3 citations · 5 across the 21 of their papers we have counts for
24 papers
VITAL: From Visual Motion to Precise Action
Ao Liang, Youquan Liu, Mingxiao Li +7
World-action models aim to turn visual anticipation into driving decisions, yet typically train action experts on trajectory representations defined independently of visual dynamic…
OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation
Youquan Liu, Weidong Yang, Ao Liang +9
LiDAR scene generation is increasingly important for scalable simulation and synthetic data creation, especially under diverse sensing conditions that are costly to capture at scal…
LightHarmony3D: Harmonizing Illumination and Shadows for Object Insertion in 3D Gaussian Splatting
Tianyu Huang, Zhenyang Ren, Zhenchen Wan +5
3D Gaussian Splatting (3DGS) enables high-fidelity reconstruction of scene geometry and appearance. Building on this capability, inserting external mesh objects into reconstructed…
Hierarchical Collaborative Fusion for 3D Instance-aware Referring Expression Segmentation
Keshen Zhou, Runnan Chen, Mingming Gong +1
Generalised 3D Referring Expression Segmentation (3D-GRES) localizes objects in 3D scenes based on natural language, even when descriptions match multiple or zero targets. Existing…
BrokenBind: Universal Modality Exploration beyond Dataset Boundaries
Zhuo Huang, Runnan Chen, Bo Han +3
Multi-modal learning combines various modalities to provide a comprehensive understanding of real-world problems. A common strategy is to directly bind different modalities togethe…
Intellectual Property Protection for 3D Gaussian Splatting Assets: A Survey
Longjie Zhao, Ziming Hong, Jiaxin Huang +3
3D Gaussian Splatting (3DGS) has become a mainstream representation for real-time 3D scene synthesis, enabling applications in virtual and augmented reality, robotics, and 3D conte…
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…
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM
Runnan Chen, Zhaoqing Wang, Jiepeng Wang +4
Understanding geometric, semantic, and instance information in 3D scenes from sequential video data is essential for applications in robotics and augmented reality. However, existi…
OVGaussian: Generalizable 3D Gaussian Segmentation with Open Vocabularies
Runnan Chen, Xiangyu Sun, Zhaoqing Wang +8
Open-vocabulary scene understanding using 3D Gaussian (3DGS) representations has garnered considerable attention. However, existing methods mostly lift knowledge from large 2D visi…
Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels
Haodong Chen, Runnan Chen, Qiang Qu +4
Recent advancements in 3D Gaussian Splatting (3DGS) have substantially improved novel view synthesis, enabling high-quality reconstruction and real-time rendering. However, blurrin…
UrbanGS: Semantic-Guided Gaussian Splatting for Urban Scene Reconstruction
Ziwen Li, Jiaxin Huang, Runnan Chen +5
Reconstructing urban scenes is challenging due to their complex geometries and the presence of potentially dynamic objects. 3D Gaussian Splatting (3DGS)-based methods have shown st…
LaVin-DiT: Large Vision Diffusion Transformer
Zhaoqing Wang, Xiaobo Xia, Runnan Chen +4
This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative fra…
An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models
Jiahao Sun, Chunmei Qing, Xiang Xu +10
In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on d…