33 citations · 74 across the 17 of their papers we have counts for
14 papers · 1 filter
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…
SLiMe: Segment Like Me
Aliasghar Khani, Saeid Asgari Taghanaki, Aditya Sanghi +2
Significant strides have been made using large vision-language models, like Stable Diffusion (SD), for a variety of downstream tasks, including image editing, image correspondence,…
TExplain: Explaining Learned Visual Features via Pre-trained (Frozen) Language Models
Saeid Asgari Taghanaki, Aliasghar Khani, Ali Saheb Pasand +4
Interpreting the learned features of vision models has posed a longstanding challenge in the field of machine learning. To address this issue, we propose a novel method that levera…
Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation
Aditya Sanghi, Pradeep Kumar Jayaraman, Arianna Rampini +4
Significant progress has recently been made in creative applications of large pre-trained models for downstream tasks in 3D vision, such as text-to-shape generation. This motivates…
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language
Aditya Sanghi, Rao Fu, Vivian Liu +5
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We intr…
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…
UNIST: Unpaired Neural Implicit Shape Translation Network
Qimin Chen, Johannes Merz, Aditya Sanghi +3
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is built on autoencoding imp…
CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation
Aditya Sanghi, Hang Chu, Joseph G. Lambourne +4
Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent progress has been made in text-to-image genera…
UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps
Peter Meltzer, Hooman Shayani, Amir Khasahmadi +3
Boundary Representations (B-Reps) are the industry standard in 3D Computer Aided Design/Manufacturing (CAD/CAM) and industrial design due to their fidelity in representing stylisti…
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
Fenggen Yu, Zhiqin Chen, Manyi Li +4
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive…
UV-Net: Learning from Boundary Representations
Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne +4
We introduce UV-Net, a novel neural network architecture and representation designed to operate directly on Boundary representation (B-rep) data from 3D CAD models. The B-rep forma…
Info3D: Representation Learning on 3D Objects using Mutual Information Maximization and Contrastive Learning
Aditya Sanghi
A major endeavor of computer vision is to represent, understand and extract structure from 3D data. Towards this goal, unsupervised learning is a powerful and necessary tool. Most…
How Powerful Are Randomly Initialized Pointcloud Set Functions?
Aditya Sanghi, Pradeep Kumar Jayaraman
We study random embeddings produced by untrained neural set functions, and show that they are powerful representations which well capture the input features for downstream tasks su…