With the rapid development of AI-generated 3D technology, a plethora of tools and platforms have emerged in the market, from Text-to-3D to Image-to-3D to AI-assisted modeling and optimization, making choosing the right tool a crucial task for product managers and technical teams. As the third article in the “AI+3D Product Manager Notes” series, this article systematically sorts out the main types and representative tools of the current AI+3D tool ecosystem, and provides a detailed evaluation framework from key dimensions such as generation quality, controllability, efficiency, ease of use, cost, and integration.
Introduction: From the technical spectrum to the bridge of landing selection
In the first two notes, we first (S2E01) discussed the driving forces, core values, and challenges of the rise of AI-generated 3D technology, and then (S2E02) deeply analyzed the principles, characteristics, and application scenarios of mainstream technology schools such as Text-to-3D, Image-to-3D, and NeRF. We have a preliminary understanding of the “technical spectrum” of AI+3D.
However, understanding the principles of technology is only the first step. As product managers, our ultimate goal is to translate technology into products or features that meet user needs and create business value. A crucial part of this process is the selection and evaluation of tools and platforms. At present, the tool ecosystem in the field of AI+3D is developing at an unprecedented speed, with various online platforms, software plugins, and open source projects emerging one after another, which is dizzying.
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With so many options available, how do we cut through the fog and find the tool or platform that best suits our business needs, technology stack, and target users? It’s not enough to just listen to a vendor’s propaganda or be attracted by a cool demo. We need to establish a systematic evaluation framework that takes a deep dive into the key capabilities, real-world limitations, and feasibility of integrating these tools into real-world workflows from the perspective of product managers.
This note (S2E03) will focus on this, aiming to:
- Sort out the main types of AI+3D tools/platforms on the market today and their representatives.
- Dissect the core competency dimensions (e.g., build quality, controllability, efficiency, integration, cost, etc.) that need to be focused on when evaluating these tools.
- Discuss the comprehensive considerations and decision-making ideas that product managers need to make when selecting technology.
- The goal is to provide product managers with a relatively clear and practical “navigation map” and “evaluation checklist” when faced with AI+3D tool selection, helping everyone make more informed and practical decisions.
1. Overview of AI+3D tool ecosystem: main types and representative players
The current AI+3D tool and service ecosystem is characterized by diversification and rapid iteration. We can classify it from different dimensions to better understand its landscape.
1. Divided by core functions and inputs and outputs
This is the most intuitive way to classify and is closely related to the schools of technology we discussed in S2E02:
a. Text-to-3D tools/platforms
Core Features: Receive text descriptions to generate 3D models.
On behalf of the player/tool: Luma AI (Genie), Masterpiece X, Meshy AI, CSM AI (Common Sense Machines), Shap-E (OpenAI, research), DreamFusion (Google Research), etc. Many online platforms offer such services, often based on web interfaces.
Peculiarity: The barrier to threshold is extremely low and creatively driven, but output quality and controllability are the main challenges.
b. Image-to-3D tools/platforms
Core Features: Receive single or multiple images to generate a 3D model.
Subcategories:
○ Single-view generation: Generate 3D from a single image. Representatives such as Luma AI, Kaedim, LeiaPix Converter (mainly generating depth maps/light fields), and applications based on Zero-1-to-3 research.
○ Multi-view reconstruction (including NeRF/scanning): Generate 3D from multiple photos or videos. Representatives such as Luma AI (NeRF), Polycam (NeRF/Scan), Kiri Engine (Scan/NeRF), RealityCapture, Metashape, and some cloud service platforms.
Peculiarity: Using image information, the results are relatively more specific; The multi-view method has high accuracy and is the mainstream of 3D scanning. The single-view approach relies on AI “brain supplementation” and varies greatly in quality and accuracy.
c. Sketch-to-3D tools
Core Features: Receive 2D sketches to generate 3D models.
On behalf of the player/tool: Google Monster Mash (fun), some research projects such as Sketch2Model, and some exploratory features integrated into modeling software.
Peculiarity: It is highly interactive and more in line with the creative habits of some users, but the technology maturity is relatively low.
d. AI-assisted modeling and editing tools
Core Features: Embed AI capabilities into traditional 3D modeling or editing processes to improve efficiency or enhance functionality.
On behalf of the player/tool: ZBrush (built-in some AI features such as topology optimization), AI plugins developed by the Blender community (such as generating textures with Stable Diffusion, AI-assisted sculpting, etc.), Masterpiece X (providing AI-assisted topology, UVs, etc.), Kaedim (providing AI optimization services).
