1 code implementation • 28 Mar 2024 • Chongjie Ye, Yinyu Nie, Jiahao Chang, Yuantao Chen, YiHao Zhi, Xiaoguang Han
We present GauStudio, a novel modular framework for modeling 3D Gaussian Splatting (3DGS) to provide standardized, plug-and-play components for users to easily customize and implement a 3DGS pipeline.
no code implementations • 28 Mar 2024 • Yujin Chen, Yinyu Nie, Benjamin Ummenhofer, Reiner Birkl, Michael Paulitsch, Matthias Müller, Matthias Nießner
In Mesh2NeRF, we propose an analytic solution to directly obtain ground-truth radiance fields from 3D meshes, characterizing the density field with an occupancy function featuring a defined surface thickness, and determining view-dependent color through a reflection function considering both the mesh and environment lighting.
no code implementations • 19 Dec 2023 • Haolin Liu, Chongjie Ye, Yinyu Nie, Yingfan He, Xiaoguang Han
Instance shape reconstruction from a 3D scene involves recovering the full geometries of multiple objects at the semantic instance level.
no code implementations • 5 Dec 2023 • Helisa Dhamo, Yinyu Nie, Arthur Moreau, Jifei Song, Richard Shaw, Yiren Zhou, Eduardo Pérez-Pellitero
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fields.
no code implementations • 2 Dec 2023 • Jiapeng Tang, Angela Dai, Yinyu Nie, Lev Markhasin, Justus Thies, Matthias Niessner
We introduce Diffusion Parametric Head Models (DPHMs), a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences.
no code implementations • 8 Aug 2023 • Mohammad Naanaa, Katharina Schmid, Yinyu Nie
Guided synthesis of high-quality 3D scenes is a challenging task.
no code implementations • CVPR 2023 • Xiangyu Zhu, Dong Du, Weikai Chen, Zhiyou Zhao, Yinyu Nie, Xiaoguang Han
We show that a simple network based on NerVE can already outperform the previous state-of-the-art methods by a great margin.
no code implementations • 24 Mar 2023 • Jiapeng Tang, Yinyu Nie, Lev Markhasin, Angela Dai, Justus Thies, Matthias Nießner
We introduce a diffusion network to synthesize a collection of 3D indoor objects by denoising a set of unordered object attributes.
no code implementations • CVPR 2023 • Yinyu Nie, Angela Dai, Xiaoguang Han, Matthias Nießner
Holistic 3D scene understanding entails estimation of both layout configuration and object geometry in a 3D environment.
1 code implementation • 10 Jun 2022 • Yuchen Rao, Yinyu Nie, Angela Dai
While 3D shape representations enable powerful reasoning in many visual and perception applications, learning 3D shape priors tends to be constrained to the specific categories trained on, leading to an inefficient learning process, particularly for general applications with unseen categories.
no code implementations • 1 Dec 2021 • Yinyu Nie, Angela Dai, Xiaoguang Han, Matthias Nießner
To this end, we propose P2R-Net to learn a probabilistic 3D model of the objects in a scene characterized by their class categories and oriented 3D bounding boxes, based on an input observed human trajectory in the environment.
1 code implementation • ICCV 2021 • Bingchen Gong, Yinyu Nie, Yiqun Lin, Xiaoguang Han, Yizhou Yu
Main-stream methods predict the missing shapes by decoding a global feature learned from the input point cloud, which often leads to deficient results in preserving topology consistency and surface details.
1 code implementation • 14 Jul 2021 • Jinglu Zhang, Yinyu Nie, Jian Chang, Jian Jun Zhang
Automatic surgical instruction generation is a prerequisite towards intra-operative context-aware surgical assistance.
1 code implementation • CVPR 2021 • Yinyu Nie, Ji Hou, Xiaoguang Han, Matthias Nießner
In this work, we introduce RfD-Net that jointly detects and reconstructs dense object surfaces directly from raw point clouds.
no code implementations • NeurIPS 2020 • Yinyu Nie, Yiqun Lin, Xiaoguang Han, Shihui Guo, Jian Chang, Shuguang Cui, Jian Jun Zhang
Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points.
1 code implementation • 13 Jul 2020 • Jinglu Zhang, Yinyu Nie, Yao Lyu, Hailin Li, Jian Chang, Xiaosong Yang, Jian Jun Zhang
The experiment results demonstrate the ability of our method on capturing long-term frame dependencies, which largely outperform the state-of-the-art methods on the frame-wise accuracy up to ~6 points and the F1@50 score ~6 points.
1 code implementation • CVPR 2020 • Yinyu Nie, Xiaoguang Han, Shihui Guo, Yujian Zheng, Jian Chang, Jian Jun Zhang
Semantic reconstruction of indoor scenes refers to both scene understanding and object reconstruction.
Ranked #2 on 3D Shape Reconstruction on Pix3D
no code implementations • 22 Feb 2020 • Yinyu Nie, Shihui Guo, Jian Chang, Xiaoguang Han, Jiahui Huang, Shi-Min Hu, Jian Jun Zhang
Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling.