Bidirectional Cross-Modal Prompting for Event-Frame Asymmetric Stereo

2026-04-16 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

Bi-CMPStereo is a novel bidirectional cross-modal prompting framework designed for event-frame asymmetric stereo, which aims to achieve reliable 3D perception in dynamic scenes. Conventional frame-based cameras provide rich contextual data but struggle with temporal resolution and motion blur, while event cameras offer high dynamic range and overcome these limitations. This framework addresses the modality gap between event and frame data by fully exploiting semantic and structural features from both domains for robust stereo matching. Bi-CMPStereo learns finely aligned stereo representations within a target canonical space and integrates complementary representations by projecting each modality into both event and frame domains. Experiments show that this approach significantly surpasses existing methods in accuracy and generalization.

Key takeaway

For research scientists developing 3D perception systems, Bi-CMPStereo offers a robust framework to overcome limitations of single-modality approaches. You should consider integrating bidirectional cross-modal prompting to leverage the complementary strengths of event and frame cameras, particularly in scenarios with fast motion or difficult illumination, to achieve superior accuracy and generalization in stereo matching.

Key insights

Bi-CMPStereo leverages bidirectional cross-modal prompting to bridge the gap between event and frame camera data for robust 3D perception.

Principles

• Complementary modalities enhance 3D perception.
• Align representations in a canonical space.

Method

Bi-CMPStereo learns aligned stereo representations in a canonical space and integrates complementary features by projecting each modality into both event and frame domains for robust cross-modal matching.

In practice

• Improve 3D perception in fast-motion scenes.
• Enhance stereo matching under challenging light.

Topics

• Bidirectional Cross-Modal Prompting
• Event-Frame Asymmetric Stereo
• Bi-CMPStereo
• Event Cameras
• 3D Perception

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.