Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Shen, Tianchang"'
We present an elastic simulator for domains defined as evolving implicit functions, which is efficient, robust, and differentiable with respect to both shape and material. This simulator is motivated by applications in 3D reconstruction: it is increa
Externí odkaz:
http://arxiv.org/abs/2410.09417
Autor:
Shen, Tianchang, Li, Zhaoshuo, Law, Marc, Atzmon, Matan, Fidler, Sanja, Lucas, James, Gao, Jun, Sharp, Nicholas
Meshes are ubiquitous in visual computing and simulation, yet most existing machine learning techniques represent meshes only indirectly, e.g. as the level set of a scalar field or deformation of a template, or as a disordered triangle soup lacking l
Externí odkaz:
http://arxiv.org/abs/2409.20562
Autor:
Wang, Zian, Shen, Tianchang, Nimier-David, Merlin, Sharp, Nicholas, Gao, Jun, Keller, Alexander, Fidler, Sanja, Müller, Thomas, Gojcic, Zan
Neural radiance fields achieve unprecedented quality for novel view synthesis, but their volumetric formulation remains expensive, requiring a huge number of samples to render high-resolution images. Volumetric encodings are essential to represent fu
Externí odkaz:
http://arxiv.org/abs/2311.10091
Autor:
Shen, Tianchang, Munkberg, Jacob, Hasselgren, Jon, Yin, Kangxue, Wang, Zian, Chen, Wenzheng, Gojcic, Zan, Fidler, Sanja, Sharp, Nicholas, Gao, Jun
Publikováno v:
ACM Transactions on Graphics, Volume 42, Issue 4, Article No.: 37, August 2023
This work considers gradient-based mesh optimization, where we iteratively optimize for a 3D surface mesh by representing it as the isosurface of a scalar field, an increasingly common paradigm in applications including photogrammetry, generative mod
Externí odkaz:
http://arxiv.org/abs/2308.05371
Autor:
Wang, Zian, Shen, Tianchang, Gao, Jun, Huang, Shengyu, Munkberg, Jacob, Hasselgren, Jon, Gojcic, Zan, Chen, Wenzheng, Fidler, Sanja
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion. Recent NeRF based methods achieve impressive fidelity of 3D reconstruction, but bake the lighti
Externí odkaz:
http://arxiv.org/abs/2304.03266
Autor:
Gao, Jun, Shen, Tianchang, Wang, Zian, Chen, Wenzheng, Yin, Kangxue, Li, Daiqing, Litany, Or, Gojcic, Zan, Fidler, Sanja
As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of 3D content is becoming evident. In our work, we aim to train performan
Externí odkaz:
http://arxiv.org/abs/2209.11163
Autor:
Munkberg, Jacob, Hasselgren, Jon, Shen, Tianchang, Gao, Jun, Chen, Wenzheng, Evans, Alex, Müller, Thomas, Fidler, Sanja
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations. Unlike recent multi-view reconstruction approaches, which typically produce entangled 3D representations encoded in neural n
Externí odkaz:
http://arxiv.org/abs/2111.12503
We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of implicit and explicit 3D representations by leveraging a novel hybrid 3D
Externí odkaz:
http://arxiv.org/abs/2111.04276
Inferring detailed 3D geometry of the scene is crucial for robotics applications, simulation, and 3D content creation. However, such information is hard to obtain, and thus very few datasets support it. In this paper, we propose an interactive framew
Externí odkaz:
http://arxiv.org/abs/2008.10719
Autor:
Burnett, Keenan, Qian, Jingxing, Du, Xintong, Liu, Linqiao, Yoon, David J., Shen, Tianchang, Sun, Susan, Samavi, Sepehr, Sorocky, Michael J., Bianchi, Mollie, Zhang, Kaicheng, Arkhangorodsky, Arkady, Sykora, Quinlan, Lu, Shichen, Huang, Yizhou, Schoellig, Angela P., Barfoot, Timothy D.
The SAE AutoDrive Challenge is a three-year collegiate competition to develop a self-driving car by 2020. The second year of the competition was held in June 2019 at MCity, a mock town built for self-driving car testing at the University of Michigan.
Externí odkaz:
http://arxiv.org/abs/2004.08752