README.md

metadata

license: other
license_name: license
license_link: LICENSE
pipeline_tag: depth-estimation

WildCross: A Cross-Modal Large Scale Benchmark for Place Recognition and Metric Depth Estimation in Natural Environments

Joshua Knights^1,2 · Joseph Reid¹ · Mark Cox¹
Kaushik Roy¹ · David Hall¹ · Peyman Moghadam^1,2

¹DATA61, CSIRO   ²Queensland University of Technology

This repository contains the pre-trained checkpoints for a variety of tasks on the WildCross benchmark.

If you find this repository useful or use the WildCross dataset in your work, please cite us using the following:

@inproceedings{wildcross2026,
  title={{WildCross: A Cross-Modal Large Scale Benchmark for Place Recognition and Metric Depth Estimation in Natural Environments}},
  author={Joshua Knights, Joseph Reid, Kaushik Roy, David Hall, Mark Cox, Peyman Moghadam},
  booktitle={Proceedings-IEEE International Conference on Robotics and Automation},
  pages={},
  year={2026}
}

Download Instructions

Our dataset can be downloaded through the CSIRO Data Access Portal. Detailed instructions for downloading the dataset can be found in the README file provided on the data access portal page.

Training and Benchmarking

Here we provide pre-trained checkpoints for a variety of tasks on WildCross.

Visual Place Recognition

Checkpoints

Model	Checkpoint Folder
NetVlad	Link
MixVPR	Link
SALAD	Link
BoQ	Link

Cross Modal Place Recognition

Checkpoints

Model	Checkpoint Folder
Lip-Loc (ResNet50)	Link
Lip-Loc (Dino-v2)	Link
Lip-Loc (Dino-v3)	Link

Metric Depth Estimation

Checkpoints

Model	Checkpoint Folder
DepthAnythingV2-vits	Link
DepthAnythingV2-vitb	Link
DepthAnythingV2-vitl	Link

For instructions on how to use these checkpoints for training or evaluation, further instructions can be found on the WildCross GitHub repository.