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Found 10 papers, 2 papers with code
Robotic Table Tennis: A Case Study into a High Speed Learning System
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets.
Instance Segmentation with Cross-Modal Consistency
We demonstrate that this formulation encourages the models to learn embeddings that are invariant to viewpoint variations and consistent across sensor modalities.
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
A whole-scene sparse input representation allows StopNet to scale to predicting trajectories for hundreds of road agents with reliable latency.
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation
It is commonly believed that high internal resolution combined with expensive operations (e. g. atrous convolutions) are necessary for accurate semantic segmentation, resulting in slow speed and large memory usage.
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving
We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving.
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion.
Future Segmentation Using 3D Structure
Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time visual data for decision making.
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Models and examples built with TensorFlow
Ranked #10 on
Unsupervised Monocular Depth Estimation
on Cityscapes
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.
Geometry-Based Next Frame Prediction from Monocular Video
We consider the problem of next frame prediction from video input.
