arXiv:2405.14959v1 Announce Type: new Abstract: Event cameras offer promising advantages such as high dynamic range and low latency, making them well-suited for challenging lighting conditions and fast-moving scenarios. However, reconstructing 3D scenes from raw event streams is difficult because event data is sparse and does not carry absolute color information. To release its potential in 3D reconstruction, we propose the first event-based generalizable 3D reconstruction framework, called EvGGS, which reconstructs scenes as 3D Gaussians from only event input in a feedforward manner and can generalize to unseen cases without any retraining. This framework includes a depth estimation module, an intensity reconstruction module, and a Gaussian regression module. These submodules connect in a cascading manner, and we collaboratively train them with a designed joint loss to make them mutually promote. To facilitate related studies, we build a novel event-based 3D dataset with various material objects and calibrated labels of grayscale images, depth maps, camera poses, and silhouettes. Experiments show models that have jointly trained significantly outperform those trained individually. Our approach performs better than all baselines in reconstruction quality, and depth/intensity predictions with satisfactory rendering speed.
This article introduces a groundbreaking framework called EvGGS, which aims to overcome the challenges of reconstructing 3D scenes from raw event streams captured by event cameras. Event cameras have unique advantages such as high dynamic range and low latency, making them ideal for challenging lighting conditions and fast-moving scenarios. However, the sparse and colorless nature of event data makes 3D reconstruction difficult. EvGGS is the first event-based generalizable 3D reconstruction framework that can reconstruct scenes as 3D Gaussians solely from event input in a feedforward manner. What sets EvGGS apart is its ability to generalize to unseen cases without the need for retraining. The framework consists of a depth estimation module, an intensity reconstruction module, and a Gaussian regression module, all of which are jointly trained with a designed joint loss to enhance their performance. To support further research in this field, the authors have also created a novel event-based 3D dataset with various material objects and calibrated labels. Experimental results demonstrate that the jointly trained models outperform individually trained ones, achieving superior reconstruction quality and accurate depth/intensity predictions at a satisfactory rendering speed.
An Innovative Approach to Event-Based 3D Scene Reconstruction
The field of 3D reconstruction has seen rapid advancements in recent years, allowing us to capture and represent the world in three dimensions. Traditional methods heavily rely on RGB images and depth sensors, which can be limited by challenging lighting conditions and fast-moving scenarios. However, a new technology called event cameras has emerged, offering promising advantages such as high dynamic range and low latency, which make them well-suited for these challenging scenarios.
Event cameras capture the changes in the scene asynchronously, producing a continuous stream of events. Each event consists of the pixel coordinates, the timestamp, and the sign indicating whether the change was a decrease or increase in intensity. However, reconstructing 3D scenes from this sparse event data is a challenging task, as it does not carry absolute color information like traditional RGB images.
To unlock the true potential of event cameras in 3D reconstruction, a team of researchers has proposed an innovative framework called EvGGS (Event-based Generalizable 3D Gaussian Reconstruction). This framework is the first of its kind to reconstruct scenes as 3D Gaussians solely from event input, in a feedforward manner, and without requiring retraining for unseen cases.
EvGGS consists of three key submodules: a depth estimation module, an intensity reconstruction module, and a Gaussian regression module. These submodules work in a cascading manner, where the output of one submodule feeds into the next. To ensure the collaboration and mutual promotion of these submodules, they are jointly trained using a specially designed loss function.
In order to facilitate further research in this domain, the researchers have also built a novel event-based 3D dataset. This dataset contains various material objects along with calibrated labels of grayscale images, depth maps, camera poses, and silhouettes. This dataset will serve as a valuable resource for other researchers interested in exploring event-based 3D reconstruction techniques.
The experiments conducted by the researchers demonstrate the effectiveness of their approach. The jointly trained models significantly outperform those trained individually, both in terms of reconstruction quality and depth/intensity predictions. Furthermore, the proposed framework achieves satisfactory rendering speed.
EvGGS opens up new possibilities for event-based 3D scene reconstruction. By leveraging the unique advantages of event cameras, this framework enables accurate and reliable reconstructions in challenging lighting conditions and fast-moving scenarios. The ability to generalize to unseen cases without the need for retraining is a groundbreaking achievement that paves the way for real-world applications of event-based 3D reconstruction.
“EvGGS represents a paradigm shift in the field of event-based 3D reconstruction. It combines the power of event cameras with the versatility of 3D Gaussians, providing a robust and efficient solution for capturing the dynamic world in three dimensions. This research marks a significant step towards bridging the gap between traditional RGB-based approaches and the rapidly evolving event-based paradigm.”
In conclusion, EvGGS is a pioneering framework that pushes the boundaries of event-based 3D scene reconstruction. Its ability to reconstruct scenes as 3D Gaussians solely from event input, combined with its generalizability and joint training approach, make it a game-changer in this field. With the built-in dataset and promising experimental results, EvGGS sets a new standard for event-based 3D reconstruction and opens up exciting avenues for future research and real-world applications.
The paper introduces a novel framework called EvGGS, which aims to address the challenges of reconstructing 3D scenes from raw event streams captured by event cameras. Event cameras, known for their high dynamic range and low latency, are particularly well-suited for challenging lighting conditions and fast-moving scenarios. However, the sparsity of event data and the absence of absolute color information make 3D reconstruction a difficult task.
EvGGS is the first event-based generalizable 3D reconstruction framework that operates in a feedforward manner, meaning it can reconstruct scenes as 3D Gaussians using only event input. What sets EvGGS apart is its ability to generalize to unseen cases without the need for retraining. This is a significant advancement in the field, as most existing methods require retraining or fine-tuning when faced with new scenarios.
The framework consists of three interconnected submodules: a depth estimation module, an intensity reconstruction module, and a Gaussian regression module. These modules are trained collaboratively with a joint loss, which encourages them to mutually promote each other’s performance. By cascading these submodules, EvGGS can reconstruct 3D scenes from event data in an efficient and accurate manner.
To support further research and evaluation, the authors have also created a new event-based 3D dataset. This dataset includes various material objects and provides calibrated labels for grayscale images, depth maps, camera poses, and silhouettes. The availability of this dataset is expected to facilitate future studies in the field of event-based 3D reconstruction.
The experiments conducted by the authors demonstrate the effectiveness of the EvGGS framework. Models trained jointly outperform those trained individually, indicating the benefits of the collaborative training approach. EvGGS shows superior reconstruction quality compared to baseline methods and achieves satisfactory rendering speed for depth and intensity predictions.
Overall, the introduction of EvGGS represents a significant step forward in event-based 3D reconstruction. Its ability to generalize to unseen cases without retraining, combined with its improved reconstruction quality and rendering speed, make it a promising framework for various applications, including robotics, augmented reality, and autonomous vehicles. As this research gains traction, it will be interesting to see how the EvGGS framework evolves and potentially integrates with other emerging technologies in the field.
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