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Research map / 3D vision / NeRF and radiance fields

TinyNeRF: Towards 100 x Compression of Voxel Radiance Fields

AAAI 2023 · 3D vision › NeRF and radiance fields · approach: Sparse-input NeRF regularization · 16 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Compress DVGO voxel grids to 2MB using block-wise DCT, pruning, and quantizationDVGO · Synthetic NeRFPSNR: 31.95 -> 31.72Hurt
Compress DVGO voxel grids to 2MB using block-wise DCT, pruning, and quantizationDVGO · Synthetic-NSVFPSNR: 35.08 -> 34.62Hurt
Compress DVGO voxel grids to 2MB using block-wise DCT, pruning, and quantizationDVGO · Blended MVSPSNR: 28.02 -> 28.02No clear effect
Compress DVGO voxel grids to 2MB using block-wise DCT, pruning, and quantizationDVGO · T&TPSNR: 28.41 -> 28.19Hurt
Compress DVGO voxel grids to 2MB using block-wise DCT, pruning, and quantizationDVGO · Synthetic NeRFmodel size (MB): 203 -> 2Helped
Use frequency-domain rather than spatial-domain pruning and quantization (ablation)TinyNeRF · Synthetic NeRFPSNRHelped
Apply block-wise DCT instead of whole-grid DCT (ablation)TinyNeRF · Synthetic NeRFPSNRHelped
Fine-tune the compressed coefficients after transforming, pruning, and quantizing (ablation)TinyNeRF · Synthetic NeRFPSNR: 31.95 -> 31.68Helped
Vary pruning ratios and quantization bit widths across the reported configurations (ablation)TinyNeRF · Synthetic NeRFPSNR: 31.72; 31.59; 31.59; 31.54No clear effect
Use block-wise DCT, pruning, and quantization at 2MBDVGO · Synthetic NeRFtraining time: 3 minutes -> 6.5 minutesHurt

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