Implicit Filtering for Learning Neural Signed Distance Functions from 3D Point Clouds

• URL: http://arxiv.org/abs/2407.13342v2
• Date: Tue, 10 Sep 2024 14:56:38 GMT
• Title: Implicit Filtering for Learning Neural Signed Distance Functions from 3D Point Clouds
• Authors: Shengtao Li, Ge Gao, Yudong Liu, Ming Gu, Yu-Shen Liu,
• Abstract summary: We propose a novel non-linear implicit filter to smooth the implicit field while preserving geometry details. Our novelty lies in that we can filter the surface (zero level set) by the neighbor input points with gradients of the signed distance field. By moving the input raw point clouds along the gradient, our proposed implicit filtering can be extended to non-zero level sets.
• Score: 34.774577477968805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural signed distance functions (SDFs) have shown powerful ability in fitting the shape geometry. However, inferring continuous signed distance fields from discrete unoriented point clouds still remains a challenge. The neural network typically fits the shape with a rough surface and omits fine-grained geometric details such as shape edges and corners. In this paper, we propose a novel non-linear implicit filter to smooth the implicit field while preserving high-frequency geometry details. Our novelty lies in that we can filter the surface (zero level set) by the neighbor input points with gradients of the signed distance field. By moving the input raw point clouds along the gradient, our proposed implicit filtering can be extended to non-zero level sets to keep the promise consistency between different level sets, which consequently results in a better regularization of the zero level set. We conduct comprehensive experiments in surface reconstruction from objects and complex scene point clouds, the numerical and visual comparisons demonstrate our improvements over the state-of-the-art methods under the widely used benchmarks.

Related papers

• Unsupervised Occupancy Learning from Sparse Point Cloud [8.732260277121547]
  Implicit Neural Representations have gained prominence as a powerful framework for capturing complex data modalities. In this paper, we propose a method to infer occupancy fields instead of Neural Signed Distance Functions. We highlight its capacity to improve implicit shape inference with respect to baselines and the state-of-the-art using synthetic and real data.
  arXiv  Detail & Related papers  (2024-04-03T14:05:39Z)
• Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment [50.892158511845466]
  We show that gradient consistency in the field, indicated by the parallelism of level sets, is the key factor affecting the inference accuracy. We propose a level set alignment loss to evaluate the parallelism of level sets, which can be minimized to achieve better gradient consistency.
  arXiv  Detail & Related papers  (2023-05-19T11:28:05Z)
• Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors [54.966603013209685]
  It is vital to infer signed distance functions (SDFs) from 3D point clouds. We present a neural network to directly infer SDFs from single sparse point clouds.
  arXiv  Detail & Related papers  (2023-03-25T15:56:50Z)
• GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation [73.77505964222632]
  We present a learning-based method, namely GeoUDF, to tackle the problem of reconstructing a discrete surface from a sparse point cloud. To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation. To extract triangle meshes from the predicted UDF, we propose a customized edge-based marching cube module.
  arXiv  Detail & Related papers  (2022-11-30T06:02:01Z)
• CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimization [54.69408516025872]
  CAP-UDF is a novel method to learn consistency-aware UDF from raw point clouds. We train a neural network to gradually infer the relationship between queries and the approximated surface. We also introduce a polygonization algorithm to extract surfaces using the gradients of the learned UDF.
  arXiv  Detail & Related papers  (2022-10-06T08:51:08Z)
• 3PSDF: Three-Pole Signed Distance Function for Learning Surfaces with Arbitrary Topologies [18.609959464825636]
  We present a novel learnable implicit representation called the three-pole signed distance function (3PSDF) It can represent non-watertight 3D shapes with arbitrary topologies while supporting easy field-to-mesh conversion. We propose a dedicated learning framework to effectively learn 3PSDF without worrying about the vanishing gradient due to the null labels.
  arXiv  Detail & Related papers  (2022-05-31T07:24:04Z)
• Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors [52.25114448281418]
  Current methods are able to reconstruct surfaces by learning Signed Distance Functions (SDFs) from single point clouds without ground truth signed distances or point normals. We propose to reconstruct highly accurate surfaces from sparse point clouds with an on-surface prior. Our method can learn SDFs from a single sparse point cloud without ground truth signed distances or point normals.
  arXiv  Detail & Related papers  (2022-04-22T09:45:20Z)
• Learning Gradient Fields for Shape Generation [69.85355757242075]
  A point cloud can be viewed as samples from a distribution of 3D points whose density is concentrated near the surface of the shape. We generate point clouds by performing gradient ascent on an unnormalized probability density. Our model directly predicts the gradient of the log density field and can be trained with a simple objective adapted from score-based generative models.
  arXiv  Detail & Related papers  (2020-08-14T18:06:15Z)

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.