Original: Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection
Revised: Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection

Original: Anonymous Author(s)
Revised: Anonymous Author(s)

Original: AffiliationAddress email
Revised: AffiliationAddress email

Original: Abstract
Revised: Abstract

Original: Most existing 3D point cloud object detection approaches heavily rely on largeamounts of labeled training data.
Revised: Most existing 3D point cloud object detection approaches heavily rely on largeamounts of labeled training data.

Original: However, the labeling process is costly andtime-consuming.
Revised: However, the labeling process is costly andtime-consuming.

Original: This paper considers few-shot 3D point cloud object detection,where only a few annotated samples of novel classes are needed with abundantsamples of base classes.
Revised: This paper considers few-shot 3D point cloud object detection,where only a few annotated samples of novel classes are needed with abundantsamples of base classes.

Original: To this end, we propose Prototypical VoteNet to recognizeand localize novel instances, which incorporates two new modules: Prototypical
Revised: To this end, we propose Prototypical VoteNet to recognizeand localize novel instances, which incorporates two new modules: Prototypical

Original: Vote Module (PVM) and Prototypical Head Module (PHM).
Revised: Vote Module (PVM) and Prototypical Head Module (PHM).

Original: Specifically, as the3D basic geometric structures can be shared among categories, PVM is designedto leverage class-agnostic geometric prototypes, which are learned from baseclasses, to refine local features of novel categories.
Revised: Specifically, as the3D basic geometric structures can be shared among categories, PVM is designedto leverage class-agnostic geometric prototypes, which are learned from baseclasses, to refine local features of novel categories.

Original: Then PHM is proposed toutilize class prototypes to enhance the global feature of each object, facilitatingsubsequent object localization and classification, which is trained by the episodictraining strategy.
Revised: Then PHM is proposed toutilize class prototypes to enhance the global feature of each object, facilitatingsubsequent object localization and classification, which is trained by the episodictraining strategy.

Original: To evaluate the model in this new setting, we contribute twonew benchmark datasets, FS-ScanNet and FS-SUNRGBD.
Revised: To evaluate the model in this new setting, we contribute twonew benchmark datasets, FS-ScanNet and FS-SUNRGBD.

Original: We conduct extensiveexperiments to demonstrate the effectiveness of Prototypical VoteNet, and ourproposed method shows significant and consistent improvements compared tobaselines on two benchmark datasets.
Revised: We conduct extensiveexperiments to demonstrate the effectiveness of Prototypical VoteNet, and ourproposed method shows significant and consistent improvements compared tobaselines on two benchmark datasets.

Original: Our code, as well as the new benchmark,will be released to facilitate future works.
Revised: Our code, as well as the new benchmark,will be released to facilitate future works.

Original:  Introduction
Revised:  Introduction

Original: 3D object detection aims to localize and recognize objects from point clouds with many applicationsin augmented reality, autonomous driving, and robotics manipulation.
Revised: 3D object detection aims to localize and recognize objects from point clouds with many applicationsin augmented reality, autonomous driving, and robotics manipulation.

Original: Recently, a number of fullysupervised 3D object detection approaches have made remarkable progress with deep learning [22,18, 31, 24].
Revised: Recently, a number of fullysupervised 3D object detection approaches have made remarkable progress with deep learning [22,18, 31, 24].

Original: Nonetheless, their success heavily relies on large amounts of labeled training data, whichare time-consuming and costly to obtain.
Revised: Nonetheless, their success heavily relies on large amounts of labeled training data, whichare time-consuming and costly to obtain.

Original: On the contrary, a human can quickly learn to recognizenovel classes by seeing only a few samples.
Revised: On the contrary, a human can quickly learn to recognizenovel classes by seeing only a few samples.

Original: To imitate such human ability, we consider few-shotpoint cloud 3D object detection which aims to train a model to recognize novel categorizes fromlimited annotated samples of novel classes together with sufficient annotated data of base classes.
Revised: To imitate such human ability, we consider few-shotpoint cloud 3D object detection which aims to train a model to recognize novel categorizes fromlimited annotated samples of novel classes together with sufficient annotated data of base classes.

Original: Few-shot learning has been extensively studied in various 2D visual understanding tasks such asobject detection [39, 41, 43, 45], image classification [14, 9, 3, 32], and semantic segmentation [23,21, 48, 19].
Revised: Few-shot learning has been extensively studied in various 2D visual understanding tasks such asobject detection [39, 41, 43, 45], image classification [14, 9, 3, 32], and semantic segmentation [23,21, 48, 19].

Original: Early attempts [9, 16, 11, 38] employ meta-learning to learn transferable knowledge froma collection of tasks and attained remarkable progress.
Revised: Early attempts [9, 16, 11, 38] employ meta-learning to learn transferable knowledge froma collection of tasks and attained remarkable progress.

Original: Recently, benefited from large-scale datasets( e.g. ImageNet [6]) and advanced pre-training methods [27, 49, 10, 53], finetuning large-scale pretrained visual models on down-stream few-shot datasets emerges as an effective approach to addressthis problem [33, 39, 54].
Revised: Recently, benefited from large-scale datasets( e.g. ImageNet [6]) and advanced pre-training methods [27, 49, 10, 53], finetuning large-scale pretrained visual models on down-stream few-shot datasets emerges as an effective approach to addressthis problem [33, 39, 54].

Original: Among different streams of work, prototype-based methods [42, 52, 20, 17]have been incorporated into both streams and show the great advantages, since they can capture the representative features of categories that can be further utilized for feature refinement [45, 50] orclassification [26, 32].
Revised: Among different streams of work, prototype-based methods [42, 52, 20, 17]have been incorporated into both streams and show the great advantages, since they can capture the representative features of categories that can be further utilized for feature refinement [45, 50] orclassification [26, 32].

Original: This motivates us to explore effective 3D cues to build prototypes for few-shot 3D detection.
Revised: This motivates us to explore effective 3D cues to build prototypes for few-shot 3D detection.

Original: Differentfrom 2D visual data, 3D data can get rid of distortions caused by perspective projections, and offergeometric cues with accurate shape and scale information.
Revised: Differentfrom 2D visual data, 3D data can get rid of distortions caused by perspective projections, and offergeometric cues with accurate shape and scale information.

Original: Besides, 3D primitives to constitute objectscan often be shared among different categories.
Revised: Besides, 3D primitives to constitute objectscan often be shared among different categories.

Original: For instance, as shown in Figure 1, rectangular platesand corners can be found in many categories.
Revised: For instance, as shown in Figure 1, rectangular platesand corners can be found in many categories.

Original: Based on these observations, in this work, we propose
Revised: Based on these observations, in this work, we propose

Original: Prototypical VoteNet, which employs such robust 3D shape and primitive clues to design geometricprototypes to facilitate representation learning in the few-shot setting.
Revised: Prototypical VoteNet, which employs such robust 3D shape and primitive clues to design geometricprototypes to facilitate representation learning in the few-shot setting.

Original: Prototypical VoteNet incorporates two new modules, namely Prototypical Vote Module (PVM) and
Revised: Prototypical VoteNet incorporates two new modules, namely Prototypical Vote Module (PVM) and

Original: Prototypical Head Module (PHM), to enhance local and global feature learning, respectively, forfew-shot 3D object detection.
Revised: Prototypical Head Module (PHM), to enhance local and global feature learning, respectively, forfew-shot 3D object detection.

Original: Specifically, based on extracted features from a backbone network( i.e. PointNet++ [25]), PVM firstly constructs a class-agnostic 3D primitive memory bank to storegeometric prototypes, which are shared by all categories and updated iteratively during training.
Revised: Specifically, based on extracted features from a backbone network( i.e. PointNet++ [25]), PVM firstly constructs a class-agnostic 3D primitive memory bank to storegeometric prototypes, which are shared by all categories and updated iteratively during training.

Original: Toexploit the transferability of geometric structures, PVM then incorporates a multi-head attentionmodule to associate geometric prototypes with points in a given scene and utilize them to refine theirfeature representations.
Revised: Toexploit the transferability of geometric structures, PVM then incorporates a multi-head attentionmodule to associate geometric prototypes with points in a given scene and utilize them to refine theirfeature representations.

