Level-Set Parameters: Novel Representation for 3D Shape Analysis

• URL: http://arxiv.org/abs/2412.13502v1
• Date: Wed, 18 Dec 2024 04:50:19 GMT
• Title: Level-Set Parameters: Novel Representation for 3D Shape Analysis
• Authors: Huan Lei, Hongdong Li, Andreas Geiger, Anthony Dick,
• Abstract summary: Recent development of neural fields brings in level-set parameters from signed distance functions as a novel, continuous, and numerical representation of 3D shapes. We establish correlations across different shapes by formulating them as a pseudo-normal distribution, and learn the distribution prior to the respective dataset. We demonstrate the promise of the novel representations through applications in shape classification, retrieval, and 6D object pose estimation.
• Score: 70.23417107911567
• License:
• Abstract: 3D shape analysis has been largely focused on traditional 3D representations of point clouds and meshes, but the discrete nature of these data makes the analysis susceptible to variations in input resolutions. Recent development of neural fields brings in level-set parameters from signed distance functions as a novel, continuous, and numerical representation of 3D shapes, where the shape surfaces are defined as zero-level-sets of those functions. This motivates us to extend shape analysis from the traditional 3D data to these novel parameter data. Since the level-set parameters are not Euclidean like point clouds, we establish correlations across different shapes by formulating them as a pseudo-normal distribution, and learn the distribution prior from the respective dataset. To further explore the level-set parameters with shape transformations, we propose to condition a subset of these parameters on rotations and translations, and generate them with a hypernetwork. This simplifies the pose-related shape analysis compared to using traditional data. We demonstrate the promise of the novel representations through applications in shape classification (arbitrary poses), retrieval, and 6D object pose estimation. Code and data in this research are provided at https://github.com/EnyaHermite/LevelSetParamData.

Related papers

• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions [5.056545768004376]
  Implicit 3D surface reconstruction of an object from its partial and noisy 3D point cloud scan is the classical geometry processing and 3D computer vision problem. We propose a neural network architecture for learning the linear implicit shape representation of the 3D surface of an object. The proposed approach achieves better Chamfer distance and comparable F-score than the state-of-the-art approach on the benchmark dataset.
  arXiv  Detail & Related papers  (2024-02-11T20:42:49Z)
• Multi-View Reconstruction using Signed Ray Distance Functions (SRDF) [22.75986869918975]
  We investigate a new computational approach that builds on a novel shape representation that is volumetric. The shape energy associated to this representation evaluates 3D geometry given color images and does not need appearance prediction. In practice we propose an implicit shape representation, the SRDF, based on signed distances which we parameterize by depths along camera rays.
  arXiv  Detail & Related papers  (2022-08-31T19:32:17Z)
• 3D Equivariant Graph Implicit Functions [51.5559264447605]
  We introduce a novel family of graph implicit functions with equivariant layers that facilitates modeling fine local details. Our method improves over the existing rotation-equivariant implicit function from 0.69 to 0.89 on the ShapeNet reconstruction task.
  arXiv  Detail & Related papers  (2022-03-31T16:51:25Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• Scene Synthesis via Uncertainty-Driven Attribute Synchronization [52.31834816911887]
  This paper introduces a novel neural scene synthesis approach that can capture diverse feature patterns of 3D scenes. Our method combines the strength of both neural network-based and conventional scene synthesis approaches.
  arXiv  Detail & Related papers  (2021-08-30T19:45:07Z)
• Ellipse Regression with Predicted Uncertainties for Accurate Multi-View 3D Object Estimation [26.930403135038475]
  This work considers objects whose three-dimensional models can be represented as ellipsoids. We present a variant of Mask R-CNN for estimating the parameters of ellipsoidal objects by segmenting each object and accurately regressing the parameters of projection ellipses.
  arXiv  Detail & Related papers  (2020-12-27T19:52:58Z)
• DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes [43.853000396885626]
  We propose a learning-based framework for predicting sharp geometric features in sampled 3D shapes. By fusing the result of individual patches, we can process large 3D models, which are impossible to process for existing data-driven methods.
  arXiv  Detail & Related papers  (2020-11-30T18:21:00Z)
• Exploring Deep 3D Spatial Encodings for Large-Scale 3D Scene Understanding [19.134536179555102]
  We propose an alternative approach to overcome the limitations of CNN based approaches by encoding the spatial features of raw 3D point clouds into undirected graph models. The proposed method achieves on par state-of-the-art accuracy with improved training time and model stability thus indicating strong potential for further research.
  arXiv  Detail & Related papers  (2020-11-29T12:56:19Z)
• DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes [54.239416488865565]
  We propose a fast single-stage 3D object detection method for LIDAR data. The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes. We find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Open dataset.
  arXiv  Detail & Related papers  (2020-04-02T17:48:50Z)

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.