VoCAPTER: Voting-based Pose Tracking for Category-level Articulated Object via Inter-frame Priors

Authors: li  zhang
yan  zhong
qiaojun  yu
xingyu  wu
xue  wang
rujing  wang
zean  han
  vocapter
Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-09 12:38:22

Abstract: Articulated objects are common in our daily life. However, current category-level articulation pose works mostly focus on predicting 9D poses on statistical point cloud observations. In this paper, we deal with the problem of category-level online robust 9D pose tracking of articulated objects, where we propose VoCAPTER, a novel 3D Voting-based Category-level Articulated object Pose TrackER.
Our VoCAPTER efficiently updates poses between adjacent frames by utilizing partial observations from the current frame and the estimated per-part 9D poses from the previous frame. Specifically, by incorporating prior knowledge of continuous motion relationships between frames, we begin by canonicalizing the input point cloud, casting the pose tracking task as an inter-frame pose increment estimation challenge. Subsequently, to obtain a robust pose-tracking algorithm, our main idea is to leverage SE(3)-invariant features during motion. This is achieved through a voting-based articulation tracking algorithm, which identifies keyframes as reference states for accurate pose updating throughout the entire video sequence. We evaluate the performance of VoCAPTER in the synthetic dataset and real-world scenarios, which demonstrates VoCAPTER's generalization ability to diverse and complicated scenes. Through these experiments, we provide evidence of VoCAPTER's superiority and robustness in multi-frame pose tracking of articulated objects. We believe that this work can facilitate the progress of various fields, including robotics, embodied intelligence, and augmented reality. All the codes will be made publicly available.

Main paper: 
 introduction: 
 Articulated objects are omnipresent in human daily life. Effectively conducting many downstream tasks relies heavily on articulated object pose estimation and tracking, encompassing human-object interaction [16, 17, 25, 43] , robotic manipulation [4, 47] , augmented reality [2, 3, 6, 7] , and 3D scene understanding [1, 5, 9] . Unlike merely predicting single-frame articulated object poses from statistical point clouds or RGB-D images [15, 36, 40] , articulated object pose tracking tasks necessitate handling multi-frame point cloud sequences, thus amplifying challenges. Specifically, point cloud sequences furnish temporal continuity information of articulated objects, hence demanding consideration of pose variations over time. Objects may undergo rotations and translations between different frames, therefore increasing task complexity due to this dynamic nature.To deal with category-level articulated object 9D pose tracking, a straightforward idea is to migrate the single-frame articulation pose estimation methods (such as keypoint based DAKDN [45] and reinforcement learning based ArtPERL [21] ) into the multiframe pose tracking task. However, these solutions face two main challenges:(i) Tracking Manner. The efficacy of these methods is hampered by the need for per-pixel representation learning, which consequently impacts the tracking speed in live point cloud streams. Consequently, these constraints obstruct the ability of category-level articulated object trackers to attain satisfactory robust and real-time tracking performance.(ii) Pose Modeling Problem. Objects have their inherent structures, which are invariant to rotations and translations (e.g., the relative position of the handle does not change during the rotation of the mug). However, prior arts often directly regress 9D poses without accounting for these intrinsic properties. This oversight results in ill-posed pose modeling issues, making convergence challenging and compromising the retention of useful features.To address the first challenge, our core idea involves leveraging the prior information between adjacent frames. While single-frame pose estimation methods can be directly applied to pose tracking tasks, this approach essentially predicts frame by frame, overlooking crucial prior information. In other words, previous methods fail to adequately consider the continuous motion relationships between frames, which hinders the improvement of inference speed. Secondly, to alleviate the difficulty of predicting point cloud poses observed in camera space, our strategy involves using the inverse pose transformation of the previous frame's point cloud to normalize the input space of the current frame. Based on this strategy, we transform the articulated object pose tracking task into an inter-frame pose increment estimation task to better utilize the temporal coherence and structural consistency of consecutive frames in video sequences, thereby improving the network's inference speed and accuracy.To handle the second challenge, the basic mechanism behind our method is to conduct a voting-based prediction, which takes the SE(3)-invariant features into consideration. The key idea is to turn the geometric features into the probability features on the point cloud, which is more friendly for learning. Concretely, we first globally model each rigid part's structure by defining key geometric relationships (i.e., some SE(3)-invariant parameters). Then we seek local geometric features by adopting a local matching strategy where each part independently casts votes for its estimated pose based on local features from point pairs and the global structure from the corresponding part. Furthermore, to get a robust prediction, we additionally introduce a part awareness mechanism to assign scores for the candidate point pairs instead of the noisy data that participates in voting.We evaluate our VoCAPTER on category-level pose tracking tasks on both point clouds dataset (PartNet-Mobility) as well as an RGB-D images dataset (ReArt-48). Experiments on real-world scenarios (RobotArm) also demonstrate the generalization capacity of our VoCAPTER. Our contributions can be summarized as follows:• We tailor a novel Voting-based Category-level Articulated objectPose TrackER (VoCAPTER) to break the performance bottleneck of existing methods.• We turn the articulated object pose tracking task into an interframe pose increment estimation task via the inter-frame priors. Besides, a universal voting strategy is applied to track the pose frame by frame to avoid the weakness of direct pose regression methods. • The efficiency and robustness of the VoCAPTER are demonstrated through the evaluation of either synthetic or real-world scenarios.The experimental results show the dramatic performance improvement and generalization capacity of our method. 
related work 2.1 category-level articulation pose estimation: 
 Category-level object pose estimation involves predicting the pose of objects that may not have been encountered previously [8, 23, 27, 35] . Unlike rigid objects [11, 28, 29] , articulated objects present a more intricate challenge due to the interdependence and coordinated movement of multiple joints [30, 32, 40] . It surely adds difficulty to the pose estimation task, as it requires considering the constrained relationships and coordinated movements among the joints in computer vision tasks [33? ]. Addressing the challenge of category-level pose estimation for articulated objects, A-NCSH [18] pioneers the extension of Normalized Object Coordinate Space (NOCS) [35] to articulate structures, enabling the estimation of part-level poses. To go further, Liu et al. [24] advance the approach towards real-world articulated object analysis by introducing part pairs to investigate previously unseen instances. Additionally, Xue et al. [42] propose a novel approach utilizing key-points as articulation models, aiming to speed up the inference while maintaining accurate pose estimation.Although the aforementioned studies effectively address categorylevel articulation pose estimation with satisfactory performance, their applicability as ready-to-use solutions in pose tracking tasks is limited. This limitation arises from the dense prediction paradigm, which constrains both the robustness and the inference speed of these methods.Unlike traditional articulated object pose estimation tasks [8, 12, 35] , category-level articulation pose tracking [26, 34, 39] extends the scope to real-time point cloud streams. Specifically, this task aims to update the pose at the frame level, utilizing the depth point cloud of the current frame along with the estimated pose from the preceding frame. One notable approach in prior research, BundleTrack [37] , leverages the complementary attributes of recent advances in deep learning for segmentation and robust feature extraction for both instance and category-level objects Pose Tracking. Alternatively, another set of methodologies aims at keypoint-based object representation and modeling. These approaches typically involve selecting appropriate key points based on the object's structural characteristics, analyzing their spatial positions and relationships, modeling the object's representation and shape, and ultimately inferring its attitude information. Examples of such techniques include tracking objects using predicted 2D keypoints in RGB sequences [20] , incorporating depth information for tracking [13] , and leveraging tracked articulated object poses for 3D reconstruction [38] .Despite significant progress, the 9D pose of an articulated object (comprising 3D translation, 3D rotation, and 3D scale) introduces additional degrees of freedom and complexity, posing challenges if a direct prediction is applied. Consequently, direct pose tracking methods often yield unstable results. Drawing inspiration from BeyondPPF [44] , we use a universal voting strategy for articulated object pose tracking in this paper. Our method employs a votingbased strategy, integrated into an end-to-end pipeline, which dynamically updates the pose relative to previous frames for per-part pose tracking. 
