OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos (2407.05615v1)

Abstract: It has long been challenging to recover the underlying dynamic 3D scene representations from a monocular RGB video. Existing works formulate this problem into finding a single most plausible solution by adding various constraints such as depth priors and strong geometry constraints, ignoring the fact that there could be infinitely many 3D scene representations corresponding to a single dynamic video. In this paper, we aim to learn all plausible 3D scene configurations that match the input video, instead of just inferring a specific one. To achieve this ambitious goal, we introduce a new framework, called OSN. The key to our approach is a simple yet innovative object scale network together with a joint optimization module to learn an accurate scale range for every dynamic 3D object. This allows us to sample as many faithful 3D scene configurations as possible. Extensive experiments show that our method surpasses all baselines and achieves superior accuracy in dynamic novel view synthesis on multiple synthetic and real-world datasets. Most notably, our method demonstrates a clear advantage in learning fine-grained 3D scene geometry. Our code and data are available at https://github.com/vLAR-group/OSN

• The paper presents OSN, a novel framework that extracts multiple valid 3D configurations from a single monocular video.
• It employs a scale-invariant representation module and an object scale network to model dynamic scene geometry accurately.
• Extensive experiments demonstrate that OSN outperforms state-of-the-art methods in metrics such as PSNR, SSIM, LPIPS, and depth accuracy.

Infinite Representations of Dynamic 3D Scenes from Monocular Videos

The paper "OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos" addresses a longstanding challenge in the field of computer vision and machine learning: extracting dynamic 3D scene representations from monocular RGB videos. Traditional methods have often sought the most plausible singular solution by introducing various constraints like depth priors and geometry constraints. Such a formulation oversimplifies the problem by ignoring the possibility of multiple, equally valid 3D configurations corresponding to a single video. This paper proposes a novel framework termed 'OSN', which seeks to capture the full spectrum of plausible 3D configurations from a given video, thus advancing beyond the singular solution paradigm.

The OSN framework consists of three integral components: an object scale-invariant representation module, an object scale network, and a joint optimization module. The scale-invariant representation module uses tensor decomposition to model per-object density and appearance in a scale-independent space, thereby allowing the network to focus on shape and appearance without concern for absolute object size. The object scale network, on the other hand, plays a critical role by predicting plausible scale ranges for each dynamic object in the scene. It leverages multi-layer perceptrons to learn and output a validity score for sampled object scales.

Joint optimization is achieved through a combination of scaled composite rendering and soft Z-buffer rendering. The former integrates information across different object scales and provides a single plausible configuration by comparing optical renderings with input videos, while the latter optimizes the object scale network by using Z-buffer techniques adapted for neural representations. This dual-pronged approach ensures that the model can generate infinite possible renditions of the given real-world scenario that agree with the observed video data.

The paper's extensive experimentation demonstrates the superiority of the OSN framework across multiple metrics, including PSNR, SSIM, and LPIPS, as well as depth accuracy measures like SSIMAE. The framework consistently outperforms existing state-of-the-art methods in both synthetic and real-world datasets involving complex dynamic scenes, highlighting its capability to achieve superior accuracy in capturing fine-grained 3D scene geometry.

Several key implications and future directions stem from this research. Firstly, it opens avenues for developing systems that are not bounded by a single reality, especially in applications requiring a comprehensive understanding of dynamic environments, such as virtual reality, robotics, and autonomous navigation systems. Furthermore, by introducing the concept of learning object scales in a flexible manner, OSN sets the groundwork for future research into dynamic scenes with deformable objects. Additionally, accommodating multiple valid scene interpretations offers robust error margins crucial for systems operating in uncertain or constrained environments.

In summary, the OSN framework presents a significant theoretical and practical advance in the field by providing a methodology capable of capturing the inherent ambiguity and multiplicity of real-world 3D scenes from monocular videos. Its ability to synthesize a wide array of plausible scene configurations displays promise for diverse applications and paves the way for further exploration into dynamic scene reconstruction.