SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Zheyun Qin $^{1*}$ , Cheng Han $^{2*}$ , Qifan Wang $^{3}$ , Nie Xiushan $^{4}$ , Yilong Yin $^{1\dagger}$ , Xiankai Lu $^{1\dagger}$ $^{1}$ Shandong University,  $^{2}$ Rochester Institute of Technology,  $^{3}$ Meta AI,  $^{4}$ Shandong Jianzhu University
The task of point cloud segmentation, comprising semantic, instance, and panoptic segmentation, has been mainly tackled by designing task-specific network architectures, which often lack the flexibility to generalize across tasks, thus resulting in a fragmented research landscape. In this paper, we introduce PROTOSEG, a prototype-based model that unifies semantic, instance, and panoptic segmentation tasks. Our approach treats these three homogeneous tasks as a classification problem with different levels of granularity. By leveraging a Transformer architecture, we extract point embeddings to optimize prototype-class distances and dynamically learn class prototypes to accommodate the end tasks. Our prototypical design enjoys simplicity and transparency, powerful representational learning, and ad-hoc explainability. Empirical results demonstrate that PROTOSEG outperforms concurrent well-known specialized architectures on 3D point cloud benchmarks, achieving  $72.3\%$ ,  $76.4\%$  and  $74.2\%$  mIoU for semantic segmentation on S3DIS, ScanNet V2 and SemanticKITTI,  $66.8\%$  mCov and  $51.2\%$  mAP for instance segmentation on S3DIS and ScanNet V2,  $62.4\%$  PQ for panoptic segmentation on SemanticKITTI, validating the strength of our concept and the effectiveness of our algorithm. The code and models are available at https://github.com/zyqin19/PROTOSEG.
Point cloud segmentation entails partitioning a collection of 3D data points into meaningful categories to interpret complex 3D scenes. Three classical subdivision segmentation tasks are involved: semantic segmentation, which assigns a class label to each point based on its class; instance segmentation, which distinguishes between individual instances of the same class; and panoptic segmentation, which combines semantic and instance segmentation to address amorphous or uncountable regions. These tasks use distinct technical approaches, which have advanced each individual task but lack flexibility to generalize to other tasks. This methodological convention leads to fragmented research efforts.
For the purpose of advancing the field of point cloud segmentation in a synergistic manner, a shift from task-specific network architectures towards a more universal framework is imperative. It is natural to question why the existing methods cannot achieve such unification, and we address this issue in § 2 by examining the relationships and distinctions between current approaches. Many of these approaches are customized for individual tasks, resulting in data representation, feature extraction, and prediction head inconsistencies that hinder integration into a unified framework [1]. Furthermore, task-specific architectures tend to overfit their respective tasks, reducing their generalizability when applied to other segmentation tasks, thus hindering the practical unification of segmentation tasks.
In light of the above discussion, our research question becomes more fundamental: what epistemological framework can effectively embrace this shift? In response, we revisit the prototype paradigm and point clouds' natural properties (irregular and sparse), and propose that point cloud segmentation naturally aligns with a classification problem. This foundational perspective shapes our research direction and motivates the creation of our prototype-anchored classification method, allowing us to unify various segmentation tasks within a single framework seamlessly. In § 3, we introduce
*Equal contribution. † Corresponding authors.
Figure 1: PROTOSEG unifies three 3D segmentation tasks (i.e., semantic, instance, and panoptic) from the prototypical view, and greatly suppresses existing specialized and unified models.
PROtoSEG, a prototype-anchored classification method that unifies semantic, instance, and panoptic segmentation. To achieve this, we employ a Transformer [2] backbone to extract point embeddings and represent classes as prototypes. This approach allows for task-specific outputs based on various granularity annotations (i.e., semantic, instance, and scene), without architectural modification. By utilizing a dynamic association and updating mechanism, PROtoSEG captures class-wise characteristics and intra-class variance. Ultimately, PROtoSEG creates well-structured embedded spaces with inter-class separation and intra-class compactness by optimizing point-prototype-class distances.
