High-res multi-view results

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

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

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

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

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

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

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

MCST-MVS		85.75 5	86.99 9	84.31 2	94.07 1	92.80 3	88.15 3	79.10 1	85.66 18	70.72 25	76.50 28	80.45 14	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

ESAPD		87.78 1	90.56 1	84.53 1	92.88 2	93.82 1	88.95 1	76.05 4	92.95 3	80.32 2	93.12 2	86.87 1	80.88 4	85.54 10	84.01 18	88.09 32	97.62 2

DELS-MVS		79.49 26	79.84 35	79.08 23	88.26 32	92.49 4	84.12 20	70.63 22	65.27 71	69.60 31	61.29 53	66.50 53	72.75 33	88.07 2	88.03 1	89.13 11	97.22 3
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
CNVR-MVS		85.96 4	87.58 7	84.06 3	92.58 4	92.40 6	87.62 4	77.77 2	88.44 10	75.93 12	79.49 21	81.97 11	81.65 3	87.04 5	86.58 3	88.79 14	97.18 4

PVSNet_BlendedMVS		76.84 45	78.47 38	74.95 46	82.37 52	89.90 25	75.45 67	65.45 55	74.99 48	70.66 26	63.07 46	58.27 82	67.60 65	84.24 18	81.70 36	88.18 28	97.10 5

PVSNet_Blended		76.84 45	78.47 38	74.95 46	82.37 52	89.90 25	75.45 67	65.45 55	74.99 48	70.66 26	63.07 46	58.27 82	67.60 65	84.24 18	81.70 36	88.18 28	97.10 5

APDe-MVS		86.37 3	88.41 4	84.00 4	91.43 9	91.83 10	88.34 2	74.67 5	91.19 4	81.76 1	91.13 3	81.94 12	80.07 5	83.38 22	82.58 29	87.69 37	96.78 7

DeepPCF-MVS	76.94 1	83.08 14	87.77 6	77.60 30	90.11 14	90.96 14	78.48 49	72.63 17	93.10 2	65.84 37	80.67 19	81.55 13	74.80 23	85.94 9	85.39 8	83.75 151	96.77 8

CHOSEN 1792x2688		72.55 60	71.98 66	73.22 54	86.57 41	92.41 5	75.63 63	66.77 44	62.08 76	52.32 76	30.27 201	50.74 110	66.14 70	86.22 7	85.41 7	91.90 1	96.75 9

SMA-MVS		84.91 7	87.95 5	81.36 10	91.75 7	90.84 15	86.35 8	73.36 13	90.22 7	72.81 18	80.70 18	85.67 2	76.69 14	86.06 8	86.14 5	87.20 50	96.05 10

abl_6				79.06 24	89.68 19	92.14 8	77.70 54	69.68 28	86.87 14	71.88 20	74.29 30	80.06 16	76.56 16			88.84 13	95.82 11

MVS_111021_HR		77.42 41	78.40 40	76.28 36	86.95 38	90.68 16	77.41 56	70.56 25	66.21 65	62.48 46	66.17 42	63.98 60	72.08 38	82.87 26	83.15 24	88.24 27	95.71 12

TSAR-MVS + GP.		82.27 18	85.98 14	77.94 28	80.72 66	88.25 38	81.12 38	67.71 40	87.10 12	73.31 15	85.23 11	83.68 5	76.64 15	80.43 51	81.47 40	88.15 30	95.66 13

HPM-MVS++		85.64 6	88.43 3	82.39 7	92.65 3	90.24 21	85.83 10	74.21 6	90.68 6	75.63 13	86.77 9	84.15 4	78.68 9	86.33 6	85.26 9	87.32 44	95.60 14

CANet		80.90 23	82.93 24	78.53 26	86.83 40	92.26 7	81.19 37	66.95 43	81.60 31	69.90 28	66.93 39	74.80 26	76.79 13	84.68 15	84.77 15	89.50 8	95.50 15

APD-MVS		84.83 8	87.00 8	82.30 8	89.61 20	89.21 29	86.51 7	73.64 11	90.98 5	77.99 7	89.89 5	80.04 17	79.18 7	82.00 38	81.37 41	86.88 54	95.49 16
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		84.16 11	85.46 16	82.64 6	92.34 5	90.57 18	86.57 6	76.51 3	86.85 15	72.91 17	77.20 27	78.69 20	79.09 8	84.64 16	84.88 14	88.44 22	95.41 17

CSCG		82.90 15	84.52 18	81.02 13	91.85 6	93.43 2	87.14 5	74.01 9	81.96 28	76.14 10	70.84 32	82.49 8	69.71 50	82.32 34	85.18 11	87.26 46	95.40 18

train_agg		83.35 13	86.93 11	79.17 22	89.70 18	88.41 35	85.60 14	72.89 16	86.31 16	66.58 36	90.48 4	82.24 10	73.06 32	83.10 24	82.64 28	87.21 49	95.30 19

GG-mvs-BLEND		54.54 190	77.58 42	27.67 227	0.03 240	90.09 24	77.20 57	0.02 237	66.83 63	0.05 242	59.90 57	73.33 30	0.04 237	78.40 65	79.30 57	88.65 16	95.20 20

DeepC-MVS	74.46 3	80.30 25	81.05 31	79.42 19	87.42 36	88.50 34	83.23 23	73.27 14	82.78 25	71.01 24	62.86 48	69.93 46	74.80 23	84.30 17	84.20 17	86.79 56	94.77 21
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_Blended_VisFu		71.76 64	73.54 62	69.69 69	79.01 72	87.16 46	72.05 84	61.80 87	56.46 93	59.66 60	53.88 72	62.48 63	59.08 130	81.17 45	78.90 59	86.53 60	94.74 22

TSAR-MVS + MP.		84.39 9	86.58 12	81.83 9	88.09 34	86.47 53	85.63 12	73.62 12	90.13 8	79.24 4	89.67 6	82.99 7	77.72 11	81.22 44	80.92 49	86.68 57	94.66 23

SD-MVS		84.31 10	86.96 10	81.22 11	88.98 26	88.68 32	85.65 11	73.85 10	89.09 9	79.63 3	87.34 8	84.84 3	73.71 28	82.66 28	81.60 38	85.48 106	94.51 24

DI_MVS_plusplus_trai		73.94 54	74.85 57	72.88 55	76.57 91	86.80 48	80.41 42	61.47 90	62.35 75	59.44 61	47.91 91	68.12 48	72.24 36	82.84 27	81.50 39	87.15 51	94.42 25

SteuartSystems-ACMMP		82.51 16	85.35 17	79.20 21	90.25 12	89.39 28	84.79 16	70.95 20	82.86 24	68.32 33	86.44 10	77.19 21	73.07 31	83.63 21	83.64 21	87.82 34	94.34 26
Skip Steuart: Steuart Systems R&D Blog.
MVS_0304		79.43 28	82.20 26	76.20 37	84.22 47	91.79 11	81.82 32	63.81 65	76.83 45	61.71 48	66.37 41	75.52 25	76.38 17	85.54 10	85.03 12	89.28 10	94.32 27

PHI-MVS		79.43 28	84.06 20	74.04 50	86.15 42	91.57 13	80.85 41	68.90 35	82.22 27	51.81 79	78.10 23	74.28 27	70.39 47	84.01 20	84.00 19	86.14 67	94.24 28

ACMMP_Plus		83.54 12	86.37 13	80.25 16	89.57 21	90.10 23	85.27 15	71.66 18	87.38 11	73.08 16	84.23 13	80.16 15	75.31 19	84.85 14	83.64 21	86.57 58	94.21 29

MP-MVS		80.94 22	83.49 21	77.96 27	88.48 27	88.16 39	82.82 27	69.34 31	80.79 34	69.67 29	82.35 15	77.13 22	71.60 41	80.97 48	80.96 48	85.87 87	94.06 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PCF-MVS	70.85 4	75.73 48	76.55 52	74.78 49	83.67 48	88.04 42	81.47 33	70.62 24	69.24 61	57.52 66	60.59 56	69.18 47	70.65 45	77.11 73	77.65 71	84.75 132	94.01 31
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DeepC-MVS_fast	75.41 2	81.69 19	82.10 28	81.20 12	91.04 11	87.81 43	83.42 22	74.04 8	83.77 22	71.09 23	66.88 40	72.44 32	79.48 6	85.08 12	84.97 13	88.12 31	93.78 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + ACMM		81.59 21	85.84 15	76.63 34	89.82 17	86.53 52	86.32 9	66.72 45	85.96 17	65.43 38	88.98 7	82.29 9	67.57 67	82.06 37	81.33 42	83.93 149	93.75 33

HyFIR lowres test		68.39 78	68.28 88	68.52 77	80.85 63	88.11 40	71.08 111	58.09 115	54.87 104	47.80 93	27.55 206	55.80 91	64.97 74	79.11 58	79.14 58	88.31 25	93.35 34

test-LLR		68.23 79	71.61 70	64.28 115	71.37 124	81.32 95	63.98 158	61.03 93	58.62 84	42.96 125	52.74 73	61.65 69	57.74 136	75.64 91	78.09 69	88.61 18	93.21 35

TESTMET0.1,1		67.38 86	71.61 70	62.45 139	66.05 172	81.32 95	63.98 158	55.36 150	58.62 84	42.96 125	52.74 73	61.65 69	57.74 136	75.64 91	78.09 69	88.61 18	93.21 35

zzz-MVS		81.65 20	83.10 22	79.97 18	88.14 33	87.62 44	83.96 21	69.90 26	86.92 13	77.67 9	72.47 31	78.74 19	74.13 27	81.59 42	81.15 45	86.01 74	93.19 37

HQP-MVS		78.26 35	80.91 32	75.17 45	85.67 44	84.33 68	83.01 25	69.38 30	79.88 37	55.83 69	79.85 20	64.90 58	70.81 44	82.46 30	81.78 35	86.30 63	93.18 38

IB-MVS	64.48 11	69.02 75	68.97 83	69.09 74	81.75 57	89.01 30	64.50 152	64.91 58	56.65 91	62.59 45	47.89 92	45.23 122	51.99 153	69.18 174	81.88 34	88.77 15	92.93 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
CDPH-MVS		79.39 31	82.13 27	76.19 38	89.22 25	88.34 36	84.20 19	71.00 19	79.67 38	56.97 68	77.77 24	72.24 36	68.50 60	81.33 43	82.74 26	87.23 47	92.84 40

CostFormer		72.18 61	73.90 60	70.18 68	79.47 69	86.19 58	76.94 59	48.62 191	66.07 67	60.40 58	54.14 70	65.82 54	67.98 62	75.84 89	76.41 78	87.67 38	92.83 41

QAPM		77.50 40	77.43 43	77.59 31	91.52 8	92.00 9	81.41 35	70.63 22	66.22 64	58.05 65	54.70 66	71.79 38	74.49 26	82.46 30	82.04 31	89.46 9	92.79 42

CP-MVS		79.44 27	81.51 30	77.02 33	86.95 38	85.96 59	82.00 29	68.44 37	81.82 29	67.39 34	77.43 25	73.68 28	71.62 40	79.56 56	79.58 55	85.73 96	92.51 43

HFP-MVS		82.48 17	84.12 19	80.56 14	90.15 13	87.55 45	84.28 18	69.67 29	85.22 19	77.95 8	84.69 12	75.94 24	75.04 21	81.85 39	81.17 44	86.30 63	92.40 44

canonicalmvs		77.65 38	79.59 36	75.39 42	81.52 58	89.83 27	81.32 36	60.74 100	80.05 36	66.72 35	68.43 36	65.09 56	74.72 25	78.87 60	82.73 27	87.32 44	92.16 45

HSP-MVS		86.82 2	89.95 2	83.16 5	89.38 22	91.60 12	85.63 12	74.15 7	94.20 1	75.52 14	94.99 1	83.21 6	85.96 1	87.67 3	85.88 6	88.32 24	92.13 46

EPNet		79.28 32	82.25 25	75.83 40	88.31 31	90.14 22	79.43 47	68.07 38	81.76 30	61.26 50	77.26 26	70.08 45	70.06 48	82.43 32	82.00 33	87.82 34	92.09 47
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
diffmvs		73.50 55	75.66 55	70.97 63	74.96 104	86.71 49	77.16 58	57.42 130	71.12 54	60.43 57	57.20 61	70.40 44	68.79 59	76.11 86	76.05 82	87.10 52	92.06 48

CPTT-MVS		75.43 49	77.13 47	73.44 52	81.43 59	82.55 78	80.96 40	64.35 60	77.95 43	61.39 49	69.20 35	70.94 41	69.38 55	73.89 110	73.32 136	83.14 162	92.06 48

test-mter		64.06 114	69.24 81	58.01 166	59.07 201	77.40 143	59.13 184	48.11 194	55.64 99	39.18 149	51.56 77	58.54 78	55.38 144	73.52 115	76.00 83	87.22 48	92.05 50

MVS_111021_LR		74.26 52	75.95 53	72.27 58	79.43 70	85.04 63	72.71 80	65.27 57	70.92 55	63.58 42	69.32 34	60.31 74	69.43 53	77.01 74	77.15 72	83.22 158	91.93 51

gg-mvs-nofinetune		62.34 136	66.19 100	57.86 170	76.15 95	88.61 33	71.18 108	41.24 220	25.74 221	13.16 224	22.91 216	63.97 61	54.52 148	85.06 13	85.25 10	90.92 3	91.78 52

ACMMPR		80.62 24	82.98 23	77.87 29	88.41 28	87.05 47	83.02 24	69.18 32	83.91 21	68.35 32	82.89 14	73.64 29	72.16 37	80.78 49	81.13 46	86.10 68	91.43 53

MVS_Test		75.22 50	76.69 50	73.51 51	79.30 71	88.82 31	80.06 44	58.74 110	69.77 58	57.50 67	59.78 58	61.35 71	75.31 19	82.07 36	83.60 23	90.13 5	91.41 54

X-MVS		78.16 36	80.55 33	75.38 43	87.99 35	86.27 55	81.05 39	68.98 33	78.33 40	61.07 52	75.25 29	72.27 33	67.52 68	80.03 53	80.52 53	85.66 103	91.20 55

MVSTER		76.92 44	79.92 34	73.42 53	74.98 102	82.97 73	78.15 50	63.41 68	78.02 41	64.41 41	67.54 37	72.80 31	71.05 43	83.29 23	83.73 20	88.53 21	91.12 56

UGNet		67.57 84	71.69 69	62.76 135	69.88 132	82.58 77	66.43 145	58.64 111	54.71 105	51.87 78	61.74 50	62.01 68	45.46 181	74.78 98	74.99 93	84.24 143	91.02 57
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
DWT-MVSNet_training		72.81 58	73.98 58	71.45 62	81.26 60	86.37 54	72.08 83	59.82 107	69.13 62	58.15 64	54.71 65	61.33 73	67.81 64	76.86 75	78.63 61	89.59 6	90.86 58

ACMMP		77.61 39	79.59 36	75.30 44	85.87 43	85.58 60	81.42 34	67.38 42	79.38 39	62.61 44	78.53 22	65.79 55	68.80 58	78.56 63	78.50 64	85.75 92	90.80 59
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
3Dnovator+	70.16 6	77.87 37	77.29 45	78.55 25	89.25 24	88.32 37	80.09 43	67.95 39	74.89 50	71.83 21	52.05 76	70.68 42	76.27 18	82.27 35	82.04 31	85.92 80	90.77 60

