mlsanityData to 7 Oct 2026

Depth from Camera Motion and Object Detection

arXiv 2103.01468 ↗First posted 2 Mar 2021cs.CV cs.ROcited by 6

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Abstract (arXiv)

This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e.g., from robot kinematics or vehicle odometry). We achieve this by 1) designing a recurrent neural network (DBox) that estimates the depth of objects using a generalized representation of bounding boxes and uncalibrated camera movement and 2) introducing the Object Depth via Motion and Detection Dataset (ODMD). ODMD training data are extensible and configurable, and the ODMD benchmark includes 21,600 examples across four validation and test sets. These sets include mobile robot experiments using an end-effector camera to locate objects from the YCB dataset and examples with perturbations added to camera motion or bounding box data. In addition to the ODMD benchmark, we evaluate DBox in other monocular application domains, achieving state-of-the-art results on existing driving and robotics benchmarks and estimating the depth of objects using a camera phone.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 35 of its 54 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

35 of 54 references.

References · 35

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23 more
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Cited by year

Citing papers per year: 2022: 5; 2023: 1.

6 papers.

  1. 2022DETRs with Hybrid Matchingcited by 149
  2. 2022STS: Surround-view Temporal Stereo for Multi-view 3D Detectioncited by 53
  3. 2022Mobile Robot Manipulation using Pure Object Detectioncited by 8
  4. 2022EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentationcited by 1
  5. 2023A survey on deep learning approaches for data integration in autonomous driving systemcited by 1
  6. 2022Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systemscited by 0