High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

ESAPD		95.11 1	95.65 1	94.48 1	97.96 3	98.62 1	96.45 1	92.82 2	96.24 3	90.25 5	96.16 2	93.09 1	93.32 3	93.93 12	92.02 18	96.07 19	99.50 3

HSP-MVS		94.69 2	95.39 2	93.88 4	96.78 13	98.11 5	94.75 7	90.91 9	96.89 2	89.12 10	96.98 1	89.47 8	94.76 1	95.24 2	93.29 9	96.98 7	97.73 29

CNVR-MVS		94.53 3	94.85 4	94.15 3	98.03 2	98.59 2	95.56 3	92.91 1	94.86 8	88.46 11	91.32 17	90.83 5	94.03 2	95.20 3	94.16 4	95.89 24	99.01 12

APDe-MVS		94.31 4	94.30 7	94.33 2	97.57 6	98.06 7	95.79 2	91.98 5	95.50 6	92.19 1	95.25 3	87.97 13	92.93 4	93.01 19	91.02 34	95.52 28	99.29 5

MCST-MVS		94.10 5	94.77 5	93.31 6	98.31 1	98.34 3	95.43 4	92.54 3	94.41 12	83.05 26	91.38 15	90.97 4	92.24 9	95.05 5	94.02 5	98.31 1	99.20 7

HPM-MVS++		94.04 6	94.96 3	92.96 8	97.93 4	97.71 12	94.65 9	91.01 8	95.91 4	87.43 13	93.52 8	92.63 2	92.29 8	94.22 11	92.34 15	94.47 47	98.37 21

NCCC		93.59 7	94.00 9	93.10 7	97.90 5	97.93 9	95.40 5	92.39 4	94.47 11	84.94 17	91.21 18	89.32 9	92.53 6	93.90 13	92.98 11	95.44 30	98.22 23

APD-MVS		93.47 8	93.44 12	93.50 5	97.06 9	97.09 21	95.27 6	91.47 6	95.71 5	89.57 7	93.66 6	86.28 18	92.81 5	92.06 26	90.70 36	94.83 43	98.60 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		93.36 9	94.33 6	92.22 10	94.68 37	97.89 11	94.56 10	90.89 10	94.80 9	90.04 6	93.53 7	90.14 6	89.78 16	92.74 21	92.17 16	93.35 96	99.07 10

TSAR-MVS + MP.		93.07 10	93.53 11	92.53 9	94.23 40	97.54 16	94.75 7	89.87 12	95.26 7	89.20 9	93.16 9	88.19 12	92.15 10	91.79 30	89.65 47	94.99 39	99.16 8

SteuartSystems-ACMMP		92.31 11	93.31 13	91.15 17	96.88 11	97.36 17	93.95 15	89.44 14	92.62 21	83.20 23	94.34 5	85.55 20	88.95 23	93.07 18	91.90 22	94.51 45	98.30 22
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_Plus		92.16 12	92.91 16	91.28 16	96.95 10	97.36 17	93.66 16	89.23 16	93.33 16	83.71 21	90.53 19	86.84 15	90.39 13	93.30 17	91.56 27	93.74 64	97.43 36

HFP-MVS		92.02 13	92.13 18	91.89 14	97.16 8	96.46 34	93.57 17	87.60 20	93.79 14	88.17 12	93.15 10	83.94 31	91.19 12	90.81 39	89.83 42	93.66 69	96.94 53

train_agg		91.99 14	93.71 10	89.98 22	96.42 22	97.03 23	94.31 13	89.05 17	93.33 16	77.75 40	95.06 4	88.27 11	88.38 29	92.02 27	91.41 29	94.00 54	98.84 15

DeepC-MVS_fast	86.59 2	91.69 15	91.39 21	92.05 13	97.43 7	96.92 26	94.05 14	90.23 11	93.31 19	83.19 24	77.91 38	84.23 27	92.42 7	94.62 8	94.83 2	95.00 38	97.88 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MPTG		91.59 16	91.12 22	92.13 11	96.76 14	96.68 29	93.39 18	88.00 19	93.63 15	90.76 4	83.97 31	85.33 22	89.89 15	91.60 32	89.65 47	94.00 54	96.97 51

TSAR-MVS + GP.		91.29 17	93.11 15	89.18 28	87.81 84	96.21 40	92.51 27	83.83 39	94.24 13	83.77 20	91.87 14	89.62 7	90.07 14	90.40 42	90.31 38	97.09 6	99.10 9

ACMMPR		91.15 18	91.44 20	90.81 18	96.61 16	96.25 38	93.09 19	87.08 22	93.32 18	84.78 18	92.08 13	82.10 37	89.71 17	90.24 43	89.82 43	93.61 74	96.30 67

DeepPCF-MVS	86.71 1	91.00 19	94.05 8	87.43 39	95.58 31	98.17 4	86.22 66	88.59 18	97.01 1	76.77 45	85.11 29	88.90 10	87.29 34	95.02 6	94.69 3	90.15 185	99.48 4

TSAR-MVS + ACMM		90.98 20	93.18 14	88.42 33	95.69 29	96.73 28	94.52 11	86.97 25	92.99 20	76.32 46	92.31 12	86.64 16	84.40 56	92.97 20	92.02 18	92.62 137	98.59 18

MP-MVS		90.81 21	91.45 19	90.06 21	96.59 17	96.33 37	92.46 28	87.19 21	90.27 33	82.54 30	91.38 15	84.88 24	88.27 30	90.58 41	89.30 52	93.30 98	97.44 34

CP-MVS		90.57 22	90.68 24	90.44 19	96.13 24	95.90 45	92.77 24	86.86 26	92.12 24	84.19 19	89.18 23	82.37 35	89.43 21	89.65 51	88.43 56	93.27 100	97.13 45

MSLP-MVS++		90.33 23	88.82 33	92.10 12	96.52 20	95.93 41	94.35 12	86.26 27	88.37 46	89.24 8	75.94 43	82.60 34	89.71 17	89.45 53	92.17 16	96.51 14	97.24 41

CANet		89.98 24	90.42 28	89.47 27	94.13 41	98.05 8	91.76 33	83.27 42	90.87 30	81.90 33	72.32 50	84.82 25	88.42 27	94.52 9	93.78 7	97.34 4	98.58 19

PGM-MVS		89.97 25	90.64 26	89.18 28	96.53 19	95.90 45	93.06 20	82.48 50	90.04 35	80.37 35	92.75 11	80.96 42	88.93 24	89.88 48	89.08 53	93.69 68	95.86 73

PHI-MVS		89.88 26	92.75 17	86.52 49	94.97 34	97.57 15	89.99 44	84.56 35	92.52 22	69.72 74	90.35 20	87.11 14	84.89 49	91.82 29	92.37 14	95.02 37	97.51 32

CSCG		89.81 27	89.69 29	89.96 23	96.55 18	97.90 10	92.89 22	87.06 23	88.74 44	86.17 14	78.24 37	86.53 17	84.75 52	87.82 75	90.59 37	92.32 143	98.01 25

X-MVS		89.73 28	90.65 25	88.66 31	96.44 21	95.93 41	92.26 30	86.98 24	90.73 31	76.32 46	89.56 22	82.05 38	86.51 40	89.98 46	89.60 49	93.43 91	96.72 61

EPNet		89.30 29	90.89 23	87.44 38	95.67 30	96.81 27	91.13 36	83.12 44	91.14 27	76.31 50	87.60 25	80.40 45	84.45 54	92.13 25	91.12 33	93.96 57	97.01 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepC-MVS	84.14 3	88.80 30	88.03 37	89.71 25	94.83 35	96.56 30	92.57 26	89.38 15	89.25 41	79.59 37	70.02 59	77.05 55	88.24 31	92.44 23	92.79 12	93.65 72	98.10 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CDPH-MVS		88.76 31	90.43 27	86.81 45	96.04 26	96.53 33	92.95 21	85.95 29	90.36 32	67.93 79	85.80 28	80.69 43	83.82 57	90.81 39	91.85 25	94.18 50	96.99 50

