Indoor 3D Reconstruction with an Unknown Camera-Projector Pair
(2407.01945)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.
Overview
-
The paper presents a novel method to self-calibrate a Camera-Projector Pair (CPP) in indoor 3D reconstruction using geometric constraints from cuboid corners (C2).
-
The approach reduces the high-dimensional optimization problem to a univariable one by leveraging the orthogonal relationships in a C2, making the self-calibration process efficient and reliable.
-
Experimental results show that the method achieves high calibration accuracy and reconstruction quality in textureless indoor scenes, outperforming traditional methods like COLMAP and learning-based methods such as DroidCalib.
Indoor 3D Reconstruction with an Unknown Camera-Projector Pair
The paper "Indoor 3D Reconstruction with an Unknown Camera-Projector Pair" authored by Qi et al. presents a novel method for solving the challenging problem of self-calibrating a Camera-Projector Pair (CPP) in indoor 3D reconstruction tasks. The key contribution lies in leveraging the geometric constraints provided by a common indoor structure, the cuboid corner (C2), to reliably estimate the CPP intrinsics from only two views. This method notably simplifies the estimation from a high-dimensional to a univariable optimization problem, thereby providing a reliable means for calibration and reconstruction in textureless indoor scenes.
Problem Context and Challenges
CPP-based structured light methods are well-regarded for generating dense and accurate 3D point clouds without requiring significant texture on the scene surfaces. Traditionally, these methods assume pre-calibrated CPP parameters, limiting their application to scenarios where the camera and projector parameters can be fixed. The challenge addressed by the authors is the self-calibration of a CPP in unknown scenes, specifically the ill-posed nature of deriving these parameters from only two views without known objects in the scene.
Method and Contributions
- Cuboid Corner Constraints: The authors demonstrate that a C2, such as a room corner, can provide sufficient constraints for self-calibration of the CPP. The crux of the approach is to use the orthogonal relationships inherent in a C2 to derive necessary constraints on the CPP's intrinsics.
- Univariable Optimization: By focusing on the principal point of the camera as a known variable, the paper reduces the multivariable problem to a simple univariable optimization problem. This transformation is significant as it turns an otherwise intractable problem into one that can be solved efficiently and reliably.
- Calibration Algorithm: The proposed algorithm starts with extracting constraints from the C2, following which the camera and projector intrinsics are estimated through an optimization process that minimizes a cycle loss function. This function ensures that the returned intrinsic parameters are consistent when mapped across the camera and projector views.
- Implementation and Results: The method’s efficacy is demonstrated on various indoor scenes with weak textures, showing both qualitative and quantitative improvements over traditional methods such as COLMAP and learning-based methods like DroidCalib, especially in two-view settings.
Experimental Results
The authors present extensive results underscoring the superiority of their approach in calibration accuracy and 3D reconstruction quality:
- Calibration Accuracy: The proposed method achieved a mean absolute error (MAE) of 4.3% in calibration, closely matching multi-view methods which assume a much simpler setup.
- Reconstruction Quality: The paper illustrates significant improvements in reconstruction fidelity in textureless indoor scenes compared to state-of-the-art two-view reconstruction methods like PlaneFormer.
- Robustness: The method is shown to be robust to changes in the number of matches and retains high accuracy even with a significantly reduced number of correspondences.
Implications and Future Directions
Theoretical Implications: The paper provides a compelling argument for exploiting simple indoor structures like C2 to address the ill-posed nature of CPP calibration in two-view settings. This has broader implications for the theory of structure from motion (SfM) and camera self-calibration, opening avenues for further research into leveraging scene-specific constraints.
Practical Implications: From a practical standpoint, the proposed method improves the flexibility and applicability of structured light systems in real-world applications where camera and projector parameters cannot be fixed and calibrated offline. This has immediate relevance in fields such as robotic vision, augmented reality, and indoor mapping.
Future Developments: The authors hint at potential extensions of their method to more general SfM problems and other self-calibration tasks. Future work might focus on automating the detection and matching of C2 structures within scenes to further streamline the calibration and reconstruction process. Moreover, exploring the use of other common indoor structures could expand the versatility and reliability of the approach.
Conclusion
The method introduced by Qi et al. marks a significant advancement in the field of 3D indoor reconstruction using CPPs. By leveraging the geometric constraints of cuboid corners, the authors provide a robust and efficient solution to the long-standing challenge of two-view self-calibration with unknown intrinsics. This work not only demonstrates immediate practical benefits but also enriches the theoretical landscape, paving the way for further innovations in computer vision and machine learning applications.
Create an account to read this summary for free: