Distance transform

About: Distance transform is a research topic. Over the lifetime, 2886 publications have been published within this topic receiving 59481 citations.

Fusion of depth maps with multiple scales

Abstract: Multi-view stereo systems can produce depth maps with large variations in viewing parameters, yielding vastly different sampling rates of the observed surface. We present a new method for surface reconstruction by integrating a set of registered depth maps with dramatically varying sampling rate. The method is based on the construction of a hierarchical signed distance field represented in an incomplete primal octree by incrementally adding triangulated depth maps. Due to the adaptive data structure, our algorithm is able to handle depth maps with varying scale and to consistently represent coarse, low-resolution regions as well as small details contained in high-resolution depth maps. A final surface mesh is extracted from the distance field by construction of a tetrahedral complex from the scattered signed distance values and applying the Marching Tetra-hedra algorithm on the partition. The output is an adaptive triangle mesh that seamlessly connects coarse and highly detailed regions while avoiding filling areas without suitable input data.

Lane detection and tracking using a new lane model and distance transform

Abstract: Lane detection is a significant component of driver assistance systems. Highway-based lane departure warning solutions are in the market since the mid-1990s. However, improving and generalizing vision-based lane detection remains to be a challenging task until recently. Among various lane detection methods developed, strong lane models, based on the global assumption of lane shape, have shown robustness in detection results, but are lack of flexibility to various shapes of lane. On the contrary, weak lane models will be adaptable to different shapes, as well as to maintain robustness. Using a typical weak lane model, particle filtering of lane boundary points has been proved to be a robust way to localize lanes. Positions of boundary points are directly used as the tracked states in the current research. This paper introduces a new weak lane model with this particle filter-based approach. This new model parameterizes the relationship between points of left and right lane boundaries, and can be used to detect all types of lanes. Furthermore, a modified version of an Euclidean distance transform is applied on an edge map to provide information for boundary point detection. In comparison to an edge map, properties of this distance transform support improved lane detection, including a novel initialization and tracking method. This paper fully explains how the application of this distance transform greatly facilitates lane detection and tracking. Two lane tracking methods are also discussed while focusing on efficiency and robustness, respectively. Finally, the paper reports about experiments on lane detection and tracking, and comparisons with other methods.

Improved alpha-tested magnification for vector textures and special effects

Abstract: A simple and efficient method is presented which allows improved rendering of glyphs composed of curved and linear elements. A distance field is generated from a high resolution image, and then stored into a channel of a lower-resolution texture. In the simplest case, this texture can then be rendered simply by using the alpha-testing and alpha-thresholding feature of modern GPUs, without a custom shader. This allows the technique to be used on even the lowest-end 3D graphics hardware. With the use of programmable shading, the technique is extended to perform various special effect renderings, including soft edges, outlining, drop shadows, multi-colored images, and sharp corners.