Change detection needs change information: improving deep 3D point cloud change detection

• URL: http://arxiv.org/abs/2304.12639v2
• Date: Mon, 29 Jan 2024 16:59:29 GMT
• Title: Change detection needs change information: improving deep 3D point cloud change detection
• Authors: Iris de G\'elis (1 and 2), Thomas Corpetti (3) and S\'ebastien Lef\`evre (2) ((1) Magellium, (2) Institut de Recherche en Informatique et Syst\`emes Al\'eatoires IRISA - UMR 6074 - Universit\'e Bretagne Sud, (3) Littoral - Environnement - T\'el\'ed\'etection - G\'eomatique LETG - UMR 6554 - Universit\'e Rennes 2)
• Abstract summary: Change detection is an important task that rapidly identifies modified areas. In this study, we focus on change segmentation using 3D point clouds (PCs) directly to avoid any information loss due to theization processes. We propose three new architectures to address 3D PC change segmentation: OneConvFusion, Triplet KPConv, and Fusion SiamKPConv.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Change detection is an important task that rapidly identifies modified areas, particularly when multi-temporal data are concerned. In landscapes with a complex geometry (e.g., urban environment), vertical information is a very useful source of knowledge that highlights changes and classifies them into different categories. In this study, we focus on change segmentation using raw three-dimensional (3D) point clouds (PCs) directly to avoid any information loss due to the rasterization processes. While deep learning has recently proven its effectiveness for this particular task by encoding the information through Siamese networks, we investigate herein the idea of also using change information in the early steps of deep networks. To do this, we first propose to provide a Siamese KPConv state-of-the-art (SoTA) network with hand-crafted features, especially a change-related one, which improves the mean of the Intersection over Union (IoU) over the classes of change by 4.70%. Considering that a major improvement is obtained due to the change-related feature, we then propose three new architectures to address 3D PC change segmentation: OneConvFusion, Triplet KPConv, and Encoder Fusion SiamKPConv. All these networks consider the change information in the early steps and outperform the SoTA methods. In particular, Encoder Fusion SiamKPConv overtakes the SoTA approaches by more than 5% of the mean of the IoU over the classes of change, emphasizing the value of having the network focus on change information for the change detection task. The code is available at https://github.com/IdeGelis/torch-points3d-SiamKPConvVariants.

Related papers

• Change Guiding Network: Incorporating Change Prior to Guide Change Detection in Remote Sensing Imagery [6.5026098921977145]
  We design the Change Guiding Network (CGNet) to tackle the insufficient expression problem of change features. CGNet generates change maps with rich semantic information to guide multi-scale feature fusion. A self-attention module named Change Guide Module (CGM) can effectively capture the long-distance dependency among pixels.
  arXiv  Detail & Related papers  (2024-04-14T08:09:33Z)
• ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection [65.59969454655996]
  We propose an efficient change detection framework, ELGC-Net, which leverages rich contextual information to precisely estimate change regions. Our proposed ELGC-Net sets a new state-of-the-art performance in remote sensing change detection benchmarks. We also introduce ELGC-Net-LW, a lighter variant with significantly reduced computational complexity, suitable for resource-constrained settings.
  arXiv  Detail & Related papers  (2024-03-26T17:46:25Z)
• MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations [50.79913333804232]
  We propose a memory-supported transformer (MS-Former) for weakly supervised change detection. MS-Former consists of a bi-directional attention block (BAB) and a patch-level supervision scheme (PSS) Experimental results on three benchmark datasets demonstrate the effectiveness of our proposed method in the change detection task.
  arXiv  Detail & Related papers  (2023-11-16T09:57:29Z)
• Explicit Change Relation Learning for Change Detection in VHR Remote Sensing Images [12.228675703851733]
  We propose a network architecture NAME for the explicit mining of change relation features. The change features of change detection should be divided into pre-changed image features, post-changed image features and change relation features. Our network performs better, in terms of F1, IoU, and OA, than those of the existing advanced networks for change detection.
  arXiv  Detail & Related papers  (2023-11-14T08:47:38Z)
• Research on self-cross transformer model of point cloud change detecter [2.3838507844983248]
  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.
  arXiv  Detail & Related papers  (2023-09-14T05:54:54Z)
• Self-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network [80.19054069988559]
  We find that self-supervised monocular depth estimation shows a direction sensitivity and environmental dependency. We propose a new Direction-aware Cumulative Convolution Network (DaCCN), which improves the depth representation in two aspects. Experiments show that our method achieves significant improvements on three widely used benchmarks.
  arXiv  Detail & Related papers  (2023-08-10T14:32:18Z)
• T-UNet: Triplet UNet for Change Detection in High-Resolution Remote Sensing Images [5.849243433046327]
  Currently, most change detection methods are based on Siamese network structure or early fusion structure. We propose a novel network, Triplet UNet(T-UNet), based on a three-branch encoder, which is capable to simultaneously extract the object features and the change features. In the decoder stage, we introduce the channel attention mechanism (CAM) and spatial attention mechanism (SAM) to fully mine and integrate detailed textures information.
  arXiv  Detail & Related papers  (2023-08-04T14:44:11Z)
• SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection [6.12477318852572]
  Change detection (CD) aims to find the difference between two images at different times and outputs a change map to represent whether the region has changed or not. Many State-of-The-Art (SoTA) methods design a deep learning model that has a powerful discriminative ability. We propose our network, the Scale and Relation-Aware Siamese Network (SARAS-Net) to deal with this issue.
  arXiv  Detail & Related papers  (2022-12-02T16:30:33Z)
• The Change You Want to See [91.3755431537592]
  Given two images of the same scene, being able to automatically detect the changes in them has practical applications in a variety of domains. We tackle the change detection problem with the goal of detecting "object-level" changes in an image pair despite differences in their viewpoint and illumination.
  arXiv  Detail & Related papers  (2022-09-28T18:10:09Z)
• 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)
• Progressive Coordinate Transforms for Monocular 3D Object Detection [52.00071336733109]
  We propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations. In this paper, we propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations.
  arXiv  Detail & Related papers  (2021-08-12T15:22:33Z)

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.