3 citations · 5 across the 21 of their papers we have counts for
12 papers · 1 filter
SketchPlay: Intuitive Creation of Physically Realistic VR Content with Gesture-Driven Sketching
Xiangwen Zhang, Xiaowei Dai, Runnan Chen +2
Creating physically realistic content in VR often requires complex modeling tools or predefined 3D models, textures, and animations, which present significant barriers for non-expe…
AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven Editing
Ziming Hong, Tianyu Huang, Runnan Chen +4
Recent studies have extended diffusion-based instruction-driven 2D image editing pipelines to 3D Gaussian Splatting (3DGS), enabling faithful manipulation of 3DGS assets and greatl…
PosA-VLA: Enhancing Action Generation via Pose-Conditioned Anchor Attention
Ziwen Li, Xin Wang, Hanlue Zhang +8
The Vision-Language-Action (VLA) models have demonstrated remarkable performance on embodied tasks and shown promising potential for real-world applications. However, current VLAs…
RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing
Longjie Zhao, Ziming Hong, Zhenyang Ren +3
3D Gaussian Splatting (3DGS) has become a leading representation for high-fidelity 3D assets, yet protecting these assets via digital watermarking remains an open challenge. Existi…
OpenInsGaussian: Open-vocabulary Instance Gaussian Segmentation with Context-aware Cross-view Fusion
Tianyu Huang, Runnan Chen, Dongting Hu +3
Understanding 3D scenes is pivotal for autonomous driving, robotics, and augmented reality. Recent semantic Gaussian Splatting approaches leverage large-scale 2D vision models to p…
La La LiDAR: Large-Scale Layout Generation from LiDAR Data
Youquan Liu, Lingdong Kong, Weidong Yang +5
Controllable generation of realistic LiDAR scenes is crucial for applications such as autonomous driving and robotics. While recent diffusion-based models achieve high-fidelity LiD…
Veila: Panoramic LiDAR Generation from a Monocular RGB Image
Youquan Liu, Lingdong Kong, Weidong Yang +8
Realistic and controllable panoramic LiDAR data generation is critical for scalable 3D perception in autonomous driving and robotics. Existing methods either perform unconditional…
SURPRISE3D: A Dataset for Spatial Understanding and Reasoning in Complex 3D Scenes
Jiaxin Huang, Ziwen Li, Hanlve Zhang +6
The integration of language and 3D perception is critical for embodied AI and robotic systems to perceive, understand, and interact with the physical world. Spatial reasoning, a ke…
When Data-Free Knowledge Distillation Meets Non-Transferable Teacher: Escaping Out-of-Distribution Trap is All You Need
Ziming Hong, Runnan Chen, Zengmao Wang +3
Data-free knowledge distillation (DFKD) transfers knowledge from a teacher to a student without access the real in-distribution (ID) data. Its common solution is to use a generator…
Intern-GS: Vision Model Guided Sparse-View 3D Gaussian Splatting
Xiangyu Sun, Runnan Chen, Mingming Gong +2
Sparse-view scene reconstruction often faces significant challenges due to the constraints imposed by limited observational data. These limitations result in incomplete information…
MLLM-For3D: Adapting Multimodal Large Language Model for 3D Reasoning Segmentation
Jiaxin Huang, Runnan Chen, Ziwen Li +5
Reasoning segmentation aims to segment target objects in complex scenes based on human intent and spatial reasoning. While recent multimodal large language models (MLLMs) have demo…
LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving
Lingdong Kong, Xiang Xu, Youquan Liu +6
Recent advancements in vision foundation models (VFMs) have revolutionized visual perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous drivi…