In our quantitative comparison on OmniObject3D, we found that our approach achieved significantly better performance compared to other state-of-the-art zero-shot 3D reconstruction methods, demonstrating its robustness across diverse object types.
For Ocrtoc3D, despite the smaller dataset size, our method still considerably outperforms previous state-of-the-art techniques, reaffirming the effectiveness of our approach even on diverse real photo inputs.
In the evaluation on Pix3D, our method maintained state-of-the-art performance across the furniture category, underscoring the need to consider object variety when analyzing comparative metrics.
While Point-E and Shap-E demonstrated competitive results on the Pix3D dataset, we suspect their performance stems from the predominance of similar furniture categories present in their training data.
#3d-reconstruction #state-of-the-art-comparison #machine-learning #computer-vision #dataset-evaluation
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