3D maps assembled from planar primitives are compact and expressive in representing man-made environments, making them suitable for a spectrum of applications. 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. NeuralPlane realizes this idea via several key designs, including: 1) a monocular module that generates geometrically smooth and semantically meaningful segments as 2D plane observations, 2) a plane-guided training procedure that implicitly learns accurate plane locations from 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 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 results in both geometry and semantics