MASt3R-Nav: WayPixel Navigation in Relative 3D Maps
A new visual navigation method called MASt3R-Nav uses pixel-relative connectivity to build geometrically accurate maps without requiring global consistency, enabling more capable navigation than traditional topological graphs.
Article intelligence
Key points
- Proposes pixel-relative connectivity map as a novel representation.
- Uses 3D grounded image matching for inter-image pixel correspondences.
- Derives WayPixel Costmap for global path planning and control.
- Demonstrates superior performance in simulation and real-world tasks.
Why it matters
This matters because proposes pixel-relative connectivity map as a novel representation.
Technical impact
May affect agent architecture, tool calling, workflow automation, and product integration.
[2605.24111] MASt3R-Nav: WayPixel Navigation in Relative 3D Maps
[Submitted on 22 May 2026]
Title:MASt3R-Nav: WayPixel Navigation in Relative 3D Maps
View a PDF of the paper titled MASt3R-Nav: WayPixel Navigation in Relative 3D Maps, by Vansh Garg and 7 other authors
View PDF HTML (experimental)
Abstract:Visual navigation ability is strongly tied to its underlying representation of the world. Unlike classical 3D maps that require globally-consistent geometry, image- or object-relative topological graphs almost entirely do away with geometric understanding. But, this comes at the cost of navigation capability, often limiting it to merely teach-and-repeat. In this work, we propose a novel map representation in the form of pixel-relative connectivity, which is geometrically accurate but does not require global geometric consistency. Inspired by recent progress in 3D grounded image matching, we construct a map from an image sequence through inter-image connectivity based on pixel correspondences in the relative 3D coordinate systems of individual image pairs. We then use this pixel-level graph to perform global path planning by approximating and sparsifying intra-image pixel connectivity. Through this, we derive a ''WayPixel Costmap'' representation and train a controller conditioned on it to predict a trajectory rollout. We show that this dense pixel-level costmap based on relative geometry is a more accurate conditioning variable for control prediction than its image- and object-level counterparts. This enables a highly capable navigation system, as validated on four types of navigation tasks in the simulator and through real world demonstrations.
Comments: 2026 IEEE International Conference on Robotics & Automation (ICRA)
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.24111 [cs.RO]
(or arXiv:2605.24111v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.24111
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Vansh Garg [view email] [v1] Fri, 22 May 2026 18:18:07 UTC (13,630 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled MASt3R-Nav: WayPixel Navigation in Relative 3D Maps, by Vansh Garg and 7 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.RO
new | recent | 2026-05
Change to browse by:
cs cs.AI
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)