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.Comment: CVPR 2016 accepted paper, project page: http://www.iis.ee.ic.ac.uk/rkouskou/6D_NBV.htm

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

Real-time Monocular Object SLAM

We present a real-time object-based SLAM system that leverages the largest object database to date. Our approach comprises two main components: 1) a monocular SLAM algorithm that exploits object rigidity constraints to improve the map and find its real scale, and 2) a novel object recognition algorithm based on bags of binary words, which provides live detections with a database of 500 3D objects. The two components work together and benefit each other: the SLAM algorithm accumulates information from the observations of the objects, anchors object features to especial map landmarks and sets constrains on the optimization. At the same time, objects partially or fully located within the map are used as a prior to guide the recognition algorithm, achieving higher recall. We evaluate our proposal on five real environments showing improvements on the accuracy of the map and efficiency with respect to other state-of-the-art techniques

Robotic Cameraman for Augmented Reality based Broadcast and Demonstration

In recent years, a number of large enterprises have gradually begun to use vari-ous Augmented Reality technologies to prominently improve the audiences’ view oftheir products. Among them, the creation of an immersive virtual interactive scenethrough the projection has received extensive attention, and this technique refers toprojection SAR, which is short for projection spatial augmented reality. However,as the existing projection-SAR systems have immobility and limited working range,they have a huge difficulty to be accepted and used in human daily life. Therefore,this thesis research has proposed a technically feasible optimization scheme so thatit can be practically applied to AR broadcasting and demonstrations. Based on three main techniques required by state-of-art projection SAR applica-tions, this thesis has created a novel mobile projection SAR cameraman for ARbroadcasting and demonstration. Firstly, by combining the CNN scene parsingmodel and multiple contour extractors, the proposed contour extraction pipelinecan always detect the optimal contour information in non-HD or blurred images.This algorithm reduces the dependency on high quality visual sensors and solves theproblems of low contour extraction accuracy in motion blurred images. Secondly, aplane-based visual mapping algorithm is introduced to solve the difficulties of visualmapping in these low-texture scenarios. Finally, a complete process of designing theprojection SAR cameraman robot is introduced. This part has solved three mainproblems in mobile projection-SAR applications: (i) a new method for marking con-tour on projection model is proposed to replace the model rendering process. Bycombining contour features and geometric features, users can identify objects oncolourless model easily. (ii) a camera initial pose estimation method is developedbased on visual tracking algorithms, which can register the start pose of robot to thewhole scene in Unity3D. (iii) a novel data transmission approach is introduced to establishes a link between external robot and the robot in Unity3D simulation work-space. This makes the robotic cameraman can simulate its trajectory in Unity3D simulation work-space and project correct virtual content. Our proposed mobile projection SAR system has made outstanding contributionsto the academic value and practicality of the existing projection SAR technique. Itfirstly solves the problem of limited working range. When the system is running ina large indoor scene, it can follow the user and project dynamic interactive virtualcontent automatically instead of increasing the number of visual sensors. Then,it creates a more immersive experience for audience since it supports the user hasmore body gestures and richer virtual-real interactive plays. Lastly, a mobile systemdoes not require up-front frameworks and cheaper and has provided the public aninnovative choice for indoor broadcasting and exhibitions

Going Further with Point Pair Features

Point Pair Features is a widely used method to detect 3D objects in point clouds, however they are prone to fail in presence of sensor noise and background clutter. We introduce novel sampling and voting schemes that significantly reduces the influence of clutter and sensor noise. Our experiments show that with our improvements, PPFs become competitive against state-of-the-art methods as it outperforms them on several objects from challenging benchmarks, at a low computational cost.Comment: Corrected post-print of manuscript accepted to the European Conference on Computer Vision (ECCV) 2016; https://link.springer.com/chapter/10.1007/978-3-319-46487-9_5

Latent-Class Hough Forests for 3D object detection and pose estimation of rigid objects

In this thesis we propose a novel framework, Latent-Class Hough Forests, for the problem of 3D object detection and pose estimation in heavily cluttered and occluded scenes. Firstly, we adapt the state-of-the-art template-based representation, LINEMOD [34, 36], into a scale-invariant patch descriptor and integrate it into a regression forest using a novel template-based split function. In training, rather than explicitly collecting representative negative samples, our method is trained on positive samples only and we treat the class distributions at the leaf nodes as latent variables. During the inference process we iteratively update these distributions, providing accurate estimation of background clutter and foreground occlusions and thus a better detection rate. Furthermore, as a by-product, the latent class distributions can provide accurate occlusion aware segmentation masks, even in the multi-instance scenario. In addition to an existing public dataset, which contains only single-instance sequences with large amounts of clutter, we have collected a new, more challenging, dataset for multiple-instance detection containing heavy 2D and 3D clutter as well as foreground occlusions. We evaluate the Latent-Class Hough Forest on both of these datasets where we outperform state-of-the art methods.Open Acces

A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds

This paper proposes a segmentation-free, automatic and efficient procedure to detect general geometric quadric forms in point clouds, where clutter and occlusions are inevitable. Our everyday world is dominated by man-made objects which are designed using 3D primitives (such as planes, cones, spheres, cylinders, etc.). These objects are also omnipresent in industrial environments. This gives rise to the possibility of abstracting 3D scenes through primitives, thereby positions these geometric forms as an integral part of perception and high level 3D scene understanding. As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure. At the center of our approach lies a closed form 3D quadric fit, operating in both primal & dual spaces and requiring as low as 4 oriented-points. Around this fit, we design a novel, local null-space voting strategy to reduce the 4-point case to 3. Voting is coupled with the famous RANSAC and makes our algorithm orders of magnitude faster than its conventional counterparts. This is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes. Results on synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201