The rise of cheaper and more accurate genotyping techniques has lead to significant advances in understanding the genotype-phenotype map. However, this is currently bottlenecked by manually intensive or slow phenotype data collection. We propose an algorithm to automatically estimate the canopy height of a row of plants in field conditions in a single pass on a moving robot. A stereo sensor pointed down collects a series of stereo image pairs. The depth images are then converted to height-above-ground images to extract height contours. Separate height contours corresponding to each frame are then concatenated to construct a height contour representing one row of plants in the plot. Since the process is automated, data can be collected throughout the growing season with very little manual labor complementing the already abundantly available genotypic data. Using experimental data from seven plots, we show our proposed approach achieves a height estimation error of approximately 3.3%.
In modern vehicles bird's view systems are widely used to show the direct car surroundings to the driver. However, state-of-the-art methods for bird's view computations suffer from heavy distortions and unnatural warping. We propose an approach towards perspectively correct bird's view images for vehicular applications. Our method uses stereo images as input and is tested using stereo datasets.
3D cameras that can capture range information, in addition to color information, are increasingly prevalent in the consumer marketplace and available in many consumer mobile imaging platforms. An interesting and important application enabled by 3D cameras is photogrammetry, where the physical distance between points can be computed using captured imagery. However, for consumer photogrammetry to succeed in the marketplace, it needs to meet the accuracy and consistency expectations of users in the real world and perform well under challenging lighting conditions, varying distances of the object from the camera etc. These requirements are exceedingly difficult to meet due to the noisy nature of range data, especially when passive stereo or multi-camera systems are used for range estimation. We present a novel and robust algorithm for point-to-point 3D measurement using range camera systems in this paper. Our algorithm utilizes the intuition that users often specify end points of an object of interest for measurement and that the line connecting the two points also belong to the same object. We analyze the 3D structure of the points along this line using robust PCA and improve measurement accuracy by fitting the endpoints to this model prior to measurement computation. We also handle situations where users attempt to measure a gap such as the arms of a sofa, width of a doorway etc. which violates our assumption. Finally, we test the performance of our proposed algorithm on a dataset of over 1800 measurements collected by humans on the Dell Venue 8 tablet with Intel RealSense Snapshot technology. Our results show significant improvements in both accuracy and consistency of measurement, which is critical in making consumer photogrammetry a reality in the marketplace.
In this paper, we present a new real-time depth estimation method using the stereo color camera and the ToF depth sensor. First, we obtain the initial depth information from the ToF depth sensor. Exploiting the initial depth information to narrow the disparity range by performing 3-D warping from the position of the ToF camera to the position of the stereo camera due to accelerating the algorithm. We construct the cost volume by calculating intensity difference and truncated absolute difference of gradients. After narrowing the disparity range, we aggregate the cost volume. Experimental results show that the proposed method can represent the disparity detail and improve the quality in the vulnerable areas of stereo matching.