Original: PVM is majorly developed to exploit shared geometric structures amongbase and novel categories to enhance feature learning of local information in the few-shot setting.
Revised: PVM is majorly developed to exploit shared geometric structures amongbase and novel categories to enhance feature learning of local information in the few-shot setting.

Original: Further, to facilitate learning discriminative features for object categorization, PHM is designed toemploy a multi-head attention network and leverage class-specific prototypes from a few supportsamples to refine global representations of objects.
Revised: Further, to facilitate learning discriminative features for object categorization, PHM is designed toemploy a multi-head attention network and leverage class-specific prototypes from a few supportsamples to refine global representations of objects.

Original: Moreover, episodic training [32, 38] is adopted tosimulate few-shot circumstances, where PHM is trained by a distribution of similar few-shot tasksinstead of only one target object detection task.
Revised: Moreover, episodic training [32, 38] is adopted tosimulate few-shot circumstances, where PHM is trained by a distribution of similar few-shot tasksinstead of only one target object detection task.

Original: In this way, supervised by the standard cross-entropyloss, PHM reduces the intra-class variation for the novel categories, with only a few support samples.
Revised: In this way, supervised by the standard cross-entropyloss, PHM reduces the intra-class variation for the novel categories, with only a few support samples.

Original: Our contributions are listed as follows:
Revised: Our contributions are listed as follows:

Original: • We are the first to study the promising few-shot 3D point cloud object detection task, whichallows a model to detect new classes, given a few examples.
Revised: • We are the first to study the promising few-shot 3D point cloud object detection task, whichallows a model to detect new classes, given a few examples.

Original: • We propose Prototypical VoteNet, which incorporates Prototypical Vote Module and Prototypical Head Module, to address this new challenge.
Revised: • We propose Prototypical VoteNet, which incorporates Prototypical Vote Module and Prototypical Head Module, to address this new challenge.

Original: Prototypical Vote Module leveragesclass-agnostic geometric prototypes to enhance the local features of novel samples.
Revised: Prototypical Vote Module leveragesclass-agnostic geometric prototypes to enhance the local features of novel samples.

Original: Prototypical Head Module utilizes the class-specific prototypes to refine the object features with
Revised: Prototypical Head Module utilizes the class-specific prototypes to refine the object features with

Original: the aid of episode training.
Revised: the aid of episode training.

Original: • We contribute two new benchmark datasets called FS-ScanNet and FS-SUNRGBD, which
Revised: • We contribute two new benchmark datasets called FS-ScanNet and FS-SUNRGBD, which

Original: are specifically designed for this problem.
Revised: are specifically designed for this problem.

Original: Our experimental results on these two benchmark
Revised: Our experimental results on these two benchmark

Original: datasets show that the proposed model effectively addresses the few-shot 3D point cloudobject detection problem, yielding significant improvement over several competitive baselineapproaches.
Revised: datasets show that the proposed model effectively addresses the few-shot 3D point cloudobject detection problem, yielding significant improvement over several competitive baselineapproaches.

Original:  Related Work
Revised:  Related Work

Original: 3D Point Cloud Object Detection.
Revised: 3D Point Cloud Object Detection.

Original: Current 3D point cloud object detection approaches can bedivided into two streams: Grid Projection/Voxelization based [46, 15, 35, 4, 51, 40] and pointbased [30, 22, 18, 8, 2].
Revised: Current 3D point cloud object detection approaches can bedivided into two streams: Grid Projection/Voxelization based [46, 15, 35, 4, 51, 40] and pointbased [30, 22, 18, 8, 2].

Original: The former projects point cloud to 2D grids or 3D voxels so that the
Revised: The former projects point cloud to 2D grids or 3D voxels so that the

Original: [Figure]
Revised: [Figure]

Original: advanced convolutional networks can be directly applied.
Revised: advanced convolutional networks can be directly applied.

Original: The latter methods take the raw point cloudfeature extraction network such as PointNet++ [25] to generate point-wise features for the subsequentdetection.
Revised: The latter methods take the raw point cloudfeature extraction network such as PointNet++ [25] to generate point-wise features for the subsequentdetection.

Original: Although these fully supervised approaches achieved promising 3D detection performance,their requirement for large amounts of training data precludes their application in many real-worldscenarios where training data is costly or hard to acquire.
Revised: Although these fully supervised approaches achieved promising 3D detection performance,their requirement for large amounts of training data precludes their application in many real-worldscenarios where training data is costly or hard to acquire.

Original: To alleviate this limitation, we explore thedirection of few-shot 3D object detection.
Revised: To alleviate this limitation, we explore thedirection of few-shot 3D object detection.

Original: In this paper, we choose VoteNet [24] as the backbonearchitecture because of its simplicity and comparable performance in 3D object detection.
Revised: In this paper, we choose VoteNet [24] as the backbonearchitecture because of its simplicity and comparable performance in 3D object detection.

Original: Few-Shot Recognition.
Revised: Few-Shot Recognition.

Original: Few-shot recognition aims to classify novel instances with abundant basesamples and a few novel samples.
Revised: Few-shot recognition aims to classify novel instances with abundant basesamples and a few novel samples.

Original: Simple pre-training and finetuning approaches first train themodel on the base classes, then finetune the model on the novel categories [3, 7] Meta-learningbased methods [9, 16, 11, 38, 32] are proposed to learn classifier across tasks and then transfer to thefew-shot classification task.
Revised: Simple pre-training and finetuning approaches first train themodel on the base classes, then finetune the model on the novel categories [3, 7] Meta-learningbased methods [9, 16, 11, 38, 32] are proposed to learn classifier across tasks and then transfer to thefew-shot classification task.

Original: The most related work is Prototypical Network [32], which represents aclass as one prototype so that classification can be performed by computing distances to the prototyperepresentation of each class.
Revised: The most related work is Prototypical Network [32], which represents aclass as one prototype so that classification can be performed by computing distances to the prototyperepresentation of each class.

Original: The above works mainly focus on 2D image understanding.
Revised: The above works mainly focus on 2D image understanding.

Original: Recently,some few-shot learning approaches for point cloud understanding [29, 50, 47] are proposed.
Revised: Recently,some few-shot learning approaches for point cloud understanding [29, 50, 47] are proposed.

Original: Forinstance, Sharma et al.
Revised: Forinstance, Sharma et al.

Original: [50] propose a graph-based method to propagate the knowledge from few-shotsamples to the input point cloud.
Revised: [50] propose a graph-based method to propagate the knowledge from few-shotsamples to the input point cloud.

Original: However, there is no work studying few-shot 3D point cloud objectdetection.
Revised: However, there is no work studying few-shot 3D point cloud objectdetection.

Original: In this paper, we first study this problem and introduce the spirit of Prototypical Networkinto few-shot 3D object detection with 3D geometric prototypes and 3D class-specific prototypes.2D Few-shot Object Detection.
Revised: In this paper, we first study this problem and introduce the spirit of Prototypical Networkinto few-shot 3D object detection with 3D geometric prototypes and 3D class-specific prototypes.2D Few-shot Object Detection.

Original: Most existing 2D few-shot detectors employ a meta-learning [41,14, 45] or fine-tuning based mechanism [44, 43, 26, 36].
Revised: Most existing 2D few-shot detectors employ a meta-learning [41,14, 45] or fine-tuning based mechanism [44, 43, 26, 36].

Original: Particularly, Kang et al.
Revised: Particularly, Kang et al.

Original: [14] propose aone-stage few-shot detector which contains a meta feature learner and a feature re-weighting module.
Revised: [14] propose aone-stage few-shot detector which contains a meta feature learner and a feature re-weighting module.

Original: Meta R-CNN [45] presents meta-learning over RoI (Region-of-Interest) features and incorporates itinto Faster R-CNN [28] and Mask R-CNN [11].
Revised: Meta R-CNN [45] presents meta-learning over RoI (Region-of-Interest) features and incorporates itinto Faster R-CNN [28] and Mask R-CNN [11].

