166 citations · 328 across the 12 of their papers we have counts for
9 papers · 1 filter
Efficient Multi-Object Pose Estimation using Multi-Resolution Deformable Attention and Query Aggregation
Arul Selvam Periyasamy, Vladimir Tsaturyan, Sven Behnke
Object pose estimation is a long-standing problem in computer vision. Recently, attention-based vision transformer models have achieved state-of-the-art results in many computer vi…
YOLOPose V2: Understanding and Improving Transformer-based 6D Pose Estimation
Arul Selvam Periyasamy, Arash Amini, Vladimir Tsaturyan +1
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network…
Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels
Arul Selvam Periyasamy, Luis Denninger, Sven Behnke
Object pose estimation is a necessary prerequisite for autonomous robotic manipulation, but the presence of symmetry increases the complexity of the pose estimation task. Existing…
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…
YOLOPose: Transformer-based Multi-Object 6D Pose Estimation using Keypoint Regression
Arash Amini, Arul Selvam Periyasamy, Sven Behnke
6D object pose estimation is a crucial prerequisite for autonomous robot manipulation applications. The state-of-the-art models for pose estimation are convolutional neural network…
T6D-Direct: Transformers for Multi-Object 6D Pose Direct Regression
Arash Amini, Arul Selvam Periyasamy, Sven Behnke
6D pose estimation is the task of predicting the translation and orientation of objects in a given input image, which is a crucial prerequisite for many robotics and augmented real…
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…
RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter
Max Schwarz, Anton Milan, Arul Selvam Periyasamy +1
Autonomous robotic manipulation in clutter is challenging. A large variety of objects must be perceived in complex scenes, where they are partially occluded and embedded among many…