Liso: Lidar-only self-supervised 3d object detection
Abstract 3D object detection is one of the most important components in any Self-Driving
stack, but current state-of-the-art (SOTA) lidar object detectors require costly & slow manual …
stack, but current state-of-the-art (SOTA) lidar object detectors require costly & slow manual …
A survey of label-efficient deep learning for 3D point clouds
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 …
learning. However, collecting large-scale precisely-annotated point clouds is extremely …
Street Gaussians without 3D Object Tracker
Realistic scene reconstruction in driving scenarios poses significant challenges due to fast-
moving objects. Most existing methods rely on labor-intensive manual labeling of object …
moving objects. Most existing methods rely on labor-intensive manual labeling of object …
LiT: Unifying LiDAR" Languages" with LiDAR Translator
LiDAR data exhibits significant domain gaps due to variations in sensors, vehicles, and
driving environments, creating “language barriers” that limit the effective use of data across …
driving environments, creating “language barriers” that limit the effective use of data across …
Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions
M Zhang, S Abdulatif, B Loesch, M Altmann… - arXiv preprint arXiv …, 2024 - arxiv.org
The rapid evolution of deep learning and its integration with autonomous driving systems
have led to substantial advancements in 3D perception using multimodal sensors. Notably …
have led to substantial advancements in 3D perception using multimodal sensors. Notably …