Original: PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
Revised: PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds

Original: Anonymous Author(s)

Original: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial tomany autonomous detection and navigation tasks.
Revised: LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous detection and navigation tasks.

Original: Though many 3D deep architectures have been developed, efficient collection and annotation of large amountsof point clouds remain one major challenge in the analytics and understandingof point cloud data.
Revised: Though many 3D deep architectures have been developed, efficient collection and annotation of large amounts of point clouds remain one major challenge in the analytics and understanding of point cloud data.

Original: This paper presents PolarMix , a point cloud augmentationtechnique that is simple and generic but can mitigate the data constraint effectivelyacross different perception tasks and scenarios.
Revised: This paper presents PolarMix , a point cloud augmentation technique that is simple and generic but can mitigate the data constraint effectively across different perception tasks and scenarios.

Original: PolarMix enriches point clouddistributions and preserves point cloud fidelity via two cross-scan augmentationstrategies that cut, edit, and mix point clouds along the scanning direction.
Revised: PolarMix enriches point cloud distributions and preserves point cloud fidelity via two cross-scan augmentation strategies that cut, edit, and mix point clouds along the scanning direction.

Original: Thefirst is scene-level swapping which exchanges point cloud sectors of two LiDARscans that are cut along the azimuth axis.
Revised: The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis.

Original: The second is instance-level rotation andpaste which crops point instances from one LiDAR scan, rotates them by multipleangles (to create multiple copies), and paste the rotated point instances into otherscans.
Revised: The second is instance-level rotation and paste which crops point instances from one LiDAR scan, rotates them by multiple angles (to create multiple copies), and paste the rotated point instances into other scans.

Original: Extensive experiments show that PolarMix achieves superior performanceconsistently across different perception tasks and scenarios.
Revised: Extensive experiments show that PolarMix achieves superior performance consistently across different perception tasks and scenarios.

Original: In addition, it canwork as a plug-and-play for various 3D deep architectures and also performs wellfor unsupervised domain adaptation.
Revised: In addition, it can work as a plug-and-play for various 3D deep architectures and also performs well for unsupervised domain adaptation.

Original: Code will be available.
Revised: Code is available at https://github.com/xiaoaoran/polarmix

Original:  Introduction
Revised:  Introduction

Original: In the past decade, LiDAR sensors have been increasingly employed in various perception relatedapplications such as autonomous driving.
Revised: In the past decade, LiDAR sensors have been increasingly employed in various perception related applications such as autonomous driving.

Original: They provide accurate and robust depth sensing of thesurrounding environments which is crucial for scene understanding for autonomous navigation indoorsand outdoors.
Revised: They provide accurate and robust depth sensing of the surrounding environments which is crucial for scene understanding for autonomous navigation indoors and outdoors.

Original: With the recent advance of deep neural networks (DNNs), point cloud understandinghas achieved significant progress in various perception tasks such as semantic segmentation  and objectdetection.
Revised: With the recent advance of deep neural networks (DNNs), point cloud understanding has achieved significant progress in various perception tasks such as semantic segmentation [47, 16, 41, 53, 26, 27] and object detection [23, 48, 9].

Original: On the other hand, training reliable DNN models requires large amount of well-annotatedtraining data, whereas collecting and annotating large amounts of point clouds is often laborious,time-consuming, and has poor scalability across tasks and domains.
Revised: On the other hand, training reliable DNN models requires large amount of well-annotated training data, whereas collecting and annotating large amounts of point clouds is often laborious, time-consuming, and has poor scalability across tasks and domains.

Original: This has become one majorconstraint in LiDAR point cloud analytics and understanding.
Revised: This has become one major constraint in LiDAR point cloud analytics and understanding.

Original: Data augmentation (DA) [26, 4], which aims to expand the distribution of the training data by modifying and creating new training samples, has been widely studied for 2D images and demonstratedgreat potential in training robust DNN models with limited training images.
Revised: Data augmentation (DA) [37, 3], which aims to expand the distribution of the training data by modifying and creating new training samples, has been widely studied for 2D images and demonstrated great potential in training robust DNN models with limited training images.

Original: However, most existing
Revised: However, most existing

Original: DA methods do not work well for LiDAR point clouds, a very meaningful but largely neglected task.
Revised: DA methods do not work well for LiDAR point clouds, a very meaningful but largely neglected task.

Original: Specifically, most existing DA methods perform global augmentation such as randomly scaling, flipping, and rotation which cannot augment local structures or model relationships across neighbouringpoint cloud scans.
Revised: Specifically, most existing DA methods perform global augmentation such as randomly scaling, flipping, and rotation which cannot augment local structures or model relationships across neighbouring [Figure] point cloud scans.

Original: Recently, several studies [38, 37, 13] explore local augmentation that creates newtraining samples via cut and mix of 2D images.
Revised: Recently, several studies [50, 49, 13] explore local augmentation that creates new training samples via cut and mix of 2D images.

Original: However, the local augmentation does not work well[Figure] for point clouds as it does not consider the unique scanning mechanism of LiDAR sensors (e.g., viacontinuous 360-degree sweeping) and specific properties of the captured point data.
Revised: However, the local augmentation does not work well for point clouds as it does not consider the unique scanning mechanism of LiDAR sensors (e.g., via continuous 360-degree sweeping) and specific properties of the captured point data.

Original: This work focuses on effective and efficient data augmentation for better learning from limited
Revised: This work focuses on effective and efficient data augmentation for better learning from limited

Original: LiDAR point cloud data.
Revised: LiDAR point cloud data.

Original: To this end, we design PolarMix , a simple yet generic DA techniquethat can effectively work across different perception tasks and datasets.
Revised: To this end, we design PolarMix , a simple yet generic DA technique that can effectively work across different perception tasks and datasets.

Original: PolarMix achieves theseunique features by capturing the essential properties of LiDAR point clouds, namely, partial visibilityand density variation that are closely associated with the sweeping mechanism of LiDAR sensors.
Revised: PolarMix achieves these unique features by capturing the essential properties of LiDAR point clouds, namely, partial visibility and density variation that are closely associated with the sweeping mechanism of LiDAR sensors.

Original: Specifically, objects in LiDAR scans are incomplete where only object sides facing the LiDAR sensorare scanned with points as illustrated in Fig.
Revised: Specifically, objects in LiDAR scans are incomplete where only object sides facing the LiDAR sensor are scanned with points as illustrated in Fig.

