  3D object detection has been widely studied due to its potential
applicability to many promising areas such as robotics and augmented reality.
Yet, the sparse nature of the 3D data poses unique challenges to this task.
Most notably, the observable surface of the 3D point clouds is disjoint from
the center of the instance to ground the bounding box prediction on. To this
end, we propose Generative Sparse Detection Network (GSDN), a
fully-convolutional single-shot sparse detection network that efficiently
generates the support for object proposals. The key component of our model is a
generative sparse tensor decoder, which uses a series of transposed
convolutions and pruning layers to expand the support of sparse tensors while
discarding unlikely object centers to maintain minimal runtime and memory
footprint. GSDN can process unprecedentedly large-scale inputs with a single
fully-convolutional feed-forward pass, thus does not require the heuristic
post-processing stage that stitches results from sliding windows as other
previous methods have. We validate our approach on three 3D indoor datasets
including the large-scale 3D indoor reconstruction dataset where our method
outperforms the state-of-the-art methods by a relative improvement of 7.14%
while being 3.78 times faster than the best prior work.
