Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

IFFNeRF: Initialisation Free and Fast 6DoF pose estimation from a single image and a NeRF model (2403.12682v1)

Published 19 Mar 2024 in cs.CV and cs.RO

Abstract: We introduce IFFNeRF to estimate the six degrees-of-freedom (6DoF) camera pose of a given image, building on the Neural Radiance Fields (NeRF) formulation. IFFNeRF is specifically designed to operate in real-time and eliminates the need for an initial pose guess that is proximate to the sought solution. IFFNeRF utilizes the Metropolis-Hasting algorithm to sample surface points from within the NeRF model. From these sampled points, we cast rays and deduce the color for each ray through pixel-level view synthesis. The camera pose can then be estimated as the solution to a Least Squares problem by selecting correspondences between the query image and the resulting bundle. We facilitate this process through a learned attention mechanism, bridging the query image embedding with the embedding of parameterized rays, thereby matching rays pertinent to the image. Through synthetic and real evaluation settings, we show that our method can improve the angular and translation error accuracy by 80.1% and 67.3%, respectively, compared to iNeRF while performing at 34fps on consumer hardware and not requiring the initial pose guess.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (26)
  1. P. Marion, P. Florence, L. Manuelli, and R. Tedrake, “Label fusion: A pipeline for generating ground truth labels for real rgbd data of cluttered scenes,” in ICRA, 2018.
  2. L. Manuelli, W. Gao, P. R. Florence, and R. Tedrake, “kpam: Keypoint affordances for category-level robotic manipulation,” in ISRR, 2019.
  3. Y. Xu, W. Wan, J. Zhang, H. Liu, Z. Shan, H. Shen, R. Wang, H. Geng, Y. Weng, J. Chen, et al., “Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,” in CVPR, 2023.
  4. S. Rajeev, Q. Wan, K. Yau, K. Panetta, and S. Agaian, “Augmented reality-based vision-aid indoor navigation system in gps denied environment,” in Mobile Multimedia/Image Processing, Security, and Applications, 2019.
  5. B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in ECCV, 2020.
  6. L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin, “iNeRF: Inverting neural radiance fields for pose estimation,” in IROS, 2021.
  7. Y. Lin, T. Müller, J. Tremblay, B. Wen, S. Tyree, A. Evans, P. A. Vela, and S. Birchfield, “Parallel inversion of neural radiance fields for robust pose estimation,” in ICRA, 2023.
  8. M. Bortolon, A. Del Bue, and F. Poiesi, “VM-NeRF: Tackling Sparsity in NeRF with View Morphing,” in ICIAP, 2023.
  9. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equation of state calculations by fast computing machines,” The journal of chemical physics, vol. 21, no. 6, 1953.
  10. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS, 2017.
  11. Y. Zhu, M. Li, W. Yao, and C. Chen, “A review of 6d object pose estimation,” in ITAIC, 2022.
  12. G. Marullo, L. Tanzi, P. Piazzolla, and E. Vezzetti, “6d object position estimation from 2d images: a literature review,” Multimedia Tools and Applications, vol. 82, no. 16, 2023.
  13. D. Maggio, M. Abate, J. Shi, C. Mario, and L. Carlone, “Loc-nerf: Monte carlo localization using neural radiance fields,” in ICRA, 2023.
  14. A. Moreau, N. Piasco, M. Bennehar, D. Tsishkou, B. Stanciulescu, and A. de La Fortelle, “Crossfire: Camera relocalization on self-supervised features from an implicit representation,” in ICCV, 2023.
  15. D. Verbin, P. Hedman, B. Mildenhall, T. Zickler, J. T. Barron, and P. P. Srinivasan, “Ref-nerf: Structured view-dependent appearance for neural radiance fields,” in CVPR, 2022.
  16. A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “Tensorf: Tensorial radiance fields,” in ECCV, 2022.
  17. T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Trans. Graph., vol. 41, no. 4, 2022.
  18. L. Masset, O. Brüls, and G. Kerschen, “Partition of the circle in cells of equal area and shape,” Structural Dynamics Research Group, Aerospace and Mechanical Engineering Department, University of Liege, ‘Institut de Mecanique et Genie Civil (B52/3), Tech. Rep., 2011.
  19. B. Beckers and P. Beckers, “Fast and accurate view factor generation,” in FICUP, An International Conference on Urban Physics, 2016.
  20. T. Malley, “A shading method for computer generated images,” Master’s thesis, Dept. of Computer Science, University of Utah, 1988.
  21. L. Jacques, L. Masset, and G. Kerschen, “Direction and surface sampling in ray tracing for spacecraft radiative heat transfer,” Aerospace Science and Technology, vol. 47, 2015.
  22. T. Tsesmelis, I. Hasan, M. Cristani, A. D. Bue, and F. Galasso, “Rgbd2lux: Dense light intensity estimation with an rgbd sensor,” in WACV, 2018.
  23. M. Tancik, P. P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. T. Barron, and R. Ng, “Fourier features let networks learn high frequency functions in low dimensional domains,” in NeurIPS, 2020.
  24. M. Oquab, T. Darcet, T. Moutakanni, H. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P.-Y. Huang, S.-W. Li, I. Misra, M. Rabbat, V. Sharma, G. Synnaeve, H. Xu, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski, “Dinov2: Learning robust visual features without supervision,” arXiv:2304.07193, 2023.
  25. A. Knapitsch, J. Park, Q.-Y. Zhou, and V. Koltun, “Tanks and temples: Benchmarking large-scale scene reconstruction,” ACM Transactions on Graphics, vol. 36, no. 4, 2017.
  26. L. Liu, J. Gu, K. Z. Lin, T.-S. Chua, and C. Theobalt, “Neural sparse voxel fields,” in NeurIPS, 2020.
Citations (2)
View on Semantic Scholar

