Optical flow computation is essential in the early stages of the video
processing pipeline. This paper focuses on a less explored problem in this
area, the 360$^\circ$ optical flow estimation using deep neural networks to
support increasingly popular VR applications. To address the distortions of
panoramic representations when applying convolutional neural networks, we
propose a novel multi-projection fusion framework that fuses the optical flow
predicted by the models trained using different projection methods. It learns
to combine the complementary information in the optical flow results under
different projections. We also build the first large-scale panoramic optical
flow dataset to support the training of neural networks and the evaluation of
panoramic optical flow estimation methods. The experimental results on our
dataset demonstrate that our method outperforms the existing methods and other
alternative deep networks that were developed for processing 360{\deg} content.

本文介绍了一种使用多投影融合框架的深度神经网络来估计 360 度光流的方法，以解决全景表示在卷积神经网络中的畸变问题。同时，建立了第一个大规模的全景光流数据集，并证明了该方法在性能上优于现有的方法和其他处理 360 度内容的深度网络。

基于多投影融合的深度 360 度光流估计

Deep 360$^\circ$ Optical Flow Estimation Based on Multi-Projection  Fusion

Semantic segmentation of 3D point cloud data is essential for enhanced
high-level perception in autonomous platforms. Furthermore, given the
increasing deployment of LiDAR sensors onboard of cars and drones, a special
emphasis is also placed on non-computationally intensive algorithms that
operate on mobile GPUs. Previous efficient state-of-the-art methods relied on
2D spherical projection of point clouds as input for 2D fully convolutional
neural networks to balance the accuracy-speed trade-off. This paper introduces
a novel approach for 3D point cloud semantic segmentation that exploits
multiple projections of the point cloud to mitigate the loss of information
inherent in single projection methods. Our Multi-Projection Fusion (MPF)
framework analyzes spherical and bird's-eye view projections using two separate
highly-efficient 2D fully convolutional models then combines the segmentation
results of both views. The proposed framework is validated on the SemanticKITTI
dataset where it achieved a mIoU of 55.5 which is higher than state-of-the-art
projection-based methods RangeNet++ and PolarNet while being 1.6x faster than
the former and 3.1x faster than the latter.

这篇论文提出了一种基于多视角融合的 3D 点云语义分割方法，通过对球面和鸟瞰图进行高效的二维卷积神经网络分割，结合两个视角的分割结果，有效缓解了单个视角方法中存在的信息损失问题，既能提高准确率又能保证速度，在 SemanticKITTI 数据集上实现了高准确率（mIoU 为 55.5），同时比目前最先进的基于投影的方法 RangeNet++ 和 PolarNet 分别快 1.6 倍和 3.1 倍。