Method Details

Details for method '3D-GCK'


Method overview

name 3D-GCK
challenge 3D vehicle detection
details 3D-GCK is based on the standard SSD 2D object detection framework and lifts the 2D detections to 3D space by predicting additional regression and classification parameters. Hence, the runtime is kept close to pure 2D object detection. The additional parameters are transformed to 3D bounding box keypoints within the network under geometric constraints. 3D-GCK features a full 3D description including all three angles of rotation without supervision by any labeled ground truth data for the object’s orientation, as it focuses on certain keypoints within the image plane.
publication Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometry Constrained Keypoints in Real-Time
Nils Gählert, Jun-Jun Wan, Nicolas Jourdan, Jan Finkbeiner, Uwe Franke, and Joachim Denzler
IV 2020
project page / code
used Cityscapes data 3D bounding boxes
used external data ImageNet
runtime 0.05 s
subsampling no
submission date October, 2020
previous submissions


Average results

Metric Value
Detection Score 30.4674
AP 34.2889
Center Distance 96.538
Size Similarity 70.9601
Orientation Similarity Yaw 82.2345
Orientation Similarity Pitch Roll 99.9749


Class results

Class Detection Score
car 66.3711
truck 21.1726
bus 25.1725
train 12.697
motorcycle 25.3173
bicycle 32.0742


Results json

This json can be visualized using the tool csPlot3dDetectionResults, which is part of the cityscapesScripts found on Github.



Download results as .csv file

Benchmark page