Search Results for author: Paul Guerrero

Found 22 papers, 13 papers with code

Points2Surf Learning Implicit Surfaces from Point Clouds

no code implementations ECCV 2020 Philipp Erler, Paul Guerrero, Stefan Ohrhallinger, Niloy J. Mitra, Michael Wimmer

Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans.

Neural Convolutional Surfaces

no code implementations5 Apr 2022 Luca Morreale, Noam Aigerman, Paul Guerrero, Vladimir G. Kim, Niloy J. Mitra

Our pipeline and architecture are designed so that disentanglement of global geometry from local details is accomplished through optimization, in a completely unsupervised manner.

Disentanglement

ATEK: Augmenting Transformers with Expert Knowledge for Indoor Layout Synthesis

no code implementations1 Feb 2022 Kurt Leimer, Paul Guerrero, Tomer Weiss, Przemyslaw Musialski

In practice, desirable layout properties may not exist in a dataset, for instance, specific expert knowledge can be missing in the data.

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

no code implementations1 Dec 2021 Kai Wang, Paul Guerrero, Vladimir Kim, Siddhartha Chaudhuri, Minhyuk Sung, Daniel Ritchie

We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning.

Differentiable Surface Triangulation

1 code implementation22 Sep 2021 Marie-Julie Rakotosaona, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov, Paul Guerrero

Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation.

ShapeMOD: Macro Operation Discovery for 3D Shape Programs

1 code implementation13 Apr 2021 R. Kenny Jones, David Charatan, Paul Guerrero, Niloy J. Mitra, Daniel Ritchie

In this paper, we present ShapeMOD, an algorithm for automatically discovering macros that are useful across large datasets of 3D shape programs.

Learning Delaunay Surface Elements for Mesh Reconstruction

1 code implementation CVPR 2021 Marie-Julie Rakotosaona, Paul Guerrero, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov

We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.

Discovering Pattern Structure Using Differentiable Compositing

1 code implementation17 Oct 2020 Pradyumna Reddy, Paul Guerrero, Matt Fisher, Wilmot Li, Miloy J. Mitra

Patterns, which are collections of elements arranged in regular or near-regular arrangements, are an important graphic art form and widely used due to their elegant simplicity and aesthetic appeal.

Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images

1 code implementation ECCV 2020 Jiahui Lei, Srinath Sridhar, Paul Guerrero, Minhyuk Sung, Niloy Mitra, Leonidas J. Guibas

We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.

Points2Surf: Learning Implicit Surfaces from Point Cloud Patches

2 code implementations20 Jul 2020 Philipp Erler, Paul Guerrero, Stefan Ohrhallinger, Michael Wimmer, Niloy J. Mitra

Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans.

StructEdit: Learning Structural Shape Variations

1 code implementation CVPR 2020 Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas J. Guibas

Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and many other applications in 3D content creation.

StructureNet: Hierarchical Graph Networks for 3D Shape Generation

2 code implementations1 Aug 2019 Kaichun Mo, Paul Guerrero, Li Yi, Hao Su, Peter Wonka, Niloy Mitra, Leonidas J. Guibas

We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.

3D Shape Generation

PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

1 code implementation4 Jan 2019 Marie-Julie Rakotosaona, Vittorio La Barbera, Paul Guerrero, Niloy J. Mitra, Maks Ovsjanikov

Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers.

Denoising Surface Reconstruction

iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos

no code implementations20 Jun 2018 Aron Monszpart, Paul Guerrero, Duygu Ceylan, Ersin Yumer, Niloy J. Mitra

A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video.

Human-Object Interaction Detection

FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchonized GANs

1 code implementation19 Jun 2018 Tom Kelly, Paul Guerrero, Anthony Steed, Peter Wonka, Niloy J. Mitra

The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods.

Graphics

PCPNET: Learning Local Shape Properties from Raw Point Clouds

35 code implementations13 Oct 2017 Paul Guerrero, Yanir Kleiman, Maks Ovsjanikov, Niloy J. Mitra

In this paper, we propose PCPNet, a deep-learning based approach for estimating local 3D shape properties in point clouds.

Computational Geometry

DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels

no code implementations22 May 2017 Paul Guerrero, Holger Winnemöller, Wilmot Li, Niloy J. Mitra

In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals.

Scene Understanding

RAID: A Relation-Augmented Image Descriptor

no code implementations5 Oct 2015 Paul Guerrero, Niloy J. Mitra, Peter Wonka

As humans, we regularly interpret images based on the relations between image regions.

Cannot find the paper you are looking for? You can Submit a new open access paper.