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

TL;DR

3D CoCa v2 introduces a robust 3D captioning framework that combines contrastive learning with test-time search, significantly enhancing generalization and performance across diverse 3D environments without retraining.

Contribution

The paper presents a novel 3D captioning model that unifies contrastive vision-language learning with caption generation and employs test-time search for improved robustness and OOD generalization.

Findings

Achieves +1.50 CIDEr on ScanRefer

Improves zero-shot OOD performance by +3.8 CIDEr

Outperforms previous models on multiple benchmarks

Abstract

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.