Radar-Guided Polynomial Fitting for Metric Depth Estimation

• URL: http://arxiv.org/abs/2503.17182v2
• Date: Thu, 25 Sep 2025 09:31:10 GMT
• Title: Radar-Guided Polynomial Fitting for Metric Depth Estimation
• Authors: Patrick Rim, Hyoungseob Park, Vadim Ezhov, Jeffrey Moon, Alex Wong,
• Abstract summary: POLAR is a novel radar-guided depth estimation method.<n>It transforms scaleless depth predictions from pretrained monocular depth estimation (MDE) models into metric depth maps.<n>It achieves state-of-the-art performance across three datasets.
• Score: 10.901638730547452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose POLAR, a novel radar-guided depth estimation method that introduces polynomial fitting to efficiently transform scaleless depth predictions from pretrained monocular depth estimation (MDE) models into metric depth maps. Unlike existing approaches that rely on complex architectures or expensive sensors, our method is grounded in a fundamental insight: although MDE models often infer reasonable local depth structure within each object or local region, they may misalign these regions relative to one another, making a linear scale and shift (affine) transformation insufficient given three or more of these regions. To address this limitation, we use polynomial coefficients predicted from cheap, ubiquitous radar data to adaptively adjust depth predictions non-uniformly across depth ranges. In this way, POLAR generalizes beyond affine transformations and is able to correct such misalignments by introducing inflection points. Importantly, our polynomial fitting framework preserves structural consistency through a novel training objective that enforces local monotonicity via first-derivative regularization. POLAR achieves state-of-the-art performance across three datasets, outperforming existing methods by an average of 24.9% in MAE and 33.2% in RMSE, while also achieving state-of-the-art efficiency in terms of latency and computational cost.

