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Shape-aware surface reconstruction from sparse 3D point-clouds
2017
Medical Image Analysis
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The reconstruction of an object's shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process ...
However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. ...
Conclusion and Outlook In this paper we have presented a methodology for a shape-aware surface reconstruction from sparse surface points. ...
doi:10.1016/j.media.2017.02.005
pmid:28282642
fatcat:3ouyknbrgbcjfawa3een5ecv74
RangeUDF: Semantic Surface Reconstruction from 3D Point Clouds
[article]
2022
arXiv
pre-print
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We present RangeUDF, a new implicit representation based framework to recover the geometry and semantics of continuous 3D scene surfaces from point clouds. ...
Extensive experiments show that RangeUDF clearly surpasses state-of-the-art approaches for surface reconstruction on four point cloud datasets. ...
As shown in these two tables, we additionally explore the impact of color and surface point density on the performance of surface reconstruction and semantic segmentation. ...
arXiv:2204.09138v1
fatcat:ncjyo32qbzd33a6yw6zx4cxsje
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2108.09481v1 fatcat:qljs6c3mz5hevb52ei3begrhra
DSP-SLAM takes as input the 3D point cloud reconstructed by a feature-based SLAM system and equips it with the ability to enhance its sparse map with dense reconstructions of detected objects. ...
We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects, and sparse landmark points to represent the background. ...
The sparse SLAM backbone provides per-frame camera poses and a 3D point cloud. ...
arXiv:2108.09481v2
fatcat:2cxcoerz6vfjnkju5oskhxxuie
FroDO: From Detections to 3D Objects
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Results on a real-world sequence from ScanNet [7]. ...
Figure 1: Given a localized input RGB sequence, FroDO dectects objects and infers their pose and a progressively fine grained and expressive object shape representation. ...
First, we optimize the energy over a sparse set of surface points, using the point decoder G s (z) as a shape prior. ...
doi:10.1109/cvpr42600.2020.01473
dblp:conf/cvpr/RunzLTMKS0ASLN20
fatcat:rax2vea64nbdhlreuauajyqebi
Multiview Rectification of Folded Documents
[article]
2016
arXiv
pre-print
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Our main contribution is a new robust rectification method based on ridge-aware 3D reconstruction of a paper sheet and unwrapping the reconstructed surface using properties of developable surfaces via ...
Prior methods either need expensive 3D scanners or model deformable surfaces using over-simplified parametric representations. ...
Our technique recovers a ridge-aware 3D reconstruction of the document surface from a sparse 3D point cloud. ...
arXiv:1606.00166v1
fatcat:ulwkig5ug5ed7iknvtjevx75ee
DIFFER: Moving Beyond 3D Reconstruction with Differentiable Feature Rendering
2019
Computer Vision and Pattern Recognition
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In this work, we propose a deep learning system that can simultaneously reason about 3D shape as well as associated properties (such as color, semantic part segments) directly from a single 2D image. ...
Perception of 3D object properties from 2D images form one of the core computer vision problems. ...
The 3D perception of machines need to go beyond just the shape reconstruction from 2D images. ...
dblp:conf/cvpr/LMJB19
fatcat:djms5h3eavfcpk4qamxfr2le34
SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering
[article]
2021
arXiv
pre-print
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Point clouds obtained from 3D sensors are usually sparse. Existing methods mainly focus on upsampling sparse point clouds in a supervised manner by using dense ground truth point clouds. ...
To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images. ...
Figure 5 : 5 Comparison results of point cloud upsampling (×4) and surface reconstruction results with different methods (b-d) from the input point clouds (a). ...
arXiv:2108.00454v2
fatcat:xswpljwwcbdzje5szfl5o5lgdm
Reconstruction of People in Loose Clothing
[article]
2021
arXiv
pre-print
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3D human body reconstruction from monocular images is an interesting and ill-posed problem in computer vision with wider applications in multiple domains. ...
In this paper, we propose SHARP, a novel end-to-end trainable network that accurately recovers the detailed geometry and appearance of 3D people in loose clothing from a monocular image. ...
