Researchers have developed a new differentiable framework for capturing an object's shape and reflectance simultaneously using structured light and a single camera. This method adaptively computes illumination conditions to reduce depth uncertainty, leading to improved depth map and reflectance parameter reconstructions. Separately, a novel privacy plug-in called VPDR has been introduced for personalized federated learning, which enhances privacy-utility trade-offs by adaptively allocating noise to preserve semantic separability. AI
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IMPACT These papers explore novel techniques in computer vision for object reconstruction and privacy-preserving federated learning, potentially advancing research in these areas.
RANK_REASON The cluster contains two distinct academic papers submitted to arXiv.