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 4	76.33 5	62.16 1	75.89 17	52.96 21	64.37 27	73.30 16	65.66 2	77.49 1	77.43 2	82.67 1	93.51 1

v1.0		69.34 16	70.08 35	68.47 2	78.83 2	83.07 2	77.86 1	58.75 4	86.89 3	56.64 8	89.08 3	83.11 1	60.69 14	74.28 7	74.11 7	78.06 32	0.00 246

CSCG		72.98 7	76.86 11	68.46 3	78.23 3	81.74 6	77.26 3	60.00 2	75.61 20	59.06 1	62.72 29	77.42 5	56.63 40	74.24 8	77.18 3	79.56 14	89.13 15

ESAPD		75.74 1	82.82 1	67.49 6	77.07 4	82.01 3	77.05 4	57.70 7	86.55 4	55.44 10	90.50 2	82.52 2	60.33 17	72.99 12	72.98 13	77.33 40	92.19 3

APDe-MVS		74.59 2	80.23 3	68.01 5	76.51 5	80.20 13	77.39 2	58.18 5	85.31 5	56.84 6	84.89 4	76.08 9	60.66 15	71.85 23	71.76 18	78.47 24	91.49 6

3Dnovator	58.39 4	65.97 32	66.85 46	64.94 11	73.72 6	79.03 18	67.73 36	54.25 21	61.52 50	52.79 23	42.27 83	60.73 49	62.01 6	71.29 25	71.75 19	79.12 19	81.34 85

MAR-MVS		66.85 27	69.81 36	63.39 20	73.56 7	80.51 12	69.87 26	51.51 33	67.78 40	46.44 42	51.09 59	61.60 45	60.38 16	72.67 19	73.61 11	78.59 22	81.44 81
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 15	73.45 8	78.56 22	73.13 12	55.24 17	70.68 30	48.93 34	70.43 17	69.10 23	54.00 51	72.78 18	72.98 13	79.14 18	88.74 18
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		73.87 5	78.60 6	68.35 4	73.32 9	81.97 5	76.19 6	59.29 3	80.12 10	56.70 7	67.09 21	76.48 7	64.26 3	75.88 3	75.75 4	80.32 9	92.93 2

QAPM		65.47 36	67.82 41	62.72 24	72.56 10	81.17 10	67.43 39	55.38 16	56.07 64	43.29 57	43.60 78	65.38 31	59.10 24	72.20 20	70.76 32	78.56 23	85.59 39

CHOSEN 1792x2688		62.48 52	64.06 56	60.64 38	72.50 11	84.18 1	62.43 65	53.77 24	47.90 84	39.85 76	25.15 202	44.76 104	53.72 52	77.29 2	77.61 1	81.60 5	91.53 5

NCCC		71.36 11	75.44 13	66.60 8	72.46 12	79.18 17	74.16 8	57.83 6	76.93 15	54.19 16	63.47 28	71.08 21	61.30 12	73.56 10	73.70 10	79.69 13	90.19 8

OpenMVS	55.62 8	62.57 48	63.76 58	61.19 35	72.13 13	78.84 20	64.42 57	50.51 41	56.44 61	45.67 48	36.88 111	56.51 59	56.66 39	68.28 51	68.96 45	77.73 36	80.44 92

HSP-MVS		74.54 3	81.12 2	66.86 7	71.93 14	78.65 21	72.60 13	55.44 14	89.94 1	54.35 14	92.24 1	77.08 6	69.84 1	75.48 4	75.01 5	76.99 46	79.45 97

HPM-MVS++		72.44 8	78.73 5	65.11 10	71.88 15	77.31 29	71.98 16	55.67 12	83.11 8	53.59 18	75.90 9	78.49 4	61.00 13	73.99 9	73.31 12	76.55 49	88.97 16

SMA-MVS		73.31 6	79.53 4	66.05 9	71.25 16	80.13 14	74.99 7	56.09 10	84.14 6	54.48 13	73.74 13	80.23 3	61.43 10	74.96 5	74.09 9	78.08 31	89.42 12

MVS_111021_HR		64.66 40	67.11 45	61.80 30	71.04 17	77.91 25	62.75 64	54.78 19	51.43 73	47.54 41	53.77 50	54.85 62	56.84 35	70.59 29	71.50 21	77.86 34	89.70 9

AdaColmap		62.79 46	62.63 62	62.98 23	70.82 18	72.90 62	67.84 35	54.09 23	65.14 45	50.71 27	41.78 84	47.64 91	60.17 19	67.41 57	66.83 60	74.28 108	76.69 111

abl_6				63.79 19	70.80 19	81.22 9	65.26 56	53.25 26	77.02 14	53.02 20	65.14 26	73.74 14	60.30 18			80.13 10	90.27 7

MS-PatchMatch		61.41 59	61.88 68	60.85 36	70.57 20	75.98 37	66.29 48	46.91 80	50.56 75	48.28 38	36.30 118	51.64 69	50.95 73	72.89 15	70.65 33	82.13 3	75.17 128

APD-MVS		71.86 9	77.91 8	64.80 12	70.39 21	75.69 41	74.02 9	56.14 9	83.59 7	52.92 22	84.67 5	73.46 15	59.30 23	69.47 37	69.66 38	76.02 56	88.84 17
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 15	70.33 22	75.16 43	72.33 15	55.78 11	75.74 18	50.41 30	80.08 8	73.15 17	57.75 32	71.96 22	70.94 30	77.25 44	88.69 19

ACMMP_NAP		71.50 10	77.27 10	64.77 13	69.64 23	79.26 15	73.53 10	54.73 20	79.32 12	54.23 15	74.81 10	74.61 13	59.40 22	73.00 11	72.17 16	77.10 45	87.72 23

TSAR-MVS + ACMM		65.95 33	72.83 18	57.93 54	69.35 24	65.85 116	73.36 11	39.84 163	76.00 16	48.69 37	82.54 7	75.03 12	49.38 93	65.33 70	63.42 113	66.94 185	81.67 78

TSAR-MVS + MP.		70.28 13	75.09 14	64.66 14	69.34 25	64.61 127	72.60 13	56.29 8	80.73 9	58.36 3	84.56 6	75.22 11	55.37 46	69.11 43	69.45 39	75.97 58	81.97 70

SD-MVS		68.30 18	72.58 19	63.31 22	69.24 26	67.85 97	70.81 22	53.65 25	79.64 11	58.52 2	74.31 11	75.37 10	53.52 57	65.63 67	63.56 111	74.13 114	81.73 77

DELS-MVS		67.36 21	70.34 34	63.89 18	69.12 27	81.55 7	70.82 21	55.02 18	53.38 68	48.83 35	56.45 42	59.35 51	60.05 21	74.93 6	74.78 6	79.51 15	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 26	71.64 25	61.65 32	69.10 28	76.84 33	71.35 20	55.42 15	67.02 41	42.83 59	65.27 25	64.60 33	53.16 60	69.70 36	71.40 22	78.02 33	86.67 30

HFP-MVS		68.75 17	72.84 17	63.98 15	68.87 29	75.09 44	71.87 17	51.22 34	73.50 24	58.17 4	68.05 20	68.67 24	57.79 31	70.49 31	69.23 41	75.98 57	84.84 44

MSLP-MVS++		61.81 57	62.19 67	61.37 34	68.33 30	63.08 145	70.75 23	38.89 171	63.96 48	57.51 5	48.59 64	61.66 44	53.67 55	62.04 122	59.92 159	79.03 20	76.08 114

CLD-MVS		64.69 39	67.25 42	61.69 31	68.22 31	78.33 23	63.09 61	47.59 67	69.64 34	53.98 17	54.87 46	53.94 65	57.87 30	72.79 16	71.34 23	79.40 16	69.87 168
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 24	72.08 22	61.56 33	66.76 32	73.58 54	71.41 18	52.98 27	69.92 33	43.85 55	70.58 16	58.75 53	56.76 37	72.90 14	71.88 17	77.57 37	86.94 29

casdiffmvs		65.76 34	68.47 39	62.59 25	66.73 33	80.56 11	69.28 29	48.36 57	57.86 58	48.25 39	54.87 46	56.69 58	61.60 8	70.70 27	71.11 26	81.33 6	87.51 25

