3D Point Cloud Frame Stabilization Using Motion Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for generating 3D volumetric videos suffer from noticeable discontinuities between frames, affecting user experience due to the lack of effective temporal corrections in independently created 3D models.
Innovation Solution
An electronic apparatus and method for sequence stabilization of 3D point cloud frames using motion information, which includes capturing image data from multiple viewpoints, extracting motion vectors, and applying iterative closest point (ICP) registration to align consecutive frames, thereby reducing artifacts and discontinuities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 3D volumetric video is created based on concatenation of independently created 3D models, then the generation process is simple and fast, but noticeable discontinuity between frames occurs affecting user experience
Solution Approach 1:
The patent applies preliminary action by performing motion estimation and compensation operations on individual 3D models before concatenation. Motion vectors are extracted and applied to pre-process each model, predicting and correcting future frame positions. This preliminary processing ensures that when frames are concatenated, they are already temporally aligned, preventing discontinuities while maintaining generation speed.
Solution Approach 2:
The patent implements feedback through iterative motion estimation and compensation. Motion vectors from consecutive frames are calculated and fed back into the system to adjust and stabilize point cloud sequences. This feedback mechanism continuously corrects temporal misalignments between frames, ensuring smooth transitions while preserving the efficiency of independent model creation.
2Stability of the object's composition
If motion estimation and compensation operations are performed, then temporal stability between frames is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies segmentation by dividing the motion estimation and compensation process into distinct modular operations: motion vector extraction, point cloud sequence generation, and frame stabilization. Each segment processes specific aspects of temporal alignment independently, allowing for optimized computation and reducing overall processing time while maintaining temporal stability.
Solution Approach 2:
The patent implements partial action by applying motion compensation selectively to key frames and critical motion regions rather than processing every frame uniformly. Motion vectors are estimated for representative frames and interpolated for intermediate frames, reducing computational load while maintaining adequate temporal stability for smooth video playback.
Data Source
AI summary
An electronic apparatus and method for sequence stabilization of point cloud frames using motion information is disclosed. The electronic apparatus receives image data that includes images of objects. The image data corresponds to a duration in which in the objects are in a dynamic state. Based on the image data, the electronic apparatus generates a point cloud sequence and extracts motion information associated with the objects. The electronic apparatus determines a first set of 3D points of a first point cloud frame that is in a static state with respect to a second set of 3D points of a second point cloud frame, based on the motion information. The first and second point cloud frames are consecutive frames of point cloud sequence. The electronic apparatus further determines a difference between the first and the second set of 3D points and updates the first point cloud frame based on the difference.