SDF-based RGB-D Camera Tracking in Neural Scene Representations
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Abstract (arXiv)
We consider the problem of tracking the 6D pose of a moving RGB-D camera in a neural scene representation. Different such representations have recently emerged, and we investigate the suitability of them for the task of camera tracking. In particular, we propose to track an RGB-D camera using a signed distance field-based representation and show that compared to density-based representations, tracking can be sped up, which enables more robust and accurate pose estimates when computation time is limited.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 14 references that are in the map. First posted on arXiv 4 May 2022.
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References · 12
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
- 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
- 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,712
- 2021Volume Rendering of Neural Implicit Surfacescited by 996
- 2020Implicit Geometric Regularization for Learning Shapescited by 710
- 2021NICE-SLAM: Neural Implicit Scalable Encoding for SLAMcited by 589
- 2021iMAP: Implicit Mapping and Positioning in Real-Timecited by 476
- 2020INeRF: Inverting Neural Radiance Fields for Pose Estimationcited by 382
- 2021Neural RGB-D Surface Reconstructioncited by 313
- 2021NeRF--: Neural Radiance Fields Without Known Camera Parameterscited by 155
- 2022iSDF: Real-Time Neural Signed Distance Fields for Robot Perceptioncited by 109
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