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Found 6 papers, 4 papers with code
The Distracting Control Suite -- A Challenging Benchmark for Reinforcement Learning from Pixels
Our experiments show that current RL methods for vision-based control perform poorly under distractions, and that their performance decreases with increasing distraction complexity, showing that new methods are needed to cope with the visual complexities of the real world.
What Matters in Unsupervised Optical Flow
We systematically compare and analyze a set of key components in unsupervised optical flow to identify which photometric loss, occlusion handling, and smoothness regularization is most effective.
Ranked #5 on
Optical Flow Estimation
on Sintel Clean unsupervised
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for 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.
Going Further with Point Pair Features
Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter.
On Pre-Trained Image Features and Synthetic Images for Deep Learning
Deep Learning methods usually require huge amounts of training data to perform at their full potential, and often require expensive manual labeling.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.
