4.7k citations · 4.8k across the 18 of their papers we have counts for
19 papers
SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them
Yang Zhou, Zixuan Huang, Sunzhu Li +10
Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that r…
Toward Deep Representation Learning for Event-Enhanced Visual Autonomous Perception: the eAP Dataset
Jinghang Li, Shichao Li, Qing Lian +3
Recent visual autonomous perception systems achieve remarkable performances with deep representation learning. However, they fail in scenarios with challenging illumination.While e…
MultiPark: Multimodal Parking Transformer with Next-Segment Prediction
Han Zheng, Zikang Zhou, Guli Zhang +6
Parking accurately and safely in highly constrained spaces remains a critical challenge. Unlike structured driving environments, parking requires executing complex maneuvers such a…
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…
SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving
Muleilan Pei, Jiayao Shan, Peiliang Li +4
Online scene perception and topology reasoning are critical for autonomous vehicles to understand their driving environments, particularly for mapless driving systems that endeavor…
SIMPL: A Simple and Efficient Multi-agent Motion Prediction Baseline for Autonomous Driving
Lu Zhang, Peiliang Li, Sikang Liu +1
This paper presents a Simple and effIcient Motion Prediction baseLine (SIMPL) for autonomous vehicles. Unlike conventional agent-centric methods with high accuracy but repetitive c…
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…
Trajectory Prediction with Graph-based Dual-scale Context Fusion
Lu Zhang, Peiliang Li, Jing Chen +1
Motion prediction for traffic participants is essential for a safe and robust automated driving system, especially in cluttered urban environments. However, it is highly challengin…
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…
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Tong Qin, Peiliang Li, Shaojie Shen
A monocular visual-inertial system (VINS), consisting of a camera and a low-cost inertial measurement unit (IMU), forms the minimum sensor suite for metric six degrees-of-freedom (…