47 citations · 187 across the 17 of their papers we have counts for
19 papers
InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts
Weipeng Zhong, Peizhou Cao, Yichen Jin +9
The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts. However, existing datasets typic…
MeshCoder: LLM-Powered Structured Mesh Code Generation from Point Clouds
Bingquan Dai, Li Ray Luo, Qihong Tang +9
Reconstructing 3D objects into editable programs is pivotal for applications like reverse engineering and shape editing. However, existing methods often rely on limited domain-spec…
STream3R: Scalable Sequential 3D Reconstruction with Causal Transformer
Yushi Lan, Yihang Luo, Fangzhou Hong +7
We present STream3R, a novel approach to 3D reconstruction that reformulates pointmap prediction as a decoder-only Transformer problem. Existing state-of-the-art methods for multi-…
Infinite Mobility: Scalable High-Fidelity Synthesis of Articulated Objects via Procedural Generation
Xinyu Lian, Zichao Yu, Ruiming Liang +9
Large-scale articulated objects with high quality are desperately needed for multiple tasks related to embodied AI. Most existing methods for creating articulated objects are eithe…
GaussianAnything: Interactive Point Cloud Flow Matching For 3D Object Generation
Yushi Lan, Shangchen Zhou, Zhaoyang Lyu +5
While 3D content generation has advanced significantly, existing methods still face challenges with input formats, latent space design, and output representations. This paper intro…
GetMesh: A Controllable Model for High-quality Mesh Generation and Manipulation
Zhaoyang Lyu, Ben Fei, Jinyi Wang +4
Mesh is a fundamental representation of 3D assets in various industrial applications, and is widely supported by professional softwares. However, due to its irregular structure, me…
Point Cloud Pre-training with Diffusion Models
Xiao Zheng, Xiaoshui Huang, Guofeng Mei +5
Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However, due to the unordered and non-uniform…
MATLABER: Material-Aware Text-to-3D via LAtent BRDF auto-EncodeR
Xudong Xu, Zhaoyang Lyu, Xingang Pan +1
Based on powerful text-to-image diffusion models, text-to-3D generation has made significant progress in generating compelling geometry and appearance. However, existing methods st…
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
Xinqi Lin, Jingwen He, Ziyan Chen +6
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework. DiffBIR decouples blind image restoration probl…
Generative Diffusion Prior for Unified Image Restoration and Enhancement
Ben Fei, Zhaoyang Lyu, Liang Pan +5
Existing image restoration methods mostly leverage the posterior distribution of natural images. However, they often assume known degradation and also require supervised training,…
Controllable Mesh Generation Through Sparse Latent Point Diffusion Models
Zhaoyang Lyu, Jinyi Wang, Yuwei An +3
Mesh generation is of great value in various applications involving computer graphics and virtual content, yet designing generative models for meshes is challenging due to their ir…
Guided Diffusion Model for Adversarial Purification
Jinyi Wang, Zhaoyang Lyu, Dahua Lin +2
With wider application of deep neural networks (DNNs) in various algorithms and frameworks, security threats have become one of the concerns. Adversarial attacks disturb DNN-based…
Accelerating Diffusion Models via Early Stop of the Diffusion Process
Zhaoyang Lyu, Xudong XU, Ceyuan Yang +2
Denoising Diffusion Probabilistic Models (DDPMs) have achieved impressive performance on various generation tasks. By modeling the reverse process of gradually diffusing the data d…
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion
Zhaoyang Lyu, Zhifeng Kong, Xudong Xu +2
3D point cloud is an important 3D representation for capturing real world 3D objects. However, real-scanned 3D point clouds are often incomplete, and it is important to recover com…
Towards Evaluating and Training Verifiably Robust Neural Networks
Zhaoyang Lyu, Minghao Guo, Tong Wu +3
Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP train…
Model-Aware Regularization For Learning Approaches To Inverse Problems
Jaweria Amjad, Zhaoyan Lyu, Miguel R. D. Rodrigues
There are various inverse problems -- including reconstruction problems arising in medical imaging -- where one is often aware of the forward operator that maps variables of intere…
Fastened CROWN: Tightened Neural Network Robustness Certificates
Zhaoyang Lyu, Ching-Yun Ko, Zhifeng Kong +3
The rapid growth of deep learning applications in real life is accompanied by severe safety concerns. To mitigate this uneasy phenomenon, much research has been done providing reli…
POPQORN: Quantifying Robustness of Recurrent Neural Networks
Ching-Yun Ko, Zhaoyang Lyu, Tsui-Wei Weng +3
The vulnerability to adversarial attacks has been a critical issue for deep neural networks. Addressing this issue requires a reliable way to evaluate the robustness of a network.…
Deep Learning for Inverse Problems: Bounds and Regularizers
Jaweria Amjad, Zhaoyan Lyu, Miguel R. D. Rodrigues
Inverse problems arise in a number of domains such as medical imaging, remote sensing, and many more, relying on the use of advanced signal and image processing approaches -- such…