Presenting DISORF, an innovative solution by Chunlin Li et al., facilitating online 3D reconstruction and scene visualization from mobile robots. DISORF addresses the needs of resource-constrained edge devices by distributing computation between the device and a remote server. It utilizes on-device SLAM for keyframe generation, while rendering is boosted by powerful NeRF models on the server side. A novel frame sampling method further enhances the quality of online NeRF training, paving the way for real-time, high-quality reconstruction and streaming from mobile robots.

• Provides a distributed framework for edge devices and remote servers.
• Employs on-device SLAM and remote NeRF models for real-time 3D reconstruction.
• Introduces a new frame sampling method to improve NeRF training quality.
• Demonstrates seamless reconstruction and visualization of unknown scenes.

With DISORF, the potential for edge devices to perform complex tasks, such as real-time mapping and environmental understanding, is greatly expanded. This framework can transform various fields, from robotics to virtual reality, enhancing interactivity and response to dynamic environments. Read more.