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 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.
2 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.
3 MDC
This method makes use of Velodyne laser scans.
85.68 % 88.65 % 77.03 % 0.2 s volta v100
4 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.
5 CONV-BOX
This method makes use of Velodyne laser scans.
84.56 % 87.54 % 77.79 % 0.2 s Tesla V100
6 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.
7 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.
8 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, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
9 Roadstar.ai 83.57 % 88.55 % 77.87 % 0.08 s GPU @ 2.0 Ghz (Python)
10 KazuaNet
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++)
11 SECOND
This method makes use of Velodyne laser scans.
79.37 % 88.07 % 77.95 % 0.05 s GPU @ 3.1 Ghz (Python)
12 AILabs3D
This method makes use of Velodyne laser scans.
79.09 % 88.32 % 77.40 % 0.6 s GPU @ >3.5 Ghz (Python)
13 D3D
This method makes use of Velodyne laser scans.
78.85 % 88.08 % 69.99 % 0.4 s 1 core @ 3.5 Ghz (Python)
14 SCANet 78.64 % 87.36 % 77.37 % 0.09s GPU @ 2.5 Ghz (Python)
15 LTT
This method makes use of Velodyne laser scans.
78.55 % 85.80 % 76.24 % 0.4 s 1 core @ 3.5 Ghz (Python)
16 SECA 78.54 % 87.52 % 77.25 % 0.09 s GPU @ 2.5 Ghz (Python)
17 FNV1_RPN 78.51 % 87.40 % 70.23 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
18 VSE 78.50 % 87.65 % 76.99 % 0.15 s GPU @ 2.5 Ghz (Python)
19 FNV1_Fusion 78.23 % 87.07 % 76.72 % 0.11 s GPU @ 2.5 Ghz (Python)
20 FNV1 77.69 % 85.53 % 69.46 % 0.11 s GPU @ 2.5 Ghz (Python)
21 AVOD-SSD
This method makes use of Velodyne laser scans.
code 77.66 % 86.14 % 75.68 % 0.09 s GPU @ 2.5 Ghz (Python)
22 NLK 77.34 % 85.03 % 74.63 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
23 Kiwoo 77.28 % 86.92 % 69.02 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
24 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.
25 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.
26 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.
27 T2Method 76.34 % 86.68 % 68.01 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
28 FNV2 74.36 % 80.68 % 65.88 % 0.18 s GPU @ 2.5 Ghz (Python)
29 CLF3D
This method makes use of Velodyne laser scans.
73.01 % 80.09 % 58.56 % 0.13 s GPU @ 2.5 Ghz (Python)
30 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.
31 anm 72.73 % 81.79 % 63.98 % 3 s 1 core @ 2.5 Ghz (C/C++)
32 avodC 69.53 % 84.54 % 67.98 % 0.1 s GPU @ 2.5 Ghz (Python)
33 tester 64.75 % 75.18 % 59.55 % 0.1
34 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.
35 TopNet-DecayRate
This method makes use of Velodyne laser scans.
55.01 % 70.40 % 47.38 % 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.
36 TopNet-HighRes
This method makes use of Velodyne laser scans.
53.71 % 67.53 % 46.54 % 0.27 s 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.
37 VoxelNet basic
This method makes use of Velodyne laser scans.
52.09 % 55.01 % 47.01 % 0.07 s GPU (Python)
38 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.
39 RT3D
This method makes use of Velodyne laser scans.
42.10 % 54.68 % 44.05 % 0.09 s GPU @ 1.8Ghz
40 Licar
This method makes use of Velodyne laser scans.
40.40 % 45.81 % 37.09 % 0.09 s GPU @ 2.0 Ghz (Python)
41 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.
42 DT3D 17.19 % 23.38 % 13.86 % 0,21s GPU @ 2.5 Ghz (Python)
43 SAITv1 13.01 % 15.96 % 11.57 % 0.18 s GPU @ 2.5 Ghz (Python, C/C++)
44 M3D 11.08 % 14.03 % 9.23 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
45 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.
46 VS3D 9.12 % 11.30 % 7.23 % 0.58 s GPU @ 2.5 Ghz (C/C++)
47 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.
48 3DVSSD 2.01 % 2.02 % 1.68 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
49 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 .
50 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 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.
2 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.
3 SECOND
This method makes use of Velodyne laser scans.
46.27 % 55.10 % 44.76 % 0.05 s GPU @ 3.1 Ghz (Python)
4 Roadstar.ai 46.18 % 50.74 % 43.36 % 0.08 s GPU @ 2.0 Ghz (Python)
5 CONV-BOX
This method makes use of Velodyne laser scans.
45.09 % 52.71 % 43.90 % 0.2 s Tesla V100
6 MDC
This method makes use of Velodyne laser scans.
45.02 % 53.51 % 43.67 % 0.2 s volta v100
7 anm 40.48 % 50.07 % 35.64 % 3 s 1 core @ 2.5 Ghz (C/C++)
8 anonymous
This method makes use of Velodyne laser scans.
35.26 % 43.26 % 32.85 % 0.75 s GPU @ 3.5 Ghz (Python)
9 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.
10 CLF3D
This method makes use of Velodyne laser scans.
34.25 % 43.11 % 33.01 % 0.13 s GPU @ 2.5 Ghz (Python)
11 NLK 24.69 % 26.99 % 23.76 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
12 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.
13 TopNet-HighRes
This method makes use of Velodyne laser scans.
19.08 % 24.30 % 18.46 % 0.27 s 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.
14 TopNet-DecayRate
This method makes use of Velodyne laser scans.
12.42 % 15.82 % 12.22 % 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.
15 DT3D 1.22 % 1.15 % 1.14 % 0,21s GPU @ 2.5 Ghz (Python)
16 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 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.
3 CONV-BOX
This method makes use of Velodyne laser scans.
61.84 % 71.60 % 55.03 % 0.2 s Tesla V100
4 Roadstar.ai 59.11 % 71.89 % 52.85 % 0.08 s GPU @ 2.0 Ghz (Python)
5 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.
6 SECOND
This method makes use of Velodyne laser scans.
56.04 % 73.67 % 48.78 % 0.05 s GPU @ 3.1 Ghz (Python)
7 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.
8 NLK 44.20 % 53.51 % 40.34 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
9 CLF3D
This method makes use of Velodyne laser scans.
41.57 % 53.29 % 35.80 % 0.13 s GPU @ 2.5 Ghz (Python)
10 anm 40.42 % 56.84 % 35.20 % 3 s 1 core @ 2.5 Ghz (C/C++)
11 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.
12 TopNet-HighRes
This method makes use of Velodyne laser scans.
12.45 % 15.70 % 12.76 % 0.27 s 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.
13 TopNet-DecayRate
This method makes use of Velodyne laser scans.
11.68 % 14.94 % 11.73 % 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.
14 DT3D 1.26 % 2.54 % 1.47 % 0,21s GPU @ 2.5 Ghz (Python)
15 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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