Recent volumetric 3D reconstruction methods can produce very accurate results, with plausible geometry even for unobserved surfaces. However, they face an undesirable trade-off when it comes to multi-view fusion. They can fuse all available view information by global averaging, thus losing fine detail, or they can heuristically cluster views for local fusion, thus restricting their ability to consider all views jointly. Our key insight is that greater detail can be retained without restricting view diversity by learning a view-fusion function conditioned on camera pose and image content. We propose to learn this multi-view fusion using a transformer. To this end, we introduce VoRTX, 1 an end-to-end volumetric 3D reconstruction network using transformers for wide-baseline, multi-view feature fusion. Our model is occlusion-aware, leveraging the transformer architecture to predict an initial, projective scene geometry estimate. This estimate is used to avoid back-projecting image features through surfaces into occluded regions. We train our model on ScanNet and show that it produces better reconstructions than state-of-the-art methods. We also demonstrate generalization without any fine-tuning, outperforming the same state-of-the-art methods on two other datasets, TUM-RGBD and ICL-NUIM.
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PSMNet: Position-aware Stereo Merging Network for Room Layout Estimation
In this paper, we propose a new deep learning-based
method for estimating room layout given a pair of 360◦
panoramas. Our system, called Position-aware Stereo Merging
Network or PSMNet, is an end-to-end joint layout-pose
estimator. PSMNet consists of a Stereo Pano Pose (SP2)
transformer and a novel Cross-Perspective Projection (CP2)
layer. The stereo-view SP2 transformer is used to implicitly
infer correspondences between views, and can handle noisy
poses. The pose-aware CP2 layer is designed to render
features from the adjacent view to the anchor (reference)
view, in order to perform view fusion and estimate the visible
layout. Our experiments and analysis validate our method,
which significantly outperforms the state-of-the-art layout
estimators, especially for large and complex room spaces.
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- Award ID(s):
- 2041307
- NSF-PAR ID:
- 10340380
- Date Published:
- Journal Name:
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
