ObjectTwin introduces a scan‑grounded protocol for measuring how well generated 3D assets match captured scenes. By fixing the scene, cameras, and depth data and only swapping the object representation, the method quantifies the asset correspondence gap—how much observation agreement is lost compared to the scan‑derived geometry. Experiments on two indoor RGB‑D datasets and two generation backends show that anchored generated assets lose 0.302–0.497 MaskIoU, with scene‑level estimates consistently positive but generator preferences varying across domains.
arXiv:2610.08684v1 Announce Type: new Abstract: Modern video models can generate realistic videos from real appearance references and proxy renders that specify scene structure, viewpoint changes, an...
SceneReGen is a new framework for reconstructing 3D scenes from a single image by generating and assembling complete object meshes within a shared observation‑aligned scene frame. It uses selective pose factorization to encode each object’s observed orientation directly into the generated mesh, while estimating translation and scale from instance‑level and global scene cues. Evaluated on the 3D‑FUTURE dataset, SceneReGen outperforms existing methods on scene‑level metrics and shows strong performance on object‑level metrics, demonstrating its effectiveness in autonomous‑driving and embodied‑AI scenarios.
arXiv:2608. 07579v1 Announce Type: cross Abstract: The AI City Challenge 2026 Track 1 evaluates multi-camera 3D perception in large indoor warehouses under a synthetic-to-real (Sim2Real) setting; depth is available only for training and validation, so inference is RGB-only.
SituHOI is a large‑scale RGB‑D dataset that captures full‑body human‑object interactions in 16 indoor rooms, comprising 18,429 annotated clips and about 86 hours of activity. The dataset pairs moving‑camera recordings with static scene scans, object geometry, language descriptions, and estimated human and object trajectories, all registered in a shared floor‑aligned frame via a pipeline that integrates camera localization, depth‑based alignment, human mesh recovery, and object tracking. The data enable the FlexHOI‑Bench benchmark for goal‑conditioned joint motion prediction, and a reference predictor, SharedHOI, demonstrates strong performance on world‑space accuracy and human‑object coordination. whyItMatters":"The dataset provides scalable, scene‑aligned supervision for modeling human‑object interaction, addressing a key limitation of existing datasets and enabling more accurate motion prediction benchmarks."
arXiv:2606.21596v2 Announce Type: replace Abstract: Recent image-to-3D scene methods recover high-fidelity 3D objects with plausible arrangements, but often leave floatings and interpenetrations that...
F2G-Pose is a real‑time, single‑pass framework for category‑level RGB‑D object pose estimation that predicts 3D rotation, translation, metric size, and a completed camera‑frame shape from a single RGB‑D observation. It lifts dense visual foundation features to partial point clouds, encodes local geometry with DGCNN tokenization, and uses a Mixture‑of‑Experts Transformer to integrate object‑level structure. Trained only on pose‑annotated synthetic SOPE data and leveraging a frozen RADIO backbone, F2G‑Pose achieves state‑of‑the‑art accuracy on SOPE and ROPE while running at 28 FPS for pose‑only inference and demonstrates strong geometry completion on HouseCat6D and real‑object transfer on HANDAL.
Syn2RealTrack addresses the synthetic‑to‑real gap in multi‑camera 3D perception for warehouses by decomposing it into three distinct issues: camera calibration, object shape prior, and known object census. The pipeline corrects lens distortion from images, fuses detections with a visibility‑weighted part‑based descriptor, measures person height directly from calibration, and uses a closed‑world cardinality prior with a causal filter to eliminate phantom boxes. These local remedies allow the system to adapt without retraining a feature extractor, achieving a 3D HOTA of 52.0118% on the AI City Challenge 2026 Track 1.
arXiv:2608.30821v1 Announce Type: cross Abstract: Composable scene modeling aims to recover a real indoor scene as complete, editable object assets arranged as observed, giving robot simulation and e...
arXiv:2609.25654v1 Announce Type: cross Abstract: Robots operating safely in cluttered everyday environments often need to infer scene geometry from partial observations. Methods that detect objects...
F2G-Pose is a real‑time, single‑pass framework for category‑level RGB‑D object pose estimation that predicts pose, metric size, and camera‑frame completed shape from a single RGB‑D observation. It lifts dense visual foundation features to partial point clouds, encodes local geometry with DGCNN tokenization, and uses a Mixture‑of‑Experts Transformer to integrate object‑level structure. Trained only on pose‑annotated synthetic SOPE data with a frozen RADIO backbone, it achieves state‑of‑the‑art accuracy on SOPE and ROPE while running at 28 FPS for pose‑only inference and demonstrates strong geometry on HouseCat6D and real‑object transfer on HANDAL.
arXiv:2606. 18429v1 Announce Type: cross Abstract: Accurately aligning CAD models to their corresponding objects in indoor RGB-D scans is a central challenge in 3D semantic reconstruction.
arXiv:2610.08213v1 Announce Type: new Abstract: Fringe Projection Profilometry (FPP) requires precise system characterisation to achieve reliable three-dimensional (3D) reconstructions; however, char...