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 …

Single frame data contains finite information which limits the performance of the existing
vision-based multi-camera 3D object detection paradigms. For fundamentally pushing the …

3D object detection from multiple image views is a fundamental and challenging task for
visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object …

Object detection through either RGB images or the LiDAR point clouds has been extensively
explored in autonomous driving. However, it remains challenging to make these two data …

Abstract 3D perception based on the representations learned from multi-camera bird's-eye-
view (BEV) is trending as cameras are cost-effective for mass production in autonomous …

Detecting objects from LiDAR point clouds is of tremendous significance in autonomous
driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved …

It is counter-intuitive that multi-modality methods based on point cloud and images perform
only marginally better or sometimes worse than approaches that solely use point cloud. This …

In this paper, we propose a long-sequence modeling framework, named StreamPETR, for
multi-view 3D object detection. Built upon the sparse query design in the PETR series, we …

Abstract We present NeRF-Det, a novel method for indoor 3D detection with posed RGB
images as input. Unlike existing indoor 3D detection methods that struggle to model scene …

By identifying four important components of existing LiDAR-camera 3D object detection
methods (LiDAR and camera candidates, transformation, and fusion outputs), we observe …