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ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data

arXiv 2202.13788 ↗First posted 25 Feb 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

Unstructured point clouds with varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR). Predicting a scalar response based on unstructured point clouds is a common problem that arises in a wide variety of applications. The current literature relies on several pre-processing steps such as structured subsampling and feature extraction to analyze the point cloud data. Those techniques lead to quantization artifacts and do not consider the relationship between the regression response and the point cloud during pre-processing. Therefore, we propose a general and holistic "Bayesian Nonlinear Tensor Learning and Modeler" (ANTLER) to model the relationship of unstructured, varying-size point cloud data with a scalar or multivariate response. The proposed ANTLER simultaneously optimizes a nonlinear tensor dimensionality reduction and a nonlinear regression model with a 3D point cloud input and a scalar or multivariate response. ANTLER has the ability to consider the complex data representation, high-dimensionality,and inconsistent size of the 3D point cloud data.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 75 references that are in the map. First posted on arXiv 25 Feb 2022.

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19 of 75 references.

References · 19

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2018Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representationscited by 941
  4. 2019Deep Learning for 3D Point Clouds: A Surveycited by 558
  5. 2016Tutorial on Variational Autoencoderscited by 409
  6. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  7. 2015Tensorizing Neural Networkscited by 250
  8. 2018Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learningcited by 215
  9. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  10. 2015Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applicationscited by 162
  11. 2019Grid-GCN for Fast and Scalable Point Cloud Learningcited by 103
  12. 2018RGCNN: Regularized Graph CNN for Point Cloud Segmentationcited by 70
7 more
  1. 2017Tensor Regression Networkscited by 44
  2. 2019GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloudcited by 15
  3. 2018A novel approach for fusion of heterogeneous sources of datacited by 14
  4. 2018Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimizationcited by 8
  5. 2015Doubly Decomposing Nonparametric Tensor Regressioncited by 2
  6. 2020Broadcasted Nonparametric Tensor Regressioncited by 2
  7. 2020Population Quasi-Monte Carlocited by 1

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  1. 2026Learning Point Cloud Geometry as a Statistical Manifold: Theory and Practice ↗
  2. 2026Mixed Precision PointPillars for Efficient 3D Object Detection with TensorRT
  3. 2024RangeLDM: Fast Realistic LiDAR Point Cloud Generation
  4. 2026PointLAM: Local Attentive Mamba for Efficient Point-based 3D Object Detection
  5. 2025Efficient Point Clouds Upsampling via Flow Matching
  6. 2024Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models
  7. 2024CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric Transformation
  8. 2024PointInfinity: Resolution-Invariant Point Diffusion Models