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…
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 propose $\textit{EventFormer}$, a computationally efficient event-based representation learning framework for asynchronously processing event camera data. EventFormer treats sparse input events as a spatially unordered set and models their spatial interactions using self-attention mechanism. An a…