mlsanityData to 7 Oct 2026

Does depth estimation help object detection?

arXiv 2204.06512 ↗First posted 13 Apr 2022cs.CVcited by 1

Not on the map. Why?

Abstract (arXiv)

Ground-truth depth, when combined with color data, helps improve object detection accuracy over baseline models that only use color. However, estimated depth does not always yield improvements. Many factors affect the performance of object detection when estimated depth is used. In this paper, we comprehensively investigate these factors with detailed experiments, such as using ground-truth vs. estimated depth, effects of different state-of-the-art depth estimation networks, effects of using different indoor and outdoor RGB-D datasets as training data for depth estimation, and different architectural choices for integrating depth to the base object detector network. We propose an early concatenation strategy of depth, which yields higher mAP than previous works' while using significantly fewer parameters.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 47 references that are in the map. First posted on arXiv 13 Apr 2022.

Builds on

28 of 47 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  4. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  5. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  6. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  7. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  8. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  9. 2015Fast R-CNNcited by 2,930
  10. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  11. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  12. 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 819
16 more
  1. 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 731
  2. 2016Multi-View 3D Object Detection Network for Autonomous Drivingcited by 721
  3. 2014Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-Scale Convolutional Architecturecited by 483
  4. 2021Categorical Depth Distribution Network for Monocular 3D Object Detectioncited by 326
  5. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  6. 2019Learning Depth-Guided Convolutions for Monocular 3D Object Detectioncited by 169
  7. 2019Stereo R-CNN based 3D Object Detection for Autonomous Drivingcited by 163
  8. 2018Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundariescited by 144
  9. 2019Self-Supervised Monocular Depth Hintscited by 127
  10. 2014Learning Rich Features from RGB-D Images for Object Detection and Segmentationcited by 124
  11. 2021Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Mergingcited by 104
  12. 2018T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Taskscited by 70
  13. 2021Ground-aware Monocular 3D Object Detection for Autonomous Drivingcited by 65
  14. 2015Multimodal Deep Learning for Robust RGB-D Object Recognitioncited by 56
  15. 2016Exploiting Depth from Single Monocular Images for Object Detection and Semantic Segmentationcited by 10
  16. 2021Stereo CenterNet based 3D Object Detection for Autonomous Drivingcited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Depth as Prior Knowledge for Object Detectioncited by 0