Search Results for author: Daniele Panozzo

Found 17 papers, 12 papers with code

Evaluating Deep Clustering Algorithms on Non-Categorical 3D CAD Models

no code implementations29 Apr 2024 Siyuan Xiang, Chin Tseng, Congcong Wen, Deshana Desai, Yifeng Kou, Binil Starly, Daniele Panozzo, Chen Feng

We introduce the first work on benchmarking and evaluating deep clustering algorithms on large-scale non-categorical 3D CAD models.

Benchmarking Clustering +1

LookUp3D: Data-Driven 3D Scanning

no code implementations5 Apr 2024 Yurii Piadyk, Giancarlo Pereira, Claudio Silva, Daniele Panozzo

We introduce a novel calibration and reconstruction procedure for structured light scanning that foregoes explicit point triangulation in favor of a data-driven lookup procedure.

Image Sculpting: Precise Object Editing with 3D Geometry Control

no code implementations CVPR 2024 Jiraphon Yenphraphai, Xichen Pan, Sainan Liu, Daniele Panozzo, Saining Xie

We present Image Sculpting, a new framework for editing 2D images by incorporating tools from 3D geometry and graphics.

3D geometry Object

An Extensible Benchmark Suite for Learning to Simulate Physical Systems

1 code implementation9 Aug 2021 Karl Otness, Arvi Gjoka, Joan Bruna, Daniele Panozzo, Benjamin Peherstorfer, Teseo Schneider, Denis Zorin

Simulating physical systems is a core component of scientific computing, encompassing a wide range of physical domains and applications.

Computational Efficiency Diversity

Orienting Point Clouds with Dipole Propagation

1 code implementation4 May 2021 Gal Metzer, Rana Hanocka, Denis Zorin, Raja Giryes, Daniele Panozzo, Daniel Cohen-Or

In the global phase, we propagate the orientation across all coherent patches using a dipole propagation.

DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes

1 code implementation30 Nov 2020 Albert Matveev, Ruslan Rakhimov, Alexey Artemov, Gleb Bobrovskikh, Vage Egiazarian, Emil Bogomolov, Daniele Panozzo, Denis Zorin, Evgeny Burnaev

We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes.

Robust & Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision

1 code implementation19 Oct 2020 Ruiqi Ni, Teseo Schneider, Daniele Panozzo, Zherong Pan, Xifeng Gao

Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits.

Robotics

A Large Scale Benchmark and an Inclusion-Based Algorithm for Continuous Collision Detection

1 code implementation28 Sep 2020 Bolun Wang, Zachary Ferguson, Teseo Schneider, Xin Jiang, Marco Attene, Daniele Panozzo

We introduce a large scale benchmark for continuous collision detection (CCD) algorithms, composed of queries manually constructed to highlight challenging degenerate cases and automatically generated using existing simulators to cover common cases.

Graphics

ACORNS: An Easy-To-Use Code Generator for Gradients and Hessians

1 code implementation9 Jul 2020 Deshana Desai, Etai Shuchatowitz, Zhongshi Jiang, Teseo Schneider, Daniele Panozzo

We demonstrate that our algorithm enables automatic, reliable, and efficient differentiation of common algorithms used in physical simulation and geometry processing.

Mathematical Software Symbolic Computation

VoronoiNet: General Functional Approximators with Local Support

no code implementations8 Dec 2019 Francis Williams, Daniele Panozzo, Kwang Moo Yi, Andrea Tagliasacchi

Voronoi diagrams are highly compact representations that are used in various Graphics applications.

Fast Tetrahedral Meshing in the Wild

2 code implementations9 Aug 2019 Yixin Hu, Teseo Schneider, Bolun Wang, Denis Zorin, Daniele Panozzo

Our method builds on the TetWild algorithm, replacing the rational triangle insertion with a new incremental approach to construct and optimize the output mesh, interleaving triangle insertion and mesh optimization.

Graphics

Gradient Dynamics of Shallow Univariate ReLU Networks

no code implementations NeurIPS 2019 Francis Williams, Matthew Trager, Claudio Silva, Daniele Panozzo, Denis Zorin, Joan Bruna

We show that the gradient dynamics of such networks are determined by the gradient flow in a non-redundant parameterization of the network function.

A Large-Scale Comparison of Tetrahedral and Hexahedral Elements for Solving Elliptic PDEs with the Finite Element Method

1 code implementation22 Mar 2019 Teseo Schneider, Yixin Hu, Xifeng Gao, Jeremie Dumas, Denis Zorin, Daniele Panozzo

The Finite Element Method (FEM) is widely used to solve discrete Partial Differential Equations (PDEs) in engineering and graphics applications.

Numerical Analysis

ABC: A Big CAD Model Dataset For Geometric Deep Learning

3 code implementations CVPR 2019 Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, Daniele Panozzo

We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications.

Deep Learning

Poly-Spline Finite Element Method

1 code implementation9 Apr 2018 Teseo Schneider, Jeremie Dumas, Xifeng Gao, Mario Botsch, Daniele Panozzo, Denis Zorin

We introduce an integrated meshing and finite element method pipeline enabling black-box solution of partial differential equations in the volume enclosed by a boundary representation.

Numerical Analysis Graphics

Surface Networks

1 code implementation CVPR 2018 Ilya Kostrikov, Zhongshi Jiang, Daniele Panozzo, Denis Zorin, Joan Bruna

We study data-driven representations for three-dimensional triangle meshes, which are one of the prevalent objects used to represent 3D geometry.

3D geometry

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