ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D Images (2410.24001v1)

Abstract: Open-vocabulary 3D object detection (OV-3Det) aims to generalize beyond the limited number of base categories labeled during the training phase. The biggest bottleneck is the scarcity of annotated 3D data, whereas 2D image datasets are abundant and richly annotated. Consequently, it is intuitive to leverage the wealth of annotations in 2D images to alleviate the inherent data scarcity in OV-3Det. In this paper, we push the task setup to its limits by exploring the potential of using solely 2D images to learn OV-3Det. The major challenges for this setup is the modality gap between training images and testing point clouds, which prevents effective integration of 2D knowledge into OV-3Det. To address this challenge, we propose a novel framework ImOV3D to leverage pseudo multimodal representation containing both images and point clouds (PC) to close the modality gap. The key of ImOV3D lies in flexible modality conversion where 2D images can be lifted into 3D using monocular depth estimation and can also be derived from 3D scenes through rendering. This allows unifying both training images and testing point clouds into a common image-PC representation, encompassing a wealth of 2D semantic information and also incorporating the depth and structural characteristics of 3D spatial data. We carefully conduct such conversion to minimize the domain gap between training and test cases. Extensive experiments on two benchmark datasets, SUNRGBD and ScanNet, show that ImOV3D significantly outperforms existing methods, even in the absence of ground truth 3D training data. With the inclusion of a minimal amount of real 3D data for fine-tuning, the performance also significantly surpasses previous state-of-the-art. Codes and pre-trained models are released on the https://github.com/yangtiming/ImOV3D.

• The paper introduces a modality conversion framework that transforms 2D images into pseudo-3D representations using monocular depth estimation and point cloud rendering.
• The method achieves over 7% mAP improvement on SUNRGBD and ScanNet datasets without using real 3D training data, highlighting its scalability.
• A two-stage training strategy combining pseudo-3D pre-training and minimal real 3D fine-tuning effectively bridges the 2D-3D domain gap.

Overview of ImOV3D: Open-Vocabulary 3D Object Detection Using 2D Images

The paper "ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D Images" by Yang et al. addresses a significant challenge in the field of open-vocabulary 3D object detection (OV-3Det): the scarcity of annotated 3D data. This research introduces a novel methodology to leverage the abundant and well-annotated 2D image datasets for 3D object detection tasks that go beyond the limited categories seen during the training phase.

Key Contributions

1. Modality Conversion Framework: The cornerstone of this work, ImOV3D, enables the conversion of 2D images into a pseudo-multimodal representation that includes both images and point clouds. This conversion utilizes monocular depth estimation and point cloud rendering to align the domains of 2D training data and 3D testing data, thereby closing the modality gap.
2. Pseudo-Multimodal Representation: By transforming 2D images into pseudo-3D representations and rendering 3D point clouds back into 2D, ImOV3D constructs a common image-PC space that incorporates both the semantic richness of 2D data and the spatial depth and structure of 3D data.
3. Benchmark Performance: The proposed ImOV3D method achieves significant improvements over state-of-the-art methods on benchmark datasets SUNRGBD and ScanNet, with [email protected] improvements of at least 7.14% and 6.78%, respectively, using no real 3D training data. Even when a small amount of real 3D data is introduced for fine-tuning, ImOV3D continues to exhibit superior performance.

Methodological Insights

Data Representation and Learning: The approach involves an innovative data transformation pipeline where 2D images are lifted to pseudo-3D coordinates, accounting for depth via monocular estimation. This is complemented by the rendering of point clouds to 2D space, enabling the use of an open-vocabulary 2D detector.

Pseudo 3D Annotations: The paper effectively addresses the annotation scarcity by generating pseudo 3D labels through various innovative means, including the use of advanced models like GPT-4 for size and orientation corrections, and leveraging 2D image datasets that are prolific and richly annotated.

Adaptation Strategies: ImOV3D employs a two-stage training strategy, including both pre-training with pseudo-3D data and a subsequent adaptation phase using minimal real 3D data, highlighting the model's ability to fine-tune and improve its robustness.

Implications and Future Research Directions

The implications of this work are substantial for various applications such as autonomous driving, robotics, and augmented reality, where 3D object detection is critical yet often hindered by inadequate training data. By utilizing 2D image datasets, this research opens up potential for more accessible and scalable solutions in detecting a wide array of objects in 3D environments.

Future research could explore more refined methods of data conversion and representation to further enhance cross-modal knowledge transfer efficiency. Additionally, addressing the limitations related to the requirement for dense point clouds in pseudo image rendering may lead to more generalized and robust models, enabling broader application across varied 3D environments.

In summary, ImOV3D presents a compelling advancement in 3D object detection, offering a resourceful utilization of existing 2D datasets and paving the way for future developments in open-vocabulary 3D vision tasks. The methodological innovations and resulting performance metrics underscore the potential of 2D-driven 3D detection methodologies in overcoming current limitations in the field.