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		74.06 4	77.71 9	69.79 1	78.95 1	81.99 3	76.33 4	62.16 1	75.89 16	52.96 20	64.37 26	73.30 15	65.66 2	77.49 1	77.43 2	82.67 1	93.51 1

ESAPD		76.46 1	83.32 1	68.47 2	78.83 2	83.07 2	77.86 1	58.75 4	86.89 3	56.64 8	89.08 2	83.11 1	60.69 13	74.28 7	74.11 8	78.06 30	92.67 3

CSCG		72.98 7	76.86 11	68.46 3	78.23 3	81.74 5	77.26 3	60.00 2	75.61 19	59.06 1	62.72 28	77.42 4	56.63 37	74.24 8	77.18 3	79.56 13	89.13 14

APDe-MVS		74.59 2	80.23 3	68.01 5	76.51 4	80.20 11	77.39 2	58.18 5	85.31 4	56.84 6	84.89 3	76.08 8	60.66 14	71.85 23	71.76 18	78.47 23	91.49 6

3Dnovator	58.39 4	65.97 31	66.85 44	64.94 10	73.72 5	79.03 16	67.73 34	54.25 20	61.52 49	52.79 22	42.27 77	60.73 49	62.01 6	71.29 25	71.75 19	79.12 18	81.34 82

MAR-MVS		66.85 26	69.81 35	63.39 19	73.56 6	80.51 9	69.87 24	51.51 32	67.78 39	46.44 39	51.09 55	61.60 46	60.38 15	72.67 18	73.61 11	78.59 21	81.44 78
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
SteuartSystems-ACMMP		69.78 15	74.76 15	63.98 14	73.45 7	78.56 20	73.13 11	55.24 16	70.68 29	48.93 32	70.43 16	69.10 22	54.00 48	72.78 17	72.98 13	79.14 17	88.74 17
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		73.87 5	78.60 6	68.35 4	73.32 8	81.97 4	76.19 5	59.29 3	80.12 9	56.70 7	67.09 20	76.48 6	64.26 4	75.88 3	75.75 4	80.32 8	92.93 2

QAPM		65.47 34	67.82 39	62.72 22	72.56 9	81.17 8	67.43 37	55.38 15	56.07 62	43.29 55	43.60 74	65.38 30	59.10 22	72.20 20	70.76 30	78.56 22	85.59 38

CHOSEN 1792x2688		62.48 50	64.06 54	60.64 36	72.50 10	84.18 1	62.43 62	53.77 23	47.90 81	39.85 72	25.15 196	44.76 98	53.72 49	77.29 2	77.61 1	81.60 4	91.53 5

NCCC		71.36 11	75.44 13	66.60 7	72.46 11	79.18 15	74.16 7	57.83 6	76.93 14	54.19 14	63.47 27	71.08 20	61.30 10	73.56 10	73.70 10	79.69 12	90.19 8

OpenMVS	55.62 8	62.57 46	63.76 55	61.19 33	72.13 12	78.84 18	64.42 54	50.51 41	56.44 59	45.67 46	36.88 105	56.51 57	56.66 36	68.28 49	68.96 43	77.73 35	80.44 89

HSP-MVS		74.54 3	81.12 2	66.86 6	71.93 13	78.65 19	72.60 12	55.44 13	89.94 1	54.35 12	92.24 1	77.08 5	69.84 1	75.48 4	75.01 5	76.99 44	79.45 93

HPM-MVS++		72.44 8	78.73 5	65.11 9	71.88 14	77.31 27	71.98 15	55.67 11	83.11 7	53.59 16	75.90 8	78.49 3	61.00 11	73.99 9	73.31 12	76.55 47	88.97 15

SMA-MVS		73.13 6	79.49 4	65.71 8	71.08 15	80.36 10	74.71 6	56.31 7	83.60 5	53.34 18	72.81 12	80.52 2	60.83 12	74.88 6	74.53 7	77.96 32	89.72 9

MVS_111021_HR		64.66 38	67.11 43	61.80 28	71.04 16	77.91 23	62.75 61	54.78 18	51.43 70	47.54 38	53.77 48	54.85 60	56.84 32	70.59 28	71.50 21	77.86 33	89.70 10

AdaColmap		62.79 44	62.63 59	62.98 21	70.82 17	72.90 59	67.84 33	54.09 22	65.14 44	50.71 26	41.78 78	47.64 86	60.17 17	67.41 55	66.83 57	74.28 104	76.69 107

abl_6				63.79 18	70.80 18	81.22 7	65.26 52	53.25 25	77.02 13	53.02 19	65.14 25	73.74 13	60.30 16			80.13 9	90.27 7

MS-PatchMatch		61.41 56	61.88 65	60.85 34	70.57 19	75.98 34	66.29 45	46.91 78	50.56 72	48.28 36	36.30 112	51.64 66	50.95 70	72.89 14	70.65 31	82.13 3	75.17 124

APD-MVS		71.86 9	77.91 8	64.80 11	70.39 20	75.69 38	74.02 8	56.14 9	83.59 6	52.92 21	84.67 4	73.46 14	59.30 21	69.47 35	69.66 36	76.02 55	88.84 16
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
train_agg		70.74 12	76.53 12	63.98 14	70.33 21	75.16 40	72.33 14	55.78 10	75.74 17	50.41 29	80.08 7	73.15 16	57.75 29	71.96 22	70.94 28	77.25 42	88.69 18

ACMMP_Plus		71.50 10	77.27 10	64.77 12	69.64 22	79.26 13	73.53 9	54.73 19	79.32 11	54.23 13	74.81 9	74.61 12	59.40 20	73.00 11	72.17 15	77.10 43	87.72 23

TSAR-MVS + ACMM		65.95 32	72.83 18	57.93 52	69.35 23	65.85 109	73.36 10	39.84 155	76.00 15	48.69 35	82.54 6	75.03 11	49.38 89	65.33 67	63.42 106	66.94 178	81.67 74

TSAR-MVS + MP.		70.28 13	75.09 14	64.66 13	69.34 24	64.61 120	72.60 12	56.29 8	80.73 8	58.36 3	84.56 5	75.22 10	55.37 42	69.11 41	69.45 37	75.97 57	81.97 66

SD-MVS		68.30 17	72.58 19	63.31 20	69.24 25	67.85 92	70.81 21	53.65 24	79.64 10	58.52 2	74.31 10	75.37 9	53.52 54	65.63 64	63.56 104	74.13 109	81.73 73

DELS-MVS		67.36 19	70.34 34	63.89 17	69.12 26	81.55 6	70.82 20	55.02 17	53.38 66	48.83 33	56.45 40	59.35 51	60.05 19	74.93 5	74.78 6	79.51 14	91.95 4
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
CDPH-MVS		67.03 25	71.64 25	61.65 30	69.10 27	76.84 31	71.35 19	55.42 14	67.02 40	42.83 57	65.27 24	64.60 32	53.16 57	69.70 34	71.40 22	78.02 31	86.67 29

HFP-MVS		68.75 16	72.84 17	63.98 14	68.87 28	75.09 41	71.87 16	51.22 33	73.50 23	58.17 4	68.05 19	68.67 23	57.79 28	70.49 30	69.23 39	75.98 56	84.84 42

MSLP-MVS++		61.81 54	62.19 64	61.37 32	68.33 29	63.08 138	70.75 22	38.89 164	63.96 47	57.51 5	48.59 61	61.66 45	53.67 52	62.04 115	59.92 151	79.03 19	76.08 110

CLD-MVS		64.69 37	67.25 40	61.69 29	68.22 30	78.33 21	63.09 58	47.59 65	69.64 33	53.98 15	54.87 45	53.94 63	57.87 27	72.79 15	71.34 23	79.40 15	69.87 161
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HQP-MVS		67.22 22	72.08 22	61.56 31	66.76 31	73.58 51	71.41 17	52.98 26	69.92 32	43.85 53	70.58 15	58.75 53	56.76 34	72.90 13	71.88 17	77.57 36	86.94 28

casdiffmvs		67.18 24	71.12 29	62.59 23	66.68 32	79.98 12	69.84 25	48.00 60	66.52 41	46.21 42	56.34 41	62.22 41	64.38 3	72.59 19	71.96 16	80.44 7	88.23 20

OPM-MVS		61.59 55	62.30 63	60.76 35	66.53 33	73.35 53	71.41 17	54.18 21	40.82 104	41.57 70	45.70 69	54.84 61	54.43 47	69.92 33	69.19 40	76.45 48	82.25 58

MP-MVS		67.34 20	73.08 16	60.64 36	66.20 34	76.62 32	69.22 28	50.92 35	70.07 30	48.81 34	69.66 17	70.12 21	53.68 51	68.41 46	69.13 41	74.98 74	87.53 24
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		67.78 18	72.46 20	62.33 26	66.09 35	74.21 44	70.05 23	51.54 31	77.27 12	54.61 11	60.30 35	71.51 19	56.73 35	69.19 39	68.63 47	74.96 75	86.11 33

3Dnovator+	55.76 7	62.70 45	65.10 51	59.90 42	65.89 36	72.15 62	62.94 60	49.82 44	62.77 48	49.06 31	43.62 73	61.47 48	58.60 25	68.51 45	66.75 58	73.08 129	80.40 90

PGM-MVS		65.35 35	70.43 33	59.43 45	65.78 37	73.75 48	69.41 26	48.18 57	68.80 36	45.37 47	65.88 23	64.04 34	52.68 63	68.94 42	68.68 46	75.18 69	82.93 55

MSDG		52.58 111	51.40 148	53.95 72	65.48 38	64.31 130	61.44 65	44.02 117	44.17 89	32.92 113	30.40 170	31.81 173	46.35 115	62.13 113	62.55 117	73.49 119	64.41 175

ACMMPR		66.20 30	71.51 28	60.00 41	65.34 39	74.04 46	69.39 27	50.92 35	71.97 27	46.04 43	66.79 21	65.68 27	53.07 58	68.93 43	69.12 42	75.21 68	84.05 49

DWT-MVSNet_training		61.22 57	63.52 57	58.53 47	65.00 40	76.55 33	59.50 89	48.22 56	51.79 69	42.14 65	47.85 64	50.21 72	55.46 41	66.16 61	67.92 51	80.85 5	84.14 48

X-MVS		63.53 42	68.62 37	57.60 55	64.77 41	73.06 56	65.82 47	50.53 40	65.77 43	42.02 66	58.20 37	63.42 37	47.83 108	68.25 50	68.50 48	74.61 91	83.16 54

CANet		67.21 23	71.83 24	61.83 27	64.51 42	79.25 14	66.72 42	48.73 50	68.49 37	50.63 28	61.40 31	66.47 26	61.44 8	69.31 38	69.90 33	78.94 20	88.00 21

tpmp4_e23		59.70 60	61.03 69	58.14 49	63.70 43	73.33 54	65.69 48	39.53 157	52.56 67	46.23 41	41.59 79	47.46 87	57.38 30	65.01 69	65.89 65	76.31 50	81.36 81

FC-MVSNet-train		55.68 75	57.00 91	54.13 71	63.37 44	66.16 105	46.77 163	52.14 29	42.36 97	37.67 78	48.50 62	41.42 111	51.28 68	61.58 120	63.22 108	73.56 117	75.76 116

ACMH	47.82 13	50.10 139	49.60 160	50.69 106	63.36 45	66.99 99	56.83 104	52.13 30	31.06 174	17.74 188	28.22 183	26.24 202	45.17 123	60.88 131	63.80 102	68.91 163	70.00 159
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		62.45 51	65.68 48	58.67 46	63.29 46	77.65 24	67.62 35	38.42 167	54.04 64	46.00 44	48.27 63	57.89 55	56.97 31	67.03 58	67.79 52	79.74 10	87.09 27

ACMM	53.73 9	57.91 65	58.27 82	57.49 56	63.10 47	66.45 103	65.65 49	49.02 47	53.69 65	42.67 62	36.41 109	46.07 94	50.38 72	64.74 72	64.63 84	74.14 108	75.91 114
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mPP-MVS					63.08 48							62.34 40

PHI-MVS		65.17 36	72.07 23	57.11 60	63.02 49	77.35 26	67.04 40	48.14 59	68.03 38	37.56 79	66.00 22	65.39 29	53.19 56	70.68 27	70.57 32	73.72 115	86.46 32

DeepC-MVS_fast	60.18 3	66.84 27	70.69 32	62.36 25	62.76 50	73.21 55	67.96 32	52.31 27	72.26 26	51.03 23	56.50 39	64.26 33	63.37 5	71.64 24	70.85 29	76.70 46	86.10 34
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
XVS					62.70 51	73.06 56	61.80 63			42.02 66		63.42 37				74.68 89

X-MVStestdata					62.70 51	73.06 56	61.80 63			42.02 66		63.42 37				74.68 89

ACMMP		63.27 43	67.85 38	57.93 52	62.64 53	72.30 61	68.23 30	48.77 49	66.50 42	43.05 56	62.07 29	57.84 56	49.98 75	66.58 59	66.46 63	74.93 79	83.17 53
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
MVS_0304		66.31 29	71.61 26	60.14 40	62.59 54	78.98 17	67.13 39	45.75 89	64.35 46	45.23 48	60.69 33	67.67 25	61.73 7	71.09 26	71.03 26	78.41 26	87.44 25

Anonymous20240521		57.48 67	58.50 79	56.29 65	62.43 55	73.55 52	61.22 68	50.86 38	45.47 85	34.58 92	32.14 154	42.75 102	52.48 64	66.50 60	67.55 54	76.18 54	81.54 76

DeepPCF-MVS	62.48 1	70.07 14	78.36 7	60.39 39	62.38 56	76.96 30	65.54 50	52.23 28	87.46 2	49.07 30	74.05 11	76.19 7	59.01 23	72.79 15	71.61 20	74.13 109	89.49 11

CP-MVS		64.37 40	69.48 36	58.39 48	62.21 57	71.81 63	67.27 38	49.51 45	69.40 35	45.76 45	60.41 34	64.96 31	51.84 66	67.33 56	67.57 53	73.78 114	84.89 41

canonicalmvs		65.55 33	70.75 31	59.49 44	62.11 58	78.26 22	66.52 43	43.82 120	71.54 28	47.84 37	61.30 32	61.68 44	58.48 26	67.56 52	69.67 35	78.16 29	85.25 40

LGP-MVS_train		59.69 61	62.59 60	56.31 64	61.94 59	68.15 91	66.90 41	48.15 58	59.75 52	38.47 75	50.38 57	48.34 84	46.87 113	65.39 66	64.93 72	75.51 66	81.21 84

Effi-MVS+		59.63 62	61.78 68	57.12 59	61.56 60	71.63 64	63.61 57	47.59 65	47.18 82	37.79 76	45.29 70	49.93 74	56.27 38	67.45 53	67.06 56	75.91 58	83.93 50

HyFIR lowres test		57.12 71	59.11 72	54.80 69	61.55 61	77.55 25	59.02 92	45.00 96	41.84 102	33.93 101	22.44 203	49.16 79	51.02 69	68.39 47	68.71 45	78.26 28	85.70 36

TSAR-MVS + GP.		66.77 28	72.21 21	60.44 38	61.23 62	70.00 71	64.26 56	47.79 61	72.98 24	56.32 9	71.35 14	72.33 17	55.68 40	65.49 65	66.66 59	77.35 38	86.62 30

