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.

APDe-MVS		86.37 3	88.41 4	84.00 5	91.43 10	91.83 11	88.34 3	74.67 6	91.19 5	81.76 1	91.13 4	81.94 13	80.07 6	83.38 23	82.58 31	87.69 38	96.78 7

ESAPD		87.60 1	90.44 1	84.29 3	92.09 6	93.44 2	88.69 2	75.11 5	93.06 3	80.80 2	94.23 2	86.70 2	81.44 4	84.84 15	83.52 24	87.64 40	97.28 2

v1.0		80.27 26	76.62 54	84.53 1	92.88 2	93.82 1	88.95 1	76.05 4	92.95 4	80.32 3	93.12 3	86.87 1	80.88 5	85.54 10	84.01 18	88.09 34	0.00 242

SD-MVS		84.31 10	86.96 10	81.22 12	88.98 27	88.68 35	85.65 12	73.85 11	89.09 10	79.63 4	87.34 9	84.84 4	73.71 31	82.66 29	81.60 41	85.48 108	94.51 24

TSAR-MVS + MP.		84.39 9	86.58 12	81.83 10	88.09 35	86.47 56	85.63 13	73.62 13	90.13 9	79.24 5	89.67 7	82.99 8	77.72 12	81.22 46	80.92 52	86.68 59	94.66 23

MSLP-MVS++		78.57 34	77.33 45	80.02 18	88.39 30	84.79 68	84.62 18	66.17 50	75.96 48	78.40 6	61.59 54	71.47 40	73.54 33	78.43 67	78.88 63	88.97 14	90.18 71

MTAPA										78.32 7		79.42 19

APD-MVS		84.83 8	87.00 8	82.30 9	89.61 21	89.21 32	86.51 9	73.64 12	90.98 6	77.99 8	89.89 6	80.04 18	79.18 8	82.00 39	81.37 44	86.88 55	95.49 16
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
HFP-MVS		82.48 17	84.12 19	80.56 15	90.15 14	87.55 48	84.28 19	69.67 30	85.22 20	77.95 9	84.69 13	75.94 25	75.04 23	81.85 40	81.17 47	86.30 65	92.40 45

zzz-MVS		81.65 20	83.10 22	79.97 19	88.14 34	87.62 47	83.96 22	69.90 27	86.92 14	77.67 10	72.47 32	78.74 20	74.13 30	81.59 44	81.15 48	86.01 76	93.19 38

CSCG		82.90 15	84.52 18	81.02 14	91.85 7	93.43 3	87.14 6	74.01 10	81.96 29	76.14 11	70.84 33	82.49 9	69.71 54	82.32 35	85.18 11	87.26 48	95.40 18

MTMP										76.04 12		76.65 24

CNVR-MVS		85.96 4	87.58 7	84.06 4	92.58 4	92.40 7	87.62 5	77.77 2	88.44 11	75.93 13	79.49 22	81.97 12	81.65 3	87.04 5	86.58 3	88.79 16	97.18 4

HPM-MVS++		85.64 6	88.43 3	82.39 8	92.65 3	90.24 23	85.83 11	74.21 7	90.68 7	75.63 14	86.77 10	84.15 5	78.68 10	86.33 6	85.26 9	87.32 46	95.60 14

HSP-MVS		86.82 2	89.95 2	83.16 6	89.38 23	91.60 13	85.63 13	74.15 8	94.20 1	75.52 15	94.99 1	83.21 7	85.96 1	87.67 3	85.88 5	88.32 26	92.13 48

SMA-MVS		85.24 7	88.27 5	81.72 11	91.74 8	90.71 16	86.71 7	73.16 15	90.56 8	74.33 16	83.07 15	85.88 3	77.16 14	86.28 7	85.58 6	87.23 49	95.77 11

TSAR-MVS + GP.		82.27 18	85.98 14	77.94 29	80.72 69	88.25 41	81.12 40	67.71 41	87.10 13	73.31 17	85.23 12	83.68 6	76.64 16	80.43 53	81.47 43	88.15 32	95.66 13

ACMMP_Plus		83.54 12	86.37 13	80.25 17	89.57 22	90.10 25	85.27 16	71.66 19	87.38 12	73.08 18	84.23 14	80.16 16	75.31 21	84.85 14	83.64 21	86.57 60	94.21 29

NCCC		84.16 11	85.46 16	82.64 7	92.34 5	90.57 20	86.57 8	76.51 3	86.85 16	72.91 19	77.20 28	78.69 21	79.09 9	84.64 17	84.88 14	88.44 24	95.41 17

3Dnovator	70.49 5	78.42 35	76.77 51	80.35 16	91.43 10	90.27 22	81.84 32	70.79 22	72.10 53	71.95 20	50.02 87	67.86 51	77.47 13	82.89 26	84.24 16	88.61 20	89.99 72

abl_6				79.06 25	89.68 20	92.14 9	77.70 58	69.68 29	86.87 15	71.88 21	74.29 31	80.06 17	76.56 17			88.84 15	95.82 10

3Dnovator+	70.16 6	77.87 38	77.29 46	78.55 26	89.25 25	88.32 40	80.09 45	67.95 40	74.89 52	71.83 22	52.05 80	70.68 43	76.27 20	82.27 36	82.04 34	85.92 82	90.77 63

AdaColmap		76.23 49	73.55 64	79.35 21	89.38 23	85.00 67	79.99 47	73.04 16	76.60 47	71.17 23	55.18 68	57.99 86	77.87 11	76.82 79	76.82 78	84.67 136	86.45 115

DeepC-MVS_fast	75.41 2	81.69 19	82.10 28	81.20 13	91.04 12	87.81 46	83.42 23	74.04 9	83.77 23	71.09 24	66.88 41	72.44 33	79.48 7	85.08 12	84.97 13	88.12 33	93.78 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS	74.46 3	80.30 25	81.05 31	79.42 20	87.42 37	88.50 37	83.23 24	73.27 14	82.78 26	71.01 25	62.86 50	69.93 46	74.80 25	84.30 18	84.20 17	86.79 58	94.77 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MCST-MVS		85.75 5	86.99 9	84.31 2	94.07 1	92.80 4	88.15 4	79.10 1	85.66 19	70.72 26	76.50 29	80.45 15	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

PVSNet_BlendedMVS		76.84 47	78.47 39	74.95 49	82.37 54	89.90 28	75.45 70	65.45 55	74.99 50	70.66 27	63.07 48	58.27 84	67.60 70	84.24 19	81.70 39	88.18 30	97.10 5

PVSNet_Blended		76.84 47	78.47 39	74.95 49	82.37 54	89.90 28	75.45 70	65.45 55	74.99 50	70.66 27	63.07 48	58.27 84	67.60 70	84.24 19	81.70 39	88.18 30	97.10 5

CANet		80.90 23	82.93 24	78.53 27	86.83 41	92.26 8	81.19 39	66.95 44	81.60 32	69.90 29	66.93 40	74.80 27	76.79 15	84.68 16	84.77 15	89.50 10	95.50 15

MP-MVS		80.94 22	83.49 21	77.96 28	88.48 28	88.16 42	82.82 28	69.34 32	80.79 35	69.67 30	82.35 17	77.13 23	71.60 44	80.97 50	80.96 51	85.87 89	94.06 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CLD-MVS		77.36 44	77.29 46	77.45 33	82.21 57	88.11 43	81.92 31	68.96 35	77.97 43	69.62 31	62.08 51	59.44 78	73.57 32	81.75 41	81.27 46	88.41 25	90.39 68
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
DELS-MVS		79.49 27	79.84 35	79.08 24	88.26 33	92.49 5	84.12 21	70.63 23	65.27 74	69.60 32	61.29 56	66.50 53	72.75 36	88.07 2	88.03 1	89.13 13	97.22 3
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
ACMMPR		80.62 24	82.98 23	77.87 30	88.41 29	87.05 51	83.02 25	69.18 33	83.91 22	68.35 33	82.89 16	73.64 30	72.16 40	80.78 51	81.13 49	86.10 70	91.43 54

