diff --git a/.github/workflows/doc.yml b/.github/workflows/doc.yml index d2637429ca..3974bd4cac 100644 --- a/.github/workflows/doc.yml +++ b/.github/workflows/doc.yml @@ -15,7 +15,7 @@ jobs: - uses: actions/checkout@v3 - uses: actions/setup-python@v3 with: - python-version: '3.9' + python-version: '3.10' - name: Install dependencies run: | pip install uv diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index b75984535a..e9039a2aa6 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -16,7 +16,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v1 with: - python-version: '3.8' + python-version: '3.10' - name: Install dependencies run: | python -m pip install build twine diff --git a/Dockerfile b/Dockerfile index 6cd4e058f0..8ed113d9eb 100644 --- a/Dockerfile +++ b/Dockerfile @@ -85,7 +85,7 @@ RUN pip install --no-cache-dir --upgrade pip 'setuptools<70.0.0' && \ pip install --no-cache-dir torch==2.1.2+cu118 torchvision==0.16.2+cu118 'numpy<2.0.0' --extra-index-url https://download.pytorch.org/whl/cu118 && \ git clone --branch master --recursive https://github.com/cvg/Hierarchical-Localization.git /opt/hloc && \ cd /opt/hloc && git checkout v1.4 && python3.10 -m pip install --no-cache-dir . && cd ~ && \ - TCNN_CUDA_ARCHITECTURES="${CUDA_ARCHITECTURES}" pip install --no-cache-dir "git+https://github.com/NVlabs/tiny-cuda-nn.git@b3473c81396fe927293bdfd5a6be32df8769927c#subdirectory=bindings/torch" && \ + TCNN_CUDA_ARCHITECTURES="${CUDA_ARCHITECTURES}" pip install --no-cache-dir --no-build-isolation "git+https://github.com/NVlabs/tiny-cuda-nn.git@b3473c81396fe927293bdfd5a6be32df8769927c#subdirectory=bindings/torch" && \ pip install --no-cache-dir pycolmap==0.6.1 pyceres==2.1 omegaconf==2.3.0 # Install gsplat and nerfstudio. @@ -98,7 +98,7 @@ COPY --from=source /tmp/nerfstudio/ /tmp/nerfstudio RUN export TORCH_CUDA_ARCH_LIST="$(echo "$CUDA_ARCHITECTURES" | tr ';' '\n' | awk '$0 > 70 {print substr($0,1,1)"."substr($0,2)}' | tr '\n' ' ' | sed 's/ $//')" && \ export MAX_JOBS=4 && \ GSPLAT_VERSION="$(sed -n 's/.*gsplat==\s*\([^," '"'"']*\).*/\1/p' /tmp/nerfstudio/pyproject.toml)" && \ - pip install --no-cache-dir git+https://github.com/nerfstudio-project/gsplat.git@v${GSPLAT_VERSION} && \ + pip install --no-cache-dir --no-build-isolation git+https://github.com/nerfstudio-project/gsplat.git@v${GSPLAT_VERSION} && \ pip install --no-cache-dir /tmp/nerfstudio 'numpy<2.0.0' && \ rm -rf /tmp/nerfstudio diff --git a/Dockerfile.blackwell b/Dockerfile.blackwell new file mode 100644 index 0000000000..2443209569 --- /dev/null +++ b/Dockerfile.blackwell @@ -0,0 +1,181 @@ +# syntax=docker/dockerfile:1 +# Blackwell (sm_120, RTX 50-series) variant of the nerfstudio Docker image. +# +# Differences from the stock Dockerfile: +# - CUDA 12.8 base images (sm_120 requires CUDA >= 12.8) +# - PyTorch 2.8 with cu128 wheels (ships sm_120 kernels) +# - COLMAP 3.12.6 / GLOMAP 1.2.0 (CUDA 12.8-compatible releases) +# - tiny-cuda-nn pinned to a Blackwell-capable master commit +# - CUDA_ARCHITECTURES defaults to "120" +# - nerfacc's JIT CUDA kernels are pre-compiled during the build (the +# runtime stage has no nvcc, so they could not be built on first use) +ARG UBUNTU_VERSION=22.04 +ARG NVIDIA_CUDA_VERSION=12.8.1 +# CUDA architectures, required by Colmap and tiny-cuda-nn. Use >= 8.0 for faster TCNN. +ARG CUDA_ARCHITECTURES="120" +ARG NERFSTUDIO_VERSION="" + +# Pull source