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SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects
Acquiring accurate depth information of transparent objects using off-the-shelf RGB-D cameras is a well-known challenge in Computer Vision and Robotics.
Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detection
To address this problem, we propose a method that trains purely on synthetic data and successfully transfers to the real world using sim2real methods by creating datasets of filled food trays using high-quality 3d models of real pieces of food for the training instance segmentation models.
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