Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Han, Zhizhong"'
Unsigned distance functions (UDFs) have been a vital representation for open surfaces. With different differentiable renderers, current methods are able to train neural networks to infer a UDF by minimizing the rendering errors on the UDF to the mult
Externí odkaz:
http://arxiv.org/abs/2407.16396
Learning signed distance functions (SDFs) from 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 noisy
Externí odkaz:
http://arxiv.org/abs/2407.14225
Diffusion models have shown remarkable results for image generation, editing and inpainting. Recent works explore diffusion models for 3D shape generation with neural implicit functions, i.e., signed distance function and occupancy function. However,
Externí odkaz:
http://arxiv.org/abs/2404.06851
Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud, which plays a critical role in 3D computer vision. Previous methods typically split a sparse point cloud into several local patches, upsampl
Externí odkaz:
http://arxiv.org/abs/2312.15133
Cross-modality registration between 2D images from cameras and 3D point clouds from LiDARs is a crucial task in computer vision and robotic. Previous methods estimate 2D-3D correspondences by matching point and pixel patterns learned by neural networ
Externí odkaz:
http://arxiv.org/abs/2312.04060
Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal supervision. However, normal supervision in benchmarks comes from synthet
Externí odkaz:
http://arxiv.org/abs/2311.00389
Autor:
Hu, Pengchong, Han, Zhizhong
Learning neural implicit representations has achieved remarkable performance in 3D reconstruction from multi-view images. Current methods use volume rendering to render implicit representations into either RGB or depth images that are supervised by m
Externí odkaz:
http://arxiv.org/abs/2310.11598
We propose Neural Gradient Learning (NGL), a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. It has excellent gradient approximation properties for the underlying geometry of th
Externí odkaz:
http://arxiv.org/abs/2309.09211
Learning implicit representations has been a widely used solution for surface reconstruction from 3D point clouds. The latest methods infer a distance or occupancy field by overfitting a neural network on a single point cloud. However, these methods
Externí odkaz:
http://arxiv.org/abs/2308.13175
Latest methods represent shapes with open surfaces using unsigned distance functions (UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the gradients around the zero level set of the UDF. However, the differential networks
Externí odkaz:
http://arxiv.org/abs/2308.11441