Going Deeper with Lean Point Networks
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%.
The paper presents an approach to improve (i) the memory footprint of point processing networks, which has been a main bottleneck for they use, and (ii) their accuracy. Specifically three point processing blocks are presented that enables wider and deeper architectures with improved information sharing. The approach is thoroughly evaluated on point segmentation benchmarks. The paper is very well written and a pleasure to read, even for a reader unfamiliar with point networks. The proposed blocks are clearly and intuitively presented and their impact convincingly demonstrated. For the sake completeness, it would be important to include memory usage and inference time in Table 2, especially as it deals with comparison against Deep CGN, so one can better assess the advantages/disadvantages of both approaches. Timing results would also be important to include in Table 3. Overall, it is a very interesting problem and the proposed approach could have broad applicability.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Going Deeper with Lean Point Networks
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%.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The paper presents an approach to improve (i) the memory footprint of point processing networks, which has been a main bottleneck for they use, and (ii) their accuracy. Specifically three point processing blocks are presented that enables wider and deeper architectures with improved information sharing. The approach is thoroughly evaluated on point segmentation benchmarks. The paper is very well written and a pleasure to read, even for a reader unfamiliar with point networks. The proposed blocks are clearly and intuitively presented and their impact convincingly demonstrated. For the sake completeness, it would be important to include memory usage and inference time in Table 2, especially as it deals with comparison against Deep CGN, so one can better assess the advantages/disadvantages of both approaches. Timing results would also be important to include in Table 3. Overall, it is a very interesting problem and the proposed approach could have broad applicability.
assistant
The paper presents an approach to improve (i) the memory footprint of point processing networks, which has been a main bottleneck for they use, and (ii) their accuracy. Specifically three point processing blocks are presented that enables wider and deeper architectures with improved information sharing. The approach is thoroughly evaluated on point segmentation benchmarks. The paper is very well written and a pleasure to read, even for a reader unfamiliar with point networks. The proposed blocks are clearly and intuitively presented and their impact convincingly demonstrated. For the sake completeness, it would be important to include memory usage and inference time in Table 2, especially as it deals with comparison against Deep CGN, so one can better assess the advantages/disadvantages of both approaches. Timing results would also be important to include in Table 3. Overall, it is a very interesting problem and the proposed approach could have broad applicability.