Title: UTILITY ANALYSIS OF NETWORK ARCHITECTURES FOR 3D POINT CLOUD PROCESSING

Abstract: In this paper, we diagnose deep neural networks for 3D point cloud processing to explore the utility of different network architectures. We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs. In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency. We conduct comparative studies based on such metrics to verify the hypotheses, which may shed new lights on the architectural design of neural networks. Experiments demonstrate the effectiveness of our method. The code will be released when this paper is accepted.

1. INTRODUCTION:

Recently, a series of works use the deep neural network (DNN) for 3D point cloud processing and have achieved superior performance in various 3D tasks. However, traditional studies usually design network architectures based on empiricism. There does not exist a rigorous and quantitative analysis about the utility of specific network architectures for 3D point cloud processing. Exploring and verifying the utility of each specific intermediate-layer architecture from the perspective of a DNN's representation capacity still present significant challenges for state-of-the-art algorithms.

In this study, we aim to bridge the gap between the intermediate-layer network architecture and its utility. Therefore, we propose a few hypotheses of the utility of specific network architectures. Table 1 lists the hypotheses to be verified in this study, towards three kinds of utilities, i.e. rotation robustness, adversarial robustness, and neighborhood inconsistency. We design and conduct comparative studies to verify the hypotheses. Finally, we obtain some new insights into the utility of specific network architectures as follows.

• The specific architecture in (Wu et al., 2019) , which uses the local density information to reweight features (Figure 1 (a) ), improves adversarial robustness (Table 1 (a) ).

• Another specific architecture in (Wu et al., 2019) , which uses local 3D coordinates' information to reweight features (Figure 1 (b) ), improves rotation robustness (Table 1 (b) ).

• The specific architecture in (Qi et al., 2017b; Liu et al., 2018) , which extracts multi-scale features (Figure 1 (c )), improves adversarial robustness and neighborhood consistency (Table 1 (c)). Neighborhood consistency measures whether a DNN assigns similar attention to neighboring points.

• The specific architecture in (Jiang et al., 2018) , which encodes the information of different orientations (Figure 1 (d) ), improves rotation robustness (Table 1 (d) ).

More specifically, in order to verify the above hypotheses, we design the following five evaluation metrics and conduct a number of comparative experiments to quantify the utility of different network architectures. 1. Information discarding and 2. information concentration: Information discarding measures how much information of an input point cloud is forgotten during the computation of a specific intermediate-layer feature. From the perspective of information propagation, the forward propagation through layers can be regarded as a hierarchical process of discarding input information (Shwartz-Ziv & Tishby, 2017) . Ideally, the DNN is supposed to discard information that is not related to the task. Let us take the task of object classification for example. The information of

Architectures:

Rotation robustness

Adversarial robustness:

Neighborhood inconsistency (a) Modules of using information of local density to reweight features.

--(b) Modules of using information of local coordinates to reweight features.

--(c) Modules of concatenating multi-scale features.

-(d) Modules of computing orientation-aware features.

--Table 1 : Illustration of the verified utilities of specific architectures. Blank regions correspond to utilities that have not been examined, instead of indicating non-existence of the utilities. Please see Figure 1 for architectural details.

foreground points is usually supposed to be related to the task, while that of background points is not related to the task and is discarded.

To this end, we further propose information concentration to measure the gap between the information related to the task and the information not related to the task. Information concentration can be used to evaluate a DNN's ability to focus on points related to the task. Contributions of our study are summarized as follows. We propose a few hypotheses on the utility of specific network architectures. Then, we design five metrics to conduct comparative studies for verifying these hypotheses, which provide a new insightful understanding of architectural utility.

2. RELATED WORK:

3D Point Cloud Processing: Recently, a number of approaches use DNNs for 3D point cloud processing and have exhibited superior performance in various 3D tasks (Qi et al., 2017a; Su et al., 2018; Valsesia et al., 2018; Yu et al., 2018; Yang et al., 2018; Gadelha et al., 2018; Wang et al., 2018a; Komarichev et al., 2019; Shi et al., 2019) . PointNet (Qi et al., 2017a) was a pioneer in this direction, which used a max pooling layer to aggregate all individual point features into a global feature. However, such architecture fell short of capturing local features. PointNet++ (Qi et al., 2017b) hierarchically used PointNet as a local descriptor to extract contextual information. Some studies (Wu et al., 2019; Jiang et al., 2018; Wang et al., 2018b; Komarichev et al., 2019) further improved the networks' ability to capture local geometric features. Other researches focused on the correlations between different regions of the 3D point cloud (Liu et al., 2018) or interaction between points (Zhao et al., 2019a) . In comparison, our study focuses on the utility analysis of intermediate-layer network architectures for point cloud processing.

