no code implementations • 20 Jan 2024 • Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, Chi-Wing Fu
We then make the representation generatable by a diffusion model by devising the subband coefficients packing scheme to layout the representation in a low-resolution grid.
1 code implementation • 6 Sep 2023 • Aliasghar Khani, Saeid Asgari Taghanaki, Aditya Sanghi, Ali Mahdavi Amiri, Ghassan Hamarneh
Then, using the extracted attention maps, the text embeddings of Stable Diffusion are optimized such that, each of them, learn about a single segmented region from the training image.
no code implementations • 1 Sep 2023 • Saeid Asgari Taghanaki, Aliasghar Khani, Amir Khasahmadi, Aditya Sanghi, Karl D. D. Willis, Ali Mahdavi-Amiri
These sentences are then used to extract the most frequent words, providing a comprehensive understanding of the learned features and patterns within the classifier.
no code implementations • 8 Jul 2023 • Aditya Sanghi, Pradeep Kumar Jayaraman, Arianna Rampini, Joseph Lambourne, Hooman Shayani, Evan Atherton, Saeid Asgari Taghanaki
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.
no code implementations • CVPR 2023 • Aditya Sanghi, Rao Fu, Vivian Liu, Karl Willis, Hooman Shayani, Amir Hosein Khasahmadi, Srinath Sridhar, Daniel Ritchie
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes.
no code implementations • 2 Sep 2022 • Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman, Longfei Zhang, Aditya Sanghi, Kamal Rahimi Malekshan
Reverse Engineering a CAD shape from other representations is an important geometric processing step for many downstream applications.
no code implementations • 26 Mar 2022 • Pradeep Kumar Jayaraman, Joseph G. Lambourne, Nishkrit Desai, Karl D. D. Willis, Aditya Sanghi, Nigel J. W. Morris
Key to achieving this is our Indexed Boundary Representation that references B-rep vertices, edges and faces in a well-defined hierarchy to capture the geometric and topological relations suitable for use with machine learning.
no code implementations • CVPR 2022 • Qimin Chen, Johannes Merz, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains.
2 code implementations • CVPR 2022 • Karl D. D. Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G. Lambourne, Armando Solar-Lezama, Wojciech Matusik
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software.
no code implementations • 23 Oct 2021 • Linh Tran, Amir Hosein Khasahmadi, Aditya Sanghi, Saeid Asgari
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning.
1 code implementation • CVPR 2022 • Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, Kamal Rahimi Malekshan
Generating shapes using natural language can enable new ways of imagining and creating the things around us.
1 code implementation • ICCV 2021 • Peter Meltzer, Hooman Shayani, Amir Khasahmadi, Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph Lambourne
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 stylistic details.
no code implementations • CVPR 2022 • Fenggen Yu, Zhiqin Chen, Manyi Li, Aditya Sanghi, Hooman Shayani, Ali Mahdavi-Amiri, Hao Zhang
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 assemblies.
2 code implementations • CVPR 2021 • Joseph G. Lambourne, Karl D. D. Willis, Pradeep Kumar Jayaraman, Aditya Sanghi, Peter Meltzer, Hooman Shayani
Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications.
Ranked #1 on B-Rep face segmentation on Fusion 360 Gallery
1 code implementation • CVPR 2021 • Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph G. Lambourne, Karl D. D. Willis, Thomas Davies, Hooman Shayani, Nigel Morris
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.
no code implementations • ECCV 2020 • Aditya Sanghi
We show that we can maximize the mutual information between 3D objects and their "chunks" to improve the representations in aligned datasets.
no code implementations • 12 May 2020 • Saeid Asgari Taghanaki, Mohammad Havaei, Alex Lamb, Aditya Sanghi, Ara Danielyan, Tonya Custis
The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks.
no code implementations • 11 Mar 2020 • 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 such as classification, and are often linearly separable.