Indoor Camera Pose Estimation from Room Layouts and Image Outer Corners.

Area of Science:

• Computer Vision
• Robotics
• Indoor Scene Understanding

Background:

• Room layouts are crucial for indoor scene understanding, defining space configurations via junctions and boundary lines.
• Camera pose estimation is fundamental for tasks like navigation and augmented reality within these spaces.

Purpose of the Study:

• To propose a novel algorithm, PnL-IOC, for accurate camera pose estimation from eight common indoor room layouts.
• To address challenges in pose estimation for layouts with limited boundary lines.

Main Methods:

• The PnL-IOC algorithm casts camera pose estimation as a Perspective-n-Line (PnL) problem, incorporating image outer corners (IOCs) as auxiliary lines.
• Two implementations are presented: one for layouts with >2 boundary lines and an extended version using NSGA-II for layouts with 2 coplanar boundaries.
• Camera pose is jointly optimized with 3D correspondence refinement of IOCs using the Gauss-Newton algorithm.

Main Results:

• The PnL-IOC method demonstrates superior accuracy and robustness in camera pose estimation compared to existing PnL methods.
• Experimental results validate the algorithm's effectiveness on both simulated and real indoor scene images across eight distinct room layouts.
• The proposed method successfully handles challenging layouts with limited boundary information.

Conclusions:

• The PnL-IOC algorithm offers a significant advancement in camera pose estimation for indoor environments.
• Its ability to leverage image outer corners improves performance, especially in complex or constrained room layouts.
• The method provides a robust and accurate solution for indoor scene understanding applications.