Cocoon is an add-on to McNeel’s Grasshopper visual scripting interface for Rhinoceros. Cocoon is a fairly straightforward implementation of the Marching Cubes algorithm for turning iso-surfaces into polygonal meshes. It is geared specifically toward wrapping existing geometric elements, and works with combinations of points, breps and curves, allowing users to vary a number of parameters that enhance sculptural potentials. It is still rough (and there are definitely a number of other approaches to level sets and isosurfacing that are faster, more robust, more elegant, and/or have more potential) but due to time constraints related to other work I am doing – now and into the near future – I thought it effective and fun enough that it was worth it to make this available to the community. As such, though, general caveats apply: it’s probably easy to break, and it will definitely generate some artifacts. But please download and have a play, and feedback on the grasshopper forum is welcome.
NVScene 2015 Session: How to Create Content with Signed Distance Functions (Johann Korndorfer)
the timeless – mercury
K3DSurf use parametric descriptions of it’s physical models. The parametric method of representing surfaces/curves uses a function to map some portion of R2 (the domain) to a patch of the surface in R3.
Because any position in the plane, and thus any position on the surface patch, can be uniquely given by two coordinates, the surface is said to be parameterized by those coordinates.
Parametric equations can be either “Implicit” or “Explicit”:
** Explicit equations:
In an explicit equations, x, y, and z are each given by separate functions of parameters u and v.
Example: X =u, Y = u+v, Z = cos(u+v)
** Implicit equations: Right now, only implicit equations like Z^n = f(X,Y) with (n mod 2 = 1) are supported by K3DSurf.
Example: Z = exp(x^2 + y^2), Z^7 = exp(x*cos(y))…
We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution – but complete – output. To this end, we introduce a 3D-EncoderPredictor Network (3D-EPN) which is composed of 3D convolutional layers. The network is trained to predict and fill in missing data, and operates on an implicit surface representation that encodes both known and unknown space. This allows us to predict global structure in unknown areas at high accuracy. We then correlate these intermediary results with 3D geometry from a shape database at test time. In a final pass, we propose a patch-based 3D shape synthesis method that imposes the 3D geometry from these retrieved shapes as constraints on the coarsely-completed mesh. This synthesis process enables us to reconstruct finescale detail and generate high-resolution output while respecting the global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms state-of-the-art completion method, the main contribution in our work lies in the combination of a data-driven shape predictor and analytic 3D shape synthesis. In our results, we show extensive evaluations on a newly-introduced shape completion benchmark for both real-world and synthetic data.
The point of this lib is that everything is structured according to patterns that we ended up using when building geometry. It makes it more easy to write code that is reusable and that somebody else can actually understand. Especially code on Shadertoy (which seems to be what everybody else is looking at for “inspiration”) tends to be really ugly. So we were forced to do something about the situation and release this lib 😉
3D Printing – The New Drive In Automotive Manufacturing
The global automotive industry sector is set back by its own internal competition. While a third of the industry revenue is accounted by the largest OEMs, a stiff competition lies among the manufacturers of automobile accessories and parts. Additive manufacturing technology brings to the industry areas to ponder about, which might turn out to become a potential game changer soon.
Here are some points that show how 3D printing is indeed the next revolution to the automotive industry:
#1 – Innovation of Flexible Design
Unlike traditional manufacturing methods, 3D printing produces components with fewer restrictions when it comes to design. The enormous flexibility allows manufacturers the freedom to include customized features and new l3functionalities such as complex geometries, electrical wiring through hollowed structures, printed parts made of multiple materials, and lightweight lattice structures, which are lighter, safer and faster.
Symvol™ for Rhino, a plugin to the Rhinoceros® 3D modeling system, is a volume-based modeling extension for the creation of both organic and mechanical objects that are always watertight and ideal for 3D printing.
With Symvol, it more like working with a malleable material, such as clay or metal, as opposed to existing modeling systems which seem more like working with a collection of paper sheets glued together.
Whatever is done during modeling in Symvol, the volume created is always a valid solid object – never any cracks and other surface issues.
At the Formlabs hackathon, we made a tiny 3D printer that uses a DLP light engine to cure resin (rather than our usual lasers and galvos). As part of this project, I wrote a small slicer that renders a mesh into a series of bitmap images (with some help from our general counsel, Martin). The slicer runs in a browser and is completely client-side. It accepts a STL file and downloads a zip file full of .png images.
It used to be hard to create applications that generate 3D printable geometry.
With ShapeJS it’s as simple as a few lines of code!