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		87.60 1	90.44 1	84.29 3	92.09 6	93.44 2	88.69 2	75.11 5	93.06 3	80.80 2	94.23 2	86.70 2	81.44 4	84.84 15	83.52 24	87.64 40	97.28 2

HSP-MVS		86.82 2	89.95 2	83.16 6	89.38 23	91.60 13	85.63 13	74.15 8	94.20 1	75.52 15	94.99 1	83.21 7	85.96 1	87.67 3	85.88 5	88.32 26	92.13 48

APDe-MVS		86.37 3	88.41 4	84.00 5	91.43 10	91.83 11	88.34 3	74.67 6	91.19 5	81.76 1	91.13 4	81.94 13	80.07 6	83.38 23	82.58 31	87.69 38	96.78 7

CNVR-MVS		85.96 4	87.58 7	84.06 4	92.58 4	92.40 7	87.62 5	77.77 2	88.44 11	75.93 13	79.49 22	81.97 12	81.65 3	87.04 5	86.58 3	88.79 16	97.18 4

MCST-MVS		85.75 5	86.99 9	84.31 2	94.07 1	92.80 4	88.15 4	79.10 1	85.66 19	70.72 26	76.50 29	80.45 15	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

HPM-MVS++		85.64 6	88.43 3	82.39 8	92.65 3	90.24 23	85.83 11	74.21 7	90.68 7	75.63 14	86.77 10	84.15 5	78.68 10	86.33 6	85.26 9	87.32 46	95.60 14

SMA-MVS		85.24 7	88.27 5	81.72 11	91.74 8	90.71 16	86.71 7	73.16 15	90.56 8	74.33 16	83.07 15	85.88 3	77.16 14	86.28 7	85.58 6	87.23 49	95.77 11

APD-MVS		84.83 8	87.00 8	82.30 9	89.61 21	89.21 32	86.51 9	73.64 12	90.98 6	77.99 8	89.89 6	80.04 18	79.18 8	82.00 39	81.37 44	86.88 55	95.49 16
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		84.39 9	86.58 12	81.83 10	88.09 35	86.47 57	85.63 13	73.62 13	90.13 9	79.24 5	89.67 7	82.99 8	77.72 12	81.22 46	80.92 52	86.68 59	94.66 23

SD-MVS		84.31 10	86.96 10	81.22 12	88.98 27	88.68 35	85.65 12	73.85 11	89.09 10	79.63 4	87.34 9	84.84 4	73.71 31	82.66 29	81.60 41	85.48 109	94.51 24

NCCC		84.16 11	85.46 16	82.64 7	92.34 5	90.57 20	86.57 8	76.51 3	86.85 16	72.91 19	77.20 28	78.69 21	79.09 9	84.64 17	84.88 14	88.44 24	95.41 17

ACMMP_Plus		83.54 12	86.37 13	80.25 17	89.57 22	90.10 25	85.27 16	71.66 19	87.38 12	73.08 18	84.23 14	80.16 16	75.31 21	84.85 14	83.64 21	86.57 60	94.21 29

train_agg		83.35 13	86.93 11	79.17 23	89.70 19	88.41 39	85.60 15	72.89 17	86.31 17	66.58 37	90.48 5	82.24 11	73.06 35	83.10 25	82.64 30	87.21 52	95.30 19

DeepPCF-MVS	76.94 1	83.08 14	87.77 6	77.60 31	90.11 15	90.96 15	78.48 53	72.63 18	93.10 2	65.84 39	80.67 20	81.55 14	74.80 25	85.94 9	85.39 8	83.75 154	96.77 8

CSCG		82.90 15	84.52 18	81.02 14	91.85 7	93.43 3	87.14 6	74.01 10	81.96 29	76.14 11	70.84 33	82.49 9	69.71 55	82.32 35	85.18 11	87.26 48	95.40 18

SteuartSystems-ACMMP		82.51 16	85.35 17	79.20 22	90.25 13	89.39 31	84.79 17	70.95 21	82.86 25	68.32 34	86.44 11	77.19 22	73.07 34	83.63 22	83.64 21	87.82 35	94.34 26
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		82.48 17	84.12 19	80.56 15	90.15 14	87.55 49	84.28 19	69.67 30	85.22 20	77.95 9	84.69 13	75.94 25	75.04 23	81.85 40	81.17 47	86.30 66	92.40 45

TSAR-MVS + GP.		82.27 18	85.98 14	77.94 29	80.72 69	88.25 42	81.12 40	67.71 41	87.10 13	73.31 17	85.23 12	83.68 6	76.64 16	80.43 53	81.47 43	88.15 32	95.66 13

DeepC-MVS_fast	75.41 2	81.69 19	82.10 28	81.20 13	91.04 12	87.81 47	83.42 23	74.04 9	83.77 23	71.09 24	66.88 41	72.44 33	79.48 7	85.08 12	84.97 13	88.12 33	93.78 33
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
zzz-MVS		81.65 20	83.10 22	79.97 19	88.14 34	87.62 48	83.96 22	69.90 27	86.92 14	77.67 10	72.47 32	78.74 20	74.13 30	81.59 44	81.15 48	86.01 77	93.19 38

TSAR-MVS + ACMM		81.59 21	85.84 15	76.63 35	89.82 18	86.53 56	86.32 10	66.72 46	85.96 18	65.43 40	88.98 8	82.29 10	67.57 73	82.06 38	81.33 45	83.93 152	93.75 34

MP-MVS		80.94 22	83.49 21	77.96 28	88.48 28	88.16 43	82.82 28	69.34 32	80.79 35	69.67 30	82.35 17	77.13 23	71.60 45	80.97 50	80.96 51	85.87 90	94.06 30
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CANet		80.90 23	82.93 24	78.53 27	86.83 41	92.26 8	81.19 39	66.95 44	81.60 32	69.90 29	66.93 40	74.80 27	76.79 15	84.68 16	84.77 15	89.50 10	95.50 15

ACMMPR		80.62 24	82.98 23	77.87 30	88.41 29	87.05 52	83.02 25	69.18 33	83.91 22	68.35 33	82.89 16	73.64 30	72.16 40	80.78 51	81.13 49	86.10 71	91.43 55

DeepC-MVS	74.46 3	80.30 25	81.05 31	79.42 20	87.42 37	88.50 37	83.23 24	73.27 14	82.78 26	71.01 25	62.86 50	69.93 46	74.80 25	84.30 18	84.20 17	86.79 58	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
v1.0		80.27 26	76.62 54	84.53 1	92.88 2	93.82 1	88.95 1	76.05 4	92.95 4	80.32 3	93.12 3	86.87 1	80.88 5	85.54 10	84.01 18	88.09 34	0.00 244

DELS-MVS		79.49 27	79.84 35	79.08 24	88.26 33	92.49 5	84.12 21	70.63 23	65.27 75	69.60 32	61.29 56	66.50 53	72.75 36	88.07 2	88.03 1	89.13 13	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
CP-MVS		79.44 28	81.51 30	77.02 34	86.95 39	85.96 63	82.00 30	68.44 38	81.82 30	67.39 35	77.43 26	73.68 29	71.62 44	79.56 59	79.58 57	85.73 99	92.51 44

MVS_0304		79.43 29	82.20 26	76.20 39	84.22 48	91.79 12	81.82 33	63.81 65	76.83 46	61.71 51	66.37 42	75.52 26	76.38 19	85.54 10	85.03 12	89.28 12	94.32 27

PHI-MVS		79.43 29	84.06 20	74.04 53	86.15 43	91.57 14	80.85 43	68.90 36	82.22 28	51.81 84	78.10 24	74.28 28	70.39 52	84.01 21	84.00 19	86.14 70	94.24 28

PGM-MVS		79.42 31	81.84 29	76.60 36	88.38 31	86.69 54	82.97 27	65.75 52	80.39 36	64.94 41	81.95 19	72.11 38	71.41 46	80.45 52	80.55 55	86.18 68	90.76 65

