Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment
Paper • 2605.08064 • Published • 1
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The JWT signature verification failed. Check the signing key and the algorithm.
Error code: JWTInvalidSignature
Exception: InvalidSignatureError
Message: Signature verification failed
Traceback: Traceback (most recent call last):
File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
decoded = jwt.decode(
jwt=token,
...<2 lines>...
options=options,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
decoded = self.decode_complete(
jwt,
...<8 lines>...
leeway=leeway,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
decoded = self._jws.decode_complete(
jwt,
...<3 lines>...
detached_payload=detached_payload,
)
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
self._verify_signature(
~~~~~~~~~~~~~~~~~~~~~~^
signing_input,
^^^^^^^^^^^^^^
...<4 lines>...
options=merged_options,
^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
raise InvalidSignatureError("Signature verification failed")
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SpaceSpan Dataset
SpaceSpan is a large-scale dataset curated for aligning 3D proxy representations with Vision-Language Models (VLMs), introduced in the paper Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment.
The dataset incorporates heterogeneous visual information into a unified format to support multi-stage training for developing spatial intelligence. It enables models to progress from simple image-text alignment to complex 3D reasoning tasks, such as 3D visual question answering (VQA) and visual grounding.
Project Page | GitHub | Paper
Dataset Description
The SpaceSpan dataset (specifically the SpaceSpan-318K version) supports four progressive training stages:
- Stage 1: Initial spatial alignment.
- Stage 2-3: Intermediate spatial reasoning development.
- Stage 4: Full-scale 3D reasoning.
Directory Structure
The dataset can be organized as follows:
data/ # Training and inference data
├── icon_image_embeds_qwen25.pt
├── number_image_embeds_qwen25.pt
├── stage_1_train.json
├── stage_2_train.json
├── stage_3_train.json
├── stage_4_train_318K.json
├── pointmaps_wo_markers
├── poses
└── ...
Citation
If you find this dataset useful for your research, please cite the following paper:
@article{proxy3d2026,
title={Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment},
author={Jiang, Jerry and Sun, Haowen and Gudovskiy, Denis and Nakata, Yohei and Okuno, Tomoyuki and Keutzer, Kurt and Zheng Wenzhao},
journal={arXiv preprint arXiv:2605.08064},
year={2026}
}
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