Related papers

• Spectral Embedding via Chebyshev Bases for Robust DeepONet Approximation [0.6752538702870791]
  SpectralEmbedded DeepONet (SEDNet) is a new variant in which the trunk is driven by a fixed Chebyshev spectral dictionary rather than coordinate inputs.<n>SEDNet consistently achieves the lowest relative L2 errors among DeepONet, FEDONet, and SEDONet with average improvements of about 30-40% over the baseline DeepONet.
  arXiv  Detail & Related papers  (2025-12-09T22:26:29Z)
• Neural Local Wasserstein Regression [16.52489456261937]
  We study the estimation problem of distribution-on-distribution regression, where both predictors and responses are probability measures.<n>Existing approaches typically rely on a global optimal transport map or tangent-space linearization.<n>We propose a flexible nonparametric framework that models regression through locally defined transport maps in Wasserstein space.
  arXiv  Detail & Related papers  (2025-11-13T21:54:18Z)
• Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues [38.954104931025704]
  We propose an Adaptive Depth Range MVS ( ADR-MVS) to improve multi-view depth estimation accuracy.<n> ADR-MVS generates adaptive range maps from depth and normal estimates using cross-attention discrepancy learning.<n> Experimental results demonstrate that ADR-MVS achieves state-of-the-art performance on the WHU, LuoJia-MVS, and M"unchen datasets.
  arXiv  Detail & Related papers  (2025-06-06T01:14:55Z)
• 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)
• Metric-Solver: Sliding Anchored Metric Depth Estimation from a Single Image [51.689871870692194]
  Metric-r is a novel sliding anchor-based metric depth estimation method. Our design enables a unified and adaptive depth representation across diverse environments.
  arXiv  Detail & Related papers  (2025-04-16T14:12:25Z)
• UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler [62.06785782635153]
  We propose a new model, UniDepthV2, capable of reconstructing metric 3D scenes from solely single images across domains.<n>UniDepthV2 directly predicts metric 3D points from the input image at inference time without any additional information.<n>Our model exploits a pseudo-spherical output representation, which disentangles the camera and depth representations.
  arXiv  Detail & Related papers  (2025-02-27T14:03:15Z)
• 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)
• 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)
• A Bayesian Approach Toward Robust Multidimensional Ellipsoid-Specific Fitting [0.0]
  This work presents a novel and effective method for fitting multidimensional ellipsoids to scattered data in the contamination of noise and outliers. We incorporate a uniform prior distribution to constrain the search for primitive parameters within an ellipsoidal domain. We apply it to a wide range of practical applications such as microscopy cell counting, 3D reconstruction, geometric shape approximation, and magnetometer calibration tasks.
  arXiv  Detail & Related papers  (2024-07-27T14:31:51Z)
• Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian [49.21866794516328]
  3D Gaussian splatting has demonstrated impressive performance in real-time novel view synthesis. Previous approaches have incorporated depth supervision into the training of 3D Gaussians to mitigate overfitting. We introduce a novel method to supervise the depth distribution of 3D Gaussians, utilizing depth priors with integrated uncertainty estimates.
  arXiv  Detail & Related papers  (2024-05-30T03:18:30Z)
• Q-SLAM: Quadric Representations for Monocular SLAM [85.82697759049388]
  We reimagine volumetric representations through the lens of quadrics. We use quadric assumption to rectify noisy depth estimations from RGB inputs. We introduce a novel quadric-decomposed transformer to aggregate information across quadrics.
  arXiv  Detail & Related papers  (2024-03-12T23:27:30Z)
• OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
  We propose an OccNeRF method for training occupancy networks without 3D supervision. We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range. For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
  arXiv  Detail & Related papers  (2023-12-14T18:58:52Z)
• Subsurface Characterization using Ensemble-based Approaches with Deep Generative Models [2.184775414778289]
  Inverse modeling is limited for ill-posed, high-dimensional applications due to computational costs and poor prediction accuracy with sparse datasets. We combine Wasserstein Geneversarative Adrial Network with Gradient Penalty (WGAN-GP) and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) WGAN-GP is trained to generate high-dimensional K fields from a low-dimensional latent space and ES-MDA updates the latent variables by assimilating available measurements.
  arXiv  Detail & Related papers  (2023-10-02T01:27:10Z)
• FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models [67.96827539201071]
  We propose a novel test-time optimization approach for 3D scene reconstruction. Our method achieves state-of-the-art cross-dataset reconstruction on five zero-shot testing datasets.
  arXiv  Detail & Related papers  (2023-08-10T17:55:02Z)
• Towards Generalizable Deepfake Detection by Primary Region Regularization [52.41801719896089]
  This paper enhances the generalization capability from a novel regularization perspective. Our method consists of two stages, namely the static localization for primary region maps, and the dynamic exploitation of primary region masks. We conduct extensive experiments over three widely used deepfake datasets - DFDC, DF-1.0, and Celeb-DF with five backbones.
  arXiv  Detail & Related papers  (2023-07-24T05:43:34Z)
• Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection [0.5156484100374059]
  Background modeling is widely used for intelligent surveillance systems to detect moving targets by subtracting the static background components. Most roadside LiDAR object detection methods filter out foreground points by comparing new data points to pre-trained background references. In this paper, we transform the raw LiDAR data into a structured representation based on the elevation and azimuth value of each LiDAR point. The proposed method was compared against two state-of-the-art roadside LiDAR background models, computer vision benchmark, and deep learning baselines, evaluated at point, object, and path levels under heavy traffic and challenging weather.
  arXiv  Detail & Related papers  (2022-04-20T22:48:05Z)
• Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth [90.33296913575818]
  In some video-based scenarios such as video depth estimation and 3D scene reconstruction from a video, the unknown scale and shift residing in per-frame prediction may cause the depth inconsistency. We propose a locally weighted linear regression method to recover the scale and shift with very sparse anchor points. Our method can boost the performance of existing state-of-the-art approaches by 50% at most over several zero-shot benchmarks.
  arXiv  Detail & Related papers  (2022-02-03T08:52:54Z)
• Improving Metric Dimensionality Reduction with Distributed Topology [68.8204255655161]
  DIPOLE is a dimensionality-reduction post-processing step that corrects an initial embedding by minimizing a loss functional with both a local, metric term and a global, topological term. We observe that DIPOLE outperforms popular methods like UMAP, t-SNE, and Isomap on a number of popular datasets.
  arXiv  Detail & Related papers  (2021-06-14T17:19:44Z)
• A Surface Geometry Model for LiDAR Depth Completion [19.33116596688515]
  LiDAR depth completion is a task that predicts depth values for every pixel on the corresponding camera frame. Most of the existing state-of-the-art solutions are based on deep neural networks, which need a large amount of data and heavy computations for training the models. In this letter, a novel non-learning depth completion method is proposed by exploiting the local surface geometry that is enhanced by an outlier removal algorithm.
  arXiv  Detail & Related papers  (2021-04-17T06:48:01Z)
• Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth Prediction [87.08227378010874]
  We show the importance of the high-order 3D geometric constraints for depth prediction. By designing a loss term that enforces a simple geometric constraint, we significantly improve the accuracy and robustness of monocular depth estimation. We show state-of-the-art results of learning metric depth on NYU Depth-V2 and KITTI.
  arXiv  Detail & Related papers  (2021-03-07T00:08:21Z)
• Balanced Depth Completion between Dense Depth Inference and Sparse Range Measurements via KISS-GP [14.158132769768578]
  Estimating a dense and accurate depth map is the key requirement for autonomous driving and robotics. Recent advances in deep learning have allowed depth estimation in full resolution from a single image. Despite this impressive result, many deep-learning-based monocular depth estimation algorithms have failed to keep their accuracy yielding a meter-level estimation error.
  arXiv  Detail & Related papers  (2020-08-12T08:07:55Z)

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.