PGM-MVS		79.42 30	81.84 29	76.60 35	88.38 30	86.69 50	82.97 26	65.75 52	80.39 35	64.94 39	81.95 17	72.11 37	71.41 42	80.45 50	80.55 52	86.18 65	90.76 61

Effi-MVS+		70.42 67	71.23 72	69.47 70	78.04 76	85.24 62	75.57 65	58.88 109	59.56 82	48.47 90	52.73 75	54.94 96	69.69 51	78.34 66	77.06 73	86.18 65	90.73 62

tpmrst		67.15 88	68.12 90	66.03 93	76.21 94	80.98 98	71.27 105	45.05 203	60.69 80	50.63 84	46.95 105	54.15 99	65.30 72	71.80 151	71.77 154	87.72 36	90.48 63

CLD-MVS		77.36 42	77.29 45	77.45 32	82.21 54	88.11 40	81.92 30	68.96 34	77.97 42	69.62 30	62.08 49	59.44 76	73.57 29	81.75 40	81.27 43	88.41 23	90.39 64
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
EPP-MVSNet		67.58 83	71.10 73	63.48 124	75.71 98	83.35 72	66.85 141	57.83 118	53.02 110	41.15 140	55.82 63	67.89 50	56.01 142	74.40 101	72.92 145	83.33 156	90.30 65

CANet_DTU		72.84 57	76.63 51	68.43 78	76.81 89	86.62 51	75.54 66	54.71 158	72.06 52	43.54 113	67.11 38	58.46 79	72.40 35	81.13 47	80.82 51	87.57 39	90.21 66

Anonymous20240521		67.93 81	65.62 107	70.63 66	76.34 93	82.96 74	73.18 79	65.78 51	53.90 108	58.48 62	42.82 118	32.14 201	62.16 89	77.34 72	80.01 54	87.86 33	90.18 67

MSLP-MVS++		78.57 33	77.33 44	80.02 17	88.39 29	84.79 65	84.62 17	66.17 49	75.96 47	78.40 5	61.59 51	71.47 39	73.54 30	78.43 64	78.88 60	88.97 12	90.18 67

3Dnovator	70.49 5	78.42 34	76.77 49	80.35 15	91.43 9	90.27 20	81.84 31	70.79 21	72.10 51	71.95 19	50.02 83	67.86 51	77.47 12	82.89 25	84.24 16	88.61 18	89.99 69

thres100view900		67.14 89	66.09 103	68.38 79	77.70 79	83.84 71	74.52 73	66.33 48	49.16 121	43.40 118	43.24 115	41.34 132	62.59 86	79.31 57	75.92 84	85.73 96	89.81 70

thres200		65.58 98	64.74 112	66.56 91	77.52 84	81.61 86	73.44 78	62.95 74	46.23 142	42.45 135	42.76 120	41.18 137	58.12 134	76.24 81	75.59 88	84.89 122	89.58 71

tpm cat1		67.47 85	67.05 96	67.98 80	76.63 90	81.51 92	74.49 75	47.65 196	61.18 78	61.12 51	42.51 125	53.02 104	64.74 77	70.11 166	71.50 157	83.22 158	89.49 72

IS_MVSNet		67.29 87	71.98 66	61.82 145	76.92 87	84.32 69	65.90 149	58.22 113	55.75 98	39.22 148	54.51 68	62.47 64	45.99 178	78.83 61	78.52 63	84.70 133	89.47 73

tpm		64.85 104	66.02 104	63.48 124	74.52 106	78.38 132	70.98 113	44.99 205	51.61 112	43.28 120	47.66 95	53.18 102	60.57 105	70.58 160	71.30 166	86.54 59	89.45 74

gm-plane-assit		54.99 185	57.99 180	51.49 195	69.27 138	54.42 221	32.32 225	42.59 212	21.18 227	13.71 222	23.61 212	43.84 126	60.21 113	87.09 4	86.55 4	90.81 4	89.28 75

OpenMVS	67.62 8	74.92 51	73.91 59	76.09 39	90.10 15	90.38 19	78.01 51	66.35 47	66.09 66	62.80 43	46.33 108	64.55 59	71.77 39	79.92 54	80.88 50	87.52 40	89.20 76

v1144		63.00 130	62.39 139	63.70 120	67.72 162	80.27 114	71.23 107	56.40 137	42.51 168	40.81 142	38.12 167	37.73 172	60.42 111	74.46 100	74.55 103	85.64 104	89.12 77

v7		63.61 119	63.02 127	64.29 114	67.88 159	80.32 113	71.60 101	56.63 136	45.37 146	42.84 130	38.54 158	38.91 168	61.05 100	74.39 102	74.52 105	85.75 92	89.10 78

ACMP	68.86 7	72.15 62	72.25 65	72.03 59	80.96 62	80.87 100	77.93 52	64.13 62	69.29 59	60.79 55	64.04 44	53.54 101	63.91 79	73.74 114	75.27 91	84.45 139	88.98 79
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v144192		62.05 144	61.46 149	62.73 137	66.59 170	79.87 117	69.30 127	55.88 142	41.50 176	39.41 147	37.23 171	36.45 180	59.62 121	72.69 137	73.51 131	85.61 105	88.93 80

LGP-MVS_train		72.02 63	73.18 64	70.67 65	82.13 55	80.26 115	79.58 46	63.04 72	70.09 56	51.98 77	65.06 43	55.62 94	62.49 87	75.97 88	76.32 79	84.80 131	88.93 80

conf0.002		67.12 90	67.13 95	67.11 86	77.95 77	82.11 81	71.71 93	63.06 70	49.16 121	43.43 115	47.76 94	48.79 113	61.42 93	76.61 77	76.55 76	85.07 116	88.92 82

v2v482		63.68 118	62.85 130	64.65 105	68.01 155	80.46 112	71.90 85	57.60 123	44.26 151	42.82 133	39.80 144	38.62 170	61.56 92	73.06 119	74.86 95	86.03 73	88.90 83

conf0.01		66.60 91	66.18 101	67.09 87	77.90 78	82.02 82	71.71 93	63.05 71	49.16 121	43.41 117	46.23 109	45.78 121	61.42 93	76.55 79	74.63 98	85.04 117	88.87 84

v1		63.49 120	62.77 133	64.32 111	68.13 149	80.70 106	71.70 99	57.43 127	43.69 157	42.89 129	39.03 152	39.77 158	59.93 117	72.93 124	74.48 109	85.86 88	88.77 85

v1192		62.25 139	61.64 147	62.96 131	66.88 167	79.72 119	69.96 122	55.77 144	41.58 175	39.42 146	37.05 173	35.96 185	60.50 110	74.30 107	74.09 124	85.24 110	88.76 86

v1141		63.48 121	62.75 135	64.32 111	68.13 149	80.69 107	71.69 100	57.43 127	43.66 159	42.83 132	39.02 153	39.74 160	59.95 115	72.94 123	74.49 107	85.86 88	88.75 87

divwei89l23v2f112		63.48 121	62.76 134	64.32 111	68.13 149	80.68 108	71.71 93	57.43 127	43.69 157	42.84 130	39.01 154	39.75 159	59.94 116	72.93 124	74.49 107	85.86 88	88.75 87

v6		64.09 110	63.40 120	64.90 97	68.28 145	80.78 101	71.85 87	57.64 122	46.73 137	45.18 104	39.40 146	40.89 143	60.54 108	72.86 127	74.40 112	85.92 80	88.72 89

v1neww		64.08 111	63.38 121	64.89 99	68.27 147	80.77 103	71.84 88	57.65 120	46.66 139	45.10 105	39.40 146	40.86 144	60.57 105	72.86 127	74.40 112	85.92 80	88.71 90

v7new		64.08 111	63.38 121	64.89 99	68.27 147	80.77 103	71.84 88	57.65 120	46.66 139	45.10 105	39.40 146	40.86 144	60.57 105	72.86 127	74.40 112	85.92 80	88.71 90

