arXiv:2610. 08339v1 Announce Type: new Abstract: Camera-based 3D perception for autonomous driving relies heavily on large annotated datasets, and deploying such a system to a new target region typically requires data collection and annotation.
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...
arXiv:2608.13147v2 Announce Type: replace Abstract: Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera str...
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.
arXiv:2606. 07366v1 Announce Type: cross Abstract: Self-driving simulations typically rely on data collected in a small number of cities or on hand-authored synthetic scenarios.
SatUnreal is a synthetic dataset created with Unreal Engine that offers 10,000 high‑resolution (0.3 m GSD) satellite stereo pairs. It addresses key limitations of existing benchmarks by ensuring physical geometry simulation, spatio‑temporal consistency, topographic diversity, and mathematically precise occlusion masks via a two‑step line‑trace algorithm. Models trained solely on SatUnreal outperform those trained on real datasets when transferred to real‑world benchmarks such as US3D and WHU‑Stereo.
GS‑Net is a lightweight plug‑and‑play module that expands sparse Structure‑from‑Motion point clouds into dense Gaussian primitives, enabling cross‑sensor view synthesis for autonomous driving. It learns a generalizable initialization for 3D Gaussian Splatting, improving rendering quality for both interpolated and extrapolated camera viewpoints. The authors introduce CARLA‑NVS, a benchmark with 12 uniformly spaced cameras, and show that GS‑Net outperforms standard 3DGS by 2.08 dB PSNR on interpolated views and 1.86 dB on extrapolated views while being 50× faster to initialize.
arXiv:2606. 00299v1 Announce Type: cross Abstract: While Video Diffusion Models (VDMs) excel at synthesizing high-fidelity videos, enabling precise camera and scene control remains challenging.
arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360$^\circ$ scene geometry, being essential for robotics and AR/VR applications. While perspective...
Grounded-Exo2Ego introduces a dual‑branch video diffusion model that combines a geometric anchoring branch with a semantic grounding branch to generate egocentric video from a single exocentric source. The framework also includes a camera re‑localization algorithm to correct reconstruction misalignment and a fully automated synthetic data engine for training. Experiments on the EgoExo4D dataset demonstrate significant performance gains over recent state‑of‑the‑art methods.
Occupancy prediction at voxel-level granularity is essential for safe robotic navigation and interaction in complex environments. Existing occupancy datasets, however, are predominantly designed for autonomous driving with vehicle-centric biases -- forward-facing cameras, far-field geometry, and static road priors -- limiting their applicability to embodied humanoid perception.
arXiv:2511.21265v2 Announce Type: replace Abstract: Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) en...
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.