  In this work we propose 3D-FFS, a novel approach to make sensor fusion based
3D object detection networks significantly faster using a class of
computationally inexpensive heuristics. Existing sensor fusion based networks
generate 3D region proposals by leveraging inferences from 2D object detectors.
However, as images have no depth information, these networks rely on extracting
semantic features of points from the entire scene to locate the object. By
leveraging aggregated intrinsic properties (e.g. point density) of the 3D point
cloud data, 3D-FFS can substantially constrain the 3D search space and thereby
significantly reduce training time, inference time and memory consumption
without sacrificing accuracy. To demonstrate the efficacy of 3D-FFS, we have
integrated it with Frustum ConvNet (F-ConvNet), a prominent sensor fusion based
3D object detection model. We assess the performance of 3D-FFS on the KITTI
dataset. Compared to F-ConvNet, we achieve improvements in training and
inference times by up to 62.84% and 56.46%, respectively, while reducing the
memory usage by up to 58.53%. Additionally, we achieve 0.59%, 2.03% and 3.34%
improvements in accuracy for the Car, Pedestrian and Cyclist classes,
respectively. 3D-FFS shows a lot of promise in domains with limited computing
power, such as autonomous vehicles, drones and robotics where LiDAR-Camera
based sensor fusion perception systems are widely used.
