NeuralPlane: Structured 3D Reconstruction in Planar Primitives with Neural Fields
Keywords: 3D Reconstruction, 3D Scene Understanding, Scene Abstraction, Neural Rendering
TL;DR: NeuralPlane rebuilds indoor scenes as arrangements of planar primitives from multi-view images.
Abstract: 3D maps assembled from planar primitives are compact and expressive in representing man-made environments. In this paper, we present **NeuralPlane**, a novel approach that explores **neural** fields for multi-view 3D **plane** reconstruction. Our method is centered upon the core idea of distilling geometric and semantic cues from inconsistent 2D plane observations into a unified 3D neural representation, which unlocks the full leverage of plane attributes. It is accomplished through several key designs, including: 1) a monocular module that generates geometrically smooth and semantically meaningful segments known as 2D plane observations, 2) a plane-guided training procedure that implicitly learns accurate 3D geometry from the multi-view plane observations, and 3) a self-supervised feature field termed *Neural Coplanarity Field* that enables the modeling of scene semantics alongside the geometry. Without relying on prior plane annotations, our method achieves high-fidelity reconstruction comprising planar primitives that are not only crisp but also well-aligned with the semantic content. Comprehensive experiments on ScanNetv2 and ScanNet++ demonstrate the superiority of our method in both geometry and semantics.
Supplementary Material: zip
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 2933
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