ACMP	56.21 5	59.78 59	61.81 67	57.41 57	61.15 63	68.88 87	65.98 46	48.85 48	58.56 55	44.19 51	48.89 60	46.31 92	48.56 100	63.61 99	64.49 95	75.75 61	81.91 67
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
IB-MVS	53.15 10	57.33 69	59.02 74	55.37 67	60.83 64	77.11 28	54.51 120	50.10 43	43.22 91	42.82 59	40.50 84	37.61 120	44.67 128	59.27 151	69.81 34	79.29 16	85.59 38
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
tpm cat1		57.41 68	58.26 83	56.42 63	60.80 65	72.56 60	64.35 55	38.43 166	49.18 78	46.36 40	36.69 107	43.50 100	54.47 44	61.39 123	62.64 115	74.11 111	81.81 71

gg-mvs-nofinetune		50.82 133	55.83 96	44.97 148	60.63 66	75.69 38	53.40 127	34.48 194	20.05 219	6.93 217	18.27 212	52.70 64	33.57 161	70.50 29	72.93 14	80.84 6	80.68 88

EPNet		64.39 39	70.93 30	56.77 62	60.58 67	75.77 35	59.28 91	50.58 39	69.93 31	40.73 71	68.59 18	61.60 46	53.72 49	68.65 44	68.07 49	75.75 61	83.87 51
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_Test		63.75 41	67.24 41	59.68 43	60.01 68	76.99 29	68.13 31	45.17 94	57.45 56	43.74 54	53.07 49	56.16 59	61.33 9	70.27 31	71.11 25	79.72 11	85.63 37

tpmrst		57.23 70	59.08 73	55.06 68	59.91 69	70.65 68	60.71 70	35.38 188	47.91 80	42.58 63	39.78 88	45.45 96	54.44 46	62.19 112	62.82 111	77.37 37	84.73 43

dps		52.84 107	52.92 131	52.74 80	59.89 70	69.49 79	54.47 121	37.38 172	42.49 96	39.53 73	35.33 114	32.71 163	51.83 67	60.45 137	61.12 137	73.33 123	68.86 165

DeepC-MVS	60.65 2	67.33 21	71.52 27	62.44 24	59.79 71	74.84 42	68.89 29	55.56 12	73.91 22	53.50 17	55.00 44	65.63 28	60.08 18	71.99 21	71.33 24	76.85 45	87.94 22
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EG-PatchMatch MVS		50.23 136	50.89 151	49.47 120	59.54 72	70.88 65	52.46 137	44.01 118	26.22 200	31.91 117	24.97 197	31.45 176	33.48 163	64.79 71	66.51 62	75.40 67	71.39 146

tpm		54.94 78	57.86 88	51.54 98	59.48 73	67.04 98	58.34 95	34.60 192	41.93 101	34.41 94	42.40 76	47.14 89	49.07 92	61.46 121	61.67 129	73.31 124	83.39 52

Effi-MVS+-dtu		53.63 96	54.85 110	52.20 90	59.32 74	61.33 160	56.42 111	40.24 153	43.84 90	34.22 97	39.49 93	46.18 93	53.00 61	58.72 156	57.49 164	69.99 157	76.91 104

PCF-MVS	55.99 6	62.31 52	66.60 45	57.32 58	59.12 75	73.68 50	67.53 36	48.71 51	61.35 50	42.83 57	51.33 54	63.48 36	53.48 55	65.64 63	64.87 73	72.22 136	85.83 35
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
PVSNet_BlendedMVS		62.53 47	66.37 46	58.05 50	58.17 76	75.70 36	61.30 66	48.67 52	58.67 53	50.93 24	55.43 42	49.39 76	53.01 59	69.46 36	66.55 60	76.24 52	89.39 12

PVSNet_Blended		62.53 47	66.37 46	58.05 50	58.17 76	75.70 36	61.30 66	48.67 52	58.67 53	50.93 24	55.43 42	49.39 76	53.01 59	69.46 36	66.55 60	76.24 52	89.39 12

TransMVSNet (Re)		47.46 155	48.94 166	45.74 142	57.96 78	64.29 131	48.26 151	48.47 54	26.33 199	19.33 177	29.45 180	31.28 181	25.31 193	63.05 105	62.70 112	75.10 72	65.47 172

DI_MVS_plusplus_trai		61.86 53	65.26 49	57.90 54	57.93 79	74.51 43	66.30 44	46.49 84	49.96 74	41.62 69	42.69 75	61.77 43	58.74 24	70.25 32	69.32 38	76.31 50	88.30 19

NR-MVSNet		48.84 147	51.76 144	45.44 144	57.66 80	60.64 163	47.39 157	47.63 63	37.26 122	13.31 197	37.31 102	29.64 191	33.53 162	63.52 101	62.09 124	73.10 128	71.89 142

PVSNet_Blended_VisFu		58.56 64	62.33 62	54.16 70	56.90 81	73.92 47	57.72 97	46.16 87	44.23 88	42.73 60	46.26 66	51.06 70	46.28 116	67.99 51	65.38 69	75.18 69	87.44 25

ACMH+	47.85 12	49.13 145	48.86 167	49.44 121	56.75 82	62.01 154	56.62 109	47.55 67	37.49 121	23.98 157	26.68 187	29.46 192	43.12 132	57.45 164	58.85 157	68.62 165	70.05 158

EPMVS		54.07 86	56.06 94	51.75 95	56.74 83	70.80 66	55.32 116	34.20 198	46.46 84	36.59 83	40.38 86	42.55 103	49.77 80	61.25 126	60.90 140	77.86 33	70.08 157

gm-plane-assit		45.41 167	48.03 170	42.34 168	56.49 84	40.48 219	24.54 224	34.15 201	14.44 228	6.59 218	17.82 213	35.32 146	49.82 78	72.93 12	74.11 8	82.47 2	81.12 85

conf0.002		51.63 123	53.79 120	49.12 123	56.33 85	64.84 118	53.05 131	47.38 69	35.86 137	24.83 148	37.86 100	38.15 117	41.08 138	61.04 129	62.70 112	72.05 137	76.06 111

conf0.01		51.32 126	53.22 123	49.11 124	56.29 86	64.78 119	53.05 131	47.37 70	35.86 137	24.83 148	36.85 106	36.64 131	41.08 138	61.01 130	61.37 131	72.03 138	76.01 112

Fast-Effi-MVS+-dtu		52.47 113	55.89 95	48.48 129	56.25 87	65.07 116	58.75 94	23.79 227	41.27 103	27.07 140	37.95 99	41.34 112	50.85 71	62.90 109	62.34 121	74.17 107	80.37 91

MVS_111021_LR		57.06 72	60.60 70	52.93 74	56.25 87	65.14 115	55.16 118	41.21 148	52.32 68	44.89 49	53.92 47	49.27 78	52.16 65	61.46 121	60.54 145	67.92 167	81.53 77

tfpn111		51.00 130	52.68 136	49.04 126	56.10 89	64.52 125	53.05 131	47.31 73	35.86 137	24.79 151	36.35 110	34.10 156	41.08 138	60.84 132	61.37 131	71.90 141	75.70 118

conf200view11		50.87 132	52.45 140	49.04 126	56.10 89	64.52 125	53.05 131	47.31 73	35.86 137	24.79 151	34.74 125	34.10 156	41.08 138	60.84 132	61.37 131	71.90 141	75.70 118

thres100view900		52.33 116	53.91 118	50.48 109	56.10 89	67.79 93	56.18 113	49.18 46	35.86 137	25.22 146	34.74 125	34.10 156	42.41 136	64.45 76	62.62 116	73.81 113	77.85 99

tfpn200view9		50.91 131	52.45 140	49.11 124	56.10 89	64.53 123	53.06 130	47.31 73	35.86 137	25.22 146	34.74 125	34.10 156	41.08 138	60.84 132	61.37 131	71.90 141	75.70 118

thres200		50.76 134	52.52 138	48.70 128	55.98 93	64.60 121	55.29 117	47.34 71	33.91 155	24.36 155	34.33 134	33.90 160	37.27 148	60.84 132	62.41 120	71.99 139	77.63 100

