title: MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific
    Generative Modeling
summary: Generative models have recently made remarkable progress in the field of 3D
  objects. However, their practical application in fields like engineering
  remains limited since they fail to deliver the accuracy, quality, and
  controllability needed for domain-specific tasks. Fine-tuning large generative
  models is a promising perspective for making these models available in these
  fields. Creating high-quality, domain-specific 3D datasets is crucial for
  fine-tuning large generative models, yet the data filtering and annotation
  process remains a significant bottleneck. We present MeshFleet, a filtered and
  annotated 3D vehicle dataset extracted from Objaverse-XL, the most extensive
  publicly available collection of 3D objects. Our approach proposes a pipeline
  for automated data filtering based on a quality classifier. This classifier is
  trained on a manually labeled subset of Objaverse, incorporating DINOv2 and
  SigLIP embeddings, refined through caption-based analysis and uncertainty
  estimation. We demonstrate the efficacy of our filtering method through a
  comparative analysis against caption and image aesthetic score-based techniques
  and fine-tuning experiments with SV3D, highlighting the importance of targeted
  data selection for domain-specific 3D generative modeling.
paper_authors._id: 67dacbe75f7f898d5eb2d0ad
paper_authors.name: Damian Boborzi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-19T14:25:21.075Z
paper_authors._id: 64a6a1d2d09682887da149ea
paper_authors.avatarUrl: /avatars/eab8441d681616fe44521f6a3c0cf60b.svg
paper_authors.fullname: Damian Boborzi
paper_authors.type: user
paper_authors.user: DamianBoborzi
paper_authors._id: 67dacbe75f7f898d5eb2d0ae
paper_authors.name: Phillip Mueller
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-20T19:17:15.432Z
paper_authors._id: 6352f30dfc9b7ad0a53e7b79
paper_authors.avatarUrl: /avatars/6dea5212d5d7017e60eb49bff7a3f9b2.svg
paper_authors.fullname: Phillip Mueller
paper_authors.type: user
paper_authors.user: P-M-Mueller24
paper_authors._id: 67dacbe75f7f898d5eb2d0af
paper_authors.name: Jonas Emrich
paper_authors._id: 67dacbe75f7f898d5eb2d0b0
paper_authors.name: Dominik Schmid
paper_authors._id: 67dacbe75f7f898d5eb2d0b1
paper_authors.name: Sebastian Mueller
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-20T10:46:02.872Z
paper_authors._id: 64f5e17e095cabd5e82e6fb5
paper_authors.avatarUrl: /avatars/f82337208e4ea3e50394752a50c41e7e.svg
paper_authors.fullname: Sebastian  Müller
paper_authors.type: user
paper_authors.user: snimu
paper_authors._id: 67dacbe75f7f898d5eb2d0b2
paper_authors.name: Lars Mikelsons
paper_title: MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific
    Generative Modeling
paper_summary: Generative models have recently made remarkable progress in the field of 3D
  objects. However, their practical application in fields like engineering
  remains limited since they fail to deliver the accuracy, quality, and
  controllability needed for domain-specific tasks. Fine-tuning large generative
  models is a promising perspective for making these models available in these
  fields. Creating high-quality, domain-specific 3D datasets is crucial for
  fine-tuning large generative models, yet the data filtering and annotation
  process remains a significant bottleneck. We present MeshFleet, a filtered and
  annotated 3D vehicle dataset extracted from Objaverse-XL, the most extensive
  publicly available collection of 3D objects. Our approach proposes a pipeline
  for automated data filtering based on a quality classifier. This classifier is
  trained on a manually labeled subset of Objaverse, incorporating DINOv2 and
  SigLIP embeddings, refined through caption-based analysis and uncertainty
  estimation. We demonstrate the efficacy of our filtering method through a
  comparative analysis against caption and image aesthetic score-based techniques
  and fine-tuning experiments with SV3D, highlighting the importance of targeted
  data selection for domain-specific 3D generative modeling.
paper_ai_summary: MeshFleet, a high-quality, domain-specific 3D vehicle dataset, uses an automated filtering pipeline with DINOv2 and SigLIP embeddings to improve fine-tuning of generative models for engineering applications.
paper_ai_keywords: MeshFleet Objaverse-XL quality classifier DINOv2 SigLIP embeddings caption-based analysis uncertainty estimation SV3D