Original: TFA [39] reveals that simply fine-tuning the boxclassifier and regressor outperforms many meta-learning based methods.
Revised: TFA [39] reveals that simply fine-tuning the boxclassifier and regressor outperforms many meta-learning based methods.

Original: Cao et al.
Revised: Cao et al.

Original: [1] improve thefew-shot detection performance by associating each novel class with a well-trained base class basedon their semantic similarity.
Revised: [1] improve thefew-shot detection performance by associating each novel class with a well-trained base class basedon their semantic similarity.

Original:  Our Approach
Revised:  Our Approach

Original: In few-shot 3D point cloud object detection (FSOD), the object class set C is split into C base and C novelsuch that C = C base ∪ C novel and C base ∩ C novel = ∅ .
Revised: In few-shot 3D point cloud object detection (FS3D), the object class set C is split into C base and C novelsuch that C = C base ∪ C novel and C base ∩ C novel = ∅ .

Original: For each class r ∈ C , its annotation dataset T rcontains all the data samples with object bounding boxes, that is T r
Revised: For each class r ∈ C , its annotation dataset T rcontains all the data samples with object bounding boxes, that is T r

Original: = { ( u, P ) | u ∈ R 6 , P ∈ R N × 3 } .
Revised: = { ( u, P ) | u ∈ R 6 , P ∈ R N × 3 } .

Original: Here, ( u, P ) is a 3D object bounding box u
Revised: Here, ( u, P ) is a 3D object bounding box u

Original: = ( x, y, z, h, w, l ) , representing box center locations andbox dimensions, in a point cloud scene P .
Revised: = ( x, y, z, h, w, l ) , representing box center locations andbox dimensions, in a point cloud scene P .

Original: There are only a few examples/shots for each novel class r ∈ C novel , which are known as supportsamples.
Revised: There are only a few examples/shots for each novel class r ∈ C novel , which are known as supportsamples.

Original: Besides, there are plenty of annotated samples for each base class r ∈ C base .
Revised: Besides, there are plenty of annotated samples for each base class r ∈ C base .

Original: Given theabove dataset, FSOD aims to train a model to detect object instances in the novel classes leveragingsuch sufficient annotations for base categories C base and limited annotations for novel categories 113 C novel .
Revised: Given theabove dataset, FS3D aims to train a model to detect object instances in the novel classes leveragingsuch sufficient annotations for base categories C base and limited annotations for novel categories 113 C novel .

Original: In the following, we introduce Prototypical VoteNet for few-shot 3D object detection.
Revised: In the following, we introduce Prototypical VoteNet for few-shot 3D object detection.

Original: We willfirstly describe VoteNet [24] in Section 3.1 which is the backbone architecture we adopt for 3Dobject detection.
Revised: We willfirstly describe VoteNet [24] in Section 3.1 which is the backbone architecture we adopt for 3Dobject detection.

Original: Then, we present Prototypical VoteNet consisting of Prototypical Vote Module(Section 3.2.1) and Prototypical Head Module (Section 3.2.2) to enhance feature learning for few-shot3D object detection.
Revised: Then, we present Prototypical VoteNet consisting of Prototypical Vote Module(Section 3.2.1) and Prototypical Head Module (Section 3.2.2) to enhance feature learning for few-shot3D object detection.

Original: 3.1 VoteNet Preliminaries
Revised: 3.1 VoteNet Preliminaries

Original: Taking a point cloud scene P i as input,VoteNet [24] is to localize and categorize 3D objects.
Revised: VoteNet [24] takes a point cloud scene P i as input, and localizes and categorizes 3D objects.

Original: Asshown in Figure 2, where the orange rectangles represent the original VoteNet modules, it firstlyincorporates a 3D backbone network ( i.e. PointNet++ [25]) parameterized by θ 1 with downsamplinglayers for point feature extraction as Equation (1).
Revised: Asshown in Figure 2, where the orange rectangles represent the original VoteNet modules, it firstlyincorporates a 3D backbone network ( i.e. PointNet++ [25]) parameterized by θ 1 with downsamplinglayers for point feature extraction as Equation (1).

Original: [Equation]
Revised: [Equation]

Original: where N and M represent the original and subsampled number of points, respectively, P i ∈ R N × 3represents an input point cloud scene i , and F i ∈ R M × (3+ d ) is the subsampled scene points (alsocalled seeds) with d -dimensional features and 3 -dimensional location coordinates.
Revised: where N and M represent the original and subsampled number of points, respectively, P i ∈ R N × 3represents an input point cloud scene i , and F i ∈ R M × (3+ d ) is the subsampled scene points (alsocalled seeds) with d -dimensional features and 3 -dimensional location coordinates.

Original: Then, F i is fed into a multi-layer perceptron (MLP) with parameters θ 2 (See Figure 2: VoteMLP)which outputs a 3 -dimensional coordinate offset ∆ d j = (∆ x j , ∆ y j , ∆ z j ) relative to its corresponding object center c = ( c x , c y , c z ) and a residual feature vector ∆ f j for each point j in F i = { f j } i asin Equation (2).
Revised: Then, F i is fed into a multi-layer perceptron (MLP) with parameters θ 2 (See Figure 2: VoteMLP)which outputs a 3 -dimensional coordinate offset ∆ d j = (∆ x j , ∆ y j , ∆ z j ) relative to its corresponding object center c = ( c x , c y , c z ) and a residual feature vector ∆ f j for each point j in F i = { f j } i asin Equation (2).

Original: { ∆
Revised: { ∆

Original: [Equation]
Revised: [Equation]

Original: Given the predicted offset ∆ d j , the estimated corresponding object center c j = ( c x j , c y j , c z j ) thatpoint j belongs to can be calculated as Equation (3).
Revised: Given the predicted offset ∆ d j , the estimated corresponding object center c j = ( c x j , c y j , c z j ) thatpoint j belongs to can be calculated as Equation (3).

Original: [Equation]
Revised: [Equation]

Original: Similarly, the point features are updated as F i ← F i + ∆ F i where ∆ F i = { ∆ f j } i .
Revised: Similarly, the point features are updated as F i ← F i + ∆ F i where ∆ F i = { ∆ f j } i .

Original: Next, VoteNet samples object centers from { ( c x j , c y j , c z j ) } i using farthest point sampling and grouppoints with nearby centers together (see Figure 2: Sampling & grouping) to form a set of objectproposals
Revised: Next, VoteNet samples object centers from { ( c x j , c y j , c z j ) } i using farthest point sampling and grouppoints with nearby centers together (see Figure 2: Sampling & grouping) to form a set of objectproposals

Original: O i =
Revised: O i =

Original: { o t } i .
Revised: { o t } i .

Original: Each object proposal is characterized by a feature vector f o t which is obtainedby applying a max pooling operation on features of all points belonging to o t .
Revised: Each object proposal is characterized by a feature vector f o t which is obtainedby applying a max pooling operation on features of all points belonging to o t .

Original: Further, equipped with object features { f o t } i , the prediction layer with parameters θ 3 (see Figure 2:
Revised: Further, equipped with object features { f o t } i , the prediction layer with parameters θ 3 (see Figure 2:

Original: Prediction Layer) is adopted to yield the bounding boxes b t , objectiveness scores s t , and classificationlogits r t for each object proposal o t following Equation (4).
Revised: Prediction Layer) is adopted to yield the bounding boxes b t , objectiveness scores s t , and classificationlogits r t for each object proposal o t following Equation (4).

Original: [Equation]
Revised: [Equation]

Original: 3.2 Prototypical VoteNet
Revised: 3.2 Prototypical VoteNet

Original: Here, we present Prototypical VoteNet which incorporates two new designs – Prototypical Vote
Revised: Here, we present Prototypical VoteNet which incorporates two new designs – Prototypical Vote

Original: Module (PVM) and Prototypical Head Module (PHM) to improve feature learning for novel categorieswith few annotated samples (see Figure 2).
Revised: Module (PVM) and Prototypical Head Module (PHM) to improve feature learning for novel categorieswith few annotated samples (see Figure 2).

