33 citations · 54 across the 13 of their papers we have counts for
13 papers
SG-Reg: Generalizable and Efficient Scene Graph Registration
Chuhao Liu, Zhijian Qiao, Jieqi Shi +3
This paper addresses the challenges of registering two rigid semantic scene graphs, an essential capability when an autonomous agent needs to register its map against a remote agen…
VDG: Vision-Only Dynamic Gaussian for Driving Simulation
Hao Li, Jingfeng Li, Dingwen Zhang +7
Dynamic Gaussian splatting has led to impressive scene reconstruction and image synthesis advances in novel views. Existing methods, however, heavily rely on pre-computed poses and…
FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models
Chuhao Liu, Ke Wang, Jieqi Shi +2
Semantic mapping based on the supervised object detectors is sensitive to image distribution. In real-world environments, the object detection and segmentation performance can lead…
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…
Efficient Implicit Neural Reconstruction Using LiDAR
Dongyu Yan, Xiaoyang Lyu, Jieqi Shi +1
Modeling scene geometry using implicit neural representation has revealed its advantages in accuracy, flexibility, and low memory usage. Previous approaches have demonstrated impre…
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…
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…
Graph-Guided Deformation for Point Cloud Completion
Jieqi Shi, Lingyun Xu, Liang Heng +1
For a long time, the point cloud completion task has been regarded as a pure generation task. After obtaining the global shape code through the encoder, a complete point cloud is g…
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.…
Coarse-To-Fine Visual Localization Using Semantic Compact Map
Ziwei Liao, Jieqi Shi, Xianyu Qi +5
Robust visual localization for urban vehicles remains challenging and unsolved. The limitation of computation efficiency and memory size has made it harder for large-scale applicat…
DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
Rong Kang, Jieqi Shi, Xueming Li +2
As the foundation of driverless vehicle and intelligent robots, Simultaneous Localization and Mapping(SLAM) has attracted much attention these days. However, non-geometric modules…
An Efficient Volumetric Mesh Representation for Real-time Scene Reconstruction using Spatial Hashing
Wei Dong, Jieqi Shi, Weijie Tang +2
Mesh plays an indispensable role in dense real-time reconstruction essential in robotics. Efforts have been made to maintain flexible data structures for 3D data fusion, yet an eff…