CDPH-MVS		79.39 32	82.13 27	76.19 40	89.22 26	88.34 40	84.20 20	71.00 20	79.67 39	56.97 71	77.77 25	72.24 37	68.50 66	81.33 45	82.74 27	87.23 49	92.84 41

EPNet		79.28 33	82.25 25	75.83 42	88.31 32	90.14 24	79.43 51	68.07 39	81.76 31	61.26 53	77.26 27	70.08 45	70.06 53	82.43 33	82.00 36	87.82 35	92.09 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		78.57 34	77.33 45	80.02 18	88.39 30	84.79 69	84.62 18	66.17 50	75.96 48	78.40 6	61.59 54	71.47 40	73.54 33	78.43 68	78.88 64	88.97 14	90.18 72

3Dnovator	70.49 5	78.42 35	76.77 51	80.35 16	91.43 10	90.27 22	81.84 32	70.79 22	72.10 53	71.95 20	50.02 88	67.86 51	77.47 13	82.89 26	84.24 16	88.61 20	89.99 73

HQP-MVS		78.26 36	80.91 32	75.17 47	85.67 45	84.33 73	83.01 26	69.38 31	79.88 38	55.83 72	79.85 21	64.90 59	70.81 48	82.46 31	81.78 38	86.30 66	93.18 39

X-MVS		78.16 37	80.55 33	75.38 45	87.99 36	86.27 59	81.05 41	68.98 34	78.33 41	61.07 55	75.25 30	72.27 34	67.52 74	80.03 56	80.52 56	85.66 106	91.20 58

3Dnovator+	70.16 6	77.87 38	77.29 46	78.55 26	89.25 25	88.32 41	80.09 45	67.95 40	74.89 52	71.83 22	52.05 81	70.68 43	76.27 20	82.27 36	82.04 34	85.92 83	90.77 64

casdiffmvs1		77.74 39	78.92 38	76.36 37	82.58 51	90.61 18	81.58 34	61.31 94	75.68 49	66.24 38	64.21 45	65.17 56	76.54 18	80.07 55	82.68 29	89.88 7	94.00 32

canonicalmvs		77.65 40	79.59 36	75.39 44	81.52 61	89.83 30	81.32 38	60.74 103	80.05 37	66.72 36	68.43 37	65.09 57	74.72 27	78.87 64	82.73 28	87.32 46	92.16 47

ACMMP		77.61 41	79.59 36	75.30 46	85.87 44	85.58 64	81.42 36	67.38 43	79.38 40	62.61 47	78.53 23	65.79 55	68.80 65	78.56 67	78.50 68	85.75 95	90.80 63
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
QAPM		77.50 42	77.43 44	77.59 32	91.52 9	92.00 10	81.41 37	70.63 23	66.22 68	58.05 68	54.70 71	71.79 39	74.49 29	82.46 31	82.04 34	89.46 11	92.79 43

MVS_111021_HR		77.42 43	78.40 41	76.28 38	86.95 39	90.68 17	77.41 61	70.56 26	66.21 69	62.48 49	66.17 43	63.98 61	72.08 41	82.87 27	83.15 25	88.24 29	95.71 12

CLD-MVS		77.36 44	77.29 46	77.45 33	82.21 57	88.11 44	81.92 31	68.96 35	77.97 43	69.62 31	62.08 51	59.44 79	73.57 32	81.75 41	81.27 46	88.41 25	90.39 69
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MAR-MVS		77.19 45	78.37 42	75.81 43	89.87 17	90.58 19	79.33 52	65.56 54	77.62 45	58.33 65	59.24 63	67.98 49	74.83 24	82.37 34	83.12 26	86.95 54	87.67 108
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
MVSTER		76.92 46	79.92 34	73.42 56	74.98 109	82.97 79	78.15 54	63.41 68	78.02 42	64.41 43	67.54 38	72.80 32	71.05 47	83.29 24	83.73 20	88.53 23	91.12 59

PVSNet_BlendedMVS		76.84 47	78.47 39	74.95 49	82.37 54	89.90 28	75.45 71	65.45 55	74.99 50	70.66 27	63.07 48	58.27 85	67.60 71	84.24 19	81.70 39	88.18 30	97.10 5

PVSNet_Blended		76.84 47	78.47 39	74.95 49	82.37 54	89.90 28	75.45 71	65.45 55	74.99 50	70.66 27	63.07 48	58.27 85	67.60 71	84.24 19	81.70 39	88.18 30	97.10 5

AdaColmap		76.23 49	73.55 65	79.35 21	89.38 23	85.00 68	79.99 47	73.04 16	76.60 47	71.17 23	55.18 69	57.99 87	77.87 11	76.82 80	76.82 79	84.67 137	86.45 116

casdiffmvs		76.13 50	76.96 49	75.15 48	82.26 56	90.09 26	79.98 48	60.64 104	70.12 58	63.58 44	62.04 52	60.30 77	74.53 28	81.62 43	82.30 32	89.90 6	92.27 46

PCF-MVS	70.85 4	75.73 51	76.55 55	74.78 52	83.67 49	88.04 46	81.47 35	70.62 25	69.24 64	57.52 69	60.59 59	69.18 47	70.65 49	77.11 77	77.65 75	84.75 135	94.01 31
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CPTT-MVS		75.43 52	77.13 48	73.44 55	81.43 62	82.55 83	80.96 42	64.35 60	77.95 44	61.39 52	69.20 36	70.94 42	69.38 61	73.89 116	73.32 143	83.14 167	92.06 50

MVS_Test		75.22 53	76.69 52	73.51 54	79.30 74	88.82 34	80.06 46	58.74 114	69.77 61	57.50 70	59.78 62	61.35 73	75.31 21	82.07 37	83.60 23	90.13 5	91.41 56

OpenMVS	67.62 8	74.92 54	73.91 62	76.09 41	90.10 16	90.38 21	78.01 55	66.35 48	66.09 70	62.80 46	46.33 113	64.55 60	71.77 43	79.92 57	80.88 53	87.52 42	89.20 80

MVS_111021_LR		74.26 55	75.95 57	72.27 61	79.43 73	85.04 67	72.71 86	65.27 57	70.92 57	63.58 44	69.32 35	60.31 76	69.43 59	77.01 78	77.15 76	83.22 162	91.93 52

diffmvs1		74.20 56	76.05 56	72.03 62	77.16 92	88.46 38	79.55 50	58.73 115	72.02 55	58.23 66	60.24 60	62.08 66	72.03 42	78.95 63	79.16 60	86.50 63	91.45 54

OMC-MVS		74.03 57	75.82 58	71.95 64	79.56 71	80.98 103	75.35 73	63.21 69	84.48 21	61.83 50	61.54 55	66.89 52	69.41 60	76.60 82	74.07 131	82.34 177	86.15 120

DI_MVS_plusplus_trai		73.94 58	74.85 60	72.88 58	76.57 98	86.80 53	80.41 44	61.47 92	62.35 79	59.44 63	47.91 96	68.12 48	72.24 39	82.84 28	81.50 42	87.15 53	94.42 25

TSAR-MVS + COLMAP		73.09 59	76.86 50	68.71 80	74.97 110	82.49 84	74.51 81	61.83 88	83.16 24	49.31 94	82.22 18	51.62 114	68.94 64	78.76 66	75.52 96	82.67 172	84.23 134

CANet_DTU		72.84 60	76.63 53	68.43 83	76.81 96	86.62 55	75.54 70	54.71 164	72.06 54	43.54 118	67.11 39	58.46 82	72.40 38	81.13 49	80.82 54	87.57 41	90.21 71

DWT-MVSNet_training		72.81 61	73.98 61	71.45 66	81.26 63	86.37 58	72.08 89	59.82 111	69.13 65	58.15 67	54.71 70	61.33 75	67.81 70	76.86 79	78.63 65	89.59 8	90.86 61

OPM-MVS		72.74 62	70.93 78	74.85 51	85.30 46	84.34 72	82.82 28	69.79 28	49.96 122	55.39 77	54.09 76	60.14 78	70.04 54	80.38 54	79.43 58	85.74 98	88.20 104

CHOSEN 1792x2688		72.55 63	71.98 70	73.22 57	86.57 42	92.41 6	75.63 67	66.77 45	62.08 80	52.32 81	30.27 207	50.74 117	66.14 77	86.22 8	85.41 7	91.90 1	96.75 9

diffmvs		72.46 64	73.75 64	70.95 67	76.33 100	87.21 50	77.96 56	58.43 118	66.25 67	55.75 73	59.11 64	56.77 91	70.42 50	77.35 76	78.90 62	86.80 57	90.64 67

CostFormer		72.18 65	73.90 63	70.18 72	79.47 72	86.19 62	76.94 63	48.62 197	66.07 71	60.40 60	54.14 75	65.82 54	67.98 68	75.84 93	76.41 85	87.67 39	92.83 42

ACMP	68.86 7	72.15 66	72.25 69	72.03 62	80.96 65	80.87 105	77.93 57	64.13 62	69.29 62	60.79 58	64.04 46	53.54 108	63.91 86	73.74 120	75.27 97	84.45 142	88.98 83
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		72.02 67	73.18 68	70.67 70	82.13 58	80.26 120	79.58 49	63.04 73	70.09 59	51.98 82	65.06 44	55.62 99	62.49 94	75.97 92	76.32 86	84.80 134	88.93 84

PVSNet_Blended_VisFu		71.76 68	73.54 66	69.69 73	79.01 75	87.16 51	72.05 90	61.80 89	56.46 97	59.66 62	53.88 77	62.48 64	59.08 136	81.17 47	78.90 62	86.53 62	94.74 22

TAPA-MVS	67.10 9	71.45 69	73.47 67	69.10 78	77.04 93	80.78 106	73.81 84	62.10 83	80.80 34	51.28 85	60.91 57	63.80 63	67.98 68	74.59 105	72.42 157	82.37 176	80.97 163
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNLPA		71.37 70	70.27 81	72.66 60	80.79 68	81.33 99	71.07 118	65.75 52	82.36 27	64.80 42	42.46 130	56.49 92	72.70 37	73.00 128	70.52 178	80.84 189	85.76 125

Effi-MVS+		70.42 71	71.23 76	69.47 74	78.04 79	85.24 66	75.57 69	58.88 113	59.56 86	48.47 95	52.73 80	54.94 102	69.69 56	78.34 70	77.06 77	86.18 68	90.73 66

ACMM	66.70 10	70.42 71	68.49 91	72.67 59	82.85 50	77.76 145	77.70 59	64.76 59	64.61 76	60.74 59	49.29 90	53.97 106	65.86 78	74.97 101	75.57 95	84.13 150	83.29 142
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet3		70.41 73	71.89 72	68.68 81	70.89 135	79.42 129	75.63 67	60.97 99	65.32 72	51.06 86	47.37 102	62.05 67	64.90 82	82.49 30	82.27 33	88.64 19	84.34 133

PMMVS		70.37 74	75.06 59	64.90 102	71.46 129	81.88 88	64.10 160	55.64 152	71.31 56	46.69 101	70.69 34	58.56 80	69.53 58	79.03 62	75.63 93	81.96 180	88.32 103

MS-PatchMatch		70.34 75	69.00 87	71.91 65	85.20 47	85.35 65	77.84 58	61.77 90	58.01 91	55.40 76	41.26 137	58.34 84	61.69 97	81.70 42	78.29 69	89.56 9	80.02 168

tpmp4_e23		69.38 76	69.47 84	69.28 76	78.20 78	82.35 85	75.92 64	49.20 196	64.15 77	59.96 61	47.93 95	55.77 97	68.06 67	73.05 127	74.53 110	84.34 144	88.50 102

GBi-Net		69.21 77	70.40 79	67.81 86	69.49 140	78.65 134	74.54 77	60.97 99	65.32 72	51.06 86	47.37 102	62.05 67	63.43 88	77.49 72	78.22 70	87.37 43	83.73 137

test1		69.21 77	70.40 79	67.81 86	69.49 140	78.65 134	74.54 77	60.97 99	65.32 72	51.06 86	47.37 102	62.05 67	63.43 88	77.49 72	78.22 70	87.37 43	83.73 137

Anonymous20240521		69.13 79	69.07 86	69.21 77	77.65 87	77.52 147	74.68 76	57.85 124	54.92 107	55.34 78	55.74 68	55.56 100	66.35 76	75.05 99	76.56 82	83.35 159	88.13 105

IB-MVS	64.48 11	69.02 80	68.97 88	69.09 79	81.75 60	89.01 33	64.50 158	64.91 58	56.65 95	62.59 48	47.89 97	45.23 129	51.99 159	69.18 181	81.88 37	88.77 17	92.93 40
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
FC-MVSNet-train		68.83 81	68.29 92	69.47 74	78.35 77	79.94 121	64.72 157	66.38 47	54.96 106	54.51 79	56.75 66	47.91 123	66.91 75	75.57 97	75.75 91	85.92 83	87.12 111

PLC	64.00 12	68.54 82	66.66 102	70.74 69	80.28 70	74.88 170	72.64 87	63.70 67	69.26 63	55.71 74	47.24 105	55.31 101	70.42 50	72.05 154	70.67 176	81.66 182	77.19 177
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous20231211		68.44 83	66.37 105	70.86 68	77.58 88	83.49 77	75.15 74	61.89 87	52.54 115	58.50 64	28.89 209	56.78 90	69.29 62	74.96 103	76.61 80	82.73 170	91.36 57

HyFIR lowres test		68.39 84	68.28 93	68.52 82	80.85 66	88.11 44	71.08 117	58.09 121	54.87 109	47.80 98	27.55 213	55.80 96	64.97 81	79.11 61	79.14 61	88.31 27	93.35 35

test-LLR		68.23 85	71.61 74	64.28 120	71.37 130	81.32 100	63.98 164	61.03 96	58.62 88	42.96 130	52.74 78	61.65 71	57.74 142	75.64 95	78.09 73	88.61 20	93.21 36

FMVSNet2		68.06 86	68.57 90	67.45 89	69.49 140	78.65 134	74.54 77	60.23 110	56.29 98	49.64 93	42.13 133	57.08 89	63.43 88	81.15 48	80.99 50	87.37 43	83.73 137

Fast-Effi-MVS+		67.59 87	67.56 97	67.62 88	73.67 115	81.14 102	71.12 115	54.79 163	58.88 87	50.61 90	46.70 111	47.05 124	69.12 63	76.06 91	76.44 84	86.43 64	86.65 114

EPP-MVSNet		67.58 88	71.10 77	63.48 129	75.71 105	83.35 78	66.85 147	57.83 125	53.02 114	41.15 146	55.82 67	67.89 50	56.01 148	74.40 107	72.92 152	83.33 160	90.30 70

UGNet		67.57 89	71.69 73	62.76 140	69.88 138	82.58 82	66.43 151	58.64 116	54.71 110	51.87 83	61.74 53	62.01 70	45.46 188	74.78 104	74.99 99	84.24 146	91.02 60
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
tpm cat1		67.47 90	67.05 101	67.98 85	76.63 97	81.51 97	74.49 82	47.65 202	61.18 82	61.12 54	42.51 129	53.02 111	64.74 84	70.11 173	71.50 164	83.22 162	89.49 76

TESTMET0.1,1		67.38 91	71.61 74	62.45 144	66.05 178	81.32 100	63.98 164	55.36 156	58.62 88	42.96 130	52.74 78	61.65 71	57.74 142	75.64 95	78.09 73	88.61 20	93.21 36

