LiDAR-based Object Detection with Real-time Voice Specifications
It addresses accessibility and safety in autonomous navigation and assistive technology by providing real-time voice output and visualizations, though it appears incremental as it builds on existing methods like PointNet.
This paper tackles LiDAR-based object detection by integrating 3D point clouds and RGB images through a multi-modal PointNet framework, achieving 87.0% validation accuracy on a 3000-sample subset, which surpasses a baseline of 67.5% on a smaller dataset.
This paper presents a LiDAR-based object detection system with real-time voice specifications, integrating KITTI's 3D point clouds and RGB images through a multi-modal PointNet framework. It achieves 87.0% validation accuracy on a 3000-sample subset, surpassing a 200-sample baseline of 67.5% by combining spatial and visual data, addressing class imbalance with weighted loss, and refining training via adaptive techniques. A Tkinter prototype provides natural Indian male voice output using Edge TTS (en-IN-PrabhatNeural), alongside 3D visualizations and real-time feedback, enhancing accessibility and safety in autonomous navigation, assistive technology, and beyond. The study offers a detailed methodology, comprehensive experimental analysis, and a broad review of applications and challenges, establishing this work as a scalable advancement in human-computer interaction and environmental perception, aligned with current research trends.