Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder
This work addresses the need for cost-effective depth estimation in autonomous delivery vehicles, though it is incremental as it builds on existing pseudo-LiDAR methods.
The paper tackles the problem of generating real-time pseudo-LiDAR point clouds from stereo images for autonomous vehicles, achieving improved performance on the KITTI benchmark with faster processing speeds.
Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain the real-time pseudo point cloud instead of the laser sensor from the image sensor. We propose an approach to use different depth estimators to obtain pseudo point clouds like LiDAR to obtain better performance. Moreover, the training and validating strategy of the depth estimator has adopted stereo imagery data to estimate more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms on KITTI benchmark while yielding point clouds significantly faster than other approaches.