Computer Science > Computer Vision and Pattern Recognition
arXiv:2411.16668 (cs)
[Submitted on 25 Nov 2024]
Title:Diffusion Features for Zero-Shot 6DoF Object Pose Estimation
View a PDF of the paper titled Diffusion Features for Zero-Shot 6DoF Object Pose Estimation, by Bernd Von Gimborn and 3 other authors
View PDFHTML (experimental)Abstract:Zero-shot object pose estimation enables the retrieval of object poses from images without necessitating object-specific training. In recent approaches this is facilitated by vision foundation models (VFM), which are pre-trained models that are effectively general-purpose feature extractors. The characteristics exhibited by these VFMs vary depending on the training data, network architecture, and training paradigm. The prevailing choice in this field are self-supervised Vision Transformers (ViT). This study assesses the influence of Latent Diffusion Model (LDM) backbones on zero-shot pose estimation. In order to facilitate a comparison between the two families of models on a common ground we adopt and modify a recent approach. Therefore, a template-based multi-staged method for estimating poses in a zero-shot fashion using LDMs is presented. The efficacy of the proposed approach is empirically evaluated on three standard datasets for object-specific 6DoF pose estimation. The experiments demonstrate an Average Recall improvement of up to 27% over the ViT baseline. The source code is available at:this https URL.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| MSC classes: | 68T45 |
| ACM classes: | I.4.8 |
| Cite as: | arXiv:2411.16668 [cs.CV] |
| (orarXiv:2411.16668v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2411.16668 arXiv-issued DOI via DataCite |
Submission history
From: Stefan Thalhammer [view email][v1] Mon, 25 Nov 2024 18:53:56 UTC (3,038 KB)
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View a PDF of the paper titled Diffusion Features for Zero-Shot 6DoF Object Pose Estimation, by Bernd Von Gimborn and 3 other authors
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