Kavli Affiliate: Feng Wang
| First 5 Authors: Chaoda Zheng, Feng Wang, Naiyan Wang, Shuguang Cui, Zhen Li
| Summary:
While 3D object bounding box (bbox) representation has been widely used in
autonomous driving perception, it lacks the ability to capture the precise
details of an object’s intrinsic geometry. Recently, occupancy has emerged as a
promising alternative for 3D scene perception. However, constructing a
high-resolution occupancy map remains infeasible for large scenes due to
computational constraints. Recognizing that foreground objects only occupy a
small portion of the scene, we introduce object-centric occupancy as a
supplement to object bboxes. This representation not only provides intricate
details for detected objects but also enables higher voxel resolution in
practical applications. We advance the development of object-centric occupancy
perception from both data and algorithm perspectives. On the data side, we
construct the first object-centric occupancy dataset from scratch using an
automated pipeline. From the algorithmic standpoint, we introduce a novel
object-centric occupancy completion network equipped with an implicit shape
decoder that manages dynamic-size occupancy generation. This network accurately
predicts the complete object-centric occupancy volume for inaccurate object
proposals by leveraging temporal information from long sequences. Our method
demonstrates robust performance in completing object shapes under noisy
detection and tracking conditions. Additionally, we show that our occupancy
features significantly enhance the detection results of state-of-the-art 3D
object detectors, especially for incomplete or distant objects in the Waymo
Open Dataset.
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