Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds

Liu, Shaowei; Gupta, Saurabh; Wang, Shenlong

doi:10.1109/cvpr52729.2023.02025

Cited by 2 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While it is reasonable to assume such prior knowledge in the case of hu- Transfer4D [MNH23] proposes a pipeline to automatically track non-rigid objects and extract a skeleton as a post-process, which is used to re-target motion to semantically similar shapes. Liu et al [LGW23] optimize part segmentation, skeleton, and kinematics from point cloud videos. As the problem is under-constrained, a two-stage approach is proposed which first optimizes the more tractable 6-DOF rigid model without kinematic constraints, later projecting it to a 1-DOF model with screw-parameterized joints and a valid kinematic tree.…”

Section: Object Pose Controlmentioning

confidence: 99%

Recent Trends in 3D Reconstruction of General Non‐Rigid Scenes

Yunus,

Lenssen,

Niemeyer

et al. 2024

Computer Graphics Forum

View full text Add to dashboard Cite

Reconstructing models of the real world, including 3D geometry, appearance, and motion of real scenes, is essential for computer graphics and computer vision. It enables the synthesizing of photorealistic novel views, useful for the movie industry and AR/VR applications. It also facilitates the content creation necessary in computer games and AR/VR by avoiding laborious manual design processes. Further, such models are fundamental for intelligent computing systems that need to interpret real‐world scenes and actions to act and interact safely with the human world. Notably, the world surrounding us is dynamic, and reconstructing models of dynamic, non‐rigidly moving scenes is a severely underconstrained and challenging problem. This state‐of‐the‐art report (STAR) offers the reader a comprehensive summary of state‐of‐the‐art techniques with monocular and multi‐view inputs such as data from RGB and RGB‐D sensors, among others, conveying an understanding of different approaches, their potential applications, and promising further research directions. The report covers 3D reconstruction of general non‐rigid scenes and further addresses the techniques for scene decomposition, editing and controlling, and generalizable and generative modeling. More specifically, we first review the common and fundamental concepts necessary to understand and navigate the field and then discuss the state‐of‐the‐art techniques by reviewing recent approaches that use traditional and machine‐learning‐based neural representations, including a discussion on the newly enabled applications. The STAR is concluded with a discussion of the remaining limitations and open challenges.

show abstract

Section: Object Pose Controlmentioning

confidence: 99%