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		97.61 1	98.19 2	96.94 3	99.03 2	99.49 2	99.00 1	95.35 1	97.97 5	92.21 10	97.50 3	99.73 1	96.95 3	97.13 9	95.61 21	99.11 6	99.87 4

HSP-MVS		97.61 1	98.30 1	96.81 4	98.66 9	99.35 4	98.00 8	94.75 8	98.45 2	92.78 5	97.99 1	98.58 4	97.41 2	98.24 1	96.48 9	99.27 4	98.99 43

CNVR-MVS		97.60 3	98.08 3	97.03 2	99.14 1	99.55 1	98.67 2	95.32 2	97.91 6	92.55 7	97.11 5	97.23 8	97.49 1	98.16 2	97.05 4	99.04 11	99.55 15

APDe-MVS		97.31 4	97.51 8	97.08 1	98.95 6	99.29 8	98.58 4	95.11 3	97.69 12	94.16 1	96.91 8	96.81 12	96.57 5	96.71 15	95.39 24	99.08 10	99.79 6

NCCC		97.01 5	97.74 5	96.16 7	99.02 3	99.35 4	98.63 3	95.04 4	97.84 9	88.95 20	96.83 10	97.02 11	96.39 9	97.44 6	96.51 8	98.90 20	99.16 35

MCST-MVS		96.93 6	98.07 4	95.61 14	98.98 4	99.44 3	98.04 7	95.04 4	98.10 3	86.55 27	97.65 2	97.56 6	95.60 18	97.67 5	96.45 10	99.43 1	99.61 14

HPM-MVS++		96.91 7	97.70 6	96.00 9	98.97 5	99.16 11	97.82 14	94.81 7	98.04 4	89.61 17	96.56 12	98.60 3	96.39 9	97.09 10	95.22 26	98.39 40	99.22 29

SD-MVS		96.87 8	97.69 7	95.92 10	96.38 41	99.25 9	97.76 15	94.75 8	97.72 10	92.46 9	95.94 13	99.09 2	96.48 8	96.01 23	96.08 15	97.68 86	99.73 9

APD-MVS		96.79 9	96.99 13	96.56 5	98.76 8	98.87 20	98.42 5	94.93 6	97.70 11	91.83 11	95.52 16	95.94 16	96.63 4	95.94 24	95.47 22	98.80 24	99.47 17
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		96.50 10	97.08 11	95.82 12	96.12 45	98.97 17	98.00 8	94.13 14	97.89 7	91.49 12	95.11 21	97.52 7	96.26 13	96.27 21	94.07 45	98.91 19	99.74 8

SteuartSystems-ACMMP		96.20 11	97.22 10	95.01 18	98.40 16	99.11 12	97.93 11	93.62 17	96.28 22	87.45 23	97.05 7	96.00 15	94.23 24	96.83 14	95.97 16	98.40 39	99.27 25
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		96.09 12	96.41 18	95.72 13	98.58 12	98.84 21	97.95 10	93.08 21	96.96 16	90.24 15	96.60 11	94.40 24	96.52 7	95.13 33	94.33 39	97.93 74	98.59 62

MPTG		95.87 13	95.63 24	96.15 8	98.60 11	98.83 22	97.89 12	93.65 16	96.24 23	93.08 4	91.13 30	95.46 21	95.72 17	95.64 25	93.67 52	97.97 71	98.46 68

ACMMP_Plus		95.81 14	96.50 17	95.01 18	98.79 7	99.17 10	97.52 20	94.20 13	96.19 24	85.71 31	93.80 26	96.20 14	95.89 14	96.62 17	94.98 32	97.93 74	98.52 65

train_agg		95.72 15	97.37 9	93.80 23	97.82 25	98.92 18	97.84 13	93.50 18	96.86 18	81.35 46	97.10 6	97.71 5	94.19 25	96.02 22	95.37 25	98.07 58	99.64 12

ACMMPR		95.59 16	95.89 20	95.25 16	98.41 15	98.74 24	97.69 18	92.73 25	96.88 17	88.95 20	95.33 18	92.91 31	95.79 15	94.73 43	94.33 39	97.92 77	98.32 73

DeepC-MVS_fast	91.53 1	95.57 17	95.67 22	95.45 15	98.57 13	99.00 16	97.76 15	94.41 11	97.06 14	86.84 26	86.39 41	92.27 36	96.38 11	97.89 4	98.06 2	98.73 30	99.01 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MSLP-MVS++		95.49 18	94.84 28	96.25 6	98.64 10	98.63 28	98.35 6	92.37 27	95.04 40	92.62 6	87.12 38	93.79 25	96.55 6	93.53 54	96.78 5	98.98 15	98.99 43

CP-MVS		95.43 19	95.67 22	95.14 17	98.24 21	98.60 29	97.45 21	92.80 23	95.98 27	89.21 19	95.22 19	93.60 26	95.43 19	94.37 47	93.22 57	97.68 86	98.72 54

MP-MVS		95.24 20	95.96 19	94.40 21	98.32 18	98.38 43	97.12 23	92.87 22	95.17 38	85.50 32	95.68 14	94.91 22	94.58 22	95.11 34	93.76 49	98.05 61	98.68 56

TSAR-MVS + ACMM		94.99 21	97.02 12	92.61 34	97.19 31	98.71 26	97.74 17	93.21 20	96.97 15	79.27 58	94.09 24	97.14 9	90.84 56	96.64 16	95.94 17	97.42 100	99.67 11

X-MVS		94.70 22	95.71 21	93.52 27	98.38 17	98.56 31	96.99 24	92.62 26	95.58 31	81.00 52	94.57 23	93.49 27	94.16 27	94.82 39	94.29 41	97.99 70	98.68 56

PGM-MVS		94.64 23	95.49 25	93.66 25	98.55 14	98.51 37	97.63 19	87.77 41	94.45 44	84.92 35	97.23 4	91.90 38	95.22 20	94.56 45	93.80 48	97.87 81	97.97 81

TSAR-MVS + GP.		94.59 24	96.60 16	92.25 35	90.25 89	98.17 49	96.22 30	86.53 47	97.49 13	87.26 24	95.21 20	97.06 10	94.07 29	94.34 49	94.20 43	99.18 5	99.71 10

PHI-MVS		94.49 25	96.72 15	91.88 37	97.06 33	98.88 19	94.99 41	89.13 35	96.15 25	79.70 55	96.91 8	95.78 18	91.87 44	94.65 44	95.68 19	98.53 34	98.98 46

AdaColmap		94.28 26	92.94 37	95.84 11	98.32 18	98.33 45	96.06 32	94.62 10	96.29 21	91.22 13	89.89 34	85.50 63	96.38 11	91.85 87	90.89 73	98.44 36	97.81 85

DeepPCF-MVS	91.00 2	94.15 27	96.87 14	90.97 45	96.82 36	99.33 7	89.40 88	92.76 24	98.76 1	82.36 42	88.74 35	95.49 20	90.58 62	98.13 3	97.80 3	93.88 193	99.88 3

CPTT-MVS		94.11 28	93.99 32	94.25 22	96.58 38	97.66 53	97.31 22	91.94 28	94.84 41	88.72 22	92.51 27	93.04 30	95.78 16	91.51 90	89.97 89	95.15 184	98.37 70

EPNet		93.69 29	95.34 26	91.76 38	96.98 35	98.47 40	95.40 38	86.79 44	95.47 32	82.84 40	95.66 15	89.17 43	90.47 63	95.25 32	94.69 35	98.10 54	98.68 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP		93.32 30	93.59 35	93.00 32	97.03 34	98.24 46	95.27 39	91.66 31	95.20 36	83.25 39	95.39 17	85.52 61	92.80 36	92.60 77	90.21 85	98.01 66	97.99 80
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
CANet		93.23 31	93.72 34	92.65 33	95.48 48	99.09 14	96.55 28	86.74 45	95.28 35	85.22 33	77.30 66	91.25 40	92.60 38	97.06 11	96.63 6	99.31 2	99.45 18