either provided or from git. +FROM scratch as source_copy +ONBUILD COPY . /tmp/nerfstudio +FROM alpine/git as source_no_copy +ARG NERFSTUDIO_VERSION +ONBUILD RUN git clone --branch ${NERFSTUDIO_VERSION} --recursive https://github.com/nerfstudio-project/nerfstudio.git /tmp/nerfstudio +ARG NERFSTUDIO_VERSION +FROM source_${NERFSTUDIO_VERSION:+no_}copy as source + +FROM nvidia/cuda:${NVIDIA_CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} as builder +ARG CUDA_ARCHITECTURES +ARG NVIDIA_CUDA_VERSION +ARG UBUNTU_VERSION + +ENV DEBIAN_FRONTEND=noninteractive +ENV QT_XCB_GL_INTEGRATION=xcb_egl +# Work around apt _apt sandbox verification failing under docker's seccomp +# profile on newer kernels (spurious "invalid signature" GPG errors). +RUN echo 'APT::Sandbox::User "root";' > /etc/apt/apt.conf.d/99docker-sandbox && \ + apt-get update && \ + apt-get install -y --no-install-recommends --no-install-suggests \ + git \ + wget \ + ninja-build \ + build-essential \ + libboost-program-options-dev \ + libboost-filesystem-dev \ + libboost-graph-dev \ + libboost-system-dev \ + libeigen3-dev \ + libflann-dev \ + libfreeimage-dev \ + libmetis-dev \ + libgoogle-glog-dev \ + libgtest-dev \ + libsqlite3-dev \ + libglew-dev \ + qtbase5-dev \ + libqt5opengl5-dev \ + libcgal-dev \ + libceres-dev \ + python3.10-dev \ + python3-pip + +# Build and install CMake +RUN wget https://github.com/Kitware/CMake/releases/download/v3.31.3/cmake-3.31.3-linux-x86_64.sh \ + -q -O /tmp/cmake-install.sh \ + && chmod u+x /tmp/cmake-install.sh \ + && mkdir /opt/cmake-3.31.3 \ + && /tmp/cmake-install.sh --skip-license --prefix=/opt/cmake-3.31.3 \ + && rm /tmp/cmake-install.sh \ + && ln -s /opt/cmake-3.31.3/bin/* /usr/local/bin + +# Build and install GLOMAP. +RUN git clone https://github.com/colmap/glomap.git && \ + cd glomap && \ + git checkout "1.2.0" && \ + mkdir build && \ + cd build && \ + mkdir -p /build && \ + cmake .. -GNinja "-DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHITECTURES}" \ + -DCMAKE_INSTALL_PREFIX=/build/glomap && \ + ninja install -j4 && \ + cd ~ + +# Build and install COLMAP. +RUN git clone https://github.com/colmap/colmap.git && \ + cd colmap && \ + git checkout "3.12.6" && \ + mkdir build && \ + cd build && \ + mkdir -p /build && \ + cmake .. -GNinja "-DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHITECTURES}" \ + -DCMAKE_INSTALL_PREFIX=/build/colmap && \ + ninja install -j4 && \ + cd ~ + +# Upgrade pip and install dependencies (torch with sm_120 kernels via cu128 wheels). +RUN pip install --no-cache-dir --upgrade pip 'setuptools<70.0.0' && \ + pip install --no-cache-dir torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128 && \ + git clone --branch master --recursive https://github.com/cvg/Hierarchical-Localization.git /opt/hloc && \ + cd /opt/hloc && git checkout v1.4 && python3.10 -m pip install --no-cache-dir . && cd ~ && \ + TCNN_CUDA_ARCHITECTURES="${CUDA_ARCHITECTURES}" pip install --no-cache-dir --no-build-isolation "git+https://github.com/NVlabs/tiny-cuda-nn.git@749dd70c5afc5a9dadb85e5652ed65d55e0ba187#subdirectory=bindings/torch" && \ + pip install --no-cache-dir pycolmap==0.6.1 pyceres==2.1 omegaconf==2.3.0 + +# Install gsplat and nerfstudio. +# NOTE: both are installed jointly in order to prevent docker cache with latest +# gsplat version (we do not expliticly specify the commit hash). +# +# We set MAX_JOBS to reduce resource usage for GH actions: +# - https://github.com/nerfstudio-project/gsplat/blob/db444b904976d6e01e79b736dd89a1070b0ee1d0/setup.py#L13-L23 +COPY --from=source /tmp/nerfstudio/ /tmp/nerfstudio +RUN export TORCH_CUDA_ARCH_LIST="$(echo "$CUDA_ARCHITECTURES" | tr ';' '\n' | awk '{if ($0 > 99) print substr($0,1,2)"."substr($0,3); else if ($0 > 70) print substr($0,1,1)"."substr($0,2)}' | tr '\n' ' ' | sed 's/ $//')" && \ + export MAX_JOBS=4 && \ + GSPLAT_VERSION="$(sed -n 's/.