IS_MVSNet		51.53 124	57.98 86	44.01 155	55.96 94	66.16 105	47.65 156	42.84 133	39.82 107	19.09 180	44.97 71	50.28 71	27.20 189	63.43 103	63.84 101	71.33 146	77.33 101

PatchmatchNet		53.37 102	55.62 98	50.75 104	55.93 95	70.54 69	51.39 140	36.41 177	44.85 86	37.26 80	39.40 95	42.54 104	47.83 108	60.29 139	60.88 142	75.69 64	70.87 151
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LS3D		49.59 141	49.75 159	49.40 122	55.88 96	59.86 171	56.31 112	45.33 92	48.57 79	28.32 135	31.54 157	36.81 130	46.27 117	57.17 165	55.88 177	64.29 187	58.42 194

diffmvs		61.00 58	65.21 50	56.08 66	55.72 97	74.18 45	64.62 53	43.25 126	56.71 57	42.71 61	50.01 58	62.20 42	54.45 45	64.56 74	64.30 98	75.75 61	84.53 46

IterMVS-LS		53.36 103	55.65 97	50.68 107	55.34 98	59.04 173	55.00 119	39.98 154	38.72 112	33.22 112	44.52 72	47.05 90	49.63 83	61.82 118	61.77 126	70.92 149	76.61 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UA-Net		47.19 158	53.02 129	40.38 178	55.31 99	60.02 170	38.41 190	38.68 165	36.42 131	22.47 165	51.95 51	58.72 54	25.62 192	54.11 181	53.40 190	61.79 200	56.51 198

MVSTER		62.51 49	67.22 42	57.02 61	55.05 100	69.23 85	63.02 59	46.88 79	61.11 51	43.95 52	59.20 36	58.86 52	56.80 33	69.13 40	70.98 27	76.41 49	82.04 60

thres400		50.39 135	52.22 142	48.26 130	55.02 101	66.32 104	52.97 135	48.33 55	32.68 163	22.94 161	33.21 147	33.38 162	37.27 148	62.74 110	61.38 130	73.04 130	75.81 115

CPTT-MVS		59.54 63	64.47 53	53.79 73	54.99 102	67.63 95	65.48 51	44.59 110	64.81 45	37.74 77	51.55 52	59.90 50	49.77 80	61.83 117	61.26 136	70.18 155	84.31 47

EPP-MVSNet		52.91 106	58.91 75	45.91 140	54.99 102	68.84 88	49.27 146	42.71 135	37.53 120	20.20 173	46.09 67	56.19 58	36.90 150	61.37 124	60.90 140	71.41 145	81.41 79

tfpn_ndepth		46.53 164	49.41 162	43.18 163	54.66 104	61.56 157	42.25 180	45.66 91	35.68 143	18.31 186	36.55 108	34.84 151	28.88 185	55.45 176	57.01 170	69.32 161	64.78 174

Fast-Effi-MVS+		55.73 74	58.26 83	52.76 79	54.33 105	68.19 90	57.05 101	34.66 190	46.92 83	38.96 74	40.53 83	41.55 109	55.69 39	65.31 68	65.99 64	75.90 59	79.34 94

view600		48.85 146	50.58 155	46.82 135	54.19 106	64.94 117	50.81 141	47.53 68	31.78 169	21.59 167	32.31 152	32.63 165	34.28 158	61.06 127	57.41 165	72.54 133	73.96 127

MDTV_nov1_ep13		52.99 105	55.59 102	49.95 116	54.08 107	70.69 67	56.47 110	38.42 167	42.78 93	30.19 127	39.56 92	43.31 101	45.78 118	60.07 144	62.11 123	74.74 84	70.62 152

thres600view7		48.44 149	50.23 156	46.35 138	54.05 108	64.60 121	50.18 144	47.34 71	31.73 170	20.74 170	32.28 153	32.62 166	33.79 160	60.84 132	56.11 175	71.99 139	73.40 131

thresconf0.02		47.89 153	50.76 154	44.54 149	53.86 109	63.96 132	46.23 167	47.72 62	33.00 162	17.08 189	36.35 110	37.80 118	29.86 179	60.01 146	60.57 144	72.49 134	63.62 180

view800		47.68 154	49.78 158	45.24 146	53.39 110	63.19 137	48.13 153	46.57 83	30.98 175	20.25 172	31.52 158	31.90 172	31.52 171	59.37 149	59.61 154	71.56 144	71.89 142

EPNet_dtu		49.85 140	56.99 92	41.52 174	52.79 111	57.06 184	41.44 183	43.13 128	56.13 61	19.24 179	52.11 50	48.38 83	22.14 199	58.19 158	58.38 159	70.35 153	68.71 166
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpnnormal		46.61 163	46.82 176	46.37 137	52.70 112	62.31 151	50.39 143	47.17 76	25.74 203	21.80 166	23.13 201	24.15 211	33.45 164	60.28 140	60.77 143	72.70 132	71.39 146

tfpn		46.62 162	49.07 165	43.75 157	52.70 112	61.49 159	45.65 170	45.68 90	30.25 180	18.84 183	30.87 165	33.67 161	29.22 184	57.80 160	59.49 155	70.44 152	69.95 160

v18		53.63 96	54.35 114	52.80 78	52.25 114	62.94 140	60.80 69	42.78 134	39.23 109	36.81 81	35.07 118	37.78 119	49.82 78	63.69 92	64.65 83	74.32 100	77.07 103

v16		53.50 98	54.17 115	52.73 81	52.24 115	62.90 141	60.67 71	42.67 137	38.72 112	36.70 82	34.84 124	37.59 122	49.69 82	63.72 89	64.68 79	74.39 98	76.72 105

v17		53.40 101	54.09 116	52.60 83	52.22 116	62.90 141	60.64 73	42.66 138	38.24 115	36.04 84	34.85 123	37.59 122	49.63 83	63.70 91	64.68 79	74.39 98	76.70 106

CANet_DTU		57.87 66	63.63 56	51.15 101	52.18 117	70.20 70	58.14 96	37.32 173	56.49 58	31.06 122	57.38 38	50.05 73	53.67 52	64.98 70	65.04 71	74.57 92	81.29 83

v8		53.77 94	54.82 111	52.54 85	52.12 118	66.95 101	60.56 77	43.23 127	37.17 126	35.35 87	34.96 119	37.50 124	49.51 88	63.67 97	64.59 85	74.48 94	78.91 97

v1neww		54.32 83	55.60 100	52.81 76	52.11 119	69.43 82	60.57 74	44.86 99	37.13 128	35.34 88	34.95 121	37.46 126	49.53 85	63.69 92	64.59 85	74.47 95	81.99 62

v7new		54.32 83	55.60 100	52.81 76	52.11 119	69.43 82	60.57 74	44.86 99	37.13 128	35.34 88	34.95 121	37.46 126	49.53 85	63.69 92	64.59 85	74.47 95	81.99 62

v6		54.32 83	55.61 99	52.82 75	52.11 119	69.44 81	60.57 74	44.86 99	37.15 127	35.40 86	34.96 119	37.47 125	49.52 87	63.68 95	64.59 85	74.47 95	81.99 62

v7		53.99 89	55.20 103	52.57 84	52.08 122	69.46 80	59.68 85	44.92 98	35.90 134	34.33 95	33.99 138	35.55 143	50.08 74	64.38 78	64.67 81	74.32 100	82.47 56

v10		53.44 100	54.40 113	52.31 89	52.08 122	66.99 99	59.68 85	43.41 123	35.90 134	34.30 96	33.98 139	35.56 142	50.10 73	64.39 77	64.67 81	74.32 100	79.30 95

v1144		53.47 99	54.65 112	52.10 91	51.93 124	69.81 73	59.32 90	44.77 107	33.21 161	32.52 114	33.55 145	34.34 155	49.29 90	64.58 73	64.81 76	74.74 84	82.27 57

v15		52.60 110	53.17 125	51.94 92	51.92 125	62.76 143	60.04 82	42.19 140	34.35 146	33.96 100	34.37 133	36.40 132	48.73 98	63.75 88	64.59 85	74.79 80	75.97 113

