In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

Abstract 3D object Detection with LiDAR-camera encounters overfitting in algorithm
development derived from violating some fundamental rules. We refer to the data annotation …

Multi-modal 3D scene understanding has gained considerable attention due to its wide
applications in many areas, such as autonomous driving and human-computer interaction …

Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D
space in autonomous driving scenarios. However objects in the BEV representation typically …

This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable
for various vision tasks only with a vanilla ViT. Motivated by the universality of the Multi-layer …

Sparse 3D detectors have received significant attention since the query-based paradigm
embraces low latency without explicit dense BEV feature construction. However, these …

Pre-training is crucial in 3D-related fields such as autonomous driving where point cloud
annotation is costly and challenging. Many recent studies on point cloud pre-training …

Combining complementary sensor modalities is crucial to providing robust perception for
safety-critical robotics applications such as autonomous driving (AD). Recent state-of-the-art …

Multimodal sensor fusion is an essential capability for autonomous robots, enabling object
detection and decision-making in the presence of failing or uncertain inputs. While recent …

Masked Autoencoders (MAE) play a pivotal role in learning potent representations,
delivering outstanding results across various 3D perception tasks essential for autonomous …