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Found 20 papers, 9 papers with code
Compact Neural Graphics Primitives with Learned Hash Probing
Neural graphics primitives are faster and achieve higher quality when their neural networks are augmented by spatial data structures that hold trainable features arranged in a grid.
Learning Radio Environments by Differentiable Ray Tracing
Ray tracing (RT) is instrumental in 6G research in order to generate spatially-consistent and environment-specific channel impulse responses (CIRs).
Adaptive Shells for Efficient Neural Radiance Field Rendering
We then extract an explicit mesh of a narrow band around the surface, with width determined by the kernel size, and fine-tune the radiance field within this band.
Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow.
Graph Neural Networks for Channel Decoding
In this work, we propose a fully differentiable graph neural network (GNN)-based architecture for channel decoding and showcase a competitive decoding performance for various coding schemes, such as low-density parity-check (LDPC) and BCH codes.
GPU-Accelerated Machine Learning in Non-Orthogonal Multiple Access
Non-orthogonal multiple access (NOMA) is an interesting technology that enables massive connectivity as required in future 5G and 6G networks.
Deep Learning-Based Synchronization for Uplink NB-IoT
We propose a neural network (NN)-based algorithm for device detection and time of arrival (ToA) and carrier frequency offset (CFO) estimation for the narrowband physical random-access channel (NPRACH) of narrowband internet of things (NB-IoT).
RTMV: A Ray-Traced Multi-View Synthetic Dataset for Novel View Synthesis
We present a large-scale synthetic dataset for novel view synthesis consisting of ~300k images rendered from nearly 2000 complex scenes using high-quality ray tracing at high resolution (1600 x 1600 pixels).
Ranked #1 on
Novel View Synthesis
on RTMV
Sionna: An Open-Source Library for Next-Generation Physical Layer Research
Sionna is a GPU-accelerated open-source library for link-level simulations based on TensorFlow.
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate.
GPU-accelerated partially linear multiuser detection for 5G and beyond URLLC systems
In this feasibility study, we have implemented a recently proposed partially linear multiuser detection algorithm in reproducing kernel Hilbert spaces (RKHSs) on a GPU-accelerated platform.
Real-time Neural Radiance Caching for Path Tracing
Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i. e. we opt for training the radiance cache while rendering.
Compressing 1D Time-Channel Separable Convolutions using Sparse Random Ternary Matrices
We demonstrate that 1x1-convolutions in 1D time-channel separable convolutions may be replaced by constant, sparse random ternary matrices with weights in $\{-1, 0,+1\}$.
Artificial Neural Networks generated by Low Discrepancy Sequences
Artificial neural networks can be represented by paths.
Neural Control Variates
We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration.
Instant Quantization of Neural Networks using Monte Carlo Methods
Low bit-width integer weights and activations are very important for efficient inference, especially with respect to lower power consumption.
Rethinking Full Connectivity in Recurrent Neural Networks
We study structurally sparse RNNs, showing that they are well suited for acceleration on parallel hardware, with a greatly reduced cost of the recurrent operations as well as orders of magnitude less recurrent weights.
Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete Probability Distributions
We compare different methods for sampling from discrete probability distributions and introduce a new algorithm which is especially efficient on massively parallel processors, such as GPUs.
Distributed, Parallel, and Cluster Computing Graphics
Integral Equations and Machine Learning
As both light transport simulation and reinforcement learning are ruled by the same Fredholm integral equation of the second kind, reinforcement learning techniques may be used for photorealistic image synthesis: Efficiency may be dramatically improved by guiding light transport paths by an approximate solution of the integral equation that is learned during rendering.
Learning Light Transport the Reinforced Way
We show that the equations of reinforcement learning and light transport simulation are related integral equations.
