Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation

Understand

Semantic segmentation of point clouds in autonomous driving datasets requires techniques that can process large numbers of points efficiently.

• Sparse 3D convolutions have become the de-facto tools to construct deep neural networks for this task: they exploit point cloud sparsity to reduce the memory and computational loads and are at the core of today's best methods.
• In this paper, we propose an alternative method that reaches the level of state-of-the-art methods without requiring sparse convolutions.
• We actually show that such level of performance is achievable by relying on tools a priori unfit for large scale and high-performing 3D perception.