AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings
<|reference_start|>AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings: Extracting planes from a 3D scene is useful for downstream tasks in robotics and augmented reality. In this paper we tackle the problem of estimating the planar surfaces in a scene from posed images. Our first finding is that a surprisingly competitive baseline results from combining popular clustering algorithms with recent improvements in 3D geometry estimation. However, such purely geometric methods are understandably oblivious to plane semantics, which are crucial to discerning distinct planes. To overcome this limitation, we propose a method that predicts multi-view consistent plane embeddings that complement geometry when clustering points into planes. We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.<|reference_end|>
@article{watson2024airplanes:,
    title={AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings},
    author={Jamie Watson, Filippo Aleotti, Mohamed Sayed, Zawar Qureshi, Oisin Mac
  Aodha, Gabriel Brostow, Michael Firman, Sara Vicente},
    journal={Proceedings of the IEEE/CVF Conference on Computer Vision and
  Pattern Recognition (CVPR), 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08960},
    primaryClass={cs.CV}
}