# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 1 Introduction
In human recognition processes, object identification typically begins with a preliminary observation, followed by the integration of historical knowledge and current sensory input to form a more accurate understanding through spatiotemporal memory construction [25]. This process is not reliant on a single type of information but is based on the dynamic integration of multiple sources. Inspired by this mechanism, this study proposes a novel spatiotemporal clue-gated encoder that efficiently aggregates historical and current point cloud data, thereby improving the segmentation accuracy. The proposed Encoder consists of a spatiotemporal clue-embedding layer that encodes serialized multi-frame point clouds to extract historical knowledge and spatiotemporal cues, while current frame data is processed using 3D convolutions to capture the latest features. A gating unit dynamically adjusts the relative importance of historical and current data, facilitating the aggregation of long-term spatiotemporal information and enhancing the model's ability to represent spatiotemporal features. Furthermore, a spatiotemporal feature select state-space module is designed to fully utilize the aggregated long-term spatiotemporal features. This module consists of an encoder and decoder based on the U-Net structure. Previous works have integrated Transformers into U-Net-based point cloud segmentation networks to enhance performance through a global attention mechanism [26; 27]. SphereFormer [15] further optimizes attention mechanisms, improving segmentation accuracy by addressing issues of sparse point cloud information and expanding the receptive field. However, these solutions come with quadratic computational complexity, which limits their efficiency and scalability. Recently, state-space models such as Mamba [28] have achieved groundbreaking advancements by providing linear complexity with selective mechanisms and hardware optimizations, making them highly suitable for the modeling of long-term spatiotemporal information in an efficient manner. Mamba has been successfully migrated from natural language processing to computer vision [29; 30], achieving competitive results while reducing memory consumption. However, its potential in handling complex, unstructured 3D point cloud data remains underexplored [31; 32]. To address this issue, this paper embeds Mamba blocks into the spatiotemporal feature select state-space module. The module's encoder consists of Mamba blocks and 3D sparse convolutions and encodes the input features, while the decoder, consisting of Mamba blocks and 3D sparse inverse convolutions, decodes the output features. Skip connections between the encoder and decoder facilitate information exchange at the spatial level. This design reduces computational costs while maintaining high segmentation accuracy, enhancing the model's suitability for practical deployment in mining operations. The contributions of this paper are summarized as follows:  
1. A spatiotemporal clue-gated encoder is proposed, efficiently aggregating long-term spatiotemporal features through the spatiotemporal clue-embedding layer. The gating unit dynamically balances the contributions of historical and current data, improving model accuracy. 2. A spatiotemporal feature select state-space module is designed, combining Mamba's global context modeling capability with its linear complexity advantage. This leads to both higher accuracy and lower computational costs. 3. The spatiotemporal feature aggregation network outperforms existing methods on our self-constructed TZMD_NUC dataset (TZ Group Co., Ltd., Taiyuan, China, Mining Dataset by North University of China) and the public SemanticKITTI dataset. Ablation experiments further demonstrate that the proposed approach surpasses Transformer-based methods and significantly reduces both temporal and spatial costs, making it better suited for the practical needs of mining operations.