LiDAR is a commonly used sensor for autonomous driving to make accurate, robust, and fast
decision-making when driving. The sensor is used in the perception system, especially …

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

Abstract 3D object detectors usually rely on hand-crafted proxies, eg, anchors or centers,
and translate well-studied 2D frameworks to 3D. Thus, sparse voxel features need to be …

Designing an efficient yet deployment-friendly 3D backbone to handle sparse point clouds is
a fundamental problem in 3D perception. Compared with the customized sparse …

False negatives (FN) in 3D object detection, eg, missing predictions of pedestrians, vehicles,
or other obstacles, can lead to potentially dangerous situations in autonomous driving. While …

Transformer, as an alternative to CNN, has been proven effective in many modalities (eg,
texts and images). For 3D point cloud transformers, existing efforts focus primarily on …

In the context of autonomous driving the significance of effective feature learning is widely
acknowledged. While conventional 3D self-supervised pre-training methods have shown …

Despite the tremendous progress of Masked Autoencoders (MAE) in developing vision tasks
such as image and video, exploring MAE in large-scale 3D point clouds remains …

A key challenge for LiDAR-based 3D object detection is to capture sufficient features from
large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts …

Recent Transformer-based 3D object detectors learn point cloud features either from point-
or voxel-based representations. However, the former requires time-consuming sampling …