V42		62.86 134	62.97 128	62.74 136	60.84 194	78.99 127	71.46 104	57.13 134	46.85 135	44.28 111	38.87 155	40.73 148	57.63 138	72.60 139	74.14 122	85.09 114	88.63 92

thres400		65.18 103	64.44 114	66.04 92	76.40 92	82.63 76	71.52 103	64.27 61	44.93 150	40.69 143	41.86 130	40.79 146	58.12 134	77.67 67	74.64 97	85.26 109	88.56 93

tfpn111		66.52 92	66.12 102	66.98 89	77.70 79	81.58 88	71.71 93	62.94 76	49.16 121	43.28 120	51.38 78	41.34 132	61.42 93	76.24 81	74.63 98	84.84 125	88.52 94

conf200view11		65.89 97	64.96 109	66.98 89	77.70 79	81.58 88	71.71 93	62.94 76	49.16 121	43.28 120	43.24 115	41.34 132	61.42 93	76.24 81	74.63 98	84.84 125	88.52 94

tfpn200view9		65.90 96	64.96 109	67.00 88	77.70 79	81.58 88	71.71 93	62.94 76	49.16 121	43.40 118	43.24 115	41.34 132	61.42 93	76.24 81	74.63 98	84.84 125	88.52 94

v1921920		61.66 151	61.10 152	62.31 140	66.32 171	79.57 121	68.41 132	55.49 148	41.03 177	38.69 153	36.64 179	35.27 191	59.60 122	73.23 117	73.41 133	85.37 107	88.51 97

tpmp4_e23		69.38 72	69.47 80	69.28 72	78.20 75	82.35 80	75.92 60	49.20 189	64.15 73	59.96 59	47.93 90	55.77 92	68.06 61	73.05 121	74.53 104	84.34 141	88.50 98

PMMVS		70.37 70	75.06 56	64.90 97	71.46 123	81.88 83	64.10 154	55.64 146	71.31 53	46.69 96	70.69 33	58.56 77	69.53 52	79.03 59	75.63 87	81.96 174	88.32 99

OPM-MVS		72.74 59	70.93 74	74.85 48	85.30 45	84.34 67	82.82 27	69.79 27	49.96 116	55.39 73	54.09 71	60.14 75	70.04 49	80.38 52	79.43 56	85.74 95	88.20 100

v1240		61.09 155	60.55 157	61.72 146	65.92 175	79.28 125	67.16 140	54.91 154	39.79 182	38.10 156	36.08 181	34.64 192	59.15 129	72.86 127	73.36 135	85.10 112	87.84 101

Fast-Effi-MVS+-dtu		63.05 128	64.72 113	61.11 149	71.21 127	76.81 151	70.72 116	43.13 211	52.51 111	35.34 173	46.55 107	46.36 118	61.40 98	71.57 153	71.44 159	84.84 125	87.79 102

MAR-MVS		77.19 43	78.37 41	75.81 41	89.87 16	90.58 17	79.33 48	65.56 54	77.62 44	58.33 63	59.24 59	67.98 49	74.83 22	82.37 33	83.12 25	86.95 53	87.67 103
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
view600		63.91 116	63.27 123	64.66 104	75.57 99	81.73 84	69.71 124	63.04 72	43.97 153	39.18 149	41.09 134	40.24 154	55.38 144	76.28 80	72.04 153	85.08 115	87.52 104

thres600view7		63.77 117	63.14 125	64.51 106	75.49 100	81.61 86	69.59 125	62.95 74	43.96 154	38.90 151	41.09 134	40.24 154	55.25 146	76.24 81	71.54 156	84.89 122	87.30 105

FC-MVSNet-train		68.83 76	68.29 87	69.47 70	78.35 74	79.94 116	64.72 151	66.38 46	54.96 102	54.51 74	56.75 62	47.91 116	66.91 69	75.57 93	75.75 85	85.92 80	87.12 106

v148		62.00 145	61.19 151	62.96 131	67.46 165	79.49 122	67.87 133	57.66 119	42.30 170	45.02 107	38.20 165	38.89 169	54.77 147	69.83 170	72.60 149	84.96 118	87.01 107

CHOSEN 280x420		62.23 141	66.57 98	57.17 175	59.88 198	68.92 195	61.20 174	42.28 213	54.17 106	39.57 145	47.78 93	64.97 57	62.68 85	73.85 111	69.52 176	77.43 201	86.75 108

Fast-Effi-MVS+		67.59 82	67.56 92	67.62 83	73.67 109	81.14 97	71.12 109	54.79 157	58.88 83	50.61 85	46.70 106	47.05 117	69.12 56	76.06 87	76.44 77	86.43 61	86.65 109

GA-MVS		64.55 107	65.76 106	63.12 130	69.68 133	81.56 91	69.59 125	58.16 114	45.23 148	35.58 172	47.01 104	41.82 131	59.41 125	79.62 55	78.54 62	86.32 62	86.56 110

AdaColmap		76.23 47	73.55 61	79.35 20	89.38 22	85.00 64	79.99 45	73.04 15	76.60 46	71.17 22	55.18 64	57.99 84	77.87 10	76.82 76	76.82 74	84.67 134	86.45 111

view800		63.02 129	62.69 136	63.39 126	74.79 105	80.76 105	67.83 134	61.93 85	43.16 164	37.78 160	40.43 139	39.73 161	53.16 151	75.01 95	73.32 136	84.87 124	86.43 112

Effi-MVS+-dtu		64.58 106	64.08 115	65.16 95	73.04 114	75.17 162	70.68 117	56.23 140	54.12 107	44.71 109	47.42 96	51.10 108	63.82 80	68.08 178	66.32 191	82.47 169	86.38 113

CDS-MVSNet		64.22 109	65.89 105	62.28 141	70.05 131	80.59 110	69.91 123	57.98 116	43.53 160	46.58 97	48.22 89	50.76 109	46.45 175	75.68 90	76.08 81	82.70 165	86.34 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
OMC-MVS		74.03 53	75.82 54	71.95 60	79.56 68	80.98 98	75.35 69	63.21 69	84.48 20	61.83 47	61.54 52	66.89 52	69.41 54	76.60 78	74.07 125	82.34 171	86.15 115

v10		63.00 130	62.22 140	63.90 119	67.88 159	77.78 139	71.59 102	54.34 159	45.37 146	42.76 134	38.53 159	38.93 167	61.05 100	74.39 102	74.52 105	85.75 92	86.04 116

IterMVS-LS		66.08 95	66.56 99	65.51 94	73.67 109	74.88 163	70.89 115	53.55 165	50.42 114	48.32 91	50.59 81	55.66 93	61.83 90	73.93 109	74.42 111	84.82 130	86.01 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Vis-MVSNet		65.53 100	69.83 79	60.52 151	70.80 130	84.59 66	66.37 147	55.47 149	48.40 129	40.62 144	57.67 60	58.43 80	45.37 182	77.49 68	76.24 80	84.47 138	85.99 118
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UniMVSNet (Re)		60.62 158	62.93 129	57.92 167	67.64 163	77.90 137	61.75 171	61.24 92	49.83 117	29.80 191	42.57 123	40.62 152	43.36 185	70.49 163	73.27 139	83.76 150	85.81 119

v8		63.44 124	62.58 137	64.43 108	68.28 145	78.07 135	71.82 90	54.85 155	46.70 138	45.20 103	39.40 146	40.91 142	60.54 108	72.85 131	74.39 117	85.92 80	85.76 120

