Depth recovery from stereo images

Induchoodan, P R; Josemartin, M J; Geetharanjin, P R

doi:10.1109/ic3i.2014.7019764

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Then the object detection algorithm is used to detect a person in the RGB frame only. Figure [3] shows the new architecture with our node included. If a person is detected, that particular RGB frame and the corresponding depth frame is skipped or discarded.…”

Section: Fig 2 Ros Node Architecture For 3d Reconstructionmentioning

confidence: 99%

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Dynamic Object Elimination based Indoor 3D Mapping System

Mane¹

2021

IJRASET

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In today's world, the need for autonomous robots is increasing at an exponential rate and the implementation of Simultaneous Localisation And Mapping (SLAM) is gaining more and more attention. One of the major component of SLAM is 3D Mapping of the environment which enables autonomous robots to perceive the environment like a human does for which many Depth cameras or RGB-D cameras prove useful. This paper proposes a continuous real-time 3D mapping system that tackles the long existing problem of point cloud distortion induced by dynamic objects in the frame. Our method uses the Microsoft Kinect V1 as the RGB-D camera and the packages in the Robotic Operating System (ROS) like the Real Time Appearance Based Mapping (RTAB-map) for 3D reconstruction. A ROS based method is used to implement dynamic object elimination in real-time. For the purpose of dynamic objects detection in the frame, two algorithms -Deep Learning based tiny YOLO-v3 and a Machine Learning based Haar Cascade classifier are used. The results from the two are compared in terms of accuracy, execution time and mean Average Precision (mAP) and it was inferred that although HaarCascade model is comparatively less accurate when detecting objects, it is two times faster than YOLO which makes the system more real-time. The real-time implementation was given more preference while selecting the model.

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Section: Fig 2 Ros Node Architecture For 3d Reconstructionmentioning

confidence: 99%

“…Another traditional method is Depth Estimation using Stereo Vision which works on the principal of triangulation [2,3]. Since the images are obtained from 2 different cameras in a stereo system, feature matching algorithms are needed in order to synchronize the two cameras in the stereo system [4].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Object Elimination based Indoor 3D Mapping System

Mane¹

2021

IJRASET

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“…In the same vein, we explore the use of depth information for improving VPR performance under vast variations in appearance and viewpoint. Depth estimation is best achieved using stereoscopic images [39], however recent advances have enabled depth estimation from monocular images. Geometric constraints [40] and non-parametric sampling [41] have been used to extract depth out of a monocular image.…”

Section: Literature Reviewmentioning

confidence: 99%

Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation

Garg

Babu

Dharmasiri

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Visual place recognition (VPR) -the act of recognizing a familiar visual place -becomes difficult when there is extreme environmental appearance change or viewpoint change. Particularly challenging is the scenario where both phenomena occur simultaneously, such as when returning for the first time along a road at night that was previously traversed during the day in the opposite direction. While such problems can be solved with panoramic sensors, humans solve this problem regularly with limited field of view vision and without needing to constantly turn around. In this paper, we present a new depth-and temporal-aware visual place recognition system that solves the opposing viewpoint, extreme appearance-change visual place recognition problem. Our system performs sequence-to-single matching by extracting depth-filtered keypoints using a stateof-the-art depth estimation pipeline, constructing a keypoint sequence over multiple frames from the reference dataset, and comparing those keypoints to those in a single query image. We evaluate the system on a challenging benchmark dataset and show that it consistently outperforms state-of-theart techniques. We also develop a range of diagnostic simulation experiments that characterize the contribution of depth-filtered keypoint sequences with respect to key domain parameters including degree of appearance change and camera motion.

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“…Active devices include sonars, emitting auditive signals and listening to its reflection [4], structured light, projecting a light pattern onto the scene and extracting depth from change of its shape [5], and LIDAR, finding the depth by measuring the time between an emitted impulse and its reflection [6]. Passive devices include stereo cameras, inferring depth by finding the disparities between two images [7], or modifying the focal length of a single camera to find the optimal sharpness for each pixel [8]. While they have competitive accuracy, each of these approaches have disadvantages like being expensive, having problems with ambient light [9] or being inaccurate in areas of low texture [10].…”

Section: Introductionmentioning

confidence: 99%

Monocular Depth Estimation with Directional Consistency by Deep Networks

Truetsch,

Schöttl

2019

Preprint

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As processing power has become more available, more human-like artificial intelligences are created to solve image processing tasks that we are inherently good at. As such we propose a model that estimates depth from a monocular image. Our approach utilizes a combination of structure from motion and stereo disparity. We estimate a pose between the source image and a different viewpoint and a dense depth map and use a simple transformation to reconstruct the image seen from said viewpoint. We can then use the real image at that viewpoint to act as supervision to train out model. The metric chosen for image comparison employs standard L1 and structural similarity and a consistency constraint between depth maps as well as smoothness constraint. We show that similar to human perception utilizing the correlation within the provided data by two different approaches increases the accuracy and outperforms the individual components.

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Depth recovery from stereo images

Cited by 4 publications

References 5 publications

Dynamic Object Elimination based Indoor 3D Mapping System

Dynamic Object Elimination based Indoor 3D Mapping System

Look No Deeper: Recognizing Places from Opposing Viewpoints under Varying Scene Appearance using Single-View Depth Estimation

Monocular Depth Estimation with Directional Consistency by Deep Networks

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