QSCA: Quantization with Self-Compensating Auxiliary for Monocular Depth Estimation

Jincheol Yang, Jaemin Choi, Matti Alexander Zinke, Suk-Ju Kang

Keywords: Quantization, Monocular depth estimation

Abstract:

Monocular depth estimation has advanced significantly with foundation models like Depth Anything, leveraging large-scale transformer architectures for the superior generalization.
However, the deployment on resource-constrained devices remains challenging due to the high computation and memory requirement.
Existing quantization methods, such as post-training quantization and quantization-aware training, often face trade-offs between efficiency and accuracy, or require extensive labeled data for retraining.
To address these limitations, we propose Quantization with Self-Compensating Auxiliary for Monocular Depth Estimation (QSCA), a novel framework for 4-bit post-training quantization of Monocular depth estimation models.
Our method integrates a lightweight Self-Compensating Auxiliary (SCA) module into both transformer encoder and decoder blocks, enabling the quantized model to recover from performance degradation without requiring ground truth.
This design enables fast adaptation while preserving structural and spatial consistency in predicted depth maps.
To our knowledge, this is the first framework to successfully apply 4-bit quantization across all layers of large-scale monocular depth estimation models.
Experimental results demonstrate that QSCA significantly improves quantized depth estimation performance. On the NYUv2 dataset, it achieves an 11\% improvement in $\delta_1$ accuracy over existing post-training quantization methods.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 25722