IS_MVSNet		67.29 92	71.98 70	61.82 150	76.92 94	84.32 74	65.90 155	58.22 119	55.75 102	39.22 154	54.51 73	62.47 65	45.99 185	78.83 65	78.52 67	84.70 136	89.47 77

tpmrst		67.15 93	68.12 95	66.03 98	76.21 101	80.98 103	71.27 111	45.05 209	60.69 84	50.63 89	46.95 110	54.15 105	65.30 79	71.80 157	71.77 161	87.72 37	90.48 68

thres100view900		67.14 94	66.09 110	68.38 84	77.70 83	83.84 76	74.52 80	66.33 49	49.16 127	43.40 123	43.24 120	41.34 139	62.59 93	79.31 60	75.92 90	85.73 99	89.81 74

conf0.002		67.12 95	67.13 100	67.11 91	77.95 81	82.11 86	71.71 99	63.06 71	49.16 127	43.43 120	47.76 99	48.79 120	61.42 99	76.61 81	76.55 83	85.07 119	88.92 86

conf0.01		66.60 96	66.18 108	67.09 92	77.90 82	82.02 87	71.71 99	63.05 72	49.16 127	43.41 122	46.23 114	45.78 128	61.42 99	76.55 83	74.63 104	85.04 120	88.87 88

tfpn111		66.52 97	66.12 109	66.98 94	77.70 83	81.58 93	71.71 99	62.94 77	49.16 127	43.28 125	51.38 83	41.34 139	61.42 99	76.24 86	74.63 104	84.84 128	88.52 98

EPMVS		66.21 98	67.49 98	64.73 107	75.81 104	84.20 75	68.94 135	44.37 213	61.55 81	48.07 97	49.21 92	54.87 103	62.88 91	71.82 156	71.40 168	88.28 28	79.37 171

EPNet_dtu		66.17 99	70.13 82	61.54 152	81.04 64	77.39 150	68.87 136	62.50 82	69.78 60	33.51 187	63.77 47	56.22 93	37.65 205	72.20 150	72.18 159	85.69 102	79.38 170
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-LS		66.08 100	66.56 104	65.51 99	73.67 115	74.88 170	70.89 121	53.55 172	50.42 120	48.32 96	50.59 86	55.66 98	61.83 96	73.93 115	74.42 117	84.82 133	86.01 122
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		65.90 101	64.96 115	67.00 93	77.70 83	81.58 93	71.71 99	62.94 77	49.16 127	43.40 123	43.24 120	41.34 139	61.42 99	76.24 86	74.63 104	84.84 128	88.52 98

conf200view11		65.89 102	64.96 115	66.98 94	77.70 83	81.58 93	71.71 99	62.94 77	49.16 127	43.28 125	43.24 120	41.34 139	61.42 99	76.24 86	74.63 104	84.84 128	88.52 98

thres200		65.58 103	64.74 118	66.56 96	77.52 90	81.61 91	73.44 85	62.95 75	46.23 148	42.45 140	42.76 124	41.18 144	58.12 140	76.24 86	75.59 94	84.89 125	89.58 75

MSDG		65.57 104	61.57 154	70.24 71	82.02 59	76.47 158	74.46 83	68.73 37	56.52 96	50.33 91	38.47 165	41.10 146	62.42 95	72.12 152	72.94 151	83.47 157	73.37 193

Vis-MVSNet		65.53 105	69.83 83	60.52 156	70.80 136	84.59 70	66.37 153	55.47 155	48.40 135	40.62 150	57.67 65	58.43 83	45.37 189	77.49 72	76.24 87	84.47 141	85.99 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PatchmatchNet		65.43 106	67.71 96	62.78 139	73.49 117	82.83 80	66.42 152	45.40 208	60.40 85	45.27 107	49.22 91	57.60 88	60.01 120	70.61 165	71.38 171	86.08 73	81.91 158
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		65.21 107	67.28 99	62.79 138	70.91 134	81.72 90	69.28 134	49.50 193	58.08 90	43.94 117	50.50 87	56.02 94	58.86 137	70.72 164	73.37 141	84.24 146	80.52 164

thres400		65.18 108	64.44 120	66.04 97	76.40 99	82.63 81	71.52 109	64.27 61	44.93 156	40.69 149	41.86 134	40.79 153	58.12 140	77.67 71	74.64 103	85.26 112	88.56 97

tpm		64.85 109	66.02 111	63.48 129	74.52 112	78.38 137	70.98 119	44.99 211	51.61 117	43.28 125	47.66 100	53.18 109	60.57 111	70.58 167	71.30 173	86.54 61	89.45 78

UA-Net		64.62 110	68.23 94	60.42 157	77.53 89	81.38 98	60.08 186	57.47 133	47.01 140	44.75 113	60.68 58	71.32 41	41.84 196	73.27 122	72.25 158	80.83 190	71.68 201

Effi-MVS+-dtu		64.58 111	64.08 121	65.16 100	73.04 120	75.17 169	70.68 123	56.23 146	54.12 112	44.71 114	47.42 101	51.10 115	63.82 87	68.08 185	66.32 198	82.47 175	86.38 118

GA-MVS		64.55 112	65.76 113	63.12 135	69.68 139	81.56 96	69.59 131	58.16 120	45.23 154	35.58 178	47.01 109	41.82 138	59.41 131	79.62 58	78.54 66	86.32 65	86.56 115

LS3D		64.54 113	62.14 147	67.34 90	80.85 66	75.79 164	69.99 127	65.87 51	60.77 83	44.35 115	42.43 131	45.95 127	65.01 80	69.88 176	68.69 186	77.97 207	71.43 203

CDS-MVSNet		64.22 114	65.89 112	62.28 146	70.05 137	80.59 115	69.91 129	57.98 122	43.53 166	46.58 102	48.22 94	50.76 116	46.45 182	75.68 94	76.08 88	82.70 171	86.34 119
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v6		64.09 115	63.40 126	64.90 102	68.28 151	80.78 106	71.85 93	57.64 129	46.73 143	45.18 109	39.40 151	40.89 150	60.54 114	72.86 133	74.40 118	85.92 83	88.72 93

v1neww		64.08 116	63.38 127	64.89 104	68.27 153	80.77 108	71.84 94	57.65 127	46.66 145	45.10 110	39.40 151	40.86 151	60.57 111	72.86 133	74.40 118	85.92 83	88.71 94

v7new		64.08 116	63.38 127	64.89 104	68.27 153	80.77 108	71.84 94	57.65 127	46.66 145	45.10 110	39.40 151	40.86 151	60.57 111	72.86 133	74.40 118	85.92 83	88.71 94

dps		64.08 116	63.22 130	65.08 101	75.27 108	79.65 125	66.68 149	46.63 207	56.94 93	55.67 75	43.96 116	43.63 135	64.00 85	69.50 180	69.82 181	82.25 178	79.02 172

test-mter		64.06 119	69.24 85	58.01 171	59.07 208	77.40 149	59.13 190	48.11 200	55.64 103	39.18 155	51.56 82	58.54 81	55.38 150	73.52 121	76.00 89	87.22 51	92.05 51

thresconf0.02		63.92 120	65.18 114	62.46 143	75.91 103	80.65 114	67.51 144	63.86 64	45.00 155	33.32 188	51.38 83	51.68 113	48.34 171	75.49 98	75.13 98	85.84 94	76.91 179

view600		63.91 121	63.27 129	64.66 109	75.57 106	81.73 89	69.71 130	63.04 73	43.97 159	39.18 155	41.09 138	40.24 161	55.38 150	76.28 84	72.04 160	85.08 118	87.52 109

thres600view7		63.77 122	63.14 131	64.51 111	75.49 107	81.61 91	69.59 131	62.95 75	43.96 160	38.90 157	41.09 138	40.24 161	55.25 152	76.24 86	71.54 163	84.89 125	87.30 110

v2v482		63.68 123	62.85 136	64.65 110	68.01 161	80.46 117	71.90 91	57.60 130	44.26 157	42.82 138	39.80 149	38.62 178	61.56 98	73.06 125	74.86 101	86.03 76	88.90 87

v7		63.61 124	63.02 133	64.29 119	67.88 165	80.32 118	71.60 107	56.63 142	45.37 152	42.84 135	38.54 163	38.91 176	61.05 106	74.39 108	74.52 111	85.75 95	89.10 82

v1		63.49 125	62.77 139	64.32 116	68.13 155	80.70 111	71.70 105	57.43 134	43.69 163	42.89 134	39.03 157	39.77 165	59.93 123	72.93 130	74.48 115	85.86 91	88.77 89

v1141		63.48 126	62.75 141	64.32 116	68.13 155	80.69 112	71.69 106	57.43 134	43.66 165	42.83 137	39.02 158	39.74 167	59.95 121	72.94 129	74.49 113	85.86 91	88.75 91

divwei89l23v2f112		63.48 126	62.76 140	64.32 116	68.13 155	80.68 113	71.71 99	57.43 134	43.69 163	42.84 135	39.01 159	39.75 166	59.94 122	72.93 130	74.49 113	85.86 91	88.75 91

