Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations (1906.01618v2)

Abstract: Unsupervised learning with generative models has the potential of discovering rich representations of 3D scenes. While geometric deep learning has explored 3D-structure-aware representations of scene geometry, these models typically require explicit 3D supervision. Emerging neural scene representations can be trained only with posed 2D images, but existing methods ignore the three-dimensional structure of scenes. We propose Scene Representation Networks (SRNs), a continuous, 3D-structure-aware scene representation that encodes both geometry and appearance. SRNs represent scenes as continuous functions that map world coordinates to a feature representation of local scene properties. By formulating the image formation as a differentiable ray-marching algorithm, SRNs can be trained end-to-end from only 2D images and their camera poses, without access to depth or shape. This formulation naturally generalizes across scenes, learning powerful geometry and appearance priors in the process. We demonstrate the potential of SRNs by evaluating them for novel view synthesis, few-shot reconstruction, joint shape and appearance interpolation, and unsupervised discovery of a non-rigid face model.

• The paper introduces a novel framework that encodes 3D scene geometry and appearance via continuous functions learned end-to-end from 2D images.
• It employs differentiable ray-marching with an LSTM-based approach to seamlessly render multi-view consistent images without explicit 3D supervision.
• Comprehensive evaluations demonstrate state-of-the-art PSNR improvements on benchmark datasets, highlighting SRNs' potential in 3D reconstruction and rendering.

Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations

This paper introduces a novel paradigm for unsupervised learning of rich 3D scene representations using neural networks, termed as Scene Representation Networks (SRNs). These representations encode both geometry and appearance and offer a framework that only requires 2D images with known camera poses for training, avoiding the necessity for explicit 3D supervision.

SRNs encapsulate scenes with continuous functions that map world coordinates directly to feature representations of local scene properties. This approach facilitates a differentiable ray-marching rendering process, ensuring an end-to-end trainable system. The network implicitly learns geometrical priors while generating multi-view consistent images from various perspectives.

Key Contributions

• Continuous 3D-Aware Representation: SRNs represent scenes as implicit continuous functions, effectively mapping 3D world coordinates to localized scene feature representations. This granularity allows interfaces with classical multi-view and projective geometry techniques, supporting high-resolution operations in a memory-efficient manner.
• Differentiable Ray-Marching Rendering: The differentiable rendering algorithm leverages a ray-marching approach to trace intersections with scene geometry. Learned via an LSTM-based approach, this enables robust and adaptable step size selection, allowing the process to be end-to-end differentiable and interpretable.
• End-to-End Training without 3D Supervision: SRNs are trained using only posed 2D images, bypassing the requirement for depth information or explicit 3D geometry. The method learns powerful priors and generates high-quality images which are multi-view consistent and can generalize to unseen intrinsic matrices and camera transformations.
• Applications and Evaluations: The network demonstrates notable efficacy across various challenging tasks including novel view synthesis, few-shot 3D scene reconstruction, and unsupervised discovery of non-rigid face models.

Numerical Results and Evaluations

The research paper provides comprehensive evaluations of SRNs across several datasets and tasks:

1. Shepard-Metzler Objects: SRNs outperformed deterministic Generative Query Networks (dGQN) on a dataset with limited observations, achieving PSNRs of up to 30.41 dB on training objects compared to 20.85 dB by the dGQN.
2. Shapenet Dataset: For the “cars” and “chairs” classes, SRNs achieved superior results in novel view synthesis, with PSNRs peaking at 26.32 dB, and maintaining multi-view consistency significantly better than existing models.
3. Non-Rigid Face Models: By conditioning on latent vectors derived from the Basel face model, SRNs accurately model and animate facial expressions, reflecting fine geometrical details in the process.

Implications and Future Directions

The introduction of SRNs presents substantial theoretical and practical implications in the sphere of 3D scene representation learning. The framework notably bridges the gap between classical geometric deep learning and contemporary neural rendering methodologies by integrating the strengths of both paradigms.

Theoretical Implications:

• Representation Learning: This work expands our understanding of how continuous, differentiable functions can serve as efficient scene representations capable of capturing both geometry and appearance with high fidelity.
• Implicit Geometry Encoding: SRNs reinforce the potential of unsupervised learning in discovering 3D structural details from 2D observations.
• Interpretable Learning Models: The use of differentiable ray-marching offers an interpretable mechanism to analyze failures and debug the geometry learning processes.

Practical Applications:

• 3D Reconstruction and Modeling: SRNs are well-suited for applications requiring high-resolution 3D reconstructions, such as virtual reality, augmented reality, and computer graphics.
• Autonomous Navigation and Robotics: Robust scene understanding capabilities are critical for navigating complex environments and performing manipulation tasks.
• Medical Imaging: Future extensions of SRNs could adapt to image formation models used in medical diagnostics, such as computed tomography (CT) and magnetic resonance imaging (MRI).

Moving forward, several enhancements and expansions to SRNs are conceivable:

• Probabilistic Framework Integration: Introducing probabilistic reasoning within SRNs could enable sampling diverse scene reconstructions under uncertain observations.
• Modeling Advanced Visual Phenomena: Extending the approach to account for effects such as translucency, lighting variations, and participative media could widen the application spectrum.
• Scalability to Complex Scenes: Addressing the challenges associated with generalization across larger and more intricate environments remains an open area for research, potentially leveraging advancements in meta-learning.

In summary, SRNs represent a significant step forward in the domain of 3D scene understanding, consolidating continuous implicit representations with the robustness of end-to-end neural rendering. The promising results achieved across various benchmarks indicate substantial opportunities for future research endeavors to build upon this foundational work.