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arXiv Computer Vision By Paul B\"uschl, Ezequiel de la Rosa, Julia Wolleb, Julian McGinnis, C\'esar Nombela-Arrieta, Bjoern Menze

Observation-Conditioned Latent Energy Priors for Sparse Implicit Neural Shape Completion

Read the original on arXiv Computer Vision →

The paper introduces a lightweight, observation-conditioned latent energy prior to improve inference for frozen implicit neural representation (INR) decoders when only sparse off‑grid signed distance function (SDF) samples are available. By standardizing latent codes based on a permutation‑invariant encoding of the sparse observations and combining this energy with a validation‑selected L2 prior, the method consistently outperforms baseline L2 and a six‑component Gaussian mixture model prior on both a controlled cell‑nucleus SDF dataset and a MedShapeNet‑derived SDF completion dataset, especially in the sparsest regimes. Ablation studies confirm that the energy term’s contribution is specific to the observed context rather than generic. whyItMatters":"The approach demonstrates that pretrained INR decoders can become more observation‑aware without retraining, improving shape completion accuracy in data‑sparse scenarios."

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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