The increasing demand on higher accuracy and the rapid growth of 3D point cloud datasets
have led to significantly higher training costs for 3D point cloud models in terms of both …

Recent autonomous driving systems heavily rely on 3D point cloud data collected from
multiple sensors for environmental awareness and decision-making. However, it is …

In pose estimation for objects with rotational symmetry, ambiguous poses may arise, and the
symmetry axes of objects are crucial for eliminating such ambiguities. Currently, in pose …

Point cloud-based machine perception applications have achieved great success in various
scenarios. In this work, we focus on point cloud k-Nearest Neighbor (kNN) search, an …

Reconstruction of surfaces from point clouds is essential in numerous practical applications.
An approach in which neural fields are trained as surface representations from point clouds …

Point cloud-based 3-D perception is poised to become a key workload on various
applications. It always leverages a feature learning network (FLN) before backbones to …

Indoor scene modification has emerged as a prominent area within computer vision,
particularly for its applications in Augmented Reality (AR) and Virtual Reality (VR) …

In point cloud video recognition (PVR) tasks, deep neural networks (DNNs) have been
widely adopted to enhance accuracy. However, real-time processing is hindered due to the …

Given a collection of points in R^ 3, KD-Tree and R-Tree are well-known nearest neighbor
search (NNS) algorithms that rely on space partitioning and spatial indexing techniques …

Point cloud is increasingly used in a variety of applications. Farthest Point Sampling (FPS) is
typically employed for down-sampling to reduce the size of point cloud and enhance the …