# LiDAR Depth Map Guided Image Compression Model

arXiv: https://arxiv.org/abs/2401.06517 · HTML: https://mlsanity.com/paper/2401.06517/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2024.

## Built on by (most cited first, 3 of 3)

- [Sparse Point Clouds Assisted Learned Image Compression](https://mlsanity.com/paper/2412.15752/) (2024)
- [Efficient and Privacy-Protecting Background Removal for 2D Video Streaming using iPhone 15 Pro Max LiDAR](https://mlsanity.com/paper/2508.20250/) (2025)
- [Event-guided Neural Video Compression](https://mlsanity.com/paper/2610.02265/) (2026)

## Abstract (arXiv)

The incorporation of LiDAR technology into some high-end smartphones has unlocked numerous possibilities across various applications, including photography, image restoration, augmented reality, and more. In this paper, we introduce a novel direction that harnesses LiDAR depth maps to enhance the compression of the corresponding RGB camera images. To the best of our knowledge, this represents the initial exploration in this particular research direction. Specifically, we propose a Transformer-based learned image compression system capable of achieving variable-rate compression using a single model while utilizing the LiDAR depth map as supplementary information for both the encoding and decoding processes. Experimental results demonstrate that integrating LiDAR yields an average PSNR gain of 0.83 dB and an average bitrate reduction of 16% as compared to its absence.