SteuartSystems-ACMMP		82.51 16	85.35 17	79.20 22	90.25 13	89.39 31	84.79 17	70.95 21	82.86 25	68.32 34	86.44 11	77.19 22	73.07 34	83.63 22	83.64 21	87.82 35	94.34 26
Skip Steuart: Steuart Systems R&D Blog.
CP-MVS		79.44 28	81.51 30	77.02 34	86.95 39	85.96 62	82.00 30	68.44 38	81.82 30	67.39 35	77.43 26	73.68 29	71.62 43	79.56 59	79.58 57	85.73 98	92.51 44

canonicalmvs		77.65 40	79.59 36	75.39 44	81.52 61	89.83 30	81.32 38	60.74 103	80.05 37	66.72 36	68.43 37	65.09 57	74.72 27	78.87 63	82.73 28	87.32 46	92.16 47

train_agg		83.35 13	86.93 11	79.17 23	89.70 19	88.41 38	85.60 15	72.89 17	86.31 17	66.58 37	90.48 5	82.24 11	73.06 35	83.10 25	82.64 30	87.21 52	95.30 19

casdiffmvs1		77.74 39	78.92 38	76.36 37	82.58 51	90.61 18	81.58 34	61.31 94	75.68 49	66.24 38	64.21 45	65.17 56	76.54 18	80.07 55	82.68 29	89.88 7	94.00 32

DeepPCF-MVS	76.94 1	83.08 14	87.77 6	77.60 31	90.11 15	90.96 15	78.48 52	72.63 18	93.10 2	65.84 39	80.67 20	81.55 14	74.80 25	85.94 9	85.39 8	83.75 153	96.77 8

TSAR-MVS + ACMM		81.59 21	85.84 15	76.63 35	89.82 18	86.53 55	86.32 10	66.72 46	85.96 18	65.43 40	88.98 8	82.29 10	67.57 72	82.06 38	81.33 45	83.93 151	93.75 34

PGM-MVS		79.42 31	81.84 29	76.60 36	88.38 31	86.69 53	82.97 27	65.75 52	80.39 36	64.94 41	81.95 19	72.11 38	71.41 45	80.45 52	80.55 55	86.18 67	90.76 64

CNLPA		71.37 69	70.27 80	72.66 60	80.79 68	81.33 98	71.07 117	65.75 52	82.36 27	64.80 42	42.46 129	56.49 91	72.70 37	73.00 127	70.52 176	80.84 187	85.76 124

MVSTER		76.92 46	79.92 34	73.42 56	74.98 108	82.97 78	78.15 53	63.41 68	78.02 42	64.41 43	67.54 38	72.80 32	71.05 46	83.29 24	83.73 20	88.53 23	91.12 58

casdiffmvs		76.13 50	76.96 49	75.15 48	82.26 56	90.09 26	79.98 48	60.64 104	70.12 57	63.58 44	62.04 52	60.30 76	74.53 28	81.62 43	82.30 32	89.90 6	92.27 46

MVS_111021_LR		74.26 55	75.95 56	72.27 61	79.43 73	85.04 66	72.71 85	65.27 57	70.92 56	63.58 44	69.32 35	60.31 75	69.43 58	77.01 77	77.15 75	83.22 161	91.93 52

OpenMVS	67.62 8	74.92 54	73.91 61	76.09 41	90.10 16	90.38 21	78.01 54	66.35 48	66.09 69	62.80 46	46.33 112	64.55 60	71.77 42	79.92 57	80.88 53	87.52 42	89.20 79

ACMMP		77.61 41	79.59 36	75.30 46	85.87 44	85.58 63	81.42 36	67.38 43	79.38 40	62.61 47	78.53 23	65.79 55	68.80 64	78.56 66	78.50 67	85.75 94	90.80 62
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
IB-MVS	64.48 11	69.02 79	68.97 87	69.09 78	81.75 60	89.01 33	64.50 157	64.91 58	56.65 94	62.59 48	47.89 96	45.23 128	51.99 158	69.18 179	81.88 37	88.77 17	92.93 40
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
MVS_111021_HR		77.42 43	78.40 41	76.28 38	86.95 39	90.68 17	77.41 60	70.56 26	66.21 68	62.48 49	66.17 43	63.98 61	72.08 41	82.87 27	83.15 25	88.24 29	95.71 12

OMC-MVS		74.03 56	75.82 57	71.95 63	79.56 71	80.98 102	75.35 72	63.21 69	84.48 21	61.83 50	61.54 55	66.89 52	69.41 59	76.60 81	74.07 130	82.34 176	86.15 119

MVS_0304		79.43 29	82.20 26	76.20 39	84.22 48	91.79 12	81.82 33	63.81 65	76.83 46	61.71 51	66.37 42	75.52 26	76.38 19	85.54 10	85.03 12	89.28 12	94.32 27

CPTT-MVS		75.43 52	77.13 48	73.44 55	81.43 62	82.55 82	80.96 42	64.35 60	77.95 44	61.39 52	69.20 36	70.94 42	69.38 60	73.89 115	73.32 141	83.14 166	92.06 50

EPNet		79.28 33	82.25 25	75.83 42	88.31 32	90.14 24	79.43 50	68.07 39	81.76 31	61.26 53	77.26 27	70.08 45	70.06 52	82.43 33	82.00 36	87.82 35	92.09 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		67.47 89	67.05 100	67.98 84	76.63 96	81.51 96	74.49 81	47.65 200	61.18 81	61.12 54	42.51 128	53.02 110	64.74 83	70.11 171	71.50 162	83.22 161	89.49 75

XVS					82.43 52	86.27 58	75.70 64			61.07 55		72.27 34				85.67 102

X-MVStestdata					82.43 52	86.27 58	75.70 64			61.07 55		72.27 34				85.67 102

X-MVS		78.16 37	80.55 33	75.38 45	87.99 36	86.27 58	81.05 41	68.98 34	78.33 41	61.07 55	75.25 30	72.27 34	67.52 73	80.03 56	80.52 56	85.66 105	91.20 57

ACMP	68.86 7	72.15 65	72.25 68	72.03 62	80.96 65	80.87 104	77.93 56	64.13 62	69.29 61	60.79 58	64.04 46	53.54 107	63.91 85	73.74 119	75.27 96	84.45 141	88.98 82
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	66.70 10	70.42 70	68.49 90	72.67 59	82.85 50	77.76 144	77.70 58	64.76 59	64.61 75	60.74 59	49.29 89	53.97 105	65.86 77	74.97 100	75.57 94	84.13 149	83.29 141
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		72.18 64	73.90 62	70.18 71	79.47 72	86.19 61	76.94 62	48.62 195	66.07 70	60.40 60	54.14 74	65.82 54	67.98 67	75.84 92	76.41 84	87.67 39	92.83 42

tpmp4_e23		69.38 75	69.47 83	69.28 75	78.20 78	82.35 84	75.92 63	49.20 194	64.15 76	59.96 61	47.93 94	55.77 96	68.06 66	73.05 126	74.53 109	84.34 143	88.50 101

PVSNet_Blended_VisFu		71.76 67	73.54 65	69.69 72	79.01 75	87.16 50	72.05 89	61.80 89	56.46 96	59.66 62	53.88 76	62.48 64	59.08 135	81.17 47	78.90 61	86.53 62	94.74 22

DI_MVS_plusplus_trai		73.94 57	74.85 59	72.88 58	76.57 97	86.80 52	80.41 44	61.47 92	62.35 78	59.44 63	47.91 95	68.12 48	72.24 39	82.84 28	81.50 42	87.15 53	94.42 25

Anonymous20231211		68.44 82	66.37 104	70.86 67	77.58 88	83.49 76	75.15 73	61.89 87	52.54 114	58.50 64	28.89 207	56.78 89	69.29 61	74.96 102	76.61 79	82.73 169	91.36 56

MAR-MVS		77.19 45	78.37 42	75.81 43	89.87 17	90.58 19	79.33 51	65.56 54	77.62 45	58.33 65	59.24 62	67.98 49	74.83 24	82.37 34	83.12 26	86.95 54	87.67 107
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
DWT-MVSNet_training		72.81 60	73.98 60	71.45 65	81.26 63	86.37 57	72.08 88	59.82 111	69.13 64	58.15 66	54.71 69	61.33 74	67.81 69	76.86 78	78.63 64	89.59 8	90.86 60

QAPM		77.50 42	77.43 44	77.59 32	91.52 9	92.00 10	81.41 37	70.63 23	66.22 67	58.05 67	54.70 70	71.79 39	74.49 29	82.46 31	82.04 34	89.46 11	92.79 43

PCF-MVS	70.85 4	75.73 51	76.55 55	74.78 52	83.67 49	88.04 45	81.47 35	70.62 25	69.24 63	57.52 68	60.59 59	69.18 47	70.65 48	77.11 76	77.65 74	84.75 134	94.01 31
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVS_Test		75.22 53	76.69 52	73.51 54	79.30 74	88.82 34	80.06 46	58.74 114	69.77 60	57.50 69	59.78 61	61.35 72	75.31 21	82.07 37	83.60 23	90.13 5	91.41 55

CDPH-MVS		79.39 32	82.13 27	76.19 40	89.22 26	88.34 39	84.20 20	71.00 20	79.67 39	56.97 70	77.77 25	72.24 37	68.50 65	81.33 45	82.74 27	87.23 49	92.84 41

HQP-MVS		78.26 36	80.91 32	75.17 47	85.67 45	84.33 72	83.01 26	69.38 31	79.88 38	55.83 71	79.85 21	64.90 59	70.81 47	82.46 31	81.78 38	86.30 65	93.18 39

diffmvs		72.46 63	73.75 63	70.95 66	76.33 99	87.21 49	77.96 55	58.43 117	66.25 66	55.75 72	59.11 63	56.77 90	70.42 49	77.35 75	78.90 61	86.80 57	90.64 66

PLC	64.00 12	68.54 81	66.66 101	70.74 68	80.28 70	74.88 168	72.64 86	63.70 67	69.26 62	55.71 73	47.24 104	55.31 100	70.42 49	72.05 153	70.67 174	81.66 181	77.19 175
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
dps		64.08 115	63.22 129	65.08 100	75.27 107	79.65 124	66.68 148	46.63 205	56.94 92	55.67 74	43.96 115	43.63 134	64.00 84	69.50 178	69.82 179	82.25 177	79.02 170

MS-PatchMatch		70.34 74	69.00 86	71.91 64	85.20 47	85.35 64	77.84 57	61.77 90	58.01 90	55.40 75	41.26 136	58.34 83	61.69 96	81.70 42	78.29 68	89.56 9	80.02 166

OPM-MVS		72.74 61	70.93 77	74.85 51	85.30 46	84.34 71	82.82 28	69.79 28	49.96 121	55.39 76	54.09 75	60.14 77	70.04 53	80.38 54	79.43 58	85.74 97	88.20 103

Anonymous20240521		69.13 78	69.07 85	69.21 76	77.65 87	77.52 146	74.68 75	57.85 123	54.92 106	55.34 77	55.74 67	55.56 99	66.35 75	75.05 98	76.56 81	83.35 158	88.13 104

FC-MVSNet-train		68.83 80	68.29 91	69.47 73	78.35 77	79.94 120	64.72 156	66.38 47	54.96 105	54.51 78	56.75 65	47.91 122	66.91 74	75.57 96	75.75 90	85.92 82	87.12 110

PatchMatch-RL		62.22 146	60.69 160	64.01 120	68.74 144	75.75 164	59.27 188	60.35 106	56.09 98	53.80 79	47.06 107	36.45 186	64.80 82	68.22 182	67.22 191	77.10 207	74.02 186

CHOSEN 1792x2688		72.55 62	71.98 69	73.22 57	86.57 42	92.41 6	75.63 66	66.77 45	62.08 79	52.32 80	30.27 205	50.74 116	66.14 76	86.22 8	85.41 7	91.90 1	96.75 9

LGP-MVS_train		72.02 66	73.18 67	70.67 69	82.13 58	80.26 119	79.58 49	63.04 73	70.09 58	51.98 81	65.06 44	55.62 98	62.49 93	75.97 91	76.32 85	84.80 133	88.93 83

UGNet		67.57 88	71.69 72	62.76 139	69.88 137	82.58 81	66.43 150	58.64 115	54.71 109	51.87 82	61.74 53	62.01 69	45.46 186	74.78 103	74.99 98	84.24 145	91.02 59
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
PHI-MVS		79.43 29	84.06 20	74.04 53	86.15 43	91.57 14	80.85 43	68.90 36	82.22 28	51.81 83	78.10 24	74.28 28	70.39 51	84.01 21	84.00 19	86.14 69	94.24 28

TAPA-MVS	67.10 9	71.45 68	73.47 66	69.10 77	77.04 92	80.78 105	73.81 83	62.10 83	80.80 34	51.28 84	60.91 57	63.80 63	67.98 67	74.59 104	72.42 155	82.37 175	80.97 161
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
GBi-Net		69.21 76	70.40 78	67.81 85	69.49 139	78.65 133	74.54 76	60.97 99	65.32 71	51.06 85	47.37 101	62.05 66	63.43 87	77.49 71	78.22 69	87.37 43	83.73 136

test1		69.21 76	70.40 78	67.81 85	69.49 139	78.65 133	74.54 76	60.97 99	65.32 71	51.06 85	47.37 101	62.05 66	63.43 87	77.49 71	78.22 69	87.37 43	83.73 136

FMVSNet3		70.41 72	71.89 71	68.68 80	70.89 134	79.42 128	75.63 66	60.97 99	65.32 71	51.06 85	47.37 101	62.05 66	64.90 81	82.49 30	82.27 33	88.64 19	84.34 132

tpmrst		67.15 92	68.12 94	66.03 97	76.21 100	80.98 102	71.27 110	45.05 207	60.69 83	50.63 88	46.95 109	54.15 104	65.30 78	71.80 156	71.77 159	87.72 37	90.48 67

Fast-Effi-MVS+		67.59 86	67.56 96	67.62 87	73.67 114	81.14 101	71.12 114	54.79 162	58.88 86	50.61 89	46.70 110	47.05 123	69.12 62	76.06 90	76.44 83	86.43 63	86.65 113

MSDG		65.57 103	61.57 153	70.24 70	82.02 59	76.47 157	74.46 82	68.73 37	56.52 95	50.33 90	38.47 163	41.10 145	62.42 94	72.12 151	72.94 149	83.47 156	73.37 191

pmmvs4		63.14 130	62.46 143	63.94 122	66.03 178	76.40 158	66.82 147	57.60 129	56.74 93	50.26 91	40.81 141	37.51 180	59.26 133	71.75 157	71.48 163	83.68 154	82.53 149

FMVSNet2		68.06 85	68.57 89	67.45 88	69.49 139	78.65 133	74.54 76	60.23 110	56.29 97	49.64 92	42.13 132	57.08 88	63.43 87	81.15 48	80.99 50	87.37 43	83.73 136

TSAR-MVS + COLMAP		73.09 58	76.86 50	68.71 79	74.97 109	82.49 83	74.51 80	61.83 88	83.16 24	49.31 93	82.22 18	51.62 113	68.94 63	78.76 65	75.52 95	82.67 171	84.23 133

Effi-MVS+		70.42 70	71.23 75	69.47 73	78.04 79	85.24 65	75.57 68	58.88 113	59.56 85	48.47 94	52.73 79	54.94 101	69.69 55	78.34 69	77.06 76	86.18 67	90.73 65

IterMVS-LS		66.08 99	66.56 103	65.51 98	73.67 114	74.88 168	70.89 120	53.55 170	50.42 119	48.32 95	50.59 85	55.66 97	61.83 95	73.93 114	74.42 116	84.82 132	86.01 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EPMVS		66.21 97	67.49 97	64.73 106	75.81 103	84.20 74	68.94 134	44.37 211	61.55 80	48.07 96	49.21 91	54.87 102	62.88 90	71.82 155	71.40 166	88.28 28	79.37 169

HyFIR lowres test		68.39 83	68.28 92	68.52 81	80.85 66	88.11 43	71.08 116	58.09 120	54.87 108	47.80 97	27.55 211	55.80 95	64.97 80	79.11 61	79.14 60	88.31 27	93.35 35

v18		63.31 129	62.02 148	64.81 105	68.48 146	73.38 177	72.14 87	54.28 165	48.99 133	47.21 98	39.56 148	41.20 142	60.80 107	72.89 131	74.46 115	85.96 81	83.64 139

v16		63.12 131	61.78 150	64.68 107	68.45 147	73.29 178	71.86 91	54.12 166	48.36 135	47.00 99	39.30 153	41.01 146	60.67 108	72.83 137	74.40 117	86.01 76	83.24 143

PMMVS		70.37 73	75.06 58	64.90 101	71.46 128	81.88 87	64.10 159	55.64 151	71.31 55	46.69 100	70.69 34	58.56 79	69.53 57	79.03 62	75.63 92	81.96 179	88.32 102

CDS-MVSNet		64.22 113	65.89 111	62.28 145	70.05 136	80.59 114	69.91 128	57.98 121	43.53 165	46.58 101	48.22 93	50.76 115	46.45 180	75.68 93	76.08 87	82.70 170	86.34 118
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v17		62.99 136	61.70 151	64.51 110	68.40 148	73.28 179	71.80 96	54.11 167	47.87 136	46.14 102	39.29 154	41.01 146	60.60 109	72.81 138	74.39 122	85.99 79	83.25 142

RPSCF		55.07 188	58.06 183	51.57 197	48.87 229	58.95 220	53.68 200	41.26 223	62.42 77	45.88 103	54.38 73	54.26 103	53.75 155	57.15 212	53.53 227	66.01 226	65.75 212

FMVSNet1		63.48 125	63.07 131	63.97 121	65.31 183	76.37 159	71.77 97	57.90 122	43.32 167	45.66 104	35.06 190	49.43 118	58.57 137	77.49 71	78.22 69	84.59 138	81.60 159

CMPMVS	43.63 17	57.67 182	55.43 190	60.28 157	72.01 126	79.00 130	62.77 173	53.23 175	41.77 179	45.42 105	30.74 204	39.03 172	53.01 157	64.81 193	64.65 202	75.26 213	68.03 207
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PatchmatchNet		65.43 105	67.71 95	62.78 138	73.49 116	82.83 79	66.42 151	45.40 206	60.40 84	45.27 106	49.22 90	57.60 87	60.01 119	70.61 163	71.38 169	86.08 72	81.91 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v8		63.44 128	62.58 142	64.43 112	68.28 150	78.07 139	71.82 95	54.85 160	46.70 143	45.20 107	39.40 149	40.91 148	60.54 113	72.85 136	74.39 122	85.92 82	85.76 124

v6		64.09 114	63.40 125	64.90 101	68.28 150	80.78 105	71.85 92	57.64 128	46.73 142	45.18 108	39.40 149	40.89 149	60.54 113	72.86 132	74.40 117	85.92 82	88.72 92

v1neww		64.08 115	63.38 126	64.89 103	68.27 152	80.77 107	71.84 93	57.65 126	46.66 144	45.10 109	39.40 149	40.86 150	60.57 110	72.86 132	74.40 117	85.92 82	88.71 93

v7new		64.08 115	63.38 126	64.89 103	68.27 152	80.77 107	71.84 93	57.65 126	46.66 144	45.10 109	39.40 149	40.86 150	60.57 110	72.86 132	74.40 117	85.92 82	88.71 93

v148		62.00 149	61.19 156	62.96 135	67.46 170	79.49 126	67.87 138	57.66 125	42.30 175	45.02 111	38.20 168	38.89 175	54.77 152	69.83 175	72.60 154	84.96 120	87.01 111

UA-Net		64.62 109	68.23 93	60.42 156	77.53 89	81.38 97	60.08 185	57.47 132	47.01 139	44.75 112	60.68 58	71.32 41	41.84 194	73.27 121	72.25 156	80.83 188	71.68 199

Effi-MVS+-dtu		64.58 110	64.08 120	65.16 99	73.04 119	75.17 167	70.68 122	56.23 145	54.12 111	44.71 113	47.42 100	51.10 114	63.82 86	68.08 183	66.32 196	82.47 174	86.38 117

LS3D		64.54 112	62.14 146	67.34 89	80.85 66	75.79 163	69.99 126	65.87 51	60.77 82	44.35 114	42.43 130	45.95 126	65.01 79	69.88 174	68.69 184	77.97 205	71.43 201

V42		62.86 138	62.97 133	62.74 140	60.84 199	78.99 131	71.46 109	57.13 139	46.85 140	44.28 115	38.87 158	40.73 154	57.63 143	72.60 144	74.14 127	85.09 116	88.63 95

MDTV_nov1_ep13		65.21 106	67.28 98	62.79 137	70.91 133	81.72 89	69.28 133	49.50 191	58.08 89	43.94 116	50.50 86	56.02 93	58.86 136	70.72 162	73.37 139	84.24 145	80.52 162

CANet_DTU		72.84 59	76.63 53	68.43 82	76.81 95	86.62 54	75.54 69	54.71 163	72.06 54	43.54 117	67.11 39	58.46 81	72.40 38	81.13 49	80.82 54	87.57 41	90.21 70

MVS-HIRNet		53.86 197	53.02 199	54.85 188	60.30 202	72.36 188	44.63 218	42.20 218	39.45 190	43.47 118	21.66 224	34.00 202	55.47 148	65.42 189	67.16 192	83.02 168	71.08 202

conf0.002		67.12 94	67.13 99	67.11 90	77.95 81	82.11 85	71.71 98	63.06 71	49.16 126	43.43 119	47.76 98	48.79 119	61.42 98	76.61 80	76.55 82	85.07 118	88.92 85

v15		62.07 147	60.70 159	63.67 125	68.09 157	73.00 180	71.27 110	53.41 171	43.70 161	43.43 119	38.77 159	39.83 162	59.87 123	72.74 141	74.25 124	85.98 80	82.61 148

conf0.01		66.60 95	66.18 107	67.09 91	77.90 82	82.02 86	71.71 98	63.05 72	49.16 126	43.41 121	46.23 113	45.78 127	61.42 98	76.55 82	74.63 103	85.04 119	88.87 87

thres100view900		67.14 93	66.09 109	68.38 83	77.70 83	83.84 75	74.52 79	66.33 49	49.16 126	43.40 122	43.24 119	41.34 138	62.59 92	79.31 60	75.92 89	85.73 98	89.81 73

tfpn200view9		65.90 100	64.96 114	67.00 92	77.70 83	81.58 92	71.71 98	62.94 77	49.16 126	43.40 122	43.24 119	41.34 138	61.42 98	76.24 85	74.63 103	84.84 127	88.52 97

tfpn111		66.52 96	66.12 108	66.98 93	77.70 83	81.58 92	71.71 98	62.94 77	49.16 126	43.28 124	51.38 82	41.34 138	61.42 98	76.24 85	74.63 103	84.84 127	88.52 97

conf200view11		65.89 101	64.96 114	66.98 93	77.70 83	81.58 92	71.71 98	62.94 77	49.16 126	43.28 124	43.24 119	41.34 138	61.42 98	76.24 85	74.63 103	84.84 127	88.52 97

