Research on self-cross transformer model of point cloud change detecter

• URL: http://arxiv.org/abs/2309.07444v1
• Date: Thu, 14 Sep 2023 05:54:54 GMT
• Title: Research on self-cross transformer model of point cloud change detecter
• Authors: Xiaoxu Ren, Haili Sun, Zhenxin Zhang
• Abstract summary: In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches. In this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer.
• Score: 2.3838507844983248
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the vigorous development of the urban construction industry, engineering deformation or changes often occur during the construction process. To combat this phenomenon, it is necessary to detect changes in order to detect construction loopholes in time, ensure the integrity of the project and reduce labor costs. Or the inconvenience and injuriousness of the road. In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Directly based on but mostly based ontraditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to convert 3D point clouds into DSM, which loses a lot of original information. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches, and neural networks are rarely applied directly. We prefer that the network is given at the level of pixels or points. Variety. Therefore, in this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer suitable for change detection. Simultaneously simulate tunneling data for change detection, and do test experiments with our network.

Related papers

• Irregular Change Detection in Sparse Bi-Temporal Point Clouds using Learned Place Recognition Descriptors and Point-to-Voxel Comparison [0.0]
  This article proposes an innovative approach for change detection in 3D point clouds. It uses deep learned place recognition descriptors and irregular object extraction based on voxel-to-point comparison. The proposed method was successfully evaluated in real-world field experiments.
  arXiv  Detail & Related papers  (2023-06-27T12:22:25Z)
• DC3DCD: unsupervised learning for multiclass 3D point cloud change detection [0.0]
  We propose an unsupervised method, called Deep 3D Change Detection (DC3DCD), to detect and categorize multiclass changes point level. Our method builds upon the DeepCluster approach, originally designed for image classification, to handle complex raw 3D PCs.
  arXiv  Detail & Related papers  (2023-05-09T13:13:53Z)
• Toward Unsupervised 3D Point Cloud Anomaly Detection using Variational Autoencoder [10.097126085083827]
  We present an end-to-end unsupervised anomaly detection framework for 3D point clouds. We propose a deep variational autoencoder-based unsupervised anomaly detection network adapted to the 3D point cloud and an anomaly score specifically for 3D point clouds.
  arXiv  Detail & Related papers  (2023-04-07T00:02:37Z)
• Hierarchical Point Attention for Indoor 3D Object Detection [111.04397308495618]
  This work proposes two novel attention operations as generic hierarchical designs for point-based transformer detectors. First, we propose Multi-Scale Attention (MS-A) that builds multi-scale tokens from a single-scale input feature to enable more fine-grained feature learning. Second, we propose Size-Adaptive Local Attention (Local-A) with adaptive attention regions for localized feature aggregation within bounding box proposals.
  arXiv  Detail & Related papers  (2023-01-06T18:52:12Z)
• On the Robustness of 3D Object Detectors [9.467525852900007]
  3D scenes exhibit a lot of variations and are prone to sensor inaccuracies as well as information loss during pre-processing. This work aims to analyze and benchmark popular point-based 3D object detectors against several data corruptions.
  arXiv  Detail & Related papers  (2022-07-20T21:47:15Z)
• Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
  In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
  arXiv  Detail & Related papers  (2021-12-13T02:12:02Z)
• Deep Point Cloud Reconstruction [74.694733918351]
  Point cloud obtained from 3D scanning is often sparse, noisy, and irregular. To cope with these issues, recent studies have been separately conducted to densify, denoise, and complete inaccurate point cloud. We propose a deep point cloud reconstruction network consisting of two stages: 1) a 3D sparse stacked-hourglass network as for the initial densification and denoising, 2) a refinement via transformers converting the discrete voxels into 3D points.
  arXiv  Detail & Related papers  (2021-11-23T07:53:28Z)
• Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud [79.39041453836793]
  We develop a novel single-stage 3D detector for point clouds in an anchor-free manner. We overcome this by converting the voxel-based sparse 3D feature volumes into the sparse 2D feature maps. We propose an IoU-based detection confidence re-calibration scheme to improve the correlation between the detection confidence score and the accuracy of the bounding box regression.
  arXiv  Detail & Related papers  (2021-08-08T13:42:13Z)
• City-scale Scene Change Detection using Point Clouds [71.73273007900717]
  We propose a method for detecting structural changes in a city using images captured from mounted cameras over two different times. A direct comparison of the two point clouds for change detection is not ideal due to inaccurate geo-location information. To circumvent this problem, we propose a deep learning-based non-rigid registration on the point clouds. Experiments show that our method is able to detect scene changes effectively, even in the presence of viewpoint and illumination differences.
  arXiv  Detail & Related papers  (2021-03-26T08:04:13Z)
• End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection [62.34374949726333]
  Pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D depth estimation with those for 3D object detection by converting 2D depth map outputs to 3D point cloud inputs. We introduce a new framework based on differentiable Change of Representation (CoR) modules that allow the entire PL pipeline to be trained end-to-end.
  arXiv  Detail & Related papers  (2020-04-07T02:18:38Z)

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.