3Dnovator+	81.14 5	88.59 32	87.49 39	89.88 24	95.83 28	96.45 36	91.94 32	82.41 51	87.09 50	85.94 16	62.80 87	85.37 21	89.46 19	91.51 33	91.89 24	93.72 66	97.30 39

ACMMP		88.48 33	88.71 34	88.22 35	94.61 38	95.53 49	90.64 40	85.60 31	90.97 28	78.62 39	89.88 21	74.20 66	86.29 41	88.16 73	86.37 76	93.57 76	95.86 73
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
AdaColmap		88.46 34	85.75 52	91.62 15	96.25 23	95.35 52	90.71 38	91.08 7	90.22 34	86.17 14	74.33 47	73.67 69	92.00 11	86.31 93	85.82 84	93.52 79	94.53 90

MVS_0304		88.43 35	89.46 30	87.21 40	91.85 53	97.60 13	92.62 25	81.10 57	87.16 49	73.80 55	72.19 52	83.36 33	87.03 35	94.64 7	93.67 8	96.88 8	97.64 31

3Dnovator	80.58 8	88.20 36	86.53 45	90.15 20	96.86 12	96.46 34	91.97 31	83.06 45	85.16 56	83.66 22	62.28 90	82.15 36	88.98 22	90.99 37	92.65 13	96.38 18	96.03 71

CPTT-MVS		88.17 37	87.84 38	88.55 32	93.33 43	93.75 63	92.33 29	84.75 34	89.87 37	81.72 34	83.93 32	81.12 41	88.45 26	85.42 103	84.07 103	90.72 177	96.72 61

MVS_111021_HR		87.82 38	88.84 32	86.62 47	94.42 39	97.36 17	88.21 53	83.26 43	83.42 59	72.52 63	82.63 33	76.93 56	84.95 48	91.93 28	91.15 32	96.39 17	98.49 20

DELS-MVS		87.75 39	86.92 43	88.71 30	94.69 36	97.34 20	92.78 23	84.50 36	77.87 76	81.94 32	67.17 64	75.49 61	82.84 62	95.38 1	95.93 1	95.55 27	99.27 6
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
MVSTER		87.68 40	89.12 31	86.01 51	88.11 81	90.05 105	89.28 47	77.05 79	91.37 25	79.97 36	76.70 41	85.25 23	84.89 49	93.53 14	91.41 29	96.73 10	95.55 78

MVS_111021_LR		87.58 41	88.67 35	86.31 50	92.58 47	95.89 47	86.20 67	82.49 49	89.08 43	77.47 42	86.20 27	74.22 65	85.49 45	90.03 45	88.52 54	93.66 69	96.74 60

QAPM		87.06 42	86.46 46	87.75 36	96.63 15	97.09 21	91.71 34	82.62 48	80.58 68	71.28 68	66.04 69	84.24 26	87.01 36	89.93 47	89.91 41	97.26 5	97.44 34

PVSNet_BlendedMVS		86.98 43	87.05 41	86.90 42	93.03 44	96.98 24	86.57 62	81.82 53	89.78 38	82.78 28	71.54 53	66.07 93	80.73 77	93.46 15	91.97 20	96.45 15	99.53 1

PVSNet_Blended		86.98 43	87.05 41	86.90 42	93.03 44	96.98 24	86.57 62	81.82 53	89.78 38	82.78 28	71.54 53	66.07 93	80.73 77	93.46 15	91.97 20	96.45 15	99.53 1

OMC-MVS		86.38 45	86.21 49	86.57 48	92.30 49	94.35 58	87.60 56	83.51 41	92.32 23	77.37 43	72.27 51	77.83 50	86.59 39	87.62 78	85.95 81	92.08 147	93.11 119

HQP-MVS		86.17 46	87.35 40	84.80 56	91.41 56	92.37 84	91.05 37	84.35 38	88.52 45	64.21 83	87.05 26	68.91 86	84.80 51	89.12 56	88.16 60	92.96 121	97.31 38

canonicalmvs		85.93 47	86.26 48	85.54 52	88.94 67	95.44 50	89.56 45	76.01 86	87.83 47	77.70 41	76.43 42	68.66 88	87.80 33	87.02 82	91.51 28	93.25 104	96.95 52

MAR-MVS		85.65 48	86.30 47	84.88 55	95.51 33	95.89 47	86.50 64	76.71 80	89.23 42	68.59 76	70.93 57	74.49 63	88.55 25	89.40 54	90.30 39	93.42 92	93.88 109
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
PCF-MVS	82.38 4	85.52 49	84.41 57	86.81 45	91.51 55	96.23 39	90.27 41	89.81 13	77.87 76	70.67 69	69.20 61	77.86 49	85.55 44	85.92 98	86.38 75	93.03 118	97.43 36
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CLD-MVS		85.43 50	84.24 58	86.83 44	87.69 87	93.16 71	90.01 43	82.72 47	87.17 48	79.28 38	71.43 56	65.81 95	86.02 42	87.33 80	86.96 69	95.25 34	97.83 28
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
OpenMVS	77.91 11	85.09 51	83.42 61	87.03 41	96.12 25	96.55 32	89.36 46	81.59 55	79.19 71	75.20 52	55.84 118	79.04 48	84.45 54	88.47 65	89.35 51	95.48 29	95.48 79

TSAR-MVS + COLMAP		84.93 52	85.79 51	83.92 59	90.90 58	93.57 66	89.25 48	82.00 52	91.29 26	61.66 88	88.25 24	59.46 114	86.71 38	89.79 49	87.09 66	93.01 119	91.09 135

TAPA-MVS	80.99 7	84.83 53	84.42 56	85.31 53	91.89 52	93.73 64	88.53 52	82.80 46	89.99 36	69.78 73	71.53 55	75.03 62	85.47 46	86.26 94	84.54 98	93.39 94	89.90 142
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PLC	81.02 6	84.81 54	81.81 75	88.31 34	93.77 42	90.35 100	88.80 50	84.47 37	86.76 51	82.17 31	66.56 66	71.01 79	88.41 28	85.48 101	84.28 101	92.26 145	88.21 167
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
CNLPA		84.72 55	82.14 71	87.73 37	92.85 46	93.83 62	84.70 84	85.07 32	90.90 29	83.16 25	56.28 114	71.53 75	88.14 32	84.19 111	84.00 106	92.48 140	94.26 95

MVS_Test		84.60 56	85.13 54	83.99 58	88.17 79	95.27 53	88.21 53	73.15 106	84.31 58	70.55 71	68.67 62	68.78 87	86.99 37	91.71 31	91.90 22	96.84 9	95.27 83

diffmvs		83.81 57	84.78 55	82.69 63	86.06 108	94.03 59	86.46 65	72.43 113	85.71 54	75.29 51	65.48 74	79.49 47	81.39 67	85.55 100	86.98 67	94.48 46	96.20 69

CANet_DTU		83.33 58	86.59 44	79.53 86	88.88 68	94.87 56	86.63 61	68.85 143	85.45 55	50.54 154	77.86 39	69.94 83	85.62 43	92.63 22	90.88 35	96.63 11	94.46 91

DI_MVS_plusplus_trai		83.32 59	82.53 69	84.25 57	86.26 104	93.66 65	90.23 42	77.16 78	77.05 83	74.06 54	53.74 127	74.33 64	83.61 59	91.40 35	89.82 43	94.17 51	97.73 29

