VoxelTrack: Exploring Multi-level Voxel Representation for 3D Point Cloud Object Tracking

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-25 05:03:50

Abstract: Current LiDAR point cloud-based 3D single object tracking (SOT) methods typically rely on point-based representation network. Despite demonstrated success, such networks suffer from some fundamental problems: 1) It contains pooling operation to cope with inherently disordered point clouds, hindering the capture of 3D spatial information that is useful for tracking, a regression task. 2) The adopted set abstraction operation hardly handles density-inconsistent point clouds, also preventing 3D spatial information from being modeled. To solve these problems, we introduce a novel tracking framework, termed VoxelTrack. By voxelizing inherently disordered point clouds into 3D voxels and extracting their features via sparse convolution blocks, VoxelTrack effectively models precise and robust 3D spatial information, thereby guiding accurate position prediction for tracked objects. Moreover, VoxelTrack incorporates a dual-stream encoder with cross-iterative feature fusion module to further explore fine-grained 3D spatial information for tracking. Benefiting from accurate 3D spatial information being modeled, our VoxelTrack simplifies tracking pipeline with a single regression loss. Extensive experiments are conducted on three widely-adopted datasets including KITTI, NuScenes and Waymo Open Dataset. The experimental results confirm that VoxelTrack achieves state-of-the-art performance (88.3%, 71.4% and 63.6% mean precision on the three datasets, respectively), and outperforms the existing trackers with a real-time speed of 36 Fps on a single TITAN RTX GPU. 
The source code and model will be released.

Main paper: 
 introduction: 
 Single object tracking (SOT) plays a pivotal role in various computer vision applications, such as autonomous driving [4, 59] and visual surveillance systems [46] . Early research works in SOT have predominantly focused on the 2D image domain [12, 60] . However, images are often disturbed by light and noise, making it difficult to track targets in the images. In recent years, with the rapid development of LiDAR sensors and considering that point cloud data is robust to light interference and environmental factors, many techniques [15, 19, 36, 41, 55, 56, 64] for 3D SOT have been proposed. Despite demonstrated success, these methods are built upon 2D SOT techniques, which may not be directly applicable to 3D SOT based on LiDAR point clouds, as point cloud data differs fundamentally from RGB image data. Therefore, it is crucial to develop tracking techniques tailored to disordered and density-inconsistent point cloud data.Currently, most of 3D SOT methods are based on point-based representation networks [34, 52, 55, 63] , such as PointNet [39] and PointNet++ [40] , which extract point features for subsequent taskoriented modules. As illustrated in the left of Fig. 1 (a), P2B [41] is an end-to-end tracking framework. It first encodes the semantic features of the point cloud in the template and search region by point representation based Siamese backbone [9] , and then performs appearance matching of the template and the search region at the feature level. The proposals [16] are generated from the resulting search region enriched by template information, where a proposal is verified as the tracking result. Based on this strong framework, a series of trackers are presented, such as BAT [63] , PTTR [65] , GLT-T [36] that consists of point representation based Siamese backbone, appearance matching, proposals generation and verification. In contrast to P2B series, M 2 Track [64] introduces a motion-centric paradigm, which explores motion cues instead of appearance matching for tracking. It predicts target motion from the concatenated point clouds of previous and current frames in a two-stage manner by using point features, as shown in the right of Fig. 1 (a) .In summary, previous research works have either used point features for appearance matching to guide tracking, or used point features to mine motion information. Although great success has been achieved in 3D SOT, point-based representation may be the sub-optimal for point cloud object tracking due to the following reasons: 1) Point representation-based networks rely on pooling operations to maintain the permutation invariance of disordered point clouds, thus encoding geometric structure information. However, the pooling operations tend to impair 3D spatial information of point clouds, which is essential for accurately regressing bounding boxes of point cloud objects. 