  Recent advances of network architecture for point cloud processing are mainly
driven by new designs of local aggregation operators. However, the impact of
these operators to network performance is not carefully investigated due to
different overall network architecture and implementation details in each
solution. Meanwhile, most of operators are only applied in shallow
architectures. In this paper, we revisit the representative local aggregation
operators and study their performance using the same deep residual
architecture. Our investigation reveals that despite the different designs of
these operators, all of these operators make surprisingly similar contributions
to the network performance under the same network input and feature numbers and
result in the state-of-the-art accuracy on standard benchmarks. This finding
stimulate us to rethink the necessity of sophisticated design of local
aggregation operator for point cloud processing. To this end, we propose a
simple local aggregation operator without learnable weights, named Position
Pooling (PosPool), which performs similarly or slightly better than existing
sophisticated operators. In particular, a simple deep residual network with
PosPool layers achieves outstanding performance on all benchmarks, which
outperforms the previous state-of-the methods on the challenging PartNet
datasets by a large margin (7.4 mIoU). The code is publicly available at
https://github.com/zeliu98/CloserLook3D