Concretely, our innovations focus on three aspects. ① Simplicity and transparency: PROTOSEG has an intuitive working mechanism [3] as the statistical meaning of class sub-centroids makes it elegant and easy to understand. This leads to the automatic discovery of underlying data structures, as the latent distribution of each class is automatically mined and fully captured as a set of representative local means (see §3.2). This contrasts with non-prototype methods [4] that learn a single weight (or query) vector per class, which may struggle to accommodate rich intra-class variations [5, 6]. ② Representation learning: PROTOSEG achieves point-wise classification through comparisons between data samples and class sub-centroids in the embedding space. This distance-based nature enables PROTOSEG to blend unsupervised sub-pattern mining – class-wise clustering (see Eqs. 13-14) with supervised representation learning – learnable prototype classification (see Eq. 15) in a synergistic manner. Significant local patterns are automatically identified to facilitate classification decisions, while the supervisory signal from classification directly optimizes the representation, in turn boosting meaningful clustering and discriminative features learning. ③ Ad-hoc explainability: PROTOSEG is a transparent classifier, imbued with a built-in case-based reasoning process, establishing  $IF \cdots$  Then rules [7]. This is achieved by allowing the class sub-centroids to serve as representative samples from the training set, thus providing human-understandable explanations for each prediction (see Fig. 3). Such an ad-hoc explainability adheres to the internal decision-making process, thereby differentiating PROTOSEG from many existing methods [8] that fail to elucidate precisely how a model works.
Incorporating these innovations, we present an elegant, general, and flexible framework for point cloud segmentation that achieves remarkable results (see Fig. 1) on widely recognized 3D point cloud semantic, instance, and panoptic segmentation tasks. Specifically, our method achieves notable performance gains in terms of  $91.5\% \rightarrow 92.2\%$  OAcc,  $75.2\% \rightarrow 76.4\%$  mIoU and  $75.4\% \rightarrow 76.3\%$  mIoU for semantic segmentation on S3DIS [9] Area 5 and ScanNet V2 [10] test/val. sets,  $64.7\% \rightarrow 66.8\%$  mCov,  $50.6\% \rightarrow 51.2\%$  mAP and  $67.6\% \rightarrow 68.4\%$  mAP<sub>50</sub> for instance segmentation on S3DIS [9] Area 5 and ScanNet V2 [10] test/val. sets, and  $61.5\% \rightarrow 62.4\%$  PQ for panoptic segmentation on SemanticKITTI [11] test set when compared to main competitors. Accompanied by a comprehensive series of ablation studies in §5.4, our extensive experiments evaluations confirm the strength of our concept and the effectiveness of our prototype-anchored classification algorithm.
2 Existing Task-Specialized Segmentation Models as Non-Prototype Learners
In this section, we provide an overview of existing methods for point cloud segmentation that are tailored to specific tasks, such as semantic, instance, or panoptic segmentation. We will discuss the key aspects of these methods, including data representation, feature extraction, and projection head.
A key challenge is the inherent customization of segmentation methods for specific tasks. From a broader perspective, it's evident that semantic segmentation methods might not directly apply to instance segmentation tasks and vice versa. For instance, the semantic segmentation method PTv2 [2] lacks modules for instance-specific localization and segmentation. This makes it inadequate for capturing the detailed spatial relationships required for precise instance boundary delineation and separation. In contrast, the instance segmentation method SoftGroup [12] uses grouping mechanisms to link points belonging to the same instance. While such a mechanism is essential for instance segmentation, it could introduce unwarranted complexity and overhead in semantic segmentation scenarios, where distinguishing individual instances isn't the primary goal.
On a finer scale, we observe inconsistencies across data representation, feature extraction, and prediction heads (Eqs. 1,2,3) in these methods. To illustrate, let's first explore the differences between the data representations that make up the initial steps of the segmentation. For a given 3D point cloud scene  $\mathcal{M}$  comprised of a set of points, data can be represented in multiple ways, including projection-based [13], voxel-based [14, 15] and point-based [16, 17, 18, 19, 2] representations. Mathematically, the input data representation through function  $\rho(\cdot)$  can be expressed as:
$$
\boldsymbol {X} = \rho (\mathcal {M}). \tag {1}
$$
After the data representation is obtained, an encoder  $\phi$  is employed to extract point cloud embeddings:
$$
\boldsymbol {E} = \phi (\boldsymbol {X}), \tag {2}
$$
where  $\phi$  incorporates techniques such as object detection [20, 21], clustering [22, 23] or graph learning [24, 25] to enhance the embedding representation based on the backbone network.
Finally, each point embedding  $\pmb{e}_i \in \pmb{E}$  is fed into the projection head for segmentation [26]. Let  $\pmb{V} = [\pmb{v}^1, \dots, \pmb{v}^C] \in \mathbb{R}^{C \times D}$  be a set of class-specific vectors, where  $C$  is the number of class,  $D$  is vector dimension,  $\pmb{v}^c \in \mathbb{R}^D$  represents the linear weight or query vector in MLP-based [18, 2, 19, 2] or Query-based models [27, 28] for the  $c^{th}$  class. The probability that a point example  $\pmb{x}_i \in \pmb{X}$  with embedding  $\pmb{e}_i \in \mathbb{R}^D$  is assigned to class  $c$  can be expressed with inner product as follows:
$$
p (c | \boldsymbol {x} _ {i}) = \frac {\exp \left(\left(\boldsymbol {v} ^ {c}\right) ^ {\top} \boldsymbol {e} _ {i}\right)}{\sum_ {c ^ {\prime} = 1} ^ {C} \exp \left(\left(\boldsymbol {v} ^ {c ^ {\prime}}\right) ^ {\top} \boldsymbol {e} _ {i}\right)}. \tag {3}
$$
These above inconsistencies in data representation, feature extraction, and projection head further complicate the unification of segmentation tasks, as the learned features may not be directly transferable across different tasks. In addition, the segmentation methods employed may not be optimal for specific tasks, limiting their applicability and performance in a unified framework. Despite these methods necessitating different processing techniques, thereby complicating the unification of the tasks, they can collectively be categorized as Non-Prototype Learners.
3 Universal Segmentation Models as Prototype Classifiers
To address the challenges, we propose a unified framework that interprets different segmentation tasks as unique granularity classification problems. This approach crafts a prototype classifier, eliminating extra computational load and task-specific architectures. Specifically, let  $\mathcal{M}$  be a 3D point cloud scene containing a set of points  $\{\pmb{x}_i\}$ . The objective of point cloud segmentation is to assign each point  $\pmb{x}_i$  to the class assignment set:
$$
\operatorname {P o i n t S e g} (\mathcal {M}): \boldsymbol {x} _ {i} \mapsto \left\{\mathcal {A} _ {c} \left(\boldsymbol {x} _ {i}\right) \mid \mathcal {A} _ {c} \left(\boldsymbol {x} _ {i}\right) \in \{0, 1 \}, c = \{1, \dots , C \} \right\} \tag {4}
$$
where  $\mathcal{A}_c(\pmb{x}_i)$  indicates whether the point  $\pmb{x}_i\in \mathcal{M}$  belongs (1) or does not belong (0) to class  $c$ . Distinct from Eqs. 1-3 in §2, we seamlessly integrate 3D point cloud segmentation under Eq.4, adopting a perspective of prototype-anchored classification, considering various levels of granularity.
3.2 Prototypical Classifiers for Segmentation
We propose a unified prototypical classifier for specific segmentation tasks. To achieve this, we leverage a set of prototypes  $\pmb{P} = \{\pmb{p}_k^c\}_{c,k=1}^{C,K} \in \mathbb{R}^{D \times C \times K}$ , where  $\pmb{p}_k^c \in \mathbb{R}^D$  denotes the center of the  $k^{th}$  sub-cluster (property) of training point samples belonging to class  $c$ . For a given point sample  $\pmb{x}_i$ ,
Figure 2: Overall pipeline of PROTOSEG. The classification of points is predicted by evaluating the minimum distance between the embedding  $e_i$  (extracted by encoder  $\phi$ ) and prototype  $p_k^c$  via our Prototype Association and Update mechanism (see Eq. 10, Eq. 12 in §3.2). Subsequently, supervision scheme optimizes  $\phi$  by minimizing the point-prototype-class distance (see Eqs. 13-15 in §3.3). Ad-hoc explains the decision-making process of our classification model, detailed in §5.4.
we make predictions by comparing point embedding  $e_i \in \mathbb{R}^D$ , extracted using a simple Transformer encoder  $\phi$ , with prototype  $\pmb{p}_k^c$  and assigning the corresponding prototype's class as the response:
$$
p (c | \boldsymbol {x} _ {i}) = \frac {\exp (- d _ {\boldsymbol {e} _ {i} , c})}{\sum_ {c ^ {\prime} = 1} ^ {C} \exp (- d _ {\boldsymbol {e} _ {i} , c ^ {\prime}})}, \text {w i t h} d _ {\boldsymbol {e} _ {i}, c} = \arg \min  \left\{\langle \boldsymbol {e} _ {i}, \boldsymbol {p} _ {k} ^ {c} \rangle \right\} _ {k = 1} ^ {K}, \tag {5}
$$
where the negative cosine distance measure  $\langle \cdot, \cdot \rangle$  is defined as  $\langle e_i, p_k^c \rangle = -e_i^\top p_k^c$ .
To get the informative prototype that can represent the properties of the class, we first associate the point embeddings to the prototypes belonging to the same class (i.e., Prototype Association) and then dynamically update prototypes according to the assignments (i.e., Prototype Update).
Prototype Association. Given embeddings of point samples  $\pmb{E}^{c} = \{e_{i}^{c}\}_{i=1}^{N_{c}} \in \mathbb{R}^{D \times N_{c}}$  in a training batch and prototypes  $\pmb{P}^{c} = \{\pmb{p}_{k}^{c}\}_{k=1}^{K} \in \mathbb{R}^{D \times K}$  for class  $c$ , we aim to maximize the similarities  $\mathcal{J}(M)$  between the associated point sample embeddings and corresponding class prototypes:
$$
\max  _ {M} \mathcal {J} (M) = \operatorname {T r} \left(\left(M ^ {c}\right) ^ {\top} \left(P ^ {c}\right) ^ {\top} E ^ {c}\right), \tag {6}
$$
where  $\mathrm{Tr}(\cdot)$  denotes the matrix trace,  $M^c = \{\pmb{m}_{k,i}^c\}_{k,i=1}^{K,N_c} \in \{0,1\}^{K \times N_c}$  is a point-to-prototype permutation matrix that denotes the association between point and prototype,  $\pmb{m}_{k,i}^c \in \{0,1\}$  denotes the one-hot assignment vector that assigns the point sample  $\pmb{x}_i$  to the prototype  $k$  of the class  $c$ .
To accommodate the representation of all classes for  $M^c$ , we impose two constraints aimed at avoiding a trivial solution in which all point samples are assigned to a single prototype [29]:
$$
\left(\boldsymbol {M} ^ {\mathrm {c}}\right) ^ {\top} \mathbf {1} _ {K} = \mathbf {1} _ {N _ {\mathrm {c}}}, \quad \left(\boldsymbol {M} ^ {\mathrm {c}}\right) ^ {\top} \mathbf {1} _ {N _ {\mathrm {c}}} = \frac {N _ {\mathrm {c}}}{K} \mathbf {1} _ {K}, \tag {7}
$$
where  $\mathbf{1}_K, \mathbf{1}_{N_c}$  denotes the tensor of all ones of  $K$  or  $N_c$  dimensions. The former Unique assignment Constraint ensures that each point embedding is assigned to one and only one prototype across all classes. The latter Equipartition Constraint enforces that each prototype is selected an equal number of times within the batch on average. The implementation of these constraints is of utmost importance as it significantly enhances the representative capability of the prototypes for each class.
For more intuitive, Eq. 6 can be rewritten as estimating the optimal  $M^{c,*}$ :
$$
M ^ {c, *} = \max  _ {M} \operatorname {T r} \left(\left(M ^ {c}\right) ^ {\top} \left(P ^ {c}\right) ^ {\top} E ^ {c}\right). \tag {8}
$$
Following [30], we smooth Eq. 8 with an entropic regularization term  $h(M^c)$ :
$$
\boldsymbol {M} ^ {c, *} = \max  _ {\boldsymbol {M} ^ {c}} \operatorname {T r} \left(\left(\boldsymbol {M} ^ {c}\right) ^ {\top} \left(\boldsymbol {P} ^ {c}\right) ^ {\top} \boldsymbol {E} ^ {c}\right) + \kappa h \left(\boldsymbol {M} ^ {c}\right), \quad \text {s . t .} \quad \boldsymbol {M} ^ {c} \in \mathbb {R} _ {+} ^ {K \times N}, \tag {9}
$$
where  $h(M^c) = \sum_{k,i} - m_{k,i}^c\log m_{k,i}^c$  reduces the randomness of association and avoids most point samples associating to one single prototype. We control the association smoothness using a small value  $\kappa$  to ensure that each point embedding is assigned to only one prototype.