V14		61.96 150	60.56 161	63.59 126	68.06 158	72.93 183	71.10 115	53.33 173	43.47 166	43.28 124	38.59 160	39.78 163	59.76 125	72.65 143	74.19 125	86.01 76	82.32 153

tpm		64.85 108	66.02 110	63.48 128	74.52 111	78.38 136	70.98 118	44.99 209	51.61 116	43.28 124	47.66 99	53.18 108	60.57 110	70.58 165	71.30 171	86.54 61	89.45 77

V9		61.85 152	60.42 164	63.51 127	68.02 159	72.85 184	70.91 119	53.24 174	43.25 168	43.27 128	38.41 164	39.73 167	59.60 127	72.55 145	74.13 128	86.04 74	82.04 155

v13		61.60 156	60.13 169	63.31 132	67.95 163	72.67 187	70.51 124	53.05 177	42.80 171	42.96 129	38.10 171	39.57 170	59.31 132	72.36 148	73.98 133	86.10 70	81.40 160

test-LLR		68.23 84	71.61 73	64.28 119	71.37 129	81.32 99	63.98 163	61.03 96	58.62 87	42.96 129	52.74 77	61.65 70	57.74 141	75.64 94	78.09 72	88.61 20	93.21 36

TESTMET0.1,1		67.38 90	71.61 73	62.45 143	66.05 177	81.32 99	63.98 163	55.36 155	58.62 87	42.96 129	52.74 77	61.65 70	57.74 141	75.64 94	78.09 72	88.61 20	93.21 36

v12		61.70 154	60.27 166	63.38 131	68.00 161	72.76 185	70.63 123	53.14 176	43.01 170	42.95 132	38.25 166	39.64 169	59.48 129	72.47 147	74.05 131	86.06 73	81.71 158

v1		63.49 124	62.77 138	64.32 115	68.13 154	80.70 110	71.70 104	57.43 133	43.69 162	42.89 133	39.03 155	39.77 164	59.93 122	72.93 129	74.48 114	85.86 90	88.77 88

divwei89l23v2f112		63.48 125	62.76 139	64.32 115	68.13 154	80.68 112	71.71 98	57.43 133	43.69 162	42.84 134	39.01 157	39.75 165	59.94 121	72.93 129	74.49 112	85.86 90	88.75 90

v7		63.61 123	63.02 132	64.29 118	67.88 164	80.32 117	71.60 106	56.63 141	45.37 151	42.84 134	38.54 161	38.91 174	61.05 105	74.39 107	74.52 110	85.75 94	89.10 81

v1141		63.48 125	62.75 140	64.32 115	68.13 154	80.69 111	71.69 105	57.43 133	43.66 164	42.83 136	39.02 156	39.74 166	59.95 120	72.94 128	74.49 112	85.86 90	88.75 90

v2v482		63.68 122	62.85 135	64.65 109	68.01 160	80.46 116	71.90 90	57.60 129	44.26 156	42.82 137	39.80 147	38.62 176	61.56 97	73.06 124	74.86 100	86.03 75	88.90 86

v10		63.00 134	62.22 145	63.90 123	67.88 164	77.78 143	71.59 107	54.34 164	45.37 151	42.76 138	38.53 162	38.93 173	61.05 105	74.39 107	74.52 110	85.75 94	86.04 120

thres200		65.58 102	64.74 117	66.56 95	77.52 90	81.61 90	73.44 84	62.95 75	46.23 147	42.45 139	42.76 123	41.18 143	58.12 139	76.24 85	75.59 93	84.89 124	89.58 74

v11		61.74 153	60.47 163	63.22 133	67.83 166	72.72 186	70.31 125	52.95 180	42.75 172	41.89 140	38.16 169	38.49 177	60.40 117	74.35 109	74.40 117	85.92 82	82.39 152

CR-MVSNet		62.31 141	64.75 116	59.47 162	68.63 145	71.29 194	67.53 141	43.18 213	55.83 99	41.40 141	41.04 139	55.85 94	57.29 144	72.76 139	73.27 144	78.77 202	83.23 144

Patchmtry						78.06 140	67.53 141	43.18 213		41.40 141

PatchT		60.46 163	63.85 121	56.51 181	65.95 179	75.68 165	47.34 210	41.39 220	53.89 112	41.40 141	37.84 172	50.30 117	57.29 144	72.76 139	73.27 144	85.67 102	83.23 144

EPP-MVSNet		67.58 87	71.10 76	63.48 128	75.71 104	83.35 77	66.85 146	57.83 124	53.02 113	41.15 144	55.82 66	67.89 50	56.01 147	74.40 106	72.92 150	83.33 159	90.30 69

pmmvs-eth3d		55.20 185	53.95 197	56.65 180	57.34 212	67.77 202	57.54 193	53.74 169	40.93 183	41.09 145	31.19 203	29.10 216	49.07 168	65.54 188	67.28 190	81.14 185	75.81 178

v1144		63.00 134	62.39 144	63.70 124	67.72 167	80.27 118	71.23 112	56.40 142	42.51 173	40.81 146	38.12 170	37.73 178	60.42 116	74.46 105	74.55 108	85.64 106	89.12 80

thres400		65.18 107	64.44 119	66.04 96	76.40 98	82.63 80	71.52 108	64.27 61	44.93 155	40.69 147	41.86 133	40.79 152	58.12 139	77.67 70	74.64 102	85.26 111	88.56 96

Vis-MVSNet		65.53 104	69.83 82	60.52 155	70.80 135	84.59 69	66.37 152	55.47 154	48.40 134	40.62 148	57.67 64	58.43 82	45.37 187	77.49 71	76.24 86	84.47 140	85.99 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CHOSEN 280x420		62.23 145	66.57 102	57.17 179	59.88 203	68.92 200	61.20 179	42.28 217	54.17 110	39.57 149	47.78 97	64.97 58	62.68 91	73.85 116	69.52 181	77.43 206	86.75 112

v1192		62.25 143	61.64 152	62.96 135	66.88 172	79.72 123	69.96 127	55.77 149	41.58 180	39.42 150	37.05 176	35.96 191	60.50 115	74.30 112	74.09 129	85.24 112	88.76 89

v144192		62.05 148	61.46 154	62.73 141	66.59 175	79.87 121	69.30 132	55.88 147	41.50 181	39.41 151	37.23 174	36.45 186	59.62 126	72.69 142	73.51 136	85.61 107	88.93 83

IS_MVSNet		67.29 91	71.98 69	61.82 149	76.92 93	84.32 73	65.90 154	58.22 118	55.75 101	39.22 152	54.51 72	62.47 65	45.99 183	78.83 64	78.52 66	84.70 135	89.47 76

view600		63.91 120	63.27 128	64.66 108	75.57 105	81.73 88	69.71 129	63.04 73	43.97 158	39.18 153	41.09 137	40.24 160	55.38 149	76.28 83	72.04 158	85.08 117	87.52 108

test-mter		64.06 118	69.24 84	58.01 170	59.07 206	77.40 148	59.13 189	48.11 198	55.64 102	39.18 153	51.56 81	58.54 80	55.38 149	73.52 120	76.00 88	87.22 51	92.05 51

thres600view7		63.77 121	63.14 130	64.51 110	75.49 106	81.61 90	69.59 130	62.95 75	43.96 159	38.90 155	41.09 137	40.24 160	55.25 151	76.24 85	71.54 161	84.89 124	87.30 109

tfpn_ndepth		62.95 137	63.75 122	62.02 147	76.89 94	79.48 127	64.09 160	60.98 98	49.48 123	38.73 156	49.92 88	44.79 129	47.37 175	71.91 154	71.66 160	84.07 150	79.00 171

v1921920		61.66 155	61.10 157	62.31 144	66.32 176	79.57 125	68.41 137	55.49 153	41.03 182	38.69 157	36.64 182	35.27 197	59.60 127	73.23 122	73.41 138	85.37 109	88.51 100

ACMH+	60.36 13	61.16 158	58.38 181	64.42 113	77.37 91	74.35 173	68.45 136	62.81 81	45.86 149	38.48 158	35.71 185	37.35 181	59.81 124	67.24 185	69.80 180	79.58 197	78.32 173