Visualization or diagnosis of representations::

The visualization of visual patterns corresponding to a feature map or the network output is the most intuitive way of interpreting DNNs (Zeiler & Fergus, 2014; Mahendran & Vedaldi, 2015; Dosovitskiy & Brox, 2016; Zhou et al., 2014) , such as gradient-based methods (Fong & Vedaldi, 2018; Selvaraju et al., 2017) , and the estimation of the saliency map (Ribeiro et al., 2016; Lundberg & Lee, 2017; Kindermans et al., 2017; Qi et al., 2017a; Zheng et al., 2019) . In comparison, our study aims to explore the utility of intermediate-layer network architectures by diagnosing the information-processing logic of DNNs.

Quantitative evaluation of representations: Recently, some studies quantified the representation similarity to help understand the neural networks (Gotmare et al., 2018; Kornblith et al., 2019; Morcos et al., 2018; Raghu et al., 2017) . The method (Li et al., 2019) quantitated the importance of different feature dimensions to guide model compression. Other studies evaluated the representations via quantifying the information they contain. The information-bottleneck theory (Tishby et al., 2000; Shwartz-Ziv & Tishby, 2017; Cheng et al., 2018) explained the trade-off between the information compression and the discrimination power of features in a neural network. Achille & Soatto (2018) designed an information Dropout layer and quantified the information transmitted through it. Ma et al. (2019) presented a method to quantify the layer-wise information discarding of DNNs and defined two model-agnostic and task-agnostic metrics of the input information. Inspired by (Ma et al., 2019) , we propose five metrics to diagnose feature representations of different DNNs and finally explore the utility of different network architectures.

3 METRICS TO DIAGNOSE NETWORKS We extend the method of (Ma et al., 2019) as the technical foundation. Based on which, a number of metrics are designed to diagnose the DNN. The method quantifies the discarding of the input information during the layerwise forward propagation by computing the entropy of the input information given the specific feature of an intermediate layer. Given a point cloud X, let f = h(X) denote the feature of a specific intermediate layer. It is assumed that f and f represent the same object concept1 when f satisfies f -f 2 < , where feature f = h(X ), X = X + δ. δ denotes a random noise.

The conditional entropy of the input information given a specific feature, which represents a specific object concept, is computed, i.e. calculating entropy

H(X ), s.t. f -f 2 < . It is assumed that X follows a Gaussian distribution X ∼ N (X, Σ = diag[σ 2 1 , σ 2 2 , . . . ])

, where Σ can be regarded as the maximum perturbation added to X following the maximum-entropy principle, which subjects to a specific concept. Considering the assumption of independent and identically distributed variables of each dimension of X , the overall entropy H(X ) can be decomposed into point-wise entropies.

EQUATION

where Hi = log σi + 1 2 log(2πe) denotes the entropy of the i-th point, which quantifies how much information of the i-th point can be discarded, when the feature h(X ) is required to represent the concept of the target object.

3.2. FIVE METRICS:

Metric 1, information discarding: The information discarding is defined as H(X ) in Equation (1). The information discarding is measured at the point level, i.e. Hi, which quantifies how much information of the i-th point is discarded during the computation of an intermediate-layer feature. The point with a lower value of Hi is regarded more important in the computation of the feature.

Metric 2, information concentration: The information concentration is based on the metric of information discarding. The information concentration is used to analyze a DNN's ability to maintain the input information related to the task, and discard redundant information unrelated to the task. For example, in the task of object classification, the background points are usually supposed not to be related to the task and are therefore more likely to be discarded by the DNN. Let Λ foreground denote the set of points in the foreground object in the point cloud X, and let Λ background denote the set of points in the background. Information concentration can be implemented as the relative background information discarding w.r.t. foreground information discarding.

E i∈Λ background [Hi] -E i∈Λ foreground [Hi].

(2)

A higher value of information concentration indicates that the DNN concentrates more on the foreground information during the computation of the feature.

Metric 3, rotation robustness: The rotation robustness is proposed to measure whether a DNN uses similar subsets of two point clouds to compute the intermediate-layer feature, if the two point clouds have the same shape but different orientations. Let X θ 1 and X θ 2 denote the point clouds that have the same global shape but different orientations θ1 and θ2. To quantify the similarity of the attention on the two point clouds, we compute the Jensen-Shannon divergence between the distributions of the perturbed inputs Xθ 1 = X θ 1 + δ1 and Xθ 2 = X θ 2 + δ2. Xθ 1 and Xθ 2 denote the perturbed inputs, which are computed to measure information discarding in Equation (1). I.e. we measure whether the DNN ignores similar sets of points to compute features of the two point clouds.

EQUATION

) x z (d) Architecture 1 Architecture 2 (c) (b) (a) F i ' MLP reweight W H1 MLP F i reweight F i ' F i W H2 Architecture 3 F i (scale1) F i (scale3) g g f i f i,(scale1) f i,(scale3) concat g f i,(scale2)

x-axis where JSD( Xθ 1 || Xθ 2 ) measures the dissimilarity between information distributions over the two point clouds.

