High-res multi-view results

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

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

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

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

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

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

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

ESAPD		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 9	84.01 17	88.09 32	97.62 2

CSCG		82.90 14	84.52 17	81.02 12	91.85 6	93.43 2	87.14 5	74.01 9	81.96 27	76.14 10	70.84 31	82.49 7	69.71 49	82.32 33	85.18 10	87.26 45	95.40 17

MCST-MVS		85.75 5	86.99 8	84.31 2	94.07 1	92.80 3	88.15 3	79.10 1	85.66 17	70.72 24	76.50 27	80.45 13	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

DELS-MVS		79.49 25	79.84 34	79.08 22	88.26 31	92.49 4	84.12 19	70.63 21	65.27 70	69.60 30	61.29 52	66.50 52	72.75 32	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
CHOSEN 1792x2688		72.55 59	71.98 65	73.22 53	86.57 40	92.41 5	75.63 62	66.77 43	62.08 75	52.32 74	30.27 199	50.74 109	66.14 69	86.22 7	85.41 6	91.90 1	96.75 9

CNVR-MVS		85.96 4	87.58 6	84.06 3	92.58 4	92.40 6	87.62 4	77.77 2	88.44 9	75.93 12	79.49 20	81.97 10	81.65 3	87.04 5	86.58 3	88.79 14	97.18 4

CANet		80.90 22	82.93 23	78.53 25	86.83 39	92.26 7	81.19 36	66.95 42	81.60 30	69.90 27	66.93 38	74.80 25	76.79 13	84.68 14	84.77 14	89.50 8	95.50 14

abl_6				79.06 23	89.68 18	92.14 8	77.70 53	69.68 27	86.87 13	71.88 19	74.29 29	80.06 15	76.56 15			88.84 13	95.82 10

QAPM		77.50 39	77.43 42	77.59 30	91.52 7	92.00 9	81.41 34	70.63 21	66.22 63	58.05 63	54.70 65	71.79 37	74.49 25	82.46 29	82.04 30	89.46 9	92.79 41

APDe-MVS		86.37 3	88.41 4	84.00 4	91.43 8	91.83 10	88.34 2	74.67 5	91.19 4	81.76 1	91.13 3	81.94 11	80.07 5	83.38 21	82.58 28	87.69 36	96.78 7

MVS_0304		79.43 27	82.20 25	76.20 36	84.22 46	91.79 11	81.82 31	63.81 63	76.83 44	61.71 47	66.37 40	75.52 24	76.38 16	85.54 9	85.03 11	89.28 10	94.32 26

HSP-MVS		86.82 2	89.95 2	83.16 5	89.38 21	91.60 12	85.63 11	74.15 7	94.20 1	75.52 14	94.99 1	83.21 5	85.96 1	87.67 3	85.88 5	88.32 24	92.13 45

PHI-MVS		79.43 27	84.06 19	74.04 49	86.15 41	91.57 13	80.85 40	68.90 34	82.22 26	51.81 77	78.10 22	74.28 26	70.39 46	84.01 19	84.00 18	86.14 65	94.24 27

DeepPCF-MVS	76.94 1	83.08 13	87.77 5	77.60 29	90.11 13	90.96 14	78.48 48	72.63 16	93.10 2	65.84 36	80.67 18	81.55 12	74.80 22	85.94 8	85.39 7	83.75 149	96.77 8

MVS_111021_HR		77.42 40	78.40 39	76.28 35	86.95 37	90.68 15	77.41 55	70.56 24	66.21 64	62.48 45	66.17 41	63.98 59	72.08 37	82.87 25	83.15 23	88.24 27	95.71 11

MAR-MVS		77.19 42	78.37 40	75.81 40	89.87 15	90.58 16	79.33 47	65.56 52	77.62 43	58.33 61	59.24 58	67.98 48	74.83 21	82.37 32	83.12 24	86.95 51	87.67 101
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
NCCC		84.16 10	85.46 15	82.64 6	92.34 5	90.57 17	86.57 6	76.51 3	86.85 14	72.91 17	77.20 26	78.69 19	79.09 8	84.64 15	84.88 13	88.44 22	95.41 16

OpenMVS	67.62 8	74.92 50	73.91 58	76.09 38	90.10 14	90.38 18	78.01 50	66.35 46	66.09 65	62.80 42	46.33 107	64.55 58	71.77 38	79.92 53	80.88 49	87.52 39	89.20 74

3Dnovator	70.49 5	78.42 33	76.77 48	80.35 14	91.43 8	90.27 19	81.84 30	70.79 20	72.10 50	71.95 18	50.02 82	67.86 50	77.47 12	82.89 24	84.24 15	88.61 18	89.99 67

HPM-MVS++		85.64 6	88.43 3	82.39 7	92.65 3	90.24 20	85.83 9	74.21 6	90.68 6	75.63 13	86.77 9	84.15 3	78.68 9	86.33 6	85.26 8	87.32 43	95.60 13

EPNet		79.28 31	82.25 24	75.83 39	88.31 30	90.14 21	79.43 46	68.07 37	81.76 29	61.26 49	77.26 25	70.08 44	70.06 47	82.43 31	82.00 32	87.82 33	92.09 46
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP_Plus		83.54 11	86.37 12	80.25 15	89.57 20	90.10 22	85.27 14	71.66 17	87.38 10	73.08 16	84.23 13	80.16 14	75.31 18	84.85 13	83.64 20	86.57 56	94.21 28

GG-mvs-BLEND		54.54 188	77.58 41	27.67 225	0.03 237	90.09 23	77.20 56	0.02 235	66.83 62	0.05 240	59.90 56	73.33 29	0.04 235	78.40 64	79.30 55	88.65 16	95.20 19

PVSNet_BlendedMVS		76.84 44	78.47 37	74.95 45	82.37 51	89.90 24	75.45 66	65.45 53	74.99 47	70.66 25	63.07 45	58.27 81	67.60 64	84.24 17	81.70 35	88.18 28	97.10 5

PVSNet_Blended		76.84 44	78.47 37	74.95 45	82.37 51	89.90 24	75.45 66	65.45 53	74.99 47	70.66 25	63.07 45	58.27 81	67.60 64	84.24 17	81.70 35	88.18 28	97.10 5

canonicalmvs		77.65 37	79.59 35	75.39 41	81.52 57	89.83 26	81.32 35	60.74 98	80.05 35	66.72 34	68.43 35	65.09 55	74.72 24	78.87 59	82.73 26	87.32 43	92.16 44

SteuartSystems-ACMMP		82.51 15	85.35 16	79.20 20	90.25 11	89.39 27	84.79 15	70.95 19	82.86 23	68.32 32	86.44 10	77.19 20	73.07 30	83.63 20	83.64 20	87.82 33	94.34 25
Skip Steuart: Steuart Systems R&D Blog.
APD-MVS		84.83 7	87.00 7	82.30 8	89.61 19	89.21 28	86.51 7	73.64 11	90.98 5	77.99 7	89.89 5	80.04 16	79.18 7	82.00 37	81.37 40	86.88 52	95.49 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
IB-MVS	64.48 11	69.02 74	68.97 82	69.09 72	81.75 56	89.01 29	64.50 150	64.91 56	56.65 90	62.59 44	47.89 91	45.23 121	51.99 151	69.18 172	81.88 33	88.77 15	92.93 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
MVS_Test		75.22 49	76.69 49	73.51 50	79.30 70	88.82 30	80.06 43	58.74 108	69.77 57	57.50 65	59.78 57	61.35 70	75.31 18	82.07 35	83.60 22	90.13 5	91.41 53

