クラウドソース車載動画のための実用的な自動キャリブレーション
Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos
クラウドソースのドライブレコーダー動画から、未知のカメラ内部パラメータを自動推定し、密な深度と自己運動の推定を可能にするシステムを提案した論文。
著者: Hemang Chawla, Matti Jukola, Shabbir Marzban, Elahe Arani, Bahram Zonooz
分類: cs.CV, cs.RO
原文アブストラクト
Spatial scene-understanding, including dense depth and ego-motion estimation, is an important problem in computer vision for autonomous vehicles and advanced driver assistance systems. Thus, it is beneficial to design perception modules that can utilize crowdsourced videos collected from arbitrary vehicular onboard or dashboard cameras. However, the intrinsic parameters corresponding to such cameras are often unknown or change over time. Typical manual calibration approaches require objects such as a chessboard or additional scene-specific information. On the other hand, automatic camera calibration does not have such requirements. Yet, the automatic calibration of dashboard cameras is challenging as forward and planar navigation results in critical motion sequences with reconstruction ambiguities. Structure reconstruction of complete visual-sequences that may contain tens of thousands of images is also computationally untenable. Here, we propose a system for practical monocular onboard camera auto-calibration from crowdsourced videos. We show the effectiveness of our proposed system on the KITTI raw, Oxford RobotCar, and the crowdsourced D$^2$-City datasets in varying conditions. Finally, we demonstrate its application for accurate monocular dense depth and ego-motion estimation on uncalibrated videos.
