Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction

Yifei Shi, Angel X. Chang, Zhelun Wu, Manolis Savva, Kai Xu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 1771-1780

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.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Shi_2019_CVPR,
author = {Shi, Yifei and Chang, Angel X. and Wu, Zhelun and Savva, Manolis and Xu, Kai},
title = {Hierarchy Denoising Recursive Autoencoders for 3D Scene Layout Prediction},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}