- The paper introduces ORG, a novel method that integrates object-ground relations with camera parameters to produce realistic 3D reconstructions from single images.
- The technique leverages pixel height representation and dense perspective fields to accurately model object geometry and eliminate floating artifacts compared to baseline methods.
- Experimental results demonstrate improved shadow rendering and reduced depth estimation errors, highlighting the method's robustness across diverse object categories and viewpoints.
Floating No More: Object-Ground Reconstruction from a Single Image
The paper "Floating No More: Object-Ground Reconstruction from a Single Image" presents a novel approach to enhance the realism of 3D object reconstruction from single-view images by addressing the often-overlooked object-ground relationship. The technique introduced, ORG (Object Reconstruction with Ground), offers promising advancements in single-image 3D reconstruction by integrating object-ground interactions and camera parameters into the reconstruction process.
Introduction and Motivation
Recent methods in 3D object reconstruction from monocular images have been focused primarily on improving the precision of the reconstructed object shapes. However, these methods frequently overlook the spatial relationship between the objects, the ground, and the camera, leading to unrealistic, floating objects when rendered in 3D spaces. To mitigate this issue, the authors propose ORG, which aims to predict the 3D geometry of objects in conjunction with the ground surface and the associated camera parameters. This approach is particularly relevant for applications involving image editing tasks such as realistic shadow rendering and object pose manipulation.
Methodology
ORG operates by leveraging two compact, pixel-level representations to capture the object-ground and camera-object relationships:
- Pixel Height Representation: This metric measures the vertical distance in pixels between the projection of a point on the object and its corresponding point on the ground plane in the image coordinates.
- Perspective Field: This field encodes camera parameters as dense fields, comprising of the elevation angle and gravity vector (up-vector).
By modeling the object as having front and back surfaces and incorporating the pixel height representation along with perspective fields, ORG is able to predict accurate object geometries relative to the ground plane. This is complemented by a Perspective Field Guided Pixel Height Re-projection module, which converts the estimated representations into common depth maps and 3D point clouds, facilitating the downstream tasks.
Experimental Results
To validate ORG, the authors conducted extensive qualitative and quantitative experiments using a dataset rendered from Objaverse, with additional evaluations on real-world images. The key findings are summarized as follows:
- Improvement in Realism: ORG demonstrates superior performance in generating realistic shadows and reflections, maintaining accurate vertical alignment and contact points with the ground. This is a significant improvement over baseline methods such as LeReS and Zero-123.
- Depth and Point Cloud Estimation: ORG outperformed state-of-the-art monocular depth estimation techniques, exhibiting lower errors in metrics such as AbsRel and LSIV.
- Generalization: The method showed robustness and generalization across various object categories and viewpoints, as well as in the wild on random web images.
Comparative Analysis
In comparing ORG to other methods, several advantages were noted:
- Unlike monocular depth estimation methods, ORG benefits from the explicit modeling of the ground, which alleviates common issues of floating objects.
- The incorporation of perspective fields allows the method to adapt to varying camera parameters, a task where traditional methods like LeReS struggle, especially with diverse viewpoints.
- ORG's joint estimation of pixel height and camera perspective fields utilizing PVTv2-b3 strengthens the parameter coherence, enhancing the accuracy of the overall 3D reconstruction process.
Implications and Future Directions
The practical implication of ORG is significant in fields requiring realistic single-view 3D reconstructions, such as augmented reality, virtual content creation, and interactive systems. Furthermore, its ability to generate realistic shadows and reflections can be employed in high-fidelity simulations and visual effects.
Theoretically, ORG sets a new precedent in the integration of scene context into object reconstruction tasks. Future work could explore the incorporation of texture and color information to further enhance photo-realism. Additionally, leveraging ORG's geometry estimations as conditioned priors for advanced image inpainting techniques presents an exciting avenue for further developments.
Conclusion
ORG represents a substantial step forward in single-image object-ground reconstruction, providing meaningful improvements in both object realism and accuracy. By addressing the previously ignored ground-plane interactions, the method paves the way for more realistic 3D reconstructions, enhancing the practical utility and reliability of 3D-aware image editing applications.