Ground segmentation of outdoor LiDAR point clouds remains challenging due to its sparse and irregular nature. This paper presents SectorGSnet: a ground segmentation framework for outdoor LiDAR point clouds, aiming to accomplish this task efficiently and effectively. The framework consists of a sector encoder module and a segmentation module. The former module introduces a novel bird’s-eye-view (BEV) sector partition strategy that discretizes the point cloud into sectors of varying sizes to enhance the 2D representation ability of the point cloud. Then, the points within each sector are fed into a multimodal PointNet encoder to obtain the corresponding sector feature map. In the latter module, a lightweight segmentation network next learns binary classification from the sector feature map, and point labels are restored from the sector segmentation results. Our proposed framework is trained and evaluated on SemanticKITTI against state-of-the-art 2D projection-based approaches and achieves an excellent balance between performance and computational complexity. We conducted inference at 170.6 Hz on a desktop PC with a GTX2080Ti GPU, and also experimented on a resource-limited platform with only 10 watts of power.

Camouflaged object detection (COD) methods suffer severe degradation on small camouflaged objects and remain incapable of real-time inference on edge devices. We propose LeanCOD, a framework that pairs a strong foundation-model encoder with a lightweight decoder to enable high-resolution inference. A size-aware composite loss further strengthens supervision on small camouflaged objects. Our size-wise experiments reveal that the 0–1% extra-small-object regime is the major performance bottleneck for existing COD methods. LeanCOD achieves an $S_{\alpha }$ of 0.915 on COD10K at a $576\times 576$ resolution, outperforming competing methods at equal or lower resolutions. Deployed with TensorRT FP16 on an NVIDIA Jetson AGX Orin, LeanCOD runs at 31.6 FPS while maintaining an $S_{\alpha }$ of 0.908 at a $576\times 576$ resolution, exceeding the 30 FPS real-time threshold.