77 research outputs found
SEETHROUGH: Finding Objects in Heavily Occluded Indoor Scene Images
Discovering 3D arrangements of objects from single indoor images is important given its many applications such as interior design and content creation for virtual environments. Although heavily researched in the recent years, existing approaches break down under medium to heavy occlusion as the core image-space region detection module fails in absence of directly visible cues. Instead, we take into account holistic contextual 3D information, exploiting the fact that objects in indoor scenes co-occur mostly in typical configurations. First, we use a neural network trained on real indoor annotated images to extract 2D keypoints, and feed them to a 3D candidate object generation stage. Then, we solve a global selection problem among these candidates using pairwise co-occurrence statistics discovered from a large 3D scene database. We iterate the process allowing for candidates with low keypoint response to be incrementally detected based on the location of the already discovered nearby objects. We demonstrate significant performance improvement over combinations of state-of-the-art methods, especially for scenes with moderately to severely occluded objects
Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene
The goal of this paper is to take a single 2D image of a scene and recover
the 3D structure in terms of a small set of factors: a layout representing the
enclosing surfaces as well as a set of objects represented in terms of shape
and pose. We propose a convolutional neural network-based approach to predict
this representation and benchmark it on a large dataset of indoor scenes. Our
experiments evaluate a number of practical design questions, demonstrate that
we can infer this representation, and quantitatively and qualitatively
demonstrate its merits compared to alternate representations.Comment: Project url with code: https://shubhtuls.github.io/factored3
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