Taking smartphone-made videos for photogrammetry is a convenient approach because of the easy image collection process for the object being reconstructed. However, the video may contain a lot of relatively similar frames. Additionally, frames may be of different quality. The primary source of quality variation in the same video is varying motion blur. Splitting the sequence of the frames into chunks and choosing the least motion-blurred frame in every chunk would reduce data redundancy and improve image data quality. Such reduction will lead to faster and more accurate reconstruction of the 3D objects. In this research, we investigated image quality evaluation in the case of human 3D head modeling. Suppose a head modeling workflow already uses a convolutional neural network for the head detection task in order to remove non-static background. In that case, features from the neural network may be reused for the quality evaluation of the same image. We proposed a motion blur evaluation method based on the LightGBM ranker model. The method was evaluated and compared with other blind image quality evaluation methods using videos of a mannequin head and real faces. Evaluation results show that the developed method in both cases outperformed sharpness-based, BRISQUE, NIQUE, and PIQUE methods in finding the least motion-blurred image.

This work addresses challenges related to camera 3D localization while reconstructing a 3D model of an ear. This work explores the potential solution of using a cap, specifically designed not to obstruct the ear, and its efficiency in enhancing the camera localization for structure-from-motion (SfM)-based object reconstruction. The proposed solution is described, and an elaboration of the experimental scenarios used to investigate the background textures is provided; data collection and software tools used in the research are reported. The results show that the proposed method is effective, and using the cap with texture leads to a reduction in the camera localization error. Errors in the 3D location reconstruction of the camera were calculated by comparing cameras localized within typical ear reconstruction situations to those of higher-accuracy reconstructions. The findings also show that caps with sparse dot patterns and a regular knitted patterned winter hat are the preferred patterns. The study provides a contribution to the field of 3D modeling, particularly in the context of creating 3D models of the human ear, and offers a step towards more accurate, reliable, and feasible 3D ear modeling and reconstruction.

This paper discusses an algorithm that attempts to automatically calculate the effect of room reverberation by training a mathematical model based on a recurrent neural network on anechoic and reverberant sound samples. Modelling the room impulse response (RIR) recorded at a 44.1 kHz sampling rate using a system identification-based approach in the time domain, even with deep learning models, is prohibitively complex and it is almost impossible to automatically learn the parameters of the model for a reverberation time longer than 1 s. Therefore, this paper presents a method to model a reverberated audio signal in the frequency domain. To reduce complexity, the spectrum is analyzed on a logarithmic scale, based on the subjective characteristics of human hearing, by calculating 10 octaves in the range 20–20,000 Hz and dividing each octave by 1/3 or 1/12 of the bandwidth. This maintains equal resolution at high, mid, and low frequencies. The study examines three different recurrent network structures: LSTM, BiLSTM, and GRU, comparing the different sizes of the two hidden layers. The experimental study was carried out to compare the modelling when each octave of the spectrum is divided into a different number of bands, as well as to assess the feasibility of using a single model to predict the spectrum of a reverberated audio in adjacent frequency bands. The paper also presents and describes in detail a new RIR dataset that, although synthetic, is calibrated with recorded impulses.

Methods based on artificial neural networks (ANN) are widely used in various audio signal processing tasks. This provides opportunities to optimize processes and save resources required for calculations. One of the main objects we need to get to numerically capture the acoustics of a room is the room impulse response (RIR). Increasingly, research authors choose not to record these impulses in a real room but to generate them using ANN, as this gives them the freedom to prepare unlimited-sized training datasets. Neural networks are also used to augment the generated impulses to make them similar to the ones actually recorded. The widest use of ANN so far is observed in the evaluation of the generated results, for example, in automatic speech recognition (ASR) tasks. This review also describes datasets of recorded RIR impulses commonly found in various studies that are used as training data for neural networks.