Unwind: Interactive Fish Straightening

The ScanAllFish project is a large-scale effort to scan all the world's 33,100 known species of fishes. It has already generated thousands of volumetric CT scans of fish species which are available on open access platforms such as the Open Science Framework. To achieve a scanning rate required for a project of this magnitude, many specimens are grouped together into a single tube and scanned all at once. The resulting data contain many fish which are often bent and twisted to fit into the scanner. Our system, Unwind, is a novel interactive visualization and processing tool which extracts, unbends, and untwists volumetric images of fish with minimal user interaction. Our approach enables scientists to interactively unwarp these volumes to remove the undesired torque and bending using a piecewise-linear skeleton extracted by averaging isosurfaces of a harmonic function connecting the head and tail of each fish. The result is a volumetric dataset of a individual, straight fish in a canonical pose defined by the marine biologist expert user. We have developed Unwind in collaboration with a team of marine biologists: Our system has been deployed in their labs, and is presently being used for dataset construction, biomechanical analysis, and the generation of figures for scientific publication

Nested Explorative Maps: A new 3D canvas for conceptual design in architecture

International audienceIn this digital age, architects still need to alternate between paper sketches and 3D modeling software for their designs. Indeed, while 3D models enable to explore different views, creating them at very early stages might reduce creativity since they do not allow to superpose several tentative designs nor to refine them progressively, as sketches do. To enable exploratory design in 3D, we introduce Nested Explorative Maps, a new system dedicated to interactive design in architecture. Our model enables coarse to fine sketching of nested architectural structures, enabling to progressively sketch a 3D building from floor plan to interior design, thanks to a series of nested maps able to spread in 3D. Each map allows the visual representation of uncertainty as well as the interactive exploration of the alternative, tentative options. We validate the model through a user study conducted with professional architects, enabling us to highlight the potential of Nested Explorative Maps for conceptual design in architecture.En cette ère du numérique, les architectes doivent encore alterner entre le croquis papier et logiciels de modélisation 3D afin de réaliser leurs conceptions. En effet, les modèles 3D permettent d’explorer différentes vues mais leur création à un stade très précoce peut impliquer une perte de la créativité car ils ne permettent pas de superposer plusieurs plans provisoires ni de les affiner progressivement, comme le font les esquisses. Pour permettre la conception exploratoire dans l'espace 3D, nous présentons Nested Explorative Maps, un nouveau système dédié à la conception interactive en architecture. Notre modèle permet de dessiner du grossier aux détails des structures architecturales imbriquées, afin de dessiner progressivement un bâtiment en 3D, du plan à la décoration intérieure, grâce à une série de cartes imbriquées capables de se répandre en 3D. Chaque carte permet de représenter visuellement l’incertitude et d’explorer de manière interactive les différentes options possibles. Une étude utilisateur réalisée auprès d'architectes professionnels nous a permis de valider notre modèle et de mettre en évidence le potentiel des cartes exploratoires imbriquées pour la conception conceptuelle en architecture

A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint

3D shape editing is widely used in a range of applications such as movie production, computer games and computer aided design. It is also a popular research topic in computer graphics and computer vision. In past decades, researchers have developed a series of editing methods to make the editing process faster, more robust, and more reliable. Traditionally, the deformed shape is determined by the optimal transformation and weights for an energy term. With increasing availability of 3D shapes on the Internet, data-driven methods were proposed to improve the editing results. More recently as the deep neural networks became popular, many deep learning based editing methods have been developed in this field, which is naturally data-driven. We mainly survey recent research works from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing methods. Both traditional methods and recent neural network based methods are reviewed