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6DoF Object Pose and Focal Length Estimation from Single RGB Images in Uncontrolled Environments.

Mayura Manawadu1, Soon-Yong Park1

  • 1Graduate School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

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|September 14, 2024
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Summary
This summary is machine-generated.

This study improves six degrees of freedom (6DoF) pose and focal length estimation for extended reality (XR) using a novel two-stage method. It effectively resolves projection scale ambiguity in RGB images, enhancing accuracy for XR applications.

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6DoFXRfocal lengthpose estimationuncontrolled RGB images

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Area of Science:

  • Computer Vision
  • Extended Reality (XR)
  • 3D Reconstruction

Background:

  • Accurate six degrees of freedom (6DoF) pose and focal length estimation are crucial for extended reality (XR) applications, enabling precise object alignment and projection scaling.
  • Estimating these parameters from single RGB images with unknown camera metadata presents significant challenges, particularly due to projection scale ambiguity.
  • Existing methods like FocalPose and Focalpose++ struggle with the inherent ambiguity between z-axis translation (tz) and focal length.

Purpose of the Study:

  • To develop an improved method for estimating 6DoF pose and focal length from uncontrolled RGB images.
  • To address and overcome the projection scale ambiguity inherent in estimating translation (tz) and focal length.
  • To enhance the accuracy and robustness of pose and focal length estimation for XR applications.

Main Methods:

  • A two-stage strategy is proposed to decouple the estimation of z-axis translation (tz) and focal length.
  • Stage 1: Arbitrarily set tz, predict other pose parameters and focal length relative to this fixed tz.
  • Stage 2: Predict the true tz and scale the focal length accordingly, utilizing iterative update rules and Huber loss.

Main Results:

  • The proposed two-stage method significantly reduces projection scale ambiguity in RGB images.
  • Experimental results on benchmark datasets show substantial improvements in median rotation and translation errors.
  • The method achieved a 7.19% improvement in projection accuracy on Pix3D datasets and reduced translation and focal length errors by 20.27% and 6.65% respectively compared to Focalpose++.

Conclusions:

  • The novel two-stage approach effectively resolves projection scale ambiguity, leading to more accurate 6DoF pose and focal length estimation.
  • The integration of iterative updates and Huber loss further enhances estimation accuracy.
  • This work provides a more robust solution for pose and focal length estimation from uncalibrated RGB images, benefiting XR applications.