Original: 1(a).
Revised: 1(a).

Original: In addition, point density varies with point depth asillustrated in Fig. 1(b).
Revised: In addition, point density varies with point depth as illustrated in Fig. 1(b).

Original: Effective data augmentation needs to cater for these LiDAR-specific featuresto ensure the fidelity and usefulness of the augmented point clouds in network training.
Revised: Effective data augmentation needs to cater for these LiDAR-specific features to ensure the fidelity and usefulness of the augmented point clouds in network training.

Original: Inspired by the above observations, PolarMix crops, edits, and mixes points along the LiDAR scanningdirection ( i.e. , the azimuth in the 3D polar system in Fig. 1(a)) to enrich point cloud distributionswhile maintaining its fidelity.
Revised: Inspired by the above observations, PolarMix crops, edits, and mixes points along the LiDAR scanning direction ( i.e. , the azimuth in the 3D polar system in Fig. 1(a)) to enrich point cloud distributions while maintaining its fidelity.

Original: It consists of two cross-scan augmentation approaches.
Revised: It consists of two cross-scan augmentation approaches.

Original: The first isscene-level swapping which exchanges point cloud sectors of two circular LiDAR scans that are cutalong the azimuth axis as illustrated in Fig. 2(a).
Revised: The first is scene-level swapping which exchanges point cloud sectors of two circular LiDAR scans that are cut along the azimuth axis as illustrated in Fig. 2(a).

Original: The second is instance-level rotation and pastewhich cuts point cloud instances from one LiDAR scan, rotates them along the scanning directionmultiple times (to create multiple copies), and pastes the rotated instances into another LiDAR scanas illustrated in Fig. 2(b).
Revised: The second is instance-level rotation and paste which cuts point cloud instances from one LiDAR scan, rotates them along the scanning direction multiple times (to create multiple copies), and pastes the rotated instances into another LiDAR scan as illustrated in Fig. 2(b).

Original: For the sample LiDAR scan in Fig.
Revised: For the sample LiDAR scan in Fig.

Original: 1(c), Fig.
Revised: 1(c), Fig.

Original: 1 (d) shows one of itsaugmentation where point cloud sectors and instances are mixed in with high fidelity.
Revised: 1 (d) shows one of its augmentation where point cloud sectors and instances are mixed in with high fidelity.

Original: Extensiveevaluations show that the PolarMix augmented point clouds improve the network training consistentlyacross various tasks and benchmarks, more details to be described in experiment part.
Revised: Extensive evaluations show that the PolarMix augmented point clouds improve the network training consistently across various tasks and benchmarks, more details to be described in experiment part.

Original: The contribution of this paper can be summarized in three aspects.
Revised: The contribution of this paper can be summarized in three aspects.

Original: First , we introduce PolarMix, asimple yet effective point cloud augmentation technique that can enrich point cloud distributions whilemaintaining point cloud fidelity concurrently.
Revised: First , we introduce PolarMix, a simple yet effective point cloud augmentation technique that can enrich point cloud distributions while maintaining point cloud fidelity concurrently.

Original: Second , PolarMix is generally applicable and can workfor different network architectures, perception tasks (e.g., object detection, semantic segmentation,etc.), and datasets/domains with consistent performance gains.
Revised: Second , PolarMix is generally applicable and can work for different network architectures, perception tasks (e.g., object detection, semantic segmentation, etc.), and datasets/domains with consistent performance gains.

Original: Third , PolarMix is easy to use andcan be incorporated as a plug and play by most existing point cloud networks.
Revised: Third , PolarMix is easy to use and can be incorporated as a plug and play by most existing point cloud networks.

Original: It also works well forunsupervised domain adaptation with state-of-the-art performance.
Revised: It also works well for unsupervised domain adaptation with state-of-the-art performance.

Original:  Related Works
Revised:  Related Works

Original: Data augmentation for 2D images .
Revised: Data augmentation for 2D images .

Original: Data augmentation has been widely studied across different2D computer vision tasks such as image classification [14, 29], object detection [23], and semanticsegmentation [5].
Revised: Data augmentation has been widely studied across different 2D computer vision tasks such as image classification [15, 40], object detection [32], and semantic segmentation [4].

Original: It plays an important role in effective and efficient deep network training sincecollecting and annotating training images is often laborious and time-consuming.
Revised: It plays an important role in effective and efficient deep network training since collecting and annotating training images is often laborious and time-consuming.

Original: One typical
Revised: One typical

Original: DA approach is global augmentation that aims to learn certain transformation invariance in imagerecognition tasks [24, 13], e.g. , random cropping [17, 27, 28], random scaling[27, 28], randomerasing [40], color jittering [28], etc.
Revised: DA approach is global augmentation that aims to learn certain transformation invariance in image recognition tasks [33, 13], e.g. , random cropping [22, 38, 39], random scaling[38, 39], random erasing [52], color jittering [39], etc.

Original: Another typical DA approach is local augmentation whichperforms various mix operations to generate new training data.
Revised: Another typical DA approach is local augmentation which performs various mix operations to generate new training data.

Original: For example, mixup [38, 32] generatesnew data via convex combinations of the input pixels/feature embeddings and the output labels.
Revised: For example, mixup [50, 43] generates new data via convex combinations of the input pixels/feature embeddings and the output labels.

Original: CutMix [37] pastes rectangular crops from other images instead of mixing the whole images.
Revised: CutMix [49] pastes rectangular crops from other images instead of mixing the whole images.

Original: Recently,several studies [39, 6, 11, 13] introduce the object concept into the cut and mix operations: [39]extends mixup and cutmix into object detection; [6, 11, 13] cut instances from one image and pastethem into another for training better instance segmentation networks.
Revised: Recently, several studies [51, 5, 10, 13] introduce the object concept into the cut and mix operations: [51] extends mixup and cutmix into object detection; [5, 10, 13] cut instances from one image and paste them into another for training better instance segmentation networks.

Original: Data augmentation for 3D point clouds .
Revised: Data augmentation for 3D point clouds .

Original: Data augmentation of point clouds has also attractedincreasing attention in recent years.
Revised: Data augmentation of point clouds has also attracted increasing attention in recent years.

Original: Similar to 2D computer vision tasks, one direct approach isto adopt global augmentation in 3D space such as random scaling, rotation, and translation, whichcan be directly incorporated for expanding 3D objects [34, 3], indoor point clouds [9], and outdoorpoint clouds [1, 2, 22].
Revised: Similar to 2D computer vision tasks, one direct approach is to adopt global augmentation in 3D space such as random scaling, rotation, and translation, which can be directly incorporated for expanding 3D objects [45, 2], indoor point clouds [8], and outdoor point clouds [1, 12, 30].

Original: Several studies also explored to augment local structures of point clouds:
Revised: Several studies also explored to augment local structures of point clouds:

Original: PointAugment [20] introduces an auto-augmentation network for shape-wise transformation and pointwise displacement; PatchAugment [25] exploits data augmentation in local areas; PointWOLF [16]applies weighted transformations in local neighbourhoods for enhancing the diversity of 3D objects.
Revised: PointAugment [25] introduces an auto-augmentation network for shape-wise transformation and pointwise displacement; PatchAugment [36] exploits data augmentation in local areas; PointWOLF [21] applies weighted transformations in local neighbourhoods for enhancing the diversity of 3D objects.

Original: However, the aforementioned works focus on object-level augmentation which are not suitable forscene-level point clouds such as those in autonomous driving.
Revised: However, the aforementioned works focus on object-level augmentation which are not suitable for scene-level point clouds such as those in autonomous driving.

Original: Recently, several studies explored the idea of mixing for augmenting point clouds.
Revised: Recently, several studies explored the idea of mixing for augmenting point clouds.

Original: For example,
Revised: For example,

Original: PointMixUp [7] interpolates 3D objects to create new samples for training.
Revised: PointMixUp [6] interpolates 3D objects to create new samples for training.

Original: PointCutMix [19] replacessubsets of point objects with that of other objects to enrich training data.
Revised: PointCutMix [24] replaces subsets of point objects with that of other objects to enrich training data.

Original: However, both work focuseson object-level augmentation only.
Revised: However, both work focuses on object-level augmentation only.

Original: Several studies [36, 12, 35, 21] also explore scene-level mix butthey are constrained with specific vision tasks.
Revised: Several studies [48, 11, 46, 29] also explore scene-level mix but they are constrained with specific vision tasks.

Original: For example, GT-Aug [36, 12] cuts instances andpastes them into other LiDAR scans for the object detection task (requiring 3D bounding boxes forobject cutting); Mix3D [21] concatenates points of two scenes as an out-of-context augmentation forthe specific task of semantic segmentation.
Revised: For example, GT-Aug [48, 11] cuts instances and pastes them into other LiDAR scans for the object detection task (requiring 3D bounding boxes for object cutting); Mix3D [29] concatenates points of two scenes as an out-of-context augmentation for the specific task of semantic segmentation.

Original: As a comparison, the proposed PolarMix can performboth object-level and scene-level augmentation.
Revised: As a comparison, the proposed PolarMix can perform both object-level and scene-level augmentation.

Original: More importantly, its designs are perfectly alignedwith LiDAR-specific data properties including partial visibility and density variation with depthwhich guarantees superior fidelity and effectiveness of the augmented point clouds.
Revised: More importantly, its designs are perfectly aligned with LiDAR-specific data properties including partial visibility and density variation with depth which guarantees superior fidelity and effectiveness of the augmented point clouds.

Original: Furthermore,
Revised: Furthermore,

Original: PolarMix is generic and applicable to various computer vision tasks such as semantic segmentationand object detection, more details to be discussed in the experiment part.
Revised: PolarMix is generic and applicable to various computer vision tasks such as semantic segmentation and object detection, more details to be discussed in the experiment part.

Original:  PolarMix
Revised:  PolarMix

Original: Problem statement .
Revised: Problem statement .

Original: Let s ∈ R N × 4 and y denote a LiDAR scan with N points and its labels,respectively.
Revised: Let s ∈ R N × 4 and y denote a LiDAR scan with N points and its labels, respectively.

Original: Each point p i in s is a 1 × 4 vector with a 3D Cartesian coordinate relative to the scanner( x i , y i , z i ) and an intensity value of returning laser beam.
Revised: Each point p i in s is a 1 × 4 vector with a 3D Cartesian coordinate relative to the scanner ( x i , y i , z i ) and an intensity value of returning laser beam.

Original: The goal of PolarMix is to generate newtraining samples (˜ s, ˜ y ) by cutting and mixing across two training samples ( s A , y A ) and ( s B , y B ) .
Revised: The goal of PolarMix is to generate new training samples (˜ s, ˜ y ) by cutting and mixing across two training samples ( s A , y A ) and ( s B , y B ) .

Original: The generated training samples (˜ s, ˜ y ) is used for network training with the original loss function.
Revised: The generated training samples (˜ s, ˜ y ) is used for network training with the original loss function.

Original: The polar coordinates.
Revised: The polar coordinates.

Original: We adopt a 3D polar coordinate system where the position of a point isdefined by three numbers ( θ, r, ϕ ) : θ is the azimuth angle from x-axis to y-axis which defines therotation scanning angle of LiDAR sweeping; r denotes the depth which is the distance of the point tothe LiDAR sensor; ϕ is the inclination angle between the z-axis and the point vector ( x i , y i , z i ) .
Revised: We adopt a 3D polar coordinate system where the position of a point is defined by three numbers ( θ, r, ϕ ) : θ is the azimuth angle from x-axis to y-axis which defines the rotation scanning angle of LiDAR sweeping; r denotes the depth which is the distance of the point to the LiDAR sensor; ϕ is the inclination angle between the z-axis and the point vector ( x i , y i , z i ) .

Original: PolarMix for LiDAR data augmentation .
Revised: PolarMix for LiDAR data augmentation .

Original: We designed two point cloud augmentation approachesin PolarMix including a scene-level swapping approach Sw () and an instance-level rotate-pasteapproach Rp () for mixing LiDAR scans s A , s B and their labels y A , y B .
Revised: We designed two point cloud augmentation approaches in PolarMix including a scene-level swapping approach Sw () and an instance-level rotate-paste approach Rp () for mixing LiDAR scans s A , s B and their labels y A , y B .

Original: The combination of the twoaugmentation approaches in PolarMix can be defined as
Revised: The combination of the two augmentation approaches in PolarMix can be defined as

Original: [Equation]
Revised: [Equation]

Original: [Figure]
Revised: [Figure]

Original: where ⊕ denotes a concatenation operation.
Revised: where ⊕ denotes a concatenation operation.

Revised: C and Ω represent class list and angle list for instancelevel rotation and paste, respectively.

