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.

MCST-MVS		85.13 19	86.62 20	83.39 14	90.55 12	89.82 12	89.29 17	73.89 19	84.38 27	76.03 24	79.01 27	85.90 16	78.47 8	87.81 13	86.11 29	92.11 1	93.29 18

CSCG		85.28 18	87.68 15	82.49 21	89.95 20	91.99 2	88.82 20	71.20 32	86.41 18	79.63 12	79.26 25	88.36 6	73.94 36	86.64 28	86.67 21	91.40 2	94.41 4

SteuartSystems-ACMMP		85.99 12	88.31 12	83.27 17	90.73 8	89.84 10	90.27 9	74.31 11	84.56 26	75.88 25	87.32 10	85.04 19	77.31 20	89.01 4	88.46 3	91.14 3	93.96 8
Skip Steuart: Steuart Systems R&D Blog.
APDe-MVS		88.00 2	90.50 2	85.08 2	90.95 6	91.58 4	92.03 1	75.53 8	91.15 1	80.10 10	92.27 3	88.34 7	80.80 2	88.00 11	86.99 15	91.09 4	95.16 3

DeepC-MVS	78.47 2	84.81 22	86.03 25	83.37 15	89.29 27	90.38 7	88.61 22	76.50 2	86.25 19	77.22 20	75.12 35	80.28 39	77.59 18	88.39 7	88.17 6	91.02 5	93.66 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ESAPD		88.46 1	91.07 1	85.41 1	91.73 2	92.08 1	91.91 2	76.73 1	90.14 4	80.33 9	92.75 1	90.44 1	80.73 3	88.97 5	87.63 9	91.01 6	95.48 1

SMA-MVS		87.56 3	90.17 4	84.52 4	91.71 3	90.57 5	90.77 4	75.19 9	90.67 3	80.50 8	86.59 13	88.86 4	78.09 12	89.92 1	89.41 1	90.84 7	95.19 2

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

ACMMPR		85.52 14	87.53 16	83.17 18	90.13 16	89.27 16	89.30 16	73.97 17	86.89 16	77.14 21	86.09 14	83.18 27	77.74 16	87.42 17	87.20 12	90.77 9	92.63 21

ACMMP		83.42 27	85.27 28	81.26 26	88.47 31	88.49 30	88.31 26	72.09 27	83.42 30	72.77 36	82.65 20	78.22 43	75.18 29	86.24 33	85.76 31	90.74 10	92.13 27
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
PGM-MVS		84.42 24	86.29 24	82.23 22	90.04 18	88.82 23	89.23 18	71.74 30	82.82 32	74.61 28	84.41 19	82.09 29	77.03 24	87.13 21	86.73 20	90.73 11	92.06 28

DeepC-MVS_fast	78.24 3	84.27 25	85.50 27	82.85 19	90.46 14	89.24 17	87.83 28	74.24 13	84.88 22	76.23 23	75.26 34	81.05 37	77.62 17	88.02 10	87.62 10	90.69 12	92.41 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SD-MVS		86.96 6	89.45 5	84.05 11	90.13 16	89.23 18	89.77 13	74.59 10	89.17 6	80.70 5	89.93 7	89.67 2	78.47 8	87.57 16	86.79 18	90.67 13	93.76 12

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

MVS_0304		81.73 34	83.86 31	79.26 37	86.22 44	89.18 20	86.41 33	67.15 57	75.28 51	70.75 48	74.59 37	83.49 26	74.42 32	87.05 24	86.34 25	90.58 15	91.08 36

APD-MVS		86.84 8	88.91 10	84.41 5	90.66 9	90.10 8	90.78 3	75.64 5	87.38 13	78.72 14	90.68 6	86.82 11	80.15 4	87.13 21	86.45 24	90.51 16	93.83 10
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
CP-MVS		84.74 23	86.43 23	82.77 20	89.48 25	88.13 34	88.64 21	73.93 18	84.92 21	76.77 22	81.94 22	83.50 25	77.29 22	86.92 27	86.49 23	90.49 17	93.14 19

XVS					86.63 40	88.68 24	85.00 43			71.81 41		81.92 31				90.47 18

X-MVStestdata					86.63 40	88.68 24	85.00 43			71.81 41		81.92 31				90.47 18

X-MVS		83.23 28	85.20 29	80.92 29	89.71 23	88.68 24	88.21 27	73.60 20	82.57 33	71.81 41	77.07 29	81.92 31	71.72 51	86.98 25	86.86 16	90.47 18	92.36 25

NCCC		85.34 16	86.59 21	83.88 12	91.48 4	88.88 21	89.79 12	75.54 7	86.67 17	77.94 19	76.55 31	84.99 20	78.07 13	88.04 9	87.68 8	90.46 21	93.31 17

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

MP-MVS		85.50 15	87.40 17	83.28 16	90.65 10	89.51 15	89.16 19	74.11 15	83.70 29	78.06 18	85.54 16	84.89 22	77.31 20	87.40 18	87.14 14	90.41 23	93.65 15
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
3Dnovator+	75.73 4	82.40 30	82.76 35	81.97 24	88.02 32	89.67 13	86.60 32	71.48 31	81.28 38	78.18 17	64.78 72	77.96 46	77.13 23	87.32 19	86.83 17	90.41 23	91.48 32

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

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

CDPH-MVS		82.64 29	85.03 30	79.86 34	89.41 26	88.31 31	88.32 25	71.84 29	80.11 41	67.47 57	82.09 21	81.44 35	71.85 49	85.89 35	86.15 28	90.24 27	91.25 34

LGP-MVS_train		79.83 40	81.22 43	78.22 45	86.28 43	85.36 53	86.76 31	69.59 41	77.34 46	65.14 64	75.68 33	70.79 67	71.37 54	84.60 42	84.01 42	90.18 28	90.74 38

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

3Dnovator	73.76 5	79.75 42	80.52 47	78.84 40	84.94 54	87.35 36	84.43 48	65.54 67	78.29 45	73.97 30	63.00 78	75.62 52	74.07 35	85.00 40	85.34 35	90.11 30	89.04 49

ACMP	73.23 7	79.79 41	80.53 46	78.94 39	85.61 47	85.68 48	85.61 38	69.59 41	77.33 47	71.00 47	74.45 38	69.16 78	71.88 47	83.15 55	83.37 48	89.92 31	90.57 41
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
train_agg		84.86 21	87.21 18	82.11 23	90.59 11	85.47 50	89.81 11	73.55 21	83.95 28	73.30 33	89.84 8	87.23 10	75.61 28	86.47 30	85.46 34	89.78 32	92.06 28

TSAR-MVS + GP.		83.69 26	86.58 22	80.32 31	85.14 49	86.96 40	84.91 46	70.25 36	84.71 25	73.91 31	85.16 17	85.63 17	77.92 14	85.44 36	85.71 32	89.77 33	92.45 23

ACMM	72.26 8	78.86 51	78.13 55	79.71 35	86.89 39	83.40 65	86.02 35	70.50 34	75.28 51	71.49 45	63.01 77	69.26 77	73.57 38	84.11 46	83.98 43	89.76 34	87.84 56
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OPM-MVS		79.68 44	79.28 53	80.15 33	87.99 33	86.77 42	88.52 24	72.72 24	64.55 84	67.65 56	67.87 62	74.33 56	74.31 34	86.37 32	85.25 36	89.73 35	89.81 45

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

CANet		81.62 35	83.41 32	79.53 36	87.06 37	88.59 28	85.47 41	67.96 53	76.59 49	74.05 29	74.69 36	81.98 30	72.98 42	86.14 34	85.47 33	89.68 37	90.42 42

abl_6				79.05 38	87.27 36	88.85 22	83.62 51	68.25 49	81.68 36	72.94 35	73.79 41	84.45 23	72.55 44			89.66 38	90.64 39

