6 accepted papers
Despite the impressive progress on understanding and generating images shown by the recent unified architectures, the integration of 3D tasks remains challenging and largely unexplored. In this paper, we introduce UniUGG, the first unified understanding and generation framework for 3D modalities. Ou…
Leveraging diverse robotic data for pretraining remains a critical challenge. Existing methods typically model the dataset’s action distribution using simple observations as inputs. However, these inputs are often incomplete, resulting in a dispersed conditional action distribution—an issue we refer…
Recent advances in LVLMs have improved vision-language understanding, but they still struggle with spatial perception, limiting their ability to reason about complex 3D scenes. Unlike previous approaches that incorporate 3D representations into models to improve spatial understanding, we aim to unlo…
Cross-modality pose estimation/localization is a critical challenge for multi-sensor-based perception systems, with applications spanning vehicle localization and online calibrations. In this paper, we introduce a hybrid approach to estimate the camera pose with respect to a point cloud with co-visi
Accurate identification of road intersections is the pivotal task for automatic construction of high-definition maps, particularly in unstructured scenes. Existing methods predominantly rely on single-modal data and thus show an obvious unimodal limitation, i.e., lack of contextual information. More…
"Generating multi-camera street-view videos is critical for augmenting autonomous driving datasets, addressing the urgent demand for extensive and varied data. Due to the limitations in diversity and challenges in handling lighting conditions, traditional rendering-based methods are increasingly bei…