Original: Specifically, PVM builds a class-agnostic memory bankof geometric prototypes G = { g k } Kk =1 with a size of K , which models transferable class-agnostic3D primitives learned from rich base categories, and further employs them to enhance local featurerepresentation for novel categories via a multi-head cross-attention module.
Revised: Specifically, PVM builds a class-agnostic memory bankof geometric prototypes G = { g k } Kk =1 with a size of K , which models transferable class-agnostic3D primitives learned from rich base categories, and further employs them to enhance local featurerepresentation for novel categories via a multi-head cross-attention module.

Original: The enhanced features arethen utilized by the Vote MLP module to output the offset of coordinates and features as Equation (2).
Revised: The enhanced features arethen utilized by the Vote MLP module to output the offset of coordinates and features as Equation (2).

Original: Second, to facilitate learning discriminative features for novel class prediction, PHM employs anattention-based design to leverage class-specific prototypes E = { e r } Rr =1 extracted from the supportset D support with R categories to refine global discriminate feature for representing each objectproposal (see Figure 2).
Revised: Second, to facilitate learning discriminative features for novel class prediction, PHM employs anattention-based design to leverage class-specific prototypes E = { e r } Rr =1 extracted from the supportset D support with R categories to refine global discriminate feature for representing each objectproposal (see Figure 2).

Original: The output features are fed to the prediction layer for producing results as
Revised: The output features are fed to the prediction layer for producing results as

Original: Equation (4).
Revised: Equation (4).

Original: To make the model more generalizable to novel classes, we exploit a meta-learningstrategy, episodic training [32, 38], to train PHM, where a distribution of similar few-shot tasks insteadof only one object detection task is learned in the training phase.
Revised: To make the model more generalizable to novel classes, we exploit a meta-learningstrategy, episodic training [32, 38], to train PHM, where a distribution of similar few-shot tasks insteadof only one object detection task is learned in the training phase.

Original: PVM and PHM are elaborated inthe following sections.
Revised: PVM and PHM are elaborated inthe following sections.

Original: 3.2.1 Prototypical Vote Module
Revised: 3.2.1 Prototypical Vote Module

Original: Given input features F i extracted by a backbone network, Prototypical Vote Module constructsclass-agnostic geometric prototypes G = { g k } Kk =1 and then uses them to enhance local point featureswith an attention module.
Revised: Given input features F i extracted by a backbone network, Prototypical Vote Module constructsclass-agnostic geometric prototypes G = { g k } Kk =1 and then uses them to enhance local point featureswith an attention module.

Original: Geometric Prototype Construction.
Revised: Geometric Prototype Construction.

Original: At the beginning, G = { g k } Kk =1 ∈ R d × K is randomlyinitialized.
Revised: At the beginning, G = { g k } Kk =1 ∈ R d × K is randomlyinitialized.

Original: During training, G is iteratively updated with a momentum based on point features offoreground objects.
Revised: During training, G is iteratively updated with a momentum based on point features offoreground objects.

Original: Specifically, for each update, given G = { g k } Kk =1 and all the foreground points{ p m } M f m =1 with features { f m } M f m =1 , where M f is the number of foreground points in the current batch,we assign each point to its nearest geometric prototype based on feature space distance.
Revised: Specifically, for each update, given G = { g k } Kk =1 and all the foreground points{ p m } M f m =1 with features { f m } M f m =1 , where M f is the number of foreground points in the current batch,we assign each point to its nearest geometric prototype based on feature space distance.

Original: Then, foreach prototype g k , we have a group of points { p m } k with features represented as { f m } k assignedto it.
Revised: Then, foreach prototype g k , we have a group of points { p m } k with features represented as { f m } k assignedto it.

Original: Point features in one group are averaged to update the corresponding geometric prototype as
Revised: Point features in one group are averaged to update the corresponding geometric prototype as

Original: Equation (5)
Revised: Equation (5)

Original: [Equation]
Revised: [Equation]

Original: f
Revised: f

Original: [Equation]
Revised: [Equation]

Original: average
Revised: average

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: [Equation]
Revised: [Equation]

Original: Here γ ∈ [0 , 1] is the momentum coefficient for updating geometric prototypes in a momentummanner, serving as a moving average over all training samples.
Revised: Here γ ∈ [0 , 1] is the momentum coefficient for updating geometric prototypes in a momentummanner, serving as a moving average over all training samples.

Original: Since one point feature is related toone geometric prototype, we call this one-hot assignment strategy as hard assignment.
Revised: Since one point feature is related toone geometric prototype, we call this one-hot assignment strategy as hard assignment.

Original: An alternativeto the hard assignment is the soft assignment, which calculates the similarity between a point featureswith all geometric prototypes.
Revised: An alternativeto the hard assignment is the soft assignment, which calculates the similarity between a point featureswith all geometric prototypes.

Original: Empirically, we found that hard assignment results in more effectivegrouping versus soft assignment.
Revised: Empirically, we found that hard assignment results in more effectivegrouping versus soft assignment.

Original: More details can be found in the supplementary file.
Revised: More details can be found in the supplementary file.

Original: Geometric Prototypical Guided Local Feature Enrichment.
Revised: Geometric Prototypical Guided Local Feature Enrichment.

Original: Given the geometric prototypes
Revised: Given the geometric prototypes

Original: G = { g k } Kk =1 and point features F i = { f j } i of a scene i , PVM further employs a multi-headcross-attention module [37] to refine the point features.
Revised: G = { g k } Kk =1 and point features F i = { f j } i of a scene i , PVM further employs a multi-headcross-attention module [37] to refine the point features.

Original: Specifically, the multi-head attention networkuses the point features F i = { f j } i as query, geometric prototypes G = { g k } Kk =1 as key and valuewhere linear transformation functions with weights represented as Q h , U h , V h are applied to encodequery, key and value respectively.
Revised: Specifically, the multi-head attention networkuses the point features F i = { f j } i as query, geometric prototypes G = { g k } Kk =1 as key and valuewhere linear transformation functions with weights represented as Q h , U h , V h are applied to encodequery, key and value respectively.

Original: Here, h represents the head index.
Revised: Here, h represents the head index.

Original: Then, for each head h , the querypoint feature is updated by softly aggregating the value features where the soft aggregation weight isdetermined by the similarity between the query point feature and corresponding key feature.
Revised: Then, for each head h , the querypoint feature is updated by softly aggregating the value features where the soft aggregation weight isdetermined by the similarity between the query point feature and corresponding key feature.

Original: Thefinal point feature f
Revised: Thefinal point feature f

Original: j is updated by summing over outputs from all heads as Equation (6).f j ← Cross_Att
Revised: j is updated by summing over outputs from all heads as Equation (6).f j ← Cross_Att

Original: [Equation]
Revised: [Equation]

Original: f j ,
Revised: f j ,

Original: [Equation] [Equation]
Revised: [Equation] [Equation]

Original: A hj,k
Revised: A hj,k

Original: [Equation]
Revised: [Equation]

Original: Here, A h j,k is the soft aggregation weight considering the similarity between the j -th query pointfeature and the k -th key feature and used to weight the k -th value feature.
Revised: Here, A h j,k is the soft aggregation weight considering the similarity between the j -th query pointfeature and the k -th key feature and used to weight the k -th value feature.

Original: Through this process, thepoint feature is refined using geometric prototypes in a weighted manner where prototypes similarto the query point feature will have higher attention weights.
Revised: Through this process, thepoint feature is refined using geometric prototypes in a weighted manner where prototypes similarto the query point feature will have higher attention weights.

Original: This mechanism transfers geometricprototypes learned from base categories with abundant data to model novel points.
Revised: This mechanism transfers geometricprototypes learned from base categories with abundant data to model novel points.

Original: The multi-headdesign enables the model to seek similarity measurements from different angles in a learnable manner
Revised: The multi-headdesign enables the model to seek similarity measurements from different angles in a learnable manner

Original: [Figure]
Revised: [Figure]

Original: to improve robustness.
Revised: to improve robustness.

