Datasets:
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"field_id": "F008",
"field": "6d object pose estimation",
"directions": [
{
"direction_id": "G0",
"name": "Pose generalization to unseen objects",
"scope": "Estimate the poses of objects unseen during training, including methods that use target-specific reference images or representations and methods that use category representations to infer pose, and where relevant size and shape, across instances."
},
{
"direction_id": "G1",
"name": "Pose recovery by observation–geometry alignment",
"scope": "Solve or refine object pose using constraints between observations and object geometry or candidate poses. This includes local correspondences, render-based comparison, and joint multi-view constraints; a method need not use all of these approaches."
},
{
"direction_id": "G2",
"name": "Pose learning with reduced real-label dependence",
"scope": "Obtain usable pose estimators when manually annotated poses in real images are scarce, using synthetic or self-supervised training evidence or adaptation with unlabeled target-domain data."
},
{
"direction_id": "G3",
"name": "Temporal object-pose tracking",
"scope": "Maintain an object's 6D pose across successive observations while handling motion, unreliable observations, or recovery after tracking failure."
},
{
"direction_id": "G4",
"name": "Structured pose representation and uncertainty",
"scope": "Represent the geometry of rotations and ambiguity in observations appropriately in pose prediction or inference, including structured rotation features, multiple plausible poses, pose distributions, or uncertainty that can be propagated."
},
{
"direction_id": "G5",
"name": "Joint multi-object pose prediction",
"scope": "Jointly localize or distinguish multiple object instances in the same scene and predict each instance's pose, rather than only improving pose recovery for objects already localized individually."
},
{
"direction_id": "G6",
"name": "Pose data and evaluation infrastructure",
"scope": "Create data, annotations, or evaluation protocols for object-pose research, with reusable research and evaluation infrastructure as the central contribution."
}
]
}
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