With the entropic regularization term in Eq. 9, the problem can be solved using the Sinkhorn-Knopp iteration [30] on the renormalization vectors  $\pmb{u}^c\in \mathbb{R}^K$  and  $\pmb{v}^{c}\in \mathbb{R}^{N^{c}}$ . After a few iterations, the vectors  $\pmb{u}^{c}$  and  $\pmb{v}^{c}$  converge, and then the permutation matrix  $M^{*}$  can be directly calculated as:
$$
M ^ {c, *} = \operatorname {d i a g} \left(\boldsymbol {u} ^ {c}\right) \exp \left(\frac {\left(\boldsymbol {P} ^ {c}\right) ^ {\top} \boldsymbol {E} ^ {c}}{\kappa}\right) \operatorname {d i a g} \left(\boldsymbol {v} ^ {c}\right), \tag {10}
$$
where  $\mathrm{diag}(\cdot)$  denotes a diagonal matrix formed by the input vector. The hyper-parameter  $\kappa$  balances the convergence speed and stability of Eq. 10 in addition to smoothing the association (Eq. 9). A smaller  $\kappa$  can lead to slower convergence but more accurate results, while a larger  $\kappa$  does the opposite. We just use  $\kappa = 0.05$  following [30] for our experiments, not extensively fine-tuned. The resulting matrix  $M^{c,*}$  satisfies both the unique assignment and equipartition constraints, effectively mapping each point sample  $\pmb{x}_i$  in the training batch to its corresponding prototype  $\pmb{p}_k^c$  for each class  $c$ .
Prototype Update. In previous solutions [31, 5], the prototypes are typically computed by determining the centers of the corresponding embedded point samples and subsequently updating them based on the online clustering results. However, this strategy may not effectively mitigate the widespread class imbalance issue, resulting in sub-optimal prototypes specifically for rare classes.
To overcome this limitation, we introduce a prototype calibration strategy with a learnable calibration factor, which estimates the representative level of each class prototype showing the importance of tailed class prototype embeddings. The calibration factor is determined by a rectified sigmoid function that adjusts the distance between the prototypes  $\pmb{P}^{c} = \{\pmb{p}_{k}^{c}\}_{k=1}^{K}$  of class  $c$  and the point embedding set  $\{e_{i}^{c}\}_{i=1}^{N_{c}}$  assigned to  $\pmb{p}_{k}^{c}$  by prototype association in current training batch:
$$
w ^ {c} = \frac {1}{N _ {c} K} \sum_ {i = 1} ^ {N _ {c}} \sum_ {k = 1} ^ {K} \frac {1}{1 + \exp \left(\left(\boldsymbol {e} ^ {c _ {i}}\right) ^ {\top} \boldsymbol {p} _ {k} ^ {c}\right)}, \quad w ^ {c} \in [ 0, 1 ]. \tag {11}
$$
Thus, the formula for the modified prototype updating method with prototype calibration is as follows:
$$
\boldsymbol {p} _ {k} ^ {c} \leftarrow \mu \boldsymbol {p} _ {k} ^ {c} + w ^ {c} (1 - \mu) \bar {\boldsymbol {E}} ^ {c}, \tag {12}
$$
where  $\mu \in [0,1]$  is a momentum coefficient, and  $\bar{\pmb{E}}^c\in \mathbb{R}^D$  is the mean vector of  $\{e_i^c\}_{i = 1}^{N_c}$ .
3.3 Supervision Scheme
Based on prototype assignment and update, we get the learned prototypes where  $\{\pmb{p}_k^c\}_{k=1}^K$  has class representation ability and is typical for their corresponding class  $c$ , and  $\pmb{p}_k^c$  represents  $k^{th}$  property within the class  $c$ . In order to shape well-structured embedded spaces, we designed a comprehensive supervision scheme based on metric learning to enhance inter-class prototypes separation (see  $\mathcal{L}_{\mathrm{PPS}}$  in Eq. 13), intra-class point-prototype compactness (see  $\mathcal{L}_{\mathrm{PPC}}$  in Eq. 14), and inter-class point-class separation (see  $\mathcal{L}_{\mathrm{PCS}}$  in Eq. 15) through optimizing point-prototype-class distances.
