FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation

1Nanjing University of Aeronautics and Astronautics 2National University of Singapore 3Nanjing University of Posts and Telecommunications
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FRNet achieves competitive performance with current arts while maintaining satisfactory efficiency for real-time processing.


LiDAR segmentation has become a crucial component in advanced autonomous driving systems. Recent range-view LiDAR segmentation approaches show promise for real-time processing. However, they inevitably suffer from corrupted contextual information and rely heavily on post-processing techniques for prediction refinement. In this work, we propose FRNet, a simple yet powerful method aimed at restoring the contextual information of range image pixels using 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, enabling 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 conducted on four popular LiDAR segmentation benchmarks under various task setups demonstrate the superiority of FRNet. Notably, FRNet achieves 73.3% and 82.5% mIoU scores on the testing sets of SemanticKITTI and nuScenes. While achieving competitive performance, FRNet operates 5 times faster than state-of-the-art approaches. Such high efficiency opens up new possibilities for more scalable LiDAR segmentation. The code has been made publicly available at


framework image

FRNet comprises three main components: 1) Frustum Feature Encoder is used to embed per-point features within the frustum region. 2) Frustum-Point Fusion Module updates per-point features hierarchically at each stage of the 2D backbone. 3) Fusion Head fuses different levels of features to predict final results.

More results

We show more qualitative results among state-of-the-art LiDAR segmentation methods.


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          title = {FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation},
          author = {Xu, Xiang and Kong, Lingdong and Shuai, Hui and Liu, Qingshan},
          journal = {arXiv preprint arXiv:2312.04484},
          year = {2023}