DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Experts
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866908958901403648 |
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| author | Xing, Mingwei Wang, Xinliang Shi, Yifeng |
| author_facet | Xing, Mingwei Wang, Xinliang Shi, Yifeng |
| contents | Constructing a unified 3D scene understanding model has long been hindered by the significant topological discrepancies across different sensor modalities. While applying the Mixture-of-Experts (MoE) architecture is an effective approach to achieving universal understanding, we observe that existing 3D MoE networks often suffer from semantics-driven routing bias. This makes it challenging to address cross-domain data characterized by "semantic consistency yet topological heterogeneity." To overcome this challenge, we propose DoReMi (Topology-Aware Domain-Representation Mixture of Experts). Specifically, we introduce a self-supervised pre-training branch based on multi attributes, such as topological and texture variations, to anchor cross-domain structural priors. Building upon this, we design a domain-aware expert branch comprising two core mechanisms: Domain Spatial-Guided Routing (DSR), which achieves an acute perception of local topological variations by extracting spatial contexts, and Entropy-controlled Dynamic Allocation (EDA), which dynamically adjusts the number of activated experts by quantifying routing uncertainty to ensure training stability. Through the synergy of these dual branches, DoReMi achieves a deep integration of universal feature extraction and highly adaptive expert allocation. Extensive experiments across various tasks, encompassing both indoor and outdoor scenes, validate the superiority of DoReMi. It achieves 80.1% mIoU on the ScanNet validation set and 77.2% mIoU on S3DIS, comprehensively outperforming existing state-of-the-art methods. The code will be released soon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Experts Xing, Mingwei Wang, Xinliang Shi, Yifeng Computer Vision and Pattern Recognition Constructing a unified 3D scene understanding model has long been hindered by the significant topological discrepancies across different sensor modalities. While applying the Mixture-of-Experts (MoE) architecture is an effective approach to achieving universal understanding, we observe that existing 3D MoE networks often suffer from semantics-driven routing bias. This makes it challenging to address cross-domain data characterized by "semantic consistency yet topological heterogeneity." To overcome this challenge, we propose DoReMi (Topology-Aware Domain-Representation Mixture of Experts). Specifically, we introduce a self-supervised pre-training branch based on multi attributes, such as topological and texture variations, to anchor cross-domain structural priors. Building upon this, we design a domain-aware expert branch comprising two core mechanisms: Domain Spatial-Guided Routing (DSR), which achieves an acute perception of local topological variations by extracting spatial contexts, and Entropy-controlled Dynamic Allocation (EDA), which dynamically adjusts the number of activated experts by quantifying routing uncertainty to ensure training stability. Through the synergy of these dual branches, DoReMi achieves a deep integration of universal feature extraction and highly adaptive expert allocation. Extensive experiments across various tasks, encompassing both indoor and outdoor scenes, validate the superiority of DoReMi. It achieves 80.1% mIoU on the ScanNet validation set and 77.2% mIoU on S3DIS, comprehensively outperforming existing state-of-the-art methods. The code will be released soon. |
| title | DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Experts |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.11232 |