*gsplat==\s*\([^," '"'"']*\).*/\1/p' /tmp/nerfstudio/pyproject.toml)" && \ + pip install --no-cache-dir --no-build-isolation git+https://github.com/nerfstudio-project/gsplat.git@v${GSPLAT_VERSION} && \ + pip install --no-cache-dir /tmp/nerfstudio 'numpy<2.0.0' && \ + rm -rf /tmp/nerfstudio + +# Pre-compile nerfacc's JIT CUDA kernels. nerfacc compiles its kernels on first +# use, but the runtime stage has no nvcc, so build them now and copy the torch +# extension cache into the runtime image. The build container has no GPU, so +# use the CUDA driver stub to satisfy the import after compilation. +RUN export TORCH_CUDA_ARCH_LIST="$(echo "$CUDA_ARCHITECTURES" | tr ';' '\n' | awk '{if ($0 > 99) print substr($0,1,2)"."substr($0,3); else if ($0 > 70) print substr($0,1,1)"."substr($0,2)}' | tr '\n' ' ' | sed 's/ $//')" && \ + export MAX_JOBS=4 && \ + ln -sf /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 && \ + LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs:${LD_LIBRARY_PATH} \ + python3.10 -c "from nerfacc.cuda._backend import _C; print('nerfacc CUDA kernels pre-compiled')" && \ + find /root/.cache/torch_extensions -name "nerfacc_cuda.so" | grep . + +# Fix permissions +RUN chmod -R go=u /usr/local/lib/python3.10 && \ + chmod -R go=u /build + +# +# Docker runtime stage. +# +FROM nvidia/cuda:${NVIDIA_CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} as runtime +ARG CUDA_ARCHITECTURES +ARG NVIDIA_CUDA_VERSION +ARG UBUNTU_VERSION + +LABEL org.opencontainers.image.source = "https://github.com/nerfstudio-project/nerfstudio" +LABEL org.opencontainers.image.licenses = "Apache License 2.0" +LABEL org.opencontainers.image.base.name="docker.io/library/nvidia/cuda:${NVIDIA_CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}" +LABEL org.opencontainers.image.documentation = "https://docs.nerf.studio/" + +# Minimal dependencies to run COLMAP binary compiled in the builder stage. +# Note: this reduces the size of the final image considerably, since all the +# build dependencies are not needed. +RUN echo 'APT::Sandbox::User "root";' > /etc/apt/apt.conf.d/99docker-sandbox && \ + apt-get update && \ + apt-get install -y --no-install-recommends --no-install-suggests \ + libboost-filesystem1.74.0 \ + libboost-program-options1.74.0 \ + libc6 \ + libceres2 \ + libfreeimage3 \ + libgcc-s1 \ + libgl1 \ + libglew2.2 \ + libgoogle-glog0v5 \ + libqt5core5a \ + libqt5gui5 \ + libqt5widgets5 \ + python3.10 \ + python3.10-dev \ + build-essential \ + python-is-python3 \ + ffmpeg + +# Copy packages from builder stage. +COPY --from=builder /build/colmap/ /usr/local/ +COPY --from=builder /build/glomap/ /usr/local/ +COPY --from=builder /usr/local/lib/python3.10/dist-packages/ /usr/local/lib/python3.10/dist-packages/ +COPY --from=builder /usr/local/bin/ns* /usr/local/bin/ +# Pre-compiled nerfacc JIT kernels (see builder stage). +COPY --from=builder /root/.cache/torch_extensions /root/.cache/torch_extensions + +# Install nerfstudio cli auto completion +RUN /bin/bash -c 'ns-install-cli --mode install' + +# Bash as default entrypoint. +CMD /bin/bash -l diff --git a/docs/quickstart/installation.md b/docs/quickstart/installation.md index de79064213..22bb7403d6 100644 --- a/docs/quickstart/installation.md +++ b/docs/quickstart/installation.md @@ -5,7 +5,7 @@ ::::::{tab-set} :::::{tab-item} Linux -Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding. +Nerfstudio requires `python >= 3.10`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding. ::::: :::::{tab-item} Windows @@ -51,7 +51,7 @@ For example: When updating, or if you close your terminal before you finish the installation and run your first `splatfacto`, you have to re-do this environment activation step. ::: -Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding. +Nerfstudio requires `python >= 3.10`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding. ::::: :::::: @@ -59,7 +59,7 @@ Nerfstudio requires `python >= 3.8`. We recommend using conda to manage dependen ## Create environment ```bash -conda create --name nerfstudio -y python=3.8 +conda create --name nerfstudio -y python=3.10 conda activate nerfstudio python -m pip install --upgrade pip @@ -79,6 +79,28 @@ pip uninstall torch torchvision functorch tinycudann ``` ::::{tab-set} +:::{tab-item} Torch 2.8+ with CUDA 12.8 (Blackwell / RTX 50-series) + +Required for NVIDIA Blackwell GPUs (RTX 50-series, compute capability sm_120), which need CUDA 12.8 or newer: + +```bash +pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128 +``` + +To build the necessary CUDA extensions, `cuda-toolkit` 12.8 is also required. We +recommend installing with conda: + +```bash +conda install -c "nvidia/label/cuda-12.8.0" cuda-toolkit +``` + +When building CUDA extensions (gsplat, tiny-cuda-nn, ...) for a Blackwell GPU, make sure the architecture list includes sm_120: + +```bash +export TORCH_CUDA_ARCH_LIST="12.0+PTX" +``` + +::: :::{tab-item} Torch 2.1.2 with CUDA 11.8 (recommended) Install PyTorch 2.1.2 with CUDA 11.8: @@ -245,6 +267,14 @@ docker build \ --file Dockerfile . ``` +For NVIDIA Blackwell GPUs (RTX 50-series, compute capability sm_120), use the CUDA 12.8-based +`Dockerfile.blackwell` variant instead — the stock image targets CUDA 11.8, which does not +support sm_120: + +```bash +docker build --tag nerfstudio-blackwell -f Dockerfile.blackwell . +``` + ### Using an interactive container The docker container can be launched with an interactive terminal where nerfstudio commands can be entered as usual. Some parameters are required and some are strongly recommended for usage as following: diff --git a/nerfstudio/data/utils/colmap_parsing_utils.py b/nerfstudio/data/utils/colmap_parsing_utils.py index 0eeaf6911b..8eabee9caf 100644 --- a/nerfstudio/data/utils/colmap_parsing_utils.py +++ b/nerfstudio/data/utils/colmap_parsing_utils.py @@ -95,6 +95,8 @@ def write_next_bytes(fid, data, format_char_sequence, endian_character="<"): """ if isinstance(data, (list, tuple)): bytes = struct.pack(endian_character + format_char_sequence, *data) + elif isinstance(data, np.ndarray): + bytes = struct.pack(endian_character + format_char_sequence, *data.tolist()) else: bytes = struct.pack(endian_character + format_char_sequence, data) fid.write(bytes) diff --git a/nerfstudio/data/utils/data_utils.py b/nerfstudio/data/utils/data_utils.py index