Robust detection and tracking of objects is crucial for the deployment of autonomous vehicle technology. Image based benchmark datasets have driven development in computer vision tasks such as object detection, tracking and segmentation of agents in the environment. Most autonomous vehicles, however, carry a combination of cameras and range sensors such as lidar and radar. As machine learning based methods for detection and tracking become more prevalent, there is a need to train and evaluate such methods on datasets containing range sensor data along with images. In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view. nuScenes comprises 1000 scenes, each 20s long and fully annotated with 3D bounding boxes for 23 classes and 8 attributes. It has 7x as many annotations and 100x as many images as the pioneering KITTI dataset. We define novel 3D detection and tracking metrics. We also provide careful dataset analysis as well as baselines for lidar and image based detection and tracking. Data, development kit and more information are available online.

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nuScenes: A Multimodal Dataset for Autonomous Driving

nuScenes: A multimodal dataset for autonomous driving

As the foundation of intelligent systems, machine vision perceives the surrounding environment and provides a basis for decision-making. Object detection is the core task in machine vision. 3D object detection can provide object steric size and location information. Compared with the 2D object detection widely studied in image coordinates, it can provide more applications of detection systems. Accurate LiDAR data has a stronger spatial capture capability and is insensitive to natural light, which makes LiDAR a potential sensor for 3D detection. Recently, deep neural network has been developed to learn powerful object features from sensor data. However, the sparsity of LiDAR point cloud data poses challenges to the network processing. Plenty of emerged efforts have been made to address this difficulty, but a comprehensive review literature is still lacking. The purpose of this article is to review the challenges and methodologies of 3D object detection networks using LiDAR data. On this account, we first give an outline of 3D detection task and LiDAR sensing techniques. Then we unfold the review of deep 3D detection networks with three kinds of LiDAR point cloud representations and their challenges. We next summarize evaluation metrics and performance of algorithms on three authoritative 3D detection benchmarks. Finally, we provide valuable insights of challenges and open issues.

Deep 3D Object Detection Networks Using LiDAR Data: A Review

Accurate 3D object detection in LiDAR based point clouds suffers from the challenges of data sparsity and irregularities. Existing methods strive to organize the points regularly, e.g. voxelize, pass them through a designed 2D/3D neural network, and then define object-level anchors that predict offsets of 3D bounding boxes using collective evidences from all the points on the objects of interest. Contrary to the state-of-the-art anchor-based methods, based on the very nature of data sparsity, we observe that even points on an individual object part are informative about semantic information of the object. We thus argue in this paper for an approach opposite to existing methods using object-level anchors. Inspired by compositional models, which represent an object as parts and their spatial relations, we propose to represent an object as composition of its interior non-empty voxels, termed hotspots, and the spatial relations of hotspots. This gives rise to the representation of Object as Hotspots (OHS). Based on OHS, we further propose an anchor-free detection head with a novel ground truth assignment strategy that deals with inter-object point-sparsity imbalance to prevent the network from biasing towards objects with more points. Experimental results show that our proposed method works remarkably well on objects with a small number of points. Notably, our approach ranked \(1^{st}\) on KITTI 3D Detection Benchmark for cyclist and pedestrian detection, and achieved state-of-the-art performance on NuScenes 3D Detection Benchmark.

Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots

Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers’ burdens and improve the safety of driving. In modern autonomous driving pipelines, the perception system is an indispensable component, aiming to accurately estimate the status of surrounding environments and provide reliable observations for prediction and planning. 3D object detection, which aims to predict the locations, sizes, and categories of the 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving. First, we introduce the background of 3D object detection and discuss the challenges in this task. Second, we conduct a comprehensive survey of the progress in 3D object detection from the aspects of models and sensory inputs, including LiDAR-based, camera-based, and multi-modal detection approaches. We also provide an in-depth analysis of the potentials and challenges in each category of methods. Additionally, we systematically investigate the applications of 3D object detection in driving systems. Finally, we conduct a performance analysis of the 3D object detection approaches, and we further summarize the research trends over the years and prospect the future directions of this area. 

3D Object Detection for Autonomous Driving: A Comprehensive Survey

SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection

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SARPNET: Shape attention regional proposal network for liDAR-based 3D object detection