Thank you! The code is unlikely to be released (it's built upon Meta-internal codebases that I no longer have access to post-internship), at least not in the form that we specifically used at submission time. The last time I caught up with the team someone was expressing interest in releasing some broadly useful rendering code, but I really can't speak on their behalf so no guarantees.

IMHO it's a really exciting time to be in the neural rendering / 3D vision space - the field is moving quickly and there's interesting work across all dimensions. My personal interests lean towards large-scale 3D reconstruction, and to that effect eliminating the need for traditional SfM/COLMAP preprocessing would be great. There's a lot of relevant recent work (https://dust3r.europe.naverlabs.com/, https://cameronosmith.github.io/flowmap/, https://vggsfm.github.io/, etc), but scaling these methods beyond several dozen images remains a challenge. I’m also really excited about using learned priors that can improve NeRF quality in underobserved regions (https://reconfusion.github.io). IMO using these priors will be super important to enabling dynamic 4D reconstruction (since it’s otherwise unfeasible to directly observe every space-time point in a scene). Finally, making NeRF environments more interactive (as other posts have described) would unlock many use cases especially in simulation (ie: for autonomous driving). This is kind of tricky for implicit representations (like the original NeRF and this work), but there have been some really cool papers in the 3D Gaussian space (https://xpandora.github.io/PhysGaussian/) that are exciting.