CDPH-MVS		93.22 32	95.08 27	91.04 44	97.57 28	98.49 39	96.74 26	89.35 34	95.19 37	73.57 81	90.26 32	91.59 39	90.68 59	95.09 36	96.15 13	98.31 45	98.81 51

CSCG		93.16 33	92.65 39	93.76 24	98.32 18	99.09 14	96.12 31	89.91 33	93.15 52	89.64 16	83.62 48	88.91 46	92.40 40	91.09 96	93.70 50	96.14 167	98.99 43

MVS_111021_LR		93.05 34	94.53 30	91.32 42	96.43 40	98.38 43	92.81 55	87.20 43	95.94 29	81.45 44	94.75 22	86.08 57	92.12 43	94.83 38	93.34 55	97.89 80	98.42 69

3Dnovator+	86.26 7	92.90 35	92.45 40	93.42 28	97.25 30	98.45 42	95.82 33	85.71 53	93.83 47	89.55 18	72.31 94	92.28 35	94.01 30	95.10 35	95.92 18	98.17 50	99.23 28

MVS_111021_HR		92.73 36	94.83 29	90.28 50	96.27 42	99.10 13	92.77 56	86.15 50	93.41 49	77.11 71	93.82 25	87.39 50	90.61 60	95.60 26	95.15 28	98.79 25	99.32 20

PLC	89.12 3	92.67 37	90.84 48	94.81 20	97.69 26	96.10 79	95.42 37	91.70 29	95.82 30	92.52 8	81.24 51	86.01 58	94.36 23	92.44 81	90.27 82	97.19 109	93.99 149
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
3Dnovator	85.78 8	92.53 38	91.96 42	93.20 30	97.99 22	98.47 40	95.78 34	85.94 51	93.07 55	86.40 28	73.43 87	89.00 45	94.08 28	94.74 42	96.44 11	99.01 14	98.57 63

DeepC-MVS	88.77 4	92.39 39	91.74 44	93.14 31	96.21 43	98.55 34	96.30 29	93.84 15	93.06 56	81.09 50	74.69 81	85.20 66	93.48 33	95.41 29	96.13 14	97.92 77	99.18 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
OMC-MVS		92.05 40	91.88 43	92.25 35	96.51 39	97.94 51	93.18 52	88.97 37	96.53 19	84.47 37	80.79 56	87.85 48	93.25 35	92.48 79	91.81 64	97.12 110	95.73 126

MVSTER		91.91 41	93.43 36	90.14 51	89.81 97	92.32 122	94.53 44	81.32 85	96.00 26	84.77 36	85.41 46	92.39 34	91.32 46	96.41 18	94.01 46	99.11 6	97.45 95

MVS_0304		91.90 42	92.93 38	90.69 49	93.66 56	98.78 23	96.73 27	85.43 57	93.13 53	78.11 67	77.02 69	89.09 44	91.10 49	96.98 12	96.54 7	99.11 6	98.96 47

QAPM		91.68 43	91.97 41	91.34 41	97.86 24	98.72 25	95.60 36	85.72 52	90.86 66	77.14 70	76.06 70	90.35 41	92.69 37	94.10 50	94.60 36	99.04 11	99.09 36

CNLPA		91.53 44	89.74 58	93.63 26	96.75 37	97.63 55	91.16 75	91.70 29	96.38 20	90.82 14	69.66 106	85.52 61	93.76 31	90.44 105	91.14 72	97.55 93	97.40 96

DELS-MVS		91.09 45	90.56 54	91.71 39	95.82 46	98.59 30	95.74 35	86.68 46	85.86 91	85.12 34	72.71 90	81.36 72	88.06 82	97.31 7	98.27 1	98.86 22	99.82 5
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
TAPA-MVS	87.40 6	90.98 46	90.71 49	91.30 43	96.14 44	97.66 53	94.80 42	89.00 36	94.74 43	77.42 69	80.22 57	86.70 53	92.27 41	91.65 89	90.17 87	98.15 53	93.83 154
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PVSNet_BlendedMVS		90.74 47	90.66 50	90.82 47	94.75 51	98.54 35	91.30 72	86.53 47	95.43 33	85.75 29	78.66 61	70.67 107	87.60 83	96.37 19	95.08 30	98.98 15	99.90 1

PVSNet_Blended		90.74 47	90.66 50	90.82 47	94.75 51	98.54 35	91.30 72	86.53 47	95.43 33	85.75 29	78.66 61	70.67 107	87.60 83	96.37 19	95.08 30	98.98 15	99.90 1

CHOSEN 280x420		90.61 49	94.27 31	86.35 79	93.12 60	98.16 50	89.99 83	69.62 171	92.48 60	76.89 74	87.28 37	96.72 13	90.31 65	94.81 40	92.33 61	98.17 50	98.08 78

MAR-MVS		90.44 50	91.17 47	89.59 54	97.48 29	97.92 52	90.96 78	79.80 92	95.07 39	77.03 72	80.83 52	79.10 80	94.68 21	93.16 61	94.46 38	97.59 92	97.63 87
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	88.14 5	90.42 51	89.56 63	91.41 40	94.44 53	98.18 48	94.35 46	94.33 12	84.55 104	76.61 75	75.84 72	88.47 47	91.29 47	90.37 107	90.66 79	97.46 94	98.88 50
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
OpenMVS	83.41 11	89.84 52	88.89 69	90.95 46	97.63 27	98.51 37	94.64 43	85.47 56	88.14 79	78.39 65	65.06 121	85.42 64	91.04 50	93.06 64	93.70 50	98.53 34	98.37 70

canonicalmvs		89.62 53	89.87 57	89.33 56	90.47 80	97.02 62	93.46 51	79.67 95	92.45 61	81.05 51	82.84 49	73.00 93	93.71 32	90.38 106	94.85 33	97.65 89	98.54 64

TSAR-MVS + COLMAP		89.59 54	89.64 60	89.53 55	93.32 59	96.51 68	95.03 40	88.53 38	95.98 27	69.10 100	91.81 28	64.53 130	93.40 34	93.53 54	91.35 70	97.77 82	93.75 159

HQP-MVS		89.57 55	90.57 53	88.41 60	92.77 61	94.71 98	94.24 47	87.97 39	93.44 48	68.18 103	91.75 29	71.54 105	89.90 67	92.31 84	91.43 68	97.39 101	98.80 52

MVS_Test		89.02 56	90.20 56	87.64 66	89.83 96	97.05 61	92.30 58	77.59 111	92.89 57	75.01 78	77.36 65	76.10 89	92.27 41	95.30 31	95.42 23	98.83 23	97.30 98

CLD-MVS		88.99 57	88.07 72	90.07 52	89.61 100	94.94 95	93.82 50	85.70 54	92.73 59	82.73 41	79.97 58	69.59 110	90.44 64	90.32 108	89.93 91	98.10 54	99.04 40
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
diffmvs		88.92 58	90.30 55	87.32 72	89.46 104	96.38 76	91.21 74	77.89 108	93.11 54	79.09 61	74.17 84	87.41 49	88.55 80	90.20 109	92.70 60	97.71 85	98.13 74

PMMVS		88.56 59	91.22 46	85.47 87	90.04 93	95.60 89	86.62 114	78.49 104	93.86 46	70.62 94	90.00 33	80.08 79	91.64 45	92.36 82	89.80 95	95.40 179	96.84 104