DWT-MVSNet_training		82.66 60	83.34 64	81.87 67	88.71 69	92.63 76	82.07 99	72.21 115	86.37 52	72.64 58	64.51 78	71.44 77	80.35 80	84.43 109	87.73 62	95.27 31	96.25 68

PVSNet_Blended_VisFu		82.55 61	83.70 60	81.21 73	89.66 62	95.15 55	82.41 97	77.36 76	72.53 103	73.64 56	61.15 95	77.19 54	70.35 147	91.31 36	89.72 46	93.84 60	98.85 14

PMMVS		82.26 62	85.48 53	78.51 95	85.92 109	91.92 88	78.30 142	70.77 129	86.30 53	61.11 93	82.46 34	70.88 80	84.70 53	88.05 74	84.78 96	90.24 184	93.98 101

ACMP	79.58 9	82.23 63	81.82 74	82.71 62	88.15 80	90.95 97	85.23 76	78.52 62	81.70 65	72.52 63	78.41 36	60.63 109	80.48 79	82.88 121	83.44 111	91.37 165	94.70 87
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CHOSEN 280x420		82.15 64	85.87 50	77.80 98	86.54 98	93.42 68	81.74 100	59.96 197	78.99 73	63.99 84	74.50 46	83.95 30	80.99 73	89.53 52	85.01 89	93.56 78	95.71 77

LGP-MVS_train		82.12 65	82.57 68	81.59 68	89.26 66	90.23 102	88.76 51	78.05 66	81.26 66	61.64 89	79.52 35	62.11 104	79.59 84	85.20 104	84.68 97	92.27 144	95.02 85

FMVSNet3		81.93 66	81.98 72	81.88 66	79.49 139	87.02 128	88.15 55	72.57 109	83.02 61	72.63 60	56.55 110	73.48 70	82.34 65	91.49 34	91.20 31	96.07 19	91.13 134

OPM-MVS		81.34 67	78.18 95	85.02 54	91.27 57	91.78 90	90.66 39	83.62 40	62.39 140	65.91 80	63.35 84	64.33 100	85.03 47	87.77 76	85.88 83	93.66 69	91.75 132

IS_MVSNet		80.92 68	84.14 59	77.16 103	87.43 88	93.90 61	80.44 105	74.64 98	75.05 89	61.10 94	65.59 71	76.89 57	67.39 158	90.88 38	90.05 40	91.95 151	96.62 64

ACMM	78.09 10	80.91 69	78.39 93	83.86 60	89.61 65	87.71 122	85.16 77	80.67 58	79.04 72	74.18 53	63.82 82	60.84 108	82.59 63	84.33 110	83.59 109	90.96 172	89.39 150
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPP-MVSNet		80.82 70	82.79 66	78.52 93	86.31 103	92.37 84	79.83 113	74.51 99	73.79 98	64.46 82	67.01 65	80.63 44	74.33 108	85.63 99	84.35 100	91.68 157	95.79 76

conf0.002		80.80 71	80.30 81	81.38 71	88.59 70	93.19 70	85.12 78	78.10 64	70.15 109	61.55 90	63.30 85	62.66 103	81.11 68	88.74 62	86.94 70	93.79 62	97.15 43

CostFormer		80.72 72	81.81 75	79.44 88	86.50 99	91.65 92	84.31 86	59.84 198	80.86 67	72.69 57	62.46 89	73.74 67	79.93 82	82.58 124	84.50 99	93.37 95	96.90 56

GBi-Net		80.72 72	80.49 79	81.00 78	78.18 143	86.19 146	86.73 58	72.57 109	83.02 61	72.63 60	56.55 110	73.48 70	80.99 73	86.57 87	86.83 71	94.89 40	90.77 136

test1		80.72 72	80.49 79	81.00 78	78.18 143	86.19 146	86.73 58	72.57 109	83.02 61	72.63 60	56.55 110	73.48 70	80.99 73	86.57 87	86.83 71	94.89 40	90.77 136

UGNet		80.71 75	83.09 65	77.93 97	87.02 92	92.71 74	80.28 109	76.53 82	73.83 97	71.35 67	70.07 58	73.71 68	58.93 185	87.39 79	86.97 68	93.48 88	96.94 53
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
tfpn111		80.42 76	79.77 86	81.18 74	88.42 73	92.55 80	85.12 78	77.94 68	70.15 109	61.00 97	74.56 44	51.22 135	81.11 68	88.23 67	84.80 92	93.50 84	96.90 56

CHOSEN 1792x2688		80.23 77	79.16 87	81.48 69	91.97 50	96.56 30	86.18 68	75.40 94	76.17 86	61.32 92	37.43 205	61.08 107	76.52 98	92.35 24	91.64 26	97.46 3	98.86 13

conf0.01		80.10 78	79.04 89	81.34 72	88.56 71	93.09 72	85.12 78	78.08 65	70.15 109	61.43 91	60.90 96	58.54 117	81.11 68	88.66 63	84.80 92	93.74 64	97.14 44

thres100view900		79.83 79	77.79 99	82.21 64	88.42 73	93.54 67	87.07 57	81.11 56	70.15 109	61.01 95	56.65 107	51.22 135	81.78 66	89.77 50	85.95 81	93.84 60	97.26 40

Effi-MVS+		79.80 80	80.04 82	79.52 87	85.53 110	93.31 69	85.28 74	70.68 131	74.15 93	58.79 107	62.03 92	60.51 110	83.37 60	88.41 66	86.09 80	93.49 87	95.80 75

FC-MVSNet-train		79.54 81	78.20 94	81.09 77	86.55 97	88.63 118	79.96 111	78.53 61	70.90 107	68.24 77	65.87 70	56.45 126	80.29 81	86.20 96	84.08 102	92.97 120	95.31 82

test-LLR		79.52 82	83.42 61	74.97 113	81.79 124	91.26 93	76.17 166	70.57 132	77.71 78	52.14 132	66.26 67	77.47 52	73.10 114	87.02 82	87.16 64	96.05 22	97.02 47

FMVSNet2		79.24 83	78.14 96	80.53 82	78.18 143	86.19 146	86.73 58	71.91 119	72.97 100	70.48 72	50.63 137	66.56 92	80.99 73	90.10 44	89.77 45	94.89 40	90.77 136

TESTMET0.1,1		79.15 84	83.42 61	74.18 123	79.81 137	91.26 93	76.17 166	67.83 154	77.71 78	52.14 132	66.26 67	77.47 52	73.10 114	87.02 82	87.16 64	96.05 22	97.02 47

tfpn200view9		79.05 85	77.21 101	81.18 74	88.42 73	92.55 80	85.12 78	77.94 68	70.15 109	61.01 95	56.65 107	51.22 135	81.11 68	88.23 67	84.80 92	93.50 84	96.90 56

conf200view11		79.04 86	77.21 101	81.18 74	88.42 73	92.55 80	85.12 78	77.94 68	70.15 109	61.00 97	56.65 107	51.22 135	81.11 68	88.23 67	84.80 92	93.50 84	96.90 56

thresconf0.02		78.87 87	80.50 78	76.96 104	87.88 83	91.71 91	82.90 96	78.51 63	67.91 118	50.85 147	74.56 44	69.93 84	67.32 159	86.86 85	85.65 85	94.32 49	86.89 175

PatchMatch-RL		78.75 88	76.47 109	81.41 70	88.53 72	91.10 95	78.09 146	77.51 75	77.33 80	71.98 65	64.38 80	48.10 151	82.55 64	84.06 112	82.35 125	89.78 187	87.97 169