SD-MVS		84.31 9	86.96 9	81.22 10	88.98 25	88.68 31	85.65 10	73.85 10	89.09 8	79.63 3	87.34 8	84.84 2	73.71 27	82.66 27	81.60 37	85.48 104	94.51 23

gg-mvs-nofinetune		62.34 134	66.19 99	57.86 168	76.15 93	88.61 32	71.18 106	41.24 218	25.74 219	13.16 222	22.91 214	63.97 60	54.52 146	85.06 12	85.25 9	90.92 3	91.78 51

DeepC-MVS	74.46 3	80.30 24	81.05 30	79.42 18	87.42 35	88.50 33	83.23 22	73.27 13	82.78 24	71.01 23	62.86 47	69.93 45	74.80 22	84.30 16	84.20 16	86.79 54	94.77 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
train_agg		83.35 12	86.93 10	79.17 21	89.70 17	88.41 34	85.60 13	72.89 15	86.31 15	66.58 35	90.48 4	82.24 9	73.06 31	83.10 23	82.64 27	87.21 48	95.30 18

CDPH-MVS		79.39 30	82.13 26	76.19 37	89.22 24	88.34 35	84.20 18	71.00 18	79.67 37	56.97 66	77.77 23	72.24 35	68.50 59	81.33 42	82.74 25	87.23 46	92.84 39

3Dnovator+	70.16 6	77.87 36	77.29 44	78.55 24	89.25 23	88.32 36	80.09 42	67.95 38	74.89 49	71.83 20	52.05 75	70.68 41	76.27 17	82.27 34	82.04 30	85.92 78	90.77 59

TSAR-MVS + GP.		82.27 17	85.98 13	77.94 27	80.72 65	88.25 37	81.12 37	67.71 39	87.10 11	73.31 15	85.23 11	83.68 4	76.64 14	80.43 50	81.47 39	88.15 30	95.66 12

MP-MVS		80.94 21	83.49 20	77.96 26	88.48 26	88.16 38	82.82 26	69.34 30	80.79 33	69.67 28	82.35 15	77.13 21	71.60 40	80.97 47	80.96 47	85.87 85	94.06 29

HyFIR lowres test		68.39 77	68.28 87	68.52 75	80.85 62	88.11 39	71.08 109	58.09 113	54.87 103	47.80 91	27.55 204	55.80 90	64.97 73	79.11 57	79.14 56	88.31 25	93.35 33

CLD-MVS		77.36 41	77.29 44	77.45 31	82.21 53	88.11 39	81.92 29	68.96 33	77.97 41	69.62 29	62.08 48	59.44 75	73.57 28	81.75 39	81.27 42	88.41 23	90.39 63
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
PCF-MVS	70.85 4	75.73 47	76.55 51	74.78 48	83.67 47	88.04 41	81.47 32	70.62 23	69.24 60	57.52 64	60.59 55	69.18 46	70.65 44	77.11 71	77.65 69	84.75 130	94.01 30
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 18	82.10 27	81.20 11	91.04 10	87.81 42	83.42 21	74.04 8	83.77 21	71.09 22	66.88 39	72.44 31	79.48 6	85.08 11	84.97 12	88.12 31	93.78 31
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MPTG		81.65 19	83.10 21	79.97 17	88.14 32	87.62 43	83.96 20	69.90 25	86.92 12	77.67 9	72.47 30	78.74 18	74.13 26	81.59 41	81.15 44	86.01 72	93.19 36

HFP-MVS		82.48 16	84.12 18	80.56 13	90.15 12	87.55 44	84.28 17	69.67 28	85.22 18	77.95 8	84.69 12	75.94 23	75.04 20	81.85 38	81.17 43	86.30 61	92.40 43

PVSNet_Blended_VisFu		71.76 63	73.54 61	69.69 67	79.01 71	87.16 45	72.05 82	61.80 85	56.46 92	59.66 59	53.88 71	62.48 62	59.08 128	81.17 44	78.90 57	86.53 58	94.74 21

ACMMPR		80.62 23	82.98 22	77.87 28	88.41 27	87.05 46	83.02 23	69.18 31	83.91 20	68.35 31	82.89 14	73.64 28	72.16 36	80.78 48	81.13 45	86.10 66	91.43 52

DI_MVS_plusplus_trai		73.94 53	74.85 56	72.88 54	76.57 90	86.80 47	80.41 41	61.47 88	62.35 74	59.44 60	47.91 90	68.12 47	72.24 35	82.84 26	81.50 38	87.15 49	94.42 24

diffmvs		73.50 54	75.66 54	70.97 62	74.96 102	86.71 48	77.16 57	57.42 128	71.12 53	60.43 56	57.20 60	70.40 43	68.79 58	76.11 84	76.05 80	87.10 50	92.06 47

PGM-MVS		79.42 29	81.84 28	76.60 34	88.38 29	86.69 49	82.97 25	65.75 50	80.39 34	64.94 38	81.95 17	72.11 36	71.41 41	80.45 49	80.55 51	86.18 63	90.76 60

CANet_DTU		72.84 56	76.63 50	68.43 76	76.81 88	86.62 50	75.54 65	54.71 156	72.06 51	43.54 111	67.11 37	58.46 78	72.40 34	81.13 46	80.82 50	87.57 38	90.21 65

TSAR-MVS + ACMM		81.59 20	85.84 14	76.63 33	89.82 16	86.53 51	86.32 8	66.72 44	85.96 16	65.43 37	88.98 7	82.29 8	67.57 66	82.06 36	81.33 41	83.93 147	93.75 32

TSAR-MVS + MP.		84.39 8	86.58 11	81.83 9	88.09 33	86.47 52	85.63 11	73.62 12	90.13 7	79.24 4	89.67 6	82.99 6	77.72 11	81.22 43	80.92 48	86.68 55	94.66 22

DWT-MVSNet_training		72.81 57	73.98 57	71.45 61	81.26 59	86.37 53	72.08 81	59.82 105	69.13 61	58.15 62	54.71 64	61.33 72	67.81 63	76.86 73	78.63 59	89.59 6	90.86 57

XVS					82.43 49	86.27 54	75.70 60			61.07 51		72.27 32				85.67 98

X-MVStestdata					82.43 49	86.27 54	75.70 60			61.07 51		72.27 32				85.67 98

X-MVS		78.16 35	80.55 32	75.38 42	87.99 34	86.27 54	81.05 38	68.98 32	78.33 39	61.07 51	75.25 28	72.27 32	67.52 67	80.03 52	80.52 52	85.66 101	91.20 54

CostFormer		72.18 60	73.90 59	70.18 66	79.47 68	86.19 57	76.94 58	48.62 189	66.07 66	60.40 57	54.14 69	65.82 53	67.98 61	75.84 87	76.41 76	87.67 37	92.83 40

CP-MVS		79.44 26	81.51 29	77.02 32	86.95 37	85.96 58	82.00 28	68.44 36	81.82 28	67.39 33	77.43 24	73.68 27	71.62 39	79.56 55	79.58 53	85.73 94	92.51 42

ACMMP		77.61 38	79.59 35	75.30 43	85.87 42	85.58 59	81.42 33	67.38 41	79.38 38	62.61 43	78.53 21	65.79 54	68.80 57	78.56 62	78.50 62	85.75 90	90.80 58
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
MS-PatchMatch		70.34 70	69.00 81	71.91 60	85.20 45	85.35 60	77.84 52	61.77 86	58.01 86	55.40 70	41.26 131	58.34 80	61.69 89	81.70 40	78.29 63	89.56 7	80.02 160

Effi-MVS+		70.42 66	71.23 71	69.47 68	78.04 75	85.24 61	75.57 64	58.88 107	59.56 81	48.47 88	52.73 74	54.94 95	69.69 50	78.34 65	77.06 71	86.18 63	90.73 61

