arXiv:2510.18999v3 Announce Type: replace-cross Abstract: Reconstructing signed distance functions (SDFs) from point cloud data benefits many robot autonomy capabilities, including localization, mapping, motion planning, and control. Methods that support online and large-scale SDF reconstruction often rely on discrete volumetric data structures, which affects the continuity and differentiability of the SDF estimates. Neural network methods have demonstrated high-fidelity differentiable SDF reconstruction but they tend to be less efficient, experience catastrophic forgetting and memory limitati
The continuous drive for more autonomous and robust robotic systems necessitates improvements in real-time environmental understanding, leading to innovations like OREN.
This development improves real-time spatial understanding for robots, which is critical for their practical deployment in complex and dynamic environments, enhancing capabilities from self-driving cars to industrial automation.
The ability to reconstruct high-fidelity, continuous, and differentiable Signed Distance Functions (SDFs) in real-time overcomes previous limitations of discrete volumetric data and less efficient neural methods.
- · Robotics companies
- · Autonomous vehicle developers
- · Logistics and manufacturing sectors
- · AI/ML researchers
- · Developers reliant on less efficient SDF reconstruction methods
- · Manual inspection industries
Robots gain more accurate and responsive spatial awareness, leading to smoother navigation and manipulation.
Increased efficiency and safety of autonomous systems accelerate their adoption across various industries, from warehousing to surgical assistance.
The widespread deployment of highly autonomous robots, underpinned by advanced spatial understanding, could reshape urban planning and human-robot interaction paradigms.
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Read at arXiv cs.AI