Bird's Eye View Evaluation 2017


The bird's eye view benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate bird's eye view detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require a bounding box overlap of 70% in bird's eye view, while for pedestrians and cyclists we require an overlap of 50%. Difficulties are defined as follows:

  • Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
  • Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
  • Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %

All methods are ranked based on the moderately difficult results.

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • Stereo: Method uses left and right (stereo) images
  • Flow: Method uses optical flow (2 temporally adjacent images)
  • Multiview: Method uses more than 2 temporally adjacent images
  • Laser Points: Method uses point clouds from Velodyne laser scanner
  • Additional training data: Use of additional data sources for training (see details)

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 MMF
This method makes use of Velodyne laser scans.
87.47 % 89.49 % 79.10 % 0.08 s GPU @ 2.5 Ghz (Python)
2 GPOD
This method makes use of Velodyne laser scans.
87.39 % 89.54 % 81.65 % 0.1 s GPU @ 2.5 Ghz (Python)
3 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
86.71 % 88.84 % 78.62 % 0.2 s GPU @ >3.5 Ghz (Python)
4 ARPNET 86.67 % 88.10 % 77.94 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
5 UberATG-HDNET
This method makes use of Velodyne laser scans.
86.57 % 89.14 % 78.32 % 0.05 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
6 PointPillars
This method makes use of Velodyne laser scans.
86.10 % 88.35 % 79.83 % 16 ms 1080ti GPU and Intel i7 CPU
7 DH-ARI 86.08 % 87.93 % 78.22 % 0.2 s 1 core @ >3.5 Ghz (Python + C/C++)
8 PointRCNN
This method makes use of Velodyne laser scans.
86.04 % 89.28 % 79.02 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
9 MVX-Net
This method makes use of Velodyne laser scans.
85.89 % 89.15 % 78.07 % 0.06 s GPU @ 3.0 Ghz (Python + C/C++)
10 UberATG-ContFuse
This method makes use of Velodyne laser scans.
85.83 % 88.81 % 77.33 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
11 MDC
This method makes use of Velodyne laser scans.
85.68 % 88.65 % 77.03 % 0.2 s volta v100
12 Fast Point R-CNN 85.53 % 87.98 % 77.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
13 AVOD
This method makes use of Velodyne laser scans.
code 85.44 % 86.80 % 77.73 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
14 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
85.22 % 87.17 % 77.71 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
15 Roadstar.ai 85.04 % 89.95 % 78.75 % 0.08 s GPU @ 2.0 Ghz (Python)
16 CONV-BOX
This method makes use of Velodyne laser scans.
84.56 % 87.54 % 77.79 % 0.2 s Tesla V100
17 F-PointNet
This method makes use of Velodyne laser scans.
code 84.00 % 88.70 % 75.33 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
18 IPOD 83.98 % 86.93 % 77.85 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
19 AVOD-FPN
This method makes use of Velodyne laser scans.
code 83.79 % 88.53 % 77.90 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
20 PIXOR++ (LIDAR)
This method makes use of Velodyne laser scans.
83.70 % 89.38 % 77.97 % 0.035 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
21 RoarNet
This method makes use of Velodyne laser scans.
code 79.41 % 88.20 % 70.02 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin, Y. Kwon and M. Tomizuka: RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement. arXiv preprint arXiv:1811.03818 2018.
22 SECOND code 79.37 % 88.07 % 77.95 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
23 ELLIOT
This method makes use of Velodyne laser scans.
79.30 % 87.06 % 77.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
24 AILabs3D
This method makes use of Velodyne laser scans.
79.09 % 88.32 % 77.40 % 0.6 s GPU @ >3.5 Ghz (Python)
25 DFD 78.92 % 86.98 % 77.22 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
26 D3D
This method makes use of Velodyne laser scans.
78.85 % 88.08 % 69.99 % 0.4 s 1 core @ 3.5 Ghz (Python)
27 SCANet 78.64 % 87.36 % 77.37 % 0.09s GPU @ 2.5 Ghz (Python)
28 LTT
This method makes use of Velodyne laser scans.
78.55 % 85.80 % 76.24 % 0.4 s 1 core @ 3.5 Ghz (Python)
29 SECA 78.54 % 87.52 % 77.25 % 0.09 s GPU @ 2.5 Ghz (Python)
30 FNV1_RPN 78.51 % 87.40 % 70.23 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
31 VSE 78.50 % 87.65 % 76.99 % 0.15 s GPU @ 2.5 Ghz (Python)
32 FNV1_Fusion 78.23 % 87.07 % 76.72 % 0.11 s GPU @ 2.5 Ghz (Python)
33 X_MD 77.80 % 87.02 % 69.66 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
34 FNV1 77.69 % 85.53 % 69.46 % 0.11 s GPU @ 2.5 Ghz (Python)
35 NLK 77.34 % 85.03 % 74.63 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
36 Kiwoo 77.28 % 86.92 % 69.02 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
37 PIXOR (LIDAR)
This method makes use of Velodyne laser scans.
77.05 % 81.70 % 72.95 % 0.035 s TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
38 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
77.00 % 85.82 % 68.94 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
39 MV3D
This method makes use of Velodyne laser scans.
76.90 % 86.02 % 68.49 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
40 T2Method 76.34 % 86.68 % 68.01 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
41 VoxelNet(Unofficial) 75.06 % 83.25 % 67.44 % 0.5 s GPU @ 2.0 Ghz (Python)
42 FNV2 74.36 % 80.68 % 65.88 % 0.18 s GPU @ 2.5 Ghz (Python)
43 CLF3D
This method makes use of Velodyne laser scans.
73.01 % 80.09 % 58.56 % 0.13 s GPU @ 2.5 Ghz (Python)
44 A3DODWTDA
This method makes use of Velodyne laser scans.
code 72.86 % 76.65 % 64.51 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
45 anm 72.73 % 81.79 % 63.98 % 3 s 1 core @ 2.5 Ghz (C/C++)
46 avodC 69.53 % 84.54 % 67.98 % 0.1 s GPU @ 2.5 Ghz (Python)
47 Complexer-YOLO
This method makes use of Velodyne laser scans.
66.07 % 74.23 % 65.70 % 0.06 s GPU @ 3.5 Ghz (C/C++)
48 TopNet-DecayRate
This method makes use of Velodyne laser scans.
64.12 % 79.76 % 56.48 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
49 3D FCN
This method makes use of Velodyne laser scans.
62.54 % 69.94 % 55.94 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
50 TopNet-HighRes
This method makes use of Velodyne laser scans.
53.71 % 67.53 % 46.54 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
51 VoxelNet basic
This method makes use of Velodyne laser scans.
52.09 % 55.01 % 47.01 % 0.07 s GPU (Python)
52 BirdNet
This method makes use of Velodyne laser scans.
50.81 % 75.52 % 50.00 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
53 PL
This method uses stereo information.
45.11 % 65.08 % 38.42 % 0.4 s GPU @ 2.5 Ghz (Python)
54 RT3D
This method makes use of Velodyne laser scans.
42.10 % 54.68 % 44.05 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
55 Licar
This method makes use of Velodyne laser scans.
40.40 % 45.81 % 37.09 % 0.09 s GPU @ 2.0 Ghz (Python)
56 Stereo R-CNN
This method uses stereo information.
34.66 % 52.94 % 29.29 % 0.4 s GPU @ 2.5 Ghz (Python)
57 CSoR
This method makes use of Velodyne laser scans.
18.69 % 23.94 % 16.30 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
58 MonoPSR 17.66 % 20.25 % 15.78 % 0.2 s GPU @ 3.5 Ghz (Python)
59 DT3D 17.19 % 23.38 % 13.86 % 0,21s GPU @ 2.5 Ghz (Python)
60 ROI-10D 12.40 % 16.77 % 11.39 % 0.2 s GPU @ 3.5 Ghz (Python)
61 A3DODWTDA (image) code 10.21 % 10.61 % 8.64 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
62 GS3D 9.12 % 11.30 % 7.23 % 0.58 s GPU @ 2.5 Ghz (C/C++)
63 OFT-Net 7.99 % 9.50 % 7.51 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
64 MF3D 5.57 % 7.88 % 5.08 % 0.03 s GPU @ 2.5 Ghz (C/C++)
65 3D-SSMFCNN code 3.19 % 3.66 % 3.45 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
66 3DVSSD 2.01 % 2.02 % 1.68 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
67 VeloFCN
This method makes use of Velodyne laser scans.
0.33 % 0.15 % 0.47 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
68 multi-task CNN 0.00 % 0.00 % 0.00 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
69 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 IPOD 51.24 % 60.83 % 45.40 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
2 AVOD-FPN
This method makes use of Velodyne laser scans.
code 51.05 % 58.75 % 47.54 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
3 PointPillars
This method makes use of Velodyne laser scans.
50.23 % 58.66 % 47.19 % 16 ms 1080ti GPU and Intel i7 CPU
4 F-PointNet
This method makes use of Velodyne laser scans.
code 50.22 % 58.09 % 47.20 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
5 GPOD
This method makes use of Velodyne laser scans.
46.39 % 53.09 % 43.62 % 0.1 s GPU @ 2.5 Ghz (Python)
6 SECOND code 46.27 % 55.10 % 44.76 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
7 Roadstar.ai 46.22 % 49.94 % 44.14 % 0.08 s GPU @ 2.0 Ghz (Python)
8 CONV-BOX
This method makes use of Velodyne laser scans.
45.09 % 52.71 % 43.90 % 0.2 s Tesla V100
