Transparent Object Depth Estimation
4 papers with code • 1 benchmarks • 3 datasets
Estimating the 3D shape of transparent objects
To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation.
SuperCaustics: Real-time, open-source simulation of transparent objects for deep learning applications
In particular, these synthetic datasets omit features such as refraction, dispersion and caustics due to limitations in the rendering pipeline.
TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline
However, the majority of current grasping algorithms would fail in this case since they heavily rely on the depth image, while ordinary depth sensors usually fail to produce accurate depth information for transparent objects owing to the reflection and refraction of light.
We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas.