Title: Novel Class Discovery for Point Cloud Segmentation via Joint Learning of Causal Representation and Reasoning
Venue: NeurIPS 2025
Accepted: yes
Keywords: Point Cloud Segmentation

Novel Class Discovery

Causal Learning
Abstract: In this paper, we focus on Novel Class Discovery for Point Cloud Segmentation (3D-NCD), aiming to learn a model that can segment unlabeled (novel) 3D classes using only the supervision from labeled (base) 3D classes. The key to this task is to setup the exact correlations between the point representations and their base class labels, as well as the representation correlations between the points from base and novel classes. A coarse or statistical correlation learning may lead to the confusion in novel class inference. lf we impose a causal relationship as a strong correlated constraint upon the learning process, the essential point cloud representations that accurately correspond to the classes should be uncovered. To this end, we introduce a structural causal model (SCM) to re-formalize the 3D-NCD problem and propose a new method, i.e., Joint Learning of Causal Representation and Reasoning. Specifically, we first analyze hidden confounders in the base class representations and the causal relationships between the base and novel classes through SCM. We devise a causal representation prototype that eliminates confounders to capture the causal representations of base classes. A graph structure is then used to model the causal relationships between the base classes' causal representation prototypes and the novel class prototypes, enabling causal reasoning from base to novel classes. Extensive experiments and visualization results on 3D and 2D NCD semantic segmentation demonstrate the superiorities of our method.


Peer reviews:
comments: SUMMARY:
This paper introduces a novel approach for Novel Class Discovery (NCD) in 3D point cloud segmentation. The proposed method uses adversarial training to eliminate confounding factors (shortcut features) in base class representations, extracting causal prototypes that generalize better to novel classes. The causal relationships between base and novel classes are modeled via a graph structure. Causal pruning and direction consistency constraints are applied to refine pseudo-label generation. Experiments on SemanticKITTI and SemanticPOSS show that the proposed method achieves state-of-the-art performance, particularly in handling long-tailed distributions and low-similarity novel classes. The method also generalizes to 2D-NCD tasks.

STRENGTHS AND WEAKNESSES:
Strengths:
1. This is the first attempt to integrate structural causal models (SCMs) into 3D-NCD, addressing spurious correlations in point cloud data.
2. The proposed method outperforms comparative methods in imbalanced and low-similarity scenarios.
3. The proposed method can be extended to 2D-NCD and demonstrate broad applicability.

Weaknesses:
1. The proposed method requires predefining the number of novel classes, limiting adaptability in dynamic open-world scenarios.
2. This paper lacks a detailed analysis of experimental results. For example, in split 2 of Table 1, SNOPS outperforms the proposed method on novel-class segmentation accuracy by a large margin.

QUESTIONS:
Are the inferred causal relationships (e.g., B→N) always valid, or could they introduce bias?

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

comments: SUMMARY:
This paper addresses novel class discovery (NCD) for 3D point cloud segmentation. The authors introduce a causality-based framework that jointly learns causal representations by removing confounding factors and performs causal reasoning to infer novel classes. The method involves causal prototype learning and a causal reasoning graph. The approach shows strong performance on both 3D and 2D benchmarks.

STRENGTHS AND WEAKNESSES:
Strengths:

+ The paper is well-written and well-organized, with clear motivations and effective presentation of the overall framework.
+ The presented framework is the first method to incorporate structural causal modeling into 3D NCD, offering new insights to the domain. The proposed modules are reasonable.
+ The proposed method achieves strong performance compared to prior approaches, as demonstrated on multiple 3D and 2D NCD benchmarks. And the ablation studies are also sufficient and detailed. 

Weaknesses:
- The effectiveness of the causal reasoning graph relies on the existence of meaningful causal relationships between base and novel classes. However, such assumptions may not hold for arbitrary class splits. A discussion or analysis on how sensitive the method is to such assumptions would strengthen the paper. 
- Missing related work. Several recent works on 3D segmentation are highly relevant and should be cited:
   - Graph Regulation Network for Point Cloud Segmentation (TPAMI 2024)
   - Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation (ICLR 2025)

QUESTIONS:
1. Providing visualizations and analysis of failure cases will further improve the paper’s quality and offer insights for future research directions.
2. Please refer to the weakness part and address the concerns there.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the problem of Novel Class Discovery (NCD) for 3D point cloud segmentation. A causality-driven framework is proposed to jointly learn causal representations and reasoning paths. Specifically, it eliminates confounding factors via adversarial training to extract causal prototypes for base classes, and constructs a causal reasoning graph to infer novel class prototypes. A GCN is then used to propagate semantic information and generate pseudo-labels. The method achieves strong performance on 3D LiDAR point cloud benchmarks and 2D NCD semantic segmentation datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nCausal Learning for Zero-Shot 3D Segmentation. This paper introduces a new causal representation learning paradigm into the 3D point cloud semantic segmentation task, using structural causal modeling and adversarial deconfounding to extract semantically aligned prototypes and improve generalization from base to novel classes. The approach is built on solid theoretical foundations and is shown to be practically effective through well-integrated model design and empirical results.\n\nGood Transferability Across Modalities. The proposed method generalizes well to 2D novel class discovery tasks (e.g., PASCAL-5i, COCO-20i), demonstrating its strong transferability across modalities without major architectural changes.\n\nComprehensive Comparisons. The approach is extensively validated on both 3D (SemanticKITTI, SemanticPOSS) and 2D benchmarks, with detailed quantitative comparisons and clear visualizations showing superior zero-shot segmentation quality over SOTA baselines.\n\nWeaknesses:\n\nConfounder Representation U is Implicit. The proposed confounding factor U is handled through adversarial training strategy, but it lacks interpretability or visual validation. The role and nature of the confounder representation U remain abstract for me.\n\nLack of analysis on base class prototypes. Since the graph nodes are constructed from base class prototypes, a lack of diversity or clear separation among them would make it difficult to establish reliable causal paths from base to novel classes. Thus one underlying assumption of the method is that the base classes are sufficiently diverse and semantically distinct to support meaningful causal reasoning. This is not explicitly discussed or analyzed, but is critical for the effectiveness of the causal graph and prototype transfer.\n\nLimited Evaluation Scope. Although the method performs strongly on outdoor LiDAR datasets (SemanticKITTI, SemanticPOSS), its generalizability to indoor 3D point clouds (e.g., S3DIS, ScanNet) remains unexplored, which limits claims about broader applicability.\n\nQUESTIONS:\nPlease also refer to the weaknesses part for more details. My main questions include:\n\nCan the authors provide more interpretation of the confounding factor U to clarify its role in the model?\n\nWhether the base class prototypes are sufficiently diverse and well-separated to support reliable base-to-novel causal reasoning?\n\nHave the authors considered testing on indoor 3D benchmarks such as S3DIS or ScanNet? Given the 3D-specific nature of the approach, evaluating on diverse 3D scenarios seems more critical than demonstrating generalization to 2D tasks. Since indoor 3D point clouds are highly relevant for applications in robotic perception.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the Novel Class Discovery (NCD) problem in 3D point cloud segmentation, where only labeled base classes are available during training. The authors introduce a causal learning framework to eliminate shortcut features (confounders) and model causal relationships between base and novel classes. The method involves: (1) learning causal representation prototypes via adversarial deconfounding; (2) constructing a graph-based causal reasoning module; and (3) using graph convolutional networks (GCN) to generate pseudo-labels for novel classes. The paper demonstrates superior performance on SemanticKITTI, SemanticPOSS, and also shows 2D extensions on PASCAL-5i and COCO-20i datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n+ The integration of SCM and causal representation learning in 3D-NCD is novel and well-justified.\n\n+ The paper provides solid theoretical grounding (e.g., ICM principle, causal DAGs) and detailed loss formulations.\n\n+ Evaluation is thorough across multiple datasets, with clear ablation studies and visualizations. The performance is solid.\n\n\nWeaknesses and questions:\n- The “shortcut features” (U) are not concretely defined or visualized; more intuitive examples or empirical evidence would strengthen the argument.\n\n- While ablations exist, the impact of individual causal constraints (e.g., pruning, direction consistency) could be analyzed in more detail.\n\n- How robust is the causal reasoning framework when base and novel classes have no obvious semantic overlap (e.g., ‘person’ vs. ‘tree’)?\n\n- The choice of MinkowskiUNet as the 3D backbone, while serviceable, is relatively outdated compared to more modern architectures like Point Transformer V3. The authors should justify this decision, and it would be better to give its potential impact on overall performance and generality.\n\n- The paper employs an adversarial loss inspired by GANs to learn confounding factors. However, further explanation is needed regarding why this approach is theoretically sound in the causal learning context. Additionally, could more recent generative models (e.g., diffusion models) offer similar inspirations?\n\n\nMinors:\n- Please spell out \"Directed Acyclic Graph (DAG)\" in full at its first occurrence in the main text to aid clarity for readers unfamiliar with causal graph terminology. Check for other ones as well.\n\n- The font style and formatting in Figures 1 and 2 significantly hinder readability. In particular, the use of italicized text throughout Figure 1 is unnecessary and visually distracting. Meanwhile, the demo point clouds are too small. Clearer typesetting and label formatting would enhance the presentation quality.\n\nQUESTIONS:\nSee Weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}