  3D object detection from raw and sparse point clouds has been far less
treated to date, compared with its 2D counterpart. In this paper, we propose a
novel framework called FVNet for 3D front-view proposal generation and object
detection from point clouds. It consists of two stages: generation of
front-view proposals and estimation of 3D bounding box parameters. Instead of
generating proposals from camera images or bird's-eye-view maps, we first
project point clouds onto a cylindrical surface to generate front-view feature
maps which retains rich information. We then introduce a proposal generation
network to predict 3D region proposals from the generated maps and further
extrude objects of interest from the whole point cloud. Finally, we present
another network to extract the point-wise features from the extruded object
points and regress the final 3D bounding box parameters in the canonical
coordinates. Our framework achieves real-time performance with 12ms per point
cloud sample. Extensive experiments on the 3D detection benchmark KITTI show
that the proposed architecture outperforms state-of-the-art techniques which
take either camera images or point clouds as input, in terms of accuracy and
inference time.
