649 research outputs found
OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas.
Recent work on depth estimation up to now has only focused on projective images ignoring 360o content which is now increasingly and more easily produced. We show that monocular depth estimation models trained on traditional images produce sub-optimal results on omnidirectional images, showcasing the need for training directly on 360o datasets, which however, are hard to acquire. In this work, we circumvent the challenges associated with acquiring high quality 360o datasets with ground truth depth annotations, by re-using recently released large scale 3D datasets and re-purposing them to 360o via rendering. This dataset, which is considerably larger than similar projective datasets, is publicly offered to the community to enable future research in this direction. We use this dataset to learn in an end-to-end fashion the task of depth estimation from 360o images. We show promising results in our synthesized data as well as in unseen realistic images
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
A dataset of annotated omnidirectional videos for distancing applications
Omnidirectional (or 360â—¦ ) cameras are acquisition devices that, in the next few years, could have a big impact on video surveillance applications, research, and industry, as they can record a spherical view of a whole environment from every perspective. This paper presents two new contributions to the research community: the CVIP360 dataset, an annotated dataset of 360â—¦ videos for distancing applications, and a new method to estimate the distances of objects in a scene from a single 360â—¦ image. The CVIP360 dataset includes 16 videos acquired outdoors and indoors, annotated by adding information about the pedestrians in the scene (bounding boxes) and the distances to the camera of some points in the 3D world by using markers at fixed and known intervals. The proposed distance estimation algorithm is based on geometry facts regarding the acquisition process of the omnidirectional device, and is uncalibrated in practice: the only required parameter is the camera height. The proposed algorithm was tested on the CVIP360 dataset, and empirical results demonstrate that the estimation error is negligible for distancing applications
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