Carview!

CARVIEW

MOTORHOMES

Select Language

HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Mon, 03 Nov 2025 02:30:14 GMT access-control-allow-origin: * strict-transport-security: max-age=31556952 etag: W/"690813b6-63a1" expires: Mon, 29 Dec 2025 21:34:23 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: 97B1:3827E5:95C9A8:A8117B:6952F187 accept-ranges: bytes age: 0 date: Mon, 29 Dec 2025 21:24:24 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210051-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1767043464.857566,VS0,VE215 vary: Accept-Encoding x-fastly-request-id: f9019fc326c4f28ea886215ea28beacab1aa9538 content-length: 6143 SpatialReasoner

we introduce SpatialReasoner, a novel large vision-language model (LVLM) that address 3D spatial reasoning with explicit 3D representations shared between stages -- 3D perception, computation, and reasoning. Explicit 3D representations provide a coherent interface that supports advanced 3D spatial reasoning and enable us to study the factual errors made by LVLMs.

Motivation — **Figure 1.** Comparing 3D spatial reasoning of our SpatialReasoner with previous state-of-the-art models.

Key Design I: Explicit Spatial Reasoning

design 1 — **Figure 2.** Comparing 3D spatial reasoning of our SpatialReasoner with previous state-of-the-art models.

Key Design II: Generalizable Spatial Reasoning

design 2 — **Figure 2.** Comparing 3D spatial reasoning of our SpatialReasoner with previous state-of-the-art models.

Key Findings

SpatialReasoner models. We experimented on a variety of SpatialReasoner models with different data and training methods.

SpatialReasoner-SFT. Qwen2.5-VL directly finetuned with SFT on synthetic spatial reasoning VQA data, as widely adopted in prior works .
SpatialReasoner-Zero. Qwen2.5-VL first warmed up with SFT on basic 3D knowledge, and then finetuned with GRPO using format, accuracy, and 3D-aware process rewards.
SpatialReasoner. Qwen2.5-VL first warmed up with SFT on synthetic spatial reasoning VQA data, and then finetuned with GRPO using format and accuracy rewards.

Evaluation on 3DSRBench .

model	3DSRBench
	Overall	Height	Location	Orientation	Multi-Object
*Open-sourced*
Qwen2.5-VL	48.4	44.1	62.7	40.6	40.5
SpatialLLM	44.8	45.8	61.6	30.0	36.7
SpatialRGPT	32.7	55.9	39.0	27.8	20.0
SpatialRGPT+Depth	48.4	55.9	60.0	34.2	42.3
*Propietary*
GPT-4o	44.2	53.2	59.6	21.6	39.0
Gemini 2.0	51.1	53.0	67.1	35.8	43.6
QwenVLMax	52.0	45.1	70.7	37.7	44.8
*Ours*
SpatialReasoner-Zero	54.0	46.4	67.3	48.4	47.2
SpatialReasoner-SFT	58.3	51.9	73.5	50.7	50.3
SpatialReasoner	60.3	52.5	75.2	55.2	51.8

Table 1. Evaluation on 3DSRBench . Our SpatialReasoner outperforms all prior open-sourced and propietary models, including GPT-4o, Gemini 2.0, and QwenVLMax.

Evaluation on 3DSRBench .

model	in-distribution				out-of-distribution
	overall	height	location	orientation	multi-object
*reference*
Qwen2.5-VL	48.4	44.1	62.7	40.6	40.5
*ours*
SpatialReasoner-Zero	53.7	40.6	68.4	50.2	46.6
SpatialReasoner-SFT	52.2	44.9	69.5	48.9	40.0
SpatialReasoner	56.4	52.5	72.6	54.1	43.4

Qualitative Comparisons

Open Source

Code for training SpatialReasoner models.

Code for generating synthetic spatial reasoning VQA data.

Huggingface collection for both training data (Basic3D, SFT, RL data) and pretrained SpatialReasoner-series models.

Miscellaneous

License. Our SpatialReasoner and SpatialReasonerDataGen is released under the Creative Commons Attribution 4.0 license. By accessing and using our SpatialReasoner and SpatialReasonerDataGen, you agree to follow the terms of access specified here.

BibTeX

@inproceedings{ma2025spatialreasoner,
  title={SpatialReasoner: Towards Explicit and Generalizable 3D Spatial Reasoning},
  author={Ma, Wufei and Chou, Yu-Cheng and Liu, Qihao and Wang, Xingrui and Melo, Celso M de and Xie, Jianwen and Yuille, Alan},
  booktitle={Advances in Neural Information Processing Systems},
  volume={38},
  year={2025},
}

Notes

This website template is adapted from Image Sculpting.

Original Source | Taken Source

SpatialReasoner: Towards Explicit and Generalizable 3D Spatial Reasoning

NeurIPS 2025