CANet_DTU		87.91 60	91.57 45	83.64 99	90.96 70	97.12 59	91.90 61	75.97 122	92.83 58	53.16 177	86.02 43	79.02 81	90.80 57	95.40 30	94.15 44	99.03 13	96.47 122

conf0.002		87.85 61	87.85 75	87.84 64	90.63 73	96.81 64	91.35 67	83.36 63	84.16 108	72.61 83	78.06 63	71.90 102	90.91 51	93.29 58	91.47 67	98.20 48	99.28 24

IS_MVSNet		87.83 62	90.66 50	84.53 92	90.08 91	96.79 65	88.16 97	79.89 91	85.44 94	72.20 85	75.50 76	87.14 51	80.21 125	95.53 27	95.22 26	96.65 132	99.02 41

EPP-MVSNet		87.72 63	89.74 58	85.37 88	89.11 107	95.57 90	86.31 116	79.44 96	85.83 92	75.73 77	77.23 67	90.05 42	84.78 99	91.22 94	90.25 83	96.83 117	98.04 79

DWT-MVSNet_training		87.65 64	88.45 71	86.71 77	90.32 87	95.64 87	87.91 100	75.69 126	93.27 51	81.43 45	74.99 79	76.48 88	86.92 87	87.74 128	92.29 62	98.00 67	98.74 53

DI_MVS_plusplus_trai		87.63 65	87.13 80	88.22 62	88.61 110	95.92 83	94.09 49	81.41 83	87.00 86	78.38 66	59.70 138	80.52 77	89.08 75	94.37 47	93.34 55	97.73 83	99.05 39

PVSNet_Blended_VisFu		87.44 66	88.72 70	85.95 84	92.02 65	97.26 57	86.88 112	82.66 75	83.86 114	79.16 59	66.96 115	84.91 67	77.26 146	94.97 37	93.48 53	97.73 83	99.64 12

tfpn111		87.30 67	87.03 82	87.61 67	90.54 75	96.39 70	91.35 67	83.15 65	84.16 108	71.65 86	86.75 39	60.49 136	90.91 51	92.89 68	89.34 97	98.05 61	99.17 31

conf0.01		87.22 68	86.71 85	87.81 65	90.61 74	96.75 66	91.35 67	83.33 64	84.16 108	72.45 84	75.61 73	68.65 113	90.91 51	93.23 59	89.34 97	98.17 50	99.27 25

FMVSNet3		87.19 69	87.32 79	87.04 74	82.82 139	90.21 136	92.88 54	76.53 114	91.69 62	81.31 47	64.81 124	80.64 74	89.79 71	94.80 41	94.76 34	98.88 21	94.32 144

LS3D		87.19 69	85.48 92	89.18 57	94.96 50	95.47 91	92.02 60	93.36 19	88.69 75	67.01 105	70.56 102	72.10 97	92.47 39	89.96 114	89.93 91	95.25 181	91.68 178

ACMP	85.16 9	87.15 71	87.04 81	87.27 73	90.80 72	94.45 102	89.41 87	83.09 71	89.15 72	76.98 73	86.35 42	65.80 124	86.94 86	88.45 122	87.52 128	96.42 157	97.56 92
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		87.04 72	89.59 62	84.07 94	90.94 71	95.95 82	86.02 118	81.65 80	85.94 90	78.54 64	78.00 64	85.40 65	69.62 184	91.83 88	91.53 66	97.63 90	98.51 66
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
LGP-MVS_train		86.95 73	87.65 76	86.12 83	91.77 68	93.84 108	93.04 53	82.77 73	88.04 80	65.33 111	87.69 36	67.09 119	86.79 88	90.20 109	88.99 110	97.05 112	97.71 86

thresconf0.02		86.84 74	89.56 63	83.67 98	90.08 91	95.66 86	89.03 90	83.62 62	87.45 83	62.19 120	86.75 39	80.81 73	78.48 134	92.24 85	91.27 71	98.60 31	92.72 174

PatchMatch-RL		86.75 75	85.43 93	88.29 61	94.06 54	96.37 77	86.82 113	82.94 72	88.94 73	79.59 56	79.83 59	59.17 146	89.46 72	91.12 95	88.81 113	96.88 116	93.78 156

tfpn_ndepth		86.61 76	87.92 74	85.08 89	90.39 83	95.45 92	88.21 96	82.30 77	90.79 67	71.22 91	82.59 50	72.09 99	80.42 124	91.37 92	88.61 116	97.93 74	94.56 141

thres100view900		86.48 77	85.08 96	88.12 63	90.54 75	96.90 63	92.39 57	84.82 58	84.16 108	71.65 86	70.86 98	60.49 136	91.23 48	93.65 52	90.19 86	98.10 54	99.32 20

ACMM	84.23 10	86.40 78	84.64 100	88.46 59	91.90 66	91.93 127	88.11 98	85.59 55	88.61 76	79.13 60	75.31 77	66.25 123	89.86 70	89.88 115	87.64 126	96.16 166	92.86 172
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
GBi-Net		86.16 79	86.00 89	86.35 79	81.81 147	89.52 143	91.40 64	76.53 114	91.69 62	81.31 47	64.81 124	80.64 74	88.72 76	90.54 102	90.72 75	98.34 41	94.08 146

test1		86.16 79	86.00 89	86.35 79	81.81 147	89.52 143	91.40 64	76.53 114	91.69 62	81.31 47	64.81 124	80.64 74	88.72 76	90.54 102	90.72 75	98.34 41	94.08 146

conf200view11		86.07 81	84.76 98	87.61 67	90.54 75	96.39 70	91.35 67	83.15 65	84.16 108	71.65 86	70.86 98	60.49 136	90.91 51	92.89 68	89.34 97	98.05 61	99.17 31

tfpn200view9		86.07 81	84.76 98	87.61 67	90.54 75	96.39 70	91.35 67	83.15 65	84.16 108	71.65 86	70.86 98	60.49 136	90.91 51	92.89 68	89.34 97	98.05 61	99.17 31

Vis-MVSNet (Re-imp)		85.89 83	89.62 61	81.55 114	89.85 95	96.08 80	87.55 104	79.80 92	84.80 101	66.55 107	73.70 86	86.71 52	68.25 191	94.40 46	94.53 37	97.32 104	97.09 101

MSDG		85.81 84	82.29 127	89.93 53	95.52 47	92.61 116	91.51 63	91.46 32	85.12 98	78.56 63	63.25 129	69.01 111	85.31 96	88.45 122	88.23 120	97.21 108	89.33 191

thres200		85.80 85	84.38 105	87.46 70	90.51 79	96.39 70	91.64 62	83.15 65	81.59 118	71.54 90	70.24 103	60.41 140	89.88 68	92.89 68	89.85 94	98.06 59	99.26 27

OPM-MVS		85.69 86	82.79 120	89.06 58	93.42 57	94.21 105	94.21 48	87.61 42	72.68 146	70.79 93	71.09 96	67.27 118	90.74 58	91.29 93	89.05 109	97.61 91	93.94 151

thres400		85.59 87	84.08 108	87.36 71	90.45 81	96.60 67	90.95 79	83.67 61	80.99 121	71.17 92	69.08 108	60.25 141	89.88 68	93.14 62	89.34 97	98.02 65	99.17 31

