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Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving
  • Yi-Nan Chen, Zhejiang University, China
  • Hang Dai, Mohamed bin Zayed University of Artificial Intelligence
  • Yong Ding, Zhejiang University, China
Document Type

Pseudo-LiDAR 3D detectors have made remarkable progress in monocular 3D detection by enhancing the capability of perceiving depth with depth estimation networks, and using LiDAR-based 3D detection architectures. The advanced stereo 3D detectors can also accurately localize 3D objects. The gap in image-to-image generation for stereo views is much smaller than that in image-to-LiDAR generation. Motivated by this, we propose a Pseudo-Stereo 3D detection framework with three novel virtual view generation methods, including image-level generation, feature-level generation, and feature-clone, for detecting 3D objects from a single image. Our analysis of depth-aware learning shows that the depth loss is effective in only feature-level virtual view generation and the estimated depth map is effective in both image-level and feature-level in our framework. We propose a disparity-wise dynamic convolution with dynamic kernels sampled from the disparity feature map to filter the features adaptively from a single image for generating virtual image features, which eases the feature degradation caused by the depth estimation errors. Till submission (November 18, 2021), our Pseudo-Stereo 3D detection framework ranks 1st on car, pedestrian, and cyclist among the monocular 3D detectors with publications on the KITTI-3D benchmark. The code is released at © 2022, CC BY-NC-SA.

Publication Date
  • Object detection,
  • Object recognition,
  • Stereo image processing,
  • 3-D detectors,
  • 3D object,
  • Depth Estimation,
  • Detection framework,
  • Feature level,
  • Objects detection,
  • Pseudo stereos,
  • Single images,
  • View generation,
  • Virtual view,
  • Optical radar,
  • Computer Vision and Pattern Recognition (cs.CV)

Preprint: arXiv

Archived with thanks to arXiv

Preprint License: CC by NC-SA 4.0

Uploaded 30 May 2022

Citation Information
Y.N. Chen, H. Dai, and Y. Ding, "Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving", arXiv, Mar 2022, doi:10.48550/arXiv.2203.02112