Temporal Lidar Depth Completion

TL;DR

This paper introduces a temporal depth completion algorithm that enhances lidar-based depth maps by incorporating information from previous frames, significantly improving accuracy especially for distant and sparse regions with minimal additional computational cost.

Contribution

It extends the PENet method with recurrency to utilize temporal information, achieving state-of-the-art results with minimal overhead.

Findings

Improved depth accuracy for faraway objects.

Enhanced performance in regions with sparse lidar data.

Significant improvements even in areas without ground truth.

Abstract

Given the lidar measurements from an autonomous vehicle, we can project the points and generate a sparse depth image. Depth completion aims at increasing the resolution of such a depth image by infilling and interpolating the sparse depth values. Like most existing approaches, we make use of camera images as guidance in very sparse or occluded regions. In addition, we propose a temporal algorithm that utilizes information from previous timesteps using recurrence. In this work, we show how a state-of-the-art method PENet can be modified to benefit from recurrency. Our algorithm achieves state-of-the-art results on the KITTI depth completion dataset while adding only less than one percent of additional overhead in terms of both neural network parameters and floating point operations. The accuracy is especially improved for faraway objects and regions containing a low amount of lidar depth samples. Even in regions without any ground truth (like sky and rooftops) we observe large improvements which are not captured by the existing evaluation metrics.