Search Results for author: Zian Wang

Found 16 papers, 3 papers with code

Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks

no code implementations22 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.

Autonomous Driving Domain Adaptation

Misalignment-Robust Frequency Distribution Loss for Image Transformation

1 code implementation28 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.

Image Enhancement Style Transfer +1

HungerGist: An Interpretable Predictive Model for Food Insecurity

no code implementations18 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.

Adaptive Shells for Efficient Neural Radiance Field Rendering

no code implementations16 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.

Novel View Synthesis Stochastic Optimization

Flexible Isosurface Extraction for Gradient-Based Mesh Optimization

no code implementations10 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.

Neural LiDAR Fields for Novel View Synthesis

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.

Novel LiDAR View Synthesis Semantic Segmentation

Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes

no code implementations6 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.

3D Reconstruction Inverse Rendering

Retire: Robust Expectile Regression in High Dimensions

no code implementations11 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.

regression Vocal Bursts Intensity Prediction

GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images

3 code implementations22 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.

Neural Light Field Estimation for Street Scenes with Differentiable Virtual Object Insertion

no code implementations19 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.

Autonomous Driving Lighting Estimation +1

Learning Intrinsic Images for Clothing

no code implementations16 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.

Intrinsic Image Decomposition

Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid

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.

Semantic Segmentation

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