🧪 Test?View on arXiv
M-plicits: Neural Implicit Surfaces via Nested Multiscale Residuals
D. Silva Vinicius, Author 2, Author 3, Author 4, Author 5
neural renderingimplicit surfacesmultiscalenoise robustness
2609.28684
Builder Relevance
1h ago80%
Abstract
M-plicits introduces a multiscale framework for modeling surfaces as a residual sum of MLPs, improving noise robustness and rendering efficiency.
Reality Card
Core Claim
M-plicits achieves superior noise robustness and rendering speed compared to existing methods while using significantly fewer parameters.
Method / Result
Achieves the best mean Chamfer distance in coarse configuration and the best median Chamfer distance and IoU in fine configuration.
Limitations
The method's reliance on a specific nested neighborhood sampling may limit generalizability to other surface types.
Paper to code
Verified implementation resources so builders can test the paper’s claims instead of stopping at the abstract.
No verified implementation link has been attached yet. AIBuzzHub will keep this panel separate from unverified search results.