3 accepted papers
In this work, we introduce an adaptive hierarchical framework for efficient 3D object detection from point cloud data, designed to dynamically balance computational efficiency and detection performance. Our approach employs a shared feature extractor and multiple detector backbones of varying widths…
We introduce RAVEN, a deep learning architecture for processing frequency-modulated continuous-wave (FMCW) radar data that is designed for high computational efficiency. RAVEN reduces computation by using a learnable antenna mixer module on independent receiver state space encoders (SSM) to compress
In this work, we introduce an adaptive hierarchical framework for efficient 3D object detection from point cloud data, designed to dynamically balance computational efficiency and detection performance. Our approach employs a shared feature extractor and multiple detector backbones of varying widths