12 accepted papers
Video depth estimation extends monocular prediction into the temporal domain to ensure coherence. However, existing methods often suffer from spatial blurring in fine-detail regions and temporal inconsistencies. We argue that current approaches, which primarily rely on temporal smoothing via Transfo…
Modern stereo matching methods have leveraged monocular depth foundation models to achieve superior zero-shot generalization performance. However, most existing methods primarily focus on extracting robust features for cost volume construction or disparity initialization. At the same time, the itera
The core of Multi-View Stereo (MVS) is to find corresponding pixels in neighboring images. However, due to challenging regions in input images such as untextured areas, repetitive patterns, or reflective surfaces, existing methods struggle to find precise pixel correspondence therein, resulting in i
State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. Directly applying 2D convolutions for cost aggregation often results in edge blurring, detail loss, and mismatches in tex…
Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames. However, existing methods still suffer from noisy and inconsistent flow matching, making it difficult to handle challenging scenarios and long-sequence estimation.To o…
In this paper, a LiDAR-inertial odometry (LIO) method that eliminates the influence of moving objects in dynamic driving scenarios is proposed. This method constructs binarized labels for 3D points of current sweep, and utilizes the label difference between each point and its surrounding points in g
Stereo matching recovers depth from image correspondences. Existing methods struggle to handle ill-posed regions with limited matching cues, such as occlusions and textureless areas. To address this, we propose MonSter, a novel method that leverages the complementary strengths of monocular depth est…
Panoramic optical flow enables a comprehensive understanding of temporal dynamics across wide fields of view. However, severe distortions caused by sphere-to-plane projections, such as the equirectangular projection (ERP), significantly degrade the performance of conventional perspective-based optic…
Multi-view depth estimation has achieved impressive performance over various benchmarks. However almost all current multi-view systems rely on given ideal camera poses which are unavailable in many real-world scenarios such as autonomous driving. In this work we propose a new robustness benchmark to…
In the current era of generative AI breakthroughs generating panoramic scenes from a single input image remains a key challenge. Most existing methods use diffusion-based iterative or simultaneous multi-view inpainting. However the lack of global scene layout priors leads to subpar outputs with dupl…
Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of high accuracy and efficiency. In this paper, we present a novel cost volume construction method which generates attention w…
In this paper, we present a novel deep learning and direct method based monocular visual odometry system named D2VO. Our system reconstructs the dense depth map of each keyframe and tracks camera poses based on these keyframes. Combining direct method and deep learning, both tracking and mapping of…