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Compressing Volumetric Radiance Fields to 1 MB

CVPR 2023 · 3D vision › NeRF and radiance fields · approach: Sparse-input NeRF regularization · 73 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Compress DVGO with voxel pruning, vector quantization, joint finetuning, and weight quantizationDVGO · Synthetic-NeRFmodel size: 105.9 -> 1.4Helped
Compress Plenoxels with VQRFPlenoxels · Synthetic-NeRFmodel size: 259.8 -> 13.7Helped
Compress TensoRF with VQRFTensoRF · Synthetic-NeRFmodel size: 67.6 -> 3.6Helped
Compress DVGO with VQRFDVGO · Synthetic-NeRFPSNR: 31.90 -> 31.77Hurt
Compress Plenoxels with VQRFPlenoxels · Synthetic-NeRFPSNR: 31.71 -> 31.53Hurt
Compress TensoRF with VQRFTensoRF · Synthetic-NeRFPSNR: 33.09 -> 32.86Hurt
Add voxel pruning (ablation)DVGO · Synthetic-NeRFmodel size: 105.9 -> 19.0Helped
Add vector quantization after pruning (ablation)DVGO · Synthetic-NeRFPSNR: 31.88 -> 31.32Hurt
Add joint finetuning after vector quantization (ablation)DVGO · Synthetic-NeRFPSNR: 31.32 -> 31.79Helped
Add weight quantization (ablation)DVGO · Synthetic-NeRFmodel size: 4.8 -> 1.4Helped
Increase codebook size from 4096 to 16384 (ablation)VQ-DVGO · Synthetic-NeRFPSNR: 31.77 -> 31.81Helped
Increase codebook size from 4096 to 16384 (ablation)VQ-DVGO · Synthetic-NeRFmodel size: 1.431 -> 1.630Hurt

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