MVS_111021_HR		80.13 38	81.46 41	78.58 42	85.77 46	85.17 54	83.45 52	69.28 44	74.08 57	70.31 49	74.31 39	75.26 53	73.13 40	86.46 31	85.15 37	89.53 39	89.81 45

IS_MVSNet		73.33 70	77.34 63	68.65 114	81.29 65	83.47 64	74.45 124	63.58 81	65.75 76	48.49 149	67.11 66	70.61 69	54.63 169	84.51 43	83.58 47	89.48 40	86.34 67

DELS-MVS		79.15 49	81.07 44	76.91 51	83.54 56	87.31 37	84.45 47	64.92 73	69.98 61	69.34 50	71.62 48	76.26 49	69.84 59	86.57 29	85.90 30	89.39 41	89.88 44
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
casdiffmvs		80.04 39	82.12 39	77.60 47	83.27 57	84.92 56	85.51 40	65.45 68	80.73 39	67.69 55	72.68 44	78.05 44	74.35 33	84.82 41	83.94 44	89.35 42	89.71 47

UniMVSNet_NR-MVSNet		70.59 86	72.19 84	68.72 112	77.72 101	80.72 88	73.81 139	69.65 40	61.99 99	43.23 177	60.54 82	57.50 114	58.57 135	79.56 103	81.07 61	89.34 43	83.97 95

PHI-MVS		82.36 31	85.89 26	78.24 44	86.40 42	89.52 14	85.52 39	69.52 43	82.38 35	65.67 62	81.35 23	82.36 28	73.07 41	87.31 20	86.76 19	89.24 44	91.56 31

canonicalmvs		79.16 48	82.37 38	75.41 56	82.33 63	86.38 46	80.80 58	63.18 83	82.90 31	67.34 58	72.79 43	76.07 50	69.62 60	83.46 54	84.41 41	89.20 45	90.60 40

MSLP-MVS++		82.09 32	82.66 36	81.42 25	87.03 38	87.22 38	85.82 37	70.04 37	80.30 40	78.66 15	68.67 58	81.04 38	77.81 15	85.19 39	84.88 39	89.19 46	91.31 33

HQP-MVS		81.19 36	83.27 33	78.76 41	87.40 35	85.45 51	86.95 30	70.47 35	81.31 37	66.91 60	79.24 26	76.63 48	71.67 52	84.43 44	83.78 45	89.19 46	92.05 30

EPP-MVSNet		74.00 68	77.41 62	70.02 101	80.53 73	83.91 61	74.99 121	62.68 101	65.06 79	49.77 146	68.68 57	72.09 62	63.06 108	82.49 60	80.73 66	89.12 48	88.91 50

NR-MVSNet		68.79 120	70.56 93	66.71 146	77.48 104	79.54 102	73.52 144	69.20 45	61.20 107	39.76 188	58.52 93	50.11 191	51.37 180	80.26 93	80.71 71	88.97 49	83.59 102

PVSNet_Blended_VisFu		76.57 57	77.90 56	75.02 58	80.56 72	86.58 44	79.24 68	66.18 61	64.81 81	68.18 53	65.61 67	71.45 63	67.05 69	84.16 45	81.80 55	88.90 50	90.92 37

TranMVSNet+NR-MVSNet		69.25 115	70.81 92	67.43 126	77.23 107	79.46 104	73.48 145	69.66 39	60.43 112	39.56 189	58.82 92	53.48 157	55.74 162	79.59 101	81.21 60	88.89 51	82.70 116

QAPM		78.47 52	80.22 50	76.43 53	85.03 51	86.75 43	80.62 59	66.00 64	73.77 58	65.35 63	65.54 69	78.02 45	72.69 43	83.71 49	83.36 49	88.87 52	90.41 43

CPTT-MVS		81.77 33	83.10 34	80.21 32	85.93 45	86.45 45	87.72 29	70.98 33	82.54 34	71.53 44	74.23 40	81.49 34	76.31 27	82.85 58	81.87 54	88.79 53	92.26 26

Effi-MVS+		75.28 63	76.20 69	74.20 64	81.15 66	83.24 66	81.11 56	63.13 85	66.37 70	60.27 78	64.30 74	68.88 82	70.93 57	81.56 65	81.69 56	88.61 54	87.35 60

UniMVSNet (Re)		69.53 109	71.90 86	66.76 144	76.42 112	80.93 84	72.59 151	68.03 52	61.75 103	41.68 185	58.34 99	57.23 122	53.27 176	79.53 104	80.62 75	88.57 55	84.90 88

PCF-MVS	73.28 6	79.42 45	80.41 48	78.26 43	84.88 55	88.17 32	86.08 34	69.85 38	75.23 53	68.43 51	68.03 61	78.38 42	71.76 50	81.26 73	80.65 74	88.56 56	91.18 35
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		79.21 47	80.32 49	77.92 46	87.46 34	88.15 33	83.95 49	67.48 56	74.28 55	68.25 52	64.70 73	77.04 47	72.17 46	85.42 37	85.00 38	88.22 57	87.62 58
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
Fast-Effi-MVS+		73.11 72	73.66 75	72.48 69	77.72 101	80.88 87	78.55 88	58.83 163	65.19 78	60.36 77	59.98 86	62.42 101	71.22 55	81.66 62	80.61 76	88.20 58	84.88 89

OMC-MVS		80.26 37	82.59 37	77.54 48	83.04 58	85.54 49	83.25 53	65.05 72	87.32 15	72.42 37	72.04 46	78.97 41	73.30 39	83.86 47	81.60 57	88.15 59	88.83 51

AdaColmap		79.74 43	78.62 54	81.05 28	89.23 28	86.06 47	84.95 45	71.96 28	79.39 44	75.51 26	63.16 76	68.84 83	76.51 26	83.55 51	82.85 50	88.13 60	86.46 66

OpenMVS	70.44 10	76.15 60	76.82 67	75.37 57	85.01 52	84.79 57	78.99 73	62.07 111	71.27 60	67.88 54	57.91 102	72.36 61	70.15 58	82.23 61	81.41 58	88.12 61	87.78 57

UA-Net		74.47 65	77.80 57	70.59 87	85.33 48	85.40 52	73.54 143	65.98 65	60.65 110	56.00 110	72.11 45	79.15 40	54.63 169	83.13 56	82.25 52	88.04 62	81.92 126

DU-MVS		69.63 104	70.91 91	68.13 118	75.99 119	79.54 102	73.81 139	69.20 45	61.20 107	43.23 177	58.52 93	53.50 155	58.57 135	79.22 107	80.45 77	87.97 63	83.97 95

FC-MVSNet-train		72.60 75	75.07 73	69.71 105	81.10 68	78.79 116	73.74 141	65.23 71	66.10 73	53.34 123	70.36 51	63.40 98	56.92 149	81.44 66	80.96 63	87.93 64	84.46 92

IB-MVS	66.94 12	71.21 82	71.66 88	70.68 84	79.18 81	82.83 70	72.61 150	61.77 115	59.66 117	63.44 70	53.26 156	59.65 108	59.16 134	76.78 151	82.11 53	87.90 65	87.33 61
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
PVSNet_BlendedMVS		76.21 58	77.52 60	74.69 61	79.46 79	83.79 62	77.50 101	64.34 77	69.88 62	71.88 39	68.54 59	70.42 70	67.05 69	83.48 52	79.63 84	87.89 66	86.87 64

PVSNet_Blended		76.21 58	77.52 60	74.69 61	79.46 79	83.79 62	77.50 101	64.34 77	69.88 62	71.88 39	68.54 59	70.42 70	67.05 69	83.48 52	79.63 84	87.89 66	86.87 64

DeepPCF-MVS	79.04 1	85.30 17	88.93 8	81.06 27	88.77 30	90.48 6	85.46 42	73.08 23	90.97 2	73.77 32	84.81 18	85.95 15	77.43 19	88.22 8	87.73 7	87.85 68	94.34 5

TAPA-MVS	71.42 9	77.69 55	80.05 51	74.94 59	80.68 71	84.52 58	81.36 55	63.14 84	84.77 23	64.82 66	68.72 56	75.91 51	71.86 48	81.62 63	79.55 88	87.80 69	85.24 81
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MVS_Test		75.37 62	77.13 65	73.31 67	79.07 82	81.32 80	79.98 61	60.12 145	69.72 64	64.11 68	70.53 50	73.22 58	68.90 63	80.14 95	79.48 90	87.67 70	85.50 76

DI_MVS_plusplus_trai		75.13 64	76.12 70	73.96 65	78.18 87	81.55 76	80.97 57	62.54 105	68.59 66	65.13 65	61.43 79	74.81 54	69.32 62	81.01 78	79.59 86	87.64 71	85.89 69

MVSTER		72.06 76	74.24 74	69.51 106	70.39 183	75.97 158	76.91 107	57.36 172	64.64 83	61.39 75	68.86 55	63.76 96	63.46 105	81.44 66	79.70 83	87.56 72	85.31 80

TSAR-MVS + ACMM		85.10 20	88.81 11	80.77 30	89.55 24	88.53 29	88.59 23	72.55 25	87.39 12	71.90 38	90.95 5	87.55 9	74.57 30	87.08 23	86.54 22	87.47 73	93.67 13

CLD-MVS		79.35 46	81.23 42	77.16 50	85.01 52	86.92 41	85.87 36	60.89 125	80.07 43	75.35 27	72.96 42	73.21 59	68.43 67	85.41 38	84.63 40	87.41 74	85.44 78
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Effi-MVS+-dtu		71.82 78	71.86 87	71.78 70	78.77 83	80.47 96	78.55 88	61.67 118	60.68 109	55.49 111	58.48 95	65.48 92	68.85 64	76.92 148	75.55 163	87.35 75	85.46 77

WR-MVS		63.03 175	67.40 149	57.92 194	75.14 128	77.60 134	60.56 207	66.10 62	54.11 179	23.88 216	53.94 148	53.58 153	34.50 212	73.93 169	77.71 114	87.35 75	80.94 132

v13		69.52 110	68.76 128	70.41 94	74.88 132	77.02 144	78.52 92	58.86 157	56.61 153	56.91 98	54.00 147	56.17 134	66.11 94	77.93 121	76.74 138	87.21 77	82.83 109

v12		69.54 108	68.79 126	70.41 94	74.88 132	77.03 142	78.54 91	58.85 159	56.71 145	56.87 100	54.13 145	56.23 133	66.15 90	77.89 122	76.74 138	87.17 78	82.80 110

v144192		69.34 114	68.68 131	70.12 99	74.06 154	80.54 92	78.08 97	60.54 130	54.99 173	54.13 117	52.92 163	52.80 169	66.73 77	77.13 145	76.72 143	87.15 79	85.63 71

EPNet		79.08 50	80.62 45	77.28 49	88.90 29	83.17 68	83.65 50	72.41 26	74.41 54	67.15 59	76.78 30	74.37 55	64.43 102	83.70 50	83.69 46	87.15 79	88.19 53
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EG-PatchMatch MVS		67.24 147	66.94 154	67.60 124	78.73 84	81.35 79	73.28 147	59.49 150	46.89 209	51.42 135	43.65 201	53.49 156	55.50 165	81.38 68	80.66 73	87.15 79	81.17 131

v1144		69.93 103	69.36 116	70.61 86	74.89 131	80.93 84	79.11 71	60.64 127	55.97 162	55.31 113	53.85 149	54.14 148	66.54 80	78.10 120	77.44 121	87.14 82	85.09 83

anonymousdsp		65.28 156	67.98 142	62.13 172	58.73 218	73.98 176	67.10 180	50.69 200	48.41 204	47.66 156	54.27 138	52.75 170	61.45 123	76.71 152	80.20 80	87.13 83	89.53 48

V9		69.58 107	68.83 124	70.46 91	74.85 135	77.04 140	78.65 86	58.85 159	56.83 144	57.12 96	54.26 140	56.31 128	66.14 92	77.83 124	76.76 133	87.13 83	82.79 112

PLC	68.99 11	75.68 61	75.31 72	76.12 55	82.94 59	81.26 81	79.94 62	66.10 62	77.15 48	66.86 61	59.13 91	68.53 84	73.73 37	80.38 87	79.04 92	87.13 83	81.68 128
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7		70.33 92	69.87 101	70.88 75	74.79 138	81.04 83	79.22 69	60.57 129	57.70 134	56.65 106	54.23 142	55.29 140	66.95 72	78.28 118	77.47 119	87.12 86	85.05 85

v10		70.22 94	69.76 105	70.74 81	74.79 138	80.30 99	79.22 69	59.81 148	57.71 133	56.58 107	54.22 144	55.31 138	66.95 72	78.28 118	77.47 119	87.12 86	85.07 84

V14		69.59 106	68.86 123	70.45 93	74.83 136	77.04 140	78.70 85	58.83 163	56.95 141	57.08 97	54.41 136	56.34 127	66.15 90	77.77 125	76.76 133	87.08 88	82.74 115

v1192		69.50 111	68.83 124	70.29 97	74.49 151	80.92 86	78.55 88	60.54 130	55.04 171	54.21 116	52.79 166	52.33 173	66.92 74	77.88 123	77.35 124	87.04 89	85.51 75

v15		69.61 105	68.88 122	70.46 91	74.81 137	77.03 142	78.75 84	58.83 163	57.06 137	57.18 95	54.55 135	56.37 126	66.13 93	77.70 126	76.76 133	87.03 90	82.69 117

v11		69.37 113	68.65 132	70.20 98	74.87 134	76.97 145	78.29 95	58.55 167	56.38 156	56.04 109	54.02 146	54.98 142	66.47 81	78.30 117	76.91 129	86.97 91	83.02 108

v1921920		69.03 117	68.32 137	69.86 102	74.03 155	80.37 97	77.55 99	60.25 139	54.62 174	53.59 122	52.36 173	51.50 184	66.75 76	77.17 144	76.69 148	86.96 92	85.56 72

v2v482		70.05 98	69.46 110	70.74 81	74.62 150	80.32 98	79.00 72	60.62 128	57.41 135	56.89 99	55.43 117	55.14 141	66.39 82	77.25 143	77.14 126	86.90 93	83.57 105

Vis-MVSNet		72.77 74	77.20 64	67.59 125	74.19 153	84.01 60	76.61 111	61.69 116	60.62 111	50.61 140	70.25 52	71.31 65	55.57 164	83.85 48	82.28 51	86.90 93	88.08 54
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1		69.97 100	69.45 112	70.59 87	74.78 140	80.51 93	78.84 76	60.30 135	56.98 138	56.81 101	54.69 132	56.29 130	65.91 98	77.37 138	76.71 146	86.89 95	83.59 102

v1141		69.96 102	69.44 113	70.58 89	74.78 140	80.50 94	78.85 74	60.30 135	56.95 141	56.74 103	54.68 133	56.26 132	65.93 96	77.38 137	76.72 143	86.88 96	83.57 105

divwei89l23v2f112		69.97 100	69.44 113	70.58 89	74.78 140	80.50 94	78.85 74	60.30 135	56.97 140	56.75 102	54.67 134	56.27 131	65.92 97	77.37 138	76.72 143	86.88 96	83.58 104

PEN-MVS		62.96 176	65.77 169	59.70 187	73.98 156	75.45 161	63.39 200	67.61 55	52.49 186	25.49 215	53.39 152	49.12 196	40.85 206	71.94 182	77.26 125	86.86 98	80.72 134

v17		70.03 99	69.43 115	70.72 83	74.75 143	77.09 137	78.78 83	58.85 159	59.53 119	58.72 85	54.87 129	57.39 116	66.38 83	77.60 130	76.75 136	86.83 99	82.80 110

v1neww		70.34 90	69.93 99	70.82 77	74.68 146	80.61 90	78.80 79	60.17 140	58.74 124	58.10 90	55.00 124	57.28 120	66.33 86	77.53 131	76.74 138	86.82 100	83.61 100

v7new		70.34 90	69.93 99	70.82 77	74.68 146	80.61 90	78.80 79	60.17 140	58.74 124	58.10 90	55.00 124	57.28 120	66.33 86	77.53 131	76.74 138	86.82 100	83.61 100

v8		70.23 93	69.86 103	70.67 85	74.69 145	79.82 101	78.79 81	59.18 153	58.80 123	58.20 88	55.00 124	57.33 117	66.31 89	77.51 134	76.71 146	86.82 100	83.88 98

v6		70.35 89	69.94 98	70.83 76	74.68 146	80.62 89	78.81 78	60.16 143	58.81 122	58.17 89	55.01 123	57.31 119	66.32 88	77.53 131	76.73 142	86.82 100	83.62 99

ACMH+	66.54 13	71.36 81	70.09 96	72.85 68	82.59 61	81.13 82	78.56 87	68.04 51	61.55 104	52.52 130	51.50 177	54.14 148	68.56 66	78.85 112	79.50 89	86.82 100	83.94 97

v16		70.07 97	69.46 110	70.79 79	74.74 144	77.08 138	78.79 81	58.86 157	59.75 116	59.15 81	54.87 129	57.33 117	66.38 83	77.61 129	76.77 131	86.81 105	82.79 112

v1240		68.64 122	67.89 144	69.51 106	73.89 157	80.26 100	76.73 109	59.97 147	53.43 183	53.08 125	51.82 176	50.84 187	66.62 79	76.79 150	76.77 131	86.78 106	85.34 79

Anonymous20240521		73.65 69	75.78 71	71.16 74	80.19 75	79.27 106	77.45 103	61.68 117	66.73 69	58.72 85	65.31 70	69.96 73	62.19 113	81.29 72	80.97 62	86.74 107	86.91 63

v18		70.10 96	69.52 108	70.77 80	74.66 149	77.06 139	78.84 76	58.84 162	60.01 115	59.23 80	55.06 122	57.47 115	66.34 85	77.50 135	76.75 136	86.71 108	82.77 114

GBi-Net		70.78 83	73.37 78	67.76 119	72.95 163	78.00 124	75.15 116	62.72 96	64.13 86	51.44 132	58.37 96	69.02 79	57.59 141	81.33 69	80.72 67	86.70 109	82.02 120

test1		70.78 83	73.37 78	67.76 119	72.95 163	78.00 124	75.15 116	62.72 96	64.13 86	51.44 132	58.37 96	69.02 79	57.59 141	81.33 69	80.72 67	86.70 109	82.02 120

FMVSNet2		70.39 88	72.67 82	67.72 122	72.95 163	78.00 124	75.15 116	62.69 100	63.29 91	51.25 136	55.64 114	68.49 85	57.59 141	80.91 79	80.35 79	86.70 109	82.02 120

v7n		67.05 149	66.94 154	67.17 135	72.35 168	78.97 107	73.26 148	58.88 156	51.16 194	50.90 137	48.21 188	50.11 191	60.96 124	77.70 126	77.38 122	86.68 112	85.05 85

WR-MVS_H		61.83 190	65.87 168	57.12 197	71.72 173	76.87 146	61.45 205	66.19 60	51.97 190	22.92 223	53.13 160	52.30 175	33.80 213	71.03 189	75.00 168	86.65 113	80.78 133

MSDG		71.52 80	69.87 101	73.44 66	82.21 64	79.35 105	79.52 65	64.59 75	66.15 72	61.87 72	53.21 158	56.09 135	65.85 99	78.94 111	78.50 96	86.60 114	76.85 170

FMVSNet3		70.49 87	72.90 80	67.67 123	72.88 166	77.98 127	74.96 122	62.72 96	64.13 86	51.44 132	58.37 96	69.02 79	57.43 144	79.43 105	79.57 87	86.59 115	81.81 127

MVS_111021_LR		78.13 54	79.85 52	76.13 54	81.12 67	81.50 78	80.28 60	65.25 70	76.09 50	71.32 46	76.49 32	72.87 60	72.21 45	82.79 59	81.29 59	86.59 115	87.91 55

DTE-MVSNet		61.85 188	64.96 179	58.22 193	74.32 152	74.39 174	61.01 206	67.85 54	51.76 192	21.91 226	53.28 155	48.17 199	37.74 209	72.22 179	76.44 149	86.52 117	78.49 157

FMVSNet1		68.84 119	70.47 95	66.94 140	71.35 180	77.68 132	74.71 123	62.35 110	56.93 143	49.94 145	50.01 183	64.59 94	57.07 147	81.33 69	80.72 67	86.25 118	82.00 123

UGNet		72.78 73	77.67 58	67.07 138	71.65 175	83.24 66	75.20 115	63.62 80	64.93 80	56.72 104	71.82 47	73.30 57	49.02 185	81.02 77	80.70 72	86.22 119	88.67 52
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
Anonymous20231211		71.90 77	72.48 83	71.21 73	80.14 76	81.53 77	76.92 106	62.89 88	64.46 85	58.94 82	43.80 200	70.98 66	62.22 112	80.70 80	80.19 81	86.18 120	85.73 70

conf0.002		67.52 144	67.64 145	67.39 128	77.80 100	78.94 108	74.28 129	62.81 89	56.64 147	46.70 159	53.65 150	46.28 206	56.59 150	80.33 90	78.37 102	86.17 121	79.23 152

conf0.01		67.72 136	67.99 141	67.39 128	77.82 98	78.94 108	74.28 129	62.81 89	56.64 147	46.70 159	53.33 154	48.59 198	56.59 150	80.34 89	78.43 97	86.16 122	79.67 148

tfpn111		68.38 123	69.23 118	67.39 128	77.83 93	78.93 110	74.28 129	62.81 89	56.64 147	46.70 159	56.24 111	53.47 158	56.59 150	80.41 82	78.43 97	86.11 123	80.53 137

conf200view11		68.11 127	68.72 129	67.39 128	77.83 93	78.93 110	74.28 129	62.81 89	56.64 147	46.70 159	52.65 168	53.47 158	56.59 150	80.41 82	78.43 97	86.11 123	80.53 137

tfpn200view9		68.11 127	68.72 129	67.40 127	77.83 93	78.93 110	74.28 129	62.81 89	56.64 147	46.82 157	52.65 168	53.47 158	56.59 150	80.41 82	78.43 97	86.11 123	80.52 139

CANet_DTU		73.29 71	76.96 66	69.00 110	77.04 109	82.06 73	79.49 66	56.30 177	67.85 67	53.29 124	71.12 49	70.37 72	61.81 121	81.59 64	80.96 63	86.09 126	84.73 90

CP-MVSNet		62.68 178	65.49 172	59.40 190	71.84 171	75.34 162	62.87 202	67.04 58	52.64 185	27.19 213	53.38 153	48.15 200	41.40 204	71.26 185	75.68 159	86.07 127	82.00 123

ACMH	65.37 14	70.71 85	70.00 97	71.54 72	82.51 62	82.47 72	77.78 98	68.13 50	56.19 159	46.06 166	54.30 137	51.20 185	68.68 65	80.66 81	80.72 67	86.07 127	84.45 93
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres600view7		67.68 137	68.43 135	66.80 142	77.90 88	78.86 114	73.84 138	62.75 94	56.07 160	44.70 174	52.85 165	52.81 168	55.58 163	80.41 82	77.77 112	86.05 129	80.28 141

thres200		67.98 130	68.55 134	67.30 133	77.89 90	78.86 114	74.18 136	62.75 94	56.35 157	46.48 164	52.98 162	53.54 154	56.46 155	80.41 82	77.97 109	86.05 129	79.78 147

CNLPA		77.20 56	77.54 59	76.80 52	82.63 60	84.31 59	79.77 63	64.64 74	85.17 20	73.18 34	56.37 110	69.81 74	74.53 31	81.12 76	78.69 95	86.04 131	87.29 62

view800		67.35 146	68.22 139	66.35 147	77.83 93	78.62 118	72.97 149	62.58 104	55.71 163	44.13 175	52.69 167	52.24 177	54.58 171	80.27 92	78.19 105	86.01 132	79.79 146

TSAR-MVS + COLMAP		78.34 53	81.64 40	74.48 63	80.13 77	85.01 55	81.73 54	65.93 66	84.75 24	61.68 73	85.79 15	66.27 90	71.39 53	82.91 57	80.78 65	86.01 132	85.98 68

LS3D		74.08 67	73.39 77	74.88 60	85.05 50	82.62 71	79.71 64	68.66 47	72.82 59	58.80 84	57.61 103	61.31 103	71.07 56	80.32 91	78.87 94	86.00 134	80.18 142

tfpn		66.58 150	67.18 151	65.88 149	77.82 98	78.45 121	72.07 154	62.52 106	55.35 167	43.21 179	52.54 172	46.12 207	53.68 172	80.02 96	78.23 104	85.99 135	79.55 150

Anonymous202405211			72.16 85		80.85 70	81.85 75	76.88 108	65.40 69	62.89 95		46.35 196	67.99 86	62.05 115	81.15 75	80.38 78	85.97 136	84.50 91

PS-CasMVS		62.38 184	65.06 176	59.25 191	71.73 172	75.21 169	62.77 203	66.99 59	51.94 191	26.96 214	52.00 175	47.52 203	41.06 205	71.16 188	75.60 162	85.97 136	81.97 125

view600		67.63 141	68.36 136	66.77 143	77.84 92	78.66 117	73.74 141	62.62 103	56.04 161	44.98 171	52.86 164	52.83 167	55.48 166	80.36 88	77.75 113	85.95 138	80.02 144

thres400		67.95 131	68.62 133	67.17 135	77.90 88	78.59 119	74.27 134	62.72 96	56.34 158	45.77 168	53.00 161	53.35 163	56.46 155	80.21 94	78.43 97	85.91 139	80.43 140

conf0.05thres1000		66.26 152	66.77 156	65.66 150	77.45 105	78.10 122	71.85 157	62.44 109	51.47 193	43.00 180	47.92 190	51.66 183	53.40 174	79.71 99	77.97 109	85.82 140	80.56 135

v52		65.23 157	66.24 160	64.06 160	61.94 208	76.42 151	72.06 155	54.30 182	49.94 198	50.04 143	47.41 193	52.42 171	60.23 131	75.71 159	76.22 153	85.78 141	85.56 72

V4		65.23 157	66.23 161	64.06 160	61.94 208	76.42 151	72.05 156	54.31 181	49.91 200	50.06 142	47.42 192	52.40 172	60.24 130	75.71 159	76.22 153	85.78 141	85.56 72

v748		65.12 159	65.24 173	64.98 154	69.77 186	76.45 150	69.47 166	57.06 174	49.93 199	50.70 138	47.87 191	49.50 195	57.14 146	73.64 172	75.18 166	85.75 143	84.14 94

thres100view900		67.60 142	68.02 140	67.12 137	77.83 93	77.75 131	73.90 137	62.52 106	56.64 147	46.82 157	52.65 168	53.47 158	55.92 159	78.77 113	77.62 116	85.72 144	79.23 152

Vis-MVSNet (Re-imp)		67.83 134	73.52 76	61.19 178	78.37 86	76.72 148	66.80 183	62.96 86	65.50 77	34.17 204	67.19 65	69.68 75	39.20 208	79.39 106	79.44 91	85.68 145	76.73 171

V42		68.76 121	69.63 106	67.74 121	64.93 204	78.01 123	78.30 94	56.48 176	58.65 126	56.30 108	54.26 140	57.03 123	64.85 101	77.47 136	77.01 128	85.60 146	84.96 87

TransMVSNet (Re)		64.74 164	65.66 170	63.66 164	77.40 106	75.33 163	69.86 162	62.67 102	47.63 207	41.21 186	50.01 183	52.33 173	45.31 196	79.57 102	77.69 115	85.49 147	77.07 168

IterMVS-LS		71.69 79	72.82 81	70.37 96	77.54 103	76.34 154	75.13 119	60.46 133	61.53 105	57.57 93	64.89 71	67.33 87	66.04 95	77.09 147	77.37 123	85.48 148	85.18 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Baseline_NR-MVSNet		67.53 143	68.77 127	66.09 148	75.99 119	74.75 172	72.43 152	68.41 48	61.33 106	38.33 193	51.31 178	54.13 150	56.03 158	79.22 107	78.19 105	85.37 149	82.45 118

Fast-Effi-MVS+-dtu		68.34 124	69.47 109	67.01 139	75.15 127	77.97 129	77.12 105	55.40 180	57.87 128	46.68 163	56.17 113	60.39 104	62.36 111	76.32 156	76.25 152	85.35 150	81.34 129

tfpn_ndepth		65.09 160	67.12 152	62.73 169	75.75 124	76.23 155	68.00 172	60.36 134	58.16 127	40.27 187	54.89 128	54.22 147	46.80 192	76.69 153	75.66 160	85.19 151	73.98 188

GA-MVS		68.14 126	69.17 119	66.93 141	73.77 158	78.50 120	74.45 124	58.28 168	55.11 170	48.44 150	60.08 84	53.99 151	61.50 122	78.43 116	77.57 117	85.13 152	80.54 136

v148		67.85 133	67.53 146	68.23 116	73.25 161	77.57 135	74.26 135	57.36 172	55.70 164	57.45 94	53.53 151	55.42 137	61.96 117	75.23 163	73.92 172	85.08 153	81.32 130

HyFIR lowres test		69.47 112	68.94 121	70.09 100	76.77 111	82.93 69	76.63 110	60.17 140	59.00 121	54.03 118	40.54 210	65.23 93	67.89 68	76.54 155	78.30 103	85.03 154	80.07 143

gg-mvs-nofinetune		62.55 179	65.05 177	59.62 188	78.72 85	77.61 133	70.83 161	53.63 183	39.71 221	22.04 225	36.36 214	64.32 95	47.53 187	81.16 74	79.03 93	85.00 155	77.17 165

tfpn1000		63.81 173	66.31 159	60.90 180	75.76 123	75.74 159	65.14 192	60.14 144	56.47 154	35.99 201	55.11 121	52.30 175	43.42 200	76.21 157	75.34 165	84.97 156	73.01 191

COLMAP_ROB	62.73 15	67.66 138	66.76 157	68.70 113	80.49 74	77.98 127	75.29 114	62.95 87	63.62 89	49.96 144	47.32 195	50.72 188	58.57 135	76.87 149	75.50 164	84.94 157	75.33 180
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
tfpnnormal		64.27 168	63.64 187	65.02 153	75.84 122	75.61 160	71.24 160	62.52 106	47.79 206	42.97 181	42.65 203	44.49 211	52.66 178	78.77 113	76.86 130	84.88 158	79.29 151

tfpnview11		64.33 167	66.17 162	62.18 171	76.25 114	75.23 166	67.45 175	61.16 120	55.50 165	36.38 198	55.35 118	51.89 179	46.96 188	77.28 142	76.10 157	84.86 159	71.85 195

pm-mvs1		65.62 154	67.42 148	63.53 165	73.66 159	76.39 153	69.66 163	60.87 126	49.73 201	43.97 176	51.24 179	57.00 124	48.16 186	79.89 97	77.84 111	84.85 160	79.82 145

thresconf0.02		64.77 163	65.90 166	63.44 166	76.37 113	75.17 171	69.51 165	61.28 119	56.98 138	39.01 191	56.24 111	48.68 197	49.78 183	77.13 145	75.61 161	84.71 161	71.53 196

diffmvs		74.38 66	76.65 68	71.74 71	77.05 108	81.86 74	79.30 67	60.54 130	69.54 65	62.16 71	69.70 53	70.74 68	66.73 77	79.18 109	78.14 107	84.63 162	87.42 59

tfpn_n400		64.23 169	66.05 163	62.12 173	76.20 115	75.24 164	67.43 176	61.15 121	54.04 180	36.38 198	55.35 118	51.89 179	46.94 189	77.31 140	76.15 155	84.59 163	72.36 192

tfpnconf		64.23 169	66.05 163	62.12 173	76.20 115	75.24 164	67.43 176	61.15 121	54.04 180	36.38 198	55.35 118	51.89 179	46.94 189	77.31 140	76.15 155	84.59 163	72.36 192

gm-plane-assit		57.00 203	57.62 210	56.28 200	76.10 118	62.43 217	47.62 226	46.57 213	33.84 229	23.24 219	37.52 211	40.19 218	59.61 133	79.81 98	77.55 118	84.55 165	72.03 194

USDC		67.36 145	67.90 143	66.74 145	71.72 173	75.23 166	71.58 158	60.28 138	67.45 68	50.54 141	60.93 80	45.20 210	62.08 114	76.56 154	74.50 170	84.25 166	75.38 179

MS-PatchMatch		70.17 95	70.49 94	69.79 103	80.98 69	77.97 129	77.51 100	58.95 155	62.33 97	55.22 114	53.14 159	65.90 91	62.03 116	79.08 110	77.11 127	84.08 167	77.91 160

TDRefinement		66.09 153	65.03 178	67.31 132	69.73 187	76.75 147	75.33 112	64.55 76	60.28 113	49.72 147	45.63 198	42.83 213	60.46 129	75.75 158	75.95 158	84.08 167	78.04 159

pmmvs4		67.89 132	67.39 150	68.48 115	71.60 177	73.57 177	74.45 124	60.98 124	64.65 82	57.97 92	54.95 127	51.73 182	61.88 118	73.78 170	75.11 167	83.99 169	77.91 160

pmmvs5		62.37 185	64.04 184	60.42 182	65.03 202	71.67 183	67.17 179	52.70 190	50.30 195	44.80 172	54.23 142	51.19 186	49.37 184	72.88 174	73.48 175	83.45 170	74.55 183

pmmvs-eth3d		63.52 174	62.44 196	64.77 156	66.82 198	70.12 188	69.41 167	59.48 151	54.34 178	52.71 126	46.24 197	44.35 212	56.93 148	72.37 175	73.77 173	83.30 171	75.91 173

pmmvs6		62.41 182	62.88 190	61.87 175	71.38 179	75.18 170	67.76 174	59.45 152	41.64 217	42.52 184	37.33 212	52.91 166	46.87 191	77.67 128	76.26 151	83.23 172	79.18 154

CDS-MVSNet		67.65 139	69.83 104	65.09 152	75.39 126	76.55 149	74.42 127	63.75 79	53.55 182	49.37 148	59.41 89	62.45 100	44.44 197	79.71 99	79.82 82	83.17 173	77.36 164
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PMMVS		65.06 161	69.17 119	60.26 184	55.25 227	63.43 210	66.71 184	43.01 224	62.41 96	50.64 139	69.44 54	67.04 88	63.29 107	74.36 167	73.54 174	82.68 174	73.99 187

SixPastTwentyTwo		61.84 189	62.45 195	61.12 179	69.20 191	72.20 180	62.03 204	57.40 171	46.54 210	38.03 195	57.14 108	41.72 215	58.12 139	69.67 200	71.58 182	81.94 175	78.30 158

IterMVS		66.36 151	68.30 138	64.10 159	69.48 190	74.61 173	73.41 146	50.79 199	57.30 136	48.28 151	60.64 81	59.92 107	60.85 128	74.14 168	72.66 178	81.80 176	78.82 156
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchMatch-RL		67.78 135	66.65 158	69.10 109	73.01 162	72.69 179	68.49 170	61.85 114	62.93 94	60.20 79	56.83 109	50.42 189	69.52 61	75.62 161	74.46 171	81.51 177	73.62 189

TinyColmap		62.84 177	61.03 202	64.96 155	69.61 188	71.69 182	68.48 171	59.76 149	55.41 166	47.69 155	47.33 194	34.20 223	62.76 110	74.52 165	72.59 179	81.44 178	71.47 197

EPNet_dtu		68.08 129	71.00 90	64.67 157	79.64 78	68.62 194	75.05 120	63.30 82	66.36 71	45.27 170	67.40 64	66.84 89	43.64 199	75.37 162	74.98 169	81.15 179	77.44 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CR-MVSNet		64.83 162	65.54 171	64.01 162	70.64 182	69.41 189	65.97 188	52.74 188	57.81 130	52.65 127	54.27 138	56.31 128	60.92 125	72.20 180	73.09 176	81.12 180	75.69 176

RPMNet		61.71 192	62.88 190	60.34 183	69.51 189	69.41 189	63.48 199	49.23 203	57.81 130	45.64 169	50.51 181	50.12 190	53.13 177	68.17 207	68.49 198	81.07 181	75.62 178

test-mter		60.84 194	64.62 181	56.42 199	55.99 225	64.18 205	65.39 190	34.23 232	54.39 177	46.21 165	57.40 106	59.49 109	55.86 160	71.02 190	69.65 189	80.87 182	76.20 172

test-LLR		64.42 165	64.36 182	64.49 158	75.02 129	63.93 207	66.61 185	61.96 112	54.41 175	47.77 153	57.46 104	60.25 105	55.20 167	70.80 192	69.33 190	80.40 183	74.38 184

TESTMET0.1,1		61.10 193	64.36 182	57.29 196	57.53 220	63.93 207	66.61 185	36.22 230	54.41 175	47.77 153	57.46 104	60.25 105	55.20 167	70.80 192	69.33 190	80.40 183	74.38 184

DWT-MVSNet_training		67.24 147	65.96 165	68.74 111	76.15 117	74.36 175	74.37 128	56.66 175	61.82 102	60.51 76	58.23 101	49.76 193	65.07 100	70.04 199	70.39 186	79.70 185	77.11 167

CostFormer		68.92 118	69.58 107	68.15 117	75.98 121	76.17 157	78.22 96	51.86 193	65.80 75	61.56 74	63.57 75	62.83 99	61.85 119	70.40 198	68.67 195	79.42 186	79.62 149

