FutureMapping: The Computational Structure of Spatial AI Systems

FutureMapping 2: Gaussian Belief Propagation for Spatial AI

We argue the case for Gaussian Belief Propagation (GBP) as a strong algorithmic framework for the distributed, generic and incremental probabilistic estimation we need in Spatial AI as we aim at high performance smart robots and devices which operate within the constraints of real products. Processor hardware is changing rapidly, and GBP has the right character to take advantage of highly distributed processing and storage while estimating global quantities, as well as great flexibility. We present a detailed tutorial on GBP, relating to the standard factor graph formulation used in robotics a…

热成像视频中的计算深度测量：通过时空解耦克服空间过拟合

Computational Depth Measurement in Thermographic Video: Overcoming Spatial Overfitting via Spatio-Temporal Decoupling

本研究针对热成像视频深度回归的空间过拟合问题，提出时空解耦架构，采用正则化XGBoost实现0.056mm MAE的高精度测量，可快速生成三维缺陷模型。

HST-HGN：基于双向状态空间模型的异构空间-时间超图网络用于全局疲劳评估

HST-HGN: Heterogeneous Spatial-Temporal Hypergraph Networks with Bidirectional State Space Models for Global Fatigue Assessment

本文提出HST-HGN，通过双向状态空间模型和异构空间-时间超图网络，有效建模长程时序依赖，实现对驾驶员疲劳的高效评估，兼顾判别力与计算效率，适用于实时车载边缘部署。

A Survey of Qualitative Spatial and Temporal Calculi -- Algebraic and Computational Properties

Qualitative Spatial and Temporal Reasoning (QSTR) is concerned with symbolic knowledge representation, typically over infinite domains. The motivations for employing QSTR techniques range from exploiting computational properties that allow efficient reasoning to capture human cognitive concepts in a computational framework. The notion of a qualitative calculus is one of the most prominent QSTR formalisms. This article presents the first overview of all qualitative calculi developed to date and their computational properties, together with generalized definitions of the fundamental concepts and…

从人类认知到神经激活：探究LLMs中空间推理的计算原语

From Human Cognition to Neural Activations: Probing the Computational Primitives of Spatial Reasoning in LLMs

研究通过分析LLMs内部空间表示机制，发现其空间推理能力有限且依赖上下文，揭示了空间推理的计算原语及跨语言机制退化问题。

RFC-Net: Learning High Resolution Global Features for Medical Image Segmentation on a Computational Budget

Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN)architectures with downstream and upstream propagation flow are popular for segmentation in medical diagnosis. However, due to performing spatial downsampling and upsampling in multiple stages, information loss is inexorable. On the contrary, connecting layers densely on high spatial resolution is computationally expensive. In this work, we devise a Loose Dense Connection Strategy to connect neurons in subsequent layers with reduced parameters. On top of that, using a m-way Tree s…