ACEFusion - Accelerated and Energy-Efficient Semantic 3D Reconstruction of Dynamic Scenes

Mihai Bujanca, Barry Lennox, Mikel Luján

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

ACEFusion is the first 3D reconstruction system able to capture the geometry and semantics of dynamic scenes using an RGB-D camera in real-time on a robotic computing platform. Harnessing the hardware accelerators of an Nvidia Jetson AGX Xavier, the system uses heterogeneous computing to achieve 30 FPS under a 30W power budget. Using a data-parallel design, we perform most image computation on the dedicated hardware accelerators, freeing the general purpose cores and GPU to process 3D geometry. To further increase efficiency, we employ a hybrid geometry representation with octrees for static-semantic reconstruction and surfels for dynamic reconstruction. ACEFusion achieves competitive results on standard benchmarks while efficiently performing a more complex overall task than existing SLAM techniques. Figure 1 shows the output of our system on a dynamic sequence.
Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Inteelligent Robots and Systems
PublisherIEEE
Publication statusPublished - Oct 2022

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