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---
task_categories:
- visual-question-answering
language:
- en
tags:
- remyx
- SpatialReasoning
- spatial-reasoning
- test-time-compute
- thinking
- reasoning
- multimodal
- vlm
- vision-language
- distance-estimation
- quantitative-spatial-reasoning
pretty_name: SpaceOm
license: apache-2.0
datasets:
- remyxai/SpaceThinker
base_model:
- UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B
pipeline_tag: image-text-to-text
library_name: transformers
model-index:
- name: SpaceOm
results:
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: 3DSRBench
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.5419
results_by_subcategory:
- name: 3D Positional Relation / Orientation
success_rate: 0.4877
- name: Object Localization / 3D Localization
success_rate: 0.6337
- name: Object Properties / Size
success_rate: 0.5043
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: BLINK
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.599
results_by_subcategory:
- name: 3D Positional Relation / Orientation
success_rate: 0.7972
- name: Counting / Object Counting
success_rate: 0.6167
- name: Depth and Distance / Relative
success_rate: 0.621
- name: Object Localization / 2D Localization
success_rate: 0.582
- name: Point and Object Tracking / Point Correspondence
success_rate: 0.3779
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: MMIU
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.388
results_by_subcategory:
- name: Camera and Image Transformation / 2D Transformation
success_rate: 0.255
- name: Camera and Image Transformation / 3D Camera Pose
success_rate: 0.4
- name: Camera and Image Transformation / Camera Motion
success_rate: 0.4436
- name: Depth and Distance / Absolute
success_rate: 0.265
- name: Object Localization / 3D Localization
success_rate: 0.3625
- name: Point and Object Tracking / 3D Tracking
success_rate: 0.725
- name: Point and Object Tracking / Point Correspondence
success_rate: 0.265
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: MMVP
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.5833
results_by_subcategory:
- name: Others / Miscellaneous
success_rate: 0.5833
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: QSpatialBench-Plus
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.4455
results_by_subcategory:
- name: Depth and Distance / Absolute
success_rate: 0.4455
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: QSpatialBench-ScanNet
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.4876
results_by_subcategory:
- name: Depth and Distance / Absolute
success_rate: 0.464
- name: Object Properties / Size
success_rate: 0.5111
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: RealWorldQA
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.6105
results_by_subcategory:
- name: Others / Miscellaneous
success_rate: 0.6105
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: SpatialSense
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.7043
results_by_subcategory:
- name: 3D Positional Relation / Orientation
success_rate: 0.7043
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: VGBench
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.3504
results_by_subcategory:
- name: Camera and Image Transformation / 2D Transformation
success_rate: 0.2568
- name: Camera and Image Transformation / 3D Camera Pose
success_rate: 0.4371
- name: Depth and Distance / Absolute
success_rate: 0.3339
- name: Depth and Distance / Relative
success_rate: 0.32
- name: Object Localization / 3D Localization
success_rate: 0.4283
- name: Point and Object Tracking / 3D Tracking
success_rate: 0.3264
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: VSI-Bench_8
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.2558
results_by_subcategory:
- name: 3D Positional Relation / Orientation
success_rate: 0.3998
- name: Counting / Object Counting
success_rate: 0.229
- name: Depth and Distance / Absolute
success_rate: 0.1562
- name: Depth and Distance / Relative
success_rate: 0.3648
- name: Object Properties / Size
success_rate: 0.1645
- name: Others / Miscellaneous
success_rate: 0.2204
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: VSR-ZeroShot
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.8085
results_by_subcategory:
- name: 3D Positional Relation / Orientation
success_rate: 0.8085
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: cvbench
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.6839
results_by_subcategory:
- name: Counting / Object Counting
success_rate: 0.6294
- name: Depth and Distance / Relative
success_rate: 0.7408
- name: Object Localization / 3D Localization