OPM-MVS		61.59 58	62.30 66	60.76 37	66.53 34	73.35 56	71.41 18	54.18 22	40.82 109	41.57 72	45.70 73	54.84 63	54.43 50	69.92 35	69.19 42	76.45 50	82.25 62

MP-MVS		67.34 22	73.08 16	60.64 38	66.20 35	76.62 34	69.22 30	50.92 36	70.07 31	48.81 36	69.66 18	70.12 22	53.68 54	68.41 48	69.13 43	74.98 76	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 19	72.46 20	62.33 28	66.09 36	74.21 48	70.05 25	51.54 32	77.27 13	54.61 12	60.30 36	71.51 20	56.73 38	69.19 41	68.63 49	74.96 77	86.11 34

casdiffmvs1		67.44 20	70.92 30	63.37 21	66.05 37	81.28 8	70.45 24	48.70 52	65.51 44	49.62 31	57.42 39	61.27 48	63.77 4	70.18 34	71.22 25	81.65 4	89.58 10

3Dnovator+	55.76 7	62.70 47	65.10 53	59.90 44	65.89 38	72.15 66	62.94 63	49.82 44	62.77 49	49.06 33	43.62 77	61.47 47	58.60 27	68.51 47	66.75 61	73.08 134	80.40 93

PGM-MVS		65.35 37	70.43 33	59.43 47	65.78 39	73.75 51	69.41 27	48.18 60	68.80 37	45.37 49	65.88 24	64.04 35	52.68 66	68.94 44	68.68 48	75.18 71	82.93 59

MSDG		52.58 117	51.40 155	53.95 76	65.48 40	64.31 137	61.44 68	44.02 123	44.17 91	32.92 119	30.40 176	31.81 180	46.35 120	62.13 120	62.55 124	73.49 124	64.41 182

ACMMPR		66.20 31	71.51 28	60.00 43	65.34 41	74.04 49	69.39 28	50.92 36	71.97 28	46.04 45	66.79 22	65.68 28	53.07 61	68.93 45	69.12 44	75.21 70	84.05 51

DWT-MVSNet_training		61.22 60	63.52 60	58.53 49	65.00 42	76.55 35	59.50 94	48.22 59	51.79 72	42.14 67	47.85 67	50.21 75	55.46 45	66.16 64	67.92 53	80.85 7	84.14 50

X-MVS		63.53 44	68.62 38	57.60 57	64.77 43	73.06 59	65.82 50	50.53 40	65.77 43	42.02 68	58.20 38	63.42 38	47.83 113	68.25 52	68.50 50	74.61 94	83.16 58

CANet		67.21 25	71.83 24	61.83 29	64.51 44	79.25 16	66.72 45	48.73 50	68.49 38	50.63 29	61.40 32	66.47 27	61.44 9	69.31 40	69.90 35	78.94 21	88.00 21

tpmp4_e23		59.70 63	61.03 72	58.14 51	63.70 45	73.33 57	65.69 51	39.53 164	52.56 69	46.23 44	41.59 85	47.46 92	57.38 33	65.01 73	65.89 68	76.31 52	81.36 84

FC-MVSNet-train		55.68 79	57.00 95	54.13 75	63.37 46	66.16 112	46.77 170	52.14 30	42.36 100	37.67 82	48.50 65	41.42 118	51.28 70	61.58 127	63.22 115	73.56 122	75.76 120

ACMH	47.82 13	50.10 145	49.60 167	50.69 110	63.36 47	66.99 105	56.83 109	52.13 31	31.06 181	17.74 194	28.22 189	26.24 210	45.17 128	60.88 138	63.80 109	68.91 169	70.00 166
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CostFormer		62.45 53	65.68 51	58.67 48	63.29 48	77.65 26	67.62 37	38.42 174	54.04 66	46.00 46	48.27 66	57.89 55	56.97 34	67.03 61	67.79 54	79.74 11	87.09 28

ACMM	53.73 9	57.91 68	58.27 85	57.49 59	63.10 49	66.45 110	65.65 52	49.02 47	53.69 67	42.67 63	36.41 115	46.07 100	50.38 76	64.74 76	64.63 92	74.14 113	75.91 118
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
mPP-MVS					63.08 50							62.34 41

PHI-MVS		65.17 38	72.07 23	57.11 63	63.02 51	77.35 28	67.04 42	48.14 62	68.03 39	37.56 83	66.00 23	65.39 30	53.19 59	70.68 28	70.57 34	73.72 120	86.46 33

DeepC-MVS_fast	60.18 3	66.84 28	70.69 32	62.36 27	62.76 52	73.21 58	67.96 34	52.31 28	72.26 27	51.03 24	56.50 41	64.26 34	63.37 5	71.64 24	70.85 31	76.70 48	86.10 35
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 53	73.06 59	61.80 66			42.02 68		63.42 38				74.68 92

X-MVStestdata					62.70 53	73.06 59	61.80 66			42.02 68		63.42 38				74.68 92

ACMMP		63.27 45	67.85 40	57.93 54	62.64 55	72.30 65	68.23 32	48.77 49	66.50 42	43.05 58	62.07 30	57.84 56	49.98 79	66.58 62	66.46 66	74.93 81	83.17 56
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 30	71.61 26	60.14 42	62.59 56	78.98 19	67.13 41	45.75 92	64.35 47	45.23 50	60.69 34	67.67 26	61.73 7	71.09 26	71.03 28	78.41 27	87.44 26

DeepPCF-MVS	62.48 1	70.07 14	78.36 7	60.39 41	62.38 57	76.96 32	65.54 54	52.23 29	87.46 2	49.07 32	74.05 12	76.19 8	59.01 25	72.79 16	71.61 20	74.13 114	89.49 11

CP-MVS		64.37 42	69.48 37	58.39 50	62.21 58	71.81 67	67.27 40	49.51 45	69.40 36	45.76 47	60.41 35	64.96 32	51.84 68	67.33 58	67.57 55	73.78 119	84.89 43

canonicalmvs		65.55 35	70.75 31	59.49 46	62.11 59	78.26 24	66.52 46	43.82 126	71.54 29	47.84 40	61.30 33	61.68 43	58.48 28	67.56 54	69.67 37	78.16 30	85.25 41

LGP-MVS_train		59.69 64	62.59 63	56.31 68	61.94 60	68.15 96	66.90 44	48.15 61	59.75 53	38.47 79	50.38 61	48.34 88	46.87 118	65.39 69	64.93 78	75.51 66	81.21 87

Effi-MVS+		59.63 65	61.78 71	57.12 62	61.56 61	71.63 68	63.61 60	47.59 67	47.18 85	37.79 80	45.29 74	49.93 77	56.27 42	67.45 55	67.06 58	75.91 59	83.93 52

HyFIR lowres test		57.12 73	59.11 75	54.80 72	61.55 62	77.55 27	59.02 97	45.00 99	41.84 106	33.93 106	22.44 210	49.16 83	51.02 72	68.39 49	68.71 47	78.26 29	85.70 37

TSAR-MVS + GP.		66.77 29	72.21 21	60.44 40	61.23 63	70.00 75	64.26 59	47.79 63	72.98 25	56.32 9	71.35 15	72.33 18	55.68 44	65.49 68	66.66 62	77.35 39	86.62 31

ACMP	56.21 5	59.78 62	61.81 70	57.41 60	61.15 64	68.88 91	65.98 49	48.85 48	58.56 56	44.19 53	48.89 63	46.31 98	48.56 104	63.61 104	64.49 103	75.75 62	81.91 71
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous202405211			56.81 98		60.91 65	73.48 55	59.82 88	48.68 53	39.26 115		24.00 205	46.77 96	50.73 75	65.28 72	65.72 69	75.37 69	83.17 56

