PTC-Depth: Pose-Refined Monocular Depth Estimation with Temporal Consistency

• URL: http://arxiv.org/abs/2604.01791v1
• Date: Thu, 02 Apr 2026 09:03:02 GMT
• Title: PTC-Depth: Pose-Refined Monocular Depth Estimation with Temporal Consistency
• Abstract summary: Existing approaches often struggle to maintain temporal consistency in depth estimation across consecutive frames.<n>This paper proposes a consistency-aware monocular depth estimation framework that leverages wheel odometry from a mobile robot to achieve stable and coherent depth predictions over time.<n>The proposed method is evaluated on the KITTI, TartanAir, and our own dataset, demonstrating robust and accurate depth estimation performance.
• Score: 5.854654986365097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Monocular depth estimation (MDE) has been widely adopted in the perception systems of autonomous vehicles and mobile robots. However, existing approaches often struggle to maintain temporal consistency in depth estimation across consecutive frames. This inconsistency not only causes jitter but can also lead to estimation failures when the depth range changes abruptly. To address these challenges, this paper proposes a consistency-aware monocular depth estimation framework that leverages wheel odometry from a mobile robot to achieve stable and coherent depth predictions over time. Specifically, we estimate camera pose and sparse depth from triangulation using optical flow between consecutive frames. The sparse depth estimates are used to update a recursive Bayesian estimate of the metric scale, which is then applied to rescale the relative depth predicted by a pre-trained depth estimation foundation model. The proposed method is evaluated on the KITTI, TartanAir, MS2, and our own dataset, demonstrating robust and accurate depth estimation performance.

Related papers

• SA4Depth: Consistent Pose-Depth Scale Alignment for Self-Supervised Monocular Depth Estimation [76.82232244682113]
  Self-supervised depth estimation from monocular sequences relies on the joint learning of a depth and a pose network.<n>We introduce SA4Depth, an approach to improve the alignment between the scene scales estimated by the pose and depth nets.<n>Our proposed method uses the depth estimated during training to reproject learnable visual features across consecutive frames.
  arXiv  Detail & Related papers  (2026-05-27T13:38:25Z)
• Seurat: From Moving Points to Depth [66.65189052568209]
  We propose a novel method that infers relative depth by examining the spatial relationships and temporal evolution of a set of tracked 2D trajectories.<n>Our approach achieves temporally smooth, high-accuracy depth predictions across diverse domains.
  arXiv  Detail & Related papers  (2025-04-20T17:37:02Z)
• A Simple yet Effective Test-Time Adaptation for Zero-Shot Monocular Metric Depth Estimation [46.037640130193566]
  We propose a new method to rescale Depth Anything predictions using 3D points provided by sensors or techniques such as low-resolution LiDAR or structure-from-motion with poses given by an IMU.<n>Our experiments highlight enhancements relative to zero-shot monocular metric depth estimation methods, competitive results compared to fine-tuned approaches and a better robustness than depth completion approaches.
  arXiv  Detail & Related papers  (2024-12-18T17:50:15Z)
• 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)
• Dusk Till Dawn: Self-supervised Nighttime Stereo Depth Estimation using Visual Foundation Models [16.792458193160407]
  Self-supervised depth estimation algorithms rely heavily on frame-warping relationships. We introduce an algorithm designed to achieve accurate self-supervised stereo depth estimation focusing on nighttime conditions.
  arXiv  Detail & Related papers  (2024-05-18T03:07:23Z)
• DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibrium [11.78276690882616]
  Self-supervised multi-frame depth estimation achieves high accuracy by computing matching costs of pixel correspondences between adjacent frames. We propose the Dual model, which tightly couples depth and pose estimation through a feedback loop. Our novel update pipeline uses a deep equilibrium model framework to iteratively refine depth estimates and a hidden state of feature maps.
  arXiv  Detail & Related papers  (2023-04-07T09:46:29Z)
• Uncertainty Guided Depth Fusion for Spike Camera [49.41822923588663]
  We propose a novel Uncertainty-Guided Depth Fusion (UGDF) framework to fuse predictions of monocular and stereo depth estimation networks for spike camera. Our framework is motivated by the fact that stereo spike depth estimation achieves better results at close range. In order to demonstrate the advantage of spike depth estimation over traditional camera depth estimation, we contribute a spike-depth dataset named CitySpike20K.
  arXiv  Detail & Related papers  (2022-08-26T13:04:01Z)
• Depth Refinement for Improved Stereo Reconstruction [13.941756438712382]
  Current techniques for depth estimation from stereoscopic images still suffer from a built-in drawback. A simple analysis reveals that the depth error is quadratically proportional to the object's distance. We propose a simple but effective method that uses a refinement network for depth estimation.
  arXiv  Detail & Related papers  (2021-12-15T12:21:08Z)
• Variational Monocular Depth Estimation for Reliability Prediction [12.951621755732544]
  Self-supervised learning for monocular depth estimation is widely investigated as an alternative to supervised learning approach. Previous works have successfully improved the accuracy of depth estimation by modifying the model structure. In this paper, we theoretically formulate a variational model for the monocular depth estimation to predict the reliability of the estimated depth image.
  arXiv  Detail & Related papers  (2020-11-24T06:23:51Z)
• Occlusion-Aware Depth Estimation with Adaptive Normal Constraints [85.44842683936471]
  We present a new learning-based method for multi-frame depth estimation from a color video. Our method outperforms the state-of-the-art in terms of depth estimation accuracy.
  arXiv  Detail & Related papers  (2020-04-02T07:10:45Z)
• Don't Forget The Past: Recurrent Depth Estimation from Monocular Video [92.84498980104424]
  We put three different types of depth estimation into a common framework. Our method produces a time series of depth maps. It can be applied to monocular videos only or be combined with different types of sparse depth patterns.
  arXiv  Detail & Related papers  (2020-01-08T16:50:51Z)

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.