5 accepted papers
Embodied 3D object detection is a fundamental perception capability for embodied agents, where observations are partial, heavily occluded, and sequential, requiring modeling of temporal continuity. However, existing benchmarks and methods are primarily designed for fully reconstructed global scenes …
Simultaneous Localization and Mapping (SLAM) is increasingly expected to provide reusable spatial representations for downstream perception. However, existing approaches often struggle with scale-consistency and producing maps that lack the geometric fidelity required for reliable perception. We pro…
3D object detection in point clouds is critical in 3D computer vision, autonomous driving, and robotics. Existing point-based detectors, tailored to handle unstructured raw point clouds, often rely on simplistic sampling strategies to select a subset of points for local representation learning and d…
Online object detection and tracking are crucial for embodied intelligence systems, including autonomous vehicles and robotics. Traditional approaches employ a pipeline structure to perform detection and tracking separately, which can not fully leverage information from the detector. Moreover, most
Point clouds have shown significant potential in various domains, including Simultaneous Localization and Mapping (SLAM). However, existing approaches either rely on dense point clouds to achieve high localization accuracy or use generalized descriptors to reduce map size. Unfortunately, these two a…