3D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916656872161280 |
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| author | Oh, Gyeongrok Kim, Sungjune Ko, Heeju Chi, Hyung-gun Kim, Jinkyu Lee, Dongwook Ji, Daehyun Choi, Sungjoon Jang, Sujin Kim, Sangpil |
| author_facet | Oh, Gyeongrok Kim, Sungjune Ko, Heeju Chi, Hyung-gun Kim, Jinkyu Lee, Dongwook Ji, Daehyun Choi, Sungjoon Jang, Sujin Kim, Sangpil |
| contents | The resolution of voxel queries significantly influences the quality of view transformation in camera-based 3D occupancy prediction. However, computational constraints and the practical necessity for real-time deployment require smaller query resolutions, which inevitably leads to an information loss. Therefore, it is essential to encode and preserve rich visual details within limited query sizes while ensuring a comprehensive representation of 3D occupancy. To this end, we introduce ProtoOcc, a novel occupancy network that leverages prototypes of clustered image segments in view transformation to enhance low-resolution context. In particular, the mapping of 2D prototypes onto 3D voxel queries encodes high-level visual geometries and complements the loss of spatial information from reduced query resolutions. Additionally, we design a multi-perspective decoding strategy to efficiently disentangle the densely compressed visual cues into a high-dimensional 3D occupancy scene. Experimental results on both Occ3D and SemanticKITTI benchmarks demonstrate the effectiveness of the proposed method, showing clear improvements over the baselines. More importantly, ProtoOcc achieves competitive performance against the baselines even with 75\% reduced voxel resolution. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_15185 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation Oh, Gyeongrok Kim, Sungjune Ko, Heeju Chi, Hyung-gun Kim, Jinkyu Lee, Dongwook Ji, Daehyun Choi, Sungjoon Jang, Sujin Kim, Sangpil Computer Vision and Pattern Recognition Artificial Intelligence The resolution of voxel queries significantly influences the quality of view transformation in camera-based 3D occupancy prediction. However, computational constraints and the practical necessity for real-time deployment require smaller query resolutions, which inevitably leads to an information loss. Therefore, it is essential to encode and preserve rich visual details within limited query sizes while ensuring a comprehensive representation of 3D occupancy. To this end, we introduce ProtoOcc, a novel occupancy network that leverages prototypes of clustered image segments in view transformation to enhance low-resolution context. In particular, the mapping of 2D prototypes onto 3D voxel queries encodes high-level visual geometries and complements the loss of spatial information from reduced query resolutions. Additionally, we design a multi-perspective decoding strategy to efficiently disentangle the densely compressed visual cues into a high-dimensional 3D occupancy scene. Experimental results on both Occ3D and SemanticKITTI benchmarks demonstrate the effectiveness of the proposed method, showing clear improvements over the baselines. More importantly, ProtoOcc achieves competitive performance against the baselines even with 75\% reduced voxel resolution. |
| title | 3D Occupancy Prediction with Low-Resolution Queries via Prototype-aware View Transformation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2503.15185 |