For each task, it contains at lease 15x larger amount of images than SOTA datasets. Compared with existing public datasets from real scenes, e.g., KITTI or Cityscapes, ApolloScape contains much large and richer labelling including holistic semantic dense point cloud for each site, stereo, per-pixel semantic labelling, lanemark labelling, instance segmentation, 3D car instance, high accurate location for every frame in various driving videos from multiple sites, cities and daytimes. In this paper, we present the ApolloScape dataset and its applications for autonomous driving. However, large scale data set for training and system evaluation is still a bottleneck for developing robust perception models. The key techniques for a self-driving car include solving tasks like 3D map construction, self-localization, parsing the driving road and understanding objects, which enable vehicles to reason and act. Īutonomous driving has attracted tremendous attention especially in the past few years. Our results are also better than other popular 3D object detectors such as AVOD and F-PointNet. When using stereo input, our input representation is able to improve the AP3D of Cars objects in the moderate category from 38.13 to 45.13. When using LiDAR input, our input representation is able to improve the AP3D of Cars objects in the moderate category from 74.99 to 76.84. With LiDAR as well as stereo input, our method outperforms PointPillars, which is one of the state-of-the art methods for 3D object detection. Achieving the accuracy of two stage detectors using a single stage approach is a important for 3D object detection as single stage approaches are simpler to implement in real-life applications. We also extend our method to stereo input and show that, aided by additional semantic segmentation input our method produces similar accuracy as state of the art stereo based detectors. This can be considered as a single shot deep learning algorithm as computing the input representation is straight forward and does not involve much computational cost. In this paper we present Single Shot 3D Object Detection (SS3D) - a single stage 3D object detection algorithm which combines a straight forward, statistically computed input representation and a single shot object detector based on PointPillars. The main downside of PointPillars is that it has a two stage approach with learned input representation based on fully connected layers followed by SSD. PointPillars is a fast 3D object detection algorithm that produces state of the art results and uses SSD adapted for 3D object detection.
The installation of the app requires a mobile phone with a 64-bit system, a 32-bit system does not support the installation of the app.Single stage deep learning algorithm for 2D object detection was made popular by Single Shot MultiBox Detector (SSD) and it was heavily adopted in several embedded applications. After testing, phones equipped with Qualcomm SDM765 5G chips have poor hardware decoding capabilities and are not supported for use, such as OPPO Reno 3 5G.ģ. Devices that do not meet the above requirements can possibly still use the app to control the camera, however, performance of some processor-intensive and AI-powered features may be sub-optimal.Ģ.
Download box shot 3d extras android#
Android devices with Exynos 9810 and above chips, including Samsung Galaxy S9, S9+, Note9 and newer models.ġ.Android devices with Snapdragon 845 and above chips, including Samsung Galaxy S9, Xiaomi Mi 8 or newer models.Android devices with Kirin 980 and above chips, including Huawei Mate 20, P30 or newer models.
Download box shot 3d extras pro#
Compatible with iOS mobile devices with chips A11 or above and iOS version 10.0 or above, including iPhone SE 2, iPhone 8, iPhone 8P, iPhone XR, iPhone XS, iPhone XS Max, iPhone X, iPhone 11, iPhone 11 Pro, iPhone 11 Pro Max, iPhone 12, iPhone 12 Pro, iPhone 12 Pro Max, iPhone 12 mini, iPad Air(2020), iPad Pro and newer iPad models.Ĭompatible with Android mobile devices that meet the following capabilities, including: