Calculating dense disparity maps from color stereo images, an efficient implementation

Muhlmann, K.; Maier, Dennis; Hesser, R.; Männer, Reinhard

doi:10.1109/smbv.2001.988760

Cited by 108 publications

(98 citation statements)

References 5 publications

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“…Since any set of passive cameras that sense visible radiation can be implemented in a stereo vision system, it is ideal for objects with varying color or texture and outdoor environments. The main limitations with this technique are its required computational time and cost for high quality results [73], its difficulty in depth discontinuity regions, and its inability to solve the correspondence problem inside regions of homogeneous intensity or color.…”

Section: Depth Sensor Classesmentioning

confidence: 99%

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Landau

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Accurate 6D object pose estimation, as well as other model-based shape matching objectives such as object detection, classification, and shape inspection, is prominent and necessary in a large number of domains and applications. This includes household and robotics applications to automate various tasks, where input depth measurements are aligned to a corresponding model of the object, such as a CAD that is composed of several interconnected parts, depending on the model's level of detail and complexity. Industrial metrology systems also compare the shapes of engineered components and equipment to their corresponding 3D models for quality assessment and anomaly detection; this often requires very costly and high resolution projection and detection mechanisms. Structuredlight laser scanners have, however, emerged as a viable option to provide cheap and reasonably accurate depth estimates, which have recently become commodity-priced within this decade due to the maturity of the technology. These sensors are also robust for semi-transparent and reflective surfaces, dynamic scenery, ambient background light, and temperature drift. Certain structured-light light coding methods unfortunately need to implement a complicated, non-invertible transformation of a distorted light pattern to generate the pixel-by-pixel depth estimates, thereby leading to a loss of information. This can result in poorly estimated or even missing depth data, especially when the object has small 3D features or non-ideal surface properties. Moreover, the informative rigid body constraint is blurred when the nonlinear 2D depth image to 3D pseudo-measurement point cloud transformation is applied, which is the domain where most model-based shape matching methods operate.Alternatively, estimating pose from information early in the structured-light sensing/processing chain has the potential to alleviate errors induced in subsequent nonlinear processing steps that are in fact locally scene dependent. This motivates one of the main contributions from this dissertation, which presents an asymptotically optimal maximum likelihood estimation method that operates directly on the raw output IR images. This is made possible by an extensive study on a commonly utilized commodity-priced structured-light sensor, which contributed to the proposed high-fidelity IR and depth image predictor and simulator that models the physics of the transmit and receive optics,

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Section: Depth Sensor Classesmentioning

confidence: 99%

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Landau

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“…By using this algorithm, reliable real-time dense disparity maps can be produced. Muhalmann et al represents a method which uses SAD correlation metric for color images, utilizing left to right reliability check with the aim of achieving improvement in both the fields of speed and quality [12]. Binaghi et al raised an advanced method which utilizes zero mean normalized cross correlation i.e.…”

Section: The Learning Modelmentioning

confidence: 99%

A Survey of Learning Approaches and Application for 3D Vision

2018

MATEC Web Conf.

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Abstract. Three-dimensional (3D) vision extracted from the stereo images or reconstructed from the twodimensional (2D) images is the most effective topic in computer vision and video surveillance. Threedimensional scene is constructed through two stereo images which existing disparity map by Stereo vision. Many methods of Stereo matching which contains median filtering, mean-shift segmentation, guided filter and joint trilateral filters [1] are used in many algorithms to construct the precise disparity map. These methods committed to figure out the image synthesis range in different Stereo matching fields and among these techniques cannot perform perfectly every turn. The paper focuses on 3D vision, introduce the background and process of 3D vision, reviews several classical datasets in the field of 3D vision, based on which the learning approaches and several types of applications of 3D vision were evaluated and analyzed.

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“…Obstacles are detected using a correlation based algorithm that uses the SAD (Sum of Absolute Differences) function as similarity measure which, for a relatively small resolution can obtain a dense stereo map in real time [8]. In our approach, several expensive refinements of the method described in [8], such as the left-right consistency check, are removed in order to reduce the computational cost of the algorithm. To remove possible inconsistencies due to occlusions, the resulting disparity map is segmented using the watershed algorithm [15] and small disparity regions are further removed from it.…”

Section: A Obstacle Mapsmentioning

confidence: 99%

Visual Registration Method for a Low Cost Robot

Aldavert

Ramisa

Toledo

et al. 2009

Lecture Notes in Computer Science

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Abstract-An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most correspondence methods first extract early features from robot sensor data, then matches between features are searched and finally the transformation that relates the maps is estimated from such matches. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by a Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated on a typical office dataset showing good performance.

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Calculating dense disparity maps from color stereo images, an efficient implementation

Cited by 108 publications

References 5 publications

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

Optimal 6D Object Pose Estimation with Commodity Depth Sensors

A Survey of Learning Approaches and Application for 3D Vision

Visual Registration Method for a Low Cost Robot

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