MVS_111021_LR		74.26 51	75.95 52	72.27 57	79.43 69	85.04 62	72.71 78	65.27 55	70.92 54	63.58 41	69.32 33	60.31 73	69.43 52	77.01 72	77.15 70	83.22 156	91.93 50

AdaColmap		76.23 46	73.55 60	79.35 19	89.38 21	85.00 63	79.99 44	73.04 14	76.60 45	71.17 21	55.18 63	57.99 83	77.87 10	76.82 74	76.82 72	84.67 132	86.45 109

MSLP-MVS++		78.57 32	77.33 43	80.02 16	88.39 28	84.79 64	84.62 16	66.17 48	75.96 46	78.40 5	61.59 50	71.47 38	73.54 29	78.43 63	78.88 58	88.97 12	90.18 66

Vis-MVSNet		65.53 98	69.83 78	60.52 149	70.80 128	84.59 65	66.37 145	55.47 147	48.40 127	40.62 142	57.67 59	58.43 79	45.37 180	77.49 67	76.24 78	84.47 136	85.99 116
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OPM-MVS		72.74 58	70.93 73	74.85 47	85.30 44	84.34 66	82.82 26	69.79 26	49.96 114	55.39 71	54.09 70	60.14 74	70.04 48	80.38 51	79.43 54	85.74 93	88.20 98

HQP-MVS		78.26 34	80.91 31	75.17 44	85.67 43	84.33 67	83.01 24	69.38 29	79.88 36	55.83 67	79.85 19	64.90 57	70.81 43	82.46 29	81.78 34	86.30 61	93.18 37

IS_MVSNet		67.29 85	71.98 65	61.82 143	76.92 86	84.32 68	65.90 147	58.22 111	55.75 97	39.22 146	54.51 67	62.47 63	45.99 176	78.83 60	78.52 61	84.70 131	89.47 71

EPMVS		66.21 91	67.49 92	64.73 100	75.81 95	84.20 69	68.94 127	44.37 205	61.55 76	48.07 90	49.21 86	54.87 96	62.88 83	71.82 148	71.40 159	88.28 26	79.37 163

thres100view900		67.14 87	66.09 102	68.38 77	77.70 78	83.84 70	74.52 72	66.33 47	49.16 119	43.40 116	43.24 114	41.34 131	62.59 85	79.31 56	75.92 82	85.73 94	89.81 68

EPP-MVSNet		67.58 81	71.10 72	63.48 122	75.71 96	83.35 71	66.85 139	57.83 116	53.02 108	41.15 138	55.82 62	67.89 49	56.01 140	74.40 99	72.92 143	83.33 154	90.30 64

MVSTER		76.92 43	79.92 33	73.42 52	74.98 100	82.97 72	78.15 49	63.41 66	78.02 40	64.41 40	67.54 36	72.80 30	71.05 42	83.29 22	83.73 19	88.53 21	91.12 55

PatchmatchNet		65.43 99	67.71 90	62.78 132	73.49 109	82.83 73	66.42 144	45.40 200	60.40 80	45.27 100	49.22 85	57.60 84	60.01 112	70.61 156	71.38 162	86.08 68	81.91 150
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
thres400		65.18 101	64.44 112	66.04 90	76.40 91	82.63 74	71.52 101	64.27 59	44.93 148	40.69 141	41.86 128	40.79 145	58.12 132	77.67 66	74.64 95	85.26 107	88.56 91

UGNet		67.57 82	71.69 68	62.76 133	69.88 130	82.58 75	66.43 143	58.64 109	54.71 104	51.87 76	61.74 49	62.01 67	45.46 179	74.78 96	74.99 91	84.24 141	91.02 56
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
CPTT-MVS		75.43 48	77.13 46	73.44 51	81.43 58	82.55 76	80.96 39	64.35 58	77.95 42	61.39 48	69.20 34	70.94 40	69.38 54	73.89 108	73.32 134	83.14 160	92.06 47

TSAR-MVS + COLMAP		73.09 55	76.86 47	68.71 73	74.97 101	82.49 77	74.51 73	61.83 84	83.16 22	49.31 87	82.22 16	51.62 106	68.94 56	78.76 61	75.52 88	82.67 164	84.23 127

tpmp4_e23		69.38 71	69.47 79	69.28 70	78.20 74	82.35 78	75.92 59	49.20 187	64.15 72	59.96 58	47.93 89	55.77 91	68.06 60	73.05 119	74.53 102	84.34 139	88.50 96

conf0.002		67.12 88	67.13 94	67.11 84	77.95 76	82.11 79	71.71 91	63.06 68	49.16 119	43.43 113	47.76 93	48.79 112	61.42 91	76.61 75	76.55 74	85.07 114	88.92 80

conf0.01		66.60 89	66.18 100	67.09 85	77.90 77	82.02 80	71.71 91	63.05 69	49.16 119	43.41 115	46.23 108	45.78 120	61.42 91	76.55 77	74.63 96	85.04 115	88.87 82

PMMVS		70.37 69	75.06 55	64.90 95	71.46 121	81.88 81	64.10 152	55.64 144	71.31 52	46.69 94	70.69 32	58.56 76	69.53 51	79.03 58	75.63 85	81.96 172	88.32 97

view600		63.91 114	63.27 121	64.66 102	75.57 97	81.73 82	69.71 122	63.04 70	43.97 151	39.18 147	41.09 132	40.24 153	55.38 142	76.28 78	72.04 151	85.08 113	87.52 102

MDTV_nov1_ep13		65.21 100	67.28 93	62.79 131	70.91 126	81.72 83	69.28 126	49.50 184	58.08 85	43.94 110	50.50 81	56.02 88	58.86 129	70.72 155	73.37 132	84.24 141	80.52 156

thres600view7		63.77 115	63.14 123	64.51 104	75.49 98	81.61 84	69.59 123	62.95 72	43.96 152	38.90 149	41.09 132	40.24 153	55.25 144	76.24 79	71.54 154	84.89 120	87.30 103

thres200		65.58 96	64.74 110	66.56 89	77.52 83	81.61 84	73.44 77	62.95 72	46.23 140	42.45 133	42.76 118	41.18 136	58.12 132	76.24 79	75.59 86	84.89 120	89.58 69

tfpn111		66.52 90	66.12 101	66.98 87	77.70 78	81.58 86	71.71 91	62.94 74	49.16 119	43.28 118	51.38 77	41.34 131	61.42 91	76.24 79	74.63 96	84.84 123	88.52 92

conf200view11		65.89 95	64.96 107	66.98 87	77.70 78	81.58 86	71.71 91	62.94 74	49.16 119	43.28 118	43.24 114	41.34 131	61.42 91	76.24 79	74.63 96	84.84 123	88.52 92

tfpn200view9		65.90 94	64.96 107	67.00 86	77.70 78	81.58 86	71.71 91	62.94 74	49.16 119	43.40 116	43.24 114	41.34 131	61.42 91	76.24 79	74.63 96	84.84 123	88.52 92

GA-MVS		64.55 105	65.76 105	63.12 128	69.68 131	81.56 89	69.59 123	58.16 112	45.23 146	35.58 170	47.01 103	41.82 130	59.41 123	79.62 54	78.54 60	86.32 60	86.56 108

tpm cat1		67.47 83	67.05 95	67.98 78	76.63 89	81.51 90	74.49 74	47.65 194	61.18 77	61.12 50	42.51 123	53.02 103	64.74 76	70.11 164	71.50 155	83.22 156	89.49 70

UA-Net		64.62 103	68.23 88	60.42 150	77.53 82	81.38 91	60.08 178	57.47 124	47.01 132	44.75 106	60.68 54	71.32 39	41.84 187	73.27 114	72.25 149	80.83 181	71.68 193

CNLPA		71.37 65	70.27 76	72.66 56	80.79 64	81.33 92	71.07 110	65.75 50	82.36 25	64.80 39	42.46 124	56.49 86	72.70 33	73.00 120	70.52 169	80.84 180	85.76 118

test-LLR		68.23 78	71.61 69	64.28 113	71.37 122	81.32 93	63.98 156	61.03 91	58.62 83	42.96 123	52.74 72	61.65 68	57.74 134	75.64 89	78.09 67	88.61 18	93.21 34

TESTMET0.1,1		67.38 84	71.61 69	62.45 137	66.05 170	81.32 93	63.98 156	55.36 148	58.62 83	42.96 123	52.74 72	61.65 68	57.74 134	75.64 89	78.09 67	88.61 18	93.21 34

Fast-Effi-MVS+		67.59 80	67.56 91	67.62 81	73.67 107	81.14 95	71.12 107	54.79 155	58.88 82	50.61 83	46.70 105	47.05 116	69.12 55	76.06 85	76.44 75	86.43 59	86.65 107

tpmrst		67.15 86	68.12 89	66.03 91	76.21 92	80.98 96	71.27 103	45.05 201	60.69 79	50.63 82	46.95 104	54.15 98	65.30 71	71.80 149	71.77 152	87.72 35	90.48 62

OMC-MVS		74.03 52	75.82 53	71.95 59	79.56 67	80.98 96	75.35 68	63.21 67	84.48 19	61.83 46	61.54 51	66.89 51	69.41 53	76.60 76	74.07 123	82.34 169	86.15 113

ACMP	68.86 7	72.15 61	72.25 64	72.03 58	80.96 61	80.87 98	77.93 51	64.13 60	69.29 58	60.79 54	64.04 43	53.54 100	63.91 78	73.74 112	75.27 89	84.45 137	88.98 77
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v6		64.09 108	63.40 118	64.90 95	68.28 143	80.78 99	71.85 85	57.64 120	46.73 135	45.18 102	39.40 144	40.89 142	60.54 106	72.86 125	74.40 110	85.92 78	88.72 87

TAPA-MVS	67.10 9	71.45 64	73.47 62	69.10 71	77.04 85	80.78 99	73.81 76	62.10 80	80.80 32	51.28 78	60.91 53	63.80 61	67.98 61	74.59 97	72.42 148	82.37 168	80.97 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
v1neww		64.08 109	63.38 119	64.89 97	68.27 145	80.77 101	71.84 86	57.65 118	46.66 137	45.10 103	39.40 144	40.86 143	60.57 103	72.86 125	74.40 110	85.92 78	88.71 88

v7new		64.08 109	63.38 119	64.89 97	68.27 145	80.77 101	71.84 86	57.65 118	46.66 137	45.10 103	39.40 144	40.86 143	60.57 103	72.86 125	74.40 110	85.92 78	88.71 88

view800		63.02 127	62.69 134	63.39 124	74.79 103	80.76 103	67.83 132	61.93 83	43.16 162	37.78 158	40.43 137	39.73 160	53.16 149	75.01 93	73.32 134	84.87 122	86.43 110

v1		63.49 118	62.77 131	64.32 109	68.13 147	80.70 104	71.70 97	57.43 125	43.69 155	42.89 127	39.03 150	39.77 157	59.93 115	72.93 122	74.48 107	85.86 86	88.77 83

v1141		63.48 119	62.75 133	64.32 109	68.13 147	80.69 105	71.69 98	57.43 125	43.66 157	42.83 130	39.02 151	39.74 159	59.95 113	72.94 121	74.49 105	85.86 86	88.75 85

divwei89l23v2f112		63.48 119	62.76 132	64.32 109	68.13 147	80.68 106	71.71 91	57.43 125	43.69 155	42.84 128	39.01 152	39.75 158	59.94 114	72.93 122	74.49 105	85.86 86	88.75 85

thresconf0.02		63.92 113	65.18 106	62.46 136	75.91 94	80.65 107	67.51 136	63.86 62	45.00 147	33.32 180	51.38 77	51.68 105	48.34 163	75.49 92	75.13 90	85.84 89	76.91 171

CDS-MVSNet		64.22 107	65.89 104	62.28 139	70.05 129	80.59 108	69.91 121	57.98 114	43.53 158	46.58 95	48.22 88	50.76 108	46.45 173	75.68 88	76.08 79	82.70 163	86.34 112
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EG-PatchMatch MVS		58.73 168	58.03 177	59.55 155	72.32 117	80.49 109	63.44 162	55.55 145	32.49 206	38.31 153	28.87 201	37.22 175	42.84 185	74.30 105	75.70 84	84.84 123	77.14 170

v2v482		63.68 116	62.85 128	64.65 103	68.01 153	80.46 110	71.90 83	57.60 121	44.26 149	42.82 131	39.80 142	38.62 169	61.56 90	73.06 117	74.86 93	86.03 71	88.90 81

v7		63.61 117	63.02 125	64.29 112	67.88 157	80.32 111	71.60 99	56.63 134	45.37 144	42.84 128	38.54 156	38.91 167	61.05 98	74.39 100	74.52 103	85.75 90	89.10 76

v1144		63.00 128	62.39 137	63.70 118	67.72 160	80.27 112	71.23 105	56.40 135	42.51 166	40.81 140	38.12 165	37.73 171	60.42 109	74.46 98	74.55 101	85.64 102	89.12 75

LGP-MVS_train		72.02 62	73.18 63	70.67 64	82.13 54	80.26 113	79.58 45	63.04 70	70.09 55	51.98 75	65.06 42	55.62 93	62.49 86	75.97 86	76.32 77	84.80 129	88.93 78

FC-MVSNet-train		68.83 75	68.29 86	69.47 68	78.35 73	79.94 114	64.72 149	66.38 45	54.96 101	54.51 72	56.75 61	47.91 115	66.91 68	75.57 91	75.75 83	85.92 78	87.12 104

v144192		62.05 142	61.46 147	62.73 135	66.59 168	79.87 115	69.30 125	55.88 140	41.50 174	39.41 145	37.23 169	36.45 179	59.62 119	72.69 135	73.51 129	85.61 103	88.93 78

tfpn		62.54 133	62.79 130	62.25 140	74.16 105	79.86 116	66.07 146	60.97 94	42.43 167	36.41 162	39.88 141	43.76 126	51.25 156	73.85 109	74.17 119	84.67 132	85.57 121

v1192		62.25 137	61.64 145	62.96 129	66.88 165	79.72 117	69.96 120	55.77 142	41.58 173	39.42 144	37.05 171	35.96 184	60.50 108	74.30 105	74.09 122	85.24 108	88.76 84

dps		64.08 109	63.22 122	65.08 94	75.27 99	79.65 118	66.68 141	46.63 199	56.94 88	55.67 69	43.96 110	43.63 127	64.00 77	69.50 171	69.82 172	82.25 170	79.02 164

v1921920		61.66 149	61.10 150	62.31 138	66.32 169	79.57 119	68.41 130	55.49 146	41.03 175	38.69 151	36.64 177	35.27 190	59.60 120	73.23 115	73.41 131	85.37 105	88.51 95

v148		62.00 143	61.19 149	62.96 129	67.46 163	79.49 120	67.87 131	57.66 117	42.30 168	45.02 105	38.20 163	38.89 168	54.77 145	69.83 168	72.60 147	84.96 116	87.01 105

tfpn_ndepth		62.95 131	63.75 115	62.02 141	76.89 87	79.48 121	64.09 153	60.98 93	49.48 116	38.73 150	49.92 83	44.79 122	47.37 168	71.91 147	71.66 153	84.07 146	79.00 165