d1a6721844..109c848cad 100644 --- a/nerfstudio/data/utils/data_utils.py +++ b/nerfstudio/data/utils/data_utils.py @@ -36,24 +36,7 @@ def pil_to_numpy(im: PILImage) -> np.ndarray: # Load in image completely (PIL defaults to lazy loading) im.load() - # Unpack data - e = Image._getencoder(im.mode, "raw", im.mode) - e.setimage(im.im) - - # NumPy buffer for the result - shape, typestr = Image._conv_type_shape(im) - data = np.empty(shape, dtype=np.dtype(typestr)) - mem = data.data.cast("B", (data.data.nbytes,)) - - bufsize, s, offset = 65536, 0, 0 - while not s: - _, s, d = e.encode(bufsize) - mem[offset : offset + len(d)] = d - offset += len(d) - if s < 0: - raise RuntimeError("encoder error %d in tobytes" % s) - - return data + return np.asarray(im) def get_image_mask_tensor_from_path(filepath: Union[Path, IO[bytes]], scale_factor: float = 1.0) -> torch.Tensor: diff --git a/nerfstudio/engine/trainer.py b/nerfstudio/engine/trainer.py index a653e1de8d..7f2f0d34d0 100644 --- a/nerfstudio/engine/trainer.py +++ b/nerfstudio/engine/trainer.py @@ -429,7 +429,7 @@ def _load_checkpoint(self) -> None: load_step = sorted(int(x[x.find("-") + 1 : x.find(".")]) for x in os.listdir(load_dir))[-1] load_path: Path = load_dir / f"step-{load_step:09d}.ckpt" assert load_path.exists(), f"Checkpoint {load_path} does not exist" - loaded_state = torch.load(load_path, map_location="cpu") + loaded_state = torch.load(load_path, map_location="cpu", weights_only=False) self._start_step = loaded_state["step"] + 1 # load the checkpoints for pipeline, optimizers, and gradient scalar self.pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"]) @@ -440,7 +440,7 @@ def _load_checkpoint(self) -> None: CONSOLE.print(f"Done loading Nerfstudio checkpoint from {load_path}") elif load_checkpoint is not None: assert load_checkpoint.exists(), f"Checkpoint {load_checkpoint} does not exist" - loaded_state = torch.load(load_checkpoint, map_location="cpu") + loaded_state = torch.load(load_checkpoint, map_location="cpu", weights_only=False) self._start_step = loaded_state["step"] + 1 # load the checkpoints for pipeline, optimizers, and gradient scalar self.pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"]) diff --git a/nerfstudio/field_components/activations.py b/nerfstudio/field_components/activations.py index e413965e24..29afaf1f03 100644 --- a/nerfstudio/field_components/activations.py +++ b/nerfstudio/field_components/activations.py @@ -22,7 +22,18 @@ from jaxtyping import Float from torch import Tensor from torch.autograd import Function -from torch.cuda.amp import custom_bwd, custom_fwd + +try: + # torch >= 2.4: custom_fwd/custom_bwd live in torch.amp and require device_type + from torch.amp import custom_bwd as _custom_bwd, custom_fwd as _custom_fwd + + def custom_fwd(*args, **kwargs): + return _custom_fwd(*args, device_type="cuda", **kwargs) + + custom_bwd = _custom_bwd(device_type="cuda") +except ImportError: + # torch < 2.4: custom_fwd/custom_bwd live in torch.cuda.amp (no device_type argument) + from torch.cuda.amp import custom_bwd, custom_fwd # type: ignore class _TruncExp(Function): diff --git a/nerfstudio/scripts/downloads/download_data.py b/nerfstudio/scripts/downloads/download_data.py index a2c9d27584..653fbf94d0 100644 --- a/nerfstudio/scripts/downloads/download_data.py +++ b/nerfstudio/scripts/downloads/download_data.py @@ -514,7 +514,7 @@ def download(self, save_dir: Path) -> None: split_filepaths = [] for image_path, new_image_path in copied_images.items(): metadata_path = image_path.parent.parent / "metadata" / f"{image_path.stem}.pt" - metadata = torch.load(metadata_path, map_location="cpu") + metadata = torch.load(metadata_path, map_location="cpu", weights_only=False) c2w = torch.eye(4) c2w[:3] = metadata["c2w"] file_path = str(Path("images") / f"{new_image_path.name}") diff --git a/nerfstudio/scripts/render.py b/nerfstudio/scripts/render.py index fc035c4a9f..4f25301d78 100644 --- a/nerfstudio/scripts/render.py +++ b/nerfstudio/scripts/render.py @@ -186,9 +186,10 @@ def _render_trajectory_video( max_idx = true_max_idx if crop_data is not None: - with renderers.background_color_override_context( - crop_data.background_color.to(pipeline.device) - ), torch.no_grad(): + with ( + renderers.background_color_override_context(crop_data.background_color.to(pipeline.device)), + torch.no_grad(), + ): outputs = pipeline.model.get_outputs_for_camera( cameras[camera_idx : camera_idx + 1], obb_box=obb_box ) diff --git a/nerfstudio/utils/eval_utils.py b/nerfstudio/utils/eval_utils.py index 11a8b23416..8d07fe842a 100644 --- a/nerfstudio/utils/eval_utils.py +++ b/nerfstudio/utils/eval_utils.py @@ -59,7 +59,7 @@ def eval_load_checkpoint(config: TrainerConfig, pipeline: Pipeline) -> Tuple[Pat load_step = config.load_step load_path = config.load_dir / f"step-{load_step:09d}.ckpt" assert load_path.exists(), f"Checkpoint {load_path} does not exist" - loaded_state = torch.load(load_path, map_location="cpu") + loaded_state = torch.load(load_path, map_location="cpu", weights_only=False) pipeline.load_pipeline(loaded_state["pipeline"], loaded_state["step"]) CONSOLE.print(f":white_check_mark: Done loading checkpoint from {load_path}") return load_path, load_step diff --git a/nerfstudio/viewer/render_state_machine.py b/nerfstudio/viewer/render_state_machine.py index ced692d453..33b2e5ac5f 100644 --- a/nerfstudio/viewer/render_state_machine.py +++ b/nerfstudio/viewer/render_state_machine.py @@ -160,9 +160,11 @@ def _render_img(self, camera_state: CameraState): [color[0] / 255.0, color[1] / 255.0, color[2] / 255.0], device=self.viewer.get_model().device, ) - with background_color_override_context( - background_color - ), torch.no_grad(), viewer_utils.SetTrace(self.check_interrupt): + with ( + background_color_override_context(background_color), + torch.no_grad(), + viewer_utils.SetTrace(self.check_interrupt), + ): outputs = self.viewer.get_model().get_outputs_for_camera(camera, obb_box=obb) else: with torch.no_grad(), viewer_utils.SetTrace(self.check_interrupt): diff --git a/pyproject.toml b/pyproject.toml index 585eee4f9c..ed0a9ae6f1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,7 +8,7 @@ version = "1.1.5" description = "All-in-one repository for state-of-the-art NeRFs" readme = "README.md" license = { text="Apache 2.0"} -requires-python = ">=3.8.0" +requires-python = ">=3.10" classifiers = [ "Development Status :: 3 - Alpha", "Programming Language :: Python", @@ -31,7 +31,7 @@ dependencies = [ "mediapy>=1.1.0", "msgpack>=1.0.4", "msgpack_numpy>=0.4.8", - "nerfacc==0.5.2", + "nerfacc>=0.5.3", "open3d>=0.16.0", "opencv-python-headless==4.10.0.84", "Pillow>=10.3.0", @@ -64,7 +64,7 @@ dependencies = [ "xatlas", "trimesh>=3.20.2", "timm==0.6.7", - "gsplat==1.4.0", + "gsplat==1.5.3", "pytorch-msssim", "pathos", "packaging",