인공지능을 활용한 3D 공간 데이터 처리 기술 동향
Trends in 3D Spatial Data Processing Technology using Artificial Intelligence
1
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
본 논문은 3D 공간 데이터 처리기술과 그 응용 분야에 대해 설명한다. LiDAR 센서를 이용한 Point Cloud 데이터 수집과 3D 공간에서의 객체 탐지 방법을 소개하고 그 중 VoxelNet, PointNet, PointRCNN 등 3D 객체 탐지에 기여하는 기술들의 구조와 기능을 설명한다. 이 기술들은 자율주행 차량의 주변 환경 인식, 의료 분야의 정밀 진단 및 수술 계획, 산업 자동화 및 로봇공학의 제조 및 품질 검사, 안전 감시 시스템의 침입 탐지 및 추적, 그리고 VR/AR 분야의 실세계 객체와의 상호작용 및 환경 매핑에 활용된다. 결론적으로, 3D 공간 데이터 처리기술은 다양한 분야에서 혁신을 주도하며 안전과 효율성, 정확한 진단 및 치료, 그리고 사용자의 경험을 풍부하게 한다.
Abstract
This paper discusses 3D spatial data processing technologies and their application areas. It introduces methods for collecting Point Cloud data using LiDAR sensors and detecting objects in 3D spaces, and describes the structure and function of technologies contributing to 3D object detection, including VoxelNet, PointNet, and PointRCNN. These technologies are utilized for environmental perception in autonomous vehicles, precise diagnostics and surgical planning in the medical field, manufacturing and quality inspection in industrial automation and robotics, intrusion detection and tracking in safety monitoring systems, and interaction with real-world objects and environment mapping in the VR/AR domain. In conclusion, 3D spatial data processing technologies are leading innovations across various fields, enhancing safety and efficiency, enabling accurate diagnostics and treatments, and enriching user experiences.
참고문헌 (18)
- Xiao. Y, "A review of object detection based on deep learning", Multimedia Tools and Applications, 79, pp.23729~23791 (2020)
- Liu. L, "Computing systems for autonomous driving : State of the art and challenges", IEEE Internet of Things Journal, 8(8), pp.6469~6486 (2020)
- Singh. S. P, "3D deep learning on medical images : a review", Sensors, 20(18), pp.5097~ (2020)
- Tian. Y, "A review of 3D printing in dentistry : Technologies, affecting factors, and applications", Scanning (2021)
- Qi, "Pointnet : Deep learning on point sets for 3d classification and segmentation", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.652~660 (2017)
- Shi. S, "Pointrcnn : 3d object proposal generation and detection from point cloud", Proceedings of the IEEE, 2019, 770~779
- Zhou. Y, "Voxelnet : End-to-end learning for point cloud based 3d object detection", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.4490~4499 (2018)
- Herrman. R, "Lower bounds on circuit depth of the quantum approximate optimization algorithm", Quantum Information Processing, 20, pp.1~17 (2021)
- Qian. G, "Pointnext : Revisiting pointnet++ with improved training and scaling strategies", Advances in Neural Information Processing Systems, 35, pp.23192~23204 (2022)
- Liu. X, "Airborne LiDAR for DEM generation : some critical issues", Progress in physical geography, 32(1), pp.31~49 (2008)
- Park. S, "GalaxyWater-CNN : Prediction of water positions on the protein structure by a 3Dconvolutional neural network", Journal of Chemical Information and Modeling, 62(13), pp.3157~3168 (2022)
- Liu. Y, "Multi-focus image fusion with a deep convolutional neural network", Information Fusion, 36, pp.191~207 (2017)
- J. W. Baek, "Trend of Object Detection Convergence Technology based on Vision Transformer", Journal of Artificial Intelligence Convergence Technology, 2(1), pp.1~6 (2022)
- H. K. Kim, "Training Methods for Artificial Intelligence Classification Models and Solving Real-life Problems Using AI Classification Models", Journal of Artificial Intelligence Convergence Technology, 2(1), pp.7~16 (2022)
- N. A Joo, "Intelligence Convergence Technology using Deep Learning based Object Recognition in Manufacturing Process", Journal of Artificial Intelligence Convergence Technology, 2(1), pp.17~22 (2022)
- K. Y. Yoo, "Development and application of artificial intelligence reinforcement learning education program for elementary students", Journal of Artificial Intelligence Convergence Technology, 2(1), pp.29~36 (2022)
- S. H. Park, "A Web-Based University Cafeteria Review Management System", Journal of Artificial Intelligence Convergence Technology, 2(3), pp.93~97 (2022)
- J. S. Lee, "Development of an Artificial Intelligence Educational Recommendation System Program using Google App Script", Journal of Artificial Intelligence Convergence Technology, 2(3), pp.98~107 (2022)