cvg/depthsplat

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1242 stars · 77 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

DepthSplat is a PyTorch research library implementing a CVPR 2025 model that connects Gaussian splatting with single/multi-view depth estimation. It provides pre-trained models for feed-forward novel view synthesis and scale-consistent depth prediction from multiple input views.

Use cases

• render novel views of unseen scenes from a few input images
• estimate multi-view depth from calibrated camera images
• pre-train depth models with Gaussian splatting on unlabeled data
• reconstruct 3D scenes as Gaussians in a feed-forward pass
• benchmark depth estimation on ScanNet, RealEstate10K, and DL3DV
• generate 3D Gaussian reconstructions without per-scene optimization

When to choose

• you need state-of-the-art feed-forward Gaussian splatting with depth-aware geometry
• you want fast novel view synthesis from sparse views without per-scene training
• you need scale-consistent multi-view depth predictions
• you are researching cross-task interactions between depth estimation and 3D reconstruction

When to avoid

• you need real-time rendering on consumer hardware without a GPU
• you want classical per-scene Gaussian splatting optimization like vanilla 3DGS
• you need a production-ready application rather than a research codebase
• you lack CUDA-capable hardware or familiarity with PyTorch research workflows

Facets

library · maturity active

machine-learning deep-learning image-processing graphics simulation computer-vision graphics machine-learning deep-learning python gaussian-splatting depth-estimation novel-view-synthesis multi-view-stereo monocular-depth feed-forward cvpr-2025 research gpu linux

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markdown · JSON · MCP: product_card(name="cvg/depthsplat")