Prototype-Prototype Distances Optimization. In order to differentiate between different prototypes within the same class (i.e., inter-class prototypes separation), we utilize a prototype-based metric learning strategy to enhance the relationship between points and their assigned prototypes within a specific class. By utilizing the point-to-prototype permutation matrix  $M^{c,*}$ , this strategy encourages each point embedding  $e_i^c$  to be similar to its assigned ('positive') prototype  $p_{k_i}^{c_i}$  and dissimilar to other irrelevant ('negative') prototypes  $\mathcal{P}^{-} = \{\pmb{p}_{k_j}^{c_j}|\pmb{p}_{k_j}^{c_j}\in \{\pmb{p}_k^c\}_{c,k = 1}^{C,K}$  and  $p_{k_j}^{c_j}\neq p_{k_i}^{c_i}\}$ :
$$
\mathcal {L} _ {\mathrm {P P S}} = - \log \frac {\exp \left(\left(\boldsymbol {e} _ {i} ^ {c}\right) ^ {\top} \boldsymbol {p} _ {k _ {i}} ^ {c _ {i}} / \tau\right)}{\exp \left(\left(\boldsymbol {e} _ {i} ^ {c}\right) ^ {\top} \boldsymbol {p} _ {k _ {i}} ^ {c _ {i}} / \tau\right) + \sum_ {\boldsymbol {p} _ {k _ {j}} ^ {c _ {j}} \in \mathcal {P} ^ {-}} \exp \left(\left(\boldsymbol {e} _ {i} ^ {c}\right) ^ {\top} \boldsymbol {p} _ {k _ {j}} ^ {c _ {j}} / \tau\right)}, \tag {13}
$$
where  $\tau$  controls the concentration level of the distributions over classes.
Point-Prototype Distance Optimization. To reduce intra-cluster variation, we directly minimize the distance between each embedded point  $e_i^c$  and its assigned prototype  $p_{k_i}^{c_i}$  (i.e., intra-class point-prototype compactness), resulting in tighter and more coherent class representations:
$$
\mathcal {L} _ {\mathrm {P P C}} = \left(1 - \left(\boldsymbol {e} _ {i} ^ {c}\right) ^ {\top} \boldsymbol {p} _ {k _ {i}} ^ {c _ {i}}\right) ^ {2}. \tag {14}
$$
Point-Class Distances Optimization. In addition to the constraints between point-prototype, for more accurate classification prediction, we aim to correctly associate points with their respective classes (i.e., inter-class point-class separation). Specifically, we push the embedded points closer to
their corresponding class (i.e.,  $c_i$ ) and enlarge the distance from other irrelevant classes (i.e.,  $c' \neq c_i$ ). Given the groundtruth class  $c_i$  of each point embedding  $e_i$ , the cross-entropy loss is used for  $\mathcal{L}_{\mathrm{PCS}}$  as:
$$
\mathcal {L} _ {\mathrm {P C S}} = - \log \frac {\exp \left(- d _ {\boldsymbol {e} _ {i} , c _ {i}}\right)}{\exp \left(- d _ {\boldsymbol {e} _ {i} , c _ {i}}\right) + \sum_ {c ^ {\prime} \neq c _ {i}} \exp \left(- d _ {\boldsymbol {e} _ {i} , c ^ {\prime}}\right)}, \text {w i t h} d _ {\boldsymbol {e} _ {i}, c} = \arg \min  \left\{\langle \boldsymbol {e} _ {i}, \boldsymbol {p} _ {k} ^ {c} \rangle \right\} _ {k = 1} ^ {K} \tag {15}
$$
where the point-class distance  $d_{\pmb{e}_i,c} \in [-1,1]$  is the distance to the closest prototype of class  $c$ .
Total Losses. Our prototypical classifier can effectively optimize the point embedding space extracted through  $\phi$  by minimizing the combined loss over all the training point samples:
$$
\mathcal {L} _ {\text {T o t a l}} = \mathcal {L} _ {\mathrm {P C S}} + \alpha \mathcal {L} _ {\mathrm {P P S}} + \beta \mathcal {L} _ {\mathrm {P P C}}, \tag {16}
$$
where  $\alpha, \beta$  are hyperparameters that control the trade-off between inter-class separation, intra-class compactness, and correct classification. By minimizing  $\mathcal{L}_{\mathrm{Total}}$ , our model segments the point cloud effectively while producing compact and discriminative class representations in the embedding space.