HyGE-Occ: Hybrid View-Transformation with 3D Gaussian and Edge Priors for 3D Panoptic Occupancy Prediction
Published:Dec 24, 2025 05:00
•1 min read
•ArXiv Vision
Analysis
This paper introduces HyGE-Occ, a novel framework designed to improve 3D panoptic occupancy prediction by enhancing geometric consistency and boundary awareness. The core innovation lies in its hybrid view-transformation branch, which combines a continuous Gaussian-based depth representation with a discretized depth-bin formulation. This fusion aims to produce better Bird's Eye View (BEV) features. The use of edge maps as auxiliary information further refines the model's ability to capture precise spatial ranges of 3D instances. Experimental results on the Occ3D-nuScenes dataset demonstrate that HyGE-Occ outperforms existing methods, suggesting a significant advancement in 3D geometric reasoning for scene understanding. The approach seems promising for applications requiring detailed 3D scene reconstruction.
Key Takeaways
- •Introduces HyGE-Occ for improved 3D panoptic occupancy prediction.
- •Utilizes a hybrid view-transformation branch with Gaussian and edge priors.
- •Demonstrates superior performance on the Occ3D-nuScenes dataset.
Reference
“...a novel framework that leverages a hybrid view-transformation branch with 3D Gaussian and edge priors to enhance both geometric consistency and boundary awareness in 3D panoptic occupancy prediction.”