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

LiDAR segmentation is crucial for autonomous driving perception. Recent trends favor point-
or voxel-based methods as they often yield better performance than the traditional range …

Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of
large-scale real-scanned 3D databases. To facilitate the development of 3D perception …

The robustness of 3D perception systems under natural corruptions from environments and
sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets …

Abstract 3D object detection is an important task in autonomous driving to perceive the
surroundings. Despite the excellent performance, the existing 3D detectors lack the …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

The computer vision, graphics, and machine learning research groups have given a
significant amount of focus to 3D object recognition (segmentation, detection, and …

In a point cloud sequence, 3D object tracking aims to predict the location and orientation of
an object in the current search point cloud given a template point cloud. Motivated by the …

Network binarization emerges as one of the most promising compression approaches
offering extraordinary computation and memory savings by minimizing the bit-width …

Accurate depth estimation under out-of-distribution (OoD) scenarios, such as adverse
weather conditions, sensor failure, and noise contamination, is desirable for safety-critical …