In recent years, consumer-level depth cameras have been adopted for various applications. However, they often produce depth maps at only a moderately high frame rate (approximately 30 frames per second), preventing them from being used for applications such as digitizing human performance involving fast motion. On the other hand, low-cost, high-frame-rate video cameras are available. This motivates us to develop a hybrid camera that consists of a high-frame-rate video camera and a low-frame-rate depth camera and to allow temporal interpolation of depth maps with the help of auxiliary color images. To achieve this, we develop a novel algorithm that reconstructs intermediate depth maps and estimates scene flow simultaneously. We test our algorithm on various examples involving fast, non-rigid motions of single or multiple objects. Our experiments show that our scene flow estimation method is more precise than a tracking-based method and the state-of-the-art techniques.
Depth sensors are often used together with video cameras to capture RGB-D images. Therefore the idea of hybrid camera is not new for 3D imaging. In fact, consumer-level depth sensors like Kinect are essentially hybrid cameras. It is well known that the depth data produced by low-cost depth sensors is often noisy and low resolution. A common approach to enhance depth maps in the spatial domain is to couple a low-resolution depth map with a high-resolution RGB image. Various solutions based on optimization (e.g., @cite_54 @cite_21 ), joint edge-preserving upsampling filters (e.g., @cite_56 @cite_60 @cite_33 ), spatialtemporal filtering @cite_36 @cite_49 , or shading cues @cite_50 @cite_8 have been explored to increase the spatial resolution of depth maps. All the methods above assume the same frame rates of color and depth maps. The exceptional case is the work by @cite_36 . However, their technique has mainly focuses on the interpolation of depth maps given sparse depth data with respect to camera frames, and has been applied to moving rigid objects only. Instead, our focus is on the estimation of new depth frames involving more complex motions. See the comparison between these two approaches in Sec. .
@cite_33 @cite_60 @cite_36 @cite_8 @cite_54 @cite_21 @cite_56 @cite_49 @cite_50
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Commercial RGB-D cameras provide the possibility of fast, accurate, and cost-effective 3-D scanning solution in a single package. These economical depth cameras provide several advantages over conventional depth sensors, such as sonars and lidars, in specific usage scenarios. In this paper, we analyze the performance of Kinect v2 time-of-flight camera while operating fully submerged underwater in a customized waterproof housing. Camera calibration has been performed for Kinect’s RGB and NIR cameras, and the effect of calibration on the generated 3-D mesh is discussed in detail. To overcome the effect of refraction of light due to the sensor housing and water, we propose a time-of-flight correction method and a fast, accurate and intuitive refraction correction method that can be applied to the acquired depth images, during 3-D mesh generation. Experimental results show that the Kinect v2 can acquire point cloud data up to 650 mm. The reconstruction results have been analyzed qualitatively and quantitatively, and confirm that the 3-D reconstruction of submerged objects at small distances is possible without the requirement of any external NIR light source. The proposed algorithms successfully generated 3-D mesh with a mean error of ±6 mm at a frame rate of nearly 10 fps. We acquired a large data set of RGB, IR and depth data from a submerged Kinect v2. The data set covers a large variety of objects scanned underwater and is publicly available for further use, along with the Kinect waterproof housing design and correction filter codes. The research is aimed toward small-scale research activities and economical solution for 3-D scanning underwater. Applications such as coral reef mapping and underwater SLAM in shallow waters for ROV’s can be a viable application area that can benefit from results achieved. Modern consumer RGB-D cameras are affordable and provide dense depth estimates at high frame rates. Hence, they are popular for building dense environment representations. Yet, the sensors often do not provide accurate depth estimates since the factory calibration exhibits a static deformation. We present a novel approach to online depth calibration that uses a visual SLAM system as reference for the measured depth. A sparse map is generated and the visual information is used to correct the static deformation of the measured depth while missing data is extrapolated using a small number of thin plate splines (TPS). The corrected depth can then be used to improve the accuracy of the sparse RGB-D map and the 3D environment reconstruction. As more data becomes available, the depth calibration is updated on the fly. Our method does not rely on a planar geometry like walls or a one-to-one-pixel correspondence between color and depth camera. Our approach is evaluated in real-world scenarios and against ground truth data. Comparison against two popular self-calibration methods is performed. Furthermore, we show clear visual improvement on aggregated point clouds with our method. Depth sensing is crucial for 3D reconstruction and scene understanding. Active depth sensors provide dense metric measurements, but often suffer from limitations such as restricted operating ranges, low spatial resolution, sensor interference, and high power consumption. In this paper, we propose a deep learning (DL) method to estimate per-pixel depth and its uncertainty continuously from a monocular video stream, with the goal of effectively turning an RGB camera into an RGB-D camera. Unlike prior DL-based methods, we estimate a depth probability distribution for each pixel rather than a single depth value, leading to an estimate of a 3D depth probability volume for each input frame. These depth probability volumes are accumulated over time under a Bayesian filtering framework as more incoming frames are processed sequentially, which effectively reduces depth uncertainty and improves accuracy, robustness, and temporal stability. Compared to prior work, the proposed approach achieves more accurate and stable results, and generalizes better to new datasets. Experimental results also show the output of our approach can be directly fed into classical RGB-D based 3D scanning methods for 3D scene reconstruction. The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizing the complex and dynamic dependency between intensity and depth images. To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance. First, we generalize the analysis representation model by including a guided weight function for dependency modeling. And the task-driven learning formulation is introduced to obtain the optimized guidance tailored to specific enhancement task. Second, the depth image is gradually enhanced along with the iterations, and thus the guidance should also be dynamically adjusted to account for the updating of depth image. To this end, stage-wise parameters are learned for dynamic guidance. Experiments on guided depth image upsampling and noisy depth image restoration validate the effectiveness of our method.