Related papers: Three-Dimensional Orientation of Compact High Velo…

We investigate the hypothesis that compact high-velocity clouds (CHVC) are the "missing" dwarf galaxies of the Local Group, by searching them for populations of resolved stars. To this end we conducted two distinct tests based on optical…

Manifold distances are very effective tools for visual object recognition. However, most of the traditional manifold distances between images are based on the pixel-level comparison and thus easily affected by image rotations and…

Estimating and rectifying the orientation angle of any image is a pretty challenging task. Initial work used the hand engineering features for this purpose, where after the invention of deep learning using convolution-based neural network…

We present a catalogue of high-velocity clouds (HVCs) from the Galactic All Sky Survey (GASS) of southern-sky neutral hydrogen, which has 57 mK sensitivity and 1 km/s velocity resolution and was obtained with the Parkes Telescope. Our…

We introduce a new deep learning method for point cloud comparison. Our approach, named Deep Point Cloud Distance (DPDist), measures the distance between the points in one cloud and the estimated surface from which the other point cloud is…

Models that reproduce the observed high-velocity clouds (HVCs) also predict clouds at lower radial velocities that may easily be confused with Galactic disk (|z| < 1 kpc) gas. We describe the first search for these low-velocity halo clouds…

We derived the dust-to-HI ratio of the intermediate-velocity clouds (IVCs), the high-velocity clouds (HVCs), and the local HI gas, by carrying out a multiple-regression analysis of the 21cm HI emission combined with the sub-mm dust optical…

3D convective cloud images form via two intertwined radiative diffusion processes. Sunlight starts in the anti-solar direction and ends in toward-sensor ones, but repeated forward-peaked scattering smears the well-collimated beams…

Accurately estimating the orientation of pedestrians is an important and challenging task for autonomous driving because this information is essential for tracking and predicting pedestrian behavior. This paper presents a flexible Virtual…

A technique is introduced for deriving the gaseous rotation curves of edge-on spiral galaxies. The entire major axis position-velocity (XV) diagram is modeled with a set of rings in a least-squares sense, allowing for the effects of…

Learning an effective representation of 3D point clouds requires a good metric to measure the discrepancy between two 3D point sets, which is non-trivial due to their irregularity. Most of the previous works resort to using the Chamfer…

We suggest that the high--velocity clouds (HVCs) are large clouds, with typical diameters of 25 kpc and containing 5e7 solar masses of neutral gas and 3e8 solar masses of dark matter, falling onto the Local Group; altogether the HVCs…

In this work, we propose a novel framework shape back-projection for computationally efficient point cloud processing in a probabilistic manner. The primary component of the technique is shape histogram and a back-projection procedure. The…

This paper proposes a hierarchical clustering approach for the segmentation of mobile LiDAR point clouds. We perform the hierarchical clustering on unorganized point clouds based on a proximity matrix. The dissimilarity measure in the…

The understanding of the nature of intermediate and high velocity gas in the Milky Way is hampered by a paucity of distance estimates to individual clouds. A project has been started at the David Dunlap Observatory to address this lack of…

The problem of determining the pattern of gas motions in the central cases -- noncircular motions in the triaxial bar potential and motions in circular orbits but with orientation parameters different from those of the main disk -- are…

We constrain gas inflow speeds in star-forming galaxies with color gradients consistent with inside-out disk growth. Our method combines new measurements of disk orientation with previously described circumgalactic absorption in background…

Chamfer Distance (CD) is widely used as a metric to quantify difference between two point clouds. In point cloud completion, Chamfer Distance (CD) is typically used as a loss function in deep learning frameworks. However, it is generally…

Semantic segmentation in autonomous driving has been undergoing an evolution from sparse point segmentation to dense voxel segmentation, where the objective is to predict the semantic occupancy of each voxel in the concerned 3D space. The…

LiDAR is an important method for autonomous driving systems to sense the environment. The point clouds obtained by LiDAR typically exhibit sparse and irregular distribution, thus posing great challenges to the detection of 3D objects,…