Search Results for author:
Found 10 papers, 6 papers with code
Deep Projective Rotation Estimation through Relative Supervision
While self-supervised learning has been used successfully for translational object keypoints, in this work, we show that naively applying relative supervision to the rotational group $SO(3)$ will often fail to converge due to the non-convexity of the rotational space.
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement
Accurate object rearrangement from vision is a crucial problem for a wide variety of real-world robotics applications in unstructured environments.
OSSID: Online Self-Supervised Instance Detection by (and for) Pose Estimation
In this paper, we propose the OSSID framework, leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm.
Self-Supervised Point Cloud Completion via Inpainting
The aim of this work is to learn to complete these partial point clouds, giving us a full understanding of the object's geometry using only partial observations.
ZePHyR: Zero-shot Pose Hypothesis Rating
We also demonstrate how our system can be used by quickly scanning and building a model of a novel object, which can immediately be used by our method for pose estimation.
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning.
Robust Instance Tracking via Uncertainty Flow
Current state-of-the-art trackers often fail due to distractorsand large object appearance changes.
Cloth Region Segmentation for Robust Grasp Selection
Our approach trains a network to segment the edges and corners of a cloth from a depth image, distinguishing such regions from wrinkles or folds.
Robotics
Learning Orientation Distributions for Object Pose Estimation
Our first method, which regresses from deep learned features to an isotropic Bingham distribution, gives the best performance for orientation distribution estimation for non-symmetric objects.
Just Go with the Flow: Self-Supervised Scene Flow Estimation
When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects.
