3 accepted papers
Vision-based Bird’s-Eye-View (BEV) 3D object detection has recently become popular in autonomous driving. However, objects with a high similarity to the background from a camera perspective cannot be detected well by existing methods. In this paper, we propose a BEV-based 3D Object Detection Network
Depth map enhancement using paired high-resolution RGB images offers a cost-effective solution for improving low-resolution depth data from lightweight ToF sensors. Nevertheless, naively adopting a depth estimation pipeline to fuse the two modalities requires groundtruth depth maps for supervision.
In this paper, we propose a cascading network for compressed sensing of images with progressive reconstruction. Specifically, we decompose the complex reconstruction mapping into the cascade of incremental detail reconstruction (IDR) modules and measurement residual updating (MRU) modules. The IDR m…