Virtual Occlusion
Virtual occlusion research focuses on accurately representing and handling the obscuring of objects in images and 3D scenes, crucial for applications like robotics, augmented reality, and 3D human modeling. Current research employs diverse approaches, including deep learning models (e.g., those leveraging diffusion priors or Gaussian splatting) and novel strategies like pseudo-stereo inputs and occlusion-enhanced distillation, to improve object detection and reconstruction even when parts are hidden. These advancements are significantly impacting fields requiring robust perception in complex, cluttered environments, enabling more accurate and efficient object manipulation, scene understanding, and human-computer interaction.
Papers
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