# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 1 Introduction  
Semantic segmentation assigns a class label to every pixel in an image, grouping areas that share the same meaning [1]. This capability enables a variety of applications, such as autonomous driving [2] and robotic grasping [3], where three-dimensional mapping with precise labeling of each pixel is essential.  
In safety-critical applications such as autonomous vehicles, segmentation neural networks are required to deliver both high accuracy and minimal inference latency [2]. This is typically measured by Intersection-over-Union (IoU), Dice coefficient, and minimal inference latency (frames per second and GPU memory footprint). Progress in road-scene understanding has been accelerated by large-scale datasets (CityScapes [4], CamVid [5]) providing detailed pixel-wise annotations. Terrain classes (paved roads, sidewalks, grassy verges) are assigned to each pixel by semantic segmentation models, while moving objects (pedestrians, cyclists, vehicles) are isolated from the static scene by dynamic-object segmentation [6]. In conventional computer-vision pipelines, every object is assumed to belong to a predefined set of categories; real-world roadways, however, are inherentlyunpredictable, and anomalous obstacles may evade detection, thereby posing critical risks to vehicle operation [7].  
In this article, we evaluate several state-of-the-art segmentation architectures, i.e., SegFormer [8], DeepLabv3+ [9], U-Net [10], and feature pyramid network (FPN) [11], on our own driving dataset. Each model's encoder (ResNet-34 [12], EfficientNet-B2 [13], and MobileNetV4 [14]) was initialized with ImageNet-pretrained weights [15], which accelerated convergence and enriched feature representations, while the decoders were trained end-to-end to guarantee pixel-level accuracy. Building on this experimental validation, we propose a system architecture that constructs a 3D map of the environment in real-time and enables efficient differentiation between object types in road and urban settings. In contrast to object segmentation in 3D LiDAR data [16], we focus on segmentation in RGB images, which are more informative, and then we project the result to 3D space using 3D LiDAR data.  
In this article, we demonstrate that efficient semantic segmentation is achievable within a designated operational area. This application scenario is relevant to autonomous vehicles operating in limited environments, such as transporting goods between buildings in a factory complex or performing autonomous parking in bus depots. Dense label maps are fused with complementary depth measurements or LiDAR point clouds to construct a probabilistic occupancy grid in real-time. The resulting unified occupancy could be fed to trajectory planners, where safe paths are computed under strict safety constraints. Specifically, semantic maps can be utilized by sampling-based motion planning algorithms such as RRT* or PRM*, augmented with semantic cost functions that prioritize safe and efficient navigation based on object classes. In addition, semantic maps can enhance obstacle detection by providing rich contextual information.  
The main contributions of this article are as follows:  
* A lightweight semantic mapping pipeline that performs segmentation on RGB images and projects LiDAR measurements onto a semantic OctoMap, using recent advancements in neural-based image segmentation in contrast to the methods from the literature [17], reducing the computational cost compared to full 3D point cloud segmentation;
* A comprehensive evaluation of multiple deep neural network architectures for semantic segmentation, comparing segmentation accuracy, inference time, memory usage, and GPU load;
* An analysis of input image resolution impact, highlighting trade-offs between model accuracy and real-time performance for 3D semantic environment reconstruction in autonomous driving scenarios.