Recovering 6D Object Pose and Predicting Next-Best-View in the Crowd
Object detection and 6D pose estimation in the crowd (scenes with multiple object instances, severe foreground occlusions and background distractors), has become an important problem in many rapidly evolving technological areas such as robotics and augmented reality. Single shot-based 6D pose estimators with manually designed features are still unable to tackle the above challenges, motivating the research towards unsupervised feature learning and next-best-view estimation. In this work, we present a complete framework for both single shot-based 6D object pose estimation and next-best-view prediction based on Hough Forests, the state of the art object pose estimator that performs classification and regression jointly. Rather than using manually designed features we a) propose an unsupervised feature learnt from depth-invariant patches using a Sparse Autoencoder and b) offer an extensive evaluation of various state of the art features. Furthermore, taking advantage of the clustering performed in the leaf nodes of Hough Forests, we learn to estimate the reduction of uncertainty in other views, formulating the problem of selecting the next-best-view. To further improve pose estimation, we propose an improved joint registration and hypotheses verification module as a final refinement step to reject false detections. We provide two additional challenging datasets inspired from realistic scenarios to extensively evaluate the state of the art and our framework. One is related to domestic environments and the other depicts a bin-picking scenario mostly found in industrial settings. We show that our framework significantly outperforms state of the art both on public and on our datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
6D Pose Estimation6D Pose Estimation using RGBClusteringObjectobject-detectionObject DetectionPose EstimationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Short-term Object Interaction Anticipation with Disentangled Object Detection @ Ego4D Short Term Object Interaction Anticipation Challenge
Short-term object interaction anticipation is an important task in egocentric video analysis, including precise predictions of future interactions and their timings as well as the categories and positions of the involved…
Objectobject-detectionObject DetectionShort-term Object Interaction AnticipationMulti-Object Discovery by Low-Dimensional Object Motion
Recent work in unsupervised multi-object segmentation shows impressive results by predicting motion from a single image despite the inherent ambiguity in predicting motion without the next image. On the other hand, the s…
Depth EstimationMonocular Depth EstimationMulti-object discoveryObject+3Predicting Next Local Appearance for Video Anomaly Detection
We present a local anomaly detection method in videos. As opposed to most existing methods that are computationally expensive and are not very generalizable across different video scenes, we propose an adversarial framew…
Anomaly DetectionObjectVideo Anomaly DetectionPredicting What You Already Know Helps: Provable Self-Supervised Learning
Self-supervised representation learning solves auxiliary prediction tasks (known as pretext tasks) without requiring labeled data to learn useful semantic representations. These pretext tasks are created solely using the…
Representation LearningSelf-Supervised LearningNext-Best-View Estimation based on Deep Reinforcement Learning for Active Object Classification
The presentation and analysis of image data from a single viewpoint are often not sufficient to solve a task. Several viewpoints are necessary to obtain more information. The next-best-view problem attempts to find the o…
Deep Reinforcement LearningObjectReinforcement Learning (RL)