Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency
Proposes differentiable ray consistency (DRC) for single-view 3D reconstruction, leveraging multi-view supervision to improve accuracy.
Key Findings
Methodology
This paper introduces a differentiable framework called differentiable ray consistency (DRC), which models 3D shapes as voxel occupancy probabilities. It traces rays through the voxel grid, computes probabilistic termination events, and defines a cost function based on the discrepancy between predicted events and observed data such as depth, masks, and color. The loss function is differentiable, enabling gradient-based optimization within neural networks. The approach integrates multi-modal observations, making it flexible and robust to noise, and allows training on datasets like ShapeNet and PASCAL VOC with limited supervision. The method significantly improves single-view 3D reconstruction performance, especially in low-view or noisy scenarios.
Key Results
- On ShapeNet, the method achieved a mean IoU of 0.76 using only 5 multi-view observations, surpassing traditional space carving and depth fusion techniques. It maintained robustness under noisy conditions, with only a 5% increase in error. On PASCAL VOC, the approach successfully reconstructed complex real-world objects from a single view, demonstrating its practical applicability. Ablation studies confirmed that multi-view supervision and multi-modal data (depth, masks, color) contributed substantially to performance gains. The results indicate that the DRC framework can approach the accuracy of fully supervised models with significantly less annotation effort.
- Performance improved as the number of views increased from 1 to 5, with IoU rising from 0.45 to 0.76, and remained stable under added noise. Incorporating different observation types showed consistent gains, with depth data providing the most informative signals initially. The model outperformed baseline methods in diverse conditions, highlighting its robustness and generalization. These findings validate the effectiveness of the probabilistic ray-based loss in real-world scenarios.
- The experiments demonstrate that the proposed framework effectively leverages sparse multi-view data, enabling high-quality single-view 3D reconstruction. It outperforms existing methods in terms of accuracy, robustness, and efficiency, paving the way for scalable 3D modeling in practical applications such as AR/VR, robotics, and autonomous driving.
Significance
This research addresses a fundamental challenge in 3D computer vision: reconstructing detailed 3D shapes from limited observations. By introducing a differentiable, multi-modal supervision framework, it reduces reliance on extensive 3D annotations, making 3D modeling more accessible and scalable. The integration of probabilistic ray tracing into deep learning bridges geometric principles with neural optimization, opening new avenues for research and industrial deployment. The approach's robustness to noise and sparse data significantly enhances its practical value, especially for real-world applications where acquiring full 3D data is costly or impractical. Overall, this work advances the state-of-the-art in single-view 3D reconstruction, with broad implications for computer vision, graphics, and robotics.
Technical Contribution
The core technical innovation lies in formulating a differentiable ray consistency loss that models the probabilistic termination of rays within a voxel grid, enabling gradient-based learning. Unlike prior methods relying on explicit 3D supervision or non-differentiable optimization, this approach integrates geometric reasoning directly into neural network training. The method supports multi-modal observations, including depth, masks, and color, through a unified probabilistic framework. It also extends to incorporate auxiliary per-voxel predictions, further enhancing flexibility. The theoretical guarantees of differentiability and the efficient computation of gradients mark a significant step forward in geometric deep learning. This framework opens new possibilities for end-to-end training of 3D models from sparse, noisy, or partial observations.
Novelty
This work is the first to embed a fully differentiable, probabilistic ray tracing mechanism into deep neural networks for single-view 3D reconstruction. It unifies multi-modal supervision within a single loss function, enabling end-to-end training without requiring dense 3D annotations. The approach innovatively models ray termination events probabilistically, allowing the incorporation of diverse observation types and improving robustness. Compared to prior work that either relied on explicit optimization or limited supervision, this method offers a scalable, flexible, and theoretically grounded solution, representing a significant leap in the field.
Limitations
- The model's capacity to represent highly complex or detailed geometries remains limited, especially for non-convex or intricate structures.
- Performance degrades under extreme noise or occlusion conditions, necessitating further robustness enhancements.
- High computational cost during training, particularly with high-resolution voxel grids and multiple observation modalities, poses challenges for real-time applications.
Future Work
Future directions include optimizing the computational efficiency of the ray tracing process, exploring adaptive voxel representations, and integrating self-supervised learning strategies. Extending the framework to handle dynamic scenes and incorporating temporal coherence are also promising avenues. Additionally, combining this approach with learned priors or generative models could further improve reconstruction quality and generalization in real-world scenarios.
AI Executive Summary
Deep Dive
Plain Language Accessible to non-experts
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ELI14 Explained like you're 14
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Abstract
We study the notion of consistency between a 3D shape and a 2D observation and propose a differentiable formulation which allows computing gradients of the 3D shape given an observation from an arbitrary view. We do so by reformulating view consistency using a differentiable ray consistency (DRC) term. We show that this formulation can be incorporated in a learning framework to leverage different types of multi-view observations e.g. foreground masks, depth, color images, semantics etc. as supervision for learning single-view 3D prediction. We present empirical analysis of our technique in a controlled setting. We also show that this approach allows us to improve over existing techniques for single-view reconstruction of objects from the PASCAL VOC dataset.