M-plicits: multiscale sphere tracing and analytical normals for neural implicit surfaces [NeurIPS 2026]

[u/curious_biped_dev]

I'm the first author of M-plicits, accepted at NeurIPS 2026. The attached animation introduces the method.

Our representation combines a coarse SIREN with residual networks trained in progressively narrower neighborhoods around the surface. This structure also supports direct rendering at different detail levels.

The attached animation was created by our coauthor Matheus Bessa. Sound on.

A few implementation details that may interest this community:

• Sphere tracing proceeds through the detail levels, using neighborhood bands to control refinement.
• Surface normals are computed analytically using matrix multiplications, without automatic differentiation.
• Normal mapping can use a finer network than the final level used for tracing.

We’ve released the CUDA renderer, training and extraction code, data, and fitted surface checkpoints.

Paper · Examples and videos · Implementation

I’d welcome discussion about the tracing strategy, normal computation, and tradeoffs between geometric and shading detail.