no code implementations • 22 Mar 2024 • Aqeel Anwar, Tae Eun Choe, Zian Wang, Sanja Fidler, Minwoo Park
The resulting augmented multi-view consistent dataset is used to train a multi-camera perception network for autonomous vehicles.
1 code implementation • 28 Feb 2024 • Zhangkai Ni, Juncheng Wu, Zian Wang, Wenhan Yang, Hanli Wang, Lin Ma
This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution, which heavily rely on precisely aligned paired datasets with pixel-level alignments.
no code implementations • 18 Nov 2023 • Yongsu Ahn, Muheng Yan, Yu-Ru Lin, Zian Wang
The escalating food insecurity in Africa, caused by factors such as war, climate change, and poverty, demonstrates the critical need for advanced early warning systems.
no code implementations • 16 Nov 2023 • Zian Wang, Tianchang Shen, Merlin Nimier-David, Nicholas Sharp, Jun Gao, Alexander Keller, Sanja Fidler, Thomas Müller, Zan Gojcic
We then extract an explicit mesh of a narrow band around the surface, with width determined by the kernel size, and fine-tune the radiance field within this band.
no code implementations • 10 Aug 2023 • Tianchang Shen, Jacob Munkberg, Jon Hasselgren, Kangxue Yin, Zian Wang, Wenzheng Chen, Zan Gojcic, Sanja Fidler, Nicholas Sharp, Jun Gao
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 modeling, and inverse physics.
no code implementations • ICCV 2023 • Shengyu Huang, Zan Gojcic, Zian Wang, Francis Williams, Yoni Kasten, Sanja Fidler, Konrad Schindler, Or Litany
We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene representation from LiDAR measurements, with the goal of synthesizing realistic LiDAR scans from novel viewpoints.
no code implementations • 6 Apr 2023 • Zian Wang, Tianchang Shen, Jun Gao, Shengyu Huang, Jacob Munkberg, Jon Hasselgren, Zan Gojcic, Wenzheng Chen, Sanja Fidler
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion.
no code implementations • CVPR 2023 • Zian Wang, Tianchang Shen, Jun Gao, Shengyu Huang, Jacob Munkberg, Jon Hasselgren, Zan Gojcic, Wenzheng Chen, Sanja Fidler
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion.
no code implementations • 11 Dec 2022 • Rebeka Man, Kean Ming Tan, Zian Wang, Wen-Xin Zhou
In this paper, we propose and study (penalized) robust expectile regression (retire), with a focus on iteratively reweighted $\ell_1$-penalization which reduces the estimation bias from $\ell_1$-penalization and leads to oracle properties.
3 code implementations • 22 Sep 2022 • Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, Sanja Fidler
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.
no code implementations • 19 Aug 2022 • Zian Wang, Wenzheng Chen, David Acuna, Jan Kautz, Sanja Fidler
In this work, we propose a neural approach that estimates the 5D HDR light field from a single image, and a differentiable object insertion formulation that enables end-to-end training with image-based losses that encourage realism.
no code implementations • 16 Nov 2021 • Kuo Jiang, Zian Wang, Xiaodong Yang
A more interpretable edge-aware metric and an annotation scheme is designed for the testing set, which allows diagnostic evaluation for intrinsic models.
no code implementations • NeurIPS 2021 • Wenzheng Chen, Joey Litalien, Jun Gao, Zian Wang, Clement Fuji Tsang, Sameh Khamis, Or Litany, Sanja Fidler
We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiable renderers.
no code implementations • ICCV 2021 • Zian Wang, Jonah Philion, Sanja Fidler, Jan Kautz
In this paper, we propose a unified, learning-based inverse rendering framework that formulates 3D spatially-varying lighting.
no code implementations • ECCV 2020 • Jun Gao, Zian Wang, Jinchen Xuan, Sanja Fidler
We also utilize DefGrid at the output layers for the task of object mask annotation, and show that reasoning about object boundaries on our predicted polygonal grid leads to more accurate results over existing pixel-wise and curve-based approaches.
1 code implementation • CVPR 2019 • Zian Wang, David Acuna, Huan Ling, Amlan Kar, Sanja Fidler
We revive the old ideas on level set segmentation which framed object annotation as curve evolution.