 16	83.17 18	90.13 16	89.27 16	89.30 16	73.97 17	86.89 16	77.14 21	86.09 13	83.18 27	77.74 16	87.42 17	87.20 12	90.77 9	92.63 21

CP-MVS		84.74 23	86.43 23	82.77 20	89.48 25	88.13 34	88.64 21	73.93 18	84.92 21	76.77 22	81.94 22	83.50 25	77.29 22	86.92 27	86.49 23	90.49 17	93.14 19

DeepC-MVS_fast	78.24 3	84.27 25	85.50 27	82.85 19	90.46 14	89.24 17	87.83 28	74.24 13	84.88 22	76.23 23	75.26 34	81.05 37	77.62 17	88.02 10	87.62 10	90.69 12	92.41 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MCST-MVS		85.13 19	86.62 20	83.39 14	90.55 12	89.82 12	89.29 17	73.89 19	84.38 27	76.03 24	79.01 27	85.90 16	78.47 8	87.81 13	86.11 29	92.11 1	93.29 18

SteuartSystems-ACMMP		85.99 12	88.31 12	83.27 17	90.73 8	89.84 10	90.27 9	74.31 11	84.56 26	75.88 25	87.32 10	85.04 19	77.31 20	89.01 4	88.46 3	91.14 3	93.96 8
Skip Steuart: Steuart Systems R&D Blog.
AdaColmap		79.74 42	78.62 53	81.05 28	89.23 28	86.06 47	84.95 44	71.96 28	79.39 43	75.51 26	63.16 74	68.84 80	76.51 26	83.55 50	82.85 49	88.13 59	86.46 63

CLD-MVS		79.35 45	81.23 41	77.16 49	85.01 52	86.92 41	85.87 36	60.89 122	80.07 42	75.35 27	72.96 42	73.21 59	68.43 66	85.41 38	84.63 40	87.41 73	85.44 74
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PGM-MVS		84.42 24	86.29 24	82.23 22	90.04 18	88.82 23	89.23 18	71.74 30	82.82 32	74.61 28	84.41 19	82.09 29	77.03 24	87.13 21	86.73 20	90.73 11	92.06 28

CANet		81.62 35	83.41 32	79.53 36	87.06 37	88.59 28	85.47 40	67.96 53	76.59 48	74.05 29	74.69 36	81.98 30	72.98 41	86.14 34	85.47 33	89.68 37	90.42 42

3Dnovator	73.76 5	79.75 41	80.52 46	78.84 40	84.94 54	87.35 36	84.43 47	65.54 67	78.29 44	73.97 30	63.00 76	75.62 52	74.07 34	85.00 40	85.34 35	90.11 30	89.04 48

TSAR-MVS + GP.		83.69 26	86.58 22	80.32 31	85.14 49	86.96 40	84.91 45	70.25 36	84.71 25	73.91 31	85.16 17	85.63 17	77.92 13	85.44 36	85.71 32	89.77 33	92.45 23

DeepPCF-MVS	79.04 1	85.30 17	88.93 8	81.06 27	88.77 30	90.48 6	85.46 41	73.08 23	90.97 2	73.77 32	84.81 18	85.95 15	77.43 19	88.22 8	87.73 7	87.85 67	94.34 5

train_agg		84.86 21	87.21 18	82.11 23	90.59 11	85.47 50	89.81 11	73.55 21	83.95 28	73.30 33	89.84 8	87.23 10	75.61 28	86.47 30	85.46 34	89.78 32	92.06 28

CNLPA		77.20 55	77.54 58	76.80 51	82.63 59	84.31 58	79.77 62	64.64 72	85.17 20	73.18 34	56.37 108	69.81 71	74.53 31	81.12 73	78.69 91	86.04 128	87.29 60

abl_6				79.05 38	87.27 36	88.85 22	83.62 50	68.25 49	81.68 36	72.94 35	73.79 41	84.45 23	72.55 43			89.66 38	90.64 39

ACMMP		83.42 27	85.27 28	81.26 26	88.47 31	88.49 30	88.31 26	72.09 27	83.42 30	72.77 36	82.65 20	78.22 43	75.18 29	86.24 33	85.76 31	90.74 10	92.13 27
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
OMC-MVS		80.26 37	82.59 37	77.54 47	83.04 57	85.54 49	83.25 52	65.05 70	87.32 15	72.42 37	72.04 45	78.97 41	73.30 38	83.86 46	81.60 56	88.15 58	88.83 50

TSAR-MVS + ACMM		85.10 20	88.81 11	80.77 30	89.55 24	88.53 29	88.59 23	72.55 25	87.39 12	71.90 38	90.95 5	87.55 9	74.57 30	87.08 23	86.54 22	87.47 72	93.67 13

PVSNet_BlendedMVS		76.21 57	77.52 59	74.69 60	79.46 75	83.79 61	77.50 100	64.34 75	69.88 62	71.88 39	68.54 58	70.42 68	67.05 68	83.48 51	79.63 80	87.89 65	86.87 61

PVSNet_Blended		76.21 57	77.52 59	74.69 60	79.46 75	83.79 61	77.50 100	64.34 75	69.88 62	71.88 39	68.54 58	70.42 68	67.05 68	83.48 51	79.63 80	87.89 65	86.87 61

XVS					86.63 40	88.68 24	85.00 42			71.81 41		81.92 31				90.47 18

X-MVStestdata					86.63 40	88.68 24	85.00 42			71.81 41		81.92 31				90.47 18

X-MVS		83.23 28	85.20 29	80.92 29	89.71 23	88.68 24	88.21 27	73.60 20	82.57 33	71.81 41	77.07 29	81.92 31	71.72 50	86.98 25	86.86 16	90.47 18	92.36 25

CPTT-MVS		81.77 33	83.10 34	80.21 32	85.93 45	86.45 45	87.72 29	70.98 33	82.54 34	71.53 44	74.23 40	81.49 34	76.31 27	82.85 57	81.87 53	88.79 52	92.26 26

ACMM	72.26 8	78.86 50	78.13 54	79.71 35	86.89 39	83.40 64	86.02 35	70.50 34	75.28 50	71.49 45	63.01 75	69.26 74	73.57 37	84.11 45	83.98 43	89.76 34	87.84 55
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVS_111021_LR		78.13 53	79.85 51	76.13 53	81.12 66	81.50 74	80.28 59	65.25 68	76.09 49	71.32 46	76.49 32	72.87 60	72.21 44	82.79 58	81.29 58	86.59 113	87.91 54

ACMP	73.23 7	79.79 40	80.53 45	78.94 39	85.61 47	85.68 48	85.61 38	69.59 41	77.33 46	71.00 47	74.45 38	69.16 75	71.88 46	83.15 54	83.37 47	89.92 31	90.57 41
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVS_0304		81.73 34	83.86 31	79.26 37	86.22 44	89.18 20	86.41 33	67.15 57	75.28 50	70.75 48	74.59 37	83.49 26	74.42 32	87.05 24	86.34 25	90.58 15	91.08 36

MVS_111021_HR		80.13 38	81.46 40	78.58 42	85.77 46	85.17 54	83.45 51	69.28 44	74.08 56	70.31 49	74.31 39	75.26 53	73.13 39	86.46 31	85.15 37	89.53 39	89.81 45

DELS-MVS		79.15 48	81.07 43	76.91 50	83.54 56	87.31 37	84.45 46	64.92 71	69.98 61	69.34 50	71.62 47	76.26 48	69.84 58	86.57 29	85.90 30	89.39 41	89.88 44
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
PCF-MVS	73.28 6	79.42 44	80.41 47	78.26 43	84.88 55	88.17 32	86.08 34	69.85 38	75.23 52	68.43 51	68.03 60	78.38 42	71.76 49	81.26 71	80.65 72	88.56 55	91.18 35
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		79.21 46	80.32 48	77.92 46	87.46 34	88.15 33	83.95 48	67.48 56	74.28 54	68.25 52	64.70 71	77.04 46	72.17 45	85.42 37	85.00 38	88.22 56	87.62 57
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
PVSNet_Blended_VisFu		76.57 56	77.90 55	75.02 57	80.56 70	86.58 44	79.24 66	66.18 61	64.81 79	68.18 53	65.61 66	71.45 63	67.05 68	84.16 44	81.80 54	88.90 49	90.92 37

OpenMVS	70.44 10	76.15 59	76.82 66	75.37 56	85.01 52	84.79 56	78.99 71	62.07 108	71.27 60	67.88 54	57.91 100	72.36 61	70.15 57	82.23 60	81.41 57	88.12 60	87.78 56

OPM-MVS		79.68 43	79.28 52	80.15 33	87.99 33	86.77 42	88.52 24	72.72 24	64.55 82	67.65 55	67.87 61	74.33 56	74.31 33	86.37 32	85.25 36	89.73 35	89.81 45

CDPH-MVS		82.64 29	85.03 30	79.86 34	89.41 26	88.31 31	88.32 25	71.84 29	80.11 40	67.47 56	82.09 21	81.44 35	71.85 48	85.89 35	86.15 28	90.24 27	91.25 34

canonicalmvs		79.16 47	82.37 38	75.41 55	82.33 62	86.38 46	80.80 57	63.18 81	82.90 31	67.34 57	72.79 43	76.07 49	69.62 59	83.46 53	84.41 41	89.20 44	90.60 40

EPNet		79.08 49	80.62 44	77.28 48	88.90 29	83.17 67	83.65 49	72.41 26	74.41 53	67.15 58	76.78 30	74.37 55	64.43 101	83.70 49	83.69 45	87.15 78	88.19 52
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP-MVS		81.19 36	83.27 33	78.76 41	87.40 35	85.45 51	86.95 30	70.47 35	81.31 37	66.91 59	79.24 26	76.63 47	71.67 51	84.43 43	83.78 44	89.19 45	92.05 30

PLC	68.99 11	75.68 60	75.31 70	76.12 54	82.94 58	81.26 77	79.94 61	66.10 62	77.15 47	66.86 60	59.13 89	68.53 81	73.73 36	80.38 83	79.04 88	87.13 82	81.68 125
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PHI-MVS		82.36 31	85.89 26	78.24 44	86.40 42	89.52 14	85.52 39	69.52 43	82.38 35	65.67 61	81.35 23	82.36 28	73.07 40	87.31 20	86.76 19	89.24 43	91.56 31

QAPM		78.47 51	80.22 49	76.43 52	85.03 51	86.75 43	80.62 58	66.00 64	73.77 57	65.35 62	65.54 68	78.02 44	72.69 42	83.71 48	83.36 48	88.87 51	90.41 43

LGP-MVS_train		79.83 39	81.22 42	78.22 45	86.28 43	85.36 53	86.76 31	69.59 41	77.34 45	65.14 63	75.68 33	70.79 66	71.37 53	84.60 41	84.01 42	90.18 28	90.74 38

DI_MVS_plusplus_trai		75.13 63	76.12 68	73.96 64	78.18 83	81.55 73	80.97 56	62.54 102	68.59 65	65.13 64	61.43 77	74.81 54	69.32 61	81.01 75	79.59 82	87.64 70	85.89 66

TAPA-MVS	71.42 9	77.69 54	80.05 50	74.94 58	80.68 69	84.52 57	81.36 54	63.14 82	84.77 23	64.82 65	68.72 55	75.91 51	71.86 47	81.62 62	79.55 84	87.80 68	85.24 77
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tpmp4_e23		68.32 122	67.08 149	69.76 101	77.86 87	75.22 165	78.37 91	56.17 177	66.06 72	64.27 66	57.15 105	54.89 139	63.40 105	70.97 188	68.29 198	78.46 188	77.00 166

MVS_Test		75.37 61	77.13 64	73.31 66	79.07 78	81.32 76	79.98 60	60.12 141	69.72 64	64.11 67	70.53 49	73.22 58	68.90 62	80.14 91	79.48 86	87.67 69	85.50 72

diffmvs		73.13 69	75.65 69	70.19 95	74.07 150	77.17 132	78.24 94	57.45 168	72.44 59	64.02 68	69.05 53	75.92 50	64.86 99	75.18 159	75.27 162	82.47 171	84.53 87

tpm cat1		65.41 152	63.81 183	67.28 131	75.61 120	72.88 175	75.32 109	52.85 185	62.97 90	63.66 69	53.24 155	53.29 162	61.83 116	65.54 207	64.14 210	74.43 204	74.60 179

IB-MVS	66.94 12	71.21 79	71.66 84	70.68 80	79.18 77	82.83 69	72.61 146	61.77 112	59.66 113	63.44 70	53.26 154	59.65 104	59.16 130	76.78 146	82.11 52	87.90 64	87.33 59
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MSDG		71.52 77	69.87 97	73.44 65	82.21 63	79.35 101	79.52 64	64.59 73	66.15 70	61.87 71	53.21 156	56.09 131	65.85 97	78.94 106	78.50 92	86.60 112	76.85 167

TSAR-MVS + COLMAP		78.34 52	81.64 39	74.48 62	80.13 73	85.01 55	81.73 53	65.93 66	84.75 24	61.68 72	85.79 14	66.27 86	71.39 52	82.91 56	80.78 63	86.01 129	85.98 65

CostFormer		68.92 115	69.58 103	68.15 114	75.98 116	76.17 154	78.22 95	51.86 191	65.80 73	61.56 73	63.57 73	62.83 95	61.85 115	70.40 195	68.67 193	79.42 184	79.62 146

MVSTER		72.06 74	74.24 72	69.51 103	70.39 180	75.97 155	76.91 104	57.36 170	64.64 81	61.39 74	68.86 54	63.76 92	63.46 104	81.44 65	79.70 79	87.56 71	85.31 76

DWT-MVSNet_training		67.24 144	65.96 162	68.74 108	76.15 112	74.36 172	74.37 124	56.66 173	61.82 98	60.51 75	58.23 99	49.76 190	65.07 98	70.04 196	70.39 183	79.70 183	77.11 164

Fast-Effi-MVS+		73.11 70	73.66 73	72.48 68	77.72 97	80.88 83	78.55 86	58.83 160	65.19 76	60.36 76	59.98 84	62.42 97	71.22 54	81.66 61	80.61 74	88.20 57	84.88 85

