Vanishing Depth: Training Generalized Depth Adapters with Sinusoidal Depth Preprocessing for Pretrained RGB Encoders
arXiv:2503.19947v2 Announce Type: replace-cross Abstract: Generalized metric depth understanding is critical for precise vision-guided robotics, which current state-of-the-art (SOTA) vision-encoders do not support. To address this, we propose a self-supervised training approach that extends pretrained RGB encoders with a depth adapter to incorporate and align metric depth into a combined latent space without interfering with the pretrained RGB feature extraction. In combination with our sinusoidal depth encoding, the depth adapter enables generalized and robust depth density and distribution i
The continuous development in AI and robotics, particularly in vision systems, necessitates advancements in generalized depth understanding for practical applications.
Precise depth understanding is critical for autonomous systems like vision-guided robotics, influencing their safety, efficiency, and reliability in real-world environments.
Vision-guided robotics and similar autonomous systems can now achieve more robust and generalized metric depth understanding, moving beyond previous limitations of vision encoders.
- · Robotics manufacturers
- · Autonomous vehicle developers
- · AI hardware developers
- · Logistics and manufacturing industries
- · Companies relying on less precise depth sensing
- · Developers neglecting advanced AI perception
Robots will be able to perform complex tasks in unprepared environments with greater accuracy and less human supervision.
This improved robotic capability could accelerate automation across various sectors, increasing efficiency and reducing operational costs.
Widespread deployment of highly autonomous robots might lead to significant shifts in labor markets and supply chain dynamics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI