https://proceedings.neurips.cc/paper_files/paper/2017/hash/d8bf84be3800d12f74d8b05e9b89836f-Abstract.html PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space Requests for name changes in the electronic proceedings will be accepted with no questions asked. However name changes may cause bibliographic tracking issues. Authors are asked to consider this carefully and disc..
https://arxiv.org/pdf/2310.17659v1.pdf 이번에 리뷰할 논문은 기존 RTNH의 성능을 높인 RTNH+이다. 4D Radar dataset인 K-Radar를 대상으로 3D object detection에 대한 모델이다. 배경 4D Radar는 3D object detection을 수행하는데 주변 날씨에 강건하다는 장점을 가진다. 하지만, Radar 측정값은 noise, interference, and clutter와 같은 요소들에 의해 corrupt되기 때문에 preprocessing 과정이 필요하다. 본 논문에서는 두 가지 알고리즘을 소개한다. Combined Constant false alarm rate (CFAR)-based Two-level Preprocessing (C..
https://arxiv.org/abs/1711.06396 VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality. To interface a highly sparse LiDAR point cloud with a region proposal network (RP arxiv.org 1. Point cloud의 '어떤 특성'때문에..
https://arxiv.org/abs/1612.00593 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In t arxiv.org Abstract point cloud는..
