Eric Brachmann

Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image

In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.

6-DOF Model Based Tracking via Object Coordinate Regression

This work investigates the problem of 6-Degrees-Of-Freedom (6-DOF) object tracking from RGB-D images, where the object is rigid and a 3D model of the object is known. As in many previous works, we utilize a Particle Filter (PF) framework. In order to have a fast tracker, the key aspect is to design a clever proposal distribution which works reliably even with a small number of particles. To achieve this we build on a recently developed state-of-the-art system for single image 6D pose estimation of known 3D objects, using the concept of so-called 3D object coordinates. The idea is to train a random forest that regresses the 3D object coordinates from the RGB-D image. Our key technical contribution is a two-way procedure to integrate the random forest predictions in the proposal distribution generation. This has many practical advantages, in particular better generalization ability with respect to occlusions, changes in lighting and fast-moving objects. We demonstrate experimentally that we exceed state-of-the-art on a given, public dataset. To raise the bar in terms of fast-moving objects and object occlusions, we also create a new dataset, which will be made publicly available.

DSAC — Differentiable RANSAC for Camera Localization

RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion. However, RANSAC has so far not been used as part of such deep learning pipelines, because its hypothesis selection procedure is non-differentiable. In this work, we present two different ways to overcome this limitation. The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w.r.t. to all learnable parameters. We call this approach DSAC, the differentiable counterpart of RANSAC. We apply DSAC to the problem of camera localization, where deep learning has so far failed to improve on traditional approaches. We demonstrate that by directly minimizing the expected loss of the output camera poses, robustly estimated by RANSAC, we achieve an increase in accuracy. In the future, any deep learning pipeline can use DSAC as a robust optimization component.

Global Hypothesis Generation for 6D Object Pose Estimation

This paper addresses the task of estimating the 6D-pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) compute local features, ii) generate a pool of pose-hypotheses, iii) select and refine a pose from the pool. This work focuses on the second step. While all existing approaches generate the hypotheses pool via local reasoning, e.g. RANSAC or Hough-Voting, we are the first to show that global reasoning is beneficial at this stage. In particular, we formulate a novel fully-connected Conditional Random Field (CRF) that outputs a very small number of pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian, we give a new, efficient two-step optimization procedure, with some guarantees for optimality. We utilize our global hypotheses generation procedure to produce results that exceed state-of-the-art for the challenging Occluded Object Dataset.

Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression.

Accurate pose estimation of object instances is a key aspect in many applications, including augmented reality or robotics. For example, a task of a domestic robot could be to fetch an item from an open drawer. The poses of both, the drawer and the item have to be known by the robot in order to fulfil the task. 6D pose estimation of rigid objects has been addressed with great success in recent years. In large part, this has been due to the advent of consumer-level RGB-D cameras, which provide rich, robust input data. However, the practical use of state-of-the-art pose estimation approaches is limited by the assumption that objects are rigid. In cluttered, domestic environments this assumption does often not hold. Examples are doors, many types of furniture, certain electronic devices and toys. A robot might encounter these items in any state of articulation. This work considers the task of one-shot pose estimation of articulated object instances from an RGB-D image. In particular, we address objects with the topology of a kinematic chain of any length, i.e. objects are composed of a chain of parts interconnected by joints. We restrict joints to either revolute joints with 1 DOF (degrees of freedom) rotational movement or prismatic joints with 1 DOF translational movement. This topology covers a wide range of common objects (see our dataset for examples). However, our approach can easily be expanded to any topology, and to joints with higher degrees of freedom.

PoseAgent: Budget-Constrained 6D Object Pose Estimation via Reinforcement Learning

State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.

Simplified authentication and authorization for RESTful services in trusted environments

In some trusted environments, such as an organizations intranet, local web services may be assumed to be trustworthy. This property can be exploited to simplify authentication and authorization protocols between resource providers and consumers, lowering the threshold for developing services and clients. Existing security solutions for RESTful services, in contrast, support untrusted services, a complexity-increasing capability that is not needed on an intranet with only trusted services. We propose a central security service with a lean API that handles both authentication and authorization for trusted RESTful services. A user trades credentials for a token that facilitates access to services. The services may query the security service for token authenticity and roles granted to a user. The system provides fine-grained access control at the level of resources, following the role-based access control (RBAC) model. Resources are identified by their URLs, making the authorization system generic. The mapping of roles to users resides with the central security service and depends on the resource to be accessed. The mapping of permissions to roles is implemented individually by the services. We rely on secure channels and the trusted intermediaries characteristic for intranets to simplify the protocols involved and to make the security features easy to use, cutting the number of required API calls in half.

Random forests versus Neural Networks — What's best for camera localization?

This work addresses the task of camera localization in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step. Random Forests (RFs) are typically used. On the other hand. Neural Networks (NNs) reign in many dense regression tasks, but are not test-time efficient. We ask the question: which of the two is best for camera localization? To address this, we make two method contributions: (1) a test-time efficient NN architecture which we term a ForestNet that is derived and initialized from a RF, and (2) a new fully-differentiable robust averaging technique for regression ensembles which can be trained end-to-end with a NN. Our experimental findings show that for scene coordinate regression, traditional NN architectures are superior to test-time efficient RFs and ForestNets, however, this does not translate to final 6D camera pose accuracy where RFs and ForestNets perform slightly better. To summarize, our best method, a ForestNet with a robust average, which has an equivalent fast and lightweight RF, improves over the state-of-the-art for camera localization on the 7-Scenes dataset [1]. While this work focuses on scene coordinate regression for camera localization, our innovations may also be applied to other continuous regression tasks.

Feature propagation on image webs for enhanced image retrieval

The bag-of-features model is often deployed in content-based image retrieval to measure image similarity. In cases where the visual appearance of semantically similar images differs largely, feature histograms mismatch and the model fails. We increase the robustness of feature histograms by automatically augmenting them with features of related images. We establish image relations by image web construction and adapt a label propagation scheme from the domain of semi-supervised learning for feature augmentation. While the benefit of feature augmentation has been shown before, our approach refrains from the use of semantic labels. Instead we show how to increase the performance of the bag-of-features model substantially on a completely unlabeled image corpus.