Search Results for author:
Found 12 papers, 4 papers with code
RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features
The inherent noisy and sparse characteristics of radar data pose challenges in finding effective representations for 3D object detection.
RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection
While LiDAR sensors have been successfully applied to 3D object detection, the affordability of radar and camera sensors has led to a growing interest in fusing radars and cameras for 3D object detection.
Ranked #4 on
3D Object Detection
on nuscenes Camera-Radar
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
InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning
We represent the map elements as a graph; we propose InstaGraM, instance-level graph modeling of HD map that brings accurate and fast end-to-end vectorized HD map learning.
3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection
Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust 3D object detection.
Ranked #1 on
3D Object Detection
on KITTI Cars Hard
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.
LaPred: Lane-Aware Prediction of Multi-Modal Future Trajectories of Dynamic Agents
In this paper, we address the problem of predicting the future motion of a dynamic agent (called a target agent) given its current and past states as well as the information on its environment.
SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Network
Since there is no pre-defined number of interacting vehicles participate in, the prediction network has to be scalable with respect to the vehicle number in order to guarantee the consistency in terms of both accuracy and computational load.
ScarfNet: Multi-scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection
Convolutional neural network (CNN) has led to significant progress in object detection.
