Scene understanding plays a crucial role in autonomous driving by utilizing sensory data for
contextual information extraction and decision making. Beyond modeling advances, the …

The perception of the surrounding environment is a key requirement for autonomous driving
systems, yet the computation of an accurate semantic representation of the scene starting …

As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …

Abstract 3D LiDAR (light detection and ranging) semantic segmentation is important in
scene understanding for many applications, such as auto-driving and robotics. For example …

LiDAR and camera are two modalities available for 3D semantic segmentation in
autonomous driving. The popular LiDAR-only methods severely suffer from inferior …

Recent works on 3D semantic segmentation propose to exploit the synergy between images
and point clouds by processing each modality with a dedicated network and projecting …

Multi-modal 3D object detection has been an active research topic in autonomous driving.
Nevertheless, it is non-trivial to explore the cross-modal feature fusion between sparse 3D …

Abstract Point-, voxel-, and range-views are three representative forms of point clouds. All of
them have accurate 3D measurements but lack color and texture information. RGB images …

Although recent point cloud analysis achieves impressive progress, the paradigm of
representation learning from single modality gradually meets its bottleneck. In this work, we …

Grounding 3D object affordance seeks to locate objects'" action possibilities" regions in the
3D space, which serves as a link between perception and operation for embodied agents …