Original: The remaining step of PVM is to feed the refined point features { f j } i to Vote
Revised: The remaining step of PVM is to feed the refined point features { f j } i to Vote

Original: MLP, which is a multi-layer perceptron, for predicting { ∆ d j , ∆ F j } i as stated in Equation (2).
Revised: MLP, which is a multi-layer perceptron, for predicting { ∆ d j , ∆ F j } i as stated in Equation (2).

Original: Inaddition, ∆ d j is explicitly supervised by a regression loss L vote used in [25].
Revised: Inaddition, ∆ d j is explicitly supervised by a regression loss L vote used in [25].

Original: What do the geometric prototypes store?
Revised: What do the geometric prototypes store?

Original: To shed more insights on what the geometric prototypesrepresent, we visualize the frequency of geometry prototypes in different categories using the“assignment”.
Revised: To shed more insights on what the geometric prototypesrepresent, we visualize the frequency of geometry prototypes in different categories using the“assignment”.

Original: The number of “assignment”s of object point features to the geometric prototypes isshown in Figure 3, where a point is assigned to the geometric prototype with the highest similarity.
Revised: The number of “assignment”s of object point features to the geometric prototypes isshown in Figure 3, where a point is assigned to the geometric prototype with the highest similarity.

Original: In each histogram, the horizontal axis represents the index of geometric prototypes and the verticalaxis represents the number of assignments.
Revised: In each histogram, the horizontal axis represents the index of geometric prototypes and the verticalaxis represents the number of assignments.

Original: Note that the first row is the novel classes and the secondrow is the base classes, which are visually similar to the novel classes in the first row correspondingly.
Revised: Note that the first row is the novel classes and the secondrow is the base classes, which are visually similar to the novel classes in the first row correspondingly.

Original: Figure 3 shows that two visually similar categories have a similar assignment histogram since theyshare the basic geometric structures.
Revised: Figure 3 shows that two visually similar categories have a similar assignment histogram since theyshare the basic geometric structures.

Original: This indicates that the memory bank of geometric prototypessuccessfully learns the 3D basic geometric knowledge, which can be a bridge to transfer the geometricknowledge from base classes to novel ones.
Revised: This indicates that the memory bank of geometric prototypessuccessfully learns the 3D basic geometric knowledge, which can be a bridge to transfer the geometricknowledge from base classes to novel ones.

Original: 3.2.2 Prototypical Head Module
Revised: 3.2.2 Prototypical Head Module

Original: As shown in Figure 2, given object proposals O i =
Revised: As shown in Figure 2, given object proposals O i =

Original: { o t } i with features { f o t } i from Sampling &
Revised: { o t } i with features { f o t } i from Sampling &

Original: Grouping module, PHM module leverages class-specific prototypes { e r } to refine the object featuresf o t for the subsequent object classification and localization.
Revised: Grouping module, PHM module leverages class-specific prototypes { e r } to refine the object featuresf o t for the subsequent object classification and localization.

Original: Moreover, for better generalizing tonovel categories, PHM is trained by the episodic training scheme, where PHM learns a large numberof similar few-shot tasks instead of only one task.
Revised: Moreover, for better generalizing tonovel categories, PHM is trained by the episodic training scheme, where PHM learns a large numberof similar few-shot tasks instead of only one task.

Original: Considering the function of PHM, we constructthe few-shot tasks that, in each iteration, PHM refines the object features { f o t } i with the aid ofclass-specific prototypes, which are extracted from randomly sampled support samples.
Revised: Considering the function of PHM, we constructthe few-shot tasks that, in each iteration, PHM refines the object features { f o t } i with the aid ofclass-specific prototypes, which are extracted from randomly sampled support samples.

Original: The detailsare given as follows.
Revised: The detailsare given as follows.

Original: In each few-shot task, class-specific prototypes are built based on support sets that are randomlysampled.
Revised: In each few-shot task, class-specific prototypes are built based on support sets that are randomlysampled.

Original: For class r , the class-specific prototype e r is obtained by averaging the instance features forall support samples in class r .
Revised: For class r , the class-specific prototype e r is obtained by averaging the instance features forall support samples in class r .

Original: The instance feature is derived by applying a max pooling operationover the features of all points belonging to that instance.
Revised: The instance feature is derived by applying a max pooling operationover the features of all points belonging to that instance.

Original: As shown in Figure 2, with class-specificprototypes E = { e r } Rr =1 for a total of R categories, PHM further employs a multi-head cross-attentionmodule to refine object features { f o t } i .
Revised: As shown in Figure 2, with class-specificprototypes E = { e r } Rr =1 for a total of R categories, PHM further employs a multi-head cross-attentionmodule to refine object features { f o t } i .

Original: Here, the object features { f o t } i serve as the query features,class-specific prototypes are used to build value features and key features similar as what has beendescribed in Section 3.2.1.
Revised: Here, the object features { f o t } i serve as the query features,class-specific prototypes are used to build value features and key features similar as what has beendescribed in Section 3.2.1.

Original: Then, the representation f o t of each proposal o t is refined using theoutputs of the multi-head attention module, which are weighted sum over the value features and theweight is proportionally to the similarity between the query feature and corresponding key features
Revised: Then, the representation f o t of each proposal o t is refined using theoutputs of the multi-head attention module, which are weighted sum over the value features and theweight is proportionally to the similarity between the query feature and corresponding key features

Original: This process can be formulated as Equation (7), which is similar to Equation (6).
Revised: This process can be formulated as Equation (7), which is similar to Equation (6).

Original: [Equation]
Revised: [Equation]

Original: Till now, the f o t is refined using class-specific prototypes, injecting class-specific features from givensupport samples into object-level features.
Revised: Until now, the f o t is refined using class-specific prototypes, injecting class-specific features fromgiven support samples into object-level features.

Original: Finally, the refined object features { f o t } i are fed into theprediction module to output the objectness score s t , bounding box locations b t , and classificationscores r t following Equation (4).
Revised: Finally, the refined object features { f o t } i are fed intothe prediction module to output the objectness score s t , bounding box locations b t , and classificationscores r t following Equation (4).

Original: What does PHM help?
Revised: What does PHM help?

Original: Figure 4 visualizes the effect of feature refinement by PHM.
Revised: Figure 4 visualizes the effect of feature refinement by PHM.

Original: The experimentis conducted on FS-ScanNet.
Revised: The experimentis conducted on FS-ScanNet.

Original: Figure 4(a) shows the object features, which have not been processedby PHM.
Revised: Figure 4(a) shows the object features, which have not been processedby PHM.

Original: Figure 4(b) shows the object features processed by PHM.
Revised: Figure 4(b) shows the object features processed by PHM.

Original: We could observe that, afterthe feature refinement by PHM, the features of each classes become more compact compared to thenon-PHM one, which further validates the effectiveness of PHM.
Revised: We could observe that, afterthe feature refinement by PHM, the features of each classes become more compact compared to thenon-PHM one, which further validates the effectiveness of PHM.

Original: 3.3 Model Training
Revised: 3.3 Model Training

Original: The model is trained by a meta-learning strategy, episodic training [32, 38].
Revised: The model is trained by a meta-learning strategy, episodic training [32, 38].

Original: The detailed trainingstrategy is included in the supplementary file.
Revised: The detailed trainingstrategy is included in the supplementary file.

Original: We use the standard cross entropy loss L cls and smoothL 1 loss [28] L reg to supervise the classification and the bounding box regression, respectively.
Revised: We use the standard cross entropy loss L cls and smoothL 1 loss [28] L reg to supervise the classification and the bounding box regression, respectively.

Original: As forthe objectness prediction, if a vote is located either within 0.3 meters to a ground truth object centeror more than 0.6 meters from any center, it is considered to be positive [24], which is supervised by across entropy loss L obj .
Revised: As forthe objectness prediction, if a vote is located either within 0.3 meters to a ground truth object centeror more than 0.6 meters from any center, it is considered to be positive [24], which is supervised by across entropy loss L obj .

Original: Note that only the positive proposals need to be supervised by L cls and L reg .
Revised: Note that only the positive proposals need to be supervised by L cls and L reg .

