In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

The large-scale deployment of autonomous vehicles is yet to come, and one of the major
remaining challenges lies in urban dense traffic scenarios. In such cases, it remains …

In this paper, we address the problem of detecting 3D objects from multi-view images.
Current query-based methods rely on global 3D position embeddings (PE) to learn the …

A major challenge in monocular 3D object detection is the limited diversity and quantity of
objects in real datasets. While augmenting real scenes with virtual objects holds promise to …

Semi-supervised learning (SSL) has promising potential for improving model performance
using both labelled and unlabelled data. Since recovering 3D information from 2D images is …

In this work, we build a modular-designed codebase, formulate strong training recipes,
design an error diagnosis toolbox, and discuss current methods for image-based 3D object …

This work explores the use of 3D generative models to synthesize training data for 3D vision
tasks. The key requirements of the generative models are that the generated data should be …

Monocular 3D object detection poses a significant challenge due to the lack of depth
information in RGB images. Many existing methods strive to enhance the object depth …

Abstract The CityGML Level of Detail 3 (LoD3), a widely adopted standard for three-
dimensional (3D) city modeling, has been accessible for an extended period. However, its …

Abstract 3D object detection and pose estimation from a single-view image is challenging
due to the high uncertainty caused by the absence of 3D perception. As a solution recent …