Ultra-Fast Single-View 3D Reconstruction with Splatter Image

Основні поняття

Splatter Image is an ultra-efficient approach for monocular 3D object reconstruction that predicts a mixture of 3D Gaussians from a single input image, enabling fast and high-quality 360° reconstructions.

Анотація

The paper introduces Splatter Image, a novel method for monocular 3D object reconstruction that achieves state-of-the-art results in terms of reconstruction quality and speed.
The key idea is to represent the 3D object as a mixture of 3D Gaussians, where the parameters of each Gaussian (opacity, position, shape, and color) are predicted by a 2D image-to-image neural network. This representation allows for efficient rendering and training, as the Gaussians can be stored in a 2D "Splatter Image" and processed using 2D operators.
The authors show that their method can produce high-quality 360° reconstructions from a single input image, outperforming slower and more expensive alternatives on several benchmark datasets, including synthetic (ShapeNet), real (CO3D), and large-scale (Objaverse) datasets. Notably, their method can be trained on a single GPU, while prior works often require distributed training on dozens or even hundreds of GPUs.
The authors also extend their method to handle multiple input views by fusing the individual Gaussian mixtures predicted from each view. This is achieved by registering the Gaussians to a common coordinate frame and allowing the network to exchange information between views via cross-attention layers.
Overall, Splatter Image demonstrates that a simple and efficient design can lead to state-of-the-art performance in single-view 3D reconstruction, with significant advantages in terms of training and inference speed.

Статистика

The paper reports the following key metrics:

On ShapeNet-SRN Cars, Splatter Image achieves a PSNR of 24.00, SSIM of 0.92, and LPIPS of 0.078.
On ShapeNet-SRN Chairs, Splatter Image achieves a PSNR of 24.43, SSIM of 0.93, and LPIPS of 0.067.
On CO3D Hydrants, Splatter Image achieves a PSNR of 21.80, SSIM of 0.80, and LPIPS of 0.150.
On CO3D Teddybears, Splatter Image achieves a PSNR of 19.44, SSIM of 0.73, and LPIPS of 0.231.
On Google Scanned Objects, Splatter Image achieves a PSNR of 21.06, SSIM of 0.88, and LPIPS of 0.111, outperforming the much more expensive OpenLRM baseline.

Цитати

"Splatter Image is an ultra-efficient approach for monocular 3D object reconstruction that uses an image-to-image neural network to map the input image to another image that holds the parameters of one coloured 3D Gaussian per pixel."
"Remarkably, the predicted 3D Gaussians provide 360° reconstructions of quality comparable or superior to much slower methods."
"Our method is more than 1000× faster in testing than PixelNeRF and VisionNeRF (while achieving equal or superior quality of reconstruction)."

Ключові висновки, отримані з

Splatter Image: Ultra-Fast Single-View 3D Reconstruction

by Stanislaw Sz... о arxiv.org 04-17-2024

https://arxiv.org/pdf/2312.13150.pdf

Глибші Запити

How could the Splatter Image representation be extended to handle more complex object geometries, such as thin structures or highly detailed surfaces?

The Splatter Image representation could be extended to handle more complex object geometries by incorporating additional information or features into the Gaussian mixture representation. For thin structures, the network could be trained to predict multiple Gaussians in close proximity to each other to capture the fine details. This would allow for a more accurate representation of thin structures and intricate details. Additionally, the network could be modified to predict Gaussian parameters that are more elongated or have different shapes to better capture the geometry of thin structures. By adjusting the parameters of the Gaussians, the Splatter Image representation can be tailored to handle a wider range of object geometries, including thin structures and highly detailed surfaces.

What are the potential limitations of the Gaussian mixture representation, and how could it be improved or combined with other 3D representations to address these limitations?

One potential limitation of the Gaussian mixture representation is its ability to accurately capture complex geometric shapes with sharp edges or discontinuities. To address this limitation, the Gaussian mixture representation could be combined with other 3D representations, such as voxel grids or implicit neural representations. By incorporating voxel grids, the representation can better capture sharp edges and fine details in the geometry. Voxel grids provide a more structured representation of 3D space, allowing for more precise modeling of complex shapes. Additionally, combining the Gaussian mixture representation with implicit neural representations can enhance the overall representation's ability to capture intricate details and complex geometries. By leveraging the strengths of each representation, the combined approach can overcome the limitations of the Gaussian mixture representation and provide a more comprehensive representation of 3D objects.

Given the efficiency of the Splatter Image method, how could it be leveraged in applications beyond single-view 3D reconstruction, such as real-time 3D scene understanding or interactive 3D modeling?

The efficiency of the Splatter Image method opens up opportunities for its application in various areas beyond single-view 3D reconstruction. In real-time 3D scene understanding, the Splatter Image representation can be utilized for fast and accurate reconstruction of dynamic scenes by continuously updating the Gaussian mixture based on new input images. This can enable real-time tracking and reconstruction of objects in motion, making it valuable for applications like augmented reality and robotics.
In interactive 3D modeling, the Splatter Image method can be leveraged for quick and intuitive manipulation of 3D objects. By allowing users to interactively modify the parameters of the Gaussians in the representation, users can sculpt and shape objects in real-time. This can enhance the user experience in 3D modeling software and enable artists and designers to create detailed and intricate 3D models efficiently. Additionally, the efficiency of the method makes it suitable for real-time rendering and visualization, enabling interactive exploration of 3D scenes and objects with high fidelity and responsiveness.