FMVSNet1		63.48 126	63.07 132	63.97 122	65.31 184	76.37 160	71.77 98	57.90 123	43.32 168	45.66 105	35.06 192	49.43 119	58.57 138	77.49 72	78.22 70	84.59 139	81.60 161

v8		63.44 129	62.58 143	64.43 113	68.28 151	78.07 140	71.82 96	54.85 161	46.70 144	45.20 108	39.40 151	40.91 149	60.54 114	72.85 137	74.39 123	85.92 83	85.76 125

v18		63.31 130	62.02 149	64.81 106	68.48 147	73.38 179	72.14 88	54.28 166	48.99 134	47.21 99	39.56 150	41.20 143	60.80 108	72.89 132	74.46 116	85.96 82	83.64 140

pmmvs4		63.14 131	62.46 144	63.94 123	66.03 179	76.40 159	66.82 148	57.60 130	56.74 94	50.26 92	40.81 142	37.51 182	59.26 134	71.75 158	71.48 165	83.68 155	82.53 150

v16		63.12 132	61.78 151	64.68 108	68.45 148	73.29 180	71.86 92	54.12 167	48.36 136	47.00 100	39.30 155	41.01 147	60.67 109	72.83 138	74.40 118	86.01 77	83.24 144

Fast-Effi-MVS+-dtu		63.05 133	64.72 119	61.11 154	71.21 133	76.81 157	70.72 122	43.13 217	52.51 116	35.34 179	46.55 112	46.36 125	61.40 104	71.57 160	71.44 166	84.84 128	87.79 107

view800		63.02 134	62.69 142	63.39 131	74.79 111	80.76 110	67.83 140	61.93 86	43.16 170	37.78 166	40.43 143	39.73 168	53.16 157	75.01 100	73.32 143	84.87 127	86.43 117

v1144		63.00 135	62.39 145	63.70 125	67.72 168	80.27 119	71.23 113	56.40 143	42.51 174	40.81 148	38.12 172	37.73 180	60.42 117	74.46 106	74.55 109	85.64 107	89.12 81

v10		63.00 135	62.22 146	63.90 124	67.88 165	77.78 144	71.59 108	54.34 165	45.37 152	42.76 139	38.53 164	38.93 175	61.05 106	74.39 108	74.52 111	85.75 95	86.04 121

v17		62.99 137	61.70 152	64.51 111	68.40 149	73.28 181	71.80 97	54.11 168	47.87 137	46.14 103	39.29 156	41.01 147	60.60 110	72.81 139	74.39 123	85.99 80	83.25 143

tfpn_ndepth		62.95 138	63.75 123	62.02 148	76.89 95	79.48 128	64.09 161	60.98 98	49.48 124	38.73 158	49.92 89	44.79 130	47.37 176	71.91 155	71.66 162	84.07 151	79.00 173

V42		62.86 139	62.97 134	62.74 141	60.84 201	78.99 132	71.46 110	57.13 140	46.85 141	44.28 116	38.87 160	40.73 155	57.63 144	72.60 145	74.14 128	85.09 117	88.63 96

tfpn		62.54 140	62.79 138	62.25 147	74.16 113	79.86 123	66.07 154	60.97 99	42.43 175	36.41 170	39.88 148	43.76 134	51.25 164	73.85 117	74.17 127	84.67 137	85.57 128

gg-mvs-nofinetune		62.34 141	66.19 107	57.86 175	76.15 102	88.61 36	71.18 114	41.24 226	25.74 228	13.16 230	22.91 223	63.97 62	54.52 154	85.06 13	85.25 10	90.92 3	91.78 53

CR-MVSNet		62.31 142	64.75 117	59.47 163	68.63 146	71.29 196	67.53 142	43.18 215	55.83 100	41.40 143	41.04 140	55.85 95	57.29 145	72.76 140	73.27 146	78.77 204	83.23 145

UniMVSNet_NR-MVSNet		62.30 143	63.51 125	60.89 155	69.48 143	77.83 143	64.07 162	63.94 63	50.03 121	31.17 193	44.82 115	41.12 145	51.37 161	71.02 162	74.81 102	85.30 111	84.95 129

v1192		62.25 144	61.64 153	62.96 136	66.88 173	79.72 124	69.96 128	55.77 150	41.58 181	39.42 152	37.05 178	35.96 193	60.50 116	74.30 113	74.09 130	85.24 113	88.76 90

Vis-MVSNet (Re-imp)		62.25 144	68.74 89	54.68 191	73.70 114	78.74 133	56.51 198	57.49 132	55.22 104	26.86 205	54.56 72	61.35 73	31.06 208	73.10 124	74.90 100	82.49 174	83.31 141

CHOSEN 280x420		62.23 146	66.57 103	57.17 181	59.88 205	68.92 202	61.20 180	42.28 219	54.17 111	39.57 151	47.78 98	64.97 58	62.68 92	73.85 117	69.52 183	77.43 208	86.75 113

PatchMatch-RL		62.22 147	60.69 161	64.01 121	68.74 145	75.75 165	59.27 189	60.35 106	56.09 99	53.80 80	47.06 108	36.45 188	64.80 83	68.22 184	67.22 193	77.10 209	74.02 188

v15		62.07 148	60.70 160	63.67 126	68.09 158	73.00 182	71.27 111	53.41 173	43.70 162	43.43 120	38.77 161	39.83 163	59.87 124	72.74 142	74.25 125	85.98 81	82.61 149

v144192		62.05 149	61.46 155	62.73 142	66.59 176	79.87 122	69.30 133	55.88 148	41.50 183	39.41 153	37.23 176	36.45 188	59.62 127	72.69 143	73.51 138	85.61 108	88.93 84

v148		62.00 150	61.19 157	62.96 136	67.46 171	79.49 127	67.87 139	57.66 126	42.30 176	45.02 112	38.20 170	38.89 177	54.77 153	69.83 177	72.60 156	84.96 121	87.01 112

V14		61.96 151	60.56 163	63.59 127	68.06 159	72.93 185	71.10 116	53.33 175	43.47 167	43.28 125	38.59 162	39.78 164	59.76 126	72.65 144	74.19 126	86.01 77	82.32 154

IterMVS		61.87 152	63.55 124	59.90 159	67.29 172	72.20 191	67.34 145	48.56 198	47.48 139	37.86 165	47.07 107	48.27 121	54.08 155	72.12 152	73.71 136	84.30 145	83.99 135
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V9		61.85 153	60.42 166	63.51 128	68.02 160	72.85 186	70.91 120	53.24 176	43.25 169	43.27 129	38.41 166	39.73 168	59.60 128	72.55 146	74.13 129	86.04 75	82.04 157

v11		61.74 154	60.47 165	63.22 134	67.83 167	72.72 188	70.31 126	52.95 182	42.75 173	41.89 142	38.16 171	38.49 179	60.40 118	74.35 110	74.40 118	85.92 83	82.39 153

v12		61.70 155	60.27 168	63.38 132	68.00 162	72.76 187	70.63 124	53.14 178	43.01 171	42.95 133	38.25 168	39.64 170	59.48 130	72.47 148	74.05 132	86.06 74	81.71 160

v1921920		61.66 156	61.10 158	62.31 145	66.32 177	79.57 126	68.41 138	55.49 154	41.03 184	38.69 159	36.64 184	35.27 199	59.60 128	73.23 123	73.41 140	85.37 110	88.51 101

v13		61.60 157	60.13 171	63.31 133	67.95 164	72.67 189	70.51 125	53.05 179	42.80 172	42.96 130	38.10 173	39.57 172	59.31 133	72.36 149	73.98 134	86.10 71	81.40 162

