PD-APE: A Parallel Decoding Framework with Adaptive Position Encoding for 3D Visual Grounding

TL;DR

This paper introduces PD-APE, a dual-branch decoding framework with adaptive position encoding for 3D visual grounding, effectively separating target object and environment understanding to improve accuracy.

Contribution

The proposed PD-APE framework uniquely decouples object and environment decoding with adaptive position encoding, enhancing focus and performance in 3D visual grounding tasks.

Findings

Outperforms state-of-the-art on ScanRefer dataset

Achieves superior results on Nr3D dataset

Demonstrates effective separation of object and environment attention

Abstract

3D visual grounding aims to identify objects in 3D point cloud scenes that match specific natural language descriptions. This requires the model to not only focus on the target object itself but also to consider the surrounding environment to determine whether the descriptions are met. Most previous works attempt to accomplish both tasks within the same module, which can easily lead to a distraction of attention. To this end, we propose PD-APE, a dual-branch decoding framework that separately decodes target object attributes and surrounding layouts. Specifically, in the target object branch, the decoder processes text tokens that describe features of the target object (e.g., category and color), guiding the queries to pay attention to the target object itself. In the surrounding branch, the queries align with other text tokens that carry surrounding environment information, making the attention maps accurately capture the layout described in the text. Benefiting from the proposed dual-branch design, the queries are allowed to focus on points relevant to each branch's specific objective. Moreover, we design an adaptive position encoding method for each branch respectively. In the target object branch, the position encoding relies on the relative positions between seed points and predicted 3D boxes. In the surrounding branch, the attention map is additionally guided by the confidence between visual and text features, enabling the queries to focus on points that have valuable layout information. Extensive experiments demonstrate that we surpass the state-of-the-art on two widely adopted 3D visual grounding datasets, ScanRefer and Nr3D.