In this paper, we address the problem of detecting 3D objects from multi-view images.
Current query-based methods rely on global 3D position embeddings (PE) to learn the …

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …

In this work, we propose PolarBEV for vision-based uneven BEV representation learning. To
adapt to the foreshortening effect of camera imaging, we rasterize the BEV space both …

Abstract 3D point clouds play a pivotal role in outdoor scene perception, especially in the
context of autonomous driving. Recent advancements in 3D LiDAR segmentation often …

3D LiDAR point cloud data is crucial for scene perception in computer vision, robotics, and
autonomous driving. Geometric and semantic scene understanding, involving 3D point …

In fields such as autonomous driving and 3D object reconstruction, complete 3D point cloud
data is crucial. Existing methods often directly reconstruct complete point clouds from partial …