Paper | Project Page | Code | Model | Data
Upload one image of a single object (vehicle, pedestrian, cyclist, or other road object) and get back a complete 3D Gaussian splat asset ready for simulation.
upload ─▶ image guard (optional) ─▶ object segmentation ─▶ recenter + pad
│
▼
3D Gaussian splat ◀── TokenGS lifting ◀── multiview diffusion ◀── camera estimation
AH_object_seg_jit.pt) — Mask2Former JIT produces a binary mask of the foreground object at the uploaded image's native resolution.AH_camera_estimator.safetensors) — predicts camera pose, distance, FOV, and object dimensions (LWH). Shares the C-RADIO backbone with multiview diffusion to avoid loading it twice.AH_multiview_diffusion.safetensors) — SparseViewDiT generates 16 novel orbit views conditioned on the input image.AH_tokengs_lifting.safetensors) — feed-forward 3D Gaussian reconstructor lifts the 16 views to a full 3DGS asset.Single NVIDIA GPU with compute capability ≥ 8.0 and ≥ 30 GB VRAM. Typical end-to-end runtime: 1-2 minutes per image on A100/H100.
meta-llama/Llama-Guard-3-11B-Vision — enabling it adds ~20-30 s per run.docker build --build-arg HF_TOKEN=$HF_TOKEN -t asset-harvester .
docker run --gpus all -e HF_TOKEN=$HF_TOKEN -p 7860:7860 asset-harvester
Checkpoints are downloaded from nvidia/asset-harvester on first run. HF_TOKEN must have access to that repo.
Use of this system is governed by the NVIDIA Open Model License Agreement.
2 commits
Paper | Project Page | Code | Model | Data
Upload one image of a single object (vehicle, pedestrian, cyclist, or other road object) and get back a complete 3D Gaussian splat asset ready for simulation.
upload ─▶ image guard (optional) ─▶ object segmentation ─▶ recenter + pad
│
▼
3D Gaussian splat ◀── TokenGS lifting ◀── multiview diffusion ◀── camera estimation
AH_object_seg_jit.pt) — Mask2Former JIT produces a binary mask of the foreground object at the uploaded image's native resolution.AH_camera_estimator.safetensors) — predicts camera pose, distance, FOV, and object dimensions (LWH). Shares the C-RADIO backbone with multiview diffusion to avoid loading it twice.AH_multiview_diffusion.safetensors) — SparseViewDiT generates 16 novel orbit views conditioned on the input image.AH_tokengs_lifting.safetensors) — feed-forward 3D Gaussian reconstructor lifts the 16 views to a full 3DGS asset.Single NVIDIA GPU with compute capability ≥ 8.0 and ≥ 30 GB VRAM. Typical end-to-end runtime: 1-2 minutes per image on A100/H100.
meta-llama/Llama-Guard-3-11B-Vision — enabling it adds ~20-30 s per run.docker build --build-arg HF_TOKEN=$HF_TOKEN -t asset-harvester .
docker run --gpus all -e HF_TOKEN=$HF_TOKEN -p 7860:7860 asset-harvester
Checkpoints are downloaded from nvidia/asset-harvester on first run. HF_TOKEN must have access to that repo.
Use of this system is governed by the NVIDIA Open Model License Agreement.
2 commits