You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: Digital fringe projection (DFP) techniques provide dense 3D measurements of dynamically changing surfaces. Like the human eyes and brain, DFP uses triangulation between matching points in two views of the same scene at different angles to compute depth. However, unlike a stereobased method, DFP uses a digital video projector to replace one of the cameras 1 . The projector rapidly projects a known sinusoidal pattern onto the subject, and the surface of the subject distorts these patterns in the camera’s field of view. Three distorted patterns (fringe images) from the camera can be used to compute the depth using triangulation. Unlike other 3D measurement methods, DFP techniques lead to systems that tend to be faster, lower in equipment cost, more flexible, and easier to develop. DFP systems can also achieve the same measurement resolution as the camera. For this reason, DFP and other digital structured light techniques have recently been the focus of intense research (as summarized in 1-5 ). Taking advantage of DFP, the graphics processing unit, and optimized algorithms, we have developed a system capable of 30 Hz 3D video data acquisition, reconstruction, and display for over 300,000 measurement points per frame 6,7 . Binary defocusing DFP methods can achieve even greater speeds 8 . Diverse applications can benefit from DFP techniques. Our collaborators have used our systems for facial function analysis 9 , facial animation 10 , cardiac mechanics studies 11 , and fluid surface measurements, but many other potential applications exist. This video will teach the fundamentals of DFP techniques and illustrate the design and operation of a binary defocusing DFP system.

[2]: This paper presents a technique that reaches 3-D shape measurement speed beyond the digital-light-processing (DLP) projector’s projection speed. In particular, a “solid-state” binary structured pattern is generated with each micro-mirror pixel always being at one status (ON or OFF). By this means, any time segment of projection can represent the whole signal, thus the exposure time can be shorter than the projection time. A sinusoidal fringe pattern is generated by properly defocusing a binary one, and the Fourier fringe analysis means is used for 3-D shape recovery. We have successfully reached 4,000 Hz rate (80 µs exposure time) 3-D shape measurement speed with an off-the-shelf DLP projector.

[3]: 

[4]: Dense 3D reconstruction of fast moving objects could contribute to various applications such as body structure analysis, accident avoidance, and so on. In this paper, we propose a technique based on a one-shot scanning method, which reconstructs 3D shapes for each frame of a high frame-rate video capturing the scenes projected by a static pattern. To avoid instability of image processing, we restrict the number of colors used in the pattern to less than two. The proposed technique comprises (1) an efficient algorithm to eliminate ambiguity of projected parallel-line patterns by using intersection points, (2) a batch reconstruction algorithm of multiple frames by using spatio-temporal constraints, and (3) an efficient detection method of color-encoded grid pattern based on de Bruijn sequence. In the experiments, the line detection algorithm worked effectively and the dense reconstruction algorithm produces accurate and robust results. We also show the improved results by using temporal constraints. Finally, the dense reconstructions of fast moving objects in a high frame-rate video are presented.

[5]: Consumer depth cameras have dramatically improved our ability to track rigid, articulated, and deformable 3D objects in real-time. However, depth cameras have a limited temporal resolution (frame-rate) that restricts the accuracy and robustness of tracking, especially for fast or unpredictable motion. In this paper, we show how to perform model-based object tracking which allows to reconstruct the object's depth at an order of magnitude higher frame-rate through simple modifications to an off-the-shelf depth camera. We focus on phase-based time-of-flight (ToF) sensing, which reconstructs each low frame-rate depth image from a set of short exposure 'raw' infrared captures. These raw captures are taken in quick succession near the beginning of each depth frame, and differ in the modulation of their active illumination. We make two contributions. First, we detail how to perform model-based tracking against these raw captures. Second, we show that by reprogramming the camera to space the raw captures uniformly in time, we obtain a 10x higher frame-rate, and thereby improve the ability to track fast-moving objects.

[6]: Active vision techniques use programmable light sources, such as projectors, whose intensities can be controlled over space and time. We present a broad framework for fast active vision using Digital Light Processing (DLP) projectors. The digital micromirror array (DMD) in a DLP projector is capable of switching mirrors "on" and "off" at high speeds (106 s). An off-the-shelf DLP projector, however, effectively operates at much lower rates (30-60Hz) by emitting smaller intensities that are integrated over time by a sensor (eye or camera) to produce the desired brightness value. Our key idea is to exploit this "temporal dithering" of illumination, as observed by a high-speed camera. The dithering encodes each brightness value uniquely and may be used in conjunction with virtually any active vision technique. We apply our approach to five well-known problems: (a) structured light-based range finding, (b) photometric stereo, (c) illumination de-multiplexing, (d) high frequency preserving motion-blur and (e) separation of direct and global scene components, achieving significant speedups in performance. In all our methods, the projector receives a single image as input whereas the camera acquires a sequence of frames.

[7]: 

[8]:
While consumer-level depth sensors are able to capture the depth at only a limited frame rate, high-speed depth cameras that already reach hundreds or even thousands of frames per second, have been explored in the fields of computer vision and optical engineering in recent years. Among various solutions, structured light illumination (e.g., [8] [6] [3] [2] [1] [4] ) is the most popular technique, which requires a DLP video projector and a synchronized video camera to acquire structured patterns (e.g., fringe images) projected by special illuminator. Compared with those approaches, our solution can be seen as a post-processing technique, and thus applicable to different types of depth sensors. St "u [5] proposed to modify a typical Time-of-Flight (ToF) camera (e.g., Kinect v2) for model-based tracking at high frame rate (300Hz). However, their solution is limited to tracking of objects with rigid motion. Our work shares close resemblance to that by Kim and Kim [7] , which uses multi-view hybrid cameras (consisting of eight high-speed RGB cameras and six ToF cameras) for motion capture. However, their technique is highly dependent on skeleton tracking, and thus is only suitable for articulated motion.