Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Neural implicit representations, which encode a surface as the level set of a neural network
applied to spatial coordinates, have proven to be remarkably effective for optimizing …

We present a novel function representation (F-Rep) based geometric modeling kernel tailor-
made to support computer aided design (CAD) and fabrication of high resolution volumetric …

Reference datasets are a key tool in the creation of new algorithms. They allow us to
compare different existing solutions and identify problems and weaknesses during the …

Neural implicit surfaces are a promising tool for geometry processing that represent a solid
object as the zero level set of a neural network. Usually trained to approximate a signed …

While signed distance fields (SDFs) in theory offer infinite level of detail, they are typically
rendered using the sphere tracing algorithm at finite resolutions, which causes the common …

We exploit the temporal coherence of closed-form animated implicit surfaces by locally re-
evaluating an octree-like discretization of the implicit field only as and where is necessary to …

We evaluate the performance of a wide set of combinations of traversal and voxel
intersection testing of signed distance function grids in a path tracing setting. In addition, we …

Significance: Monte Carlo radiation transfer (MCRT) is the gold standard for modeling light
transport in turbid media. Typical MCRT models use voxels or meshes to approximate …

Using Lipschitz bounds as linear inclusion functions, we show that both Lipschitz-based ray-
tracing and bottom-up inclusion functions can be used together in the same framework. We …