OptiX: a general purpose ray tracing engine

By S. Parker, James Bigler, Andreas Dietrich, Heiko Friedrich, Jared Hoberock, David P. Luebke, David K. McAllister, M. McGuire, Keith Morley, Austin Robison and 1 more. ACM Transactions on Graphics, 2010

910 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/aa88eb88dbe1a24e92b85e66c4e8059192aa2aee

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

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Citations in the corpus

Ray Tracing for Radio Propagation Modeling: Principles and Applications (2015, methodology): “Although most applications of OptiX are in computer graphics, recently, it has been used to accelerate ray tracing for radio propagation modeling [89].”
Embree (2014, background): “These results indicate that Embree can reach interactive build rates on CPUs for multi-million triangle scenes, and this performance compares favorably to existing methods on CPUs and GPUs.”
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review (2022, neutral): “They also included hardware accelerated ray tracing [96] which skipped empty spaces, and stopped when the ray’s transmittance was saturated.”
The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial (2019, neutral): “The HPC contains 96 computing nodes (1600 CPUs and 10 GPUs), 1 managing node and 1 Web server.”
Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder (2017, methodology): “After this rasterization pass, we continue tracing the path using an NVIDIA OptiX-based GPU ray tracer [Parker et al. 2010].”
UniSim: A Neural Closed-Loop Sensor Simulator (2023, methodology): “Most prior sensor simulation systems use 3D-scanned or manually built synthetic environments for small indoor environments [33, 37, 67], and perform rasterization or ray-tracing [58,69] to simulate various sensor data [20,22,30].”
Orbit: A Unified Simulation Framework for Interactive Robot Learning Environments (2023, methodology): “Recent techniques [25] simulate the interaction of rays with object’s textures in a physically correct”
FastNeRF: High-Fidelity Neural Rendering at 200FPS (2021, methodology): “To further improve performance, we use hardware accelerated ray tracing [32] to skip empty space, starting to integrate points along the ray only after the first hit with a collision mesh derived from the density volume, and visiting every voxel thereafter until a ray’s transmittance is saturated.”
GPU-based out-of-core many-lights rendering (2013, neutral): “Recently, many GPU-based or GPU-friendly global illumination algorithms and systems [Zhou et al. 2008; Wang et al. 2009; Parker et al. 2010; Hachisuka et al. 2008; Kaplanyan and Dachsbacher 2013] have been presented to utilize the parallel computational power.”
Crowdsourcing in Computer Vision (2016, neutral): “These days, the major microprocessor vendors provide basic libraries for efficient ray tracing on CPUs and GPUs—for example Embree [127] and OptiX [90].”
Optimizing Data Warehousing Applications for GPUs Using Kernel Fusion/Fission (2012, background): “This expectation is motivated by the fact that GPUs have demonstrated significant performance improvements for data intensive applications such as molecular dynamics [3], physical simulations [4] in science, options pricing [5] in finance, and ray tracing [6] in graphics.”