2) The set abstraction operation employed in point presentation networks learns key point features through down-sampling, grouping and feed-forward blocks. Nonetheless, such set abstraction operation struggles to effectively handle the inherently density-inconsistent point clouds, thereby preventing 3D spatial information from being modeling.To solve the above problems, we propose to leverage voxelized point clouds as input and employ voxel-based representation network for 3D SOT. We therefore introduce a novel tracking framework, termed VoxelTrack, which fully explores 3D spatial information of point clouds to guide target box regression for tracking, as shown in Fig. 1 (b ). We first voxelize point clouds cropped from two consecutive frames and align them spatially, and then extract voxel features by a series of sparse convolution blocks. Leveraging the derived features incorporated by rich 3D spatial information of point clouds, we could perform box regression to predict target bounding box without any task-oriented module, such as proposal generation and verification, motion prediction and refinement. To further enhance 3D spatial information for accurate tracking, we incorporate dual-stream voxel representation learning network to explore fine-grained 3D spatial information. In addition, we perform layer-by-layer feature interaction for the two streams through a cross-iterative feature fusion module, enhancing the synchronization between dual-stream voxel features and thereby guiding a more accurate box regression.We evaluate our proposed VoxelTrack on three widely-adopted datasets, including KITTI, NuScenes and Waymo Open Dataset. Experimental results demonstrate that VoxelTrack outperforms P2B/M 2 Track by a significant margin of 28.0%/7.5%, 22.5%/9.8% mean success on the KITTI and NuScenes, respectively. Our method could also accurately track objects in complex point clouds scenes, such as those with sparse point clouds and distractors. Moreover, thanks to the removal of task-oriented modules, the proposed Vox-elTrack runs at a real-time speed of 36 Fps on a single TITAN RTX GPU.The main contributions of this work are summarized as follows:  
related work 2.1 2d single object tracking: 
 In recent years, research in the study of 2D object tracking [9, 12, 22, 50, 57, 60, 67] has become very mature. Most of the trackers [23, 24, 50, 57, 67] follow a Siamese-based network. SiamFC [1] as a pioneering work in the field, introduces a fully-convolution framework to achieve feature fusion and matching of template and search areas for object tracking. SiamRPN [25] first introduces a single-stage region proposal network (RPN) [16] to achieve object tracking by comparing the similarity between the current frame features and template features. By using a spatial-aware sampling strategy, SiamRPN++ [23] further improves the siamese-based network to remove the disturbance factors such as padding. Due to the extraordinary correlation modeling ability of Transformer [47] and the proposal of VIT [11] , SimViT-Track [5] uses a similar method to feed templates and search areas into the ViT backbone to predict the location of targets. After that, many other notable variants [6, 13, 35, 49, 54] in 2D SOT also achieve the considerable success. However, it is non-trivial to apply these techniques to 3D SOT on LiDAR point clouds.Early 3D single object tracking (SOT) methods [2, 21, 29, 37] primarily operate within the RGB-D domain. While these approaches offer a promising research direction, RGB-D trackers may falter in tracking targets when the RGB-D information is compromised by factors such as lighting conditions or noise. With the advancements in LiDAR sensor technology, the point cloud domain has emerged as a pivotal domain for the evolution of 3D SOT, overcoming the aforementioned limitations. Pioneering this shift, SC3D [15] introduces the first Siamese tracker based solely on point clouds, matching feature distances between template and search regions while regularizing training through shape completion. Subsequently, P2B [41] introduces a 3D Region Proposal Network (RPN). Diverging from SC3D, P2B adopts an end-to-end framework, employing a VoteNet [38] to generate a batch of high-quality candidate target boxes. Inspired by the success of P2B, numerous networks [18, 19, 32, 34, 36, 42, 48, 52, 63, 