RI-Fusion: 3D Object Detection Using Enhanced Point Features With Range-Image Fusion for Autonomous Driving

Light detection and ranging (LiDAR) is widely used in the automotive industry as it can provide point cloud information containing precise distances. Three-dimensional (3D) object detection based on LiDAR point clouds is significant for environment perception tasks. However, feature learning for point clouds remains challenging. This paper proposes a two-stage voxel-based LiDAR 3D object detector, referred to as density-aware and neighborhood attention (DenNet), that focuses on the neighborhood information of objects. DenNet mainly integrates two modules: voxel density-aware (VDA) and neighborhood attention (NA). VDA introduces density information of the point cloud. Here, point cloud density information was added as voxel features in the voxel-based framework to alleviate the information loss during voxelization. Additionally, to extract neighbor information, the characteristics of 3D objects were analyzed for traffic scenes. The NA mechanism was adopted, which localizes the receptive field for each query to its nearest neighboring points. DenNet yielded competitive results, as compared with state-of-the-art methods, for the KITTI and One Million Scenes (ONCE) datasets; notably, it afforded an improvement of 3.96% relative to the baseline mean average precision on the more challenging ONCE dataset.

Voxel is one of the common structural representation of 3D point cloud. Due to the sparsity of point cloud generated by light detection and ranging (LiDAR), there is the extreme imbalance in the foreground and background voxels. It decreases the accuracy of 3D object detection, has the negative effect on intelligent driving safety. To overcome this problem, we present a saliency prediction based 3D object detector SP-Det in this article. Although foreground voxels have the sufficient feature of object, it is difficult to localize the foreground region from voxel space with the larger background region. We design an auxiliary learning task, saliency prediction (SP). It benefits 3D detector in identifying the foreground region. SP task uses label diffusion to alleviate the label imbalance. It reduces the learning difficulty of saliency in voxel and bird's eye view (BEV) spaces. After that, to strengthen feature interaction from the sparse foreground region, we design saliency fusion (SF) module to fuse the learning result in SP task. It utilizes voxel and BEV saliency maps as progressive attention to resist the redundant feature from background region. To aggregate more foreground feature inside 3D and BEV region of interest (RoI), we design hybrid grid maps based RoI pooling (Hybrid-RoI pooling). Experiments are conducted in STF dataset. The adverse weather enlarges the sparsity of LiDAR point cloud, increasing the difficulty of object detection. SP-Det identifies and leverages foreground region, and achieves the performance better than the current methods. Hence, we believe that SP-Det benefits to LiDAR based 3D scene understanding in the adverse weather.

Geometric information constraint 3D object detection from LiDAR point cloud for autonomous vehicles under adverse weather

Recent growth in unmanned aerial vehicle (UAV) technology have promoted the detailed mapping of individual tree species. However, the in-depth mining and comprehending of the significance of features derived from high-resolution UAV data for tree species discrimination remains a difficult task. In this study, a state-of-the-art approach combining UAV-borne light detection and ranging (LiDAR) and hyperspectral was used to classify 11 common tree species in a typical natural secondary forest in Northeast China. First, comprehensive relevant structural and spectral features were extracted. Then, the most valuable feature sets were selected by using a hybrid approach combining correlation-based feature selection with the optimized recursive feature elimination algorithm. The random forest algorithm was used to assess feature importance and perform the classification. Finally, the robustness of features derived from point clouds with different structures and hyperspectral images with different spatial resolutions was tested. Our results showed that the best classification accuracy was obtained by combining LiDAR and hyperspectral data (75.7%) compared to that based on LiDAR (60.0%) and hyperspectral (64.8%) data alone. The mean intensity of single returns and the visible atmospherically resistant index for red-edge band were the most influential LiDAR and hyperspectral derived features, respectively. The selected features were robust in point clouds with a density not lower than 5% (~5 pts/m2) and a resolution not lower than 0.3 m in hyperspectral data. Although canopy surface features were slightly different from original LiDAR features, canopy surface information was also important for tree species classification. This study proved the capabilities of UAV-borne LiDAR and hyperspectral data in natural secondary forest tree species discrimination and the potential for this approach to be transferable to other study areas.

Point cloud-based three-dimensional (3D) object detection is a critical task in autonomous driving, robotics, and augmented reality, where accurate localization and classification of objects are essential under diverse and challenging scenarios. This work introduces a point cloud-based multi-target 3D object detection framework using Light Detection and Ranging (LiDAR) sensors. The key contribution lies in improving the encoding technique to preserve spatial information and adapt to varying point densities, enabling efficient processing of raw LiDAR data for robust 3D detection. Specifically, we design an efficient network architecture that incorporates a single unified classification head to jointly handle positive and negative samples, simplifying the design and improving training stability. Our model detects all classes (car, pedestrian, cyclist) simultaneously within a single network, enhancing computational efficiency while avoiding separate networks for each class. Furthermore, the proposed feature extraction pipeline combines a voxel-based encoder with a sparsely embedded convolutional backbone and a feature pyramid network, facilitating multi-scale feature representation and effective detection of objects at different scales. This encoding method facilitates the efficient processing of raw LiDAR data, enabling accurate object detection across diverse scenarios. As a result, our model achieves state-of-the-art performance on the KITTI dataset, surpassing baseline methods such as PointPillars and VoxelNet by delivering superior Average Precision (AP) across all difficulty levels for the car class in both bird’s-eye view (BEV) and 3D object detection tasks. Specifically, it achieves AP40 scores of 92.84%, 88.63%, and 85.68% in BEV detection and 87.84%, 76.65%, and 73.60% in 3D detection for Easy, Moderate, and Hard levels, respectively. These results highlight the effectiveness of our encoding technique in enhancing model efficiency and detection accuracy across diverse scenarios.

