nanodrr

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A performance-oriented reimplementation of DiffDRR with the following improvements:

  • Optimized, pure PyTorch implementation (~5× faster than DiffDRR at baseline)
  • Fused Triton rendering kernel, used by default on CUDA (up to ~80× faster than DiffDRR)
  • Modular design (freely swap subjects, extrinsics, and intrinsics during rendering)
  • Compatibility with torch.compile and mixed precision
  • Extensive type hints with jaxtyping
  • Standard Python package structure managed with uv

All projective geometry is implemented internally using the standard Hartley and Zisserman pinhole camera formulation.

Installation

[!NOTE]

On pytorch<2.9, torch.compile with bfloat16 is slower than eager for the pure PyTorch backend due to a CUDA graph capture issue (see Benchmarks). The fused Triton backend is unaffected.

To strictly install the renderer:

pip install nanodrr

To install the optional plotting or 3D visualization module:

pip install "nanodrr[plot]"   # 2D visualization (matplotlib, opencv)
pip install "nanodrr[scene]"  # 3D visualization (VTK, PyVista)
pip install "nanodrr[all]"    # All extras

Benchmarks

[!IMPORTANT]

  • ~5× faster than DiffDRR with the pure PyTorch backend (1,093 FPS vs 223 FPS)
  • ~57× faster with the fused Triton kernel, without compilation (12,600 FPS vs 223 FPS)
  • ~80× faster with torch.compile and bfloat16 (17,820 FPS vs 223 FPS)
  • ~3.5× less memory than DiffDRR (322 MB vs 1,170 MB peak reserved with bfloat16 + compile)
Benchmarking runtime, FPS, and memory usage.

FPS computed from per-frame GPU kernel time (torch.profiler); median wall-clock timings are also recorded in tests/benchmark/benchmark.csv. Benchmarked by rendering 200×200 DRRs on an NVIDIA RTX 6000 Ada (48 GB) with Python 3.12. Compile represents torch.compile(mode="reduce-overhead", fullgraph=True). Full experiment at tests/benchmark/.

Docs

To test the docs locally, run

uv run --group docs jupyter nbconvert --to markdown tutorials/*.ipynb --output-dir docs/tutorials/
uv run --group docs zensical serve

Roadmap

  • Implement a fully optimized renderer
  • Port strictly necessary modules from DiffDRR (e.g., SE(3) utilities, loss functions, and 2D plotting)
  • Migrate 3D plotting functions to an optional module
  • Integrate with xvr to speed up network training and registration
  • Integrate with polypose to speed up registration
  • Release as v1.0.0 of DiffDRR!

Key dates

PyPI data
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  • Released:
Latest release

1 maintainer

PyPI data
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Avatar for vgopala4 from gravatar.com vgopala4

Requires

Python >=3.10

Provides Extra

all plot scene

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