mlsanityData to 7 Oct 2026

Domain Generalization for 6D Pose Estimation Through NeRF-based Image Synthesis

arXiv 2407.10762 ↗First posted 15 Jul 2024cs.CV eess.IVcited by 5

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Abstract (arXiv)

This work introduces a novel augmentation method that increases the diversity of a train set to improve the generalization abilities of a 6D pose estimation network. For this purpose, a Neural Radiance Field is trained from synthetic images and exploited to generate an augmented set. Our method enriches the initial set by enabling the synthesis of images with (i) unseen viewpoints, (ii) rich illumination conditions through appearance extrapolation, and (iii) randomized textures. We validate our augmentation method on the challenging use-case of spacecraft pose estimation and show that it significantly improves the pose estimation generalization capabilities. On the SPEED+ dataset, our method reduces the error on the pose by 50% on both target domains.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 35 of its 45 references that are in the map. First posted on arXiv 15 Jul 2024.

Builds on

35 of 45 references.

References · 35

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  12. 2013Domain Generalization via Invariant Feature Representationcited by 622
23 more
  1. 2020Learning to Generate Novel Domains for Domain Generalizationcited by 296
  2. 2020Learning to Learn Single Domain Generalizationcited by 295
  3. 2020A Brief Review of Domain Adaptationcited by 287
  4. 2018Generalizing Across Domains via Cross-Gradient Trainingcited by 268
  5. 2021Learning to Diversify for Single Domain Generalizationcited by 170
  6. 2018Style Augmentation: Data Augmentation via Style Randomizationcited by 79
  7. 2021MixStyle Neural Networks for Domain Generalization and Adaptationcited by 64
  8. 2019Satellite Pose Estimation Challenge: Dataset, Competition Design and Resultscited by 62
  9. 2021SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gapcited by 57
  10. 2019Satellite Pose Estimation with Deep Landmark Regression and Nonlinear Pose Refinementcited by 56
  11. 2019Deep Learning for Spacecraft Pose Estimation from Photorealistic Renderingcited by 54
  12. 2020EfficientPose: An efficient, accurate and scalable end-to-end 6D multi object pose estimation approachcited by 46
  13. 2022Robust Multi-Task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gapcited by 44
  14. 2018Hide-and-Seek: A Data Augmentation Technique for Weakly-Supervised Localization and Beyondcited by 36
  15. 2022Neural-Sim: Learning to Generate Training Data with NeRFcited by 27
  16. 2022Bridging the Domain Gap in Satellite Pose Estimation: a Self-Training Approach based on Geometrical Constraintscited by 21
  17. 2020Domain Shift in Computer Vision models for MRI data analysis: An Overviewcited by 16
  18. 2021Robotic Testbed for Rendezvous and Optical Navigation: Multi-Source Calibration and Machine Learning Use Casescited by 14
  19. 2022Spacecraft Pose Estimation Based on Unsupervised Domain Adaptation and on a 3D-Guided Loss Combinationcited by 12
  20. 20236D Object Pose Estimation from Approximate 3D Models for Orbital Roboticscited by 10
  21. 2024Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operationscited by 7
  22. 2019Illumination-Based Data Augmentation for Robust Background Subtractioncited by 6
  23. 2024Domain Generalization for In-Orbit 6D Pose Estimationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025NeRF-based Visualization of 3D Cues Supporting Data-Driven Spacecraft Pose Estimationcited by 2
  2. 2025WALDO: Where Unseen Model-based 6D Pose Estimation Meets Occlusioncited by 0
  3. 2026EventHub: Data Factory for Generalizable Event-Based Stereo Networks without Active Sensorscited by 0
  4. 2026NeRF-based Spacecraft Reconstruction from Monocular Imagery Under Illumination Variability and Pose Uncertaintycited by 0
  5. 2026CAD-Free Learning of Spacecraft Pose Estimators via NeRF-Based Augmentationscited by 0