Depth Sensor
Depth sensors, devices that measure the distance to objects, are crucial for various applications, from robotics and autonomous driving to augmented reality and medical imaging. Current research focuses on improving depth sensor accuracy and robustness, particularly in challenging environments (e.g., low light, transparent objects), often employing techniques like sensor fusion (combining data from multiple sensors), deep learning (neural networks for depth enhancement and completion), and novel algorithms for efficient data processing and noise reduction. These advancements are driving significant improvements in 3D scene understanding and object manipulation, impacting fields ranging from warehouse automation to minimally invasive surgery.
Papers
Self-Supervised Monocular Depth Underwater
Shlomi Amitai, Itzik Klein, Tali Treibitz
Unsupervised confidence for LiDAR depth maps and applications
Andrea Conti, Matteo Poggi, Filippo Aleotti, Stefano Mattoccia
FloatingFusion: Depth from ToF and Image-stabilized Stereo Cameras
Andreas Meuleman, Hakyeong Kim, James Tompkin, Min H. Kim