The rotation non-robustness is defined as the average of the dissimilarity of attention on any two point clouds with different orientations, i.e. E ∀θ 1 ,θ 2 [JSD( Xθ 1 || Xθ 2 )]. Note that we do not directly compare the attention on features, because there is no mechanism to ensure that dimensions of X θ 1 and X θ 2 are semantically aligned. In this study, we use the method in Hershey & Olsen (2007) to approximate the Jensen-Shannon divergence.

Metric 4, adversarial robustness: We use the method in (Szegedy et al., 2013) to perform adversarial attacks. The objective is

EQUATION

where C(•) is the predicted label; l * is the correct label of X; l is a target incorrect label. In this study, we perform adversarial attacks against all incorrect classes. We use the average of 2 over all incorrect classes to measure the adversarial robustness.

Metric 5, neighborhood inconsistency: The neighborhood inconsistency is proposed to evaluate a DNN's ability to assign similar attention to neighboring points during the computation of an intermediate-layer feature, i.e. a well-trained DNN should have a low value of neighborhood inconsistency. Ideally, for a DNN, except for special points (e.g. those on the edge), most neighboring points in a small region of a point cloud usually have similar shape contexts, so they are supposed to make similar contributions to the classification and receive similar attention. Let N(i) denote a set of K nearest neighboring points of the i-th point. We define the neighborhood inconsistency as the difference between the maximum and minimum point-wise information discarding within N(i).

Ei[ max:

j∈N(i) Hj -min j∈N(i) Hj].

(5)

4. HYPOTHESES AND COMPARATIVE STUDY:



4.1. INTRODUCTION OF SPECIFIC ARCHITECTURES:

• Notation:

Let xi ∈ R 3 denote the i-th point, i = 1, 2, . . . , n; let N(i) denote a set of K nearest points of xi; let Fi ∈ R d×K denote intermediate-layer features that correspond to neighboring points in N(i),

where each column of Fi represents the feature of a specific point in N(i).

• Architecture 1, features reweighted by the information of the local density: Architecture 1 focuses on the use of the local density information to reweight features (Wu et al., 2019) . As shown in Figure 1 (a), for each point xi, Architecture 1 uses the local density w.r.t. neighboring points of xi to compute W H1 ∈ R K , which reweights intermediate-layer features Fi.

F i = Fi diag[W H1 ],

where

EQUATION

where diag[W H1 ] transforms the vector W H1 into a diagonal matrix; density(N(i)) is a density vector w.r.t. points in N(i); the M LP is a two-layer perceptron network.

• Architecture 2, features reweighted by the information of local coordinates: As shown in Figure 1 (b), for each point xi, Architecture 2 uses the information of local 3D coordinates to compute W H2 ∈ R M ×K to reweight intermediate-layer features Fi.

EQUATION

where the M LP is a single-layer perceptron network.

• Architecture 3, multi-scale features: Architecture 3 focuses on the use of multi-scale contextual information (Qi et al., 2017b; Liu et al., 2018) . As illustrated in Figure 1 (c),

{F scale=K 1 i , ..., F scale=K T i

} denote features that are extracted using contexts of xi at different scales, . Architecture 3 concatenates these multi-scale features to obtain f upper i .

F scale=K t i ∈ R d×K t .

f upper i = concat          f upper i,(scale=K 1 ) f upper i,(scale=K 2 ) . . . f upper i,(scale=K T )          ,

where

EQUATION

where concat indicates the concatenation operator; g(•) is a function for feature extraction (Qi et al., 2017a) . Please see Appendix B for details about this function.

• Architecture 4, orientation-aware features: Architecture 4 focuses on the use of orientation information (Jiang et al., 2018) . As illustrated in Figure 1 (d), for each point xi, F oe i ∈ R d×O denotes the feature of xi, which encodes the information of various orientations, where O is the number of orientations. Architecture 4 uses F oe i to compute the orientation-aware feature

EQUATION

where Conv oe is a special convolution operator. Please see (Jiang et al., 2018) or Appendix C.4 for details about this operator and the computation of f oe i .

4.2. FOUR HYPOTHESES AND COMPARATIVE STUDY DESIGN:

Hypothesis 1: Architecture 1 designed by (Wu et al., 2019) , as shown in Figure 1 (a), increases the adversarial robustness.

This hypothesis is proposed based on the observation that PointConv (Wu et al., 2019) has good performance in adversarial robustness, which may stem from Architecture 1. To verify this hypothesis, we design comparative studies on PointConv, PointNet++ (Qi et al., 2017b) , and Point2Sequence (Liu et al., 2018) . For each network, we construct two versions for comparison, i.e. one with Architecture 1 and the other without Architecture 1.

