  This paper proposes a computationally efficient approach to detecting objects
natively in 3D point clouds using convolutional neural networks (CNNs). In
particular, this is achieved by leveraging a feature-centric voting scheme to
implement novel convolutional layers which explicitly exploit the sparsity
encountered in the input. To this end, we examine the trade-off between
accuracy and speed for different architectures and additionally propose to use
an L1 penalty on the filter activations to further encourage sparsity in the
intermediate representations. To the best of our knowledge, this is the first
work to propose sparse convolutional layers and L1 regularisation for efficient
large-scale processing of 3D data. We demonstrate the efficacy of our approach
on the KITTI object detection benchmark and show that Vote3Deep models with as
few as three layers outperform the previous state of the art in both laser and
laser-vision based approaches by margins of up to 40% while remaining highly
competitive in terms of processing time.
