RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing

• URL: http://arxiv.org/abs/2310.03507v1
• Date: Thu, 5 Oct 2023 12:39:27 GMT
• Title: RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path Tracing
• Authors: Antoine Scardigli, Lukas Cavigelli, Lorenz K. M\"uller
• Abstract summary: MonteCarlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts. We propose a framework with end-to-end training of a sampling importance network, a latent space encoder network, and a denoiser network.
• Score: 1.534667887016089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monte-Carlo path tracing is a powerful technique for realistic image synthesis but suffers from high levels of noise at low sample counts, limiting its use in real-time applications. To address this, we propose a framework with end-to-end training of a sampling importance network, a latent space encoder network, and a denoiser network. Our approach uses reinforcement learning to optimize the sampling importance network, thus avoiding explicit numerically approximated gradients. Our method does not aggregate the sampled values per pixel by averaging but keeps all sampled values which are then fed into the latent space encoder. The encoder replaces handcrafted spatiotemporal heuristics by learned representations in a latent space. Finally, a neural denoiser is trained to refine the output image. Our approach increases visual quality on several challenging datasets and reduces rendering times for equal quality by a factor of 1.6x compared to the previous state-of-the-art, making it a promising solution for real-time applications.

Related papers

• High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction [20.431360489828975]
  We propose a novel Monte Carlo sampling strategy to accelerate the sampling process and a corresponding denoiser, subpixel sampling reconstruction (SSR) Our method significantly outperforms previous approaches in denoising quality and reduces overall time costs, enabling real-time rendering capabilities at 2K resolution.
  arXiv  Detail & Related papers  (2023-01-03T10:43:22Z)
• AdaNeRF: Adaptive Sampling for Real-time Rendering of Neural Radiance Fields [8.214695794896127]
  Novel view synthesis has recently been revolutionized by learning neural radiance fields directly from sparse observations. rendering images with this new paradigm is slow due to the fact that an accurate quadrature of the volume rendering equation requires a large number of samples for each ray. We propose a novel dual-network architecture that takes an direction by learning how to best reduce the number of required sample points.
  arXiv  Detail & Related papers  (2022-07-21T05:59:13Z)
• Learning Neural Light Fields with Ray-Space Embedding Networks [51.88457861982689]
  We propose a novel neural light field representation that is compact and directly predicts integrated radiance along rays. Our method achieves state-of-the-art quality on dense forward-facing datasets such as the Stanford Light Field dataset.
  arXiv  Detail & Related papers  (2021-12-02T18:59:51Z)
• SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring [23.86692375792203]
  Image deblurring is a computer vision problem that aims to recover a sharp image from a blurred image. Our model uses dilated convolution to enable the obtainment of the large receptive field with high spatial resolution. We propose a novel module using the wavelet transform, which effectively helps the network to recover clear high-frequency texture details.
  arXiv  Detail & Related papers  (2021-10-12T07:58:10Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• NeRF in detail: Learning to sample for view synthesis [104.75126790300735]
  Neural radiance fields (NeRF) methods have demonstrated impressive novel view synthesis. In this work we address a clear limitation of the vanilla coarse-to-fine approach -- that it is based on a performance and not trained end-to-end for the task at hand. We introduce a differentiable module that learns to propose samples and their importance for the fine network, and consider and compare multiple alternatives for its neural architecture.
  arXiv  Detail & Related papers  (2021-06-09T17:59:10Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• Anytime Sampling for Autoregressive Models via Ordered Autoencoding [88.01906682843618]
  Autoregressive models are widely used for tasks such as image and audio generation. The sampling process of these models does not allow interruptions and cannot adapt to real-time computational resources. We propose a new family of autoregressive models that enables anytime sampling.
  arXiv  Detail & Related papers  (2021-02-23T05:13:16Z)
• Normalized Convolution Upsampling for Refined Optical Flow Estimation [23.652615797842085]
  Normalized Convolution UPsampler (NCUP) is an efficient joint upsampling approach to produce the full-resolution flow during the training of optical flow CNNs. Our proposed approach formulates the upsampling task as a sparse problem and employs the normalized convolutional neural networks to solve it. We achieve state-of-the-art results on Sintel benchmark with 6% error reduction, and on-par on the KITTI dataset, while having 7.5% fewer parameters.
  arXiv  Detail & Related papers  (2021-02-13T18:34:03Z)
• Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising [104.59305271099967]
  We present a pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising. We develop a pixel aggregation network for video denoising to sample pixels across the spatial-temporal space. Our method is able to solve the misalignment issues caused by large motion in dynamic scenes.
  arXiv  Detail & Related papers  (2021-01-26T13:00:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.