CVMVSNet		62.55 179	65.89 167	58.64 192	66.95 196	69.15 191	66.49 187	56.29 178	52.46 187	32.70 205	59.27 90	58.21 113	50.09 182	71.77 183	71.39 183	79.31 187	78.99 155

CHOSEN 1792x2688		69.20 116	69.26 117	69.13 108	76.86 110	78.93 110	77.27 104	60.12 145	61.86 101	54.42 115	42.54 204	61.61 102	66.91 75	78.55 115	78.14 107	79.23 188	83.23 107

PM-MVS		60.48 195	60.94 203	59.94 185	58.85 217	66.83 200	64.27 197	51.39 196	55.03 172	48.03 152	50.00 185	40.79 217	58.26 138	69.20 203	67.13 205	78.84 189	77.60 162

tpmp4_e23		68.32 125	67.08 153	69.76 104	77.86 91	75.22 168	78.37 93	56.17 179	66.06 74	64.27 67	57.15 107	54.89 143	63.40 106	70.97 191	68.29 200	78.46 190	77.00 169

PatchT		61.97 187	64.04 184	59.55 189	60.49 212	67.40 197	56.54 214	48.65 207	56.69 146	52.65 127	51.10 180	52.14 178	60.92 125	72.20 180	73.09 176	78.03 191	75.69 176

RPSCF		67.64 140	71.25 89	63.43 167	61.86 210	70.73 186	67.26 178	50.86 198	74.20 56	58.91 83	67.49 63	69.33 76	64.10 103	71.41 184	68.45 199	77.61 192	77.17 165

MDTV_nov1_ep13_2view		60.16 196	60.51 204	59.75 186	65.39 201	69.05 192	68.00 172	48.29 209	51.99 188	45.95 167	48.01 189	49.64 194	53.39 175	68.83 204	66.52 206	77.47 193	69.55 202

MDTV_nov1_ep13		64.37 166	65.24 173	63.37 168	68.94 192	70.81 185	72.40 153	50.29 202	60.10 114	53.91 120	60.07 85	59.15 110	57.21 145	69.43 202	67.30 202	77.47 193	69.78 201

LTVRE_ROB	59.44 16	61.82 191	62.64 193	60.87 181	72.83 167	77.19 136	64.37 196	58.97 154	33.56 230	28.00 212	52.59 171	42.21 214	63.93 104	74.52 165	76.28 150	77.15 195	82.13 119
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
MDA-MVSNet-bldmvs		53.37 212	53.01 215	53.79 209	43.67 235	67.95 196	59.69 210	57.92 169	43.69 213	32.41 206	41.47 205	27.89 232	52.38 179	56.97 228	65.99 208	76.68 196	67.13 206

MVS-HIRNet		54.41 208	52.10 216	57.11 198	58.99 216	56.10 222	49.68 223	49.10 204	46.18 211	52.15 131	33.18 221	46.11 208	56.10 157	63.19 215	59.70 223	76.64 197	60.25 219

dps		64.00 172	62.99 189	65.18 151	73.29 160	72.07 181	68.98 169	53.07 186	57.74 132	58.41 87	55.55 116	47.74 202	60.89 127	69.53 201	67.14 204	76.44 198	71.19 198

test0.0.03 1		58.80 199	61.58 200	55.56 202	75.02 129	68.45 195	59.58 211	61.96 112	52.74 184	29.57 208	49.75 186	54.56 145	31.46 215	71.19 186	69.77 188	75.75 199	64.57 210

EU-MVSNet		54.63 207	58.69 206	49.90 213	56.99 221	62.70 215	56.41 215	50.64 201	45.95 212	23.14 220	50.42 182	46.51 205	36.63 210	65.51 210	64.85 209	75.57 200	74.91 181

PatchmatchNet		64.21 171	64.65 180	63.69 163	71.29 181	68.66 193	69.63 164	51.70 195	63.04 92	53.77 121	59.83 88	58.34 112	60.23 131	68.54 205	66.06 207	75.56 201	68.08 205
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Anonymous20231206		56.36 205	57.80 209	54.67 205	70.08 184	66.39 201	60.46 208	57.54 170	49.50 203	29.30 209	33.86 220	46.64 204	35.18 211	70.44 196	68.88 194	75.47 202	68.88 204

CMPMVS	47.78 17	62.49 181	62.52 194	62.46 170	70.01 185	70.66 187	62.97 201	51.84 194	51.98 189	56.71 105	42.87 202	53.62 152	57.80 140	72.23 178	70.37 187	75.45 203	75.91 173
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
MIMVSNet		58.52 201	61.34 201	55.22 203	60.76 211	67.01 199	66.81 182	49.02 205	56.43 155	38.90 192	40.59 209	54.54 146	40.57 207	73.16 173	71.65 181	75.30 204	66.00 208

TAMVS		59.58 198	62.81 192	55.81 201	66.03 200	65.64 204	63.86 198	48.74 206	49.95 197	37.07 197	54.77 131	58.54 111	44.44 197	72.29 177	71.79 180	74.70 205	66.66 207

tpm cat1		65.41 155	63.81 186	67.28 134	75.61 125	72.88 178	75.32 113	52.85 187	62.97 93	63.66 69	53.24 157	53.29 165	61.83 120	65.54 209	64.14 212	74.43 206	74.60 182

test20.03		53.93 210	56.28 211	51.19 211	72.19 170	65.83 202	53.20 218	61.08 123	42.74 215	22.08 224	37.07 213	45.76 209	24.29 228	70.44 196	69.04 192	74.31 207	63.05 214

FMVSNet5		57.24 202	60.02 205	53.99 207	56.45 222	62.74 214	65.27 191	47.03 212	55.14 169	39.55 190	40.88 207	53.42 162	41.83 201	72.35 176	71.10 185	73.79 208	64.50 211

testgi		54.39 209	57.86 208	50.35 212	71.59 178	67.24 198	54.95 216	53.25 185	43.36 214	23.78 217	44.64 199	47.87 201	24.96 224	70.45 195	68.66 196	73.60 209	62.78 215

MIMVSNet1		49.27 214	53.25 214	44.62 219	44.61 232	61.52 218	53.61 217	52.18 191	41.62 218	18.68 228	28.14 228	41.58 216	25.50 222	68.46 206	69.04 192	73.15 210	62.37 216

pmmvs3		47.65 215	49.08 220	45.99 217	44.61 232	54.79 225	50.04 221	31.95 235	33.91 228	29.90 207	30.37 222	33.53 224	46.31 194	63.50 213	63.67 213	73.14 211	63.77 213

FC-MVSNet-test		56.90 204	65.20 175	47.21 215	66.98 195	63.20 212	49.11 224	58.60 166	59.38 120	11.50 235	65.60 68	56.68 125	24.66 227	71.17 187	71.36 184	72.38 212	69.02 203

LP		53.62 211	53.43 212	53.83 208	58.51 219	62.59 216	57.31 213	46.04 216	47.86 205	42.69 183	36.08 216	36.86 221	46.53 193	64.38 212	64.25 211	71.92 213	62.00 217

GG-mvs-BLEND		46.86 220	67.51 147	22.75 233	0.05 242	76.21 156	64.69 194	0.04 239	61.90 100	0.09 243	55.57 115	71.32 64	0.08 239	70.54 194	67.19 203	71.58 214	69.86 200

ambc			53.42 213		64.99 203	63.36 211	49.96 222		47.07 208	37.12 196	28.97 224	16.36 238	41.82 202	75.10 164	67.34 201	71.55 215	75.72 175

tpmrst		62.00 186	62.35 197	61.58 176	71.62 176	64.14 206	69.07 168	48.22 211	62.21 98	53.93 119	58.26 100	55.30 139	55.81 161	63.22 214	62.62 215	70.85 216	70.70 199

tpm		62.41 182	63.15 188	61.55 177	72.24 169	63.79 209	71.31 159	46.12 215	57.82 129	55.33 112	59.90 87	54.74 144	53.63 173	67.24 208	64.29 210	70.65 217	74.25 186

