1,670 research outputs found
Enhancing Perception and Immersion in Pre-Captured Environments through Learning-Based Eye Height Adaptation
Pre-captured immersive environments using omnidirectional cameras provide a
wide range of virtual reality applications. Previous research has shown that
manipulating the eye height in egocentric virtual environments can
significantly affect distance perception and immersion. However, the influence
of eye height in pre-captured real environments has received less attention due
to the difficulty of altering the perspective after finishing the capture
process. To explore this influence, we first propose a pilot study that
captures real environments with multiple eye heights and asks participants to
judge the egocentric distances and immersion. If a significant influence is
confirmed, an effective image-based approach to adapt pre-captured real-world
environments to the user's eye height would be desirable. Motivated by the
study, we propose a learning-based approach for synthesizing novel views for
omnidirectional images with altered eye heights. This approach employs a
multitask architecture that learns depth and semantic segmentation in two
formats, and generates high-quality depth and semantic segmentation to
facilitate the inpainting stage. With the improved omnidirectional-aware
layered depth image, our approach synthesizes natural and realistic visuals for
eye height adaptation. Quantitative and qualitative evaluation shows favorable
results against state-of-the-art methods, and an extensive user study verifies
improved perception and immersion for pre-captured real-world environments.Comment: 10 pages, 13 figures, 3 tables, submitted to ISMAR 202
Application of augmented reality and robotic technology in broadcasting: A survey
As an innovation technique, Augmented Reality (AR) has been gradually deployed in the broadcast, videography and cinematography industries. Virtual graphics generated by AR are dynamic and overlap on the surface of the environment so that the original appearance can be greatly enhanced in comparison with traditional broadcasting. In addition, AR enables broadcasters to interact with augmented virtual 3D models on a broadcasting scene in order to enhance the performance of broadcasting. Recently, advanced robotic technologies have been deployed in a camera shooting system to create a robotic cameraman so that the performance of AR broadcasting could be further improved, which is highlighted in the paper
Design and Analysis of a Single-Camera Omnistereo Sensor for Quadrotor Micro Aerial Vehicles (MAVs)
We describe the design and 3D sensing performance of an omnidirectional stereo (omnistereo) vision system applied to Micro Aerial Vehicles (MAVs). The proposed omnistereo sensor employs a monocular camera that is co-axially aligned with a pair of hyperboloidal mirrors (a vertically-folded catadioptric configuration). We show that this arrangement provides a compact solution for omnidirectional 3D perception while mounted on top of propeller-based MAVs (not capable of large payloads). The theoretical single viewpoint (SVP) constraint helps us derive analytical solutions for the sensor’s projective geometry and generate SVP-compliant panoramic images to compute 3D information from stereo correspondences (in a truly synchronous fashion). We perform an extensive analysis on various system characteristics such as its size, catadioptric spatial resolution, field-of-view. In addition, we pose a probabilistic model for the uncertainty estimation of 3D information from triangulation of back-projected rays. We validate the projection error of the design using both synthetic and real-life images against ground-truth data. Qualitatively, we show 3D point clouds (dense and sparse) resulting out of a single image captured from a real-life experiment. We expect the reproducibility of our sensor as its model parameters can be optimized to satisfy other catadioptric-based omnistereo vision under different circumstances
Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework
Surround-view fisheye perception under valet parking scenes is fundamental
and crucial in autonomous driving. Environmental conditions in parking lots
perform differently from the common public datasets, such as imperfect light
and opacity, which substantially impacts on perception performance. Most
existing networks based on public datasets may generalize suboptimal results on
these valet parking scenes, also affected by the fisheye distortion. In this
article, we introduce a new large-scale fisheye dataset called Fisheye Parking
Dataset(FPD) to promote the research in dealing with diverse real-world
surround-view parking cases. Notably, our compiled FPD exhibits excellent
characteristics for different surround-view perception tasks. In addition, we
also propose our real-time distortion-insensitive multi-task framework Fisheye
Perception Network (FPNet), which improves the surround-view fisheye BEV
perception by enhancing the fisheye distortion operation and multi-task
lightweight designs. Extensive experiments validate the effectiveness of our
approach and the dataset's exceptional generalizability.Comment: 12 pages, 11 figure
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