4 accepted papers
Nighttime self-supervised monocular depth estimation has received increasing attention in recent years. However, using night images for self-supervision is unreliable because the photometric consistency assumption is usually violated in the videos taken under complex lighting conditions. Even with d…
Most nighttime semantic segmentation studies are based on domain adaptation approaches and image input. However, limited by the low dynamic range of conventional cameras, images fail to capture structural details and boundary information in low-light conditions. Event cameras, as a new form of visio…
This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture. We ease the learning process by obtaining coarse camera poses from monocular sequences through multi-view geometry to deal with the former. Howeve…
Manipulating deformable objects, such as ropes (1D), fabrics (2D), and bags (3D), poses a significant challenge in robotics research due to their high degree of freedom in physical state and nonlinear dynamics. Compared with single-dimensional deformable objects, multi-dimensional object manipulatio…