  Significant progress has been made recently in point cloud segmentation
utilizing an encoder-decoder framework, which initially encodes point clouds
into low-resolution representations and subsequently decodes high-resolution
predictions. Inspired by the success of high-resolution architectures in image
dense prediction, which always maintains a high-resolution representation
throughout the entire learning process, we consider it also highly important
for 3D dense point cloud analysis. Therefore, in this paper, we explore
high-resolution architectures for 3D point cloud segmentation. Specifically, we
generalize high-resolution architectures using a unified pipeline named
PointHR, which includes a knn-based sequence operator for feature extraction
and a differential resampling operator to efficiently communicate different
resolutions. Additionally, we propose to avoid numerous on-the-fly computations
of high-resolution architectures by pre-computing the indices for both sequence
and resampling operators. By doing so, we deliver highly competitive
high-resolution architectures while capitalizing on the benefits of
well-designed point cloud blocks without additional effort. To evaluate these
architectures for dense point cloud analysis, we conduct thorough experiments
using S3DIS and ScanNetV2 datasets, where the proposed PointHR outperforms
recent state-of-the-art methods without any bells and whistles. The source code
is available at \url{https://github.com/haibo-qiu/PointHR}.
