3D multi-object visual grounding aims to identify and localize all objects in a 3D scene that correspond to a given text description. Unlike traditional single-object grounding, this task presents additional challenges as point clouds inherently lack fine-grained details, making it difficult to capture subtle object features and contextual information. Moreover, textual descriptions are inherently limited in perceiving and understanding complex 3D environments, especially in scenarios with high object similarity or intricate spatial arrangements. To tackle the above challenges, we propose SGMG, a Rendered 2D Semantic and Generative priors guided 3D Multi-object Grounding Framework. The SGMG framework introduces two key innovations that work cohesively to enhance grounding accuracy. First, a Generative-Assistant(GA) Module leverages the capabilities of a generative model to provide enriched scene prior information and capture the fine-grained scene details. Second, the Semantic-Augment Fusion(SAF) Module is designed to improve the representation of text features from the vision features, thereby boosting the accuracy of multimodal information interactions. Furthermore, we introduce a multi-level fusion mechanism, ensuring that semantic and spatial relationships between objects are preserved and effectively leveraged during the grounding process. Experimental results demonstrate that SGMG achieves state-of-the-art performance in multi-object 3D grounding and competitive results in traditional single-object tasks, highlighting its effectiveness in diverse scenarios.

3D dense captioning requires a model to translate its understanding of an input 3D scene into several captions associated with different object regions. Existing methods adopt a sophisticated "detect-then-describe" pipeline, which builds explicit relation modules upon a 3D detector with numerous hand-crafted components. While these methods have achieved initial success, the cascade pipeline tends to accumulate errors because of duplicated and inaccurate box estimations and messy 3D scenes. In this paper, we first propose Vote2Cap-DETR, a simple-yet-effective transformer framework that decouples the decoding process of caption generation and object localization through parallel decoding. Moreover, we argue that object localization and description generation require different levels of scene understanding, which could be challenging for a shared set of queries to capture. To this end, we propose an advanced version, Vote2Cap-DETR++, which decouples the queries into localization and caption queries to capture task-specific features. Additionally, we introduce the iterative spatial refinement strategy to vote queries for faster convergence and better localization performance. We also insert additional spatial information to the caption head for more accurate descriptions. Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate Vote2Cap-DETR and Vote2Cap-DETR++ surpass conventional "detect-then-describe" methods by a large margin.