Region-aware Depth Scale Adaptation with Sparse Measurements

• URL: http://arxiv.org/abs/2507.14879v1
• Date: Sun, 20 Jul 2025 09:36:57 GMT
• Title: Region-aware Depth Scale Adaptation with Sparse Measurements
• Authors: Rizhao Fan, Tianfang Ma, Zhigen Li, Ning An, Jian Cheng,
• Abstract summary: We introduce a non-learning-based approach to adapt the relative-scale predictions of foundation models into metric-scale depth.<n>Our method requires neither retraining nor fine-tuning, thereby preserving the strong generalization ability of the original foundation models.
• Score: 8.532410904912922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, the emergence of foundation models for depth prediction has led to remarkable progress, particularly in zero-shot monocular depth estimation. These models generate impressive depth predictions; however, their outputs are often in relative scale rather than metric scale. This limitation poses challenges for direct deployment in real-world applications. To address this, several scale adaptation methods have been proposed to enable foundation models to produce metric depth. However, these methods are typically costly, as they require additional training on new domains and datasets. Moreover, fine-tuning these models often compromises their original generalization capabilities, limiting their adaptability across diverse scenes. In this paper, we introduce a non-learning-based approach that leverages sparse depth measurements to adapt the relative-scale predictions of foundation models into metric-scale depth. Our method requires neither retraining nor fine-tuning, thereby preserving the strong generalization ability of the original foundation models while enabling them to produce metric depth. Experimental results demonstrate the effectiveness of our approach, high-lighting its potential to bridge the gap between relative and metric depth without incurring additional computational costs or sacrificing generalization ability.

Related papers

• Depth Anything with Any Prior [64.39991799606146]
  Prior Depth Anything is a framework that combines incomplete but precise metric information in depth measurement with relative but complete geometric structures in depth prediction.<n>We develop a conditioned monocular depth estimation (MDE) model to refine the inherent noise of depth priors.<n>Our model showcases impressive zero-shot generalization across depth completion, super-resolution, and inpainting over 7 real-world datasets.
  arXiv  Detail & Related papers  (2025-05-15T17:59:50Z)
• Multi-view Reconstruction via SfM-guided Monocular Depth Estimation [92.89227629434316]
  We present a new method for multi-view geometric reconstruction.<n>We incorporate SfM information, a strong multi-view prior, into the depth estimation process.<n>Our method significantly improves the quality of depth estimation compared to previous monocular depth estimation works.
  arXiv  Detail & Related papers  (2025-03-18T17:54:06Z)
• Relative Pose Estimation through Affine Corrections of Monocular Depth Priors [69.59216331861437]
  We develop three solvers for relative pose estimation that explicitly account for independent affine (scale and shift) ambiguities.<n>We propose a hybrid estimation pipeline that combines our proposed solvers with classic point-based solvers and epipolar constraints.
  arXiv  Detail & Related papers  (2025-01-09T18:58:30Z)
• Amodal Depth Anything: Amodal Depth Estimation in the Wild [39.27552294431748]
  Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene.<n>We propose a novel formulation of amodal depth estimation in the wild, focusing on relative depth prediction to improve model generalization across diverse natural images.<n>We present two complementary frameworks: Amodal-DAV2, a deterministic model based on Depth Anything V2, and Amodal-DepthFM, a generative model that integrates conditional flow matching principles.
  arXiv  Detail & Related papers  (2024-12-03T09:56:38Z)
• MetricGold: Leveraging Text-To-Image Latent Diffusion Models for Metric Depth Estimation [9.639797094021988]
  MetricGold is a novel approach that harnesses generative diffusion model's rich priors to improve metric depth estimation.<n>Our experiments demonstrate robust generalization across diverse datasets, producing sharper and higher quality metric depth estimates.
  arXiv  Detail & Related papers  (2024-11-16T20:59:01Z)
• GroCo: Ground Constraint for Metric Self-Supervised Monocular Depth [2.805351469151152]
  We propose a novel constraint on ground areas designed specifically for the self-supervised paradigm. This mechanism not only allows to accurately recover the scale but also ensures coherence between the depth prediction and the ground prior.
  arXiv  Detail & Related papers  (2024-09-23T09:30:27Z)
• TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs [5.6168844664788855]
  This work presents TanDepth, a practical scale recovery method for obtaining metric depth results from relative estimations at inference-time.<n>Our method leverages sparse measurements from Global Digital Elevation Models (GDEM) by projecting them to the camera view.<n>An adaptation to the Cloth Filter Simulation is presented, which allows selecting ground points from the estimated depth map to then correlate with the projected reference points.
  arXiv  Detail & Related papers  (2024-09-08T15:54:43Z)
• ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation [62.600382533322325]
  We propose a novel monocular depth estimation method called ScaleDepth. Our method decomposes metric depth into scene scale and relative depth, and predicts them through a semantic-aware scale prediction module. Our method achieves metric depth estimation for both indoor and outdoor scenes in a unified framework.
  arXiv  Detail & Related papers  (2024-07-11T05:11:56Z)
• Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering [93.94371335579321]
  We propose a learning framework that trains models to predict geometry-preserving depth without requiring extra data or annotations. Comprehensive experiments underscore our framework's superior generalization capabilities. Our innovative loss functions empower the model to autonomously recover domain-specific scale-and-shift coefficients.
  arXiv  Detail & Related papers  (2023-09-18T12:36:39Z)
• Calibrating Self-supervised Monocular Depth Estimation [77.77696851397539]
  In the recent years, many methods demonstrated the ability of neural networks to learn depth and pose changes in a sequence of images, using only self-supervision as the training signal. We show that incorporating prior information about the camera configuration and the environment, we can remove the scale ambiguity and predict depth directly, still using the self-supervised formulation and not relying on any additional sensors.
  arXiv  Detail & Related papers  (2020-09-16T14:35:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.