Original: The scene-level swapping Sw ( s A , s B | α, β ) aims to cut a point cloud sector from azimuth angle α toazimuth angle β from a LiDAR scan s A , and switch it with the similarly cut point cloud sector fromanother LiDAR scan s B .
Revised: The scene-level swapping Sw ( s A , s B | α, β ) aims to cut a point cloud sector from azimuth angle α to azimuth angle β from a LiDAR scan s A , and switch it with the similarly cut point cloud sector from another LiDAR scan s B .

Original: The swapping operation can be defined as follows
Revised: The swapping operation can be defined as follows

Original: [Equation]
Revised: [Equation]

Original: (2) where M α,βA denotes a binary mask indicating the azimuth range [ α, β ] that is cut out from LiDARscan s A , and ⊙ is element-wise multiplication.
Revised: where M α,βA denotes a binary mask indicating the azimuth range [ α, β ] that is cut out from LiDAR scan s A , and ⊙ is element-wise multiplication.

Original: The scene-level swapping thus exchanges two pointcloud sectors of the same size from two LiDAR scans in a bird’s-eye-view as illustrated in Fig.
Revised: The scene-level swapping thus exchanges two point cloud sectors of the same size from two LiDAR scans in a bird’s-eye-view as illustrated in Fig.

Original: 2 (a)since the maximum scanning area in the horizontal plane is a circle.
Revised: 2 (a) since the maximum scanning area in the horizontal plane is a circle.

Original: Note the angles α, β should bewithin the horizontal field-of-view of LiDAR sensor ( e.g. within 360 ◦ ).
Revised: Note the angles α, β should be within the horizontal field-of-view of LiDAR sensor ( e.g. within 360 ◦ ).

Original: The instance-level augmentation Rp ( s A , s B | C, Ω) , as illustrated in Fig.
Revised: The instance-level augmentation Rp ( s A , s B | C, Ω) , as illustrated in Fig.

Original: 2 (c), crops point instancesof semantic classes C from LiDAR scan s B , rotates them about z-axis by multiple azimuth anglesΩ = { ω 1 , . .
Revised: 2 (c), crops point instances of semantic classes C from LiDAR scan s B , rotates them about z-axis by multiple azimuth angles Ω = { ω 1 , . .

Original: . , ω J } to create multiple copies, and pastes all cropped and rotated point instances intoanother scan s A .
Revised: . , ω J } to create multiple copies, and pastes all cropped and rotated point instances into another scan s A .

Original: This augmentation operation can be defined by
Revised: This augmentation operation can be defined by

Original: [Equation]
Revised: [Equation]

Original: where M CB is a binary mask indicating which semantic classes of points instances to crop from
Revised: where M CB is a binary mask indicating which semantic classes of points instances to crop from

Original: LiDAR scan B , and R ω represents the rotation matrix around the z-axis for an azimuth angle ω .
Revised: LiDAR scan B , and R ω represents the rotation matrix around the z-axis for an azimuth angle ω .

Original: Using a polar coordinate system to augment LiDAR points has two desirable features.
Revised: Using a polar coordinate system to augment LiDAR points has two desirable features.

Original: First, it allowspoint cutting, rotating, and mixing to be perfectly aligned with the LiDAR scanning mechanismwhich greatly helps to preserve LiDAR-specific data properties such as partial visibility and densityvariation along the depth.
Revised: First, it allows point cutting, rotating, and mixing to be perfectly aligned with the LiDAR scanning mechanism which greatly helps to preserve LiDAR-specific data properties such as partial visibility and density variation along the depth.

Original: Second, it simplifies the augmentation process with negligible computational overhead: The scene-level swapping involves point slicing and concatenating only while the  instance-level augmentation can be achieved by simple dot products followed by point concatenation.
Revised: Second, it simplifies the augmentation process with negligible computational overhead: The scene-level swapping involves point slicing and concatenating only while the Algorithm instance-level augmentation can be achieved by simple dot products followed by point concatenation.

Original: Beyond that, PolarMix works in the input space which is naturally compatible with different networkarchitectures.
Revised: Beyond that, PolarMix works in the input space which is naturally compatible with different network architectures.

Original: Algorithm 1 summarizes the pipeline of the proposed PolarMix.Algorithm
Revised: Algorithm 1 summarizes the pipeline of the proposed PolarMix.

Original: PolarMix for unsupervised domain adaptation .
Revised: PolarMix for unsupervised domain adaptation .

Original: The proposed PolarMix can be directly appliedfor unsupervised domain adaptation via self-training [42].
Revised: The proposed PolarMix can be directly applied for unsupervised domain adaptation via self-training [54].

Original: With LiDAR data from a labeled sourcedomain , a supervised network model can be trained and applied to predict pseudo labels for LiDARdata from an unlabeled target domain .
Revised: With LiDAR data from a labeled source domain , a supervised network model can be trained and applied to predict pseudo labels for LiDAR data from an unlabeled target domain .

Original: PolarMix can then cut and mix LiDAR scans between thesource domain (with ground-truth labels) and the target domain (with pseudo labels).
Revised: PolarMix can then cut and mix LiDAR scans between the source domain (with ground-truth labels) and the target domain (with pseudo labels).

Original: Such augmented
Revised: Such augmented

Original: LiDAR data mitigates the inter-domain discrepancy which facilitates unsupervised domain adaptationeffectively, more details to be described in the ensuing experiments.
Revised: LiDAR data mitigates the inter-domain discrepancy which facilitates unsupervised domain adaptation effectively, more details to be described in the ensuing experiments.

Original:  Experiments
Revised:  Experiments

Original: We evaluate how PolarMix benefits deep neural network training for LiDAR point cloud understanding.
Revised: We evaluate how PolarMix benefits deep neural network training for LiDAR point cloud understanding.

Original: In Section 4.1, we evaluate it over the semantic segmentation task across different deep architecturesand benchmarking datasets.
Revised: In Section 4.1, we evaluate it over the semantic segmentation task across different deep architectures and benchmarking datasets.

Original: In Section 4.2, we evaluate it over object detection, mainly to examineits generalization capability across different computer vision tasks.
Revised: In Section 4.2, we evaluate it over object detection, mainly to examine its generalization capability across different computer vision tasks.