CNLPA		71.37 66	70.27 77	72.66 57	80.79 65	81.33 94	71.07 112	65.75 52	82.36 26	64.80 40	42.46 126	56.49 87	72.70 34	73.00 122	70.52 171	80.84 182	85.76 120

anonymousdsp		54.99 185	57.24 181	52.36 192	53.82 215	71.75 188	51.49 197	48.14 193	33.74 205	33.66 180	38.34 162	36.13 183	47.54 169	64.53 190	70.60 170	79.53 193	85.59 122

tfpn		62.54 135	62.79 132	62.25 142	74.16 107	79.86 118	66.07 148	60.97 96	42.43 169	36.41 164	39.88 143	43.76 127	51.25 158	73.85 111	74.17 121	84.67 134	85.57 123

UniMVSNet_NR-MVSNet		62.30 138	63.51 119	60.89 150	69.48 137	77.83 138	64.07 156	63.94 63	50.03 115	31.17 187	44.82 110	41.12 138	51.37 155	71.02 155	74.81 96	85.30 108	84.95 124

DU-MVS		60.87 157	61.82 144	59.76 156	66.69 168	75.87 156	64.07 156	61.96 83	49.31 119	31.17 187	42.76 120	36.95 177	51.37 155	69.67 171	73.20 142	83.30 157	84.95 124

NR-MVSNet		61.08 156	62.09 142	59.90 154	71.96 122	75.87 156	63.60 162	61.96 83	49.31 119	27.95 196	42.76 120	33.85 197	48.82 164	74.35 104	74.05 126	85.13 111	84.45 126

RPMNet		58.63 173	62.80 131	53.76 191	67.59 164	71.29 189	54.60 194	38.13 224	55.83 96	35.70 171	41.58 132	53.04 103	47.89 167	66.10 182	67.38 184	78.65 199	84.40 127

FMVSNet3		70.41 69	71.89 68	68.68 76	70.89 129	79.42 124	75.63 63	60.97 96	65.32 68	51.06 81	47.37 97	62.05 65	64.90 75	82.49 29	82.27 30	88.64 17	84.34 128

TSAR-MVS + COLMAP		73.09 56	76.86 48	68.71 75	74.97 103	82.49 79	74.51 74	61.83 86	83.16 23	49.31 89	82.22 16	51.62 107	68.94 57	78.76 62	75.52 90	82.67 166	84.23 129

IterMVS		61.87 147	63.55 118	59.90 154	67.29 166	72.20 184	67.34 139	48.56 192	47.48 133	37.86 159	47.07 102	48.27 114	54.08 149	72.12 146	73.71 129	84.30 142	83.99 130
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
conf0.05thres1000		60.33 161	59.42 171	61.40 148	73.15 113	78.25 134	65.29 150	60.30 103	36.61 193	35.75 170	33.25 189	39.23 165	50.35 161	72.18 145	72.67 148	83.57 153	83.74 131

GBi-Net		69.21 73	70.40 75	67.81 81	69.49 134	78.65 129	74.54 70	60.97 96	65.32 68	51.06 81	47.37 97	62.05 65	63.43 81	77.49 68	78.22 66	87.37 41	83.73 132

test1		69.21 73	70.40 75	67.81 81	69.49 134	78.65 129	74.54 70	60.97 96	65.32 68	51.06 81	47.37 97	62.05 65	63.43 81	77.49 68	78.22 66	87.37 41	83.73 132

FMVSNet2		68.06 80	68.57 85	67.45 84	69.49 134	78.65 129	74.54 70	60.23 106	56.29 94	49.64 88	42.13 129	57.08 86	63.43 81	81.15 46	80.99 47	87.37 41	83.73 132

v18		63.31 125	62.02 143	64.81 101	68.48 141	73.38 172	72.14 82	54.28 160	48.99 128	47.21 94	39.56 145	41.20 136	60.80 102	72.89 126	74.46 110	85.96 79	83.64 135

Vis-MVSNet (Re-imp)		62.25 139	68.74 84	54.68 185	73.70 108	78.74 128	56.51 191	57.49 125	55.22 100	26.86 199	54.56 67	61.35 71	31.06 201	73.10 118	74.90 94	82.49 168	83.31 136

ACMM	66.70 10	70.42 67	68.49 86	72.67 56	82.85 49	77.76 140	77.70 54	64.76 59	64.61 72	60.74 56	49.29 85	53.97 100	65.86 71	74.97 96	75.57 89	84.13 147	83.29 137
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v17		62.99 132	61.70 146	64.51 106	68.40 143	73.28 174	71.80 91	54.11 162	47.87 131	46.14 98	39.29 151	41.01 140	60.60 104	72.81 133	74.39 117	85.99 77	83.25 138

v16		63.12 127	61.78 145	64.68 103	68.45 142	73.29 173	71.86 86	54.12 161	48.36 130	47.00 95	39.30 150	41.01 140	60.67 103	72.83 132	74.40 112	86.01 74	83.24 139

CR-MVSNet		62.31 137	64.75 111	59.47 158	68.63 140	71.29 189	67.53 136	43.18 209	55.83 96	41.40 137	41.04 136	55.85 90	57.29 139	72.76 134	73.27 139	78.77 197	83.23 140

PatchT		60.46 159	63.85 116	56.51 177	65.95 174	75.68 160	47.34 205	41.39 216	53.89 109	41.40 137	37.84 169	50.30 111	57.29 139	72.76 134	73.27 139	85.67 100	83.23 140

tfpnnormal		58.97 166	56.48 184	61.89 144	71.27 126	76.21 155	66.65 144	61.76 89	32.90 207	36.41 164	27.83 205	29.14 210	50.64 160	73.06 119	73.05 143	84.58 137	83.15 142

Baseline_NR-MVSNet		59.47 164	60.28 160	58.54 164	66.69 168	73.90 169	61.63 172	62.90 79	49.15 127	26.87 198	35.18 186	37.62 173	48.20 166	69.67 171	73.61 130	84.92 119	82.82 143

v15		62.07 143	60.70 154	63.67 121	68.09 152	73.00 175	71.27 105	53.41 166	43.70 156	43.43 115	38.77 156	39.83 156	59.87 118	72.74 136	74.25 119	85.98 78	82.61 144

pmmvs4		63.14 126	62.46 138	63.94 118	66.03 173	76.40 153	66.82 142	57.60 123	56.74 90	50.26 87	40.81 138	37.51 174	59.26 128	71.75 152	71.48 158	83.68 152	82.53 145

TranMVSNet+NR-MVSNet		60.38 160	61.30 150	59.30 159	68.34 144	75.57 161	63.38 165	63.78 66	46.74 136	27.73 197	42.56 124	36.84 178	47.66 168	70.36 164	74.59 102	84.91 121	82.46 146

pmmvs5		59.72 162	60.24 162	59.11 161	62.77 188	77.33 145	63.17 166	54.00 163	40.21 180	37.23 161	40.41 140	35.99 184	51.75 154	72.55 140	72.74 147	85.72 98	82.45 147

v11		61.74 149	60.47 158	63.22 129	67.83 161	72.72 181	70.31 120	52.95 175	42.75 167	41.89 136	38.16 166	38.49 171	60.40 112	74.35 104	74.40 112	85.92 80	82.39 148

V14		61.96 146	60.56 156	63.59 122	68.06 153	72.93 178	71.10 110	53.33 168	43.47 161	43.28 120	38.59 157	39.78 157	59.76 120	72.65 138	74.19 120	86.01 74	82.32 149

v7n		57.04 180	56.64 183	57.52 172	62.85 187	74.75 165	61.76 170	51.80 179	35.58 200	36.02 169	32.33 193	33.61 198	50.16 162	67.73 179	70.34 173	82.51 167	82.12 150

V9		61.85 148	60.42 159	63.51 123	68.02 154	72.85 179	70.91 114	53.24 169	43.25 163	43.27 124	38.41 161	39.73 161	59.60 122	72.55 140	74.13 123	86.04 72	82.04 151

