Rigorous Projection for Image Stitching: a 3D-Informed Approach for Accurate Panoramic Photogrammetry

Beyond Reprojection Error: Camera Calibration with 3D Targets

In 3D reconstruction, camera calibration is an essential element for achieving high fidelity and accuracy of the reconstructed geometry. While existing approaches rely upon 2D planar calibration, this work proposes a framework tailored for 3D reconstruction that is based on predicting scene rays, which adds flexibility to the reconstruction pipeline and enables the use of recent advances in camera models. Novel metrics, reconstruction and intersection error, derived from predicted scene rays are employed in combination with a bootstrapping procedure that statistically evaluates different calibration objects and calibration pipelines for both intrinsic and extrinsic camera parameters. The results show that the generalized distortion model more faithfully captures physical camera effects and yields an improvement in calibration accuracy. Reprojection error is shown to be a potentially misleading indicator of 3D accuracy, and the proposed ray-based metrics provide a more holistic assessment. An icosahedron calibration target is designed to enrich calibration information for 3D reconstruction together with a ring-feature-based detector. The icosahedral target yields approximately 40% lower mean intersection and more stable calibration across bootstrap trials on synthetic data, while real-data performance demands very tight fabrication tolerances.

Insights into the PAS Pana Stitching Algorithm

Abstract. In this paper, we describe a modern, efficient, accurate and reliable stitching algorithm that JOANNEUM RESEARCH (JR) has developed for the Phase One PAS Pana multi-camera system. The goal is to generate one large output image (four-channels including RGB and NIR) in nadir geometry. Based on the experience from a workflow developed in 2024, an improved stitching algorithm has been designed in 2025 which uses so called “controlled” projective transformations and simultaneous adjustment of all available image overlaps. We give insight into the role of projective transformations for fusing the images of calibrated cameras in a virtual image plane and how we moved on from the classical eight parameter transformation to a “controlled” transformation using only six, physically meaningful parameters. Experiments with real image data from a flight mission over Denver, USA demonstrate the accuracy and stability of the new stitching algorithm.

Combining Photogrammetry and Gaussian Splatting

Abstract. Among the image-based methods, photogrammetry is a consolidated 3D reconstruction technique able to provide highly accurate metric products, widely exploited in many domains. Photogrammetry is, however, conditioned by the characteristics of the captured scene, with good performance in well-textured areas and limits when non-collaborative surfaces, such as reflective or transparent, are present. In such cases, the photogrammetric reconstruction is often affected by noise, incomplete geometry and artifacts, reducing its final reconstruction quality. In recent years, different AI-based reconstruction methods have emerged as alternative (or complementary) 3D reconstruction and rendering solutions. In particular, 3D Gaussian Splatting (GS) has demonstrated impressive capabilities in rendering photorealistic scenes in challenging situations with high visual fidelity. However, its application in large-scale scenarios or when highly accurate 3D metric products are required is still limited, due to the high computational resources needed and the intrinsic optimization of GS methods for photometric rendering quality. To address these bottlenecks, this work proposes a hybrid reconstruction pipeline, leveraging the strengths and benefits of each technique. The method exploits the accurate geometry of photogrammetry in well-textured regions and the GS capabilities to improve completeness and visual aspect in areas featuring non-collaborative surfaces. A fusion strategy is proposed to combine the two results into a single 3D model, presenting examples from two aerial and one terrestrial dataset.

Beyond Checkerboards: Advantages of Photogrammetric Camera Calibration for Robust 3D Vision and Novel-View Generation

Imagery is fundamental to modern scientific research, making robust intrinsic camera calibration indispensable for accurate visual inference. The checkerboard-based calibration method has long been favored for its simplicity and ease of deployment and is widely used even in mission-critical computer vision pipelines. However, its limitations in modeling high-precision camera geometry can compromise downstream performance in tasks requiring geometric accuracy. In this work, camera calibration is revisited through the lens of photogrammetric self-calibration (PSC), and it is demonstrated that the PSC consistently outperforms the checkerboard method in both accuracy and precision across a range of vision tasks, including 3D reconstruction with structure from motion (SfM), visual simultaneous localization and mapping (SLAM), and novel-view synthesis and reconstruction. Our findings advocate for a paradigm shift toward calibration methods that better reflect the physical and projective properties of camera systems in real-world deployments for critical computer vision applications.

ODGS-SLAM: Omnidirectional Gaussian Splatting SLAM

Recent Advances in Image-Based 3D Reconstruction: a Photogrammetric Perspective on Conventional and Learning-Based Techniques

Image-based 3D reconstruction is vital in many applications, such as digital twins, smart cities, machine vision, and autonomous driving. In recent years, it has undergone a paradigm shift, propelled by advancements in both conventional photogrammetry and deep learning. This review provides a comprehensive photogrammetric perspective on both conventional and learning-based techniques, a viewpoint that prioritizes geometric fidelity, robustness, handling of uncertainty, and suitability for real-world applications. We first systematically revisit the fundamentals of traditional pipelines: Structure from Motion (SfM), Multi-View Stereo (MVS), and surface reconstruction. The review then details recent progress in conventional methods, highlighting innovations in scalable and efficient SfM, specialized camera models for MVS, and robust surface reconstruction algorithms. Subsequently, we explore the transformative evolution brought by learning-based techniques, including deep SfM, learning-based MVS, differentiable rendering-based scene representation methods (NeRF, 3DGS), groundbreaking feed-forward 3D reconstruction models (e.g., DUSt3R, VGGT), and surface reconstruction including explicit and implicit methods. Emphasis is placed on evaluating whether learning-based approaches genuinely meet photogrammetric requirements such as metric accuracy and reliability, rather than optimizing solely for visual realism. Furthermore, we conclude by identifying key challenges and research frontiers including generalization across domains, scalability to high-resolution imagery, real-time performance, and uncertainty quantification. By bridging the gap between classical photogrammetry and data-driven 3D vision, this work aims to guide future research toward robust, accurate, and certifiable 3D reconstruction systems suitable for engineering, industrial, and geospatial applications.