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Found 14 papers, 3 papers with code
RANRAC: Robust Neural Scene Representations via Random Ray Consensus
We demonstrate the compatibility and potential of our solution for both photo-realistic robust multi-view reconstruction from real-world images based on neural radiance fields and for single-shot reconstruction based on light-field networks.
Neural inverse procedural modeling of knitting yarns from images
We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples.
Efficient 3D Reconstruction, Streaming and Visualization of Static and Dynamic Scene Parts for Multi-client Live-telepresence in Large-scale Environments
Despite the impressive progress of telepresence systems for room-scale scenes with static and dynamic scene entities, expanding their capabilities to scenarios with larger dynamic environments beyond a fixed size of a few square-meters remains challenging.
BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statistics
Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand.
Incomplete Gamma Kernels: Generalizing Locally Optimal Projection Operators
We present incomplete gamma kernels, a generalization of Locally Optimal Projection (LOP) operators.
Occlusion Fields: An Implicit Representation for Non-Line-of-Sight Surface Reconstruction
Non-line-of-sight reconstruction (NLoS) is a novel indirect imaging modality that aims to recover objects or scene parts outside the field of view from measurements of light that is indirectly scattered off a directly visible, diffuse wall.
Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs
Second, convolutional neural networks provide fast inference and generalize but either require large amounts of training data or a physics-constrained loss based on finite differences that can lead to inaccuracies and discretization artifacts.
FaDIV-Syn: Fast Depth-Independent View Synthesis using Soft Masks and Implicit Blending
Novel view synthesis is required in many robotic applications, such as VR teleoperation and scene reconstruction.
Physics-Informed Deep Learning of Incompressible Fluid Dynamics
Moreover, the trained neural networks offer a differentiable update step to advance the fluid simulation in time and, thus, can be used as efficient differentiable fluid solvers.
Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3D
Our method indicates strong improvements in terms of accuracy, speed and generalization capabilities over current 3D NN-based fluid models.
Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize
Our models significantly outperform a recent differentiable fluid solver in terms of computational speed and accuracy.
Bonn Activity Maps: Dataset Description
The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest.
Orthogonal Wasserstein GANs
In current state-of-the-art Wasserstein-GANs this constraint is enforced via gradient norm regularization.
SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence
Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in VR/AR settings, has seen tremendous progress in recent years.
Human-Computer Interaction