65] utilizing it as a baseline have been proposed. OSP2B [34] enhances P2B into a one-stage Siamese tracker by simultaneously generating 3D proposals and predicting center-ness scores. BAT [63] encodes distance information from point-to-box to augment correlation learning between template and search areas. PTTR [65] , CMT [18] , and STNet [20] explore different attention mechanisms to improve feature propagation. CXTrack [55] underscores the significance of contextual information on tracking performance, designing a target-centric transformer network to explore such information. MBPTrack [56] employs a memory mechanism for enhancing aggregation of previous spatial and temporal information. Despite the impressive performance achieved by these methods, they adhere to the Siamese paradigm, focusing either on designing intricate modules or incorporating additional point cloud features. However, owing to the sparse nature of point clouds, they may lack sufficient texture information for appearance matching. Fortunately, information pertaining to object motion is well retained. M 2 Track introduces a motion-centric paradigm that explicitly models target motion between successive frames by directly fusing point clouds from two consecutive frames as input and utilizing PointNet [39] to predict relative target motion from the cropped target area. While these point representation-based tracking methods have demonstrated superior performance, they may still face challenges to deal with inherently disordered and sparsityinconsistent point clouds. In this paper, we propose VoxelTrack, a novel voxel representation-based tracking framework.Presently, 3D perception approaches employing point cloud representation are primarily categorized into point-based methods and voxel-based methods. PointNet [39] and PointNet++ [40] have paved the way for a multitude of vision tasks, including classification, segmentation, and detection, to be conducted directly on raw point cloud data. Building upon the success of these frameworks, a plethora of point-based representation methods have emerged [27, 31, 43, 45, 51, 53, 61, 62] . However, such point-based representation networks encounter inherent challenges. Given the sparsity and inconsistent density of point clouds, these methods heavily rely on point sampling and regional point aggregation operations, which are susceptible to time-consuming and computationintensive point-wise feature duplication. On the other hand, voxelbased representation methods in 3D perception entail partitioning the input point cloud into a grid of uniformly sized voxels based on a predefined coordinate system, addressing the irregular data format issue. VoxelNet [66] lays the groundwork for target detection frameworks and serves as the backbone for numerous 3D detection methods. Sparse convolution techniques are employed in SECOND [58] to proficiently learn sparse voxel features from point clouds. Compared to point-based methods, voxel-based methods [8, 10, 30, 33] demand fewer memory and computing resources, rendering them potentially superior choices for point cloud object tracking task. To the best of our knowledge, VoxelTrack is the first attempt to utilize a pure voxel representation network for 3D SOT. 
methodology: 
 In this section, we present our proposed VoxelTrack to implement the 3D SOT task. We first explicitly define the 3D SOT task in Sec. 3.1, followed by a review of two popular model series using point-based representation in Sec. 3.2. In Sec. 3.3, we describe our proposed framework VoxelTrack, consisting of three key components: Voxel Division, Multi-level Voxel Representation Learning and Box Regression. Finally, the implementation of the three components are detailed in Sec. 3.4, 3.5 and 3.6, respectively.The task of 3D SOT is defined: In a 𝑇 -frame sequence of point clouds{P ∈ R 𝑁 ×3 } 𝑇 𝑡 =1 , an initial 3D bounding box 𝐵 1 =(𝑥 1 , 𝑦 1 , 𝑧 1 ,𝑤 1 , ℎ 1 , 𝑙 1 , 𝜃 1 )of a specific target is given in the first frame, tracking needs to accurately localize the target in subsequent frame and predict bounding boxes{𝐵 𝑡 =(𝑥 𝑡 , 𝑦 𝑡 , 𝑧 𝑡 , 𝑤 𝑡 , ℎ 𝑡 , 𝑙 𝑡 , 𝜃 𝑡 )} 𝑇 𝑡 =2for the tracked target. The point cloud P ∈ R 𝑁 ×3 is composed of 𝑁 points. (𝑥, 𝑦, 𝑧), (𝑤, ℎ, 𝑙) and 𝜃 in bounding box represent the center coordinates, box size and angle. In the community, it is assumed that the target size remains constant across all frame. Therefore, only (𝑥, 𝑦, 𝑧) and 𝜃 are needed to predict for 3D bounding box. . The proposals are generated from search