Efficient RGB-D scene understanding via multi-task adaptive learning and cross-dimensional feature guidance

2025-07-01

Knowledge-Based Systems

AI Summary

This work addresses the challenges of occlusion, ambiguous boundaries, and task-sample discrepancies that hinder adaptive attention in RGB-D scene understanding. To overcome these issues, the authors propose a unified multi-task model that jointly performs semantic segmentation, instance segmentation, orientation estimation, panoptic segmentation, and scene classification. Key innovations include an enhanced RGB-D fusion encoder, a normalized focal channel layer, a context-aware feature interaction mechanism, a non-bottleneck 1D convolutional structure, and a dynamic multi-task adaptive loss function. These components collectively enable effective cross-modal feature fusion and task-aware dynamic learning. Extensive experiments on NYUv2, SUN RGB-D, and Cityscapes demonstrate that the proposed model surpasses existing methods in both segmentation accuracy and inference speed.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Scene understanding plays a critical role in enabling intelligence and autonomy in robotic systems. Traditional approaches often face challenges, including occlusions, ambiguous boundaries, and the inability to adapt attention based on task-specific requirements and sample variations. To address these limitations, this paper presents an efficient RGB-D scene understanding model that performs a range of tasks, including semantic segmentation, instance segmentation, orientation estimation, panoptic segmentation, and scene classification. The proposed model incorporates an enhanced fusion encoder, which effectively leverages redundant information from both RGB and depth inputs. For semantic segmentation, we introduce normalized focus channel layers and a context feature interaction layer, designed to mitigate issues such as shallow feature misguidance and insufficient local-global feature representation. The instance segmentation task benefits from a non-bottleneck 1D structure, which achieves superior contour representation with fewer parameters. Additionally, we propose a multi-task adaptive loss function that dynamically adjusts the learning strategy for different tasks based on scene variations. Extensive experiments on the NYUv2, SUN RGB-D, and Cityscapes datasets demonstrate that our approach outperforms existing methods in both segmentation accuracy and processing speed.

Problem

• RGB-D scene understanding
• occlusions
• ambiguous boundaries
• task-specific attention
• multi-task learning

Innovation

• multi-task adaptive learning
• cross-dimensional feature guidance
• enhanced fusion encoder
• non-bottleneck 1D structure
• adaptive loss function