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Found 4 papers, 2 papers with code
Pose-Free Generalizable Rendering Transformer
To address this challenge, we introduce PF-GRT, a new Pose-Free framework for Generalizable Rendering Transformer, eliminating the need for pre-computed camera poses and instead leveraging feature-matching learned directly from data.
Learning to Estimate 6DoF Pose from Limited Data: A Few-Shot, Generalizable Approach using RGB Images
The accurate estimation of six degrees-of-freedom (6DoF) object poses is essential for many applications in robotics and augmented reality.
POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference
To mitigate this issue, we propose a general paradigm for object pose estimation, called Promptable Object Pose Estimation (POPE).
StegaNeRF: Embedding Invisible Information within Neural Radiance Fields
Recent advances in neural rendering imply a future of widespread visual data distributions through sharing NeRF model weights.
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