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RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering
Di Chang, Aljaž Božič, T. Zhang, Q. Yan, Y. Chen, S. Süsstrunk, Matthias Nießner
ECCV 2022
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bibtex
We introduce RC-MVSNet, an unsupervised multi-view stereo reconstruction approach
that leverages NeRF-like rendering to generate consistent photometric supervision
for non-Lambertian surfaces, and propose an improved Gaussian-Uniform sampling
to overcome occlusion artifacts present in existing approaches.
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TransformerFusion: Monocular RGB Scene Reconstruction using Transformers
Aljaž Božič, Pablo Palafox, Justus Thies, Angela Dai, Matthias Nießner
NeurIPS 2021
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We introduce TransformerFusion, a transformer-based 3D scene reconstruction approach.
The input monocular RGB video frames are fused into a volumetric feature representation
of the scene by a transformer network that learns to attend to the most relevant image
observations, resulting in an accurate online surface reconstruction.
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NPMs: Neural Parametric Models for 3D Deformable Shapes
Pablo Palafox, Aljaž Božič, Justus Thies, Matthias Nießner, Angela Dai
ICCV 2021
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We propose Neural Parametric Models (NPMs), a learned alternative to traditional, parametric 3D models.
4D dynamics are disentangled into latent-space representations of shape and pose, leveraging the flexibility
of recent developments in learned implicit functions. Once learned, NPMs enable optimization over the learned
spaces to fit to new observations.
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Neural Deformation Graphs for Globally-consistent Non-rigid Reconstruction
Aljaž Božič, Pablo Palafox, Michael Zollhöfer, Justus Thies, Angela Dai, Matthias Nießner
CVPR 2021 (Oral)
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We introduce Neural Deformation Graphs for globally-consistent deformation tracking and 3D
reconstruction of non-rigid objects. Specifically, we implicitly model a deformation graph via a
deep neural network and empose per-frame viewpoint consistency as well as inter-frame graph and
surface consistency constraints in a self-supervised fashion.
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Neural Non-Rigid Tracking
Aljaž Božič*, Pablo Palafox*, Michael Zollhöfer, Angela Dai, Justus Thies, Matthias Nießner
NeurIPS 2020
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We introduce a novel, end-to-end learnable, differentiable non-rigid tracker that enables
state-of-the-art non-rigid reconstruction. By enabling gradient back-propagation through a non-rigid
as-rigid-as-possible optimization solver, we are able to learn correspondences in an end-to-end
manner such that they are optimal for the task of non-rigid tracking.
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Learning to Optimize Non-Rigid Tracking
Yang Li, Aljaž Božič, Tianwei Zhang, Yanli Ji, Tatsuya Harada, Matthias Nießner
CVPR 2020 (Oral)
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We learn the tracking of non-rigid objects by differentiating through the underlying non-rigid
solver. Specifically, we propose ConditionNet which learns to generate a problem-specific
preconditioner using a large number of training samples from the Gauss-Newton update equation. The
learned preconditioner increases PCG’s convergence speed by a significant margin.
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DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data
Aljaž Božič, Michael Zollhöfer, Christian Theobalt, Matthias Nießner
CVPR 2020
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We present a large dataset of 400 scenes, over 390,000 RGB-D frames, and 5,533 densely aligned frame
pairs, and introduce a data-driven non-rigid RGB-D reconstruction approach using learned heatmap
correspondences, achieving state-of-the-art reconstruction results on a newly established
quantitative benchmark.
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Semantic Monocular SLAM for Highly Dynamic Environments
Aljaž Božič*, Nikolas Brasch*, Joe Lallemand, Federico Tombari
IROS 2018
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We propose a semantic monocular SLAM framework designed to deal with highly dynamic environments,
combining feature-based and direct approaches to achieve robustness under challenging conditions.
Our approach uses deep-learned semantic information extracted from the scene to cope with outliers
on dynamic objects.
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Real-time Variational Stereo Reconstruction with Applications to Large-Scale Dense SLAM
Georg Kuschk, Aljaž Božič, Daniel Cremers
IEEE Intelligent Vehicles Symposium (IV), 2017
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bibtex
We propose an algorithm for dense and direct large-scale visual SLAM that runs in real-time on a
commodity notebook. A fast variational dense 3D reconstruction algorithm was developed which
robustly integrates data terms from multiple images. Embedded into a keyframe-based SLAM framework
it enables us to densely reconstruct large scenes.
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