Lightweight HSV-Based 3D Color Classification Module for Zero-Shot 3D Visual Grounding
Minho Park, Jongmin Lim, Soobin Cha, Inho Oh, Sehun Chang, Kwangsu Kim · IEEE Access · 2025
Zero-shot 3D visual grounding (ZS-3DVG) refers to the task of grounding an object within a 3D scene based on a natural-language description, particularly for categories unseen during training. In this task, color serves as a crucial cue, but its use is hindered by challenges in both accuracy and speed. Accuracy is degraded by the instability of the RGB color space under varying illumination and the semantic gap between the standard color definitions and human perception. Speed is hampered by the computational overhead of rendering 3D point clouds into multiple 2D views. Although more views can improve color recognition, this incurs substantial latency, making real-time applications infeasible. To address these challenges, we propose a lightweight module that enhances both speed and accuracy. With respect to speed, our module bypasses the rendering pipeline and operates directly on HSV histograms extracted from 3D point clouds, significantly reducing the computation. For accuracy, our module leverages a key property of the HSV color space: chromatic colors concentrate along the hue axis, whereas achromatic colors lie on the value axis. Based on this, we mitigate the RGB instability by routing the input data to specialized classifiers focusing on the relevant axis. Furthermore, we resolved the semantic mismatch by grouping perceptually similar colors. The experimental results show that our method achieves higher accuracy and faster inference than RGB- or CLIP-based baselines, enabling robust and efficient color recognition in ZS-3DVG.