mlsanityData to 7 Oct 2026

N-QGN: Navigation Map from a Monocular Camera using Quadtree Generating Networks

arXiv 2202.11982 ↗First posted 24 Feb 2022cs.CVcited by 0

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Abstract (arXiv)

Monocular depth estimation has been a popular area of research for several years, especially since self-supervised networks have shown increasingly good results in bridging the gap with supervised and stereo methods. However, these approaches focus their interest on dense 3D reconstruction and sometimes on tiny details that are superfluous for autonomous navigation. In this paper, we propose to address this issue by estimating the navigation map under a quadtree representation. The objective is to create an adaptive depth map prediction that only extract details that are essential for the obstacle avoidance. Other 3D space which leaves large room for navigation will be provided with approximate distance. Experiment on KITTI dataset shows that our method can significantly reduce the number of output information without major loss of accuracy.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 24 Feb 2022.

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14 of 21 references.

References · 14

  1. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  2. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
  3. 2017Unsupervised Learning of Depth and Ego-Motion from Videocited by 778
  4. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  5. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  6. 2016Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescuecited by 368
  7. 2020D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometrycited by 206
  8. 2017Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputscited by 189
  9. 2020Hierarchical Neural Architecture Search for Deep Stereo Matchingcited by 166
  10. 2014Deep Convolutional Neural Fields for Depth Estimation from a Single Imagecited by 106
  11. 2017Occlusion Aware Unsupervised Learning of Optical Flowcited by 101
  12. 2021Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimationcited by 48
2 more
  1. 2019Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutionscited by 7
  2. 2020RocNet: Recursive Octree Network for Efficient 3D Deep Representationcited by 1

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