notations and problem statement: 
 In this paper, we address the task of tracking per-part 9D pose of articulated objects belonging to known categories. We build upon the category-level articulated object and part definitions introduced in A-NCSH [18] and assume a constant number of rigid parts and kinematic structures for all objects within the same category. The problem of pose tracking is defined as follows: the task takes the live point cloud stream {P 𝑡 } 𝑡 ≥0 as input, where 𝑡 denotes the 𝑡-th frame in the video. Each point cloud P 𝑡 consists of 𝐾 rigid parts, and 𝑆 (𝑘 ) represents the points of the 𝑘-th part. Along with each point cloud, there exists a per-part pose 𝑇(𝑘 ) 𝑡 = [𝑅 (𝑘 ) 𝑡 | t (𝑘 ) 𝑡 ] 𝐾𝑘=1 at the 𝑡-th frame. Here, 𝑅 𝑇 (𝑘 ) 𝑡 = Δ𝑇 (𝑘 ) 𝑡 • 𝑇 (𝑘 ) 𝑡 -1 = Δ𝑇 (𝑘 ) 𝑡 • Δ𝑇 (𝑘 ) 𝑡 -1 • • • 𝑇 (𝑘 ) 0 (1)To track the pose in the current frame, our key idea is to generate the SE(3)-invariant parameters 𝜇(𝑘 ) 𝑡 , 𝜈 (𝑘 ) 𝑡 , 𝛼 (𝑘 ) 𝑡 , 𝛽 (𝑘 ) 𝑡 , 𝛾 (𝑘 ) 𝑡and conduct the voting procedure based on these parameters (Details can be seen in Section 4.4). To this end, we can effectively predict the pose of the current frame with the assistance of the last frame. 
method: 
 The overview of our VoCAPTER can be seen in Figure 3 , in this section, we will introduce each module in detail. Formally, taking a 3D partial point cloud or an RGB-D image as input, the 𝑡-th frame point cloud is first canonicalized via the estimated 9D pose 𝑇 𝑘 𝑡 -1 from the last frame, as discussed in Section 4.1. Then we use PointNet++ to conduct the segmentation at the per-part level, as elaborated in Section 4.2. We propose a part awareness mechanism to filter the noisy point pairs in Section 4.3. Finally, given the SE(3)-invariant parameters, we perform the pose voting frame by frame, with details in Section 4.4. Canonicalization of the point cloud helps simplify the training and optimization of the network, which is widely used in previous arts [18, 21, 42] . However, It's impractical to canonicalize the current frame directly. To take full advantage of the priors between adjacent frames, we pseudo-canonicalize the point cloud at the 𝑡-th frame via the pose from the last frame. Here, we give the theorem with the corresponding proof that this all holds in Theorem 4.1: THEOREM 4.1. During the object motion, the transformation matrix between the adjacent frames is approximately equal to the transformation matrix from the canonical space to the pseudo-canonical space.PROOF. As depicted in Figure 2 , denote the transformation matrix of the adjacent frames by Δ𝑇 , whose action on the 𝑡 -1-th frame point cloud P 𝑡 -1 can transform the 𝑡 -1-th frame point cloud into the 𝑡-th frame point cloud P 𝑡 . By the assumption in the task of tracking, Δ𝑇 is approximately the identity matrix 𝐼 , which means that the transformation matrix 𝑇 𝑡 -1 : canonical space -→ P 𝑡 -1 is approximately equal to the transformation matrix 𝑇 𝑡 : canonical space -→ P 𝑡 . Since the action of 𝑇 𝑡 -1 on P 𝑡 can get the pseudo-canonical space and the action of 𝑇 𝑡 on P 𝑡 can get the canonical space, the transformation matrix Δ T from the canonical space to the pseudocanonical space is approximately equal to the identity matrix 𝐼 .Then there exists 𝜖, such that:EQUATIONThen,EQUATIONHere 𝜖 is a threshold for the approximation. Then we can draw the conclusion that Δ𝑇 is approximately equal to Δ T . □ Therefore, the foundational idea behind our canonicalization is as follows: firstly, we canonicalize the 𝑡-th frame point cloud {P 𝑡 } 𝑡 ≥0 with the per-part pose 𝑇 𝑡 -1 on P 𝑡 . We conduct this procedure at the per-part level with the relative transformation matrix Δ𝑇 of the adjacent frames