Object-Compositional Neural Implicit Surfaces (2207.09686v2)

Abstract: The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images. However, most approaches focus on holistic scene representation yet ignore individual objects inside it, thus limiting potential downstream applications. In order to learn object-compositional representation, a few works incorporate the 2D semantic map as a cue in training to grasp the difference between objects. But they neglect the strong connections between object geometry and instance semantic information, which leads to inaccurate modeling of individual instance. This paper proposes a novel framework, ObjectSDF, to build an object-compositional neural implicit representation with high fidelity in 3D reconstruction and object representation. Observing the ambiguity of conventional volume rendering pipelines, we model the scene by combining the Signed Distance Functions (SDF) of individual object to exert explicit surface constraint. The key in distinguishing different instances is to revisit the strong association between an individual object's SDF and semantic label. Particularly, we convert the semantic information to a function of object SDF and develop a unified and compact representation for scene and objects. Experimental results show the superiority of ObjectSDF framework in representing both the holistic object-compositional scene and the individual instances. Code can be found at https://qianyiwu.github.io/objectsdf/

• The paper introduces ObjectSDF, a framework using object-specific SDFs to distinctly model individual objects in 3D scenes.
• It integrates geometric and semantic constraints to enhance reconstruction accuracy, outperforming state-of-the-art methods in PSNR, mIOU, and Chamfer Distance.
• The framework paves the way for advanced applications in robotics, AR, and VR by enabling detailed object-aware scene reconstruction and manipulation.

Object-Compositional Neural Implicit Surfaces: A Detailed Analysis

The paper "Object-Compositional Neural Implicit Surfaces" introduces ObjectSDF, a novel framework aimed at bridging the gap between holistic 3D scene representation and object-focused modeling. This paper focuses on capturing the individual characteristics of objects within a scene using neural implicit representations, specifically utilizing Signed Distance Functions (SDFs).

Problem Definition and Approach

Traditional neural implicit representation techniques, exemplified by methods like Neural Radiance Fields (NeRF), excel at synthesizing novel views and performing 3D reconstruction from multi-view images. However, these approaches generally treat scenes holistically and do not distinctly represent individual objects, limiting their applicability in tasks that necessitate object-aware understanding, such as robotic manipulation, object editing, and augmented/virtual reality.

To address this limitation, the authors propose ObjectSDF, which utilizes an object-compositional framework leveraging Signed Distance Functions (SDFs) associated with individual objects in a scene. The key innovation of ObjectSDF is its strategy of associating each object's SDF with semantic labels to enforce explicit surface constraints. To improve the separability and modeling fidelity of objects within the scene, the representation explicitly links an object's geometry, as described by its SDF, to its semantic identity.

Methodological Details

ObjectSDF models the entire scene by compositing the SDFs of the respective objects. This approach significantly contrasts with previous methods that use a singular SDF for the entire scene. By associating semantic labels with object SDFs, ObjectSDF achieves a unified and compact representation that enhances the accuracy of both scene and object reconstructions. The proposed framework capitalizes on a novel integration of geometric and semantic constraints to inform the training process. This is achieved by rendering semantic information as a function derived from object-specific SDFs, effectively guiding the network toward more reliable 3D scene understanding and manipulation capabilities.

Experimental Validation

The authors validate their framework on the ToyDesk and ScanNet datasets, demonstrating substantial improvements in both scene-level and object-level representation tasks. ObjectSDF outperforms state-of-the-art methods in metrics such as PSNR for rendering quality, mIOU for segmentation accuracy, and Chamfer Distance for 3D model fidelity. Notably, the proposed method excels in the accurate reconstruction of individual objects, effectively handling occlusions, and supporting the robust training of the network with only slight computational overhead.

Implications and Future Work

The ObjectSDF framework presents considerable advancements in the field of neural implicit representations by introducing an object-compositional perspective. This method has profound implications for applications requiring detailed object-aware scene reconstruction, such as robotics and AR/VR. Furthermore, by facilitating the extraction and manipulation of scene objects, ObjectSDF paves the way for novel interaction paradigms in digital environments.

Future developments could address the limitations highlighted in the paper, such as enhancing the accuracy of reconstruction in occluded or non-visible regions, possibly through integrating additional physical or causal constraints. Moreover, extending this framework to handle dynamic or deformable objects could significantly expand its applicability to various domains.

In conclusion, this paper provides a compelling approach to neural implicit scene representation by foregrounding object compositionality, setting a solid foundation for further exploration and refinement in object-aware 3D modeling technologies.