33 citations · 33 across the 5 of their papers we have counts for
5 papers
Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Data
Mohammadmehdi Ataei, Farzaneh Askari, Kamal Rahimi Malekshan +1
Computer-Aided Design (CAD) models are defined by their construction history: a parametric recipe that encodes design intent. However, existing large-scale 3D datasets predominantl…
Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodings
Aditya Sanghi, Aliasghar Khani, Pradyumna Reddy +5
Large-scale 3D generative models require substantial computational resources yet often fall short in capturing fine details and complex geometries at high resolutions. We attribute…
Make-A-Shape: a Ten-Million-scale 3D Shape Model
Ka-Hei Hui, Aditya Sanghi, Arianna Rampini +4
Significant progress has been made in training large generative models for natural language and images. Yet, the advancement of 3D generative models is hindered by their substantia…
Generalizable Pose Estimation Using Implicit Scene Representations
Vaibhav Saxena, Kamal Rahimi Malekshan, Linh Tran +1
6-DoF pose estimation is an essential component of robotic manipulation pipelines. However, it usually suffers from a lack of generalization to new instances and object types. Most…
Reconstructing editable prismatic CAD from rounded voxel models
Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman +3
Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications. In this work, we introduce a novel neural net…