Effi-MVS+		75.28 62	76.20 67	74.20 63	81.15 65	83.24 65	81.11 55	63.13 83	66.37 68	60.27 77	64.30 72	68.88 79	70.93 56	81.56 64	81.69 55	88.61 53	87.35 58

PatchMatch-RL		67.78 132	66.65 155	69.10 106	73.01 159	72.69 176	68.49 166	61.85 111	62.93 91	60.20 78	56.83 107	50.42 186	69.52 60	75.62 156	74.46 168	81.51 174	73.62 187

v18		70.10 93	69.52 104	70.77 76	74.66 144	77.06 135	78.84 74	58.84 159	60.01 111	59.23 79	55.06 120	57.47 111	66.34 83	77.50 130	76.75 131	86.71 106	82.77 110

v16		70.07 94	69.46 106	70.79 75	74.74 139	77.08 134	78.79 79	58.86 154	59.75 112	59.15 80	54.87 127	57.33 113	66.38 81	77.61 124	76.77 126	86.81 104	82.79 108

RPSCF		67.64 137	71.25 85	63.43 164	61.86 208	70.73 183	67.26 174	50.86 196	74.20 55	58.91 81	67.49 62	69.33 73	64.10 102	71.41 181	68.45 197	77.61 190	77.17 162

LS3D		74.08 65	73.39 75	74.88 59	85.05 50	82.62 70	79.71 63	68.66 47	72.82 58	58.80 82	57.61 101	61.31 99	71.07 55	80.32 87	78.87 90	86.00 131	80.18 139

v17		70.03 96	69.43 111	70.72 79	74.75 138	77.09 133	78.78 81	58.85 156	59.53 115	58.72 83	54.87 127	57.39 112	66.38 81	77.60 125	76.75 131	86.83 98	82.80 106

dps		64.00 169	62.99 186	65.18 148	73.29 157	72.07 178	68.98 165	53.07 184	57.74 128	58.41 84	55.55 114	47.74 199	60.89 123	69.53 198	67.14 202	76.44 196	71.19 196

v8		70.23 90	69.86 99	70.67 81	74.69 140	79.82 97	78.79 79	59.18 150	58.80 119	58.20 85	55.00 122	57.33 113	66.31 87	77.51 129	76.71 141	86.82 99	83.88 94

v6		70.35 86	69.94 94	70.83 72	74.68 141	80.62 85	78.81 76	60.16 139	58.81 118	58.17 86	55.01 121	57.31 115	66.32 86	77.53 126	76.73 137	86.82 99	83.62 95

v1neww		70.34 87	69.93 95	70.82 73	74.68 141	80.61 86	78.80 77	60.17 136	58.74 120	58.10 87	55.00 122	57.28 116	66.33 84	77.53 126	76.74 133	86.82 99	83.61 96

v7new		70.34 87	69.93 95	70.82 73	74.68 141	80.61 86	78.80 77	60.17 136	58.74 120	58.10 87	55.00 122	57.28 116	66.33 84	77.53 126	76.74 133	86.82 99	83.61 96

pmmvs4		67.89 129	67.39 146	68.48 112	71.60 174	73.57 174	74.45 120	60.98 121	64.65 80	57.97 89	54.95 125	51.73 179	61.88 114	73.78 167	75.11 164	83.99 165	77.91 157

IterMVS-LS		71.69 76	72.82 79	70.37 92	77.54 99	76.34 151	75.13 115	60.46 129	61.53 101	57.57 90	64.89 69	67.33 83	66.04 93	77.09 142	77.37 118	85.48 145	85.18 78
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v148		67.85 130	67.53 142	68.23 113	73.25 158	77.57 130	74.26 131	57.36 170	55.70 160	57.45 91	53.53 149	55.42 133	61.96 113	75.23 158	73.92 169	85.08 150	81.32 127

v15		69.61 102	68.88 118	70.46 87	74.81 132	77.03 138	78.75 82	58.83 160	57.06 133	57.18 92	54.55 133	56.37 122	66.13 91	77.70 121	76.76 128	87.03 89	82.69 113

V9		69.58 104	68.83 120	70.46 87	74.85 130	77.04 136	78.65 84	58.85 156	56.83 140	57.12 93	54.26 138	56.31 124	66.14 90	77.83 119	76.76 128	87.13 82	82.79 108

V14		69.59 103	68.86 119	70.45 89	74.83 131	77.04 136	78.70 83	58.83 160	56.95 137	57.08 94	54.41 134	56.34 123	66.15 88	77.77 120	76.76 128	87.08 87	82.74 111

v13		69.52 107	68.76 124	70.41 90	74.88 127	77.02 140	78.52 90	58.86 154	56.61 149	56.91 95	54.00 145	56.17 130	66.11 92	77.93 116	76.74 133	87.21 76	82.83 105

v2v482		70.05 95	69.46 106	70.74 77	74.62 145	80.32 94	79.00 70	60.62 125	57.41 131	56.89 96	55.43 115	55.14 137	66.39 80	77.25 138	77.14 121	86.90 92	83.57 101

v12		69.54 105	68.79 122	70.41 90	74.88 127	77.03 138	78.54 89	58.85 156	56.71 141	56.87 97	54.13 143	56.23 129	66.15 88	77.89 117	76.74 133	87.17 77	82.80 106

v1		69.97 97	69.45 108	70.59 83	74.78 135	80.51 89	78.84 74	60.30 131	56.98 134	56.81 98	54.69 130	56.29 126	65.91 96	77.37 133	76.71 141	86.89 94	83.59 98

divwei89l23v2f112		69.97 97	69.44 109	70.58 85	74.78 135	80.50 90	78.85 72	60.30 131	56.97 136	56.75 99	54.67 132	56.27 127	65.92 95	77.37 133	76.72 138	86.88 95	83.58 100

v1141		69.96 99	69.44 109	70.58 85	74.78 135	80.50 90	78.85 72	60.30 131	56.95 137	56.74 100	54.68 131	56.26 128	65.93 94	77.38 132	76.72 138	86.88 95	83.57 101

UGNet		72.78 71	77.67 57	67.07 135	71.65 172	83.24 65	75.20 111	63.62 78	64.93 78	56.72 101	71.82 46	73.30 57	49.02 181	81.02 74	80.70 70	86.22 117	88.67 51
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
CMPMVS	47.78 17	62.49 179	62.52 191	62.46 167	70.01 182	70.66 184	62.97 197	51.84 192	51.98 186	56.71 102	42.87 199	53.62 149	57.80 136	72.23 175	70.37 184	75.45 201	75.91 170
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7		70.33 89	69.87 97	70.88 71	74.79 133	81.04 79	79.22 67	60.57 126	57.70 130	56.65 103	54.23 140	55.29 136	66.95 71	78.28 113	77.47 114	87.12 85	85.05 81

v10		70.22 91	69.76 101	70.74 77	74.79 133	80.30 95	79.22 67	59.81 144	57.71 129	56.58 104	54.22 142	55.31 134	66.95 71	78.28 113	77.47 114	87.12 85	85.07 80

V42		68.76 118	69.63 102	67.74 118	64.93 202	78.01 118	78.30 92	56.48 174	58.65 122	56.30 105	54.26 138	57.03 119	64.85 100	77.47 131	77.01 123	85.60 143	84.96 83

v11		69.37 110	68.65 128	70.20 94	74.87 129	76.97 141	78.29 93	58.55 164	56.38 152	56.04 106	54.02 144	54.98 138	66.47 79	78.30 112	76.91 124	86.97 90	83.02 104

