1 research outputs found
Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints
We propose Shift R-CNN, a hybrid model for monocular 3D object detection,
which combines deep learning with the power of geometry. We adapt a Faster
R-CNN network for regressing initial 2D and 3D object properties and combine it
with a least squares solution for the inverse 2D to 3D geometric mapping
problem, using the camera projection matrix. The closed-form solution of the
mathematical system, along with the initial output of the adapted Faster R-CNN
are then passed through a final ShiftNet network that refines the result using
our newly proposed Volume Displacement Loss. Our novel, geometrically
constrained deep learning approach to monocular 3D object detection obtains top
results on KITTI 3D Object Detection Benchmark, being the best among all
monocular methods that do not use any pre-trained network for depth estimation.Comment: v1: Accepted to be published in 2019 IEEE International Conference on
Image Processing, Sep 22-25, 2019, Taipei. IEEE Copyright notice added. Minor
changes for camera-ready version. (updated May. 15, 2019