In this paper we introduce a novel way to predict semantic information from sparse, single-shot LiDAR measurements in the context of autonomous driving. In particular, we fuse learned features from complementary representations. The approach is aimed specifically at improving the semantic segmentation of top-view grid maps. Towards this goal the 3D LiDAR point cloud is projected onto two orthogonal 2D representations. For each representation a tailored deep learning architecture is developed to effectively extract semantic information which are fused by a superordinate deep neural network. The contribution of this work is threefold: (1) We examine different stages within the segmentation network for fusion. (2) We quantify the impact of embedding different features. (3) We use the findings of this survey to design a tailored deep neural network architecture leveraging respective advantages of different representations. Our method is evaluated using the SemanticKITTI dataset which provides a point-wise semantic annotation of more than 23.000 LiDAR measurements.
University of Chinese Academy of Sciences（中国科学院大学）；Damo Academy, Alibaba Group（阿里巴巴集团达摩院）
LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms. Previous point-based or sparse voxel-based methods are far away from real-time applications since time-consuming neighbor searching or sparse 3D convolution are employed. Recent 2D projection-based methods, including range view and multi-view fusion, can run in real time, but suffer from lower accuracy due to information loss during the 2D projection. Besides, to improve the performance, previous methods usually adopt test time augmentation (TTA), which…
Stellantis, Opel Automobile GmbH（斯泰兰蒂斯集团欧宝汽车有限公司）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
Semantic understanding of the surrounding environment is essential for automated vehicles. The recent publication of the SemanticKITTI dataset stimulates the research on semantic segmentation of LiDAR point clouds in urban scenarios. While most existing approaches predict sparse pointwise semantic classes for the sparse input LiDAR scan, we propose PillarSegNet to be able to output a dense semantic grid map. In contrast to a previously proposed grid map method, PillarSegNet uses PointNet to learn features directly from the 3D point cloud and then conducts 2D semantic segmentation in the top vi…
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）
In this paper, we consider the transformation of laser range measurements into a top-view grid map representation to approach the task of LiDAR-only semantic segmentation. Since the recent publication of the SemanticKITTI data set, researchers are now able to study semantic segmentation of urban LiDAR sequences based on a reasonable amount of data. While other approaches propose to directly learn on the 3D point clouds, we are exploiting a grid map framework to extract relevant information and represent them by using multi-layer grid maps. This representation allows us to use well-studied deep…
BiCo-Fusion: Bidirectional Complementary LiDAR-Camera Fusion for Semantic- and Spatial-Aware 3D Object Detection
AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州）人工智能方向）；School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU)（南洋理工大学电子与电气工程学院）
Understanding driving situations regardless the conditions of the traffic scene is a cornerstone on the path towards autonomous vehicles; however, despite common sensor setups already include complementary devices such as LiDAR or radar, most of the research on perception systems has traditionally focused on computer vision. We present a LiDAR-based 3D object detection pipeline entailing three stages. First, laser information is projected into a novel cell encoding for bird's eye view projection. Later, both object location on the plane and its heading are estimated through a convolutional neu…
Data augmentations are important in training high-performance 3D object detectors for point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly, most state-of-the-art 3D detectors only use a few simple data augmentations. In particular, different from 2D image data augmentations, 3D data augmentations need to account for different representations of input data and require being customized for different models, which introduces significant overhead. In this paper, we resort to a search-based approach, and propose LidarAugment, a practical and effective data …
Huazhong University of Science and Technology（华中科技大学）；SenseTime Research（商汤科技研究院）；Chinese University of Hong Kong（香港中文大学）
Camera and 3D LiDAR sensors have become indispensable devices in modern autonomous driving vehicles, where the camera provides the fine-grained texture, color information in 2D space and LiDAR captures more precise and farther-away distance measurements of the surrounding environments. The complementary information from these two sensors makes the two-modality fusion be a desired option. However, two major issues of the fusion between camera and LiDAR hinder its performance, \ie, how to effectively fuse these two modalities and how to precisely align them (suffering from the weak spatiotempora…
When localizing and detecting 3D objects for autonomous driving scenes, obtaining information from multiple sensor (e.g. camera, LIDAR) typically increases the robustness of 3D detectors. However, the efficient and effective fusion of different features captured from LIDAR and camera is still challenging, especially due to the sparsity and irregularity of point cloud distributions. This notwithstanding, point clouds offer useful complementary information. In this paper, we would like to leverage the advantages of LIDAR and camera sensors by proposing a deep neural network architecture for the …
LiteFusion: Taming 3D Object Detectors from Vision-Based to Multi-Modal with Minimal Adaptation
China Ministry of Education (MOE) Key Laboratory of Artificial Intelligence（中国教育部人工智能重点实验室）；Artificial Intelligence Institute, Shanghai Jiao Tong University（上海交通大学人工智能学院）；Huawei Noah’s Ark Lab（华为诺亚实验室）
LiDAR has become one of the primary 3D object detection sensors in autonomous driving. However, LiDAR's diverging point pattern with increasing distance results in a non-uniform sampled point cloud ill-suited to discretized volumetric feature extraction. Current methods either rely on voxelized point clouds or use inefficient farthest point sampling to mitigate detrimental effects caused by density variation but largely ignore point density as a feature and its predictable relationship with distance from the LiDAR sensor. Our proposed solution, Point Density-Aware Voxel network (PDV), is an en…
Spatially-Aware Evaluation Framework for Aerial LiDAR Point Cloud Semantic Segmentation: Distance-Based Metrics on Challenging Regions
Department of Statistics, Computer Science and Mathematics and Institute of Smart Cities (ISC), Public University of Navarre (UPNA)（统计、计算机科学与数学系和智能城市研究所（ISC），纳瓦拉公共大学）；Tracasa Instrumental
Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shanghai University（上海大学）；Tencent Map, T Lab（腾讯地图T实验室）
As camera and LiDAR sensors capture complementary information used in autonomous driving, great efforts have been made to develop semantic segmentation algorithms through multi-modality data fusion. However, fusion-based approaches require paired data, i.e., LiDAR point clouds and camera images with strict point-to-pixel mappings, as the inputs in both training and inference, which seriously hinders their application in practical scenarios. Thus, in this work, we propose the 2D Priors Assisted Semantic Segmentation (2DPASS), a general training scheme, to boost the representation learning on po…
Considerable research effort has been devoted to LiDAR-based 3D object detection and empirical performance has been significantly improved. While progress has been encouraging, we observe an overlooked issue: it is not yet common practice to compare different 3D detectors under the same cost, e.g., inference latency. This makes it difficult to quantify the true performance gain brought by recently proposed architecture designs. The goal of this work is to conduct a cost-aware evaluation of LiDAR-based 3D object detectors. Specifically, we focus on SECOND, a simple grid-based one-stage detector…
Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection
School of Computer Science and Technology, Tongji University, Shanghai 201804, China（同济大学计算机科学与技术学院）；School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China（同济大学电子信息工程学院）
Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. While prevalent multi-modal methods simply decorate raw lidar point clouds with camera features and feed them directly to existing 3D detection models, our study shows that fusing camera features with deep lidar features instead of raw points, can lead to better performance. However, as those features are often augmented and aggregated, a key challenge in fusion is how to effectively align the transformed features from two modalities. In this paper, we propose two novel techni…
There are two critical sensors for 3D perception in autonomous driving, the camera and the LiDAR. The camera provides rich semantic information such as color, texture, and the LiDAR reflects the 3D shape and locations of surrounding objects. People discover that fusing these two modalities can significantly boost the performance of 3D perception models as each modality has complementary information to the other. However, we observe that current datasets are captured from expensive vehicles that are explicitly designed for data collection purposes, and cannot truly reflect the realistic data di…
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）；FZI Research Center for Information Technology（FZI信息技术研究中心）
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras. Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately. We specify the estimation steps for input data represented by point sets, but mainly focus on input data represented by images such as disparity maps or LiDAR range images. Instead of relying on an external ground segmentation only, we deduce occupancy evidence by analyzing the surface orientation around measurements. We conduct experiments and evaluate the presented method using LiDA…
LiDAR has become a standard sensor for autonomous driving applications as they provide highly precise 3D point clouds. LiDAR is also robust for low-light scenarios at night-time or due to shadows where the performance of cameras is degraded. LiDAR perception is gradually becoming mature for algorithms including object detection and SLAM. However, semantic segmentation algorithm remains to be relatively less explored. Motivated by the fact that semantic segmentation is a mature algorithm on image data, we explore sensor fusion based 3D segmentation. Our main contribution is to convert the RGB i…
Detecting objects of interest, such as human survivors, safety equipment, and structure access points, is critical to any search-and-rescue operation. Robots deployed for such time-sensitive efforts rely on their onboard sensors to perform their designated tasks. However, as disaster response operations are predominantly conducted under perceptually degraded conditions, commonly utilized sensors such as visual cameras and LiDARs suffer in terms of performance degradation. In response, this work presents a method that utilizes the complementary nature of vision and depth sensors to leverage mul…
University of California at Riverside（加州大学河滨分校）；Toyota North America R&D Labs（丰田北美研发实验室）
