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project_baldur
ilessio-aiflowlab/project_baldur
project_baldur is a depth estimation model from ilessio-aiflowlab. Use it for the depth estimation task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as apache-2.0.
Part of the ANIMA Perception Suite by Robot Flow Labs.
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Public
Updated Apr 2, 2026
Repo size
487 MB
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.pth254 MB · 52%
At a glance
- Task
- Depth Estimation
- Library
- pytorch
- License
- apache-2.0
- Access
- Public
- Created
- Mar 28, 2026
- Updated
- Apr 2, 2026
- SHA
- a2a8f103
Model card
From the Hugging Face model README
BALDUR — Dynamic Visual SLAM
Part of the ANIMA Perception Suite by Robot Flow Labs.
Model Description
BALDUR enhances monocular visual SLAM for dynamic environments (crowds, traffic, moving robots). It combines a feed-forward depth + dynamic mask prediction network with patch-based bundle adjustment.
- Paper: Dynamic Visual SLAM using a General 3D Prior (Dec 2025, arXiv:2512.06868)
- Architecture: ResNet-18 encoder → dual U-Net decoders (depth + dynamic mask)
- Parameters: 15.9M
- Input: RGB image [B, 3, 480, 640]
- Output: Depth map [B, 1, 480, 640] (metres) + Dynamic mask [B, 1, 480, 640] (probability)
Training
| Setting | Value |
|---|---|
| Dataset | TUM-RGBD Dynamic (3,784 frames, 4 sequences) |
| Split | 90/5/5 (train/val/test) |
| Best val_loss | 0.2680 (epoch 183) |
| Optimizer | AdamW (lr=0.0003, wd=0.05) |
| Scheduler | CosineAnnealingWarmRestarts (T_0=20) |
| Regularization | Dropout 0.3, heavy augmentation |
| Precision | bf16 mixed precision |
| Hardware | NVIDIA L4 (23GB) |
| Training time | ~5.3 hours |
| Date | 2026-04-02 |
Exported Formats
| Format | File | Use Case |
|---|---|---|
| PyTorch (.pth) | pytorch/baldur_v1.pth | Training, fine-tuning |
| SafeTensors | pytorch/baldur_v1.safetensors | Fast loading, safe |
| ONNX | onnx/baldur_v1.onnx | Cross-platform inference |
| TensorRT FP32 | tensorrt/baldur_v1_fp32.trt | Full precision inference |
| TensorRT FP16 | tensorrt/baldur_v1_fp16.trt | Edge deployment (Jetson/L4) |
Usage
import torch
from anima_baldur.models.feed_forward import FeedForwardNetwork
model = FeedForwardNetwork(pretrained=False, max_depth=10.0)
model.load_state_dict(torch.load("pytorch/baldur_v1.pth"))
model.eval()
# Inference
rgb = torch.randn(1, 3, 480, 640) # ImageNet-normalized
depth, dynamic_mask = model(rgb)
# depth: metres, dynamic_mask: 0=static, 1=dynamic
Dynamic Mask
The dynamic mask identifies moving objects (people, vehicles) in the scene.
Trained self-supervised: the model learns to mask regions where depth prediction is unreliable.
Use dynamic_mask > 0.5 to filter dynamic regions from SLAM mapping.
License
Apache 2.0 — Robot Flow Labs / AIFLOW LABS LIMITED
Citation
@article{baldur2025,
title={Dynamic Visual SLAM using a General 3D Prior},
author={Zhong, Xingguang and Jin, Liren and Popovi\'c, Marija and Behley, Jens and Stachniss, Cyrill},
year={2025},
journal={arXiv:2512.06868}
}
- Task
- Depth Estimation
- Library
- pytorch
- License
- apache-2.0
- Created
- Mar 28, 2026
- Updated
- Apr 2, 2026