Original: Therefore, the overall loss for Prototypical VoteNet is given by,
Revised: Therefore, the overall loss for Prototypical VoteNet is given by,

Original: [Equation]
Revised: [Equation]

Original: where α 1 , α 2 , α 3 is the coefficients to balance the loss contributions.
Revised: where α 1 , α 2 , α 3 is the coefficients to balance the loss contributions.

Original:  Experiments
Revised:  Experiments

Original: To our best knowledge, there is no prior study of few-shot point cloud object detection.
Revised: To our best knowledge, there is no prior study of few-shot point cloud object detection.

Original: Therefore,we setup a new benchmark which is described in Section 4.1 & 4.2.
Revised: Therefore,we setup a new benchmark which is described in Section 4.1 & 4.2.

Original: Then, we conduct experimentsand compare our method with baseline methods in Section 4.3.
Revised: Then, we conduct experimentsand compare our method with baseline methods in Section 4.3.

Original: Third, a series of ablation studies areperformed for further analyzing Prototypical VoteNet in Section 4.4.
Revised: Third, a series of ablation studies areperformed for further analyzing Prototypical VoteNet in Section 4.4.

Original: In addition, the implementationdetails are included in the supplementary file.
Revised: In addition, the implementationdetails are included in the supplementary file.

Original: 4.1 Benchmark Setup
Revised: 4.1 Benchmark Setup

Original: Datasets.
Revised: Datasets.

Original: We construct two new benchmark datasets FS-SUNRGBD and FS-ScanNet.
Revised: We construct two new benchmark datasets FS-SUNRGBD and FS-ScanNet.

Original: Specifically,
Revised: Specifically,

Original: FS-SUNRGBD is derived from SUNRGBD [34].
Revised: FS-SUNRGBD is derived from SUNRGBD [34].

Original: SUNRGBD consists of 5K RGB-D trainingimages annotated, and the standard evaluation protocol reports performance on 10 categories.
Revised: SUNRGBD consists of 5K RGB-D trainingimages annotated, and the standard evaluation protocol reports performance on 10 categories.

Original: Werandomly select 4 classes as the novel ones while keeping the remaining ones as the base.
Revised: Werandomly select 4 classes as the novel ones while keeping the remaining ones as the base.

Original: In thetraining set, only K annotated bounding boxes for each novel class are given, where k equals 1, 2,3, 4 and 5.
Revised: In thetraining set, only K annotated bounding boxes for each novel class are given, where k equals 1, 2,3, 4 and 5.

Original: FS-ScanNet is derived from ScanNet [5].
Revised: FS-ScanNet is derived from ScanNet [5].

Original: ScanNet consists of 1,513 point clouds, andthe annotation of the point clouds corresponds to 18 semantic classes plus one for the unannotatedspace.
Revised: ScanNet consists of 1,513 point clouds, andthe annotation of the point clouds corresponds to 18 semantic classes plus one for the unannotatedspace.

Original: Out of its 18 object categories, we randomly select 6 classes as the novel ones, while keepingthe remaining as the base.
Revised: Out of its 18 object categories, we randomly select 6 classes as the novel ones, while keepingthe remaining as the base.

Original: We evaluate with 2 different base/novel splits.
Revised: We evaluate with 2 different base/novel splits.

Original: In the training set, only Kannotated bounding boxes for each novel class are given, where k equals 1, 3 and 5.
Revised: In the training set, only Kannotated bounding boxes for each novel class are given, where k equals 1, 3 and 5.

Original: More detailsabout the new benchmark datasets can be referred to the supplementary file.
Revised: More detailsabout the new benchmark datasets can be referred to the supplementary file.

Original: Evaluation Metrics.
Revised: Evaluation Metrics.

Original: We follow the standard evaluation protocol [24] in 3D point cloud objectdetection by using mean Average Precision(mAP) under different IoU thresholds ( i.e. 0.25, 0.50),denoted as AP 25 and AP 50 .
Revised: We follow the standard evaluation protocol [24] in 3D point cloud objectdetection by using mean Average Precision(mAP) under different IoU thresholds ( i.e. 0.25, 0.50),denoted as AP 25 and AP 50 .

Original: In addition, in the inference stage, we detect both novel classes and baseclasses.
Revised: In addition, in the inference stage, we detect both novel classes and baseclasses.

Original: The performance on base classes is included in the supplementary file.
Revised: The performance on base classes is included in the supplementary file.

Original: 4.2 Benchmark Few-shot 3D Object Detection
Revised: 4.2 Benchmark Few-shot 3D Object Detection

Original: We build the first benchmark for few-shot 3D object detection.
Revised: We build the first benchmark for few-shot 3D object detection.

Original: The benchmark incorporates 4competitive baselines, and three of them are built upon the vanilla VoteNet detector with few-shotlearning strategies, which have been shown to be successful in 2D few-shot object detection.
Revised: The benchmark incorporates 4competitive baselines, and three of them are built upon the vanilla VoteNet detector with few-shotlearning strategies, which have been shown to be successful in 2D few-shot object detection.

Original: • VoteNet-joint: The first one is to train the detector on the base and novel classes together.
Revised: • VoteNet-joint: The first one is to train the detector on the base and novel classes together.

Original: In
Revised: In

Original: this way, it can learn good features from the
Revised: this way, it can learn good features from the

Original: base
Revised: base

Original: classes that are applicable for detecting
Revised: classes that are applicable for detecting

Original: novel classes.
Revised: novel classes.

Original: We term this baseline as VoteNet-joint.
Revised: We term this baseline as VoteNet-joint.

Original: • VoteNet+TFA [39]: We incorporate a well-designed few-shot object detection method
Revised: • VoteNet+TFA [39]: We incorporate a well-designed few-shot object detection method

Original: TFA [39] with VoteNet.
Revised: TFA [39] with VoteNet.

Original: TFA first trains VoteNet on the training set with abundant samplesof base classes.
Revised: TFA first trains VoteNet on the training set with abundant samplesof base classes.

Original: Then only the
Revised: Then only the

Original: classifier and the regressor are finetuned with a small balanceset containing both base classes and novel classes.
Revised: classifier and the regressor are finetuned with a small balanceset containing both base classes and novel classes.

Original: [Table]
Revised: [Table]

Original: [Table]
Revised: [Table]

Original: • VoteNet+PT+TFA: The pretraining is proven to be important in 2D few-shot learning, asit learns more generic features, facilitating knowledge transfer from base classes to novelclasses.
Revised: • VoteNet+PT+TFA: The pretraining is proven to be important in 2D few-shot learning, asit learns more generic features, facilitating knowledge transfer from base classes to novelclasses.

Original: Therefore, we add a pretraining stage, which is borrowed from a self-supervisedpoint cloud contrastive learning method [13], before the training stage of VoteNet+TFA.
Revised: Therefore, we add a pretraining stage, which is borrowed from a self-supervisedpoint cloud contrastive learning method [13], before the training stage of VoteNet+TFA.

Original: • Meta VoteNet: Meta VoteNet is inspired by Meta RCNN [45].
Revised: • Meta VoteNet: Meta VoteNet is inspired by Meta RCNN [45].

Original: Meta RCNN develops ameta-learning prediction head, which leverages class prototypes to multiply the RoI featuresand then predicts if a RoI feature is the category that the class prototype belongs to.
Revised: Meta RCNN develops ameta-learning prediction head, which leverages class prototypes to multiply the RoI featuresand then predicts if a RoI feature is the category that the class prototype belongs to.

Original: InVoteNet, the grouped point features after the vote stage can be treated as the RoI features.
Revised: InVoteNet, the grouped point features after the vote stage can be treated as the RoI features.

Original: Therefore, We implement a meta-learning prediction head on VoteNet as a strong baseline.
Revised: Therefore, We implement a meta-learning prediction head on VoteNet as a strong baseline.

Original: 4.3 Main Results
Revised: 4.3 Main Results

Original: FS-ScanNet.
Revised: FS-ScanNet.

Original: We first compare the proposed approach with the baselines on FS-ScanNet in Table 1.
Revised: We first compare the proposed approach with the baselines on FS-ScanNet in Table 1.

Original: These results show: 1) VoteNet+TFA has the worst performance.
Revised: These results show: 1) VoteNet+TFA has the worst performance.

