4.7k citations · 4.8k across the 18 of their papers we have counts for
12 papers · 1 filter
GoIRL: Graph-Oriented Inverse Reinforcement Learning for Multimodal Trajectory Prediction
Muleilan Pei, Shaoshuai Shi, Lu Zhang +2
Trajectory prediction for surrounding agents is a challenging task in autonomous driving due to its inherent uncertainty and underlying multimodality. Unlike prevailing data-driven…
Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
Shichao Li, Peiliang Li, Qing Lian +2
Perceiving pedestrians in highly crowded urban environments is a difficult long-tail problem for learning-based autonomous perception. Speeding up 3D ground truth generation for su…
Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion
Jieqi Shi, Peiliang Li, Xiaozhi Chen +1
In the practical application of point cloud completion tasks, real data quality is usually much worse than the CAD datasets used for training. A small amount of noisy data will usu…
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning
Jieqi Shi, Peiliang Li, Xiaozhi Chen +1
The image-based 3D object detection task expects that the predicted 3D bounding box has a ``tightness'' projection (also referred to as cuboid), which fits the object contour well…
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
Qing Lian, Peiliang Li, Xiaozhi Chen
Due to the inherent ill-posed nature of 2D-3D projection, monocular 3D object detection lacks accurate depth recovery ability. Although the deep neural network (DNN) enables monocu…
Temporal Point Cloud Completion with Pose Disturbance
Jieqi Shi, Lingyun Xu, Peiliang Li +2
Point clouds collected by real-world sensors are always unaligned and sparse, which makes it hard to reconstruct the complete shape of object from a single frame of data. In this w…
Tracking from Patterns: Learning Corresponding Patterns in Point Clouds for 3D Object Tracking
Jieqi Shi, Peiliang Li, Shaojie Shen
A robust 3D object tracker which continuously tracks surrounding objects and estimates their trajectories is key for self-driving vehicles. Most existing tracking methods employ a…
Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking
Peiliang Li, Jieqi Shi, Shaojie Shen
Directly learning multiple 3D objects motion from sequential images is difficult, while the geometric bundle adjustment lacks the ability to localize the invisible object centroid.…
Multi-Sensor 3D Object Box Refinement for Autonomous Driving
Peiliang Li, Siqi Liu, Shaojie Shen
We propose a 3D object detection system with multi-sensor refinement in the context of autonomous driving. In our framework, the monocular camera serves as the fundamental sensor f…
Stereo R-CNN based 3D Object Detection for Autonomous Driving
Peiliang Li, Xiaozhi Chen, Shaojie Shen
We propose a 3D object detection method for autonomous driving by fully exploiting the sparse and dense, semantic and geometry information in stereo imagery. Our method, called Ste…
Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving
Peiliang Li, Tong Qin, Shaojie Shen
We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios. Instead of directly regressing the 3D…
Relocalization, Global Optimization and Map Merging for Monocular Visual-Inertial SLAM
Tong Qin, Perliang Li, Shaojie Shen
The monocular visual-inertial system (VINS), which consists one camera and one low-cost inertial measurement unit (IMU), is a popular approach to achieve accurate 6-DOF state estim…