3D CoCa v2：采用测试时搜索的对比学习器实现可泛化空间智能

3D CoCa v2: Contrastive Learners with Test-Time Search for Generalizable Spatial Intelligence

January 10, 2026

作者: Hao Tang, Ting Huang, Zeyu Zhang

cs.AI

摘要

空间智能指在三维环境中感知、推理并描述物体及其相互关系的能力，是具身感知与场景理解的基础。三维描述生成技术旨在用自然语言描述三维场景，但由于点云的稀疏性与不规则性，以及现有描述器在差异显著的室内外三维场景中存在弱定位性和有限分布外泛化能力，该技术仍面临挑战。为此，我们提出可泛化三维描述框架3D CoCa v2，通过统一对比式视觉语言学习与三维描述生成，并采用不更新描述器参数的自适应测试时搜索机制提升鲁棒性。该框架基于冻结的CLIP语义先验、具备空间感知能力的几何三维场景编码器，以及通过对比学习和描述生成联合优化的多模态解码器，无需外部检测器或人工提案。推理时，测试时搜索机制生成多样化描述候选，并基于紧凑场景摘要进行奖励引导的选择。实验显示：在ScanRefer和Nr3D数据集上CIDEr@0.5IoU指标分别提升1.50和1.61分，在TOD3Cap的零样本分布外评估中CIDEr@0.25指标提升3.8分。代码将发布于https://github.com/AIGeeksGroup/3DCoCav2。

English

Spatial intelligence refers to the ability to perceive, reason about, and describe objects and their relationships within three-dimensional environments, forming a foundation for embodied perception and scene understanding. 3D captioning aims to describe 3D scenes in natural language; however, it remains challenging due to the sparsity and irregularity of point clouds and, more critically, the weak grounding and limited out-of-distribution (OOD) generalization of existing captioners across drastically different environments, including indoor and outdoor 3D scenes. To address this challenge, we propose 3D CoCa v2, a generalizable 3D captioning framework that unifies contrastive vision-language learning with 3D caption generation and further improves robustness via test-time search (TTS) without updating the captioner parameters. 3D CoCa v2 builds on a frozen CLIP-based semantic prior, a spatially-aware 3D scene encoder for geometry, and a multimodal decoder jointly optimized with contrastive and captioning objectives, avoiding external detectors or handcrafted proposals. At inference, TTS produces diverse caption candidates and performs reward-guided selection using a compact scene summary. Experiments show improvements over 3D CoCa of +1.50 CIDEr@0.5IoU on ScanRefer and +1.61 CIDEr@0.5IoU on Nr3D, and +3.8 CIDEr@0.25 in zero-shot OOD evaluation on TOD3Cap. Code will be released at https://github.com/AIGeeksGroup/3DCoCav2.