Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views

מושגי ליבה

A novel framework - MonoSelfRecon - that achieves explicit 3D mesh reconstruction for generalizable indoor scenes with monocular RGB views by purely self-supervised training on voxel-SDF, without requiring any depth or SDF annotations.

תקציר

The content presents a novel framework called MonoSelfRecon that addresses the challenge of efficient and accurate 3D scene reconstruction from monocular RGB views. The key highlights are:

MonoSelfRecon is the first to achieve explicit 3D mesh reconstruction for generalizable indoor scenes using purely self-supervised training on voxel-SDF, without requiring any depth or SDF annotations.

The framework follows an Autoencoder-based architecture, decoding voxel-SDF and a generalizable Neural Radiance Field (NeRF), where the NeRF guides the voxel-SDF in self-supervision.

Novel self-supervised losses are proposed, which not only enable pure self-supervision, but can also be used together with supervised signals to further boost supervised training.

Experiments show that MonoSelfRecon trained in pure self-supervision outperforms current best self-supervised indoor depth estimation models and is comparable to 3D reconstruction models trained in full supervision with depth annotations.

The framework is not restricted to a specific model design, and can be used to extend any models with voxel-SDF for purely self-supervised 3D reconstruction.

סטטיסטיקה

The content does not provide any specific numerical data or statistics. The focus is on the novel framework and self-supervised training approach.

ציטוטים

The content does not contain any striking quotes that support the key logics.

תובנות מפתח מזוקקות מ:

MonoSelfRecon

by Runfa Li,Upa... ב- arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.06753.pdf

שאלות מעמיקות

How can the self-supervised losses proposed in MonoSelfRecon be extended to other 3D reconstruction tasks beyond indoor scenes

The self-supervised losses proposed in MonoSelfRecon can be extended to other 3D reconstruction tasks beyond indoor scenes by adapting the framework to different environments and scenarios. One way to achieve this is by modifying the training data and annotations to suit the specific characteristics of the new scenes. For example, for outdoor scenes, additional data augmentation techniques such as weather conditions, lighting variations, and different types of objects can be incorporated into the training process. This will help the model learn to generalize better across diverse environments. Additionally, the self-supervised losses can be adjusted to focus on different aspects of the scene, such as object detection, semantic segmentation, or instance segmentation, depending on the requirements of the new reconstruction task. By fine-tuning the self-supervised losses and training data, MonoSelfRecon can be adapted to handle a wide range of 3D reconstruction tasks beyond indoor scenes.

What are the potential limitations of the self-supervised approach, and how can they be addressed to further improve the generalization and robustness of the framework

One potential limitation of the self-supervised approach in MonoSelfRecon is the reliance on monocular RGB views for reconstruction, which may lead to challenges in capturing depth information accurately, especially in complex or dynamic scenes. To address this limitation and improve the generalization and robustness of the framework, several strategies can be implemented. Firstly, incorporating additional sensor data such as depth sensors or LiDAR scans can provide complementary information to enhance the reconstruction accuracy. Secondly, introducing temporal consistency constraints in the self-supervised losses can help improve the model's ability to handle dynamic scenes by capturing motion and changes over time. Furthermore, integrating semantic information into the reconstruction process can enhance scene understanding and object recognition, leading to more comprehensive and detailed reconstructions. By addressing these limitations and incorporating additional data sources and constraints, the self-supervised approach in MonoSelfRecon can be strengthened for better generalization and robustness.

Given the focus on explicit 3D mesh reconstruction, how can the framework be adapted to handle dynamic scenes or incorporate semantic information for more comprehensive scene understanding

To adapt the framework of MonoSelfRecon for dynamic scenes or incorporate semantic information for more comprehensive scene understanding, several modifications can be made. For dynamic scenes, the framework can be extended to incorporate motion estimation techniques to capture the movement of objects and elements within the scene. This can involve integrating optical flow algorithms or incorporating recurrent neural networks to handle temporal information effectively. Additionally, the self-supervised losses can be adjusted to include constraints for dynamic objects, such as object tracking and motion prediction, to improve the reconstruction accuracy in dynamic environments.
Incorporating semantic information can be achieved by integrating semantic segmentation networks into the framework to provide object-level understanding of the scene. By combining the 3D mesh reconstruction with semantic segmentation results, the framework can generate more detailed and informative reconstructions that include object labels and categories. This can enhance scene interpretation and enable applications such as augmented reality and virtual reality with enriched semantic information. By adapting the framework to handle dynamic scenes and incorporate semantic information, MonoSelfRecon can offer a more comprehensive and detailed understanding of complex 3D environments.