Geometric Neural Process Fields

• URL: http://arxiv.org/abs/2502.02338v1
• Date: Tue, 04 Feb 2025 14:17:18 GMT
• Title: Geometric Neural Process Fields
• Authors: Wenzhe Yin, Zehao Xiao, Jiayi Shen, Yunlu Chen, Cees G. M. Snoek, Jan-Jakob Sonke, Efstratios Gavves,
• Abstract summary: Geometric Neural Process Fields (G-NPF) is a probabilistic framework for neural radiance fields that explicitly captures uncertainty. Building on these bases, we design a hierarchical latent variable model, allowing G-NPF to integrate structural information across multiple spatial levels. Experiments on novel-view synthesis for 3D scenes, as well as 2D image and 1D signal regression, demonstrate the effectiveness of our method.
• Score: 58.77241763774756
• License:
• Abstract: This paper addresses the challenge of Neural Field (NeF) generalization, where models must efficiently adapt to new signals given only a few observations. To tackle this, we propose Geometric Neural Process Fields (G-NPF), a probabilistic framework for neural radiance fields that explicitly captures uncertainty. We formulate NeF generalization as a probabilistic problem, enabling direct inference of NeF function distributions from limited context observations. To incorporate structural inductive biases, we introduce a set of geometric bases that encode spatial structure and facilitate the inference of NeF function distributions. Building on these bases, we design a hierarchical latent variable model, allowing G-NPF to integrate structural information across multiple spatial levels and effectively parameterize INR functions. This hierarchical approach improves generalization to novel scenes and unseen signals. Experiments on novel-view synthesis for 3D scenes, as well as 2D image and 1D signal regression, demonstrate the effectiveness of our method in capturing uncertainty and leveraging structural information for improved generalization.

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