MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific
  Generative Modeling
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.
67dacbe75f7f898d5eb2d0ad
Damian Boborzi
claimed_verified
2025-03-19T14:25:21.075Z
64a6a1d2d09682887da149ea
/avatars/eab8441d681616fe44521f6a3c0cf60b.svg
Damian Boborzi
user
DamianBoborzi
67dacbe75f7f898d5eb2d0ae
Phillip Mueller
claimed_verified
2025-03-20T19:17:15.432Z
6352f30dfc9b7ad0a53e7b79
/avatars/6dea5212d5d7017e60eb49bff7a3f9b2.svg
Phillip Mueller
user
P-M-Mueller24
67dacbe75f7f898d5eb2d0af
Jonas Emrich
67dacbe75f7f898d5eb2d0b0
Dominik Schmid
67dacbe75f7f898d5eb2d0b1
Sebastian Mueller
claimed_verified
2025-03-20T10:46:02.872Z
64f5e17e095cabd5e82e6fb5
/avatars/f82337208e4ea3e50394752a50c41e7e.svg
Sebastian  Müller
user
snimu
67dacbe75f7f898d5eb2d0b2
Lars Mikelsons
MeshFleet: Filtered and Annotated 3D Vehicle Dataset for Domain Specific
  Generative Modeling
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.
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.
MeshFleet Objaverse-XL quality classifier DINOv2 SigLIP embeddings caption-based analysis uncertainty estimation SV3D