The paper introduces Generative Semantic Scene Completion (GSSC), a framework that recasts outdoor LiDAR semantic scene completion as a discrete diffusion process. It comprises three components: (1) paired sparse‑dense scene synthesis (PS³) to generate synthetic training data, (2) semantic‑guided generative scene completion (SGSC) that generates scenes from noise conditioned on sparse scans, and (3) structured source discrete diffusion (S²D²) that refines existing completions in a single flow‑matching step. Using this approach, the authors achieve state‑of‑the‑art performance on the SemanticKITTI benchmark, reaching 38.8% mIoU in a single‑sweep, single‑sample setting and 39.2% with limited augmentation.
By Shi Chen, Weifeng Ge
arXiv:2605.17630v3 Announce Type: replace
Abstract: Frozen segmentation foundation models often fail when the target class appears in a form that is weakly represented during pretraining. To address...
By Abderrahmene Boudiaf, Irfan Hussain, Sajid Javed
ReVisIT is a train‑free framework that turns retrieved image‑label pairs into units of visual thought, combining structured class definitions, multimodal retrieval, and alternating user/assistant injection before joint decoding. On several benchmarks—including Fast Open MiniImageNet, Bongard‑OpenWorld, and the newly released MAAC‑Bench—ReVisIT achieves performance comparable to or surpassing large, trained models while using far fewer parameters. The approach demonstrates that high‑quality retrieval and a simple turns layer can provide a universal performance boost across diverse multimodal tasks.
By Bingchen Huang, Zhiling Wang, Yifu Chen, Yuanchao Du
arXiv:2608. 02830v1 Announce Type: cross Abstract: Many-shot in-context learning (ICL) lets vision-language models (VLMs) adapt from image--label demonstrations without weight updates, and is widely assumed to improve as more demonstrations are supplied.
By Mohammad Rostami
FuseAdapt is a parameter‑efficient framework for multimodal semantic segmentation that performs fusion within the adaptation space of a frozen vision foundation model (VFM). Instead of a separate fusion network, each modality receives a lightweight adaptation branch, and only low‑rank residual activations are routed across modalities inside the frozen backbone. The method also introduces Predictive Modality Modeling (PMM) to handle missing modalities during training, achieving significant mIoU gains on three benchmarks.
By Xin Zhang, Robby T. Tan
VOIM (Voxel‑Grounded Online Instance Manager) is a training‑free system that builds open‑vocabulary 3D instance maps from RGB‑D or monocular RGB input by deferring label and instance decisions until sufficient soft evidence accumulates per voxel across views. Across four perception configurations on ScanNet++, VOIM outperforms the strongest online RGB‑D system, OVO‑SLAM, by 4.8–11.7 mIoU, and achieves 44.07 mIoU under a like‑for‑like protocol, winning all ten scenes. The method also runs unchanged on monocular RGB, matching baseline performance on Replica, and produces exportable occupancy grids that support free‑form instance queries.
By Sangmin Song, Sarath Kodagoda, Marc G. Carmichael, Karthick Thiyagarajan, Amal Gunatilake, Kelly Prentice, Jodi Martin