arXiv Computer Vision
Sep 28

GraphWrit3R: End-to-End 3D Scene Graph Writing

arXiv:2609.31595v1 Announce Type: new Abstract: 3D scene graphs provide a structured representation of complex environments by encoding objects, their semantic attributes, and the spatial and functio...

By Luka Milivojevic, Nikola Popovic, Sayan Deb Sarkar, Sebastian Koch, Iro Armeni, Luc Van Gool, Danda Pani Paudel
arXiv AI
Oct 26

Beyond Pattern Matching in 3D MLLMs: Part-Level Functional Reasoning for Generalizable Recognition

The paper introduces PFIR, a Part-level Functional Inference for Recognition framework that reverses the typical function-as-annotation flow in 3D multimodal large language models (MLLMs). By leveraging PartNeXt segmentation and a curriculum of 507K QA pairs with functional chains of thought, PFIR trains a dual-objective reinforcement learning model (GRPO) to perform strict functional reasoning and descriptive grounding. In zero‑shot tests, PFIR improves accuracy from 60.5% to 66.7%, especially on geometrically ambiguous samples, demonstrating that part‑level functional reasoning enhances generalizable 3D recognition.

By Weihong Li, Hao Jiang, Yaohua Zha, Tao Dai, Shu-Tao Xia
arXiv Computer Vision
Oct 16

Training-Free Target-Aware Refinement of 3D Gaussian Language Map

The paper introduces a training‑free, target‑aware refinement method for 3D Gaussian language maps distilled from vision‑language features. By blending the visual embedding within user‑specified target regions with the corresponding text embedding using a convex combination, the method improves target localization while preserving open‑vocabulary queryability. The authors derive the optimal blending coefficient analytically, avoid hyperparameters, and demonstrate consistent gains across three benchmarks, including a real‑world robot navigation demo.

By Sibaek Lee, Kyeongsu Kang, Seongbo Ha, Yewon Jeon, Joonyeol Choi, Daegeol Ko, Jaehyuk Hur, Taemin Choi, Minsu Kim, Hyungjoo Ahn, Wonsok Yoo, Hyeonwoo Yu