ACMH	59.42 14	61.59 158	59.22 181	64.36 115	78.92 76	78.26 138	67.65 141	67.48 42	39.81 188	30.98 195	38.25 168	34.59 201	61.37 105	70.55 168	73.47 139	79.74 198	79.59 169
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	60.36 13	61.16 159	58.38 183	64.42 114	77.37 91	74.35 175	68.45 137	62.81 81	45.86 150	38.48 160	35.71 187	37.35 183	59.81 125	67.24 187	69.80 182	79.58 199	78.32 175

v1240		61.09 160	60.55 164	61.72 151	65.92 181	79.28 130	67.16 146	54.91 160	39.79 189	38.10 162	36.08 186	34.64 200	59.15 135	72.86 133	73.36 142	85.10 115	87.84 106

NR-MVSNet		61.08 161	62.09 148	59.90 159	71.96 128	75.87 162	63.60 168	61.96 84	49.31 125	27.95 202	42.76 124	33.85 205	48.82 170	74.35 110	74.05 132	85.13 114	84.45 131

DU-MVS		60.87 162	61.82 150	59.76 161	66.69 174	75.87 162	64.07 162	61.96 84	49.31 125	31.17 193	42.76 124	36.95 185	51.37 161	69.67 178	73.20 149	83.30 161	84.95 129

UniMVSNet (Re)		60.62 163	62.93 135	57.92 172	67.64 169	77.90 142	61.75 177	61.24 95	49.83 123	29.80 197	42.57 127	40.62 159	43.36 192	70.49 170	73.27 146	83.76 153	85.81 124

PatchT		60.46 164	63.85 122	56.51 183	65.95 180	75.68 166	47.34 212	41.39 222	53.89 113	41.40 143	37.84 174	50.30 118	57.29 145	72.76 140	73.27 146	85.67 103	83.23 145

TranMVSNet+NR-MVSNet		60.38 165	61.30 156	59.30 164	68.34 150	75.57 168	63.38 171	63.78 66	46.74 142	27.73 203	42.56 128	36.84 186	47.66 174	70.36 171	74.59 108	84.91 124	82.46 151

conf0.05thres1000		60.33 166	59.42 178	61.40 153	73.15 119	78.25 139	65.29 156	60.30 107	36.61 200	35.75 176	33.25 194	39.23 173	50.35 167	72.18 151	72.67 155	83.57 156	83.74 136

pmmvs5		59.72 167	60.24 169	59.11 166	62.77 195	77.33 151	63.17 172	54.00 169	40.21 187	37.23 167	40.41 144	35.99 192	51.75 160	72.55 146	72.74 154	85.72 101	82.45 152

USDC		59.69 168	60.03 172	59.28 165	64.04 189	71.84 194	63.15 173	55.36 156	54.90 108	35.02 182	48.34 93	29.79 216	58.16 139	70.60 166	71.33 172	79.99 196	73.42 192

Baseline_NR-MVSNet		59.47 169	60.28 167	58.54 169	66.69 174	73.90 176	61.63 178	62.90 80	49.15 133	26.87 204	35.18 191	37.62 181	48.20 172	69.67 178	73.61 137	84.92 122	82.82 148

thisisatest0515		59.37 170	60.68 162	57.84 176	64.39 188	75.65 167	58.56 193	53.86 170	41.55 182	42.12 141	40.40 145	39.59 171	47.09 180	71.69 159	73.79 135	81.02 188	82.08 156

pm-mvs1		59.21 171	59.58 177	58.77 168	67.97 163	77.07 156	64.12 159	57.20 138	34.73 208	36.86 168	35.34 189	40.54 160	43.34 193	74.32 112	73.30 145	83.13 168	81.77 159

tfpnnormal		58.97 172	56.48 191	61.89 149	71.27 132	76.21 161	66.65 150	61.76 91	32.90 214	36.41 170	27.83 212	29.14 217	50.64 166	73.06 125	73.05 150	84.58 140	83.15 147

tfpnview11		58.92 173	59.60 176	58.13 170	72.99 121	77.11 154	60.48 181	60.37 105	42.10 178	29.10 199	43.45 117	40.72 156	41.67 197	70.53 169	70.43 179	84.17 149	72.85 195

FMVSNet5		58.86 174	60.24 169	57.25 180	52.66 224	66.25 208	63.77 167	52.86 183	57.85 92	37.92 164	36.12 185	52.22 112	51.37 161	70.88 163	71.43 167	84.92 122	66.91 211

TAMVS		58.86 174	60.91 159	56.47 184	62.38 197	77.57 146	58.97 191	52.98 180	38.76 194	36.17 173	42.26 132	47.94 122	46.45 182	70.23 172	70.79 175	81.86 181	78.82 174

EG-PatchMatch MVS		58.73 176	58.03 186	59.55 162	72.32 125	80.49 116	63.44 170	55.55 153	32.49 215	38.31 161	28.87 210	37.22 184	42.84 194	74.30 113	75.70 92	84.84 128	77.14 178

tfpn_n400		58.64 177	59.27 179	57.89 173	72.83 122	77.26 152	60.35 182	60.29 108	39.77 190	29.10 199	43.45 117	40.72 156	41.61 198	70.06 174	71.39 169	83.17 165	72.26 198

tfpnconf		58.64 177	59.27 179	57.89 173	72.83 122	77.26 152	60.35 182	60.29 108	39.77 190	29.10 199	43.45 117	40.72 156	41.61 198	70.06 174	71.39 169	83.17 165	72.26 198

RPMNet		58.63 179	62.80 137	53.76 197	67.59 170	71.29 196	54.60 201	38.13 230	55.83 100	35.70 177	41.58 136	53.04 110	47.89 173	66.10 189	67.38 191	78.65 206	84.40 132

ADS-MVSNet		58.40 180	59.16 182	57.52 178	65.80 182	74.57 174	60.26 184	40.17 227	50.51 119	38.01 163	40.11 147	44.72 131	59.36 132	64.91 193	66.55 196	81.53 183	72.72 197

tfpn1000		58.35 181	59.96 173	56.47 184	72.78 124	77.51 148	56.66 197	59.16 112	43.74 161	29.76 198	42.79 123	42.49 136	37.04 206	68.92 182	68.98 184	83.45 158	75.25 183

TransMVSNet (Re)		57.83 182	56.90 189	58.91 167	72.26 126	74.69 173	63.57 169	61.42 93	32.30 216	32.65 190	33.97 193	35.96 193	39.17 203	73.84 119	72.84 153	84.37 143	74.69 186

MIMVSNet		57.78 183	59.71 175	55.53 188	54.79 216	77.10 155	63.89 166	45.02 210	46.59 147	36.79 169	28.36 211	40.77 154	45.84 186	74.97 101	76.58 81	86.87 56	73.60 191

CMPMVS	43.63 17	57.67 184	55.43 192	60.28 158	72.01 127	79.00 131	62.77 174	53.23 177	41.77 180	45.42 106	30.74 206	39.03 174	53.01 158	64.81 195	64.65 204	75.26 215	68.03 209
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 1		57.35 185	59.89 174	54.38 193	71.37 130	73.45 178	52.71 203	61.03 96	46.11 149	26.33 206	41.73 135	44.08 132	29.72 211	71.43 161	70.90 174	85.10 115	71.56 202

v7n		57.04 186	56.64 190	57.52 178	62.85 194	74.75 172	61.76 176	51.80 186	35.58 207	36.02 175	32.33 198	33.61 206	50.16 168	67.73 186	70.34 180	82.51 173	82.12 155

pmmvs-eth3d		55.20 187	53.95 199	56.65 182	57.34 214	67.77 204	57.54 195	53.74 171	40.93 185	41.09 147	31.19 205	29.10 218	49.07 169	65.54 190	67.28 192	81.14 186	75.81 180

v748		55.19 188	54.63 195	55.85 186	61.44 200	72.97 184	58.72 192	51.62 187	34.48 210	36.39 172	32.09 199	33.05 207	45.48 187	61.85 205	67.87 189	81.45 184	80.08 167

