<b>NeRF</b><ahref="https://www.matthewtancik.com/nerf">[Mildenhall et al. 2020]</a> uses 2D images and their camera poses to reconstruct a volumetric radiance-and-density field that is visualized using ray marching.
<b>NeRF</b><ahref="https://www.matthewtancik.com/nerf">[Mildenhall et al. 2020]</a> uses 2D images and their camera poses to reconstruct a volumetric radiance-and-density field that is visualized using ray marching.
Lastly, <b>neural volume</b> learns a denoised radiance and density field directly from a volumetric path tracer.
Lastly, <b>neural volume</b> learns a denoised radiance and density field directly from a volumetric path tracer.
In all tasks, our encoding and its efficient implementation provide clear benefits: instant training, high quality, and simplicity. Our encoding is task-agnostic: we use the same implementation and hyperparameters across all tasks and only vary the hash table size which trades off quality and performance.
In all tasks, our encoding and its efficient implementation provide clear benefits: instant training, high quality, and simplicity. Our encoding is task-agnostic: we use the same implementation and hyperparameters across all tasks and only vary the hash table size which trades off quality and performance.
Real-time training progress on the image task where the neural network learns the mapping from 2D coordinates to RGB colors of a high-resolution image. Note that in this video, the network is trained from scratch—but converges so quickly you may miss it if you blink!<br/>
Real-time training progress on the image task where the neural network learns the mapping from 2D coordinates to RGB colors of a high-resolution image. Note that in this video, the network is trained from scratch—but converges so quickly you may miss it if you blink!
We also support training NeRF-like radiance fields from the noisy output of a volumetric path tracer. Rays are fed in real-time to the network during training, which learns a denoised radiance field.
We also support training NeRF-like radiance fields from the noisy output of a volumetric path tracer. Rays are fed in real-time to the network during training, which learns a denoised radiance field.<br/>
Real-time training progress on various SDF datsets. Training data is generated on the fly from the ground-truth mesh using the <ahref="https://developer.nvidia.com/optix">NVIDIA OptiX raytracing framework</a>.
Real-time training progress on various SDF datsets. Training data is generated on the fly from the ground-truth mesh using the <ahref="https://developer.nvidia.com/optix">NVIDIA OptiX raytracing framework</a>.
<em>Lucy</em> model from the <ahref="http://graphics.stanford.edu/data/3Dscanrep/">Stanford 3D scan repository</a>
<em>Lucy</em> model from the <ahref="http://graphics.stanford.edu/data/3Dscanrep/">Stanford 3D scan repository</a>
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<em>Factory robot</em> dataset by Arman Toorians and Saurabh Jain.
<em>Factory robot</em> dataset by Arman Toorians and Saurabh Jain
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<em>Disney Cloud</em> model by Walt Disney Animation Studios. (<ahref="https://media.disneyanimation.com/uploads/production/data_set_asset/6/asset/License_Cloud.pdf">CC BY-SA 3.0</a>)