LS3D		78.72 89	75.79 115	82.15 65	91.91 51	89.39 114	83.66 89	85.88 30	76.81 84	59.22 106	57.67 104	58.53 118	83.72 58	82.07 129	81.63 138	88.50 196	84.39 183

thres200		78.69 90	76.71 105	80.99 80	88.35 77	92.56 78	86.03 69	77.94 68	66.27 121	60.66 99	56.08 115	51.11 139	79.45 85	88.23 67	85.54 87	93.52 79	97.20 42

tpmp4_e23		78.57 91	78.48 92	78.68 91	85.38 112	89.14 116	84.69 85	60.32 196	78.81 74	70.65 70	57.89 102	65.54 96	79.63 83	80.09 148	83.24 114	91.41 164	94.63 89

IB-MVS	74.10 12	78.52 92	78.51 91	78.52 93	90.15 60	95.39 51	71.95 185	77.53 74	74.95 90	77.25 44	58.93 100	55.92 127	58.37 187	79.01 168	87.89 61	95.88 25	97.47 33
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
EPNet_dtu		78.49 93	81.96 73	74.45 120	92.57 48	88.74 117	82.98 91	78.83 60	83.28 60	44.64 192	77.40 40	67.73 89	53.98 197	85.44 102	84.91 90	93.71 67	86.22 177
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
thres400		78.39 94	76.39 110	80.73 81	88.02 82	92.94 73	84.77 83	78.88 59	65.20 129	59.70 103	55.20 120	50.85 140	79.45 85	88.81 59	84.81 91	93.57 76	96.91 55

UA-Net		78.30 95	80.92 77	75.25 112	87.42 89	92.48 83	79.54 125	75.49 93	60.47 146	60.52 100	68.44 63	84.08 29	57.54 188	88.54 64	88.45 55	90.96 172	83.97 188

Vis-MVSNet (Re-imp)		78.28 96	82.68 67	73.16 143	86.64 95	92.68 75	78.07 147	74.48 100	74.05 94	53.47 123	64.22 81	76.52 58	54.28 193	88.96 58	88.29 58	92.03 149	94.00 100

tfpn_ndepth		78.22 97	78.84 90	77.49 100	88.32 78	90.95 97	80.79 104	76.31 84	74.24 92	59.50 105	69.52 60	60.02 113	67.11 160	85.06 105	82.95 120	92.94 126	89.18 155

MSDG		78.11 98	73.17 133	83.86 60	91.78 54	86.83 133	85.25 75	86.02 28	72.84 101	69.69 75	51.43 134	54.00 132	77.61 90	81.95 133	82.27 127	92.83 133	82.91 193

HyFIR lowres test		78.08 99	76.81 103	79.56 85	90.77 59	94.64 57	82.97 92	69.85 136	69.81 115	59.53 104	33.52 210	64.66 97	78.97 87	88.77 61	88.38 57	95.27 31	97.86 27

test-mter		77.90 100	82.44 70	72.60 148	78.52 141	90.24 101	73.85 178	65.31 172	76.37 85	51.29 136	65.58 72	75.94 60	71.36 127	85.98 97	86.26 77	95.26 33	96.71 63

view600		77.68 101	75.68 116	80.01 83	87.72 85	92.57 77	83.79 87	77.95 67	64.41 132	58.72 108	54.32 125	50.54 141	78.25 88	88.23 67	83.13 116	93.64 73	96.59 65

thres600view7		77.66 102	75.67 117	79.98 84	87.71 86	92.56 78	83.79 87	77.94 68	64.41 132	58.69 109	54.32 125	50.54 141	78.23 89	88.23 67	83.06 118	93.52 79	96.55 66

MS-PatchMatch		77.47 103	76.48 108	78.63 92	89.89 61	90.42 99	85.42 73	69.53 138	70.79 108	60.43 101	50.05 139	70.62 82	70.66 140	86.71 86	82.54 122	95.86 26	84.23 184

tfpn		77.45 104	76.23 112	78.87 90	87.15 91	91.90 89	82.17 98	76.59 81	62.98 138	56.93 112	53.08 131	57.31 123	76.41 100	87.26 81	85.20 88	93.95 58	95.89 72

Fast-Effi-MVS+		77.37 105	76.68 106	78.17 96	82.84 121	89.94 106	81.47 102	68.01 151	72.99 99	60.26 102	55.07 121	53.20 133	82.99 61	86.47 92	86.12 79	93.46 89	92.98 122

Vis-MVSNet		77.24 106	79.99 85	74.02 127	84.62 115	93.92 60	80.33 108	72.55 112	62.58 139	55.25 117	64.45 79	69.49 85	57.00 189	88.78 60	88.21 59	94.36 48	92.54 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
view800		77.22 107	75.35 118	79.41 89	87.42 89	92.21 86	82.94 94	77.19 77	63.67 136	57.78 110	53.68 128	50.19 143	77.32 91	87.70 77	83.84 107	93.79 62	96.19 70

MDTV_nov1_ep13		77.20 108	80.04 82	73.90 129	82.22 122	90.14 103	79.25 131	61.52 190	78.63 75	56.98 111	65.52 73	72.80 73	73.05 116	80.93 141	83.20 115	90.36 181	89.05 157

EPMVS		77.16 109	79.08 88	74.92 114	86.73 93	91.98 87	78.62 138	55.44 208	79.43 69	56.59 114	61.24 94	70.73 81	76.97 95	80.59 144	81.43 150	95.15 36	88.17 168

tpm cat1		76.93 110	76.19 113	77.79 99	85.08 114	88.58 119	82.96 93	59.33 199	75.72 88	72.64 58	51.25 135	64.41 99	75.74 103	77.90 177	80.10 173	90.97 171	95.35 80

PatchmatchNet		76.85 111	80.03 84	73.15 144	84.08 117	91.04 96	77.76 151	55.85 207	79.43 69	52.74 128	62.08 91	76.02 59	74.56 106	79.92 149	81.41 151	93.92 59	90.29 141
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		76.80 112	76.33 111	77.35 102	84.07 118	84.11 167	81.54 101	68.52 145	66.17 122	61.74 87	57.84 103	64.31 101	74.88 104	83.48 119	86.21 78	93.34 97	92.16 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		76.57 113	76.78 104	76.32 107	80.94 131	89.75 110	82.94 94	72.64 108	59.01 157	62.95 86	58.60 101	62.67 102	66.91 162	86.26 94	87.20 63	91.57 159	93.97 102
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
tpmrst		76.27 114	77.65 100	74.66 116	86.13 106	89.53 113	79.31 130	54.91 209	77.19 82	56.27 115	55.87 117	64.58 98	77.25 92	80.85 142	80.21 170	94.07 52	95.32 81

dps		75.76 115	75.02 121	76.63 106	84.51 116	88.12 120	77.51 153	58.33 201	75.91 87	71.98 65	57.37 105	57.85 120	76.81 97	77.89 178	78.40 182	90.63 178	89.63 145

tfpn1000		75.39 116	76.18 114	74.47 119	86.71 94	90.10 104	77.57 152	74.78 96	68.76 117	53.33 124	63.57 83	58.37 119	60.84 181	83.80 115	81.24 155	93.58 75	87.42 171

tfpnview11		74.85 117	75.06 120	74.61 117	86.58 96	89.54 112	79.98 110	75.81 88	64.95 131	47.47 171	64.85 75	54.72 128	63.86 169	84.54 108	82.20 129	93.97 56	84.64 180

CR-MVSNet		74.84 118	77.91 97	71.26 170	81.77 126	85.52 155	78.32 140	54.14 211	74.05 94	51.09 140	50.00 140	71.38 78	70.77 137	86.48 90	84.03 104	91.46 163	93.92 105

