VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

Colour-colour diagrams and extragalactic globular cluster ages. Systematic uncertainties using the (V-K)-(V-I) diagram

(abridged) We investigate biases in cluster ages and [Fe/H] estimated from the (V-K)-(V-I) diagram, arising from inconsistent Horizontal Branch morphology, metal mixture, treatment of core convection between observed clusters and the theoretical colour grid employed for age and metallicity determinations. We also study the role played by statistical fluctuations of the observed colours, caused by the low total mass of typical globulars. Horizontal Branch morphology is potentially the largest source of uncertainty. A single-age system harbouring a large fraction of clusters with an HB morphology systematically bluer than the one accounted for in the theoretical colour grid, can simulate a bimodal population with an age difference as large as 8 Gyr. When only the redder clusters are considered, this uncertainty is almost negligible, unless there is an extreme mass loss along the Red Giant Branch phase. The metal mixture affects mainly the redder clusters; the effect of colour fluctuations becomes negligible for the redder clusters, or when the integrated Mv is brighter than -8.5 mag. The treatment of core convection is relevant for ages below ~4 Gyr. The retrieved [Fe/H] distributions are overall only mildly affected. Colour fluctuations and convective core extension have the largest effect. When 1sigma photometric errors reach 0.10 mag, all biases found in our analysis are erased, and bimodal age populations with age differences of up to ~8 Gyr go undetected. The use of both (U-I)-(V-K) and (V-I)-(V-K) diagrams may help disclosing the presence of blue HB stars unaccounted for in the theoretical colour calibration.Comment: 20 pages, including 26 figures. A&A in pres

Unsupervised Monocular Depth Estimation with Left-Right Consistency

Learning based methods have shown very promising results for the task of depth estimation in single images. However, most existing approaches treat depth prediction as a supervised regression problem and as a result, require vast quantities of corresponding ground truth depth data for training. Just recording quality depth data in a range of environments is a challenging problem. In this paper, we innovate beyond existing approaches, replacing the use of explicit depth data during training with easier-to-obtain binocular stereo footage. We propose a novel training objective that enables our convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data. Exploiting epipolar geometry constraints, we generate disparity images by training our network with an image reconstruction loss. We show that solving for image reconstruction alone results in poor quality depth images. To overcome this problem, we propose a novel training loss that enforces consistency between the disparities produced relative to both the left and right images, leading to improved performance and robustness compared to existing approaches. Our method produces state of the art results for monocular depth estimation on the KITTI driving dataset, even outperforming supervised methods that have been trained with ground truth depth.Comment: CVPR 2017 ora

Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements

The upcoming Large Synoptic Survey Telescope (LSST) will detect many strongly lensed Type Ia supernovae (LSNe Ia) for time-delay cosmography. This will provide an independent and direct way for measuring the Hubble constant $H_0$, which is necessary to address the current $4.4 \sigma$ tension in $H_0$ between the local distance ladder and the early Universe measurements. We present a detailed analysis of different observing strategies for the LSST, and quantify their impact on time-delay measurement between multiple images of LSNe Ia. For this, we produced microlensed mock-LSST light curves for which we estimated the time delay between different images. We find that using only LSST data for time-delay cosmography is not ideal. Instead, we advocate using LSST as a discovery machine for LSNe Ia, enabling time delay measurements from follow-up observations from other instruments in order to increase the number of systems by a factor of 2 to 16 depending on the observing strategy. Furthermore, we find that LSST observing strategies, which provide a good sampling frequency (the mean inter-night gap is around two days) and high cumulative season length (ten seasons with a season length of around 170 days per season), are favored. Rolling cadences subdivide the survey and focus on different parts in different years; these observing strategies trade the number of seasons for better sampling frequency. In our investigation, this leads to half the number of systems in comparison to the best observing strategy. Therefore rolling cadences are disfavored because the gain from the increased sampling frequency cannot compensate for the shortened cumulative season length. We anticipate that the sample of lensed SNe Ia from our preferred LSST cadence strategies with rapid follow-up observations would yield an independent percent-level constraint on $H_0$.Comment: 25 pages, 22 figures; accepted for publication in A&