The results demonstrate the feasibility of integrating perception, 3D reconstruction, semantic assessment, and scalable deployment into a single applied pipeline, while remaining proof-of-concept and requiring extension to larger object and scene corpora, baselines, real-photograph evaluation, and human-centered assessment before broad claims about general interior-scene reconstruction can be made.
Abstract
This study presents a modular AI-assisted workflow for converting single 2D interior images into textured 3D assets and for evaluating those assets when ground-truth 3D meshes are unavailable. The proposed pipeline combines object detection, instance isolation, monocular-depth estimation, image-to-3D generation, texture synthesis, mesh export, and cloud-based execution to support early-stage interior-design and real-estate visualization tasks. A reference-free validation protocol is introduced, based on rendered multi-view comparisons, silhouette Intersection-over-Union, automated captioning, and multimodal embedding similarity, and is complemented by a composite validation framework that benchmarks reconstructed scenes against 200 panoramic indoor scenes from the Structured3D dataset using Hungarian-matched placement, size, recall, and relative-distance metrics. The workflow was implemented and tested using contemporary computer-vision and generative 3D components, with Hunyuan3D 2.0 used as the main reconstruction model. Proof-of-concept experiments on a representative corpus of 178 synthetically generated single-object images spanning a range of interior furniture categories show comparable silhouette IoU for textured and non-textured outputs and indicate that texture-preserving renderings improve visual and semantic similarity scores across CLIP-based evaluations. The 200-scene dataset evaluation reveals stable spatial localization (placement error ≈ 1.18 m, relative-distance error ≈ 0.54 m) alongside systematic over-prediction and size-calibration errors. Beyond the applied pipeline, the study contributes a reference-free, ground-truth-free protocol for 3D-asset evaluation and a first quantified account of where object-centric single-image reconstruction is reliable—spatial placement—and where it is not—object scale and spurious detection—at interior-scene scale. The results demonstrate the feasibility of integrating perception, 3D reconstruction, semantic assessment, and scalable deployment into a single applied pipeline, while remaining proof-of-concept and requiring extension to larger object and scene corpora, baselines, real-photograph evaluation, and human-centered assessment before broad claims about general interior-scene reconstruction can be made.
Immersive digital experiences rely increasingly on high-quality 3D content, yet traditional 3D authoring demands specialized expertise, multi-camera capture rigs, and prolonged processing pipelines that remain out of reach for most developers. This paper presents a lightweight, end-to-end artificial intelligence framework that automatically reconstructs a textured 3D polygon model from a single RGB photograph and renders it interactively through a browser-native WebAR interface. The system integrates a deep learning model utilizing convolutional layers to capture representative patterns and features. with a monocular depth-estimation module, Marching-Cubes mesh synthesis, UV-texture projection, and progressive mesh-simplification optimized for delivery over A-Frame, Three.js, and the WebXR Device API. Empirical evaluation confirms a reconstruction accuracy of 96.8% across benchmark image sets, with processing times within practical thresholds on commodity mobile hardware. The proposed architecture eliminates dedicated application installation and offers a scalable, cost-efficient route to AI-powered 3D asset generation.
Shaik Farheen Taj, Ramesh Shahabadkar· International Research Journ...· 0 citations
OmniMech is introduced, the first million-scale benchmark for evaluating VLMs on executable CAD generation from industrial manufacturing data, and experiments show that current VLMs and CAD-specialized models still struggle with executable program synthesis, fine-grained 3D reconstruction, and reliable enforcement of dimensions and tolerances.
