Are We Ready for Real-Time LiDAR Semantic Segmentation in Autonomous Driving?

• URL: http://arxiv.org/abs/2410.08365v1
• Date: Thu, 10 Oct 2024 20:47:33 GMT
• Title: Are We Ready for Real-Time LiDAR Semantic Segmentation in Autonomous Driving?
• Authors: Samir Abou Haidar, Alexandre Chariot, Mehdi Darouich, Cyril Joly, Jean-Emmanuel Deschaud,
• Abstract summary: Scene semantic segmentation can be achieved by directly integrating 3D spatial data with specialized deep neural networks. We investigate various 3D semantic segmentation methodologies and analyze their performance and capabilities for resource-constrained inference on embedded NVIDIA Jetson platforms.
• Score: 42.348499880894686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Within a perception framework for autonomous mobile and robotic systems, semantic analysis of 3D point clouds typically generated by LiDARs is key to numerous applications, such as object detection and recognition, and scene reconstruction. Scene semantic segmentation can be achieved by directly integrating 3D spatial data with specialized deep neural networks. Although this type of data provides rich geometric information regarding the surrounding environment, it also presents numerous challenges: its unstructured and sparse nature, its unpredictable size, and its demanding computational requirements. These characteristics hinder the real-time semantic analysis, particularly on resource-constrained hardware architectures that constitute the main computational components of numerous robotic applications. Therefore, in this paper, we investigate various 3D semantic segmentation methodologies and analyze their performance and capabilities for resource-constrained inference on embedded NVIDIA Jetson platforms. We evaluate them for a fair comparison through a standardized training protocol and data augmentations, providing benchmark results on the Jetson AGX Orin and AGX Xavier series for two large-scale outdoor datasets: SemanticKITTI and nuScenes.

Related papers

• Open-Vocabulary High-Resolution 3D (OVHR3D) Data Segmentation and Annotation Framework [1.1280113914145702]
  This research aims to design and develop a comprehensive and efficient framework for 3D segmentation tasks. The framework integrates Grounding DINO and Segment anything Model, augmented by an enhancement in 2D image rendering via 3D mesh.
  arXiv  Detail & Related papers  (2024-12-09T07:39:39Z)
• 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)
• A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design [77.34726150561087]
  We conduct a survey on the most relevant and recent advances in Deep Semantic in the context of vision for autonomous vehicles. Our main objective is to provide a comprehensive discussion on the main methods, advantages, limitations, results and challenges faced from each perspective.
  arXiv  Detail & Related papers  (2023-03-08T01:29:55Z)
• LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection [36.77084564823707]
  deep learning methods heavily rely on annotated data and often face domain generalization issues. LiDAR-CS dataset is the first dataset that addresses the sensor-related gaps in the domain of 3D object detection in real traffic.
  arXiv  Detail & Related papers  (2023-01-29T19:10:35Z)
• MetaGraspNet: A Large-Scale Benchmark Dataset for Vision-driven Robotic Grasping via Physics-based Metaverse Synthesis [78.26022688167133]
  We present a large-scale benchmark dataset for vision-driven robotic grasping via physics-based metaverse synthesis. The proposed dataset contains 100,000 images and 25 different object types. We also propose a new layout-weighted performance metric alongside the dataset for evaluating object detection and segmentation performance.
  arXiv  Detail & Related papers  (2021-12-29T17:23:24Z)
• 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)
• 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)
• H3D: Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and textured Meshes from UAV LiDAR and Multi-View-Stereo [4.263987603222371]
  This paper introduces a 3D dataset which is unique in three ways. It depicts the village of Hessigheim (Germany) henceforth referred to as H3D. It is designed for promoting research in the field of 3D data analysis on one hand and to evaluate and rank emerging approaches.
  arXiv  Detail & Related papers  (2021-02-10T09:33:48Z)
• RELLIS-3D Dataset: Data, Benchmarks and Analysis [16.803548871633957]
  RELLIS-3D is a multimodal dataset collected in an off-road environment. The data was collected on the Rellis Campus of Texas A&M University.
  arXiv  Detail & Related papers  (2020-11-17T18:28:01Z)
• Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection [102.62963605429508]
  Point cloud semantic segmentation plays an essential role in autonomous driving. Current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes. We propose a novel Aware 3D Semantic Detection (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task.
  arXiv  Detail & Related papers  (2020-09-22T14:17:40Z)

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.