研究生: |
蕭子謦 Hsiao, Tsu-Ching |
---|---|
論文名稱: |
基於特殊歐幾里得三維群之分數擴散模型解決六維物體姿態估計中的模糊性 Confronting Ambiguity in 6D Object Pose Estimation via Score-Based Diffusion on SE(3) |
指導教授: |
李濬屹
Lee, Chun-Yi |
口試委員: |
陳煥宗
Chen, Hwann-Tzong 劉育綸 Liu, Yu-Lun |
學位類別: |
碩士 Master |
系所名稱: |
電機資訊學院 - 資訊工程學系 Computer Science |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 英文 |
論文頁數: | 45 |
中文關鍵詞: | 電腦視覺 、物體姿態估計 、擴散模型 、李群 |
外文關鍵詞: | Computer Vision, Object Pose Estimation, Diffusion Model, Lie Group |
相關次數: | 點閱:2 下載:0 |
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從單一RGB圖像中,解決由物體的對稱性或遮擋所造成的姿態模糊性,並精準預測物體的6D姿態是一個重大的挑戰。為了應對這一挑戰,我們提出了一種新穎的基於特殊歐幾里得三維群 SE(3) 的分數擴散模型。這是首次將基於 SE(3) 的分數擴散模型應用到圖像領域中解決姿態估計問題。實驗數據顯示,該方法在處理姿態模糊性與減輕影像透視引起的模糊性上能達到卓越的效果,同時也展示我們對 SE(3) 提出的替代斯坦分數公式具有很好的穩健性。這種公式不僅提高了朗之萬動力學方程式在 SE(3) 上的收歛性,也增強了斯坦分數的計算效率。因此,我們開發出一種有潛力的6D物體姿態估計方法。
Addressing accuracy limitations and pose ambiguity in 6D object pose estimation from single RGB images presents a significant challenge, particularly due to object symmetries or occlusions. In response, we introduce a novel score-based diffusion method applied to the SE(3) group, marking the first application of diffusion models to SE(3) within the image domain, specifically tailored for pose estimation tasks. Extensive evaluations demonstrate the method's efficacy in handling pose ambiguity, mitigating perspective-induced ambiguity, and showcasing the robustness of our surrogate Stein score formulation on SE(3). This formulation not only improves the convergence of Langevin dynamics but also enhances computational efficiency. Thus, we pioneer a promising strategy for 6D object pose estimation.
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