AnyDepth: Depth Estimation Made Easy

• URL: http://arxiv.org/abs/2601.02760v1
• Date: Tue, 06 Jan 2026 06:51:35 GMT
• Title: AnyDepth: Depth Estimation Made Easy
• Authors: Zeyu Ren, Zeyu Zhang, Wukai Li, Qingxiang Liu, Hao Tang,
• Abstract summary: Monocular depth estimation aims to recover the depth information of 3D scenes from 2D images.<n>Recent work has made significant progress, but its reliance on large-scale datasets and complex decoders has limited its efficiency and generalization ability.<n>We propose a lightweight and data-centric framework for zero-shot monocular depth estimation.
• Score: 14.853297988186682
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Monocular depth estimation aims to recover the depth information of 3D scenes from 2D images. Recent work has made significant progress, but its reliance on large-scale datasets and complex decoders has limited its efficiency and generalization ability. In this paper, we propose a lightweight and data-centric framework for zero-shot monocular depth estimation. We first adopt DINOv3 as the visual encoder to obtain high-quality dense features. Secondly, to address the inherent drawbacks of the complex structure of the DPT, we design the Simple Depth Transformer (SDT), a compact transformer-based decoder. Compared to the DPT, it uses a single-path feature fusion and upsampling process to reduce the computational overhead of cross-scale feature fusion, achieving higher accuracy while reducing the number of parameters by approximately 85%-89%. Furthermore, we propose a quality-based filtering strategy to filter out harmful samples, thereby reducing dataset size while improving overall training quality. Extensive experiments on five benchmarks demonstrate that our framework surpasses the DPT in accuracy. This work highlights the importance of balancing model design and data quality for achieving efficient and generalizable zero-shot depth estimation. Code: https://github.com/AIGeeksGroup/AnyDepth. Website: https://aigeeksgroup.github.io/AnyDepth.

Related papers

• Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation [108.04354143020886]
  We introduce prompting into depth foundation models, creating a new paradigm for metric depth estimation termed Prompt Depth Anything.<n>We use a low-cost LiDAR as the prompt to guide the Depth Anything model for accurate metric depth output, achieving up to 4K resolution.
  arXiv  Detail & Related papers  (2024-12-18T16:32:12Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth Estimation [0.0]
  This paper introduces a novel deep learning-based approach using an enhanced encoder-decoder architecture.<n>It incorporates multi-scale feature extraction to enhance depth prediction accuracy across various object sizes and distances.<n> Experimental results on the KITTI dataset show that our model achieves a significantly faster inference time of 0.019 seconds.
  arXiv  Detail & Related papers  (2024-10-15T13:46:19Z)
• Depth Pro: Sharp Monocular Metric Depth in Less Than a Second [45.6690958201871]
  We present a foundation model for zero-shot metric monocular depth estimation.<n>Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-frequency details.<n>It produces a 2.25-megapixel depth map in 0.3 seconds on a standard GPU.
  arXiv  Detail & Related papers  (2024-10-02T22:42:20Z)
• NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth Supervision for Indoor Multi-View 3D Detection [72.0098999512727]
  NeRF-Det has achieved impressive performance in indoor multi-view 3D detection by utilizing NeRF to enhance representation learning. We present three corresponding solutions, including semantic enhancement, perspective-aware sampling, and ordinal depth supervision. The resulting algorithm, NeRF-Det++, has exhibited appealing performance in the ScanNetV2 and AR KITScenes datasets.
  arXiv  Detail & Related papers  (2024-02-22T11:48:06Z)
• Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data [87.61900472933523]
  This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. We scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data. We evaluate its zero-shot capabilities extensively, including six public datasets and randomly captured photos.
  arXiv  Detail & Related papers  (2024-01-19T18:59:52Z)
• Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth Estimation [1.6775954077761863]
  We present a fully convolutional depth estimation network using contextual feature fusion. Compared to UNet++ and HRNet, we use high-resolution and low-resolution features to reserve information on small targets and fast-moving objects. Our method reduces the parameters without sacrificing accuracy.
  arXiv  Detail & Related papers  (2023-09-17T13:40:15Z)
• Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusion [6.491470878214977]
  This paper benchmarks various transformer-based models for the depth estimation task on an indoor NYUV2 dataset and an outdoor KITTI dataset. We propose a novel attention-based architecture, Depthformer for monocular depth estimation. Our proposed method improves the state-of-the-art by 3.3%, and 3.3% respectively in terms of Root Mean Squared Error (RMSE)
  arXiv  Detail & Related papers  (2022-07-10T20:49:11Z)
• HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model [3.5290359800552946]
  HiMODE is a novel monocular omnidirectional depth estimation model based on a CNN+Transformer architecture. We show that HiMODE can achieve state-of-the-art performance for 360deg monocular depth estimation.
  arXiv  Detail & Related papers  (2022-04-11T11:11:43Z)
• Progressive Coordinate Transforms for Monocular 3D Object Detection [52.00071336733109]
  We propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations. In this paper, we propose a novel and lightweight approach, dubbed em Progressive Coordinate Transforms (PCT) to facilitate learning coordinate representations.
  arXiv  Detail & Related papers  (2021-08-12T15:22:33Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)

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.