Efficient 3D Reconstruction from Image Pairs using Differentiable Gaussian Splatting

Belangrijkste concepten

Our method, pixelSplat, reconstructs a 3D radiance field parameterized by Gaussian primitives from just a pair of input images. This enables real-time rendering and an interpretable 3D representation, while outperforming state-of-the-art light field rendering methods.

Samenvatting

The paper introduces pixelSplat, a method for generalizable novel view synthesis that reconstructs a 3D radiance field from just a pair of input images. The key innovations are:

Epipolar Encoding: The method uses a multi-view encoder that resolves scale ambiguity by finding cross-view pixel correspondences and associating them with positionally encoded depth values. This allows the model to infer the correct 3D geometry consistent with the arbitrary scale of the input camera poses.

Differentiable Gaussian Prediction: To overcome the issue of local minima in primitive-based 3D reconstruction, the method predicts the locations of Gaussian primitives probabilistically. A neural network predicts a discrete probability distribution over depth buckets, and Gaussian locations are sampled from this distribution. This sampling is made differentiable via a reparameterization trick, allowing gradients to flow through the representation.

Real-time Rendering: The 3D radiance field is represented using Gaussian primitives, which can be efficiently rendered via rasterization. This enables real-time rendering and a memory-efficient training process, in contrast to the high computational cost of prior generalizable novel view synthesis methods.

The paper demonstrates that pixelSplat outperforms state-of-the-art light field rendering methods on the RealEstate10k and ACID datasets, while being significantly faster and more memory-efficient during both training and inference.

Statistieken

Our method requires 650x less time to render 100 novel views compared to the next-fastest baseline.
Our method uses significantly less memory per ray at training time compared to the baselines.

Citaten

"We significantly outperform previous black-box based light field transformers on the real-world ACID and RealEstate10k datasets while drastically reducing both training and rendering cost and generating explicit 3D scenes."
"To overcome local minima inherent to sparse and locally supported representations, we predict a dense probability distribution over 3D and sample Gaussian means from that probability distribution."

Belangrijkste Inzichten Gedestilleerd Uit

pixelSplat

by David Charat... om arxiv.org 04-03-2024

https://arxiv.org/pdf/2312.12337.pdf

Diepere vragen

How could the probabilistic Gaussian prediction approach be extended to other types of 3D primitives beyond Gaussians

The probabilistic Gaussian prediction approach could be extended to other types of 3D primitives by adapting the prediction mechanism to suit the characteristics of different primitives. For example, for primitives with more complex shapes or structures, the neural network could be designed to predict parameters that are specific to those shapes. Additionally, the discrete probability distribution over depth buckets could be replaced with a more continuous distribution for primitives that require a finer level of detail. By customizing the prediction process for each type of primitive, the model could effectively handle a variety of 3D representations beyond Gaussians.

What are the potential limitations of the epipolar encoding scheme, and how could it be further improved to handle more challenging real-world scenarios

The epipolar encoding scheme, while effective in resolving scale ambiguity and finding cross-view correspondences, may have limitations in handling more challenging real-world scenarios. One potential limitation is the reliance on accurate depth estimation, which can be challenging in scenes with complex geometry or occlusions. To improve the scheme, incorporating additional cues or information, such as semantic segmentation or object detection, could enhance the accuracy of depth predictions. Furthermore, integrating multi-view consistency constraints or leveraging temporal information from video sequences could help improve the robustness of the encoding scheme in challenging scenarios.

Could the insights from this work on differentiable 3D reconstruction be applied to other inverse graphics problems, such as 3D object detection or segmentation

The insights from this work on differentiable 3D reconstruction could be applied to other inverse graphics problems, such as 3D object detection or segmentation, by leveraging similar principles of implicit representation learning and differentiable rendering. For 3D object detection, the model could learn to predict object shapes and poses in a scene by reconstructing 3D representations from multiple views and optimizing for object localization. Similarly, for 3D object segmentation, the model could infer object boundaries and segmentations by reconstructing detailed 3D structures and incorporating semantic information. By adapting the techniques and methodologies from this work, it is possible to address a wide range of inverse graphics tasks with improved accuracy and efficiency.