straight lines within a constrained 2-dimensional space.As the II.RELATED WORK antenna is moving,by extracting the phase differences from A.Computer Vision and Sensor-based Approach the specified tags at different time points,we build the angle Computer-vision-based solutions mainly leverage the depth profiles to depict the geometry angles between the antenna-tag camera to perform 3D reconstruction of multiple objects pairs.By comparing the angle profiles of different reference [1,2].To avoid the blind angles in 3D reconstruction for the tags,we are able to derive the relative positions of these tags specified objects.usually multiple depth cameras are deployed on the specified package,and further figure out the package at different positions to perform multi-view reconstruction for orientation and package stacking for multiple packages. their 3D models [1],or a moving depth camera is used to There are three key challenges to realize 3D reconstruction build the 3D models in a mobile approach [2].In a word, via RFID systems.The first challenge is to determine the these approaches suffer from the line-of-sight (LOS)constraint package orientation according to the RF-signals from the reference tags attached to a specified package.To tackle this in 3D perception,and they are vulnerable to the limitation of the light intensity.Sensor-based solutions [3,4]mainly challenge,we extract angle profiles from the phases of the attach the battery-powered sensors (such as inertial sensors or RF-signals,then we build an angle-profile-based model to GPS modules)to the surface of the objects,and continuously transform the RF-signals into the indicators for the relative monitor the 3D placement of the specified objects,so as to localization among the reference tags.Thus,after performing track the orientation variation [3],or the stacking situation 1-dimensional mobile scanning along a straight line,we can among multiple objects. determine the relative positions of the reference tag pairs on the package,and use this information to further derive the B.RFID-based Approach package orientation.The second challenge is to determine Orientation tracking:By attaching multiple RFID tags onto the stacking situation among multiple packages,according to the specified object,it is possible to track the orientation the RF-signals from the reference tags attached to multiple variation of the object according to the variation of the packages.To tackle this challenge,we further perform a 2- corresponding RF-signals [5,6].Tagball [5]is proposed as dimensional mobile scanning to scan the packages along the a 3D human-computer interaction system,where multiple orthogonal direction of the previous scanning direction,such passive tags are attached to a controlling ball,such that the that the relative 3D positions of the reference tags from motions of the ball rotation from users can be detected from different packages can be determined.In this way,we can the phase changes of multiple tags.Tagyro [6]attaches an estimate the centers of packages according to the reference array of passive RFID tags as orientation sensors on the tags,and derive the relative locations of different packages in objects,by transforming the runtime phase offsets between the 3D space.The third challenge is to select effective refer- tags into the orientation angle.Compared with our RF-3DScan ence tag pairs for accurately deriving the package orientation system,these approaches track the orientation variation of the and stacking situation.To tackle this challenge,we filter out dynamically moving objects,whereas our approach aims to those reference tags with unstable phases.which are located determine the orientation of statically placed packages. outside the field of major antenna beam during scanning,by Localization:RFID localization generally falls into two cat- referring to the received signal strength(RSS).Further,as our egories:absolute localization [7-10]and relative localization empirical study shows that the absolute phase of the RF-signal [11-15].By attaching multiple tags and pinpointing each tag's varies with different orientations of the reference tag,thus we 3D coordinates,the absolute localization can be tailored to our measure the phase differences to extract the angle profiles from problem for 3D reconstruction.However,this approach suffers the reference tags during the mobile scanning. from complicated system deployment and collaboration.For To the best of our knowledge,this paper presents the example,the state-of-the-art absolute localization schemes first study of using RFID for 3D reconstruction on tagged PinIt [7]and Tagoram [10]are able to achieve cm-level packages.We make three contributions as follows.1)For 3D localization accuracy,however,they either need to deploy reconstruction on the packages,we attach multiple reference many reference tags or require sophisticated calibration of RFID tags onto the packages,and respectively tackle the issues multiple readers.Rather than absolute localization,recent of package orientation and package stacking,by leveraging RFID researches start to focus on the relative localization the angle profiles extracted from the RF-signals.We build an of multiple objects without any pre-deployment of reference angle-profile-based model to depict the relationship between nodes.Relative localization investigates the relative locations the RF-signals from the reference tags and the package ori- of a set of objects as oppose to their absolute coordinates entation/stacking.2)We propose a mobile scanning solution STPP [13]is the first work to tackle 2D relative localization.It to realize the 3D reconstruction of tagged packages.We are investigates the spatial-temporal dynamics in the phase profiles able to determine the package orientation via /-dimensional However,this approach leverages large-range scanning to mobile scanning,and further determine the package stacking detect the Vzone from the phase sequences,as it requires the via 2-dimensional mobile scanning.3)We have implemented a antenna to cross the perpendicular point during the scanning prototype system to evaluate the performance,the experiment to collect enough phases.Compared with STPP,our approach results in real settings show that RF-3DScan achieves about performs 3D relative localization by leveraging the angle 92.5%bottom face accuracy and about 4.08 angle error. profiles from rather small-range scanning.straight lines within a constrained 2-dimensional