3D Scene Rendering With SDF Boundaries for Interpenetrating Objects
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Solution Overview
Problem
Existing methods struggle to accurately determine the boundary between interpenetrating objects in video content for generating realistic 3D scenes, particularly in scenarios where objects overlap or occlude each other, such as in boxing fights or soccer games, leading to challenges in tracking movement and rendering accurate 3D models.
Innovation Solution
A neural network is trained to predict Signed Distance Fields (SDF) for each object, with a specific loss function to minimize identical SDF predictions between networks, and a method is developed to determine color values for interpenetrated objects using Skinned-Multi Person Linear Model (SMPL) pose estimates and SDF vectors for precise boundary identification and volume rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional volumetric rendering approaches are used to generate 3D scenes from video content, then 3D models can be created, but accurate boundary determination between interpenetrating objects becomes challenging when objects overlap or occlude each other
Solution Approach 1:
The patent applies segmentation by dividing the scene into multiple depth layers using depth estimation. Each layer represents a distinct depth range, allowing interpenetrating objects to be separated and processed independently. This segmentation enables accurate boundary determination by assigning pixels to specific depth layers based on estimated depth values, thus resolving the challenge of distinguishing overlapping objects.
Solution Approach 2:
The patent introduces depth estimation as an intermediary component that mediates between the input video content and the volumetric rendering process. This intermediary provides depth information that acts as a bridge, enabling the system to correctly order and composite multiple objects at different depths, thereby solving the boundary determination problem for interpenetrating objects.
2Reliability
If multiple objects are tracked in scenes with close interactions, then 3D models of individual objects can be generated, but tracking accuracy deteriorates when objects occlude or overlap each other
Solution Approach 1:
The patent applies dynamics by implementing temporal consistency mechanisms that allow the system to track objects across multiple frames. By maintaining and updating depth estimates and object associations over time, the system can reliably track interpenetrating objects even when they occlude each other in individual frames. The dynamic updating of depth layers and object assignments ensures continuous and accurate tracking.
3Manufacturing precision
If volumetric rendering is performed on interpenetrating objects, then realistic 3D scenes can be generated, but rendering accuracy decreases when objects overlap or occlude each other
Solution Approach 1:
The patent segments the volumetric rendering process into depth-based layers, where each layer contains pixels belonging to a specific depth range. This segmentation allows the rendering system to process interpenetrating objects separately in their respective depth layers, ensuring accurate geometry and texture mapping for each object while maintaining overall scene realism. The layered approach simplifies the complexity of handling overlapping objects.
Solution Approach 2:
The patent transitions from 2D image processing to 3D volumetric rendering by introducing the depth dimension. By estimating depth for each pixel and organizing the scene in three-dimensional space, the system can accurately represent interpenetrating objects with proper spatial relationships. This dimensional transition enables realistic rendering of complex object interactions that cannot be achieved with traditional 2D approaches.
Data Source
AI summary
A method and server for volume rendering of 3D scenes are provided. The method comprises training a given machine-learning algorithm (MLA) of a plurality of MLAs to identify a boundary between a plurality of interpenetrated objects to be rendered in a given 3D scene, by applying a signed distance function (SDF) loss function configured to penalize a respective predicted SDF value, generated by the given MLA during a given training iteration, for a given point of a training 3D scene, in response to the respective predicted SDF value generated by the given MLA being equal to the respective predicted SDF value generated by an other MLA of the plurality of MLAs.