Summary

  • The paper introduces IFFNeRF, a method that leverages Metropolis-Hasting sampling and isocell-based ray casting for real-time, initialization-free 6DoF pose estimation.
  • It employs an attention-based ray-to-image matching and least squares optimization, achieving up to 80.1% angular and 67.3% translation error improvements over prior methods.
  • IFFNeRF runs at 34 FPS on consumer-grade hardware, making it highly applicable for robotics, augmented reality, and autonomous vehicle systems.

Introducing IFFNeRF for Real-time Initialization-Free 6DoF Pose Estimation with NeRF

Introduction to IFFNeRF

The quest for precise camera pose estimation has driven significant research efforts within the computer vision community. State-of-the-art methodologies predominantly utilize Neural Radiance Fields (NeRF) to leverage the photorealistic rendering of scenes for pose estimation tasks. Despite their accuracy, such methods often suffer from the need for proximate initial guesses and prolonged computational times, hindering their applicability in real-time scenarios. In this context, the paper at hand introduces a novel methodology, IFFNeRF (Initialization Free and Fast NeRF), aimed at estimating the six degrees-of-freedom (6DoF) camera pose from a single image and a NeRF model. IFFNeRF distinguishes itself by operating in real-time while eliminating the prerequisite of an initial pose guess, addressing two critical limitations of current NeRF-based pose estimation approaches.

Key Contributions and Methodology

IFFNeRF’s design encompasses several innovative components that coalesce to achieve real-time performance without the need for an initial camera pose:

  1. Surface Point Sampling via Metropolis-Hasting (M-H) Algorithm: Surface points within the scene are sampled utilizing the M-H algorithm, based on the density outputs from the NeRF model. This approach ensures that sampled points accurately represent the scene's structure.
  2. Isocell-based Ray Casting: From each sampled surface point, multiple rays are cast in directions determined by the surface normal and arranged in an isocell pattern. This method ensures a comprehensive sampling of potential views with a minimal number of rays.
  3. Attention-based Ray to Image Matching: Utilizing a learned attention mechanism, the embedding of each cast ray is matched with the query image embedding. This process efficiently identifies a subset of rays that are most relevant to the image, thus directly contributing to the pose estimation accuracy.
  4. Least Squares Pose Estimation: The final pose is computed through a Least Squares optimization over the selected rays, providing a closed-form solution that contributes to the method's real-time performance.

The evaluation of IFFNeRF on synthetic and real datasets illustrates its superiority over existing NeRF-based pose estimation methods, particularly in terms of angular and translation error accuracy improvements by 80.1% and 67.3%, respectively, compared to iNeRF. Furthermore, IFFNeRF’s computational performance enables operations at 34 frames per second on consumer-grade hardware, a significant enhancement over current standards.

Theoretical and Practical Implications

IFFNeRF's proposed methodology harbors substantial theoretical and practical implications. Theoretically, it presents a paradigm shift in pose estimation by proving the feasibility of initialization-free, real-time NeRF-based methods. Practically, the ability to estimate camera pose without prior information and within tight time constraints opens up new avenues in robotics, autonomous vehicles, and augmented reality applications, where quick and accurate pose estimation is paramount.

Speculations on Future Developments

Looking towards the future, IFFNeRF sets the stage for advancements in multi-scene adaptability and further refinements in computational efficiency. The exploration into generalized models capable of handling diverse scenes without specific training for each scenario could enhance the method’s versatility. Additionally, incremental improvements in the attention mechanism and ray sampling process could yield further reductions in computational overhead and memory usage, solidifying IFFNeRF's position at the forefront of real-time pose estimation methodologies.

Conclusion

The introduction of IFFNeRF marks a significant milestone in the field of NeRF-based camera pose estimation. By delivering on the promise of real-time performance devoid of initialization requirements, it paves the way for broader adoption and integration of NeRF methodologies in time-sensitive and resource-constrained applications. Future research endeavors inspired by IFFNeRF could potentially unravel new capabilities and optimizations, further bridging the gap between theoretical excellence and practical utility in the domain of camera pose estimation.