Effect of Upsampling on the Predicted Point-cloud The predicted point-cloud P, generated from the fused depth maps, can sometimes be sparse and contain holes. ...
arXiv:2106.04778v3
fatcat:5as3byi76ve5tez6qzio3mwb7y
ANISE: Assembly-based Neural Implicit Surface rEconstruction
[article]
2022
arXiv
pre-print
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We present ANISE, a method that reconstructs a 3D shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. ...
The part implicit functions can then be combined into a single, coherent shape, enabling part-aware shape reconstructions from images and point clouds. ...
We present ANISE, a method that reconstructs a 3D shape from partial observations (images or sparse point clouds) using a part-aware neural implicit shape representation. ...
arXiv:2205.13682v1
fatcat:73xrvfyeeffd3ijkpo3vckttii
Point Scene Understanding via Disentangled Instance Mesh Reconstruction
[article]
2022
arXiv
pre-print
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Semantic scene reconstruction from point cloud is an essential and challenging task for 3D scene understanding. ...
Based on the accurate proposals, we leverage a mesh-aware latent code space to disentangle the processes of shape completion and mesh generation, relieving the ambiguity caused by the incomplete point ...
-We design a disentangled instance mesh reconstruction strategy to mitigate the ambiguity of learning complete shapes from incomplete point cloud observations, by leveraging a mesh-aware latent code space ...
arXiv:2203.16832v2
fatcat:umhqlzcierh6pclnaf77avvqm4
FroDO: From Detections to 3D Objects
[article]
2020
arXiv
pre-print
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Key to FroDO is to embed object shapes in a novel learnt space that allows seamless switching between sparse point cloud and dense DeepSDF decoding. ...
We introduce FroDO, a method for accurate 3D reconstruction of object instances from RGB video that infers object location, pose and shape in a coarse-to-fine manner. ...
First, we optimize the energy over a sparse set of surface points, using the point decoder G s (z) as a shape prior. ...
arXiv:2005.05125v1
fatcat:ein67ktbl5bcla5l3kizbstjee
Deep Point Cloud Simplification for High-quality Surface Reconstruction
[article]
2022
arXiv
pre-print
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In this paper, we propose a novel point cloud simplification network (PCS-Net) dedicated to high-quality surface mesh reconstruction while maintaining geometric fidelity. ...
Therefore, simplifying point clouds for achieving compact, clean, and uniform points is becoming increasingly important for 3D vision and graphics tasks. ...
We propose a new point cloud simplification network (PCS-Net) to produce sparse and uniform point clouds to take advantage of compact storage and reconstruct surfaces faithfully. ...
arXiv:2203.09088v1
fatcat:nmtgbd73kvfzjh4qz7ary7ixfq
Learning to Infer Implicit Surfaces without 3D Supervision
[article]
2019
arXiv
pre-print
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While explicit representations, such as point clouds and voxels, can span a wide range of shape variations, their resolutions are often limited. ...
Recent advances in 3D deep learning have shown that it is possible to train highly effective deep models for 3D shape generation, directly from 2D images. ...
For point cloud representation, we also visualize the meshes reconstructed from the output point cloud. Implementation details. ...
arXiv:1911.00767v1
fatcat:vq33mtgfvvc7llzkigbvpljo4i
Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis
[article]
2022
arXiv
pre-print
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Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. ...
The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. ...
This method could be reiterated many times to reconstruct a 3D shape from a combination of numerous surface elements. ...
arXiv:2203.03311v2
fatcat:e2kvryolufearetp4ujlw2gwwy
SLAM-based dense surface reconstruction in monocular Minimally Invasive Surgery and its application to Augmented Reality
2018
Computer Methods and Programs in Biomedicine
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A robust global 3D surface reconstruction framework has been developed for building a dense surface using only unorganized sparse point clouds extracted from the SLAM. ...
Both camera tracking and surface reconstruction based on a sparse point cloud are effective and operated in real-time. ...
method to generate the smooth surface from an unstructured sparse point cloud. ...
doi:10.1016/j.cmpb.2018.02.006
pmid:29544779
fatcat:v4bhbs7wzzej7pifadeffhwzqm
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