Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds

• URL: http://arxiv.org/abs/2304.14132v2
• Date: Fri, 28 Apr 2023 01:39:05 GMT
• Title: Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds
• Authors: Pengfei Song, Luoyu Mei, Han Cheng
• Abstract summary: This paper presents a framework for semantic segmentation on sparse sequential point clouds of millimeter-wave radar. The sparsity and capturing temporal-topological features of mmWave data is still a problem. We introduce graph structure and topological features to the point cloud and propose a semantic segmentation framework. Our model achieves mean accuracy on a custom dataset by $mathbf82.31%$ and outperforms state-of-the-art algorithms.
• Score: 3.3888257250564364
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents a framework for semantic segmentation on sparse sequential point clouds of millimeter-wave radar. Compared with cameras and lidars, millimeter-wave radars have the advantage of not revealing privacy, having a strong anti-interference ability, and having long detection distance. The sparsity and capturing temporal-topological features of mmWave data is still a problem. However, the issue of capturing the temporal-topological coupling features under the human semantic segmentation task prevents previous advanced segmentation methods (e.g PointNet, PointCNN, Point Transformer) from being well utilized in practical scenarios. To address the challenge caused by the sparsity and temporal-topological feature of the data, we (i) introduce graph structure and topological features to the point cloud, (ii) propose a semantic segmentation framework including a global feature-extracting module and a sequential feature-extracting module. In addition, we design an efficient and more fitting loss function for a better training process and segmentation results based on graph clustering. Experimentally, we deploy representative semantic segmentation algorithms (Transformer, GCNN, etc.) on a custom dataset. Experimental results indicate that our model achieves mean accuracy on the custom dataset by $\mathbf{82.31}\%$ and outperforms the state-of-the-art algorithms. Moreover, to validate the model's robustness, we deploy our model on the well-known S3DIS dataset. On the S3DIS dataset, our model achieves mean accuracy by $\mathbf{92.6}\%$, outperforming baseline algorithms.

Related papers

• A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing [1.96862953848735]
  We benchmark the ability of graph neural networks to simultaneously capture local clustering environments and long-range correlations. We find that current architectures fail to capture information from long-range correlations as effectively as domain-specific baselines.
  arXiv  Detail & Related papers  (2024-10-27T16:58:48Z)
• ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining [104.34751911174196]
  We build a large-scale dataset of 3DGS using ShapeNet and ModelNet datasets. Our dataset ShapeSplat consists of 65K objects from 87 unique categories. We introduce textbftextitGaussian-MAE, which highlights the unique benefits of representation learning from Gaussian parameters.
  arXiv  Detail & Related papers  (2024-08-20T14:49:14Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• Neural-prior stochastic block model [0.0]
  We propose to model the communities as being determined by the node attributes rather than the opposite. We propose an algorithm, stemming from statistical physics, based on a combination of belief propagation and approximate message passing. The proposed model and algorithm can be used as a benchmark for both theory and algorithms.
  arXiv  Detail & Related papers  (2023-03-17T14:14:54Z)
• 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds [95.54285993019843]
  We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
  arXiv  Detail & Related papers  (2022-11-01T20:59:38Z)
• 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)
• 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)
• S3Net: 3D LiDAR Sparse Semantic Segmentation Network [1.330528227599978]
  S3Net is a novel convolutional neural network for LiDAR point cloud semantic segmentation. It adopts an encoder-decoder backbone that consists of Sparse Intra-channel Attention Module (SIntraAM) and Sparse Inter-channel Attention Module (SInterAM)
  arXiv  Detail & Related papers  (2021-03-15T22:15:24Z)
• Learning Robust Feature Representations for Scene Text Detection [0.0]
  We present a network architecture derived from the loss to maximize conditional log-likelihood. By extending the layer of latent variables to multiple layers, the network is able to learn robust features on scale. In experiments, the proposed algorithm significantly outperforms state-of-the-art methods in terms of both recall and precision.
  arXiv  Detail & Related papers  (2020-05-26T01:06:47Z)

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.