FMVSNet3		70.41 68	71.89 67	68.68 74	70.89 127	79.42 122	75.63 62	60.97 94	65.32 67	51.06 79	47.37 96	62.05 64	64.90 74	82.49 28	82.27 29	88.64 17	84.34 126

v1240		61.09 153	60.55 155	61.72 144	65.92 173	79.28 123	67.16 138	54.91 152	39.79 180	38.10 154	36.08 179	34.64 191	59.15 127	72.86 125	73.36 133	85.10 110	87.84 99

CMPMVS	43.63 17	57.67 176	55.43 183	60.28 151	72.01 119	79.00 124	62.77 166	53.23 168	41.77 172	45.42 99	30.74 198	39.03 165	53.01 150	64.81 186	64.65 195	75.26 206	68.03 201
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
V42		62.86 132	62.97 126	62.74 134	60.84 191	78.99 125	71.46 102	57.13 132	46.85 133	44.28 109	38.87 153	40.73 147	57.63 136	72.60 137	74.14 120	85.09 112	88.63 90

Vis-MVSNet (Re-imp)		62.25 137	68.74 83	54.68 183	73.70 106	78.74 126	56.51 189	57.49 123	55.22 99	26.86 197	54.56 66	61.35 70	31.06 199	73.10 116	74.90 92	82.49 166	83.31 134

GBi-Net		69.21 72	70.40 74	67.81 79	69.49 132	78.65 127	74.54 69	60.97 94	65.32 67	51.06 79	47.37 96	62.05 64	63.43 80	77.49 67	78.22 64	87.37 40	83.73 130

test1		69.21 72	70.40 74	67.81 79	69.49 132	78.65 127	74.54 69	60.97 94	65.32 67	51.06 79	47.37 96	62.05 64	63.43 80	77.49 67	78.22 64	87.37 40	83.73 130

FMVSNet2		68.06 79	68.57 84	67.45 82	69.49 132	78.65 127	74.54 69	60.23 104	56.29 93	49.64 86	42.13 127	57.08 85	63.43 80	81.15 45	80.99 46	87.37 40	83.73 130

tpm		64.85 102	66.02 103	63.48 122	74.52 104	78.38 130	70.98 111	44.99 203	51.61 110	43.28 118	47.66 94	53.18 101	60.57 103	70.58 158	71.30 164	86.54 57	89.45 72

ACMH	59.42 14	61.59 151	59.22 172	64.36 108	78.92 72	78.26 131	67.65 133	67.48 40	39.81 179	30.98 187	38.25 161	34.59 192	61.37 97	70.55 159	73.47 130	79.74 189	79.59 161
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
conf0.05thres1000		60.33 159	59.42 169	61.40 146	73.15 111	78.25 132	65.29 148	60.30 101	36.61 191	35.75 168	33.25 187	39.23 164	50.35 159	72.18 143	72.67 146	83.57 151	83.74 129

v8		63.44 122	62.58 135	64.43 106	68.28 143	78.07 133	71.82 88	54.85 153	46.70 136	45.20 101	39.40 144	40.91 141	60.54 106	72.85 129	74.39 115	85.92 78	85.76 118

Patchmtry						78.06 134	67.53 134	43.18 207		41.40 135

UniMVSNet (Re)		60.62 156	62.93 127	57.92 165	67.64 161	77.90 135	61.75 169	61.24 90	49.83 115	29.80 189	42.57 121	40.62 151	43.36 183	70.49 161	73.27 137	83.76 148	85.81 117

UniMVSNet_NR-MVSNet		62.30 136	63.51 117	60.89 148	69.48 135	77.83 136	64.07 154	63.94 61	50.03 113	31.17 185	44.82 109	41.12 137	51.37 153	71.02 153	74.81 94	85.30 106	84.95 122

v10		63.00 128	62.22 138	63.90 117	67.88 157	77.78 137	71.59 100	54.34 157	45.37 144	42.76 132	38.53 157	38.93 166	61.05 98	74.39 100	74.52 103	85.75 90	86.04 114

ACMM	66.70 10	70.42 66	68.49 85	72.67 55	82.85 48	77.76 138	77.70 53	64.76 57	64.61 71	60.74 55	49.29 84	53.97 99	65.86 70	74.97 94	75.57 87	84.13 145	83.29 135
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
TAMVS		58.86 166	60.91 151	56.47 176	62.38 187	77.57 139	58.97 183	52.98 171	38.76 185	36.17 165	42.26 126	47.94 114	46.45 173	70.23 163	70.79 166	81.86 173	78.82 166

tfpn1000		58.35 173	59.96 164	56.47 176	72.78 116	77.51 140	56.66 188	59.16 106	43.74 153	29.76 190	42.79 117	42.49 128	37.04 197	68.92 173	68.98 175	83.45 153	75.25 175

test-mter		64.06 112	69.24 80	58.01 164	59.07 198	77.40 141	59.13 182	48.11 192	55.64 98	39.18 147	51.56 76	58.54 77	55.38 142	73.52 113	76.00 81	87.22 47	92.05 49

EPNet_dtu		66.17 92	70.13 77	61.54 145	81.04 60	77.39 142	68.87 128	62.50 79	69.78 56	33.51 179	63.77 44	56.22 87	37.65 196	72.20 142	72.18 150	85.69 97	79.38 162
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
pmmvs5		59.72 160	60.24 160	59.11 159	62.77 185	77.33 143	63.17 164	54.00 161	40.21 178	37.23 159	40.41 138	35.99 183	51.75 152	72.55 138	72.74 145	85.72 96	82.45 145

tfpn_n400		58.64 169	59.27 170	57.89 166	72.83 114	77.26 144	60.35 174	60.29 102	39.77 181	29.10 191	43.45 111	40.72 148	41.61 189	70.06 165	71.39 160	83.17 158	72.26 190

tfpnconf		58.64 169	59.27 170	57.89 166	72.83 114	77.26 144	60.35 174	60.29 102	39.77 181	29.10 191	43.45 111	40.72 148	41.61 189	70.06 165	71.39 160	83.17 158	72.26 190

tfpnview11		58.92 165	59.60 167	58.13 163	72.99 113	77.11 146	60.48 173	60.37 99	42.10 170	29.10 191	43.45 111	40.72 148	41.67 188	70.53 160	70.43 170	84.17 144	72.85 187

MIMVSNet		57.78 175	59.71 166	55.53 180	54.79 206	77.10 147	63.89 158	45.02 202	46.59 139	36.79 161	28.36 202	40.77 146	45.84 177	74.97 94	76.58 73	86.87 53	73.60 183

pm-mvs1		59.21 163	59.58 168	58.77 161	67.97 155	77.07 148	64.12 151	57.20 130	34.73 199	36.86 160	35.34 182	40.54 152	43.34 184	74.32 104	73.30 136	83.13 161	81.77 151

Fast-Effi-MVS+-dtu		63.05 126	64.72 111	61.11 147	71.21 125	76.81 149	70.72 114	43.13 209	52.51 109	35.34 171	46.55 106	46.36 117	61.40 96	71.57 151	71.44 157	84.84 123	87.79 100

MSDG		65.57 97	61.57 146	70.24 65	82.02 55	76.47 150	74.46 75	68.73 35	56.52 91	50.33 84	38.47 158	41.10 138	62.42 87	72.12 144	72.94 142	83.47 152	73.37 185

pmmvs4		63.14 124	62.46 136	63.94 116	66.03 171	76.40 151	66.82 140	57.60 121	56.74 89	50.26 85	40.81 136	37.51 173	59.26 126	71.75 150	71.48 156	83.68 150	82.53 143

