Uber Technologies, Inc.

Thommen Korah

Radek Grzeszczuk

Vasudev Parameswaran

Jingchen Liu

Varsha Hedau

Yanxi Liu

Timo Pekka Pylvaenaeinen

Jerome Berclaz

Myra Nam

Architecture that detects entrances on building facades. In a first stage, scene geometry is exploited and the multi-dimensional problem is reduced down to a one-dimensional (1D) problem. Entrance hypotheses are generated by considering pairs of locations along lines exhibiting strong gradients in the transverse direction. In a second stage, a rich set of discriminative image features for entrances is explored according to constructed designs, specifically focusing on properties such as symmetry and color consistency, for example. Classifiers (e.g., random forest) are utilized to perform automatic feature selection and entrance classification. In another stage, a joint model is formulated in three dimensions (3D) for entrances on a given facade, which enables the exploitation of physical constraints between different entrances on the same facade in a systematic manner to prune false positives, and thereby select an optimum set of entrances on a given facade.

Entrance detection from street-level imagery

Architecture that summarizes a large amount (e.g., thousands of miles) of street-level image/video data of different perspectives and types (e.g., continuous scan-type data and panorama-type data) into a single view that resembles aerial imagery. Polygons surfaces are generated from the scan patterns and the image data is projected onto the surfaces, and then rendered into the desired orthographic projection. The street-level data is processed using a distributed computing approach across cluster nodes. The collection is processed into image tiles on the separate cluster nodes representing an orthographic map projection that can be viewed at various levels of detail. Map features such as lower-level roads, that are at lower elevations than higher-level roads, and are hidden by higher-level overpassing roads, can be navigated in the map. With the summarized data, the maps can be navigated and zoomed efficiently.

Map-like summary visualization of street-level distance data and panorama data

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San Ramon, CA, United States of America

Thommen Korah