RGB Image
RGB images, representing color information in red, green, and blue channels, are fundamental to computer vision, serving as input for a wide range of tasks. Current research focuses on leveraging RGB data for diverse applications, including 3D object reconstruction (often employing transformer networks and Gaussian splatting), human pose estimation (using graph convolutional networks and privileged information), and robotic manipulation (through Sim2Real transfer and foundation models). These advancements significantly impact fields like robotics, medical imaging, and remote sensing by enabling more robust and efficient solutions for tasks ranging from automated object grasping to flood detection and surgical skill assessment.
Papers
PressureVision++: Estimating Fingertip Pressure from Diverse RGB Images
Patrick Grady, Jeremy A. Collins, Chengcheng Tang, Christopher D. Twigg, Kunal Aneja, James Hays, Charles C. Kemp
A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image
Ivan Bilić, Filip Marić, Ivan Marković, Ivan Petrović
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models
Utkarsh Singhal, Stella X. Yu, Zackery Steck, Scott Kangas, Aaron A. Reite
Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels
Arul Selvam Periyasamy, Luis Denninger, Sven Behnke
DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image
Yijin Li, Xinyang Liu, Wenqi Dong, Han Zhou, Hujun Bao, Guofeng Zhang, Yinda Zhang, Zhaopeng Cui
Simultaneous Acquisition of High Quality RGB Image and Polarization Information using a Sparse Polarization Sensor
Teppei Kurita, Yuhi Kondo, Legong Sun, Yusuke Moriuchi