FMVSNet1		63.48 119	63.07 124	63.97 115	65.31 176	76.37 152	71.77 90	57.90 115	43.32 160	45.66 98	35.06 185	49.43 111	58.57 130	77.49 67	78.22 64	84.59 134	81.60 153

tfpnnormal		58.97 164	56.48 182	61.89 142	71.27 124	76.21 153	66.65 142	61.76 87	32.90 205	36.41 162	27.83 203	29.14 208	50.64 158	73.06 117	73.05 141	84.58 135	83.15 140

DU-MVS		60.87 155	61.82 142	59.76 154	66.69 166	75.87 154	64.07 154	61.96 81	49.31 117	31.17 185	42.76 118	36.95 176	51.37 153	69.67 169	73.20 140	83.30 155	84.95 122

NR-MVSNet		61.08 154	62.09 140	59.90 152	71.96 120	75.87 154	63.60 160	61.96 81	49.31 117	27.95 194	42.76 118	33.85 196	48.82 162	74.35 102	74.05 124	85.13 109	84.45 124

LS3D		64.54 106	62.14 139	67.34 83	80.85 62	75.79 156	69.99 119	65.87 49	60.77 78	44.35 108	42.43 125	45.95 119	65.01 72	69.88 167	68.69 177	77.97 198	71.43 195

PatchMatch-RL		62.22 140	60.69 153	64.01 114	68.74 137	75.75 157	59.27 181	60.35 100	56.09 94	53.80 73	47.06 102	36.45 179	64.80 75	68.22 175	67.22 184	77.10 200	74.02 180

PatchT		60.46 157	63.85 114	56.51 175	65.95 172	75.68 158	47.34 202	41.39 214	53.89 107	41.40 135	37.84 167	50.30 110	57.29 137	72.76 132	73.27 137	85.67 98	83.23 138

TranMVSNet+NR-MVSNet		60.38 158	61.30 148	59.30 157	68.34 142	75.57 159	63.38 163	63.78 64	46.74 134	27.73 195	42.56 122	36.84 177	47.66 166	70.36 162	74.59 100	84.91 119	82.46 144

Effi-MVS+-dtu		64.58 104	64.08 113	65.16 93	73.04 112	75.17 160	70.68 115	56.23 138	54.12 106	44.71 107	47.42 95	51.10 107	63.82 79	68.08 176	66.32 189	82.47 167	86.38 111

IterMVS-LS		66.08 93	66.56 98	65.51 92	73.67 107	74.88 161	70.89 113	53.55 163	50.42 112	48.32 89	50.59 80	55.66 92	61.83 88	73.93 107	74.42 109	84.82 128	86.01 115
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PLC	64.00 12	68.54 76	66.66 96	70.74 63	80.28 66	74.88 161	72.64 79	63.70 65	69.26 59	55.71 68	47.24 99	55.31 94	70.42 45	72.05 146	70.67 167	81.66 174	77.19 169
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
v7n		57.04 178	56.64 181	57.52 170	62.85 184	74.75 163	61.76 168	51.80 177	35.58 198	36.02 167	32.33 191	33.61 197	50.16 160	67.73 177	70.34 171	82.51 165	82.12 148

TransMVSNet (Re)		57.83 174	56.90 180	58.91 160	72.26 118	74.69 164	63.57 161	61.42 89	32.30 207	32.65 182	33.97 186	35.96 184	39.17 194	73.84 111	72.84 144	84.37 138	74.69 178

ADS-MVSNet		58.40 172	59.16 173	57.52 170	65.80 174	74.57 165	60.26 176	40.17 219	50.51 111	38.01 155	40.11 140	44.72 123	59.36 124	64.91 184	66.55 187	81.53 175	72.72 189

ACMH+	60.36 13	61.16 152	58.38 174	64.42 107	77.37 84	74.35 166	68.45 129	62.81 78	45.86 142	38.48 152	35.71 180	37.35 174	59.81 117	67.24 178	69.80 173	79.58 190	78.32 167

Baseline_NR-MVSNet		59.47 162	60.28 158	58.54 162	66.69 166	73.90 167	61.63 170	62.90 77	49.15 125	26.87 196	35.18 184	37.62 172	48.20 164	69.67 169	73.61 128	84.92 117	82.82 141

MDTV_nov1_ep13_2view		54.47 189	54.61 187	54.30 188	60.50 192	73.82 168	57.92 185	43.38 206	39.43 184	32.51 183	33.23 188	34.05 194	47.26 169	62.36 194	66.21 190	84.24 141	73.19 186

test0.0.03 1		57.35 177	59.89 165	54.38 185	71.37 122	73.45 169	52.71 193	61.03 91	46.11 141	26.33 198	41.73 129	44.08 124	29.72 202	71.43 152	70.90 165	85.10 110	71.56 194

v18		63.31 123	62.02 141	64.81 99	68.48 139	73.38 170	72.14 80	54.28 158	48.99 126	47.21 92	39.56 143	41.20 135	60.80 100	72.89 124	74.46 108	85.96 77	83.64 133

v16		63.12 125	61.78 143	64.68 101	68.45 140	73.29 171	71.86 84	54.12 159	48.36 128	47.00 93	39.30 148	41.01 139	60.67 101	72.83 130	74.40 110	86.01 72	83.24 137

v17		62.99 130	61.70 144	64.51 104	68.40 141	73.28 172	71.80 89	54.11 160	47.87 129	46.14 96	39.29 149	41.01 139	60.60 102	72.81 131	74.39 115	85.99 75	83.25 136

v15		62.07 141	60.70 152	63.67 119	68.09 150	73.00 173	71.27 103	53.41 164	43.70 154	43.43 113	38.77 154	39.83 155	59.87 116	72.74 134	74.25 117	85.98 76	82.61 142

pmmvs6		54.20 190	53.54 191	54.97 181	63.22 183	72.98 174	60.17 177	52.32 176	26.77 218	34.30 176	23.29 213	36.23 181	40.33 192	68.77 174	68.76 176	79.47 192	78.00 168

v748		55.19 180	54.63 186	55.85 178	61.44 190	72.97 175	58.72 184	51.62 178	34.48 201	36.39 164	32.09 192	33.05 198	45.48 178	61.85 196	67.87 180	81.45 176	80.08 159

V14		61.96 144	60.56 154	63.59 120	68.06 151	72.93 176	71.10 108	53.33 166	43.47 159	43.28 118	38.59 155	39.78 156	59.76 118	72.65 136	74.19 118	86.01 72	82.32 147

V9		61.85 146	60.42 157	63.51 121	68.02 152	72.85 177	70.91 112	53.24 167	43.25 161	43.27 122	38.41 159	39.73 160	59.60 120	72.55 138	74.13 121	86.04 70	82.04 149

v12		61.70 148	60.27 159	63.38 125	68.00 154	72.76 178	70.63 116	53.14 169	43.01 163	42.95 126	38.25 161	39.64 162	59.48 122	72.47 140	74.05 124	86.06 69	81.71 152

v11		61.74 147	60.47 156	63.22 127	67.83 159	72.72 179	70.31 118	52.95 173	42.75 165	41.89 134	38.16 164	38.49 170	60.40 110	74.35 102	74.40 110	85.92 78	82.39 146

v13		61.60 150	60.13 162	63.31 126	67.95 156	72.67 180	70.51 117	53.05 170	42.80 164	42.96 123	38.10 166	39.57 163	59.31 125	72.36 141	73.98 126	86.10 66	81.40 154

MVS-HIRNet		53.86 191	53.02 192	54.85 182	60.30 194	72.36 181	44.63 210	42.20 212	39.45 183	43.47 112	21.66 217	34.00 195	55.47 141	65.42 182	67.16 185	83.02 162	71.08 196

IterMVS		61.87 145	63.55 116	59.90 152	67.29 164	72.20 182	67.34 137	48.56 190	47.48 131	37.86 157	47.07 101	48.27 113	54.08 147	72.12 144	73.71 127	84.30 140	83.99 128
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v52		54.79 186	55.15 184	54.36 187	54.07 210	72.13 183	59.84 179	49.39 185	34.50 200	35.08 173	31.63 195	35.74 186	47.21 171	63.90 191	67.92 178	80.59 183	80.23 157

