ML Anthology

Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction

Yifei Shi, Angel X. Chang, Zhelun Wu, Manolis Savva, Kai Xu
CVPR 2019
doi:10.1109/CVPR.2019.00187 /cvpr/2019/shi2019cvpr-hierarchy/

Abstract

Indoor scenes exhibit rich hierarchical structure in 3D object layouts. Many tasks in 3D scene understanding can benefit from reasoning jointly about the hierarchical context of a scene, and the identities of objects. We present a variational denoising recursive autoencoder (VDRAE) that generates and iteratively refines a hierarchical representation of 3D object layouts, interleaving bottom-up encoding for context aggregation and top-down decoding for propagation. We train our VDRAE on large-scale 3D scene datasets to predict both instance-level segmentations and a 3D object detections from an over-segmentation of an input point cloud. We show that our VDRAE improves object detection performance on real-world 3D point cloud datasets compared to baselines from prior work.

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Cite

Text

Shi et al. "Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019. doi:10.1109/CVPR.2019.00187

Markdown

[Shi et al. "Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.](https://mlanthology.org/cvpr/2019/shi2019cvpr-hierarchy/) doi:10.1109/CVPR.2019.00187

BibTeX

@inproceedings{shi2019cvpr-hierarchy,
  title     = {{Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction}},
  author    = {Shi, Yifei and Chang, Angel X. and Wu, Zhelun and Savva, Manolis and Xu, Kai},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2019},
  doi       = {10.1109/CVPR.2019.00187},
  url       = {https://mlanthology.org/cvpr/2019/shi2019cvpr-hierarchy/}
}