DecomPose: A Framework for Category-Level 6D Object Pose Estimation
A recent study presents DecomPose, a framework that accounts for difficulty in decomposing tasks, aimed at enhancing category-level 6D object pose estimation. Released on arXiv, the research tackles the issue of geometric variability among categories in multi-category joint learning, which can result in gradient conflicts and detrimental transfer effects. DecomPose employs gradient-based diagnostics to measure cross-category contention at the module level and introduces two primary elements: difficulty-aware gradient decoupling, which organizes categories based on a difficulty proxy derived from data and directs instances to specific correspondence branches, and stability-driven asymmetric branching, which allocates more capable branches to simpler categories to ensure stable optimization. This framework seeks to reduce optimization contention and boost pose estimation precision.
Key facts
- DecomPose is a difficulty-aware decomposition framework for category-level 6D object pose estimation.
- The paper is published on arXiv with ID 2605.15728.
- It addresses geometric heterogeneity across categories in multi-category joint learning.
- Gradient conflicts and negative transfer are key challenges in shared model parameters.
- Gradient-based diagnostics quantify module-level cross-category contention.
- Difficulty-aware gradient decoupling groups categories using a data-driven difficulty proxy.
- Stability-driven asymmetric branching assigns higher-capacity branches to simple categories.
- The framework isolates incompatible updates to improve optimization.
Entities
Institutions
- arXiv