# Indoor 3D Reconstruction with an Unknown Camera-Projector Pair

arXiv: https://arxiv.org/abs/2407.01945 · HTML: https://mlsanity.com/paper/2407.01945/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras](https://mlsanity.com/paper/1904.04998/) (2019)
- [PlaneRCNN: 3D Plane Detection and Reconstruction from a Single Image](https://mlsanity.com/paper/1812.04072/) (2018)
- [PlaneTR: Structure-Guided Transformers for 3D Plane Recovery](https://mlsanity.com/paper/2107.13108/) (2021)
- [StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints](https://mlsanity.com/paper/2209.05277/) (2022)
- [PlaneFormers: From Sparse View Planes to 3D Reconstruction](https://mlsanity.com/paper/2208.04307/) (2022)
- [NOPE-SAC: Neural One-Plane RANSAC for Sparse-View Planar 3D Reconstruction](https://mlsanity.com/paper/2211.16799/) (2022)
- [Deep Learning for Camera Calibration and Beyond: A Survey](https://mlsanity.com/paper/2303.10559/) (2023)
- [A Minimal Solution for Two-view Focal-length Estimation using Two Affine Correspondences](https://mlsanity.com/paper/1706.01649/) (2017)
- [A linear method for camera pair self-calibration and multi-view reconstruction with geometrically verified correspondences](https://mlsanity.com/paper/1906.12075/) (2019)

## Built on by (most cited first, 1 of 1)

- [AxisPose: Model-Free Matching-Free Single-Shot 6D Object Pose Estimation via Axis Generation](https://mlsanity.com/paper/2503.06660/) (2025)

## Abstract (arXiv)

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.