Recent advances in large-scale pretraining have yielded visual foundation models with
strong capabilities. Not only can recent models generalize to arbitrary images for their …

Holistic understanding of urban scenes based on RGB images is a challenging yet important
problem. It encompasses understanding both the geometry and appearance to enable novel …

Dense prediction tasks, such as semantic segmentation, depth estimation, and surface
normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or …

The 3-D object detection with light detection and ranging (LiDAR) point clouds is a
challenging problem, which requires 3-D scene understanding, yet this task is critical to …

Single image depth estimation is a foundational task in computer vision and generative
modeling. However prevailing depth estimation models grapple with accommodating the …

Estimating depth from single RGB images and videos is of widespread interest due to its
applications in many areas, including autonomous driving, 3D reconstruction, digital …

LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing
complementary information between depth-aware LiDAR points and semantically rich …

Monocular depth estimation has experienced significant progress on terrestrial images in
recent years thanks to deep learning advancements. But it remains inadequate for …

Multi-task approaches to joint depth and segmentation prediction are well-studied for
monocular images. Yet, predictions from a single-view are inherently limited, while multiple …

The systematic evaluation and understanding of computer vision models under varying
conditions require large amounts of data with comprehensive and customized labels which …