title: Point-MoE: Towards Cross-Domain Generalization in 3D Semantic
    Segmentation via Mixture-of-Experts
summary: While scaling laws have transformed natural language processing and computer
  vision, 3D point cloud understanding has yet to reach that stage. This can be
  attributed to both the comparatively smaller scale of 3D datasets, as well as
  the disparate sources of the data itself. Point clouds are captured by diverse
  sensors (e.g., depth cameras, LiDAR) across varied domains (e.g., indoor,
  outdoor), each introducing unique scanning patterns, sampling densities, and
  semantic biases. Such domain heterogeneity poses a major barrier towards
  training unified models at scale, especially under the realistic constraint
  that domain labels are typically inaccessible at inference time. In this work,
  we propose Point-MoE, a Mixture-of-Experts architecture designed to enable
  large-scale, cross-domain generalization in 3D perception. We show that
  standard point cloud backbones degrade significantly in performance when
  trained on mixed-domain data, whereas Point-MoE with a simple top-k routing
  strategy can automatically specialize experts, even without access to domain
  labels. Our experiments demonstrate that Point-MoE not only outperforms strong
  multi-domain baselines but also generalizes better to unseen domains. This work
  highlights a scalable path forward for 3D understanding: letting the model
  discover structure in diverse 3D data, rather than imposing it via manual
  curation or domain supervision.
paper_authors._id: 683d33be277ad05e5a672f79
paper_authors.name: Xuweiyi Chen
paper_authors._id: 683d33be277ad05e5a672f7a
paper_authors.name: Wentao Zhou
paper_authors._id: 683d33be277ad05e5a672f7b
paper_authors.name: Aruni RoyChowdhury
paper_authors._id: 683d33be277ad05e5a672f7c
paper_authors.name: Zezhou Cheng
paper_title: Point-MoE: Towards Cross-Domain Generalization in 3D Semantic
    Segmentation via Mixture-of-Experts
paper_summary: While scaling laws have transformed natural language processing and computer
  vision, 3D point cloud understanding has yet to reach that stage. This can be
  attributed to both the comparatively smaller scale of 3D datasets, as well as
  the disparate sources of the data itself. Point clouds are captured by diverse
  sensors (e.g., depth cameras, LiDAR) across varied domains (e.g., indoor,
  outdoor), each introducing unique scanning patterns, sampling densities, and
  semantic biases. Such domain heterogeneity poses a major barrier towards
  training unified models at scale, especially under the realistic constraint
  that domain labels are typically inaccessible at inference time. In this work,
  we propose Point-MoE, a Mixture-of-Experts architecture designed to enable
  large-scale, cross-domain generalization in 3D perception. We show that
  standard point cloud backbones degrade significantly in performance when
  trained on mixed-domain data, whereas Point-MoE with a simple top-k routing
  strategy can automatically specialize experts, even without access to domain
  labels. Our experiments demonstrate that Point-MoE not only outperforms strong
  multi-domain baselines but also generalizes better to unseen domains. This work
  highlights a scalable path forward for 3D understanding: letting the model
  discover structure in diverse 3D data, rather than imposing it via manual
  curation or domain supervision.
paper_ai_summary: Point-MoE, a Mixture-of-Experts architecture, enables large-scale, cross-domain generalization in 3D perception by automatically specializing experts without domain labels.
paper_ai_keywords: Mixture-of-Experts Point-MoE point cloud backbones 3D perception domain heterogeneity domain labels top-k routing multi-domain baselines