DEGround: An Effective Baseline for Ego-centric 3D Visual Grounding with a Homogeneous Framework

Understand

A core task in embodied intelligence is ego-centric 3D visual grounding.

• Existing methods typically adopt two-stage, heterogeneous pipelines that pair a detector with a separate grounding model.
• Incompatible decoders and box heads hinder the transfer of object-level priors, and the split training causes redundant re-optimization.
• To overcome these limitations, we present DEGround, a straight, elegant, and effective framework that centers on object-level sharing over detection and grounding.