  Recently, promising applications in robotics and augmented reality have
attracted considerable attention to 3D object detection from point clouds. In
this paper, we present FCAF3D - a first-in-class fully convolutional
anchor-free indoor 3D object detection method. It is a simple yet effective
method that uses a voxel representation of a point cloud and processes voxels
with sparse convolutions. FCAF3D can handle large-scale scenes with minimal
runtime through a single fully convolutional feed-forward pass. Existing 3D
object detection methods make prior assumptions on the geometry of objects, and
we argue that it limits their generalization ability. To get rid of any prior
assumptions, we propose a novel parametrization of oriented bounding boxes that
allows obtaining better results in a purely data-driven way. The proposed
method achieves state-of-the-art 3D object detection results in terms of
<EMAIL_ADDRESS>on ScanNet V2 (+4.5), SUN RGB-D (+3.5), and S3DIS (+20.5) datasets. The
code and models are available at https://github.com/samsunglabs/fcaf3d.
