Paper:
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
Few prior works study deep learning on point sets. PointNet [20]  is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.

Review:
As clearly indicated in the title, this paper submission is an extension of the PointNet work of [19], to appear at CVPR 2017. The goal is to classify and segment (3D) point clouds. Novel contributions over [19] are the use of a hierarchical network, leveraging neighbourhoods at different scales, and a mechanism to deal with varying sampling densities, effectively generating receptive fields that vary in a data dependent manner. All this leads to state-of-the-art results.
PointNet++ seems an important extension over PointNet, in that it allows to properly exploit local spatial information. Yet the impact on the overall performance is just 1-2 percent.
Some more experimental or theoretical analysis would have been appreciated. For instance: 
- A number of alternative sampling options, apart from most distant point, are mentioned in the text, but not compared against in the experiments. 
- In the conclusions, it is mentioned that MSG is more robust than MRG but worse in terms of computational efficiency. I'd like to see such a claim validated by a more in-depth analysis of the computational load, or at least some indicative numbers.