Unbiased IoU for Spherical Image Object Detection

Field-of-View IoU for Object Detection in 360° Images

360° cameras have gained popularity over the last few years. In this paper, we propose two fundamental techniques -- Field-of-View IoU (FoV-IoU) and 360Augmentation for object detection in 360° images. Although most object detection neural networks designed for the perspective images are applicable to 360° images in equirectangular projection (ERP) format, their performance deteriorates owing to the distortion in ERP images. Our method can be readily integrated with existing perspective object detectors and significantly improves the performance. The FoV-IoU computes the intersection-over-unio…

用于语义分割的球面特征金字塔网络

Spherical Feature Pyramid Networks For Semantic Segmentation

本文针对球面语义分割问题，提出球面特征金字塔网络（Spherical FPNs），利用基于图的球面CNN的金字塔层级结构，在Stanford 2D-3D-S数据集上以更少参数取得48.75 mIOU的最先进性能，超越球面UNet。

动态类感知主动学习用于无偏卫星图像分割

Dynamic Class-Aware Active Learning for Unbiased Satellite Image Segmentation

本文提出动态类感知主动学习方法，通过实时跟踪类性能差距，解决类别不平衡问题，提升分割精度和标注效率。

Towards Unbiased Label Distribution Learning for Facial Pose Estimation Using Anisotropic Spherical Gaussian

Facial pose estimation refers to the task of predicting face orientation from a single RGB image. It is an important research topic with a wide range of applications in computer vision. Label distribution learning (LDL) based methods have been recently proposed for facial pose estimation, which achieve promising results. However, there are two major issues in existing LDL methods. First, the expectations of label distributions are biased, leading to a biased pose estimation. Second, fixed distribution parameters are applied for all learning samples, severely limiting the model capability. In t…

STD: Sparse-to-Dense 3D Object Detector for Point Cloud

We present a new two-stage 3D object detection framework, named sparse-to-dense 3D Object Detector (STD). The first stage is a bottom-up proposal generation network that uses raw point cloud as input to generate accurate proposals by seeding each point with a new spherical anchor. It achieves a high recall with less computation compared with prior works. Then, PointsPool is applied for generating proposal features by transforming their interior point features from sparse expression to compact representation, which saves even more computation time. In box prediction, which is the second stage, …

用于语义分割的标准CNN的等距柱状图像构建方法

Equirectangular image construction method for standard CNNs for Semantic Segmentation

针对等距柱状图像畸变和数据集稀缺问题，提出将透视图像转换为等距柱状图像的方法，使标准CNN能学习畸变特征进行语义分割，最优参数φ为6π/16，平均IoU达43.76%，优于其他三类方法。