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arxiv: 2604.16954 · v1 · submitted 2026-04-18 · 💻 cs.CV

Recognition: unknown

TSM-Pose: Topology-Aware Learning with Semantic Mamba for Category-Level Object Pose Estimation

Jinshuo Liu , Bingtao Ma , Junlin Su , Guanyuan Pan , Beining Wu , Cheng Yang , Jiaxuan Lu , Chenggang Yan
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Shuai Wang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 06:46 UTC · model grok-4.3

classification 💻 cs.CV
keywords category-level object pose estimationtopology extractionsemantic mambapoint cloudkeypoint modelingglobal feature aggregationembodied intelligencegeneralization to unseen instances
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The pith

A topology extractor combined with a semantic Mamba aggregator enables better generalization to unseen objects in category-level pose estimation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Category-level object pose estimation aims to determine the 3D position and orientation of objects from a known category even when the specific instance is new. Existing approaches often rely on basic feature handling that misses consistent shapes across a category and fails to use semantic information effectively at keypoints. The proposed TSM-Pose adds a Topology Extractor that derives a global topological view from the input point cloud and merges it with local geometric details. It pairs this with a Mamba-based Global Semantic Aggregator that embeds semantic priors into keypoints and applies TwinMamba blocks to capture long-range connections across the data. Experiments across three standard datasets show these additions yield higher accuracy than prior techniques, pointing toward more reliable performance in settings that require handling object variation.

Core claim

TSM-Pose introduces a Topology Extractor to capture the global topological representation of the point cloud and integrate it into local geometry features for robust category-level structural representation, together with a Mamba-based Global Semantic Aggregator that injects semantic priors into keypoints to boost their expressiveness and employs multiple TwinMamba blocks to model long-range dependencies for more effective global feature aggregation.

What carries the argument

The Topology Extractor for global topological representations from point clouds integrated with local features, and the Mamba-based Global Semantic Aggregator using TwinMamba blocks to incorporate semantic priors and long-range dependency modeling.

If this is right

  • The method produces more robust category-level structural representations that support generalization to novel object instances.
  • Semantic priors injected via the aggregator increase the usefulness of modeled keypoints for pose determination.
  • Long-range dependency modeling through TwinMamba blocks improves the quality of aggregated global features over the point cloud.
  • The overall pipeline delivers higher accuracy than prior state-of-the-art approaches on the REAL275, CAMERA25, and HouseCat6D benchmarks.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same topology and semantic-aggregation ideas could be tested on related 3D tasks such as part segmentation or shape completion where category-consistent structures are useful.
  • Because Mamba blocks are used for efficiency in long-range modeling, the approach may scale to larger point clouds or real-time robotic settings with lower compute cost than attention-based alternatives.
  • If the gains persist outside the three benchmarks, the framework could lower the data requirements for training pose estimators in varied real-world environments.

Load-bearing premise

The reported performance gains arise specifically because the Topology Extractor and Mamba aggregator capture category-shared topological structures and semantic priors rather than from other unmentioned details of the training process or evaluation setup.

What would settle it

An ablation study on the REAL275, CAMERA25, or HouseCat6D datasets in which the Topology Extractor or the TwinMamba blocks are removed and the pose estimation accuracy shows no meaningful decrease compared to the full model.

Figures

Figures reproduced from arXiv: 2604.16954 by Beining Wu, Bingtao Ma, Chenggang Yan, Cheng Yang, Guanyuan Pan, Jiaxuan Lu, Jinshuo Liu, Junlin Su, Shuai Wang.

Figure 1
Figure 1. Figure 1: TUMAP visualizations of local geometric features (Point [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Overall framework of TSM-Pose. We fuse multimodal features [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: We visualized and compared TSM-Pose and our baseline [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Topological representations of six object categories. For each category, the Betti curves (top) and the corresponding persistence [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
read the original abstract

Category-level object pose estimation is fundamental for embodied intelligence, yet achieving robust generalization to unseen instances remains challenging. However, existing methods mainly rely on simple feature extraction and aggregation, which struggle to capture category-shared topological structures and conduct semantic keypoint modeling, limiting their generalization. To address these, we propose a \textbf{T}opology-Aware Learning with \textbf{S}emantic \textbf{M}amba for Category-Level \textbf{P}ose Estimation framework (TSM-Pose). Specifically, we introduce a Topology Extractor to capture the global topological representation of the point cloud, which is integrated into local geometry features and enables robust category-level structural representation. Simultaneously, we propose a Mamba-based Global Semantic Aggregator that injects semantics priors into keypoints to enhance their expressiveness and leverages multiple TwinMamba blocks to model long-range dependencies for more effective global feature aggregation. Extensive experiments on three benchmark datasets (REAL275, CAMERA25, and HouseCat6D) demonstrate that TSM-Pose outperforms existing state-of-the-art methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes TSM-Pose, a framework for category-level object pose estimation from point clouds. It introduces a Topology Extractor to capture global topological representations and integrate them with local geometry features for robust category-level structural modeling. It also presents a Mamba-based Global Semantic Aggregator that injects semantic priors into keypoints and employs multiple TwinMamba blocks to model long-range dependencies for global feature aggregation. Extensive experiments on the REAL275, CAMERA25, and HouseCat6D benchmarks are reported to show outperformance over existing state-of-the-art methods.

