We introduce a framework for multi-camera 3D object detection. In contrast to existing works,
which estimate 3D bounding boxes directly from monocular images or use depth prediction …

3D object detection from images, one of the fundamental and challenging problems in
autonomous driving, has received increasing attention from both industry and academia in …

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 …

While recent camera-only 3D detection methods leverage multiple timesteps, the limited
history they use significantly hampers the extent to which temporal fusion can improve object …

Monocular 3D object detection has received increasing attention due to the wide application
in autonomous driving. Existing works mainly focus on introducing geometry projection to …

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 …

In this paper we propose an approach for monocular 3D object detection from a single RGB
image, which leverages a novel disentangling transformation for 2D and 3D detection losses …

In the field of monocular 3D detection, it is common practice to utilize scene geometric clues
to enhance the detector's performance. However, many existing works adopt these clues …

Monocular 3D object detection is an essential component in autonomous driving while
challenging to solve, especially for those occluded samples which are only partially visible …

Visual perception plays an important role in autonomous driving. One of the primary tasks is
object detection and identification. Since the vision sensor is rich in color and texture …