success_rate: 0.6815
- task:
type: visual-question-answering
name: Spatial Reasoning
dataset:
name: spatialbench
type: benchmark
metrics:
- type: success_rate
name: Overall Success Rate
value: 0.6553
results_by_subcategory:
- name: 3D Positional Relation / Orientation
success_rate: 0.6765
- name: Counting / Object Counting
success_rate: 0.75
- name: Object Properties / Existence
success_rate: 0.925
- name: Object Properties / Reachability
success_rate: 0.55
- name: Object Properties / Size
success_rate: 0.375
---
[](https://remyx.ai/?model_id=SpaceThinker-Qwen2.5VL-3B&sha256=abc123def4567890abc123def4567890abc123def4567890abc123def4567890)
# SpaceOm
<img src="https://cdn-uploads.huggingface.co/production/uploads/647777304ae93470ffc28913/5cPsHwrmzqPOjd7zUgzss.gif" width="500"/>
## 📚 Contents
- [🧠 Model Overview](#model-overview)
- [📊 Evaluation & Benchmarks](#model-evaluation)
- [🏃♀️ Running SpaceOm](#running-spaceom)
- [🏋️♂️ Training Configuration](#training-spaceom)
- [📂 Dataset Info](#dataset-info)
- [⚠️ Limitations](#limitations)
- [📜 Citation](#citation)
## Model Overview
**SpaceOm** improves over **SpaceThinker** by adding:
* the target module `o_proj` in LoRA fine-tuning
* **SpaceOm** [dataset](https://huggingface.co/datasets/salma-remyx/SpaceOm) for longer reasoning traces
* **Robo2VLM-Reasoning** [dataset](https://huggingface.co/datasets/salma-remyx/Robo2VLM-Reasoning) for more robotics domain and MCVQA examples
The choice to include `o_proj` among the target modules in LoRA finetuning was inspired by the study [here](https://arxiv.org/pdf/2505.20993v1), which argues for
the importance of this module in reasoning models.
The reasoning traces in the SpaceThinker dataset average ~200 "thinking" tokens so now we've included longer reasoning traces in the training data
to help the model use more tokens in reasoning.
Aiming to improve alignment for robotics applications, we've trained with synthetic reasoning traces, derived from the **Robo2VLM-1** [dataset](https://huggingface.co/datasets/keplerccc/Robo2VLM-1).
## Running SpaceOm
### Ollama
To launch with ollama, run:
```bash
ollama run hf.co/remyxai/SpaceOm:latest
```
or
```bash
ollama run remyxai/spaceom
```
### llama.cpp
To run locally with **llama.cpp**, install and build this [branch](https://github.com/HimariO/llama.cpp.qwen2.5vl/tree/qwen25-vl) and download the [.gguf weights here](https://huggingface.co/remyxai/SpaceThinker-Qwen2.5VL-3B/tree/main/gguf)
```bash
./llama-qwen2vl-cli -m spaceom-F16.gguf
--mmproj spaceom-vision.gguf
--image images/example_1.jpg --threads 24 -ngl 9
-p "Does the man in blue shirt working have a greater \\
height compared to the wooden pallet with boxes on floor?"
```
### Transformers
Run locally using **Transformers**
```python
import torch
from PIL import Image
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
import requests
from io import BytesIO
# Configuration
model_id = "remyxai/SpaceOm"
image_path = "images/example_1.jpg" # or local path
prompt = "What can you infer from this image about the environment?"
system_message = (
"You are VL-Thinking 🤔, a helpful assistant with excellent reasoning ability. "
"You should first think about the reasoning process and then provide the answer. "
"Use <think>...</think> and <answer>...</answer> tags."
)
# Load model and processor
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
model_id, device_map="auto", torch_dtype=torch.bfloat16
)
processor = AutoProcessor.from_pretrained(model_id)
# Load and preprocess image
if image_path.startswith("http"):
image = Image.open(BytesIO(requests.get(image_path).content)).convert("RGB")
else:
image = Image.open(image_path).convert("RGB")
if image.width > 512:
ratio = image.height / image.width
image = image.resize((512, int(512 * ratio)), Image.Resampling.LANCZOS)
# Format input
chat = [
{"role": "system", "content": [{"type": "text", "text": system_message}]},
{"role": "user", "content": [{"type": "image", "image": image},
{"type": "text", "text": prompt}]}
]
text_input = processor.apply_chat_template(chat, tokenize=False,
add_generation_prompt=True)
# Tokenize
inputs = processor(text=[text_input], images=[image],
return_tensors="pt").to("cuda")
# Generate response
generated_ids = model.generate(**inputs, max_new_tokens=1024)
output = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
print("Response:\n", output)
```
## Dataset Info
The [SpaceThinker](https://huggingface.co/datasets/remyxai/SpaceThinker) dataset includes over 12K samples synthesized using VQASynth on a subset of images in the localized narratives split of [the cauldron](https://huggingface.co/datasets/HuggingFaceM4/the_cauldron).
