arXiv AI
Error-Driven Scene Editing for 3D Grounding in Large Language Models
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 2
CoEvolve: Construct-to-Edit Visual Grounding with Bidirectional State Refinement
CoEvolve is a construct-to-edit framework for visual grounding that separates the task into explicit state construction and state editing. It uses Region‑Evolution Reinforcement to progressively refine candidate regions and Bidirectional Denoising Refiner to adjust coordinate fields based on fixed semantic context. The approach achieves high grounding accuracy with a 9B backbone, rivaling much larger models, and can recover over 27 percentage points in mean box overlap after a single refinement pass.
arXiv Computer Vision
3d ago
Grounding Generative Verification in 3D Content
The paper introduces 3DVerifierBench, a benchmark covering ten tasks that evaluates 3D content generation for correctness, rejection explanations, and responsiveness to specification changes. It highlights a significant performance gap between humans and current models, and presents a scalable pipeline that generates over 14,000 training examples with contradiction explanations. The study also demonstrates how verification can improve 3D generation via candidate selection and recursive refinement, raising factual success rates dramatically.
arXiv AI
Jun 15
3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding
arXiv:2603. 04976v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its potential in 3D scene understanding remains under-explored.
arXiv AI
Jul 1
PruneGround: Plug-and-play Spatial Pruning for 3D Visual Grounding
arXiv:2606. 31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions.
arXiv AI
Oct 2
Task-Adaptive Grounded 3D-Programmers Using 2D VLMs
The paper introduces 3D-Prog, a framework that adapts powerful 2D vision‑language models (VLMs) for reliable 3D understanding, manipulation, and generation. It does so by employing Canonical Coordinate Framing (CCF) to anchor inputs and outputs in a shared Euclidean coordinate system, resolving axis ambiguity, metric scale, and reference issues, and Task‑Adaptive Feedback (TAF) to close the reasoning loop with dynamic, task‑specific feedback. Together, CCF and TAF enable 2D VLMs to perform open‑vocabulary 3D tasks without retraining, yielding consistent, interpretable, and high‑quality results across diverse 3D scenarios.
arXiv AI
Oct 7
ElasticFit: Fit-Aware 3D Object Insertion via VLM Reasoning and Generative Adaptation
arXiv:2610.07460v1 Announce Type: cross Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location: the inserted object must also fit local geometry while...