using the Equation 7:P (𝑘 ) 𝑡 = (𝑇 (𝑘 ) 𝑡 -1 ) -1 P (𝑘 ) 𝑡 = (Δ𝑇 (𝑘 ) 𝑡 -1 • Δ𝑇 (𝑘 ) 𝑡 -2 • • • 𝑇 (𝑘 ) 0 ) -1 • P (𝑘 ) 0 (7)As specified in Theorem 4.1, to predict the Δ𝑇 here, we can predict the Δ T which transforms the canonical space to the pseudocanonical space.After the canonicalization at per-part level, we only need to predict the per-part pose increment Δ𝑇(𝑘 ) 𝑡 = [Δ𝑅 (𝑘 ) 𝑡 | Δt (𝑘 )𝑡 ] between 𝑡th and 𝑡 -1-th frame. Given the minimal rotation angle and translation between adjacent frames along the XYZ-axis, the neural network becomes more sensitive to subtle pose changes, thereby significantly improving tracking performance.Given the canonicalized point cloud P 𝑡 ∈ R 𝑁 ×3 in Section 4.1, we use the PointNet++ [31] to extract the helpful feature F 1 . Once we get the features, we use a decoder to decouple F 1 into 𝐾 masks 𝑚 (𝑘 ) 𝑡 ∈ R 𝑁 ×1 |𝑘 = 1, ..., 𝐾 . Mathematically, the part-level point cloud can be retrieved by Equation 8:P (𝑘 ) 𝑡 = P 𝑡 • 𝑚 (𝑘 )𝑡 , where 𝑚EQUATIONLoss Function. During the training, we adopt the MSE loss to conduct the part segmentation optimization:EQUATIONwhere m(𝑘 )denotes the predicted mask and 𝑚 (𝑘 ) 𝑡 denotes as the GT mask.As mentioned before, our method needs to sample the point pairs to conduct the per-part pose tracking. Intuitively, only the point pair chosen from the same part is valid. Upon the consideration of such situation, we propose the part awareness mechanism to filter the noisy point pairs (point pairs that come from different parts) before the pose voting.Concretely, denote all the sampled point pairs by T , we first define a point pair score {𝑐 𝑗 | 𝑗 = 1, ..., 𝐽 } (𝐽 is the number of sampled point pairs), which serves as a filter for noisy point pairs originating from different parts. Mathematically, given the part segmentation {P (𝑘 ) 𝑡 |𝑘 = 1, ..., 𝐾 }, point pair score 𝑐 𝑗 can be formulated as Equation 10: During the inference stage, we retain only point pairs with scores 𝑐 𝑗 greater than 0.5 to feed into the pose voting. Point pairs above this threshold are considered to be sampled from the same rigid part, effectively reducing interference from noisy point pairs.EQUATIONLoss Function. The part awareness loss L 𝑝𝑎𝑟𝑡 is defined as the binary entropy loss of the ground truth 𝑐 𝑗 and the predicted score ĉ 𝑗 :EQUATIONFor each rigid-part P (𝑘 ) 𝑡 at 𝑡-th frame, we sample the point pairs randomly from the same part as mentioned in Section 4.3. Subsequently, the point pair feature 𝐹 (𝑝 1 , 𝑝 2 ) [10] and the masked feature F 2 are fed into the PPF encoder to get the re-modulated feature. Then, we use a three-layer MLP to output the SE(3)-invariant features, which is applied to generate the key parameters of voting𝜇 (𝑘 ) 𝑡 , 𝜈 (𝑘 ) 𝑡 , 𝛼 (𝑘 ) 𝑡 , 𝛽 (𝑘 ) 𝑡 , 𝛾 (𝑘 )𝑡 . We use the following strategy to obtain the Δ𝑇 (𝑘 ) 𝑡 . Universal Voting Strategy. In this section, we use a universal voting strategy that creates a global model description via randomly sampled oriented points. The essence of this method lies in leveraging feature sparsity. Concretely, pose tracking tasks for non-uniformly distributed objects (e.g., points of a mug primarily concentrated on the body rather than the handle) can be transformed into a sparse feature modeling problem with uniform distributions of local point pairs.We transform local point pairs into probability distributions via bins. For translation voting, A circle is divided into bins to determine the center. For orientation voting, bins are evenly distributed in a Fibonacci sphere to count the votes. The final prediction of the tracked pose will emerge with the largest vote count. For optimization, we place each part in a zero-center