3D Object Detection Evaluation 2017


The 3D object detection 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 3D object 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 an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box 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.
76.75 % 86.81 % 68.41 % 0.08 s GPU @ 2.5 Ghz (Python)
2 PointRCNN
This method makes use of Velodyne laser scans.
75.42 % 84.32 % 67.86 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
3 PointPillars
This method makes use of Velodyne laser scans.
74.99 % 79.05 % 68.30 % 16 ms 1080ti GPU and Intel i7 CPU
4 DH-ARI 74.90 % 80.02 % 68.46 % 0.2 s 1 core @ >3.5 Ghz (Python + C/C++)
5 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
74.87 % 84.02 % 67.55 % 0.2 s GPU @ >3.5 Ghz (Python)
6 Fast Point R-CNN 74.43 % 83.45 % 66.38 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
7 ARPNET 73.87 % 83.33 % 65.65 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
8 AILabs3D
This method makes use of Velodyne laser scans.
73.70 % 83.32 % 65.77 % 0.6 s GPU @ >3.5 Ghz (Python)
9 SECOND code 73.66 % 83.13 % 66.20 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
10 RoarNet
This method makes use of Velodyne laser scans.
code 73.04 % 83.71 % 59.16 % 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.
11 MVX-Net
This method makes use of Velodyne laser scans.
72.67 % 83.19 % 65.22 % 0.06 s GPU @ 3.0 Ghz (Python + C/C++)
12 MDC
This method makes use of Velodyne laser scans.
72.67 % 82.07 % 64.60 % 0.2 s volta v100
13 IPOD 72.57 % 79.75 % 66.33 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
14 AVOD-FPN
This method makes use of Velodyne laser scans.
code 71.88 % 81.94 % 66.38 % 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.
15 CONV-BOX
This method makes use of Velodyne laser scans.
70.47 % 79.98 % 64.49 % 0.2 s Tesla V100
16 F-PointNet
This method makes use of Velodyne laser scans.
code 70.39 % 81.20 % 62.19 % 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.
17 D3D
This method makes use of Velodyne laser scans.
67.90 % 83.51 % 59.59 % 0.4 s 1 core @ 3.5 Ghz (Python)
18 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
66.98 % 76.09 % 64.92 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
19 ELLIOT
This method makes use of Velodyne laser scans.
66.86 % 76.38 % 64.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
20 DFD 66.56 % 76.36 % 64.11 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
21 SCANet 66.30 % 76.09 % 58.68 % 0.09s GPU @ 2.5 Ghz (Python)
22 UberATG-ContFuse
This method makes use of Velodyne laser scans.
66.22 % 82.54 % 64.04 % 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.
23 AVOD
This method makes use of Velodyne laser scans.
code 65.78 % 73.59 % 58.38 % 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.
24 VSE 65.75 % 75.98 % 58.44 % 0.15 s GPU @ 2.5 Ghz (Python)
25 LTT
This method makes use of Velodyne laser scans.
65.42 % 74.07 % 59.68 % 0.4 s 1 core @ 3.5 Ghz (Python)
26 FNV1_RPN 65.18 % 74.61 % 57.75 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
27 FNV1_Fusion 65.07 % 74.78 % 57.74 % 0.11 s GPU @ 2.5 Ghz (Python)
28 X_MD 64.82 % 74.06 % 57.49 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
29 SECA 64.59 % 73.70 % 57.21 % 0.09 s GPU @ 2.5 Ghz (Python)
30 Kiwoo 64.21 % 75.62 % 57.19 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
31 MV3D
This method makes use of Velodyne laser scans.
62.35 % 71.09 % 55.12 % 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.
32 T2Method 62.08 % 74.36 % 55.14 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
33 FNV1 61.69 % 71.93 % 55.41 % 0.11 s GPU @ 2.5 Ghz (Python)
34 FNV2 59.26 % 67.67 % 51.97 % 0.18 s GPU @ 2.5 Ghz (Python)
35 CLF3D
This method makes use of Velodyne laser scans.
58.48 % 65.54 % 46.54 % 0.13 s GPU @ 2.5 Ghz (Python)
36 A3DODWTDA
This method makes use of Velodyne laser scans.
code 56.81 % 59.35 % 50.51 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
37 anm 56.76 % 68.02 % 49.39 % 3 s 1 core @ 2.5 Ghz (C/C++)
38 avodC 55.47 % 65.71 % 48.74 % 0.1 s GPU @ 2.5 Ghz (Python)
39 VoxelNet(Unofficial) 52.87 % 67.27 % 46.62 % 0.5 s GPU @ 2.0 Ghz (Python)
40 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
52.73 % 66.77 % 51.31 % 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.
41 Complexer-YOLO
This method makes use of Velodyne laser scans.
49.44 % 55.63 % 44.13 % 0.06 s GPU @ 3.5 Ghz (C/C++)
42 NLK 47.82 % 55.84 % 43.93 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
43 PL
This method uses stereo information.
34.04 % 49.68 % 28.45 % 0.4 s GPU @ 2.5 Ghz (Python)
44 VoxelNet basic
This method makes use of Velodyne laser scans.
24.35 % 29.70 % 23.52 % 0.07 s GPU (Python)
45 Stereo R-CNN
This method uses stereo information.
23.21 % 37.87 % 19.73 % 0.4 s GPU @ 2.5 Ghz (Python)
46 RT3D
This method makes use of Velodyne laser scans.
21.27 % 23.49 % 19.81 % 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.
47 BirdNet
This method makes use of Velodyne laser scans.
13.44 % 14.75 % 12.04 % 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.
48 Licar
This method makes use of Velodyne laser scans.
12.88 % 16.25 % 13.67 % 0.09 s GPU @ 2.0 Ghz (Python)
49 TopNet-HighRes
This method makes use of Velodyne laser scans.
12.58 % 15.29 % 12.25 % 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.
50 MonoPSR 10.85 % 12.57 % 9.06 % 0.2 s GPU @ 3.5 Ghz (Python)
51 ROI-10D 10.30 % 12.30 % 9.39 % 0.2 s GPU @ 3.5 Ghz (Python)
52 DT3D 9.92 % 15.37 % 9.26 % 0,21s GPU @ 2.5 Ghz (Python)
53 CSoR
This method makes use of Velodyne laser scans.
6.79 % 6.76 % 6.14 % 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.
54 A3DODWTDA (image) code 6.45 % 6.76 % 4.87 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
55 GS3D 6.29 % 7.69 % 6.16 % 0.58 s GPU @ 2.5 Ghz (C/C++)
56 MF3D 3.17 % 3.81 % 3.25 % 0.03 s GPU @ 2.5 Ghz (C/C++)
57 OFT-Net 2.50 % 3.28 % 2.27 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
58 3D-SSMFCNN code 2.28 % 2.39 % 1.52 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
59 3DVSSD 1.14 % 1.38 % 1.27 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
60 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 F-PointNet
This method makes use of Velodyne laser scans.
code 44.89 % 51.21 % 40.23 % 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.
2 IPOD 44.68 % 56.92 % 42.39 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
3 PointPillars
This method makes use of Velodyne laser scans.
43.53 % 52.08 % 41.49 % 16 ms 1080ti GPU and Intel i7 CPU
4 AVOD-FPN
This method makes use of Velodyne laser scans.
code 42.81 % 50.80 % 40.88 % 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.
5 SECOND code 42.56 % 51.07 % 37.29 % 0.05 s 4 cores @ 3.5 Ghz (C/C++)
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
6 MDC
This method makes use of Velodyne laser scans.
42.54 % 50.79 % 36.56 % 0.2 s volta v100
7 CONV-BOX
This method makes use of Velodyne laser scans.
41.01 % 47.74 % 35.98 % 0.2 s Tesla V100
8 ELLIOT
This method makes use of Velodyne laser scans.