CostFormer		85.47 88	86.98 83	83.71 97	88.70 109	94.02 107	88.07 99	62.72 202	89.78 70	78.68 62	72.69 91	78.37 83	87.35 85	85.96 141	89.32 106	96.73 123	98.72 54

tfpn		85.32 89	84.47 103	86.31 82	90.24 90	95.99 81	89.39 89	82.28 78	79.44 129	69.50 98	66.59 117	67.71 115	88.20 81	92.47 80	90.22 84	98.26 46	98.89 49

view600		85.15 90	83.59 114	86.96 75	90.38 84	96.39 70	90.33 80	83.15 65	80.46 122	70.61 95	67.96 111	60.04 142	89.22 73	92.89 68	88.30 118	98.10 54	99.08 37

thres600view7		85.14 91	83.58 115	86.96 75	90.37 86	96.39 70	90.33 80	83.15 65	80.46 122	70.60 96	67.96 111	60.04 142	89.22 73	92.89 68	88.28 119	98.06 59	99.08 37

test-LLR		85.11 92	89.49 65	80.00 122	85.32 130	94.49 100	82.27 164	74.18 135	87.83 81	56.70 140	75.55 74	86.26 54	82.75 112	93.06 64	90.60 80	98.77 27	98.65 60

tfpn1000		84.98 93	86.47 86	83.24 100	89.93 94	94.98 93	86.58 115	81.22 86	88.54 77	67.35 104	79.39 60	70.93 106	76.07 166	90.70 99	87.37 130	98.32 44	93.37 164

FMVSNet2		84.89 94	84.02 110	85.91 85	81.81 147	89.52 143	91.40 64	75.79 123	84.45 105	79.39 57	58.75 141	74.35 92	88.72 76	93.51 56	93.46 54	98.34 41	94.08 146

FC-MVSNet-train		84.88 95	84.08 108	85.82 86	89.21 106	91.74 128	85.87 119	81.20 87	81.71 117	74.66 80	73.38 88	64.99 128	86.60 90	90.75 98	88.08 122	97.36 102	97.90 83

EPNet_dtu		84.87 96	89.01 67	80.05 121	95.25 49	92.88 114	88.84 92	84.11 59	91.69 62	49.28 194	85.69 44	78.95 82	65.39 196	92.22 86	91.66 65	97.43 99	89.95 188
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
view800		84.86 97	83.35 117	86.63 78	90.31 88	96.17 78	89.86 85	82.67 74	79.95 128	70.04 97	67.25 114	59.75 144	88.72 76	92.64 76	88.72 115	98.19 49	98.95 48

Effi-MVS+		84.80 98	85.71 91	83.73 96	87.94 115	95.76 84	90.08 82	73.45 140	85.12 98	62.66 119	72.39 93	64.97 129	90.59 61	92.95 67	90.69 78	97.67 88	98.12 75

UA-Net		84.69 99	87.64 77	81.25 116	90.38 84	95.67 85	87.33 108	79.41 97	72.07 149	66.48 108	75.09 78	92.48 33	66.88 193	94.03 51	94.25 42	97.01 115	89.88 189

TESTMET0.1,1		84.62 100	89.49 65	78.94 131	82.18 143	94.49 100	82.27 164	70.94 160	87.83 81	56.70 140	75.55 74	86.26 54	82.75 112	93.06 64	90.60 80	98.77 27	98.65 60

CHOSEN 1792x2688		84.59 101	84.30 107	84.93 90	93.71 55	98.23 47	89.91 84	77.96 107	84.81 100	65.93 109	45.19 204	71.76 104	83.13 110	95.46 28	95.13 29	98.94 18	99.53 16

MDTV_nov1_ep13		84.17 102	88.03 73	79.66 124	86.00 125	94.41 103	85.05 124	66.01 193	90.36 68	64.34 116	77.13 68	84.56 68	82.71 114	87.12 132	88.92 111	93.84 196	93.69 160

test-mter		84.06 103	89.00 68	78.29 136	81.92 145	94.23 104	81.07 175	70.38 164	87.12 85	56.10 150	74.75 80	85.80 59	81.81 117	92.52 78	90.10 88	98.43 37	98.49 67

tfpnview11		83.86 104	85.36 94	82.10 111	89.66 99	94.55 99	87.73 101	81.81 79	85.72 93	58.99 127	80.80 53	66.64 120	76.13 165	90.79 97	88.15 121	98.26 46	90.90 182

IB-MVS	79.58 12	83.83 105	84.81 97	82.68 103	91.85 67	97.35 56	75.75 194	82.57 76	86.55 88	84.01 38	70.90 97	65.43 126	63.18 202	84.19 155	89.92 93	98.74 29	99.31 22
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
tpmp4_e23		83.72 106	84.45 104	82.86 101	88.25 112	92.54 118	88.95 91	63.01 200	88.20 78	74.83 79	68.07 110	71.99 101	86.65 89	84.11 157	88.74 114	95.47 177	97.51 94

EPMVS		83.71 107	86.76 84	80.16 120	89.72 98	95.64 87	84.68 125	59.73 209	89.61 71	62.67 118	72.65 92	81.80 71	86.22 92	86.23 137	88.03 124	97.96 72	93.35 165

HyFIR lowres test		83.43 108	82.94 119	84.01 95	93.41 58	97.10 60	87.21 109	74.04 137	80.15 127	64.98 112	41.09 212	76.61 87	86.51 91	93.31 57	93.01 59	97.91 79	99.30 23

tfpn_n400		83.32 109	84.61 101	81.81 112	89.50 102	94.81 96	87.41 106	81.65 80	80.24 125	58.99 127	80.80 53	66.64 120	75.84 167	90.09 111	89.33 104	97.46 94	90.37 184

tfpnconf		83.32 109	84.61 101	81.81 112	89.50 102	94.81 96	87.41 106	81.65 80	80.24 125	58.99 127	80.80 53	66.64 120	75.84 167	90.09 111	89.33 104	97.46 94	90.37 184

PatchmatchNet		83.28 111	87.57 78	78.29 136	87.46 120	94.95 94	83.36 134	59.43 212	90.20 69	58.10 133	74.29 83	86.20 56	84.13 102	85.27 147	87.39 129	97.25 106	94.67 140
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CDS-MVSNet		83.13 112	83.73 113	82.43 109	84.52 135	92.92 113	88.26 95	77.67 110	72.08 148	69.08 101	66.96 115	74.66 91	78.61 131	90.70 99	91.96 63	96.46 156	96.86 103
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
RPSCF		82.91 113	81.86 129	84.13 93	88.25 112	88.32 167	87.67 102	80.86 88	84.78 102	76.57 76	85.56 45	76.00 90	84.61 100	78.20 206	76.52 213	86.81 222	83.63 209

Vis-MVSNet		82.88 114	86.04 88	79.20 129	87.77 118	96.42 69	86.10 117	76.70 113	74.82 141	61.38 122	70.70 101	77.91 84	64.83 197	93.22 60	93.19 58	98.43 37	96.01 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
dps		82.63 115	82.64 123	82.62 105	87.81 117	92.81 115	84.39 126	61.96 203	86.43 89	81.63 43	69.72 105	67.60 117	84.42 101	82.51 183	83.90 181	95.52 175	95.50 132

IterMVS-LS		82.62 116	82.75 122	82.48 106	87.09 121	87.48 180	87.19 110	72.85 143	79.09 130	66.63 106	65.22 119	72.14 96	84.06 104	88.33 125	91.39 69	97.03 114	95.60 131
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Fast-Effi-MVS+		82.61 117	82.51 125	82.72 102	85.49 129	93.06 112	87.17 111	71.39 157	84.18 107	64.59 114	63.03 130	58.89 147	90.22 66	91.39 91	90.83 74	97.44 97	96.21 123

tpm cat1		82.39 118	82.32 126	82.47 107	88.13 114	92.42 121	87.43 105	62.79 201	85.30 95	78.05 68	60.14 136	72.10 97	83.20 109	82.26 186	85.67 145	95.23 182	98.35 72

MS-PatchMatch		82.16 119	82.18 128	82.12 110	91.65 69	93.50 110	89.51 86	71.95 151	81.48 119	64.45 115	59.58 140	77.54 85	77.23 147	89.88 115	85.62 146	97.94 73	87.68 195

conf0.05thres1000		81.86 120	79.55 135	84.56 91	89.39 105	94.15 106	87.57 103	81.36 84	69.95 162	65.78 110	56.38 149	59.38 145	86.04 93	90.58 101	88.49 117	97.22 107	97.97 81

tpmrst		81.71 121	83.87 112	79.20 129	89.01 108	93.67 109	84.22 127	60.14 207	87.45 83	59.49 126	64.97 122	71.86 103	85.30 97	84.72 151	86.30 137	97.04 113	98.09 77

RPMNet		81.47 122	86.24 87	75.90 174	86.72 122	92.12 124	82.82 155	55.76 220	85.21 96	53.73 173	63.45 128	83.16 70	80.13 126	92.34 83	89.52 96	96.23 164	97.90 83

CR-MVSNet		81.44 123	85.29 95	76.94 158	86.53 123	92.12 124	83.86 128	58.37 214	85.21 96	56.28 145	59.60 139	80.39 78	80.50 122	92.77 74	89.32 106	96.12 168	97.59 90

Effi-MVS+-dtu		81.18 124	82.77 121	79.33 127	84.70 134	92.54 118	85.81 120	71.55 155	78.84 131	57.06 138	71.98 95	63.77 132	85.09 98	88.94 119	87.62 127	91.79 211	95.68 127

test0.0.03 1		80.99 125	84.37 106	77.05 155	85.32 130	89.79 140	78.43 185	74.18 135	84.78 102	57.98 136	76.06 70	72.88 94	69.14 188	88.02 126	87.70 125	97.27 105	91.37 179

Fast-Effi-MVS+-dtu		80.57 126	83.44 116	77.22 151	83.98 137	91.52 130	85.78 122	64.54 198	80.38 124	50.28 190	74.06 85	62.89 134	82.00 116	89.10 118	88.91 112	96.75 121	97.21 100

FMVSNet5		80.56 127	82.53 124	78.26 138	73.80 212	81.52 209	82.26 167	68.36 179	88.85 74	64.21 117	69.09 107	84.38 69	83.49 108	87.13 131	86.76 135	97.44 97	79.95 215

ADS-MVSNet		80.25 128	82.96 118	77.08 154	87.86 116	92.60 117	81.82 172	56.19 219	86.95 87	56.16 148	68.19 109	72.42 95	83.70 107	82.05 187	85.45 151	96.75 121	93.08 170

FMVSNet1		80.18 129	78.07 140	82.65 104	78.55 182	87.57 179	88.41 94	73.93 138	70.16 160	73.57 81	49.80 180	64.45 131	85.35 95	90.54 102	90.72 75	96.10 169	93.21 168

USDC		80.10 130	79.33 136	81.00 118	86.36 124	91.71 129	88.74 93	75.77 124	81.90 116	54.90 158	67.67 113	52.05 159	83.94 105	88.44 124	86.25 138	96.31 160	87.28 199

COLMAP_ROB	75.69 15	79.47 131	76.90 148	82.46 108	92.20 62	90.53 132	85.30 123	83.69 60	78.27 134	61.47 121	58.26 143	62.75 135	78.28 136	82.41 184	82.13 196	93.83 198	83.98 207
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pmmvs4		79.32 132	77.78 142	81.11 117	80.18 155	88.96 161	83.39 132	76.07 120	81.27 120	69.35 99	58.66 142	51.19 162	82.01 115	87.16 130	84.39 178	95.66 173	92.82 173

PatchT		79.28 133	83.88 111	73.93 181	85.54 128	90.95 131	66.14 212	56.53 218	83.21 115	56.28 145	56.50 148	76.80 86	80.50 122	92.77 74	89.32 106	98.57 33	97.59 90

ACMH	78.51 14	79.27 134	78.08 139	80.65 119	89.52 101	90.40 133	80.45 177	79.77 94	69.54 166	54.85 159	64.83 123	56.16 153	83.94 105	84.58 153	86.01 142	95.41 178	95.03 137
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		79.23 135	78.95 138	79.56 125	81.89 146	92.52 120	82.97 149	73.70 139	67.27 183	64.97 113	61.66 135	65.06 127	78.61 131	87.12 132	88.07 123	95.23 182	90.95 181

ACMH+	79.09 13	79.12 136	77.22 146	81.35 115	88.50 111	90.36 134	82.14 169	79.38 99	72.78 145	58.59 130	62.31 133	56.44 152	84.10 103	82.03 188	84.05 179	95.40 179	92.55 175

UniMVSNet_NR-MVSNet		78.89 137	78.04 141	79.88 123	79.40 160	89.70 141	82.92 151	80.17 89	76.37 139	58.56 131	57.10 146	54.92 155	81.44 118	83.51 161	87.12 132	96.76 120	97.60 88

tpm		78.87 138	81.33 132	76.00 171	85.57 127	90.19 137	82.81 156	59.66 210	78.35 133	51.40 184	66.30 118	67.92 114	80.94 120	83.28 173	85.73 143	95.65 174	97.56 92

GA-MVS		78.86 139	80.42 133	77.05 155	83.27 138	92.17 123	83.24 137	75.73 125	73.75 142	46.27 205	62.43 131	57.12 149	76.94 154	93.14 62	89.34 97	96.83 117	95.00 138

IterMVS		78.85 140	81.36 131	75.93 172	84.27 136	85.74 189	83.83 130	66.35 192	76.82 135	50.48 187	63.48 127	68.82 112	73.99 171	89.68 117	89.34 97	96.63 136	95.67 128
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet (Re)		78.00 141	77.52 143	78.57 134	79.66 159	90.36 134	82.09 170	77.86 109	76.38 138	60.26 123	54.63 154	52.07 158	75.31 169	84.97 150	86.10 140	96.22 165	98.11 76

DU-MVS		77.98 142	76.71 149	79.46 126	78.68 177	89.26 155	82.92 151	79.06 101	76.52 136	58.56 131	54.89 152	48.35 192	81.44 118	83.16 176	87.21 131	96.08 170	97.60 88

FC-MVSNet-test		77.95 143	81.85 130	73.39 186	82.31 141	88.99 160	79.33 181	74.24 134	78.75 132	47.40 202	70.22 104	72.09 99	60.78 208	86.66 134	85.62 146	96.30 161	90.61 183

NR-MVSNet		77.21 144	76.41 150	78.14 140	80.18 155	89.26 155	83.38 133	79.06 101	76.52 136	56.59 143	54.89 152	45.32 205	72.89 174	85.39 146	86.12 139	96.71 124	97.36 97

gg-mvs-nofinetune		77.08 145	79.79 134	73.92 182	85.95 126	97.23 58	92.18 59	52.65 225	46.19 224	27.79 231	38.27 216	85.63 60	85.67 94	96.95 13	95.62 20	99.30 3	98.67 59

TranMVSNet+NR-MVSNet		77.02 146	75.76 152	78.49 135	78.46 187	88.24 168	83.03 148	79.97 90	73.49 144	54.73 162	54.00 157	48.74 187	78.15 138	82.36 185	86.90 134	96.59 141	96.55 110

CVMVSNet		76.86 147	79.09 137	74.26 179	85.29 132	89.44 149	79.91 180	78.47 105	68.94 170	44.45 210	62.35 132	69.70 109	64.50 199	85.82 142	87.03 133	92.94 204	90.33 186