Taiting Lu, Runze Liu, Ziwei Dong et al.· 0 citations
Editable 3D scene creation requires object instances and lights that can be inspected, moved, and imported into standard engines, yet existing single-image methods largely stop at room-scale geometry, baked/global illumination, or text-driven generation. We introduce Lumera (Light-aware Unified Engine-native Reconstruction and Assembly), a benchmark and reference pipeline for engine-native, light-aware 3D scene parsing from a single image. Lumera-2K is built from 2,513 UE5 projects and provides 3.73M components, 63M object instances, 102.6K engine-native parametric lights, and 95.1K camera views. On this data, Lumera-Box and Lumera-Light adapt VLM to parse object boxes and parametric light tuples (x,y,z,r,g,b,I), which are assembled with per-object mesh reconstruction, HDR environment estimation, and a bounded agentic refinement loop. In a sanitized box benchmark against DetAny3D, SpatialLM, N3D-VLM, and WildDet3D, Lumera-Box obtains the strongest overall detection, geometry, semantic, and layout scores (merged mAP 0.1141, IoU-B 0.2472, F-score 0.2762), while WildDet3D remains stronger on anchor recall. For lights, Lumera-Light recovers almost all non-empty scenes (recall 0.998) but remains limited at individual-light localization (F1 0.209 at 0.5 m); matched lights have median position error 0.261 m, median {\Delta}E2000 4.59, and intensity Pearson r=0.628. These results establish parametric lights as a measurable editable-scene target and expose remaining bottlenecks in relation structure, light recall/intensity, and cross-engine generalization.
Text-driven 3D indoor scene synthesis has witnessed significant progress through Large Language Model (LLM)-based frameworks like ReSpace. However, current paradigms heavily rely on retrieving objects from pre-defined, static 3D asset libraries, which fundamentally constrains the diversity and personalization of generated scenes due to the closed-set nature of existing databases. Conversely, recent breakthroughs in promptable segmentation (SAM 3) and single-image 3D reconstruction (SAM 3D) have empowered the extraction of high-fidelity 3D geometry and texture from in-the-wild images. In this paper, we bridge the gap between text-driven scene layout generation and single-view object reconstruction by proposing OpenAsset. This novel framework converts single images of real-world objects into reusable, canonicalized 3D assets that seamlessly integrate into the scene synthesis workflow. Specifically, given a user concept prompt or target region, OpenAsset leverages SAM3 for precise instance isolation and SAM3D for geometry and texture recovery. To ensure compatibility with structured scene representations (SSR), we introduce an automated canonicalization module that normalizes the scale, orientation, and coordinate systems of the reconstructed meshes. By transforming “wild” visual percepts into standardized assets, OpenAsset effectively expands the controllable vocabulary of indoor scene synthesis beyond curated datasets, offering a practical pathway toward user-sourced open 3D scene generation that supports custom objects outside fixed predefined asset libraries. It is worth noting that the satisfactory performance of OpenAsset critically depends on effective segmentation and reconstruction results, which serve as essential prerequisites for our method.
Abstract. Open-vocabulary 3D segmentation offers an attractive alternative to closed-set scene parsing, yet directly transferring 2D vision-language models to outdoor point clouds remains difficult because projection disrupts geometric continuity and sparse sampling weakens mask quality. This paper presents GeoOpen3D, a geometry-guided and training-free framework for open-vocabulary 3D point cloud segmentation. GeoOpen3D constructs a geometry-preserving RGB-D representation through projection, super-sampling, and depth enhancement to improve alignment between 3D structure and 2D foundation models. It then combines GroundingDINO for language-driven proposal generation with SAM for mask extraction, while introducing depth-aware regularization to favor structurally coherent regions and clearer boundaries. The selected masks are back-projected to the original point cloud through pixel-to-point correspondence, yielding point-wise semantic labels without any 3D model training. Experiments on the SensatUrban dataset show that GeoOpen3D achieves 42.1% mIoU, including 98.5% IoU for buildings and 97.3% IoU for vegetation, outperforming existing training-free open-vocabulary baselines. Additional experiments on a custom island dataset further demonstrate promising transferability to unseen categories. These results indicate that geometry-guided 2D-to-3D transfer provides an effective and scalable path towards open-vocabulary understanding of large-scale outdoor scenes.
Shuai Zhang, Zhuo Li, Jing Ou et al.· The International Archives o...· 0 citations
This work proposes a decompose-before-reconstruct approach, which significantly improves mesh fidelity and novel-view synthesis and novel-view synthesis, while supporting object-wise modifiability and interactivity.
Minhas Kamal, Hiranya Garbha Kumar, Mahedi Kamal et al.· 0 citations
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