ReconSplat: Generalizable 3D Scene Reconstruction Beyond Observed Views

Giuseppe Stracquadanio, Kevin Raj, Julia Grabinski, Stefan Roth  ·  ECCV 2026

Paper · Project page · Code

ReconSplat reconstructs a scene from a handful of posed images, synthesizing novel views and depth inside and outside the observed frusta. It pairs a feed-forward Gaussian reconstruction with a multi-view latent diffusion model that jointly generates appearance and geometry, so unobserved regions are completed rather than left empty.

This repository holds the released weights. Two stages are trained, and running the model needs one checkpoint from each:

• Stage 1, multi-view latent field. From sparse input views with known cameras, we predict variational 3D Gaussians, enconding latent appeareance and geometry, that can be rasterized to any view.
• Stage 2, multi-view latent diffusion. Conditioned on input views and on rasterized features for target views, a multi-view diffusion models renders novel views and depth.

Checkpoint hierarchy

Below we describe our checkpoint hierarchy. The "Init. from" column denotes the weights we use for initializing the corresponding checkpoint. N is the number of context (input) views, M the number of target views, f the VAE downsampling factor relating image to latent resolution, and Beff the effective batch size. All files live under stage2-diffusion/.

• -upsample-ft halves the effective downsampling factor (f = 8 → 4) by bilinearly upsampling the input images 2×, so the denoiser runs on a 4× larger token grid and recovers finer detail. Predictions are mapped back to the native resolution.
• -video-ft fine-tunes on 10-frame sequences for temporally consistent dense camera trajectories. Only the "temporal" residual blocks (i.e., 3D convolutions) are trained; every other parameter stays frozen.
• DL3DV inputs are center-cropped from 256 × 480 to 256 × 448 so both spatial dimensions are multiples of the patch and downsampling factors.

[!TIP] -video-ft: Use these for rendering videos and for point-cloud extraction (recommended).

The table covers only what differs between checkpoints. For the shared training setup, including objective, optimizer and schedule, noise schedule, guidance, and Stage 1 VAE recipe, see the paper.

Files

Usage

Clone the code and follow its setup instructions (this includes building a custom CUDA rasterization kernel), then download the pair you need:

git clone https://github.com/visinf/reconsplat.git reconsplat
cd reconsplat

pip install -U "huggingface_hub[cli]"

# e.g., to download the base RE10K model.
hf download gpstracquadanio/reconsplat \
    stage1-vae/re10k.ckpt stage2-diffusion/re10k-base.ckpt \
    --local-dir checkpoints

Each checkpoint has a matching evaluation config, which already points at its default saving location:

python -m src.main +experiment=re10k_diffusion_release_evals \
    mode=test \
    dataset/view_sampler=evaluation \
    dataset.view_sampler.index_path=assets/re10k_evaluation/<index>.json \
    test.compute_scores=true

[!NOTE] To render videos instead of scoring individual views, add test.save_video=true test.save_image=false test.compute_scores=false and point index_path at a video evaluation index.

[!NOTE] Stage 2 checkpoints contain both the EMA and the raw denoiser weights. Inference uses the EMA weights by default (load_ema_weights always resolves to true in test mode); you can use the raw weights for further fine-tuning.

Camera poses for test splits

To simplify evaluation with VGGT-estimated cameras, we release cached camera predictions for the RE10K and DL3DV test splits. They are stored under poses/re10k/test/ and poses/dl3dv/test/.

Download them with:

mkdir -p datasets   # or symlink it to wherever you keep the RE10K/DL3DV data
hf download gpstracquadanio/reconsplat \
    --include "poses/re10k/*" \
    --local-dir datasets

Then run evaluation with:

dataset.cameras_root=datasets/poses/re10k \
dataset.load_depth_labels=false

For DL3DV, replace re10k with dl3dv.

License

Apache 2.0. The codebase also incorporates third-party MIT-licensed components, whose copyright notices are retained in the files concerned. Dataset terms remain with their original providers.

Citation

@article{stracquadanio2026reconsplat,
    title   = {ReconSplat: Generalizable 3D scene reconstruction beyond observed views},
    author  = {Stracquadanio, Giuseppe and Raj, Kevin and Grabinski, Julia and Roth, Stefan},
    journal = {{ECCV}},
    year    = {2026},
}