Part of International Conference on Representation Learning 2025 (ICLR 2025) Conference
Hanqiao Ye, Yuzhou Liu, Yangdong Liu, Shuhan Shen
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.