GESS: Multi-cue Guided Local Feature Learning via Geometric and Semantic Synergy

2026-04-06

AI Summary

This work addresses the limitations of existing local feature methods that rely solely on appearance cues, resulting in unstable keypoints and poorly discriminative descriptors. To overcome this, the authors propose a multi-cue guided local feature learning framework that innovatively couples semantic segmentation with surface normal prediction and incorporates a depth-aware stability assessment. This leads to a Semantic–Depth-Aware Keypoint selection mechanism (SDAK) and a Unified Three-Cue Fusion descriptor module (UTCF). Built upon a lightweight backbone and a joint prediction head, the proposed approach significantly improves local feature detection and matching performance across four benchmark datasets, demonstrating the effectiveness of jointly modeling semantic, geometric, and depth cues.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Local Search
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Robust local feature detection and description are foundational tasks in computer vision. Existing methods primarily rely on single appearance cues for modeling, leading to unstable keypoints and insufficient descriptor discriminability. In this paper, we propose a multi-cue guided local feature learning framework that leverages semantic and geometric cues to synergistically enhance detection robustness and descriptor discriminability. Specifically, we construct a joint semantic-normal prediction head and a depth stability prediction head atop a lightweight backbone. The former leverages a shared 3D vector field to deeply couple semantic and normal cues, thereby resolving optimization interference from heterogeneous inconsistencies. The latter quantifies the reliability of local regions from a geometric consistency perspective, providing deterministic guidance for robust keypoint selection. Based on these predictions, we introduce the Semantic-Depth Aware Keypoint (SDAK) mechanism for feature detection. By coupling semantic reliability with depth stability, SDAK reweights keypoint responses to suppress spurious features in unreliable regions. For descriptor construction, we design a Unified Triple-Cue Fusion (UTCF) module, which employs a semantic-scheduled gating mechanism to adaptively inject multi-attribute features, improving descriptor discriminability. Extensive experiments on four benchmarks validate the effectiveness of the proposed framework. The source code and pre-trained model will be available at: https://github.com/yiyscut/GESS.git.

Problem

• local feature detection
• descriptor discriminability
• multi-cue learning
• semantic cues
• geometric consistency

Innovation

• multi-cue fusion
• semantic-geometric synergy
• local feature learning
• keypoint reliability
• descriptor discriminability