EG-PatchMatch MVS		58.73 174	58.03 184	59.55 161	72.32 124	80.49 115	63.44 169	55.55 152	32.49 213	38.31 159	28.87 208	37.22 182	42.84 192	74.30 112	75.70 91	84.84 127	77.14 176

v1240		61.09 159	60.55 162	61.72 150	65.92 180	79.28 129	67.16 145	54.91 159	39.79 187	38.10 160	36.08 184	34.64 198	59.15 134	72.86 132	73.36 140	85.10 114	87.84 105

ADS-MVSNet		58.40 178	59.16 180	57.52 176	65.80 181	74.57 172	60.26 183	40.17 225	50.51 118	38.01 161	40.11 145	44.72 130	59.36 131	64.91 191	66.55 194	81.53 182	72.72 195

FMVSNet5		58.86 172	60.24 167	57.25 178	52.66 222	66.25 206	63.77 166	52.86 181	57.85 91	37.92 162	36.12 183	52.22 111	51.37 160	70.88 161	71.43 165	84.92 121	66.91 209

IterMVS		61.87 151	63.55 123	59.90 158	67.29 171	72.20 189	67.34 144	48.56 196	47.48 138	37.86 163	47.07 106	48.27 120	54.08 154	72.12 151	73.71 134	84.30 144	83.99 134
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view800		63.02 133	62.69 141	63.39 130	74.79 110	80.76 109	67.83 139	61.93 86	43.16 169	37.78 164	40.43 142	39.73 167	53.16 156	75.01 99	73.32 141	84.87 126	86.43 116

pmmvs5		59.72 166	60.24 167	59.11 165	62.77 193	77.33 150	63.17 171	54.00 168	40.21 185	37.23 165	40.41 143	35.99 190	51.75 159	72.55 145	72.74 152	85.72 100	82.45 151

pm-mvs1		59.21 169	59.58 175	58.77 167	67.97 162	77.07 155	64.12 158	57.20 137	34.73 206	36.86 166	35.34 187	40.54 159	43.34 191	74.32 111	73.30 143	83.13 167	81.77 157

MIMVSNet		57.78 181	59.71 173	55.53 186	54.79 214	77.10 154	63.89 165	45.02 208	46.59 146	36.79 167	28.36 209	40.77 153	45.84 184	74.97 100	76.58 80	86.87 56	73.60 189

tfpnnormal		58.97 170	56.48 189	61.89 148	71.27 131	76.21 160	66.65 149	61.76 91	32.90 212	36.41 168	27.83 210	29.14 215	50.64 165	73.06 124	73.05 148	84.58 139	83.15 146

tfpn		62.54 139	62.79 137	62.25 146	74.16 112	79.86 122	66.07 153	60.97 99	42.43 174	36.41 168	39.88 146	43.76 133	51.25 163	73.85 116	74.17 126	84.67 136	85.57 127

v748		55.19 186	54.63 193	55.85 184	61.44 198	72.97 182	58.72 191	51.62 185	34.48 208	36.39 170	32.09 197	33.05 205	45.48 185	61.85 203	67.87 187	81.45 183	80.08 165

TAMVS		58.86 172	60.91 158	56.47 182	62.38 195	77.57 145	58.97 190	52.98 178	38.76 192	36.17 171	42.26 131	47.94 121	46.45 180	70.23 170	70.79 173	81.86 180	78.82 172

PM-MVS		50.11 205	50.38 209	49.80 202	47.23 231	62.08 218	50.91 204	44.84 210	41.90 178	36.10 172	35.22 188	26.05 223	46.83 179	57.64 210	55.42 226	72.90 218	74.32 185

v7n		57.04 184	56.64 188	57.52 176	62.85 192	74.75 170	61.76 175	51.80 184	35.58 205	36.02 173	32.33 196	33.61 204	50.16 167	67.73 184	70.34 178	82.51 172	82.12 154

conf0.05thres1000		60.33 165	59.42 176	61.40 152	73.15 118	78.25 138	65.29 155	60.30 107	36.61 198	35.75 174	33.25 192	39.23 171	50.35 166	72.18 150	72.67 153	83.57 155	83.74 135

RPMNet		58.63 177	62.80 136	53.76 195	67.59 169	71.29 194	54.60 199	38.13 228	55.83 99	35.70 175	41.58 135	53.04 109	47.89 172	66.10 187	67.38 189	78.65 204	84.40 131

GA-MVS		64.55 111	65.76 112	63.12 134	69.68 138	81.56 95	69.59 130	58.16 119	45.23 153	35.58 176	47.01 108	41.82 137	59.41 130	79.62 58	78.54 65	86.32 64	86.56 114

Fast-Effi-MVS+-dtu		63.05 132	64.72 118	61.11 153	71.21 132	76.81 156	70.72 121	43.13 215	52.51 115	35.34 177	46.55 111	46.36 124	61.40 103	71.57 158	71.44 164	84.84 127	87.79 106

V4		54.78 193	55.14 192	54.37 192	54.07 217	72.13 190	59.83 187	49.39 192	34.46 209	35.11 178	31.64 199	35.72 194	47.22 177	63.90 198	67.92 185	80.59 190	80.23 163

v52		54.79 192	55.15 191	54.36 193	54.07 217	72.13 190	59.84 186	49.39 192	34.50 207	35.08 179	31.63 200	35.74 193	47.21 178	63.90 198	67.92 185	80.59 190	80.23 163

USDC		59.69 167	60.03 170	59.28 164	64.04 187	71.84 192	63.15 172	55.36 155	54.90 107	35.02 180	48.34 92	29.79 214	58.16 138	70.60 164	71.33 170	79.99 194	73.42 190

COLMAP_ROB	51.17 15	55.13 187	52.90 201	57.73 175	73.47 117	67.21 204	62.13 174	55.82 148	47.83 137	34.39 181	31.60 201	34.24 200	44.90 188	63.88 200	62.52 210	75.67 211	63.02 217
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs6		54.20 196	53.54 198	54.97 187	63.22 191	72.98 181	60.17 184	52.32 183	26.77 225	34.30 182	23.29 220	36.23 188	40.33 199	68.77 181	68.76 183	79.47 199	78.00 174

TDRefinement		52.70 198	51.02 207	54.66 190	57.41 211	65.06 210	61.47 178	54.94 157	44.03 157	33.93 183	30.13 206	27.57 217	46.17 182	61.86 202	62.48 211	74.01 217	66.06 211

anonymousdsp		54.99 189	57.24 186	52.36 196	53.82 219	71.75 193	51.49 202	48.14 197	33.74 210	33.66 184	38.34 165	36.13 189	47.54 174	64.53 195	70.60 175	79.53 198	85.59 126

EPNet_dtu		66.17 98	70.13 81	61.54 151	81.04 64	77.39 149	68.87 135	62.50 82	69.78 59	33.51 185	63.77 47	56.22 92	37.65 203	72.20 149	72.18 157	85.69 101	79.38 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thresconf0.02		63.92 119	65.18 113	62.46 142	75.91 102	80.65 113	67.51 143	63.86 64	45.00 154	33.32 186	51.38 82	51.68 112	48.34 170	75.49 97	75.13 97	85.84 93	76.91 177

LP		48.21 211	46.65 218	50.03 201	60.39 201	63.86 215	48.73 206	38.71 227	35.60 204	32.99 187	23.31 219	24.95 225	40.07 200	57.73 209	61.56 212	79.29 200	59.51 223

TransMVSNet (Re)		57.83 180	56.90 187	58.91 166	72.26 125	74.69 171	63.57 168	61.42 93	32.30 214	32.65 188	33.97 191	35.96 191	39.17 201	73.84 118	72.84 151	84.37 142	74.69 184

MDTV_nov1_ep13_2view		54.47 195	54.61 194	54.30 194	60.50 200	73.82 175	57.92 192	43.38 212	39.43 191	32.51 189	33.23 193	34.05 201	47.26 176	62.36 201	66.21 197	84.24 145	73.19 192

