Abstract We introduce Equivariant Neural Field Expectation Maximization (EFEM), a simple,
effective, and robust geometric algorithm that can segment objects in 3D scenes without …

If a robot masters folding a kitchen towel, we would expect it to master folding a large beach
towel. However, existing policy learning methods that rely on data augmentation still don't …

Equivariance has gained strong interest as a desirable network property that inherently
ensures robust generalization. However, when dealing with complex systems such as …

Research into dynamic 3D scene understanding has primarily focused on short-term change
tracking from dense observations while little attention has been paid to long-term changes …

Representing multimodal behaviors is a critical challenge for pedestrian trajectory
prediction. Previous methods commonly represent this multimodality with multiple latent …

Accurate trajectory prediction is crucial for the safe and efficient operation of autonomous
vehicles. The growing popularity of deep learning has led to the development of numerous …

We introduce a framework for intrinsic latent diffusion models operating directly on the
surfaces of 3D shapes with the goal of synthesizing high-quality textures. Our approach is …

Invariance against rotations of 3D objects is an important property in analyzing 3D point set
data. Conventional 3D point set DNNs having rotation invariance typically obtain accurate …

Deep neural networks (DNNs) in 3D scenes show a strong capability of extracting high-level
semantic features and significantly promote research in the 3D field. 3D shapes and scenes …

Equivariant networks have recently made significant strides in computer vision tasks related
to robotic grasping, molecule generation, and 6D pose tracking. In this paper, we explore 3D …