Transparent object perception is a rapidly developing research problem in artificial
intelligence. The ability to perceive transparent objects enables robots to achieve higher …

Glass surface detection is challenging as glass normally borrows similar visual appearances
from the arbitrary objects/scenes behind it. Although some methods have been proposed to …

Meta AI Research has recently released SAM (Segment Anything Model) which is trained on
a large segmentation dataset of over 1 billion masks. As a foundation model in the field of …

Transparent objects widely exist in the world. The task of transparent object segmentation is
challenging as the object lacks its own texture. The cue of shape information therefore gets …

Recent research has shown significant interest in image-based glass surface detection
(GSD). However, detecting glass surfaces in dynamic scenes remains largely unexplored …

Reflective surfaces present a persistent challenge for reliable 3D mapping and perception in
robotics and autonomous systems. However, existing reflection datasets and benchmarks …

Concealed object segmentation (COS) is a challenging task that involves localizing and
segmenting those concealed objects that are visually blended with their surrounding …

Glass surfaces are ubiquitous in human life environment, such as glass windows, glass
doors, glass guardrails and glass walls. Most glass surfaces are transparent without intrinsic …

Mirror detection aims to discover mirror regions in images to avoid misidentifying reflected
objects. Existing methods mainly mine clues from spatial domain. We observe that the …

Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly
characterized by their visual appearances because they contain the visual appearance of …