LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices

• URL: http://arxiv.org/abs/2108.03917v1
• Date: Mon, 9 Aug 2021 10:17:27 GMT
• Title: LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices
• Authors: Radu Alexandru Rosu, Peer Sch\"utt, Jan Quenzel and Sven Behnke
• Abstract summary: Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. Here, we propose LatticeNet, a novel approach for 3D semantic segmentation, which takes raw point clouds as input. We present results of 3D segmentation on multiple datasets where our method achieves state-of-the-art performance.
• Score: 27.048998326468688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images. Applying the same methods on 3D data still poses challenges due to the heavy memory requirements and the lack of structured data. Here, we propose LatticeNet, a novel approach for 3D semantic segmentation, which takes raw point clouds as input. A PointNet describes the local geometry which we embed into a sparse permutohedral lattice. The lattice allows for fast convolutions while keeping a low memory footprint. Further, we introduce DeformSlice, a novel learned data-dependent interpolation for projecting lattice features back onto the point cloud. We present results of 3D segmentation on multiple datasets where our method achieves state-of-the-art performance. We also extend and evaluate our network for instance and dynamic object segmentation.

Related papers

• Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
  Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding. An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
  arXiv  Detail & Related papers  (2024-11-25T10:14:10Z)
• Spherical Frustum Sparse Convolution Network for LiDAR Point Cloud Semantic Segmentation [62.258256483231484]
  LiDAR point cloud semantic segmentation enables the robots to obtain fine-grained semantic information of the surrounding environment. Many works project the point cloud onto the 2D image and adopt the 2D Convolutional Neural Networks (CNNs) or vision transformer for LiDAR point cloud semantic segmentation. In this paper, we propose a novel spherical frustum structure to avoid quantized information loss.
  arXiv  Detail & Related papers  (2023-11-29T09:55:13Z)
• Dynamic Clustering Transformer Network for Point Cloud Segmentation [23.149220817575195]
  We propose a novel 3D point cloud representation network, called Dynamic Clustering Transformer Network (DCTNet) It has an encoder-decoder architecture, allowing for both local and global feature learning. Our method was evaluated on an object-based dataset (ShapeNet), an urban navigation dataset (Toronto-3D), and a multispectral LiDAR dataset.
  arXiv  Detail & Related papers  (2023-05-30T01:11:05Z)
• Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis [66.49788145564004]
  We present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology. Our methods perform favorably against the current state-of-the-art competitors.
  arXiv  Detail & Related papers  (2022-12-17T15:05:25Z)
• CpT: Convolutional Point Transformer for 3D Point Cloud Processing [10.389972581905]
  We present CpT: Convolutional point Transformer - a novel deep learning architecture for dealing with the unstructured nature of 3D point cloud data. CpT is an improvement over existing attention-based Convolutions Neural Networks as well as previous 3D point cloud processing transformers. Our model can serve as an effective backbone for various point cloud processing tasks when compared to the existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-21T17:45:55Z)
• Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [122.53774221136193]
  State-of-the-art methods for driving-scene LiDAR-based perception often project the point clouds to 2D space and then process them via 2D convolution. A natural remedy is to utilize the 3D voxelization and 3D convolution network. We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern.
  arXiv  Detail & Related papers  (2021-09-12T06:25:11Z)
• Learning point embedding for 3D data processing [2.12121796606941]
  Current point-based methods are essentially spatial relationship processing networks. Our architecture, PE-Net, learns the representation of point clouds in high-dimensional space. Experiments show that PE-Net achieves the state-of-the-art performance in multiple challenging datasets.
  arXiv  Detail & Related papers  (2021-07-19T00:25:28Z)
• 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)
• Local Grid Rendering Networks for 3D Object Detection in Point Clouds [98.02655863113154]
  CNNs are powerful but it would be computationally costly to directly apply convolutions on point data after voxelizing the entire point clouds to a dense regular 3D grid. We propose a novel and principled Local Grid Rendering (LGR) operation to render the small neighborhood of a subset of input points into a low-resolution 3D grid independently. We validate LGR-Net for 3D object detection on the challenging ScanNet and SUN RGB-D datasets.
  arXiv  Detail & Related papers  (2020-07-04T13:57:43Z)
• Learning to Segment 3D Point Clouds in 2D Image Space [20.119802932358333]
  We show how to efficiently project 3D point clouds into a 2D image space. Traditional 2D convolutional neural networks (CNNs) such as U-Net can be applied for segmentation.
  arXiv  Detail & Related papers  (2020-03-12T03:18:59Z)

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.