UA-Net		74.47 64	77.80 56	70.59 83	85.33 48	85.40 52	73.54 139	65.98 65	60.65 106	56.00 107	72.11 44	79.15 40	54.63 165	83.13 55	82.25 51	88.04 61	81.92 123

Effi-MVS+-dtu		71.82 75	71.86 83	71.78 69	78.77 79	80.47 92	78.55 86	61.67 115	60.68 105	55.49 108	58.48 93	65.48 88	68.85 63	76.92 143	75.55 159	87.35 74	85.46 73

tpm		62.41 180	63.15 185	61.55 174	72.24 166	63.79 207	71.31 155	46.12 213	57.82 125	55.33 109	59.90 85	54.74 140	53.63 169	67.24 205	64.29 208	70.65 215	74.25 183

v1144		69.93 100	69.36 112	70.61 82	74.89 126	80.93 80	79.11 69	60.64 124	55.97 158	55.31 110	53.85 147	54.14 145	66.54 78	78.10 115	77.44 116	87.14 81	85.09 79

MS-PatchMatch		70.17 92	70.49 90	69.79 100	80.98 68	77.97 124	77.51 99	58.95 152	62.33 93	55.22 111	53.14 157	65.90 87	62.03 112	79.08 105	77.11 122	84.08 163	77.91 157

CHOSEN 1792x2688		69.20 113	69.26 113	69.13 105	76.86 105	78.93 105	77.27 102	60.12 141	61.86 97	54.42 112	42.54 201	61.61 98	66.91 74	78.55 110	78.14 103	79.23 186	83.23 103

v1192		69.50 108	68.83 120	70.29 93	74.49 146	80.92 82	78.55 86	60.54 127	55.04 167	54.21 113	52.79 165	52.33 170	66.92 73	77.88 118	77.35 119	87.04 88	85.51 71

v144192		69.34 111	68.68 127	70.12 96	74.06 151	80.54 88	78.08 96	60.54 127	54.99 169	54.13 114	52.92 161	52.80 166	66.73 76	77.13 140	76.72 138	87.15 78	85.63 67

HyFIR lowres test		69.47 109	68.94 117	70.09 97	76.77 106	82.93 68	76.63 106	60.17 136	59.00 117	54.03 115	40.54 207	65.23 89	67.89 67	76.54 150	78.30 99	85.03 151	80.07 140

tpmrst		62.00 184	62.35 194	61.58 173	71.62 173	64.14 204	69.07 164	48.22 209	62.21 94	53.93 116	58.26 98	55.30 135	55.81 157	63.22 212	62.62 213	70.85 214	70.70 197

MDTV_nov1_ep13		64.37 163	65.24 170	63.37 165	68.94 189	70.81 182	72.40 149	50.29 200	60.10 110	53.91 117	60.07 83	59.15 106	57.21 141	69.43 199	67.30 200	77.47 191	69.78 199

PatchmatchNet		64.21 168	64.65 177	63.69 160	71.29 178	68.66 190	69.63 160	51.70 193	63.04 89	53.77 118	59.83 86	58.34 108	60.23 127	68.54 202	66.06 205	75.56 199	68.08 203
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1921920		69.03 114	68.32 133	69.86 99	74.03 152	80.37 93	77.55 98	60.25 135	54.62 170	53.59 119	52.36 172	51.50 181	66.75 75	77.17 139	76.69 143	86.96 91	85.56 68

FC-MVSNet-train		72.60 73	75.07 71	69.71 102	81.10 67	78.79 111	73.74 137	65.23 69	66.10 71	53.34 120	70.36 50	63.40 94	56.92 145	81.44 65	80.96 61	87.93 63	84.46 88

CANet_DTU		73.29 68	76.96 65	69.00 107	77.04 104	82.06 72	79.49 65	56.30 175	67.85 66	53.29 121	71.12 48	70.37 70	61.81 117	81.59 63	80.96 61	86.09 123	84.73 86

v1240		68.64 119	67.89 140	69.51 103	73.89 154	80.26 96	76.73 105	59.97 143	53.43 179	53.08 122	51.82 175	50.84 184	66.62 77	76.79 145	76.77 126	86.78 105	85.34 75

pmmvs-eth3d		63.52 171	62.44 193	64.77 153	66.82 196	70.12 185	69.41 163	59.48 147	54.34 174	52.71 123	46.24 195	44.35 209	56.93 144	72.37 172	73.77 170	83.30 167	75.91 170

CR-MVSNet		64.83 159	65.54 168	64.01 159	70.64 179	69.41 186	65.97 184	52.74 186	57.81 126	52.65 124	54.27 136	56.31 124	60.92 121	72.20 177	73.09 173	81.12 177	75.69 173

Patchmtry						65.80 201	65.97 184	52.74 186		52.65 124

PatchT		61.97 185	64.04 181	59.55 186	60.49 210	67.40 194	56.54 211	48.65 205	56.69 142	52.65 124	51.10 179	52.14 175	60.92 121	72.20 177	73.09 173	78.03 189	75.69 173

ACMH+	66.54 13	71.36 78	70.09 92	72.85 67	82.59 60	81.13 78	78.56 85	68.04 51	61.55 100	52.52 127	51.50 176	54.14 145	68.56 65	78.85 107	79.50 85	86.82 99	83.94 93

MVS-HIRNet		54.41 206	52.10 213	57.11 196	58.99 214	56.10 220	49.68 221	49.10 202	46.18 208	52.15 128	33.18 218	46.11 205	56.10 153	63.19 213	59.70 221	76.64 195	60.25 217

GBi-Net		70.78 80	73.37 76	67.76 116	72.95 160	78.00 119	75.15 112	62.72 93	64.13 83	51.44 129	58.37 94	69.02 76	57.59 137	81.33 68	80.72 65	86.70 107	82.02 116

test1		70.78 80	73.37 76	67.76 116	72.95 160	78.00 119	75.15 112	62.72 93	64.13 83	51.44 129	58.37 94	69.02 76	57.59 137	81.33 68	80.72 65	86.70 107	82.02 116

FMVSNet3		70.49 84	72.90 78	67.67 120	72.88 163	77.98 122	74.96 118	62.72 93	64.13 83	51.44 129	58.37 94	69.02 76	57.43 140	79.43 101	79.57 83	86.59 113	81.81 124

EG-PatchMatch MVS		67.24 144	66.94 151	67.60 121	78.73 80	81.35 75	73.28 143	59.49 146	46.89 206	51.42 132	43.65 198	53.49 153	55.50 161	81.38 67	80.66 71	87.15 78	81.17 128

FMVSNet2		70.39 85	72.67 80	67.72 119	72.95 160	78.00 119	75.15 112	62.69 97	63.29 88	51.25 133	55.64 112	68.49 82	57.59 137	80.91 76	80.35 76	86.70 107	82.02 116

v7n		67.05 146	66.94 151	67.17 132	72.35 165	78.97 102	73.26 144	58.88 153	51.16 191	50.90 134	48.21 187	50.11 188	60.96 120	77.70 121	77.38 117	86.68 110	85.05 81

v748		65.12 156	65.24 170	64.98 151	69.77 183	76.45 147	69.47 162	57.06 172	49.93 196	50.70 135	47.87 190	49.50 192	57.14 142	73.64 169	75.18 163	85.75 139	84.14 90

