Simultaneous Color Registration and Depth Completion of Point Clouds with Curriculum Learning.

Area of Science:

• Computer Vision
• Deep Learning
• Robotics

Background:

• Dense depth completion is crucial for 3D computer vision tasks.
• Sparse and misaligned depth data from sensors pose significant challenges.
• Existing methods struggle with real-world sensor noise and alignment issues.

Purpose of the Study:

• To develop a robust method for dense depth completion and color image registration.
• To address challenges posed by sparse depth maps and misaligned RGB inputs.
• To bridge the simulation-to-real gap in depth estimation.

Main Methods:

• A fully convolutional neural network architecture for joint depth completion and color image registration.
• A novel synthetic depth generation strategy mimicking Time-of-Flight (ToF) sensor noise and artifacts.
• A staged curriculum learning paradigm to progressively increase task complexity.

Main Results:

• The joint model outperforms separate single-task approaches by leveraging shared features.
• Achieved competitive accuracy on the KITTI Depth Completion benchmark.
• Demonstrated significantly fewer parameters and faster inference compared to existing methods.

Conclusions:

• The proposed approach effectively performs dense depth completion and color image registration.
• The synthetic data generation and curriculum learning strategies improve robustness and bridge the sim-to-real gap.
• The model offers an effective and efficient solution for real-world 3D computer vision applications.