3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition

• URL: http://arxiv.org/abs/2407.04833v4
• Date: Tue, 22 Oct 2024 03:41:08 GMT
• Title: 3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition
• Authors: Younggun Kim, Beomsik Cho, Seonghoon Ryoo, Soomok Lee,
• Abstract summary: 3D Adaptive Structural Convolution Network (3D-ASCN) is a cutting-edge framework for 3D point cloud recognition. It combines 3D convolution kernels, a structural tree structure, and adaptive neighborhood sampling for effective geometric feature extraction.
• Score: 3.3748750222488657
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Adapting deep learning networks for point cloud data recognition in self-driving vehicles faces challenges due to the variability in datasets and sensor technologies, emphasizing the need for adaptive techniques to maintain accuracy across different conditions. In this paper, we introduce the 3D Adaptive Structural Convolution Network (3D-ASCN), a cutting-edge framework for 3D point cloud recognition. It combines 3D convolution kernels, a structural tree structure, and adaptive neighborhood sampling for effective geometric feature extraction. This method obtains domain-invariant features and demonstrates robust, adaptable performance on a variety of point cloud datasets, ensuring compatibility across diverse sensor configurations without the need for parameter adjustments. This highlights its potential to significantly enhance the reliability and efficiency of self-driving vehicle technology.

Related papers

• Multi-view Structural Convolution Network for Domain-Invariant Point Cloud Recognition of Autonomous Vehicles [3.3748750222488657]
  Multi-View Structural Convolution Network (MSCN) designed for domain-invariant point cloud recognition. MSCN comprises Structural Convolution Layers (SCL) that extract local context geometric features from point clouds. MSCN enhances feature representation by training with unseen domain point clouds derived from source domain point clouds.
  arXiv  Detail & Related papers  (2025-01-27T18:25:35Z)
• TraIL-Det: Transformation-Invariant Local Feature Networks for 3D LiDAR Object Detection with Unsupervised Pre-Training [21.56675189346088]
  We introduce Transformation-Invariant Local (TraIL) features and the associated TraIL-Det architecture. TraIL features exhibit rigid transformation invariance and effectively adapt to variations in point density. They utilize the inherent isotropic radiation of LiDAR to enhance local representation. Our method outperforms contemporary self-supervised 3D object detection approaches in terms of mAP on KITTI.
  arXiv  Detail & Related papers  (2024-08-25T17:59:17Z)
• 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)
• Learning-Based Biharmonic Augmentation for Point Cloud Classification [79.13962913099378]
  Biharmonic Augmentation (BA) is a novel and efficient data augmentation technique. BA diversifies point cloud data by imposing smooth non-rigid deformations on existing 3D structures. We present AdvTune, an advanced online augmentation system that integrates adversarial training.
  arXiv  Detail & Related papers  (2023-11-10T14:04:49Z)
• UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation [113.35352122662752]
  We present an efficient multi-modal backbone for outdoor 3D perception named UniTR. UniTR processes a variety of modalities with unified modeling and shared parameters. UniTR is also a fundamentally task-agnostic backbone that naturally supports different 3D perception tasks.
  arXiv  Detail & Related papers  (2023-08-15T12:13:44Z)
• Nearest Neighbors Meet Deep Neural Networks for Point Cloud Analysis [14.844183458784235]
  We present an alternative to enhance existing deep neural networks without redesigning or extra parameters, termed as Spatial-Neighbor Adapter (SN-Adapter) Building on any trained 3D network, we utilize its learned encoding capability to extract features of the training dataset and summarize them as spatial knowledge. For a test point cloud, the SN-Adapter retrieves k nearest neighbors (k-NN) from the pre-constructed spatial prototypes and linearly interpolates the k-NN prediction with prototypical that of the original 3D network.
  arXiv  Detail & Related papers  (2023-03-01T17:57:09Z)
• DuEqNet: Dual-Equivariance Network in Outdoor 3D Object Detection for Autonomous Driving [4.489333751818157]
  We propose DuEqNet, which first introduces the concept of equivariance into 3D object detection network. The dual-equivariant of our model can extract the equivariant features at both local and global levels. Our model presents higher accuracy on orientation and better prediction efficiency.
  arXiv  Detail & Related papers  (2023-02-27T08:30:02Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• 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)
• Spherical Interpolated Convolutional Network with Distance-Feature Density for 3D Semantic Segmentation of Point Clouds [24.85151376535356]
  Spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. The proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset.
  arXiv  Detail & Related papers  (2020-11-27T15:35:12Z)
• InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [65.47126868838836]
  We propose a novel 3D object detection framework with dynamic information modeling. Coarse predictions are generated in the first stage via a voxel-based region proposal network. Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
  arXiv  Detail & Related papers  (2020-07-16T18:27:08Z)
• Triangle-Net: Towards Robustness in Point Cloud Learning [0.0]
  We propose a novel approach for 3D classification that can simultaneously achieve invariance towards rotation, positional shift, scaling, and is robust to point sparsity. We show that our approach outperforms PointNet and 3DmFV by 35.0% and 28.1% respectively in ModelNet 40 classification tasks.
  arXiv  Detail & Related papers  (2020-02-27T20:42:32Z)

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.