ISOD contains 2,000 manually labelled RGB-D images from 20 diverse sites, each featuring over 30 types of small objects randomly placed amidst the items already present in the scenes. These objects, typically ≤3cm in height, include LEGO blocks, rags, slippers, gloves, shoes, cables, crayons, chalk, glasses, smartphones (and their cases), fake banana peels, fake pet waste, and piles of toilet paper, among others. These items were chosen because they either threaten the safe operation of indoor mobile robots or create messes if run over.
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MlGesture is a dataset for hand gesture recognition tasks, recorded in a car with 5 different sensor types at two different viewpoints. The dataset contains over 1300 hand gesture videos from 24 participants and features 9 different hand gesture symbols. One sensor cluster with five different cameras is mounted in front of the driver in the center of the dashboard. A second sensor cluster is mounted on the ceiling looking straight down.
SEmantic Salient Instance Video (SESIV) dataset is obtained by augmenting the DAVIS-2017 benchmark dataset by assigning semantic ground-truth for salient instance labels. The SESIV dataset consists of 84 high-quality video sequences with pixel-wisely per-frame ground-truth labels.
Overview The goal: using simulation data to train neural networks to estimate the pose of a rover's camera with respect to a known target object
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