10 accepted papers
Amodal segmentation is an image-based algorithm that aims to predict masks for both visible and occluded parts of objects. Existing methods typically rely on supervised learning with annotated amodal masks or synthetic data. The effectiveness of these methods relies heavily on the quality of the dat
We have witnessed significant progress in deep learning-based 3D vision ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However existing scene-level datasets for deep learning-based 3D vision limited to either synthetic enviro…
Bokeh is widely used in photography to draw attention to the subject while effectively isolating distractions in the background. Computational methods can simulate bokeh effects without relying on a physical camera lens but the inaccurate lens modeling in existing filtering-based methods leads to ar…
Efficiently representing and reconstructing the 3D geometry of biological trees remains a challenging problem in computer vision and graphics. We propose a novel approach for generating realistic tree models from single-view photographs. We cast the 3D information inference problem to a semantic vox…
Lighting effects such as shadows or reflections are key in making synthetic images realistic and visually appealing. To generate such effects, traditional computer graphics uses a physically-based renderer along with 3D geometry. To compensate for the lack of geometry in 2D Image compositing, recent…
We present a novel approach to perform instance segmentation, and counting, for densely packed self-similar trees using a top-view RGB image sequence. We propose a solution that leverages pixel content, shape, and self-occlusion. First, we perform an initial over-segmentation of the image sequence a…
"Shadows are essential for realistic image compositing. Physics based shadow rendering methods require 3D geometries, which are not always available. Deep learning-based shadow synthesis methods learn a mapping from the light information to an object’s shadow without explicitly modeling the shadow g…
We introduce an interactive Soft Shadow Network (SSN) to generates controllable soft shadows for image compositing. SSN takes a 2D object mask as input and thus is agnostic to image types such as painting and vector art. An environment light map is used to control the shadow's characteristics, such…
We introduce FlyCam-a novel framework-for gimbal drone camera photography. Our approach abstracts the camera and the drone into a single flying camera object so that the user does not need to think about the drone movement and camera control as two separate actions. The camera is controlled from a s
Thanks to the recent advances in computational photography and remote sensing, point clouds of buildings are becoming increasingly available, yet their processing poses various challenges. In our work, we tackle the problem of point cloud completion and editing and we approach it via inverse procedu…