A Lidar-enhanced neural radiance field to transform drive videos into photorealistic sensor simulation testbeds.

Instantaneous subtle motion detection and estimation for vehicles in street scene.

Image features with differential privacy guarantee for visual localization and structure-from-motion.

Revisiting NOCS-based 3D object detection and localization in driving scenes with categorical object NeRF.

Test-time domain adapation for 3D semantic segmentation leveraging image-Lidar cross-modal consistency.

Geometric transformation creates dense occlusion-aware semantic map from compact parametric annotation, facilitating road layout estimation.

A cross-domain and cross-modal (geometry and appearance) constrastive learning framework for video domain adpatation.

A principled way to fuse the geometric camera pose estimation with CNN predictions, via learning geometry-guided uncertainty, driven by a self-attention graph neural network.

Geometric SLAM and self-supervised monocular CNN-depth learning can benefit each other.

Joint rolling shutter image stitching and distortion removal, by deriving a new rolling-shutter-aware homography and a minimal 5-point solver.

Learning parametric road layout from non-parametric semantic 3D reconstruction obtained by Structure-from-Motion.

Theoretical degeneracy on SfM with a rolling-shutter camera and leveraging data-driven priors through a network that learns camera motion and scene structure to undistort a single rolling shutter image.

Theoretical degeneracy on radial distortion self-calibration in forward motion and a network to learn radial distortion parameters and camera intrinsics for SLAM.

A baseline-insensitive bilinear objective function for translation averaging in global SfM. Theoretically revealing the underlying subtle difference that leads to the performance gap between two convex methods, LUD and Shapefit/kick.

Develop a rolling-shutter-aware differential SfM method for depth and motion recovery, which is further leveraged to remove rolling shutter distortion.