Rethinking Inductive Biases for Surface Normal Estimation (2403.00712v1)

Abstract: Despite the growing demand for accurate surface normal estimation models, existing methods use general-purpose dense prediction models, adopting the same inductive biases as other tasks. In this paper, we discuss the inductive biases needed for surface normal estimation and propose to (1) utilize the per-pixel ray direction and (2) encode the relationship between neighboring surface normals by learning their relative rotation. The proposed method can generate crisp - yet, piecewise smooth - predictions for challenging in-the-wild images of arbitrary resolution and aspect ratio. Compared to a recent ViT-based state-of-the-art model, our method shows a stronger generalization ability, despite being trained on an orders of magnitude smaller dataset. The code is available at https://github.com/baegwangbin/DSINE.

• The paper introduces pixel-wise ray direction inputs and rotational constraints to improve the architectural design for surface normal estimation.
• The method enforces piecewise smoothness while preserving sharp boundaries, yielding robust performance in unconstrained, in-the-wild settings.
• Empirical results show the approach outperforms state-of-the-art models using a smaller dataset, demonstrating efficiency and enhanced generalization.

Enhancing Surface Normal Estimation via Inductive Biases and Rotational Constraints

Introduction

Surface normal estimation is a key task in computer vision, underpinning a range of applications from 3D reconstruction to robotic manipulation. Despite its importance, the task has been traditionally approached with models carrying general-purpose inductive biases, which, as this paper highlights, might limit their performance and generalization capabilities, especially when dealing with unconstrained, in-the-wild scenarios. Herein, we describe an innovative approach that rethinks the inductive biases necessary for accurate surface normal estimation, proposing a method that directly incorporates pixel-wise ray direction and models the relative rotation between neighboring pixels' normals. This architectural innovation enables the generation of highly detailed, crisp yet piecewise smooth surface normal predictions across images of arbitrary resolution and aspect ratio.

Related Work

The landscape of surface normal estimation has been shaped significantly by the advent of deep learning, with early efforts leveraging handcrafted features and discretized output spaces. Gradually, the field has moved towards convolutional neural networks (CNNs) and, more recently, transformer models, aiming to capitalize on their capacity for handling complex spatial hierarchies and relationships. However, state-of-the-art approaches often borrow inductive biases from proximal tasks such as depth estimation and semantic segmentation - a practice that, while beneficial in certain contexts, might not be fully aligned with the unique characteristics and requirements of surface normal estimation.

Inductive Biases for Surface Normal Estimation

The essence of this work is the identification and integration of task-specific inductive biases into a deep learning framework for improved surface normal estimation. Key to this approach are two architectural novelties:

1. Encoding per-pixel ray direction as input to the network facilitates camera intrinsics-aware inference, enhancing the model's ability to generalize across varying camera configurations and viewing conditions.
2. The introduction of a rotation estimation component that models the relative rotation between neighboring pixels' normals as a form of axis-angle representation. This granularity enables the model to generate predictions that are simultaneously smooth within surfaces and sharply delineated at their boundaries.

Methodology and Approach

The proposed method, detailed through a network architecture incorporating convolutional layers for initial prediction and recurrent units for iterative refinement, is noteworthy for several reasons. The integration of per-pixel ray direction into the network input directly tackles the challenge of camera intrinsics variability. Furthermore, the novel use of rotational constraints between pixels offers a structured way to enforce piecewise smoothness in the estimated normals, an attribute often desired but hard to ensure in practice.

Empirically, the approach is validated against a recent state-of-the-art model based on the Vision Transformer (ViT) and is found to deliver superior generalization capability. This is particularly evident in challenging in-the-wild scenarios, where our method excels in predicting highly detailed and accurate surface normals. Notably, the proposed model achieves these results despite being trained on a significantly smaller dataset, highlighting its efficiency and robustness.

Implications and Future Directions

The research presents a compelling case for a more nuanced consideration of inductive biases in the design of models for surface normal estimation. By aligning the architectural features closely with the task-specific demands, it demonstrates the possibility of achieving high generalization capability and robust performance across diverse imaging conditions. Looking ahead, this work opens several avenues for exploration, including the potential for camera calibration using the model itself and extending the approach to other vision tasks where geometric understanding is critical.

Conclusion

In summary, this paper marks a significant step forward in the quest for accurate and robust surface normal estimation, offering a methodology that is both practically effective and theoretically grounded. The proposed approach underscores the importance of task-specific inductive biases and opens up new possibilities for advancing state-of-the-art surface normal estimation and its applications in computer vision and beyond.