33 citations · 78 across the 13 of their papers we have counts for
12 papers · 1 filter
Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction
Jiayang Bai, Zhen He, Shan Yang +4
Predicting panoramic indoor lighting from a single perspective image is a fundamental but highly ill-posed problem in computer vision and graphics. To achieve locale-aware and robu…
Self-NeRF: A Self-Training Pipeline for Few-Shot Neural Radiance Fields
Jiayang Bai, Letian Huang, Wen Gong +2
Recently, Neural Radiance Fields (NeRF) have emerged as a potent method for synthesizing novel views from a dense set of images. Despite its impressive performance, NeRF is plagued…
Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation
Changfeng Ma, Yang Yang, Jie Guo +2
Most existing point cloud completion methods are only applicable to partial point clouds without any noises and outliers, which does not always hold in practice. We propose in this…
Deep Point Cloud Simplification for High-quality Surface Reconstruction
Yuanqi Li, Jianwei Guo, Xinran Yang +4
The growing size of point clouds enlarges consumptions of storage, transmission, and computation of 3D scenes. Raw data is redundant, noisy, and non-uniform. Therefore, simplifying…
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction
Jiayang Bai, Jie Guo, Chenchen Wan +6
Lighting prediction from a single image is becoming increasingly important in many vision and augmented reality (AR) applications in which shading and shadow consistency between vi…
GLPanoDepth: Global-to-Local Panoramic Depth Estimation
Jiayang Bai, Shuichang Lai, Haoyu Qin +2
In this paper, we propose a learning-based method for predicting dense depth values of a scene from a monocular omnidirectional image. An omnidirectional image has a full field-of-…
SVBRDF Recovery From a Single Image With Highlights using a Pretrained Generative Adversarial Network
Tao Wen, Beibei Wang, Lei Zhang +2
Spatially-varying bi-directional reflectance distribution functions (SVBRDFs) are crucial for designers to incorporate new materials in virtual scenes, making them look more realis…
A Transductive Maximum Margin Classifier for Few-Shot Learning
Fei Pan, Chunlei Xu, Jie Guo +1
Few-shot learning aims to train a classifier that can generalize well when just a small number of labeled examples per class are given. We introduce a transductive maximum margin c…
Temporal Alignment Prediction for Few-Shot Video Classification
Fei Pan, Chunlei Xu, Jie Guo +1
The goal of few-shot video classification is to learn a classification model with good generalization ability when trained with only a few labeled videos. However, it is difficult…
Image Stitching Based on Planar Region Consensus
Aocheng Li, Jie Guo, Yanwen Guo
Image stitching for two images without a global transformation between them is notoriously difficult. In this paper, noticing the importance of planar structure under perspective g…
FA-GANs: Facial Attractiveness Enhancement with Generative Adversarial Networks on Frontal Faces
Jingwu He, Chuan Wang, Yang Zhang +2
Facial attractiveness enhancement has been an interesting application in Computer Vision and Graphics over these years. It aims to generate a more attractive face via manipulations…
Hybrid Models for Open Set Recognition
Hongjie Zhang, Ang Li, Jie Guo +1
Open set recognition requires a classifier to detect samples not belonging to any of the classes in its training set. Existing methods fit a probability distribution to the trainin…