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Found 5 papers, 5 papers with code
Embodied Understanding of Driving Scenarios
Hereby, we introduce the Embodied Language Model (ELM), a comprehensive framework tailored for agents' understanding of driving scenes with large spatial and temporal spans.
Leveraging Vision-Centric Multi-Modal Expertise for 3D Object Detection
Current research is primarily dedicated to advancing the accuracy of camera-only 3D object detectors (apprentice) through the knowledge transferred from LiDAR- or multi-modal-based counterparts (expert).
Ranked #6 on
3D Object Detection
on nuScenes Camera Only
Geometric-aware Pretraining for Vision-centric 3D Object Detection
We also conduct experiments on various image backbones and view transformations to validate the efficacy of our approach.
VoxelFormer: Bird's-Eye-View Feature Generation based on Dual-view Attention for Multi-view 3D Object Detection
In recent years, transformer-based detectors have demonstrated remarkable performance in 2D visual perception tasks.
You Only Segment Once: Towards Real-Time Panoptic Segmentation
To reduce the computational overhead, we design a feature pyramid aggregator for the feature map extraction, and a separable dynamic decoder for the panoptic kernel generation.
