title: Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective
    Distillation and Unlabeled Data Augmentation
summary: Accurately estimating depth in 360-degree imagery is crucial for virtual
  reality, autonomous navigation, and immersive media applications. Existing
  depth estimation methods designed for perspective-view imagery fail when
  applied to 360-degree images due to different camera projections and
  distortions, whereas 360-degree methods perform inferior due to the lack of
  labeled data pairs. We propose a new depth estimation framework that utilizes
  unlabeled 360-degree data effectively. Our approach uses state-of-the-art
  perspective depth estimation models as teacher models to generate pseudo labels
  through a six-face cube projection technique, enabling efficient labeling of
  depth in 360-degree images. This method leverages the increasing availability
  of large datasets. Our approach includes two main stages: offline mask
  generation for invalid regions and an online semi-supervised joint training
  regime. We tested our approach on benchmark datasets such as Matterport3D and
  Stanford2D3D, showing significant improvements in depth estimation accuracy,
  particularly in zero-shot scenarios. Our proposed training pipeline can enhance
  any 360 monocular depth estimator and demonstrates effective knowledge transfer
  across different camera projections and data types. See our project page for
  results: https://albert100121.github.io/Depth-Anywhere/
paper_authors._id: 6672e9972376de4b7ab47afd
paper_authors.name: Ning-Hsu Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-19T14:36:18.321Z
paper_authors._id: 64647db4fdbe694bc7537504
paper_authors.avatarUrl: /avatars/4df13f878599bc7310ddecdb29a381b0.svg
paper_authors.fullname: Albert Wang
paper_authors.type: user
paper_authors.user: Albert-NHWang
paper_authors._id: 6672e9972376de4b7ab47afe
paper_authors.name: Yu-Lun Liu
paper_title: Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective
    Distillation and Unlabeled Data Augmentation
paper_summary: Accurately estimating depth in 360-degree imagery is crucial for virtual
  reality, autonomous navigation, and immersive media applications. Existing
  depth estimation methods designed for perspective-view imagery fail when
  applied to 360-degree images due to different camera projections and
  distortions, whereas 360-degree methods perform inferior due to the lack of
  labeled data pairs. We propose a new depth estimation framework that utilizes
  unlabeled 360-degree data effectively. Our approach uses state-of-the-art
  perspective depth estimation models as teacher models to generate pseudo labels
  through a six-face cube projection technique, enabling efficient labeling of
  depth in 360-degree images. This method leverages the increasing availability
  of large datasets. Our approach includes two main stages: offline mask
  generation for invalid regions and an online semi-supervised joint training
  regime. We tested our approach on benchmark datasets such as Matterport3D and
  Stanford2D3D, showing significant improvements in depth estimation accuracy,
  particularly in zero-shot scenarios. Our proposed training pipeline can enhance
  any 360 monocular depth estimator and demonstrates effective knowledge transfer
  across different camera projections and data types. See our project page for
  results: https://albert100121.github.io/Depth-Anywhere/
paper_ai_keywords: six-face cube projection teacher models pseudo labels semi-supervised joint training offline mask generation zero-shot scenarios knowledge transfer