region by offset learning. Finally, the proposal with the highest confidence is selected to generate tracking result. The whole process can be represented by the following equation:EQUATIONwhere into one input 𝑃 𝑡 -1,𝑡 . This approach distinguishes between previous frame target, current frame target and background points via joint spatial-temporal learning. Afterwards, they leverage a two-stage network to explore motion cue instead of appearance matching for tracking. A coarse box offset (Δ𝑥 𝑡 ,Δ𝑦 𝑡 ,Δ𝑧 𝑡 ,Δ𝜃 𝑡 ) 𝑐𝑜𝑎𝑟𝑠𝑒 and a fine box offset (Δ𝑥 𝑡 ,Δ𝑦 𝑡 ,Δ𝑧 𝑡 ,Δ𝜃 𝑡 ) 𝑓 𝑖𝑛𝑒 are predicted from the two stages, respectively. The whole process can be represented by the following equation:EQUATIONwhere 𝐹 𝑠𝑒𝑔 and 𝐹 𝑚𝑜𝑡𝑖𝑜𝑛 denote the segmentation network and motion inference network. Different from the above two series of trackers, our proposed Vox-elTrack leverages voxel representation for point clouds to perform tracking and simplify the tracking framework into:EQUATIONwhere 𝐹 𝑣 and 𝐹 𝑟𝑒𝑔 denote the voxel representation backbone and box regression network. The overall architecture of our proposed framework is illustrated in Fig. The previous frame point cloud 𝑃 𝑡 -1 and current frame point cloudsEQUATIONWhere ⌊•⌋ denotes the floor function, (i, j, k) is the voxelized coordinate, (x,y,z) is the original coordinate of the point, Δ is the voxel size. Then we concatenate the previous frame V 𝑡 -1 ∈ R 𝑊 ×𝐿×𝐻 ×3 and the current frame V 𝑡 ∈ R 𝑊 ×𝐿×𝐻 ×3 along the channel dimension as input V 𝑡 -1,𝑡 ∈ R 𝑊 ×𝐿×𝐻 ×6 . Considering that the point clouds in each frame may be relatively sparse, especially in distant scenes, we find that the previous pooling and set abstraction operations (involving farthest point sampling and feature forward) in point-based methods should be improved, preventing 3D spatial information from being modeled. We therefore introduce a simple dynamic voxel feature encoder to tackle non-empty voxels after voxelization. In each voxel, it averages the values of all points and then reassigns the average value to each point. As a result, the number of points in each voxel is dynamic and will not be reduced:EQUATIONBy leveraging the dynamic voxel feature encoder, all points and spatial information are well retained without introducing information loss, so that VoxelTrack can learn rich features with 3D spatial information and guide accurate box regression for tracking.Dual-stream Encoder. To extract rich spatial information from voxelized point clouds by making full use of their ordered spatial structure, we design a dual-stream encoder specifically for the modeling of fine-grained 3D spatial information. As shown in Fig. 3 , we first divide the point clouds into two scales V 𝑙𝑎𝑟𝑔𝑒 and V 𝑠𝑚𝑎𝑙𝑙 . After that, we generate two inputsV 𝑙𝑎𝑟𝑔𝑒 𝑡 -1,𝑡 ∈ R 𝑊 𝑙 ×𝐿 𝑙 ×𝐻 𝑙 ×6 and V 𝑠𝑚𝑎𝑙𝑙 𝑡 -1,𝑡 ∈ R 𝑊 𝑠 ×𝐿 𝑠 ×𝐻 𝑠 ×6for the dual-stream encoder. Due to the sparsity of the point cloud, a lot voxels are empty. We then employ slightly-modified VoxelNext [7] as feature extraction backbone, which uses 3D sparse convolution layers [17] instead of 3D convolution layers to encode voxel features. According to our design, two different scales of voxel inputs are extracted features by two similar backbones, respectively to focus on learning multi-scale features {F 𝑙𝑎𝑟𝑔𝑒 𝑠 , F 𝑠𝑚𝑎𝑙𝑙 𝑠 } 𝑁 𝑠=1 . Specifically, our backbone is consisted of 𝑁 stages, where the spatial information F 𝐿 𝑡 is down-sampled to half after each stage. Meanwhile, the number of channels is doubled to enhance the voxel feature representation ability. The scale transformation for F 𝑙𝑎𝑟𝑔𝑒 𝑠 can be formulated as:F 𝑙𝑎𝑟𝑔𝑒 𝑠+1 ∈ R 𝑊 2 × 𝐿 2 × 𝐻 2 ×2𝐶 = SpConv(F 𝑙𝑎𝑟𝑔𝑒 𝑠 ∈ R 𝑊 ×𝐿×𝐻 ×𝐶 ) (6)where 𝑠 ∈ {0, 1, 2}, and the scale transformation for F 𝑠𝑚𝑎𝑙𝑙 𝑠 is similar to Eq. 6. with low-resolution by linear interpolation operation. Compared to using the convolution layers for sampling, our method is able to better preserve features of different scale and reduce the consumption of computational resources. Detailed experiments and analysis can refer to Tab. 6). It is important to note that we use only the down-sampling in the last stage to obtain the output features of the backbone. To make a better fusion, we then employ convolution layer to further process features.With the obtained encoded features, we convert 3D voxel features to 2D BEV features F 𝐵𝐸𝑉 . Thanks to 3D spatial information being modeled, we directly predict the target position (Δ𝑥, Δ𝑦, Δ𝑧, Δ𝜃 ) in a one-stage manner. Specifically, we first aggregate the