Abstract Free and open-source software (FOSS) is a preferred tool for individuals and companies. The advantages of FOSS are minimal expenses, multi-platform and community support, transparent privacy policies, no vendor-related limitations, etc. Blender is a FOSS computer graphics 3D and 2D editor. It offers functions such as modeling, animating, video editing, simulations, image processing, scripting, rendering, etc. It is widely used as a free alternative to existing commercial products. This comprehensive survey examines Blender and its features and explores its usage in research, academic, and industrial projects, based on hundreds of collected and referenced sources. A comparison of Blender with alternative proprietary tools was conducted in terms of rendering performance, popularity, support, and feature set. According to this survey, Blender can be used as an efficient tool in film industry for visual effects composition, for dataset production for scientific experiments or deep learning methods, for educational purposes as a 3D geometrical problems demonstration tool, for the design of industrial models prepared, for example, for 3D printing, usage in augmented or virtual reality applications, etc. More specialized features are available as community-developed add-ons. The main reason why Blender is not used more often is that many professionals are used to other software.

This paper proposes a novel content-aware method for automatic focusing of the scene on a 3D display. The method addresses a common problem that visualized content is often out of focus, which adversely affects perceived 3D content. The method outperforms existing focusing method, having the error lower by almost 30%. The existing and novel focusing is extended with depth-of-field enhancement of the scene to mitigate out-of-focus artifacts. The relation between the total depth range of the scene and the visual quality of the result is discussed and evaluated according to human perception experiments. A space-warping method for synthetic scenes is proposed to reduce out-of-focus artifacts while maintaining the scene appearance. A user study was conducted to evaluate the proposed methods and identify the crucial parameters in the scene-focusing process on the 3D stereoscopic display by Looking Glass Factory. The study confirmed the efficiency of the proposals and discovered that the depth-of-field artifact mitigation might not be suitable for all scenes despite theoretical hypotheses. The overall proposal of this paper is a set of methods that can be used to produce the best user experience with an arbitrary scene displayed on a 3D display.

Abstract This paper proposes a method for an automatic detection of 3D-display-friendly scenes from video sequences. Manual selection of such scenes by a human user would be extremely time consuming and would require additional evaluation of the result on 3D display. The input videos can be intentionally captured or taken from other sources, such as films. First, the input video is analyzed and the camera trajectory is estimated. The optimal frame sequence that follows defined rules, based on optical attributes of the display, is then extracted. This ensures the best visual quality and viewing comfort. The following identification of a correct focusing distance is an important step to produce a sharp and artifact-free result on a 3D display. Two novel and equally efficient focus metrics for 3D displays are proposed and evaluated. Further scene enhancements are proposed to correct the unsuitably captured video. Multiple image analysis approaches used in the proposal are compared in terms of both quality and time performance. The proposal is experimentally evaluated on a state-of-the-art 3D display by Looking Glass Factory and is suitable even for other multi-view devices. The problem of optimal scene detection, which includes the input frames extraction, resampling, and focusing, was not addressed in any previous research. Separate stages of the proposal were compared with existing methods, but the results show that the proposed scheme is optimal and cannot be replaced by other state-of-the-art approaches.

Light field rendering is an image-based rendering method that does not use 3D models but only images of the scene as input to render new views. Light field approximation, represented as a set of images, suffers from so-called refocusing artifacts due to different depth values of the pixels in the scene. Without information about depths in the scene, proper focusing of the light field scene is limited to a single focusing distance. The correct focusing method is addressed in this work and a real-time solution is proposed for focusing of light field scenes, based on statistical analysis of the pixel values contributing to the final image. Unlike existing techniques, this method does not need precomputed or acquired depth information. Memory requirements and streaming bandwidth are reduced and real-time rendering is possible even for high resolution light field data, yielding visually satisfactory results. Experimental evaluation of the proposed method, implemented on a GPU, is presented in this paper.