Replication Data for: 3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization

The high-level concept of stereo matching pipeline involves 2D feature extraction from the stereo pair, obtaining pixel correspondence, and finally disparity computation. In this paper, we present (1) Input Fusion and (2) Conditional Cost Volume Normalization that are closely integrated with stereo matching networks.

By leveraging the complementary nature of LiDAR and stereo modalities, our model produces high-precision disparity estimation.