PatchmatchNet		65.43 101	67.71 91	62.78 134	73.49 111	82.83 75	66.42 146	45.40 202	60.40 81	45.27 102	49.22 86	57.60 85	60.01 114	70.61 158	71.38 164	86.08 70	81.91 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pm-mvs1		59.21 165	59.58 170	58.77 163	67.97 157	77.07 150	64.12 153	57.20 132	34.73 201	36.86 162	35.34 184	40.54 153	43.34 186	74.32 106	73.30 138	83.13 163	81.77 153

v12		61.70 150	60.27 161	63.38 127	68.00 156	72.76 180	70.63 118	53.14 171	43.01 165	42.95 128	38.25 163	39.64 163	59.48 124	72.47 142	74.05 126	86.06 71	81.71 154

FMVSNet1		63.48 121	63.07 126	63.97 117	65.31 178	76.37 154	71.77 92	57.90 117	43.32 162	45.66 100	35.06 187	49.43 112	58.57 132	77.49 68	78.22 66	84.59 136	81.60 155

v13		61.60 152	60.13 164	63.31 128	67.95 158	72.67 182	70.51 119	53.05 172	42.80 166	42.96 125	38.10 168	39.57 164	59.31 127	72.36 143	73.98 128	86.10 68	81.40 156

TAPA-MVS	67.10 9	71.45 65	73.47 63	69.10 73	77.04 86	80.78 101	73.81 77	62.10 82	80.80 33	51.28 80	60.91 54	63.80 62	67.98 62	74.59 99	72.42 150	82.37 170	80.97 157
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MDTV_nov1_ep13		65.21 102	67.28 94	62.79 133	70.91 128	81.72 85	69.28 128	49.50 186	58.08 86	43.94 112	50.50 82	56.02 89	58.86 131	70.72 157	73.37 134	84.24 143	80.52 158

v52		54.79 188	55.15 186	54.36 189	54.07 213	72.13 185	59.84 181	49.39 187	34.50 202	35.08 175	31.63 197	35.74 187	47.21 173	63.90 193	67.92 180	80.59 185	80.23 159

V4		54.78 189	55.14 187	54.37 188	54.07 213	72.13 185	59.83 182	49.39 187	34.46 204	35.11 174	31.64 196	35.72 188	47.22 172	63.90 193	67.92 180	80.59 185	80.23 159

v748		55.19 182	54.63 188	55.85 180	61.44 193	72.97 177	58.72 186	51.62 180	34.48 203	36.39 166	32.09 194	33.05 199	45.48 180	61.85 198	67.87 182	81.45 178	80.08 161

MS-PatchMatch		70.34 71	69.00 82	71.91 61	85.20 46	85.35 61	77.84 53	61.77 88	58.01 87	55.40 72	41.26 133	58.34 81	61.69 91	81.70 41	78.29 65	89.56 7	80.02 162

ACMH	59.42 14	61.59 153	59.22 174	64.36 110	78.92 73	78.26 133	67.65 135	67.48 41	39.81 181	30.98 189	38.25 163	34.59 193	61.37 99	70.55 161	73.47 132	79.74 191	79.59 163
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		66.17 94	70.13 78	61.54 147	81.04 61	77.39 144	68.87 130	62.50 81	69.78 57	33.51 181	63.77 45	56.22 88	37.65 198	72.20 144	72.18 152	85.69 99	79.38 164
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
EPMVS		66.21 93	67.49 93	64.73 102	75.81 97	84.20 70	68.94 129	44.37 207	61.55 77	48.07 92	49.21 87	54.87 97	62.88 84	71.82 150	71.40 161	88.28 26	79.37 165

dps		64.08 111	63.22 124	65.08 96	75.27 101	79.65 120	66.68 143	46.63 201	56.94 89	55.67 71	43.96 111	43.63 128	64.00 78	69.50 173	69.82 174	82.25 172	79.02 166

tfpn_ndepth		62.95 133	63.75 117	62.02 143	76.89 88	79.48 123	64.09 155	60.98 95	49.48 118	38.73 152	49.92 84	44.79 123	47.37 170	71.91 149	71.66 155	84.07 148	79.00 167

TAMVS		58.86 168	60.91 153	56.47 178	62.38 190	77.57 141	58.97 185	52.98 173	38.76 187	36.17 167	42.26 128	47.94 115	46.45 175	70.23 165	70.79 168	81.86 175	78.82 168

ACMH+	60.36 13	61.16 154	58.38 176	64.42 109	77.37 85	74.35 168	68.45 131	62.81 80	45.86 144	38.48 154	35.71 182	37.35 175	59.81 119	67.24 180	69.80 175	79.58 192	78.32 169

pmmvs6		54.20 192	53.54 193	54.97 183	63.22 186	72.98 176	60.17 179	52.32 178	26.77 220	34.30 178	23.29 215	36.23 182	40.33 194	68.77 176	68.76 178	79.47 194	78.00 170

PLC	64.00 12	68.54 77	66.66 97	70.74 64	80.28 67	74.88 163	72.64 81	63.70 67	69.26 60	55.71 70	47.24 100	55.31 95	70.42 46	72.05 148	70.67 169	81.66 176	77.19 171
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
EG-PatchMatch MVS		58.73 170	58.03 179	59.55 157	72.32 119	80.49 111	63.44 164	55.55 147	32.49 208	38.31 155	28.87 203	37.22 176	42.84 187	74.30 107	75.70 86	84.84 125	77.14 172

thresconf0.02		63.92 115	65.18 108	62.46 138	75.91 96	80.65 109	67.51 138	63.86 64	45.00 149	33.32 182	51.38 78	51.68 106	48.34 165	75.49 94	75.13 92	85.84 91	76.91 173

pmmvs-eth3d		55.20 181	53.95 192	56.65 176	57.34 207	67.77 197	57.54 188	53.74 164	40.93 178	41.09 141	31.19 199	29.10 211	49.07 163	65.54 183	67.28 185	81.14 180	75.81 174

Anonymous20231206		52.23 196	52.80 197	51.56 194	64.70 181	69.41 193	51.01 198	58.60 112	36.63 192	22.44 206	21.80 218	31.42 205	30.52 202	66.79 181	67.83 183	82.10 173	75.73 175

CP-MVSNet		50.57 199	52.60 199	48.21 203	58.77 203	65.82 203	48.17 203	56.29 139	37.41 189	16.59 215	37.14 172	31.95 203	29.21 205	56.60 209	63.71 200	80.22 187	75.56 176

tfpn1000		58.35 175	59.96 166	56.47 178	72.78 118	77.51 142	56.66 190	59.16 108	43.74 155	29.76 192	42.79 119	42.49 129	37.04 199	68.92 175	68.98 177	83.45 155	75.25 177

PS-CasMVS		50.17 200	52.02 200	48.02 204	58.60 204	65.54 204	48.04 204	56.19 141	36.42 195	16.42 217	35.68 183	31.33 206	28.85 207	56.42 211	63.54 201	80.01 188	75.18 178

CVMVSNet		54.92 187	58.16 177	51.13 196	62.61 189	68.44 196	55.45 193	52.38 177	42.28 171	21.45 207	47.10 101	46.10 119	37.96 197	64.42 191	63.81 199	76.92 204	75.01 179

TransMVSNet (Re)		57.83 176	56.90 182	58.91 162	72.26 120	74.69 166	63.57 163	61.42 91	32.30 209	32.65 184	33.97 188	35.96 185	39.17 196	73.84 113	72.84 146	84.37 140	74.69 180

PM-MVS		50.11 201	50.38 204	49.80 198	47.23 227	62.08 213	50.91 199	44.84 206	41.90 173	36.10 168	35.22 185	26.05 218	46.83 174	57.64 205	55.42 222	72.90 213	74.32 181

PatchMatch-RL		62.22 142	60.69 155	64.01 116	68.74 139	75.75 159	59.27 183	60.35 102	56.09 95	53.80 75	47.06 103	36.45 180	64.80 76	68.22 177	67.22 186	77.10 202	74.02 182

