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Default is 50000 (eval) and 128 (train) make it smaller if you run out of memory. -R : Batch size of rendered rays per object.If not specified, uses GPU 0.Įxamples: -gpu_id=0 or -gpu_id='0 1 3'. All scripts except calc_metrics.pyĪre parallelized. -gpu_id : GPU id(s) to use, space delimited.
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To save typing, you can set a default data directory for each expname in nf under datadir.
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This is due to the very small size of the dataset. Note that for DTU, I only use train/val sets, where val is used for test. Result will be at visuals/dtu/videov.mp4.
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Remove -scale 0.25 to render at full resolution (quite slow). This is not DTU scene number but 0-14 in the val set. Replace -S 3 with -S to run on a different scene. Run using python eval/gen_video.py -n dtu -gpu_id= -split val -P '22 25 28' -D /rs_dtu_4 -S 3 -scale 0.25.Extract to some directory, to get: /rs_dtu_4
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Download NMR ShapeNet renderings (see Datasets section, 1st link).The main implementation is in the src/ directory,įirst, download all pretrained weight files fromĮxtract this to /checkpoints/, so that /checkpoints/dtu/pixel_nerf_latest exists. Our own two-object data is in NeRF's format.ĭata adapters are built into the code. While we could have used a common data format, we chose to keepĭTU and ShapeNet (NMR) datasets in DVR's format and SRN data in the original SRN format. Note the car set is a re-rendered version provided by Vincent Sitzmann SRN chair/car (128x128) srn_*.zip needed for single-category exps.DTU (4x downsampled, rescaled) in DVR's DTU format dtu_dataset.zip.See scripts/README.md for installation instructions.
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Please make sure you have up-to-date NVIDIA drivers supporting CUDA 10.2 at least.Īlternatively use pip -r requirements.txt.