Effi-MVS+-dtu		74.57 119	74.60 125	74.53 118	81.38 128	86.74 135	80.39 107	67.70 155	67.36 120	53.06 125	59.86 98	57.50 121	75.84 102	80.19 146	78.62 180	88.79 195	91.95 131

tfpn_n400		74.36 120	74.39 127	74.32 121	86.37 101	89.86 107	79.71 115	75.69 90	60.00 148	47.47 171	64.85 75	54.72 128	63.70 172	83.80 115	83.35 112	92.96 121	84.16 185

tfpnconf		74.36 120	74.39 127	74.32 121	86.37 101	89.86 107	79.71 115	75.69 90	60.00 148	47.47 171	64.85 75	54.72 128	63.70 172	83.80 115	83.35 112	92.96 121	84.16 185

RPSCF		74.27 122	73.24 132	75.48 111	81.01 130	80.18 191	76.24 165	72.37 114	74.84 91	68.24 77	72.47 49	67.39 90	73.89 109	71.05 205	69.38 214	81.14 221	77.37 204

FMVSNet1		74.26 123	71.95 138	76.95 105	74.28 191	83.94 169	83.61 90	69.99 134	57.08 162	65.08 81	42.39 186	57.41 122	76.98 94	86.57 87	86.83 71	91.77 156	89.42 148

conf0.05thres1000		74.20 124	71.44 141	77.43 101	86.09 107	89.85 109	80.82 103	75.79 89	53.51 186	54.71 118	44.37 163	49.78 144	74.67 105	85.02 106	83.47 110	92.49 139	94.10 98

GA-MVS		73.62 125	74.52 126	72.58 149	79.93 135	89.29 115	78.02 148	71.67 125	60.79 145	42.68 196	54.41 124	49.07 147	70.07 150	89.39 55	86.55 74	93.13 115	92.12 128

Fast-Effi-MVS+-dtu		73.56 126	75.32 119	71.50 166	80.35 133	86.83 133	79.72 114	58.07 202	67.64 119	44.83 189	60.28 97	54.07 131	73.59 113	81.90 135	82.30 126	92.46 141	94.18 96

tpm		73.50 127	74.85 122	71.93 160	83.19 120	86.84 132	78.61 139	55.91 206	65.64 124	48.90 163	56.30 113	61.09 106	72.31 118	79.10 167	80.61 169	92.68 135	94.35 94

RPMNet		73.46 128	77.85 98	68.34 178	81.71 127	85.52 155	73.83 179	50.54 219	74.05 94	46.10 181	53.03 132	71.91 74	66.31 164	83.55 118	82.18 130	91.55 161	94.71 86

USDC		73.43 129	72.31 136	74.73 115	80.86 132	86.21 144	80.42 106	71.83 121	71.69 105	46.94 175	59.60 99	42.58 188	76.47 99	82.66 123	81.22 157	91.88 153	82.24 198

pmmvs4		73.38 130	71.53 140	75.55 110	75.95 171	85.24 159	77.25 156	71.59 126	71.03 106	63.10 85	49.09 145	44.22 176	73.73 112	82.04 130	80.18 171	91.68 157	88.89 161

UniMVSNet_NR-MVSNet		73.11 131	72.59 134	73.71 131	76.90 152	86.58 139	77.01 157	75.82 87	65.59 125	48.82 164	50.97 136	48.42 149	71.61 124	79.19 165	83.03 119	92.11 146	94.37 92

FMVSNet5		72.83 132	73.89 130	71.59 164	67.42 210	76.28 206	75.88 170	63.74 182	77.27 81	54.59 120	53.32 129	71.48 76	73.85 110	81.95 133	81.69 136	94.06 53	75.20 210

PatchT		72.66 133	76.58 107	68.09 180	79.02 140	86.09 150	59.81 208	51.78 217	72.00 104	51.09 140	46.84 150	66.70 91	70.77 137	86.48 90	84.03 104	96.07 19	93.92 105

ACMH	71.22 14	72.65 134	70.13 149	75.59 109	86.19 105	86.14 149	75.76 171	77.63 73	54.79 179	46.16 180	53.28 130	47.28 153	77.24 93	78.91 170	81.18 159	90.57 179	89.33 151
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IterMVS		72.43 135	74.05 129	70.55 174	80.34 134	81.17 186	77.44 154	61.00 192	63.57 137	46.82 177	55.88 116	59.09 116	65.03 166	83.15 120	83.83 108	92.67 136	91.65 133
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH+	72.14 13	72.38 136	69.34 160	75.93 108	85.21 113	84.89 162	76.96 160	76.04 85	59.76 150	51.63 135	50.37 138	48.69 148	76.90 96	76.06 186	78.69 178	88.85 194	86.90 174

DU-MVS		72.19 137	71.35 142	73.17 142	75.95 171	86.02 151	77.01 157	74.42 101	65.39 127	48.82 164	49.10 143	42.81 186	71.61 124	78.67 171	83.10 117	91.22 167	94.37 92

UniMVSNet (Re)		72.12 138	72.28 137	71.93 160	76.77 153	87.38 124	75.73 172	73.51 105	65.76 123	50.24 156	48.65 146	46.49 154	63.85 170	80.10 147	82.47 123	91.49 162	95.13 84

ADS-MVSNet		72.11 139	73.72 131	70.24 175	81.24 129	86.59 138	74.75 175	50.56 218	72.58 102	49.17 161	55.40 119	61.46 105	73.80 111	76.01 187	78.14 183	91.93 152	85.86 178

gg-mvs-nofinetune		72.10 140	74.79 123	68.97 177	83.31 119	95.22 54	85.66 72	48.77 221	35.68 221	22.17 230	30.49 214	77.73 51	76.37 101	94.30 10	93.03 10	97.55 2	97.05 46

TAMVS		72.06 141	71.76 139	72.41 153	76.68 155	88.12 120	74.82 174	68.09 150	53.52 185	56.91 113	52.94 133	56.93 125	66.91 162	81.37 138	82.44 124	91.07 169	86.99 173

v6		72.04 142	70.26 145	74.11 124	76.46 159	87.06 125	79.60 117	71.75 122	59.48 152	52.69 129	44.61 156	45.79 159	71.01 135	79.57 155	81.45 148	93.16 110	93.85 112

v1neww		72.02 143	70.23 147	74.10 125	76.45 160	87.06 125	79.59 120	71.75 122	59.35 153	52.60 130	44.59 158	45.74 160	71.06 132	79.57 155	81.46 146	93.16 110	93.84 113

v7new		72.02 143	70.23 147	74.10 125	76.45 160	87.06 125	79.59 120	71.75 122	59.35 153	52.60 130	44.59 158	45.74 160	71.06 132	79.57 155	81.46 146	93.16 110	93.84 113

v2v482		71.73 145	69.80 152	73.99 128	75.88 178	86.66 137	79.58 123	71.90 120	57.58 161	50.41 155	45.35 153	43.24 184	73.05 116	79.69 150	82.18 130	93.08 117	93.87 110

test0.0.03 1		71.70 146	74.68 124	68.23 179	81.79 124	83.81 170	68.64 191	70.57 132	68.81 116	43.47 193	62.77 88	60.09 112	51.77 203	82.48 125	81.67 137	93.16 110	83.13 191

V42		71.58 147	70.11 150	73.30 140	75.66 184	86.68 136	79.17 133	69.92 135	59.29 156	52.80 127	44.36 164	45.66 162	68.83 152	79.48 161	81.49 145	93.44 90	93.82 115

v1		71.54 148	69.71 153	73.66 134	76.08 165	86.88 129	79.60 117	72.06 118	57.00 164	50.75 151	44.23 167	44.79 165	70.61 142	79.62 151	81.52 141	92.88 130	93.93 103

v1141		71.53 149	69.69 154	73.68 132	76.08 165	86.86 130	79.59 120	72.07 117	57.01 163	50.78 149	44.23 167	44.70 168	70.68 139	79.61 153	81.52 141	92.89 127	93.92 105

divwei89l23v2f112		71.53 149	69.69 154	73.68 132	76.09 164	86.86 130	79.60 117	72.08 116	56.96 165	50.78 149	44.24 166	44.70 168	70.65 141	79.62 151	81.53 139	92.89 127	93.93 103

v7		71.49 151	69.98 151	73.25 141	75.89 176	86.45 140	79.44 128	69.29 141	58.07 159	50.08 157	43.87 174	43.67 178	71.94 120	82.03 132	81.70 134	92.88 130	94.04 99

NR-MVSNet		71.47 152	71.11 143	71.90 162	77.73 148	86.02 151	76.88 161	74.42 101	65.39 127	46.09 182	49.10 143	39.87 200	64.27 168	81.40 137	82.24 128	91.99 150	93.75 116

v8		71.42 153	69.69 154	73.43 138	76.45 160	85.12 161	79.53 126	67.47 158	59.34 155	52.90 126	44.60 157	45.82 158	71.05 134	79.56 158	81.45 148	93.17 108	91.96 130

v18		71.13 154	68.98 162	73.63 135	76.66 156	79.78 193	79.95 112	65.98 166	61.34 142	54.71 118	44.75 155	46.06 155	71.27 128	79.59 154	81.51 144	93.21 106	89.81 143

TranMVSNet+NR-MVSNet		71.12 155	70.24 146	72.15 157	76.01 169	84.80 164	76.55 163	75.65 92	61.99 141	45.29 185	48.42 147	43.07 185	67.55 156	78.28 174	82.83 121	91.85 154	92.29 125

v10		70.97 156	69.44 157	72.75 145	75.90 175	84.58 166	79.43 129	66.45 163	58.07 159	49.93 158	43.87 174	43.68 177	71.91 121	82.04 130	81.70 134	92.89 127	92.11 129

v1144		70.93 157	69.42 159	72.70 146	75.48 185	86.26 142	79.22 132	69.39 140	55.61 176	48.05 169	43.47 180	42.55 189	71.51 126	82.11 128	81.74 133	92.56 138	94.17 97

v16		70.93 157	68.76 166	73.47 137	76.60 157	79.66 195	79.57 124	65.81 169	60.85 143	54.44 121	44.50 162	45.90 157	71.15 129	79.50 159	81.39 152	93.27 100	89.51 147

v17		70.82 159	68.69 167	73.31 139	76.53 158	79.67 194	79.45 127	65.80 170	60.32 147	53.75 122	44.51 161	45.92 156	71.09 131	79.49 160	81.38 153	93.26 103	89.54 146

Baseline_NR-MVSNet		70.61 160	68.87 164	72.65 147	75.95 171	80.49 189	75.92 169	74.75 97	65.10 130	48.78 166	41.28 194	44.28 175	68.45 153	78.67 171	79.64 174	92.04 148	92.62 123

v148		70.34 161	68.46 169	72.54 151	76.04 168	86.38 141	74.83 173	72.73 107	55.88 175	55.26 116	43.32 183	43.49 179	64.52 167	76.93 184	80.11 172	91.85 154	93.11 119

v1192		70.32 162	68.77 165	72.12 159	74.76 187	85.62 154	78.73 136	68.53 144	55.08 178	46.34 179	42.39 186	40.67 196	71.90 122	82.27 126	81.53 139	92.43 142	93.86 111

v144192		70.10 163	68.55 168	71.90 162	74.55 188	85.67 153	77.81 149	68.22 149	54.65 180	46.91 176	42.76 184	41.27 195	70.95 136	80.48 145	81.11 164	92.96 121	93.90 108

pmmvs5		70.01 164	69.31 161	70.82 173	75.80 181	86.26 142	72.94 180	67.91 152	53.84 184	47.22 174	47.31 149	41.47 194	67.61 155	83.93 114	81.93 132	93.42 92	90.42 140

v15		70.00 165	67.82 174	72.55 150	76.06 167	79.37 197	79.10 134	65.30 173	56.89 166	51.18 138	43.96 173	44.76 166	70.52 144	79.40 162	81.22 157	93.13 115	89.14 156

V14		69.91 166	67.71 176	72.47 152	76.01 169	79.30 198	78.92 135	65.17 174	56.74 167	51.08 143	43.82 176	44.73 167	70.44 146	79.31 163	81.14 162	93.20 107	88.91 160

COLMAP_ROB	66.31 15	69.91 166	66.61 180	73.76 130	86.44 100	82.76 174	76.59 162	76.46 83	63.82 135	50.92 146	45.60 152	49.13 146	65.87 165	74.96 191	74.45 202	86.30 208	75.57 208
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
v1921920		69.85 168	68.38 170	71.58 165	74.35 189	85.39 157	77.78 150	67.88 153	54.64 181	45.39 184	42.11 189	39.97 199	71.10 130	81.68 136	81.17 161	92.96 121	93.69 118

v11		69.84 169	67.85 173	72.17 156	75.78 182	79.15 200	78.20 145	64.76 180	56.10 173	49.50 159	43.54 178	43.36 182	71.62 123	82.21 127	81.52 141	93.17 108	89.05 157

V9		69.79 170	67.57 177	72.38 154	75.95 171	79.21 199	78.72 137	65.06 175	56.51 169	51.06 144	43.66 177	44.70 168	70.28 148	79.22 164	81.06 165	93.24 105	88.67 164

v12		69.66 171	67.45 178	72.23 155	75.89 176	79.13 201	78.29 143	64.96 178	56.40 170	50.75 151	43.53 179	44.60 171	70.21 149	79.11 166	80.99 166	93.27 100	88.41 165

pm-mvs1		69.62 172	68.07 172	71.44 167	77.21 150	85.32 158	76.11 168	71.05 127	46.55 209	51.17 139	41.83 191	48.20 150	61.81 178	84.00 113	81.14 162	91.28 166	89.42 148

v13		69.55 173	67.33 179	72.14 158	75.83 179	79.04 202	78.22 144	64.85 179	56.16 172	50.60 153	43.43 181	44.56 172	70.05 151	79.01 168	80.92 168	93.28 99	88.22 166

tfpnnormal		69.29 174	65.58 182	73.62 136	79.87 136	84.82 163	76.97 159	75.12 95	45.29 211	49.03 162	35.57 208	37.20 209	68.02 154	82.70 122	81.24 155	92.69 134	92.20 126

v1240		69.28 175	67.82 174	71.00 172	74.09 192	85.13 160	76.54 164	67.28 160	53.17 187	44.70 190	41.55 193	39.38 201	70.51 145	81.29 139	81.18 159	92.88 130	93.02 121

CVMVSNet		68.95 176	70.79 144	66.79 186	79.69 138	83.75 171	72.05 184	70.90 128	56.20 171	36.30 207	54.94 123	59.22 115	54.03 196	78.33 173	78.65 179	87.77 202	84.44 182

MIMVSNet		68.66 177	69.43 158	67.76 181	64.92 215	84.68 165	74.16 176	54.10 213	60.85 143	51.27 137	39.47 198	49.48 145	67.48 157	84.86 107	85.57 86	94.63 44	81.10 199

TDRefinement		67.82 178	64.91 188	71.22 171	82.08 123	81.45 182	77.42 155	73.79 104	59.62 151	48.35 168	42.35 188	42.40 190	60.87 180	74.69 192	74.64 201	84.83 212	79.20 202

anonymousdsp		67.61 179	68.94 163	66.04 190	71.44 204	83.97 168	66.45 196	63.53 184	50.54 196	42.42 197	49.39 141	45.63 163	62.84 175	77.99 176	81.34 154	89.59 190	93.75 116

