DecomPose: Disentangling Cross-Category Optimization Contention for Category-Level 6D Object Pose Estimation

TL;DR

DecomPose introduces a framework that reduces optimization conflicts in category-level 6D object pose estimation by decoupling categories based on difficulty and structural complexity, improving accuracy.

Contribution

The paper presents a novel difficulty-aware decomposition framework that mitigates cross-category optimization contention through gradient decoupling and asymmetric branching strategies.

Findings

DecomPose significantly improves pose estimation accuracy across multiple benchmarks.

Gradient-based diagnostics effectively quantify cross-category contention.

The approach reduces negative transfer and enhances training stability.

Abstract

Category-level 6D object pose estimation is typically formulated as a multi-category joint learning problem with fully shared model parameters. However, pronounced geometric heterogeneity across categories entangles incompatible optimization signals in shared modules, resulting in gradient conflicts and negative transfer during training. To address this challenge, we first introduce gradient-based diagnostics to quantify module-level cross-category contention. Building on results of diagnostics, we propose DecomPose, a difficulty-aware decomposition framework that mitigates optimization contention via: (1) difficulty-aware gradient decoupling, which groups categories using a data-driven difficulty proxy and routes each instance to a group-specific correspondence branch to isolate incompatible updates; and (2) stability-driven asymmetric branching, which assigns higher-capacity branches to structurally simple categories as stable optimization anchors while constraining complex categories with lightweight branches to suppress noisy updates and alleviate negative transfer. Extensive experiments on REAL275, CAMERA25, and HouseCat6D demonstrate that DecomPose effectively reduces cross-category optimization contention and delivers superior pose estimation performance across multiple benchmarks.