37 citations · 61 across the 10 of their papers we have counts for
11 papers
Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies
Galbot Team, Xuchuan Chen, Xiaoqian Cheng +31
GPT-6 Astra exhibits a remarkable ability to generate numerical robot actions, extending its role beyond high-level planning. To assess Astra's capabilities as general-purpose embo…
RoboSynChallenge: Mastering Real-World Dexterity via Generalizing Synthesized Manipulation Skills
Runyi Zhao, Ruixin Wu, Chengkun Li +15
Achieving generalizable robotic manipulation remains a central challenge in embodied intelligence. Despite rapid advances in model architectures and learning algorithms, progress i…
FullPart: Generating each 3D Part at Full Resolution
Lihe Ding, Shaocong Dong, Yaokun Li +10
Part-based 3D generation holds great potential for various applications. Previous part generators that represent parts using implicit vector-set tokens often suffer from insufficie…
From One to More: Contextual Part Latents for 3D Generation
Shaocong Dong, Lihe Ding, Xiao Chen +10
Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth da…
Interactive3D: Create What You Want by Interactive 3D Generation
Shaocong Dong, Lihe Ding, Zhanpeng Huang +3
3D object generation has undergone significant advancements, yielding high-quality results. However, fall short of achieving precise user control, often yielding results that do no…
MsSVT++: Mixed-scale Sparse Voxel Transformer with Center Voting for 3D Object Detection
Jianan Li, Shaocong Dong, Lihe Ding +1
Accurate 3D object detection in large-scale outdoor scenes, characterized by considerable variations in object scales, necessitates features rich in both long-range and fine-graine…
Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors
Lihe Ding, Shaocong Dong, Zhanpeng Huang +5
Most 3D generation research focuses on up-projecting 2D foundation models into the 3D space, either by minimizing 2D Score Distillation Sampling (SDS) loss or fine-tuning on multi-…
Sample-adaptive Augmentation for Point Cloud Recognition Against Real-world Corruptions
Jie Wang, Lihe Ding, Tingfa Xu +4
Robust 3D perception under corruption has become an essential task for the realm of 3D vision. While current data augmentation techniques usually perform random transformations on…
RDFNet: Regional Dynamic FISTA-Net for Spectral Snapshot Compressive Imaging
Shiyun Zhou, Tingfa Xu, Shaocong Dong +1
Deep convolutional neural networks have recently shown promising results in compressive spectral reconstruction. Previous methods, however, usually adopt a single mapping function…
CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds
Haiyang Wang, Lihe Ding, Shaocong Dong +5
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D. Our proposed method first generates some high-quality 3D proposals by levera…
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection
Xinli Xu, Shaocong Dong, Lihe Ding +3
3D object detection with multi-sensors is essential for an accurate and reliable perception system of autonomous driving and robotics. Existing 3D detectors significantly improve t…