Shape-From-Template in Flatland

2015-06-01 · CVPR 2015 6 · Mathias Gallardo, Daniel Pizarro, Adrien Bartoli, Toby Collins

Shape-from-Template (SfT) is the problem of inferring the shape of a deformable object as observed in an image using a shape template. We call 2DSfT the 'usual' instance of SfT where the shape is a surface embedded in 3D and the image a 2D projection. We introduce 1DSfT, a novel instance of SfT where the shape is a curve embedded in 2D and the image a 1D projection. We focus on isometric deformations, for which 2DSfT is a well-posed problem, and admits an analytical local solution which may be used to initialize nonconvex refinement. Through a complete theoretical study of 1DSfT with perspective projection, we show that it is related to 2DSfT, but may have very different properties: (i) 1DSfT cannot be exactly solved locally and (ii) 1DSfT cannot be solved uniquely, as it has a discrete amount of at least two solutions. We then propose two convex initialization algorithms, a local analytical one based on infinitesimal planarity and a global one based on inextensibility. We show how nonconvex refinement can be implemented where, contrarily to current 2DSfT methods, one may enforce isometry exactly using a novel angle-based parameterization. Finally, our method is tested with simulated and real data.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

2018-09-03 · Hugo Caselles-Dupré, Louis Annabi, Oksana Hagen, Michael Garcia-Ortiz 외

Flatland is a simple, lightweight environment for fast prototyping and testing of reinforcement learning agents. It is of lower complexity compared to similar 3D platforms (e.g. DeepMind Lab or VizDoom), but emulates phy…

Lifelong learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Flatland-RL : Multi-Agent Reinforcement Learning on Trains

2020-12-10 · Sharada Mohanty, Erik Nygren, Florian Laurent, Manuel Schneider 외

Efficient automated scheduling of trains remains a major challenge for modern railway systems. The underlying vehicle rescheduling problem (VRSP) has been a major focus of Operations Research (OR) since decades. Traditio…

Imitation Learning Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning+2

3DoF Localization from a Single Image and an Object Map: the Flatlandia Problem and Dataset

2023-04-13 · Matteo Toso, Matteo Taiana, Stuart James, Alessio Del Bue

Efficient visual localization is crucial to many applications, such as large-scale deployment of autonomous agents and augmented reality. Traditional visual localization, while achieving remarkable accuracy, relies on ex…

Privacy Preserving Visual Localization

FlatLands: Generative Floormap Completion From a Single Egocentric View

2026-03-16 · Subhransu S. Bhattacharjee, Dylan Campbell, Rahul Shome arxiv

A single egocentric image typically captures only a small portion of the floor, yet a complete metric traversability map of the surroundings would better serve applications such as indoor navigation. We introduce FlatLan…

FlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space

2026-08-21 · Jiahong Liu, Ram Samarth B B, Xinyu Fu, Menglin Yang 외 arxiv

Federated learning enables privacy-preserving collaborative training, but highly heterogeneous client data remain challenging, especially in graph federated learning where clients possess structurally diverse graphs. Exi…

Personalized Federated Learning Graph Learning