IB-MVS	53.15 10	57.33 71	59.02 77	55.37 70	60.83 66	77.11 30	54.51 125	50.10 43	43.22 93	42.82 61	40.50 90	37.61 127	44.67 133	59.27 158	69.81 36	79.29 17	85.59 39
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 70	58.26 86	56.42 67	60.80 67	72.56 63	64.35 58	38.43 173	49.18 81	46.36 43	36.69 113	43.50 107	54.47 48	61.39 130	62.64 122	74.11 116	81.81 75

gg-mvs-nofinetune		50.82 139	55.83 103	44.97 154	60.63 68	75.69 41	53.40 132	34.48 201	20.05 227	6.93 223	18.27 220	52.70 66	33.57 169	70.50 30	72.93 15	80.84 8	80.68 91

EPNet		64.39 41	70.93 29	56.77 65	60.58 69	75.77 38	59.28 96	50.58 39	69.93 32	40.73 73	68.59 19	61.60 45	53.72 52	68.65 46	68.07 51	75.75 62	83.87 54
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVS_Test		63.75 43	67.24 43	59.68 45	60.01 70	76.99 31	68.13 33	45.17 97	57.45 59	43.74 56	53.07 52	56.16 61	61.33 11	70.27 32	71.11 26	79.72 12	85.63 38

Anonymous20240521		56.80 75	58.96 78	54.28 73	59.96 71	66.74 108	60.37 81	44.87 102	41.01 108	36.81 85	47.57 68	47.87 90	48.23 109	64.41 80	65.17 75	75.45 67	79.95 95

tpmrst		57.23 72	59.08 76	55.06 71	59.91 72	70.65 72	60.71 73	35.38 195	47.91 83	42.58 64	39.78 94	45.45 102	54.44 49	62.19 119	62.82 118	77.37 38	84.73 45

dps		52.84 113	52.92 138	52.74 84	59.89 73	69.49 83	54.47 126	37.38 179	42.49 99	39.53 77	35.33 120	32.71 170	51.83 69	60.45 144	61.12 145	73.33 128	68.86 172

diffmvs1		62.37 54	66.47 48	57.59 58	59.86 74	76.31 36	66.92 43	44.21 120	58.21 57	42.24 66	53.51 51	57.41 57	58.12 29	67.29 59	67.44 56	74.18 111	85.09 42

DeepC-MVS	60.65 2	67.33 23	71.52 27	62.44 26	59.79 75	74.84 46	68.89 31	55.56 13	73.91 23	53.50 19	55.00 45	65.63 29	60.08 20	71.99 21	71.33 24	76.85 47	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
Anonymous20231211		56.40 76	57.00 95	55.70 69	59.78 76	72.49 64	61.29 71	46.83 82	40.50 110	40.46 74	22.12 212	49.73 78	51.07 71	64.39 81	65.30 74	74.74 86	84.44 48

EG-PatchMatch MVS		50.23 142	50.89 158	49.47 124	59.54 77	70.88 69	52.46 143	44.01 124	26.22 208	31.91 123	24.97 203	31.45 184	33.48 171	64.79 75	66.51 65	75.40 68	71.39 153

tpm		54.94 82	57.86 91	51.54 102	59.48 78	67.04 104	58.34 100	34.60 199	41.93 105	34.41 99	42.40 82	47.14 94	49.07 96	61.46 128	61.67 137	73.31 129	83.39 55

Effi-MVS+-dtu		53.63 100	54.85 117	52.20 94	59.32 79	61.33 167	56.42 116	40.24 161	43.84 92	34.22 102	39.49 99	46.18 99	53.00 64	58.72 164	57.49 172	69.99 162	76.91 108

PCF-MVS	55.99 6	62.31 55	66.60 47	57.32 61	59.12 80	73.68 53	67.53 38	48.71 51	61.35 51	42.83 59	51.33 58	63.48 37	53.48 58	65.64 66	64.87 79	72.22 141	85.83 36
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		60.59 61	63.99 57	56.62 66	58.59 81	75.05 45	65.61 53	44.19 121	51.88 71	40.39 75	51.77 55	52.12 67	56.36 41	66.32 63	66.88 59	74.55 96	83.89 53

PVSNet_BlendedMVS		62.53 49	66.37 49	58.05 52	58.17 82	75.70 39	61.30 69	48.67 54	58.67 54	50.93 25	55.43 43	49.39 80	53.01 62	69.46 38	66.55 63	76.24 54	89.39 13

PVSNet_Blended		62.53 49	66.37 49	58.05 52	58.17 82	75.70 39	61.30 69	48.67 54	58.67 54	50.93 25	55.43 43	49.39 80	53.01 62	69.46 38	66.55 63	76.24 54	89.39 13

TransMVSNet (Re)		47.46 161	48.94 174	45.74 148	57.96 84	64.29 138	48.26 158	48.47 56	26.33 207	19.33 183	29.45 186	31.28 189	25.31 201	63.05 110	62.70 119	75.10 74	65.47 179

DI_MVS_plusplus_trai		61.86 56	65.26 52	57.90 56	57.93 85	74.51 47	66.30 47	46.49 87	49.96 77	41.62 71	42.69 81	61.77 42	58.74 26	70.25 33	69.32 40	76.31 52	88.30 20

NR-MVSNet		48.84 153	51.76 151	45.44 150	57.66 86	60.64 170	47.39 164	47.63 65	37.26 129	13.31 203	37.31 108	29.64 199	33.53 170	63.52 106	62.09 131	73.10 133	71.89 148

PVSNet_Blended_VisFu		58.56 67	62.33 65	54.16 74	56.90 87	73.92 50	57.72 102	46.16 90	44.23 90	42.73 62	46.26 70	51.06 73	46.28 121	67.99 53	65.38 73	75.18 71	87.44 26

ACMH+	47.85 12	49.13 151	48.86 175	49.44 125	56.75 88	62.01 161	56.62 114	47.55 69	37.49 128	23.98 163	26.68 193	29.46 200	43.12 139	57.45 172	58.85 165	68.62 172	70.05 165

EPMVS		54.07 90	56.06 101	51.75 99	56.74 89	70.80 70	55.32 121	34.20 205	46.46 87	36.59 88	40.38 92	42.55 110	49.77 84	61.25 133	60.90 148	77.86 34	70.08 164

gm-plane-assit		45.41 174	48.03 178	42.34 175	56.49 90	40.48 226	24.54 231	34.15 208	14.44 236	6.59 224	17.82 221	35.32 153	49.82 82	72.93 13	74.11 7	82.47 2	81.12 88

conf0.002		51.63 129	53.79 127	49.12 128	56.33 91	64.84 125	53.05 136	47.38 71	35.86 144	24.83 154	37.86 106	38.15 124	41.08 145	61.04 136	62.70 119	72.05 142	76.06 115

conf0.01		51.32 132	53.22 130	49.11 129	56.29 92	64.78 126	53.05 136	47.37 72	35.86 144	24.83 154	36.85 112	36.64 138	41.08 145	61.01 137	61.37 139	72.03 143	76.01 116

Fast-Effi-MVS+-dtu		52.47 119	55.89 102	48.48 135	56.25 93	65.07 123	58.75 99	23.79 234	41.27 107	27.07 146	37.95 105	41.34 119	50.85 74	62.90 115	62.34 128	74.17 112	80.37 94

MVS_111021_LR		57.06 74	60.60 73	52.93 78	56.25 93	65.14 122	55.16 123	41.21 155	52.32 70	44.89 51	53.92 49	49.27 82	52.16 67	61.46 128	60.54 153	67.92 174	81.53 80

tfpn111		51.00 136	52.68 143	49.04 131	56.10 95	64.52 132	53.05 136	47.31 75	35.86 144	24.79 157	36.35 116	34.10 163	41.08 145	60.84 139	61.37 139	71.90 146	75.70 122

conf200view11		50.87 138	52.45 147	49.04 131	56.10 95	64.52 132	53.05 136	47.31 75	35.86 144	24.79 157	34.74 131	34.10 163	41.08 145	60.84 139	61.37 139	71.90 146	75.70 122

thres100view900		52.33 122	53.91 125	50.48 113	56.10 95	67.79 98	56.18 118	49.18 46	35.86 144	25.22 152	34.74 131	34.10 163	42.41 143	64.45 79	62.62 123	73.81 118	77.85 103

tfpn200view9		50.91 137	52.45 147	49.11 129	56.10 95	64.53 130	53.06 135	47.31 75	35.86 144	25.22 152	34.74 131	34.10 163	41.08 145	60.84 139	61.37 139	71.90 146	75.70 122

thres200		50.76 140	52.52 145	48.70 134	55.98 99	64.60 128	55.29 122	47.34 73	33.91 162	24.36 161	34.33 140	33.90 167	37.27 155	60.84 139	62.41 127	71.99 144	77.63 104

