Preliminary Analysis of SOHO/STEREO Observations of Sungrazing Comet ISON (C/2012 S1) Around Perihelion

We present photometric and morphological analysis of the behavior of sungrazing comet C/2012 S1 ISON in SOHO and STEREO images around its perihelion on 2013 November 28.779 UT. ISON brightened gradually November 20-26 with a superimposed outburst on November 21.3-23.5. The slope of brightening changed about November 26.7 and was significantly steeper in SOHO's orange and clear filter images until November 27.9 when it began to flatten out, reaching a peak about November 28.1 ($r_H{\sim}17 R_\odot$), then fading before brightening again from November 28.6 ($r_H{\sim}5 R_\odot$) until disappearing behind the occulting disc. ISON brightened continuously as it approached perihelion while visible in all other telescopes/filters. The central condensation disappeared about November 28.5 and the leading edge became progressively more elongated until perihelion. These photometric and morphological behaviors are reminiscent of the tens of meter sized Kreutz comets regularly observed by SOHO and STEREO and strongly suggest that the nucleus of ISON was destroyed prior to perihelion. This is much too small to support published gas production rates and implies significant mass loss and/or disruption in the days and weeks leading up to perihelion. No central condensation was seen post-perihelion. The post-perihelion lightcurve was nearly identical in all telescopes/filters and fell slightly steeper than $r_H^{-2}$. This implies that the brightness was dominated by reflected solar continuum off of remnant dust in the coma/tail and that any remaining active nucleus was <10 m in radius.Comment: Accepted by ApJL; 11 pages of text (pre-print style), 3 figures, 1 tabl

Deep Learning Based Photometric Stereo from Many Images and Under Unknown Illumination

Shape from X is an interesting area of research in computer vision community. This topic is divided into passive and active methods. Example of passive methods is shape from texture, shape from defocus and shape from the silhouette. For active methods, the important categories are shape from shading and photometric stereo. In shape from shading, the cue for shape reconstruction is shading which is the relation between intensity and shape. In this case, only one image is considered. In photometric stereo, where multiple vantage points exist, 3D reconstruction considers multiple images (at least three). Photometric stereo on its own can be categorised depending on existing information of illumination directions, illumination intensities, Lambertian surfaces or non-Lambertian surfaces. This paper presents a method employing deep learning for photometric stereo where lighting and surface conditions are unknown. The proposed method is applied to a public dataset. Based on the experimental results, this method outperforms currently existing techniques

Depth Super-Resolution Meets Uncalibrated Photometric Stereo

A novel depth super-resolution approach for RGB-D sensors is presented. It disambiguates depth super-resolution through high-resolution photometric clues and, symmetrically, it disambiguates uncalibrated photometric stereo through low-resolution depth cues. To this end, an RGB-D sequence is acquired from the same viewing angle, while illuminating the scene from various uncalibrated directions. This sequence is handled by a variational framework which fits high-resolution shape and reflectance, as well as lighting, to both the low-resolution depth measurements and the high-resolution RGB ones. The key novelty consists in a new PDE-based photometric stereo regularizer which implicitly ensures surface regularity. This allows to carry out depth super-resolution in a purely data-driven manner, without the need for any ad-hoc prior or material calibration. Real-world experiments are carried out using an out-of-the-box RGB-D sensor and a hand-held LED light source.Comment: International Conference on Computer Vision (ICCV) Workshop, 201