Metric-Solver: Sliding Anchored Metric Depth Estimation from a Single Image

• URL: http://arxiv.org/abs/2504.12103v1
• Date: Wed, 16 Apr 2025 14:12:25 GMT
• Title: Metric-Solver: Sliding Anchored Metric Depth Estimation from a Single Image
• Authors: Tao Wen, Jiepeng Wang, Yabo Chen, Shugong Xu, Chi Zhang, Xuelong Li,
• Abstract summary: Metric-r is a novel sliding anchor-based metric depth estimation method.<n>Our design enables a unified and adaptive depth representation across diverse environments.
• Score: 51.689871870692194
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate and generalizable metric depth estimation is crucial for various computer vision applications but remains challenging due to the diverse depth scales encountered in indoor and outdoor environments. In this paper, we introduce Metric-Solver, a novel sliding anchor-based metric depth estimation method that dynamically adapts to varying scene scales. Our approach leverages an anchor-based representation, where a reference depth serves as an anchor to separate and normalize the scene depth into two components: scaled near-field depth and tapered far-field depth. The anchor acts as a normalization factor, enabling the near-field depth to be normalized within a consistent range while mapping far-field depth smoothly toward zero. Through this approach, any depth from zero to infinity in the scene can be represented within a unified representation, effectively eliminating the need to manually account for scene scale variations. More importantly, for the same scene, the anchor can slide along the depth axis, dynamically adjusting to different depth scales. A smaller anchor provides higher resolution in the near-field, improving depth precision for closer objects while a larger anchor improves depth estimation in far regions. This adaptability enables the model to handle depth predictions at varying distances and ensure strong generalization across datasets. Our design enables a unified and adaptive depth representation across diverse environments. Extensive experiments demonstrate that Metric-Solver outperforms existing methods in both accuracy and cross-dataset generalization.

Related papers

• Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion [51.69876947593144]
  Existing methods for depth completion operate in tightly constrained settings.<n>Inspired by advances in monocular depth estimation, we reframe depth completion as an image-conditional depth map generation.<n>Marigold-DC builds on a pretrained latent diffusion model for monocular depth estimation and injects the depth observations as test-time guidance.
  arXiv  Detail & Related papers  (2024-12-18T00:06:41Z)
• SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation [17.88798247412388]
  SharpDepth combines the metric accuracy of discriminative depth estimation methods with the fine-grained boundary sharpness typically achieved by generative methods.<n>Our approach bridges these limitations by integrating metric accuracy with detailed boundary preservation, resulting in depth predictions that are both metrically precise and visually sharp.
  arXiv  Detail & Related papers  (2024-11-27T11:07:27Z)
• 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)
• Progressive Depth Decoupling and Modulating for Flexible Depth Completion [28.693100885012008]
  Image-guided depth completion aims at generating a dense depth map from sparse LiDAR data and RGB image. Recent methods have shown promising performance by reformulating it as a classification problem with two sub-tasks: depth discretization and probability prediction. We propose a progressive depth decoupling and modulating network, which incrementally decouples the depth range into bins and adaptively generates multi-scale dense depth maps.
  arXiv  Detail & Related papers  (2024-05-15T13:45:33Z)
• ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval [19.28042366225802]
  Multi-View Stereo(MVS) is a fundamental problem in geometric computer vision. We present a novel multi-stage coarse-to-fine framework to achieve adaptive all-pixel depth range and depth interval. Our model achieves state-of-the-art performance and yields competitive generalization ability.
  arXiv  Detail & Related papers  (2023-08-17T14:52:11Z)
• Blur aware metric depth estimation with multi-focus plenoptic cameras [8.508198765617196]
  We present a new metric depth estimation algorithm using only raw images from a multi-focus plenoptic camera. The proposed approach is especially suited for the multi-focus configuration where several micro-lenses with different focal lengths are used.
  arXiv  Detail & Related papers  (2023-08-08T13:38:50Z)
• Monocular Visual-Inertial Depth Estimation [66.71452943981558]
  We present a visual-inertial depth estimation pipeline that integrates monocular depth estimation and visual-inertial odometry. Our approach performs global scale and shift alignment against sparse metric depth, followed by learning-based dense alignment. We evaluate on the TartanAir and VOID datasets, observing up to 30% reduction in RMSE with dense scale alignment.
  arXiv  Detail & Related papers  (2023-03-21T18:47:34Z)
• Improving Monocular Visual Odometry Using Learned Depth [84.05081552443693]
  We propose a framework to exploit monocular depth estimation for improving visual odometry (VO) The core of our framework is a monocular depth estimation module with a strong generalization capability for diverse scenes. Compared with current learning-based VO methods, our method demonstrates a stronger generalization ability to diverse scenes.
  arXiv  Detail & Related papers  (2022-04-04T06:26:46Z)
• DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data [110.29043712400912]
  We present a method for depth estimation with monocular images, which can predict high-quality depth on diverse scenes up to an affine transformation. Experiments show that our method outperforms previous methods on 8 datasets by a large margin with the zero-shot test setting.
  arXiv  Detail & Related papers  (2020-02-03T05:38:33Z)

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.