LMSeg: A deep graph message-passing network for efficient and accurate semantic segmentation of large-scale 3D landscape meshes

• URL: http://arxiv.org/abs/2407.04326v2
• Date: Wed, 10 Jul 2024 05:52:25 GMT
• Title: LMSeg: A deep graph message-passing network for efficient and accurate semantic segmentation of large-scale 3D landscape meshes
• Authors: Zexian Huang, Kourosh Khoshelham, Gunditj Mirring Traditional Owners Corporation, Martin Tomko,
• Abstract summary: This paper presents an end-to-end deep graph message-passing network, LMSeg, designed to efficiently and accurately perform semantic segmentation on large-scale 3D landscape meshes. The hierarchical and local pooling of the barycentric graph, along with the effective geometry aggregation modules, enable fast inference and accurate segmentation of small-sized and irregular mesh objects.
• Score: 3.482371041476053
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic segmentation of large-scale 3D landscape meshes is pivotal for various geospatial applications, including spatial analysis, automatic mapping and localization of target objects, and urban planning and development. This requires an efficient and accurate 3D perception system to understand and analyze real-world environments. However, traditional mesh segmentation methods face challenges in accurately segmenting small objects and maintaining computational efficiency due to the complexity and large size of 3D landscape mesh datasets. This paper presents an end-to-end deep graph message-passing network, LMSeg, designed to efficiently and accurately perform semantic segmentation on large-scale 3D landscape meshes. The proposed approach takes the barycentric dual graph of meshes as inputs and applies deep message-passing neural networks to hierarchically capture the geometric and spatial features from the barycentric graph structures and learn intricate semantic information from textured meshes. The hierarchical and local pooling of the barycentric graph, along with the effective geometry aggregation modules of LMSeg, enable fast inference and accurate segmentation of small-sized and irregular mesh objects in various complex landscapes. Extensive experiments on two benchmark datasets (natural and urban landscapes) demonstrate that LMSeg significantly outperforms existing learning-based segmentation methods in terms of object segmentation accuracy and computational efficiency. Furthermore, our method exhibits strong generalization capabilities across diverse landscapes and demonstrates robust resilience against varying mesh densities and landscape topologies.

Related papers

• Large Spatial Model: End-to-end Unposed Images to Semantic 3D [79.94479633598102]
  Large Spatial Model (LSM) processes unposed RGB images directly into semantic radiance fields. LSM simultaneously estimates geometry, appearance, and semantics in a single feed-forward operation. It can generate versatile label maps by interacting with language at novel viewpoints.
  arXiv  Detail & Related papers  (2024-10-24T17:54:42Z)
• ALSTER: A Local Spatio-Temporal Expert for Online 3D Semantic Reconstruction [62.599588577671796]
  We propose an online 3D semantic segmentation method that incrementally reconstructs a 3D semantic map from a stream of RGB-D frames. Unlike offline methods, ours is directly applicable to scenarios with real-time constraints, such as robotics or mixed reality.
  arXiv  Detail & Related papers  (2023-11-29T20:30:18Z)
• Semi-Weakly Supervised Object Kinematic Motion Prediction [56.282759127180306]
  Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. We propose a graph neural network to learn the map between hierarchical part-level segmentation and mobile parts parameters. The network predictions yield a large scale of 3D objects with pseudo labeled mobility information.
  arXiv  Detail & Related papers  (2023-03-31T02:37:36Z)
• 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)
• GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs [49.55919802779889]
  We propose a Graph Convolution based Spatial Propagation Network (GraphCSPN) as a general approach for depth completion. In this work, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning. Our method achieves the state-of-the-art performance, especially when compared in the case of using only a few propagation steps.
  arXiv  Detail & Related papers  (2022-10-19T17:56:03Z)
• MDA-Net: Multi-Dimensional Attention-Based Neural Network for 3D Image Segmentation [4.221871357181261]
  We propose a multi-dimensional attention network (MDA-Net) to efficiently integrate slice-wise, spatial, and channel-wise attention into a U-Net based network. We evaluate our model on the MICCAI iSeg and IBSR datasets, and the experimental results demonstrate consistent improvements over existing methods.
  arXiv  Detail & Related papers  (2021-05-10T16:58:34Z)
• PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation [0.9137554315375922]
  We propose a inception based deep network architecture called PIG-Net, that effectively characterizes the local and global geometric details of the point clouds. We perform an exhaustive experimental analysis of the PIG-Net architecture on two state-of-the-art datasets.
  arXiv  Detail & Related papers  (2021-01-28T13:27:55Z)
• Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes [36.07733308424772]
  The deficiency of 3D segmentation labels is one of the main obstacles to effective point cloud segmentation. We propose a novel deep graph convolutional network-based framework for large-scale semantic scene segmentation in point clouds with sole 2D supervision.
  arXiv  Detail & Related papers  (2020-04-26T23:02:23Z)
• Real-Time High-Performance Semantic Image Segmentation of Urban Street Scenes [98.65457534223539]
  We propose a real-time high-performance DCNN-based method for robust semantic segmentation of urban street scenes. The proposed method achieves the accuracy of 73.6% and 68.0% mean Intersection over Union (mIoU) with the inference speed of 51.0 fps and 39.3 fps.
  arXiv  Detail & Related papers  (2020-03-11T08:45:53Z)

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.