no code implementations • 2 Dec 2023 • Pengsheng Guo, Hans Hao, Adam Caccavale, Zhongzheng Ren, Edward Zhang, Qi Shan, Aditya Sankar, Alexander G. Schwing, Alex Colburn, Fangchang Ma
Our analysis identifies the core of these challenges as the interaction among noise levels in the 2D diffusion process, the architecture of the diffusion network, and the 3D model representation.
no code implementations • 12 Oct 2023 • Xiaoming Zhao, Alex Colburn, Fangchang Ma, Miguel Angel Bautista, Joshua M. Susskind, Alexander G. Schwing
In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.
1 code implementation • ICCV 2023 • Noah Stier, Anurag Ranjan, Alex Colburn, Yajie Yan, Liang Yang, Fangchang Ma, Baptiste Angles
Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency.
1 code implementation • ICCV 2023 • Ziya Erkoç, Fangchang Ma, Qi Shan, Matthias Nießner, Angela Dai
HyperDiffusion operates directly on MLP weights and generates new neural implicit fields encoded by synthesized MLP parameters.
1 code implementation • 21 Jul 2022 • Xiaoming Zhao, Fangchang Ma, David Güera, Zhile Ren, Alexander G. Schwing, Alex Colburn
What is really needed to make an existing 2D GAN 3D-aware?
no code implementations • 5 Apr 2022 • Yawar Siddiqui, Justus Thies, Fangchang Ma, Qi Shan, Matthias Nießner, Angela Dai
Texture cues on 3D objects are key to compelling visual representations, with the possibility to create high visual fidelity with inherent spatial consistency across different views.
1 code implementation • ICCV 2021 • Yawar Siddiqui, Justus Thies, Fangchang Ma, Qi Shan, Matthias Nießner, Angela Dai
3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks.
1 code implementation • 8 Mar 2019 • Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze
In this paper, we address the problem of fast depth estimation on embedded systems.
1 code implementation • NeurIPS 2018 • Fangchang Ma, Ulas Ayaz, Sertac Karaman
In this work, we present new theoretical results on convolutional generative neural networks, in particular their invertibility (i. e., the recovery of input latent code given the network output).
2 code implementations • 1 Jul 2018 • Fangchang Ma, Guilherme Venturelli Cavalheiro, Sertac Karaman
Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving.
Ranked #6 on Depth Completion on VOID
6 code implementations • 21 Sep 2017 • Fangchang Ma, Sertac Karaman
We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image.
1 code implementation • 4 Mar 2017 • Fangchang Ma, Luca Carlone, Ulas Ayaz, Sertac Karaman
We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements?