flexdepth

Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation

Overview

FlexDepth is a family of self-supervised monocular depth estimation models designed for robust driving perception. It introduces a Scale-Driven Decoder (SDD) with adaptive component selection, enabling a single architecture to span five model scales — from ultra-lightweight (1.5M params, 0.7 GFLOPs) to high-accuracy (32.3M params, 24.6 GFLOPs).

Key Features

• Five model scales: Nano (N), Small (S), Medium (M), Large (L), X-Large (X)
• Scale-Driven Decoder (SDD): Adaptive component selection based on model scale
• High-Efficiency Bottleneck (HEB): For small models (N, S) — maximizes efficiency
• High-Performance Bottleneck (HPB): For large models (M, L, X) — maximizes accuracy
• Dynamic upsampling: Sharper depth boundaries via learned upsampling
• Two-stage static-dynamic decoupled training: Handles dynamic scenes in driving scenarios
• YOLO11-based encoder: Leverages rich visual representations from YOLO segmentation pretraining

Model Zoo

KITTI-trained Models

Cityscapes-trained Models

Efficiency

Model Files

Each model consists of two weight files:

├── kitti/
│   ├── flex_n/
│   │   ├── encoder.pth    # YOLO11-based encoder weights
│   │   └── depth.pth      # Scale-Driven Decoder weights
│   ├── flex_s/
│   ├── flex_m/
│   ├── flex_l/
│   └── flex_x/
└── cs/
    ├── flex_n/
    ├── flex_s/
    ├── flex_m/
    ├── flex_l/
    └── flex_x/

Usage

Installation

conda create -n flexdepth python=3.10
conda activate flexdepth

pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

Download Weights

from huggingface_hub import snapshot_download

# Download all models
snapshot_download(repo_id="StarNew/flexdepth", local_dir="./models")

# Or download a specific model
from huggingface_hub import hf_hub_download

hf_hub_download(
    repo_id="StarNew/flexdepth",
    filename="kitti/flex_n/encoder.pth",
    local_dir="./models"
)
hf_hub_download(
    repo_id="StarNew/flexdepth",
    filename="kitti/flex_n/depth.pth",
    local_dir="./models"
)

Evaluation

# Flex-Nano on KITTI
python evaluate_depth.py --png --eval_mono --scale 4 \
    --encoder_model_type yolo11n-seg --decoder_model_type flexn \
    --load_weights_folder ./models/kitti/flex_n \
    --data_path <kitti_data_path> --split_path <splits_path>

# Flex-X-Large on KITTI
python evaluate_depth.py --png --eval_mono --scale 4 \
    --encoder_model_type yolo11x-seg --decoder_model_type flexx \
    --load_weights_folder ./models/kitti/flex_x \
    --data_path <kitti_data_path> --split_path <splits_path>

ONNX Export

python export_onnx.py --encoder_model_type yolo11n-seg --decoder_model_type flexn \
    --load_weights_folder ./models/kitti/flex_n --scales 4 --export_name flex-n

Comparison with Depth Anything V2

On the KITTI Eigen benchmark with dense ground truth and least-squares alignment:

FlexDepth achieves comparable or better accuracy than Depth Anything V2 with ~13× fewer parameters and ~78× fewer GFLOPs at similar resolution.

Citation

@InProceedings{flexdepth,
  author={Zhu, Zhaowen and Zhang, Li and Chen, Yujie and Zhang, Tian and Wang, Yingjie and Zhan, Mingxia},
  editor={Favaro, Paolo and Kukelova, Zuzana and Maki, Atsuto and Rohrbach, Anna and Schindler, Konrad and Tombari, Federico},
  title={Towards Robust Driving Perception: A Flexible Scale-Driven Family for Self-Supervised Monocular Depth Estimation},
  booktitle={Computer Vision -- ECCV 2026},
  year={2026},
  publisher={Springer Nature Switzerland},
  address={Cham},
  pages={110--129},
  isbn={978-3-032-36846-1},
  doi={10.1007/978-3-032-36846-1_7}
}

Acknowledgment

This work is supported by the National Natural Science Foundation of China under Grant 62332016.

Our code is built upon Monodepth2, Manydepth, and Ultralytics.