**SpaceThinker** is formatted similar to the [Llama-Nemotron-Post-Training-Dataset-v1](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset) to toggle reasoning.
The [SpaceOm](https://huggingface.co/datasets/remyxai/SpaceOm) dataset includes ~1K samples synthesized using VQASynth to include longer reasoning traces.
The [Robo2VLM-Reasoning](https://huggingface.co/datasets/remyxai/Robo2VLM-Reasoning) datasert is a subset of the original [Robo2VLM](https://huggingface.co/datasets/remyxai/Robo2VLM-Reasoning) dataset modified to include reasoning traces.
These datasets were combined to create the final training data for this model.
The model builds upon the ideas from [SpatialVLM (Chen et al., 2024)](https://spatial-vlm.github.io/), introducing synthetic reasoning traces grounded on a 3D scene reconstruction pipeline using **Molmo, VGGT, SAM2**.
## Training SpaceOm
**PEFT Configuration**
- Architecture: Qwen2.5-VL-3B
- Base model: UCSC-VLAA/VLAA-Thinker-Qwen2.5VL-3B
- Method: LoRA finetuning (PEFT)
- LoRA Alpha: 256
- LoRA Rank: 128
- Target Modules: q_proj, v_proj, o_proj
- Optimizer: AdamW (lr=2e-5), batch size = 1, epochs = 3
- Max input length: 1024 tokens
Reproduce LoRA SFT training with included script:
```bash
python train.py
```
## Model Evaluation
### OmniSpatial
Benchmark leaderboard with **SpaceOm** highlighted.
| Model | Avg | Manip | Motion | Traffic | Locate | Geospatial | Pattern | Geometric | Ego | Allo | Hypo |
|-----------------------------|--------|--------|--------|---------|--------|------------|---------|-----------|--------|--------|--------|
| 🥇 o3-2025-04-16 | 56.33 | 71.89 | 66.18 | 61.18 | 68.57 | 65.45 | 40.21 | 29.68 | 77.06 | 48.40 | 48.19 |
| 🥈 Gemini-2.5-pro-preview-05-06 | 55.19 | 67.57 | 71.39 | 62.35 | 75.24 | 64.55 | 43.30 | 34.84 | 74.51 | 38.03 | 37.35 |
| 🥉 Gemini-2.5-flash-thinking-05-20 | 53.16 | 70.27 | 64.74 | 61.18 | 72.38 | 58.18 | 35.05 | 36.13 | 74.12 | 40.96 | 32.53 |
| o4-mini-04-16 | 52.77 | 72.97 | 59.83 | 60.00 | 73.33 | 61.82 | 34.02 | 36.77 | 73.53 | 40.69 | 40.96 |
| Gemini-2.5-flash-preview-05-20 | 52.12 | 67.57 | 62.72 | 68.24 | 73.33 | 60.91 | 38.14 | 34.19 | 75.49 | 35.90 | 33.73 |
| GPT-4.1-2025-04-14 | 51.78 | 66.22 | 64.74 | 60.00 | 65.33 | 60.18 | 31.75 | 30.06 | 70.98 | 40.64 | 39.04 |
| o1-2024-12-17 | 50.36 | 71.62 | 60.98 | 57.65 | 63.81 | 60.00 | 39.18 | 27.10 | 71.57 | 38.03 | 36.14 |