TinyColmap		67.16 180	63.51 198	71.42 168	77.94 146	79.54 196	72.80 181	69.78 137	56.58 168	45.52 183	44.53 160	33.53 218	74.45 107	76.91 185	77.06 190	88.03 201	76.41 205

FC-MVSNet-test		67.04 181	72.47 135	60.70 207	76.92 151	81.41 183	61.52 204	69.45 139	65.58 126	26.74 225	61.79 93	60.40 111	41.17 216	77.60 180	77.78 185	88.41 197	82.70 195

TransMVSNet (Re)		66.87 182	64.30 193	69.88 176	78.32 142	81.35 185	73.88 177	74.34 103	43.19 215	45.20 187	40.12 195	42.37 191	55.97 191	80.85 142	79.15 175	91.56 160	83.06 192

CMPMVS	50.59 17	66.74 183	62.72 202	71.42 168	85.40 111	89.72 111	72.69 182	70.72 130	51.24 192	51.75 134	38.91 201	44.40 173	63.74 171	70.84 206	71.52 206	84.19 213	72.45 215
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v7n		66.43 184	65.51 183	67.51 182	71.63 203	83.10 172	70.89 188	65.02 176	50.13 199	44.68 191	39.59 197	38.77 203	62.57 176	77.59 181	78.91 176	90.29 183	90.44 139

EG-PatchMatch MVS		66.23 185	65.20 185	67.43 183	77.74 147	86.20 145	72.51 183	63.68 183	43.95 213	43.44 194	36.22 207	45.43 164	54.04 195	81.00 140	80.95 167	93.15 114	82.67 196

v52		65.34 186	64.59 190	66.21 188	69.63 208	82.41 178	69.22 189	62.80 186	49.63 200	45.15 188	39.31 200	41.85 192	60.68 182	72.61 196	77.02 192	89.75 189	89.33 151

V4		65.34 186	64.59 190	66.21 188	69.64 207	82.42 177	69.22 189	62.80 186	49.60 201	45.21 186	39.33 199	41.82 193	60.66 183	72.61 196	77.03 191	89.76 188	89.32 153

v748		65.00 188	63.86 197	66.33 187	71.85 201	82.15 180	66.80 194	65.64 171	48.50 205	47.98 170	39.62 196	39.20 202	56.44 190	71.25 203	77.53 187	89.29 191	88.74 163

WR-MVS		64.98 189	66.59 181	63.09 199	74.34 190	82.68 175	64.98 202	69.17 142	54.42 182	36.18 208	44.32 165	44.35 174	44.65 206	73.60 193	77.83 184	89.21 193	88.96 159

gm-plane-assit		64.86 190	68.15 171	61.02 206	76.44 163	68.29 216	41.60 225	53.37 214	34.68 223	26.19 227	33.22 211	57.09 124	71.97 119	95.12 4	93.97 6	96.54 13	94.66 88

CP-MVSNet		64.84 191	64.97 186	64.69 194	72.09 198	81.04 187	66.66 195	67.53 157	52.45 189	37.40 203	44.00 172	38.37 205	53.54 198	72.26 200	76.93 193	90.94 174	89.75 144

MDTV_nov1_ep13_2view		64.72 192	64.94 187	64.46 195	71.14 205	81.94 181	67.53 192	54.54 210	55.92 174	43.29 195	44.02 171	43.27 183	59.87 184	71.85 202	74.77 200	90.36 181	82.82 194

MVS-HIRNet		64.63 193	64.03 196	65.33 192	75.01 186	82.84 173	58.54 212	52.10 216	55.42 177	49.29 160	29.83 217	43.48 180	66.97 161	78.28 174	78.81 177	90.07 186	79.52 201

LTVRE_ROB	63.07 16	64.49 194	63.16 201	66.04 190	77.47 149	82.64 176	70.98 187	65.02 176	34.01 224	29.61 217	49.12 142	35.58 214	70.57 143	75.10 190	78.45 181	82.60 216	87.24 172
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
PEN-MVS		64.35 195	64.29 194	64.42 196	72.67 194	79.83 192	66.97 193	68.24 148	51.21 193	35.29 210	44.09 169	38.51 204	52.36 201	71.06 204	77.65 186	90.99 170	87.68 170

pmmvs6		64.24 196	61.77 206	67.12 184	72.39 197	81.39 184	71.33 186	65.95 168	36.05 220	48.48 167	30.55 213	43.45 181	58.75 186	77.88 179	76.36 196	85.83 209	86.70 176

pmmvs-eth3d		64.24 196	61.96 204	66.90 185	66.35 212	76.04 208	66.09 198	66.31 164	52.59 188	50.94 145	37.61 203	32.79 220	62.43 177	75.78 188	75.48 198	89.27 192	83.39 190

PS-CasMVS		64.22 198	64.19 195	64.25 197	71.86 200	80.67 188	66.42 197	67.43 159	50.64 195	36.48 205	42.60 185	37.46 208	52.56 200	71.98 201	76.69 195	90.76 175	89.29 154

WR-MVS_H		64.14 199	65.36 184	62.71 201	72.47 196	82.33 179	65.13 199	66.99 161	51.81 191	36.47 206	43.33 182	42.77 187	43.99 208	72.41 199	75.99 197	91.20 168	88.86 162

SixPastTwentyTwo		63.75 200	63.42 199	64.13 198	72.91 193	80.34 190	61.29 205	63.90 181	49.58 202	40.42 200	54.99 122	37.13 210	60.90 179	68.46 210	70.80 209	85.37 211	82.65 197

PM-MVS		63.52 201	62.51 203	64.70 193	64.79 217	76.08 207	65.07 200	62.08 188	58.13 158	46.56 178	44.98 154	31.31 221	62.89 174	72.58 198	69.93 213	86.81 206	84.55 181

DTE-MVSNet		63.26 202	63.41 200	63.08 200	72.59 195	78.56 203	65.03 201	68.28 147	50.53 197	32.38 214	44.03 170	37.79 207	49.48 204	70.83 207	76.73 194	90.73 176	85.42 179

testgi		63.11 203	64.88 189	61.05 205	75.83 179	78.51 204	60.42 207	66.20 165	48.77 204	34.56 212	56.96 106	40.35 197	40.95 217	77.46 182	77.22 189	88.37 199	74.86 212

GG-mvs-BLEND		62.08 204	88.31 36	31.46 229	0.16 237	98.10 6	91.57 35	0.09 235	85.07 57	0.21 240	73.90 48	83.74 32	0.19 237	88.98 57	89.39 50	96.58 12	99.02 11

Anonymous20231206		62.05 205	61.83 205	62.30 203	72.09 198	77.84 205	63.10 203	67.62 156	50.20 198	36.68 204	29.59 218	37.05 211	43.90 209	77.33 183	77.31 188	90.41 180	83.49 189

N_pmnet		60.52 206	58.83 210	62.50 202	68.97 209	75.61 209	59.72 210	66.47 162	51.90 190	41.26 198	35.42 209	35.63 213	52.25 202	67.07 214	70.08 212	86.35 207	76.10 206

LP		59.72 207	58.23 211	61.44 204	75.67 183	74.97 210	61.05 206	48.34 222	54.02 183	40.82 199	31.61 212	36.92 212	54.69 192	67.52 212	71.18 208	88.08 200	71.42 218

testpf		59.38 208	64.51 192	53.40 215	76.71 154	66.40 218	50.18 219	38.98 232	64.13 134	35.10 211	47.91 148	51.41 134	43.16 210	66.37 215	71.23 207	76.25 224	84.14 187