Original: In Section 4.3, we evaluatehow it facilitates unsupervised domain adaptation over multiple synthetic-to-real domain adaptationbenchmarks of LiDAR data.
Revised: In Section 4.3, we evaluate how it facilitates unsupervised domain adaptation over multiple synthetic-to-real domain adaptation benchmarks of LiDAR data.

Original: Finally, we provide an in-depth analysis of different components in
Revised: Finally, we provide an in-depth analysis of different components in

Original: PolarMix in Section 4.4.
Revised: PolarMix in Section 4.4.

Original: 4.1 PolarMix helps learn better representations for semantic segmentation
Revised: 4.1 PolarMix helps learn better representations for semantic segmentation

Original: We first study how PolarMix helps learn better representation for semantic segmentation.
Revised: We first study how PolarMix helps learn better representation for semantic segmentation.

Original: Theexperiments were conducted over multiple deep architectures and public datasets.
Revised: The experiments were conducted over multiple deep architectures and public datasets.

Original: 4.1.1 Experimental Settings
Revised: 4.1.1 Experimental Settings

Original: Dataset.
Revised: Dataset.

Original: We evaluate PolarMix over three LiDAR datasets of driving scenes that have been widelyadopted for benchmarking in semantic segmentation.
Revised: We evaluate PolarMix over three LiDAR datasets of driving scenes that have been widely adopted for benchmarking in semantic segmentation.

Original: The first is SemanticKITTI [1] which is alarge-scale dataset collected in a city of Germany.
Revised: The first is SemanticKITTI [1] which is a large-scale dataset collected in a city of Germany.

Original: It has 43,551 LiDAR scans with 64 beams withpoint-wise annotations of 19 semantic classes.
Revised: It has 43,551 LiDAR scans with 64 beams with point-wise annotations of 19 semantic classes.

Original: We follow the widely-adopted split and use sequences00-07, 09-10 as the training set and sequence 08 for validation.
Revised: We follow the widely-adopted split and use sequences 00-07, 09-10 as the training set and sequence 08 for validation.

Original: The second is nuScenes-lidarseg [2]dataset which has 40,000 scans captured in 1000 scenes of 20s duration.
Revised: The second is nuScenes-lidarseg [12] dataset which has 40,000 scans captured in 1000 scenes of 20s duration.

Original: It is collected with a 32 beams
Revised: It is collected with a 32 beams

Original: LiDAR sensor at 20Hz frequency with point-wise annotations of 16 semantic classes.
Revised: LiDAR sensor at 20Hz frequency with point-wise annotations of 16 semantic classes.

Original: We follow theofficially split of training data and validation data.
Revised: We follow the officially split of training data and validation data.

Original: The third is SemanticPOSS [22] which consists of2,988 annotated point cloud scans of 14 semantic classes.
Revised: The third is SemanticPOSS [30] which consists of 2,988 annotated point cloud scans of 14 semantic classes.

Original: We follow the official benchmark setting,
Revised: We follow the official benchmark setting,

Original: [Table]
Revised: [Table]

Original: .e

Original: Architectures and implementation details.
Revised: Architectures and implementation details.

Original: We evaluate PolarMix over four widely adoptedsemantic segmentation networks: 1) MinkNet [8] which is a typical voxel-based sparse CNN; 2)
Revised: We evaluate PolarMix over four widely adopted semantic segmentation networks: 1) MinkNet [7] which is a typical voxel-based sparse CNN; 2)

Original: SPVCNN [30] which is a hybrid network with a sparse convolutional and a point-based sub-network;3) RandLA-Net [15] which is a standard point-based network; and 4) Cylinder3D [41] which a stateof-the-art cylindrical and asymmetrical 3D CNN.
Revised: SPVCNN [41] which is a hybrid network with a sparse convolutional and a point-based sub-network; 3) RandLA-Net [16] which is a standard point-based network; and 4) Cylinder3D [53] which a stateof-the-art cylindrical and asymmetrical 3D CNN.

Original: We adopt the default training hyper-parametersin the open-source repositories 123 for all four networks, and the only modification is the batch sizefor PolarMix and MinkNet (we change it to 8).
Revised: We adopt the default training hyper-parameters in the open-source repositories 234 for all four networks, and the only modification is the batch size for SPVCNN and MinkNet (we change it to 8).

Original: We conducted experiments with a single Tesla2080Ti GPU forPolarMix and MinkNet and  a Tesla V100 GPU for RandLA-Net and Cylinder3D.
Revised: We conducted experiments with a single Tesla 2080Ti GPU for MinkNet and SPVCNN and a Tesla V100 GPU for RandLA-Net and Cylinder3D.

Original: Note training RandLA-Net and Cylinder3D takes much longer time ( e.g. , more than two weeks for Cylinder3D while less than 1 day for MinkNet). We therefore uniformly sub-sampled the same 10%of SemanticKITTI for faster experiments with these two networks.
Revised: Note training RandLA-Net and Cylinder3D takes relatively longer time, we therefore uniformly sub-sampled the same 10% of SemanticKITTI for faster experiments with these two networks.

Original: For augmentation with scene-level swapping, we randomly crop 180 ◦ sectors from 360 ◦ for [ α, β ] forpoint swapping.
Revised: For augmentation with scene-level swapping, we randomly crop 180 ◦ sectors from 360 ◦ for [ α, β ] for point swapping.

Original: For augmentation with instance-level rotate and paste, we take three rotation anglesfor dataset SemanticKITTI ( 0 ◦ , and another two rotation angles randomly picked from (0 ◦ , 120 ◦ ]and (120 ◦ , 240 ◦ ] ), and two rotation angles for datasets SemanticPOSS and nuScenes-lidarseg ( 0 ◦and another rotation angle randomly chosen from either +90 ◦ or − 90 ◦ ).
Revised: For augmentation with instance-level rotate and paste, we take three rotation angles for dataset SemanticKITTI ( 0 ◦ , and another two rotation angles randomly picked from (0 ◦ , 120 ◦ ] and (120 ◦ , 240 ◦ ] ), and two rotation angles for datasets SemanticPOSS and nuScenes-lidarseg ( 0 ◦ and another rotation angle randomly chosen from either +90 ◦ or − 90 ◦ ).

Original: We set σ 1 , σ 2 as 0.5, 1,respectively.
Revised: We set δ 1 , δ 2 as 0.5, 1, respectively.

Original: We also examine hyper-parameters of PolarMix with details provided in the appendix.
Revised: We also examine hyper-parameters of PolarMix with details provided in the appendix.

Original: 4.1.2 Results
Revised: 4.1.2 Results

Original: PolarMix improves semantic segmentation by large margins .
Revised: PolarMix improves semantic segmentation by large margins .

Revised: Since data augmentation for LiDAR semantic segmentation is a relatively under-explored task with few existing works, we selected the highly-related mixing-based methods including Cut-Mix [49] and Copy-paste [13] in 2D vision and the pioneering work Mix3D [29] in 3D vision as baseline augmentation methods, and compared the proposed PolarMix with them.

Original: Tables 1 and 2 show experimentalresults across two networks MinkNet and SPVCNN and three datasets SemanticKITTI, nuSceneslidarseg, and SemanticPOSS.
Revised: Tables 1 and 2 show experimental results across two networks MinkNet and SPVCNN and three datasets SemanticKITTI, nuScenes-lidarseg, and SemanticPOSS.

Original: It can be observed that the baseline with either MinkNet or SPVCNN(without involving any data augmentation in network training) produces fair semantic segmentationfor all three datasets.
Revised: It can be observed that the baseline with either MinkNet or SPVCNN (without involving any data augmentation in network training) produces fair semantic segmentation for all three datasets.

Original: However, including global augmentation with random scaling and rotation(i.e. , +CGA in the two tables) improves semantic segmentation consistently across the two networksand the three datasets.
Revised: However, including global augmentation with random scaling and rotation (i.e., +CGA in the two tables) improves semantic segmentation consistently across the two networks and the three datasets.

Original: On top of the global augmentation, further including local augmentation(i.e., +CutMix † , +CopyPaste † , and+Mix3D † ) further improves the semantic segmentation in mostcases. As a comparison, PolarMix introduces the best performance gains consistently across the two
Revised: On top of the global augmentation, further including local augmentation (i.e., +CutMix † , +CopyPaste † , and

Original: [Table]
Revised: [Table]

Original: +CGA
Revised: +CGA

Original: [Table]
Revised: [Table]

Original: +CutMix † [37]
Revised: +CutMix † [49]

Original: [Table]
Revised: [Table]

Original: +Copy-Paste † [13]
Revised: +Copy-Paste † [13]

Original: [Table]
Revised: [Table]

Original: +Mix3D † [21]
Revised: +Mix3D † [29]

Original: [Table]
Revised: [Table]

Original: +PolarMix † (Ours)
Revised: +PolarMix † (Ours)

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table] 
Revised: [Table] [Figure] [Table] [Figure]

Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original: [Table]
Revised: [Table]

Original: [Figure]
Revised: [Figure]

Original:  baseline networks and the three evaluated benchmarking datasets, demonstrating its great robustnessand generalization capabilities across different network architectures and datasets.
Revised: +Mix3D † ) further improves the semantic segmentation in most cases. As a comparison, PolarMix introduces the best performance gains consistently across the two baseline networks and the three evaluated benchmarking datasets, demonstrating its great robustness and generalization capabilities across different network architectures and datasets.

Original: PolarMix improves data-efficiency .
Revised: PolarMix improves data-efficiency .

Original: The proposed PolarMix works reliably with different amountsof training data, and it also improves data efficiency by reducing training data and annotationseffectively.
Revised: The proposed PolarMix works reliably with different amounts of training data, and it also improves data efficiency by reducing training data and annotations effectively.

Original: As shown in Fig.
Revised: As shown in Fig.

Original: 3, the data augmentation with PolarMix consistently helps acrossdifferent proportions of training data of SemanticKITTI as well as two different segmentationnetworks MinkNet and SPVCNN.
Revised: 3, the data augmentation with PolarMix consistently helps across different proportions of training data of SemanticKITTI as well as two different segmentation networks MinkNet and SPVCNN.

Original: In addition, including PolarMix can achieve similar segmentationmIoU while using 75% of SemanticKITTI in network training (as compared with training with 100%
Revised: In addition, including PolarMix can achieve similar segmentation mIoU while using 75% of SemanticKITTI in network training (as compared with training with 100%

Original: SemanticKITTI without using PolarMix) for both networks, hence saves 25% efforts in training datacollection and annotations.
Revised: SemanticKITTI without using PolarMix) for both networks, hence saves 25% efforts in training data collection and annotations.

Original: PolarMix works across deep architectures .
Revised: PolarMix works across deep architectures .

Original: The proposed PolarMix can work across differentdeep architectures beyond the voxel-based MinkNet and the sparse convolution network SPVCNN.
Revised: The proposed PolarMix can work across different deep architectures beyond the voxel-based MinkNet and the sparse convolution network SPVCNN.

Original: We evaluate this feature by experimenting with two new deep architectures including the pointbased network RandLA-Net [15] and the more recent 3D cylindrical convolutional architectureof Cylinder3D [41].
Revised: We evaluate this feature by experimenting with two new deep architectures including the pointbased network RandLA-Net [16] and the more recent 3D cylindrical convolutional architecture of Cylinder3D [53].

Original: We train the two networks with default settings as in the officially releasedrepositories.
Revised: We train the two networks with default settings as in the officially released repositories.

Original: As shown in Table 3, incorporating PolarMix improves the segmentation performanceconsistently by large margins for both strong baselines (trained with 10% of SemanticKITTI data).
Revised: As shown in Table 3, incorporating PolarMix improves the segmentation performance consistently by large margins for both strong baselines (trained with 10% of SemanticKITTI data).

Original: This further verifies that PolarMix has superior generalization capability across different 3D deeparchitectures.
Revised: This further verifies that PolarMix has superior generalization capability across different 3D deep architectures.

Original: [Table]
Revised: [Table]

Original:  [Table]2 PolarMix helps learn effective representations for object detection
Revised: Methods [Table]

Original: Setup.
Revised: Setup.

Original: The proposed PolarMix works in the input space withsuperior independence of specific tasks.
Revised: The proposed PolarMix works in the input space with independence of specific tasks.

Original: We verify thisnice property by evaluating it over object detection, another classical 3D understandingtask that aims to predict 3D bounding box and label for each interested object instance.
Revised: We verify this property by evaluating it over object detection, another classical 3D understanding task that aims to predict 3D bounding box and label for each interested object instance.

Original: We performexperiments with dataset nuScenes [2] and three classical deep networks including PointPillar [18],
Revised: We perform experiments with dataset nuScenes [12] and three classical deep networks including PointPillar [23],

Original: Second [36], and CenterNet[10].
Revised: Second [48], and CenterNet[9].