PointConv w/ and w/o Architecture 1: To obtain the PointConv without Architecture 1, we remove all the modules of Architecture 1 from the original network (see the footnote2 ), which are located behind the 2-nd, 5-th, 8-th, 11-th, and 14-th nonlinear transformation layers. Please see Appendix D.2 for the global architectures of different versions of PointConv.

EQUATION

where f upper i is the feature in the upper layer; diag[W H1 ] transforms the vector W H1 into a diagonal matrix.

PointNet++ w/ and w/o Architecture 1: To obtain the PointNet++ with Architecture 1, we add three modules of Architecture 1, which are located behind the 3-rd, 6-th, and 9-th nonlinear transformation layers. Please see Appendix D.1 for the global architectures of different versions of PointNet++. Hypothesis 2: Architecture 2 designed by (Wu et al., 2019) , as shown in Figure 1 (b), increases the rotation robustness.

EQUATION

This hypothesis is proposed based on the observation that PointConv (Wu et al., 2019) 

EQUATION

)

PointNet++ w/ and w/o Architecture 2: Just like Hypothesis 1, to obtain the PointNet++ with Architecture 2, we add three modules of Architecture 2, which are located behind the 3-rd, 6-th, and 9-th nonlinear transformation layers. Please see Appendix D.1 for the global architectures of different versions of PointNet++. The baseline network of Point2Sequence concatenates features of 4 different scales to compute the feature in the upper layer, {f upper i,(scale=K 1 ) , f upper i,(scale=K 2 ) , f upper i,(scale=K 3 ) , f upper i,(scale=K 4 ) }. In this study, we set K1 = 128, K2 = 64, K3 = 32, and K4 = 16. The first network for comparison extracts three different scale features, {f upper i,(scale=K 1 ) , f upper i,(scale=K 2 ) , f upper i,(scale=K 3 ) }, and the second one extracts two different scale features, {f upper i,(scale=K 1 ) , f upper i,(scale=K 2 ) }.

EQUATION

Hypothesis 4: Architecture 4 designed by (Jiang et al., 2018) , as shown in Figure 1 (d) , increases the rotation robustness. This hypothesis is proposed based on the observation that PointSIFT (Jiang et al., 2018) performs well in rotation robustness, which may stem from Architecture 4. Because Architecture 4 ensures that features contain information from various orientations. To verify this hypothesis, we design comparative studies on PointSIFT, PointNet++, and Point2Sequence as follows. 

5. EXPERIMENTS:

To demonstrate the broad applicability of our method, we applied our method to diagnose six widely used DNNs, including PointNet, PointNet++, PointConv, DGCNN, PointSIFT, and Point2Sequence. These DNNs were trained using three benchmark datasets, including the ModelNet40 dataset (Wu et al., 2015) , the ShapeNet4 dataset (Chang et al., 2015) , the 3D MNIST5 dataset. In sum, we conducted four comparative experiments. The first experiment was to analyze DNNs using the proposed five metrics. The remaining three experiments were conducted to verify the effects of different network architectures on DNNs' rotation robustness, adversarial robustness, and neighborhood inconsistency, respectively.

Implementation details::

To analyze the information concentration of DNNs, we generated a new dataset that contained both the foreground objects and the background, since most widely used benchmark datasets for point cloud classification only contain foreground objects. Specifically, for each sample (i.e. the foreground object) in the ModelNet40, we used the following three steps to generate the background. First, we randomly sampled a set of 500 points from point clouds, which had different labels from the foreground object. Second, we resized this set of points to the density of the foreground object. Finally, we randomly located it around the foreground object. The dataset will be released when this paper is accepted.

The entropy-based method (Ma et al., 2019) quantified the layerwise information discarding. This method assumed the feature space of the concept of a specific object satisfied f -f 2 < , where f = h(X), f = h(X ), X = X + δ. δ denotes a random noise. For point cloud processing, each dimension of the intermediate-layer feature is computed using the context of a specific point xi. However, adding noise to a point cloud will change the context of each point. In order to extend the entropy-based method to point cloud processing, we selected the same set of points as the contexts w.r.t. xi and x i , so as to generate a convincing evaluation. Please see Appendix E for details.

Experiment 1, quantifying the representation capacity of DNNs: As shown in Table 2 , we measured information discarding, information concentration, rotation robustness, and neighborhood inconsistency of the representation of the fully connected layer close to the network output, which had 512 hidden units. We measured adversarial robustness by performing adversarial attacks over all Table 4 : Comparisons of the adversarial robustness between DNNs trained using the ModelNet40, ShapeNet, and 3D MNIST datasets. The column of ∆ denotes the increase of the adversarial robustness of the network with the specific architecture w.r.t. the network without the specific architecture. ∆ > 0 indicates that the corresponding hypothesis has been verified. Experimental results show that Architecture 1 and Architecture 3 improved the adversarial robustness. Please see Appendix F (Table 15 ) for the classification accuracy of these DNNs. Experiment 2, verifying the effects on rotation robustness: For the computation of rotation robustness, during the training and testing phases, each point cloud was rotated by random angles. Table 3 shows that Architecture 2 and Architecture 4 improved the rotation robustness.

