EgoPoint-Ground: A New Breakthrough in Multimodal Visual Localization for AI to Understand 'Where the Finger Points'

This article introduces EgoPoint-Ground, a new work focusing on gesture pointing understanding and visual localization from a first-person perspective. This work includes the first large-scale multimodal dataset (over 15000 interaction samples) and proposes the SV-CoT structured visual reasoning method, which achieves an 11.7% performance improvement compared to the current best solution, promoting the shift of visual localization from pure language to "language + gesture" multimodal understanding.

Traditional visual localization (VG) relies on pure language descriptions, which are prone to judgment errors due to ambiguity. However, real human interactions often combine gestures and language, but existing multimodal models ignore such non-verbal cues. Understanding gesture pointing from a first-person perspective also faces challenges such as complex dynamic scenes, severe occlusions, multi-granularity requirements, and real-time demands.

EgoPoint-Ground is the first large-scale dataset for indicative visual localization from a first-person perspective, containing over 15000 interaction samples covering multiple scenes such as indoor homes and kitchens. Each sample provides fine-grained annotations like hand-target bounding box pairs and dense semantic descriptions, supporting research on joint gesture-language understanding and scene reasoning.

SV-CoT (Structured Visual Chain of Thought) decomposes visual localization into four-step reasoning: gesture parsing, spatial reasoning, semantic matching, and context validation. Its innovation lies in extending the language chain of thought to the visual domain, generating visualizable intermediate results at each step, with advantages of strong interpretability, traceable errors, and modular design.

Tests on the EgoPoint-Ground dataset show that SV-CoT improves by 11.7% compared to the current best method. Compared to baselines like pure language, pure gesture, and simple fusion, the effect of structured fusion is significant. Ablation experiments verify that removing the gesture parsing, spatial reasoning, and semantic matching modules leads to 6%, 4%, and 5% performance drops respectively.

This achievement can be applied to scenarios such as smart AR glasses (understanding gesture + language navigation), home service robots (accurate execution of instructions), and visual impairment assistance technology (precise object description), providing a foundation for natural interaction AI systems.

Current limitations include scenarios concentrated indoors, single gesture type (only finger pointing), and no coverage of continuous dynamic interactions. Future directions: expanding outdoor/industrial scenarios, supporting more gesture types, modeling temporal dynamics, and exploring lightweight models adapted to edge devices.