  Object detectors based on sparse object proposals have recently been proven
to be successful in the 2D domain, which makes it possible to establish a fully
end-to-end detector without time-consuming post-processing. This development is
also attractive for 3D object detectors. However, considering the remarkably
larger search space in the 3D domain, whether it is feasible to adopt the
sparse method in the 3D object detection setting is still an open question. In
this paper, we propose SparsePoint, the first sparse method for 3D object
detection. Our SparsePoint adopts a number of learnable proposals to encode
most likely potential positions of 3D objects and a foreground embedding to
encode shared semantic features of all objects. Besides, with the attention
module to provide object-level interaction for redundant proposal removal and
Hungarian algorithm to supply one-one label assignment, our method can produce
sparse and accurate predictions. SparsePoint sets a new state-of-the-art on
four public datasets, including ScanNetV2, SUN RGB-D, S3DIS, and Matterport3D.
Our code will be publicly available soon.
