Existing techniques to compress point cloud attributes leverage either geometric or video-based compression tools. In this work, we explore a radically different approach inspired by recent advances in point cloud representation learning. A point cloud can be interpreted as a 2D manifold in a 3D space. As that, its attributes could be mapped onto a folded 2D grid; compressed through a conventional 2D image codec; and mapped back at the decoder side to recover attributes on 3D points. The folding operation is optimized by employing a deep neural network as a parametric folding function. As mapping is lossy in nature, we propose several strategies to refine it in such a way that attributes in 3D can be mapped to the 2D grid with minimal distortion. This approach can be flexibly applied to portions of point clouds in order to better adapt to local geometric complexity, and thus has a potential for being used as a tool in existing or future coding pipelines. Our preliminary results show that the proposed folding-based coding scheme can already reach performance similar to the latest MPEG GPCC codec.
Cloud storing of all your photos is about to get a whole lot better, and about to become a whole new experience. PixelDrive uses cutting edge Machine Learning (ML) to optimise your photos to much smaller file sizes, whilst preserving the quality and their resolution. The end result is beautifully enhanced photos at a fraction of the storage. This makes cloud storing of all your photos possible, and you can even share them super fast because the data required for transmission is significantly lower. We are good with Machine Learning, but we also love our media!
Many attempts have been done to extend the great success of convolutional neural networks (CNNs) achieved on high-end GPU servers to portable devices such as smart phones. Providing compression and acceleration service of deep learning models on the cloud is therefore of significance and is attractive for end users. ImageNet), which could be more cumbersome than the network itself and cannot be easily uploaded to the cloud. In this paper, we present a novel positive-unlabeled (PU) setting for addressing this problem. In practice, only a small portion of the original training set is required as positive examples and more useful training examples can be obtained from the massive unlabeled data on the cloud through a PU classifier with an attention based multi-scale feature extractor.
Abstract: Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilization of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artifacts usually exist in point cloud data from real 3D LiDAR sensors. As a result, the performance of the trained models on this data for perception tasks when tested on real point cloud data is degraded due to the domain shift between simulated and real environments.
Recent models for learned image compression are based on autoencoders that learn approximately invertible mappings from pixels to a quantized latent representation. The transforms are combined with an entropy model, which is a prior on the latent representation that can be used with standard arithmetic coding algorithms to generate a compressed bitstream. Recently, hierarchical entropy models were introduced as a way to exploit more structure in the latents than previous fully factorized priors, improving compression performance while maintaining end-to-end optimization. Inspired by the success of autoregressive priors in probabilistic generative models, we examine autoregressive, hierarchical, and combined priors as alternatives, weighing their costs and benefits in the context of image compression. While it is well known that autoregressive models can incur a significant computational penalty, we find that in terms of compression performance, autoregressive and hierarchical priors are complementary and can be combined to exploit the probabilistic structure in the latents better than all previous learned models.