EgoPathBench

Yang Zhao, Zhuo Chen, and Xubo Yang, Shanghai Jiao Tong University.

Release upload complete. All 36 archives were verified against SHA-256 checksums. See UPLOAD_COMPLETE.json and archive_manifest.json.

31,852 train, 1,345 validation, and 1,111 benchmark questions across point and embodied traversability, point and embodied paths, and intent paths.

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Download the full snapshot with hf download runder1/EgoPathBench --repo-type dataset --local-dir downloads. Verify and extract the downloaded archives using python scripts/unpack_dataset.py --source downloads --output dataset from the code repository (Python 3.12+). Downloads require roughly 34 GB, plus another 34 GB for extraction. The 32 asset tar shards extract into one shared assets/ tree.

This is a file-based release containing JSONL records and referenced assets; it is not an automatically inferred load_dataset configuration.

Read release/{split}/vqa/vqa_next_{task}.jsonl for task questions. Each row contains a question ID, prompts, a relative image path, visible-waypoint metadata, and ground truth. Ground truth and evaluator sidecars are for evaluation or training supervision only; never pass them to a zero-shot model.

For benchmark scoring use benchmark_evalfix/benchmark/vqa/ and its matching gt/ and sidecars/, rather than the unpatched benchmark labels. Run evaluation from this dataset root so asset paths resolve.

Original scene meshes, source-pool archives, provider credentials, and experiment logs are not included. Historical construction provenance may contain original machine paths; executable image/waypoint/evaluation references are local to this release.

The benchmark reference-route integrity check passed on all 819 route questions after packaging. This validates packaging, not model performance.

Training resource

training/ contains 31,852 direct-answer and 31,852 spatial CoT training conversations, with portable image paths and the original export report.

License and sources

CC BY-NC-SA 4.0, with applicable upstream terms retained. Scene geometry derives through InternScenes from 3RScan, ScanNet, ARKitScenes, and Matterport3D. See THIRD_PARTY_NOTICES.md. Original scene assets must be obtained upstream.

Code and paper

Code

Project page

Paper · arXiv:2609.16610

Citation

@misc{zhao2026egopathbench,
  title={EgoPathBench: Evaluating Zero-Shot Egocentric Waypoint Decision-Making in Vision-Language Models},
  author={Yang Zhao and Zhuo Chen and Xubo Yang},
  year={2026},
  eprint={2609.16610},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  url={https://arxiv.org/abs/2609.16610}
}

Contact: runder1103@sjtu.edu.cn; yangxubo@sjtu.edu.cn.