3 citations · 4 across the 6 of their papers we have counts for
7 papers · 1 filter
ConvPoseCNN2: Prediction and Refinement of Dense 6D Object Poses
Arul Selvam Periyasamy, Catherine Capellen, Max Schwarz +1
Object pose estimation is a key perceptual capability in robotics. We propose a fully-convolutional extension of the PoseCNN method, which densely predicts object translations and…
Stillleben: Realistic Scene Synthesis for Deep Learning in Robotics
Max Schwarz, Sven Behnke
Training data is the key ingredient for deep learning approaches, but difficult to obtain for the specialized domains often encountered in robotics. We describe a synthesis pipelin…
Visual Descriptor Learning from Monocular Video
Umashankar Deekshith, Nishit Gajjar, Max Schwarz +1
Correspondence estimation is one of the most widely researched and yet only partially solved area of computer vision with many applications in tracking, mapping, recognition of obj…
ConvPoseCNN: Dense Convolutional 6D Object Pose Estimation
Catherine Capellen, Max Schwarz, Sven Behnke
6D object pose estimation is a prerequisite for many applications. In recent years, monocular pose estimation has attracted much research interest because it does not need depth me…
Refining 6D Object Pose Predictions using Abstract Render-and-Compare
Arul Selvam Periyasamy, Max Schwarz, Sven Behnke
Robotic systems often require precise scene analysis capabilities, especially in unstructured, cluttered situations, as occurring in human-made environments. While current deep-lea…
Robust 6D Object Pose Estimation in Cluttered Scenes using Semantic Segmentation and Pose Regression Networks
Arul Selvam Periyasamy, Max Schwarz, Sven Behnke
Object pose estimation is a crucial prerequisite for robots to perform autonomous manipulation in clutter. Real-world bin-picking settings such as warehouses present additional cha…