V14		52.51 112	53.06 127	51.87 93	51.91 126	62.70 145	59.93 83	42.13 141	34.16 149	33.88 102	34.22 135	36.35 135	48.62 99	63.72 89	64.59 85	74.77 81	75.69 121

v1141		53.78 91	55.02 107	52.33 87	51.89 127	69.75 75	60.24 79	44.81 104	34.04 153	33.34 109	34.57 128	36.17 140	48.76 95	63.90 84	64.81 76	74.94 76	81.87 70

divwei89l23v2f112		53.78 91	55.02 107	52.33 87	51.89 127	69.74 77	60.25 78	44.82 103	34.06 151	33.33 110	34.56 131	36.18 138	48.75 96	63.90 84	64.82 74	74.94 76	81.88 68

v1		53.78 91	55.02 107	52.34 86	51.89 127	69.75 75	60.24 79	44.81 104	34.05 152	33.39 108	34.57 128	36.18 138	48.75 96	63.90 84	64.82 74	74.94 76	81.88 68

V9		52.42 115	52.94 130	51.81 94	51.89 127	62.64 147	59.81 84	42.08 143	33.97 154	33.85 103	34.05 137	36.28 136	48.49 101	63.68 95	64.59 85	74.76 82	75.41 122

conf0.05thres1000		45.26 169	46.99 174	43.24 161	51.87 131	60.52 166	45.17 172	45.24 93	27.06 195	18.60 184	26.24 189	31.23 182	28.82 186	56.88 168	58.52 158	69.71 159	68.50 167

v12		52.31 117	52.81 133	51.72 96	51.87 131	62.56 148	59.66 87	42.02 144	33.77 156	33.70 106	33.88 140	36.20 137	48.36 102	63.64 98	64.56 92	74.73 87	75.08 125

v13		52.22 119	52.69 135	51.67 97	51.85 133	62.48 150	59.57 88	41.97 145	33.61 158	33.69 107	33.71 143	36.11 141	48.23 105	63.57 100	64.55 93	74.72 88	74.77 126

Vis-MVSNet (Re-imp)		44.31 177	51.67 146	35.72 196	51.82 134	55.24 192	34.57 200	41.63 147	39.10 110	8.84 212	45.93 68	46.63 91	14.45 214	54.09 182	57.03 169	63.00 195	63.65 179

v11		52.23 118	52.83 132	51.53 99	51.73 135	62.49 149	58.82 93	41.81 146	33.53 159	33.23 111	33.73 142	35.10 148	49.07 92	64.49 75	64.71 78	74.49 93	75.75 117

v2v482		54.00 87	55.12 104	52.69 82	51.73 135	69.42 84	60.65 72	45.09 95	34.56 145	33.73 105	35.29 115	35.36 145	49.92 76	64.05 83	65.16 70	75.00 73	81.98 65

test-LLR		54.62 81	58.66 77	49.89 117	51.68 137	65.89 107	47.88 154	46.35 85	42.51 94	29.84 128	41.41 80	48.87 80	45.20 121	62.91 107	64.43 96	78.43 24	84.62 44

test0.0.03 1		43.07 183	46.95 175	38.54 186	51.68 137	58.77 176	35.28 195	46.35 85	32.05 167	12.44 199	28.53 182	35.52 144	14.40 215	57.12 167	56.93 171	71.11 147	59.69 187

GA-MVS		53.77 94	56.41 93	50.70 105	51.63 139	69.96 72	57.55 99	44.39 111	34.31 147	27.15 138	40.99 82	36.40 132	47.65 110	67.45 53	67.16 55	75.83 60	78.60 98

UniMVSNet_NR-MVSNet		49.56 142	53.04 128	45.49 143	51.59 140	64.42 129	46.97 161	51.01 34	37.87 116	16.42 190	39.87 87	34.91 150	33.43 165	59.59 148	62.70 112	73.52 118	71.94 139

v1192		52.69 108	53.86 119	51.31 100	51.22 141	69.76 74	57.37 100	44.39 111	32.21 165	31.39 121	32.41 151	32.44 167	49.19 91	64.25 79	64.17 99	74.31 103	81.81 71

tfpnview11		42.71 186	45.29 184	39.71 179	51.06 142	58.61 180	38.47 188	44.80 106	30.44 179	13.60 193	31.25 161	30.97 184	22.40 196	54.20 180	55.04 183	67.90 168	56.51 198

v144192		52.43 114	53.63 121	51.03 102	51.06 142	69.60 78	56.94 103	44.84 102	32.15 166	30.88 123	32.45 150	32.71 163	48.36 102	62.98 106	63.52 105	74.10 112	82.02 61

tfpn_n400		42.55 187	45.11 186	39.55 181	50.95 144	58.68 178	38.40 191	44.75 108	29.29 184	13.60 193	31.25 161	30.97 184	22.38 197	53.96 184	55.66 179	67.20 175	56.00 201

tfpnconf		42.55 187	45.11 186	39.55 181	50.95 144	58.68 178	38.40 191	44.75 108	29.29 184	13.60 193	31.25 161	30.97 184	22.38 197	53.96 184	55.66 179	67.20 175	56.00 201

tfpn1000		41.76 191	45.01 188	37.96 190	50.95 144	58.44 181	34.94 197	44.09 116	30.68 177	12.08 200	30.14 171	31.96 171	18.67 203	51.96 192	53.45 189	67.05 177	58.40 195

v1921920		51.95 120	53.19 124	50.51 108	50.82 147	69.14 86	55.45 115	44.34 115	31.53 171	30.53 125	31.96 155	31.67 174	48.31 104	63.12 104	63.28 107	73.59 116	81.60 75

FMVSNet3		55.66 76	59.68 71	50.96 103	50.59 148	66.49 102	57.57 98	46.61 80	49.30 75	28.77 131	39.61 89	51.42 67	43.85 130	68.29 48	68.80 44	78.35 27	73.86 128

TranMVSNet+NR-MVSNet		48.06 152	51.36 149	44.21 153	50.38 149	62.09 153	47.28 158	50.88 37	36.11 132	13.25 198	37.51 101	31.60 175	30.70 175	59.34 150	62.53 118	72.81 131	70.31 154

v1240		51.42 125	52.66 137	49.97 115	50.31 150	68.70 89	54.05 124	43.85 119	30.78 176	30.22 126	31.43 159	31.03 183	47.98 106	62.62 111	63.16 109	73.40 121	80.93 86

v148		51.72 121	53.15 126	50.05 113	50.15 151	67.51 96	56.98 102	42.85 132	32.60 164	32.41 116	33.88 140	34.71 152	44.45 129	61.06 127	63.00 110	73.45 120	79.24 96

PatchT		48.11 151	51.27 150	44.43 150	50.13 152	61.58 156	33.59 201	32.92 207	40.38 105	31.74 118	30.60 169	36.93 129	45.00 125	58.80 154	61.11 138	73.19 126	69.47 162

CNLPA		54.00 87	57.08 90	50.40 110	49.83 153	61.75 155	53.47 126	37.27 174	74.55 20	44.85 50	33.58 144	45.42 97	52.94 62	58.89 153	53.66 188	64.06 188	71.68 145

our_test_3					49.68 154	61.50 158	45.84 169

pmmvs4		51.28 127	52.50 139	49.85 118	49.54 155	63.02 139	52.83 136	43.41 123	44.65 87	35.71 85	34.38 132	32.25 168	45.14 124	60.21 143	60.03 149	72.44 135	72.98 137

