A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving

TL;DR: This survey paper reviews the research progress for infrastructure-based object detection and tracking systems and highlights current opportunities, open problems, and anticipated future trends.

TL;DR: PillarGrid as discussed by the authors proposes a cooperative perception method fusing information from multiple 3D LiDARs (both on-board and roadside), to enhance the situation awareness for connected and automated vehicles.

TL;DR: In this article , the authors investigate the role of attention mechanisms for the task of 3D semantic segmentation for autonomous driving, by identifying the significance of different attention mechanisms when adopted in existing deep learning networks.

TL;DR: Multi-modality 3D object detection in autonomous driving reviews existing research on LiDAR and camera fusion approaches for 3D object detection. It identifies key challenges and promising research directions in the field.

TL;DR: Evaluated on the KITTI 3D object detection benchmark, the Patch Refinement submission from January 28, 2019, outperformed all previous entries on all three difficulties of the class car, using only 50 % of the available training data and only LiDAR information.

TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.

TL;DR: In this paper, a vision-based algorithm for autonomous tracking and landing on a moving platform in varying environmental conditions is presented. And the authors use an energy-aware approach, where the design of the algorithm is based on an evaluation of the energy consumption and Quality of Service (QoS) of each computational component.

Abstract: Few prior works study deep learning on point sets. PointNet by Qi et al. is a pioneer in this direction. However, by design PointNet does not capture local structures induced by the metric space points live in, limiting its ability to recognize fine-grained patterns and generalizability to complex scenes. In this work, we introduce a hierarchical neural network that applies PointNet recursively on a nested partitioning of the input point set. By exploiting metric space distances, our network is able to learn local features with increasing contextual scales. With further observation that point sets are usually sampled with varying densities, which results in greatly decreased performance for networks trained on uniform densities, we propose novel set learning layers to adaptively combine features from multiple scales. Experiments show that our network called PointNet++ is able to learn deep point set features efficiently and robustly. In particular, results significantly better than state-of-the-art have been obtained on challenging benchmarks of 3D point clouds.