Siddhartha Chaudhuri

research

∙ 03/03/2023

Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly

Representing a 3D shape with a set of primitives can aid perception of s...

0 Xianghao Xu, et al. ∙

research

∙ 07/24/2022

PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation

This paper introduces a data-driven shape completion approach that focus...

0 Bo Sun, et al. ∙

research

∙ 05/05/2022

Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes

This paper introduces a framework designed to accurately predict piecewi...

0 Noam Aigerman, et al. ∙

research

∙ 12/01/2021

The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts

We present the Shape Part Slot Machine, a new method for assembling nove...

0 Kai Wang, et al. ∙

research

∙ 11/23/2021

ReGroup: Recursive Neural Networks for Hierarchical Grouping of Vector Graphic Primitives

Selection functionality is as fundamental to vector graphics as it is fo...

0 Sumit Chaturvedi, et al. ∙

research

∙ 11/12/2021

Temporally-Consistent Surface Reconstruction using Metrically-Consistent Atlases

We propose a method for unsupervised reconstruction of a temporally-cons...

9 Jan Bednařík, et al. ∙

research

∙ 10/11/2021

BuildingNet: Learning to Label 3D Buildings

We introduce BuildingNet: (a) a large-scale dataset of 3D building model...

13 Pratheba Selvaraju, et al. ∙

research

∙ 08/06/2021

GLASS: Geometric Latent Augmentation for Shape Spaces

We investigate the problem of training generative models on a very spars...

13 Sanjeev Muralikrishnan, et al. ∙

research

∙ 04/14/2021

Temporally-Coherent Surface Reconstruction via Metric-Consistent Atlases

We propose a method for the unsupervised reconstruction of a temporally-...

0 Jan Bednařík, et al. ∙

research

∙ 02/15/2021

A Unified Batch Selection Policy for Active Metric Learning

Active metric learning is the problem of incrementally selecting high-ut...

6 Priyadarshini Kumari, et al. ∙

research

∙ 01/19/2021

Joint Learning of 3D Shape Retrieval and Deformation

We propose a novel technique for producing high-quality 3D models that m...

2 Mikaela Angelina Uy, et al. ∙

research

∙ 05/20/2020

Batch Decorrelation for Active Metric Learning

We present an active learning strategy for training parametric models of...

15 Priyadarshini K, et al. ∙

research

∙ 05/04/2020

Neural Subdivision

This paper introduces Neural Subdivision, a novel framework for data-dri...

0 Hsueh-Ti Derek Liu, et al. ∙

research

∙ 03/26/2020

ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

We propose a novel, end-to-end trainable, deep network called ParSeNet t...

0 Gopal Sharma, et al. ∙

research

∙ 03/19/2020

Affinity Graph Supervision for Visual Recognition

Affinity graphs are widely used in deep architectures, including graph c...

0 Chu Wang, et al. ∙

research

∙ 12/13/2019

Neural Cages for Detail-Preserving 3D Deformations

We propose a novel learnable representation for detail-preserving shape ...

36 Wang Yifan, et al. ∙

research

∙ 05/27/2019

FAN: Focused Attention Networks

Attention networks show promise for both vision and language tasks, by e...

0 Chu Wang, et al. ∙

research

∙ 05/08/2019

PerceptNet: Learning Perceptual Similarity of Haptic Textures in Presence of Unorderable Triplets

In order to design haptic icons or build a haptic vocabulary, we require...

0 Priyadarshini K, et al. ∙

research

∙ 03/27/2019

BAE-NET: Branched Autoencoder for Shape Co-Segmentation

We treat shape co-segmentation as a representation learning problem and ...

10 Zhiqin Chen, et al. ∙

research

∙ 03/25/2019

CoSegNet: Deep Co-Segmentation of 3D Shapes with Group Consistency Loss

We introduce CoSegNet, a deep neural network architecture for co-segment...

28 Chenyang Zhu, et al. ∙

research

∙ 10/20/2018

Learning Material-Aware Local Descriptors for 3D Shapes

Material understanding is critical for design, geometric modeling, and a...

4 Hubert Lin, et al. ∙

research

∙ 09/14/2018

SCORES: Shape Composition with Recursive Substructure Priors

We introduce SCORES, a recursive neural network for shape composition. O...

4 Chenyang Zhu, et al. ∙

research

∙ 07/24/2018

GRAINS: Generative Recursive Autoencoders for INdoor Scenes

We present a generative neural network which enables us to generate plau...

0 Manyi Li, et al. ∙

research

∙ 04/28/2018

Generalizing Across Domains via Cross-Gradient Training

We present CROSSGRAD, a method to use multi-domain training data to lear...

0 Shiv Shankar, et al. ∙

research

∙ 12/09/2017

A Deep Recurrent Framework for Cleaning Motion Capture Data

We present a deep, bidirectional, recurrent framework for cleaning noisy...

0 Utkarsh Mall, et al. ∙

research

∙ 08/22/2017

Tags2Parts: Discovering Semantic Regions from Shape Tags

We propose a novel method for discovering shape regions that strongly co...

0 Sanjeev Muralikrishnan, et al. ∙

research

∙ 08/06/2017

ComplementMe: Weakly-Supervised Component Suggestions for 3D Modeling

Assembly-based tools provide a powerful modeling paradigm for non-expert...

0 Minhyuk Sung, et al. ∙

research

∙ 06/14/2017

Learning Local Shape Descriptors from Part Correspondences With Multi-view Convolutional Networks

We present a new local descriptor for 3D shapes, directly applicable to ...

0 Haibin Huang, et al. ∙

research

∙ 05/05/2017

GRASS: Generative Recursive Autoencoders for Shape Structures

We introduce a novel neural network architecture for encoding and synthe...

0 Jun Li, et al. ∙

research

∙ 12/08/2016

3D Shape Segmentation with Projective Convolutional Networks

This paper introduces a deep architecture for segmenting 3D objects into...

0 Evangelos Kalogerakis, et al. ∙

Siddhartha Chaudhuri

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