spatial information by global max pooling. Then we apply a MLP to derive the target position. Finally, we leverage a residual log-likelihood estimation [26] function F to calculate training loss L:EQUATIONwhere (Δ𝑥 𝑡 ,Δ𝑦 𝑡 ,Δ𝑧 𝑡 ,Δ𝜃 𝑡 ) and ( Δ 𝑥 𝑡 , Δ 𝑦 𝑡 , Δ 𝑧 𝑡 , Δ 𝜃 𝑡 ) denote the prediction parameters and label, respectively. [66] . We train our VoxelTrack using a AdamW optimizer on eight TITAN RTX GPUs, with a batch size of 128. The initial learning rate is set to 1𝑒-4, which is reduced by a factor of 5 every 40 epochs. More implementation details can be found in appendix. Datasets. To evaluate the performance of our tracking network, we conducted comprehensive and detailed experiments and analyses on three large-scale and widely used datasets, including KITTI [14] , NuScenes [3] and Waymo Open Dataset (WOD) [44] . KITTI consists of 21 training scene sequences and 29 test scenes sequences. Metrics. We use one pass evaluation (OPE) as an approach to evaluate the performance of our model, simultaneously use of both Success and Precision metrics. Success is calculated by the intersection over union (IOU) between the ground truth bonding box and the predicted bounding box. Precision is calculated as the distance between the centers of the two bounding boxes. 
comparison with state-of-the-arts: 
 Results on KITTI. We compare the proposed VoxelTrack with existing point representation-based state-of-the-art methods, and present a comprehensive analysis towards the performance of these methods on all categories, including Car, Pedestrian, Van and Cyclist. As show in Tab. 1, our VoxelTrack demonstrates predominant performance across various categories, achieving a mean Success rate of 70.4% and a mean Precision rate of 88.3% in the KITTI dataset, respectively. This is due to that voxel-based representation captures accurate 3D spatial information, which is more suited for disordered and density-inconsistent point clouds. Compared to point-based representation based methods P2B [41] and M 2 Track [64] , Voxel-Track achieves 28.0% and 7.5% performance gains in terms of mean Success, while running at a real-time speed. In addition, our method exhibits significant performance advantage compared to recent P2B series work CXTrack [55] . Note that, VoxelTrack obviously outperforms the previous best method by in the Van category, which implies that our method can achieve good performance without the large dataset training.Results on WOD. To demonstrate the applicability of our proposed VoxelTrack method, we evaluate the KITTI trained Car and Pedestrian model on WOD, following common practice in the community [19, 55] . We select some representative methods for comparison, including CXTrack [55] , STNet [20] , V2B [19] , BAT [63] and P2B [41] . The experiment results are shown in the Tab. 2. Our VoxelTrack achieves best performance with a mean Success and Precision of 47.5% and 63.6%. Compared to existing point representation base methods, VoxelTrack presents performance improvements across vehicle and pedestrian categories with varying degrees of complexity. This is attributed to the higher generalization of voxel representation compared to point representation to unseen scenes, proving the potential of the proposed voxel representation-based tracking framework. Results on NuScenes. We further explore the various capabilities of VoxelTrack on the NuScenes dataset. Because NuScenes contains a large number of complex and diverse scenes, it becomes a more challenging dataset for 3D SOT. We choose the state-ofthe-art trackers that have reported performance on this dataset as comparisons: SC3D [15] , P2B [41] , PTT [42] , BAT [63] , GLT-T [36] , PTTR [65] and M 2 Track [64] . As show in Tab. 3, our VoxelTrack demonstrates great performance with the mean Success and Precision rates of 59.06% and 71.39%. Notably, VoxelTrack exhibits the leading performance in all categories. The results of this experiment demonstrate that our method can accurately and robustly track objects even in complex scenes. when the dual-stream features in previous stages are not fused, the high-resolution spatial information is distracted, which can lead to less accurate predictions of yaw angle, thus affecting Success metric more compared to Precision metric. The reason for the small impact on Success metric for the Car category is that the yaw angle of the cars does not change significantly in a point cloud sequence and is relatively easy to predict. Variant Design of CIF Module. In our CIF