Experiment 3, verifying the effects on adversarial robustness: Table 4 shows that both Architecture 1 and Architecture 3 improved the adversarial robustness. For Architecture 3, we found that the adversarial robustness increased with the scale number of features.

Experiment 4, verifying the effects on neighborhood inconsistency: For the computation of neighborhood inconsistency, we used k-NN search to select 16 neighbors for each point. Table 5 shows that networks with Architecture 3 usually had lower neighborhood inconsistency than those without Architecture 3. Besides, DNNs, which extracted features from contexts of more scales, usually exhibited lower neighborhood inconsistency.

6. CONCLUSION:

In this paper, we have verified a few hypotheses of the utility of four specific network architectures for 3D point cloud processing. Comparative studies are conducted to prove the utility of the specific architectures, including rotation robustness, adversarial robustness, and neighborhood inconsistency. In preliminary experiments, we have verified that Architecture 2 and Architecture 4 mainly improve the rotation robustness; Architecture 1 and Architecture 3 have positive effects on adversarial robustness; Architecture 3 usually alleviates the neighborhood inconsistency.

A OVERVIEW:

This Appendix provides more details about comparative studies in the main paper and includes more implementation details about experiments. In Section B, we introduce a special element-wise max operator widely used in point cloud processing. In Section C, we briefly introduce DNNs used in comparative studies. In Section D, we show details about different versions of DNNs for comparison. In Section E, we show implementation details about extending the entropy-based method (Ma et al., 2019) , can be formulated as follows, which is the local feature of N(i).

EQUATION

where M LP is an MLP network with a few layers; M LP (Fi) ∈ R D×K ; MAX is an element-wise max operator as follows. Let The Sampling layer uses the farthest point sampling algorithm to select a subset of points from the input points, which defines the centroids of local regions. Then, for each selected point, the Grouping layer constructs a local region by using k-NN search to find K neighboring points. For each local region, the MLP transforms features of points in the local region into higher dimension features. Different from PointNet++, PointConv uses the information of density (i.e. Architecture 1) and local 3D coordinates (i.e. Architecture 2) to reweight the features learned by the MLP. Finally, a 1 × 1 convolution is used to compute the output feature of each local region. Please see Table 8 (left column) for details about the network architecture.

EQUATION

f 1 f 2 f K KxD f 1 ' f 2 ' f K '

C.3 POINT2SEQUENCE:

Point2Sequence (Liu et al., 2018) Specifically, given a point cloud X = {xi}, i = 1, 2, ..., N , Point2Sequence first uses the farthest point sampling algorithm to select N points from the input point cloud, X = {x j }, j = 1, 2, ..., N , to define the centroids of local regions {N(j)}, j = 1, 2, ...N . For each local region N(j), T different scale areas {A(j) 1 , ..., A(j) t , ..., A(j) T } are established by using k-NN search to select [K1, ..., Kt, ..., KT ] nearest points of x j . In this way, multi-scale areas are established. Then, Point2Sequence extracts a feature f upper j,scale=K t ∈ R d for each scale area A(j) t by the MLP and the Maxpooling layer introduced in Appendix C.1. Therefore, for each local region N(j), a feature sequence f upper j = {f upper j,scale=K 1 , ..., f upper j,scale=K t , ..., f upper j,scale=K T } is acquired. Then, f upper j is aggregated into a d-dimensional feature rj by the encoder-decoder feature aggregation part. The sequence encoder-decoder structure used here is an LSTM network, where an attention mechanism is proposed to highlight the importance of different area scales (please see (Liu et al., 2018) for details). Then, a 1024-dimensional global feature is aggregated from the features rj of all local regions by the local region feature aggregation part. Finally, the global feature is used for shape classification. Please see Table 9 for details about the network architecture.

C.4 POINTSIFT:

PointSIFT (Jiang et al., 2018) adopts the similar hierarchical structure as PointNet++, which is composed of a number of SA modules. The difference is that PointSIFT uses a special orientation encoding unit, i.e., Architecture 4, to learn an orientation-aware feature for each point.

Architecture 4 is a point-wise local feature descriptor that encodes information of eight orientations. Unlike the unordered operator, e.g. max pooling, which discards all inputs except for the maximum, Architecture 4 is an ordered operator, which could be more informative.

Architecture 4 first selects 8-nearest points of x i from eight octants partitioned by the ordering of three coordinates. Since distant points provide little information for the description of local patterns, when no point exists within searching radius r in some octant, x i will be duplicated as the nearest neighbor of itself. Then, Architecture 4 processes features of 8-nearest neighboring points, F oe i ∈ R d×2×2×2 , which reside in a 2 × 2 × 2 cube for local pattern description centering at x i (as shown in Figure 1 (d) ), the three dimensions 2 × 2 × 2 correspond to three axes. An orientation-

:

In this study, the concept of an object is referred to as a small range of features that represent the same object instance.