coordinate before voting. The universal voting strategy for each rigid part at 𝑡-th frame is conducted as follows:⋆ Translation Voting. Denote the center by 𝑜 with the input point cloud. Therefore, the translation voting can be regarded as voting for the center of the part P (𝑘 ) 𝑡 in the camera space for the 𝑡-th frame point cloud P 𝑡 .For each point pair 𝑝 1 and 𝑝 2 , we estimate the following two offsets: ). Please note that we conduct the voting scheme on each part but only one part is on demonstrating for simplicity's sake.EQUATION)𝑤ℎ𝑒𝑟𝑒 𝑑 = 𝑝 2 -𝑝 1 ∥𝑝 2 -𝑝 1 ∥ 2 (14)As depicted in Figure 4 (𝑎 1 ), once we get the key parameters 𝜇(𝑘 ) 𝑡 , 𝜈 (𝑘 )𝑡 , we can determine the center with one degree-of-freedom ambiguity. To elaborate further, as depicted in Figure 4 (𝑎 2 ) the object center will lie on a circle with its center 𝑐 and radius ∥𝑜 -𝑐 ∥ 2 . During inference, divide the circle into bins to generate multiple votes. The bin with the most votes will be regarded as the final prediction.⋆ Orientation Voting.During the rotation process, the direction of upward and rightward exhibit invariance under SO(3), thus enabling voting for the prediction of these directions. To elaborate further, we vote for the upward and rightward direction in the camera space for all the parts of P 𝑡 . The remaining direction is derived using the cross product. Mathematically, we define the upward orientation as 𝑒 1 and rightward orientation as 𝑒 2 (they are both unit vectors). Then we estimate the two angles using the point pair 𝑝 1 and 𝑝 2 :EQUATIONEQUATIONAfterward, we conduct an elaborate partition with a Fibonacci sphere [14] . Bins can be evenly distributed on the sphere, which makes our orientation voting more accurate. As depicted in Figure 4 (𝑏 1 ), once we get the key parameters 𝛼(𝑘 ) 𝑡 , 𝛽 (𝑘 )𝑡 , we can determine the orientation up to one degree-of-freedom ambiguity, which lies in a cone. During inference, we may generate multiple candidates with a constant degree interval around the sphere for each point tuple, demonstrated in Figure 4 (𝑏 2 ). The final prediction will be the orientation with the most votes. Here, the orientation bin size is set to be 1.5 • . ⋆ Scales. Denoting the average bounding box scales as s(𝑘 ) 𝑡 ∈ R 3 and the bounding box scale in a particular stream as s (𝑘 ) 𝑡 ∈ R 3 , we predict the following statistic:EQUATIONDuring inference, 𝛾 Obtain the nearest reference state 𝑡 ′ for 𝑡-th frame from reference state pool 𝐵. EQUATIONwhere ⊙ is the element-wise production. Loss Function. During training, the 𝜇 𝑡𝑖 is GT(𝑖 refers to the 𝑖-th probability of the 𝑖-th bin in the distribution).For the scale, we adopt the MSE loss as the objective function:EQUATIONThe total loss for voting is the sum of all the loss functions above: EQUATION 
experiment 5.1 experimental setup: 
 Datasets. To train our VoCAPTER, we generate the corresponding datasets for training and validation following [22] . Concretely, we first conduct experiments on a synthetic articulated object tracking dataset with the objects from PartNet-Mobility [41] . Next, we generate a semi-synthetic dataset for the articulated object tracking task from ReArt-48 repository [24] , which generates more than 50K frames for each category. To further validate the generalization capacity of our VoCAPTER in real-world scenarios, we test the model trained by RobotArm. Please refer to the supplementary materials for more details about the datasets.Metrics. We use the average precision to conduct the comparison between different methods, which is reported under both intersections over union (3D IoU) and pose error (translation and rotation error). 