6 research outputs found
KiloNeuS: A Versatile Neural Implicit Surface Representation for Real-Time Rendering
NeRF-based techniques fit wide and deep multi-layer perceptrons (MLPs) to a
continuous radiance field that can be rendered from any unseen viewpoint.
However, the lack of surface and normals definition and high rendering times
limit their usage in typical computer graphics applications. Such limitations
have recently been overcome separately, but solving them together remains an
open problem. We present KiloNeuS, a neural representation reconstructing an
implicit surface represented as a signed distance function (SDF) from
multi-view images and enabling real-time rendering by partitioning the space
into thousands of tiny MLPs fast to inference. As we learn the implicit surface
locally using independent models, resulting in a globally coherent geometry is
non-trivial and needs to be addressed during training. We evaluate rendering
performance on a GPU-accelerated ray-caster with in-shader neural network
inference, resulting in an average of 46 FPS at high resolution, proving a
satisfying tradeoff between storage costs and rendering quality. In fact, our
evaluation for rendering quality and surface recovery shows that KiloNeuS
outperforms its single-MLP counterpart. Finally, to exhibit the versatility of
KiloNeuS, we integrate it into an interactive path-tracer taking full advantage
of its surface normals. We consider our work a crucial first step toward
real-time rendering of implicit neural representations under global
illumination.Comment: 9 pages, 8 figure
Efficient Generation of Multimodal Fluid Simulation Data
Applying the representational power of machine learning to the prediction of
complex fluid dynamics has been a relevant subject of study for years. However,
the amount of available fluid simulation data does not match the notoriously
high requirements of machine learning methods. Researchers have typically
addressed this issue by generating their own datasets, preventing a consistent
evaluation of their proposed approaches. Our work introduces a generation
procedure for synthetic multi-modal fluid simulations datasets. By leveraging a
GPU implementation, our procedure is also efficient enough that no data needs
to be exchanged between users, except for configuration files required to
reproduce the dataset. Furthermore, our procedure allows multiple modalities
(generating both geometry and photorealistic renderings) and is general enough
for it to be applied to various tasks in data-driven fluid simulation. We then
employ our framework to generate a set of thoughtfully designed benchmark
datasets, which attempt to span specific fluid simulation scenarios in a
meaningful way. The properties of our contributions are demonstrated by
evaluating recently published algorithms for the neural fluid simulation and
fluid inverse rendering tasks using our benchmark datasets. Our contribution
aims to fulfill the community's need for standardized benchmarks, fostering
research that is more reproducible and robust than previous endeavors.Comment: 10 pages, 7 figure
Massive Uniform Mesh Decimation via a Fast Intrinsic Delaunay Triangulation
Triangular meshes are still today the data structure at the main foundations
of many computer graphics applications. With the increasing demand in content
variety, a lot of effort has been and is being put into developing new
algorithms to automatically generate and edit geometric assets, with a
particular focus on 3D scans. However, this kind of content is often generated
with a dramatically high resolution, making it impractical for a large variety
of tasks. Furthermore, procedural assets and 3D scans largely suffer from poor
geometry quality, which makes them unsuitable in various applications. We
propose a new efficient technique for massively decimating dense meshes with
high vertex count very quickly. The proposed method relies on a fast algorithm
for computing geodesic farthest point sampling and Voronoi partitioning, and
generates simplified meshes with high-quality uniform triangulations
Fluid Dynamics Network: Topology-Agnostic 4D Reconstruction via Fluid Dynamics Priors
Representing 3D surfaces as level sets of continuous functions over R3 is the common denominator of neural implicit representations, which recently enabled remarkable progress in geometric deep learning and computer vision tasks. In order to represent 3D motion within this framework, it is often assumed (either explicitly or implicitly) that the transformations which a surface may undergo are homeomorphic: this is not necessarily true, for instance, in the case of fluid dynamics. In order to represent more general classes of deformations, we propose to apply this theoretical framework as regularizers for the optimization of simple 4D implicit functions (such as signed distance fields). We show that our representation is capable of capturing both homeomorphic and topology-changing deformations, while also defining correspondences over the continuously-reconstructed surfaces
KiloNeuS: Implicit Neural Representations with Real-Time Global Illumination
The latest trends in inverse rendering techniques for reconstruction use neural networks to learn 3D representations as neural fields. NeRF-based techniques fit multi-layer perceptrons (MLPs) to a set of training images to estimate a radiance field which can then be rendered from any virtual camera by means of volume rendering algorithms. Major drawbacks of these representations are the lack of well-defined surfaces and non-interactive rendering times, as wide and deep MLPs must be queried millions of times per single frame. These limitations have recently been singularly overcome, but managing to accomplish this simultaneously opens up new use cases. We present KiloNeuS, a new neural object representation that can be rendered in path-traced scenes at interactive frame rates. KiloNeuS enables the simulation of realistic light interactions between neural and classic primitives in shared scenes, and it demonstrably performs in real-time with plenty of room for future optimizations and extensions