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Title: Going Deeper with Lean Point Networks

Paper content:
We introduce three generic point cloud processing blocks that improve both accuracy and memory consumption of multiple state-of-the-art networks, thus allowing to design deeper and more accurate networks.
The novel processing blocks that facilitate efficient information flow are a convolution-type operation block for point sets that blends neighborhood information in a memory-efficient manner; a multi-resolution point cloud processing block; and a crosslink block that efficiently shares information across low- and high-resolution processing branches.
Combining these blocks, we design significantly wider and deeper architectures.
We extensively evaluate the proposed architectures on multiple point segmentation benchmarks (ShapeNetPart, ScanNet, PartNet) and report systematic improvements in terms of both accuracy and memory consumption by using our generic modules in conjunction with multiple recent architectures (PointNet++, DGCNN, SpiderCNN, PointCNN).
We report a 9.7% increase in IoU on the PartNet dataset, which is the most complex, while decreasing memory footprint  by  57%.
Geometry processing has recently started profiting from applying deep learning to graphics and 3D shape analysis (Qi et al., 2017b; Wang et al., 2018b; with networks that guarantee desirable properties of point cloud processing, such as permutation-invariance and quantization-free representation Wang et al., 2017; .
Despite these advances, several differences still impede the breakthroughs made in computer vision.
The different nature of 3D data dictates re-inventing for geometry processing the functionality of basic image processing blocks, such as multi-resolution processing or convolution operations.
When operating with unstructured point clouds, one has to resort to elementary local pooling operations that group information within a neighborhood based on Euclidean distance.
Exemplary methods such as the PointNet/PointNet++ architectures (Qi et al., 2017a; make design choices that potentially compromise performance.
In particular, the computation and memory demands of point network blocks can affect both training speed and, more crucially, inference time.
One of the main bottlenecks for point networks is their memory-intensive nature: as detailed in Sec. 3.1, the PointNet++ architecture and its variants replicate point neighborhood information, letting every node carry in its feature vector information about all of its neighborhood.
This results in significant memory overhead, and limits the number of layers, features and feature compositions one can compute.
In this work, we enhance point processing networks by introducing a set of modules that improve memory footprint and accuracy, without compromising on inference speed.
We call the result architectures Lean Point Networks, to highlight their lightweight memory budget.
We build on the decreased memory budget to go deeper with point networks.
As has been witnessed repeatedly in the image domain Huang et al., 2016; Zagoruyko & Komodakis, 2016) , we show that going deep also increases the prediction accuracy of point networks.
We start in Sec. 3.2 by replacing the grouping operation used in point cloud processing networks with a low-memory alternative that is the point cloud processing counterpart of efficient image processing implementations of convolution.
The resulting 'point convolution block' is 67% more memory-efficient and 41% faster than its PointNet++ counterpart, while exhibiting favorable training properties due to more effective mixing of information across neighborhoods.
We then turn in Sec. 3.3 to improving the information flow across layers and scales within point networks through three techniques: a multi-resolution variant for multi-scale network which still delivers the multi-scale context but at a reduced memory and computational cost, residual links, and a new cross-link block that broadcasts multi-scale information across the network branches.
By combining these advances we are able to successfully train deeper point networks that allow us to leverage upon larger, recently introduced datasets.
In Sec. 4 we thoroughly validate our contributions on the ShapeNet-Part, ScanNet and PartNet segmentation benchmarks, reporting systematic improvements over the PointNet++ baseline.
As shown in Fig. 1 , when combined these contributions deliver multifold reductions in memory consumption while improving performance, allowing us in a second stage to train increasingly wide and deep networks.
On PartNet, the most complex dataset, our deep architecture achieves a 9.7% relative increase in IoU while decreasing memory footprint by 57% and inference time by 47%.
Having thoroughly ablated our design choices on the PartNet++ baseline, in Sec. 4.3 we turn to confirming the generic nature of our blocks.
We extend the scope of our experiments to three additional networks,
(i) DGCNN (Wang et al., 2018b) ,
(ii) SpiderCNN (Xu et al., 2018) and
(iii) PointCNN (Li et al., 2018b) and report systematic improvements in memory efficiency and performance.
In this work we have introduced new generic building blocks for point processing networks, that exhibit favorable memory, computation, and optimization properties when compared to the current counterparts of state-of-the-art point processing networks.
When based on PointNet++, our lean architecture convPN wins on all counts, memory efficiency (-67% wrt. PointNet++) and speed (-41% and -68% on inference time and length of backward pass).
Its deep counterpart has a marginal cost in terms of efficiency and achieves the best IoU on PartNet (+9.7% over PointNet++).
Those generic and modular blocks exhibit similar performance on all of the additional tested architectures with a significant decrease in memory (up to -69%) and increase in IoU (up to +8.0%).
From the promising results on PartNet and the extremely low cost of depth in our architectures, we anticipate that adding these components to the armament of the deep geometry processing community will allow researchers to train the next generation of point processing networks by leveraging upon the advent of larger shape datasets (Mo et al., 2018; Koch et al., 2018

assistant: text: We introduce three generic point cloud processing blocks that improve both accuracy and memory consumption of multiple state-of-the-art networks, thus allowing to design deeper and more accurate networks.