Aron Monszpart

RAPter: rebuilding man-made scenes with regular arrangements of planes

With the proliferation of acquisition devices, gathering massive volumes of 3D data is now easy. Processing such large masses of pointclouds, however, remains a challenge. This is particularly a problem for raw scans with missing data, noise, and varying sampling density. In this work, we present a simple, scalable, yet powerful data reconstruction algorithm. We focus on reconstruction of man-made scenes as regular arrangements of planes (RAP), thereby selecting both local plane-based approximations along with their global inter-plane relations. We propose a novel selection formulation to directly balance between data fitting and the simplicity of the resulting arrangement of extracted planes. The main technical contribution is a formulation that allows less-dominant orientations to still retain their internal regularity, and not become overwhelmed and regularized by the dominant scene orientations. We evaluate our approach on a variety of complex 2D and 3D pointclouds, and demonstrate the advantages over existing alternative methods.

Imagining the unseen: stability-based cuboid arrangements for scene understanding

Missing data due to occlusion is a key challenge in 3D acquisition, particularly in cluttered man-made scenes. Such partial information about the scenes limits our ability to analyze and understand them. In this work we abstract such environments as collections of cuboids and hallucinate geometry in the occluded regions by globally analyzing the physical stability of the resultant arrangements of the cuboids. Our algorithm extrapolates the cuboids into the un-seen regions to infer both their corresponding geometric attributes (e.g., size, orientation) and how the cuboids topologically interact with each other (e.g., touch or fixed). The resultant arrangement provides an abstraction for the underlying structure of the scene that can then be used for a range of common geometry processing tasks. We evaluate our algorithm on a large number of test scenes with varying complexity, validate the results on existing benchmark datasets, and demonstrate the use of the recovered cuboid-based structures towards object retrieval, scene completion, etc.

T-tubule remodelling disturbs localized β2-adrenergic signalling in rat ventricular myocytes during the progression of heart failure

Aims Cardiomyocyte β2-adrenergic receptor (β2AR) cyclic adenosine monophosphate (cAMP) signalling is regulated by the receptors’ subcellular location within transverse tubules (T-tubules), via interaction with structural and regulatory proteins, which form a signalosome. In chronic heart failure (HF), β2ARs redistribute from T-tubules to the cell surface, which disrupts functional signalosomes and leads to diffuse cAMP signalling. However, the functional consequences of structural changes upon β2AR-cAMP signalling during progression from hypertrophy to advanced HF are unknown. Methods and results Rat left ventricular myocytes were isolated at 4-, 8-, and 16-week post-myocardial infarction (MI), β2ARs were stimulated either via whole-cell perfusion or locally through the nanopipette of the scanning ion conductance microscope. cAMP release was measured via a Förster Resonance Energy Transfer-based sensor Epac2-camps. Confocal imaging of di-8-ANNEPS-stained cells and immunoblotting were used to determine structural alterations. At 4-week post-MI, T-tubule regularity, density and junctophilin-2 (JPH2) expression were significantly decreased. The amplitude of local β2AR-mediated cAMP in T-tubules was reduced and cAMP diffused throughout the cytosol instead of being locally confined. This was accompanied by partial caveolin-3 (Cav-3) dissociation from the membrane. At 8-week post-MI, the β2AR-mediated cAMP response was observed at the T-tubules and the sarcolemma (crest). Finally, at 16-week post-MI, the whole cell β2AR-mediated cAMP signal was depressed due to adenylate cyclase dysfunction, while overall Cav-3 levels were significantly increased and a substantial portion of Cav-3 dissociated into the cytosol. Overexpression of JPH2 in failing cells in vitro or AAV9.SERCA2a gene therapy in vivo did not improve β2AR-mediated signal compartmentation or reduce cAMP diffusion. Conclusion Although changes in T-tubule structure and β2AR-mediated cAMP signalling are significant even at 4-week post-MI, progression to the HF phenotype is not linear. At 8-week post-MI the loss of β2AR-mediated cAMP is temporarily reversed. Complete disorganization of β2AR-mediated cAMP signalling due to changes in functional receptor localization and cellular structure occurs at 16-week post-MI.

String Actuated Curved Folded Surfaces

Curved folded surfaces, given their ability to produce elegant freeform shapes by folding flat sheets etched with curved creases, hold a special place in computational Origami. Artists and designers have proposed a wide variety of different fold patterns to create a range of interesting surfaces. The creative process, design, as well as fabrication is usually only concerned with the static surface that emerges once folding has completed. Folding such patterns, however, is difficult as multiple creases have to be folded simultaneously to obtain a properly folded target shape. We introduce string actuated curved folded surfaces that can be shaped by pulling a network of strings, thus, vastly simplifying the process of creating such surfaces and making the folding motion an integral part of the design. Technically, we solve the problem of which surface points to string together and how to actuate them by locally expressing a desired folding path in the space of isometric shape deformations in terms of novel string actuation modes. We demonstrate the validity of our approach by computing string actuation networks for a range of well-known crease patterns and testing their effectiveness on physical prototypes. All the examples in this article can be downloaded for personal use from http://geometry.cs.ucl.ac.uk/projects/2017/string-actuated/.

SMASH: physics-guided reconstruction of collisions from videos

Collision sequences are commonly used in games and entertainment to add drama and excitement. Authoring even two body collisions in the real world can be difficult, as one has to get timing and the object trajectories to be correctly synchronized. After tedious trial-and-error iterations, when objects can actually be made to collide, then they are difficult to capture in 3D. In contrast, synthetically generating plausible collisions is difficult as it requires adjusting different collision parameters (e.g., object mass ratio, coefficient of restitution, etc.) and appropriate initial parameters. We present SMASH to directly read off appropriate collision parameters directly from raw input video recordings. Technically we enable this by utilizing laws of rigid body collision to regularize the problem of lifting 2D trajectories to a physically valid 3D reconstruction of the collision. The reconstructed sequences can then be modified and combined to easily author novel and plausible collisions. We evaluate our system on a range of synthetic scenes and demonstrate the effectiveness of our method by accurately reconstructing several complex real world collision events.

MCGraph: multi-criterion representation for scene understanding

The field of scene understanding endeavours to extract a broad range of information from 3D scenes. Current approaches exploit one or at most a few different criteria (e.g., spatial, semantic, functional information) simultaneously for analysis. We argue that to take scene understanding to the next level of performance, we need to take into account many different, and possibly previously unconsidered types of knowledge simultaneously. A unified representation for this type of processing is as of yet missing. In this work we propose MCGraph: a unified multi-criterion data representation for understanding and processing of large-scale 3D scenes. Scene abstraction and prior knowledge are kept separated, but highly connected. For this purpose, primitives (i.e., proxies) and their relationships (e.g., contact, support, hierarchical) are stored in an abstraction graph, while the different categories of prior knowledge necessary for processing are stored separately in a knowledge graph. These graphs complement each other bidirectionally, and are processed concurrently. We illustrate our approach by expressing previous techniques using our formulation, and present promising avenues of research opened up by using such a representation. We also distribute a set of MCGraph annotations for a small number of NYU2 scenes, to be used as ground truth multi-criterion abstractions.