Hyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detection
Researchers have introduced HGC-Det, a novel hyperbolic constrained cross-modal distillation method designed to enhance multimodal 3D object detection. This approach addresses common limitations in existing techniques, such as modality heterogeneity, spatial misalignment, and representation crises, by integrating point cloud and image features more effectively. The HGC-Det framework consists of separate image and point cloud branches for semantic feature extraction. The point cloud branch features three key components: 2D Semantic-Guided Voxel Optimization (SGVO) to refine spatial representations using image cues; Hyperbolic Geometry Constrained Cross-Modal Feature Transfer (HFT) to reduce semantic loss during feature fusion by leveraging hyperbolic space properties; and Feature Aggregation-based Geometry Optimization (FAGO) to compensate for spatial feature degradation. Extensive experiments conducted on both indoor datasets (SUN RGB-D, ARKitScenes) and outdoor datasets (KITTI, nuScenes) demonstrate that HGC-Det achieves a superior trade-off between detection accuracy and computational cost compared to current methods, marking a significant advancement in robust 3D perception tasks.
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Hyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detection
Researchers have introduced HGC-Det, a novel hyperbolic constrained cross-modal distillation method designed to enhance multimodal 3D object detection. This approach addresses common limitations in existing techniques, such as modality heterogeneity, spatial misalignment, and representation crises, by integrating point cloud and image features more effectively. The HGC-Det framework consists of separate image and point cloud branches for semantic feature extraction. The point cloud branch features three key components: 2D Semantic-Guided Voxel Optimization (SGVO) to refine spatial representations using image cues; Hyperbolic Geometry Constrained Cross-Modal Feature Transfer (HFT) to reduce semantic loss during feature fusion by leveraging hyperbolic space properties; and Feature Aggregation-based Geometry Optimization (FAGO) to compensate for spatial feature degradation. Extensive experiments conducted on both indoor datasets (SUN RGB-D, ARKitScenes) and outdoor datasets (KITTI, nuScenes) demonstrate that HGC-Det achieves a superior trade-off between detection accuracy and computational cost compared to current methods, marking a significant advancement in robust 3D perception tasks.
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