Turning a set of ordinary 2D photos into a navigable 3D scene used to require specialised scanning rigs, careful calibration, and time-consuming modelling. Today, generative approaches are changing that workflow. Models such as NeRFs (Neural Radiance Fields) can learn how a scene looks from different viewpoints and then render new views that were never captured by a camera. This capability is quickly becoming useful in product visualisation, virtual production, robotics, and AR/VR. For learners exploring this space through a generative AI course in Pune, understanding the “why” and “how” behind 2D-to-3D generation is an important foundation.
Why 2D-to-3D Generation Is Hard
A single image collapses depth into a flat projection. Many different 3D shapes can produce the same 2D picture, which is why the problem is fundamentally ambiguous. To recover 3D structure, a model needs extra cues, such as:
- Multi-view images (photos taken from different angles)
- Camera pose information (where each image was taken from)
- Lighting and shading cues (how surfaces interact with light)
- Priors learned from data (common patterns of real-world objects and scenes)
Traditional photogrammetry can reconstruct geometry by matching features across images, but it often struggles with reflective surfaces, textureless objects, moving elements, and inconsistent lighting. Generative 3D approaches aim to be more flexible by learning a representation that can fill gaps and handle imperfect inputs.
NeRFs: The Core Idea in Simple Terms
A Neural Radiance Field represents a scene as a function that maps a 3D position and viewing direction to two things: colour and density. Instead of building a mesh first, NeRF learns a continuous “volume” representation. The typical pipeline looks like this:
1) Input preparation
You start with multiple images of a scene and estimate camera parameters (intrinsics and poses). Many toolchains automate this step, but accurate camera alignment remains important.
2) Learning the radiance field
The neural network is trained so that, when you “render” rays through the scene (similar to how a camera works), the predicted colours match the training images. Rendering is done by sampling points along each ray and integrating predicted colour and density.
3) Novel view synthesis
Once trained, the NeRF can generate new viewpoints smoothly—effectively allowing you to orbit around an object or move through a scene even if those exact camera views were never captured.
NeRFs are especially good at producing realistic view-dependent effects (like subtle specular highlights). However, classic NeRF training can be slow, and outputs are not always immediately usable in real-time applications without further optimisation. If you are taking a generative AI course in Pune, it helps to treat NeRF as the conceptual baseline: it clarifies how learned 3D representations differ from polygon meshes and why rendering-based supervision works.
Beyond NeRF: Other Models for 3D Synthesis
NeRFs are not the only approach. Depending on your goal—fast generation, editable assets, or compatibility with game engines—other model families may be a better fit.
1) 3D Gaussian Splatting
This approach represents a scene using many small 3D Gaussians that can be rendered efficiently. It often trains faster than classic NeRFs and can support real-time viewing in many cases. It is popular for creating immersive scene captures where speed matters.
2) Diffusion-based 3D generation
Diffusion models, widely used for 2D image synthesis, are also being adapted for 3D. They can generate 3D-consistent outputs from text prompts or limited images, often through intermediate representations (multi-view images, depth maps) or by optimising a 3D representation to match diffusion-guided views. These methods can be powerful for concept generation, but controlling geometry precisely can be challenging.
3) Mesh and implicit surface models
Some pipelines predict explicit meshes directly, while others learn implicit surfaces (such as signed distance functions) that can be converted into meshes. These are attractive when you need assets that can be rigged, animated, or imported into standard 3D workflows.
A Practical Workflow from 2D Inputs to a Usable 3D Asset
In real projects, “generate 3D” usually means more than rendering pretty views. A practical workflow often includes:
- Capture strategy: consistent lighting, enough viewpoints, minimal motion blur, and good coverage.
- Reconstruction/training: NeRF, Gaussian splats, or a hybrid approach based on constraints.
- Post-processing: clean-up, removing floaters/artifacts, and improving texture consistency.
- Asset conversion: converting to a mesh or a format usable in engines, sometimes via surface extraction and retopology.
- Validation: checking scale, alignment, and performance (poly count, texture size, rendering speed).
These steps mirror the skills expected in production teams, which is why hands-on practice in a generative AI course in Pune can be valuable—especially when it includes both modelling fundamentals and ML-based reconstruction.
Key Challenges and What to Watch For
Even with modern models, a few issues show up repeatedly:
- Occlusions and missing views: unseen surfaces will be guessed and can look wrong.
- Reflective/transparent objects: mirrors, glass, and shiny metals remain difficult.
- Consistency for editing: generated scenes may look real but be hard to edit cleanly.
- Compute cost: training and rendering can be resource-intensive without optimisation.
- Evaluation: image similarity is not always the same as correct geometry.
Conclusion
3D object and scene generation from 2D inputs is shifting from manual modelling to learned reconstruction and synthesis. NeRFs introduced a clear and influential way to learn scenes via rendering supervision, while newer methods like Gaussian splatting and diffusion-based pipelines expand the range of what is practical—faster training, more flexibility, and new ways to generate assets. As tools mature, the key advantage will come from knowing which representation fits your use case and how to take outputs into real production pipelines. For anyone building these skills through a generative AI course in Pune, focusing on fundamentals—camera geometry, representations, and evaluation—will make the difference between impressive demos and dependable results.