3,783 research outputs found
3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform
Significant geometric structures can be compactly described by global
wireframes in the estimation of 3D room layout from a single panoramic image.
Based on this observation, we present an alternative approach to estimate the
walls in 3D space by modeling long-range geometric patterns in a learnable
Hough Transform block. We transform the image feature from a cubemap tile to
the Hough space of a Manhattan world and directly map the feature to the
geometric output. The convolutional layers not only learn the local
gradient-like line features, but also utilize the global information to
successfully predict occluded walls with a simple network structure. Unlike
most previous work, the predictions are performed individually on each cubemap
tile, and then assembled to get the layout estimation. Experimental results
show that we achieve comparable results with recent state-of-the-art in
prediction accuracy and performance. Code is available at
https://github.com/Starrah/DMH-Net.Comment: Accepted by ECCV 202
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer
Panoramic image enables deeper understanding and more holistic perception of
surrounding environment, which can naturally encode enriched scene
context information compared to standard perspective image. Previous work has
made lots of effort to solve the scene understanding task in a bottom-up form,
thus each sub-task is processed separately and few correlations are explored in
this procedure. In this paper, we propose a novel method using depth prior for
holistic indoor scene understanding which recovers the objects' shapes,
oriented bounding boxes and the 3D room layout simultaneously from a single
panorama. In order to fully utilize the rich context information, we design a
transformer-based context module to predict the representation and relationship
among each component of the scene. In addition, we introduce a real-world
dataset for scene understanding, including photo-realistic panoramas,
high-fidelity depth images, accurately annotated room layouts, and oriented
object bounding boxes and shapes. Experiments on the synthetic and real-world
datasets demonstrate that our method outperforms previous panoramic scene
understanding methods in terms of both layout estimation and 3D object
detection
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