The PointConv for classification is revised from the code for segmentation released by(Wu et al., 2019).

The PointSIFT for classification is revised from the code for segmentation released by(Jiang et al., 2018).

The ShapeNet dataset for classification is converted from the ShapeNet part segmentation dataset.

https://www.kaggle.com/daavoo/3d-mnist/version/13

Figures and Tables:

PRELIMINARY: ENTROPY-BASED INFORMATION DISCARDING QUANTIFICATION

Figure 1: Illustration of the specific intermediate-layer architectures. Please see texts in Section 4.1, as well as Appendix C for architectural details.

Point2Sequence w/ and w/o Architecture 1: To obtain the Point2Sequence with Architecture 1, we add the module of Architecture 1 behind the last nonlinear transformation layer, as shown in Equation (11). Please see Appendix D.3 for the global architectures of different versions of Point2Sequence.

Point2Sequence w/ and w/o Architecture 2: Just like Hypothesis 1, to obtain the Point2Sequence with Architecture 2, we add the module of Architecture 2 behind the last nonlinear transformation layer, as shown in Equation (13). Please see Appendix D.3 for the global architectures of different versions of Point2Sequence.Hypothesis 3: Architecture 3 designed by(Qi et al., 2017b), as shown in Figure1 (c), increases the adversarial robustness and the neighborhood consistency.This hypothesis is proposed inspired by(Qi et al., 2017b;Liu et al., 2018), which encode multi-scale contextual information. To verify this hypothesis, we design the following comparative studies on Point2Sequence and PointNet++. PointNet++ w/ and w/o Architecture 3: To obtain the PointNet++ with Architecture 3, we use the multi-scale version of PointNet++ (Qi et al., 2017b), which extracts features at three scales. Point2Sequence w/ and w/o Architecture 3: To obtain different versions of Point2Sequence, we use three networks as follows.

PointSIFT w/ and w/o Architecture 4: To get the PointSIFT without Architecture 4, we remove all the modules of Architecture 4 from the original network (see the footnote 3 ), which are located before the 1-st, 3-rd, 5-th, and 7-th nonlinear transformation layers. Please see Appendix D.4 for the global architectures of different versions of PointSIFT. PointNet++ w/ and w/o Architecture 4: To obtain the PointNet++ with Architecture 4, we add the module of Architecture 4 before the 7-th nonlinear transformation layer. Point2Sequence w/ and w/o Architecture 4: To obtain the Point2Sequence with Architecture 4, we add the module of Architecture 4 before the 14-th nonlinear transformation layer.

Figure 2: Comparisons of layerwise information discarding and layerwise information concentration between DNNs. Table 3: Comparisons of the rotation non-robustness between DNNs trained using the ModelNet40, ShapeNet, and 3D MNIST datasets. The column of ∆ denotes the increase of the rotation robustness of the network with the specific architecture w.r.t. the network without the specific architecture. ∆ > 0 indicates that the corresponding hypothesis has been verified. Experimental results show that Architecture 2 and Architecture 4 improved the rotation robustness, i.e. reporting relatively lower values of rotation non-robustness. Please see Appendix F (Table14) for the classification accuracy of these DNNs.



Figure 3: Illustration of the special max pooling operator. Fi denotes features correspond to points in neighborhood N(i) w.r.t. point xi. Each column of Fi represents the feature of a specific point in N(i).

is composed of five parts: (a) multi-scale area establishment, (b) area feature extraction, (c) encoder-decoder feature aggregation, (d) local region feature aggregation, and (e) shape classification, where parts (a) and (b) makes up Architecture 3 in our study.

3. Rotation robustness: Rotation robustness measures whether a DNN will use the same logic to recognize the same object when a point cloud has been rotated by a random angle. In other words, if two point clouds have the same global shape but different orientations, the DNN is supposed to select the same regions/points to compute the intermediate-layer feature. Unlike images with rich color information, point clouds usually only use spatial contexts for classification. Therefore, a well-trained DNN is supposed to have rotation robustness.

Table content: <table/>

Each specific context w.r.t. xi is composed of Kt nearest neighboring points around xi. Then, f upper i,(scale=K t ) ∈ R D in the upper layer is computed using F scale=K t

Table content: <table><tr><td>i</td></tr></table>

has good performance in rotation robustness, which may stem from Architecture 2. To verify this hypothesis, we design comparative studies on PointConv, PointNet++, and Point2Sequence.

Table content: <table><tr><td>PointConv w/ and w/o Architecture 2: To obtain the PointConv without Architecture 2, we remove</td></tr><tr><td>all the modules of Architecture 2, which are located before the 3-rd, 6-th, 9-th, 12-th, and 15-th</td></tr><tr><td>nonlinear transformation layers. Please see Appendix D.2 for the global architectures of different</td></tr><tr><td>versions of PointConv.</td></tr></table>

Quantification of the representation capacity of different DNNs on the ModelNet40 dataset.

