Following the introduction of VecSet, extensive work has been done to propose enhancements. This project is designed to incorporate these novel designs and to provide a unifed framework for VecSet-based representations.

• [2025-10-03] Released the inference script.
• [2025-04-09] Released the pretrained model point_vec1024x32_dim1024_depth24_sdf_nb and learnable_vec1024_dim1024_depth24_sdf.
• [2025-04-06] Released traing code and a pretrained model learnable_vec1024x32_dim1024_depth24_sdf_nb.

conda create -y -n vecset python=3.11 -y
pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124
conda install cuda-nvcc=12.4 -c nvidia -y
conda install libcusparse-dev -y
conda install libcublas-dev -y
conda install libcusolver-dev -y
conda install libcurand-dev -y # torch_cluster
pip install flash-attn --no-build-isolation
pip install torch-cluster -f https://data.pyg.org/whl/torch-2.6.0+cu124.html
pip install tensorboard
pip install einops
pip install trimesh
pip install tqdm
pip install PyMCubes

16 GPUs (4 GPUs with accum_iter 4)

cd vecset
torchrun \
    --nproc_per_node=4 \
    main_ae.py \
    --accum_iter=4  \
    --model learnable_vec1024x16_dim1024_depth24_nb \
    --output_dir output/ae/learnable_vec1024x16_dim1024_depth24_sdf_nb \
    --log_dir output/ae/learnable_vec1024x16_dim1024_depth24_sdf_nb \
    --num_workers 24 \
    --point_cloud_size 8192 \
    --batch_size 16 \
    --epochs 500 \
    --warmup_epochs 1 --blr 5e-5 --clip_grad 1

The base model design is from VecSet. I have incorporated the following features list:

• Faster training with Flash Attention
• Normalized Bottleneck (NBAE) from LaGeM. No need to tune the KL weight anymore!
• SDF regression instead of occupancy classification suggested by TripoSG. For now, I only use Eikonal regularization.

I am planning to incorporate the following features:

• Edge sampling from Dora-VAE
• Multiresolution training from CLAY
• Compact autoencoder from COD-VAE
• Quantized bottleneck (VQ).
• (Start an issue if you have any ideas!)

The following models will be released in this link:

• (Other models are training!)

If you want to test the autoencoder, make sure the input surface point cloud is normalized,

## surface: N x 3
shifts = (surface.max(axis=0) + surface.min(axis=0)) / 2
surface = surface - shifts
distances = np.linalg.norm(surface, axis=1)
scale = 1 / np.max(distances)
surface *= scale

Here is the inference script,

python infer.py --input input_point_cloud.ply --output output_mesh.obj

The available model definitions can be found in autoencoder.py. Note that the script assumes the input file is a point cloud instead of a mesh file.

• Removed layernorm on KV suggested by Youkang Kong
• Added layernorm before final output layer.
• Added zero initialization on the final output layer.
• Added random rotations as the data augmentations as in LaGeM.
• Adjusted code for latest version of PyTorch.