module, the sampling method will affect the synchronization of feature interaction. As show in Tab. 6, we ablate two commonly used up-sampling methods and down-sampling methods, respectively. We choose 3D transposed convolution and 3D linear interpolation as alternatives for up-sampling methods, 3D convolution and 3D maxpooling for down-sampling methods. We generate four candidate variants by permuting and combining them. These variants will be applied to the CIF module fuse the feature maps extracted by the dual-stream branches. It can be seen that the combination of 3D linear interpolation and 3D maxpooling achieves the best performance. In contrast to the convolutional approach, these two methods maintain feature semantic space, which facilitates the collaborative modeling of 3D spatial information required for tracking between the dual-stream features.Robustness to Sparsity and Distractors. Considering that most point clouds in real scenes are sparse and contained with distractors, generally testing the performance on the test dataset may lack reliability in practical applications. Therefore, it is necessary to analyze the robustness of model to sparse point clouds and distractors. Following [41] , we divide the Car category dataset into six sparsity levels, while divide the Pedestrian category dataset into three inter-class and extra-class distractors levels. As show in Fig. 6 , we compare our VoxelTrack with M 2 Track [64] and P2B [41] . Voxel-Track performs better in complex scenes (a), specially for extremely sparse scenes with fewer than 20 points. For (b) and (c), our method exhibits consistent performance advantage regardless of how many interfering objects the scene contains. These all results demonstrate the potential of the proposed method for practical applications. Visualization Analysis. In Fig. 5 , we show visualization results on the KITTI dataset. We obtain LiDAR point cloud sequences from each category, and then compare the ground truth box with three prediction bounding boxes of P2B, M 2 Track and VoxelTrack. Our VoxelTrack can more accurately and robustly track objects across all categories than comparative methods, intuitively demonstrating the effectiveness of our proposed framework. 
conclusion: 
 This paper presents a novel voxel representation based tracking framework, termed VoxelTrack. The novel framework leverages voxel representation to explore 3D spatial information to guide direct box regression for tracking. Moreover, It incorporates a dualstream encoder with a cross-iterative feature fusion module to further model fine-grained 3D spatial information. Through extensive experiments and analyses, we prove that our proposed VoxelTrack effectively handles disordered and density-inconsistent point clouds, thereby exhibiting the state-of-the-art performance on three published datasets and significantly outperforming the previous point representation based methods. 
: 
 Track series. P2B series employs appearance matching techniques to generate proposals and verifies one as tracking result, while M 2 Track series models motion relation for tracking in a two-stage manner. In contrast, our VoxelTrack explores 3D spatial information through voxel-based representation for tracking, thereby simplifying the tracking pipeline with a single regression loss function. 
 
 Appendix:  


Decision: Three reviewers have reviewed the paper. The advantages include: The proposed method simplifies previous approaches using Voxel Representation; The Cross-Iterative Feature Fusion (CIF) module aggregates multi-scale features, enhancing effectiveness; VoxelTrack is computationally efficient, making it suitable for real-world applications like vehicle deployment; The paper is well-organized, technically sound, and easy to understand; Extensive experimental results support the effectiveness of VoxelTrack and its superiority over point-based backbones; The dual-stream encoder enhances multi-scale information, boosting overall performance. The disadvantages include the following: The authors need to provide a more solid justification for using voxel-based methods over point-based algorithms; The authors should clarify the claim that the set abstraction operation struggles with density-inconsistent point clouds.

Overall, the paper has a good quality and can be accepted to the conference. All reviewers support accepting this paper. The ACs do not find significant reasons to overturn reviewers' comments and thus recommend accepting this paper.

