MNEW: Multi-domain Neighborhood Embedding and Weighting for Sparse Point Clouds Segmentation

• URL: http://arxiv.org/abs/2004.03401v1
• Date: Sun, 5 Apr 2020 18:02:07 GMT
• Title: MNEW: Multi-domain Neighborhood Embedding and Weighting for Sparse Point Clouds Segmentation
• Authors: Yang Zheng, Izzat H. Izzat, Sanling Song
• Abstract summary: We propose MNEW, including multi-domain neighborhood embedding, and attention weighting based on their geometry distance, feature similarity, and neighborhood sparsity. MNEW achieves the top performance for sparse point clouds, which is important to the application of LiDAR-based automated driving perception.
• Score: 1.2380933178502298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point clouds have been widely adopted in 3D semantic scene understanding. However, point clouds for typical tasks such as 3D shape segmentation or indoor scenario parsing are much denser than outdoor LiDAR sweeps for the application of autonomous driving perception. Due to the spatial property disparity, many successful methods designed for dense point clouds behave depreciated effectiveness on the sparse data. In this paper, we focus on the semantic segmentation task of sparse outdoor point clouds. We propose a new method called MNEW, including multi-domain neighborhood embedding, and attention weighting based on their geometry distance, feature similarity, and neighborhood sparsity. The network architecture inherits PointNet which directly process point clouds to capture pointwise details and global semantics, and is improved by involving multi-scale local neighborhoods in static geometry domain and dynamic feature space. The distance/similarity attention and sparsity-adapted weighting mechanism of MNEW enable its capability for a wide range of data sparsity distribution. With experiments conducted on virtual and real KITTI semantic datasets, MNEW achieves the top performance for sparse point clouds, which is important to the application of LiDAR-based automated driving perception.

Related papers

• PointeNet: A Lightweight Framework for Effective and Efficient Point Cloud Analysis [28.54939134635978]
  PointeNet is a network designed specifically for point cloud analysis. Our method demonstrates flexibility by seamlessly integrating with a classification/segmentation head or embedding into off-the-shelf 3D object detection networks. Experiments on object-level datasets, including ModelNet40, ScanObjectNN, ShapeNet KITTI, and the scene-level dataset KITTI, demonstrate the superior performance of PointeNet over state-of-the-art methods in point cloud analysis.
  arXiv  Detail & Related papers  (2023-12-20T03:34:48Z)
• Clustering based Point Cloud Representation Learning for 3D Analysis [80.88995099442374]
  We propose a clustering based supervised learning scheme for point cloud analysis. Unlike current de-facto, scene-wise training paradigm, our algorithm conducts within-class clustering on the point embedding space. Our algorithm shows notable improvements on famous point cloud segmentation datasets.
  arXiv  Detail & Related papers  (2023-07-27T03:42:12Z)
• Quadric Representations for LiDAR Odometry, Mapping and Localization [93.24140840537912]
  Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes. We propose a novel method of describing scenes using quadric surfaces, which are far more compact representations of 3D objects. Our method maintains low latency and memory utility while achieving competitive, and even superior, accuracy.
  arXiv  Detail & Related papers  (2023-04-27T13:52:01Z)
• Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network [22.9434434107516]
  We develop a new multi-layer transformer network for few-shot point cloud semantic segmentation. Our method achieves the new state-of-the-art performance, with 15% less inference time, over existing few-shot 3D point cloud segmentation models.
  arXiv  Detail & Related papers  (2023-03-28T00:27:54Z)
• Towards Robust 3D Object Recognition with Dense-to-Sparse Deep Domain Adaptation [5.763876449960417]
  Three-dimensional (3D) object recognition is crucial for intelligent autonomous agents. Most state-of-art approaches rely on relatively dense point clouds and performance drops significantly for sparse point clouds. Unsupervised domain adaption allows to minimise the discrepancy between dense and sparse point clouds.
  arXiv  Detail & Related papers  (2022-05-07T13:42:43Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)
• ODFNet: Using orientation distribution functions to characterize 3D point clouds [0.0]
  We leverage on point orientation distributions around a point in order to obtain an expressive local neighborhood representation for point clouds. New ODFNet model achieves state-of-the-art accuracy for object classification on ModelNet40 and ScanObjectNN datasets.
  arXiv  Detail & Related papers  (2020-12-08T19:54:20Z)
• 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)
• LiDAR-based Panoptic Segmentation via Dynamic Shifting Network [56.71765153629892]
  LiDAR-based panoptic segmentation aims to parse both objects and scenes in a unified manner. We propose the Dynamic Shifting Network (DS-Net), which serves as an effective panoptic segmentation framework in the point cloud realm. Our proposed DS-Net achieves superior accuracies over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-11-24T08:44:46Z)
• ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation [111.56730703473411]
  Training deep neural networks (DNNs) on LiDAR data requires large-scale point-wise annotations. Simulation-to-real domain adaptation (SRDA) trains a DNN using unlimited synthetic data with automatically generated labels. ePointDA consists of three modules: self-supervised dropout noise rendering, statistics-invariant and spatially-adaptive feature alignment, and transferable segmentation learning.
  arXiv  Detail & Related papers  (2020-09-07T23:46:08Z)
• Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision [68.35777836993212]
  We propose a Pseudo-LiDAR point cloud network to generate temporally and spatially high-quality point cloud sequences. By exploiting the scene flow between point clouds, the proposed network is able to learn a more accurate representation of the 3D spatial motion relationship.
  arXiv  Detail & Related papers  (2020-06-20T03:11:04Z)

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.