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DXPR: Depth-Based Vision-LiDAR Cross-Modal Place Recognition Using Vision Foundation Models

Paper recorded by Signals 4 on 2026-09-08 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-08 on arXiv · recorded by Signals 4 on 2026-09-09

Category: cs.CV · 计算机视觉 · first seen 2026-09-09

Abstract

We present DXPR, a depth-based cross-modal place recognition (CMPR) framework that uses vision foundation models (VFMs) to match monocular camera queries against a LiDAR map without modality-specific encoders. This enables robots and autonomous vehicles to robustly localize using only cameras within pre-built LiDAR maps, even under severe seasonal, weather, and illumination changes. The key idea i

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#120 most recent of 237 cs.CV papers we have recorded · ↑ newer: A Joint 2D-3D Statistical Shape Model for Orthopedic Reconstruction · ↓ older: Concentrate After Imagination: Text-Conditioned Evidence Grounding for
Cite this page: DXPR: Depth-Based Vision-LiDAR Cross-Modal Place Recognition Using Vision Foundation Models: the #120 most recent of 237 cs.CV papers we have recorded (as of 2026-09-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/dxpr-depth-based-vision-lidar-cross-modal-place-recognition-using-vision-foundat.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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