GaussianOcc3D: A Gaussian-Based Adaptive Multi-modal 3D Occupancy Prediction

• URL: http://arxiv.org/abs/2601.22729v1
• Date: Fri, 30 Jan 2026 09:05:30 GMT
• Title: GaussianOcc3D: A Gaussian-Based Adaptive Multi-modal 3D Occupancy Prediction
• Authors: A. Enes Doruk, Hasan F. Ates,
• Abstract summary: We present a memory-efficient, continuous 3D Gaussian representation framework for semantic occupancy prediction.<n>GaussianOcc3D exhibits superior robustness across challenging rainy and nighttime conditions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D semantic occupancy prediction is a pivotal task in autonomous driving, providing a dense and fine-grained understanding of the surrounding environment, yet single-modality methods face trade-offs between camera semantics and LiDAR geometry. Existing multi-modal frameworks often struggle with modality heterogeneity, spatial misalignment, and the representation crisis--where voxels are computationally heavy and BEV alternatives are lossy. We present GaussianOcc3D, a multi-modal framework bridging camera and LiDAR through a memory-efficient, continuous 3D Gaussian representation. We introduce four modules: (1) LiDAR Depth Feature Aggregation (LDFA), using depth-wise deformable sampling to lift sparse signals onto Gaussian primitives; (2) Entropy-Based Feature Smoothing (EBFS) to mitigate domain noise; (3) Adaptive Camera-LiDAR Fusion (ACLF) with uncertainty-aware reweighting for sensor reliability; and (4) a Gauss-Mamba Head leveraging Selective State Space Models for global context with linear complexity. Evaluations on Occ3D, SurroundOcc, and SemanticKITTI benchmarks demonstrate state-of-the-art performance, achieving mIoU scores of 49.4%, 28.9%, and 25.2% respectively. GaussianOcc3D exhibits superior robustness across challenging rainy and nighttime conditions.

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