V4		54.78 187	55.14 185	54.37 186	54.07 210	72.13 183	59.83 180	49.39 185	34.46 202	35.11 172	31.64 194	35.72 187	47.22 170	63.90 191	67.92 178	80.59 183	80.23 157

USDC		59.69 161	60.03 163	59.28 158	64.04 180	71.84 185	63.15 165	55.36 148	54.90 102	35.02 174	48.34 87	29.79 207	58.16 131	70.60 157	71.33 163	79.99 187	73.42 184

anonymousdsp		54.99 183	57.24 179	52.36 190	53.82 212	71.75 186	51.49 194	48.14 191	33.74 203	33.66 178	38.34 160	36.13 182	47.54 167	64.53 188	70.60 168	79.53 191	85.59 120

CR-MVSNet		62.31 135	64.75 109	59.47 156	68.63 138	71.29 187	67.53 134	43.18 207	55.83 95	41.40 135	41.04 134	55.85 89	57.29 137	72.76 132	73.27 137	78.77 195	83.23 138

RPMNet		58.63 171	62.80 129	53.76 189	67.59 162	71.29 187	54.60 191	38.13 222	55.83 95	35.70 169	41.58 130	53.04 102	47.89 165	66.10 180	67.38 182	78.65 197	84.40 125

LTVRE_ROB	47.26 16	49.41 202	49.91 204	48.82 198	64.76 178	69.79 189	49.05 197	47.12 196	20.36 227	16.52 214	36.65 176	26.96 211	50.76 157	60.47 198	63.16 200	64.73 221	72.00 192
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
Anonymous20231206		52.23 194	52.80 195	51.56 192	64.70 179	69.41 190	51.01 195	58.60 110	36.63 190	22.44 204	21.80 216	31.42 203	30.52 200	66.79 179	67.83 181	82.10 171	75.73 173

WR-MVS		51.02 196	54.56 188	46.90 204	63.84 181	69.23 191	44.78 209	56.38 136	38.19 186	14.19 218	37.38 168	36.82 178	22.39 216	60.14 199	66.20 191	79.81 188	73.95 182

CHOSEN 280x420		62.23 139	66.57 97	57.17 173	59.88 195	68.92 192	61.20 172	42.28 211	54.17 105	39.57 143	47.78 92	64.97 56	62.68 84	73.85 109	69.52 174	77.43 199	86.75 106

CVMVSNet		54.92 185	58.16 175	51.13 194	62.61 186	68.44 193	55.45 190	52.38 175	42.28 169	21.45 205	47.10 100	46.10 118	37.96 195	64.42 189	63.81 197	76.92 202	75.01 177

pmmvs-eth3d		55.20 179	53.95 190	56.65 174	57.34 204	67.77 194	57.54 186	53.74 162	40.93 176	41.09 139	31.19 197	29.10 209	49.07 161	65.54 181	67.28 183	81.14 178	75.81 172

WR-MVS_H		49.62 201	52.63 196	46.11 207	58.80 199	67.58 195	46.14 207	54.94 150	36.51 192	13.63 221	36.75 175	35.67 188	22.10 217	56.43 208	62.76 201	81.06 179	72.73 188

COLMAP_ROB	51.17 15	55.13 181	52.90 194	57.73 169	73.47 110	67.21 196	62.13 167	55.82 141	47.83 130	34.39 175	31.60 196	34.24 193	44.90 181	63.88 193	62.52 203	75.67 204	63.02 212
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
testgi		48.51 204	50.53 201	46.16 206	64.78 177	67.15 197	41.54 213	54.81 154	29.12 213	17.03 211	32.07 193	31.98 200	20.15 220	65.26 183	67.00 186	78.67 196	61.10 217

FMVSNet5		58.86 166	60.24 160	57.25 172	52.66 215	66.25 198	63.77 159	52.86 174	57.85 87	37.92 156	36.12 178	52.22 104	51.37 153	70.88 154	71.43 158	84.92 117	66.91 203

PEN-MVS		51.04 195	52.94 193	48.82 198	61.45 189	66.00 199	48.68 199	57.20 130	36.87 189	15.36 216	36.98 172	32.72 199	28.77 206	57.63 204	66.37 188	81.44 177	74.00 181

CP-MVSNet		50.57 197	52.60 197	48.21 201	58.77 200	65.82 200	48.17 200	56.29 137	37.41 187	16.59 213	37.14 170	31.95 201	29.21 203	56.60 207	63.71 198	80.22 185	75.56 174

PS-CasMVS		50.17 198	52.02 198	48.02 202	58.60 201	65.54 201	48.04 201	56.19 139	36.42 193	16.42 215	35.68 181	31.33 204	28.85 205	56.42 209	63.54 199	80.01 186	75.18 176

TDRefinement		52.70 192	51.02 200	54.66 184	57.41 203	65.06 202	61.47 171	54.94 150	44.03 150	33.93 177	30.13 200	27.57 210	46.17 175	61.86 195	62.48 204	74.01 210	66.06 205

test20.03		47.23 208	48.69 206	45.53 208	63.28 182	64.39 203	41.01 215	56.93 133	29.16 212	15.21 217	23.90 209	30.76 206	17.51 225	64.63 187	65.26 192	79.21 194	62.71 213

DTE-MVSNet		49.82 200	51.92 199	47.37 203	61.75 188	64.38 204	45.89 208	57.33 129	36.11 194	12.79 223	36.87 173	31.93 202	25.73 211	58.01 201	65.22 193	80.75 182	70.93 197

SixPastTwentyTwo		49.11 203	49.22 205	48.99 197	58.54 202	64.14 205	47.18 203	47.75 193	31.15 209	24.42 200	41.01 135	26.55 212	44.04 182	54.76 215	58.70 210	71.99 214	68.21 199

TinyColmap		52.66 193	50.09 203	55.65 179	59.72 196	64.02 206	57.15 187	52.96 172	40.28 177	32.51 183	32.42 190	20.97 222	56.65 139	63.95 190	65.15 194	74.91 207	63.87 209

LP		48.21 205	46.65 211	50.03 195	60.39 193	63.86 207	48.73 198	38.71 221	35.60 197	32.99 181	23.31 212	24.95 218	40.07 193	57.73 202	61.56 205	79.29 193	59.51 218

N_pmnet		47.67 206	47.00 210	48.45 200	54.72 207	62.78 208	46.95 204	51.25 179	36.01 195	26.09 199	26.59 207	25.93 217	35.50 198	55.67 211	59.01 208	76.22 203	63.04 211

MDA-MVSNet-bldmvs		44.15 211	42.27 217	46.34 205	38.34 227	62.31 209	46.28 205	55.74 143	29.83 211	20.98 206	27.11 206	16.45 229	41.98 186	41.11 227	57.47 212	74.72 208	61.65 216

PM-MVS		50.11 199	50.38 202	49.80 196	47.23 224	62.08 210	50.91 196	44.84 204	41.90 171	36.10 166	35.22 183	26.05 216	46.83 172	57.64 203	55.42 220	72.90 211	74.32 179

new-patchmatchnet		42.21 214	42.97 214	41.33 213	53.05 214	59.89 211	39.38 216	49.61 183	28.26 215	12.10 224	22.17 215	21.54 221	19.22 221	50.96 220	56.04 218	74.61 209	61.92 215

RPSCF		55.07 182	58.06 176	51.57 191	48.87 222	58.95 212	53.68 192	41.26 217	62.42 73	45.88 97	54.38 68	54.26 97	53.75 148	57.15 205	53.53 221	66.01 220	65.75 206

