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Neural Ray-Tracing: Learning Surfaces and Reflectance …
https://arxiv.org/abs/2104.13562
Points-connecting neural network ray tracing - PubMed
https://pubmed.ncbi.nlm.nih.gov/34469953/
Unsupervised neural network ray tracing (NNRT) to calculate a light ray path connecting given points in a gradient-index medium is proposed here. If two points are given, the NNRT can provide a light ray path passing through these points without knowledge of the light ray direction.
Neural network algorithm for solving ray-tracing problem
https://ieeexplore.ieee.org/document/1201979/
Abstract: This work is dedicated to the study of neural network method for solving of ray-tracing task, which appears in 3D visualization algorithms. Physical representation of the task is the problem of finding the nearest point of the "vision" ray crossing with the surfaces of …
GitHub - princeton-computational …
https://github.com/princeton-computational-imaging/neural_raytracing
Neural Ray-tracing is an extension on top of NeRF & VolSDF to allow for efficient ray-marching, so that dynamic lighting conditions can be rendered. This is done by adding an additional network that accounts for lighting based on position and viewing direction, as well as learning correct surfaces such that an SDF can be quickly raymarched.
Raytracing for Deep Neural Networks Training
https://resources.nvidia.com/gtcd-2020/GTC2020t22124
GTC 2020. Learn how to prepare virtual scene and render images dedicated to train deep convolutional neural networks. Make sure that the model trained in virtual reality can be efficient and accurate in the real world. We present Sky Engine, the very first raytracer designed for data generation for deep neural networks training. Our NVIDIA OptiX based raytracer is tightly …
End-to-end sensor and neural network design using …
https://pubmed.ncbi.nlm.nih.gov/34809257/
The proposed ray tracing model makes no thin lens nor paraxial approximation, and is valid for any field of view and point source position. Using the gradient backpropagation framework for neural network optimization, any of the lens parameter can then be jointly optimized along with the neural network parameters.
Take a Deep Dive into Ray Tracing, Machine Learning and …
https://developer.nvidia.com/blog/take-a-deep-dive-into-ray-tracing-machine-learning-and-neural-networks-through-siggraph-frontiers/
Starting on May 24, you can take part in industry-leading discussions around ray tracing, machine learning and neural networks. These pre-conference webinars are ongoing educational events that will take place through June 18, and feature several NVIDIA guest speakers. Both courses are free. Simply add the events to your calendar of choice here and show up to experience the …
Localization with Deep Neural Networks using mmWave …
https://ieeexplore.ieee.org/document/9249699/
The world is moving towards faster data transformation with more efficient localization of a user being the preliminary requirement. This work investigates the use of a deep learning technique for wireless localization, considering both millimeter-wave (mmWave) and sub-6 GHz frequencies. The capability of learning a new neural network model makes the …
SIGGRAPH in a Nutshell: Real-Time Ray Tracing Gets Realistic and ...
https://www.studiodaily.com/2018/08/siggraph-nutshell-real-time-ray-tracing-gets-realistic-machine-learning-gets-practical/
Nvidia. Showgoers’ time at the Vancouver Convention Centre was rewarded this year with an especially lively event. Nvidia dominated the news cycle with the release of its new Turing GPU architecture and RTX ray-tracing technology — and the widespread feeling, supported by any number of tech demos on the show floor, that hardware-accelerated real-time ray …
(PDF) DeepRay: Deep Learning Meets Ray-Tracing
https://www.researchgate.net/publication/358799082_DeepRay_Deep_Learning_Meets_Ray-Tracing
ray-tracing simulator and the computational efficiency of deep convolutional neural networks. Our goal is to de velop a data- driven model that (i) can …
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