State of the Art in Dense Monocular Non-Rigid 3D Reconstruction (2210.15664v2)

Abstract: 3D reconstruction of deformable (or non-rigid) scenes from a set of monocular 2D image observations is a long-standing and actively researched area of computer vision and graphics. It is an ill-posed inverse problem, since -- without additional prior assumptions -- it permits infinitely many solutions leading to accurate projection to the input 2D images. Non-rigid reconstruction is a foundational building block for downstream applications like robotics, AR/VR, or visual content creation. The key advantage of using monocular cameras is their omnipresence and availability to the end users as well as their ease of use compared to more sophisticated camera set-ups such as stereo or multi-view systems. This survey focuses on state-of-the-art methods for dense non-rigid 3D reconstruction of various deformable objects and composite scenes from monocular videos or sets of monocular views. It reviews the fundamentals of 3D reconstruction and deformation modeling from 2D image observations. We then start from general methods -- that handle arbitrary scenes and make only a few prior assumptions -- and proceed towards techniques making stronger assumptions about the observed objects and types of deformations (e.g. human faces, bodies, hands, and animals). A significant part of this STAR is also devoted to classification and a high-level comparison of the methods, as well as an overview of the datasets for training and evaluation of the discussed techniques. We conclude by discussing open challenges in the field and the social aspects associated with the usage of the reviewed methods.

• The paper surveys the state of the art in dense monocular non-rigid 3D reconstruction methods, highlighting the core challenge of reconstructing deformable shapes from single 2D images due to the ill-posed nature of the problem.
• Key methodological approaches covered include Shape from Template (SfT), Non-Rigid Structure-from-Motion (NRSfM), data-driven models, and recent advancements using neural rendering techniques like NeRF.
• The survey discusses significant challenges remaining in the field, such as robustness to occlusions and ambiguities, scaling to complex scenes, and incorporating ethical considerations, while pointing towards potential future directions like using event cameras and physics-based modeling.

State of the Art in Dense Monocular Non-Rigid 3D Reconstruction

The paper offers a comprehensive survey on the current state of dense monocular non-rigid 3D reconstruction methods, a significant domain within computer vision and computer graphics. The challenge of reconstructing 3D geometry from 2D images, particularly for deformable objects, is intricate due to the inverse problem's ill-posed nature, as without constraints, multiple 3D configurations can correspond to the same 2D projection. This survey presents advancements in methodologies that address this challenge and reviews their applications across various fields.

Monocular 3D reconstruction employs a single camera setup, offering ease of use compared to multi-view systems. The paper categorizes the methods into several key approaches: Shape from Template (SfT), Non-Rigid Structure-from-Motion (NRSfM), data-driven models using neural representations like Neural Radiance Fields (NeRF), and various approaches that utilize parametric models for specific categories such as humans, faces, hands, and animals. Each approach exploits inherent properties and prior knowledge about the objects to mitigate ambiguities and achieve robustness in 3D reconstruction.

Key Highlights and Results

1. Shape from Template (SfT): SfT approaches leverage a known template to reconstruct deformable object shapes from monocular images. These methods are particularly effective when high-resolution texture maps are available, enabling accurate capturing of local folds and wrinkles, as demonstrated by $\boldsymbol{\phi}$-SfT. Analytical and neural network approaches within SfT have developed solutions for isometric and elastically deformable surfaces.
2. Non-Rigid Structure-from-Motion (NRSfM): NRSfM techniques do not rely on templates but use point tracks across frames to reconstruct non-rigid motion. The survey outlines how methods like Jumping Manifolds and Neural Trajectory Priors extend NRSfM's applicability by incorporating smooth trajectories and neural models for deformations, substantially improving scalability and accuracy.
3. Neural Rendering Methods: Recent breakthroughs in NeRF-based methods have revolutionized dynamic scene reconstruction by learning dense 3D representations. These techniques, such as Neural Scene Flow Fields and NR-NeRF, demonstrate success in handling dynamic deformations and rendering novel views, although challenges remain in achieving topological changes and high-fidelity geometry.
4. Data-Driven Models: Using data-driven priors from large-scale image collections, approaches like Canonical Surface Mappings (CSM) and subsequent derivatives extend monocular reconstruction to categorical models, particularly focusing on birds using datasets such as CUB. These methods leverage weaker annotations like silhouettes and semantic keypoints to achieve generalized 3D reconstructions.
5. Human and Facial Reconstruction: The field of human performance capture is enriched with methods ranging from template-free models like PIFu, to parametric models integrated robustly into frameworks like ICON and ARCH. These approaches provide real-time solutions and capture significant details in human representation, including clothing and motion dynamics.
6. Event Cameras and Physics-Based Modeling: Emerging directions in using event cameras for high-speed motion capture and incorporating physics-based models for physically plausible deformation modeling demonstrate new paths. Event cameras, with their high temporal resolution, and differentiable physics simulators present new methodologies that could significantly advance 3D tracking tasks under challenging scenarios.

Implications and Future Directions

The paper highlights that while many existing methods achieve impressive visual results, several open challenges warrant further research attention. Among these are improving the robustness of methods against occlusions and ambiguities in depth and motion, scaling to large and complex scenes with multiple dynamic objects, and integrating adaptable, interactive editing tools for reconstructed models. Furthermore, ethical considerations such as data bias, environmental concerns regarding computational resources, and ensuring privacy and consent in dataset usage are pivotal in steering the community towards responsible innovation.

The survey posits that as methods continue to adopt more sophisticated neural representations, coupled with physics-based understanding and potentially augmented by novel sensors like event cameras, the fidelity and applicability of monocular non-rigid 3D reconstruction will improve, broadening the scope of immersive and interactive applications in AI-driven environments.