Point-NeRF: High-quality Rendering with Neural Point Clouds

Table of Contents

1. Introduction
2. NeRF Model
3. Issues with NeRF Model
4. Point-NeRF Model
5. Multi-View Reconstruction Network
6. Feature Extraction Network
7. Optimization
8. Point Pruning and Growing Techniques
9. Advantages of Point-NeRF Model
10. Comparison with Other Rendering Methods
11. Scalability of Point-NeRF Model
12. Effectiveness of Point Growing
13. Conclusion
14. FAQs

Point-NeRF: A Point-Based Neural Radiance Fields Model

Neural Radiance Fields (NeRF) is a popular model for optimizing a scene by taking hundreds of multi-view images. However, NeRF has some issues with shading sampling efficiency and scalability. To address these issues, the University of Southern California and Adobe Research have developed Point-NeRF, a point-Based neural radiance fields model that installs neural features on each point and obtains a neural point cloud.

NeRF Model

The original NeRF model encodes the entire radiance field as MLPs, which take the shading location and the ray direction as input and output RGB colors and the volume density. However, since NeRF is unaware of scene geometry, its shading sampling is inefficient. Using a single network to encode the scene also makes it hard to Scale and converge.

Issues with NeRF Model

The inefficient sampling strategy and lack of scene geometry awareness in NeRF model make it difficult to scale and converge. To address these issues, Point-NeRF leverages multi-view construction methods to quickly reconstruct a point cloud.

Point-NeRF Model

Point-NeRF installs neural features on each point and obtains a neural point cloud. During ray marching, Point-NeRF skips empty regions and only computes shading near the geometry prior. Once the ray arrives at the nearby regions of the geometry prior, Point-NeRF queries K nearest neural points and uses their relative positions, point features, and the ray directions as input to compute the RGB color and the volume density.

Multi-View Reconstruction Network

To get the Shape prior from the 2D images input, a multi-view reconstruction network generates depth images and combines them into a point cloud. We can also use an existing point cloud here.

Feature Extraction Network

Another feature extraction network projects the image features onto the points and finally creates a neural point cloud. During optimization, we only sample the shading points near the shade prior.

Optimization

The K nearest neural points are queried, and their features are aggregated according to their relative positions to the shading location and a scalar value, the point confidence. The MLPs use the aggregated features to generate view-dependent colors and volume density. After aggregating the radiance along the ray, we can calculate the rendering loss between the generated and the ground-truth color on the pixel. The gradients will optimize not only the MLPs but also the point features.

Point Pruning and Growing Techniques

We also propose point pruning and growing techniques to address outliers and holes in the initial point cloud. If our model predicts a high volume density at a shading location and its neural point neighbors are relatively far away from it, we can confidently add a new point at its location. The process can expand the boundary of our point cloud to fill the holes and improve the final results.

Advantages of Point-NeRF Model

The efficient sampling strategy, the radiance field localization, and the flexibility of points help Point-NeRF achieve fast convergence, high rendering quality, flexibility for editing, and strong scalability for large scenes. Since the radiance field is distributed as point features, Point-NeRF can encode more fine-grained details and high-frequency information, thus finally achieving better rendering quality than NeRF, sparse voxel-based NeRF methods, and other point-based rendering methods.

Comparison with Other Rendering Methods

Point-NeRF also achieves a convergence speed, which is 30 times faster than NeRF. On other datasets such as DTU and Tanks & Temples, Point-NeRF also consistently achieves high-quality rendering results with fast convergent speed.

Scalability of Point-NeRF Model

Because our point representation skips all the empty regions and only encodes radiance fields near the surface of objects, Point-NeRF is scalable to large-scale scenes, such as ScanNet.

Effectiveness of Point Growing

Here we also demonstrate the effectiveness of point growing. With only rendering loss, Point-NeRF can even grow out the entire chair from only 1000 initial points.

Conclusion

Point-NeRF is a point-based neural radiance fields model that addresses the issues with the original NeRF model. It achieves fast convergence, high rendering quality, flexibility for editing, and strong scalability for large scenes.

FAQs

Q: What is Point-NeRF? A: Point-NeRF is a point-based neural radiance fields model that installs neural features on each point and obtains a neural point cloud.

Q: What are the issues with the original NeRF model? A: The original NeRF model has issues with shading sampling efficiency and scalability.

Q: How does Point-NeRF address the issues with the original NeRF model? A: Point-NeRF leverages multi-view construction methods to quickly reconstruct a point cloud and installs neural features on each point to obtain a neural point cloud.

Q: What are the advantages of Point-NeRF? A: The advantages of Point-NeRF include fast convergence, high rendering quality, flexibility for editing, and strong scalability for large scenes.

Q: How does Point-NeRF compare to other rendering methods? A: Point-NeRF achieves a convergence speed, which is 30 times faster than NeRF, and consistently achieves high-quality rendering results with fast convergent speed on other datasets such as DTU and Tanks & Temples.