Learning Descriptors for Object Recognition and 3D Pose Estimation

Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.Comment: CVPR 201

A Survey on Joint Object Detection and Pose Estimation using Monocular Vision

In this survey we present a complete landscape of joint object detection and pose estimation methods that use monocular vision. Descriptions of traditional approaches that involve descriptors or models and various estimation methods have been provided. These descriptors or models include chordiograms, shape-aware deformable parts model, bag of boundaries, distance transform templates, natural 3D markers and facet features whereas the estimation methods include iterative clustering estimation, probabilistic networks and iterative genetic matching. Hybrid approaches that use handcrafted feature extraction followed by estimation by deep learning methods have been outlined. We have investigated and compared, wherever possible, pure deep learning based approaches (single stage and multi stage) for this problem. Comprehensive details of the various accuracy measures and metrics have been illustrated. For the purpose of giving a clear overview, the characteristics of relevant datasets are discussed. The trends that prevailed from the infancy of this problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and Pattern Recognition (CCVPR) 201

A systems engineering approach to robotic bin picking

In recent times the presence of vision and robotic systems in industry has become common place, but in spite of many achievements a large range of industrial tasks still remain unsolved due to the lack of flexibility of the vision systems when dealing with highly adaptive manufacturing environments. An important task found across a broad range of modern flexible manufacturing environments is the need to present parts to automated machinery from a supply bin. In order to carry out grasping and manipulation operations safely and efficiently we need to know the identity, location and spatial orientation of the objects that lie in an unstructured heap in a bin. Historically, the bin picking problem was tackled using mechanical vibratory feeders where the vision feedback was unavailable. This solution has certain problems with parts jamming and more important they are highly dedicated. In this regard if a change in the manufacturing process is required, the changeover may include an extensive re-tooling and a total revision of the system control strategy (Kelley et al., 1982). Due to these disadvantages modern bin picking systems perform grasping and manipulation operations using vision feedback (Yoshimi & Allen, 1994). Vision based robotic bin picking has been the subject of research since the introduction of the automated vision controlled processes in industry and a review of existing systems indicates that none of the proposed solutions were able to solve this classic vision problem in its generality. One of the main challenges facing such a bin picking system is its ability to deal with overlapping objects. The object recognition in cluttered scenes is the main objective of these systems and early approaches attempted to perform bin picking operations for similar objects that are jumbled together in an unstructured heap using no knowledge about the pose or geometry of the parts (Birk et al., 1981). While these assumptions may be acceptable for a restricted number of applications, in most practical cases a flexible system must deal with more than one type of object with a wide scale of shapes. A flexible bin picking system has to address three difficult problems: scene interpretation, object recognition and pose estimation. Initial approaches to these tasks were based on modeling parts using the 2D surface representations. Typical 2D representations include invariant shape descriptors (Zisserman et al., 1994), algebraic curves (Tarel & Cooper, 2000), 2 Name of the book (Header position 1,5) conics (Bolles & Horaud, 1986; Forsyth et al., 1991) and appearance based models (Murase & Nayar, 1995; Ohba & Ikeuchi, 1997). These systems are generally better suited to planar object recognition and they are not able to deal with severe viewpoint distortions or objects with complex shapes/textures. Also the spatial orientation cannot be robustly estimated for objects with free-form contours. To address this limitation most bin picking systems attempt to recognize the scene objects and estimate their spatial orientation using the 3D information (Fan et al., 1989; Faugeras & Hebert, 1986). Notable approaches include the use of 3D local descriptors (Ansar & Daniilidis, 2003; Campbell & Flynn, 2001; Kim & Kak, 1991), polyhedra (Rothwell & Stern, 1996), generalized cylinders (Ponce et al., 1989; Zerroug & Nevatia, 1996), super-quadrics (Blane et al., 2000) and visual learning methods (Johnson & Hebert, 1999; Mittrapiyanuruk et al., 2004). The most difficult problem for 3D bin picking systems that are based on a structural description of the objects (local descriptors or 3D primitives) is the complex procedure required to perform the scene to model feature matching. This procedure is usually based on complex graph-searching techniques and is increasingly more difficult when dealing with object occlusions, a situation when the structural description of the scene objects is incomplete. Visual learning methods based on eigenimage analysis have been proposed as an alternative solution to address the object recognition and pose estimation for objects with complex appearances. In this regard, Johnson and Hebert (Johnson & Hebert, 1999) developed an object recognition scheme that is able to identify multiple 3D objects in scenes affected by clutter and occlusion. They proposed an eigenimage analysis approach that is applied to match surface points using the spin image representation. The main attraction of this approach resides in the use of spin images that are local surface descriptors; hence they can be easily identified in real scenes that contain clutter and occlusions. This approach returns accurate results but the pose estimation cannot be inferred, as the spin images are local descriptors and they are not robust to capture the object orientation. In general the pose sampling for visual learning methods is a problem difficult to solve as the numbers of views required to sample the full 6 degree of freedom for object pose is prohibitive. This issue was addressed in the paper by Edwards (Edwards, 1996) when he applied eigenimage analysis to a one-object scene and his approach was able to estimate the pose only in cases where the tilt angle was limited to 30 degrees with respect to the optical axis of the sensor. In this chapter we describe the implementation of a vision sensor for robotic bin picking where we attempt to eliminate the main problem faced by the visual learning methods, namely the pose sampling problem. This paper is organized as follows. Section 2 outlines the overall system. Section 3 describes the implementation of the range sensor while Section 4 details the edge-based segmentation algorithm. Section 5 presents the viewpoint correction algorithm that is applied to align the detected object surfaces perpendicular on the optical axis of the sensor. Section 6 describes the object recognition algorithm. This is followed in Section 7 by an outline of the pose estimation algorithm. Section 8 presents a number of experimental results illustrating the benefits of the approach outlined in this chapter

