Search Results for author: Sven Dickinson

Found 14 papers, 2 papers with code

Representing 3D Shapes with Probabilistic Directed Distance Fields

no code implementations CVPR 2022 Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson

On the other hand, implicit representations (occupancy, distance, or radiance fields) preserve greater fidelity, but suffer from complex or inefficient rendering processes, limiting scalability.

3D Reconstruction

Disentangling Geometric Deformation Spaces in Generative Latent Shape Models

no code implementations27 Feb 2021 Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson, Allan Jepson

In this work, we improve on a prior generative model of geometric disentanglement for 3D shapes, wherein the space of object geometry is factorized into rigid orientation, non-rigid pose, and intrinsic shape.

Disentanglement Pose Transfer +1

Appearance Shock Grammar for Fast Medial Axis Extraction from Real Images

no code implementations CVPR 2020 Charles-Olivier Dufresne Camaro, Morteza Rezanejad, Stavros Tsogkas, Kaleem Siddiqi, Sven Dickinson

We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image;iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes.

Geometric Disentanglement for Generative Latent Shape Models

no code implementations ICCV 2019 Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, Sven Dickinson

Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics.

3D Object Retrieval 3D Shape Generation +4

DeepFlux for Skeletons in the Wild

2 code implementations CVPR 2019 Yukang Wang, Yongchao Xu, Stavros Tsogkas, Xiang Bai, Sven Dickinson, Kaleem Siddiqi

In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms.

Edge Detection Object +3

AMAT: Medial Axis Transform for Natural Images

1 code implementation ICCV 2017 Stavros Tsogkas, Sven Dickinson

We introduce Appearance-MAT (AMAT), a generalization of the medial axis transform for natural images, that is framed as a weighted geometric set cover problem.

Clustering

Learning to Combine Mid-Level Cues for Object Proposal Generation

no code implementations ICCV 2015 Tom Lee, Sanja Fidler, Sven Dickinson

In this paper, we introduce Parametric Min-Loss (PML), a novel structured learning framework for parametric energy functions.

Object Proposal Generation Object Recognition

A Framework for Symmetric Part Detection in Cluttered Scenes

no code implementations5 Feb 2015 Tom Lee, Sanja Fidler, Alex Levinshtein, Cristian Sminchisescu, Sven Dickinson

The role of symmetry in computer vision has waxed and waned in importance during the evolution of the field from its earliest days.

3D Object Detection and Viewpoint Estimation with a Deformable 3D Cuboid Model

no code implementations NeurIPS 2012 Sanja Fidler, Sven Dickinson, Raquel Urtasun

We demonstrate the effectiveness of our approach in indoor and outdoor scenarios, and show that our approach outperforms the state-of-the-art in both 2D[Felz09] and 3D object detection[Hedau12].

3D Object Detection Object +2

Detecting Reduplication in Videos of American Sign Language

no code implementations LREC 2012 Zoya Gavrilov, Stan Sclaroff, Carol Neidle, Sven Dickinson

A framework is proposed for the detection of reduplication in digital videos of American Sign Language (ASL).

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