VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations
view2space_4b is an ECCV 2026 VIEW2SPACE model built on top of
Qwen/Qwen3-VL-4B-Instruct.
It is designed for grounded multi-view visual reasoning from sparse
observations.
Quick start
Please see the VIEW2SPACE GitHub repository for evaluation code and usage:
Quick links
Overview
VIEW2SPACE studies how vision-language models reason across sparse and heterogeneous viewpoints. Instead of solving a task from a single image, the model must integrate partial observations from multiple views to form a more complete spatial understanding.
This checkpoint is the Qwen3-VL-4B VIEW2SPACE model release and is intended
for multi-view visual reasoning under sparse observations.
Model Summary
- Model name:
view2space_4b - Base model:
Qwen/Qwen3-VL-4B-Instruct - Architecture:
Qwen3VLForConditionalGeneration - Project: VIEW2SPACE
- Use case: multi-view visual reasoning from sparse observations
- Venue: ECCV 2026
Highlights
- Built for grounded multi-view reasoning rather than single-image prediction.
- Targets sparse observations and heterogeneous viewpoints.
- Released together with the public VIEW2SPACE testing set and evaluation code.
Resources
- Public testing release:
view2space-v1 - Official repository:
https://github.com/pokerme7777/VIEW2SPACE - Public eval pipeline:
src/evalin the official repository
Usage Notes
- Use the official VIEW2SPACE repository for evaluation scripts and prompt formatting.
- The current public testing release is
view2space-v1. - If you need another public data format, please open an issue in the GitHub repository.
Framework versions
- TRL: 0.26.2
- Transformers: 4.57.0
- Pytorch: 2.7.1+cu126
- Datasets: 4.4.2
- Tokenizers: 0.22.1
Citations
@inproceedings{ke2026view2space,
title={View2space: Studying multi-view visual reasoning from sparse observations},
author={Ke, Fucai and Cai, Zhixi and Li, Boying and Chen, Long and Lin, Beibei and Wang, Weiqing and Haghighi, Pari Delir and Haffari, Gholamreza and Rezatofighi, Hamid},
booktitle={European Conference on Computer Vision},
pages={520--538},
year={2026},
organization={Springer}
}