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..
https://arxiv.org/abs/2111.09621 SimpleTrack: Understanding and Rethinking 3D Multi-object Tracking 3D multi-object tracking (MOT) has witnessed numerous novel benchmarks and approaches in recent years, especially those under the "tracking-by-detection" paradigm. Despite their progress and usefulness, an in-depth analysis of their strengths and weaknesse arxiv.org Abstract 3D MOT methods는 4 part..
Paper : https://arxiv.org/abs/2011.07491 Github : https://github.com/lilygeorgescu/AED-SSMTL Anomaly Detection in Video via Self-Supervised and Multi-Task Learning Anomaly detection in video is a challenging computer vision problem. Due to the lack of anomalous events at training time, anomaly detection requires the design of learning methods without full supervision. In this paper, we approach ..
