LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning (2504.18424v1)

Abstract: We present layered ray intersections (LaRI), a new method for unseen geometry reasoning from a single image. Unlike conventional depth estimation that is limited to the visible surface, LaRI models multiple surfaces intersected by the camera rays using layered point maps. Benefiting from the compact and layered representation, LaRI enables complete, efficient, and view-aligned geometric reasoning to unify object- and scene-level tasks. We further propose to predict the ray stopping index, which identifies valid intersecting pixels and layers from LaRI's output. We build a complete training data generation pipeline for synthetic and real-world data, including 3D objects and scenes, with necessary data cleaning steps and coordination between rendering engines. As a generic method, LaRI's performance is validated in two scenarios: It yields comparable object-level results to the recent large generative model using 4% of its training data and 17% of its parameters. Meanwhile, it achieves scene-level occluded geometry reasoning in only one feed-forward.

An Overview of Layered Ray Intersections (LaRI) for Single-view 3D Geometric Reasoning

The paper "LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning" introduces Layered Ray Intersections (LaRI), a novel approach for reasoning on unseen geometry from a single image. Challenging traditional depth estimation methods, LaRI directly models multiple surfaces intersected by camera rays using layered point maps. This methodology offers a comprehensive model enabling seamless, efficient, and view-aligned geometric reasoning capable of integrating object and scene-level tasks.

Methodological Approach

The core innovation of LaRI lies in its representation of the entire geometric structure by recording all possible ray-surface interactions along the camera's line of sight. This significantly contrasts typical representations limited to visible surfaces. The resulting layered structure is encoded in a compact format comprising depth-ordered, ray-surface intersection coordinates which can be estimated as a standard 2D regression task. LaRI thereby transcends the constraints of conventional depth maps and complex generative models, offering robust capabilities for unseen geometry estimation.

Training and Evaluation

A detailed pipeline for generating synthetic and real-world training data was established, allowing for rendering LaRI maps from 3D objects and scenes with appropriate preprocessing. Noteworthy is LaRI's proficiency in achieving object-level results comparative to recent generative models using significantly fewer training resources: merely 4% of the training data and 17% of the parameters. Furthermore, LaRI exhibits remarkable efficiency in scene-level occluded geometry reasoning, completed in a single feed-forward pass.

Competitive Performance

On object-level evaluation from the Google Scanned Objects dataset, LaRI effectively balances high precision with low computational footprint. LaRI's outputs naturally align to camera poses, bypassing the limitations inherent in point cloud registration required by other models, thereby achieving improved scores in practical, real-world scenarios. Scene-level results further demonstrated LaRI's adeptness in handling occluded geometric reasoning, suggesting its utility in applications demanding dynamic spatial comprehension, such as robotics and virtual reality.

Implications and Future Directions

LaRI’s ability to reason about unseen surfaces offers significant practical benefits, particularly in fields like autonomous navigation and interactive media, where understanding beyond direct observation is crucial. Its streamlined approach alleviates efficiency bottlenecks and reduces manual intervention, presenting a unified framework for both object and scene-level 3D reconstruction tasks. Future developments could explore enhancing LaRI’s integration with larger datasets and diverse environmental contexts, further expanding its applicability and precision in complex real-world settings.

This intricate alignment of layered ray intersections presents a promising avenue for advancing machine capabilities in geometric reasoning, providing a foundation for more adaptive and efficient 3D modeling technologies.