Robotics: Science and Systems XXII

FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction

Zeyu Jiang, Changqing Zhou, Xingxing Zuo, Changhao Chen

Abstract:

Existing learning-based occupancy prediction methods rely on large-scale 3D annotations and generalize poorly across environments. We present FreeOcc, a training-free framework for open-vocabulary occupancy prediction from monocular or RGB-D sequences. Unlike prior approaches that require voxel-level supervision and ground-truth camera poses, FreeOcc operates without 3D annotations, pose ground truth, or any learning stage. FreeOcc incrementally builds a globally consistent occupancy map via a four-layer pipeline: a SLAM backbone estimates poses and sparse geometry; a geometrically consistent Gaussian update constructs dense 3D Gaussian maps; open-vocabulary semantics from off-the-shelf vision–language models are associated with Gaussian primitives; and a probabilistic Gaussian-to-occupancy projection produces dense voxel occupancy. Despite being entirely training-free and pose-agnostic, FreeOcc achieves over 2× improvements in IoU and mIoU on EmbodiedOcc-ScanNet compared to prior self-supervised methods. We further introduce ReplicaOcc, a benchmark for indoor open-vocabulary occupancy prediction, and show that FreeOcc transfers zero-shot to novel environments, substantially outperforming both supervised and self-supervised baselines. Code will be released.

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Bibtex:

  
@INPROCEEDINGS{JiangZ-RSS-26, 
    AUTHOR    = {Zeyu Jiang AND Changqing Zhou AND Xingxing Zuo AND Changhao Chen}, 
    TITLE     = {{FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.166} 
}