IS_MVSNet		51.53 130	57.98 89	44.01 161	55.96 100	66.16 112	47.65 163	42.84 140	39.82 113	19.09 186	44.97 75	50.28 74	27.20 197	63.43 108	63.84 108	71.33 151	77.33 105

PatchmatchNet		53.37 107	55.62 105	50.75 108	55.93 101	70.54 73	51.39 147	36.41 184	44.85 88	37.26 84	39.40 101	42.54 111	47.83 113	60.29 146	60.88 150	75.69 64	70.87 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
LS3D		49.59 147	49.75 166	49.40 126	55.88 102	59.86 179	56.31 117	45.33 95	48.57 82	28.32 141	31.54 163	36.81 137	46.27 122	57.17 173	55.88 185	64.29 195	58.42 201

IterMVS-LS		53.36 108	55.65 104	50.68 111	55.34 103	59.04 181	55.00 124	39.98 162	38.72 119	33.22 118	44.52 76	47.05 95	49.63 87	61.82 125	61.77 133	70.92 154	76.61 113
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UA-Net		47.19 164	53.02 136	40.38 185	55.31 104	60.02 178	38.41 198	38.68 172	36.42 138	22.47 171	51.95 54	58.72 54	25.62 200	54.11 189	53.40 198	61.79 208	56.51 205

MVSTER		62.51 51	67.22 44	57.02 64	55.05 105	69.23 89	63.02 62	46.88 81	61.11 52	43.95 54	59.20 37	58.86 52	56.80 36	69.13 42	70.98 29	76.41 51	82.04 64

thres400		50.39 141	52.22 149	48.26 136	55.02 106	66.32 111	52.97 140	48.33 58	32.68 170	22.94 167	33.21 153	33.38 169	37.27 155	62.74 116	61.38 138	73.04 135	75.81 119

CPTT-MVS		59.54 66	64.47 55	53.79 77	54.99 107	67.63 100	65.48 55	44.59 114	64.81 46	37.74 81	51.55 56	59.90 50	49.77 84	61.83 124	61.26 144	70.18 160	84.31 49

EPP-MVSNet		52.91 112	58.91 79	45.91 146	54.99 107	68.84 92	49.27 153	42.71 142	37.53 127	20.20 179	46.09 71	56.19 60	36.90 157	61.37 131	60.90 148	71.41 150	81.41 82

tfpn_ndepth		46.53 171	49.41 170	43.18 170	54.66 109	61.56 164	42.25 188	45.66 94	35.68 150	18.31 192	36.55 114	34.84 158	28.88 193	55.45 184	57.01 178	69.32 167	64.78 181

Fast-Effi-MVS+		55.73 78	58.26 86	52.76 83	54.33 110	68.19 95	57.05 106	34.66 197	46.92 86	38.96 78	40.53 89	41.55 116	55.69 43	65.31 71	65.99 67	75.90 60	79.34 98

view600		48.85 152	50.58 162	46.82 141	54.19 111	64.94 124	50.81 148	47.53 70	31.78 176	21.59 173	32.31 159	32.63 172	34.28 166	61.06 134	57.41 173	72.54 138	73.96 132

MDTV_nov1_ep13		52.99 111	55.59 109	49.95 120	54.08 112	70.69 71	56.47 115	38.42 174	42.78 95	30.19 133	39.56 98	43.31 109	45.78 123	60.07 151	62.11 130	74.74 86	70.62 159

thres600view7		48.44 155	50.23 163	46.35 144	54.05 113	64.60 128	50.18 151	47.34 73	31.73 177	20.74 176	32.28 160	32.62 173	33.79 168	60.84 139	56.11 183	71.99 144	73.40 137

thresconf0.02		47.89 159	50.76 161	44.54 155	53.86 114	63.96 139	46.23 174	47.72 64	33.00 169	17.08 195	36.35 116	37.80 125	29.86 187	60.01 153	60.57 152	72.49 139	63.62 187

view800		47.68 160	49.78 165	45.24 152	53.39 115	63.19 144	48.13 160	46.57 86	30.98 182	20.25 178	31.52 164	31.90 179	31.52 179	59.37 156	59.61 162	71.56 149	71.89 148

EPNet_dtu		49.85 146	56.99 97	41.52 181	52.79 116	57.06 192	41.44 191	43.13 135	56.13 63	19.24 185	52.11 53	48.38 87	22.14 207	58.19 166	58.38 167	70.35 158	68.71 173
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpnnormal		46.61 170	46.82 184	46.37 143	52.70 117	62.31 158	50.39 150	47.17 78	25.74 211	21.80 172	23.13 208	24.15 219	33.45 172	60.28 147	60.77 151	72.70 137	71.39 153

tfpn		46.62 169	49.07 173	43.75 163	52.70 117	61.49 166	45.65 178	45.68 93	30.25 188	18.84 189	30.87 171	33.67 168	29.22 192	57.80 168	59.49 163	70.44 157	69.95 167

v18		53.63 100	54.35 121	52.80 82	52.25 119	62.94 147	60.80 72	42.78 141	39.23 116	36.81 85	35.07 124	37.78 126	49.82 82	63.69 97	64.65 91	74.32 104	77.07 107

v16		53.50 103	54.17 122	52.73 85	52.24 120	62.90 148	60.67 74	42.67 144	38.72 119	36.70 87	34.84 130	37.59 129	49.69 86	63.72 94	64.68 87	74.39 102	76.72 109

v17		53.40 106	54.09 123	52.60 87	52.22 121	62.90 148	60.64 76	42.66 145	38.24 122	36.04 89	34.85 129	37.59 129	49.63 87	63.70 96	64.68 87	74.39 102	76.70 110

CANet_DTU		57.87 69	63.63 59	51.15 105	52.18 122	70.20 74	58.14 101	37.32 180	56.49 60	31.06 128	57.38 40	50.05 76	53.67 55	64.98 74	65.04 77	74.57 95	81.29 86

v8		53.77 98	54.82 118	52.54 89	52.12 123	66.95 107	60.56 80	43.23 134	37.17 133	35.35 92	34.96 125	37.50 131	49.51 92	63.67 102	64.59 93	74.48 98	78.91 101

v1neww		54.32 87	55.60 107	52.81 80	52.11 124	69.43 86	60.57 77	44.86 103	37.13 135	35.34 93	34.95 127	37.46 133	49.53 89	63.69 97	64.59 93	74.47 99	81.99 66

v7new		54.32 87	55.60 107	52.81 80	52.11 124	69.43 86	60.57 77	44.86 103	37.13 135	35.34 93	34.95 127	37.46 133	49.53 89	63.69 97	64.59 93	74.47 99	81.99 66

v6		54.32 87	55.61 106	52.82 79	52.11 124	69.44 85	60.57 77	44.86 103	37.15 134	35.40 91	34.96 125	37.47 132	49.52 91	63.68 100	64.59 93	74.47 99	81.99 66

v7		53.99 93	55.20 110	52.57 88	52.08 127	69.46 84	59.68 90	44.92 101	35.90 141	34.33 100	33.99 144	35.55 150	50.08 78	64.38 83	64.67 89	74.32 104	82.47 60

v10		53.44 105	54.40 120	52.31 93	52.08 127	66.99 105	59.68 90	43.41 129	35.90 141	34.30 101	33.98 145	35.56 149	50.10 77	64.39 81	64.67 89	74.32 104	79.30 99

v1144		53.47 104	54.65 119	52.10 95	51.93 129	69.81 77	59.32 95	44.77 111	33.21 168	32.52 120	33.55 151	34.34 162	49.29 94	64.58 77	64.81 84	74.74 86	82.27 61

v15		52.60 116	53.17 132	51.94 96	51.92 130	62.76 150	60.04 86	42.19 147	34.35 153	33.96 105	34.37 139	36.40 139	48.73 102	63.75 93	64.59 93	74.79 82	75.97 117

