March 18, 2024Open Access

SceneSense: Diffusion Models for 3D Occupancy Synthesis from Partial Observation

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Local occupancy predictions represent the ground truth occupancy distribution better than the running occupancy map, enhancing robotic navigation.
Test exploration trajectories reveal that SceneSense outperforms existing methods that require multiple views or offline synthesis.
Utilizing a single RGB-D camera, SceneSense synthesizes occupancy information from partial observations, ensuring that known areas remain intact in the map context. As a flexible framework, it adapts to various modalities and retrains for new applications for optimal performance.

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Randomized trial demonstrates improved 3D occupancy predictions in robotics, suggesting effective spatial understanding.

Reed et al. (2024) studied this question.

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