Original: For example, it only achieves 8.07%
Revised: For example, it only achieves 8.07%

Original: AP 25 and 1.03% AP 50 on 3-shot in Novel Split-1.
Revised: AP 25 and 1.03% AP 50 on 3-shot in Novel Split-1.

Original: The reason is that VoteNet+TFA is trained fromscratch, and only a few layers are funetuned by novel samples.
Revised: The reason is that VoteNet+TFA is trained fromscratch, and only a few layers are funetuned by novel samples.

Original: Therefore, it tends to be overfittingwith poor generalization ability on novel classes.
Revised: Therefore, it tends to be overfittingwith poor generalization ability on novel classes.

Original: Even adding a pretaining stage to VoteNet+TFA,which is termed as VoteNet+PT+TFA, it still performs poorly.
Revised: Even adding a pretaining stage to VoteNet+TFA,which is termed as VoteNet+PT+TFA, it still performs poorly.

Original: For example, it only contributes theimprovement of +2.30% AP 25 and +1.10% AP 50 to VoteNet+TFA on 3-shot in Novel Split-1.
Revised: For example, it only contributes theimprovement of +2.30% AP 25 and +1.10% AP 50 to VoteNet+TFA on 3-shot in Novel Split-1.

Original: Thisis different from 2D few-shot object detection.
Revised: Thisis different from 2D few-shot object detection.

Original: This is potentially caused by the lack of large-scaledatasets for pre-training in 3D, while the 2D backbone ResNet [12] has a dataset of over one millionimages for pre-training.
Revised: This is potentially caused by the lack of large-scaledatasets for pre-training in 3D, while the 2D backbone ResNet [12] has a dataset of over one millionimages for pre-training.

Original: Besides our designed method, Meta VoteNet is superior.
Revised: Besides our designed method, Meta VoteNet is superior.

Original: For example,
Revised: For example,

Original: Meta VoteNet outperforms VoteNet+joint by +3.09% AP 25 and +1.95% AP 50 on 3-shot in Novel
Revised: Meta VoteNet outperforms VoteNet+joint by +3.09% AP 25 and +1.95% AP 50 on 3-shot in Novel

Original: Split-1.
Revised: Split-1.

Original: This shows the meta-learning prediction head, derived from Meta RCNN [45], helps togeneralize from base classes to novel classes.
Revised: This shows the meta-learning prediction head, derived from Meta RCNN [45], helps togeneralize from base classes to novel classes.

Original: Our new model Prototypical VoteNet surpasses allcompetitors by significant margins.
Revised: Our new model Prototypical VoteNet surpasses allcompetitors by significant margins.

Original: For example, Prototypical VoteNet achieves 32.25% AP 25 and16.01% AP 50 on 3-shot in Novel Split-1.
Revised: For example, Prototypical VoteNet achieves 32.25% AP 25 and16.01% AP 50 on 3-shot in Novel Split-1.

Original: This is because our proposed method is equipped witha more generic vote module by learning geometric prototypes, and leverage class prototypes andepisode learning scheme to promote the discriminative feature learning.
Revised: This is because our proposed method is equipped witha more generic vote module by learning geometric prototypes, and leverage class prototypes andepisode learning scheme to promote the discriminative feature learning.

Original: FS-SUNRGBD.
Revised: FS-SUNRGBD.

Original: Table 2 shows the result comparison on FS-SUNRGBD.
Revised: Table 2 shows the result comparison on FS-SUNRGBD.

Original: In FS-SUNRGBD, wefind that VoteNet+TFA and VoteNet+PT+TFA still have a large gap with other methods due tothe overfitting and insufficient pretraining.
Revised: In FS-SUNRGBD, wefind that VoteNet+TFA and VoteNet+PT+TFA still have a large gap with other methods due tothe overfitting and insufficient pretraining.

Original: Meanwhile, Meta VoteNet outperforms other baselinemethods.
Revised: Meanwhile, Meta VoteNet outperforms other baselinemethods.

Original: For example, on 3-shot, Meta VoteNet surpasses VoteNet+PT+TFA by +8.34% AP 25and
Revised: For example, on 3-shot, Meta VoteNet surpasses VoteNet+PT+TFA by +8.34% AP 25and

Original: +0.81%
Revised: +0.81%

Original: AP 50 and VoteNet+joint by +2.51% AP 25 and +0.92% AP 50 .
Revised: AP 50 and VoteNet+joint by +2.51% AP 25 and +0.92% AP 50 .

Original: Finally, our designed
Revised: Finally, our designed

Original: Prototypical VoteNet outperforms all the methods.
Revised: Prototypical VoteNet outperforms all the methods.

Original: For instance, on 3-shot, Prototypical VoteNet surpasses Meta VoteNet by +5.37% AP 25 and +3.01% AP 50 , which further validates the effectivenessof our proposed PVM and PHM.
Revised: For instance, on 3-shot, Prototypical VoteNet surpasses Meta VoteNet by +5.37% AP 25 and +3.01% AP 50 , which further validates the effectivenessof our proposed PVM and PHM.

Original: 4.4 Ablation Study
Revised: 4.4 Ablation Study

Original: [Table]
Revised: [Table]

Original: +PVM
Revised: +PVM

Original: [Table]
Revised: [Table]

Original: +
Revised: +

Original: [Table]
Revised: [Table]

Original: +PVM+PHM
Revised: +PVM+PHM

Original: [Table]
Revised: [Table]

Original: [Table]
Revised: [Table]

Original: Contributions of individual components.
Revised: Contributions of individual components.

Original: We conduct ablation studies on 3-shot and 5-shot insplit-1 of FS-ScanNet with results shown in Table 3.
Revised: We conduct ablation studies on 3-shot and 5-shot insplit-1 of FS-ScanNet with results shown in Table 3.

Original: The results show that both of them are effectiveon their own.
Revised: The results show that both of them are effectiveon their own.

Original: For example, on 3-shot, PVM contributes the improvement of +4.19% AP 25 and+4.59% AP 50 , and PHM contributes the improvement of +6.12% AP 25 and +5.00% AP 50 .
Revised: For example, on 3-shot, PVM contributes the improvement of +4.19% AP 25 and+4.59% AP 50 , and PHM contributes the improvement of +6.12% AP 25 and +5.00% AP 50 .

Original: Moreover,when combined, the best performance, 31.25% AP 25 and 16.01% AP 50 , is achieved.
Revised: Moreover,when combined, the best performance, 31.25% AP 25 and 16.01% AP 50 , is achieved.

Original: [Table]
Revised: [Table]

Original: =30.
Revised: =30.

Original: [Table]
Revised: [Table]

Original: K =
Revised: K =

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

Original: K =
Revised: K =

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

Original: K =31.
Revised: K =31.

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

Original: Effectiveness of Prototypes.
Revised: Effectiveness of Prototypes.

Original: Table 4 shows the effectiveness of two kinds of prototypes.
Revised: Table 4 shows the effectiveness of two kinds of prototypes.

Original: Specifically, in order to validate the geometric prototypes, we first displace them by the self-learning ones,which are randomly initialized and updated by the gradient descend during the model training.
Revised: Specifically, in order to validate the geometric prototypes, we first displace them by the self-learning ones,which are randomly initialized and updated by the gradient descend during the model training.

Original: Theexperiment results show that the performance significantly degrades.
Revised: Theexperiment results show that the performance significantly degrades.

Original: To validate the effectiveness ofclass prototypes, we also alter them by the randomly initialized self-learning prototypes.
Revised: To validate the effectiveness ofclass prototypes, we also alter them by the randomly initialized self-learning prototypes.

Original: Similarly,the performance drops drastically due to the lack of class prototypes.
Revised: Similarly,the performance drops drastically due to the lack of class prototypes.

Original: Size of Memory Bank.
Revised: Size of Memory Bank.

Original: Table 5 reports the performance of our approach with regard to the size ofthe memory bank containing the geometric prototypes.
Revised: Table 5 reports the performance of our approach with regard to the size ofthe memory bank containing the geometric prototypes.

