TL;DR
This paper benchmarks and analyzes the challenges of adapting indoor 3D object detectors across different datasets and domain gaps, proposing approaches to improve cross-domain generalization.
Contribution
It introduces a comprehensive benchmark with multiple datasets, including new large-scale datasets, and analyzes various domain gaps affecting indoor 3D object detection.
Findings
Domain gaps significantly impact detection performance
Synthetic-to-real and quality variations are key challenges
Proposed baseline methods improve cross-domain adaptation
Abstract
As a fundamental task for indoor scene understanding, 3D object detection has been extensively studied, and the accuracy on indoor point cloud data has been substantially improved. However, existing researches have been conducted on limited datasets, where the training and testing sets share the same distribution. In this paper, we consider the task of adapting indoor 3D object detectors from one dataset to another, presenting a comprehensive benchmark with ScanNet, SUN RGB-D and 3D Front datasets, as well as our newly proposed large-scale datasets ProcTHOR-OD and ProcFront generated by a 3D simulator. Since indoor point cloud datasets are collected and constructed in different ways, the object detectors are likely to overfit to specific factors within each dataset, such as point cloud quality, bounding box layout and instance features. We conduct experiments across datasets on…
Peer Reviews
Decision·Submitted to ICLR 2025
The paper provides a detailed analysis of the impact of domain gaps on model performance. This is useful for understanding specific adaptation challenges and will benefit future work to address these potential issues in real practice. Besides showing the challenges, the proposed synthetic datasets, SimRoom and SimHouse, also offer more diverse and scalable data compared to existing real-world datasets, enabling controlled experiments for domain adaptation. Overall, this paper presents a novel
The baseline domain adaptation methods implemented are straightforward and lack complexity. Methods like the mean teacher framework and size priors are standard and do not demonstrate significant innovation or exploration of recent advancements in domain adaptation, using such basic adaptation methods might be insufficient to challenge future models. In addition, although the paper claims that the synthetic datasets have high-quality annotations, there is a lack of discussion about how faithfull
1. The paper generated a large-scale synthetic dataset for ablating indoor 3D object detection task, which may help multiple potential tasks for indoor scene understanding. 2. The paper demonstrates extensive experiments to ablate different basic approaches on the newly proposed domain adaptation benchmark.
1. This paper does not provide adequate discussions about the applications and importance of the task of domain adaptation in indoor 3D object detection. Moreover, it also doesn't discuss the differences and unique values compared with the task of outdoor domain adaptation of 3D object detection. 2. It seems that the generation process of the SimRoom / SimHouse dataset is a simple usage of ProcTHOR framework. 3. For the domain adaptation benchmark, it is also the application of multiple exist
This paper focuses on indoor 3D object detection, moving beyond the traditional emphasis on outdoor environments. The authors introduce two larger, novel synthetic datasets to facilitate exploration in this area. Through experiments conducted across multiple adaptation scenarios, they analyze critical factors such as point cloud quality and object size, thoroughly investigating their impact on model adaptability.The structure of the paper is clear, with a rigorous logical flow and fluent express
1.The authors emphasize the low annotation costs associated with their synthetic datasets. However, a comparative analysis of the annotation costs, speeds, and methodologies between their simulation data generation approach and other synthetic data generation techniques, as well as traditional manual annotation methods, would strengthen their argument. Including such an analysis could provide insights into the efficiency and scalability of their approach relative to existing methods, highlightin
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