Peculiarity: Aimed at professional users, designed to optimize existing workflows, not replace them entirely.
e. AI texture/material generation tools
Core Features: Generate PBR texture maps based on text descriptions, reference maps, or model semantics.
On behalf of the player/tool: Scenario.gg, WithPoly, Adobe Substance 3D (integrated with Firefly AI), Polyhive, and some plugins that integrate into game engines or DCC software.
Peculiarity: Focusing on the material aspect greatly improves the efficiency of texture creation, especially for quickly filling scenes or generating stylized materials.
f. AI 3D data processing/optimization platform
Core Features: It provides AI-based model repair, format conversion, lightweight (LOD generation), automatic UV unfolding, automatic binding, and more.
On behalf of the player/tool: Kaedim, Masterpiece X, Simplygon (traditional optimization tools, may also incorporate AI), and some platforms that offer API services.
Peculiarity: Address model “availability” and performance issues for the back-end of the 3D asset production pipeline.
2. Divided by service form and target users
a. Online SaaS platform
Morphology: Users access through a web browser, upload inputs (text, images), generate and download results online. It is usually subscription-based or charged per generation/compute resource.
Target Users: It has a wide range of coverage, from individual enthusiasts and indie developers to small and medium-sized design teams. Ease of use is usually better.
Representative: Luma AI, Polycam Web, Meshy AI, Scenario.gg, etc.
b. Desktop software/plugins
Morphology: Install as a standalone desktop app or integrate as a plug-in into existing 3D software such as Blender, Maya, UE, Unity. You may need local compute resources (GPUs) or call cloud APIs. Various pricing models (buyout, subscription, on-demand).
Target Users: More professional users (3D artists, game developers), emphasizing deep integration with existing workflows.
Representative: RealityCapture, Metashape, ZBrush, Substance 3D, and various Blender/UE/Unity plugins.
c. API/SDK services
Morphology: Provide API interfaces or SDK development kits, allowing developers to integrate AI+3D capabilities into their own applications or services. Typically charged per API call or throughput.
Target Users: Enterprise developers, platforms that require customized solutions or apply AI+3D capabilities at scale.
Representative: Kaedim APIs, Getimg.ai (3D-related APIs), Nvidia Omniverse-related APIs, and a number of API providers that focus on specific features.
d. Open Source Projects and Research Code
Morphology: Exposing the source code on platforms like GitHub for researchers and developers to learn, modify, and use. You often need to configure your own environment, train a model, or run inference.
Target Users: Researchers, technology enthusiasts, and teams with strong development capabilities.
Representative: NeRF (and its many variants of open source implementations), Point-E, Shap-E, Gaussian Splatting open source code, some 3D generation projects based on Stable Diffusion, etc.
Understanding these different classifications and representative players helps us more quickly locate the types of tools or services that may be relevant to our specific needs.
3. Representative Technologies/Models/Tools/Cases/Literature and Discussions (for Part 1, “Tool Ecosystem Overview”)
AI 3D Generator Tool Overview and Review
Tools/platforms are categorized by function type and service type, and a link to the official website is attached.
[Source: 9 Best AI 3D Generators You Need To Try – eWeek – https://www.eweek.com/artificial-intelligence/best-ai-3d-generators/]
Introduction to Representative Tools (Luma AI, Kaedim, Scenario.gg)
2-3 representative tools in different categories (e.g., Luma AI for NeRF/Generation, Kaedim for Optimization/Image-to-3D, and Scenario.gg for Texture Generation) with a brief overview of their core features, target users, and business model
[Source: Luma AI – https://lumalabs.ai/ ; Kaedim – https://www.kaedim3d.com/ ; Scenario.gg – https://www.scenario.com/]
List of excellent open source projects related to AI+3D
Links to a list of excellent open-source projects related to AI+3D, such as the GitHub Awesome List, for tech enthusiasts.
