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Found 4 papers, 1 papers with code
Acceleration of Actor-Critic Deep Reinforcement Learning for Visual Grasping in Clutter by State Representation Learning Based on Disentanglement of a Raw Input Image
For a robotic grasping task in which diverse unseen target objects exist in a cluttered environment, some deep learning-based methods have achieved state-of-the-art results using visual input directly.
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation
We show that the proposed method outperforms the state-of-the-art works with significant margin evaluating on challenging benchmarks.
Ranked #9 on
Depth Estimation
on NYU-Depth V2
Efficient Feature Matching by Progressive Candidate Search
We present a novel feature matching algorithm that systematically utilizes the geometric properties of features such as position, scale, and orientation, in addition to the conventional descriptor vectors.
Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks
Kinect skeleton tracker is able to achieve considerable human body tracking performance in convenient and a low-cost manner.
