This is used for evaluation, ablations, and hypertuning Schedule many tasks across sets of scenes or hyperparameters. We provide a parallel task executor based on the task manager from PlenOctrees to automatically Python render_imgs_circle.py Parallel task executor The dataset format will be auto-detected from data_dir.įor pretrained checkpoints please see: Evaluationīy default this saves all frames, which is very slow. Where should be configs/syn.json for NeRF-synthetic scenes,Ĭonfigs/tnt.json for tanks and temples scenes, for example. To convert instant-ngp data, please try our scriptįor training a single scene, see opt/opt.py. Using it will trigger the nerf-synthetic (Blender) data loaderĭue to similarity, but will not train properly. Note: we currently do not support the instant-ngp format data (since the project was released before NGP). Note this data should be identical to that in NeRF++įinally, the real Lego capture can be downloaded from:
We provide a processed Tanks and temples dataset (with background) in NSVF format at: ( nerf_synthetic.zip and nerf_llff_data.zip). Please get the NeRF-synthetic and LLFF datasets from:
We have backends for NeRF-Blender, LLFF, NSVF, and CO3D dataset formats, and the dataset will be auto-detected. Adding support would be welcome.įirst create the virtualenv we recommend using conda:
#Apt install github cli windows#
Windows is not officially supported, and we have only tested with Linux. However, note that the JAX version is currently feature-limited, running in about 1 hour per epoch and only supporting bounded scenes (at present).Ĭheck out PeRFCeption, which uses Plenoxels with tuned parameters to generate a largeĪrtistic Radiance Fields by Kai Zhang et al This contains the official optimization code.Ī JAX implementation is also available at. Note that the joint first-authors decided to swap the order of names between arXiv and CVPR proceedings. Radiance Fields without Neural Networks},Īuthor=,
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