3D IoU is calculated between the predicted and GT bounding boxes with a threshold 𝛿. 3𝐷 𝛿 is used to represent the Average Precision (%) of over 𝛿% IOU. For pose error, we report the Average Precision (%) under (5 • , 5 cm), (10 • , 5 cm), (15 • , 5 cm) following [19, 48] .Implementation Details. During data pre-processing, input point clouds are downsampled to 2,048 points, and objects in RGB-D images are cropped and projected into the point cloud as network inputs. We set the initial learning rate to 0.001 and utilize cosine learning rate decay during training, with a total of 100 training epochs. All experiments were conducted on four NVIDIA GeForce RTX 4090 GPUs with 24GB of memory.In this section, we conduct experiments on the synthetic dataset containing the articulated objects from PartNet-Mobility [41] . Quantitative experimental results of our VoCAPTER are reported in Table 1. Compared with single-frame pose estimation methods such as those proposed by A-NCSH [18] and OMAD [42] , VoCAPTER demonstrates a significant improvement in per-part pose tracking performance, as evidenced by the 3𝐷 25 and 3𝐷 50 Average Precision metrics. For instance, considering the category Scissors, VoCAPTER achieves Average Precision scores of 65.3%, 68.3%, 65.8%, surpassing the scores obtained by A-NCSH [18] (41.8%, 31.2%, 30.8%) and OMAD [42] (21.0%, 19.8%, 20.6%) in the 3𝐷 25 Average Precision metric. In terms of inference time, VoCAPTER achieves the fastest speed, averaging only 0.08 seconds per frame, surpassing both A-NCSH and OMAD. While our method exhibits only a slight improvement in pose tracking compared to PA-Pose, it demonstrates superior real-time performance. Therefore, we conclude that VoCAPTER effectively implements the voting strategy and fully leverages it in the pose tracking task. Figure 5 shows the qualitative results. It is evident from the results that VoCAPTER closely approximates the GT. This can be attributed to the effectiveness of the universal voting scheme in accurately modeling the geometry of rigid parts, thus enhancing registration accuracy.In this section, we conduct various ablation studies on our Vo-CAPTER. Only the results of base part from the category Dishwasher are reported, which are conducted in the PartNet-Mobility dataset. Orientation bin size. As mentioned in Section 4.4, bins are used for orientation voting and the bin size is set to be 1.5 • . To verify the effects of orientation bin size, the ablation study results can be seen in Table 2 (I -III). From it, we infer that 1.5 • is a better choice for orientation bin size, while larger or smaller bin sizes tend to introduce a big marginal error.Part Awareness. With the aid of the part awareness mechanism, we filter the sampled point pairs for per-part pose tracking (i.e., point pairs for per-part pose tracking should be from the target part meantime). Table 2 (IV -V) shows the ablation experiment results. It can be concluded that the proposed part awareness mechanism helps to achieve better performance. we conjecture this can be attributed to the elimination of noisy point pairs.Experiments on Semi-Synthetic Scenarios. We evaluate the effect of our VoCAPTER on the dataset ReArt-48 with semi-synthetic scenarios. Qualitative results are shown in Figure 6 (Top). The tracking results show that our method can perform well in the semi-synthetic scenarios.Experiments on Real-world Scenarios. To investigate the tracking performance in real-world scenarios, we train and evaluate Vo-CAPTER on the 7-part RobotArm dataset [24] . Figure 6 (Down) shows the qualitative results. It is undeniable to suffer from the effect of the multi-depth structure of the robot arm instance.  