Significance. If the reported gains are attributable to the Topology Extractor and TwinMamba components rather than uncontrolled factors, the work could advance category-level pose estimation by demonstrating how explicit topological and semantic modeling with efficient Mamba blocks improves generalization to unseen instances, with relevance to embodied AI and robotics applications.

major comments (2)
  1. [Experiments] The central claim of outperformance on REAL275, CAMERA25, and HouseCat6D lacks ablation studies that disable or replace the Topology Extractor and TwinMamba blocks (while freezing all other hyperparameters, data splits, and optimization settings). Without these controlled comparisons, it is impossible to attribute the gains specifically to the claimed topological and semantic mechanisms rather than implementation details or baseline re-implementations.
  2. [Method] The description of the Topology Extractor (global topology integrated into local geometry) and the Mamba-based aggregator remains high-level in the method; no explicit equations or pseudocode detail the fusion operation or how TwinMamba blocks inject semantic priors, making it difficult to verify that these components capture category-shared structures beyond standard point-cloud processing.
minor comments (1)
  1. [Abstract] The abstract introduces 'TwinMamba blocks' without a brief definition or forward reference, which may reduce immediate clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the paper to strengthen the experimental validation and methodological clarity.

read point-by-point responses

  1. Referee: [Experiments] The central claim of outperformance on REAL275, CAMERA25, and HouseCat6D lacks ablation studies that disable or replace the Topology Extractor and TwinMamba blocks (while freezing all other hyperparameters, data splits, and optimization settings). Without these controlled comparisons, it is impossible to attribute the gains specifically to the claimed topological and semantic mechanisms rather than implementation details or baseline re-implementations.

    Authors: We agree that the current experiments do not include the specific controlled ablations requested, which limits direct attribution of gains to the Topology Extractor and TwinMamba components. In the revised manuscript, we will add these studies: one variant with the Topology Extractor disabled (relying only on local geometry features) and another replacing TwinMamba blocks with standard transformer-based aggregation, while strictly freezing all hyperparameters, data splits, and optimization settings. Results will be reported on all three benchmarks to quantify the contribution of each module. revision: yes

  2. Referee: [Method] The description of the Topology Extractor (global topology integrated into local geometry) and the Mamba-based aggregator remains high-level in the method; no explicit equations or pseudocode detail the fusion operation or how TwinMamba blocks inject semantic priors, making it difficult to verify that these components capture category-shared structures beyond standard point-cloud processing.

    Authors: We acknowledge that the method descriptions are high-level and lack the requested explicit details. In the revision, we will expand Section 3 with mathematical formulations: equations for the Topology Extractor showing how global topological representations (e.g., via graph-based or persistent homology features) are fused with local point features through concatenation or attention-based integration; and equations for semantic prior injection in the Mamba aggregator, including the state-space model updates in TwinMamba blocks. Pseudocode for the full pipeline and TwinMamba forward pass will also be added to clarify the long-range dependency modeling. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical performance claims rest on external benchmarks, not self-referential definitions or derivations

full rationale

The paper is a standard empirical CV contribution proposing a new architecture (Topology Extractor + TwinMamba aggregator) and reporting accuracy on three public datasets (REAL275, CAMERA25, HouseCat6D). No equations, parameter-fitting steps, or derivation chain appear in the abstract or described claims. Performance is asserted via direct comparison to external SOTA methods rather than any quantity that reduces to the model's own fitted outputs or self-citations. The central attribution of gains to the proposed modules is an empirical question addressed by experiments, not a logical reduction to inputs by construction. This is the normal non-circular case for applied ML papers.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 2 invented entities

The central claim rests on standard deep-learning assumptions plus two newly introduced modules whose effectiveness is asserted via benchmark results.

free parameters (1)
  • hyperparameters of TwinMamba blocks and Topology Extractor
    Typical learned weights and architectural choices in the proposed neural network components.
axioms (2)
  • domain assumption Point clouds of objects within a category share extractable global topological structures that improve pose estimation when integrated with local features.
    Invoked to justify the Topology Extractor design.
  • domain assumption Injecting semantic priors into keypoints via Mamba blocks enhances expressiveness and long-range dependency modeling for global aggregation.
    Basis for the Global Semantic Aggregator.
invented entities (2)
  • Topology Extractor no independent evidence
    purpose: Capture global topological representation of the point cloud for category-level structural features.
    New module introduced to address limitations of simple feature aggregation.
  • TwinMamba blocks no independent evidence
    purpose: Model long-range dependencies while incorporating semantic priors for keypoint and feature aggregation.
    Novel Mamba variant proposed for the semantic aggregator.

pith-pipeline@v0.9.0 · 5511 in / 1487 out tokens · 46686 ms · 2026-05-10T06:46:35.909848+00:00 · methodology

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