Robust Category-Level 6D Pose Estimation with Coarse-to-Fine Rendering of Neural Features

Ma, Wufei; Wang, Angtian; Yuille, Alan; Kortylewski, Adam

doi:10.1007/978-3-031-20077-9_29

Cited by 6 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Category-level pose estimation aims at generalizing to unknown objects of the same category [32], [16], [61], [69], [24], [106]. As compared to instance-level object pose estimation, which learn to predict poses of known objects.…”

Section: G Category-level Trainingmentioning

confidence: 99%

See 1 more Smart Citation

Open Challenges for Monocular Single-shot 6D Object Pose Estimation

Thalhammer¹,

Peter²,

Weibel³

et al. 2023

Preprint

View full text Add to dashboard Cite

Object pose estimation is a non-trivial task that enables robotic manipulation, bin picking, augmented reality, and scene understanding, to name a few use cases. Monocular object pose estimation gained considerable momentum with the rise of high-performing deep learning-based solutions and is particularly interesting for the community since sensors are inexpensive and inference is fast. Prior works establish the comprehensive state of the art for diverse pose estimation problems. Their broad scopes make it difficult to identify promising future directions. We narrow down the scope to the problem of singleshot monocular 6D object pose estimation, which is commonly used in robotics, and thus are able to identify such trends. By reviewing recent publications in robotics and computer vision, the state of the art is established at the union of both fields. Following that, we identify promising research directions in order to help researchers to formulate relevant research ideas and effectively advance the state of the art. Findings include that methods are sophisticated enough to overcome the domain shift and that occlusion handling is a fundamental challenge. We also highlight problems such as novel object pose estimation and challenging materials handling as central challenges to advance robotics.

show abstract

Section: G Category-level Trainingmentioning

confidence: 99%

“…As compared to instance-level object pose estimation, which learn to predict poses of known objects. Common principles are encoding canonical or category-specific features [61], [16], [24], [106], render-and-compare [69] and fast adaptation [32].…”

Section: G Category-level Trainingmentioning

confidence: 99%