Foundational feature fusion for conditional flow matching in 6D pose estimation
Conditional flow matching has enabled a step forward in object 6D pose estimation, achieving state-of-the-art performance by progressively denoising and registering object representations to observed scenes. Existing methods require training task-specific encoders supervised on object-scene overlap and rely on trivial feature fusion strategies to resolve pose ambiguities. We present FunFlow6D, a novel flow matching-based formulation that leverages features from geometric and appearance foundation models for pose estimation, eliminating the need for task-specific encoder training. We also introduce a cross attention-based fusion mechanism that dynamically combines geometric and appearance ...
| Authors | Amir Hamza, Davide Boscaini, Fabio Poiesi |
|---|---|
| Primary category | cs.CV |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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