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🔗 Source: proceedings.mlr.press
📊 Size: 2.3 MB
👤 Author: Anonymous Submission
⬇️ Downloads: 81

Summary

The study reveals that inaccurate instance depth estimation is the primary bottleneck in this task, and simplifies monocular 3D detection as an instance depth estimation problem. The authors propose Probabilistic and Geometric Depth (PGD), a method that jointly leverages probabilistic depth uncertainty and geometric relationships across co-existed objects for accurate depth estimation. PGD constructs a geometric relation graph to enhance depth estimation and uses a probabilistic representation to capture uncertainty. The method achieves significant improvements on KITTI and nuScenes benchmarks, obtaining 1st place out of all monocular vision-only methods while maintaining real-time efficiency.

Description

The study reveals that inaccurate instance depth estimation is the primary bottleneck in this task, and simplifies monocular 3D detection as an instance depth...

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  • File Format: PDF
  • File Size: 2.3 MB
  • Pages: 11
  • Language: EN
  • Author: Anonymous Submission
  • Total Downloads: 81
  • Last Updated: 1 month ago

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