M2H: Multi-Task Learning with Efficient Window-Based Cross-Task Attention for Monocular Spatial Perception

• URL: http://arxiv.org/abs/2510.17363v1
• Date: Mon, 20 Oct 2025 10:03:31 GMT
• Title: M2H: Multi-Task Learning with Efficient Window-Based Cross-Task Attention for Monocular Spatial Perception
• Authors: U. V. B. L Udugama, George Vosselman, Francesco Nex,
• Abstract summary: Multi-Mono-Hydra (M2H) is a novel multi-task learning framework for semantic segmentation and depth, edge, and surface normal estimation from a single monocular image.<n>Built on a lightweight ViT-based DINOv2 backbone, M2H is optimized for real-time deployment.
• Score: 4.329662126907974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying real-time spatial perception on edge devices requires efficient multi-task models that leverage complementary task information while minimizing computational overhead. This paper introduces Multi-Mono-Hydra (M2H), a novel multi-task learning framework designed for semantic segmentation and depth, edge, and surface normal estimation from a single monocular image. Unlike conventional approaches that rely on independent single-task models or shared encoder-decoder architectures, M2H introduces a Window-Based Cross-Task Attention Module that enables structured feature exchange while preserving task-specific details, improving prediction consistency across tasks. Built on a lightweight ViT-based DINOv2 backbone, M2H is optimized for real-time deployment and serves as the foundation for monocular spatial perception systems supporting 3D scene graph construction in dynamic environments. Comprehensive evaluations show that M2H outperforms state-of-the-art multi-task models on NYUDv2, surpasses single-task depth and semantic baselines on Hypersim, and achieves superior performance on the Cityscapes dataset, all while maintaining computational efficiency on laptop hardware. Beyond benchmarks, M2H is validated on real-world data, demonstrating its practicality in spatial perception tasks.

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