Add to ORKGRepresenting 3D surfaces as level sets of continuous functions over R3 is the common denominator of neural implicit representations, which recently enabled remarkable progress in geometric deep learning and computer vision tasks. In order to represent 3D motion within this framework, it is often assumed (either explicitly or implicitly) that the transformations which a surface may undergo are homeomorphic: this is not necessarily true, for instance, in the case of fluid dynamics. In order to represent more general classes of deformations, we propose to apply this theoretical framework as regularizers for the optimization of simple 4D implicit functions (such as signed distance fields). We show that our representation is capable of capturing...
We present new insights and a novel paradigm (StEik) for learning implicit neural representations (I...
We present a novel 3D mapping method leveraging the recent progress in neural implicit representatio...
Recent advances in learning 3D shapes using neural implicit functions have achieved impressive resul...
Representing 3D surfaces as level sets of continuous functions over R3 is the common denominator of ...
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view re...
Submitted to T-PAMIIn deformable registration, the geometric framework - large deformation diffeomor...
Recent neural networks based surface reconstruction can be roughly divided into two categories, one ...
Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces...
Implicit shape representations, such as Level Sets, provide a very elegant formulation for performin...
Neural implicit functions have recently shown promising results on surface reconstructions from mult...
Neural implicit fields have recently emerged as a useful representation for 3D shapes. These fields ...
Neural 3D implicit representations learn priors that are useful for diverse applications, such as si...
In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensi...
The distance function induced by a surface in R^n is known to carry a great deal of topological info...
We introduce a novel approach for rendering static and dynamic 3D neural signed distance functions (...
We present new insights and a novel paradigm (StEik) for learning implicit neural representations (I...
We present a novel 3D mapping method leveraging the recent progress in neural implicit representatio...
Recent advances in learning 3D shapes using neural implicit functions have achieved impressive resul...
Representing 3D surfaces as level sets of continuous functions over R3 is the common denominator of ...
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view re...
Submitted to T-PAMIIn deformable registration, the geometric framework - large deformation diffeomor...
Recent neural networks based surface reconstruction can be roughly divided into two categories, one ...
Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces...
Implicit shape representations, such as Level Sets, provide a very elegant formulation for performin...
Neural implicit functions have recently shown promising results on surface reconstructions from mult...
Neural implicit fields have recently emerged as a useful representation for 3D shapes. These fields ...
Neural 3D implicit representations learn priors that are useful for diverse applications, such as si...
In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensi...
The distance function induced by a surface in R^n is known to carry a great deal of topological info...
We introduce a novel approach for rendering static and dynamic 3D neural signed distance functions (...
We present new insights and a novel paradigm (StEik) for learning implicit neural representations (I...
We present a novel 3D mapping method leveraging the recent progress in neural implicit representatio...
Recent advances in learning 3D shapes using neural implicit functions have achieved impressive resul...