FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation
Xiang Xu Lingdong Kong Hui Shuai Qingshan Liu
LiDAR segmentation is crucial for autonomous driving systems. The recent
range-view approaches are promising for real-time processing. However, they
suffer inevitably from corrupted contextual information and rely heavily on
post-processing techniques for prediction refinement. In this work, we propose
a simple yet powerful FRNet that restores the contextual information of the
range image pixels with corresponding frustum LiDAR points. Firstly, a frustum
feature encoder module is used to extract per-point features within the frustum
region, which preserves scene consistency and is crucial for point-level
predictions. Next, a frustum-point fusion module is introduced to update
per-point features hierarchically, which enables each point to extract more
surrounding information via the frustum features. Finally, a head fusion module
is used to fuse features at different levels for final semantic prediction.
Extensive experiments on four popular LiDAR segmentation benchmarks under
various task setups demonstrate our superiority. FRNet achieves competitive
performance while maintaining high efficiency. The code is publicly available.