Rig3R: Rig-Aware Conditioning for Learned 3D Reconstruction (2506.02265v1)

Abstract: Estimating agent pose and 3D scene structure from multi-camera rigs is a central task in embodied AI applications such as autonomous driving. Recent learned approaches such as DUSt3R have shown impressive performance in multiview settings. However, these models treat images as unstructured collections, limiting effectiveness in scenarios where frames are captured from synchronized rigs with known or inferable structure. To this end, we introduce Rig3R, a generalization of prior multiview reconstruction models that incorporates rig structure when available, and learns to infer it when not. Rig3R conditions on optional rig metadata including camera ID, time, and rig poses to develop a rig-aware latent space that remains robust to missing information. It jointly predicts pointmaps and two types of raymaps: a pose raymap relative to a global frame, and a rig raymap relative to a rig-centric frame consistent across time. Rig raymaps allow the model to infer rig structure directly from input images when metadata is missing. Rig3R achieves state-of-the-art performance in 3D reconstruction, camera pose estimation, and rig discovery, outperforming both traditional and learned methods by 17-45% mAA across diverse real-world rig datasets, all in a single forward pass without post-processing or iterative refinement.

• The paper introduces a rig-aware latent space that leverages camera metadata to enhance 3D reconstruction and pose estimation.
• The novel dual-raymap representation provides robust supervision for both global pose and rig-centric estimations in challenging scenes.
• The approach processes unordered and calibrated image sets in a single forward pass, achieving state-of-the-art performance improvements of 17-45% mAA.

Rig3R: Rig-Aware Conditioning for Learned 3D Reconstruction

The paper "Rig3R: Rig-Aware Conditioning for Learned 3D Reconstruction" introduces Rig3R, a sophisticated model for multiview 3D reconstruction and pose estimation that notably integrates rig-related constraints. In the context of embodied AI applications like autonomous driving, accurately estimating agent poses and 3D scene structures from multi-camera setups is crucial. Although past approaches such as DUSt3R have demonstrated commendable abilities in multiview scenarios, they neglect potential rig structure that is often inherent in real-world settings involving synchronized multi-camera arrangements. Rig3R aims to address this limitation by utilizing available rig metadata and inferring rig structures from images when such metadata is unavailable.

Rig3R is designed to operate under diverse input configurations, encompassing both ordered and unordered image sets and calibrated rigs. A core innovation of this approach lies in its ability to learn a rig-aware latent space through conditioning on metadata that includes camera IDs, timestamps, and rig poses, while demonstrating robustness to missing information. Rig3R predicts dense pointmaps and two types of raymaps, namely a pose raymap based on a global reference frame and a rig raymap rooted in a rig-centric framework consistent over time. This dual-raymap approach affords it the flexibility to infer rig structure directly from input images in the absence of metadata, facilitating rig calibration discovery from unordered images—a feature unparalleled in existing learned or traditional methodologies.

The computational efficacy of Rig3R is highlighted through its single forward pass inference capability, executing without the need for post-processing or iterative refinement. The model achieves state-of-the-art performance in 3D reconstruction tasks and pose estimation, asserting superiority over both learned and classical approaches in real-world rig datasets by a margin of 17-45% in mean Average Accuracy (mAA).

Technical Contributions and Experimental Findings

Rig3R comprises several technical advancements:

1. Rig-Aware Conditioning: Integration of rig constraints into the modeling process significantly enhances 3D reconstruction and pose estimation tasks, while remaining adaptable to partial or absent metadata.
2. Raymap Representation: Raymap encoding provides spatially consistent and stable supervision, allowing robust per-pixel and camera intrinsic and extrinsic estimations under varying conditions, including ambiguous regions like sky or dynamic pixel arrangements.
3. Transformer Architecture: A dual-stage architecture harnesses both image patch encoding and joint multiview attention, permitting Rig3R to aggregate and synthesize complex multiview data sets.
4. Rig Calibration Discovery: The unique ability of Rig3R to extrapolate rig structure from unordered image collections offers a transformative tool for modern AI applications.

Concluding Remarks and Future Directions

Rig3R's innovative fusion of rig-aware embeddings within a learned multiview reconstruction framework sets a new benchmark in computer vision applications. Nevertheless, the current model's dependence on diverse rig configurations across existing datasets for optimal performance is a notable limitation that future research could address through synthetic data augmentations or alternative training paradigms. Further exploration in dynamic scene modeling could enhance robustness in scenarios typified by rapid environmental changes.

Collectively, the Rig3R model presents a robust architecture imbued with flexibility that demonstrates strong potential for applied and theoretical advancements in AI-driven spatial understanding and localization challenges, particularly within autonomous systems. As researchers continue to push boundaries in computer vision, the rig-aware conditioning approach may inspire novel methodologies tailored to both constrained and unconstrained multiview imaging environments.