Data-Driven Shape Analysis and Processing

Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure

SuperPoint: Self-Supervised Interest Point Detection and Description

This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.Comment: Camera-ready version for CVPR 2018 Deep Learning for Visual SLAM Workshop (DL4VSLAM2018

Voids in cosmological simulations over cosmic time

We study evolution of voids in cosmological simulations using a new method for tracing voids over cosmic time. The method is based on tracking watershed basins (contiguous regions around density minima) of well developed voids at low redshift, on a regular grid of density field. It enables us to construct a robust and continuous mapping between voids at different redshifts, from initial conditions to the present time. We discuss how the new approach eliminates strong spurious effects of numerical origin when voids evolution is traced by matching voids between successive snapshots (by analogy to halo merger trees). We apply the new method to a cosmological simulation of a standard LambdaCDM cosmological model and study evolution of basic properties of typical voids (with effective radii between 6Mpc/h and 20Mpc/h at redshift z=0) such as volumes, shapes, matter density distributions and relative alignments. The final voids at low redshifts appear to retain a significant part of the configuration acquired in initial conditions. Shapes of voids evolve in a collective way which barely modifies the overall distribution of the axial ratios. The evolution appears to have a weak impact on mutual alignments of voids implying that the present state is in large part set up by the primordial density field. We present evolution of dark matter density profiles computed on iso-density surfaces which comply with the actual shapes of voids. Unlike spherical density profiles, this approach enables us to demonstrate development of theoretically predicted bucket-like shape of the final density profiles indicating a wide flat core and a sharp transition to high-density void walls.Comment: 13 pages, 13 figures; accepted for publication in MNRA