Topographical technology by Airborne LIDAR (Light Detection and Ranging) generates a precise points cloud with a density of several points per square meter, LIDAR data processing is a crucial step to be used. Extraction of 3D information in automatic way and especially in urban areas from LIDAR data is one of the most difficult problems in computer vision; it is also a necessary step for implementation of several applications that require a high level interpretation of LASER data. Therefore, there is recently an increased interest in this research field and a vast literature. The problematic discussed in this article lies in the differentiation between the sets of points that represent a specified layer of information (construction, vegetation, roads, lines, etc.). This step is called segmentation. The aim of this study is to provide a set of automatic segmentation techniques tailored to different types of 3D data and proposes a methodology to classify LIDAR data with a maximum degree of automaticity using only point cloud data.

Automatic building extraction is an important topic for many applications such as urban planning, disaster management, 3D building modeling and updating GIS databases. Its approaches mainly depend on two data sources: light detection and ranging (LiDAR) point cloud and aerial imagery both of which have advantages and disadvantages of their own. In this study, in order to benefit from the advantages of each data sources, LiDAR and image data combined together. And then, the building boundaries were extracted with the automated active contour algorithm implemented in MATLAB. Active contour algorithm uses initial contour positions to segment an object in the image. Initial contour positions were detected without user interaction by a series of image enhancements, band ratio and morphological operations. Four test areas with varying building and background levels of detail were selected from ISPRS’s benchmark Vaihingen and Istanbul datasets. Vegetation and shadows were removed from all the datasets by band ratio to improve segmentation quality. Subsequently, LiDAR point cloud data was converted to raster format and added to the aerial imagery as an extra band. Resulting merged image and initial contour positions were given to the active contour algorithm to extract building boundaries. In order to compare the contribution of LiDAR to the proposed method, the boundaries of the buildings were extracted from the input image before and after adding LiDAR data to the image as a layer. Finally extracted building boundaries were smoothed by the Awrangjeb (Int J Remote Sen 37(3): 551–579. https://doi.org/10.1080/01431161.2015.1131868 , 2016) boundary regularization algorithm. Correctness (Corr), completeness (Comp) and accuracy (Q) metrics were used to assess accuracy of segmented building boundaries by comparing extracted building boundaries with manually digitized building boundaries. Proposed approach shows the promising results with over 93% correctness, 92% completeness and 89% quality.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Light Detection And Ranging (LiDAR) sensors can generate a number of sequential 3D point clouds, which are widely deployed in many real-world systems. 3D object detection in point clouds, is one of the most fundamental tasks. Unfortunately, the existing 3D object detection methods degrade in snowy weather, because in that situation the annotated samples are difficult to collect. To solve this issue, we propose a novel GAN-based Snowfall Point-cloud AugmentOR (GAN spaor ) to generate high-quality synthetic snowfall point clouds as augmentations. The basic idea of GAN spaor is to transfer annotated point clouds to snowfall versions by simultaneously learning the global style of real snowfall point clouds and the local details of physics-induced ones. Our framework fuses data-driven and physical modeling methods for rapidly generating data in snowy weather. To evaluate the effectiveness of GAN spaor , we employ a number of recent 3D object detection methods and train them by using the synthetic samples of GAN spaor as auxiliary augmentations. Moreover, we conduct a comparative analysis of the characteristics of the data distributions of the snowy point clouds synthesized by GAN spaor . Experimental results demonstrate that GAN spaor can improve the performance of 3D object detection methods compared with other existing snowfall point cloud simulators.

Chapter 5 - Data Preprocessing and Feature Extraction

Segmenting or detecting objects in sparse Lidar point clouds are two important tasks in autonomous driving to allow a vehicle to act safely in its 3D environment. The best performing methods in 3D semantic segmentation or object detection rely on a large amount of annotated data. Yet annotating 3D Lidar data for these tasks is tedious and costly. In this context, we propose a self-supervised pretraining method for 3D perception models that is tailored to autonomous driving data. Specifically, we leverage the availability of synchronized and calibrated image and Lidar sensors in autonomous driving setups for distilling self-supervised pre-trained image representations into 3D models. Hence, our method does not require any point cloud nor image annotations. The keyingredient of our method is the use of superpixels which are used to pool 3D point features and 2D pixel features in visually similar regions. We then train a 3D network on the self-supervised task of matching these pooled point features with the corresponding pooled image pixel features. The advantages of contrasting regions obtained by superpixels are that: (1) grouping together pixels and points of visually coherent regions leads to a more meaningful contrastive task that produces features well adapted to 3D semantic segmentation and 3D object detection; (2) all the different regions have the same weight in the contrastive loss regardless of the number of 3D points sampled in these regions; (3) it mitigates the noise produced by incorrect matching of points and pixels due to occlusions between the different sensors. Extensive experiments on autonomous driving datasets demonstrate the ability of our image-to-Lidar distillation strategy to produce 3D representations that transfer well on semantic segmentation and object detection tasks.

A Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV) can map the overflown environment in point clouds. Mapped canopy heights allow for the estimation of crop biomass in agriculture. The work presented in this paper contributes to sensory UAV setup design for mapping and textual analysis of agricultural fields. LiDAR data are combined with data from Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors to conduct environment mapping for point clouds. The proposed method facilitates LiDAR recordings in an experimental winter wheat field. Crop height estimates ranging from 0.35–0.58 m are correlated to the applied nitrogen treatments of 0–300 . The LiDAR point clouds are recorded, mapped, and analysed using the functionalities of the Robot Operating System (ROS) and the Point Cloud Library (PCL). Crop volume estimation is based on a voxel grid with a spatial resolution of 0.04 × 0.04 × 0.001 m. Two different flight patterns are evaluated at an altitude of 6 m to determine the impacts of the mapped LiDAR measurements on crop volume estimations.

Civil engineering includes a wide range of applications that require three-dimensional (3D) information. With the advent of LiDAR (Light Detection And Ranging) technology, obtaining 3D terrain information accurately in a cost and time effective manner became more popular. In this paper, three case studies demonstrate the roles and applications of airborne LiDAR data including highway, transmission line and hydropower dam development projects. In first case study, the highway alignment was considerably changed after the initial design stage. LiDAR data was able to do the re-alignment survey quickly, allowing for prompt changes in design. In the next case study, a comparison is made between conventional transmission route alignment design and the design using PLS-CADD with LiDAR data. Utilizing the LiDAR data in PLSCADD allows transmission line engineers to model the construction costs of several alignments options effectively and efficiently compared to the conventional method. The last case study uses LiDAR for obtaining the necessary information for proposed dam. Using LiDAR data maximizes topological accuracy and volume measurements while minimizing survey time and costs. Furthermore, utilizing the multi-temporal LiDAR data can reveal valuable information such as topography changes. LiDAR survey technology has proven that it is the best method to capture accurate data from the earth surface which provides maximum density of topographical & digital imagery information for various applications.

In autonomous systems and robotic applications, accurate extrinsic calibration between light detection and ranging (LiDAR) sensors and cameras is crucial for reliable sensor fusion. Several techniques have been developed, including target-based and targetless calibration, but they are either impractical for real-world applications or limited in extracting complex and diverse features. This study presents TransCalib, an innovative deep-learning method for targetless and automatic extrinsic calibration. TransCalib predicts the misalignment between the camera and LiDAR by leveraging EfficientNetV2 to obtain features from the RGB camera image and LiDAR point cloud projection image (depth image), owing to its performance and parameter efficiency.We also developed an innovative feature-matching module that comprises a calibration convolutional feature aggregation block (Calib-CFAB) and a convolutional self-attention (CSA) transformer. Calib-CFAB enriches the combined feature map of the RGB and depth images, while the CSA transformer obtains the correlation in the feature maps. Trained and tested on the KITTI odometry dataset, TransCalib achieved a mean absolute rotation error of 0.14° and a mean translation error of 1.8 cm, outperforming existing methods. The proposed method allows for a robust fusion of LiDAR and camera data, improving the perception abilities of autonomous systems.

Hyperspectral image classification has been an active topic of research. In recent years, it has been found that light detection and ranging (LiDAR) data provide a source of complementary information that can greatly assist in the classification of hyperspectral data, in particular when it is difficult to separate complex classes. This is because, in addition to the spatial and the spectral information provided by hyperspectral data, LiDAR can provide very valuable information about the height of the surveyed area that can help with the discrimination of classes and their separability. In the past, several efforts have been investigated for fusion of hyperspectral and LiDAR data, with some efforts driven by the morphological information that can be derived from both data sources. However, a main challenge for the learning approaches is how to exploit the information coming from multiple features. Specifically, it has been found that simple concatenation or stacking of features such as morphological attribute profiles (APs) may contain redundant information. In addition, a significant increase in the number of features may lead to very high-dimensional input features. This is in contrast with the limited number of training samples often available in remote-sensing applications, which may lead to the Hughes effect. In this work, we develop a new efficient strategy for fusion and classification of hyperspectral and LiDAR data. Our approach has been designed to integrate multiple types of features extracted from these data. An important characteristic of the presented approach is that it does not require any regularization parameters, so that different types of features can be efficiently exploited and integrated in a collaborative and flexible way. Our experimental results, conducted using a hyperspectral image and a LiDAR-derived digital surface model (DSM) collected over the University of Houston campus and the neighboring urban area, indicate that the proposed framework for multiple feature learning provides state-of-the-art classification results.