The Flow has not summarised this story yet — read it at arXiv Computer Vision.
arXiv:2607. 02561v1 Announce Type: cross Abstract: Consumer depth sensors such as the LiDAR scanner on recent iPhones provide metric range, but their useful range is short and their returns are sparse.
arXiv:2607. 13682v2 Announce Type: cross Abstract: Radiative Gaussian splatting reconstructs sparse-view CT fast and accurately, and recent work attaches per-Gaussian posteriors to yield per-voxel uncertainty maps.
The paper introduces SightCAD, a framework for parametric CAD reconstruction that explicitly handles view feasibility constraints in realistic indoor scenes. It learns a view selector to choose a small set of feasible viewpoints for a vision‑language model that generates executable CadQuery code, and jointly trains the selector and the VLM using a reward that depends on the final geometric fidelity. The approach outperforms baseline methods on several benchmarks, achieving higher intersection‑over‑union scores and a lower invalid‑code rate.
The study investigates which layers of feed‑forward geometry models—specifically VGGT, π³, and VGGT‑Ω—are essential for preserving camera poses and dense 3D structure. By pruning 3,018 configurations and evaluating seven metrics across indoor and outdoor datasets, the authors identify two redundancy regions (early and late) and show that combined deletions degrade performance additively, enabling more efficient pruning. They also demonstrate that CKA can serve as a cheaper proxy for interval degradation, and that closed‑form linear calibration can recover accuracy without retraining, reducing aggregator parameters by up to 44% while maintaining comparable performance.
arXiv:2605.08371v2 Announce Type: replace Abstract: Multi-view geometry transformers are feed-forward 3D foundation models that jointly predict depth maps, point maps, and camera poses for N images i...
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.