PEN-MVS		51.04 197	52.94 195	48.82 200	61.45 192	66.00 202	48.68 202	57.20 132	36.87 191	15.36 218	36.98 174	32.72 200	28.77 208	57.63 206	66.37 190	81.44 179	74.00 183

WR-MVS		51.02 198	54.56 190	46.90 206	63.84 183	69.23 194	44.78 212	56.38 138	38.19 188	14.19 220	37.38 170	36.82 179	22.39 218	60.14 201	66.20 193	79.81 190	73.95 184

MIMVSNet		57.78 177	59.71 168	55.53 182	54.79 209	77.10 149	63.89 160	45.02 204	46.59 141	36.79 163	28.36 204	40.77 147	45.84 179	74.97 96	76.58 75	86.87 55	73.60 185

USDC		59.69 163	60.03 165	59.28 160	64.04 182	71.84 187	63.15 167	55.36 150	54.90 103	35.02 176	48.34 88	29.79 209	58.16 133	70.60 159	71.33 165	79.99 189	73.42 186

MSDG		65.57 99	61.57 148	70.24 67	82.02 56	76.47 152	74.46 76	68.73 36	56.52 92	50.33 86	38.47 160	41.10 139	62.42 88	72.12 146	72.94 144	83.47 154	73.37 187

MDTV_nov1_ep13_2view		54.47 191	54.61 189	54.30 190	60.50 195	73.82 170	57.92 187	43.38 208	39.43 186	32.51 185	33.23 190	34.05 195	47.26 171	62.36 196	66.21 192	84.24 143	73.19 188

tfpnview11		58.92 167	59.60 169	58.13 165	72.99 115	77.11 148	60.48 175	60.37 101	42.10 172	29.10 193	43.45 112	40.72 149	41.67 190	70.53 162	70.43 172	84.17 146	72.85 189

WR-MVS_H		49.62 203	52.63 198	46.11 209	58.80 202	67.58 198	46.14 210	54.94 152	36.51 194	13.63 223	36.75 177	35.67 189	22.10 219	56.43 210	62.76 203	81.06 181	72.73 190

ADS-MVSNet		58.40 174	59.16 175	57.52 172	65.80 176	74.57 167	60.26 178	40.17 221	50.51 113	38.01 157	40.11 142	44.72 124	59.36 126	64.91 186	66.55 189	81.53 177	72.72 191

tfpn_n400		58.64 171	59.27 172	57.89 168	72.83 116	77.26 146	60.35 176	60.29 104	39.77 183	29.10 193	43.45 112	40.72 149	41.61 191	70.06 167	71.39 162	83.17 160	72.26 192

tfpnconf		58.64 171	59.27 172	57.89 168	72.83 116	77.26 146	60.35 176	60.29 104	39.77 183	29.10 193	43.45 112	40.72 149	41.61 191	70.06 167	71.39 162	83.17 160	72.26 192

LTVRE_ROB	47.26 16	49.41 204	49.91 206	48.82 200	64.76 180	69.79 192	49.05 200	47.12 198	20.36 229	16.52 216	36.65 178	26.96 213	50.76 159	60.47 200	63.16 202	64.73 223	72.00 194
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
UA-Net		64.62 105	68.23 89	60.42 152	77.53 83	81.38 93	60.08 180	57.47 126	47.01 134	44.75 108	60.68 55	71.32 40	41.84 189	73.27 116	72.25 151	80.83 183	71.68 195

test0.0.03 1		57.35 179	59.89 167	54.38 187	71.37 124	73.45 171	52.71 196	61.03 93	46.11 143	26.33 200	41.73 131	44.08 125	29.72 204	71.43 154	70.90 167	85.10 112	71.56 196

LS3D		64.54 108	62.14 141	67.34 85	80.85 63	75.79 158	69.99 121	65.87 50	60.77 79	44.35 110	42.43 127	45.95 120	65.01 73	69.88 169	68.69 179	77.97 200	71.43 197

MVS-HIRNet		53.86 193	53.02 194	54.85 184	60.30 197	72.36 183	44.63 213	42.20 214	39.45 185	43.47 114	21.66 219	34.00 196	55.47 143	65.42 184	67.16 187	83.02 164	71.08 198

DTE-MVSNet		49.82 202	51.92 201	47.37 205	61.75 191	64.38 207	45.89 211	57.33 131	36.11 196	12.79 225	36.87 175	31.93 204	25.73 213	58.01 203	65.22 195	80.75 184	70.93 199

testpf		43.39 214	47.17 211	38.98 218	65.58 177	47.38 229	36.09 222	31.67 231	36.97 190	19.47 210	33.01 191	35.62 190	23.61 217	50.86 223	56.08 219	57.48 228	70.27 200

EU-MVSNet		44.84 212	47.85 209	41.32 216	49.26 222	56.59 220	43.07 214	47.64 197	33.03 206	13.82 221	36.78 176	30.99 207	24.37 216	53.80 218	55.57 221	69.78 218	68.21 201

SixPastTwentyTwo		49.11 205	49.22 207	48.99 199	58.54 205	64.14 208	47.18 206	47.75 195	31.15 211	24.42 202	41.01 137	26.55 214	44.04 184	54.76 217	58.70 212	71.99 216	68.21 201

CMPMVS	43.63 17	57.67 178	55.43 185	60.28 153	72.01 121	79.00 126	62.77 168	53.23 170	41.77 174	45.42 101	30.74 200	39.03 166	53.01 152	64.81 188	64.65 197	75.26 208	68.03 203
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ambc			42.30 217		50.36 219	49.51 226	35.47 223		32.04 210	23.53 203	17.36 224	8.95 236	29.06 206	64.88 187	56.26 218	61.29 225	67.12 204

FMVSNet5		58.86 168	60.24 162	57.25 174	52.66 218	66.25 201	63.77 161	52.86 176	57.85 88	37.92 158	36.12 180	52.22 105	51.37 155	70.88 156	71.43 160	84.92 119	66.91 205

test2356		46.29 211	47.37 210	45.03 211	54.38 211	57.99 218	42.03 215	50.32 183	30.78 212	16.65 214	27.40 207	23.70 221	29.86 203	61.20 199	64.31 198	76.93 203	66.22 206

TDRefinement		52.70 194	51.02 202	54.66 186	57.41 206	65.06 205	61.47 173	54.94 152	44.03 152	33.93 179	30.13 202	27.57 212	46.17 177	61.86 197	62.48 206	74.01 212	66.06 207

RPSCF		55.07 184	58.06 178	51.57 193	48.87 225	58.95 215	53.68 195	41.26 219	62.42 74	45.88 99	54.38 69	54.26 98	53.75 150	57.15 207	53.53 223	66.01 222	65.75 208

pmmvs3		41.86 217	42.29 218	41.36 214	39.80 228	52.66 223	38.93 221	35.85 230	23.40 224	20.22 209	19.30 220	20.84 225	40.56 193	55.98 212	58.79 211	72.80 214	65.03 209

FC-MVSNet-test		47.24 209	54.37 191	38.93 219	59.49 200	58.25 217	34.48 224	53.36 167	45.66 145	6.66 234	50.62 80	42.02 130	16.62 228	58.39 202	61.21 208	62.99 224	64.40 210

TinyColmap		52.66 195	50.09 205	55.65 181	59.72 199	64.02 209	57.15 189	52.96 174	40.28 179	32.51 185	32.42 192	20.97 224	56.65 141	63.95 192	65.15 196	74.91 209	63.87 211

Anonymous20231211		40.44 219	39.25 220	41.84 213	54.29 212	57.29 219	41.10 217	49.06 190	17.67 232	10.15 229	10.63 231	16.79 230	25.15 215	52.14 219	56.70 217	71.30 217	63.51 212

