UniDet3D: Multi-dataset Indoor 3D Object Detection

Abstract: Growing customer demand for smart solutions in robotics and augmented reality has attracted considerable attention to 3D object detection from point clouds. Yet, existing indoor datasets taken individually are too small and insufficiently diverse to train a powerful and general 3D object detection model. In the meantime, more general approaches utilizing foundation models are still inferior in quality to those based on supervised training for a specific task. In this work, we propose \ours{}, a simple yet effective 3D object detection model, which is trained on a mixture of indoor datasets and is capable of working in various indoor environments. By unifying different label spaces, \ours{} enables learning a strong representation across multiple datasets through a supervised joint training scheme. The proposed network architecture is built upon a vanilla transformer encoder, making it easy to run, customize and extend the prediction pipeline for practical use. Extensive experiments demonstrate that \ours{} obtains significant gains over existing 3D object detection methods in 6 indoor benchmarks: ScanNet (+1.1 mAP50), ARKitScenes (+19.4 mAP25), S3DIS (+9.1 mAP50), MultiScan (+9.3 mAP50), 3RScan (+3.2 mAP50), and ScanNet++ (+2.7 mAP50). Code is available at https://github.com/filapro/unidet3d .

• The paper presents a unified multi-dataset training paradigm that significantly enhances indoor 3D object detection accuracy.
• It employs a streamlined transformer encoder with self-attention and efficient disentangled matching, reducing computational overhead.
• Experimental results show robust improvements, including +1.1 mAP50 on ScanNet and +19.4 mAP25 on ARKitScenes, demonstrating effective generalization.

UniDet3D: Multi-dataset Indoor 3D Object Detection

The paper presents UniDet3D, a novel approach for 3D object detection trained on a compendium of indoor datasets. 3D object detection is pivotal for applications within robotics, augmented reality (AR), and 3D scanning, promising enhanced scene understanding through accurate localization and recognition of objects within point cloud data. Various indoor datasets have historically posed challenges for training robust models due to their limited size, diversity, and the intrinsic complexity of indoor scenes arising from the disparate types and arrangements of objects. UniDet3D tackles these challenges through a unified framework that learns from heterogeneous datasets to produce a versatile and high-precision 3D object detection model.

Methodology Overview

The methodology behind UniDet3D is centered on unifying label spaces from disparate datasets and employing a supervised joint training mechanism to enhance the model's learning capability across diverse datasets. This approach bridges the domain gap typically observed among datasets due to variations in data collection methodologies — from Kinect captures to smartphone-based ones — which influence point cloud density and scene coverage.

The model architecture is noteworthy for its simplicity and effectiveness. At its core is a vanilla transformer encoder, a deliberate choice that facilitates ease of implementation, customization, and extension. Unlike conventional transformer-based 3D detection methods that often incorporate positional encoding and complex cross-attention mechanisms, UniDet3D adopts a streamlined architecture. This design choice eliminates unnecessary computational expenditure while maintaining competitive accuracy, enabled by features such as self-attention without positional encoding and efficient disentangled matching, replacing the traditional Hungarian matching approach.

Numerical Results and Impact

UniDet3D demonstrates substantial performance improvements across six indoor benchmarks, notably ScanNet, ARKitScenes, S3DIS, MultiScan, 3RScan, and ScanNet++. The results are compelling, with UniDet3D achieving improvements like +1.1 mAP\textsubscript{50} over existing methodologies on ScanNet and even more significant gains on ARKitScenes (+19.4 mAP\textsubscript{25}) and S3DIS (+9.1 mAP\textsubscript{50}). These advancements underscore the model's ability to generalize effectively across datasets, which is indicative of its strong representation learning.

Implications and Future Directions

The practical implications of this research are extensive, given the current trajectory towards integrating 3D scene understanding into consumer-facing technologies like AR and advanced robotic systems. UniDet3D not only offers tangible improvements in 3D object detection accuracy but also promises reduced computational overheads due to its streamlined architecture.

Theoretically, this work reinforces the potential of multi-dataset training paradigms in mitigating dataset-specific limitations and broadening the generalization capabilities of machine learning models. The unification of label spaces across multiple datasets in the training process is a pivotal contribution that future research could expand upon, potentially adapting similar methodologies to other domains of machine learning where data diversity poses a challenge.

As the AI field progresses, subsequent work could explore further reduction in computational costs while maintaining or improving accuracy, improved robustness to dataset-specific variances, and application to new domains that straddle indoor and outdoor environments or incorporate real-time processing constraints. Such efforts would drive forward the practical applicability and deployment of reconceptualized 3D object detection models across various real-world applications.

UniDet3D sets a foundation that others in the field can build upon, both in exploring the nuances of training multi-dataset models and in the broader context of enhancing scene understanding capabilities. As we continue to bridge the divide between theoretical potential and real-world application, approaches such as presented in this paper will play an integral role in shaping the future capabilities of AI systems.