TinyColmap		52.66 199	50.09 210	55.65 185	59.72 204	64.02 214	57.15 194	52.96 179	40.28 184	32.51 189	32.42 195	20.97 229	56.65 146	63.95 197	65.15 201	74.91 214	63.87 215

UniMVSNet_NR-MVSNet		62.30 142	63.51 124	60.89 154	69.48 142	77.83 142	64.07 161	63.94 63	50.03 120	31.17 191	44.82 114	41.12 144	51.37 160	71.02 160	74.81 101	85.30 110	84.95 128

DU-MVS		60.87 161	61.82 149	59.76 160	66.69 173	75.87 161	64.07 161	61.96 84	49.31 124	31.17 191	42.76 123	36.95 183	51.37 160	69.67 176	73.20 147	83.30 160	84.95 128

ACMH	59.42 14	61.59 157	59.22 179	64.36 114	78.92 76	78.26 137	67.65 140	67.48 42	39.81 186	30.98 193	38.25 166	34.59 199	61.37 104	70.55 166	73.47 137	79.74 196	79.59 167
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
tmp_tt				16.09 237	13.07 242	8.12 245	13.61 242	2.08 240	55.09 104	30.10 194	40.26 144	22.83 227	5.35 239	29.91 234	25.25 236	32.33 238

UniMVSNet (Re)		60.62 162	62.93 134	57.92 171	67.64 168	77.90 141	61.75 176	61.24 95	49.83 122	29.80 195	42.57 126	40.62 158	43.36 190	70.49 168	73.27 144	83.76 152	85.81 123

tfpn1000		58.35 179	59.96 171	56.47 182	72.78 123	77.51 147	56.66 195	59.16 112	43.74 160	29.76 196	42.79 122	42.49 135	37.04 204	68.92 180	68.98 182	83.45 157	75.25 181

tfpn_n400		58.64 175	59.27 177	57.89 172	72.83 121	77.26 151	60.35 181	60.29 108	39.77 188	29.10 197	43.45 116	40.72 155	41.61 196	70.06 172	71.39 167	83.17 164	72.26 196

tfpnconf		58.64 175	59.27 177	57.89 172	72.83 121	77.26 151	60.35 181	60.29 108	39.77 188	29.10 197	43.45 116	40.72 155	41.61 196	70.06 172	71.39 167	83.17 164	72.26 196

tfpnview11		58.92 171	59.60 174	58.13 169	72.99 120	77.11 153	60.48 180	60.37 105	42.10 177	29.10 197	43.45 116	40.72 155	41.67 195	70.53 167	70.43 177	84.17 148	72.85 193

NR-MVSNet		61.08 160	62.09 147	59.90 158	71.96 127	75.87 161	63.60 167	61.96 84	49.31 124	27.95 200	42.76 123	33.85 203	48.82 169	74.35 109	74.05 131	85.13 113	84.45 130

TranMVSNet+NR-MVSNet		60.38 164	61.30 155	59.30 163	68.34 149	75.57 166	63.38 170	63.78 66	46.74 141	27.73 201	42.56 127	36.84 184	47.66 173	70.36 169	74.59 107	84.91 123	82.46 150

Baseline_NR-MVSNet		59.47 168	60.28 165	58.54 168	66.69 173	73.90 174	61.63 177	62.90 80	49.15 132	26.87 202	35.18 189	37.62 179	48.20 171	69.67 176	73.61 135	84.92 121	82.82 147

Vis-MVSNet (Re-imp)		62.25 143	68.74 88	54.68 189	73.70 113	78.74 132	56.51 196	57.49 131	55.22 103	26.86 203	54.56 71	61.35 72	31.06 206	73.10 123	74.90 99	82.49 173	83.31 140

test0.0.03 1		57.35 183	59.89 172	54.38 191	71.37 129	73.45 176	52.71 201	61.03 96	46.11 148	26.33 204	41.73 134	44.08 131	29.72 209	71.43 159	70.90 172	85.10 114	71.56 200

N_pmnet		47.67 212	47.00 217	48.45 206	54.72 215	62.78 216	46.95 212	51.25 186	36.01 202	26.09 205	26.59 214	25.93 224	35.50 205	55.67 218	59.01 215	76.22 210	63.04 216

SixPastTwentyTwo		49.11 209	49.22 212	48.99 203	58.54 210	64.14 213	47.18 211	47.75 199	31.15 216	24.42 206	41.01 140	26.55 219	44.04 189	54.76 222	58.70 217	71.99 221	68.21 205

ambc			42.30 222		50.36 223	49.51 230	35.47 227		32.04 215	23.53 207	17.36 229	8.95 240	29.06 211	64.88 192	56.26 222	61.29 229	67.12 208

FPMVS		39.11 223	36.39 228	42.28 216	55.97 213	45.94 234	46.23 214	41.57 219	35.73 203	22.61 208	23.46 218	19.82 231	28.32 216	43.57 229	40.67 232	58.96 230	45.54 228

PMVS	27.44 18	32.08 228	29.07 231	35.60 227	48.33 230	24.79 239	26.97 236	41.34 221	20.45 233	22.50 209	17.11 231	18.64 233	20.44 225	41.99 232	38.06 233	54.02 235	42.44 232
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Anonymous20231206		52.23 200	52.80 202	51.56 198	64.70 186	69.41 198	51.01 203	58.60 116	36.63 197	22.44 210	21.80 223	31.42 210	30.52 207	66.79 186	67.83 188	82.10 178	75.73 179

CVMVSNet		54.92 191	58.16 182	51.13 200	62.61 194	68.44 201	55.45 198	52.38 182	42.28 176	21.45 211	47.10 105	46.10 125	37.96 202	64.42 196	63.81 204	76.92 209	75.01 183

MDA-MVSNet-bldmvs		44.15 217	42.27 224	46.34 211	38.34 234	62.31 217	46.28 213	55.74 150	29.83 218	20.98 212	27.11 213	16.45 235	41.98 193	41.11 233	57.47 219	74.72 215	61.65 221

pmmvs3		41.86 221	42.29 223	41.36 217	39.80 232	52.66 227	38.93 225	35.85 234	23.40 229	20.22 213	19.30 225	20.84 230	40.56 198	55.98 217	58.79 216	72.80 219	65.03 213

testpf		43.39 218	47.17 216	38.98 221	65.58 182	47.38 233	36.09 226	31.67 235	36.97 195	19.47 214	33.01 194	35.62 196	23.61 221	50.86 227	56.08 223	57.48 232	70.27 204

1111		38.93 224	38.98 225	38.86 223	50.10 224	50.42 228	29.52 231	38.00 229	22.67 230	17.99 215	17.40 227	26.26 221	28.72 214	54.86 220	58.20 218	68.82 225	43.08 231

.test1245		25.86 231	24.56 234	27.39 232	50.10 224	50.42 228	29.52 231	38.00 229	22.67 230	17.99 215	17.40 227	26.26 221	28.72 214	54.86 220	0.05 239	0.01 243	0.24 240

testgi		48.51 210	50.53 208	46.16 212	64.78 184	67.15 205	41.54 221	54.81 161	29.12 220	17.03 217	32.07 198	31.98 207	20.15 226	65.26 190	67.00 193	78.67 203	61.10 222

test2356		46.29 215	47.37 215	45.03 215	54.38 216	57.99 223	42.03 220	50.32 188	30.78 217	16.65 218	27.40 212	23.70 226	29.86 208	61.20 204	64.31 203	76.93 208	66.22 210

CP-MVSNet		50.57 203	52.60 204	48.21 207	58.77 208	65.82 208	48.17 208	56.29 144	37.41 194	16.59 219	37.14 175	31.95 208	29.21 210	56.60 214	63.71 205	80.22 192	75.56 180