Original: For implementation, we adopted default  hyper-parameters  in the OpenPCDet repository4 and test with two Tesla 2080Ti GPUs.
Revised: For implementation, we adopted default training hyper-parameters and optimizer in the OpenPCDet repository and training with two Tesla 2080Ti GPUs (11GB).

Revised: We used random flip along the X and Y axis, random rotation, and random scaling for basic data augmentation.

Revised: For a fair comparison, PolarMix is directly implemented on top of the baseline with the same configurations.

Original: For evaluation metrics, we adoptedthe widely used mean Average Precision (mAP) and nuScenes detection score (NDS).
Revised: For evaluation metrics, we adopted the widely used mean Average Precision (mAP) and nuScenes detection score (NDS).

Original: Results.
Revised: Results.

Original: Table 4 shows experimental results.
Revised: Table 4 shows experimental results.

Original: It can be observed that incorporating PolarMix improvesboth mAP and NDS consistently across the three tested deep networks.
Revised: It can be observed that incorporating PolarMix improves both mAP and NDS consistently across the three tested deep networks.

Original: This experiment shows thatthe proposed PolarMix has superior generalization capability across different computer vision tasks,largely due to its cut-edit-mix strategy which enriches the distribution of the training data in the inputspace without changing LiDAR-specific data properties.
Revised: This experiment shows that the proposed PolarMix has superior generalization capability across different computer vision tasks, largely due to its cut-edit-mix strategy which enriches the distribution of the training data in the input space without changing LiDAR-specific data properties.

Original: 4.3 PolarMix helps reduce domain gap
Revised: 4.3 PolarMix helps reduce domain gap

Revised: Unsupervised domain adaptation (UDA) is an important research topic, aiming to solve the noticeable performance drops of deep neural networks while training and testing across different domains, as a result of the distribution bias (domain shift).

Revised: UDA has been widely studied in both 2D vision [55, 56, 17, 18, 14, 19] and 3D vision [31, 20, 28, 46, 34, 35].

Original: The proposed PolarMix can be easily extended for unsupervised domain adaptation by mixinglabelled source point data and unlabeled target point data.
Revised: The proposed PolarMix can be easily extended for UDA by mixing labelled source point data and unlabeled target point data.

Original: We evaluate this nice feature by conductingexperiments over two challenging synthetic-to-real point cloud segmentation benchmarks including SynLiDAR → SemanticKITTI and SynLiDAR → SemanticPOSS.
Revised: We evaluate this nice feature by conducting experiments over two challenging synthetic-to-real point cloud segmentation benchmarks including SynLiDAR → SemanticKITTI and SynLiDAR → SemanticPOSS.

Original: SynLiDAR [35] is a synthetic LiDAR point cloud dataset that consists of 198k scans as collected from virtual scenes.
Revised: SynLiDAR [46] is a synthetic LiDAR point cloud dataset that consists of 198k scans as collected from virtual scenes.

Original: It shares 19common point classes with the SemanticKITTI and 13 common point classes with the SemanticPOSS.
Revised: It shares 19 common point classes with the SemanticKITTI and 13 common point classes with the SemanticPOSS.

Original: In the experiments, we train networks with the labelled SynLiDAR data (as the source data) and theunlabeled SemanticKITTI and SemanticPOSS data (as the target data), and perform evaluations overthe validation set of SemanticKITTI and  SemanticPOSS.
Revised: In the experiments, we train networks with the labelled SynLiDAR data (as the source data) and the unlabeled SemanticKITTI and SemanticPOSS data (as the target data), and perform evaluations over the validation set of SemanticKITTI and [Table] SemanticPOSS.

Original: We follow the existing benchmarks [35]and adopt MinkNet as the segmentation model.
Revised: We follow the existing benchmarks [46] and adopt MinkNet as the segmentation model.

Original: We adopt the self-training approach as described in section 3 for unsupervised domain adaptation.
Revised: We adopt the self-training approach as described in section 3 for unsupervised domain adaptation.

Original: Specifically, we first train a supervised model with the labelled source data and apply the supervisedmodel to predict pseudo labels for the unlabelled target data.
Revised: Specifically, we first train a supervised model with the labelled source data and apply the supervised model to predict pseudo labels for the unlabelled target data.

Original: We then apply PolarMix to cut andmix between the labelled source data and the pseudo-labelled target data, and further train the model
Revised: We then apply PolarMix to cut and mix between the labelled source data and the pseudo-labelled target data

Original: [Table]
Revised: [Table]

Original:  with all augmented point data.
Revised: and further train the model with all augmented point data.

Original: As shown in Table 5, incorporating PolarMix achieves state-of-the-artmIoUs for both SemanticKITTI and SemanticPOSS.
Revised: As shown in Table 5, incorporating PolarMix achieves state-of-the-art mIoUs for both SemanticKITTI and SemanticPOSS.

Original: The superior segmentation performance islargely attributed to the cut-and-mix strategy in PolarMix which effectively mitigates the distributiondiscrepancy across LiDAR scans  two 
Revised: The superior segmentation performance is largely attributed to the cut-and-mix strategy in PolarMix which effectively mitigates the distribution discrepancy across LiDAR scans of two different domains [Table]

Original: 4.4 Ablation study
Revised: 4.4 Ablation study

Original: [Table]
Revised: [Table]

Original: We perform several ablation studies to examine the contribution of the two data augmentationcomponents in the proposed PolarMix.
Revised: We perform several ablation studies to examine the contribution of the two data augmentation components in the proposed PolarMix.

Original: In the ablation studies, we train SPVCNN with the sequence00 of SemanticKITTI and evaluate the trained models over the validation set of SemanticKITTI.
Revised: In the ablation studies, we train SPVCNN with the sequence 00 of SemanticKITTI and evaluate the trained models over the validation set of SemanticKITTI.

Original: Weadopt the same training configurations as described in Section 4.1 and Table 6 shows experimentalresults.
Revised: We adopt the same training configurations as described in Section 4.1 and Table 6 shows experimental results.

Original: With the conventional global augmentation including random rotation and random scaling,the trained SPVCNN model achieves a mIoU of 48.9%.
Revised: With the conventional global augmentation including random rotation and random scaling, the trained SPVCNN model achieves a mIoU of 48.9%.

Original: On top of that, including the proposedscene-level swapping alone improves the mIoU by 1.4%.
Revised: On top of that, including the proposed scene-level swapping alone improves the mIoU by 1.4%.