COLMAP_ROB	51.17 15	55.13 189	52.90 203	57.73 177	73.47 118	67.21 206	62.13 175	55.82 149	47.83 138	34.39 183	31.60 203	34.24 202	44.90 190	63.88 202	62.52 212	75.67 213	63.02 219
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		55.07 190	58.06 185	51.57 199	48.87 231	58.95 222	53.68 202	41.26 225	62.42 78	45.88 104	54.38 74	54.26 104	53.75 156	57.15 214	53.53 229	66.01 228	65.75 214

gm-plane-assit		54.99 191	57.99 187	51.49 201	69.27 144	54.42 227	32.32 231	42.59 218	21.18 234	13.71 228	23.61 219	43.84 133	60.21 119	87.09 4	86.55 4	90.81 4	89.28 79

anonymousdsp		54.99 191	57.24 188	52.36 198	53.82 221	71.75 195	51.49 204	48.14 199	33.74 212	33.66 186	38.34 167	36.13 191	47.54 175	64.53 197	70.60 177	79.53 200	85.59 127

CVMVSNet		54.92 193	58.16 184	51.13 202	62.61 196	68.44 203	55.45 200	52.38 184	42.28 177	21.45 213	47.10 106	46.10 126	37.96 204	64.42 198	63.81 206	76.92 211	75.01 185

v52		54.79 194	55.15 193	54.36 195	54.07 219	72.13 192	59.84 187	49.39 194	34.50 209	35.08 181	31.63 202	35.74 195	47.21 179	63.90 200	67.92 187	80.59 192	80.23 165

V4		54.78 195	55.14 194	54.37 194	54.07 219	72.13 192	59.83 188	49.39 194	34.46 211	35.11 180	31.64 201	35.72 196	47.22 178	63.90 200	67.92 187	80.59 192	80.23 165

GG-mvs-BLEND		54.54 196	77.58 43	27.67 232	0.03 246	90.09 26	77.20 62	0.02 243	66.83 66	0.05 247	59.90 61	73.33 31	0.04 243	78.40 69	79.30 59	88.65 18	95.20 20

MDTV_nov1_ep13_2view		54.47 197	54.61 196	54.30 196	60.50 202	73.82 177	57.92 194	43.38 214	39.43 193	32.51 191	33.23 195	34.05 203	47.26 177	62.36 203	66.21 199	84.24 146	73.19 194

pmmvs6		54.20 198	53.54 200	54.97 189	63.22 193	72.98 183	60.17 185	52.32 185	26.77 227	34.30 184	23.29 222	36.23 190	40.33 201	68.77 183	68.76 185	79.47 201	78.00 176

MVS-HIRNet		53.86 199	53.02 201	54.85 190	60.30 204	72.36 190	44.63 220	42.20 220	39.45 192	43.47 119	21.66 226	34.00 204	55.47 149	65.42 191	67.16 194	83.02 169	71.08 204

TDRefinement		52.70 200	51.02 209	54.66 192	57.41 213	65.06 212	61.47 179	54.94 158	44.03 158	33.93 185	30.13 208	27.57 219	46.17 184	61.86 204	62.48 213	74.01 219	66.06 213

TinyColmap		52.66 201	50.09 212	55.65 187	59.72 206	64.02 216	57.15 196	52.96 181	40.28 186	32.51 191	32.42 197	20.97 231	56.65 147	63.95 199	65.15 203	74.91 216	63.87 217

Anonymous20231206		52.23 202	52.80 204	51.56 200	64.70 187	69.41 200	51.01 205	58.60 117	36.63 199	22.44 212	21.80 225	31.42 212	30.52 209	66.79 188	67.83 190	82.10 179	75.73 181

PEN-MVS		51.04 203	52.94 202	48.82 206	61.45 199	66.00 209	48.68 209	57.20 138	36.87 198	15.36 224	36.98 179	32.72 208	28.77 215	57.63 213	66.37 197	81.44 185	74.00 189

WR-MVS		51.02 204	54.56 197	46.90 212	63.84 190	69.23 201	44.78 219	56.38 144	38.19 195	14.19 226	37.38 175	36.82 187	22.39 224	60.14 208	66.20 200	79.81 197	73.95 190

CP-MVSNet		50.57 205	52.60 206	48.21 209	58.77 210	65.82 210	48.17 210	56.29 145	37.41 196	16.59 221	37.14 177	31.95 210	29.21 212	56.60 216	63.71 207	80.22 194	75.56 182

PS-CasMVS		50.17 206	52.02 207	48.02 210	58.60 211	65.54 211	48.04 211	56.19 147	36.42 202	16.42 223	35.68 188	31.33 213	28.85 214	56.42 218	63.54 208	80.01 195	75.18 184

PM-MVS		50.11 207	50.38 211	49.80 204	47.23 233	62.08 220	50.91 206	44.84 212	41.90 179	36.10 174	35.22 190	26.05 225	46.83 181	57.64 212	55.42 228	72.90 220	74.32 187

DTE-MVSNet		49.82 208	51.92 208	47.37 211	61.75 198	64.38 214	45.89 218	57.33 137	36.11 203	12.79 231	36.87 180	31.93 211	25.73 220	58.01 210	65.22 202	80.75 191	70.93 205

WR-MVS_H		49.62 209	52.63 205	46.11 215	58.80 209	67.58 205	46.14 217	54.94 158	36.51 201	13.63 229	36.75 182	35.67 197	22.10 225	56.43 217	62.76 210	81.06 187	72.73 196

LTVRE_ROB	47.26 16	49.41 210	49.91 213	48.82 206	64.76 186	69.79 199	49.05 207	47.12 204	20.36 236	16.52 222	36.65 183	26.96 220	50.76 165	60.47 207	63.16 209	64.73 229	72.00 200
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
SixPastTwentyTwo		49.11 211	49.22 214	48.99 205	58.54 212	64.14 215	47.18 213	47.75 201	31.15 218	24.42 208	41.01 141	26.55 221	44.04 191	54.76 224	58.70 219	71.99 223	68.21 207

testgi		48.51 212	50.53 210	46.16 214	64.78 185	67.15 207	41.54 223	54.81 162	29.12 222	17.03 219	32.07 200	31.98 209	20.15 228	65.26 192	67.00 195	78.67 205	61.10 224

LP		48.21 213	46.65 220	50.03 203	60.39 203	63.86 217	48.73 208	38.71 229	35.60 206	32.99 189	23.31 221	24.95 227	40.07 202	57.73 211	61.56 214	79.29 202	59.51 225

N_pmnet		47.67 214	47.00 219	48.45 208	54.72 217	62.78 218	46.95 214	51.25 188	36.01 204	26.09 207	26.59 216	25.93 226	35.50 207	55.67 220	59.01 217	76.22 212	63.04 218

FC-MVSNet-test		47.24 215	54.37 198	38.93 224	59.49 207	58.25 224	34.48 230	53.36 174	45.66 151	6.66 239	50.62 85	42.02 137	16.62 234	58.39 209	61.21 215	62.99 230	64.40 216

test20.03		47.23 216	48.69 215	45.53 216	63.28 192	64.39 213	41.01 224	56.93 141	29.16 221	15.21 225	23.90 218	30.76 215	17.51 233	64.63 196	65.26 201	79.21 203	62.71 220

test2356		46.29 217	47.37 217	45.03 217	54.38 218	57.99 225	42.03 222	50.32 190	30.78 219	16.65 220	27.40 214	23.70 228	29.86 210	61.20 206	64.31 205	76.93 210	66.22 212