Baseline_NR-MVSNet		76.71 148	74.56 166	79.23 128	78.68 177	84.15 195	82.45 160	78.87 103	75.83 140	60.05 124	47.92 196	50.18 173	79.06 130	83.16 176	83.86 182	96.26 162	96.80 105

v6		76.41 149	75.11 155	77.93 142	79.08 166	89.48 148	83.25 136	75.62 127	70.21 157	55.94 153	50.48 170	50.81 168	77.01 153	83.32 168	84.97 165	96.66 129	96.50 117

v1neww		76.39 150	75.09 156	77.91 143	79.08 166	89.49 146	83.21 138	75.62 127	70.20 158	55.81 154	50.43 171	50.74 169	77.05 151	83.33 166	84.99 162	96.66 129	96.48 120

v7new		76.39 150	75.09 156	77.91 143	79.08 166	89.49 146	83.21 138	75.62 127	70.20 158	55.81 154	50.43 171	50.74 169	77.05 151	83.33 166	84.99 162	96.66 129	96.48 120

v2v482		76.25 152	74.78 160	77.96 141	78.50 185	89.14 158	83.05 147	76.02 121	68.78 171	54.11 169	51.36 163	48.59 189	79.49 128	83.53 160	85.60 149	96.59 141	96.49 119

V42		76.21 153	75.04 158	77.58 148	78.68 177	89.33 151	82.93 150	74.64 132	69.84 163	56.13 149	50.42 174	50.93 165	76.30 163	83.32 168	84.89 170	96.83 117	96.54 111

v1		76.04 154	74.65 163	77.66 145	78.77 173	89.33 151	83.18 141	76.22 117	68.17 174	54.58 165	50.10 176	49.99 174	76.70 158	83.38 164	85.05 160	96.50 151	96.51 114

v1141		76.03 155	74.64 164	77.66 145	78.78 171	89.32 154	83.14 145	76.22 117	68.27 172	54.56 166	50.06 178	49.84 179	76.78 156	83.40 162	85.07 157	96.50 151	96.51 114

divwei89l23v2f112		76.03 155	74.64 164	77.65 147	78.78 171	89.33 151	83.15 143	76.21 119	68.26 173	54.55 167	50.08 177	49.86 177	76.73 157	83.39 163	85.06 159	96.51 150	96.51 114

v7		76.00 157	75.01 159	77.15 153	78.73 174	88.87 162	83.15 143	72.40 147	69.20 168	53.57 174	49.73 182	49.23 183	78.49 133	86.15 140	85.17 156	96.53 148	96.73 107

v8		75.89 158	74.74 161	77.23 150	79.09 165	88.00 171	83.19 140	71.08 159	70.03 161	56.29 144	50.50 168	50.88 167	77.06 150	83.32 168	84.99 162	96.68 128	95.49 133

TinyColmap		75.75 159	73.19 181	78.74 133	84.82 133	87.69 175	81.59 173	74.62 133	71.81 150	54.01 171	55.79 151	44.42 210	82.89 111	84.61 152	83.76 183	94.50 190	84.22 206

MIMVSNet		75.71 160	77.26 144	73.90 183	70.93 213	88.71 165	79.98 179	57.67 217	73.58 143	58.08 135	53.93 158	58.56 148	79.41 129	90.04 113	89.97 89	97.34 103	86.04 200

pm-mvs1		75.61 161	74.19 168	77.26 149	80.16 157	88.79 163	81.49 174	75.49 131	59.49 209	58.09 134	48.32 194	55.53 154	72.35 175	88.61 121	85.48 150	95.99 171	93.12 169

v10		75.57 162	74.67 162	76.62 162	78.73 174	87.46 181	83.14 145	69.41 172	69.27 167	53.44 175	49.73 182	49.21 184	78.44 135	86.17 139	85.18 155	96.53 148	95.65 130

v1144		75.54 163	74.55 167	76.69 160	78.33 189	88.77 164	82.89 153	72.76 144	67.18 185	51.73 181	49.34 189	48.37 190	78.10 139	86.22 138	85.24 153	96.35 159	96.74 106

TDRefinement		75.54 163	73.22 179	78.25 139	87.65 119	89.65 142	85.81 120	79.28 100	71.14 153	56.06 151	52.17 161	51.96 160	68.74 190	81.60 189	80.58 202	91.94 209	85.45 201

v18		75.49 165	74.04 169	77.18 152	79.31 162	82.47 198	83.66 131	68.68 175	71.77 151	57.43 137	50.71 166	51.01 163	77.31 145	83.35 165	85.03 161	96.70 126	93.91 152

pmmvs5		75.46 166	75.12 154	75.87 175	79.39 161	89.44 149	78.12 187	72.27 149	65.98 190	51.54 182	55.83 150	46.23 198	76.80 155	88.77 120	85.73 143	97.07 111	93.84 153

v16		75.32 167	73.90 171	76.98 157	79.23 163	82.37 201	83.27 135	68.48 176	71.54 152	57.06 138	50.43 171	50.93 165	77.18 148	83.30 171	84.92 168	96.70 126	93.79 155

tfpnnormal		75.27 168	72.12 191	78.94 131	82.30 142	88.52 166	82.41 161	79.41 97	58.03 210	55.59 156	43.83 210	44.71 207	77.35 143	87.70 129	85.45 151	96.60 140	96.61 109

v17		75.24 169	73.83 172	76.89 159	79.15 164	82.38 200	83.16 142	68.48 176	70.93 155	56.69 142	50.53 167	50.98 164	77.13 149	83.29 172	84.93 167	96.71 124	93.77 157

anonymousdsp		75.14 170	77.25 145	72.69 189	76.68 199	89.26 155	75.26 198	68.44 178	65.53 193	46.65 204	58.16 144	56.67 151	73.96 172	87.84 127	86.05 141	95.13 185	97.22 99

v148		74.98 171	73.52 176	76.69 160	78.84 170	89.02 159	78.78 183	76.82 112	67.22 184	59.61 125	49.18 191	47.94 194	70.57 183	80.76 193	83.99 180	95.52 175	96.52 113

v1192		74.96 172	73.92 170	76.17 165	77.76 192	88.19 170	82.54 159	71.94 152	66.84 186	50.07 192	48.10 195	46.14 199	78.28 136	86.30 136	85.23 154	96.41 158	96.67 108

v144192		74.76 173	73.64 173	76.06 168	77.58 193	88.23 169	81.87 171	71.63 154	66.03 189	51.08 185	48.63 193	46.77 197	77.59 142	84.53 154	84.76 171	96.64 134	96.54 111

v11		74.62 174	73.41 178	76.03 169	78.54 183	81.97 205	82.34 162	67.33 189	68.08 175	53.39 176	49.73 182	48.87 186	78.01 141	86.66 134	84.97 165	96.56 146	93.58 162

v1921920		74.60 175	73.56 175	75.81 176	77.43 195	87.94 172	82.18 168	71.33 158	66.48 188	49.23 196	47.84 197	45.56 203	78.03 140	85.70 144	84.92 168	96.65 132	96.50 117

v15		74.54 176	73.06 183	76.26 163	78.70 176	82.14 202	82.89 153	68.05 180	68.07 176	54.77 160	49.76 181	49.88 176	76.56 159	83.19 175	84.76 171	96.59 141	93.60 161

V14		74.48 177	73.00 185	76.20 164	78.65 180	82.09 203	82.79 157	67.88 183	68.04 177	54.75 161	49.68 185	49.92 175	76.51 160	83.12 178	84.67 173	96.63 136	93.44 163