9 MDC
This method makes use of Velodyne laser scans.
45.02 % 53.51 % 43.67 % 0.2 s volta v100
10 ARPNET 43.35 % 50.80 % 37.79 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
11 ELLIOT
This method makes use of Velodyne laser scans.
42.66 % 50.68 % 39.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 anm 40.48 % 50.07 % 35.64 % 3 s 1 core @ 2.5 Ghz (C/C++)
13 anonymous
This method makes use of Velodyne laser scans.
35.58 % 42.50 % 35.06 % 0.75 s GPU @ 3.5 Ghz (C/C++)
14 X_MD 35.57 % 43.28 % 34.25 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
15 35.26 % 43.26 % 32.85 %
16 AVOD
This method makes use of Velodyne laser scans.
code 35.24 % 42.51 % 33.97 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
17 CLF3D
This method makes use of Velodyne laser scans.
34.25 % 43.11 % 33.01 % 0.13 s GPU @ 2.5 Ghz (Python)
18 NLK 24.69 % 26.99 % 23.76 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
19 BirdNet
This method makes use of Velodyne laser scans.
21.35 % 26.07 % 19.96 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
20 Complexer-YOLO
This method makes use of Velodyne laser scans.
20.88 % 22.00 % 20.81 % 0.06 s GPU @ 3.5 Ghz (C/C++)
21 TopNet-HighRes
This method makes use of Velodyne laser scans.
19.08 % 24.30 % 18.46 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
22 TopNet-DecayRate
This method makes use of Velodyne laser scans.
12.59 % 15.09 % 12.23 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
23 MonoPSR 11.22 % 14.27 % 10.54 % 0.2 s GPU @ 3.5 Ghz (Python)
24 OFT-Net 1.55 % 1.93 % 1.65 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
25 DT3D 1.22 % 1.15 % 1.14 % 0,21s GPU @ 2.5 Ghz (Python)
26 mBoW
This method makes use of Velodyne laser scans.
0.01 % 0.01 % 0.01 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 MDC
This method makes use of Velodyne laser scans.
64.78 % 77.38 % 56.77 % 0.2 s volta v100
2 ARPNET 62.76 % 77.99 % 55.55 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
3 PointPillars
This method makes use of Velodyne laser scans.
62.25 % 79.14 % 56.00 % 16 ms 1080ti GPU and Intel i7 CPU
4 F-PointNet
This method makes use of Velodyne laser scans.
code 61.96 % 75.38 % 54.68 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
5 CONV-BOX
This method makes use of Velodyne laser scans.
61.84 % 71.60 % 55.03 % 0.2 s Tesla V100
6 GPOD
This method makes use of Velodyne laser scans.
60.73 % 68.84 % 55.05 % 0.1 s GPU @ 2.5 Ghz (Python)
7 Roadstar.ai 60.26 % 67.31 % 54.62 % 0.08 s GPU @ 2.0 Ghz (Python)
8 IPOD 58.92 % 77.10 % 51.01 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
9 ELLIOT
This method makes use of Velodyne laser scans.
57.63 % 76.27 % 52.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.48 % 68.09 % 50.77 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
11 SECOND code 56.04 % 73.67 % 48.78 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
12 AVOD
This method makes use of Velodyne laser scans.
code 47.74 % 63.66 % 46.55 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
13 X_MD 44.49 % 54.22 % 38.48 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
14 NLK 44.20 % 53.51 % 40.34 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
15 CLF3D
This method makes use of Velodyne laser scans.
41.57 % 53.29 % 35.80 % 0.13 s GPU @ 2.5 Ghz (Python)
16 anm 40.42 % 56.84 % 35.20 % 3 s 1 core @ 2.5 Ghz (C/C++)
17 Complexer-YOLO
This method makes use of Velodyne laser scans.
30.16 % 36.12 % 26.01 % 0.06 s GPU @ 3.5 Ghz (C/C++)
18 BirdNet
This method makes use of Velodyne laser scans.
27.18 % 38.93 % 25.51 % 0.11 s Titan Xp GPU
J. Beltran, C. Guindel, F. Moreno, D. Cruzado, F. Garcia and A. Escalera: BirdNet: a 3D Object Detection Framework from LiDAR information. arXiv preprint arXiv:1805.01195 2018.
19 TopNet-DecayRate
This method makes use of Velodyne laser scans.
19.92 % 28.06 % 19.13 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
20 TopNet-HighRes
This method makes use of Velodyne laser scans.
12.45 % 15.70 % 12.76 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, J. Frias and C. Stiller: Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks. 2018.
21 MonoPSR 12.17 % 14.75 % 11.35 % 0.2 s GPU @ 3.5 Ghz (Python)
22 DT3D 1.26 % 2.54 % 1.47 % 0,21s GPU @ 2.5 Ghz (Python)
23 OFT-Net 0.43 % 0.79 % 0.43 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
24 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Related Datasets

Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}



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