Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion
This addresses cost reduction for lidar sensors in applications like autonomous vehicles, but it is incremental as it optimizes an existing setup.
The paper tackles the problem of reducing the number of lidar lines needed for depth completion while maintaining accuracy, achieving comparable depth accuracy with only half the lines.
Lidar is a vital sensor for estimating the depth of a scene. Typical spinning lidars emit pulses arranged in several horizontal lines and the monetary cost of the sensor increases with the number of these lines. In this work, we present the new problem of optimizing the positioning of lidar lines to find the most effective configuration for the depth completion task. We propose a solution to reduce the number of lines while retaining the up-to-the-mark quality of depth completion. Our method consists of two components, (1) line selection based on the marginal contribution of a line computed via the Shapley value and (2) incorporating line position spread to take into account its need to arrive at image-wide depth completion. Spatially-aware Shapley values (SaS) succeed in selecting line subsets that yield a depth accuracy comparable to the full lidar input while using just half of the lines.