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.

翻译：自动驾驶数据集中点云的语义分割需要能够高效处理大量点的技术。稀疏3D卷积已成为构建此类深度神经网络的事实标准工具：它们利用点云稀疏性降低内存和计算负载，并成为当下最优方法的核心。本文提出一种无需稀疏卷积即可达到当前最优方法水平的替代方案。我们实际证明，通过依赖理论上不适于大规模高性能3D感知的工具，也能实现同等性能水平。具体而言，我们提出新型3D骨干网络WaffleIron，其几乎完全由MLP和稠密2D卷积构成，并阐述如何通过训练使其在SemanticKITTI和nuScenes数据集上达到高性能。我们坚信，在稀疏3D卷积难以部署的框架和硬件环境中，WaffleIron是传统采用稀疏3D卷积骨干网络的有力替代方案。