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 In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained end-to-end fashion. However, has so far not used as part of such because its hypothesis selection procedure non-differentiable. this work, we present two different ways to overcome limitation. The most promising approach inspired reinforcement...

In recent years, the task of estimating 6D pose 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 scenes. work, we show that RGB is sufficient to achieve visually convincing results. Our key concept model exploit uncertainty system at all stages processing pipeline. The comes in form continuous distributions over 3D coordinates...

Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object RGB-D image which is the topic this work. The idea to compare observation with output forward process, rendered interest particular pose. Due occlusion or complicated sensor noise, it can be difficult perform comparison meaningful way. We propose that "learns compare", while taking these difficulties into account. This done by describing posterior density...

Article Free Access Share on Real time compression of triangle mesh connectivity Authors: Stefan Gumhold WSI/GRIS Univ. Tübingen TübingenView Profile , Wolfgang Straßer Authors Info & Claims SIGGRAPH '98: Proceedings the 25th annual conference Computer graphics and interactive techniquesJuly 1998 Pages 133–140https://doi.org/10.1145/280814.280836Online:24 July 1998Publication History 208citation1,088DownloadsMetricsTotal Citations208Total Downloads1,088Last 12 Months46Last 6 weeks13 Get...

This paper addresses the task of estimating 6D-pose a known 3D object from single RGB-D image. Most modern approaches solve this in three steps: i) compute local features, ii) generate pool pose-hypotheses, iii) select and refine pose pool. work focuses on second step. While all existing hypotheses via reasoning, e.g. RANSAC or Hough-Voting, we are first to show that global reasoning is beneficial at stage. In particular, formulate novel fully-connected Conditional Random Field (CRF) outputs...

We address a core problem of computer vision: Detection and description 2D feature points for image matching. For long time, hand-crafted designs, like the seminal SIFT algorithm, were unsurpassed in accuracy efficiency. Recently, learned detectors emerged that implement detection using neural networks. Training these networks usually resorts to optimizing low-level matching scores, often pre-defining sets patches which should or not match, contain key points. Unfortunately, increased scores...

Polygonal models acquired with emerging 3D scanning technology or from large scale CAD applications easily reach sizes of several gigabytes and do not fit in the address space common 32-bit desktop PCs. In this paper we propose an out-of-core mesh compression technique that converts such gigantic meshes into a streamable, highly compressed representation. During decompression only small portion needs to be kept memory at any time. As full connectivity information is available along...

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In this paper we show how out-of-core mesh processing techniques can be adapted to perform their computations based on the new sequence paradigm (Isenburg, et al., 2003), using simplification as an example. We believe that concept will also prove useful for other tasks, such a parameterization, remeshing, or smoothing, which currently only in-core solutions exist. A represents particular interleaved ordering of indexed triangles and vertices. This representation allows streaming very large...

This paper presents Pearl, a mixed-reality approach for the analysis of human movement data in situ. As physical environment shapes motion and behavior, such can benefit from direct inclusion analytical process. We present methods exploring relation to surrounding regions interest, as objects, furniture, architectural elements. introduce concepts selecting filtering through interaction with environment, suite visualizations revealing aggregated emergent spatial temporal relations. More...

Finding the best viewing parameters for a scene is quite difficult but very important problem. Fully automatic procedures seem to be impossible as notion of strongly depends on human judgment well application. In this paper solution sub-problem placing light sources given camera proposed. A position defined optimal, when resulting illumination reveals more about illuminations from all other positions, i.e. maximizes information that added image through illumination. With help an experiment...

Accurate pose estimation of object instances is a key aspect in many applications, including augmented reality or robotics. For example, task domestic robot could be to fetch an item from open drawer. The poses both, the drawer and have known by order fulfil task. 6D rigid objects has been addressed with great success recent years. In large part, this due advent consumer-level RGB-D cameras, which provide rich, robust input data. However, practical use state-of-the-art approaches limited...

Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose like Faster R-CNN are prone to providing overconfident predictions outlier objects. Recent outlier-aware detection approaches estimate the density instance-wide features with class-conditional Gaussians and train on synthesized from their low-likelihood regions. this strategy does not guarantee that will have a low likelihood according other Gaussians. We...

Discriminative learning effectively predicts true object class for image classification. However, it often results in false positives outliers, posing critical concerns applications like autonomous driving and video surveillance systems. Previous attempts to address this challenge involved training classifiers through contrastive using actual outlier data or synthesizing outliers self-supervised learning. Furthermore, unsupervised generative modeling of inliers pixel space has shown limited...

Line primitives are a very powerful visual attribute used for scientific visualization and in particular 3D vector-field visualization. We extend the basic line with additional attributes including color, width, texture orientation. To implement we represent stylized as generalized cylinders. One important contribution of our work is an efficient rendering algorithm lines, which hybrid sense that it uses both CPU GPU based rendering. improve depth perception shadow algorithm. present several...