Leon Jungemeyer, Alejandro Magaña, Gautham Mohan +2 more
Working out an object's position and orientation from an image is routine in a lab and difficult on a factory floor. The methods that work well need resource-heavy data pipelines, textured 3D models of the exact object, and objects that match those models closely.
Industrial reality breaks all three. Parts get scratched and dented, assemblies deviate from the nominal geometry, and the textured model frequently does not exist because what engineering has is a CAD file describing shape and nothing about appearance.
PIXIE estimates pose from an RGB image using only an untextured model, which targets exactly that gap. Texture invariance is the right property to want here: the same part arrives painted differently, worn differently and lit differently across a plant, and a method keyed to appearance will keep failing in ways that look arbitrary from the outside.
6D pose estimation remains a key challenge in robotics and computer vision, particularly in industrial environments. The deployment of currently available data-driven methods is often limited by resource-intensive data pipelines, reliance on textured 3D models, and sensitivity to geometric deviations caused by damages or assembly defects. We present PIXIE, a zero-shot framework that estimates the 6D pose of an object from an RGB image using only an untextured 3D model.…
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