mlsanityData to 7 Oct 2026

EffLoc: Lightweight Vision Transformer for Efficient 6-DOF Camera Relocalization

arXiv 2402.13537 ↗First posted 21 Feb 2024cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

Camera relocalization is pivotal in computer vision, with applications in AR, drones, robotics, and autonomous driving. It estimates 3D camera position and orientation (6-DoF) from images. Unlike traditional methods like SLAM, recent strides use deep learning for direct end-to-end pose estimation. We propose EffLoc, a novel efficient Vision Transformer for single-image camera relocalization. EffLoc's hierarchical layout, memory-bound self-attention, and feed-forward layers boost memory efficiency and inter-channel communication. Our introduced sequential group attention (SGA) module enhances computational efficiency by diversifying input features, reducing redundancy, and expanding model capacity. EffLoc excels in efficiency and accuracy, outperforming prior methods, such as AtLoc and MapNet. It thrives on large-scale outdoor car-driving scenario, ensuring simplicity, end-to-end trainability, and eliminating handcrafted loss functions.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 36 of its 46 references that are in the map. First posted on arXiv 21 Feb 2024.

Builds on

36 of 46 references.

References · 36

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24 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D Scenescited by 12
  2. 2025MVL-Loc: Leveraging Vision-Language Model for Generalizable Multi-Scene Camera Relocalizationcited by 1
  3. 2025ThermalLoc: A Vision Transformer-Based Approach for Robust Thermal Camera Relocalization in Large-Scale Environmentscited by 0