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 …

Compared with previous two-stream trackers, the recent one-stream tracking pipeline, which
allows earlier interaction between the template and search region, has achieved a …

As the perception range of LiDAR increases, LiDAR-based 3D object detection becomes a
dominant task in the long-range perception task of autonomous driving. The mainstream 3D …

Abstract 3D object detection received increasing attention in autonomous driving recently.
Objects in 3D scenes are distributed with diverse orientations. Ordinary detectors do not …

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 …

Advanced 3D object detection methods usually rely on large-scale, elaborately labeled
datasets to achieve good performance. However, labeling the bounding boxes for the 3D …

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 …

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

Current 3D object detection models follow a single dataset-specific training and testing
paradigm, which often faces a serious detection accuracy drop when they are directly …

Human-centric scene understanding is significant for real-world applications, but it is
extremely challenging due to the existence of diverse human poses and actions, complex …