Zhongbin Guo

Zhongbin Guo, Jiahe Liu, Wenyu Gao et al.

Text-driven 3D reconstruction demands a mask generator that simultaneously understands open-vocabulary instructions and remains consistent across viewpoints. We present LISA-3D, a two-stage framework that lifts language-image segmentation into 3D by retrofitting the instruction-following model LISA with geometry-aware Low-Rank Adaptation (LoRA) layers and reusing a frozen SAM-3D reconstructor. During training we exploit off-the-shelf RGB-D sequences and their camera poses to build a differentiable reprojection loss that enforces cross-view agreement without requiring any additional 3D-text supervision. The resulting masks are concatenated with RGB images to form RGBA prompts for SAM-3D, which outputs Gaussian splats or textured meshes without retraining. Across ScanRefer and Nr3D, LISA-3D improves language-to-3D accuracy by up to +15.6 points over single-view baselines while adapting only 11.6M parameters. The system is modular, data-efficient, and supports zero-shot deployment on unseen categories, providing a practical recipe for language-guided 3D content creation. Our code will be available at https://github.com/binisalegend/LISA-3D.

Zhongbin Guo, Jiahe Liu, Yushan Li et al.

Existing Vision Language Models (VLMs) architecturally rooted in "flatland" perception, fundamentally struggle to comprehend real-world 3D spatial intelligence. This failure stems from a dual-bottleneck: input-stage conflict between computationally exorbitant geometric-aware encoders and superficial 2D-only features, and output-stage misalignment where discrete tokenizers are structurally incapable of producing precise, continuous numerical values. To break this impasse, we introduce GEODE (Geometric-Output and Decoupled-Input Engine), a novel architecture that resolves this dual-bottleneck by decoupling 3D reasoning from numerical generation. GEODE augments main VLM with two specialized, plug-and-play modules: Decoupled Rationale Module (DRM) that acts as spatial co-processor, aligning explicit 3D data with 2D visual features via cross-attention and distilling spatial Chain-of-Thought (CoT) logic into injectable Rationale Tokens; and Direct Regression Head (DRH), an "Embedding-as-Value" paradigm which routes specialized control tokens to a lightweight MLP for precise, continuous regression of scalars and 3D bounding boxes. The synergy of these modules allows our 1.5B parameter model to function as a high-level semantic dispatcher, achieving state-of-the-art spatial reasoning performance that rivals 7B+ models.