  In this work, we propose a novel method termed \emph{Frustum ConvNet
(F-ConvNet)} for amodal 3D object detection from point clouds. Given 2D region
proposals in an RGB image, our method first generates a sequence of frustums
for each region proposal, and uses the obtained frustums to group local points.
F-ConvNet aggregates point-wise features as frustum-level feature vectors, and
arrays these feature vectors as a feature map for use of its subsequent
component of fully convolutional network (FCN), which spatially fuses
frustum-level features and supports an end-to-end and continuous estimation of
oriented boxes in the 3D space. We also propose component variants of
F-ConvNet, including an FCN variant that extracts multi-resolution frustum
features, and a refined use of F-ConvNet over a reduced 3D space. Careful
ablation studies verify the efficacy of these component variants. F-ConvNet
assumes no prior knowledge of the working 3D environment and is thus
dataset-agnostic. We present experiments on both the indoor SUN-RGBD and
outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD,
and at the time of submission it outperforms all published works on the KITTI
benchmark. Code has been made available at:
{\url{https://github.com/zhixinwang/frustum-convnet}.}