Original: This ablation study is performed on 3-shot insplit-1 of FS-ScanNet.
Revised: This ablation study is performed on 3-shot insplit-1 of FS-ScanNet.

Original: For K = 30 , the memory bank only contains 30 geometric prototypes, whichonly achieves 29.98% AP 25 and 15.01% AP 50 .
Revised: For K = 30 , the memory bank only contains 30 geometric prototypes, whichonly achieves 29.98% AP 25 and 15.01% AP 50 .

Original: Further, when using more prototypes (i.e., K = 120 ),we see a clear performance improvement, which reaches 31.25% AP 25 and 16.01% AP 50 .
Revised: Further, when using more prototypes (i.e., K = 120 ),we see a clear performance improvement, which reaches 31.25% AP 25 and 16.01% AP 50 .

Original: However,when continuous increasing K, there will be no improvement.
Revised: However,when continuous increasing K, there will be no improvement.

Original: Coefficient γ .
Revised: Coefficient γ .

Original: Table 6 shows the effect of momentum coefficient ( γ in Equation (5)).
Revised: Table 6 shows the effect of momentum coefficient ( γ in Equation (5)).

Original: The experiment is performed on 3-shot in split 1 of FS-ScanNet.
Revised: The experiment is performed on 3-shot in split 1 of FS-ScanNet.

Original: The results show that, when using a relativelylarge coefficient ( i.e. , γ ∈ [0 . 999 , 0 . 9999] ), the model performs well, compared with the model usinga small momentum coefficient ( i.e. , γ ∈ [0 . 9 , 0 . 99] ).
Revised: The results show that, when using a relativelylarge coefficient ( i.e. , γ ∈ [0 . 999 , 0 . 9999] ), the model performs well, compared with the model usinga small momentum coefficient ( i.e. , γ ∈ [0 . 9 , 0 . 99] ).

Original: Moreover, the performance drops considerablyat the extreme case of γ = 0 .
Revised: Moreover, the performance drops considerablyat the extreme case of γ = 0 .

Original: This indicates that a slow updating process is beneficial.
Revised: This indicates that a slow updating process is beneficial.

Original:  Conclusion
Revised:  Conclusion

Original: We have presented Prototypical VoteNet for few-shot 3D object detection along with a new benchmarkfor evaluation.
Revised: We have presented Prototypical VoteNet for FS3D along with a new benchmark for evaluation.

Original: Prototypical VoteNet enjoys the advantages of two new designs, namely Prototypical Vote Module (PVM) and Prototypical Head Module (PHM), for enhancing feature learning in thefew-shot setting.
Revised: Prototypical VoteNet enjoys the advantages of two new designs, namely Prototypical Vote Module(PVM) and Prototypical Head Module (PHM), for enhancing feature learning in the few-shot setting.

Original: Specifically, PVM exploits geometric prototypes learned from base categories torefine local features of novel categories.
Revised: Specifically, PVM exploits geometric prototypes learned from base categories to refine local featuresof novel categories.

Original: PHM is proposed to utilize class-specific prototypes topromote discriminativeness of object-level features.
Revised: PHM is proposed to utilize class-specific prototypes to promote discriminativeness of object-level features.

Original: Extensive experiments on two new benchmarkdatasets demonstrate the superiority of our approach.
Revised: Extensive experiments on two new benchmark datasets demonstrate thesuperiority of our approach.

Original: We hope our studies on 3D propotypes andproposed new benchmark could inspire further investigations in 3D few-shot learning.
Revised: We hope our studies on 3D propotypes and proposed new benchmarkcould inspire further investigations in 3D few-shot learning.

Original: Checklist
Revised: Checklist

Original: The checklist follows the references.
Revised: The checklist follows the references.

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Revised: Please read the checklist guidelines carefully for information onhow to answer these questions.

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Revised: For each question, change the default [TODO] to [Yes] , [No] , or[N/A] .

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Revised: You are strongly encouraged to include a justification to your answer , either by referencingthe appropriate section of your paper or providing a brief inline description.

Original: For example:
Revised: For example:

Original: • Did you include the license to the code and datasets?
Revised: • Did you include the license to the code and datasets?

Original: [Yes] See Section ?? .•
Revised: [Yes] See Section ?? .•

Original: Did you include the license to the code and datasets?
Revised: Did you include the license to the code and datasets?

Original: [No] The code and the data areproprietary.•
Revised: [No] The code and the data areproprietary.•

Original: Did you include the license to the code and datasets?
Revised: Did you include the license to the code and datasets?

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

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Original: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s
Revised: (a) Do the main claims made in the abstract and introduction accurately reflect the paper’s

Original: contributions and scope?
Revised: contributions and scope?

Original: [Yes]
Revised: [Yes]

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

Original: [N/A]
Revised: [Yes] we have included limitationdiscussion in the supplementary material Section A.7.

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: [N/A]
Revised: [N/A](d)

Original: (d) Have you read the ethics review guidelines and ensured that your paper conforms tothem?
Revised: Have you read the ethics review guidelines and ensured that your paper conforms tothem?

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

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Revised: If you are including theoretical results...

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

Original: [N/A](b)
Revised: [N/A](b)

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Revised: Did you include complete proofs of all theoretical results?

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

Original: If you ran experiments...
Revised: If you ran experiments...

Original: (a) Did you include the code, data, and instructions needed to reproduce the main experi-
Revised: (a) Did you include the code, data, and instructions needed to reproduce the main experi-

Original: mental results (either in the supplemental material or as a URL)?
Revised: mental results (either in the supplemental material or as a URL)?

Original: [Yes](b) Did you specify all the training details (e.g., data splits, hyperparameters, how theywere chosen)?
Revised: [Yes] (b) Did you specify all the training details (e.g., data splits, hyperparameters, how theywere chosen)?

Original: [Yes]
Revised: [Yes]

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

Original: [Yes]
Revised: [Table]

Original: (d) Did you include the total amount of compute and the type of resources used (e.g., typeof GPUs, internal cluster, or cloud provider)?
Revised: (d) Did you include the total amount of compute and the type of resources used (e.g., typeof GPUs, internal cluster, or cloud provider)?

Original: [Yes]
Revised: [Yes]

Revised: [Table]

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

Original: [Yes]
Revised: [Yes]

Revised: [Table]

Original: (b) Did you mention the license of the assets?
Revised: (b) Did you mention the license of the assets?

Original: [Yes]
Revised: [Yes]

Revised: [Table]

Original: (c) Did you include any new assets either in the supplemental material or as a URL?
Revised: (c) Did you include any new assets either in the supplemental material or as a URL?

Original: [Yes]
Revised: [Yes]

Original:  (d) Did you discuss whether and how consent was obtained from people whose data you’reusing/curating?
Revised: [Table] (d) Did you discuss whether and how consent was obtained from people whose data you’re [Table] using/curating?

Original: [Yes]
Revised: [Yes]

Revised: [Table]

Original: (e) Did you discuss whether the data you are using/curating contains personally identifiable
Revised: (e) Did you discuss whether the data you are using/curating contains personally identifiable

Revised: [Table]

Original: information or offensive content?
Revised: information or offensive content?

Original: [Yes]
Revised: [Yes]

Revised: [Table]

Original: If you used crowdsourcing or conducted research with human subjects...
Revised: If you used crowdsourcing or conducted research with human subjects...

Original: (a) Did you include the full text of instructions given to participants and screenshots, if

Original: [Table]
Revised: [Table]

Original: applicable?
Revised: (a) Did you include the full text of instructions given to participants and screenshots, ifapplicable?

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

Revised: [Table]

Original: (b) Did you describe any potential participant risks, with links to Institutional Review
Revised: (b) Did you describe any potential participant risks, with links to Institutional Review

Revised: [Table]

Original: Board (IRB) approvals, if applicable?
Revised: Board (IRB) approvals, if applicable?

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Original: (c) Did you include the estimated hourly wage paid to participants and the total amount
Revised: Did you include the estimated hourly wage paid to participants and the total amount

Revised: [Table]

Original: spent on participant compensation?
Revised: spent on participant compensation?

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

Revised: [Table]