GBi-Net		54.66 79	58.42 80	50.26 111	49.36 156	65.81 110	56.80 105	46.61 80	49.30 75	28.77 131	39.61 89	51.42 67	42.71 133	64.25 79	65.54 66	77.32 39	73.03 134

test1		54.66 79	58.42 80	50.26 111	49.36 156	65.81 110	56.80 105	46.61 80	49.30 75	28.77 131	39.61 89	51.42 67	42.71 133	64.25 79	65.54 66	77.32 39	73.03 134

FMVSNet2		53.94 90	57.29 89	50.03 114	49.36 156	65.81 110	56.80 105	45.95 88	43.13 92	28.04 136	35.68 113	48.18 85	42.71 133	67.23 57	67.95 50	77.32 39	73.03 134

pm-mvs1		46.14 165	49.34 164	42.41 167	48.93 159	62.22 152	44.98 173	42.68 136	27.66 191	20.76 169	29.88 176	34.96 149	26.41 191	60.03 145	60.42 146	70.70 151	70.20 155

testpf		31.84 215	34.86 215	28.32 215	48.89 160	32.91 229	26.53 220	25.77 226	21.99 210	10.05 209	23.39 199	25.55 206	14.07 216	39.23 224	42.32 220	44.58 226	58.65 191

Anonymous20231206		40.63 192	43.29 193	37.53 191	48.88 161	55.81 189	34.99 196	44.98 97	28.16 188	10.16 208	17.26 217	27.50 198	18.28 204	54.00 183	55.07 182	67.85 169	65.23 173

ADS-MVSNet		45.39 168	46.42 179	44.19 154	48.74 162	57.52 183	43.91 177	31.93 211	35.89 136	27.11 139	30.12 172	32.06 170	45.30 119	53.13 190	55.19 181	68.15 166	61.07 186

v7n		47.22 157	48.38 168	45.87 141	48.20 163	63.58 133	50.69 142	40.93 151	26.60 198	26.44 142	26.52 188	29.65 190	38.19 146	58.22 157	60.23 148	70.79 150	73.83 129

Vis-MVSNet		51.13 128	58.04 85	43.06 164	47.68 164	67.71 94	49.10 149	39.09 163	37.75 118	22.57 163	51.03 56	48.78 82	32.42 169	62.12 114	61.80 125	67.49 172	77.12 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		43.98 179	43.63 192	44.39 152	47.66 165	59.31 172	32.66 207	33.88 203	30.15 181	33.75 104	16.82 219	28.39 196	45.25 120	53.92 187	55.00 184	73.16 127	61.80 183

IterMVS		50.23 136	53.27 122	46.68 136	47.59 166	60.58 165	53.10 129	36.62 176	36.07 133	25.89 143	39.42 94	40.05 115	43.65 131	60.22 142	61.35 135	73.23 125	75.23 123
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		49.25 143	53.97 117	43.75 157	47.53 167	64.53 123	48.59 150	42.27 139	33.77 156	26.64 141	40.46 85	42.26 106	30.01 177	61.77 119	61.71 127	67.48 173	73.28 133
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
testgi		34.51 208	37.42 208	31.12 211	47.37 168	50.34 199	24.38 225	41.21 148	20.32 217	5.64 223	20.56 205	26.55 201	8.06 230	49.28 197	52.65 191	60.05 203	42.23 224

CR-MVSNet		48.82 148	51.85 143	45.29 145	46.74 169	55.95 187	52.06 138	34.21 196	42.17 98	31.74 118	32.92 149	42.53 105	45.00 125	58.80 154	61.11 138	61.99 199	69.47 162

MDTV_nov1_ep13_2view		44.44 173	45.75 182	42.91 165	46.13 170	63.43 135	46.53 166	34.20 198	29.08 187	19.95 176	26.23 190	27.89 197	35.88 154	53.36 189	56.43 174	74.74 84	63.86 178

test20.03		36.00 204	38.92 202	32.60 204	45.92 171	50.99 197	28.05 218	43.69 122	21.62 213	6.03 219	17.61 215	25.91 204	8.34 229	51.26 193	52.60 193	63.58 190	52.46 209

UniMVSNet (Re)		46.89 161	51.65 147	41.34 175	45.60 172	62.71 144	44.05 176	47.10 77	37.24 124	13.55 196	36.90 104	34.54 154	26.76 190	57.56 161	59.90 152	70.98 148	72.69 138

FMVSNet1		50.14 138	52.78 134	47.06 133	45.56 173	63.56 134	54.22 122	43.74 121	34.10 150	25.37 145	29.79 177	42.06 107	38.70 144	64.25 79	65.54 66	74.75 83	70.18 156

RPMNet		43.70 180	48.17 169	38.48 187	45.52 174	55.95 187	37.66 193	26.63 223	42.17 98	25.47 144	29.59 179	37.61 120	33.87 159	50.85 195	52.02 195	61.75 201	69.00 164

v748		44.90 170	46.14 181	43.46 160	45.37 175	60.89 162	48.15 152	39.42 158	25.81 202	24.36 155	25.90 192	28.48 195	34.44 156	53.39 188	57.35 166	69.00 162	71.14 150

TESTMET0.1,1		53.30 104	58.66 77	47.04 134	44.94 176	65.89 107	47.88 154	35.95 182	42.51 94	29.84 128	41.41 80	48.87 80	45.20 121	62.91 107	64.43 96	78.43 24	84.62 44

V42		52.63 109	55.08 105	49.76 119	44.93 177	67.49 97	60.19 81	42.13 141	37.21 125	34.08 99	34.57 128	37.30 128	47.29 111	63.48 102	64.15 100	69.96 158	81.38 80

TAMVS		44.27 178	49.35 163	38.35 188	44.74 178	61.04 161	39.07 187	31.82 212	29.95 182	18.34 185	33.55 145	39.94 116	30.01 177	56.85 169	57.58 163	66.13 180	66.54 169

pmmvs6		41.90 190	44.01 191	39.43 183	44.45 179	58.77 176	41.92 181	39.22 162	21.74 211	19.08 181	17.40 216	31.33 179	24.28 195	55.94 172	56.67 172	67.60 171	66.24 170

DU-MVS		47.33 156	50.86 152	43.20 162	44.43 180	60.64 163	46.97 161	47.63 63	37.26 122	16.42 190	37.31 102	31.39 177	33.43 165	57.53 162	59.98 150	70.35 153	71.94 139

Baseline_NR-MVSNet		47.14 159	50.83 153	42.84 166	44.43 180	63.31 136	44.50 175	50.36 42	37.71 119	11.25 204	30.84 166	32.09 169	30.96 173	57.53 162	63.73 103	75.53 65	70.60 153

PMMVS		55.74 73	62.68 58	47.64 132	44.34 182	65.58 113	47.22 160	37.96 169	56.43 60	34.11 98	61.51 30	47.41 88	54.55 43	65.88 62	62.49 119	67.67 170	79.48 92

Anonymous20231211		34.02 212	32.10 221	36.26 194	44.26 183	46.14 210	29.39 213	39.72 156	14.08 231	28.33 134	7.94 232	13.30 229	16.12 208	43.07 215	47.88 209	59.72 204	52.28 211

CMPMVS	33.64 16	44.39 176	46.41 180	42.03 169	44.21 184	56.50 186	46.73 164	26.48 224	34.20 148	35.14 90	24.22 198	34.64 153	40.52 143	56.50 171	56.07 176	59.12 206	62.74 182
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LP		38.21 196	37.72 206	38.79 185	44.07 185	51.16 195	35.54 194	31.37 214	25.38 206	23.73 158	18.64 210	18.03 221	29.31 183	47.85 200	52.63 192	68.71 164	50.34 216

WR-MVS		37.61 197	42.15 194	32.31 207	43.64 186	51.85 193	29.39 213	43.35 125	27.65 192	4.40 226	29.90 175	29.80 189	10.46 222	46.73 203	51.98 196	62.60 197	57.16 196

pmmvs5		47.02 160	50.02 157	43.51 159	43.48 187	62.65 146	47.24 159	37.78 171	30.59 178	24.80 150	35.26 116	30.43 187	34.36 157	59.05 152	60.28 147	73.40 121	71.92 141

LTVRE_ROB	32.83 17	35.10 206	37.46 207	32.35 206	43.12 188	49.99 201	28.52 216	33.23 206	12.73 232	8.18 213	27.71 185	21.34 214	32.64 168	46.92 202	48.11 206	48.41 222	55.45 204
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
PLC	44.22 14	49.14 144	51.75 145	46.10 139	42.78 189	55.60 191	53.11 128	34.46 195	55.69 63	32.47 115	34.16 136	41.45 110	48.91 94	57.13 166	54.09 186	64.84 185	64.10 176
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs-eth3d		44.67 172	45.27 185	43.98 156	42.56 190	55.72 190	44.97 174	40.81 152	31.96 168	29.13 130	26.09 191	25.27 208	36.69 151	55.13 178	56.62 173	69.68 160	66.12 171

OMC-MVS		55.48 77	61.85 66	48.04 131	41.55 191	60.32 167	56.80 105	31.78 213	75.67 18	42.30 64	51.52 53	54.15 62	49.91 77	60.28 140	57.59 162	65.91 181	73.42 130

DTE-MVSNet		36.91 200	40.44 198	32.79 203	40.74 192	47.55 207	30.71 211	44.39 111	27.03 196	4.32 227	30.88 164	25.99 203	12.73 217	45.58 205	50.80 199	63.86 189	55.23 205

UGNet		51.04 129	58.79 76	42.00 170	40.59 193	65.32 114	46.65 165	39.26 161	39.90 106	27.30 137	54.12 46	52.03 65	30.93 174	59.85 147	59.62 153	67.23 174	80.70 87
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
PEN-MVS		38.23 195	41.72 195	34.15 198	40.56 194	50.07 200	33.17 204	44.35 114	27.64 193	5.54 224	30.84 166	26.67 200	14.99 212	45.64 204	52.38 194	66.29 179	58.83 190

1111		29.41 221	30.75 222	27.85 216	39.46 195	37.63 224	22.26 226	32.15 209	17.93 224	7.92 214	13.48 223	20.98 215	17.30 206	44.76 208	46.51 215	47.99 223	33.96 228

.test1245		19.53 228	19.26 230	19.85 228	39.46 195	37.63 224	22.26 226	32.15 209	17.93 224	7.92 214	13.48 223	20.98 215	17.30 206	44.76 208	0.01 236	0.00 240	0.03 237

v52		44.41 174	46.76 177	41.67 171	38.93 197	60.06 168	49.26 147	36.02 179	25.57 204	24.73 154	25.66 193	31.34 178	35.93 152	55.52 174	57.99 160	65.14 183	71.21 148

V4		44.41 174	46.76 177	41.67 171	38.93 197	60.05 169	49.26 147	36.02 179	25.55 205	24.75 153	25.66 193	31.33 179	35.93 152	55.52 174	57.99 160	65.14 183	71.21 148

ambc			35.52 214		38.36 199	40.40 220	28.38 217		25.20 207	14.87 192	13.22 225	7.54 236	19.34 202	55.63 173	47.79 210	47.91 224	58.89 189

MIMVSNet		45.62 166	49.56 161	41.02 176	38.17 200	64.43 128	49.48 145	35.43 187	36.53 130	20.06 175	22.58 202	35.16 147	28.75 187	61.97 116	62.20 122	74.20 105	64.07 177

PatchMatch-RL		43.37 181	44.93 189	41.56 173	37.94 201	51.70 194	40.02 185	35.75 183	39.04 111	30.71 124	35.14 117	27.43 199	46.58 114	51.99 191	50.55 200	58.38 208	58.64 192

WR-MVS_H		36.29 202	40.35 200	31.55 209	37.80 202	49.94 202	30.57 212	41.11 150	26.90 197	4.14 228	30.72 168	28.85 193	10.45 223	42.47 217	47.99 208	65.24 182	55.54 203

CP-MVSNet		37.09 199	40.62 197	32.99 200	37.56 203	48.25 205	32.75 205	43.05 129	27.88 190	5.93 220	31.27 160	25.82 205	15.09 210	43.37 213	48.82 202	63.54 192	58.90 188

PS-CasMVS		36.84 201	40.23 201	32.89 201	37.44 204	48.09 206	32.68 206	42.97 131	27.36 194	5.89 221	30.08 174	25.48 207	14.96 213	43.28 214	48.71 203	63.39 193	58.63 193

FC-MVSNet-test		30.97 217	37.38 209	23.49 224	37.42 205	33.68 228	19.43 231	39.27 160	31.37 173	1.67 238	38.56 98	28.85 193	6.06 234	41.40 220	43.80 219	37.10 229	44.03 223

N_pmnet		34.09 210	35.74 213	32.17 208	37.25 206	43.17 216	32.26 209	35.57 185	26.22 200	10.60 207	20.44 206	19.38 219	20.20 201	44.59 210	47.00 213	57.13 211	49.35 218

SixPastTwentyTwo		36.11 203	37.80 205	34.13 199	37.13 207	46.72 209	34.58 199	34.96 189	21.20 215	11.66 201	29.15 181	19.88 218	29.77 180	44.93 206	48.34 205	56.67 212	54.41 207

testus		29.45 220	32.20 220	26.23 218	37.01 208	37.90 223	17.56 232	35.70 184	18.23 222	3.39 231	17.04 218	14.78 225	11.78 219	42.48 216	49.38 201	51.92 221	45.62 221

new-patchmatchnet		30.47 218	32.80 219	27.75 217	36.81 209	43.98 213	24.85 223	39.29 159	20.52 216	4.06 229	15.94 220	16.05 224	9.57 224	41.32 221	42.05 222	51.94 220	49.74 217

TAPA-MVS	47.92 11	51.66 122	57.88 87	44.40 151	36.46 210	58.42 182	53.82 125	30.83 215	69.51 34	34.97 91	46.90 65	49.67 75	46.99 112	58.00 159	54.64 185	63.33 194	68.00 168
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
COLMAP_ROB	34.79 15	38.65 194	40.72 196	36.23 195	36.41 211	49.22 204	45.51 171	27.60 221	37.81 117	20.54 171	23.37 200	24.25 210	28.11 188	51.02 194	48.55 204	59.22 205	50.82 214
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test2356		34.09 210	36.84 211	30.87 212	36.25 212	43.59 215	27.92 219	35.44 186	21.73 212	6.94 216	19.31 209	18.23 220	17.77 205	49.28 197	51.58 197	60.94 202	54.17 208

anonymousdsp		43.03 184	47.19 172	38.18 189	36.00 213	56.92 185	38.44 189	34.56 193	24.22 208	22.53 164	29.69 178	29.92 188	35.21 155	53.96 184	58.98 156	62.32 198	76.66 108

CVMVSNet		38.91 193	44.49 190	32.40 205	34.57 214	47.20 208	34.81 198	34.20 198	31.45 172	8.95 211	38.86 97	36.38 134	24.30 194	47.77 201	46.94 214	57.59 210	62.85 181

USDC		42.80 185	45.57 183	39.58 180	34.55 215	51.13 196	42.61 179	36.21 178	39.59 108	23.65 159	33.13 148	20.87 217	37.86 147	55.35 177	57.16 168	62.61 196	61.75 184

