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README.md

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 license: apache-2.0
 pipeline_tag: image-to-image
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+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+Joint RGB and Depth Representation Autoencoder (RAE-d) and Conditional Flow Matching (CFM) model for underwater benthic environment generation.  
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+BenthicFlow is a unified generative pipeline for benthic (seafloor) environments.
+A **single conditional flow matching model** jointly synthesizes aligned RGB and
+depth, and a MultiDiffusion-inspired windowed sampler extends generation to
+scenes of unbounded spatial extent — no separate inpainting or stitching network.
+Generated RGB-D mosaics are lifted into continuous 3D scenes with
+**surface-aligned Gaussian surfels**.
+
+
+- **Developed by:** Joaquín Figueira Chacon, Camile C. Lendering, Giacomo D'Amicantontion, Erkut Akdag and Egor Bondarev 
+- **Funded by:** ITEA Advisor and Xecs Marisens projects winthin the Eindhoven University of Technology.
+- **Model type:** Generative model
+- **License:** Apache 2.0
+- **Adapted from model:** facebook/dinov2-base
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/jacomof/BenthicFlow
+- **Paper [optional]:** To be released soon in Arxiv and ECCV 2026 proceedings.
+
+## Uses
+
+Use to create RGBD mosaics of benthic environments.
+
+## How to Get Started with the Model
+
+Download the Github repository and follow the provided instructions.
+
+## Training Details
+
+### Training Data
+
+Data from the Squidle+ benthic survey framework is used for training. Images are downsampled to 518 resolution, and 224 crops of the the downsampled images are used for
+training. Further details on the precise subset used are present in the Github repository and paper.
+
+### Training Procedure
+
+- RAE: A representation autoencoder (using a DINOv2-B encoder and a convolutional decoder) is trained to reconstruct images following a 3 stage regime for 16 epochs with a batch size of 16
+  crops.
+Following standard RAE practices, phase 1 applies an L1 
+reconstruction loss on RGB and depth. From epoch 6, phase 2 adds a Learned Perceptual Image Patch Similarity (LPIPS) term on RGB. 
+From epoch 8, phase 3 adds a hinge adversarial term on RGB, with a
+discriminator formed from a frozen DINO-S/8 backbone and a small trainable
+convolutional head, and DiffAugment applied identically to real and reconstructed
+inputs.
+- CFM:  A DIBOv2 average pooled token of produced by encoding a reference image is computed and appended to the timestep encoder of a CFM network. The CFM network is implemente
+  using a UNet architecture. 
+
+
+#### Training Hyperparameters
+
+- **Training regime:** fp16 mixed precision.
+- **Batch size:** 16 (d-RAE) and 256 (CFM).
+- **Learning rate:** 2e-4 (d-RAE) and 1e-4 (CFM).
+- **Exponential Moving Average:** 0.9999 decay.
+
+## Evaluation
+
+Please refer to the paper when published.
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Estimated as 34560.0 g eq. CO2 using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
+
+- **Hardware Type:** H100 GPUs
+- **Hours used:** Approx. 200 H100 GPU hours.
+- **Carbon Emitted:** 34560.0 g eq. CO2
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+- The RAE-d architecture consists of a frozen DINOv2-B RGB encoder, a depth encoder consisting of 8 attention blocks with embedding dimension 256 and 8 attention heads.
+  The decoder consists of a convolutional network with two pre-norm GroupNorm/SiLU residual up-convolution blocks (inspired by the SD-VAE decoder) with upsampling factors
+  2 and 7 and hidden dimensions 256 and 128.  
+- The CFM's UNet consists of 2 downsampling residual blocks, 1 middle residual block and 2 up-sampling residual blocks. Adaptive Group Normalization is used in all the
+   blocks.
+
+### Compute Infrastructure
+
+[Snellius: the National Supercomputer of the Netherlands](https://www.surf.nl/en/services/compute/snellius-the-national-supercomputer). It uses a SLURM management system.
+
+#### Hardware
+
+4 H100 GPUs, 64 CPU cores, 512 GB of RAM are used for training.
+
+## Citation
+
+To be released soon in Arxiv and ECCV 2026 proceedings.
+
+## Model Card Contact
+
+- email: j.figueira@tue.nl