CodeNeRF: Disentangled Neural Radiance Fields for Object Categories (2109.01750v1)

Abstract: CodeNeRF is an implicit 3D neural representation that learns the variation of object shapes and textures across a category and can be trained, from a set of posed images, to synthesize novel views of unseen objects. Unlike the original NeRF, which is scene specific, CodeNeRF learns to disentangle shape and texture by learning separate embeddings. At test time, given a single unposed image of an unseen object, CodeNeRF jointly estimates camera viewpoint, and shape and appearance codes via optimization. Unseen objects can be reconstructed from a single image, and then rendered from new viewpoints or their shape and texture edited by varying the latent codes. We conduct experiments on the SRN benchmark, which show that CodeNeRF generalises well to unseen objects and achieves on-par performance with methods that require known camera pose at test time. Our results on real-world images demonstrate that CodeNeRF can bridge the sim-to-real gap. Project page: \url{https://github.com/wayne1123/code-nerf}

• The paper presents a novel method that disentangles shape and texture using separate latent embeddings, enabling editable 3D object synthesis directly from 2D images.
• It jointly optimizes shape, texture, and camera parameters, overcoming the fixed viewpoint limitations of previous models.
• Experiments on the ShapeNet-SRN dataset show competitive reconstruction performance, emphasizing practical benefits for AR, VR, and digital content creation.

Disentangling Shape and Texture in Neural Radiance Fields with CodeNeRF

The paper "CodeNeRF: Disentangled Neural Radiance Fields for Object Categories" introduces an advanced approach to 3D neural representations that improves upon existing models such as NeRF, SRN, and DeepSDF by effectively disentangling shape and texture in 3D object synthesis. This work is situated within the broader context of neural scene representations in computer vision, specifically concerning the synthesis of new views from sparse or single images of unseen objects. At the core of this paper lies the novel architecture of CodeNeRF, which builds on the rich existing literature yet addresses several key limitations related to camera viewpoint independence and joint optimization capabilities.

CodeNeRF distinguishes itself from traditional Neural Radiance Fields (NeRF) by not being scene-specific and instead generalizing across object classes. Importantly, CodeNeRF disentangles geometry and appearance using separate latent embeddings, much akin to DeepSDF, but without the dependence on 3D supervision, relying solely on 2D images. This disentanglement ensures that shape and appearance are independently editable, providing more control in the synthesis task. Moreover, unlike conventional methods that necessitate known camera poses during testing, CodeNeRF estimates these parameters in conjunction with shape and texture codes through optimization, making it more adaptable and comprehensive for real-world applications.

Experimentally, CodeNeRF’s prowess is demonstrated on the ShapeNet-SRN dataset, where it achieves competitive results on one- and two-view reconstruction tasks when compared to state-of-the-art models that require fixed camera poses during testing, such as PixelNeRF and SRN. Notably, CodeNeRF matches or exceeds these benchmarks while freeing itself from the constraints of needing pre-defined camera parameters, highlighting its efficacy in generalizing to new instances. The disentanglement of shape and texture is further exemplified through rendered interpolations between latent spaces, illustrating the finesse with which CodeNeRF can manipulate and synthesize variations of object visuals.

In practical terms, CodeNeRF has significant implications for fields reliant on advanced 3D modeling and rendering techniques, such as augmented reality, virtual reality, simulation, and digital content creation. The ability to edit and synthesize textures and shapes offers detailed customization that can benefit design and entertainment industries. Furthermore, the flexibility to handle unknown camera viewpoints makes it appealing for applications requiring dynamic and non-static environments, marking an advancement in the ability to bridge synthetic and real-world visual scenarios, as demonstrated on datasets like Stanford-Cars and Pix3D.

Theoretically, CodeNeRF’s introduction of separate latent embeddings for shape and texture potentially opens new research directions in disentangled representation learning within neural networks. The work aligns with ongoing efforts to refine neural representations not just for photorealism but for greater structural understanding and manipulation capabilities.

While this research marks a substantial step forward, future work might explore scaling CodeNeRF to handle more diverse and complex object categories or refining its optimization processes for even faster adaptation to new viewpoints and conditions. Additionally, extending its capabilities to handle more intricate texture and lighting variations could further enhance its real-world applicability.

CodeNeRF’s contribution lies in its effective synthesis methodology that merges the strengths of previous models while addressing critical gaps related to disentanglement and viewpoint estimation, enriching both the theoretical foundations and practical applications within the domain of 3D neural rendering.