  In this paper, we propose SparseDet for end-to-end 3D object detection from
point cloud. Existing works on 3D object detection rely on dense object
candidates over all locations in a 3D or 2D grid following the mainstream
methods for object detection in 2D images. However, this dense paradigm
requires expertise in data to fulfill the gap between label and detection. As a
new detection paradigm, SparseDet maintains a fixed set of learnable proposals
to represent latent candidates and directly perform classification and
localization for 3D objects through stacked transformers. It demonstrates that
effective 3D object detection can be achieved with none of post-processing such
as redundant removal and non-maximum suppression. With a properly designed
network, SparseDet achieves highly competitive detection accuracy while running
with a more efficient speed of 34.5 FPS. We believe this end-to-end paradigm of
SparseDet will inspire new thinking on the sparsity of 3D object detection.
