Nowadays, robots are dominating the manufacturing, entertainment, and healthcare
industries. Robot vision aims to equip robots with the capabilities to discover information …

This paper presents ShapeLLM, the first 3D Multimodal Large Language Model (LLM)
designed for embodied interaction, exploring a universal 3D object understanding with 3D …

We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-
grained, instance-level, and hierarchical 3D part information. Our dataset consists of …

The ability to understand the ways to interact with objects from visual cues, aka visual
affordance, is essential to vision-guided robotic research. This involves categorizing …

This report surveys advances in deep learning‐based modelling techniques that address
four different 3D indoor scene analysis tasks, as well as synthesis of 3D indoor scenes. We …

A critical aspect of human visual perception is the ability to parse visual scenes into
individual objects and further into object parts, forming part-whole hierarchies. Such …

For the task of mobility analysis of 3D shapes, we propose joint analysis for simultaneous
motion part segmentation and motion attribute estimation, taking a single 3D model as input …

Data-driven methods serve an increasingly important role in discovering geometric,
structural, and semantic relationships between shapes. In contrast to traditional approaches …

Rapidly developing technologies are realizing a 3D telepresence, in which geographically
separated users can interact with each other through their virtual avatars. In this article, we …

We introduce a framework for action-driven evolution of 3D indoor scenes, where the goal is
to simulate how scenes are altered by human actions, and specifically, by object placements …