State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the point clouds to 2D space and then process them via 2D convolution. Although this corporation shows the competitiveness in the point cloud, it inevitably alters and abandons the 3D topology and geometric relations. A natural remedy is to utilize the 3D voxelization and 3D convolution network. However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited. An important reason is the property of the outdoor point cloud, namely sparsity and varying density. Motivated by this investigation, we propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern while maintaining these inherent properties. Moreover, a point-wise refinement module is introduced to alleviate the interference of lossy voxel-based label encoding. We evaluate the proposed model on two large-scale datasets, i.e., SemanticKITTI and nuScenes. Our method achieves the 1st place in the leaderboard of SemanticKITTI 1 and outperforms existing methods on nuScenes with a noticeable margin, about 4%. Furthermore, the proposed 3D framework also generalizes well to LiDAR panoptic segmentation and LiDAR 3D detection.

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Most proxy caches for streaming videos do not cache the entire video but only a portion of it. This is partly due to the large size of video objects. Another reason is that the popularity of different part of a video can be different, e.g. the prefix is generally more popular. Therefore, the development of efficient cache mechanisms requires an understanding of the internal popularity characteristics of streaming videos. This paper has two major contributions. Firstly, we analyze two 6-month long traces of RTSP video requests recorded at different streaming video servers of an entertainment video-on-demand provider, and show that the traces provide evidence that the internal popularity of the majority of the most popular videos obeys a k-transformed Zipf-like distribution. Secondly, we propose a caching algorithm which exploits this empirical internal popularity distribution. We find that this algorithm has similar performance compare with fine-grained caching but requires significantly less state information.