EU-MVSNet		58.73 209	60.92 207	56.17 211	66.17 213	72.39 213	58.85 211	61.24 191	48.47 206	27.91 222	46.70 151	40.06 198	39.07 218	68.27 211	70.34 211	83.77 214	80.23 200

test2356		58.43 210	59.52 208	57.16 209	66.71 211	68.00 217	54.69 214	60.91 194	49.22 203	28.63 220	41.86 190	33.68 217	44.36 207	72.98 194	75.47 199	87.69 203	75.40 209

test20.03		57.93 211	59.22 209	56.44 210	71.84 202	73.78 212	53.55 216	65.96 167	43.02 216	28.46 221	37.50 204	38.17 206	30.41 225	75.25 189	74.42 203	88.41 197	72.37 216

testus		55.91 212	56.38 212	55.37 213	65.15 214	65.88 220	50.07 220	60.92 193	45.62 210	26.99 224	41.74 192	24.43 227	42.08 213	69.50 209	73.60 204	86.97 205	73.91 213

MDA-MVSNet-bldmvs		54.99 213	52.66 215	57.71 208	52.74 228	74.87 211	55.61 213	68.41 146	43.65 214	32.54 213	37.93 202	22.11 229	54.11 194	48.85 227	67.34 216	82.85 215	73.88 214

new-patchmatchnet		53.91 214	52.69 214	55.33 214	64.83 216	70.90 214	52.24 218	61.75 189	41.09 217	30.82 215	29.90 216	28.22 223	36.69 220	61.52 220	65.08 219	85.64 210	72.14 217

MIMVSNet1		52.76 215	53.95 213	51.38 218	41.96 232	70.79 215	53.56 215	63.03 185	39.36 218	27.83 223	22.73 227	33.07 219	34.47 222	70.49 208	72.69 205	87.41 204	68.51 219

pmmvs3		52.59 216	52.43 216	52.78 216	54.53 226	64.49 222	50.07 220	46.89 225	35.31 222	30.19 216	27.27 220	26.96 225	53.02 199	67.28 213	70.54 210	81.96 217	75.20 210

new_pmnet		50.32 217	51.36 217	49.11 219	49.19 229	64.89 221	48.66 223	47.99 224	47.55 207	26.27 226	29.51 219	28.66 222	44.89 205	61.12 221	62.74 223	77.66 223	65.03 222

FPMVS		50.25 218	45.67 223	55.58 212	70.48 206	60.12 223	59.78 209	59.33 199	46.66 208	37.94 201	30.22 215	27.51 224	35.94 221	50.98 226	47.90 226	70.02 227	56.31 224

Anonymous20231211		49.72 219	47.45 220	52.38 217	60.54 220	66.16 219	52.47 217	60.87 195	25.32 230	25.16 228	15.98 229	23.66 228	37.00 219	61.01 222	64.41 221	78.25 222	75.60 207

1111		48.34 220	47.93 219	48.83 220	58.14 222	59.33 225	37.54 226	43.85 226	31.76 225	29.36 218	23.26 224	34.58 215	42.20 211	65.15 216	68.72 215	81.86 218	52.66 227

testmv		46.89 221	46.37 221	47.48 221	60.96 218	58.36 227	36.71 228	56.94 203	27.16 228	17.93 232	23.94 222	18.84 231	31.06 223	61.55 218	66.72 217	81.28 219	68.05 220

test1235678		46.88 222	46.36 222	47.48 221	60.96 218	58.35 228	36.71 228	56.94 203	27.15 229	17.93 232	23.93 223	18.82 232	31.06 223	61.55 218	66.71 218	81.27 220	68.04 221

test12356		41.15 223	41.46 224	40.78 224	53.10 227	49.87 229	33.37 231	52.25 215	25.12 231	15.64 234	22.76 226	15.01 233	15.81 230	52.97 224	64.54 220	74.50 226	59.96 223

PMVS	36.83 18	40.62 224	36.39 225	45.56 223	58.40 221	33.20 233	32.62 232	56.02 205	28.25 227	37.92 202	22.29 228	26.15 226	25.29 227	48.49 228	43.82 229	63.13 230	52.53 228
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		35.20 225	33.96 226	36.65 226	43.30 231	32.51 234	26.96 234	48.31 223	38.87 219	20.08 231	8.08 232	7.41 237	26.44 226	53.60 223	58.43 224	54.81 232	38.79 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
.test1245		33.05 226	31.21 228	35.20 227	58.14 222	59.33 225	37.54 226	43.85 226	31.76 225	29.36 218	23.26 224	34.58 215	42.20 211	65.15 216	0.77 233	0.11 237	3.62 235

PMMVS2		32.52 227	33.92 227	30.88 230	34.15 235	44.70 232	27.79 233	39.69 231	22.21 232	4.31 239	15.73 230	14.13 234	12.45 234	40.11 229	47.00 227	66.88 228	53.54 225

no-one		32.08 228	31.09 229	33.23 228	46.10 230	46.90 231	20.80 235	49.13 220	16.27 233	7.85 236	10.62 231	10.68 235	13.65 233	31.50 231	51.31 225	61.83 231	50.38 229

E-PMN		21.42 229	17.56 231	25.94 231	36.25 234	19.02 237	11.56 236	43.72 229	15.25 235	6.99 237	8.04 233	4.53 239	21.77 229	16.13 233	26.16 231	35.34 234	33.77 232

MVE	25.07 19	21.25 230	23.51 230	18.62 233	15.07 236	29.77 236	10.67 238	34.60 233	12.51 236	9.46 235	7.84 234	3.82 240	14.38 232	27.45 232	42.42 230	27.56 236	40.74 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		20.61 231	16.32 232	25.62 232	36.41 233	18.93 238	11.51 237	43.75 228	15.65 234	6.53 238	7.56 235	4.68 238	22.03 228	14.56 234	23.10 232	33.51 235	29.77 233

testmvs		0.76 232	1.23 233	0.21 234	0.05 238	0.21 239	0.38 240	0.09 235	0.94 237	0.05 241	2.13 237	0.08 241	0.60 236	0.82 235	0.77 233	0.11 237	3.62 235

test123		0.67 233	1.11 234	0.16 235	0.01 239	0.14 240	0.20 241	0.04 237	0.77 238	0.02 242	2.15 236	0.02 242	0.61 235	0.23 236	0.72 235	0.07 239	3.76 234

sosnet-low-res		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 243	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

sosnet		0.00 234	0.00 235	0.00 236	0.00 240	0.00 241	0.00 242	0.00 238	0.00 239	0.00 243	0.00 238	0.00 243	0.00 238	0.00 237	0.00 236	0.00 240	0.00 237

ambc			50.35 218		55.61 225	59.93 224	48.73 222		44.08 212	35.81 209	24.01 221	10.64 236	41.57 215	72.83 195	63.35 222	74.99 225	77.61 203

MTAPA										91.14 2		85.84 19

MTMP										90.95 3		84.13 28

Patchmatch-RL test							8.17 239

tmp_tt				39.78 225	56.31 224	31.71 235	35.84 230	15.08 234	82.57 64	50.83 148	63.07 86	47.51 152	15.28 231	52.23 225	44.24 228	65.35 229

XVS					89.65 63	95.93 41	85.97 70			76.32 46		82.05 38				93.51 82

X-MVStestdata					89.65 63	95.93 41	85.97 70			76.32 46		82.05 38				93.51 82

abl_6				89.54 26	95.55 32	97.59 14	89.01 49	85.00 33	94.67 10	83.04 27	84.70 30	91.47 3	89.46 19			95.20 35	98.63 16

mPP-MVS					95.90 27							80.22 46

NP-MVS									89.55 40

Patchmtry						87.41 123	78.32 140	54.14 211		51.09 140

DeepMVS_CX						48.96 230	43.77 224	40.58 230	50.93 194	24.67 229	36.95 206	20.18 230	41.60 214	38.92 230		52.37 233	53.31 226

High-resolution multi-view benchmark