V9		74.37 178	72.87 186	76.11 167	78.58 181	82.02 204	82.68 158	67.75 185	67.80 179	54.63 163	49.50 187	49.86 177	76.40 161	83.05 179	84.59 174	96.63 136	93.30 166

v12		74.29 179	72.82 187	76.02 170	78.52 184	81.96 206	82.27 164	67.65 186	67.88 178	54.63 163	49.40 188	49.74 181	76.40 161	82.99 180	84.52 175	96.64 134	93.23 167

v13		74.20 180	72.72 189	75.92 173	78.49 186	81.90 207	82.28 163	67.55 187	67.64 181	54.29 168	49.25 190	49.75 180	76.30 163	82.92 182	84.47 176	96.63 136	93.08 170

v1240		74.04 181	73.04 184	75.20 178	77.19 197	87.69 175	80.93 176	70.72 163	65.08 195	48.47 197	47.31 198	44.71 207	77.33 144	85.50 145	85.07 157	96.59 141	95.94 125

testgi		73.22 182	75.84 151	70.16 202	81.67 150	85.50 191	71.45 203	70.81 161	69.56 165	44.74 209	74.52 82	49.25 182	58.45 209	84.10 158	83.37 187	93.86 195	84.56 205

CP-MVSNet		73.19 183	72.37 190	74.15 180	77.54 194	86.77 186	76.34 190	72.05 150	65.66 192	51.47 183	50.49 169	43.66 212	70.90 177	80.93 192	83.40 186	96.59 141	95.66 129

WR-MVS		72.93 184	73.57 174	72.19 194	78.14 190	87.71 174	76.21 192	73.02 142	67.78 180	50.09 191	50.35 175	50.53 171	61.27 207	80.42 196	83.10 190	94.43 191	95.11 136

TransMVSNet (Re)		72.90 185	70.51 199	75.69 177	80.88 151	85.26 193	79.25 182	78.43 106	56.13 216	52.81 178	46.81 199	48.20 193	66.77 194	85.18 149	83.70 184	95.98 172	88.28 194

WR-MVS_H		72.69 186	72.80 188	72.56 191	77.94 191	87.83 173	75.26 198	71.53 156	64.75 196	52.19 180	49.83 179	48.62 188	61.96 206	81.12 191	82.44 193	96.50 151	95.00 138

SixPastTwentyTwo		72.65 187	73.22 179	71.98 197	78.40 188	87.64 177	70.09 205	70.37 165	66.49 187	47.60 200	65.09 120	45.94 200	73.09 173	78.94 199	78.66 208	92.33 207	89.82 190

LTVRE_ROB	71.82 16	72.62 188	71.77 192	73.62 184	80.74 152	87.59 178	80.42 178	70.37 165	49.73 220	37.12 220	59.76 137	42.52 217	80.92 121	83.20 174	85.61 148	92.13 208	93.95 150
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		72.37 189	71.47 195	73.43 185	77.32 196	86.43 187	75.99 193	71.94 152	63.37 199	49.24 195	49.07 192	42.42 218	69.60 185	80.59 195	83.18 189	96.48 155	95.23 135

MVS-HIRNet		72.32 190	73.45 177	71.00 201	80.58 153	89.97 138	68.51 209	55.28 221	70.89 156	52.27 179	39.09 214	57.11 150	75.02 170	85.76 143	86.33 136	94.36 192	85.00 203

PEN-MVS		72.24 191	71.30 196	73.33 187	77.08 198	85.57 190	76.75 188	72.52 146	63.89 198	48.12 198	50.79 164	43.09 215	69.03 189	78.54 201	83.46 185	96.50 151	93.76 158

v7n		72.11 192	71.66 193	72.63 190	75.26 205	86.85 182	76.74 189	68.77 174	62.70 202	49.40 193	45.92 203	43.51 213	70.63 182	84.16 156	83.21 188	94.99 186	95.25 134

EG-PatchMatch MVS		71.81 193	71.54 194	72.12 195	80.53 154	89.94 139	78.51 184	66.56 191	57.38 212	47.46 201	44.28 209	52.22 157	63.10 203	85.22 148	84.42 177	96.56 146	87.35 198

CMPMVS	54.54 17	71.74 194	67.94 207	76.16 166	90.41 82	93.25 111	78.32 186	75.60 130	59.81 208	53.95 172	44.64 207	51.22 161	70.70 179	74.59 214	75.88 214	88.01 216	76.23 218
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
v52		71.67 195	71.16 197	72.26 193	73.90 211	86.80 185	75.72 196	68.04 181	62.53 204	50.43 189	46.15 202	47.83 195	70.73 178	78.53 203	81.76 197	94.75 189	94.53 142

V4		71.67 195	71.15 198	72.27 192	73.91 210	86.82 183	75.73 195	68.04 181	62.49 205	50.47 188	46.20 200	47.74 196	70.70 179	78.54 201	81.76 197	94.76 188	94.52 143

MDTV_nov1_ep13_2view		71.65 197	73.08 182	69.97 203	75.22 206	86.81 184	73.98 200	59.61 211	69.75 164	48.01 199	54.21 156	53.06 156	69.19 187	78.50 204	80.43 203	93.84 196	88.79 192

gm-plane-assit		71.33 198	75.18 153	66.83 207	79.06 169	75.57 217	48.05 226	60.33 204	48.28 221	34.67 225	44.34 208	67.70 116	79.78 127	97.25 8	96.21 12	99.10 9	96.92 102

DTE-MVSNet		71.19 199	70.45 200	72.06 196	76.61 200	84.59 194	75.61 197	72.32 148	63.12 201	45.70 207	50.72 165	43.02 216	65.89 195	77.53 210	82.23 195	96.26 162	91.93 177

testpf		71.11 200	76.92 147	64.33 209	81.95 144	78.78 214	61.99 214	43.97 232	84.31 106	46.81 203	61.76 134	63.32 133	62.03 205	77.13 211	80.68 201	89.25 215	92.50 176

v748		70.94 201	70.25 201	71.75 199	75.58 203	86.28 188	72.12 201	70.25 168	60.25 207	54.08 170	46.18 201	44.41 211	64.61 198	77.92 208	82.49 192	93.87 194	94.19 145

pmmvs6		70.29 202	67.90 208	73.07 188	76.17 201	85.31 192	76.29 191	70.75 162	47.39 223	55.33 157	37.15 220	50.49 172	69.55 186	82.96 181	80.85 199	90.34 214	91.18 180

PM-MVS		70.17 203	69.42 204	71.04 200	70.82 214	81.26 211	71.25 204	67.80 184	69.16 169	51.04 186	53.15 160	34.93 222	72.19 176	80.30 197	76.95 212	93.16 203	90.21 187

pmmvs-eth3d		69.59 204	67.57 210	71.95 198	70.04 216	80.05 212	71.48 202	70.00 170	62.57 203	55.99 152	44.92 205	35.73 221	70.64 181	81.56 190	79.69 204	93.55 199	88.43 193

N_pmnet		68.54 205	67.83 209	69.38 204	75.77 202	81.90 207	66.21 211	72.53 145	65.91 191	46.09 206	44.67 206	45.48 204	63.82 201	74.66 213	77.39 211	91.87 210	84.77 204

LP		68.35 206	68.20 206	68.53 205	82.61 140	82.93 196	69.42 206	53.36 224	71.06 154	45.32 208	41.19 211	49.10 185	67.20 192	73.89 215	78.16 209	93.25 201	81.04 213

Anonymous20231206		68.09 207	68.68 205	67.39 206	75.16 207	82.55 197	69.33 207	70.06 169	63.34 200	42.28 212	37.91 218	43.12 214	52.67 213	83.56 159	82.71 191	94.84 187	87.59 196

EU-MVSNet		68.07 208	70.25 201	65.52 208	74.68 209	81.30 210	68.53 208	70.31 167	62.40 206	37.43 219	54.62 155	48.36 191	51.34 217	78.32 205	79.27 205	90.84 212	87.47 197

test2356		66.34 209	69.50 203	62.65 211	70.77 215	74.02 219	61.29 215	64.23 199	67.61 182	33.88 228	56.51 147	44.92 206	53.09 212	80.01 198	82.24 194	92.66 206	81.22 212

