EgoTrack: Robust 6D Object Tracking via Reliability-Aware Pose Hypothesis Reasoning in Extreme Egocentric Vision

Robust 6D object pose estimation is critical for egocentric and embodied systems operating under challenging conditions such as low light, motion blur, smoke and occlusion. Under such degradation, frame-wise pose estimates can become unreliable: selecting a single estimate may discard useful alternatives, while even the entire candidate set may be uninformative. We introduce EgoTrack, which retains multiple pose hypotheses and keeps their influence revisable during trajectory reconstruction. For each frame, a learned policy selects the object template appearance most useful for pose recovery, and and a frozen estimator retains its top-K ranked pose hypotheses. Trajectory diffusion then repeatedly retrieves candidate evidence conditioned on the evolving trajectory state and reliability cues; temporal denoising updates the trajectory, which changes subsequent retrieval. A learned NULL token provides an alternative to real-candidate evidence when the local set is uninformative. Experiments show substantial improvements in 6D pose accuracy and trajectory consistency over frame-wise and temporal baselines under severe visual degradation.