DINO-VO: A Feature-based Visual Odometry Leveraging a Visual Foundation Model

• URL: http://arxiv.org/abs/2507.13145v1
• Date: Thu, 17 Jul 2025 14:09:34 GMT
• Title: DINO-VO: A Feature-based Visual Odometry Leveraging a Visual Foundation Model
• Authors: Maulana Bisyir Azhari, David Hyunchul Shim,
• Abstract summary: Learning-based monocular visual odometry (VO) poses robustness, generalization, and efficiency challenges in robotics.<n>Recent advances in visual foundation models, such as DINOv2, have improved robustness and generalization in various vision tasks.<n>We present DINO-VO, a feature-based VO system leveraging DINOv2 visual foundation model for its sparse feature matching.
• Score: 2.163881720692685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based monocular visual odometry (VO) poses robustness, generalization, and efficiency challenges in robotics. Recent advances in visual foundation models, such as DINOv2, have improved robustness and generalization in various vision tasks, yet their integration in VO remains limited due to coarse feature granularity. In this paper, we present DINO-VO, a feature-based VO system leveraging DINOv2 visual foundation model for its sparse feature matching. To address the integration challenge, we propose a salient keypoints detector tailored to DINOv2's coarse features. Furthermore, we complement DINOv2's robust-semantic features with fine-grained geometric features, resulting in more localizable representations. Finally, a transformer-based matcher and differentiable pose estimation layer enable precise camera motion estimation by learning good matches. Against prior detector-descriptor networks like SuperPoint, DINO-VO demonstrates greater robustness in challenging environments. Furthermore, we show superior accuracy and generalization of the proposed feature descriptors against standalone DINOv2 coarse features. DINO-VO outperforms prior frame-to-frame VO methods on the TartanAir and KITTI datasets and is competitive on EuRoC dataset, while running efficiently at 72 FPS with less than 1GB of memory usage on a single GPU. Moreover, it performs competitively against Visual SLAM systems on outdoor driving scenarios, showcasing its generalization capabilities.

Related papers

• Feature Hallucination for Self-supervised Action Recognition [37.20267786858476]
  We propose a deep translational action recognition framework that enhances recognition accuracy by jointly predicting action concepts and auxiliary features from RGB video frames.<n>Our framework achieves state-of-the-art performance on multiple benchmarks, including Kinetics-400, Kinetics-600, and Something-Something V2, demonstrating its effectiveness in capturing fine-grained action dynamics.
  arXiv  Detail & Related papers  (2025-06-25T11:50:23Z)
• SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification [61.753607285860944]
  We propose a novel two-stage feature learning framework named SD-ReID for AG-ReID.<n>In the first stage, we train a simple ViT-based model to extract coarse-grained representations and controllable conditions.<n>In the second stage, we fine-tune the SD model to learn complementary representations guided by the controllable conditions.
  arXiv  Detail & Related papers  (2025-04-13T12:44:50Z)
• ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations [33.74746234704817]
  Video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description.<n>This is challenging as it involves deep vision-level understanding, pixel-level dense prediction andtemporal reasoning.<n>We propose bfReferDINO RVOS that inherits region-level vision-text alignment from foundational visual grounding models.
  arXiv  Detail & Related papers  (2025-01-24T16:24:15Z)
• BRIGHT-VO: Brightness-Guided Hybrid Transformer for Visual Odometry with Multi-modality Refinement Module [11.898515581215708]
  Visual odometry (VO) plays a crucial role in autonomous driving, robotic navigation, and other related tasks.<n>We introduce BrightVO, a novel VO model based on Transformer architecture, which performs front-end visual feature extraction.<n>Using pose graph optimization, this module iteratively refines pose estimates to reduce errors and improve both accuracy and robustness.
  arXiv  Detail & Related papers  (2025-01-15T08:50:52Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• HRVMamba: High-Resolution Visual State Space Model for Dense Prediction [60.80423207808076]
  State Space Models (SSMs) with efficient hardware-aware designs have demonstrated significant potential in computer vision tasks. These models have been constrained by three key challenges: insufficient inductive bias, long-range forgetting, and low-resolution output representation. We introduce the Dynamic Visual State Space (DVSS) block, which employs deformable convolution to mitigate the long-range forgetting problem. We also introduce High-Resolution Visual State Space Model (HRVMamba) based on the DVSS block, which preserves high-resolution representations throughout the entire process.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments [55.864869961717424]
  It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. We design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these problems.
  arXiv  Detail & Related papers  (2023-10-23T16:30:39Z)
• An Efficient and Scalable Collection of Fly-inspired Voting Units for Visual Place Recognition in Changing Environments [20.485491385050615]
  Low-overhead VPR techniques would enable platforms equipped with low-end, cheap hardware. Our goal is to provide an algorithm of extreme compactness and efficiency while achieving state-of-the-art robustness to appearance changes and small point-of-view variations.
  arXiv  Detail & Related papers  (2021-09-22T19:01:20Z)
• Efficient Person Search: An Anchor-Free Approach [86.45858994806471]
  Person search aims to simultaneously localize and identify a query person from realistic, uncropped images. To achieve this goal, state-of-the-art models typically add a re-id branch upon two-stage detectors like Faster R-CNN. In this work, we present an anchor-free approach to efficiently tackling this challenging task, by introducing the following dedicated designs.
  arXiv  Detail & Related papers  (2021-09-01T07:01:33Z)
• Full-Duplex Strategy for Video Object Segmentation [141.43983376262815]
  Full- Strategy Network (FSNet) is a novel framework for video object segmentation (VOS) Our FSNet performs the crossmodal feature-passing (i.e., transmission and receiving) simultaneously before fusion decoding stage. We show that our FSNet outperforms other state-of-the-arts for both the VOS and video salient object detection tasks.
  arXiv  Detail & Related papers  (2021-08-06T14:50:50Z)
• Early Bird: Loop Closures from Opposing Viewpoints for Perceptually-Aliased Indoor Environments [35.663671249819124]
  We present novel research that simultaneously addresses viewpoint change and perceptual aliasing. We show that our integration of VPR with SLAM significantly boosts the performance of VPR, feature correspondence, and pose graph submodules. For the first time, we demonstrate a localization system capable of state-of-the-art performance despite perceptual aliasing and extreme 180-degree-rotated viewpoint change.
  arXiv  Detail & Related papers  (2020-10-03T20:18:55Z)
• ASFD: Automatic and Scalable Face Detector [129.82350993748258]
  We propose a novel Automatic and Scalable Face Detector (ASFD) ASFD is based on a combination of neural architecture search techniques as well as a new loss design. Our ASFD-D6 outperforms the prior strong competitors, and our lightweight ASFD-D0 runs at more than 120 FPS with Mobilenet for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-03-25T06:00:47Z)

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.