3D Composite Scene Insertion for Realistic Non-Overlapping Objects
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Solution Overview
Problem
The challenge in 3D computer vision is producing high-quality annotations for 3D data, which is time-consuming and complicated by scene diversity, occlusions, and the reality gap between synthetic and real domains, especially for large-scale datasets.
Innovation Solution
A method for generating realistic composite scenes by inserting objects within a scene without overlap, using 3D point comparisons and randomization to determine insertion positions, ensuring realistic integration of objects like avatars within environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If objects are inserted into 3D scenes using traditional methods, then the scene composition process is simple, but objects may overlap with existing objects or environment causing unrealistic scenes
Solution Approach 1:
The patent applies preliminary action by pre-computing valid insertion positions for objects in the 3D scene before actual composition. The system calculates all possible positions where an object can be placed without overlapping existing objects or environment, stores these pre-computed positions, and then randomly selects from them during scene generation. This eliminates the need for complex real-time collision detection and ensures realistic non-overlapping scenes.
2Measurement precision
If manual annotation methods are used for 3D data, then annotation quality can be high, but the process is extremely time-consuming for large-scale datasets
Solution Approach 1:
The patent applies copying by generating synthetic 3D scenes with automatically computed annotations instead of manually annotating real scenes. The system creates realistic 3D composite scenes with objects inserted at pre-computed positions, and automatically generates ground truth annotations including object positions, orientations, and occlusion information. This synthetic data can be used to train and evaluate 3D vision models, providing both high quality annotations and large-scale data generation capability.
3Productivity
If synthetic data generation methods are used, then large-scale data collection is efficient, but the reality gap between synthetic and real domains remains
Solution Approach 1:
The patent applies local quality by ensuring that each inserted object in the synthetic scene has realistic local properties including correct lighting, shadows, and occlusions specific to its position and orientation. The system computes realistic interactions between objects and environment, such as occlusion relationships and shadow casting, for each object at its inserted position. This creates locally realistic scenes that bridge the gap between synthetic and real domains, even while generating large-scale data efficiently.
Data Source
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AI summary
The present disclosure relates to a method for generating a three-dimensional (3D) composite scene by inserting at least a first object within a scene, the method comprising: - determining at least one position within said scene where said first object is to be inserted based on of a number of 3D points comprised in an intersection of: • a surface of an envelope comprising a 3D representation of said first object, and of • a surface of an envelope comprising a 3D representation of said at least a second object of said scene; inserting said first object at said at least one determined position.