Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.12941 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914160491626496 |
|---|---|
| author | Hu, Chunyong Luo, Qi Xu, Jianyun Wang, Song Li, Qiang Yang, Sheng |
| author_facet | Hu, Chunyong Luo, Qi Xu, Jianyun Wang, Song Li, Qiang Yang, Sheng |
| contents | In the realm of autonomous driving, accurately detecting surrounding obstacles is crucial for effective decision-making. Traditional methods primarily rely on 3D bounding boxes to represent these obstacles, which often fail to capture the complexity of irregularly shaped, real-world objects. To overcome these limitations, we present GUIDE, a novel framework that utilizes 3D Gaussians for instance detection and occupancy prediction. Unlike conventional occupancy prediction methods, GUIDE also offers robust tracking capabilities. Our framework employs a sparse representation strategy, using Gaussian-to-Voxel Splatting to provide fine-grained, instance-level occupancy data without the computational demands associated with dense voxel grids. Experimental validation on the nuScenes dataset demonstrates GUIDE's performance, with an instance occupancy mAP of 21.61, marking a 50\% improvement over existing methods, alongside competitive tracking capabilities. GUIDE establishes a new benchmark in autonomous perception systems, effectively combining precision with computational efficiency to better address the complexities of real-world driving environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving Hu, Chunyong Luo, Qi Xu, Jianyun Wang, Song Li, Qiang Yang, Sheng Robotics In the realm of autonomous driving, accurately detecting surrounding obstacles is crucial for effective decision-making. Traditional methods primarily rely on 3D bounding boxes to represent these obstacles, which often fail to capture the complexity of irregularly shaped, real-world objects. To overcome these limitations, we present GUIDE, a novel framework that utilizes 3D Gaussians for instance detection and occupancy prediction. Unlike conventional occupancy prediction methods, GUIDE also offers robust tracking capabilities. Our framework employs a sparse representation strategy, using Gaussian-to-Voxel Splatting to provide fine-grained, instance-level occupancy data without the computational demands associated with dense voxel grids. Experimental validation on the nuScenes dataset demonstrates GUIDE's performance, with an instance occupancy mAP of 21.61, marking a 50\% improvement over existing methods, alongside competitive tracking capabilities. GUIDE establishes a new benchmark in autonomous perception systems, effectively combining precision with computational efficiency to better address the complexities of real-world driving environments. |
| title | GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving |
| topic | Robotics |
| url | https://arxiv.org/abs/2511.12941 |