The proliferation of 3D representations, from explicit meshes to implicit neural fields and
more, motivates the need for simulators agnostic to representation. We present a data …

Signed Distance Fields (SDFs) parameterized by neural networks have recently gained
popularity as a fundamental geometric representation. However, editing the shape encoded …

At the beginning of a project, architects convey design ideas via quick 2D diagrams, front
views, floor plans, and sketches. Consequently, many stakeholders have difficulty …

Stochastic geometry models have enjoyed immense success in graphics for modeling
interactions of light with complex phenomena such as participating media, rough surfaces …

Signed distance fields (SDFs) are a widely used implicit surface representation, with broad
applications in computer graphics, computer vision, and applied mathematics. To …

We introduce differentiable indirection–a novel learned primitive that employs differentiable
multi-scale lookup tables as an effective substitute for traditional compute and data …

This paper proposes an approach to bridge the gap between industrial applications of
Computer Aided Design (CAD) and Topology Optimization (TO) by enabling fast and …

We introduce RMMI, a novel reactive control framework for mobile manipulators operating in
complex, static environments. Our approach leverages a neural Signed Distance Field …

Implicit volumes are known for their ability to represent smooth shapes of arbitrary topology
thanks to hierarchical combinations of primitives using a structure called a blobtree. We …

Today's architectural engineering and construction (AEC) software require a learning curve
to generate a three-dimension building representation. This limits the ability to quickly …