54 research outputs found
Topology-Aware Surface Reconstruction for Point Clouds
We present an approach to inform the reconstruction of a surface from a point
scan through topological priors. The reconstruction is based on basis functions
which are optimized to provide a good fit to the point scan while satisfying
predefined topological constraints. We optimize the parameters of a model to
obtain likelihood function over the reconstruction domain. The topological
constraints are captured by persistence diagrams which are incorporated in the
optimization algorithm promote the correct topology. The result is a novel
topology-aware technique which can: 1.) weed out topological noise from point
scans, and 2.) capture certain nuanced properties of the underlying shape which
could otherwise be lost while performing surface reconstruction. We showcase
results reconstructing shapes with multiple potential topologies, compare to
other classical surface construction techniques, and show the completion of
real scan data
A Survey of Surface Reconstruction from Point Clouds
International audienceThe area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations – not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques, and provide directions for future work in surface reconstruction
VNT-Net: Rotational Invariant Vector Neuron Transformers
Learning 3D point sets with rotational invariance is an important and
challenging problem in machine learning. Through rotational invariant
architectures, 3D point cloud neural networks are relieved from requiring a
canonical global pose and from exhaustive data augmentation with all possible
rotations. In this work, we introduce a rotational invariant neural network by
combining recently introduced vector neurons with self-attention layers to
build a point cloud vector neuron transformer network (VNT-Net). Vector neurons
are known for their simplicity and versatility in representing SO(3) actions
and are thereby incorporated in common neural operations. Similarly,
Transformer architectures have gained popularity and recently were shown
successful for images by applying directly on sequences of image patches and
achieving superior performance and convergence. In order to benefit from both
worlds, we combine the two structures by mainly showing how to adapt the
multi-headed attention layers to comply with vector neurons operations. Through
this adaptation attention layers become SO(3) and the overall network becomes
rotational invariant. Experiments demonstrate that our network efficiently
handles 3D point cloud objects in arbitrary poses. We also show that our
network achieves higher accuracy when compared to related state-of-the-art
methods and requires less training due to a smaller number of hyperparameters
in common classification and segmentation tasks.Comment: arXiv admin note: text overlap with arXiv:2104.12229 by other author
Non-local Scan Consolidation for 3D Urban Scenes
Recent advances in scanning technologies, in particular devices that extract depth through active sensing, allow fast scanning of urban scenes. Such rapid acquisition incurs imperfections: large regions remain missing, significant variation in sampling density is common, and the data is often corrupted with noise and outliers. However, buildings often exhibit large scale repetitions and selfsimilarities. Detecting, extracting, and utilizing such large scale repetitions provide powerful means to consolidate the imperfect data. Our key observation is that the same geometry, when scanned multiple times over reoccurrences of instances, allow application of a simple yet effective non-local filtering. The multiplicity of the geometry is fused together and projected to a base-geometry defined by clustering corresponding surfaces. Denoising is applied by separating the process into off-plane and in-plane phases. We show that the consolidation of the reoccurrences provides robust denoising and allow reliable completion of missing parts. We present evaluation results of the algorithm on several LiDAR scans of buildings of varying complexity and styles.
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