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Found 10 papers, 2 papers with code
M2DA: Multi-Modal Fusion Transformer Incorporating Driver Attention for Autonomous Driving
By incorporating driver attention, we empower the human-like scene understanding ability to autonomous vehicles to identify crucial areas within complex scenarios precisely and ensure safety.
GraphBEV: Towards Robust BEV Feature Alignment for Multi-Modal 3D Object Detection
Additionally, we propose a Global Align module to rectify the misalignment between LiDAR and camera BEV features.
Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook
In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed prediction and planning.
RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM
To align SAM or SAM-AD with multi-modal methods, we then introduce AD-FPN for upsampling the image features extracted by SAM.
VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection
In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features.
VGA: Vision and Graph Fused Attention Network for Rumor Detection
Moreover, these methods usually only extract visual features in a basic manner, seldom consider tampering or textual information in images.
Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS
The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process.
GraphAlign: Enhancing Accurate Feature Alignment by Graph matching for Multi-Modal 3D Object Detection
Through the projection calibration between the image and point cloud, we project the nearest neighbors of point cloud features onto the image features.
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving
Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception.
Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection
Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds to enhance detection accuracy.
