PointSee: Image Enhances Point Cloud

Area of Science:

• Computer Vision
• Artificial Intelligence
• Robotics

Background:

• Multi-modal fusion is trending for 3D object detection (3OD).
• Existing fusion networks face challenges in computational efficiency, plug-and-play capability, and feature alignment.
• There's a need for effective and adaptable multi-modal fusion solutions for 3OD.

Purpose of the Study:

• To introduce PointSee, a novel lightweight, flexible, and effective multi-modal fusion solution for 3D object detection.
• To enhance point clouds with semantic features from scene images for improved 3OD performance.
• To enable seamless integration with existing 3OD networks with minimal modifications.

Main Methods:

• PointSee employs a two-module architecture: a hidden module (HM) for offline point cloud decoration with 2D image data and a seen module (SM) for point-wise semantic feature enrichment.
• HM facilitates minimal adaptation of existing 3OD networks.
• SM further refines point clouds by incorporating representative semantic features.

Main Results:

• PointSee demonstrates significant quantitative and qualitative improvements on popular outdoor and indoor benchmarks.
• The proposed training strategy effectively addresses potential inaccuracies in 2D object detection regressions.
• PointSee achieved superior performance compared to thirty-five state-of-the-art methods.

Conclusions:

• PointSee offers a lightweight, flexible, and effective solution for multi-modal fusion in 3D object detection.
• The HM and SM modules provide a robust framework for semantic feature enhancement of point clouds.
• PointSee significantly advances the state-of-the-art in 3D object detection by improving accuracy and efficiency.