Source-linked AI summary
Point-E: A System for Generating 3D Point Clouds from Complex Prompts
Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, Mark Chen
TL;DR
Text-to-3D generation seeks to extend rapid, complex-prompt image generation to 3D content creation. Point·E uses a two-stage text-to-image and image-conditioned point-cloud pipeline, producing diverse complex shapes efficiently but with low-resolution point-cloud limitations.
Problem
Text-to-3D generation aims to support 3D content creation for applications including virtual reality, gaming, and industrial design.
Method
Point·E first samples a synthetic image from text and then generates coarse and fine colored point clouds conditioned on that image.
Results
Point·E can often produce consistent, diverse, and high-quality colored 3D shapes for simple, complex, and diverse text prompts.
Takeaways & Limitations
The approach offers an efficient starting point for further work in text-to-3D synthesis and can produce point clouds suitable for some practical uses.
Takeaways & Limitations
The pipeline requires synthetic renderings and produces relatively low-resolution point clouds that do not capture fine-grained shape or texture.
Abstract
from arXiv · showhide
While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model, and then produces a 3D point cloud using a second diffusion model which conditions on the generated image. While our method still falls short of the state-of-the-art in terms of sample quality, it is one to two orders of magnitude faster to sample from, offering a practical trade-off for some use cases. We release our pre-trained point cloud diffusion models, as well as evaluation code and models, at https://github.com/openai/point-e.
1. Introduction
Point·E addresses text-to-3D generation by combining text-to-image and image-to-3D diffusion models. This two-stage pipeline targets diverse prompts while avoiding expensive per-sample optimization and can often produce colored point clouds matching simple and complex descriptions.
- 1. Introduction: Text-to-3D methods either struggle with diverse prompts because large-scale 3D datasets are scarce or require expensive optimization for each sample.The first category trains directly on 3D data; the second leverages text-image models but can encounter local minima that do not correspond to coherent objects.
- 1. Introduction: Point·E pairs a text-to-image model with an image-to-3D model to combine broad prompt coverage with efficient generation.The text-to-image model uses large-scale text-image data, while the image-to-3D model uses a smaller image-3D dataset.
- 1. Introduction: The pipeline first generates a synthetic rendered view from the text prompt, then conditions a point cloud diffusion model on that image.Figure 1 depicts this two-stage process; the image-to-3D stage produces an RGB point cloud.
- 1. Introduction: For rendering-based evaluations, Point·E converts generated point clouds into meshes using a regression-based approach.This additional conversion supports evaluation in settings based on rendered outputs.
- 1. Introduction: Point·E can often produce colored 3D point clouds that match both simple and complex text prompts.The paper releases its point cloud diffusion models, evaluation code, and evaluation models.
2. Background
The paper uses Gaussian diffusion, which gradually adds noise and learns to reverse that process for sampling. It also uses accelerated ODE sampling and classifier-free guidance to control generation.
- 2. Background: Gaussian diffusion defines a timestep-indexed noising process that gradually adds Gaussian noise according to a schedule β_t.At the final timestep, the sample contains almost no information and resembles Gaussian noise.
- 2. Background: A direct jump to a selected noising timestep is possible without running the entire forward chain.This property is described for the Gaussian diffusion setup used by the paper.
- 2. Background: Diffusion sampling starts from random Gaussian noise and repeatedly applies a learned reverse process to obtain a noiseless sample.The model predicts the effective noise added to each noisy sample to parameterize the reverse transition.
- 2. Background: Point·E uses a carefully designed second-order ODE solver to trade off diffusion sample quality and sampling efficiency.The sampler follows the differential-equation view of diffusion sampling.
- 2. Background: Classifier-free guidance increases fidelity at the expense of diversity by combining conditional and unconditional predictions during sampling.The paper applies this technique throughout its models with conditioning dropped at probability 0.1.
3. Related Work
Related work spans direct generative modeling of 3D data, text-image-guided optimization, point-cloud generation, and reconstruction from images. These approaches differ in their data requirements, prompt coverage, representations, and sampling procedures.
- 3. Related Work: Point-cloud generation has used autoencoders with latent GANs or GMMs, VAEs, flow models, and diffusion-based stages.PVD is identified as a particularly similar prior method because it generates point clouds directly with diffusion.
- 3. Related Work: 3D-aware GANs trained from 2D images typically target novel-view synthesis in forward-facing scenes rather than full 360-degree object reconstruction.This distinguishes that literature from methods focused on complete objects.
- 3. Related Work: Text-conditional 3D methods can optimize NeRFs or meshes using text-image matching objectives such as CLIP, sometimes without 3D training data.These methods optimize a 3D representation against a pretrained text-image model.
- 3. Related Work: Other text-conditional methods train GANs, flow models, or VQ-VAEs on 3D data paired with text or image-derived embeddings.Limited 3D training data tends to restrict many such methods to simple prompts or narrow object categories.
- 3. Related Work: Single- or few-image 3D reconstruction is underspecified because the model must infer details absent from the conditioning images.Several regression-based methods nevertheless report promising results on this task.
4. Method
Point·E generates colored 3D point clouds through a staged pipeline: text-conditioned view synthesis, coarse point-cloud diffusion, and 4K upsampling. It trains on rendered views and point clouds, using diffusion models conditioned on images and point-cloud context.
- Generation pipeline: The pipeline first generates a synthetic view from text, then a 1,024-point cloud, and finally a 4,096-point cloud conditioned on the coarse cloud and view.This decomposes text-to-3D generation into view synthesis, coarse generation, and upsampling stages.
- View synthesis: The view-synthesis model is fine-tuned from a 3-billion-parameter GLIDE model on rendered 3D data while retaining the original image dataset.3D renders are sampled 5% of the time during fine-tuning, and a special token selects in-distribution 3D renders at test time.
- Data processing: The models are trained on several million 3D models processed into rendered views, text descriptions, and colored point clouds.The dataset includes standardized renderings and RGB-colored point clouds.
- Data processing: Each object is rendered from 20 random camera angles and converted into a uniform 4K colored point cloud using farthest point sampling.Rendering standardizes formats, camera views, lighting, and point-cloud representation.
- Point-cloud diffusion: Point clouds are represented as K × 6 tensors containing normalized xyz coordinates and RGB colors, then generated directly by diffusion from noise.All coordinates and colors are normalized to [−1, 1].
- Point-cloud diffusion: A permutation-invariant Transformer predicts ϵ and Σ from the image, timestep, and noised point cloud, using CLIP grid features as image context.The model omits positional encodings, and its output tokens corresponding to the input points produce the diffusion predictions.
- Point-cloud diffusion: A large base diffusion model generates 1K points, while a smaller upsampler conditions on them and generates 3K additional points to reach 4K.The upsampler distinguishes conditioning points from newly generated points through separate linear embeddings.
- Mesh reconstruction: For rendering-based evaluation, generated point clouds are converted into textured meshes by predicting a signed distance field and applying marching cubes.Vertex colors are assigned from the nearest original point-cloud colors.
5. Results
Point·E’s ablations show that image conditioning and model scale improve text-to-3D evaluation, while qualitative results reveal both strong shape reconstruction and characteristic failures. Compared with other methods, it trades lower evaluation performance for much faster sampling.
- 5.1. Model Scaling and Ablations: Image conditioning substantially improves CLIP R-Precision over text-only conditioning, while grid-based image embeddings outperform a single image embedding.The grid provides more spatial information from the conditioning image.
- 5.1. Model Scaling and Ablations: Scaling the point-cloud diffusion model speeds P-FID convergence and increases final CLIP R-Precision.The evaluated base models range from 40M to 1B parameters, using full image conditioning for the scaled models.
- 5.2. Qualitative Results: Point·E often generates consistent, high-quality 3D shapes for complex prompts while binding colors to the relevant shape parts.The qualitative samples demonstrate varied inferred shapes and correct color association.
- 5.2. Qualitative Results: The model can misinterpret object proportions or infer occluded geometry incorrectly, causing generated shapes to diverge from the prompt.Examples include a tall dog instead of a short, long dog and a mirrored cone inferred beneath a traffic cone.
- 5.3. Comparison to Other Methods: Lower CLIP R-Precision may partly reflect that Point·E does not explicitly optimize every view to match the text prompt.Its point clouds also require preprocessing for rendering, and mesh conversion can lose information from the original point clouds.
- 5.3. Comparison to Other Methods: Point·E performs worse than current state-of-the-art methods on the reported evaluation, but produces samples in a small fraction of the time.The comparison uses CLIP R-Precision and reported sampling compute requirements; no standard benchmark set yet exists for text-conditional 3D synthesis.
6. Limitations and Future Work
Point·E has limitations in its input requirements, output fidelity, and potential misuse. Future work includes conditioning on real-world images and producing higher-quality 3D representations.
- The pipeline currently requires synthetic renderings, though future 3D generators could condition on real-world images.
- Point clouds are produced at relatively low resolution and do not capture fine-grained shape or texture.
- Higher-quality representations such as meshes or NeRFs could broaden the applications of the model’s outputs.
- The model may inherit biases from the DALL·E 2 system because many biases originate in the dataset.
- Point clouds could support real-world fabrication, creating risks when dangerous-object blueprints are produced or trusted without empirical validation.
7. Conclusion
Point·E generates 3D point clouds by first producing synthetic views and then conditioning colored point-cloud generation on those views. The authors conclude that it efficiently produces diverse and complex 3D shapes from text prompts.
- Point·E first generates synthetic views and then produces colored point clouds conditioned on those views.
- Point·E efficiently produces diverse and complex 3D shapes conditioned on text prompts.
- The diffusion models use batch size 64 for 1,300,000 training iterations.
- Evaluation samples use stochastic DDPM for P-FID and P-IS, while figures and CLIP R-Precision use Heun sampling.
B. P-FID and P-IS Metrics
The P-FID and P-IS evaluation pipeline uses a modified PointNet++ model trained on ModelNet40. The reported P-IS of approximately 13 is near the expected upper bound implied by the training data.
- P-FID and P-IS use a PointNet++ model trained on ModelNet40 with roughly 16 million parameters and additional augmentations.
- Table 4 reports sampling performance for different model components using the Karras sampler for base and upsampler models, but not GLIDE.
- The evaluation model applies random rotations and Gaussian point noise with standard deviation sampled from U[0, 0.01].
- P-FID uses features before the final ReLU activation, while P-IS uses predicted probabilities for 40 ModelNet40 classes.
- The generative training dataset has P-IS 12.95, making the best reported P-IS score of ∼13 near the expected upper bound.
C. Mesh Extraction
Point·E converts point clouds into meshes by predicting signed distance fields and applying marching cubes. The approach often smooths meshes and removes outliers, but may miss thin or sparse object parts.
- A Transformer-based encoder-decoder predicts signed distance values from an unordered input point cloud and queried 3D coordinates.
- The SDF regression model is trained on 2.4 million manifold meshes with Gaussian noise augmentation and a weighted L1 objective.
- The SDF uses negative values outside the surface, and its asymmetric loss encourages predicting inside the surface under uncertainty.
- The inside-favoring objective helps prevent meshes from ignoring thin or noisy point-cloud parts.
- Generated meshes are often smooth and remove outliers, but can sometimes miss thin or sparse object parts.
- A 128 × 128 × 128 grid produces 128^3 SDF queries for mesh extraction.
D. Conditioning on DALL·E 2 Samples
The point cloud model can reconstruct 3D objects from DALL·E 2-generated conditioning images, but shadows and image framing can distort the result.
- DALL·E 2-generated images can condition the model to produce reconstructed 3D point clouds.Examples include an avocado chair and a corgi.
- Shadows beneath objects are often interpreted as a dark ground plane in the reconstructed point cloud.
- When objects occupy too much of the conditioning image, the model can misinterpret their shapes.
- Adding a border around the generated image can improve reconstructed shapes in these cases.
E. Pure Text-Conditional Generation
The pure text-conditional point cloud model produces non-trivial results for simple prompts but struggles with multiple concepts and may inherit dataset biases.
- The pure text-conditional model performs worse on evaluations than the full system but still achieves non-trivial results.
- It can produce point clouds matching prompts that describe single objects, such as a desk lamp or guitar.
- It struggles with prompts that combine multiple concepts, including a small red cube atop a large blue cube.