16 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Transparent objects
We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera; and second, we develop a deep neural network, called $KeyPose$, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects.
This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.
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.
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.
Inspired by affinity-based method and the successes of contextual attention in inpainting, we develop a novel end-to-end approach for natural image matting with a guided contextual attention module, which is specifically designed for image matting.
To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10K, consisting of 10, 428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets.
Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem.