SliceSemOcc: Vertical Slice Based Multimodal 3D Semantic Occupancy Representation

• URL: http://arxiv.org/abs/2509.03999v1
• Date: Thu, 04 Sep 2025 08:27:54 GMT
• Title: SliceSemOcc: Vertical Slice Based Multimodal 3D Semantic Occupancy Representation
• Authors: Han Huang, Han Sun, Ningzhong Liu, Huiyu Zhou, Jiaquan Shen,
• Abstract summary: SliceSemOcc is a novel vertical slice based multimodal framework for 3D semantic occupancy representation.<n>We propose the SE3D module, which preserves height-wise resolution through average pooling and assigns dynamic channel attention weights to each height layer.
• Score: 26.38332949554491
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driven by autonomous driving's demands for precise 3D perception, 3D semantic occupancy prediction has become a pivotal research topic. Unlike bird's-eye-view (BEV) methods, which restrict scene representation to a 2D plane, occupancy prediction leverages a complete 3D voxel grid to model spatial structures in all dimensions, thereby capturing semantic variations along the vertical axis. However, most existing approaches overlook height-axis information when processing voxel features. And conventional SENet-style channel attention assigns uniform weight across all height layers, limiting their ability to emphasize features at different heights. To address these limitations, we propose SliceSemOcc, a novel vertical slice based multimodal framework for 3D semantic occupancy representation. Specifically, we extract voxel features along the height-axis using both global and local vertical slices. Then, a global local fusion module adaptively reconciles fine-grained spatial details with holistic contextual information. Furthermore, we propose the SEAttention3D module, which preserves height-wise resolution through average pooling and assigns dynamic channel attention weights to each height layer. Extensive experiments on nuScenes-SurroundOcc and nuScenes-OpenOccupancy datasets verify that our method significantly enhances mean IoU, achieving especially pronounced gains on most small-object categories. Detailed ablation studies further validate the effectiveness of the proposed SliceSemOcc framework.

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