| InternVL3-78B | 49.33 | 63.78 | 63.12 | 56.24 | 59.24 | 51.45 | 27.63 | 30.19 | 74.51 | 38.46 | 35.90 |
| GPT-4.1-mini-2025-04-14 | 48.87 | 64.32 | 56.53 | 59.06 | 60.19 | 56.36 | 29.28 | 30.19 | 72.55 | 39.57 | 39.28 |
| Claude-3-7-thinking-20250219| 48.62 | 57.21 | 59.73 | 53.73 | 67.94 | 57.27 | 30.24 | 28.17 | 68.63 | 37.94 | 36.95 |
| InternVL3-38B | 48.48 | 63.42 | 63.58 | 54.59 | 58.29 | 50.55 | 29.90 | 28.52 | 72.16 | 36.76 | 33.49 |
| Gemini-2.0-flash-exp | 48.40 | 61.89 | 56.01 | 51.76 | 63.43 | 59.09 | 20.82 | 33.81 | 72.75 | 39.20 | 39.28 |
| Qwen-VL2.5-72B | 47.85 | 58.38 | 60.12 | 50.12 | 59.81 | 53.64 | 26.19 | 33.03 | 71.37 | 36.81 | 36.39 |
| GPT-4o-2024-11-20 | 47.81 | 65.54 | 57.23 | 56.47 | 52.38 | 54.09 | 26.29 | 25.48 | 75.98 | 39.49 | 39.76 |
| Claude-3-7-sonnet-20250219 | 47.53 | 57.57 | 55.95 | 56.71 | 63.81 | 59.09 | 29.48 | 28.39 | 72.16 | 36.06 | 36.63 |
| Qwen-VL2.5-32B | 47.36 | 63.06 | 55.09 | 51.76 | 66.29 | 56.91 | 26.39 | 27.48 | 68.04 | 37.50 | 40.24 |
| Claude-3-5-sonnet-20241022 | 46.86 | 54.05 | 54.57 | 58.12 | 68.38 | 53.09 | 26.60 | 31.74 | 70.00 | 34.79 | 39.52 |
| InternVL3-14B | 45.94 | 54.32 | 60.17 | 50.35 | 51.81 | 51.45 | 28.04 | 28.26 | 68.04 | 35.37 | 34.46 |
| LLaVA-onevision-qwen2-72B | 45.66 | 62.16 | 50.29 | 54.12 | 60.95 | 56.36 | 22.68 | 25.81 | 76.47 | 37.23 | 33.73 |
| SoFar-Qwen2.5-3B | 45.14 | 56.49 | 51.16 | 54.12 | 53.14 | 52.73 | 31.75 | 22.88 | 71.60 | 36.56 | 41.69 |
| Gemma-3-27B | 44.75 | 56.76 | 55.78 | 57.65 | 50.48 | 52.73 | 27.84 | 29.03 | 64.71 | 33.51 | 32.53 |
| Gemini-2.0-flash-lite | 44.03 | 59.19 | 46.71 | 60.24 | 49.52 | 53.27 | 21.65 | 31.23 | 66.47 | 36.81 | 38.80 |
| Gemma-3-12B | 43.71 | 54.05 | 54.91 | 54.12 | 47.62 | 45.45 | 16.49 | 30.32 | 63.73 | 36.70 | 33.73 |
| GPT-4o-mini-2024-07-18 | 42.64 | 55.95 | 50.29 | 54.59 | 43.43 | 44.91 | 22.47 | 29.42 | 61.57 | 36.76 | 34.22 |
| GPT-4.1-nano-2025-04-14 | 42.62 | 50.90 | 53.85 | 54.90 | 40.95 | 42.42 | 24.40 | 30.11 | 53.59 | 37.23 | 33.73 |
| 🧘♂️ **SpaceOm** | 41.79 | 51.89 | 47.98 | 50.82 | 39.62 | 43.64 | 27.63 | 27.61 | 70.00 | 35.74 | 33.73 |
| InternVL3-8B | 41.60 | 52.43 | 40.87 | 48.94 | 51.05 | 44.77 | 24.95 | 28.63 | 64.20 | 38.62 | 40.96 |
| SpaceThinker-Qwen2.5-3B | 40.42 | 47.84 | 53.06 | 43.29 | 35.43 | 38.73 | 24.33 | 28.00 | 58.04 | 35.11 | 31.08 |
| Qwen-VL2.5-3B | 40.30 | 55.41 | 47.51 | 46.12 | 42.29 | 44.73 | 32.16 | 23.87 | 59.41 | 33.30 | 30.84 |
