Indoor 3D Reconstruction with an Unknown Camera-Projector Pair

TL;DR

This paper introduces a novel method for indoor 3D reconstruction using an unknown camera-projector pair, leveraging room corners to reliably calibrate intrinsics from only two views, enabling accurate 3D modeling without prior calibration.

Contribution

It presents the first approach to derive sufficient constraints for CPP intrinsics from a single room corner, simplifying calibration to a univariable optimization and improving indoor 3D reconstruction accuracy.

Findings

Outperforms traditional calibration methods in indoor scenes.

Enables reliable 3D reconstruction with minimal views.

Shows potential for sparse-view structure from motion.

Abstract

Structured light-based method with a camera-projector pair (CPP) plays a vital role in indoor 3D reconstruction, especially for scenes with weak textures. Previous methods usually assume known intrinsics, which are pre-calibrated from known objects, or self-calibrated from multi-view observations. It is still challenging to reliably recover CPP intrinsics from only two views without any known objects. In this paper, we provide a simple yet reliable solution. We demonstrate that, for the first time, sufficient constraints on CPP intrinsics can be derived from an unknown cuboid corner (C2), e.g. a room's corner, which is a common structure in indoor scenes. In addition, with only known camera principal point, the complex multi-variable estimation of all CPP intrinsics can be simplified to a simple univariable optimization problem, leading to reliable calibration and thus direct 3D reconstruction with unknown CPP. Extensive results have demonstrated the superiority of the proposed method over both traditional and learning-based counterparts. Furthermore, the proposed method also demonstrates impressive potential to solve similar tasks without active lighting, such as sparse-view structure from motion.