Table content: <table><tr><td>Models</td><td>Information discarding</td><td>Information concentration</td><td>Rotation non-robustness</td><td>Adversarial robustness</td><td>Neighborhood inconsistency</td></tr><tr><td>PointNet</td><td>-8128.10</td><td>1.043</td><td>8.409</td><td>1.994</td><td>2.946</td></tr><tr><td>PointNet++</td><td>-8578.97</td><td>1.116</td><td>5.000</td><td>2.504</td><td>3.451</td></tr><tr><td>PointConv</td><td>-8720.98</td><td>0.380</td><td>3.918</td><td>2.878</td><td>3.735</td></tr><tr><td>DGCNN</td><td>-9165.82</td><td>1.042</td><td>4.383</td><td>2.421</td><td>1.445</td></tr><tr><td>PointSIFT</td><td>-8391.08</td><td>0.032</td><td>4.747</td><td>2.839</td><td>2.387</td></tr><tr><td>Point2Sequence</td><td>-8145.27</td><td>1.141</td><td>5.786</td><td>2.526</td><td>3.655</td></tr></table>

for the classification accuracy of these DNNs.

Table content: <table><tr><td>Architecture</td><td>Model</td><td>w/</td><td>ModelNet40 w/o</td><td>∆</td><td>w/</td><td>ShapeNet w/o</td><td>∆</td><td>w/</td><td>3D MNIST w/o</td><td>∆</td></tr><tr><td/><td>PointConv</td><td colspan="9">3.918 3.954 0.036 4.250 2.703 -1.547 5.140 6.221 1.081</td></tr><tr><td>Architecture 2</td><td>PointNet++</td><td colspan="9">2.658 5.000 2.342 4.186 6.709 2.523 5.256 6.754 1.498</td></tr><tr><td/><td colspan="10">Point2Sequence 4.020 5.786 1.766 2.821 5.222 2.401 4.590 7.410 2.820</td></tr><tr><td/><td>PointSIFT</td><td colspan="9">4.747 7.090 2.343 4.598 5.118 0.520 7.851 6.154 -1.697</td></tr><tr><td>Architecture 4</td><td>PointNet++</td><td colspan="9">5.505 5.000 -0.505 6.152 6.709 0.557 5.298 6.754 1.456</td></tr><tr><td/><td colspan="10">Point2Sequence 2.917 5.786 2.869 2.909 5.222 2.313 5.942 7.410 1.468</td></tr></table>

Comparisons of the neighborhood inconsistency between DNNs trained using the Model-Net40, ShapeNet, and 3D MNIST datasets. The column of ∆ denotes the increase of the neighborhood consistency of the network with the specific architecture w.r.t. the network without the specific architecture. ∆ > 0 indicates that the corresponding hypothesis has been verified. Experimental results show that Architecture 3 increased the neighborhood consistency, i.e. reporting relatively lower values of neighborhood inconsistency. Please see Appendix F (Table15) for the classification accuracy of these DNNs.

Table content: <table><tr><td>Model</td><td>w/</td><td colspan="2">ModelNet40 w/o</td><td>∆</td><td>w/</td><td>ShapeNet w/o</td><td>∆</td><td>w/</td><td>3D MNIST w/o</td><td>∆</td></tr><tr><td>PointNet++</td><td colspan="10">3.149 3.451 0.302 3.321 3.346 0.025 3.496 3.519 0.023</td></tr><tr><td colspan="3">Point2Sequence (4 scales vs. 3 scales) 3.655 Point2Sequence (4 scales vs. 2 scales)</td><td colspan="3">4.182 0.527 3.091 4.253 0.598</td><td colspan="3">3.179 0.088 3.226 3.199 0.108</td><td colspan="2">3.411 0.185 3.537 0.311</td></tr><tr><td colspan="11">incorrect classes. Figure 2 compares layerwise information discarding and layerwise information</td></tr><tr><td colspan="11">concentration of different layers in different DNNs. We found that PointNet and Point2Sequence</td></tr><tr><td colspan="11">had relatively higher values of information discarding. PointConv and PointSIFT discarded more</td></tr><tr><td colspan="11">information of points in the background. PointConv, DGCNN, and PointSIFT performed well in</td></tr><tr><td colspan="11">rotation robustness. PointConv, PointSIFT, and Point2Sequence exhibited higher adversarial robust-</td></tr><tr><td colspan="8">ness. DGCNN and PointSIFT exhibited lower neighborhood inconsistency.</td><td/><td/></tr></table>

to cloud processing. In Section F, we compare the accuracy of different versions of DNNs. In Section G, we supplement related work about learning interpretable representations.B A SPECIAL ELEMENT-WISE MAX OPERATOR IN POINT CLOUD PROCESSINGIn point cloud processing, a special element-wise max operator is widely used for aggregating a set of neighboring points' features into a local feature. As shown in Figure3, given a set of K nearest neighboring points of xi, N(i), let Fi ∈ R d×K denote intermediate-layer features that correspond to the set of neighboring points in N(i) w.r.t. the point xi. Each specific column of Fi represents the feature of a specific point in N(i). The process of extracting the feature in the upper layer, i.e.

