You can’t really compare HyperNeRFs to Plenoxels because the former does so much more. Vanilla NeRF has just under half a million weights (if we ignore the coa

You can’t really compare HyperNeRFs to Plenoxels because the former does so much more. Vanilla NeRF has just under half a million weights (if we ignore the coarse network), although more recent work has shown comparable results with only four layers. To compare apples-to-apples, we should probably also apply the (hand-tuned) TVD regularization used in plenoxels. Plenoxels store 28 parameters per grid point, so at a 256^3 grid size this would be just under half a billion parameters. You don’t know which voxels you can prune at the outset (btw, DNNs can be pruned as well), but for real scenes they show sparsity reaching ~5% after 12.8k iterations. For a real 360 deg scene, because the voxelized volume is quite bounded, you also need to consider the 64 layer multi-sphere image required to capture the background. Basically, you need a fairly large GPU card to train plenoxels on any kind of real scene. The option to make this space-compute tradeoff is still useful.