conclusion: 
 In this paper, we propose a novel framework, VoCAPTER, to conduct the category-level articulated object tracking task. Our method first leverages the inter-frame priors to conduct adjacent pose increment estimation task. Afterward, we perform the segmentation at per-part level and filter the noisy point pairs via proposed part awareness. Finally, we use the SE(3)-invariant parameters to vote for the pose at all t frames (𝑡 > 0). Empirical results demonstrate the superiority of our VoCAPTER compared to state-of-the-art methods not only on the synthetic dataset but also on real-world scenarios, which turns out to be a robust and real-time tracking framework. 
: 
 Figure 1 : Given a live point cloud stream or RGB-D images as input, our VoCAPTER can track the per-part pose at all frames 𝑡 > 0 in an online manner. Our main contributions are: 1) we turn the articulated object pose tracking task into an inter-frame pose increment estimation task instead of tracking frame by frame. 2) We leverage the SE(3)-invariant parameters to conduct per-part pose voting instead of direct pose regress. Experiments demonstrate the superiority of our VoCAPTER, not only on the synthetic dataset but also on real-world scenarios. 
 
 Appendix:  


Decision: The submission presents a voting-based pose tracking method for category-level articulated objects, demonstrating its effectiveness through experiments on both synthetic and real-world datasets. The proposed method shows notable accuracy improvements and robust pose estimation by leveraging inter-frame motion information and SE(3)-invariant parameters.

However, some concerns regarding the novelty of certain components and the clarity of explanations were noted by reviewers. Specifically, the canonicalization and part segmentation methods appear to follow existing practices, and the comparative analysis on real-world datasets requires further enhancement. Despite these issues, the paper provides a solid foundation and offers a valuable contribution to the field.

Reviews: review: paper_summary: paper_summary: This paper addresses pose tracking for category-level articulated objects. It proposes a voting-based inter-frame incremental pose estimation method using decomposed features based on Category-based Point Pair Feature (CPPF).
strength_weakness: strength_weakness: - The method incorporates voting-based pose tracking, enabling efficient and accurate pose estimation.
- Comparative experiments on three datasets, PartNet-Mobility, ReArt-48, and RobotArm, show that the method outperforms the existing methods.
limitations: limitations: - The main concern is that the novelty of proposed method is unclear. The novelty of the canonicalization component seems limited, as it is a common practice to use the displacement from the previous frame. The part segmentation component uses PointNet++. A detailed explanation of the part awareness component needs to be included. The pose voting component is a minor tweak of existing methods Beyond PPF and CPFF.
- Figure 4 ($a_2$) and ($b_1$) are misleading. In Fig. 4($a_2$), points $p_1$ and $p_2$ are not necessarily on the circle. In Fig. 4 ($b_1$), points $p_2$ and $p_3$ should be at the centers of the circles, as in Figure 2 in the BeyondPPF paper.
- Minor comments
    - I wonder why 9D pose estimation is necessary for the pose voting step. Isn’t 6D enough if there is no part deformation? I wonder how the mask features $F_2$ and $F(p_1, p_2)$ are fused? In l. 552, how do we calculate the center $\mathbf{c}$ and radius $\lVert \mathbf{o} - \mathbf{c} \rVert_2$ from $\mu_t$, $\nu_t$?
