Correct Depth Perception Research Articles

Towards Non-Lambertian Scenes for Tensor Displays

Tensor displays are screens able to render a light field with correct depth perception without wearing glasses. Such devices have already been shown to be able to accurately render a scene composed of Lambertian objects. This paper presents the model and prototyping of a tensor display with three layers, using repurposed computer monitors, and extends the light field factorization method to non-Lambertian objects. Furthermore, we examine the relation and limitations between the depth-of-field and the depth range with Lambertian and non-Lambertian scenes. Non-Lambertian scenes contain out-of-range disparities that can not be properly rendered with the usual optimization method. We propose to artificially compress the disparity range of the scene by using two light fields focused on different depths, effectively solving the problem and allowing to render the scene clearly on both simulated and prototyped tensor display.

View-Consistent MeshFlow for Stereoscopic Video Stabilization

This paper presents a method to stabilize shaky stereoscopic videos captured by hand-held stereo cameras. It is often problematic to apply a traditional monocular video stabilization techniques directly to the stereoscopic views independently. This is mainly because some undesirable vertical disparities and inaccurate horizontal disparities are produced, which violates the original stereoscopic disparity constraint, leading to erroneous depth perceptions. In this paper, we show that the MeshFlow stabilization method for monocular videos can be extended for stereoscopic videos by taking additional disparity constraints during the stabilization. In particular, we first estimate disparities between two views. Then, we compute camera motions by the MeshFlow motion model, in which the camera paths can be extracted from the meshes at each view. Next, we smooth these paths of two views separately. After path optimization, we adjust the meshes of one view by our proposed joint the disparity and stability mesh warp, so that the temporal stability and the correct depth perception can be achieved simultaneously. We evaluate our method on various challenging stereoscopic videos with different camera motions and scene types. In the experiment, we adapt the objective quality assessment of single videos to evaluate our stereoscopic video stabilization. We further propose an objective evaluation method to assess the quality of the disparities in terms of the spatial and temporal coherence after the stabilization. The experimental results demonstrate the effectiveness of our method both quantitatively and qualitatively.

Autostereoscopic augmented reality visualization for depth perception in endoscopic surgery

Augmented reality (AR) has received increasing attention in minimally invasive surgery (MIS) applications. The goal of applying AR techniques to MIS is to enhance a surgeon's perception of the spatial relationship by overlaying invisible structures (e.g. tumor or vessels) onto the in vivo endoscopic video acquired during the surgery. One of primary issues of AR visualization is to provide correct depth perception for visible and invisible structures. In this paper, we present a video-based AR system consisting of functional modules for real-time 3D surface capture, reconstruction, and registration with pre-operative segmented CT model. The real-time 3D registration allows precise overlay of invisible structures onto 2D video for AR visualization. The AR overlay result is displayed on a multi-view autostereoscopic lenticular LCD. To study and compare the efficacy of AR visualization techniques, we investigated five different AR visualization modes. Both simulated and in vivo experiments were carried out and autostereoscopic AR visualization results were given. Evaluation and comparison for depth perception between five AR visualization modes are presented. Finally, we conclude the characteristics of these visualization modes. The novelty of our work lies in successful implementation of an end-to-end 3D autostereoscopic AR system from real-time reconstruction and registration with our multi-channel 3D endoscope, and systematic evaluation and comparison of five different visualization modes for depth perception.

Single-view X-ray depth recovery: toward a novel concept for image-guided interventions.

X-ray imaging is widely used for guiding minimally invasive surgeries. Despite ongoing efforts in particular toward advanced visualization incorporating mixed reality concepts, correct depth perception from X-ray imaging is still hampered due to its projective nature. In this paper, we introduce a new concept for predicting depth information from single-view X-ray images. Patient-specific training data for depth and corresponding X-ray attenuation information are constructed using readily available preoperative 3D image information. The corresponding depth model is learned employing a novel label-consistent dictionary learning method incorporating atlas and spatial prior constraints to allow for efficient reconstruction performance. We have validated our algorithm on patient data acquired for different anatomy focus (abdomen and thorax). Of 100 image pairs per each of 6 experimental instances, 80 images have been used for training and 20 for testing. Depth estimation results have been compared to ground truth depth values. We have achieved around [Formula: see text] and [Formula: see text] mean squared error on abdomen and thorax datasets, respectively, and visual results of our proposed method are very promising. We have therefore presented a new concept for enhancing depth perception for image-guided interventions.

The Limits of Transparent Depth Perception with Restricted Field of View: Application in an Augmented Reality Surgical Microscope

This paper describes an investigation into the effect of reduced field of view and target size in transparent stereo augmented reality systems. Six subjects were asked to identify the maximum disparity limit of correct 3D perception for a random dot target of different sizes, viewed through a transparent scene with a varying field of view (FOV). The results showed that the maximum disparity limit increased linearly up to 25 degrees FOV after which it showed no significant increase for increasing FOV. There was no significant difference in the maximum disparity values obtained using a 4, 8, or 10 degree target. The maximum disparity limit of 5.9 ±1 (mean ± SD) degrees visual angle was used in an equation to predict the range in which correct depth perception occurs for transparent AR in a binocular surgical microscope. We suggest that the optimum image position to give the greatest range for correct 3D depth perception is 25cm when viewing transparent targets behind a transparent real scene.