[Source: Awesome 3D Generation (GitHub) – https://github.com/justimyhxu/awesome-3D-generation]
2. Key competency evaluation dimension: the “perspective lens” of product managers
In the face of numerous AI+3D tools, how to effectively evaluate and compare? We need a structured evaluation framework that delves into the true capabilities and applicability of the tool from multiple dimensions. Here are a few key capability dimensions that we recommend focusing on from a product manager’s perspective:
1. Quality of Generation
This is the most intuitive and core evaluation dimension, but “quality” itself is a multi-level concept that needs to be examined in detail:
a. Visual Fidelity
Does the generated 3D model look accurate, realistic, or in the intended style in terms of shape, scale, color, light and shadow, etc.? How well does it match the text description or reference image you entered? Are there obvious distortions, artifacts, or irrationalities?
b. Geometric Detail & Texture Resolution
Does the model produce sufficiently rich geometric details (e.g., sculpted lines, folds of fabric, mechanical parts)? What is the resolution of the generated texture maps? Is it clear and sharp, and can it express the subtle characteristics of the material?
c. 3D Consistency
Looking at the model from different angles, is its structure consistent and reasonable? Are there “Janus problems” (different perspectives look like different objects) or other perspective-related geometry errors?
d. Usability Quality (Extremely Important)
As we highlighted in S2E01 and will be detailed in subsequent S2E08, it is about whether the model can be used smoothly for downstream professional processes. Needs to be assessed:
- Topology: Does the output mesh have a clean, optimized topology (ideally quad faces)? Are there serious errors such as nonmanifold, hole, self-intersecting, etc.? Is it suitable for editing, rigging, and animating?
- UV Coordinates: Are effective, well-laid out, low-stretch UVs generated? Do you need a lot of manual fixes?
- Material System: Is the output material PBR compliant? Are the texture channels complete? Can it be used directly in the target rendering engine?
- Performance Cost: Is the number of faces and vertices of the output model within an acceptable range? Is a lot of optimization required?
Evaluation methodology: You can’t just look at the official demo or renders. Be sure to try it out for yourself and export and import the generated results into standard 3D software (e.g., Blender) and target engines (e.g., UE, Unity) for careful examination and testing. Compare the output quality of different tools with the same input.
2. Controllability & Editability
The AI-generated “black box” nature makes controllability a challenge. We need to evaluate how precisely users can guide and modify the generated results:
a. Particle size and precision of input control
Can users more precisely control the shape, size, scale, part relationships, style, etc. of the generated results through richer inputs such as detailed parameter settings, structured descriptions, multimodal input combinations? Is the prompt performance stable and predictable?
b. Iterative modification and optimization capabilities
Does the tool support further modifications and optimizations to the generated results? For example, is it possible to lock certain parts and only regenerate others? Are there interactive editing tools (e.g., smart brushes, parameter adjustment sliders) to fine-tune the results? Is the modification process reversible?
c. Friendliness of the output format
Is the output 3D model format an industry standard format (e.g., glTF, FBX, OBJ)? Does it contain the necessary metadata (e.g., material information, bone information)? Does its internal structure (e.g., topology, UVs) benefit subsequent manual editing?
d. Style consistency and control
Can models for specific art styles be generated consistently? Can users upload their own style references and have the AI imitate them? Can you maintain style uniformity when generating in bulk?
Evaluation methodology: Design a series of test cases with clear, detailed requirements (e.g., “Generate a cylinder 10cm high, 5cm in diameter, with a specific pattern on the surface”) to observe the tool in action. Try to modify and edit the generated results to assess their difficulty and effectiveness.
3. Efficiency & Speed
One of the core values of AI is to improve efficiency, so generation speed and overall workflow efficiency are important considerations:
a. Generation Time
How long does it take from submitting input to getting initial results? Is it seconds, minutes, or hours? For scenarios that require rapid iteration, generation time is critical.
b. Training/Optimization Time
For some optimization-based methods (e.g., NeRF, SDS), the training or optimization process can be time-consuming. You need to understand the total time cost required to complete a high-quality build.
c. Workflow Integration Efficiency
Can the tool be seamlessly integrated into an existing 3D design or development pipeline? Is it easy to import and export? What is format compatibility? Is there a need for extensive manual data transformation or pre-processing/post-processing steps? A tool that appears to be fast to generate but requires a lot of processing time may not be very efficient in practice.
d. Batch Processing
For scenarios that require large-scale production, does the tool support batch input and batch generation? How automated is it?
Evaluation methodology: Actual test of build times for different inputs. Simulate a complete workflow that evaluates the total time from input to final available output. Examine whether the tool provides APIs or scripting interfaces to support automation and batch processing.
4. Usability & User Experience
No matter how powerful they are, hard-to-use, counter-intuitive tools are difficult to accept:
a. Learning Curve
Is the tool’s interface clear and intuitive? Is the operating logic easy to understand? Does the user need a deep background in AI or 3D to use it effectively? Is there good documentation, tutorials, and community support?
b. Friendliness of interaction methods
Is it easy to input (text, image, sketch)? Is there guidance or suggestion for writing prompts? Are the previews and feedback generated in a timely manner?
c. Error Handling and Robertability
When input is improper or generation fails, can the tool provide helpful error prompts and guidance? Is the system stable and reliable?
d. Design for the target user
Is the tool designed to meet the usage habits and needs of its target user base (e.g., a tool for beginners should be simpler and more intuitive, and a tool for professionals with more advanced options)?
Evaluation methodology: Get started and try it out to simulate the usage scenarios of your target users. Check the documentation and community for reviews and FAQs from other users. Pay attention to whether the overall interaction flow of the tool is smooth and comfortable.
5. Cost & Business Model
Cost is an unavoidable factor in business decisions:
a. Pricing Model
Is it free, one-time buyout, monthly/yearly subscription, pay-per-generation/API calls/compute resource usage, or hybrid model? Which model is more in line with the expected frequency and budget?
b. Total Cost of Ownership (TCO)
In addition to direct purchase or subscription costs, there are potential hardware investments (such as on-premises GPUs), cloud resource consumption, and overhead costs such as integration, maintenance, and personnel training.
c. Usage Limits and Quotas
What are the features, builds, or quality limitations that are typically available in free or low-priced editions? Can the paid quota meet the demand? How are fees for exceeding quotas calculated?
d. Licensing
How is copyright ownership defined for generated content? What are the license terms for commercial use? Are there any potential legal risks?
Evaluation methodology: Read the tool’s pricing page and licensing agreement carefully. Calculate the total cost of different scenarios based on estimated usage. Evaluate whether the free or trial version is sufficient for adequate functionality and quality validation.
6. Integration & Extensibility
Will the tool fit well into the existing technology ecosystem and support future expansion needs?
a. Format Compatibility
What standard 3D file formats (e.g. glTF, FBX, USD, OBJ) are supported for import and export? How is it compatible with other commonly used software (DCC tools, game engines)?
b. API/SDK support
Are there APIs or SDKs available for developers to develop or integrate into custom processes? Is the API well documented? Is it feature-rich? Is the call stable?
c. Plugin ecosystem
Does it support or have an active ecosystem of plugins that can easily extend its functionality?
d. Platform Compatibility
Does the tool only support the web, or does it offer multi-platform clients such as Windows, macOS, Linux, etc.? Is there a mobile version?
Evaluation methodology: Consult the tool’s technical documentation to learn about its supported formats and interfaces. Test data interactions with other critical software. Evaluate the quality and potential of their API/SDK.
7. Support & Community
Can I get timely help when I encounter a problem? Is there an active community to exchange and learn?
a. Official Support
Do you provide official technical support channels (e.g. tickets, emails, phones)? How responsive and problem-solving are they?
b. Documentation and Tutorials
Are there comprehensive, clear, and up-to-date official documentation and tutorial resources?
c. Community ecology
Do you have an active user community (e.g., forums, Discord servers, social media groups)? What is the community like? Are users willing to share experiences and solve problems?
d. Update the iteration frequency
Is the tool’s development team active? How often are versions updated? Can you keep up with the latest technological advancements and fix known issues?
Evaluation methodology: Browse the official website, documentation, and community platforms. Try contacting technical support. Observe community discussion activity and user feedback. View the version update history.
Through systematic evaluation of these dimensions, product managers can gain a more comprehensive and objective understanding of the true capabilities, advantages and disadvantages, and applicable scenarios of an AI+3D tool, providing a solid foundation for final selection decisions.
8. Representative technologies/models/tools/cases/literature and discussions (for Part 2, “Key Capability Assessment Dimensions”)
Technical assessment checklist template
Evaluation checklist or scorecard template as a reference when using the actual evaluation tool.
[Source: Technology Checklist Templates – Checklist.com – https://checklist.com/templates/technology]
Game Engine Asset Creation and Import Guide (Unreal Engine as an example)
Articles or official documentation that discuss game engines (such as UE or Unity) or the film and television industry’s technical specifications for 3D model assets (such as topology, UV, material requirements).
[Source: Unreal Engine – Asset Creation and Import Guidelines – https://docs.unrealengine.com/5.3/en-US/asset-creation-and-import-guidelines-in-unreal-engine/]
Cloud GPU price comparison
Analyze cloud computing costs, specifically GPU/TPU instances, or compare different AI service pricing models
[Source: Cloud GPU Price Comparison [2025] – GetDeploying.com – https://getdeploying.com/reference/cloud-gpu]
AI 3D Generator Tool Review
Horizontal evaluation reports of certain AI+3D tools published by trusted third parties (e.g., technology media, research institutions, well-known bloggers).
[Source: 9 Best AI 3D Generators You Need To Try – eWeek – https://www.eweek.com/artificial-intelligence/best-ai-3d-generators/]
3. Selection consideration and decision-making: the art of weighing for product managers
After completing a systematic evaluation of potential tools, the final selection decision is often not a simple “best” or “worst” multiple-choice question, but a complex process that requires trade-offs and trade-offs between multiple dimensions. Product managers need to make decisions that best suit the current stage based on specific business goals, user needs, team capabilities, resource constraints, and other factors.
1. Clarify core needs and target scenarios
This is the premise of selection. First of all, answer clearly:
- What core problem do we want to solve with AI+3D? Is it to produce a concept prototype quickly? Is it to improve the efficiency of a specific link (e.g., texture, optimization)? Is it to lower the creative threshold for non-professional users? Or is it for mass content generation?
- Who are the target users? Are you an experienced 3D artist? Is it a designer or developer? Or an ordinary C-end user? What are their requirements for quality, efficiency, and ease of use?
- What is the final deliverable? Is it a concept draft for internal review? Are you an optimized asset that needs to be imported into the game engine? Is it direct-to-consumer personalized content? What are its quality and format requirements?
- What are the key constraints for use cases? Do you need to generate in real time? Are you extremely cost-sensitive? Do you need tight integration with existing complex workflows?
Only by clarifying the core requirements and scenario constraints can the matching degree of different tools be evaluated in a targeted manner. For example, a team that pursues extreme efficiency for rapid concept design may value generation speed and ease of use, and has a higher tolerance for the topological quality of the output model; A team that needs to use generative assets directly for game development must prioritize the “usability” quality (topology, UV, PBR) of the output model.
2. Assess technology maturity and risk
AI+3D technology is still evolving rapidly, and many tools may be based on very cutting-edge but also unstable technologies.
- Choose a technology with moderate maturity: For core business or scenarios with high stability requirements, prefer tools or technical paths that are relatively mature, have more validation, and have successful cases. For exploratory or non-critical functions, try more novel and bold technologies, but do a good job of risk management and failure planning.
- Be wary of “over-promise”: Be cautious about the manufacturer’s advertising and verify its claimed capabilities through practical testing. Pay attention to the true limitations of the technology and avoid being misled by short-term hype or hype.
- Consider technology lock-in risks: Over-reliance on a specific vendor’s closed-source technology or platform may pose a risk of being “locked in” in the future. Where possible, prioritize solutions that support open standards, provide APIs, or have better interoperability.
3. Weigh “self-development vs. procurement vs. open source”
Teams typically have several options for how to acquire AI+3D capabilities:
a. Direct procurement of business tools/services (SaaS/Software/API)
Merit: Quick access to usable features often has good ease of use and technical support, and does not require extensive R&D resources.
Shortcoming: It may be costly, especially for large-scale use, limited customization capabilities, and potential technology lock-in risks.
b. Secondary development based on open source projects
Merit: High flexibility, deep customization, no direct software licensing fees, and the ability to master core technologies.
Shortcoming: It requires a strong R&D team (algorithms, engineering), a long development cycle, and needs to solve problems such as deployment, maintenance, and updates by itself, and the stability and continuous maintenance of open source projects also need to be evaluated.
c. Completely self-developed
Merit: Full control of technology and products can build unique barriers to competition.
Shortcoming: With huge R&D investment, extremely high technical thresholds, and the greatest risks, it is only suitable for large companies or research institutions with top AI R&D strength and long-term strategic investment.
Product managers need to weigh the pros and cons of these paths based on the company/team’s technical strength, capital budget, project cycle, technical control requirements, and strategic goals. For most teams, it is more realistic to adopt commercial tools or develop based on established open source projects in the early stages.
4. Consider team capabilities and learning costs
The introduction of new tools or technologies is bound to be accompanied by the team’s learning and adaptation process.
- Assess your team’s existing skills stack: Do team members have the foundational knowledge needed to use the new tools (e.g., 3D basics, AI concepts, specific software operations)? Do I need to invest in additional training resources?
- Choose a tool that matches your team’s capabilities: For teams with relatively weak technical skills or those who need to get started quickly, prioritize tools with good ease of use, a gentle learning curve, and good documentation and community support.
- Step-by-step introduction and iteration: Consider piloting the introduction of new tools in small-scale or non-core projects to gradually familiarize and master them as the team accumulates experience before rolling them out to a wider range of applications.
5. Establish a continuous evaluation and adjustment mechanism
Technologies and tools in AI+3D are iterating extremely fast, and today’s best options may be lagging behind tomorrow. Therefore, selection decisions should not be static.
- Keep the market focused: Product managers need to keep an eye on industry trends, new technological advancements, and emerging tools.
- Regular re-evaluation: Is it still optimal to reassess the tools or technology stack currently in use (e.g., quarterly or semi-annually)? Are there better alternatives that emerge?
- Embrace Change, Adapt with Agile: Establish a flexible technology selection and product iteration mechanism, which can adjust tool selection or technical routes in a timely manner according to technological development and changes in business needs.
The selection process itself is a process that requires a combination of technical understanding, business judgment, risk awareness and communication and coordination skills. Product managers play a key role as decision-makers and enablers in this process.
6. Representative Techniques/Models/Tools/Cases/Literature and Discussions (for Part 3, “Selection Considerations and Decision-Making”)
Product Requirements Analysis Methodology (User Story Map, Kano Model)
A general product requirements analysis methodology or template (such as user story map, Kano model, etc.) and explain how to apply it to the requirements definition stage of AI+3D tool selection.
[Source: User Story Mapping by Jeff Patton – https://www.jpattonassociates.com/user-story-mapping/ ; The Kano Model Guide – https://kanomodel.com/]
Technology Maturity Curve (Gartner Hype Cycle)
The Gartner Hype Cycle or similar technology maturity curve model highlights its value in assessing technology risk and developing adoption strategies.
[Source: Gartner Hype Cycle – https://www.gartner.com/en/research/methodologies/gartner-hype-cycle]
Build vs Buy
Discuss the considerations for “build vs buy” decisions (such as cost, time, core competencies, risks, etc.) when selecting technologies
[Source: Build vs Buy: The 6 steps framework to avoid disasters – DivByZero – https://divbyzero.com/blog/build-vs-buy/]
Agile development principles
Agile Development Manifesto, or related principles, emphasizes the importance of staying flexible and adaptable in a rapidly changing technology landscape.
[Source: Principles behind the Agile Manifesto – https://agilemanifesto.org/principles.html]
Conclusion: In the jungle of tools, find the path to value
The ecosystem of AI-generated 3D tools and services is expanding and evolving at an unprecedented rate, offering us endless possibilities but also confusion about choice. From text-to-3D platforms that can “turn stone into gold” to refined 3D scanning and reconstruction applications, to traditional software plugins that seamlessly integrate AI capabilities, and efficiency tools that focus on specific aspects such as texture and optimization, this “jungle of tools” is both exciting and disorienting.
The core purpose of this note is to provide product managers with a “map” through this jungle and a “compass” for a set of evaluation tools. We have sorted out the main landscape of the tool ecosystem and identified the key dimensions to focus on when evaluating tools – looking beyond the cool effect at the surface, and delving into their core capabilities such as generation quality (especially “usability” quality), controllability, efficiency, ease of use, cost, integration, and technical support. In the end, we emphasize that selection decisions are not simply prioritizing the pros and cons, but an “art” that requires comprehensive trade-offs based on specific business needs, user scenarios, team capabilities, and risk appetite.
For product managers, understanding and leveraging tools is a key part of realizing product value. In the emerging field of AI+3D, we need to:
- Stay curious and keep learning: Proactively understand and experiment with emerging tools and technologies.
- Establish a framework and systematic evaluation: Use structured evaluation dimensions to conduct objective and in-depth analysis.
- Focus on needs and make rational decisions: Always take solving real problems and meeting user needs as the starting point to make the most suitable choice for the current situation.
- Embrace iteration and adapt flexibly: Stay observant and ready to adjust your strategy as needed in a rapidly changing environment.
Only by mastering the methodology of evaluating and selecting tools can we ride the AI + 3D technology wave with greater confidence and transform it into excellent products that truly empower users, drive innovation, and create value. In the next note (S2E04), we will begin an in-depth exploration of NeRF, a revolutionary technology, dissecting its principles, challenges, and potential applications.
