3D Point Cloud Duplicate Removal via Depth Analysis
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Solution Overview
Problem
Existing point cloud compression techniques generate a large number of redundant and duplicate vertices, increasing codec complexity and data size, which wastes computing resources and degrades the visual quality of reconstructed 3D models.
Innovation Solution
A decoder and encoder system that identifies and removes duplicate points in 3D point cloud data by analyzing depth values and corresponding pixels across multiple geometry frames, reducing redundant points without degrading visual quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing point cloud compression techniques are used, then compression is achieved, but a large number of redundant and duplicate vertices are generated, increasing codec complexity and data size
Solution Approach 1:
The patent applies preliminary action by identifying and removing duplicate points during the encoding process before transmission. The encoder compares points across multiple views and eliminates duplicates in advance, so that the decoder receives already-optimized data without needing to perform complex duplicate removal operations, thereby reducing overall codec complexity.
Solution Approach 2:
The patent extracts and removes redundant duplicate vertices from the point cloud data during encoding. By identifying points that appear in multiple views and removing them before compression, the system reduces the total number of vertices and decreases codec complexity while maintaining reconstruction quality.
2Quantity of substance
If existing point cloud compression techniques are used, then compression is achieved, but data size increases due to redundant points
Solution Approach 1:
The patent performs duplicate point removal during the encoding stage rather than after decoding. By eliminating redundant points before transmission, the actual data size is reduced, and the decoder processes fewer points, thereby improving processing efficiency without compromising reconstruction quality.
Solution Approach 2:
The patent discards redundant duplicate points during encoding and recovers only the necessary unique points for accurate 3D reconstruction. This selective discarding reduces data size and improves processing efficiency by eliminating unnecessary information that would otherwise require storage and processing.
3Reliability
If duplicate points are not removed, then processing is simpler, but visual quality of reconstructed 3D models degrades
Solution Approach 1:
The patent performs duplicate point removal during encoding as a preliminary step before transmission. This ensures that only unique, non-redundant points are transmitted and reconstructed, guaranteeing high visual quality. The processing complexity is managed by performing the removal operation during encoding rather than requiring complex post-processing at the decoder.
Solution Approach 2:
The patent uses feedback from multiple view comparisons during encoding to identify duplicate points. By analyzing point cloud data from different viewpoints and using this feedback to eliminate duplicates, the system ensures high reconstruction quality while managing processing complexity through structured comparison algorithms.
Data Source
AI summary
A decoder, an encoder and a method for decoding 3D point cloud data. The decoder includes a communication interface configured to receive an encoded bit stream comprising the 3D point cloud data and a processor. The processor is configured to decode the bit stream to identify a plurality of 2D frames; identify corresponding pixels in two or more geometry frames in the plurality of 2D frames based on locations of the corresponding pixels in the two or more geometry frames; identify depth values of the corresponding pixels in the two or more geometry frames; identify duplicate points in at least one of the two or more geometry frames based on the identified depth values of the corresponding pixels in the two or more geometry frames; and remove or ignore the identified duplicate points while reconstructing the 3D point cloud data.