A High Resolution Survey of the Galactic Plane at 408 MHz

The interstellar medium is a complex 'ecosystem' with gas constituents in the atomic, molecular, and ionized states, dust, magnetic fields, and relativistic particles. The Canadian Galactic Plane Survey has imaged these constituents with angular resolution of the order of arcminutes. This paper presents radio continuum data at 408 MHz over the area 52 degrees < longitude < 193 degrees, -6.5 degrees < latitude < 8.5 degrees, with an extension to latitude = 21 degrees in the range 97 degrees < longitude < 120 degrees, with angular resolution 2.8' x 2.8' cosec(declination). Observations were made with the Synthesis Telescope at the Dominion Radio Astrophysical Observatory as part of the Canadian Galactic Plane Survey. The calibration of the survey using existing radio source catalogs is described. The accuracy of 408-MHz flux densities from the data is 6%. Information on large structures has been incorporated into the data using the single-antenna survey of Haslam (1982). The paper presents the data, describes how it can be accessed electronically, and gives examples of applications of the data to ISM research.Comment: Accepted for publication in the Astronomical Journa

Progressive Refinement Imaging

This paper presents a novel technique for progressive online integration of uncalibrated image sequences with substantial geometric and/or photometric discrepancies into a single, geometrically and photometrically consistent image. Our approach can handle large sets of images, acquired from a nearly planar or infinitely distant scene at different resolutions in object domain and under variable local or global illumination conditions. It allows for efficient user guidance as its progressive nature provides a valid and consistent reconstruction at any moment during the online refinement process. // Our approach avoids global optimization techniques, as commonly used in the field of image refinement, and progressively incorporates new imagery into a dynamically extendable and memory‐efficient Laplacian pyramid. Our image registration process includes a coarse homography and a local refinement stage using optical flow. Photometric consistency is achieved by retaining the photometric intensities given in a reference image, while it is being refined. Globally blurred imagery and local geometric inconsistencies due to, e.g. motion are detected and removed prior to image fusion. // We demonstrate the quality and robustness of our approach using several image and video sequences, including handheld acquisition with mobile phones and zooming sequences with consumer cameras

Probabilistic Triangulation for Uncalibrated Multi-View 3D Human Pose Estimation

3D human pose estimation has been a long-standing challenge in computer vision and graphics, where multi-view methods have significantly progressed but are limited by the tedious calibration processes. Existing multi-view methods are restricted to fixed camera pose and therefore lack generalization ability. This paper presents a novel Probabilistic Triangulation module that can be embedded in a calibrated 3D human pose estimation method, generalizing it to uncalibration scenes. The key idea is to use a probability distribution to model the camera pose and iteratively update the distribution from 2D features instead of using camera pose. Specifically, We maintain a camera pose distribution and then iteratively update this distribution by computing the posterior probability of the camera pose through Monte Carlo sampling. This way, the gradients can be directly back-propagated from the 3D pose estimation to the 2D heatmap, enabling end-to-end training. Extensive experiments on Human3.6M and CMU Panoptic demonstrate that our method outperforms other uncalibration methods and achieves comparable results with state-of-the-art calibration methods. Thus, our method achieves a trade-off between estimation accuracy and generalizability. Our code is in https://github.com/bymaths/probabilistic_triangulationComment: 9pages, 5figures, conferenc

Flexible SVBRDF Capture with a Multi-Image Deep Network

International audienceEmpowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast with the tens or hundreds of pictures required by traditional optimization-based approaches. However, a single image is often simply not enough to observe the rich appearance of real-world materials. We present a deep-learning method capable of estimating material appearance from a variable number of uncalibrated and unordered pictures captured with a handheld camera and flash. Thanks to an order-independent fusing layer, this architecture extracts the most useful information from each picture, while benefiting from strong priors learned from data. The method can handle both view and light direction variation without calibration. We show how our method improves its prediction with the number of input pictures, and reaches high quality reconstructions with as little as 1 to 10 images-a sweet spot between existing single-image and complex multi-image approaches