Hashmod: A Hashing Method for Scalable 3D Object Detection

We present a scalable method for detecting objects and estimating their 3D poses in RGB-D data. To this end, we rely on an efficient representation of object views and employ hashing techniques to match these views against the input frame in a scalable way. While a similar approach already exists for 2D detection, we show how to extend it to estimate the 3D pose of the detected objects. In particular, we explore different hashing strategies and identify the one which is more suitable to our problem. We show empirically that the complexity of our method is sublinear with the number of objects and we enable detection and pose estimation of many 3D objects with high accuracy while outperforming the state-of-the-art in terms of runtime.Comment: BMVC 201

확률적인 3차원 자세 복원과 행동인식

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 오성회.These days, computer vision technology becomes popular and plays an important role in intelligent systems, such as augment reality, video and image analysis, and to name a few. Although cost effective depth cameras, like a Microsoft Kinect, have recently developed, most computer vision algorithms assume that observations are obtained from RGB cameras, which make 2D observations. If, somehow, we can estimate 3D information from 2D observations, it might give better solutions for many computer vision problems. In this dissertation, we focus on estimating 3D information from 2D observations, which is well known as non-rigid structure from motion (NRSfM). More formally, NRSfM finds the three dimensional structure of an object by analyzing image streams with the assumption that an object lies in a low-dimensional space. However, a human body for long periods of time can have complex shape variations and it makes a challenging problem for NRSfM due to its increased degree of freedom. In order to handle complex shape variations, we propose a Procrustean normal distribution mixture model (PNDMM) by extending a recently proposed Procrustean normal distribution (PND), which captures the distribution of non-rigid variations of an object by excluding the effects of rigid motion. Unlike existing methods which use a single model to solve an NRSfM problem, the proposed PNDMM decomposes complex shape variations into a collection of simpler ones, thereby model learning can be more tractable and accurate. We perform experiments showing that the proposed method outperforms existing methods on highly complex and long human motion sequences. In addition, we extend the PNDMM to a single view 3D human pose estimation problem. While recovering a 3D structure of a human body from an image is important, it is a highly ambiguous problem due to the deformation of an articulated human body. Moreover, before estimating a 3D human pose from a 2D human pose, it is important to obtain an accurate 2D human pose. In order to address inaccuracy of 2D pose estimation on a single image and 3D human pose ambiguities, we estimate multiple 2D and 3D human pose candidates and select the best one which can be explained by a 2D human pose detector and a 3D shape model. We also introduce a model transformation which is incorporated into the 3D shape prior model, such that the proposed method can be applied to a novel test image. Experimental results show that the proposed method can provide good 3D reconstruction results when tested on a novel test image, despite inaccuracies of 2D part detections and 3D shape ambiguities. Finally, we handle an action recognition problem from a video clip. Current studies show that high-level features obtained from estimated 2D human poses enable action recognition performance beyond current state-of-the-art methods using low- and mid-level features based on appearance and motion, despite inaccuracy of human pose estimation. Based on these findings, we propose an action recognition method using estimated 3D human pose information since the proposed PNDMM is able to reconstruct 3D shapes from 2D shapes. Experimental results show that 3D pose based descriptors are better than 2D pose based descriptors for action recognition, regardless of classification methods. Considering the fact that we use simple 3D pose descriptors based on a 3D shape model which is learned from 2D shapes, results reported in this dissertation are promising and obtaining accurate 3D information from 2D observations is still an important research issue for reliable computer vision systems.