Pdf Augmenting Implicit Neural Shape Representations With Explicit

Geometric Modeling Explicit Implicit Representations Pdf In this paper we advocate deformation aware regularization for implicit neural representations, aiming at producing plausible deformations as latent code changes. View a pdf of the paper titled augmenting implicit neural shape representations with explicit deformation fields, by matan atzmon and 3 other authors.

Pdf On Quantizing Implicit Neural Representations In this paper we advocate deformation aware regularization for implicit neural representations, aiming at producing plausible deformations as latent code changes. This paper introduces a framework for augmenting implicit neural shape representations with explicit deformation fields to improve shape generalization and plausible deformations. In this paper we advocate deformation aware regularization for implicit neural representations, aiming at producing plausible deformations as latent code changes. I am a research scientist at nvidia, part of the nvidia spatial intelligence lab. prior to that, i completed my ph.d. studies at the department of computer science and applied mathematics at the weizmann institute of science under the supervision of prof. yaron lipman.

Implicit Neural Representations In this paper we advocate deformation aware regularization for implicit neural representations, aiming at producing plausible deformations as latent code changes. I am a research scientist at nvidia, part of the nvidia spatial intelligence lab. prior to that, i completed my ph.d. studies at the department of computer science and applied mathematics at the weizmann institute of science under the supervision of prof. yaron lipman. Augmenting implicit neural shape representations with explicit deformation fields. Augmenting implicit neural shape representations with explicit deformation fields: paper and code. implicit neural representation is a recent approach to learn shape collections as zero level sets of neural networks, where each shape is represented by a latent code. Our main contribution is a new shape space regularization framework that encourages implicit neural shape representations to generate plausible interpolations of the training shapes. In this work, we propose a novel 3d representation method, neural vector fields (nvf), which leverages the explicit learning process of direct manipulation on meshes and the implicit representation of udfs to enjoy the ad vantages of both approaches.