Originality - I think the primary originality of the paper is limited to the engineering set-up specific to obtaining the dataset. It appears quite comprehensive and addresses a number of different geometric patterns that could prove to be a challenge for ultrasound-based image reconstruction. The biomimetic portion of the network also comes across as novel. Although, the specific impact of that portion of the network architecture is not clear. The rest of the paper applies standard supervised learning techniques to a labeled dataset and is not novel.

The reviews are moderately positive and the AC recommends accepting this paper as a poster. The authors are requested to address the reviewers' concerns and integrate material from the rebuttal into the revision.

In this paper, a bat-inspired high-resolution ultrasound 3D imaging system is presented. Live bats demonstrate that the properly used ultrasound can be used to perceive 3D space. With this in mind, a neural network referred to as a Bat-G network is implemented to reconstruct the 3D representation of target objects from the hyperbolic FM (HFM) chirped ultrasonic echoes. The Bat-G network consists of an encoder emulating a bat's central auditory pathway, and a 3D graphical visualization decoder. For the acquisition of the ultrasound data, a custom-made Bat-I sensor module is used.

In this paper, a bat-inspired high-resolution ultrasound 3D imaging system is presented. Live bats demonstrate that the properly used ultrasound can be used to perceive 3D space. With this in mind, a neural network referred to as a Bat-G network is implemented to reconstruct the 3D representation of target objects from the hyperbolic FM (HFM) chirped ultrasonic echoes. The Bat-G network consists of an encoder emulating a bat's central auditory pathway, and a 3D graphical visualization decoder. For the acquisition of the ultrasound data, a custom-made Bat-I sensor module is used.