PMMVS		65.06 158	69.17 115	60.26 181	55.25 225	63.43 208	66.71 180	43.01 222	62.41 92	50.64 136	69.44 52	67.04 84	63.29 106	74.36 163	73.54 171	82.68 170	73.99 184

Vis-MVSNet		72.77 72	77.20 63	67.59 122	74.19 148	84.01 59	76.61 107	61.69 114	60.62 107	50.61 137	70.25 51	71.31 65	55.57 160	83.85 47	82.28 50	86.90 92	88.08 53
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
USDC		67.36 142	67.90 139	66.74 142	71.72 170	75.23 163	71.58 154	60.28 134	67.45 67	50.54 138	60.93 78	45.20 207	62.08 111	76.56 149	74.50 167	84.25 162	75.38 176

V4		65.23 154	66.23 158	64.06 157	61.94 206	76.42 148	72.05 152	54.31 179	49.91 197	50.06 139	47.42 191	52.40 169	60.24 126	75.71 154	76.22 149	85.78 137	85.56 68

v52		65.23 154	66.24 157	64.06 157	61.94 206	76.42 148	72.06 151	54.30 180	49.94 195	50.04 140	47.41 192	52.42 168	60.23 127	75.71 154	76.22 149	85.78 137	85.56 68

COLMAP_ROB	62.73 15	67.66 135	66.76 154	68.70 110	80.49 72	77.98 122	75.29 110	62.95 85	63.62 86	49.96 141	47.32 194	50.72 185	58.57 131	76.87 144	75.50 160	84.94 154	75.33 177
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FMVSNet1		68.84 116	70.47 91	66.94 137	71.35 177	77.68 127	74.71 119	62.35 107	56.93 139	49.94 142	50.01 182	64.59 90	57.07 143	81.33 68	80.72 65	86.25 116	82.00 120

EPP-MVSNet		74.00 66	77.41 61	70.02 98	80.53 71	83.91 60	74.99 117	62.68 98	65.06 77	49.77 143	68.68 56	72.09 62	63.06 107	82.49 59	80.73 64	89.12 47	88.91 49

TDRefinement		66.09 150	65.03 175	67.31 129	69.73 184	76.75 143	75.33 108	64.55 74	60.28 109	49.72 144	45.63 196	42.83 210	60.46 125	75.75 153	75.95 154	84.08 163	78.04 156

CDS-MVSNet		67.65 136	69.83 100	65.09 149	75.39 121	76.55 146	74.42 123	63.75 77	53.55 178	49.37 145	59.41 87	62.45 96	44.44 193	79.71 95	79.82 78	83.17 169	77.36 161
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
IS_MVSNet		73.33 67	77.34 62	68.65 111	81.29 64	83.47 63	74.45 120	63.58 79	65.75 74	48.49 146	67.11 65	70.61 67	54.63 165	84.51 42	83.58 46	89.48 40	86.34 64

GA-MVS		68.14 123	69.17 115	66.93 138	73.77 155	78.50 115	74.45 120	58.28 165	55.11 166	48.44 147	60.08 82	53.99 148	61.50 118	78.43 111	77.57 112	85.13 149	80.54 133

IterMVS		66.36 148	68.30 134	64.10 156	69.48 187	74.61 170	73.41 142	50.79 197	57.30 132	48.28 148	60.64 79	59.92 103	60.85 124	74.14 164	72.66 175	81.80 173	78.82 153
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PM-MVS		60.48 193	60.94 200	59.94 182	58.85 215	66.83 197	64.27 193	51.39 194	55.03 168	48.03 149	50.00 184	40.79 214	58.26 134	69.20 200	67.13 203	78.84 187	77.60 159

test-LLR		64.42 162	64.36 179	64.49 155	75.02 124	63.93 205	66.61 181	61.96 109	54.41 171	47.77 150	57.46 102	60.25 101	55.20 163	70.80 189	69.33 188	80.40 180	74.38 181

TESTMET0.1,1		61.10 191	64.36 179	57.29 194	57.53 218	63.93 205	66.61 181	36.22 228	54.41 171	47.77 150	57.46 102	60.25 101	55.20 163	70.80 189	69.33 188	80.40 180	74.38 181

TinyColmap		62.84 175	61.03 199	64.96 152	69.61 185	71.69 179	68.48 167	59.76 145	55.41 162	47.69 152	47.33 193	34.20 220	62.76 109	74.52 161	72.59 176	81.44 175	71.47 195

anonymousdsp		65.28 153	67.98 138	62.13 169	58.73 216	73.98 173	67.10 176	50.69 198	48.41 201	47.66 153	54.27 136	52.75 167	61.45 119	76.71 147	80.20 77	87.13 82	89.53 47

thres100view900		67.60 139	68.02 136	67.12 134	77.83 89	77.75 126	73.90 133	62.52 103	56.64 143	46.82 154	52.65 167	53.47 155	55.92 155	78.77 108	77.62 111	85.72 140	79.23 149

tfpn200view9		68.11 124	68.72 125	67.40 124	77.83 89	78.93 105	74.28 125	62.81 86	56.64 143	46.82 154	52.65 167	53.47 155	56.59 146	80.41 78	78.43 93	86.11 120	80.52 136

tfpn111		68.38 120	69.23 114	67.39 125	77.83 89	78.93 105	74.28 125	62.81 86	56.64 143	46.70 156	56.24 109	53.47 155	56.59 146	80.41 78	78.43 93	86.11 120	80.53 134

conf0.01		67.72 133	67.99 137	67.39 125	77.82 94	78.94 103	74.28 125	62.81 86	56.64 143	46.70 156	53.33 152	48.59 195	56.59 146	80.34 85	78.43 93	86.16 119	79.67 145

conf0.002		67.52 141	67.64 141	67.39 125	77.80 96	78.94 103	74.28 125	62.81 86	56.64 143	46.70 156	53.65 148	46.28 203	56.59 146	80.33 86	78.37 98	86.17 118	79.23 149

conf200view11		68.11 124	68.72 125	67.39 125	77.83 89	78.93 105	74.28 125	62.81 86	56.64 143	46.70 156	52.65 167	53.47 155	56.59 146	80.41 78	78.43 93	86.11 120	80.53 134

Fast-Effi-MVS+-dtu		68.34 121	69.47 105	67.01 136	75.15 122	77.97 124	77.12 103	55.40 178	57.87 124	46.68 160	56.17 111	60.39 100	62.36 110	76.32 151	76.25 148	85.35 147	81.34 126

thres200		67.98 127	68.55 130	67.30 130	77.89 86	78.86 109	74.18 132	62.75 91	56.35 153	46.48 161	52.98 160	53.54 151	56.46 151	80.41 78	77.97 104	86.05 126	79.78 144

test-mter		60.84 192	64.62 178	56.42 197	55.99 223	64.18 203	65.39 186	34.23 230	54.39 173	46.21 162	57.40 104	59.49 105	55.86 156	71.02 187	69.65 186	80.87 179	76.20 169

ACMH	65.37 14	70.71 82	70.00 93	71.54 70	82.51 61	82.47 71	77.78 97	68.13 50	56.19 155	46.06 163	54.30 135	51.20 182	68.68 64	80.66 77	80.72 65	86.07 124	84.45 89
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MDTV_nov1_ep13_2view		60.16 194	60.51 201	59.75 183	65.39 199	69.05 189	68.00 168	48.29 207	51.99 185	45.95 164	48.01 188	49.64 191	53.39 171	68.83 201	66.52 204	77.47 191	69.55 200

thres400		67.95 128	68.62 129	67.17 132	77.90 84	78.59 114	74.27 130	62.72 93	56.34 154	45.77 165	53.00 159	53.35 160	56.46 151	80.21 90	78.43 93	85.91 135	80.43 137

RPMNet		61.71 190	62.88 187	60.34 180	69.51 186	69.41 186	63.48 195	49.23 201	57.81 126	45.64 166	50.51 180	50.12 187	53.13 173	68.17 204	68.49 196	81.07 178	75.62 175

EPNet_dtu		68.08 126	71.00 86	64.67 154	79.64 74	68.62 191	75.05 116	63.30 80	66.36 69	45.27 167	67.40 63	66.84 85	43.64 195	75.37 157	74.98 166	81.15 176	77.44 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
view600		67.63 138	68.36 132	66.77 140	77.84 88	78.66 112	73.74 137	62.62 100	56.04 157	44.98 168	52.86 163	52.83 164	55.48 162	80.36 84	77.75 108	85.95 134	80.02 141

pmmvs5		62.37 183	64.04 181	60.42 179	65.03 200	71.67 180	67.17 175	52.70 188	50.30 192	44.80 169	54.23 140	51.19 183	49.37 180	72.88 171	73.48 172	83.45 166	74.55 180

EPMVS		60.00 195	61.97 195	57.71 193	68.46 190	63.17 211	64.54 191	48.23 208	63.30 87	44.72 170	60.19 81	56.05 132	50.85 177	65.27 209	62.02 215	69.44 217	63.81 210

thres600view7		67.68 134	68.43 131	66.80 139	77.90 84	78.86 109	73.84 134	62.75 91	56.07 156	44.70 171	52.85 164	52.81 165	55.58 159	80.41 78	77.77 107	86.05 126	80.28 138

view800		67.35 143	68.22 135	66.35 144	77.83 89	78.62 113	72.97 145	62.58 101	55.71 159	44.13 172	52.69 166	52.24 174	54.58 167	80.27 88	78.19 101	86.01 129	79.79 143

pm-mvs1		65.62 151	67.42 144	63.53 162	73.66 156	76.39 150	69.66 159	60.87 123	49.73 198	43.97 173	51.24 178	57.00 120	48.16 182	79.89 93	77.84 106	84.85 157	79.82 142

UniMVSNet_NR-MVSNet		70.59 83	72.19 81	68.72 109	77.72 97	80.72 84	73.81 135	69.65 40	61.99 95	43.23 174	60.54 80	57.50 110	58.57 131	79.56 99	81.07 60	89.34 42	83.97 91

DU-MVS		69.63 101	70.91 87	68.13 115	75.99 114	79.54 98	73.81 135	69.20 45	61.20 103	43.23 174	58.52 91	53.50 152	58.57 131	79.22 103	80.45 75	87.97 62	83.97 91

tfpn		66.58 147	67.18 147	65.88 146	77.82 94	78.45 116	72.07 150	62.52 103	55.35 163	43.21 176	52.54 171	46.12 204	53.68 168	80.02 92	78.23 100	85.99 132	79.55 147

conf0.05thres1000		66.26 149	66.77 153	65.66 147	77.45 101	78.10 117	71.85 153	62.44 106	51.47 190	43.00 177	47.92 189	51.66 180	53.40 170	79.71 95	77.97 104	85.82 136	80.56 132

tfpnnormal		64.27 165	63.64 184	65.02 150	75.84 117	75.61 157	71.24 156	62.52 103	47.79 203	42.97 178	42.65 200	44.49 208	52.66 174	78.77 108	76.86 125	84.88 155	79.29 148

ADS-MVSNet		55.94 204	58.01 204	53.54 208	62.48 205	58.48 217	59.12 209	46.20 212	59.65 114	42.88 179	52.34 173	53.31 161	46.31 190	62.00 216	60.02 220	64.23 226	60.24 218

LP		53.62 209	53.43 209	53.83 206	58.51 217	62.59 214	57.31 210	46.04 214	47.86 202	42.69 180	36.08 213	36.86 218	46.53 189	64.38 210	64.25 209	71.92 211	62.00 215

pmmvs6		62.41 180	62.88 187	61.87 172	71.38 176	75.18 167	67.76 170	59.45 148	41.64 214	42.52 181	37.33 209	52.91 163	46.87 187	77.67 123	76.26 147	83.23 168	79.18 151

UniMVSNet (Re)		69.53 106	71.90 82	66.76 141	76.42 107	80.93 80	72.59 147	68.03 52	61.75 99	41.68 182	58.34 97	57.23 118	53.27 172	79.53 100	80.62 73	88.57 54	84.90 84

TransMVSNet (Re)		64.74 161	65.66 167	63.66 161	77.40 102	75.33 160	69.86 158	62.67 99	47.63 204	41.21 183	50.01 182	52.33 170	45.31 192	79.57 98	77.69 110	85.49 144	77.07 165

tfpn_ndepth		65.09 157	67.12 148	62.73 166	75.75 119	76.23 152	68.00 168	60.36 130	58.16 123	40.27 184	54.89 126	54.22 144	46.80 188	76.69 148	75.66 156	85.19 148	73.98 185

NR-MVSNet		68.79 117	70.56 89	66.71 143	77.48 100	79.54 98	73.52 140	69.20 45	61.20 103	39.76 185	58.52 91	50.11 188	51.37 176	80.26 89	80.71 69	88.97 48	83.59 98

TranMVSNet+NR-MVSNet		69.25 112	70.81 88	67.43 123	77.23 103	79.46 100	73.48 141	69.66 39	60.43 108	39.56 186	58.82 90	53.48 154	55.74 158	79.59 97	81.21 59	88.89 50	82.70 112

FMVSNet5		57.24 200	60.02 202	53.99 205	56.45 220	62.74 212	65.27 187	47.03 210	55.14 165	39.55 187	40.88 204	53.42 159	41.83 197	72.35 173	71.10 182	73.79 206	64.50 209

thresconf0.02		64.77 160	65.90 163	63.44 163	76.37 108	75.17 168	69.51 161	61.28 116	56.98 134	39.01 188	56.24 109	48.68 194	49.78 179	77.13 140	75.61 157	84.71 158	71.53 194

MIMVSNet		58.52 199	61.34 198	55.22 201	60.76 209	67.01 196	66.81 178	49.02 203	56.43 151	38.90 189	40.59 206	54.54 142	40.57 203	73.16 170	71.65 178	75.30 202	66.00 206