GG-mvs-BLEND		65.67 210	93.78 33	32.89 230	0.47 237	99.35 4	96.92 25	0.22 237	93.28 50	0.51 240	84.07 47	92.50 32	0.62 237	93.59 53	93.86 47	98.59 32	99.79 6

test20.03		65.17 211	67.41 211	62.55 212	75.35 204	79.31 213	62.22 213	68.83 173	56.50 215	35.35 224	51.97 162	44.70 209	40.01 223	80.69 194	79.25 206	93.55 199	79.47 217

testus		64.41 212	66.39 212	62.10 213	70.01 217	72.88 220	59.74 220	64.99 196	65.18 194	33.49 229	57.35 145	30.48 228	51.71 216	78.09 207	80.75 200	92.69 205	79.97 214

MDA-MVSNet-bldmvs		62.23 213	61.13 215	63.52 210	58.94 228	82.44 199	60.71 218	73.28 141	57.22 213	38.42 217	49.63 186	27.64 230	62.83 204	54.98 227	74.16 216	86.96 221	81.83 211

new_pmnet		61.60 214	62.68 213	60.35 216	63.02 222	74.93 218	60.97 217	58.86 213	64.21 197	35.38 223	39.51 213	39.89 219	57.37 210	72.78 216	72.56 217	86.49 223	74.85 220

new-patchmatchnet		60.74 215	59.78 217	61.87 214	69.52 218	76.67 216	57.99 223	65.78 194	52.63 218	38.47 216	38.08 217	32.92 225	48.88 219	68.50 219	69.87 222	90.56 213	79.75 216

pmmvs3		60.52 216	60.87 216	60.12 217	61.38 223	71.62 222	57.42 224	53.94 223	48.09 222	35.95 221	38.62 215	32.19 227	64.12 200	75.33 212	77.99 210	87.89 218	82.28 210

MIMVSNet1		60.51 217	61.43 214	59.44 218	48.75 232	77.21 215	60.98 216	66.84 190	52.09 219	38.74 215	29.29 227	39.40 220	48.08 220	77.60 209	78.87 207	93.22 202	75.56 219

FPMVS		56.54 218	52.82 223	60.87 215	74.90 208	67.58 225	67.69 210	65.38 195	57.86 211	41.51 213	37.83 219	34.19 223	41.21 222	55.88 226	53.09 228	74.55 229	63.31 226

Anonymous20231211		56.40 219	54.23 220	58.92 219	64.68 221	71.87 221	59.09 222	64.63 197	34.66 231	35.73 222	21.99 229	29.42 229	45.81 221	67.46 222	70.30 221	83.57 224	83.94 208

1111		54.82 220	55.44 219	54.10 221	61.33 225	64.37 226	42.52 227	46.65 230	42.29 225	34.21 226	29.57 225	45.65 201	51.95 214	71.47 217	74.60 215	87.95 217	60.10 227

testmv		53.23 221	53.37 221	53.06 222	64.78 219	63.76 228	42.27 229	60.18 205	38.40 227	24.60 232	33.04 221	23.85 231	39.28 224	68.05 220	72.53 218	87.23 219	73.98 221

test1235678		53.22 222	53.36 222	53.05 223	64.78 219	63.75 229	42.27 229	60.17 206	38.36 228	24.60 232	33.03 222	23.84 232	39.28 224	68.04 221	72.52 219	87.23 219	73.96 222

test12356		48.96 223	49.54 224	48.28 225	59.74 227	57.59 231	42.10 231	58.32 216	36.65 230	23.11 234	31.44 223	19.22 233	23.46 231	61.17 225	71.98 220	82.97 225	68.75 223

PMVS	42.57 18	45.71 224	42.61 226	49.32 224	61.35 224	37.82 235	36.96 233	60.10 208	37.20 229	41.50 214	28.53 228	33.11 224	28.82 230	53.45 228	48.70 230	67.22 232	59.42 228
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		43.95 225	42.62 225	45.50 226	50.79 230	41.20 234	35.55 234	52.51 226	52.95 217	29.09 230	12.92 232	11.48 237	38.15 226	62.01 224	66.62 224	66.89 233	51.17 230
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		41.25 226	42.55 227	39.74 227	43.25 233	55.05 232	38.15 232	47.11 229	31.78 232	11.83 237	21.16 230	19.12 234	20.98 233	49.95 230	56.09 226	77.09 227	64.68 225

.test1245		40.04 227	40.41 228	39.60 228	61.33 225	64.37 226	42.52 227	46.65 230	42.29 225	34.21 226	29.57 225	45.65 201	51.95 214	71.47 217	5.65 233	0.92 237	23.86 235

no-one		36.24 228	35.28 229	37.36 229	49.42 231	52.08 233	23.67 235	54.16 222	20.93 235	12.98 236	13.94 231	12.99 235	16.68 234	34.98 232	55.52 227	67.24 231	56.51 229

E-PMN		27.87 229	24.36 231	31.97 231	41.27 235	25.56 238	16.62 237	49.16 227	22.00 234	9.90 238	11.75 234	7.86 239	29.57 229	22.22 233	34.70 231	45.27 234	46.41 232

MVE	32.98 19	27.61 230	29.89 230	24.94 233	21.97 236	37.22 236	15.56 239	38.83 233	17.49 236	14.72 235	11.64 236	5.62 240	21.26 232	35.20 231	50.95 229	37.29 236	51.13 231
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
EMVS		26.96 231	22.96 232	31.63 232	41.91 234	25.73 237	16.30 238	49.10 228	22.38 233	9.03 239	11.22 237	8.12 238	29.93 228	20.16 234	31.04 232	43.49 235	42.04 233

testmvs		5.16 232	8.14 233	1.69 234	0.36 238	1.65 239	3.02 240	0.66 235	7.17 237	0.50 241	12.58 233	0.69 241	4.67 235	5.42 235	5.65 233	0.92 237	23.86 235

test123		4.39 233	7.11 234	1.21 235	0.11 239	1.16 240	1.67 241	0.35 236	5.91 238	0.16 242	11.65 235	0.16 242	4.45 236	1.72 236	4.92 235	0.51 239	24.28 234

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			57.08 218		58.68 229	67.71 224	60.07 219		57.13 214	42.79 211	30.00 224	11.64 236	50.18 218	78.89 200	69.14 223	82.64 226	85.02 202

MTAPA										93.37 3		95.71 19

MTMP										93.84 2		94.86 23

Patchmatch-RL test							19.65 236

tmp_tt				57.89 220	79.94 158	59.29 230	52.84 225	36.65 234	94.77 42	68.22 102	72.96 89	65.62 125	33.65 227	66.20 223	58.02 225	76.06 228

XVS					92.16 63	98.56 31	91.04 76			81.00 52		93.49 27				98.00 67

X-MVStestdata					92.16 63	98.56 31	91.04 76			81.00 52		93.49 27				98.00 67

abl_6				93.25 29	97.12 32	98.71 26	94.40 45	87.81 40	97.86 8	87.19 25	91.07 31	95.80 17	94.18 26			98.78 26	99.36 19

mPP-MVS					97.95 23							92.24 37

NP-MVS									94.12 45

Patchmtry						92.08 126	83.86 128	58.37 214		56.28 145

DeepMVS_CX						70.68 223	59.61 221	67.36 188	72.12 147	38.41 218	53.88 159	32.44 226	55.15 211	50.88 229		74.35 230	68.42 224

High-resolution multi-view benchmark