Benchmarking 6D Object Pose Estimation for Robotics

Benchmarking 6D object pose estimation for robotics is not straightforward as sufficient accuracy depends on many factors, e.g., the selected gripper, dimensions, weight and material of an object, grasping point, and the robot task itself. We formulate the problem as a successful grasp, i.e. for a fixed set of factors affecting the task, will the given pose estimate provide sufficiently good grasp to complete the task. The successful grasp is modelled in a probabilistic framework by sampling in the pose error space and executing the task and automatically detecting success or failure. Hours of sampling and thousands of samples are used to construct a non-parametric probability of a successful grasp given the pose residual. The framework is experimentally validated with real objects and assembly tasks and comparison of several state-of-the-art point cloud based 3D pose estimation methods.

Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF) exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease at every iteration of our framework, while their output is regularized by a nonlocal prior implicit within the NLF. Unlike complicated neural networks that embed the nonlocality prior within the layers of the network, our framework is modular, it uses standard pre-trained CNNs together with standard nonlocal filters. An instance of the proposed framework, called NN3D, is evaluated over large grayscale image datasets showing state-of-the-art performance.