license: cc-by-4.0
pipeline_tag: image-text-to-text
library_name: transformers
LEGO: A Model for Multi-View 3D Scene Understanding
This repository contains the official weights for LEGO, a baseline method for multi-view reasoning in 3D scene understanding. LEGO leverages knowledge from pre-trained 2D LVLMs (specifically fine-tuning a Fuyu-8B model) and is trained using the TripAlign pre-training dataset. It is evaluated on MV-ScanQA, a novel 3D question answering dataset designed to rigorously test multi-view compositional reasoning.
LEGO achieves state-of-the-art performance on MV-ScanQA, as well as on existing benchmarks for 3D dense captioning and question answering.
This model was presented in the paper Advancing 3D Scene Understanding with MV-ScanQA Multi-View Reasoning Evaluation and TripAlign Pre-training Dataset.
Overview of LEGO, MV-ScanQA, and TripAlign
The MV-ScanQA dataset addresses limitations in existing 3D vision-language datasets by introducing questions that explicitly require integrating information from multiple views, thus rigorously testing multi-view compositional reasoning over distant objects.
To facilitate training for such demanding scenarios, the TripAlign dataset is introduced. This large-scale, low-cost 2D-3D-language pre-training corpus contains 1M <2D view, set of 3D objects, text> triplets, providing richer, view-grounded multi-object multimodal alignment signals than previous single-object annotations.
LEGO (Large-scale Multi-View Grounding Objective) is the baseline method developed to tackle the multi-view reasoning challenge in MV-ScanQA. It transfers knowledge from pre-trained 2D LVLMs (like Fuyu-8B, which this model fine-tunes) to the 3D domain with TripAlign.
Usage
This model is a PEFT (Parameter-Efficient Fine-Tuning) LoRA adapter built on top of the adept/fuyu-8b base model. You can load and use it with the transformers and peft libraries.
First, ensure you have the necessary libraries installed:
pip install transformers accelerate peft torch torchvision pillow
Below is a sample code for inference. Please note that the image pre-processing functions (build_transform, find_closest_aspect_ratio, dynamic_preprocess, load_image) are adapted from the original repository's usage patterns for Fuyu-based models.
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(image_file, input_size=448, max_num=12):
image = Image.open(image_file).convert('RGB')
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
# Define the base model and the LoRA adapter ID
base_model_name_or_path = "adept/fuyu-8b"
# Replace 'your-org/your-repo' with the actual model ID on Hugging Face Hub
peft_model_id = "your-org/your-repo" # e.g., kmichiru/LEGO
# Load the base model
print(f"Loading base model: {base_model_name_or_path}...")
base_model = AutoModelForCausalLM.from_pretrained(
base_model_name_or_path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True,
device_map="auto" # Use 'auto' to load across available devices
)
tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path, trust_remote_code=True, use_fast=False)
# Load the PEFT adapter weights on top of the base model
print(f"Loading LoRA adapter: {peft_model_id}...")
model = PeftModel.from_pretrained(base_model, peft_model_id).eval()
print("Model loaded successfully!")
# Example usage (replace with your image path and question)
# You might need to download a sample image, e.g., from the GitHub repo
# A dummy image for testing:
# from PIL import ImageDraw
# dummy_image = Image.new('RGB', (800, 600), color = 'red')
# draw = ImageDraw.Draw(dummy_image)
# draw.text((10,10), "Sample Image", fill=(0,0,0))
# dummy_image.save("sample_image.png")
image_path = "sample_image.png" # Replace with path to a real image
if not Path(image_path).exists():
print(f"Warning: Image '{image_path}' not found. Please provide a valid image path or create a dummy image.")
# Exit or handle gracefully if no image is available for execution
exit()
pixel_values = load_image(image_path, max_num=6).to(torch.bfloat16).cuda() # Ensure image is on GPU
generation_config = dict(max_new_tokens=1024, do_sample=True)
question = "Describe the main objects in this 3D scene." # Example question
# For a Fuyu model, the prompt format might be specific. Refer to Fuyu documentation.
# This example uses a basic chat format.
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(f'User: {question}
Assistant: {response}')
# Example for 3D question answering (assuming the model outputs bounding box coordinates)
question_with_bbox = "What is the bounding box of the chair in this scene?"
response_bbox, history_bbox = model.chat(tokenizer, pixel_values, question_with_bbox, generation_config, history=None, return_history=True)
print(f'User: {question_with_bbox}
Assistant: {response_bbox}')
Citation
If you find this codebase useful, please consider citing our work:
@inproceedings{mo2025mvscanqa,
title={Advancing 3D Scene Understanding with MV-ScanQA Multi-View Reasoning Evaluation and TripAlign Pre-training Dataset},
author={Mo, Wentao and Chen, QingChao and Peng, Yuxin and Huang, Siyuan and Liu, Yang},
booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
year={2025},
}
License
This code repository and datasets are licensed under a CC-BY-4.0 license.
Copyright (c) 2025 Wentao Mo.