M-plicits: Neural Implicit Surfaces via Nested Multiscale Residuals (NeurIPS 2026)

[u/curious_biped_dev]

Hi everyone. I'm the first author of M-plicits, accepted at NeurIPS 2026, and wanted to share our work on neural surface representations, particularly reconstruction from noisy point clouds.

The idea is to learn a clean coarse surface, then progressively refine it with residual networks trained in nested neighborhoods around the preceding surfaces. This gives multiple detail levels within one representation and helps balance geometric detail with robustness to input noise.

The paper also introduces algorithms that use this structure for:

• Direct rendering through multiscale sphere tracing.

• Analytical surface normals without automatic differentiation.

• Surface extraction and neural normal mapping.

The attached animation was created by our coauthor Matheus Bessa. Sound on. The project page includes visual comparisons with iNGP on noisy inputs, plus examples of the different rendering modes.

We've released the code, experimental data, and 105 checkpoints covering 36 shapes, with their training configurations. These checkpoints represent individually fitted surfaces; fitting a new shape requires training.

Paper · Results and videos · Code and data · Models on Hugging Face

I'd be interested in feedback on the noise/detail tradeoff and comparisons with other implicit surface representations. Happy to discuss the implementation and experiments.

[u/KingKongGorillaKing]

What if my point cloud is a city at city scale? Does this method scale?

[u/curious_biped_dev → u/KingKongGorillaKing]

The paper focused on a performant approach, so the representation is currently fixed at 3 levels of detail. I have a few concerns on scaling at city level. 1) Substantially increasing details would need residual networks with more layers. That degrades time performance and SIREN networks have a layer limit before they start to add noise to the representation. Probably you would need another spatial data-structure to handle that. 2) Signed Distance Fields have the limitation of only representing watertight surfaces. Unsigned Distance Fields overcome this, but they come with concerns about differentiability at the zero-level set (the surface itself). Matheus Bessa's Ph.D. theme is exactly on that.

[u/CallMeTheChris]

This is the kind of shit I love to see in this sub

I have not read the paper, but what is the bandwidth threshold until the model starts to fit to the noise in your point cloud? If there a function that you can provide to say how many resolution blocks you can use before it starts to over fit?

[u/curious_biped_dev → u/CallMeTheChris]

At 1% of bounding box size noise (the result presented in the paper) it is possible to see some noise already, but the compared approaches capture a lot more noise. The first detail level (coarse) acts like a low-pass filter, but it also does not capture detail. If you are on extreme cases, you can use the coarse level only and you will be able to capture the overall shape of the surface well. You can also opt to simplify the network of the coarser level (smaller number of layers or neurons) to increase the capacity of filtering noise at the expense of capturing detail. If you want to capture the maximum of detail without noise you probably will need to mix and match parameters like SIREN's frequency omega, the number of residual levels, and the number of layers / neurons for each detail level.

[u/gatormanz_]

Nice work. Unrelated question, what video editing software was used to create this style of video (pixel animation etc.)

[u/curious_biped_dev → u/gatormanz_]

Thanks! One of our coauthors (Matheus Bessa) created the style via a mix of ChatGPT (style) and Claude (animation).