3D dense captioning is an emerging field in computer vision that aims to generate descriptive textual annotations for multiple regions of interest within a three-dimensional (3D) scene. Unlike traditional 2D image captioning, which focuses on describing objects and their interactions in flat images, 3D dense captioning deals with complex spatial information inherent in 3D environments. This involves recognizing and describing objects, their relationships, and contextual details in a volumetric space, often derived from sources such as LiDAR scans, stereo cameras, or depth sensors. This blog provides an insightful overview of 3d dense captioning topic

Introduction

Humans can easily explore and describe the scene and the objects around them in detail. However, making computers understand the whole scene and local entities like humans remains challenges.

Image captioning, as a representation of the vision-language generation task, aims to generate accurate and contextually relevant textual descriptions for an entire image, hence bridging the visual understanding with natural language description. Subsequently, dense captioning, as a natural extension of image captioning, focuses on local regions in the images and provides more detailed descriptions and instructions.

These two fields are widely explored and many methods achieve good performance and significantly advanced vision-language development. However, they still have limitations, and one of them is that they heavily rely on 2D images, which are obtained with a single view only and then can result in misaligned, distorted, and obscured, appearances. Consequently, it is a challenge to exactly explore the relations between local entities.

However, with the advancement of 3D point cloud data collection techniques and 3D data processing methods, bridging 3D information and language has become an interesting topic for exploration. The 3D point cloud data provides geometry information and spatial relations between objects. 3D dense captioning is first proposed by Chen et al. as a novel task for generating dense captions in 3D scenes, elevating the traditional dense captioning task from 2D to 3D and resulting in more customized and detailed descriptions.

Recently, the research on 3D dense captioning has been growing rapidly. Development methods have overcome the challenges and pushed the boundaries of 3D dense captioning. This article is intended to delve deeper into 3D dense captioning, including task definition, used framework, implemented methods, and datasets.

Method Overview

Task definition

3D dense captioning aims to understand the 3D scene at the object-level by generating natural descriptions for an object in the scene. The input required is a 3D point cloud, which represents the geometric and appearance characteristics of objects in the scene, along with other features such as colors, normal vectors, ...

The point cloud input data can be represented as a matrix $P \in \mathbb{R}^{N \times F}$ where $N$ denotes the number of points (usually 40,000) and $F$ is the dimensionality of point features, including point coordinates $(x,y,z)$ and other features if needed. The final output comprises $M$ tuples $(B_i, D_i)_{i=1}^{M}$, where $B_i = [\bm{c}_i, \bm{s}_i]$ is the $i$-th bounding box with center $\bm{c}_i \in \mathbb{R}^3$ and box size $\bm{s}_i \in \mathbb{R}^3$, and $D_i$ is the corresponding text description.

Architecture

Most implemented methods follow the encoder-decoder architecture, which can be divided into three components: scene encoder, relative module, and feature decoder:

• Scene encoder: The scene encoder extracts the scene details such as object-level features or other features from the scene point cloud. Various 3D detection methods can be used such as PointNet, PointNet++, PointGroup, VoteNet, and 3DETR, and achieve notable performance. In this step, most methods aggregate object proposals and produce bounding boxes, which can be used later in the feature decoding step.
• Relation module: The relation module enables the modeling of intricate connections and cross-modal interactions within indoor scenes. Choosing the suitable relation approach depends on the specific requirements of the 3D dense captioning task and the characteristics of the scene understanding problem being addressed. Commonly, many methods use graph neural networks, transformers, or knowledge distillation.
• Feature decoder: The feature encoder generates the bounding boxes and captions for objects in the scene, manipulating the features from the scene encoder and relation module.

Figure 1:General framework and pipeline for 3D dense captioning task. Source image here

Next

In the next part, we will discuss about different state-of-the-art methods and well-known metrics used for validate the effectiveness of these methods.