V14		52.51 118	53.06 134	51.87 97	51.91 131	62.70 152	59.93 87	42.13 148	34.16 156	33.88 107	34.22 141	36.35 142	48.62 103	63.72 94	64.59 93	74.77 83	75.69 125

v1141		53.78 95	55.02 114	52.33 91	51.89 132	69.75 79	60.24 83	44.81 108	34.04 160	33.34 115	34.57 134	36.17 147	48.76 99	63.90 89	64.81 84	74.94 78	81.87 74

divwei89l23v2f112		53.78 95	55.02 114	52.33 91	51.89 132	69.74 81	60.25 82	44.82 107	34.06 158	33.33 116	34.56 137	36.18 145	48.75 100	63.90 89	64.82 82	74.94 78	81.88 72

v1		53.78 95	55.02 114	52.34 90	51.89 132	69.75 79	60.24 83	44.81 108	34.05 159	33.39 114	34.57 134	36.18 145	48.75 100	63.90 89	64.82 82	74.94 78	81.88 72

V9		52.42 121	52.94 137	51.81 98	51.89 132	62.64 154	59.81 89	42.08 150	33.97 161	33.85 109	34.05 143	36.28 143	48.49 105	63.68 100	64.59 93	74.76 84	75.41 126

conf0.05thres1000		45.26 176	46.99 182	43.24 168	51.87 136	60.52 173	45.17 180	45.24 96	27.06 203	18.60 190	26.24 195	31.23 190	28.82 194	56.88 176	58.52 166	69.71 164	68.50 174

v12		52.31 123	52.81 140	51.72 100	51.87 136	62.56 155	59.66 92	42.02 151	33.77 163	33.70 112	33.88 146	36.20 144	48.36 106	63.64 103	64.56 100	74.73 90	75.08 129

v13		52.22 125	52.69 142	51.67 101	51.85 138	62.48 157	59.57 93	41.97 152	33.61 165	33.69 113	33.71 149	36.11 148	48.23 109	63.57 105	64.55 101	74.72 91	74.77 130

Vis-MVSNet (Re-imp)		44.31 184	51.67 153	35.72 202	51.82 139	55.24 200	34.57 208	41.63 154	39.10 117	8.84 218	45.93 72	46.63 97	14.45 221	54.09 190	57.03 177	63.00 203	63.65 186

v11		52.23 124	52.83 139	51.53 103	51.73 140	62.49 156	58.82 98	41.81 153	33.53 166	33.23 117	33.73 148	35.10 155	49.07 96	64.49 78	64.71 86	74.49 97	75.75 121

v2v482		54.00 91	55.12 111	52.69 86	51.73 140	69.42 88	60.65 75	45.09 98	34.56 152	33.73 111	35.29 121	35.36 152	49.92 80	64.05 88	65.16 76	75.00 75	81.98 69

test-LLR		54.62 85	58.66 81	49.89 121	51.68 142	65.89 114	47.88 161	46.35 88	42.51 97	29.84 134	41.41 86	48.87 84	45.20 126	62.91 113	64.43 104	78.43 25	84.62 46

test0.0.03 1		43.07 190	46.95 183	38.54 193	51.68 142	58.77 184	35.28 203	46.35 88	32.05 174	12.44 205	28.53 188	35.52 151	14.40 222	57.12 175	56.93 179	71.11 152	59.69 194

GA-MVS		53.77 98	56.41 100	50.70 109	51.63 144	69.96 76	57.55 104	44.39 115	34.31 154	27.15 144	40.99 88	36.40 139	47.65 115	67.45 55	67.16 57	75.83 61	78.60 102

UniMVSNet_NR-MVSNet		49.56 148	53.04 135	45.49 149	51.59 145	64.42 136	46.97 168	51.01 35	37.87 123	16.42 196	39.87 93	34.91 157	33.43 173	59.59 155	62.70 119	73.52 123	71.94 145

thisisatest0530		53.61 102	57.22 93	49.40 126	51.30 146	68.22 94	52.72 142	43.34 132	42.72 96	35.31 95	43.57 79	44.14 106	44.37 135	63.00 111	64.86 80	69.34 166	74.00 131

v1192		52.69 114	53.86 126	51.31 104	51.22 147	69.76 78	57.37 105	44.39 115	32.21 172	31.39 127	32.41 158	32.44 174	49.19 95	64.25 84	64.17 106	74.31 107	81.81 75

tfpnview11		42.71 193	45.29 192	39.71 186	51.06 148	58.61 188	38.47 196	44.80 110	30.44 186	13.60 199	31.25 167	30.97 192	22.40 204	54.20 188	55.04 191	67.90 175	56.51 205

v144192		52.43 120	53.63 128	51.03 106	51.06 148	69.60 82	56.94 108	44.84 106	32.15 173	30.88 129	32.45 157	32.71 170	48.36 106	62.98 112	63.52 112	74.10 117	82.02 65

tfpn_n400		42.55 194	45.11 194	39.55 188	50.95 150	58.68 186	38.40 199	44.75 112	29.29 192	13.60 199	31.25 167	30.97 192	22.38 205	53.96 192	55.66 187	67.20 182	56.00 208

tfpnconf		42.55 194	45.11 194	39.55 188	50.95 150	58.68 186	38.40 199	44.75 112	29.29 192	13.60 199	31.25 167	30.97 192	22.38 205	53.96 192	55.66 187	67.20 182	56.00 208

tfpn1000		41.76 198	45.01 196	37.96 197	50.95 150	58.44 189	34.94 205	44.09 122	30.68 184	12.08 206	30.14 177	31.96 178	18.67 211	51.96 200	53.45 197	67.05 184	58.40 202

v1921920		51.95 126	53.19 131	50.51 112	50.82 153	69.14 90	55.45 120	44.34 119	31.53 178	30.53 131	31.96 161	31.67 181	48.31 108	63.12 109	63.28 114	73.59 121	81.60 79

FMVSNet3		55.66 80	59.68 74	50.96 107	50.59 154	66.49 109	57.57 103	46.61 83	49.30 78	28.77 138	39.61 95	51.42 70	43.85 137	68.29 50	68.80 46	78.35 28	73.86 133

TranMVSNet+NR-MVSNet		48.06 158	51.36 156	44.21 159	50.38 155	62.09 160	47.28 165	50.88 38	36.11 139	13.25 204	37.51 107	31.60 183	30.70 183	59.34 157	62.53 125	72.81 136	70.31 161

tttt0517		53.05 110	56.73 99	48.76 133	50.35 156	67.51 101	51.96 146	43.34 132	42.00 104	33.88 107	43.19 80	43.49 108	44.37 135	62.58 118	64.86 80	68.67 171	73.46 135

v1240		51.42 131	52.66 144	49.97 119	50.31 157	68.70 93	54.05 129	43.85 125	30.78 183	30.22 132	31.43 165	31.03 191	47.98 111	62.62 117	63.16 116	73.40 126	80.93 89

v148		51.72 127	53.15 133	50.05 117	50.15 158	67.51 101	56.98 107	42.85 139	32.60 171	32.41 122	33.88 146	34.71 159	44.45 134	61.06 134	63.00 117	73.45 125	79.24 100

PatchT		48.11 157	51.27 157	44.43 156	50.13 159	61.58 163	33.59 209	32.92 214	40.38 111	31.74 124	30.60 175	36.93 136	45.00 130	58.80 161	61.11 146	73.19 131	69.47 169

CNLPA		54.00 91	57.08 94	50.40 114	49.83 160	61.75 162	53.47 131	37.27 181	74.55 21	44.85 52	33.58 150	45.42 103	52.94 65	58.89 160	53.66 196	64.06 196	71.68 151

our_test_3					49.68 161	61.50 165	45.84 177

pmmvs4		51.28 133	52.50 146	49.85 122	49.54 162	63.02 146	52.83 141	43.41 129	44.65 89	35.71 90	34.38 138	32.25 175	45.14 129	60.21 150	60.03 157	72.44 140	72.98 143

GBi-Net		54.66 83	58.42 83	50.26 115	49.36 163	65.81 117	56.80 110	46.61 83	49.30 78	28.77 138	39.61 95	51.42 70	42.71 140	64.25 84	65.54 70	77.32 41	73.03 140

test1		54.66 83	58.42 83	50.26 115	49.36 163	65.81 117	56.80 110	46.61 83	49.30 78	28.77 138	39.61 95	51.42 70	42.71 140	64.25 84	65.54 70	77.32 41	73.03 140

FMVSNet2		53.94 94	57.29 92	50.03 118	49.36 163	65.81 117	56.80 110	45.95 91	43.13 94	28.04 142	35.68 119	48.18 89	42.71 140	67.23 60	67.95 52	77.32 41	73.03 140

pm-mvs1		46.14 172	49.34 172	42.41 174	48.93 166	62.22 159	44.98 181	42.68 143	27.66 199	20.76 175	29.88 182	34.96 156	26.41 199	60.03 152	60.42 154	70.70 156	70.20 162

testpf		31.84 221	34.86 223	28.32 221	48.89 167	32.91 236	26.53 227	25.77 233	21.99 218	10.05 215	23.39 206	25.55 214	14.07 223	39.23 231	42.32 227	44.58 233	58.65 198

Anonymous20231206		40.63 199	43.29 201	37.53 198	48.88 168	55.81 197	34.99 204	44.98 100	28.16 196	10.16 214	17.26 225	27.50 206	18.28 212	54.00 191	55.07 190	67.85 176	65.23 180

ADS-MVSNet		45.39 175	46.42 187	44.19 160	48.74 169	57.52 191	43.91 185	31.93 218	35.89 143	27.11 145	30.12 178	32.06 177	45.30 124	53.13 198	55.19 189	68.15 173	61.07 193

v7n		47.22 163	48.38 176	45.87 147	48.20 170	63.58 140	50.69 149	40.93 159	26.60 206	26.44 148	26.52 194	29.65 198	38.19 153	58.22 165	60.23 156	70.79 155	73.83 134

Vis-MVSNet		51.13 134	58.04 88	43.06 171	47.68 171	67.71 99	49.10 156	39.09 170	37.75 125	22.57 169	51.03 60	48.78 86	32.42 177	62.12 121	61.80 132	67.49 179	77.12 106
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS-HIRNet		43.98 186	43.63 200	44.39 158	47.66 172	59.31 180	32.66 215	33.88 210	30.15 189	33.75 110	16.82 227	28.39 204	45.25 125	53.92 195	55.00 192	73.16 132	61.80 190

IterMVS		50.23 142	53.27 129	46.68 142	47.59 173	60.58 172	53.10 134	36.62 183	36.07 140	25.89 149	39.42 100	40.05 122	43.65 138	60.22 149	61.35 143	73.23 130	75.23 127
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CDS-MVSNet		49.25 149	53.97 124	43.75 163	47.53 174	64.53 130	48.59 157	42.27 146	33.77 163	26.64 147	40.46 91	42.26 113	30.01 185	61.77 126	61.71 134	67.48 180	73.28 139
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
testgi		34.51 215	37.42 216	31.12 217	47.37 175	50.34 207	24.38 232	41.21 155	20.32 225	5.64 229	20.56 213	26.55 209	8.06 237	49.28 205	52.65 199	60.05 211	42.23 230

CR-MVSNet		48.82 154	51.85 150	45.29 151	46.74 176	55.95 195	52.06 144	34.21 203	42.17 101	31.74 124	32.92 155	42.53 112	45.00 130	58.80 161	61.11 146	61.99 207	69.47 169

MDTV_nov1_ep13_2view		44.44 180	45.75 190	42.91 172	46.13 177	63.43 142	46.53 173	34.20 205	29.08 195	19.95 182	26.23 196	27.89 205	35.88 161	53.36 197	56.43 182	74.74 86	63.86 185

test20.03		36.00 211	38.92 210	32.60 210	45.92 178	50.99 205	28.05 225	43.69 128	21.62 221	6.03 225	17.61 223	25.91 212	8.34 236	51.26 201	52.60 201	63.58 198	52.46 216

UniMVSNet (Re)		46.89 167	51.65 154	41.34 182	45.60 179	62.71 151	44.05 184	47.10 79	37.24 131	13.55 202	36.90 110	34.54 161	26.76 198	57.56 169	59.90 160	70.98 153	72.69 144

FMVSNet1		50.14 144	52.78 141	47.06 139	45.56 180	63.56 141	54.22 127	43.74 127	34.10 157	25.37 151	29.79 183	42.06 114	38.70 151	64.25 84	65.54 70	74.75 85	70.18 163

RPMNet		43.70 187	48.17 177	38.48 194	45.52 181	55.95 195	37.66 201	26.63 230	42.17 101	25.47 150	29.59 185	37.61 127	33.87 167	50.85 203	52.02 203	61.75 209	69.00 171

v748		44.90 177	46.14 189	43.46 167	45.37 182	60.89 169	48.15 159	39.42 165	25.81 210	24.36 161	25.90 198	28.48 203	34.44 164	53.39 196	57.35 174	69.00 168	71.14 157

thisisatest0515		46.88 168	49.57 168	43.74 165	45.33 183	60.46 174	46.19 175	41.06 158	30.34 187	29.73 136	32.50 156	31.63 182	35.43 162	58.75 163	61.71 134	64.70 194	71.59 152

TESTMET0.1,1		53.30 109	58.66 81	47.04 140	44.94 184	65.89 114	47.88 161	35.95 189	42.51 97	29.84 134	41.41 86	48.87 84	45.20 126	62.91 113	64.43 104	78.43 25	84.62 46

V42		52.63 115	55.08 112	49.76 123	44.93 185	67.49 103	60.19 85	42.13 148	37.21 132	34.08 104	34.57 134	37.30 135	47.29 116	63.48 107	64.15 107	69.96 163	81.38 83

TAMVS		44.27 185	49.35 171	38.35 195	44.74 186	61.04 168	39.07 195	31.82 219	29.95 190	18.34 191	33.55 151	39.94 123	30.01 185	56.85 177	57.58 171	66.13 187	66.54 176

pmmvs6		41.90 197	44.01 199	39.43 190	44.45 187	58.77 184	41.92 189	39.22 169	21.74 219	19.08 187	17.40 224	31.33 187	24.28 203	55.94 180	56.67 180	67.60 178	66.24 177

DU-MVS		47.33 162	50.86 159	43.20 169	44.43 188	60.64 170	46.97 168	47.63 65	37.26 129	16.42 196	37.31 108	31.39 185	33.43 173	57.53 170	59.98 158	70.35 158	71.94 145

Baseline_NR-MVSNet		47.14 165	50.83 160	42.84 173	44.43 188	63.31 143	44.50 183	50.36 42	37.71 126	11.25 210	30.84 172	32.09 176	30.96 181	57.53 170	63.73 110	75.53 65	70.60 160

PMMVS		55.74 77	62.68 61	47.64 138	44.34 190	65.58 120	47.22 167	37.96 176	56.43 62	34.11 103	61.51 31	47.41 93	54.55 47	65.88 65	62.49 126	67.67 177	79.48 96

CMPMVS	33.64 16	44.39 183	46.41 188	42.03 176	44.21 191	56.50 194	46.73 171	26.48 231	34.20 155	35.14 96	24.22 204	34.64 160	40.52 150	56.50 179	56.07 184	59.12 213	62.74 189
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LP		38.21 203	37.72 214	38.79 192	44.07 192	51.16 203	35.54 202	31.37 221	25.38 214	23.73 164	18.64 218	18.03 229	29.31 191	47.85 208	52.63 200	68.71 170	50.34 222

