Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Three-dimensional point cloud registration is an important field in computer vision. Recently,
due to the increasingly complex scenes and incomplete observations, many partial-overlap …

Three-dimensional point cloud classification is fundamental but still challenging in 3-D
vision. Existing graph-based deep learning methods fail to learn both low-level extrinsic and …

Recent advancements in diffusion models have significantly enhanced the data synthesis
with 2D control. Yet, precise 3D control in street view generation, crucial for 3D perception …

As the noisy intermediate-scale quantum (NISQ) era has begun, a quantum neural network
(QNN) is definitely a promising solution to many problems that classical neural networks …

Self-supervised depth estimation draws a lot of attention recently as it can promote the 3D
sensing capa-bilities of self-driving vehicles. However, it intrinsically relies upon the …

Observing that the existing model compression approaches only focus on reducing the
redundancies in convolutional neural networks (CNNs) along one particular dimension (eg …

Recent advances in cross-modal 3D object detection rely heavily on anchor-based methods,
and however, intractable anchor parameter tuning and computationally expensive …

In stereo matching, various learning-based approaches have shown impressive
performance in solving traditional difficulties on multiple datasets. While most progress is …

In autonomous driving, LiDAR and radar are crucial for environmental perception. LiDAR
offers precise 3D spatial sensing information but struggles in adverse weather like fog …