Open vocabulary 3D scene understanding is essential for next-generation interactive systems, empowering users to intuitively query and navigate reconstructed environments using natural language. However, current 3D Gaussian frameworks are often bottlenecked by restrictive multiview capture requirements, costly scene-specific optimization, and the massive memory overhead of storing dense language features.
GoDeep is an annotation‑free method for open‑vocabulary 3D scene understanding that uses a vision‑language model solely as a translator to generate structured, entity‑level descriptions of each image. These descriptions are projected and aggregated in a language‑only embedding space, eliminating the need for a 3D training corpus or domain‑specific encoder. The approach achieves competitive performance on ScanNet++ and a cultural heritage benchmark, accurately localizes out‑of‑vocabulary objects, and offers explainable, point‑level predictions.
By Thodoris Betsas, Anastasios Doulamis, Andreas Georgopoulos
arXiv:2608.21136v1 Announce Type: new
Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...
By Jie Xu, Na Zhao
arXiv:2606. 19733v1 Announce Type: cross Abstract: Efficiently retrieving specific 3D instances from large-scale scenes via natural language prompts remains a formidable challenge in multimedia analysis.
By Xiuyuan Zhu, Ke Lu, Zijie Yang, Chao Yue, Jian Xue, Dongming Zhang
Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current benchmarks. First, 3D datasets often rely on point clo...
Indoor visual relocalization plays a critical role in emerging spatial and embodied AI applications. However, prior research was predominantly devoted to low-level vision schemes, struggling to perceive scene semantics and compositions, which limits both interpretability and applicability.
arXiv:2608.20720v1 Announce Type: new
Abstract: Open-world 3D affordance grounding requires localizing functional object parts in 3D given free-form language queries. Existing methods typically assum...
By Junqi Wu, Kaihua Tang, Xuanwen Chen, Hongzhi Li, Jianqiang Huang, Xian-Sheng Hua
Building memory is essential for long-horizon planning in zero-shot embodied navigation. Detector-centric scene graphs often compress observations into sparse nodes, discarding fine-grained visual evidence and accumulating noise, while 3D reconstruction-based methods remain computationally prohibitive.
arXiv:2609.16233v1 Announce Type: cross
Abstract: Vision-language models excel at 2D image understanding but remain limited in 3D spatial reasoning. Progress is hindered by limitations in current ben...
By Anubhav Khanal, Prabigya Acharya, Roshni Poudel, Sujan Kapali, Bigyan Bhatta, Pramish Paudel, Francois Rameau, Danda Pani Paudel
Real-world spatial intelligence requires agents to understand scenes from continuous video streams, where objects move, persist, disappear, and reappear over time. While recent spatial foundation models have enabled generalizable feed-forward 3D reconstruction, most streaming methods remain geometry-centric and lack temporally consistent object-level understanding.
ScenePilot introduces a retrieval‑augmented Grow‑and‑Repair framework for text‑driven 3D indoor scene generation. It uses a Hierarchical Retrieval‑Augmented Planning module to fetch room, group, and anchor layout priors, then incrementally inserts object groups with a base generator, while a Reinforcement Multimodal Repair module performs lightweight local corrections after each insertion and a final global repair. The approach is trained on a new SceneReverse‑17k dataset of perturbed scenes, enabling the policy to predict structured move‑rotate‑scale actions from rendered views, scene state, retrieved priors, and edit history, thereby improving physical plausibility, functional coherence, and controllability without heavy full‑scene optimization.
By Jiawei Zhang, Hongsong Wang, Pan Zhou
arXiv:2510. 01483v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) demonstrate strong image-level scene understanding, but reasoning over long egocentric video remains costly: because VLMs maintain no persistent memory or explicit spatial representation, all sampled frames must be re-processed for every new query.
By Mohamad Al Mdfaa, Svetlana Lukina, Timur Akhtyamov, Arthur Nigmatzyanov, Dmitrii Nalberskii, Sergey Zagoruyko, Gonzalo Ferrer