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Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion

Paper recorded by Signals 4 on 2026-10-09 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-09 on arXiv · recorded by Signals 4 on 2026-10-12

Category: cs.CV · 计算机视觉 · first seen 2026-10-12

Abstract

Accurate detection of drivable area is essential for autonomous driving and advanced driver assistance systems. While state-of-the-art methods focus on general traversability estimation, they prove inadequate for snow-covered roads where following existing wheel tracks offers crucial advantages: improved traction, predictable vehicle dynamics, and verified path stability. To address this, we propo

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#298 most recent of 756 cs.CV papers we have recorded · ↑ newer: Beyond the Snowflakes: Disentangling Weather Effects from Confounding · ↓ older: Make It Flow: Animating Water in 3D Gaussian Splatting Scenes
Cite this page: Tracking the Invisible: Self-Supervised Wheel Track Detection with Thermal-RGB Fusion: the #298 most recent of 756 cs.CV papers we have recorded (as of 2026-10-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/tracking-the-invisible-self-supervised-wheel-track-detection-with-thermal-rgb-fu.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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