space. As the antenna is moving, by extracting the phase differences from the specified tags at different time points, we build the angle profiles to depict the geometry angles between the antenna-tag pairs. By comparing the angle profiles of different reference tags, we are able to derive the relative positions of these tags on the specified package, and further figure out the package orientation and package stacking for multiple packages. There are three key challenges to realize 3D reconstruction via RFID systems. The first challenge is to determine the package orientation according to the RF-signals from the reference tags attached to a specified package. To tackle this challenge, we extract angle profiles from the phases of the RF-signals, then we build an angle-profile-based model to transform the RF-signals into the indicators for the relative localization among the reference tags. Thus, after performing 1-dimensional mobile scanning along a straight line, we can determine the relative positions of the reference tag pairs on the package, and use this information to further derive the package orientation. The second challenge is to determine the stacking situation among multiple packages, according to the RF-signals from the reference tags attached to multiple packages. To tackle this challenge, we further perform a 2- dimensional mobile scanning to scan the packages along the orthogonal direction of the previous scanning direction, such that the relative 3D positions of the reference tags from different packages can be determined. In this way, we can estimate the centers of packages according to the reference tags, and derive the relative locations of different packages in the 3D space. The third challenge is to select effective refer￾ence tag pairs for accurately deriving the package orientation and stacking situation. To tackle this challenge, we filter out those reference tags with unstable phases, which are located outside the field of major antenna beam during scanning, by referring to the received signal strength (RSS). Further, as our empirical study shows that the absolute phase of the RF-signal varies with different orientations of the reference tag, thus we measure the phase differences to extract the angle profiles from the reference tags during the mobile scanning. To the best of our knowledge, this paper presents the first study of using RFID for 3D reconstruction on tagged packages. We make three contributions as follows. 1) For 3D reconstruction on the packages, we attach multiple reference RFID tags onto the packages, and respectively tackle the issues of package orientation and package stacking, by leveraging the angle profiles extracted from the RF-signals. We build an angle-profile-based model to depict the relationship between the RF-signals from the reference tags and the package ori￾entation/stacking. 2) We propose a mobile scanning solution to realize the 3D reconstruction of tagged packages. We are able to determine the package orientation via 1-dimensional mobile scanning, and further determine the package stacking via 2-dimensional mobile scanning. 3) We have implemented a prototype system to evaluate the performance, the experiment results in real settings show that RF-3DScan achieves about 92.5% bottom face accuracy and about 4.08◦ angle error. II. RELATED WORK A. Computer Vision and Sensor-based Approach Computer-vision-based solutions mainly leverage the depth camera to perform 3D reconstruction of multiple objects [1, 2]. To avoid the blind angles in 3D reconstruction for the specified objects, usually multiple depth cameras are deployed at different positions to perform multi-view reconstruction for their 3D models [1], or a moving depth camera is used to build the 3D models in a mobile approach [2]. In a word, these approaches suffer from the line-of-sight (LOS) constraint in 3D perception, and they are vulnerable to the limitation of the light intensity. Sensor-based solutions [3, 4] mainly attach the battery-powered sensors (such as inertial sensors or GPS modules) to the surface of the objects, and continuously monitor the 3D placement of the specified objects, so as to track the orientation variation [3], or the stacking situation among multiple objects. B. RFID-based Approach Orientation tracking: By attaching multiple RFID tags onto the specified object, it is possible to track the orientation variation of the object according to the variation of the corresponding RF-signals [5, 6]. Tagball [5] is proposed as a 3D human-computer interaction system, where multiple passive tags are attached to a controlling ball, such that the motions of the ball rotation from users can be detected from the phase changes of multiple tags. Tagyro [6] attaches an array of passive RFID tags as orientation sensors on the objects, by transforming the runtime phase offsets between tags into the orientation angle. Compared with our RF-3DScan system, these approaches track the orientation variation of the dynamically moving objects, whereas our approach aims to determine the orientation of statically placed packages. Localization: RFID localization generally falls into two cat￾egories: absolute localization [7–10] and relative localization [11–15]. By attaching multiple tags and pinpointing each tag’s 3D coordinates, the absolute localization can be tailored to our problem for 3D reconstruction. However, this approach suffers from complicated system deployment and collaboration. For example, the state-of-the-art absolute localization schemes PinIt [7] and Tagoram [10] are able to achieve cm-level localization accuracy, however, they either need to deploy many reference tags or require sophisticated calibration of multiple readers. Rather than absolute localization, recent RFID researches start to focus on the relative localization of multiple objects without any pre-deployment of reference nodes. Relative localization investigates the relative locations of a set of objects as oppose to their absolute coordinates. STPP [13] is the first work to tackle 2D relative localization. It investigates the spatial-temporal dynamics in the phase profiles However, this approach leverages large-range scanning to detect the V-zone from the phase sequences, as it requires the antenna to cross the perpendicular point during the scanning to collect enough phases. Compared with STPP, our approach performs 3D relative localization by leveraging the angle profiles from rather small-range scanning