    - In l. 869, the details of the RobotArm dataset are missing. Reference [22] seems to be incorrect.
paper_summary: paper_summary: This paper proposes VoCAPTER, a category-level articulated object tracking method based on inter-frame priors and pose voting strategy. VoCAPTER could conduct real-time tracking manner by utilizing inter-frame information and address ill-posed pose modeling problem by using a voting-based prediction. Experiments on the synthetic and real-word datasets demonstrate the effectiveness of the proposed method.
strength_weakness: strength_weakness: Better accuracy over baseline methods on synthetic datasets
limitations: limitations: 1. Missing comparisons on the semi-synthetic and real-world datasets. Table 3 and Table 4 only contain experimental results of the proposed method, which lack comparison with other methods or any standards. Could the authors provide more comparisons or analyses on these datasets?
2. Some expression of the paper is not clear. Why voting-based prediction is better than regression-based prediction? In lines 165-166, I didn’t understand why the transformation from geometric features to probability features is “more friendly for learning”?
3. I am wondering why the authors didn’t compare the method with CAPTRA, which also could conduct 9DoF articulated object pose tracking? 

Reference: Yijia Weng, He Wang, Qiang Zhou, Yuzhe Qin, Yueqi Duan, Qingnan Fan, Baoquan Chen, Hao Su, Leonidas J. Guibas, CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds, ICCV, 2021.
paper_summary: paper_summary: This research focuses on the problem of category-level joint object position tracking, especially for applications in multi-frame data streaming environments. The paper proposes a new pose tracking framework, VoCAPTER, by transforming the tracking task into an inter-frame pose increment estimation task and introducing SE(3)-invariant parameters for joint part pose voting. VoCAPTER utilizes a priori the motion relations between consecutive frames to update the position of the joint objects through a voting mechanism, providing a new approach to avoid direct pose regression. The experimental results demonstrate the effectiveness and generalization ability of the method in both synthetic and practical application scenarios. The proposal of VoCAPTER provides new perspectives and tools for the problem of joint object position tracking at the category level, and provides effective methodological support for research in this area.
strength_weakness: strength_weakness: 1. Unlike traditional methods that track each frame independently, VoCAPTER utilizes a priori knowledge of continuous motion between frames. This approach not only improves the inference speed, but also improves the accuracy of the pose estimation by considering temporal coherence.
2. Using SE(3)-invariant parameters for component pose voting, VoCAPTER avoids the problems common to direct pose regression, which often struggles to deal with the nonlinearity and complexity of articulated object motion. This feature makes the model more robust to different orientations and motions.
3. the method introduces a probabilistic voting mechanism for pose estimation that effectively handles the uncertainty inherent in joint object tracking across sequential frames. This leads to better generalization and stability in pose prediction.
4. VoCAPTER not only performs well on synthetic datasets, but is also validated in real-world scenarios. This shows its practical applicability and robustness under diverse conditions.
limitations: limitations: 1. The method's dependence on the inter-frame a priori and the voting mechanism may increase the computational overhead, which may affect its real-time applicability in resource-limited environments compared to simpler direct regression methods.
2. the performance of VoCAPTER relies heavily on the quality of the inter-frame motion information. In scenarios with drastic or highly unpredictable inter-frame variations, the accuracy of the system may be degraded.
3. while effective for the objects tested, it is unclear how the method can be extended to objects with more joints or more complex motion chains.
4. Although VoCAPTER shows good generalization ability in experiments, it is still a challenge to further verify its performance in more real-world scenarios .
5. When dealing with noisy or abnormal data, although some sensing mechanisms are introduced to improve the stability of prediction, how to ensure that it can still maintain high efficiency and accuracy in extreme situations is a problem that needs further research.