EU-MVSNet		44.84 218	47.85 216	41.32 221	49.26 228	56.59 226	43.07 221	47.64 203	33.03 213	13.82 227	36.78 181	30.99 214	24.37 222	53.80 225	55.57 227	69.78 224	68.21 207

MDA-MVSNet-bldmvs		44.15 219	42.27 226	46.34 213	38.34 236	62.31 219	46.28 215	55.74 151	29.83 220	20.98 214	27.11 215	16.45 237	41.98 195	41.11 235	57.47 221	74.72 217	61.65 223

testpf		43.39 220	47.17 218	38.98 223	65.58 183	47.38 235	36.09 228	31.67 237	36.97 197	19.47 216	33.01 196	35.62 198	23.61 223	50.86 229	56.08 225	57.48 234	70.27 206

testus		42.30 221	43.69 221	40.67 222	53.21 222	53.50 228	31.81 232	49.96 191	27.06 225	11.55 233	25.67 217	19.00 234	25.20 221	55.34 221	62.59 211	72.31 222	62.69 221

new-patchmatchnet		42.21 222	42.97 223	41.33 220	53.05 223	59.89 221	39.38 225	49.61 192	28.26 224	12.10 232	22.17 224	21.54 230	19.22 229	50.96 228	56.04 226	74.61 218	61.92 222

pmmvs3		41.86 223	42.29 225	41.36 219	39.80 234	52.66 229	38.93 227	35.85 236	23.40 231	20.22 215	19.30 227	20.84 232	40.56 200	55.98 219	58.79 218	72.80 221	65.03 215

MIMVSNet1		40.84 224	43.46 222	37.79 226	32.14 238	58.92 223	39.24 226	50.83 189	27.00 226	11.29 234	16.76 236	26.53 222	17.75 232	57.14 215	61.12 216	75.46 214	56.78 228

FPMVS		39.11 225	36.39 230	42.28 218	55.97 215	45.94 236	46.23 216	41.57 221	35.73 205	22.61 210	23.46 220	19.82 233	28.32 218	43.57 231	40.67 234	58.96 232	45.54 230

1111		38.93 226	38.98 227	38.86 225	50.10 226	50.42 230	29.52 233	38.00 231	22.67 232	17.99 217	17.40 229	26.26 223	28.72 216	54.86 222	58.20 220	68.82 227	43.08 233

testmv		37.40 227	37.95 228	36.76 227	48.97 229	49.33 233	28.65 236	46.74 205	18.34 237	7.68 237	16.80 234	14.47 238	19.18 230	51.72 226	56.93 222	69.36 225	58.09 226

test1235678		37.40 227	37.94 229	36.76 227	48.97 229	49.30 234	28.65 236	46.73 206	18.33 238	7.68 237	16.79 235	14.46 239	19.18 230	51.72 226	56.92 223	69.36 225	58.07 227

new_pmnet		33.19 229	35.52 231	30.47 230	27.55 242	45.31 237	29.29 235	30.92 238	29.00 223	9.88 236	18.77 228	17.64 236	26.77 219	44.07 230	45.98 232	58.41 233	47.87 229

PMVS	27.44 18	32.08 230	29.07 233	35.60 229	48.33 232	24.79 241	26.97 238	41.34 223	20.45 235	22.50 211	17.11 233	18.64 235	20.44 227	41.99 234	38.06 235	54.02 237	42.44 234
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		29.92 231	31.49 232	28.08 231	38.46 235	37.74 239	21.36 239	40.17 227	16.83 239	5.61 241	15.66 237	11.48 240	6.60 240	42.01 233	51.23 230	56.29 235	45.52 231

no-one		26.96 232	26.51 234	27.49 233	37.87 237	39.14 238	17.12 241	41.31 224	12.02 241	3.68 243	8.04 239	8.42 243	10.67 238	28.11 237	45.96 233	54.27 236	43.89 232

.test1245		25.86 233	24.56 236	27.39 234	50.10 226	50.42 230	29.52 233	38.00 231	22.67 232	17.99 217	17.40 229	26.26 223	28.72 216	54.86 222	0.05 241	0.01 245	0.24 242

Gipuma		24.91 234	24.61 235	25.26 235	31.47 239	21.59 242	18.06 240	37.53 233	25.43 229	10.03 235	4.18 243	4.25 245	14.85 235	43.20 232	47.03 231	39.62 239	26.55 238
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		20.45 235	22.31 237	18.27 238	20.52 243	26.73 240	14.85 243	28.43 240	13.69 240	0.79 246	10.35 238	9.10 241	3.83 242	27.64 238	32.87 236	41.17 238	35.81 235

E-PMN		15.08 236	11.65 239	19.08 236	28.73 240	12.31 245	6.95 246	36.87 235	10.71 243	3.63 244	5.13 240	2.22 248	13.81 237	11.34 241	18.50 239	24.49 241	21.32 239

EMVS		14.40 237	10.71 240	18.70 237	28.15 241	12.09 246	7.06 245	36.89 234	11.00 242	3.56 245	4.95 241	2.27 247	13.91 236	10.13 242	16.06 240	22.63 242	18.51 240

MVE	15.98 19	14.37 238	16.36 238	12.04 240	7.72 245	20.24 243	5.90 247	29.05 239	8.28 244	3.92 242	4.72 242	2.42 246	9.57 239	18.89 240	31.46 237	16.07 244	28.53 237
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.05 239	0.08 241	0.01 241	0.00 247	0.01 248	0.03 249	0.01 244	0.05 245	0.00 248	0.14 245	0.01 249	0.03 245	0.05 243	0.05 241	0.01 245	0.24 242

test123		0.05 239	0.08 241	0.01 241	0.00 247	0.01 248	0.01 250	0.00 245	0.05 245	0.00 248	0.16 244	0.00 250	0.04 243	0.02 244	0.05 241	0.00 247	0.26 241

sosnet-low-res		0.00 241	0.00 243	0.00 243	0.00 247	0.00 250	0.00 251	0.00 245	0.00 247	0.00 248	0.00 246	0.00 250	0.00 246	0.00 245	0.00 244	0.00 247	0.00 244

sosnet		0.00 241	0.00 243	0.00 243	0.00 247	0.00 250	0.00 251	0.00 245	0.00 247	0.00 248	0.00 246	0.00 250	0.00 246	0.00 245	0.00 244	0.00 247	0.00 244

Anonymous202405211			66.35 106		78.00 80	84.41 71	74.85 75	63.18 70	51.00 118		31.37 204	53.73 107	69.67 57	76.28 84	76.84 78	83.21 164	90.85 62

our_test_3					63.32 191	71.07 198	55.90 199

ambc			42.30 224		50.36 225	49.51 232	35.47 229		32.04 217	23.53 209	17.36 231	8.95 242	29.06 213	64.88 194	56.26 224	61.29 231	67.12 210

MTAPA										78.32 7		79.42 19

MTMP										76.04 12		76.65 24

Patchmatch-RL test							2.17 248

tmp_tt				16.09 239	13.07 244	8.12 247	13.61 244	2.08 242	55.09 105	30.10 196	40.26 146	22.83 229	5.35 241	29.91 236	25.25 238	32.33 240

XVS					82.43 52	86.27 59	75.70 65			61.07 55		72.27 34				85.67 103

X-MVStestdata					82.43 52	86.27 59	75.70 65			61.07 55		72.27 34				85.67 103

abl_6				79.06 25	89.68 20	92.14 9	77.70 59	69.68 29	86.87 15	71.88 21	74.29 31	80.06 17	76.56 17			88.84 15	95.82 10

mPP-MVS					86.96 38							70.61 44

NP-MVS									81.60 32

Patchmtry						78.06 141	67.53 142	43.18 215		41.40 143

DeepMVS_CX						19.81 244	17.01 242	10.02 241	23.61 230	5.85 240	17.21 232	8.03 244	21.13 226	22.60 239		21.42 243	30.01 236

High-resolution multi-view benchmark