用多样本先验优化单目深度估计，提升室内场景重建精度。
MultiDepth: Multi-Sample Priors for Refining Monocular Metric Depth Estimations in Indoor Scenes
- 通过多样本输入与轻量编码解码结构，迭代优化初始深度图。
- 在四个数据集上实现超过45%的精度提升，计算开销极低。
- 适合资源受限设备部署，尤其适用于室内场景深度重建。
单目度量深度估计（MMDE）是边缘设备上实现室内场景重建的关键任务。尽管其重要性突出，现有模型仍易受场景边界频率和复杂度影响，难以充分捕捉多种室内环境。本文提出单目度量深度精炼（MMDR）任务，利用先进MMDE模型的初始预测结果，通过采样图像及初始深度图构建多样本先验。与传统迭代精炼方法不同，MultiDepth不依赖法向图预测，有效降低模型规模与计算开销，同时实现显著精炼效果。该方法采用轻量级编码解码架构，处理来自图像的多个样本并结合分割掩码。在四个数据集上的实验表明，相比当前最优方法，其精度提升超过45%，且计算成本极低。
原文摘要 · Abstract (English)
Monocular metric depth estimation (MMDE) is a crucial task to solve for indoor scene reconstruction on edge devices. Despite this importance, existing models are sensitive to factors such as boundary frequency of objects in the scene and scene complexity, failing to fully capture many indoor scenes. In this work, we propose to close this gap through the task of monocular metric depth refinement (MMDR) by leveraging state-of-the-art MMDE models. MultiDepth proposes a solution by taking samples of the image along with the initial depth map prediction made by a pre-trained MMDE model. Compared to existing iterative depth refinement techniques, MultiDepth does not employ normal map prediction as part of its architecture, effectively lowering the model size and computation overhead while outputting impactful changes from refining iterations. MultiDepth implements a lightweight encoder-decoder architecture for the refinement network, processing multiple samples from the given image, including segmentation masking. We evaluate MultiDepth on four datasets and compare them to state-of-the-art methods to demonstrate its effective refinement with minimal overhead, displaying accuracy improvement upward of 45%.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。