A Decade of Transformation: How Deep Learning Redefined Stereo Matching in the Twenties
What is stereo matching used for in computer vision?
Stereo matching in computer vision is used for calculating dense disparity maps from two corrected images, which helps in determining the depth of objects in a scene. This technique plays a crucial role in various applications such as autonomous driving, robotics, and augmented reality, enabling better understanding of the 3D environment and facilitating precise decision-making.
How do 2D and 3D classes differ in stereo matching?
In stereo matching, 2D and 3D classes differ primarily in their cost-volume computation and optimization strategies1. 2D architectures process the left and right images separately, focusing on pixel-level correspondence within a volumetric space3. On the other hand, 3D architectures consider both images simultaneously, often using correlation layers to compute matching costs and leveraging 3D convolutions for cost aggregation. The choice between 2D and 3D approaches depends on the specific application and desired efficiency-accuracy trade-off.
What major problem does deep stereo matching face with domain transitions?
Deep stereo matching faces a major problem with domain transitions, particularly in its inability to generalize effectively between actual and synthetic data2. This limitation hinders the models' performance when dealing with high-resolution images, non-Lambertian objects, and challenging weather conditions. Efforts are being made to address these issues and improve the adaptability of deep stereo matching models across different domains.