33 citations · 54 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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.…
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…