Summary Colorizing Radiance Fields using Knowledge Distillation arxiv.org
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CoRF is a superior method for colorizing radiance fields with potential applications in AR, VR, and content restoration, surpassing traditional methods.
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Key Points
- CoRF is a method for colorizing radiance fields in multi-view images.
- The goal is to generate colorized novel views while maintaining consistency across views.
- CoRF proposes a distillation-based method to transfer color knowledge from existing 2D colorization methods to the radiance field network.
- The method outperforms baselines in terms of 3D consistency and produces better colorized novel views.
- It is effective for colorizing multi-view IR images and in-the-wild grey-scale content.
Summaries
28 word summary
CoRF colorizes radiance fields using knowledge distillation, addressing limitations of traditional methods. It outperforms baselines and has potential applications in AR, VR, infra-red sensors, and legacy content restoration.
75 word summary
CoRF is a distillation-based method for colorizing radiance fields in multi-view images. It addresses limitations of traditional methods by transferring color knowledge from 2D colorization methods. CoRF utilizes a two-stage training process and multi-scale self-regularization to mitigate color inconsistency. It outperforms baselines in terms of 3D consistency and produces superior colorized novel views for indoor, outdoor, infra-red, and old grey-scale multi-view images. CoRF has potential applications in AR, VR, infra-red sensors, and restoring legacy content.
135 word summary
CoRF is a method for colorizing radiance fields in multi-view images while maintaining consistency across views. It addresses the limitations of traditional colorization methods by proposing a distillation-based approach that transfers color knowledge from existing 2D colorization methods to the radiance field network. The method consists of a two-stage training process and utilizes multi-scale self-regularization to mitigate spatial color inconsistency. CoRF outperforms baselines in terms of 3D consistency and produces superior colorized novel views for both indoor and outdoor scenes, as well as for infra-red (IR) multi-view images and old grey-scale multi-view image sequences. It has potential applications in augmented reality (AR), virtual reality (VR), and can be useful for infra-red sensors and restoring legacy content. CoRF is implemented using Plenoxels as the radiance field representation, which involves a sparse voxel grid and spherical harmonics.
394 word summary
CoRF is a method for colorizing radiance fields in multi-view images to generate colorized novel views while maintaining consistency across views. Traditional colorization methods often result in artifacts and inconsistencies when applied directly to grey-scale novel views. Training a radiance field network on colorized grey-scale image sequences also does not solve the consistency issue. To address this problem, CoRF proposes a distillation-based method that transfers color knowledge from existing 2D colorization methods to the radiance field network. Experimental results show that CoRF produces superior colorized novel views compared to baselines for both indoor and outdoor scenes, as well as for infra-red (IR) multi-view images and old grey-scale multi-view image sequences.
Colorization is a well-studied problem in computer graphics, but colorizing novel views has not been well addressed. Colorizing grey-scale multi-view image sequences has potential applications in augmented reality (AR) and virtual reality (VR), especially in restoring legacy content. It can also be useful for infra-red sensors that capture shapes and objects in scenes but not color information.
CoRF proposes a two-stage training process. In the first stage, a radiance field network is trained on grey-scale multi-view images. In the second stage, color knowledge is distilled from a pre-trained colorization network into the radiance field network. Multi-scale self-regularization is used to mitigate spatial color inconsistency. The approach is effective on various grey-scale image sequences and performs well on downstream tasks such as colorizing multi-view IR images and in-the-wild grey-scale content.
Image and video colorization methods often fail to model the ill-posed problem of finding natural and aesthetically pleasing colors that maintain spatial consistency. Deep learning techniques have extended traditional methods for colorizing black-and-white images, proving effective due to their understanding of content and large-scale real-world video datasets. Knowledge distillation has been widely used to transfer knowledge from larger networks to smaller networks.
CoRF is implemented using Plenoxels as the radiance field representation, which uses a sparse voxel grid and spherical harmonics. The training process involves training the radiance field network on grey-scale multi-view images and then distilling color knowledge from a pre-trained colorization network. Multi-scale regularization is applied to address spatial color inconsistency.
Experimental results demonstrate that CoRF outperforms baselines in terms of 3D consistency and produces better colorized novel views. The method is effective for colorizing multi-view IR images and in-the-wild grey-scale content. CoRF has applications in augmented reality, virtual reality, and other domains.
626 word summary
CoRF is a method for colorizing radiance fields, which are used for high-quality novel-view synthesis in multi-view images. The goal is to generate colorized novel views from input grey-scale multi-view images while maintaining consistency across views. Applying image or video-based colorization methods directly to the generated grey-scale novel views often results in artifacts and inconsistencies. Training a radiance field network on colorized grey-scale image sequences also does not solve the consistency issue. To address this problem, CoRF proposes a distillation-based method that transfers color knowledge from existing 2D colorization methods to the radiance field network. The experimental results show that CoRF produces superior colorized novel views compared to baselines, both for indoor and outdoor scenes. The method is also effective for colorizing radiance field networks trained from infra-red (IR) multi-view images and old grey-scale multi-view image sequences.
Colorization is a well-studied problem in computer graphics, where the objective is to add color to a monochromatic signal. NeRF-based methods have become popular for generating novel views of a scene while learning the underlying geometry implicitly. However, colorizing novel views has not been well addressed in the literature. Colorizing grey-scale multi-view image sequences has great potential in applications like augmented reality (AR) and virtual reality (VR), especially in restoring legacy content. It can also be useful for modalities like infra-red sensors that capture shapes and objects in scenes but not color information.
Colorization is an ill-posed problem because there can be multiple possibilities of color given a grey-scale observation. The objective is to find a natural and aesthetically pleasing color that maintains spatial consistency. Image and video colorization methods often fail to model this aspect, resulting in inconsistencies across views. Traditional methods for colorizing black-and-white images have been extended using deep learning techniques, which have proven to be effective due to their understanding of content and large-scale real-world video datasets.
CoRF proposes a two-stage training process. In the first stage, a radiance field network is trained on input grey-scale multi-view images. In the second stage, color knowledge is distilled from a pre-trained colorization network into the radiance field network. Multi-scale self-regularization is used to mitigate spatial color inconsistency. The effectiveness of the approach is demonstrated on various grey-scale image sequences from existing datasets. The method also performs well on downstream tasks such as colorizing multi-view IR images and in-the-wild grey-scale content.
In terms of related work, image colorization has been extensively studied, with traditional methods solving an objective function using sparse inputs or deep learning methods that learn rich representations for large-scale real-world video datasets. Video colorization is more challenging as it requires maintaining temporal and spatial consistency. Knowledge distillation has been widely used to transfer knowledge from larger networks to smaller networks, and various approaches have been proposed based on hidden layer activations, intermediate representations, and adversarial loss functions.
The method is implemented using Plenoxels as the radiance field representation, which uses a sparse voxel grid and spherical harmonics for each voxel grid. The training process involves training the radiance field network on grey-scale multi-view images in the first stage and then distilling color knowledge from a pre-trained colorization network in the second stage. Multi-scale regularization is applied to address spatial color inconsistency.
The experimental results show that CoRF outperforms baselines in terms of 3D consistency and produces better colorized novel views. Quantitative metrics for cross-view consistency and a user study support the effectiveness of the method. The method is also effective for colorizing multi-view IR images and in-the-wild grey-scale content.
In conclusion, CoRF is a novel method for colorizing radiance fields using knowledge distillation. It addresses the problem of inconsistency in colorizing novel views from grey-scale multi-view images and achieves superior results compared to baselines. The method has applications in various domains, including augmented reality, virtual