RevTet: Adaptive and Reversible Tetrahedral Neural Representations

Novel-view synthesis enables immersive exploration of scenes reconstructed from images, making radiance fields an important representation for visual computing. However, directly learned volumetric attributes remain difficult to combine with movable geometric support and reversible resolution changes on a conforming mesh. We introduce RevTet, a tetrahedral radiance representation that couples constrained vertex optimization with compatible newest-vertex bisection, explicit attribute transfer, and checked inverse events for capacity reuse and levels of detail. Experiments show that importance-guided refinement and vertex motion improve reconstruction in controlled continuations, while hierarchical coarsening retains much of the learned image quality at reduced cell counts without retraining. These results support hierarchical tetrahedral adaptation as a means of controlling representation capacity while retaining direct field parameters and explicit connectivity, with lossy coarsening providing a tunable quality–size trade-off.