WR-MVS		37.61 204	42.15 202	32.31 213	43.64 193	51.85 201	29.39 221	43.35 131	27.65 200	4.40 232	29.90 181	29.80 197	10.46 229	46.73 211	51.98 204	62.60 205	57.16 203

pmmvs5		47.02 166	50.02 164	43.51 166	43.48 194	62.65 153	47.24 166	37.78 178	30.59 185	24.80 156	35.26 122	30.43 195	34.36 165	59.05 159	60.28 155	73.40 126	71.92 147

LTVRE_ROB	32.83 17	35.10 213	37.46 215	32.35 212	43.12 195	49.99 209	28.52 223	33.23 213	12.73 239	8.18 219	27.71 191	21.34 222	32.64 176	46.92 210	48.11 214	48.41 229	55.45 211
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	49.14 150	51.75 152	46.10 145	42.78 196	55.60 199	53.11 133	34.46 202	55.69 65	32.47 121	34.16 142	41.45 117	48.91 98	57.13 174	54.09 194	64.84 192	64.10 183
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
pmmvs-eth3d		44.67 179	45.27 193	43.98 162	42.56 197	55.72 198	44.97 182	40.81 160	31.96 175	29.13 137	26.09 197	25.27 216	36.69 158	55.13 186	56.62 181	69.68 165	66.12 178

OMC-MVS		55.48 81	61.85 69	48.04 137	41.55 198	60.32 175	56.80 110	31.78 220	75.67 19	42.30 65	51.52 57	54.15 64	49.91 81	60.28 147	57.59 170	65.91 188	73.42 136

DTE-MVSNet		36.91 207	40.44 206	32.79 209	40.74 199	47.55 215	30.71 219	44.39 115	27.03 204	4.32 233	30.88 170	25.99 211	12.73 224	45.58 213	50.80 207	63.86 197	55.23 212

UGNet		51.04 135	58.79 80	42.00 177	40.59 200	65.32 121	46.65 172	39.26 168	39.90 112	27.30 143	54.12 48	52.03 68	30.93 182	59.85 154	59.62 161	67.23 181	80.70 90
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		38.23 202	41.72 203	34.15 204	40.56 201	50.07 208	33.17 212	44.35 118	27.64 201	5.54 230	30.84 172	26.67 208	14.99 219	45.64 212	52.38 202	66.29 186	58.83 197

1111		29.41 227	30.75 229	27.85 222	39.46 202	37.63 231	22.26 233	32.15 216	17.93 232	7.92 220	13.48 231	20.98 223	17.30 214	44.76 216	46.51 222	47.99 230	33.96 234

.test1245		19.53 234	19.26 237	19.85 234	39.46 202	37.63 231	22.26 233	32.15 216	17.93 232	7.92 220	13.48 231	20.98 223	17.30 214	44.76 216	0.01 243	0.00 247	0.03 243

v52		44.41 181	46.76 185	41.67 178	38.93 204	60.06 176	49.26 154	36.02 186	25.57 212	24.73 160	25.66 199	31.34 186	35.93 159	55.52 182	57.99 168	65.14 190	71.21 155

V4		44.41 181	46.76 185	41.67 178	38.93 204	60.05 177	49.26 154	36.02 186	25.55 213	24.75 159	25.66 199	31.33 187	35.93 159	55.52 182	57.99 168	65.14 190	71.21 155

ambc			35.52 222		38.36 206	40.40 227	28.38 224		25.20 215	14.87 198	13.22 233	7.54 243	19.34 210	55.63 181	47.79 217	47.91 231	58.89 196

MIMVSNet		45.62 173	49.56 169	41.02 183	38.17 207	64.43 135	49.48 152	35.43 194	36.53 137	20.06 181	22.58 209	35.16 154	28.75 195	61.97 123	62.20 129	74.20 109	64.07 184

PatchMatch-RL		43.37 188	44.93 197	41.56 180	37.94 208	51.70 202	40.02 193	35.75 190	39.04 118	30.71 130	35.14 123	27.43 207	46.58 119	51.99 199	50.55 208	58.38 215	58.64 199

WR-MVS_H		36.29 209	40.35 208	31.55 215	37.80 209	49.94 210	30.57 220	41.11 157	26.90 205	4.14 234	30.72 174	28.85 201	10.45 230	42.47 224	47.99 216	65.24 189	55.54 210

CP-MVSNet		37.09 206	40.62 205	32.99 206	37.56 210	48.25 213	32.75 213	43.05 136	27.88 198	5.93 226	31.27 166	25.82 213	15.09 217	43.37 221	48.82 210	63.54 200	58.90 195

PS-CasMVS		36.84 208	40.23 209	32.89 207	37.44 211	48.09 214	32.68 214	42.97 138	27.36 202	5.89 227	30.08 180	25.48 215	14.96 220	43.28 222	48.71 211	63.39 201	58.63 200

FC-MVSNet-test		30.97 223	37.38 217	23.49 230	37.42 212	33.68 235	19.43 238	39.27 167	31.37 180	1.67 244	38.56 104	28.85 201	6.06 241	41.40 227	43.80 226	37.10 236	44.03 229

N_pmnet		34.09 217	35.74 221	32.17 214	37.25 213	43.17 223	32.26 217	35.57 192	26.22 208	10.60 213	20.44 214	19.38 227	20.20 209	44.59 218	47.00 220	57.13 218	49.35 224

SixPastTwentyTwo		36.11 210	37.80 213	34.13 205	37.13 214	46.72 217	34.58 207	34.96 196	21.20 223	11.66 207	29.15 187	19.88 226	29.77 188	44.93 214	48.34 213	56.67 219	54.41 214

testus		29.45 226	32.20 228	26.23 224	37.01 215	37.90 230	17.56 239	35.70 191	18.23 230	3.39 237	17.04 226	14.78 233	11.78 226	42.48 223	49.38 209	51.92 228	45.62 227

new-patchmatchnet		30.47 224	32.80 227	27.75 223	36.81 216	43.98 220	24.85 230	39.29 166	20.52 224	4.06 235	15.94 228	16.05 232	9.57 231	41.32 228	42.05 229	51.94 227	49.74 223

TAPA-MVS	47.92 11	51.66 128	57.88 90	44.40 157	36.46 217	58.42 190	53.82 130	30.83 222	69.51 35	34.97 97	46.90 69	49.67 79	46.99 117	58.00 167	54.64 193	63.33 202	68.00 175
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
COLMAP_ROB	34.79 15	38.65 201	40.72 204	36.23 201	36.41 218	49.22 212	45.51 179	27.60 228	37.81 124	20.54 177	23.37 207	24.25 218	28.11 196	51.02 202	48.55 212	59.22 212	50.82 220
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test2356		34.09 217	36.84 219	30.87 218	36.25 219	43.59 222	27.92 226	35.44 193	21.73 220	6.94 222	19.31 217	18.23 228	17.77 213	49.28 205	51.58 205	60.94 210	54.17 215

anonymousdsp		43.03 191	47.19 180	38.18 196	36.00 220	56.92 193	38.44 197	34.56 200	24.22 216	22.53 170	29.69 184	29.92 196	35.21 163	53.96 192	58.98 164	62.32 206	76.66 112

CVMVSNet		38.91 200	44.49 198	32.40 211	34.57 221	47.20 216	34.81 206	34.20 205	31.45 179	8.95 217	38.86 103	36.38 141	24.30 202	47.77 209	46.94 221	57.59 217	62.85 188

USDC		42.80 192	45.57 191	39.58 187	34.55 222	51.13 204	42.61 187	36.21 185	39.59 114	23.65 165	33.13 154	20.87 225	37.86 154	55.35 185	57.16 176	62.61 204	61.75 191

