  Hidden features in neural network usually fail to learn informative
representation for 3D segmentation as supervisions are only given on output
prediction, while this can be solved by omni-scale supervision on intermediate
layers. In this paper, we bring the first omni-scale supervision method to
point cloud segmentation via the proposed gradual Receptive Field Component
Reasoning (RFCR), where target Receptive Field Component Codes (RFCCs) are
designed to record categories within receptive fields for hidden units in the
encoder. Then, target RFCCs will supervise the decoder to gradually infer the
RFCCs in a coarse-to-fine categories reasoning manner, and finally obtain the
semantic labels. Because many hidden features are inactive with tiny magnitude
and make minor contributions to RFCC prediction, we propose a Feature
Densification with a centrifugal potential to obtain more unambiguous features,
and it is in effect equivalent to entropy regularization over features. More
active features can further unleash the potential of our omni-supervision
method. We embed our method into four prevailing backbones and test on three
challenging benchmarks. Our method can significantly improve the backbones in
all three datasets. Specifically, our method brings new state-of-the-art
performances for S3DIS as well as Semantic3D and ranks the 1st in the ScanNet
benchmark among all the point-based methods. Code will be publicly available at
https://github.com/azuki-miho/RFCR.
