IReNe: Instant Recoloring of Neural Radiance Fields [2024] Conference on Computer Vision and Pattern Recognition (CVPR) Advances in NERFs have allowed for 3D scene reconstructions and novel view synthesis. Yet, efficiently editing these representations while retaining photorealism is an emerging challenge. Recent methods face three primary limitations: they’re slow for interactive use, lack precision at object boundaries, and struggle to ensure multi-view consistency. We introduce IReNe to address these limitations, enabling swift, near real-time color editing in NeRF. Leveraging a pre-trained NeRF model and a single training image with user-applied color edits, IReNe swiftly adjusts network parameters in seconds. This adjustment allows the model to generate new scene views, accurately representing the color changes from the training image while also controlling object boundaries and view-specific effects. Object boundary control is achieved by integrating a trainable segmentation module into the model. The process gains efficiency by retraining only the weights of the last network layer. We observed that neurons in this layer can be classified into those responsible for view-dependent appearance and those contributing to diffuse appearance. We introduce an automated classification approach to identify these neuron types and exclusively fine-tune the weights of the diffuse neurons. This further accelerates training and ensures consistent color edits across different views. A thorough validation on a new dataset, with edited object colors, shows significant quantitative and qualitative advancements over competitors, accelerating speeds by 5x to 500x. NeRFLight: Fast and Light Neural Radiance Fields using a Shared Feature Grid [2023] Conference on Computer Vision and Pattern Recognition (CVPR) While original Neural Radiance Fields (NeRF) have shown impressive results in modeling the appearance of a scene with compact MLP architectures, they are not able to achieve real-time rendering. This has been recently addressed by either baking the outputs of NeRF into a data structure or arranging trainable parameters in an explicit feature grid. These strategies, however, significantly increase the memory footprint of the model which prevents their deployment on bandwidth-constrained applications. In this paper, we extend the grid-based approach to achieve real-time view synthesis at more than 150 FPS using a lightweight model. Our main contribution is a novel architecture in which the density field of NeRF-based representations is split into N regions and the density is modeled using N different decoders which reuse the same feature grid. This results in a smaller grid where each feature is located in more than one spatial position, forcing them to learn a compact representation that is valid for different parts of the scene. We further reduce the size of the final model by disposing of the features symmetrically on each region, which favors feature pruning after training while also allowing smooth gradient transitions between neighboring voxels. An exhaustive evaluation demonstrates that our method achieves real-time performance and quality metrics on a pair with state-of-the-art with an improvement of more than 2× in the FPS/MB ratio. ICE: Implicit Coordinate Encoder for Multiple Image Neural Representation [2023] IEEE Transactions on Image Processing (TIP) In recent years, implicit neural representations (INR) have shown their great potential to solve many computer graphics and computer vision problems. With this technique, signals such as 2D images or 3D shapes can be fit by training multi-layer perceptrons (MLP) on continuous functions, providing many advantages over conventional discrete representations. Despite being considered a promising approach to 2D image encoding and compression, the application of INR to image collections remains a challenge, since the number of parameters needed rapidly grow with the number of images. In this paper, we propose a fully implicit approach to INR which drastically reduces the size of MLP models in multiple image representation tasks. We introduce the concept of implicit coordinate encoder (ICE) and show it can be used to scale INR with the image number; specifically, by learning a common feature space between images. Furthermore, we show that our method is valid not only for image collections but also for large (gigapixel) images by applying a “divide-and-conquer” strategy. We propose an auto-encoder deep neural network architecture, with a single ICE (encoder) and multiple MLP (decoders), which are jointly trained following a multi-task learning strategy. We demonstrate the benefits coming from ICE when it is implemented as a one-dimensional convolutional encoder, including a better performance of the downstream MLP models with an order of magnitude fewer parameters. Our method is the first one to make use of convolutional blocks in INR networks, unlike the conventional approach of using MLP architectures only. We show the benefits of ICE in two experimental scenarios: a collection of twenty-four small ( 768×512 ) images (Kodak dataset), and a single large ( 3072×3072 ) image (dwarf planet Pluto), achieving better quality than previous fully-implicit methods, using up to 50% fewer parameters. Reference: Fernando Rivas-Manzaneque, Angela Ribeiro, Orlando Avila-García, IEEE Transactions on Image Processing.