Baseline_NR-MVSNet		67.53 140	68.77 123	66.09 145	75.99 114	74.75 169	72.43 148	68.41 48	61.33 102	38.33 190	51.31 177	54.13 147	56.03 154	79.22 103	78.19 101	85.37 146	82.45 114

CHOSEN 280x420		58.70 198	61.88 196	54.98 202	55.45 224	50.55 228	64.92 189	40.36 224	55.21 164	38.13 191	48.31 186	63.76 92	63.03 108	73.73 168	68.58 195	68.00 220	73.04 188

SixPastTwentyTwo		61.84 187	62.45 192	61.12 176	69.20 188	72.20 177	62.03 200	57.40 169	46.54 207	38.03 192	57.14 106	41.72 212	58.12 135	69.67 197	71.58 179	81.94 172	78.30 155

ambc			53.42 210		64.99 201	63.36 209	49.96 220		47.07 205	37.12 193	28.97 221	16.36 236	41.82 198	75.10 160	67.34 199	71.55 213	75.72 172

TAMVS		59.58 196	62.81 189	55.81 199	66.03 198	65.64 202	63.86 194	48.74 204	49.95 194	37.07 194	54.77 129	58.54 107	44.44 193	72.29 174	71.79 177	74.70 203	66.66 205

tfpn_n400		64.23 166	66.05 160	62.12 170	76.20 110	75.24 161	67.43 172	61.15 118	54.04 176	36.38 195	55.35 116	51.89 176	46.94 185	77.31 135	76.15 151	84.59 159	72.36 190

tfpnconf		64.23 166	66.05 160	62.12 170	76.20 110	75.24 161	67.43 172	61.15 118	54.04 176	36.38 195	55.35 116	51.89 176	46.94 185	77.31 135	76.15 151	84.59 159	72.36 190

tfpnview11		64.33 164	66.17 159	62.18 168	76.25 109	75.23 163	67.45 171	61.16 117	55.50 161	36.38 195	55.35 116	51.89 176	46.96 184	77.28 137	76.10 153	84.86 156	71.85 193

tfpn1000		63.81 170	66.31 156	60.90 177	75.76 118	75.74 156	65.14 188	60.14 140	56.47 150	35.99 198	55.11 119	52.30 172	43.42 196	76.21 152	75.34 161	84.97 153	73.01 189

PMVS	39.38 18	46.06 220	43.30 225	49.28 213	62.93 203	38.75 234	41.88 227	53.50 182	33.33 228	35.46 199	28.90 222	31.01 225	33.04 212	58.61 225	54.63 227	68.86 218	57.88 222
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
FPMVS		51.87 211	50.00 216	54.07 204	66.83 195	57.25 218	60.25 205	50.91 195	50.25 193	34.36 200	36.04 214	32.02 222	41.49 199	58.98 224	56.07 224	70.56 216	59.36 219

Vis-MVSNet (Re-imp)		67.83 131	73.52 74	61.19 175	78.37 82	76.72 144	66.80 179	62.96 84	65.50 75	34.17 201	67.19 64	69.68 72	39.20 204	79.39 102	79.44 87	85.68 142	76.73 168

CVMVSNet		62.55 177	65.89 164	58.64 189	66.95 194	69.15 188	66.49 183	56.29 176	52.46 184	32.70 202	59.27 88	58.21 109	50.09 178	71.77 180	71.39 180	79.31 185	78.99 152

MDA-MVSNet-bldmvs		53.37 210	53.01 212	53.79 207	43.67 233	67.95 193	59.69 206	57.92 166	43.69 210	32.41 203	41.47 202	27.89 230	52.38 175	56.97 226	65.99 206	76.68 194	67.13 204

Anonymous20240521		62.94 174	66.99 150	58.22 190	74.13 149	76.58 145	59.13 208	61.72 113	52.53 182	32.20 204	52.87 162	54.34 143	36.44 207	73.90 166	76.66 144	85.71 141	82.02 116

pmmvs3		47.65 214	49.08 218	45.99 216	44.61 230	54.79 223	50.04 219	31.95 233	33.91 225	29.90 205	30.37 219	33.53 221	46.31 190	63.50 211	63.67 211	73.14 209	63.77 211

test0.0.03 1		58.80 197	61.58 197	55.56 200	75.02 124	68.45 192	59.58 207	61.96 109	52.74 180	29.57 206	49.75 185	54.56 141	31.46 213	71.19 183	69.77 185	75.75 197	64.57 208

Anonymous20231206		56.36 203	57.80 206	54.67 203	70.08 181	66.39 199	60.46 204	57.54 167	49.50 200	29.30 207	33.86 217	46.64 201	35.18 209	70.44 193	68.88 192	75.47 200	68.88 202

testpf		47.41 215	48.47 221	46.18 215	66.30 197	50.67 227	48.15 223	42.60 223	37.10 222	28.75 208	40.97 203	39.01 217	30.82 214	52.95 229	53.74 228	60.46 227	64.87 207

tmp_tt				14.50 235	14.68 238	7.17 241	10.46 241	2.21 236	37.73 221	28.71 209	25.26 228	16.98 234	4.37 236	31.49 232	29.77 232	26.56 236

LTVRE_ROB	59.44 16	61.82 189	62.64 190	60.87 178	72.83 164	77.19 131	64.37 192	58.97 151	33.56 227	28.00 210	52.59 170	42.21 211	63.93 103	74.52 161	76.28 146	77.15 193	82.13 115
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		62.68 176	65.49 169	59.40 187	71.84 168	75.34 159	62.87 198	67.04 58	52.64 181	27.19 211	53.38 151	48.15 197	41.40 200	71.26 182	75.68 155	86.07 124	82.00 120

PS-CasMVS		62.38 182	65.06 173	59.25 188	71.73 169	75.21 166	62.77 199	66.99 59	51.94 188	26.96 212	52.00 174	47.52 200	41.06 201	71.16 185	75.60 158	85.97 133	81.97 122

PEN-MVS		62.96 173	65.77 166	59.70 184	73.98 153	75.45 158	63.39 196	67.61 55	52.49 183	25.49 213	53.39 150	49.12 193	40.85 202	71.94 179	77.26 120	86.86 97	80.72 131

WR-MVS		63.03 172	67.40 145	57.92 192	75.14 123	77.60 129	60.56 203	66.10 62	54.11 175	23.88 214	53.94 146	53.58 150	34.50 210	73.93 165	77.71 109	87.35 74	80.94 129

testgi		54.39 207	57.86 205	50.35 211	71.59 175	67.24 195	54.95 214	53.25 183	43.36 211	23.78 215	44.64 197	47.87 198	24.96 222	70.45 192	68.66 194	73.60 207	62.78 213

test2356		47.20 217	48.62 220	45.54 217	56.38 221	54.89 222	50.62 218	45.08 217	38.65 219	23.40 216	36.23 212	31.10 224	29.31 216	62.76 214	62.49 214	68.48 219	54.23 225

gm-plane-assit		57.00 201	57.62 207	56.28 198	76.10 113	62.43 215	47.62 224	46.57 211	33.84 226	23.24 217	37.52 208	40.19 215	59.61 129	79.81 94	77.55 113	84.55 161	72.03 192

EU-MVSNet		54.63 205	58.69 203	49.90 212	56.99 219	62.70 213	56.41 212	50.64 199	45.95 209	23.14 218	50.42 181	46.51 202	36.63 206	65.51 208	64.85 207	75.57 198	74.91 178

