  Identifying spatially complete planar primitives from visual data is a
crucial task in computer vision. Prior methods are largely restricted to either
2D segment recovery or simplifying 3D structures, even with extensive plane
annotations. We present PlanarNeRF, a novel framework capable of detecting
dense 3D planes through online learning. Drawing upon the neural field
representation, PlanarNeRF brings three major contributions. First, it enhances
3D plane detection with concurrent appearance and geometry knowledge. Second, a
lightweight plane fitting module is proposed to estimate plane parameters.
Third, a novel global memory bank structure with an update mechanism is
introduced, ensuring consistent cross-frame correspondence. The flexible
architecture of PlanarNeRF allows it to function in both 2D-supervised and
self-supervised solutions, in each of which it can effectively learn from
sparse training signals, significantly improving training efficiency. Through
extensive experiments, we demonstrate the effectiveness of PlanarNeRF in
various scenarios and remarkable improvement over existing works.