Original: In addition, including the basic version ofthe proposed instance-level cut-and-mix (i.e., without multiple rotations to create multiple copiesof the cropped object instances) alone improves the mIoU by 2.0% while incorporating the fullinstance-level cut-and-mix improves the mIoU by 4.3%.
Revised: In addition, including the basic version of the proposed instance-level cut-and-mix (i.e., without multiple rotations to create multiple copies of the cropped object instances) alone improves the mIoU by 2.0% while incorporating the full instance-level cut-and-mix improves the mIoU by 4.3%.

Original: Finally, incorporating both augmentationcomponents (i.e., the full PolarMix) improves the mIoU by 5.9%, demonstrating the complementaryproperty of the two approaches in point data augmentation
Revised: Finally, incorporating both augmentation components (i.e., the full PolarMix) improves the mIoU by 5.9%, demonstrating the complementary property of the two approaches in point data augmentation. 4.5 Discussion

Revised: We conducted experiments to examine whether mixing more than two LiDAR scans further improves the segmentation performances.

Revised: Specifically, we increased the mixed LiDAR scans and benchmarking them without using PolarMix.

Original: Conclusion
Revised: The experiments were conducted with SPVCNN that is trained with sequence 00 of SemanticKITTI. As Table 7 shows, mixing two scans produces clearly the best performance. We examined the mixed data and found that mixing more scans introduces more [Table]

Revised: hardly distinguishable objects.

Revised: The experimental results are consistent with other mixing-based augmentation works [50, 49, 6, 29].

Original: This paper presents PolarMix which is a data augmentation method for LiDAR point cloud learning.
Revised: This paper presents PolarMix which is a data augmentation method for LiDAR point cloud learning.

Original: It produces new point cloud data by mixing LiDAR scans for training networks.
Revised: It produces new point cloud data by mixing LiDAR scans for training networks.

Original: There are twoapproaches that are designed based on LiDAR data properties in PolarMix.
Revised: There are two approaches that are designed based on LiDAR data properties in PolarMix.

Original: Specifically, the scenelevel swapping exchanges points within the same range of azimuth angles while the instance-levelrotating-pasting selects points of certain classes from one LiDAR scan and rotates for multiple copiesbefore pasting into another scan.
Revised: Specifically, the scenelevel swapping exchanges points within the same range of azimuth angles while the instance-level rotating-pasting selects points of certain classes from one LiDAR scan and rotates for multiple copies before pasting into another scan.

Original: Extensive experiments show the superiority of our method indata augmentation for both semantic segmentation and object detection across a variety of deepframeworks and public datasets.
Revised: Extensive experiments show the superiority of our method in data augmentation for both semantic segmentation and object detection across a variety of deep frameworks and public datasets.

Original: We also extended PolarMix into unsupervised domain adaptationand achieved state-of-the-art performances in multiple synthetic-to-real LiDAR data segmentationbenchmarks.
Revised: We also extended PolarMix into unsupervised domain adaptation and achieved state-of-the-art performances in multiple synthetic-to-real LiDAR data segmentation benchmarks.

Original: We hope that the idea of PolarMix can encourage future research to provide deeperinsights on data augmentation for deep point cloud learning.
Revised: We hope that the idea of PolarMix can encourage future research to provide deeper insights on data augmentation for deep point cloud learning.

Original: Checklist
Revised: Acknowledgments and Disclosure of Funding

Revised: This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from Singapore

Original: For all authors...
Revised: Telecommunications Limited (Singtel), through Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU).

Original:  (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contribu- tions and scope?
Revised: For all authors... (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s contribu- tions and scope?

Original: [Yes] (b) Did you describe the limitations of your work?
Revised: [Yes] (b) Did you describe the limitations of your work?

Original: [Yes] Please refer to the parameter learningin the appendix, where we claimed that the effectiveness of our work may be influenced by the parameter settings including swapping angular range and the number of copies.
Revised: [Yes] Please refer to the parameter learning in the appendix, where we claimed that the effectiveness of our work may be influenced by the parameter settings including swapping angular range and the number of copies.

Original: (c) Did you discuss any potential negative societal impacts of your work?
Revised: (c) Did you discuss any potential negative societal impacts of your work?

Original: [Yes] Please refer to thebroader impact section in the Appendix for more details (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?
Revised: [Yes] Please refer to the broader impact section in the Appendix for more details (d) Have you read the ethics review guidelines and ensured that your paper conforms to them?

Original: [Yes] 
Revised: [Yes] 2.

Original: If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results?
Revised: If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results?

Original: [N/A](b) Did you include complete proofs of all theoretical results?
Revised: [N/A] (b) Did you include complete proofs of all theoretical results?

Original: [N/A]3.
Revised: [N/A] 3.

Original: If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimentalresults (either in the supplemental material or as a URL)?
Revised: If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?

Original: [N/A]
Revised: [N/A]

Original: The code will be available soon.
Revised: The code will be available soon.

Original: (b) Did you specify all the training details (e
Revised: (b) Did you specify all the training details (e

Original: g., data splits, hyperparameters, how they werechosen)?
Revised: g., data splits, hyperparameters, how they were chosen)?

Original: [Yes]
Revised: [Yes]

Original: (c) Did you report error bars (e g., with respect to the random seed after running experimentsmultiple times)?
Revised: (c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?

Original: [No] (d) Did you include the total amount of compute and the type of resources used (e g., type of GPUs,internal cluster, or cloud provider)?
Revised: [No] (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)?

Original: [Yes] 
Revised: [Yes] 4.

Original: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators?
Revised: If you are using existing assets (e.g., code, data, models) or curating/releasing new assets... (a) If your work uses existing assets, did you cite the creators?

Original: [Yes] 
Revised: [Yes] (b) Did you mention the license of the assets? [Yes] (c) Did you include any new assets either in the supplemental material or as a URL? [N/A] (d) Did you discuss whether and how consent was obtained from people whose data you’re us- ing/curating?

Original: (b) Did you mention the license of the assets?
Revised: [N/A] (e) Did you discuss whether the data you are using/curating contains personally identifiable informa- tion or offensive content?

Original: [Yes]
Revised: [N/A] 5.

Original: [Table]
Revised: If you used crowdsourcing or conducted research with human subjects... (a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A] (b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A] (c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation?

Original: [Table]
Revised: [N/A]