| SpaceQwen2.5-VL-3B | 40.25 | 58.11 | 39.88 | 41.18 | 40.95 | 40.91 | 29.90 | 25.81 | 63.73 | 38.83 | 39.76 |
| Gemma-3-4B | 39.79 | 41.89 | 49.71 | 56.47 | 27.62 | 36.36 | 23.71 | 24.52 | 59.80 | 36.17 | 38.55 |
| Qwen-VL2.5-7B | 39.18 | 58.38 | 35.09 | 50.12 | 45.33 | 44.00 | 31.13 | 29.42 | 64.51 | 33.19 | 37.35 |
| InternVL3-2B | 37.98 | 50.00 | 40.58 | 43.29 | 40.00 | 40.55 | 21.86 | 28.52 | 55.49 | 35.11 | 33.01 |
| SpaceMantis-13B | 36.36 | 47.03 | 36.59 | 40.94 | 34.86 | 33.09 | 22.27 | 24.39 | 49.22 | 38.25 | 39.28 |
| RoboPoint-vicuna-7B | 35.85 | 57.03 | 28.61 | 34.82 | 37.33 | 40.55 | 29.90 | 22.71 | 50.20 | 38.72 | 40.96 |
| LLaVA-onevision-qwen2-7B | 35.68 | 43.24 | 38.15 | 32.94 | 29.52 | 41.82 | 28.87 | 22.58 | 47.06 | 36.17 | 37.35 |
| SpatialBot-3B | 35.68 | 43.24 | 38.15 | 32.94 | 29.52 | 41.82 | 28.87 | 22.58 | 47.06 | 36.17 | 37.35 |
| LLaVA-1.5-vicuna-7B | 34.97 | 54.46 | 31.23 | 35.29 | 36.19 | 33.94 | 29.01 | 24.18 | 55.60 | 34.66 | 36.14 |
| RoboPoint-vicuna-13B | 34.60 | 55.68 | 28.15 | 42.82 | 32.19 | 32.55 | 24.12 | 27.74 | 49.02 | 37.66 | 33.49 |
See full **SpaceOm** [results here](https://huggingface.co/datasets/salma-remyx/SpaceOm_OmniSpatial/blob/main/OmniSpatial_spaceom_results.json) for the
**OmniSpatial** [benchmark](https://qizekun.github.io/omnispatial/).
### SpatialScore
Top scores in each category are **bolded** in partial table of 3B/4B models.
| **Model** | **Overall** | **Count.** | **Obj.-Loc.** | **Pos.-Rel.** | **Dist.** | **Obj.-Prop.** | **Cam.&IT.** | **Tracking** | **Others** |
|------------------------|-------------|------------|----------------|----------------|-----------|----------------|---------------|---------------|------------|
| InternVL2.5-4B | 49.82 | **53.32** | **62.02** | **62.82** | **42.30** | 27.00 | 32.49 | 37.02 | **48.95** |
| 🧘♂️ **SpaceOm** | 48.15 | 47.84 | 55.24 | 61.83 | 41.48 | 30.97 | 32.94 | **37.20** | 43.74 |
| Qwen2.5-VL-3B | 47.90 | 46.62 | 55.55 | 62.23 | 37.53 | 32.59 | **35.85** | 36.90 | 42.19 |
| SpaceQwen2.5-VL-3B | 42.31 | 45.01 | 49.78 | 57.88 | 27.36 | **34.11** | 26.34 | 26.44 | 43.58 |
| SpatialBot-Phi2-3B | 41.65 | 53.25 | 54.32 | 55.40 | 27.12 | 26.10 | 24.21 | 27.57 | 41.66 |
See [all results](https://huggingface.co/datasets/salma-remyx/SpaceOm_SpatialScore) for evaluating **SpaceOm** on the **SpatialScore** [benchmark](https://haoningwu3639.github.io/SpatialScore/).
