Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Han, Zhizhong"'
We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clouds. Almost all existing methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec3f040a39d54eae09fdb1a65617cd17
http://arxiv.org/abs/2305.05873
http://arxiv.org/abs/2305.05873
It is vital to infer signed distance functions (SDFs) from 3D point clouds. The latest methods rely on generalizing the priors learned from large scale supervision. However, the learned priors do not generalize well to various geometric variations th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::963949e76cfe96c9d9c33280a81fe64f
http://arxiv.org/abs/2303.14505
http://arxiv.org/abs/2303.14505
Learning signed distance functions (SDFs) from 3D point clouds is an important task in 3D computer vision. However, without ground truth signed distances, point normals or clean point clouds, current methods still struggle from learning SDFs from noi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0885488624bb519b86667c912e94f16
We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations. Previous methods focus on learning point weights to fit neighborhoods into a geometric surface app
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13e06079c4da9fc019061e0d1943700a
http://arxiv.org/abs/2210.07158
http://arxiv.org/abs/2210.07158
Surface reconstruction for point clouds is an important task in 3D computer vision. Most of the latest methods resolve this problem by learning signed distance functions (SDF) from point clouds, which are limited to reconstructing shapes or scenes wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a94cd7043328443682f9386b5264578f
http://arxiv.org/abs/2210.02757
http://arxiv.org/abs/2210.02757
Publikováno v:
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
It is an important task to reconstruct surfaces from 3D point clouds. Current methods are able to reconstruct surfaces by learning Signed Distance Functions (SDFs) from single point clouds without ground truth signed distances or point normals. Howev
Surface reconstruction from point clouds is vital for 3D computer vision. State-of-the-art methods leverage large datasets to first learn local context priors that are represented as neural network-based signed distance functions (SDFs) with some par
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0caf803f66273427f7512ac3921659d9
http://arxiv.org/abs/2204.11015
http://arxiv.org/abs/2204.11015
Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image. So far, most of the previous methods still struggle to extract semantic attribute
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=arXiv_______::64ee64eb1aad7e4106ff992d2c2575f1
http://arxiv.org/abs/2203.15190
http://arxiv.org/abs/2203.15190
The manual annotation for large-scale point clouds is still tedious and unavailable for many harsh real-world tasks. Self-supervised learning, which is used on raw and unlabeled data to pre-train deep neural networks, is a promising approach to addre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e9e15df54e28661d1e89bd620c9accf
http://arxiv.org/abs/2203.14084
http://arxiv.org/abs/2203.14084
Normal estimation for unstructured point clouds is an important task in 3D computer vision. Current methods achieve encouraging results by mapping local patches to normal vectors or learning local surface fitting using neural networks. However, these
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea14aa6039f49a0606ab9b580e766f8e