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Found 5 papers, 2 papers with code
Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images
To address this limitation, we propose a novel 3D object detection model, P2D (Predict to Detect), that integrates a prediction scheme into a detection framework to explicitly extract and leverage motion features.
CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception
Autonomous driving requires an accurate and fast 3D perception system that includes 3D object detection, tracking, and segmentation.
Ranked #2 on
3D Multi-Object Tracking
on nuscenes Camera-Radar
Boosting Monocular 3D Object Detection with Object-Centric Auxiliary Depth Supervision
In this way, our 3D detection network can be supervised by more depth supervision from raw LiDAR points, which does not require any human annotation cost, to estimate accurate depth without explicitly predicting the depth map.
CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer
Camera and radar sensors have significant advantages in cost, reliability, and maintenance compared to LiDAR.
Ranked #6 on
3D Object Detection
on nuscenes Camera-Radar
Diverse Multiple Trajectory Prediction Using a Two-stage Prediction Network Trained with Lane Loss
Prior arts in the field of motion predictions for autonomous driving tend to focus on finding a trajectory that is close to the ground truth trajectory.