Table content: <table><tr><td>f upper i</td></tr></table>

,...,KQi et al., 2017b) is a hierarchical structure composed of a number of set abstraction modules (SA module). For each SA module, a set of points is processed and abstracted to produce a new set with fewer elements. An SA module includes four parts: the Sampling layer, the Grouping layer, the MLP, and the Maxpooling layer. Given a set of N input points, the Sampling layer uses the farthest point sampling algorithm to select a subset of points from the input points, which defines the centroids of local regions, {xi}, i = 1, . . . , N . Then, for each selected point, the Grouping layer constructs a local region by using ball query search to find K neighboring points within a radius r.For each local region N(i) centered at xi, Fi ∈ R d×K denotes the intermediate-layer features that correspond to points in N(i). The MLP transforms Fi into higher dimension features F i ∈ R D×K , where D > d. Finally, the Maxpooling layer encodes F i into a local feature f upper i , which will be fed to the upper SA module. Please see Appendix B for details about the Maxpooling layer.In this study, the baseline network of PointNet++ is composed of three SA modules and a few fully connected layers. Please see Table6(left column) for details about the network architecture.Wu et al., 2019) has a similar architecture with PointNet++, i.e. hierarchically using a few blocks to extract contextual information. In this study, the baseline network of PointConv is composed of five blocks. Each block is constructed as [Sample layer→Group layer→MLP→Architecture 1→Architecture 2→Conv layer].

Table content: <table><tr><td>f Dk &gt;</td><td>(15)</td></tr><tr><td>C INTRODUCTION OF DNNS USED IN COMPARATIVE STUDIES</td><td/></tr><tr><td colspan="2">For a better understanding of different versions of DNNs in the next section, we briefly intro-</td></tr><tr><td colspan="2">duce DNNs used in comparative studies, including PointNet++, PointConv, Point2Sequence, and</td></tr><tr><td>PointSIFT.</td><td/></tr><tr><td>C.1 POINTNET++</td><td/></tr><tr><td>PointNet++ (C.2 POINTCONV</td><td/></tr><tr><td>PointConv (</td><td/></tr></table>

Peer review (iclr2020):
The paper addresses the task of evaluation of point-based architectures for deep learning, aiming at establishing the contribution of various architectural blocks (e.g., local density reweighting) to a number of desirable properties of the resulting models (such as adversarial robustness, rotation robustness, etc.). To assess the influence of such blocks on the resulting models, authors formulate and test a variety hypotheses while measuring model performance using a number of existing measures, such as information discarding/concentration, etc. I believe the paper cannot be accepted in its present form, as (1) no clear motivation for the work is presented, (2) the problem statement and the research questions are very blurry, and consequently (3) the results do not provide significant new knowledge related to the point-based neural architectures. While the specific architectural choices do influence the characteristics of commonly used models and a systematic evaluation of the former is indeed useful for practice, the paper does not indicate a particular application context, which I believe is crucial to the entire following narrative. Do the authors aim at evaluating in the recognition/labeling context? In the synthesis context (e.g., novel shape synthesis, completion, or upsampling)? In the geometry processing context (normal estimation, patch decomposition, or denoising)? Would the claimed hypothesis, let alone the results, even be formulated the same way, once one switches to a different class of applications? If yes, it would be hard to understand from the paper. If no, this could potentially diminish the value of the entire work significantly. If the paper targeted a particular class of applications or simply a specific task, the hypotheses might have looked more natural; however, currently I find the motivation for these hypotheses rather vague. New insights into neural architectures or other models could emerge from (empirical and/or theoretical) regularities revealed by a purposeful experiment; however, the authors perform a seemingly random evaluation of point-based architectures which is of a fairly limited value per se. The reader might wonder: what is the exact problem that the study aims to address? Thus, I believe, a clarification of the problem statement and the basic research questions for the entire work is needed. The paper does a good job of thoroughly presenting results of numerous computational experiments and values of the selected measures. However, as stated before, the results fail to provide significant insights into point-based neural architectures due to (1) lack of specific applications the authors are trying to solve and (2) arbitrariness of the chosen experiments lead to equally arbitrary conclusions. If “architecture of We et al., which uses local density information, improves adversarial robustness”, what difference would it make? Would it open novel opportunities for point-based deep nets, and if yes, which exactly? One minor additions is that since the paper focuses on empirical evaluation of machine learning models, significance measures such a statistical confidence intervals would be very useful to present in the results.