MIMVSNet1		40.84 216	43.46 213	37.79 219	32.14 229	58.92 213	39.24 217	50.83 180	27.00 217	11.29 226	16.76 227	26.53 213	17.75 224	57.14 206	61.12 207	75.46 205	56.78 221

FC-MVSNet-test		47.24 207	54.37 189	38.93 217	59.49 197	58.25 214	34.48 221	53.36 165	45.66 143	6.66 232	50.62 79	42.02 129	16.62 226	58.39 200	61.21 206	62.99 222	64.40 208

test2356		46.29 209	47.37 208	45.03 209	54.38 208	57.99 215	42.03 212	50.32 181	30.78 210	16.65 212	27.40 205	23.70 219	29.86 201	61.20 197	64.31 196	76.93 201	66.22 204

Anonymous20231211		40.44 217	39.25 218	41.84 211	54.29 209	57.29 216	41.10 214	49.06 188	17.67 230	10.15 227	10.63 229	16.79 228	25.15 213	52.14 217	56.70 215	71.30 215	63.51 210

EU-MVSNet		44.84 210	47.85 207	41.32 214	49.26 219	56.59 217	43.07 211	47.64 195	33.03 204	13.82 219	36.78 174	30.99 205	24.37 214	53.80 216	55.57 219	69.78 216	68.21 199

gm-plane-assit		54.99 183	57.99 178	51.49 193	69.27 136	54.42 218	32.32 222	42.59 210	21.18 225	13.71 220	23.61 210	43.84 125	60.21 111	87.09 4	86.55 4	90.81 4	89.28 73

testus		42.30 213	43.69 212	40.67 215	53.21 213	53.50 219	31.81 223	49.96 182	27.06 216	11.55 225	25.67 208	19.00 225	25.20 212	55.34 212	62.59 202	72.31 213	62.69 214

pmmvs3		41.86 215	42.29 216	41.36 212	39.80 225	52.66 220	38.93 218	35.85 228	23.40 222	20.22 207	19.30 218	20.84 223	40.56 191	55.98 210	58.79 209	72.80 212	65.03 207

1111		38.93 219	38.98 219	38.86 218	50.10 217	50.42 221	29.52 224	38.00 223	22.67 223	17.99 209	17.40 220	26.26 214	28.72 207	54.86 213	58.20 211	68.82 219	43.08 226

.test1245		25.86 226	24.56 228	27.39 227	50.10 217	50.42 221	29.52 224	38.00 223	22.67 223	17.99 209	17.40 220	26.26 214	28.72 207	54.86 213	0.05 233	0.01 237	0.24 235

ambc			42.30 215		50.36 216	49.51 223	35.47 220		32.04 208	23.53 201	17.36 222	8.95 234	29.06 204	64.88 185	56.26 216	61.29 223	67.12 202

testmv		37.40 220	37.95 220	36.76 220	48.97 220	49.33 224	28.65 227	46.74 197	18.34 228	7.68 230	16.80 225	14.47 230	19.18 222	51.72 218	56.93 213	69.36 217	58.09 219

test1235678		37.40 220	37.94 221	36.76 220	48.97 220	49.30 225	28.65 227	46.73 198	18.33 229	7.68 230	16.79 226	14.46 231	19.18 222	51.72 218	56.92 214	69.36 217	58.07 220

testpf		43.39 212	47.17 209	38.98 216	65.58 175	47.38 226	36.09 219	31.67 229	36.97 188	19.47 208	33.01 189	35.62 189	23.61 215	50.86 221	56.08 217	57.48 226	70.27 198

FPMVS		39.11 218	36.39 222	42.28 210	55.97 205	45.94 227	46.23 206	41.57 213	35.73 196	22.61 202	23.46 211	19.82 224	28.32 209	43.57 223	40.67 226	58.96 224	45.54 223

new_pmnet		33.19 222	35.52 223	30.47 223	27.55 233	45.31 228	29.29 226	30.92 230	29.00 214	9.88 229	18.77 219	17.64 227	26.77 210	44.07 222	45.98 224	58.41 225	47.87 222

no-one		26.96 225	26.51 226	27.49 226	37.87 228	39.14 229	17.12 232	41.31 216	12.02 233	3.68 236	8.04 231	8.42 235	10.67 230	28.11 229	45.96 225	54.27 228	43.89 225

test12356		29.92 224	31.49 224	28.08 224	38.46 226	37.74 230	21.36 230	40.17 219	16.83 231	5.61 234	15.66 228	11.48 232	6.60 232	42.01 225	51.23 222	56.29 227	45.52 224

PMMVS2		20.45 228	22.31 229	18.27 231	20.52 234	26.73 231	14.85 234	28.43 232	13.69 232	0.79 239	10.35 230	9.10 233	3.83 234	27.64 230	32.87 228	41.17 230	35.81 228

PMVS	27.44 18	32.08 223	29.07 225	35.60 222	48.33 223	24.79 232	26.97 229	41.34 215	20.45 226	22.50 203	17.11 224	18.64 226	20.44 219	41.99 226	38.06 227	54.02 229	42.44 227
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		24.91 227	24.61 227	25.26 228	31.47 230	21.59 233	18.06 231	37.53 225	25.43 220	10.03 228	4.18 235	4.25 237	14.85 227	43.20 224	47.03 223	39.62 231	26.55 231
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	15.98 19	14.37 231	16.36 230	12.04 233	7.72 236	20.24 234	5.90 238	29.05 231	8.28 236	3.92 235	4.72 234	2.42 238	9.57 231	18.89 232	31.46 229	16.07 236	28.53 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX						19.81 235	17.01 233	10.02 233	23.61 221	5.85 233	17.21 223	8.03 236	21.13 218	22.60 231		21.42 235	30.01 229

E-PMN		15.08 229	11.65 231	19.08 229	28.73 231	12.31 236	6.95 237	36.87 227	10.71 235	3.63 237	5.13 232	2.22 240	13.81 229	11.34 233	18.50 231	24.49 233	21.32 232

EMVS		14.40 230	10.71 232	18.70 230	28.15 232	12.09 237	7.06 236	36.89 226	11.00 234	3.56 238	4.95 233	2.27 239	13.91 228	10.13 234	16.06 232	22.63 234	18.51 233

tmp_tt				16.09 232	13.07 235	8.12 238	13.61 235	2.08 234	55.09 100	30.10 188	40.26 139	22.83 220	5.35 233	29.91 228	25.25 230	32.33 232

testmvs		0.05 232	0.08 233	0.01 234	0.00 238	0.01 239	0.03 240	0.01 236	0.05 237	0.00 241	0.14 237	0.01 241	0.03 237	0.05 235	0.05 233	0.01 237	0.24 235

test123		0.05 232	0.08 233	0.01 234	0.00 238	0.01 239	0.01 241	0.00 237	0.05 237	0.00 241	0.16 236	0.00 242	0.04 235	0.02 236	0.05 233	0.00 239	0.26 234

sosnet-low-res		0.00 234	0.00 235	0.00 236	0.00 238	0.00 241	0.00 242	0.00 237	0.00 239	0.00 241	0.00 238	0.00 242	0.00 238	0.00 237	0.00 236	0.00 239	0.00 237

sosnet		0.00 234	0.00 235	0.00 236	0.00 238	0.00 241	0.00 242	0.00 237	0.00 239	0.00 241	0.00 238	0.00 242	0.00 238	0.00 237	0.00 236	0.00 239	0.00 237

MTAPA										78.32 6		79.42 17

MTMP										76.04 11		76.65 22

Patchmatch-RL test							2.17 239

mPP-MVS					86.96 36							70.61 42

NP-MVS									81.60 30

High-resolution multi-view benchmark