6 accepted papers
Point cloud completion seeks to recover geometrically consistent shapes from partial or sparse 3D observations. Although recent methods have achieved reasonable global shape reconstruction, they often rely on Euclidean proximity and overlook the intrinsic nonlinear geometric structure of point cloud
Trajectory prediction is critical for autonomous driving, enabling safe and efficient planning in dense, dynamic traffic. Most existing methods optimize prediction accuracy under fixed-length observations. However, real-world driving often yields variable-length, incomplete observations, posing a ch
With the growing interest in embodied and spatial intelligence, accurately predicting trajectories in 3D environments has become increasingly critical. However, no datasets have been explicitly designed to study 3D trajectory prediction. To this end, we contribute a 3D motion trajectory (3DMoTraj) d…
Path planning in real road traffic refers to the navigation of an autonomous vehicle through an obstacle-filled environment. It is crucial for the comfort, safety, and efficiency of the autonomous driving experience. It is advantageous to plan paths in the reference line-based Frenet frames rather t
Motion prediction of multiple agents in a dynamic scene is a crucial component in many real applications, including intelligent monitoring and autonomous driving. Due to the complex interactions among the agents and their interactions with the surrounding scene, accurate trajectory prediction is sti…
Accurate trajectory prediction plays a key role in robot navigation. It is beneficial for planning a collision-free and appropriate path for the autonomous robots, especially in crowded scenes. However, it is a particularly challenging task because there are complex and subtle interactions among ped