Budget-aware Correspondence-driven View Selection for Real-time 3D Perception of Mobile Robots
Vaishnav Ramesh, Jacob Carter, Shivali J. Patel, Sanjeev J. Koppal, Md Jahidul Islam
原文摘要
3D visual perception and scene reconstruction are critical for autonomous robots and intelligent transportation, yet real-world deployments often generate long, redundant image sequences that hinder downstream processing. This report investigates BCVS (budget-aware correspondence-driven view sampling), a pose-free view selection algorithm that offers 22+ FPS on a CPU, enabling online filtering for efficient 3D perception by mobile robots. Unlike existing approaches that rely on full structure-from-motion (SfM) pipelines or camera pose estimation, BCVS employs a purely correspondence-based scoring strategy that prioritizes view novelty while maintaining sufficient overlap for geometric stability. Given a user-specified budget, BCVS iteratively selects the most distinct frames and suppresses near-duplicates, producing compact subsets that preserve connectivity and minimize reconstruction loss. Extensive experiments on benchmark show that BCVS outperforms CPU-friendly baselines at tight budgets and remains competitive as budgets scale. Field trials with large aerial and marine robotics data further validate its practicality and scalability: BCVS reduces redundancy up to 16% while sustaining robust SfM registration and high-quality 3D reconstructions.
cs.RO · cs.CV