3D object detection plays a fundamental role in enabling autonomous driving, which is regarded as the significant key to unlocking the bottleneck of contemporary transportation systems from the perspectives of safety, mobility, and sustainability. Most of the state-of-the-art (SOTA) object detection methods from point clouds are developed based on a single onboard LiDAR, whose performance will be inevitably limited by the range and occlusion, especially in dense traffic scenarios. In this paper, we propose \textit{PillarGrid}, a novel cooperative perception method fusing information from multi…
With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics. Camera and LIDAR, with complementary semantic and depth information, are the typical choices for detection tasks in complicated driving environments. For most camera-LIDAR fusion algorithms, however, the calibration of the sensor suite will greatly impact the performance. More specifically, the detection algorithm usually requires an accurate geometric relationship among multiple sensors as the input, and it is often assumed that the contents from …
In this paper, we propose a new deep architecture for fusing camera and LiDAR sensors for 3D object detection. Because the camera and LiDAR sensor signals have different characteristics and distributions, fusing these two modalities is expected to improve both the accuracy and robustness of 3D object detection. One of the challenges presented by the fusion of cameras and LiDAR is that the spatial feature maps obtained from each modality are represented by significantly different views in the camera and world coordinates; hence, it is not an easy task to combine two heterogeneous feature maps w…
University of Central Florida（中佛罗里达大学）；U.S. Army Research Laboratory（美国陆军研究实验室）；University of California, Berkeley（加州大学伯克利分校）；Google（谷歌）
The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR. Despite the emerging datasets and technological advancements, it remains challenging due to three reasons: (1) the need for near-real-time latency with limited hardware; (2) uneven or even long-tailed distribution of LiDAR points across space; and (3) an increasing number of extremely fine-grained semantic classes. In an attempt to jointly tackle all the aforementioned challenges, we propose a new LiDAR-specific, nearest-neighbor-f…
SARFusion: Scene-Aware Routing Fusion for Robust Camera-LiDAR 3D Object Detection
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan College（武汉学院）
Opel Automobile GmbH（欧宝汽车公司）；Institute of Measurement and Control Systems, Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院测量与控制系统研究所）
3D pedestrian detection is a challenging task in automated driving because pedestrians are relatively small, frequently occluded and easily confused with narrow vertical objects. LiDAR and camera are two commonly used sensor modalities for this task, which should provide complementary information. Unexpectedly, LiDAR-only detection methods tend to outperform multisensor fusion methods in public benchmarks. Recently, PointPainting has been presented to eliminate this performance drop by effectively fusing the output of a semantic segmentation network instead of the raw image information. In thi…
Semantic grids can be useful representations of the scene around an autonomous system. By having information about the layout of the space around itself, a robot can leverage this type of representation for crucial tasks such as navigation or tracking. By fusing information from multiple sensors, robustness can be increased and the computational load for the task can be lowered, achieving real time performance. Our multi-scale LiDAR-Aided Perspective Transform network uses information available in point clouds to guide the projection of image features to a top-view representation, resulting in…
HeuDiaSyc（HeuDiaSyc实验室）；Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Renault S.A.S（雷诺公司）
LIDAR sensors are usually used to provide autonomous vehicles with 3D representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR scans, at the cost of a manual tuning of numerical constraints, and a lack of flexibility. We instead propose an evidential pipeline, to accumulate road detection results obtained from neural networks. First, we introduce RoadSeg, a new convolutional architecture that is optimized for road detection in LIDAR scans. RoadSeg is used to classify individual LIDAR points as either belonging to the road, or not. Yet, such p…
Semantic grids are a useful representation of the environment around a robot. They can be used in autonomous vehicles to concisely represent the scene around the car, capturing vital information for downstream tasks like navigation or collision assessment. Information from different sensors can be used to generate these grids. Some methods rely only on RGB images, whereas others choose to incorporate information from other sensors, such as radar or LiDAR. In this paper, we present an architecture that fuses LiDAR and camera information to generate semantic grids. By using the 3D information fr…
The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception. Instead of directly fusing estimated depths across LiDAR and stereo modalities, we take advantages of the stereo matching network with two enhanced techniques: Input Fusion and Conditional Cost Volume Normalization (CCVNorm) on the LiDAR information. The proposed framework is generic and closely integrated with the cost volume component that is commonly utilized in stereo matching neural networks. We experimentally verify the…
LFP: Efficient and Accurate End-to-End Lane-Level Planning via Camera-LiDAR Fusion
School of Computer Science and Technology, University of Science and Technology of China (USTC)（中国科学技术大学计算机科学与技术学院）；SenseTime Research（商汤科技研究院）；Shanghai AI Laboratory（上海人工智能实验室）；School of Artificial Intelligence and Data Science, University of Science and Technology of China (USTC)（中国科学技术大学人工智能与数据科学学院）