TSAR-MVS + COLMAP		54.37 82	62.43 61	44.98 147	34.33 216	58.94 175	54.11 123	34.15 201	74.06 21	34.57 93	71.63 13	42.03 108	47.88 107	61.26 125	57.33 167	64.83 186	71.74 144

test-mter		48.31 150	55.04 106	40.45 177	34.12 217	59.02 174	41.77 182	28.05 219	38.43 114	22.67 162	39.35 96	44.40 99	41.88 137	60.30 138	61.68 128	74.20 105	82.12 59

CHOSEN 280x420		42.39 189	47.40 171	36.54 193	33.56 218	39.66 222	40.67 184	26.88 222	34.66 144	18.03 187	30.09 173	45.59 95	44.82 127	54.46 179	54.00 187	55.28 215	73.32 132

EU-MVSNet		33.00 214	36.49 212	28.92 213	33.10 219	42.86 217	29.32 215	35.99 181	22.94 209	5.83 222	25.29 195	24.43 209	15.21 209	41.22 222	41.65 223	54.08 216	57.01 197

testmv		27.97 222	29.98 223	25.62 220	32.54 220	36.86 226	20.53 228	33.33 204	14.11 229	2.64 233	12.76 227	11.77 231	11.07 220	42.34 218	45.44 217	53.60 218	46.60 219

test1235678		27.96 223	29.97 224	25.62 220	32.54 220	36.83 227	20.53 228	33.33 204	14.10 230	2.64 233	12.75 228	11.76 232	11.07 220	42.34 218	45.43 218	53.60 218	46.59 220

TinyColmap		37.18 198	37.37 210	36.95 192	31.17 222	45.21 212	39.71 186	34.65 191	29.83 183	20.20 173	18.54 211	13.72 228	38.27 145	50.33 196	51.57 198	57.71 209	52.42 210

FMVSNet5		43.29 182	47.07 173	38.87 184	30.46 223	50.99 197	45.87 168	37.19 175	42.17 98	19.32 178	26.77 186	40.51 113	30.26 176	56.82 170	55.81 178	70.10 156	56.46 200

FPMVS		26.87 224	28.19 225	25.32 222	27.09 224	29.49 232	32.28 208	17.79 232	28.09 189	11.33 202	19.38 208	14.69 226	20.88 200	35.11 226	32.82 228	42.56 227	37.75 226

no-one		21.91 226	22.52 229	21.20 226	25.97 225	30.78 231	13.29 235	32.75 208	9.08 235	1.84 235	6.18 234	7.00 237	8.03 231	25.56 231	40.16 224	45.29 225	38.83 225

MDA-MVSNet-bldmvs		34.31 209	34.11 216	34.54 197	24.73 226	49.66 203	33.42 203	43.03 130	21.59 214	11.10 205	19.81 207	12.68 230	31.41 172	35.59 225	48.05 207	63.56 191	51.39 213

pmmvs3		31.22 216	33.62 217	28.43 214	22.82 227	40.26 221	26.40 221	22.05 230	16.89 226	10.99 206	14.72 221	16.26 223	29.70 181	44.82 207	47.39 211	58.61 207	54.98 206

PM-MVS		34.96 207	38.17 204	31.22 210	22.78 228	40.82 218	33.56 202	23.61 228	29.16 186	21.43 168	28.00 184	21.43 213	31.90 170	44.33 211	42.12 221	54.07 217	61.34 185

test12356		20.09 227	22.80 227	16.93 229	22.59 229	24.43 233	13.32 234	25.93 225	12.67 233	1.58 240	11.53 230	9.25 234	2.29 235	33.15 229	37.05 225	35.85 231	31.54 229

PMVS	18.18 18	21.95 225	22.85 226	20.90 227	21.92 230	14.78 235	19.95 230	17.31 233	15.69 227	11.32 203	13.70 222	13.91 227	15.02 211	34.92 227	31.72 229	39.85 228	35.20 227
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		10.66 232	8.30 234	13.42 232	19.91 231	7.87 239	4.30 240	29.47 217	8.37 238	1.70 237	3.67 235	1.29 243	9.12 226	8.98 236	13.59 233	16.03 235	14.30 235

MIMVSNet1		29.60 219	33.37 218	25.20 223	19.52 232	43.94 214	26.29 222	37.92 170	19.95 220	3.79 230	12.64 229	21.99 212	7.70 232	43.83 212	46.32 216	55.97 214	44.92 222

EMVS		10.15 233	7.67 235	13.05 233	19.22 233	7.77 240	4.48 238	29.34 218	8.65 237	1.67 238	3.55 236	1.36 242	9.15 225	8.15 237	11.79 235	14.44 236	12.43 236

TDRefinement		35.76 205	38.23 203	32.88 202	19.09 234	46.04 211	43.29 178	29.49 216	33.49 160	19.04 182	22.29 204	17.82 222	29.69 182	48.60 199	47.24 212	56.65 213	52.12 212

Gipuma		17.16 230	17.83 231	16.36 230	18.76 235	12.15 238	11.97 236	27.78 220	17.94 223	4.86 225	2.53 238	2.73 240	8.90 227	34.32 228	36.09 227	25.92 233	19.06 232
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
RPSCF		33.61 213	40.43 199	25.65 219	16.00 236	32.41 230	31.73 210	13.33 235	50.13 73	23.12 160	31.56 156	40.09 114	32.73 167	41.14 223	37.05 225	36.99 230	50.63 215

PMMVS2		12.25 231	14.17 232	10.00 234	11.39 237	14.35 236	8.21 237	19.29 231	9.31 234	0.19 242	7.38 233	6.19 238	1.10 237	19.26 232	21.13 232	19.85 234	21.56 231

new_pmnet		19.10 229	22.71 228	14.89 231	10.93 238	24.08 234	14.22 233	13.94 234	18.68 221	2.93 232	12.84 226	11.27 233	11.94 218	30.57 230	30.58 230	35.38 232	30.93 230

MVE	10.35 19	9.76 234	11.08 233	8.22 235	4.43 239	13.04 237	3.36 241	23.57 229	5.74 239	1.76 236	3.09 237	1.75 241	6.78 233	12.78 234	23.04 231	9.44 237	18.09 233
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				4.41 236	2.56 240	1.81 242	2.61 242	0.27 237	20.12 218	9.81 210	17.69 214	9.04 235	1.96 236	12.88 233	12.11 234	9.23 238

GG-mvs-BLEND		44.87 171	64.59 52	21.86 225	0.01 241	73.70 49	55.99 114	0.01 238	50.70 71	0.01 243	49.18 59	63.61 35	0.01 238	63.83 87	64.50 94	75.13 71	86.62 30

sosnet-low-res		0.00 237	0.00 238	0.00 237	0.00 242	0.00 243	0.00 244	0.00 239	0.00 242	0.00 244	0.00 241	0.00 244	0.00 240	0.00 239	0.00 239	0.00 240	0.00 240

sosnet		0.00 237	0.00 238	0.00 237	0.00 242	0.00 243	0.00 244	0.00 239	0.00 242	0.00 244	0.00 241	0.00 244	0.00 240	0.00 239	0.00 239	0.00 240	0.00 240

testmvs		0.01 235	0.01 236	0.00 237	0.00 242	0.00 243	0.00 244	0.00 239	0.01 240	0.00 244	0.02 239	0.00 244	0.00 240	0.01 238	0.01 236	0.00 240	0.03 237

test123		0.01 235	0.01 236	0.00 237	0.00 242	0.00 243	0.00 244	0.00 239	0.01 240	0.00 244	0.02 239	0.00 244	0.01 238	0.00 239	0.01 236	0.00 240	0.03 237

MTAPA										54.82 10		71.98 18

MTMP										50.64 27		68.31 24

Patchmatch-RL test							0.69 243

NP-MVS									72.62 25

Patchmtry						64.49 127	52.06 138	34.21 196		31.74 118

DeepMVS_CX						5.87 241	4.32 239	1.74 236	9.04 236	1.30 241	7.97 231	3.16 239	8.56 228	9.74 235		6.30 239	14.51 234

High-resolution multi-view benchmark