ICRA挑战赛技术报告：用DINOv3搞定户外机器人场景的细粒度语义分割，鲁棒又精准。
arXiv:2606.18582v1 Announce Type: new Abstract: The GOOSE 2D Fine-Grained Semantic Segmentation Challenge at the ICRA 2026 Workshop on Field Robotics …
用全景画布统一多视图特征，无需复杂几何编码器，实现轻量级3D场景理解。
arXiv:2606.19253v1 Announce Type: cross Abstract: Existing approaches to 3D scene understanding in Vision-Language Models (VLMs) either rely on comple…
零样本本体对齐新突破，用LLM将USD场景自动转化为知识图谱，机器人语义理解再进一步。
arXiv:2606.09134v1 Announce Type: cross Abstract: Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but t…
突破性提出统一3D多模态大模型，部件感知表示让场景理解更细粒度。
arXiv:2606.06485v1 Announce Type: new Abstract: Recent advances in 3D multimodal large language models (3D-MLLMs) have enabled unified solutions for 3…
利用图像基础模型实现可扩展的3D人体放置，虚拟场景与AI融合的前沿突破。
arXiv:2604.19673v2 Announce Type: replace Abstract: Training embodied agents to understand 3D scenes as humans do requires large-scale data of people …
EgoVis 2026 CASTLE挑战赛亚军方案的技术报告，详解多模态场景理解新方法MARS，视觉AI进阶必读
arXiv:2605.18176v1 Announce Type: new Abstract: This report presents MARS, short for Multimodal Agentic Reasoning with Source selection, our system fo…
实时理解多模态世界的AI模型，开创实时世界模型新范式
The first real-time multimodal world model Discussion | Link