upload a_badcases sample

a_badcases/a_badcase1/18_0069_psnr19.10_iiith_soccer_004_5_877_925/ego_GT.mp4

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a_badcases/a_badcase1/18_0069_psnr19.10_iiith_soccer_004_5_877_925/exo.mp4

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a_badcases/a_badcase1/18_0069_psnr19.10_iiith_soccer_004_5_877_925/target.mp4

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a_badcases/a_badcase1/description.txt

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+由于算法设计（recam），exo输入需要与ego同分辨率，而原先的exo（732*448）与ego（448*448）并不一致，故采取对exo进行裁剪的方案。
+具体裁剪方式为，确定ego相机在exo画面内的平均位置（其可通过ego外参、exo外参、exo内参计算得到），再按此位置作为中心，对exo进行裁剪。
+此法让ego相机都存在于exo画面内，但被裁剪掉的exo部分则成了不可见区域，模型需自己推断该不可见区域的布局。
+如图 原exo 和 exo_crop 所示，右边的围栏成了不可见区域，在 target预测 中恢复不到与 GT 完全一致的场景。但核心区域的ego视角还是能得到较好的生成。

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+  "name": "AUTOLab_84bd5053_2023-07-21-13h-18m-54s",
+  "prompt": "Remove the blue object from the dowel"
+}

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a_badcases/a_badcase3/description.txt

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+在此抓取场景中，由于exo视角较远，导致目标区域的清晰度较低，模型也难以仅依赖exo恢复到真实的物体状态。