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Issues 4 1.3 Organization of the Dissertation 6 Chapter 2 Preliminary 9 2.1 Generalized Procrustes Analysis (GPA) 11 2.2 EM-GPA Algorithm 12 2.2.1 Objective function 12 2.2.2 E-step 15 2.2.3 M-step 16 2.3 Implementation Considerations for EM-GPA 18 2.3.1 Preprocessing stage 18 2.3.2 Small update rate for the covariance matrix 20 2.4 Experiments 21 2.4.1 Shape alignment with the missing information 23 2.4.2 3D shape modeling 24 2.4.3 2D+3D active appearance models 28 2.5 Chapter Summary and Discussion 32 Chapter 3 Procrustean Normal Distribution Mixture Model 33 3.1 Non-Rigid Structure from Motion 35 3.2 Procrustean Normal Distribution (PND) 38 3.3 PND Mixture Model 41 3.4 Learning a PNDMM 43 3.4.1 E-step 44 3.4.2 M-step 46 3.5 Learning an Adaptive PNDMM 48 3.6 Experiments 50 3.6.1 Experimental setup 50 3.6.2 CMU Mocap database 53 3.6.3 UMPM dataset 69 3.6.4 Simple and short motions 74 3.6.5 Real sequence - qualitative representation 77 3.7 Chapter Summary 78 Chapter 4 Recovering a 3D Human Pose from a Novel Image 83 4.1 Single View 3D Human Pose Estimation 85 4.2 Candidate Generation 87 4.2.1 Initial pose generation 87 4.2.2 Part recombination 88 4.3 3D Shape Prior Model 89 4.3.1 Procrustean mixture model learning 89 4.3.2 Procrustean mixture model fitting 91 4.4 Model Transformation 92 4.4.1 Model normalization 92 4.4.2 Model adaptation 95 4.5 Result Selection 96 4.6 Experiments 98 4.6.1 Implementation details 98 4.6.2 Evaluation of the joint 2D and 3D pose estimation 99 4.6.3 Evaluation of the 2D pose estimation 104 4.6.4 Evaluation of the 3D pose estimation 106 4.7 Chapter Summary 108 Chapter 5 Application to Action Recognition 109 5.1 Appearance and Motion Based Descriptors 112 5.2 2D Pose Based Descriptors 113 5.3 Bag-of-Features with a Multiple Kernel Method 114 5.4 Classification - Kernel Group Sparse Representation 115 5.4.1 Group sparse representation for classification 116 5.4.2 Kernel group sparse (KGS) representation for classification 118 5.5 Experiment on sub-JHMDB Dataset 120 5.5.1 Experimental setup 120 5.5.2 3D pose based descriptor 122 5.5.3 Experimental results 123 5.6 Chapter Summary 129 Chapter 6 Conclusion and Future Work 131 Appendices 135 A Proof of Propositions in Chapter 2 137 A.1 Proof of Proposition 1 137 A.2 Proof of Proposition 3 138 A.3 Proof of Proposition 4 139 B Calculation of p(XijDii) in Chapter 3 141 B.1 Without the Dirac-delta term 141 B.2 With the Dirac-delta term 142 C Procrustean Mixture Model Learning and Fitting in Chapter 4 145 C.1 Procrustean Mixture Model Learning 145 C.2 Procrustean Mixture Model Fitting 147 Bibliography 153 초 록 167Docto

Generic 3D Representation via Pose Estimation and Matching

Though a large body of computer vision research has investigated developing generic semantic representations, efforts towards developing a similar representation for 3D has been limited. In this paper, we learn a generic 3D representation through solving a set of foundational proxy 3D tasks: object-centric camera pose estimation and wide baseline feature matching. Our method is based upon the premise that by providing supervision over a set of carefully selected foundational tasks, generalization to novel tasks and abstraction capabilities can be achieved. We empirically show that the internal representation of a multi-task ConvNet trained to solve the above core problems generalizes to novel 3D tasks (e.g., scene layout estimation, object pose estimation, surface normal estimation) without the need for fine-tuning and shows traits of abstraction abilities (e.g., cross-modality pose estimation). In the context of the core supervised tasks, we demonstrate our representation achieves state-of-the-art wide baseline feature matching results without requiring apriori rectification (unlike SIFT and the majority of learned features). We also show 6DOF camera pose estimation given a pair local image patches. The accuracy of both supervised tasks come comparable to humans. Finally, we contribute a large-scale dataset composed of object-centric street view scenes along with point correspondences and camera pose information, and conclude with a discussion on the learned representation and open research questions.Comment: Published in ECCV16. See the project website http://3drepresentation.stanford.edu/ and dataset website https://github.com/amir32002/3D_Street_Vie