We introduce ReRef-3D, a benchmark for language-guided placement in 3D scenes. It contains 33,826 instructions across 998 CLEVR-derived scenes, spanning 16 placement families and direct, one-hop, and two-hop references.
VoxProg introduces voxel programming for 3D spatial reasoning, enabling language models to translate task interpretations into executable programs that operate on local voxel views of scene surfaces. By indexing surface geometry on regular grids, the approach isolates task-relevant regions and boundaries, allowing downstream pose programs to compute target SE(3) poses. The method is evaluated on the 71-task VoxProg-Bench benchmark, showing consistent superiority over relation-based, keypoint-based, and mesh-based baselines.
arXiv:2610.28280v1 Announce Type: cross Abstract: Reasoning about fine-grained placement constraints, such as support and containment, is essential for realistic 3D scene creation. It remains a signi...
Reasoning about fine-grained placement constraints, such as support and containment, is essential for realistic 3D scene creation. It remains a significant challenge for existing methods that rely on...
arXiv:2606. 31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions.
SceneBloom is an agentic framework that generates realistic, varied, and simulation-ready indoor environments for embodied learning. It combines diffusion-based visual layout sampling with a contract-guided 3D compiler that uses extracted relations for geometric fitting and pose correction. The method outperforms baselines across five room types, achieving higher visual quality, physical stability, and better alignment with real room distributions, and improves object navigation policy success rates on real scans.
The paper introduces LEGO, a benchmark dataset pairing user text descriptions with human‑annotated fine‑grained constraints and reference 3D scenes, and LEGO‑Eval, an evaluation framework that decomposes descriptions into atomic constraints and verifies each using grounding and spatial reasoning tools. It demonstrates that LEGO‑Eval detects misalignment more accurately than existing methods and that current 3D scene synthesis approaches achieve at most a 10% success rate on this benchmark.
arXiv:2608.21543v1 Announce Type: cross Abstract: Object placement is critical in image composition, requiring spatially and semantically coherent positioning of objects within diverse scenes. Existi...
arXiv:2610.08446v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have made significant progress in visual understanding, but precise 3D spatial reasoning integrated with physi...
arXiv:2610.28018v1 Announce Type: new Abstract: Examining existing benchmarks for grounding in 3D VLMs reveals a pervasive "semantic leakage" in the evaluation and prompting protocols: a heavy relian...
arXiv:2606.22694v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) remain unreliable when spatial reasoning requires composing relations whose meanings depend on frames of reference. E...
Multimodal large language models (MLLMs) have made significant progress in visual understanding, but precise 3D spatial reasoning integrated with physical environment remains difficult. Furniture asse...