N_pmnet		47.67 208	47.00 212	48.45 202	54.72 210	62.78 211	46.95 207	51.25 181	36.01 197	26.09 201	26.59 209	25.93 219	35.50 200	55.67 213	59.01 210	76.22 205	63.04 213

COLMAP_ROB	51.17 15	55.13 183	52.90 196	57.73 171	73.47 112	67.21 199	62.13 169	55.82 143	47.83 132	34.39 177	31.60 198	34.24 194	44.90 183	63.88 195	62.52 205	75.67 206	63.02 214
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
test20.03		47.23 210	48.69 208	45.53 210	63.28 185	64.39 206	41.01 218	56.93 135	29.16 214	15.21 219	23.90 211	30.76 208	17.51 227	64.63 189	65.26 194	79.21 196	62.71 215

testus		42.30 215	43.69 214	40.67 217	53.21 216	53.50 222	31.81 226	49.96 184	27.06 218	11.55 227	25.67 210	19.00 227	25.20 214	55.34 214	62.59 204	72.31 215	62.69 216

new-patchmatchnet		42.21 216	42.97 216	41.33 215	53.05 217	59.89 214	39.38 219	49.61 185	28.26 217	12.10 226	22.17 217	21.54 223	19.22 223	50.96 222	56.04 220	74.61 211	61.92 217

MDA-MVSNet-bldmvs		44.15 213	42.27 219	46.34 207	38.34 230	62.31 212	46.28 208	55.74 145	29.83 213	20.98 208	27.11 208	16.45 231	41.98 188	41.11 229	57.47 214	74.72 210	61.65 218

testgi		48.51 206	50.53 203	46.16 208	64.78 179	67.15 200	41.54 216	54.81 156	29.12 215	17.03 213	32.07 195	31.98 202	20.15 222	65.26 185	67.00 188	78.67 198	61.10 219

LP		48.21 207	46.65 213	50.03 197	60.39 196	63.86 210	48.73 201	38.71 223	35.60 199	32.99 183	23.31 214	24.95 220	40.07 195	57.73 204	61.56 207	79.29 195	59.51 220

testmv		37.40 222	37.95 222	36.76 222	48.97 223	49.33 227	28.65 230	46.74 199	18.34 230	7.68 232	16.80 227	14.47 232	19.18 224	51.72 220	56.93 215	69.36 219	58.09 221

test1235678		37.40 222	37.94 223	36.76 222	48.97 223	49.30 228	28.65 230	46.73 200	18.33 231	7.68 232	16.79 228	14.46 233	19.18 224	51.72 220	56.92 216	69.36 219	58.07 222

MIMVSNet1		40.84 218	43.46 215	37.79 221	32.14 232	58.92 216	39.24 220	50.83 182	27.00 219	11.29 228	16.76 229	26.53 215	17.75 226	57.14 208	61.12 209	75.46 207	56.78 223

new_pmnet		33.19 224	35.52 225	30.47 225	27.55 236	45.31 231	29.29 229	30.92 232	29.00 216	9.88 231	18.77 221	17.64 229	26.77 212	44.07 224	45.98 226	58.41 227	47.87 224

FPMVS		39.11 220	36.39 224	42.28 212	55.97 208	45.94 230	46.23 209	41.57 215	35.73 198	22.61 204	23.46 213	19.82 226	28.32 211	43.57 225	40.67 228	58.96 226	45.54 225

test12356		29.92 226	31.49 226	28.08 226	38.46 229	37.74 233	21.36 233	40.17 221	16.83 233	5.61 236	15.66 230	11.48 234	6.60 234	42.01 227	51.23 224	56.29 229	45.52 226

no-one		26.96 227	26.51 228	27.49 228	37.87 231	39.14 232	17.12 235	41.31 218	12.02 235	3.68 238	8.04 233	8.42 237	10.67 232	28.11 231	45.96 227	54.27 230	43.89 227

1111		38.93 221	38.98 221	38.86 220	50.10 220	50.42 224	29.52 227	38.00 225	22.67 225	17.99 211	17.40 222	26.26 216	28.72 209	54.86 215	58.20 213	68.82 221	43.08 228

PMVS	27.44 18	32.08 225	29.07 227	35.60 224	48.33 226	24.79 235	26.97 232	41.34 217	20.45 228	22.50 205	17.11 226	18.64 228	20.44 221	41.99 228	38.06 229	54.02 231	42.44 229
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
PMMVS2		20.45 230	22.31 231	18.27 233	20.52 237	26.73 234	14.85 237	28.43 234	13.69 234	0.79 241	10.35 232	9.10 235	3.83 236	27.64 232	32.87 230	41.17 232	35.81 230

DeepMVS_CX						19.81 238	17.01 236	10.02 235	23.61 223	5.85 235	17.21 225	8.03 238	21.13 220	22.60 233		21.42 237	30.01 231

MVE	15.98 19	14.37 233	16.36 232	12.04 235	7.72 239	20.24 237	5.90 241	29.05 233	8.28 238	3.92 237	4.72 236	2.42 240	9.57 233	18.89 234	31.46 231	16.07 238	28.53 232
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
Gipuma		24.91 229	24.61 229	25.26 230	31.47 233	21.59 236	18.06 234	37.53 227	25.43 222	10.03 230	4.18 237	4.25 239	14.85 229	43.20 226	47.03 225	39.62 233	26.55 233
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
E-PMN		15.08 231	11.65 233	19.08 231	28.73 234	12.31 239	6.95 240	36.87 229	10.71 237	3.63 239	5.13 234	2.22 242	13.81 231	11.34 235	18.50 233	24.49 235	21.32 234

EMVS		14.40 232	10.71 234	18.70 232	28.15 235	12.09 240	7.06 239	36.89 228	11.00 236	3.56 240	4.95 235	2.27 241	13.91 230	10.13 236	16.06 234	22.63 236	18.51 235

test123		0.05 234	0.08 235	0.01 236	0.00 241	0.01 242	0.01 244	0.00 239	0.05 239	0.00 243	0.16 238	0.00 244	0.04 237	0.02 238	0.05 235	0.00 241	0.26 236

.test1245		25.86 228	24.56 230	27.39 229	50.10 220	50.42 224	29.52 227	38.00 225	22.67 225	17.99 211	17.40 222	26.26 216	28.72 209	54.86 215	0.05 235	0.01 239	0.24 237

testmvs		0.05 234	0.08 235	0.01 236	0.00 241	0.01 242	0.03 243	0.01 238	0.05 239	0.00 243	0.14 239	0.01 243	0.03 239	0.05 237	0.05 235	0.01 239	0.24 237

sosnet-low-res		0.00 236	0.00 237	0.00 238	0.00 241	0.00 244	0.00 245	0.00 239	0.00 241	0.00 243	0.00 240	0.00 244	0.00 240	0.00 239	0.00 238	0.00 241	0.00 239

sosnet		0.00 236	0.00 237	0.00 238	0.00 241	0.00 244	0.00 245	0.00 239	0.00 241	0.00 243	0.00 240	0.00 244	0.00 240	0.00 239	0.00 238	0.00 241	0.00 239

our_test_3					63.32 184	71.07 191	55.90 192

MTAPA										78.32 6		79.42 18

MTMP										76.04 11		76.65 23

Patchmatch-RL test							2.17 242

tmp_tt				16.09 234	13.07 238	8.12 241	13.61 238	2.08 236	55.09 101	30.10 190	40.26 141	22.83 222	5.35 235	29.91 230	25.25 232	32.33 234

XVS					82.43 50	86.27 55	75.70 61			61.07 52		72.27 33				85.67 100

X-MVStestdata					82.43 50	86.27 55	75.70 61			61.07 52		72.27 33				85.67 100

mPP-MVS					86.96 37							70.61 43

NP-MVS									81.60 31

Patchmtry						78.06 136	67.53 136	43.18 209		41.40 137

High-resolution multi-view benchmark