  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. In particular, we propose a novel 3D
backbone, WaffleIron, made almost exclusively of MLPs and dense 2D convolutions
and present how to train it to reach high performance on SemanticKITTI and
nuScenes. We believe that WaffleIron is a compelling alternative to backbones
using sparse 3D convolutions, especially in frameworks and on hardware where
those convolutions are not readily available.