LTVRE_ROB	47.26 16	49.41 208	49.91 211	48.82 204	64.76 185	69.79 197	49.05 205	47.12 202	20.36 234	16.52 220	36.65 181	26.96 218	50.76 164	60.47 205	63.16 207	64.73 227	72.00 198
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
PS-CasMVS		50.17 204	52.02 205	48.02 208	58.60 209	65.54 209	48.04 209	56.19 146	36.42 200	16.42 221	35.68 186	31.33 211	28.85 212	56.42 216	63.54 206	80.01 193	75.18 182

PEN-MVS		51.04 201	52.94 200	48.82 204	61.45 197	66.00 207	48.68 207	57.20 137	36.87 196	15.36 222	36.98 177	32.72 206	28.77 213	57.63 211	66.37 195	81.44 184	74.00 187

test20.03		47.23 214	48.69 213	45.53 214	63.28 190	64.39 211	41.01 222	56.93 140	29.16 219	15.21 223	23.90 216	30.76 213	17.51 231	64.63 194	65.26 199	79.21 201	62.71 218

WR-MVS		51.02 202	54.56 195	46.90 210	63.84 188	69.23 199	44.78 217	56.38 143	38.19 193	14.19 224	37.38 173	36.82 185	22.39 222	60.14 206	66.20 198	79.81 195	73.95 188

EU-MVSNet		44.84 216	47.85 214	41.32 219	49.26 226	56.59 224	43.07 219	47.64 201	33.03 211	13.82 225	36.78 179	30.99 212	24.37 220	53.80 223	55.57 225	69.78 222	68.21 205

gm-plane-assit		54.99 189	57.99 185	51.49 199	69.27 143	54.42 225	32.32 229	42.59 216	21.18 232	13.71 226	23.61 217	43.84 132	60.21 118	87.09 4	86.55 4	90.81 4	89.28 78

WR-MVS_H		49.62 207	52.63 203	46.11 213	58.80 207	67.58 203	46.14 215	54.94 157	36.51 199	13.63 227	36.75 180	35.67 195	22.10 223	56.43 215	62.76 208	81.06 186	72.73 194

gg-mvs-nofinetune		62.34 140	66.19 106	57.86 174	76.15 101	88.61 36	71.18 113	41.24 224	25.74 226	13.16 228	22.91 221	63.97 62	54.52 153	85.06 13	85.25 10	90.92 3	91.78 53

DTE-MVSNet		49.82 206	51.92 206	47.37 209	61.75 196	64.38 212	45.89 216	57.33 136	36.11 201	12.79 229	36.87 178	31.93 209	25.73 218	58.01 208	65.22 200	80.75 189	70.93 203

new-patchmatchnet		42.21 220	42.97 221	41.33 218	53.05 221	59.89 219	39.38 223	49.61 190	28.26 222	12.10 230	22.17 222	21.54 228	19.22 227	50.96 226	56.04 224	74.61 216	61.92 220

testus		42.30 219	43.69 219	40.67 220	53.21 220	53.50 226	31.81 230	49.96 189	27.06 223	11.55 231	25.67 215	19.00 232	25.20 219	55.34 219	62.59 209	72.31 220	62.69 219

MIMVSNet1		40.84 222	43.46 220	37.79 224	32.14 236	58.92 221	39.24 224	50.83 187	27.00 224	11.29 232	16.76 234	26.53 220	17.75 230	57.14 213	61.12 214	75.46 212	56.78 226

Gipuma		24.91 232	24.61 233	25.26 233	31.47 237	21.59 240	18.06 238	37.53 231	25.43 227	10.03 233	4.18 241	4.25 243	14.85 233	43.20 230	47.03 229	39.62 237	26.55 236
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new_pmnet		33.19 227	35.52 229	30.47 228	27.55 240	45.31 235	29.29 233	30.92 236	29.00 221	9.88 234	18.77 226	17.64 234	26.77 217	44.07 228	45.98 230	58.41 231	47.87 227

testmv		37.40 225	37.95 226	36.76 225	48.97 227	49.33 231	28.65 234	46.74 203	18.34 235	7.68 235	16.80 232	14.47 236	19.18 228	51.72 224	56.93 220	69.36 223	58.09 224

test1235678		37.40 225	37.94 227	36.76 225	48.97 227	49.30 232	28.65 234	46.73 204	18.33 236	7.68 235	16.79 233	14.46 237	19.18 228	51.72 224	56.92 221	69.36 223	58.07 225

FC-MVSNet-test		47.24 213	54.37 196	38.93 222	59.49 205	58.25 222	34.48 228	53.36 172	45.66 150	6.66 237	50.62 84	42.02 136	16.62 232	58.39 207	61.21 213	62.99 228	64.40 214

DeepMVS_CX						19.81 242	17.01 240	10.02 239	23.61 228	5.85 238	17.21 230	8.03 242	21.13 224	22.60 237		21.42 241	30.01 234

test12356		29.92 229	31.49 230	28.08 229	38.46 233	37.74 237	21.36 237	40.17 225	16.83 237	5.61 239	15.66 235	11.48 238	6.60 238	42.01 231	51.23 228	56.29 233	45.52 229

MVE	15.98 19	14.37 236	16.36 236	12.04 238	7.72 243	20.24 241	5.90 245	29.05 237	8.28 242	3.92 240	4.72 240	2.42 244	9.57 237	18.89 238	31.46 235	16.07 242	28.53 235
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
no-one		26.96 230	26.51 232	27.49 231	37.87 235	39.14 236	17.12 239	41.31 222	12.02 239	3.68 241	8.04 237	8.42 241	10.67 236	28.11 235	45.96 231	54.27 234	43.89 230

E-PMN		15.08 234	11.65 237	19.08 234	28.73 238	12.31 243	6.95 244	36.87 233	10.71 241	3.63 242	5.13 238	2.22 246	13.81 235	11.34 239	18.50 237	24.49 239	21.32 237

EMVS		14.40 235	10.71 238	18.70 235	28.15 239	12.09 244	7.06 243	36.89 232	11.00 240	3.56 243	4.95 239	2.27 245	13.91 234	10.13 240	16.06 238	22.63 240	18.51 238

PMMVS2		20.45 233	22.31 235	18.27 236	20.52 241	26.73 238	14.85 241	28.43 238	13.69 238	0.79 244	10.35 236	9.10 239	3.83 240	27.64 236	32.87 234	41.17 236	35.81 233

GG-mvs-BLEND		54.54 194	77.58 43	27.67 230	0.03 244	90.09 26	77.20 61	0.02 241	66.83 65	0.05 245	59.90 60	73.33 31	0.04 241	78.40 68	79.30 59	88.65 18	95.20 20

sosnet-low-res		0.00 239	0.00 241	0.00 241	0.00 245	0.00 248	0.00 249	0.00 243	0.00 245	0.00 246	0.00 244	0.00 248	0.00 244	0.00 243	0.00 242	0.00 245	0.00 242

sosnet		0.00 239	0.00 241	0.00 241	0.00 245	0.00 248	0.00 249	0.00 243	0.00 245	0.00 246	0.00 244	0.00 248	0.00 244	0.00 243	0.00 242	0.00 245	0.00 242

testmvs		0.05 237	0.08 239	0.01 239	0.00 245	0.01 246	0.03 247	0.01 242	0.05 243	0.00 246	0.14 243	0.01 247	0.03 243	0.05 241	0.05 239	0.01 243	0.24 240

test123		0.05 237	0.08 239	0.01 239	0.00 245	0.01 246	0.01 248	0.00 243	0.05 243	0.00 246	0.16 242	0.00 248	0.04 241	0.02 242	0.05 239	0.00 245	0.26 239

Anonymous202405211			66.35 105		78.00 80	84.41 70	74.85 74	63.18 70	51.00 117		31.37 202	53.73 106	69.67 56	76.28 83	76.84 77	83.21 163	90.85 61

our_test_3					63.32 189	71.07 196	55.90 197

Patchmatch-RL test							2.17 246

mPP-MVS					86.96 38							70.61 44

NP-MVS									81.60 32

High-resolution multi-view benchmark