I kind of agree with you. I'd summarize it "lack of knowledge in mathematics" instead of "the code". But I disagree about the rate of progress in the field. Th

I kind of agree with you. I'd summarize it "lack of knowledge in mathematics" instead of "the code". But I disagree about the rate of progress in the field. This work is very impressive, I do agree. But I remain skeptical because they only used synthetic data for the robot demo. And that golden necklace can already be reconstructed in similar quality with other tools that use explicit algorithms. For example, the block matching and voxel volume tracing inside Agisoft Photoscan works wonders for reflective mostly smooth objects with high frequency surface detail. And the idea of using known geometry + pictures to estimate a lighting envmap and PBR material maps was already published 10+ years ago. The only difference between then and now is the growth in compute power. We already showed that this works on toy examples years ago, and now we have the compute power to use it in practice. But despite all that research, give any of these algorithms (including the instant-NGP NeRF, that this appears to be based on) a few pictures of a tree, and it'll fail. What all of these approaches still lack is a way to use pre-acquired knowledge of the real world. And that's crucial to resolve ambiguities, like differentiating between lots of similar-looking similar-colored leaves.