1111		43.08 222	44.02 224	41.98 221	59.22 212	49.27 230	41.48 228	45.63 215	35.01 223	23.06 219	28.60 223	30.15 226	27.22 217	60.42 220	57.97 222	55.27 231	46.74 228

.test1245		30.81 229	29.14 231	32.77 228	59.22 212	49.27 230	41.48 228	45.63 215	35.01 223	23.06 219	28.60 223	30.15 226	27.22 217	60.42 220	0.10 235	0.01 239	0.43 237

WR-MVS_H		61.83 188	65.87 165	57.12 195	71.72 170	76.87 142	61.45 201	66.19 60	51.97 187	22.92 221	53.13 158	52.30 172	33.80 211	71.03 186	75.00 165	86.65 111	80.78 130

test20.03		53.93 208	56.28 208	51.19 210	72.19 167	65.83 200	53.20 216	61.08 120	42.74 212	22.08 222	37.07 210	45.76 206	24.29 226	70.44 193	69.04 190	74.31 205	63.05 212

gg-mvs-nofinetune		62.55 177	65.05 174	59.62 185	78.72 81	77.61 128	70.83 157	53.63 181	39.71 218	22.04 223	36.36 211	64.32 91	47.53 183	81.16 72	79.03 89	85.00 152	77.17 162

DTE-MVSNet		61.85 186	64.96 176	58.22 190	74.32 147	74.39 171	61.01 202	67.85 54	51.76 189	21.91 224	53.28 153	48.17 196	37.74 205	72.22 176	76.44 145	86.52 115	78.49 154

N_pmnet		47.35 216	50.13 215	44.11 219	59.98 211	51.64 226	51.86 217	44.80 218	49.58 199	20.76 225	40.65 205	40.05 216	29.64 215	59.84 222	55.15 225	57.63 228	54.00 226

MIMVSNet1		49.27 213	53.25 211	44.62 218	44.61 230	61.52 216	53.61 215	52.18 189	41.62 215	18.68 226	28.14 225	41.58 213	25.50 220	68.46 203	69.04 190	73.15 208	62.37 214

testus		45.61 221	49.06 219	41.59 222	56.13 222	55.28 221	43.51 226	39.64 226	37.74 220	18.23 227	35.52 216	31.28 223	24.69 224	62.46 215	62.90 212	67.33 221	58.26 221

Gipuma		36.38 226	35.80 229	37.07 225	45.76 229	33.90 235	29.81 234	48.47 206	39.91 217	18.02 228	8.00 237	8.14 239	25.14 221	59.29 223	61.02 218	55.19 232	40.31 230
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
Anonymous20231211		51.46 212	50.59 214	52.46 209	67.30 192	66.70 198	55.00 213	59.22 149	29.96 229	17.62 229	19.11 231	28.74 229	35.72 208	66.42 206	69.52 187	79.92 182	73.71 186

new-patchmatchnet		46.97 218	49.47 217	44.05 220	62.82 204	56.55 219	45.35 225	52.01 190	42.47 213	17.04 230	35.73 215	35.21 219	21.84 231	61.27 217	54.83 226	65.26 225	60.26 216

testmv		42.58 223	44.36 222	40.49 223	54.63 226	52.76 224	41.21 230	44.37 219	28.83 230	12.87 231	27.16 226	25.03 231	23.01 227	60.83 218	61.13 216	66.88 222	54.81 223

test1235678		42.57 224	44.36 222	40.49 223	54.63 226	52.75 225	41.21 230	44.37 219	28.82 231	12.87 231	27.15 227	25.01 232	23.01 227	60.83 218	61.13 216	66.88 222	54.81 223

new_pmnet		38.40 225	42.64 226	33.44 227	37.54 236	45.00 232	36.60 232	32.72 232	40.27 216	12.72 233	29.89 220	28.90 228	24.78 223	53.17 228	52.90 229	56.31 229	48.34 227

FC-MVSNet-test		56.90 202	65.20 172	47.21 214	66.98 193	63.20 210	49.11 222	58.60 163	59.38 116	11.50 234	65.60 67	56.68 121	24.66 225	71.17 184	71.36 181	72.38 210	69.02 201

test12356		35.10 228	38.50 227	31.13 229	44.14 232	43.70 233	32.27 233	34.42 229	26.51 233	9.47 235	25.22 229	20.34 233	10.86 234	53.47 227	56.15 223	55.59 230	44.11 229

DeepMVS_CX						18.74 240	18.55 237	8.02 235	26.96 232	7.33 236	23.81 230	13.05 238	25.99 219	25.17 234		22.45 238	36.25 233

no-one		36.35 227	37.59 228	34.91 226	46.13 228	49.89 229	27.99 235	43.56 221	20.91 235	7.03 237	14.64 233	15.50 237	18.92 232	42.95 230	60.20 219	65.84 224	59.03 220

E-PMN		21.77 231	18.24 233	25.89 230	40.22 234	19.58 238	12.46 239	39.87 225	18.68 237	6.71 238	9.57 234	4.31 242	22.36 230	19.89 235	27.28 233	33.73 234	28.34 234

EMVS		20.98 232	17.15 234	25.44 231	39.51 235	19.37 239	12.66 238	39.59 227	19.10 236	6.62 239	9.27 235	4.40 241	22.43 229	17.99 236	24.40 234	31.81 235	25.53 235

MVE	19.12 19	20.47 233	23.27 232	17.20 234	12.66 239	25.41 237	10.52 240	34.14 231	14.79 238	6.53 240	8.79 236	4.68 240	16.64 233	29.49 233	41.63 230	22.73 237	38.11 231
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		25.60 230	29.75 230	20.76 233	28.00 237	30.93 236	23.10 236	29.18 234	23.14 234	1.46 241	18.23 232	16.54 235	5.08 235	40.22 231	41.40 231	37.76 233	37.79 232

GG-mvs-BLEND		46.86 219	67.51 143	22.75 232	0.05 240	76.21 153	64.69 190	0.04 237	61.90 96	0.09 242	55.57 113	71.32 64	0.08 237	70.54 191	67.19 201	71.58 212	69.86 198

testmvs		0.09 234	0.15 235	0.02 236	0.01 241	0.02 242	0.05 243	0.01 238	0.11 239	0.01 243	0.26 239	0.01 243	0.06 239	0.10 237	0.10 235	0.01 239	0.43 237

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 242	0.00 244	0.00 245	0.00 240	0.00 241	0.00 244	0.00 240	0.00 244	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 242	0.00 244	0.00 245	0.00 240	0.00 241	0.00 244	0.00 240	0.00 244	0.00 240	0.00 239	0.00 238	0.00 242	0.00 239

test123		0.09 234	0.14 236	0.02 236	0.00 242	0.02 242	0.02 244	0.01 238	0.09 240	0.00 244	0.30 238	0.00 244	0.08 237	0.03 238	0.09 237	0.01 239	0.45 236

our_test_3					67.93 191	70.99 181	66.89 177

Patchmatch-RL test							2.85 242

mPP-MVS					89.90 21							81.29 36

NP-MVS									80.10 41

High-resolution multi-view benchmark