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.

翻译：LiDAR分割对自动驾驶系统至关重要。近年来基于范围视图的方法在实时处理方面展现出前景，但这些方法不可避免地存在上下文信息受损的问题，且严重依赖后处理技术进行预测优化。本文提出了一种简洁而高效的FRNet，通过将范围图像像素与对应截锥体LiDAR点进行关联来恢复上下文信息。首先，采用截锥体特征编码器模块提取截锥体区域内的逐点特征，该模块能保持场景一致性，对点级预测至关重要；其次，引入截锥体-点融合模块层次化地更新逐点特征，使每个点能通过截锥体特征获取更多周围信息；最后，通过头融合模块融合不同层级的特征进行最终语义预测。在四种主流LiDAR分割基准数据集及多种任务场景下的广泛实验证明了本方法的优越性。FRNet在保持高效性的同时实现了具有竞争力的性能。代码已开源。