Reviews: review: paper_summary: paper_summary: In this paper, a novel framework called VoxelTrack is introduced. VoxelTrack voxelizes point clouds into 3D voxels and extracts their features using sparse convolution blocks, effectively capturing precise 3D spatial information for accurate position prediction of tracked objects. Additionally, VoxelTrack employs a dual-stream encoder with a cross-iterative feature fusion module to enhance fine-grained 3D spatial information modeling. This approach simplifies the tracking pipeline with a single regression loss.
strength_weakness: strength_weakness: 1. It simplifies the previous method by using Voxel Representation.
2. "CIF” (cross-iterative feature fusion module) helps to aggregate multi-scale features.
limitations: limitations: 1. Are there any similar designs of CIF in point-based methods? Please summarize it in related works and compare them in experiments. 
2. I think "Dual-Stream Voxel Representation Learning" is time-consuming since you need to extract features twice. With this consideration, how to extend your framework to perform the multi-object tracking, which is more useful in practices.
paper_summary: paper_summary: This paper proposes a new voxel-based framework for 3D point cloud object tracking. Authors claim that the previous widely-used point-based methods struggle to exploit 3D spatial information due to the permutation invariance pooling and set abstraction operations. Therefore, a new method named VoxelTrack is proposed to model 3D spatial information precisely. The VoxelTrack utilizes a voxel-based backbone and can consume current and previous frames using a Dual-Stream Voxel Representation Learning module. Finally, the Box Regression module can predict the tracklet by utilizing the output of the cross-iterative feature fusion module. The extensive experimental results demonstrate the effectiveness of the proposed method. Better, the VoxelTrack achieves a 36FPS of inference efficiency when using a single TITAN RTX GPU.
strength_weakness: strength_weakness: 1. The proposed VoxelTrack is computing saving, which have a strong potential make a deployment on the vehicle or other real-world applications.
2. The paper is well-organized, and the proposed framework is technically sound.
3. The extensive experimental results demonstrate the effectiveness of the proposed VoxelTrack.
limitations: limitations: First, the authors should explain why the point-based algorithms cannot capture 3D spatial information well. To my knowledge, PointNet also adopted a concatenation operation of the permutation invariance pooled feature with the **original points**, which can capture both geometric and spatial information. The second claim, *the adopted set abstraction operation hardly handles density-inconsistent point clouds, also prevents 3D spatial information from being modeled*, is vague. The authors should provide more explanation to make it clean. Therefore, the motivation of authors using the voxel-based method to substitute the point-based method is not solid.

Second, as shown in Fig 4, The large and small voxel branch consumes two tensors in $\mathbb{R}^{W\times L\times H\times C}$ and $\mathbb{R}^{2W\times 2L\times 2H\times C}$ respectively, and output two feature maps with identical dimensions. However, the cascade pipeline of the Dual-stream Voxel Representation Learning module has a feature pyramid that down-samples the feature map at each level. These two figures state inconsistently. 

Third, VoxelTrack is not the first paper to use a voxel-based method for object detection or tracking (or both), which limits the novelty of the proposed method.
paper_summary: paper_summary: Compared to the mainstream point-based 3D point cloud tracking, this paper proposed a novel voxel-based framework to capture rich spatial information. Besides, a dual-stream encoder is proposed to extract multi-level voxel features in a cross-iterative fusion manner. Extensive experiments are conducted on three datasets to demonstrate the effectiveness of the proposed VoxelTrack.
strength_weakness: strength_weakness: 1. Using a voxel-based backbone to extract features is reasonable and has been demonstrated to be more effective than a point-based backbone. 
2. The paper is well-written and easy to understand.
3. The experiments are well conducted and reasonable to support the goals of the proposed components.
4. The dual-stream encoder enhances the multi-scale information and boosts the performance.
limitations: limitations: 1. It is suggested to replace the backbone of point-based methods with a voxel-based backbone or replace the backbone of VoxelTrack with the point-based backbone to further demonstrate the effectiveness of the proposed voxel representation.
2. It is better to provide more discussion about the motivation for using dual-stream in the introduction section.
3. It is recommended to conduct ablation studies on the impact of voxel grid size, as this can offer further insights.