MIM4D: Masked Multi-View Video Modeling for Autonomous Driving Representation Learning

Why Autonomous Driving Needs Better Visual Representation Learning?

In autonomous driving systems, multi-view video data captured by cameras forms the backbone of environmental perception. However, current approaches face two critical challenges:

1. Dependency on Expensive 3D Annotations: Traditional supervised learning requires massive labeled 3D datasets, limiting scalability.
2. Ignored Temporal Dynamics: Single-frame or monocular methods fail to capture motion patterns in dynamic scenes.

MIM4D (Masked Modeling with Multi-View Video for Autonomous Driving) introduces an innovative solution. Through dual-path masked modeling (spatial + temporal) and 3D volumetric rendering, it learns robust geometric representations using only unlabeled multi-view videos. Experiments demonstrate significant improvements in BEV segmentation, 3D object detection, and other tasks on the nuScenes dataset.

Core Design Principles of MIM4D

1. Dual Masked Modeling Architecture

MIM4D consists of three key modules:

2. Temporal Modeling Innovations

• Short/Long-Term Feature Synergy
  Short-term branch focuses on local motion (e.g., t-1 and t+1 frames) via deformable attention.
  Long-term branch analyzes global scene flow (e.g., 5-frame window) with dimension reduction for efficiency.

• Height-Channel Transformation
  Compresses 3D voxels (C×Z×H×W) into BEV features (C’×H×W) for query-based temporal modeling, then restores 3D structures via inverse transformation.

3. Self-Supervised Training Strategy

• Depth-Aware Sampling: Supervises only LiDAR-projected pixel regions to reduce background noise.
• Hybrid Loss Function:
  [
  Loss = \lambda_{RGB} \cdot \text{Color Error} + \lambda_{Depth} \cdot \text{Depth Error}
  ]
  Optimal convergence achieved at (\lambda_{RGB}=10, \lambda_{Depth}=10).

Performance Validation: Benchmark Comparisons

1. Pre-Training Effectiveness

Using ConvNeXt-S backbone on nuScenes validation set:

Key Findings:

• 9.2% mAP improvement over ImageNet baseline
• SOTA in unsupervised methods, surpassing UniPAD by 0.8% mAP

2. Downstream Task Performance

BEV Segmentation

(Setting 1: 100m×50m@25cm; Setting 2: 100m×100m@50cm)

3D Object Detection

Technical Breakthroughs

Innovation 1: 4D Spatiotemporal Modeling

Extends MAE to temporal dimensions via continuous scene flow. Expanding the time window from 1 to 5 frames boosts detection accuracy by 8.8%:

Innovation 2: Geometry-Aware Rendering

Implements neural implicit surface reconstruction:

1. Samples 96 points per ray with bicubic interpolation for 3D features.
2. Predicts SDF and RGB via MLP networks.
3. Volumetric rendering formulas:
   [
   \hat{C}_i = \sum T_j\alpha_j c_j, \quad \hat{D}_i = \sum T_j\alpha_j t_j
   ]
   This enables precise geometry learning without 3D labels.

FAQ: 6 Key Questions for Developers

Q1: How does MIM4D handle dynamic scene modeling?

A: Dual-branch Transformer architecture:

• Short-term branch tracks instant motions (vehicles, pedestrians).
• Long-term branch models global patterns (traffic light cycles, road topology).

Q2: Advantages over NeRF-based methods?

A: Unlike NeRF for novel view synthesis, MIM4D:

1. Incorporates temporal dynamics.
2. Uses SDF for sharper surface reconstruction.
3. Enhances generalization via masked learning.

Q3: Computational requirements for deployment?

Paper configuration:

• Input resolution: 800×450 (optimized for RTX 3090 VRAM).
• Training: 12 epochs (AdamW, lr=2e-4).
• Voxel dimensions: 128×128×5 (channels×height×resolution).

Q4: Real-time inference capability?

At nuScenes’ 2Hz frame rate:

• Voxel encoder uses sparse convolutions for acceleration.
• Neural rendering: 512 rays/view, 96 samples/ray.
  Meets real-time requirements (specific FPS pending official release).

Q5: How to reproduce results?

Key steps:

1. Data: nuScenes dataset (700 training + 150 validation scenes).
2. Masking strategy:

   • Depth-aware sampling (512 rays/image).
   • Local masking (16×16 patches).
   • Global masking (30% ratio, 32×32 blocks).
3. Code: https://github.com/hustvl/MIM4D

Conclusion: A New Paradigm for Autonomous Driving Pre-Training

MIM4D achieves three major advancements through spatiotemporal masked modeling and geometry-aware rendering:

1. Lower Annotation Costs: Fully self-supervised using multi-view videos.
2. Enhanced Motion Perception: 5-frame windows improve moving object detection by 8.8%.
3. Multi-Task Versatility: Outperforms SOTA methods in BEV segmentation, HD map construction, etc.

This work demonstrates that extending 2D vision pre-training to 4D spatiotemporal domains is crucial for advancing autonomous perception. With evolving multimodal fusion techniques, MIM4D paves the way for more robust L4 autonomous driving systems.