13 accepted papers
Precise drone rephotography technology aims to recover camera poses from a reference sequence and obtain well-aligned image sequences, playing a crucial role in autonomous drone inspection tasks. However, existing rephotography methods rely on static image inputs, resulting in low efficiency and lim
We present See, Point, Fly (SPF), a training-free aerial vision-and-language navigation (AVLN) framework built atop vision-language models (VLMs). SPF is capable of navigating to any goal based on any type of free-form instructions in any kind of environment. In contrast to existing VLM-based approa…
This paper presents CollabLoc, a novel approach for real-time multi-user visual localization. Typically, localization systems employ a client-server design for locating cameras. In these systems, lightweight simultaneous localization and mapping computations are performed on the client side, while t…
LiDAR-camera fusion plays a pivotal role in 3D reconstruction for self-driving applications. A fundamental prerequisite for effective fusion is the precise calibration between LiDAR and camera systems. Many existing calibration methods are constrained by predefined mis-calibration ranges in the trai…
This paper proposes a precise drone rephotography system for fixed-domain patrolling scenarios. The proposed system integrates computer-vision-based localization and fine-tuned pixel-level dense flow prediction to achieve consistent and precise rephotography images with viewpoints that closely align…
Recently, many research studies have been carried out on using deep learning methods for 3D point cloud understanding. However, there is still no remarkable result on 3D point cloud semantic segmentation compared to those of 2D research. One important reason is that 3D data has higher dimensionality…
Correspondence selection, a crucial step in many computer vision tasks, aims to distinguish between inliers and outliers from putative correspondences. The coherence of correspondences is often used for predicting inlier probability, but it is difficult for neural networks to extract coherence conte…
Local feature detection and description are two essential steps in many visual applications. Most learned local feature methods require high-quality labeled data to achieve superior performance, but such labels are often expensive. To address this problem, we propose MUFeat, an unsupervised learning…
This paper presents a novel method for object-level unknown obstacle detection in driving scenes that reduces false positives. The proposed method combines existing anomaly detectors, depth estimation, and object detection techniques to achieve object-level predictions. Our method can predict anomal…
In this paper, we aim to apply deep saliency prediction to automatic drone exploration, which should consider not only one single image, but multiple images from different view angles or localizations in order to determine the exploration direction. However, little attention has been paid to such sa…
Most of the existing saliency prediction research focuses on either single images or videos (or more precisely multiple images in sequence). However, to apply saliency prediction to drone exploration that has to consider multiple images from different view angles or localizations to determine the di…
2D-to-3D correspondence estimation is the key step of 3D model-based image localization, and most of the existing research in this field focuses on improving the feature matching performance. Even with the best feature matching method, there are still some outliers, and thus, almost all the methods…
Feature descriptor matching is a critical step is many computer vision applications such as image stitching, image retrieval and visual localization. However, it is often affected by many practical factors which will degrade its performance. Among these factors, illumination variations are the most…