37.78 % 45.94 % 34.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
9 ARPNET 36.42 % 47.34 % 35.49 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
10 anm 34.71 % 45.89 % 32.43 % 3 s 1 core @ 2.5 Ghz (C/C++)
11 anonymous
This method makes use of Velodyne laser scans.
33.37 % 40.19 % 27.90 % 0.75 s GPU @ 3.5 Ghz (C/C++)
12 X_MD 33.23 % 40.34 % 28.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
13 CLF3D
This method makes use of Velodyne laser scans.
31.65 % 35.85 % 26.94 % 0.13 s GPU @ 2.5 Ghz (Python)
14 AVOD
This method makes use of Velodyne laser scans.
code 31.51 % 38.28 % 26.98 % 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.
15 31.30 % 38.00 % 28.77 %
16 Complexer-YOLO
This method makes use of Velodyne laser scans.
15.32 % 19.45 % 14.80 % 0.06 s GPU @ 3.5 Ghz (C/C++)
17 NLK 14.51 % 16.80 % 13.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
18 BirdNet
This method makes use of Velodyne laser scans.
11.80 % 14.31 % 10.55 % 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 MonoPSR 10.66 % 12.65 % 10.08 % 0.2 s GPU @ 3.5 Ghz (Python)
20 TopNet-HighRes
This method makes use of Velodyne laser scans.
9.66 % 13.45 % 9.64 % 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 DT3D 1.14 % 1.14 % 1.14 % 0,21s GPU @ 2.5 Ghz (Python)
22 OFT-Net 1.11 % 1.06 % 1.06 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
23 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

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 ARPNET 59.53 % 74.56 % 52.79 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
2 PointPillars
This method makes use of Velodyne laser scans.
59.07 % 75.78 % 52.92 % 16 ms 1080ti GPU and Intel i7 CPU
3 MDC
This method makes use of Velodyne laser scans.
57.27 % 75.27 % 49.75 % 0.2 s volta v100
4 F-PointNet
This method makes use of Velodyne laser scans.
code 56.77 % 71.96 % 50.39 % 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.
54.45 % 68.27 % 52.26 % 0.2 s Tesla V100
6 SECOND code 53.85 % 70.51 % 46.90 % 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 IPOD 53.46 % 71.40 % 48.34 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
8 AVOD-FPN
This method makes use of Velodyne laser scans.
code 52.18 % 64.00 % 46.61 % 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.
9 ELLIOT
This method makes use of Velodyne laser scans.
51.17 % 68.87 % 46.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 AVOD
This method makes use of Velodyne laser scans.
code 44.90 % 60.11 % 38.80 % 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.
11 X_MD 37.22 % 51.69 % 36.44 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
12 anm 35.86 % 50.06 % 31.11 % 3 s 1 core @ 2.5 Ghz (C/C++)
13 CLF3D
This method makes use of Velodyne laser scans.
35.39 % 50.58 % 33.55 % 0.13 s GPU @ 2.5 Ghz (Python)
14 NLK 34.74 % 44.10 % 32.30 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
15 Complexer-YOLO
This method makes use of Velodyne laser scans.
23.48 % 28.36 % 22.85 % 0.06 s GPU @ 3.5 Ghz (C/C++)
16 BirdNet
This method makes use of Velodyne laser scans.
12.43 % 18.35 % 11.88 % 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.
17 MonoPSR 11.01 % 13.43 % 9.93 % 0.2 s GPU @ 3.5 Ghz (Python)
18 TopNet-HighRes
This method makes use of Velodyne laser scans.
5.98 % 4.48 % 6.18 % 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.
19 DT3D 1.20 % 1.76 % 1.26 % 0,21s GPU @ 2.5 Ghz (Python)
20 OFT-Net 0.43 % 0.43 % 0.43 % 0.5 s 8 cores @ 2.5 Ghz (Python + C/C++)
21 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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