251 citations · 315 across the 8 of their papers we have counts for
8 papers
IMR-LLM: Industrial Multi-Robot Task Planning and Program Generation using Large Language Models
Xiangyu Su, Juzhan Xu, Oliver van Kaick +2
In modern industrial production, multiple robots often collaborate to complete complex manufacturing tasks. Large language models (LLMs), with their strong reasoning capabilities,…
Diverse Part Synthesis for 3D Shape Creation
Yanran Guan, Oliver van Kaick
Methods that use neural networks for synthesizing 3D shapes in the form of a part-based representation have been introduced over the last few years. These methods represent shapes…
Active Self-Training for Weakly Supervised 3D Scene Semantic Segmentation
Gengxin Liu, Oliver van Kaick, Hui Huang +1
Since the preparation of labeled data for training semantic segmentation networks of point clouds is a time-consuming process, weakly supervised approaches have been introduced to…
Photo-to-Shape Material Transfer for Diverse Structures
Ruizhen Hu, Xiangyu Su, Xiangkai Chen +2
We introduce a method for assigning photorealistic relightable materials to 3D shapes in an automatic manner. Our method takes as input a photo exemplar of a real object and a 3D o…
Predictive and Generative Neural Networks for Object Functionality
Ruizhen Hu, Zihao Yan, Jingwen Zhang +4
Humans can predict the functionality of an object even without any surroundings, since their knowledge and experience would allow them to "hallucinate" the interaction or usage sce…
RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud
Zihao Yan, Ruizhen Hu, Xingguang Yan +4
We introduce RPM-Net, a deep learning-based approach which simultaneously infers movable parts and hallucinates their motions from a single, un-segmented, and possibly partial, 3D…
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Yanran Guan, Han Liu, Kun Liu +8
We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D…
Graph2Plan: Learning Floorplan Generation from Layout Graphs
Ruizhen Hu, Zeyu Huang, Yuhan Tang +3
We introduce a learning framework for automated floorplan generation which combines generative modeling using deep neural networks and user-in-the-loop designs to enable human user…