TSAR-MVS + COLMAP		54.37 86	62.43 64	44.98 153	34.33 223	58.94 183	54.11 128	34.15 208	74.06 22	34.57 98	71.63 14	42.03 115	47.88 112	61.26 132	57.33 175	64.83 193	71.74 150

test-mter		48.31 156	55.04 113	40.45 184	34.12 224	59.02 182	41.77 190	28.05 226	38.43 121	22.67 168	39.35 102	44.40 105	41.88 144	60.30 145	61.68 136	74.20 109	82.12 63

CHOSEN 280x420		42.39 196	47.40 179	36.54 200	33.56 225	39.66 229	40.67 192	26.88 229	34.66 151	18.03 193	30.09 179	45.59 101	44.82 132	54.46 187	54.00 195	55.28 222	73.32 138

EU-MVSNet		33.00 220	36.49 220	28.92 219	33.10 226	42.86 224	29.32 222	35.99 188	22.94 217	5.83 228	25.29 201	24.43 217	15.21 216	41.22 229	41.65 230	54.08 223	57.01 204

testmv		27.97 228	29.98 230	25.62 226	32.54 227	36.86 233	20.53 235	33.33 211	14.11 237	2.64 239	12.76 235	11.77 238	11.07 227	42.34 225	45.44 224	53.60 225	46.60 225

test1235678		27.96 229	29.97 231	25.62 226	32.54 227	36.83 234	20.53 235	33.33 211	14.10 238	2.64 239	12.75 236	11.76 239	11.07 227	42.34 225	45.43 225	53.60 225	46.59 226

TinyColmap		37.18 205	37.37 218	36.95 199	31.17 229	45.21 219	39.71 194	34.65 198	29.83 191	20.20 179	18.54 219	13.72 236	38.27 152	50.33 204	51.57 206	57.71 216	52.42 217

FMVSNet5		43.29 189	47.07 181	38.87 191	30.46 230	50.99 205	45.87 176	37.19 182	42.17 101	19.32 184	26.77 192	40.51 120	30.26 184	56.82 178	55.81 186	70.10 161	56.46 207

FPMVS		26.87 230	28.19 232	25.32 228	27.09 231	29.49 239	32.28 216	17.79 239	28.09 197	11.33 208	19.38 216	14.69 234	20.88 208	35.11 233	32.82 235	42.56 234	37.75 232

no-one		21.91 232	22.52 236	21.20 232	25.97 232	30.78 238	13.29 242	32.75 215	9.08 242	1.84 241	6.18 241	7.00 244	8.03 238	25.56 238	40.16 231	45.29 232	38.83 231

MDA-MVSNet-bldmvs		34.31 216	34.11 224	34.54 203	24.73 233	49.66 211	33.42 211	43.03 137	21.59 222	11.10 211	19.81 215	12.68 237	31.41 180	35.59 232	48.05 215	63.56 199	51.39 219

pmmvs3		31.22 222	33.62 225	28.43 220	22.82 234	40.26 228	26.40 228	22.05 237	16.89 234	10.99 212	14.72 229	16.26 231	29.70 189	44.82 215	47.39 218	58.61 214	54.98 213

PM-MVS		34.96 214	38.17 212	31.22 216	22.78 235	40.82 225	33.56 210	23.61 235	29.16 194	21.43 174	28.00 190	21.43 221	31.90 178	44.33 219	42.12 228	54.07 224	61.34 192

test12356		20.09 233	22.80 234	16.93 235	22.59 236	24.43 240	13.32 241	25.93 232	12.67 240	1.58 246	11.53 238	9.25 241	2.29 242	33.15 236	37.05 232	35.85 238	31.54 235

PMVS	18.18 18	21.95 231	22.85 233	20.90 233	21.92 237	14.78 242	19.95 237	17.31 240	15.69 235	11.32 209	13.70 230	13.91 235	15.02 218	34.92 234	31.72 236	39.85 235	35.20 233
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
E-PMN		10.66 238	8.30 241	13.42 238	19.91 238	7.87 246	4.30 247	29.47 224	8.37 245	1.70 243	3.67 242	1.29 250	9.12 233	8.98 243	13.59 240	16.03 242	14.30 241

MIMVSNet1		29.60 225	33.37 226	25.20 229	19.52 239	43.94 221	26.29 229	37.92 177	19.95 228	3.79 236	12.64 237	21.99 220	7.70 239	43.83 220	46.32 223	55.97 221	44.92 228

EMVS		10.15 239	7.67 242	13.05 239	19.22 240	7.77 247	4.48 245	29.34 225	8.65 244	1.67 244	3.55 243	1.36 249	9.15 232	8.15 244	11.79 242	14.44 243	12.43 242

TDRefinement		35.76 212	38.23 211	32.88 208	19.09 241	46.04 218	43.29 186	29.49 223	33.49 167	19.04 188	22.29 211	17.82 230	29.69 190	48.60 207	47.24 219	56.65 220	52.12 218

Gipuma		17.16 236	17.83 238	16.36 236	18.76 242	12.15 245	11.97 243	27.78 227	17.94 231	4.86 231	2.53 245	2.73 247	8.90 234	34.32 235	36.09 234	25.92 240	19.06 238
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
RPSCF		33.61 219	40.43 207	25.65 225	16.00 243	32.41 237	31.73 218	13.33 242	50.13 76	23.12 166	31.56 162	40.09 121	32.73 175	41.14 230	37.05 232	36.99 237	50.63 221

PMMVS2		12.25 237	14.17 239	10.00 240	11.39 244	14.35 243	8.21 244	19.29 238	9.31 241	0.19 248	7.38 240	6.19 245	1.10 244	19.26 239	21.13 239	19.85 241	21.56 237

new_pmnet		19.10 235	22.71 235	14.89 237	10.93 245	24.08 241	14.22 240	13.94 241	18.68 229	2.93 238	12.84 234	11.27 240	11.94 225	30.57 237	30.58 237	35.38 239	30.93 236

MVE	10.35 19	9.76 240	11.08 240	8.22 241	4.43 246	13.04 244	3.36 248	23.57 236	5.74 246	1.76 242	3.09 244	1.75 248	6.78 240	12.78 241	23.04 238	9.44 244	18.09 239
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
tmp_tt				4.41 242	2.56 247	1.81 249	2.61 249	0.27 244	20.12 226	9.81 216	17.69 222	9.04 242	1.96 243	12.88 240	12.11 241	9.23 245

GG-mvs-BLEND		44.87 178	64.59 54	21.86 231	0.01 248	73.70 52	55.99 119	0.01 245	50.70 74	0.01 249	49.18 62	63.61 36	0.01 245	63.83 92	64.50 102	75.13 73	86.62 31

sosnet-low-res		0.00 243	0.00 245	0.00 243	0.00 249	0.00 250	0.00 251	0.00 246	0.00 249	0.00 250	0.00 248	0.00 251	0.00 247	0.00 246	0.00 246	0.00 247	0.00 246

sosnet		0.00 243	0.00 245	0.00 243	0.00 249	0.00 250	0.00 251	0.00 246	0.00 249	0.00 250	0.00 248	0.00 251	0.00 247	0.00 246	0.00 246	0.00 247	0.00 246

testmvs		0.01 241	0.01 243	0.00 243	0.00 249	0.00 250	0.00 251	0.00 246	0.01 247	0.00 250	0.02 246	0.00 251	0.00 247	0.01 245	0.01 243	0.00 247	0.03 243

test123		0.01 241	0.01 243	0.00 243	0.00 249	0.00 250	0.00 251	0.00 246	0.01 247	0.00 250	0.02 246	0.00 251	0.01 245	0.00 246	0.01 243	0.00 247	0.03 243

MTAPA										54.82 11		71.98 19

MTMP										50.64 28		68.31 25

Patchmatch-RL test							0.69 250

NP-MVS									72.62 26

Patchmtry						64.49 134	52.06 144	34.21 203		31.74 124

DeepMVS_CX						5.87 248	4.32 246	1.74 243	9.04 243	1.30 247	7.97 239	3.16 246	8.56 235	9.74 242		6.30 246	14.51 240

High-resolution multi-view benchmark