activity
20162024
most citedDelving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling

2 citations · 8 across the 9 of their papers we have counts for

collaborators

7 papers

cs.LG2023

Learning Gradient Fields for Scalable and Generalizable Irregular Packing

Tianyang Xue, Mingdong Wu, Lin Lu +3

The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly…

cs.CV20231 cited

Lazy Visual Localization via Motion Averaging

Siyan Dong, Shaohui Liu, Hengkai Guo +2

Visual (re)localization is critical for various applications in computer vision and robotics. Its goal is to estimate the 6 degrees of freedom (DoF) camera pose for each query imag…

cs.GR2023

Patch-based 3D Natural Scene Generation from a Single Example

Weiyu Li, Xuelin Chen, Jue Wang +1

We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of h…

cs.CV20232 cited

Delving into Discrete Normalizing Flows on SO(3) Manifold for Probabilistic Rotation Modeling

Yulin Liu, Haoran Liu, Yingda Yin +3

Normalizing flows (NFs) provide a powerful tool to construct an expressive distribution by a sequence of trackable transformations of a base distribution and form a probabilistic m…

cs.CV20232 cited

A Laplace-inspired Distribution on SO(3) for Probabilistic Rotation Estimation

Yingda Yin, Yang Wang, He Wang +1

Estimating the 3DoF rotation from a single RGB image is an important yet challenging problem. Probabilistic rotation regression has raised more and more attention with the benefit…

cs.CV20222 cited

Visual Localization via Few-Shot Scene Region Classification

Siyan Dong, Shuzhe Wang, Yixin Zhuang +3

Visual (re)localization addresses the problem of estimating the 6-DoF (Degree of Freedom) camera pose of a query image captured in a known scene, which is a key building block of m…