SuperQuadricOcc: Multi-Layer Gaussian Approximation of Superquadrics for Real-Time Self-Supervised Occupancy Estimation

• URL: http://arxiv.org/abs/2511.17361v2
• Date: Tue, 25 Nov 2025 10:31:55 GMT
• Title: SuperQuadricOcc: Multi-Layer Gaussian Approximation of Superquadrics for Real-Time Self-Supervised Occupancy Estimation
• Authors: Seamie Hayes, Reenu Mohandas, Tim Brophy, Alexandre Boulch, Ganesh Sistu, Ciaran Eising,
• Abstract summary: We propose a superquadric-based occupancy model to enable real-time inference.<n>On the Occ3D dataset, SuperQuadricOcc achieves a 75% reduction in memory footprint and a 5.9% improvement in mIoU.<n>To our knowledge, this is the first occupancy model to enable real-time inference while maintaining competitive performance.
• Score: 38.85929062825556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic occupancy estimation enables comprehensive scene understanding for automated driving, providing dense spatial and semantic information essential for perception and planning. While Gaussian representations have been widely adopted in self-supervised occupancy estimation, the deployment of a large number of Gaussian primitives drastically increases memory requirements and is not suitable for real-time inference. In contrast, superquadrics permit reduced primitive count and lower memory requirements due to their diverse shape set. However, implementation into a self-supervised occupancy model is nontrivial due to the absence of a superquadric rasterizer to enable model supervision. Our proposed method, SuperQuadricOcc, employs a superquadric-based scene representation. By leveraging a multi-layer icosphere-tessellated Gaussian approximation of superquadrics, we enable Gaussian rasterization for supervision during training. On the Occ3D dataset, SuperQuadricOcc achieves a 75% reduction in memory footprint, 124% faster inference, and a 5.9% improvement in mIoU compared to previous Gaussian-based methods, without the use of temporal labels. To our knowledge, this is the first occupancy model to enable real-time inference while maintaining competitive performance. The use of superquadrics reduces the number of primitives required for scene modeling by 84% relative to Gaussian-based approaches. Finally, evaluation against prior methods is facilitated by our fast superquadric voxelization module. The code will be made available at https://github.com/seamie6/SuperQuadricOcc.

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