Semantic Structure from Motion: A Novel Framework for Joint Object Recognition and 3D Reconstruction

Bao, Sid Yingze; Savarese, Silvio

doi:10.1007/978-3-642-34091-8_17

Cited by 3 publications

(3 citation statements)

References 33 publications

(40 reference statements)

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“…Some of them include smoothness in disparity/depth priors [4], [11], [17] or Manhatton/piewise-planar priors [6], [18], [7], [19], [8]. Another line of work [20], [9], [21], [10], [22], [23] used a limited set of detected object classes as a prior for reconstruction.…”

Section: Background and Related Workmentioning

confidence: 99%

Dense monocular reconstruction using surface normals

Weerasekera

Latif

Garg

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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PSPB accepted the recommendations of an ad hoc committee, which reviewed the policy that had previously been revised in November 2010. The highlights of the current policy are as follows: The policy reaffirms the principle that authors are free to post their own version of their IEEE periodical or conference articles on their personal Web sites, those of their employers, or their funding agencies for the purpose of meeting public availability requirements prescribed by their funding agencies. Authors may post their version of an article as accepted for publication in an IEEE periodical or conference proceedings. Posting of the final PDF, as published by IEEE Xplore®, continues to be prohibited, except for open-access journal articles supported by payment of an article processing charge (APC), whose authors may freely post the final version.  The policy provides that IEEE periodicals will make available to each author a preprint version of that person's article that includes the Digital Object Identifier, IEEE's copyright notice, and a notice showing the article has been accepted for publication.  The policy states that authors are allowed to post versions of their articles on approved third-party servers that are operated by not-for-profit organizations. Because IEEE policy provides that authors are free to follow public access mandates of government funding agencies, IEEE authors may follow requirements to deposit their accepted manuscripts in those government repositories.IEEE distributes accepted versions of journal articles for author posting through the Author Gateway, now used by all journals produced by IEEE Publishing Operations. (Some journals use services from external vendors, and these journals are encouraged to adopt similar services for the convenience of authors.) Authors' versions distributed through the Author Gateway include a live link to articles in IEEE Xplore. Most conferences do not use the Author Gateway; authors of conference articles should feel free to post their own version of their articles as accepted for publication by an IEEE conference, with the addition of a copyright notice and a Digital Object Identifier to the version of record in IEEE Xplore.

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Section: Background and Related Workmentioning

confidence: 99%

Dense monocular reconstruction using surface normals

Weerasekera

Latif

Garg

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…Nüchter [12] first proposed the concept of constructing semantic maps for mobile robots in 2008. In 2011, Bao [13] proposed the reconstruction of semantic scenes using semantic information and SFM. Salas-Moreno [14] proposed an "object-oriented" SLAM system (SLAM++) in 2013.…”

Section: Introductionmentioning

confidence: 99%

A Computationally Efficient Semantic SLAM Solution for Dynamic Scenes

Wang

Zhang

et al. 2019

Remote Sensing

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In various dynamic scenes, there are moveable objects such as pedestrians, which may challenge simultaneous localization and mapping (SLAM) algorithms. Consequently, the localization accuracy may be degraded, and a moving object may negatively impact the constructed maps. Maps that contain semantic information of dynamic objects impart humans or robots with the ability to semantically understand the environment, and they are critical for various intelligent systems and location-based services. In this study, we developed a computationally efficient SLAM solution that is able to accomplish three tasks in real time: (1) complete localization without accuracy loss due to the existence of dynamic objects and generate a static map that does not contain moving objects, (2) extract semantic information of dynamic objects through a computionally efficient approach, and (3) eventually generate semantic maps, which overlay semantic objects on static maps. The proposed semantic SLAM solution was evaluated through four different experiments on two data sets, respectively verifying the tracking accuracy, computational efficiency, and the quality of the generated static maps and semantic maps. The results show that the proposed SLAM solution is computationally efficient by reducing the time consumption for building maps by 2/3; moreover, the relative localization accuracy is improved, with a translational error of only 0.028 m, and is not degraded by dynamic objects. Finally, the proposed solution generates static maps of a dynamic scene without moving objects and semantic maps with high-precision semantic information of specific objects.

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“…In the context of SfM-based 3-D ATR, this paper suggests a semantic SfM has been proposed, which simultaneously considers the geometric and semantic cues provided by 2-D images [15]. However, the processing burden is 20 min per scene, making it unsuitable for UAV applications that require near-real-time processing.…”

Section: Introductionmentioning

confidence: 99%

A New Passive 3-D Automatic Target Recognition Architecture for Aerial Platforms

Kechagias‐Stamatis

Aouf

2019

IEEE Trans. Geosci. Remote Sensing

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The 3-D automatic target recognition (ATR) has many advantages over its 2-D counterpart, but there are several constraints in the context of small low-cost unmanned aerial vehicles (UAVs). These limitations include the requirement for active rather than passive monitoring, high equipment costs, sensor packaging size, and processing burden. We, therefore, propose a new structure from motion (SfM) 3-D ATR architecture that exploits the UAV's onboard sensors, i.e., the visual band camera, gyroscope, and accelerometer, and meets the requirements of a small UAV system. We tested the proposed 3-D SfM ATR using simulated UAV reconnaissance scenarios and found that the performance was better than classic 3-D light detection and ranging (LIDAR) ATR, combining the advantages of 3-D LIDAR ATR and passive 2-D ATR. The main advantages of the proposed architecture include the rapid processing, target pose invariance, small template size, passive scene sensing, and inexpensive equipment. We implemented the SfM module under two keypoint detection, description and matching schemes, with the 3-D ATR module exploiting several current techniques. By comparing SfM 3-D ATR, 3-D LIDAR ATR, and 2-D ATR, we confirmed the superior performance of our new architecture.

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Semantic Structure from Motion: A Novel Framework for Joint Object Recognition and 3D Reconstruction

Cited by 3 publications

References 33 publications

Dense monocular reconstruction using surface normals

Dense monocular reconstruction using surface normals

A Computationally Efficient Semantic SLAM Solution for Dynamic Scenes

A New Passive 3-D Automatic Target Recognition Architecture for Aerial Platforms

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