OpenNovelty.org

LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
3D reconstrctionunseen scene reconstructiondepth estimationpoint maps
Abstract:

We present Layered Ray Intersections (LaRI), a fully supervised method for occluded geometry reasoning from a single image. Unlike conventional depth estimation, which is limited to visible surfaces, LaRI predicts multiple surfaces intersected by the camera rays using layered point maps. Compared to the existing approaches that leverage neural implicit representations or iterative refinement, LaRI achieves complete scene reconstruction in one feed-forward pass, enabling efficient and view-aligned geometric reasoning to underpin both object-level and scene-level tasks. We further propose to predict the ray stopping index, which identifies valid intersecting pixels and layers from LaRI’s output. To better underpin and evaluate this task, we build an annotation pipeline using rendering engines, construct annotations for five public datasets, including synthetic and real-world data covering 3D objects and scenes. As a generic method, LaRI’s performance is validated in object-level and scene-level reconstruction tasks.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Layered Ray Intersections (LaRI), a feed-forward method predicting multiple depth layers along camera rays from a single image, plus a ray stopping index to identify valid intersections. It resides in the Multi-Layer Surface Prediction leaf under Layered and Occluded Geometry Reasoning. This leaf currently contains only the original paper itself, indicating a sparse research direction within the broader taxonomy of fifty papers. The taxonomy shows that most prior work addresses single-surface depth estimation or implicit volumetric reconstruction, leaving explicit multi-layer surface prediction relatively underexplored.

The taxonomy reveals neighboring branches that handle occlusion differently. Single-Image Neural Implicit Surface Reconstruction (e.g., MonoSDF, GeoRecon) infers hidden geometry through global scene integration rather than explicit layering. Monocular 3D Object Detection methods (e.g., MonoDETR, SMOKE) focus on visible bounding boxes without reasoning about occluded surfaces behind objects. Occlusion-Aware Object and Layout Reasoning jointly models room layout and object occlusion relationships but does not predict multiple depth layers per ray. LaRI's explicit multi-layer representation diverges from these implicit or object-centric approaches, targeting complete scene geometry in a view-aligned manner.

Among twenty-nine candidates examined, the layered representation contribution shows four refutable candidates out of ten examined, suggesting some overlap with prior multi-layer or layered depth work. The ray stopping index contribution examined nine candidates with zero refutations, indicating less direct prior work on this specific mechanism. The annotation pipeline contribution examined ten candidates with zero refutations, suggesting novelty in the benchmark construction. The limited search scope means these statistics reflect top-K semantic matches and citation expansion, not an exhaustive literature review. The layered representation appears to have the most substantial prior work among the three contributions.

Overall, the analysis suggests LaRI occupies a sparsely populated research direction within the taxonomy, with the layered representation showing moderate overlap among examined candidates while the stopping index and annotation pipeline appear more novel. The search covered twenty-nine candidates, providing a snapshot of closely related work but not a comprehensive field survey. The taxonomy context indicates that explicit multi-layer surface prediction remains less explored compared to implicit volumetric or single-surface methods, though the refutable candidates for the core representation warrant careful examination of how LaRI differentiates itself from prior layered approaches.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
29
Contribution Candidate Papers Compared
4
Refutable Paper

Research Landscape Overview

Core task: occluded geometry reasoning from single images. The field addresses the challenge of inferring complete three-dimensional structure from monocular views when parts of the scene are hidden or overlapped. The taxonomy reveals a diverse landscape organized around application domains and methodological emphases. Monocular 3D Scene Reconstruction (e.g., NeuralRecon[1], GeoRecon[5]) focuses on holistic environment modeling, often leveraging volumetric or neural implicit representations. Monocular 3D Object Detection and Localization (e.g., MonoDETR[22], SMOKE[46]) targets individual object pose and bounding box estimation in cluttered scenes. Monocular 3D Human and Garment Reconstruction (e.g., Human Mesh Survey[4], Photorealistic Clothed Humans[25]) specializes in articulated body and clothing geometry. Monocular 3D Lane Detection (e.g., Anchor3DLane[26], Freq-3DLane[19]) addresses structured road elements for autonomous driving. Specialized Domain Reconstruction covers niche applications such as civil infrastructure monitoring (e.g., Civil Structure Displacement[9], Building Monocular Vision[38]) and underwater or remote sensing scenarios (e.g., Underwater Reconstruction[36], Building Reconstruction Remote[33]). Depth Estimation and Foundational Methods (e.g., Depth Estimation Survey[27], Make3D[28]) provide core techniques that underpin many branches. Finally, Layered and Occluded Geometry Reasoning explicitly tackles multi-surface and visibility reasoning. Recent work increasingly grapples with how to represent and predict geometry that lies behind visible surfaces—a problem central to occlusion handling. Some approaches adopt multi-layer depth or surface representations to capture front and back geometry simultaneously, while others rely on probabilistic or uncertainty-aware frameworks (e.g., Geometry Uncertainty Projection[15], Categorical Depth Distribution[10]) to model ambiguous regions. LaRI[0] sits within the Layered and Occluded Geometry Reasoning branch, specifically under Multi-Layer Surface Prediction, emphasizing explicit reasoning about multiple depth layers from a single view. This contrasts with volumetric scene reconstruction methods like NeuralRecon[1] or MonoSDF[16], which infer occluded structure implicitly through global scene integration, and with object-centric detectors like MonoDETR[22] that focus on visible bounding boxes rather than layered surfaces. The main open question remains how to balance computational efficiency with the richness of multi-layer representations, especially when ground-truth layered annotations are scarce.

Claimed Contributions

Layered Ray Intersections (LaRI) representation for single-view 3D geometric reasoning

The authors introduce LaRI, a representation that predicts multiple surfaces intersected by camera rays using layered point maps. Unlike conventional depth estimation limited to visible surfaces, LaRI achieves complete scene reconstruction in one feed-forward pass, enabling efficient view-aligned geometric reasoning for both object-level and scene-level tasks.

10 retrieved papers
Can Refute
Ray stopping index prediction for identifying valid intersections

The authors propose a ray stopping index network that identifies the last valid surface intersection for each camera ray. This approach enforces depth ordering and enables the model to distinguish valid intersection points from invalid ones in the fixed-layer LaRI representation.

9 retrieved papers
Annotation pipeline and benchmark datasets for occluded geometry reasoning

The authors develop a complete data annotation pipeline using graphics rendering engines to construct training and evaluation data from five public datasets. This includes both synthetic 3D assets and real-world scans, addressing the lack of proper datasets for training and evaluating occluded geometry reasoning tasks.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Layered Ray Intersections (LaRI) representation for single-view 3D geometric reasoning

The authors introduce LaRI, a representation that predicts multiple surfaces intersected by camera rays using layered point maps. Unlike conventional depth estimation limited to visible surfaces, LaRI achieves complete scene reconstruction in one feed-forward pass, enabling efficient view-aligned geometric reasoning for both object-level and scene-level tasks.

Contribution

Ray stopping index prediction for identifying valid intersections

The authors propose a ray stopping index network that identifies the last valid surface intersection for each camera ray. This approach enforces depth ordering and enables the model to distinguish valid intersection points from invalid ones in the fixed-layer LaRI representation.

Contribution

Annotation pipeline and benchmark datasets for occluded geometry reasoning

The authors develop a complete data annotation pipeline using graphics rendering engines to construct training and evaluation data from five public datasets. This includes both synthetic 3D assets and real-world scans, addressing the lack of proper datasets for training and evaluating occluded geometry reasoning tasks.

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