People
Tero Karras
Tero Karras is a principal research scientist at NVIDIA Research, which he joined in 2009. His current research interests revolve around deep learning, generative models, and digital content creation. He has also had a pivotal role on NVIDIA's real-time ray tracing efforts, especially related to efficient acceleration structure construction and dedicated hardware units.
Main Field of Interest:
Machine Learning and Artificial Intelligence
Additional Research Areas:
Computer Graphics
Ray Tracing
Google Scholar:
https://scholar.google.fi/citations?user=-50qJW8AAAAJ
Publications
A Style-Based Generator Architecture for Generative Adversarial Networks
Texture Level of Detail Strategies for Real-Time Ray Tracing
Noise2Noise: Learning Image Restoration without Clean Data
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Audio-Driven Facial Animation by Joint End-to-End Learning of Pose and Emotion
Efficient Incoherent Ray Traversal on GPUs Through Compressed Wide BVHs
Production-Level Facial Performance Capture Using Deep Convolutional Neural Networks
Pruning Convolutional Neural Networks for Resource Efficient Inference
Apex Point Map for Constant-Time Bounding Plane Approximation
On Quality Metrics of Bounding Volume Hierarchies
Megakernels Considered Harmful: Wavefront Path Tracing on GPUs
Gradient-Domain Metropolis Light Transport
Fast Parallel Construction of High-Quality Bounding Volume Hierarchies
Understanding the Efficiency of Ray Traversal on GPUs - Kepler and Fermi Addendum
Maximizing Parallelism in the Construction of BVHs, Octrees, and k-d Trees
Improved Dual-Space Bounds for Simultaneous Motion and Defocus Blur
Efficient Triangle Coverage Tests for Stochastic Rasterization
Clipless Dual-Space Bounds for Faster Stochastic Rasterization
High-Performance Software Rasterization on GPUs
Stratified Sampling for Stochastic Transparency
Architecture Considerations for Tracing Incoherent Rays
Two Methods for Fast Ray-Cast Ambient Occlusion
Efficient Sparse Voxel Octrees - Analysis, Extensions, and Implementation
Efficient Sparse Voxel Octrees
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