Object localization plays a crucial role in computational perception, enabling applications ranging from surveillance to autonomous navigation. This can be leveraged by fusing data cameras and LiDARs (Light Detection Ranging). However, there are challenges employing current fusion methods edge devices, while keeping the process flexible modular. paper presents method for multiple object that fu...

Multi-modal 3D object detection has been an active research topic in autonomous driving. Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse points and dense 2D pixels. Recent approaches either fuse image features with point cloud that are projected onto plane or combine These often suffer from severe information loss, thus causing sub-optimal performance. T...

This paper presents the first physiologically motivated pulse coupled neural network (PCNN)-based image fusion network for object detection. Primate vision processing principles, such as expectation driven filtering, state dependent modulation, temporal synchronization, and multiple processing paths are applied to create a physiologically motivated image fusion network. PCNN's are used to fuse ...