PVCap: Towards Accurate 3D Dense Captioning via PseudoCap and VoxelCapNet

Jul 16, 2026

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3D dense captioning generates descriptive sentences for each object in a 3D scene. Prior methods face two challenges: limited data augmentation that relies on global rigid transformations without changing spatial layouts, and uneven model design where a simple backbone and detection head restrict semantic feature extraction for captioning. PVCap addresses both by using PseudoCap to create diverse pseudo frames through instance-level random mixing with teacher-student pseudo label supervision, and VoxelCapNet to leverage voxel features with an adapted caption head.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

Introduction

Task Overview

Prior Work and Limitations

Proposed Framework: PVCap

PseudoCap Data Augmentation

VoxelCapNet Network Architecture

PVCap: Towards Accurate 3D Dense Captioning via PseudoCap and VoxelCapNet Xiaopei Wu 1,2 , Chenshu Hou2 , Liang Peng2 , Dan Xu3 , Binbin Lin2 , Xiaoshui Huang4 Yuenan Hou 1 , Yu Li 1 , Wenxiao Wang2 , Haifeng Liu2 , Deng Cai2 , Wanli Ouyang 1 1 SH AI Lab 2ZJU 3HKUST 4 SJTU arXiv :2607 .06097v 1 [ cs .CV] 7 Jul 2026 Abstract 3D dense captioning, an emerging vision-language task, aims to generate descriptive sentences for each object in the 3D scene. Despite the impressive results achieved by previous methods, they suffer from two limitations. First, current research often employs global rigid transformations, such as rotation, to augment scenes without changing their spatial layouts. However, diverse spatial layouts are crucial for training a 3D dense captioning model to describe spatial relations between objects. Second, previous works mainly focus on the design of the caption generation pipeline while utilizing a simple network architecture for other components, i.e., backbone and detection head, which is crucial for extracting rich semantic information for captioning. In this paper, we propose PVCap to alleviate the aforementioned problems. Our PVCap consists of PseudoCap and VoxelCapNet. Specifically, PseudoCap employs a random mixing technique on instances within the dataset, generating numerous pseudo frames with diverse spatial layoutsat the instance level. By utilizing a teacher-student framework, PseudoCap obtains pseudo caption labels for these pseudo frames. This data augmentation approach significantly increases the number of training samples and enhances the model’s ability to describe the environment effectively. Regarding VoxelCapNet, we introduce a robust caption network that utilizes voxel features and adapts the caption head to the voxel-based network architecture. Our VoxelCapNet can serve as a competitive baseline for future research on 3D dense captioning. Extensive experiments are conducted on two prevalent benchmarks, i.e., ScanRefer and Nr3D. Notably, our method surpasses current state-ofthe-art by 11.41% and 13.99% in CIDEr@0.5IoU, respectively. Codes will be made publicly available. 1. Introduction 3D dense captioning [3, 5, 10, 23, 44, 50] aims to describe individual objects in 3D point cloud scenes by natural language. It can be divided into two tasks: object detection and object caption generation. Scan2Cap[10], MORE[23] and SpaCap3D[44] propose well-designed relation reasoning modules to model relations among object proposals efficiently. [52] introduces contextual information from two branches to improve the caption. 3DJCG[3] and D3Net[5] study the correlation between 3D visual grounding and 3D dense captioning and demonstrate the synergistic effect of these two tasks. Additionally, Vote2Cap[6] and Vote2Cap++[7] jointly train object detection and caption generation, enabling the mutual promotion of the two tasks. Though previous methods have achieved remarkable results, they suffer from two primary issues: insufficient data augmentation and poor network architecture. Regarding data augmentation, current 3D dense captioning methods often rely on rigid transformations (such as rotation) . However, these transformations do not alter the spatial layout of the scene, and describing the environment surrounding an object is crucial for accurate captioning. Simple data augmentation techniques provide only limited variation in the data space available for the caption model, which restricts its ability to generate precise captions. As to the network architecture, we observe that existing works primarily focus on the design of the caption generation pipeline while ignoring the importance of other components. They usually leverage a simple network architecture, such as PointNet++[35] as the backbone and 3DETR[32] as the detection head, to extract features for the caption head. These architectures cannot provide sufficient information for accurate captioning, leading to unsatisfactory resul

FAQs

What problem does 3D dense captioning aim to solve?

It generates natural-language sentences for individual objects in 3D point cloud scenes.

What two main limitations of prior methods does PVCap target?

Insufficient augmentation due to rigid transformations that keep spatial layouts unchanged, and inadequate network architectures that underuse components beyond the caption pipeline.

How do PseudoCap and VoxelCapNet improve the model?

PseudoCap creates instance-level pseudo frames and uses a teacher-student framework to produce filtered pseudo caption labels for supervision. VoxelCapNet introduces a voxel-based caption network that extracts voxel features and adapts the caption head to the voxel architecture.

Jul 16, 2026

Upload by Riley