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Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://github.com/danini/absolute-pose-from-oriented-and-scaled-features. 

Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://github.com/jonathanventura/P1AC/ . 

In augmented reality applications it is essential to know the position and orientation of the user to correctly register virtual 3D content in the user’s field of view. For this purpose, visual tracking through simultaneous localization and mapping (SLAM) is often used. However, when applied to the commonly occurring situation where the users are mostly stationary, many methods presented in previous research have two key limitations. First, SLAM techniques alone do not address the problem of global localization with respect to prior models of the environment. Global localization is essential in many applications where multiple users are expected to track within a shared space, such as spectators at a sporting event. Secondly, these methods often assume significant translational movement to accurately reconstruct and track from a local model of the environment, causing challenges for many stationary applications. In this paper, we extend recent research on Spherical Localization and Tracking to support relocalization after tracking failure, as well as global localization in large shared environments, and optimize the method for operation on mobile hardware. We also evaluate various state-of-the-art localization approaches, the robustness of our visual tracking method, and demonstrate the effectiveness of our system in real-life scenarios. 

Recent advances in Neural Radiance Field (NeRF)-based methods have enabled high-fidelity novel view synthesis for video with dynamic elements. However, these methods often require expensive hardware, take days to process a second-long video and do not scale well to longer videos. We create an end-to-end pipeline for creating dynamic 3D video from a monocular video that can be run on consumer hardware in minutes per second of footage, not days. Our pipeline handles the estimation of the camera parameters, depth maps, 3D reconstruction of dynamic foreground and static background elements, and the rendering of the 3D video on a computer or VR headset. We use a state-of-the-art visual transformer model to estimate depth maps which we use to scale COLMAP poses and enable RGB-D fusion with estimated depth data. In our preliminary experiments, we rendered the output in a VR headset and visually compared the method against ground-truth datasets and state-of-the-art NeRF-based methods. 

We investigate how real-time, 360 degree view synthesis can be achieved on current virtual reality hardware from a single panoramic image input. We introduce a light-weight method to automatically convert a single panoramic input into a multi-cylinder image representation that supports real-time, free-viewpoint view synthesis rendering for virtual reality. We apply an existing convolutional neural network trained on pinhole images to a cylindrical panorama with wrap padding to ensure agreement between the left and right edges. The network outputs a stack of semi-transparent panoramas at varying depths which can be easily rendered and composited with over blending. Quantitative experiments and a user study show that the method produces convincing parallax and fewer artifacts than a textured mesh representation.