Search Results for author: Jiayang Bai

Found 6 papers, 1 papers with code

360-GS: Layout-guided Panoramic Gaussian Splatting For Indoor Roaming

no code implementations1 Feb 2024 Jiayang Bai, Letian Huang, Jie Guo, Wen Gong, Yuanqi Li, Yanwen Guo

This technique typically takes perspective images as input and optimizes a set of 3D elliptical Gaussians by splatting them onto the image planes, resulting in 2D Gaussians.

Novel View Synthesis

On the Error Analysis of 3D Gaussian Splatting and an Optimal Projection Strategy

no code implementations1 Feb 2024 Letian Huang, Jiayang Bai, Jie Guo, Yuanqi Li, Yanwen Guo

This paper addresses the projection error function of 3D Gaussian Splatting, commencing with the residual error from the first-order Taylor expansion of the projection function.

Neural Rendering

Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction

no code implementations18 Mar 2023 Jiayang Bai, Zhen He, Shan Yang, Jie Guo, Zhenyu Chen, Yan Zhang, Yanwen Guo

Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama.

HDR Reconstruction

Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields

no code implementations10 Mar 2023 Jiayang Bai, Letian Huang, Wen Gong, Jie Guo, Yanwen Guo

Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for synthesizing novel views from a dense set of images.

Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction

no code implementations13 Feb 2022 Jiayang Bai, Jie Guo, Chenchen Wan, Zhenyu Chen, Zhen He, Shan Yang, Piaopiao Yu, Yan Zhang, Yanwen Guo

At its core is a new lighting model (dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph Convolutional Network (GCN) that infers the new lighting representation from a single LDR image of limited field-of-view.

Graph Learning Lighting Estimation

GLPanoDepth: Global-to-Local Panoramic Depth Estimation

1 code implementation6 Feb 2022 Jiayang Bai, Shuichang Lai, Haoyu Qin, Jie Guo, Yanwen Guo

In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image.

Depth Estimation

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