### SpaCE-10
Top scores in each category are **bolded** in partial table of 3B/4B models.
[](https://colab.research.google.com/drive/1YpIOjJFZ-Zaomg77ImeQHSqYBLB8T1Ce?usp=sharing)
| **Model** | **Overall** | **EQ** | **SQ** | **SA** | **OO** | **OS** | **EP** | **FR** | **SP** | **Source** |
|--------------------------|-------------|----------|----------|----------|----------|----------|----------|----------|----------|-------------|
| InternVL2.5-4B | **36.01** | **34.30**| 34.40 | 43.60 | 44.40 | 16.50 | **31.10**| **50.10**| **33.70**| Table |
| SpaceThinker | 32.72 | 32.73 | 24.81 | 47.26 | 50.33 | 33.63 | 9.25 | 37.54 | 26.25 | GPT Eval |
| 🧘♂️ **SpaceOm** | 32.32 | 32.47 | 24.81 | **47.63**| 50.00 | 32.52 | 9.12 | 37.04 | 25.00 | GPT Eval |
| SpaceQwen | 31.98 | 31.19 | 25.89 | 41.61 | **51.98**| **35.18**| 10.97 | 36.54 | 22.50 | GPT Eval |
| Qwen2.5-VL-3B-Instruct | 30.00 | 31.70 | **45.50**| 39.00 | 43.00 | 25.30 | 11.50 | 22.80 | 21.20 | Table |
**Legend:**
- EQ: Entity Quantification
- SQ: Scene Quantification
- SA: Size Assessment
- OO: Object-Object spatial relations
- OS: Object-Scene spatial relations
- EP: Entity Presence
- FR: Functional Reasoning
- SP: Spatial Planning
> ℹ️ Note: Scores for SpaceQwen, SpaceThinker, SpaceOm are generated via `gpt_eval_score` on single-choice (`*-single`) versions of the SpaCE-10 benchmark tasks. Other entries reflect leaderboard accuracy scores from the official SpaCE-10 evaluation table.
Read more about the [SpaCE-10 benchmark](https://arxiv.org/pdf/2506.07966v1) or see [results here](https://huggingface.co/datasets/salma-remyx/SpaceOm_SpaCE-10_Results/blob/main/20250611_041721_results.json)
## Limitations
- Performance may degrade in cluttered environments or camera perspective.
- This model was fine-tuned using synthetic reasoning over an internet image dataset.
- Multimodal biases inherent to the base model (Qwen2.5-VL) may persist.
- Not intended for use in safety-critical or legal decision-making.
> Users are encouraged to evaluate outputs critically and consider fine-tuning for domain-specific safety and performance. Distances estimated using autoregressive
> transformers may help in higher-order reasoning for planning and behavior but may not be suitable replacements for measurements taken with high-precision sensors,
> calibrated stereo vision systems, or specialist monocular depth estimation models capable of more accurate, pixel-wise predictions and real-time performance.
## Citation
```
@article{chen2024spatialvlm,
title = {SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities},
author = {Chen, Boyuan and Xu, Zhuo and Kirmani, Sean and Ichter, Brian and Driess, Danny and Florence, Pete and Sadigh, Dorsa and Guibas, Leonidas and Xia, Fei},
journal = {arXiv preprint arXiv:2401.12168},
year = {2024},
url = {https://arxiv.org/abs/2401.12168},
}
@misc{qwen2.5-VL,
title = {Qwen2.5-VL},
url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
author = {Qwen Team},
month = {January},
year = {2025}
}
@misc{vl-thinking2025,
title={SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models },
author={Hardy Chen and Haoqin Tu and Fali Wang and Hui Liu and Xianfeng Tang and Xinya Du and Yuyin Zhou and Cihang Xie},
year = {2025},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/UCSC-VLAA/VLAA-Thinking}},
}
@article{wu2025spatialscore,
author = {Wu, Haoning and Huang, Xiao and Chen, Yaohui and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
title = {SpatialScore: Towards Unified Evaluation for Multimodal Spatial Understanding},
journal = {arXiv preprint arXiv:2505.17012},
year = {2025},
}
``` |