FPMVS		51.87 213	50.00 218	54.07 206	66.83 197	57.25 220	60.25 209	50.91 197	50.25 196	34.36 203	36.04 217	32.02 225	41.49 203	58.98 226	56.07 226	70.56 218	59.36 221

EPMVS		60.00 197	61.97 198	57.71 195	68.46 193	63.17 213	64.54 195	48.23 210	63.30 90	44.72 173	60.19 83	56.05 136	50.85 181	65.27 211	62.02 217	69.44 219	63.81 212

PMVS	39.38 18	46.06 221	43.30 227	49.28 214	62.93 205	38.75 236	41.88 229	53.50 184	33.33 231	35.46 202	28.90 225	31.01 228	33.04 214	58.61 227	54.63 229	68.86 220	57.88 224
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test2356		47.20 218	48.62 222	45.54 218	56.38 223	54.89 224	50.62 220	45.08 219	38.65 222	23.40 218	36.23 215	31.10 227	29.31 218	62.76 216	62.49 216	68.48 221	54.23 227

CHOSEN 280x420		58.70 200	61.88 199	54.98 204	55.45 226	50.55 230	64.92 193	40.36 226	55.21 168	38.13 194	48.31 187	63.76 96	63.03 109	73.73 171	68.58 197	68.00 222	73.04 190

testus		45.61 222	49.06 221	41.59 223	56.13 224	55.28 223	43.51 228	39.64 228	37.74 223	18.23 229	35.52 219	31.28 226	24.69 226	62.46 217	62.90 214	67.33 223	58.26 223

testmv		42.58 224	44.36 224	40.49 224	54.63 228	52.76 226	41.21 232	44.37 221	28.83 232	12.87 232	27.16 229	25.03 233	23.01 229	60.83 220	61.13 218	66.88 224	54.81 225

test1235678		42.57 225	44.36 224	40.49 224	54.63 228	52.75 227	41.21 232	44.37 221	28.82 233	12.87 232	27.15 230	25.01 234	23.01 229	60.83 220	61.13 218	66.88 224	54.81 225

no-one		36.35 228	37.59 230	34.91 227	46.13 230	49.89 231	27.99 237	43.56 223	20.91 237	7.03 238	14.64 235	15.50 239	18.92 234	42.95 232	60.20 221	65.84 226	59.03 222

new-patchmatchnet		46.97 219	49.47 219	44.05 221	62.82 206	56.55 221	45.35 227	52.01 192	42.47 216	17.04 231	35.73 218	35.21 222	21.84 233	61.27 219	54.83 228	65.26 227	60.26 218

ADS-MVSNet		55.94 206	58.01 207	53.54 210	62.48 207	58.48 219	59.12 212	46.20 214	59.65 118	42.88 182	52.34 174	53.31 164	46.31 194	62.00 218	60.02 222	64.23 228	60.24 220

testpf		47.41 216	48.47 223	46.18 216	66.30 199	50.67 229	48.15 225	42.60 225	37.10 225	28.75 210	40.97 206	39.01 220	30.82 216	52.95 231	53.74 230	60.46 229	64.87 209

N_pmnet		47.35 217	50.13 217	44.11 220	59.98 213	51.64 228	51.86 219	44.80 220	49.58 202	20.76 227	40.65 208	40.05 219	29.64 217	59.84 224	55.15 227	57.63 230	54.00 228

new_pmnet		38.40 226	42.64 228	33.44 228	37.54 238	45.00 234	36.60 234	32.72 234	40.27 219	12.72 234	29.89 223	28.90 231	24.78 225	53.17 230	52.90 231	56.31 231	48.34 229

test12356		35.10 229	38.50 229	31.13 230	44.14 234	43.70 235	32.27 235	34.42 231	26.51 235	9.47 236	25.22 232	20.34 235	10.86 236	53.47 229	56.15 225	55.59 232	44.11 231

1111		43.08 223	44.02 226	41.98 222	59.22 214	49.27 232	41.48 230	45.63 217	35.01 226	23.06 221	28.60 226	30.15 229	27.22 219	60.42 222	57.97 224	55.27 233	46.74 230

Gipuma		36.38 227	35.80 231	37.07 226	45.76 231	33.90 237	29.81 236	48.47 208	39.91 220	18.02 230	8.00 239	8.14 241	25.14 223	59.29 225	61.02 220	55.19 234	40.31 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		25.60 231	29.75 232	20.76 234	28.00 239	30.93 238	23.10 238	29.18 236	23.14 236	1.46 242	18.23 234	16.54 237	5.08 237	40.22 233	41.40 233	37.76 235	37.79 234

E-PMN		21.77 232	18.24 235	25.89 231	40.22 236	19.58 240	12.46 241	39.87 227	18.68 239	6.71 239	9.57 236	4.31 244	22.36 232	19.89 237	27.28 235	33.73 236	28.34 236

EMVS		20.98 233	17.15 236	25.44 232	39.51 237	19.37 241	12.66 240	39.59 229	19.10 238	6.62 240	9.27 237	4.40 243	22.43 231	17.99 238	24.40 236	31.81 237	25.53 237

tmp_tt				14.50 236	14.68 240	7.17 243	10.46 243	2.21 238	37.73 224	28.71 211	25.26 231	16.98 236	4.37 238	31.49 234	29.77 234	26.56 238

MVE	19.12 19	20.47 234	23.27 234	17.20 235	12.66 241	25.41 239	10.52 242	34.14 233	14.79 240	6.53 241	8.79 238	4.68 242	16.64 235	29.49 235	41.63 232	22.73 239	38.11 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX						18.74 242	18.55 239	8.02 237	26.96 234	7.33 237	23.81 233	13.05 240	25.99 221	25.17 236		22.45 240	36.25 235

.test1245		30.81 230	29.14 233	32.77 229	59.22 214	49.27 232	41.48 230	45.63 217	35.01 226	23.06 221	28.60 226	30.15 229	27.22 219	60.42 222	0.10 237	0.01 241	0.43 239

testmvs		0.09 235	0.15 237	0.02 237	0.01 243	0.02 244	0.05 245	0.01 240	0.11 241	0.01 244	0.26 241	0.01 245	0.06 241	0.10 239	0.10 237	0.01 241	0.43 239

test123		0.09 235	0.14 238	0.02 237	0.00 244	0.02 244	0.02 246	0.01 240	0.09 242	0.00 245	0.30 240	0.00 246	0.08 239	0.03 240	0.09 239	0.01 241	0.45 238

sosnet-low-res		0.00 237	0.00 239	0.00 239	0.00 244	0.00 246	0.00 247	0.00 242	0.00 243	0.00 245	0.00 242	0.00 246	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

sosnet		0.00 237	0.00 239	0.00 239	0.00 244	0.00 246	0.00 247	0.00 242	0.00 243	0.00 245	0.00 242	0.00 246	0.00 242	0.00 241	0.00 240	0.00 244	0.00 241

our_test_3					67.93 194	70.99 184	66.89 181

MTAPA										83.48 1		86.45 13

MTMP										82.66 3		84.91 21

Patchmatch-RL test							2.85 244

mPP-MVS					89.90 21							81.29 36

NP-MVS									80.10 42

Patchmtry						65.80 203	65.97 188	52.74 188		52.65 127

High-resolution multi-view benchmark