12 papers
Spacecraft Pose Tracking via Kinematic Prediction and Selective Event Correction
Yonglong Zhang, Guanning Yao, Shuo Zhang +3
In initialized spacecraft tracking, a short-horizon motion prediction remains available as event support changes. Observation-Aligned Event Innovation (OA-EI) uses events to correc…
GeoDistill-Refine: Silhouette-First Geometry Distillation for Annotation-Free Spacecraft Segmentation
Yonglong Zhang, Zongwu Xie, Yang Liu
Foundation segmentation models can provide supervision for spacecraft imagery without manual training masks, but their predictions vary with textual prompts and may contain geometr…
GAP-GDRNet: Geometry-aware monocular 6D pose estimation for spacecraft using synthetic geometric supervision
Zongwu Xie, Yonglong Zhang, Yifan Yang +2
Monocular spacecraft 6D pose estimation remains difficult under weak texture, thin structures, illumination variation, and occlusion. This article presents GAP-GDRNet, a geometry-a…
Precision-Aware Illumination-Disentangled Vision Transformer for Spacecraft 6D Pose Estimation
Zongwu Xie, Yifan Yang, Yonglong Zhang +3
Vision sensors provide a lightweight solution for spacecraft proximity operations, but monocular spacecraft 6D pose estimation remains difficult under illumination variation, specu…
STELLAR: Scaling 3D Perception Large Models for Autonomous Driving
Yingwei Li, Xin Huang, Yang Liu +13
Model scaling has demonstrated remarkable success through large-scale training on diverse datasets. It remains an open question whether the same paradigm would apply to autonomous…
Component-Aware Structure-Preserving Style Transfer for Satellite Visual Sim2Real Data Construction
Zongwu Xie, Yonglong Zhang, Yifan Yang +3
For camera-based satellite visual sensing, Sim2Real data construction requires images that approach real-domain sensor appearance while retaining the annotations inherited from sim…
Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels
Wandong Sun, Yongbo Su, Leoric Huang +10
Achieving robust vision-based humanoid locomotion remains challenging due to two fundamental issues: the sim-to-real gap introduces significant perception noise that degrades perfo…
HDCNet: A Hybrid Depth Completion Network for Grasping Transparent and Reflective Objects
Guanghu Xie, Mingxu Li, Songwei Wu +4
Depth perception of transparent and reflective objects has long been a critical challenge in robotic manipulation.Conventional depth sensors often fail to provide reliable measurem…
DCIRNet: Depth Completion with Iterative Refinement for Dexterous Grasping of Transparent and Reflective Objects
Guanghu Xie, Zhiduo Jiang, Yonglong Zhang +4
Transparent and reflective objects in everyday environments pose significant challenges for depth sensors due to their unique visual properties, such as specular reflections and li…
HTMNet: A Hybrid Network with Transformer-Mamba Bottleneck Multimodal Fusion for Transparent and Reflective Objects Depth Completion
Guanghu Xie, Yonglong Zhang, Zhiduo Jiang +4
Transparent and reflective objects pose significant challenges for depth sensors, resulting in incomplete depth information that adversely affects downstream robotic perception and…
Learning Perceptive Humanoid Locomotion over Challenging Terrain
Wandong Sun, Baoshi Cao, Long Chen +4
Humanoid robots are engineered to navigate terrains akin to those encountered by humans, which necessitates human-like locomotion and perceptual abilities. Currently, the most reli…
Learning Humanoid Locomotion with World Model Reconstruction
Wandong Sun, Long Chen, Yongbo Su +3
Humanoid robots are designed to navigate environments accessible to humans using their legs. However, classical research has primarily focused on controlled laboratory settings, re…