Progressive Encoding for Neural Optimization

• URL: http://arxiv.org/abs/2104.09125v1
• Date: Mon, 19 Apr 2021 08:22:55 GMT
• Title: Progressive Encoding for Neural Optimization
• Authors: Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung and Daniel Cohen-Or
• Abstract summary: We show the competence of the PPE layer for mesh transfer and its advantages compared to contemporary surface mapping techniques. Most importantly, our technique is a parameterization-free method, and thus applicable to a variety of target shape representations.
• Score: 92.55503085245304
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce a Progressive Positional Encoding (PPE) layer, which gradually exposes signals with increasing frequencies throughout the neural optimization. In this paper, we show the competence of the PPE layer for mesh transfer and its advantages compared to contemporary surface mapping techniques. Our approach is simple and requires little user guidance. Most importantly, our technique is a parameterization-free method, and thus applicable to a variety of target shape representations, including point clouds, polygon soups, and non-manifold meshes. We demonstrate that the transferred meshing remains faithful to the source mesh design characteristics, and at the same time fits the target geometry well.

Related papers

• Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks [53.67497327319569]
  We introduce a novel neural rendering technique to solve image-to-3D from a single view. Our approach employs the signed distance function as the surface representation and incorporates generalizable priors through geometry-encoding volumes and HyperNetworks. Our experiments show the advantages of our proposed approach with consistent results and rapid generation.
  arXiv  Detail & Related papers  (2023-12-24T08:42:37Z)
• EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization [3.3998740964877463]
  Quantization is a key method for deploying deep neural networks on edge devices with limited memory and computation resources. We propose a new method for enhanced Post-Training Quantization (EPTQ) that employs a network-wise quantization optimization process.
  arXiv  Detail & Related papers  (2023-09-20T10:50:28Z)
• Flexible Isosurface Extraction for Gradient-Based Mesh Optimization [65.76362454554754]
  This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field. We introduce FlexiCubes, an isosurface representation specifically designed for optimizing an unknown mesh with respect to geometric, visual, or even physical objectives.
  arXiv  Detail & Related papers  (2023-08-10T06:40:19Z)
• Learned Vertex Descent: A New Direction for 3D Human Model Fitting [64.04726230507258]
  We propose a novel optimization-based paradigm for 3D human model fitting on images and scans. Our approach is able to capture the underlying body of clothed people with very different body shapes, achieving a significant improvement compared to state-of-the-art. LVD is also applicable to 3D model fitting of humans and hands, for which we show a significant improvement to the SOTA with a much simpler and faster method.
  arXiv  Detail & Related papers  (2022-05-12T17:55:51Z)
• Mesh Draping: Parametrization-Free Neural Mesh Transfer [92.55503085245304]
  Mesh Draping is a neural method for transferring existing mesh structure from one shape to another. We show that by leveraging gradually increasing frequencies to guide the neural optimization, we are able to achieve stable and high quality mesh transfer.
  arXiv  Detail & Related papers  (2021-10-11T17:24:52Z)
• Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations [21.64457003420851]
  We develop a hybrid neural surface representation that allows us to impose geometry-aware sampling and regularization. We demonstrate that our method can be adopted to improve techniques for reconstructing neural implicit surfaces from multi-view images or point clouds.
  arXiv  Detail & Related papers  (2020-12-11T15:51:04Z)
• Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector [15.900382629390297]
  We propose to develop a novel model that integrates the optimization to learning, aiming to address the technical challenges in 3D registration. In addition to the deep transformation decoding network, our framework introduce an optimizable deep underlineSpatial underlineCorrelation underlineRepresentation.
  arXiv  Detail & Related papers  (2020-09-29T22:44:38Z)
• Neural Subdivision [58.97214948753937]
  This paper introduces Neural Subdivision, a novel framework for data-driven coarseto-fine geometry modeling. We optimize for the same set of network weights across all local mesh patches, thus providing an architecture that is not constrained to a specific input mesh, fixed genus, or category. We demonstrate that even when trained on a single high-resolution mesh our method generates reasonable subdivisions for novel shapes.
  arXiv  Detail & Related papers  (2020-05-04T20:03:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.