Drive3R: Block-wise Streaming Geometric Foundation Models for Autonomous Driving

Key points

• While recent feed-forward geometric foundation models have revolutionized per-scene optimization, applying them to continuous, long-horizon driving sequences is computationally intractable due to GPU memory limits and the quadratic complexity of global attention.1
• To address these limitations, we introduce Drive3R, a block-wise streaming geometric foundation model.1
• Its block-wise causal attention resolves the scalability bottleneck of fully bidirectional attention while avoiding the loss of local future context in purely causal streaming.1
• Extensive experiments demonstrate that Drive3R establishes a new state-of-the-art on nuScenes, KITTI, and Waymo.1

Sources (2)

• [1]Drive3R: Block-wise Streaming Geometric Foundation Models for Autonomous Driving
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 06:35 PM

  High-fidelity closed-loop simulation for autonomous driving increasingly relies on dense multi-view 3D reconstruction.

  While recent feed-forward geometric foundation models have revolutionized per-scene optimization, applying them to continuous, long-horizon driving sequences is computationally intractable due to GPU memory limits and the quadratic complexity of global attention.

• [2]Drive3R: Block-wise Streaming Geometric Foundation Models for Autonomous Driving
  Hugging Face Daily Papers · Oct 10, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.