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Domain Generalization for 6D Pose Estimation Through NeRF-based Image Synthesis
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.
Domain Generalization for In-Orbit 6D Pose Estimation
We address the problem of estimating the relative 6D pose, i. e., position and orientation, of a target spacecraft, from a monocular image, a key capability for future autonomous Rendezvous and Proximity Operations.
Leveraging Neural Radiance Fields for Pose Estimation of an Unknown Space Object during Proximity Operations
We train the NeRF model using a sparse collection of images that depict the target, and in turn generate a large dataset that is diverse both in terms of viewpoint and illumination.
Forward Error Correction applied to JPEG-XS codestreams
JPEG-XS offers low complexity image compression for applications with constrained but reasonable bit-rate, and low latency.
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