8 accepted papers
Self-supervised stereo matching has drawn attention due to its ability to estimate disparity without needing ground-truth data. However, existing self-supervised stereo matching methods heavily rely on the photo-metric consistency assumption, which is vulnerable to natural disturbances, resulting in…
While human vision inherently achieves robust cross-domain depth estimation through binocular coordination, robotic systems employing stereo matching still confront significant challenges in maintaining robustness across domains when performing real-time environmental depth perception. Furthermore,
"Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have greatly advanced novel view synthesis, which is capable of photo-realistic rendering. However, these methods require the foundational assumption of the static scene (, consistent lighting condition and persistent object positions),…
"State-of-the-art stereo matching networks trained on in-domain data often underperform on cross-domain scenes. Intuitively, leveraging the zero-shot capacity of a foundation model can alleviate the cross-domain generalization problem. The main challenge of incorporating a foundation model into ster…
Estimating disparities in challenging areas is difficult and limits the performance of stereo matching models. In this paper we exploit local structure information (LSI) to enhance stereo matching. Specifically our LSI comprises a series of key elements including the slant plane (parameterised by di…
Pose estimation plays a critical role in self-supervised monocular depth estimation for indoor scenes, especially those involving complex ego-motion. In this letter, we leverage the two-view geometry constraints into pose estimation to boost the accuracy of pose estimation, which ultimately improves
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences. However, since weak supervision cannot distinguish the losses caused by the detection and description steps, directly conducting weakly supervis…
Acquiring dense and precise depth information in real time is highly demanded for robotic perception and automatic driving. Motivated by the complementary nature of stereo images and LiDAR point clouds, we propose an efficient stereo-LiDAR fusion network (SLFNet) to predict a dense depth map of a sc