Abstract Implicit Neural Representations have gained prominence as a powerful framework
for capturing complex data modalities encompassing a wide range from 3D shapes to …

Occupancy prediction has increasingly garnered attention in recent years for its fine-grained
understanding of 3D scenes. Traditional approaches typically rely on dense, regular grid …

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 …

A neural signed distance function (SDF) is a convenient shape representation for many
tasks such as surface reconstruction editing and generation. However neural SDFs are …

Implicit Neural Representations have gained prominence as a powerful framework for
capturing complex data modalities, encompassing a wide range from 3D shapes to images …

This paper explores the problem of reconstructing temporally consistent surfaces from a 3D
point cloud sequence without correspondence. To address this challenging task, we …

3D representations of large-scale and urban scenes are crucial across various industries,
including autonomous driving, urban planning, natural resource supervision and many …

Abstract We introduce LGSur‐Net, an end‐to‐end deep learning architecture, engineered
for the upsampling of sparse point clouds. LGSur‐Net harnesses a trainable Gaussian local …

We consider the challenging problem of learning Signed Distance Functions (SDF) from
sparse and noisy 3D point clouds. In contrast to recent methods that depend on smoothness …

Reconstructing continuous surfaces from unoriented and unordered 3D points is a
fundamental challenge in computer vision and graphics. Recent advancements address this …