Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending
and drawing extensive attention both from industry and academia. Conventional …

We present cross-view transformers, an efficient attention-based model for map-view
semantic segmentation from multiple cameras. Our architecture implicitly learns a map** …

Monocular 3D object detection is an important task for autonomous driving considering its
advantage of low cost. It is much more challenging than conventional 2D cases due to its …

Recent progress in 3D object detection from single images leverages monocular depth
estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors …

Monocular 3D object detection is a key problem for autonomous vehicles, as it provides a
solution with simple configuration compared to typical multi-sensor systems. The main …

Abstract 6D pose estimation from a single RGB image is a fundamental task in computer
vision. The current top-performing deep learning-based methods rely on an indirect strategy …

Recent advances in 3D semantic segmentation with deep neural networks have shown
remarkable success, with rapid performance increase on available datasets. However …

Abstract 3D object detection is receiving increasing attention from both industry and
academia thanks to its wide applications in various fields. In this paper, we propose Point …

The precise localization of 3D objects from a single image without depth information is a
highly challenging problem. Most existing methods adopt the same approach for all objects …