NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization

• URL: http://arxiv.org/abs/2502.12534v2
• Date: Wed, 19 Feb 2025 03:34:42 GMT
• Title: NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization
• Authors: Zhen Li, Weiwei Sun, Shrisudhan Govindarajan, Shaobo Xia, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi,
• Abstract summary: We develop an efficient framework that converts an irregular point cloud into a signed distance field (SDF) We efficiently predict the SDF value at a point by aggregating nearby tokens. We show that aggregating across multiple scales is critical to overcome the approximations introduced by the serialization.
• Score: 31.537870456314796
• License:
• Abstract: We present a novel approach to large-scale point cloud surface reconstruction by developing an efficient framework that converts an irregular point cloud into a signed distance field (SDF). Our backbone builds upon recent transformer-based architectures (i.e., PointTransformerV3), that serializes the point cloud into a locality-preserving sequence of tokens. We efficiently predict the SDF value at a point by aggregating nearby tokens, where fast approximate neighbors can be retrieved thanks to the serialization. We serialize the point cloud at different levels/scales, and non-linearly aggregate a feature to predict the SDF value. We show that aggregating across multiple scales is critical to overcome the approximations introduced by the serialization (i.e. false negatives in the neighborhood). Our frameworks sets the new state-of-the-art in terms of accuracy and efficiency (better or similar performance with half the latency of the best prior method, coupled with a simpler implementation), particularly on outdoor datasets where sparse-grid methods have shown limited performance.

Related papers

• P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising [81.92854168911704]
  We tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr"odinger bridges to points clouds. Experiments on object datasets show that P2P-Bridge achieves significant improvements over existing methods.
  arXiv  Detail & Related papers  (2024-08-29T08:00:07Z)
• INPC: Implicit Neural Point Clouds for Radiance Field Rendering [5.64500060725726]
  We introduce a new approach for reconstruction and novel-view synthesis of real-world scenes. We propose a hybrid scene representation, which implicitly encodes a point cloud in a continuous octree-based probability field and a multi-resolution hash grid. Our method achieves fast inference at interactive frame rates, and can extract explicit point clouds to further enhance performance.
  arXiv  Detail & Related papers  (2024-03-25T15:26:32Z)
• Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling [55.05706827963042]
  Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud. Previous methods typically split a sparse point cloud into several local patches, upsample patch points, and merge all upsampled patches. We propose a novel approach that learns an unsigned distance field guided by local priors for point cloud upsampling.
  arXiv  Detail & Related papers  (2023-12-23T01:52:14Z)
• 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)
• CloudAttention: Efficient Multi-Scale Attention Scheme For 3D Point Cloud Learning [81.85951026033787]
  We set transformers in this work and incorporate them into a hierarchical framework for shape classification and part and scene segmentation. We also compute efficient and dynamic global cross attentions by leveraging sampling and grouping at each iteration. The proposed hierarchical model achieves state-of-the-art shape classification in mean accuracy and yields results on par with the previous segmentation methods.
  arXiv  Detail & Related papers  (2022-07-31T21:39:15Z)
• 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)
• Stratified Transformer for 3D Point Cloud Segmentation [89.9698499437732]
  Stratified Transformer is able to capture long-range contexts and demonstrates strong generalization ability and high performance. To combat the challenges posed by irregular point arrangements, we propose first-layer point embedding to aggregate local information. Experiments demonstrate the effectiveness and superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets.
  arXiv  Detail & Related papers  (2022-03-28T05:35:16Z)
• Attention-based Transformation from Latent Features to Point Clouds [6.547680885781582]
  AXform is an attention-based method to transform latent features to point clouds. It takes both parameter sharing and data flow into account, which makes it has fewer outliers, fewer network parameters, and a faster convergence speed. AXform does not have the strong 2-manifold constraint, which improves the generation of non-smooth surfaces. Considerable experiments on different datasets show that our methods achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2021-12-10T03:59:04Z)

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.