Depth-Based Vehicle Visualization for Reduced Surround-View Artifacts

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Solution Overview

Problem

Existing vehicle Surround View Systems suffer from visual artifacts such as geometric, texture, and color distortions, which obscure or omit useful information and can be distracting, interfering with safe vehicle operation.

Innovation Solution

The environment surrounding a vehicle is visualized by texturizing a detected 3D surface topology, with dynamic objects rendered by warping rigid objects and representing non-rigid objects as flat 2D surfaces, using depth maps and neural networks to estimate accurate depth and model the environment as a 3D surface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Shape

If images are projected onto a 3D bowl shape using conventional stitching techniques, then a top-down 360° surround view visualization is achieved, but geometric distortions and texture artifacts are introduced

Engineering Contradiction:

Improve3D visualization representationVSAvoidgeometric accuracy

Core Design Contradiction:

ShapeVSManufacturing precision

Solution Approach 1:

The environment is divided into multiple depth ranges (foreground, midground, background) and each range is processed separately through depth-based segmentation. This allows different regions to be rendered with appropriate depth information without forcing all regions into a uniform 3D bowl shape, thereby reducing geometric distortions while maintaining 3D visualization.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transitions from traditional 2D image stitching to a depth-based 3D representation system. By introducing depth as a fundamental dimension for organizing and rendering environment data, the system achieves more accurate geometric representation without the artifacts of conventional stitching techniques.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Area of stationary object

If conventional image stitching is used to combine camera frames, then a surround view visualization is generated, but texture distortions including blur and object disappearance occur

Engineering Contradiction:

Improvesurround view coverageVSAvoidtexture accuracy

Core Design Contradiction:

Area of stationary objectVSManufacturing precision

Solution Approach 1:

Depth information is extracted and processed in advance before the actual rendering process. By pre-computing depth maps and organizing environment data by depth ranges, the system prepares accurate spatial information that prevents texture distortions during the final visualization, eliminating blur and object disappearance artifacts.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces the mechanical image stitching process with a depth-based spatial organization system. Instead of mechanically blending overlapping images, the system uses depth information to properly position and render objects in 3D space, achieving texture accuracy while maintaining full surround view coverage.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If a 3D bowl shape model is used to represent the environment, then a virtual camera view can be rendered, but color distortions and visual artifacts are introduced

Engineering Contradiction:

Improvevirtual camera rendering capabilityVSAvoidcolor accuracy

Core Design Contradiction:

Ease of operationVSManufacturing precision

Solution Approach 1:

Different regions of the environment are assigned different rendering properties based on their depth characteristics. Foreground, midground, and background regions are processed and rendered with appropriate depth information and color accuracy for their respective distances, eliminating the color distortions that occur when forcing all regions into a uniform 3D bowl model.

Inventive Principle:

Principle #3Local quality

4Manufacturing precision

If depth-based segmentation is applied to separate environment regions, then geometric accuracy is improved, but processing complexity increases

Engineering Contradiction:

Improvegeometric accuracyVSAvoidprocessing pipeline complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The environment is segmented into depth-based regions (foreground, midground, background) which simplifies the processing pipeline by allowing each region to be handled with appropriate depth information. This segmentation approach achieves geometric accuracy while maintaining manageable processing complexity through systematic region-based processing.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS20250292498A1Depth-based vehicle environment visualization using generative ai

Publication Date: 2025.09.18 NVIDIA CORP

AI summary

In various examples, systems and methods are disclosed relating to geometry estimation and dynamic object rendering for vehicle environment visualization. In embodiments, the environment surrounding an ego-machine may be visualized by extracting one or more depth maps from image data, converting the depth map(s) into a 3D surface topology of the surrounding environment, and/or texturizing the detected 3D surface topology with image data. Dynamic objects such as moving vehicles or pedestrians may be detected and masked from a first pass of texturization. Rigid dynamic objects may be visualized by warping corresponding depth values using corresponding trajectories, inserting or fusing the resulting warped 3D representation of each such object into the (e.g., texturized) 3D surface topology, and texturizing the warped 3D representation of each object using corresponding image data. Non-rigid dynamic objects may be represented as flat 2D surfaces and texturized with corresponding image data.