Rooms from Motion: Un-posed Indoor 3D Object Detection as Localization and Mapping (2505.23756v1)

Abstract: We revisit scene-level 3D object detection as the output of an object-centric framework capable of both localization and mapping using 3D oriented boxes as the underlying geometric primitive. While existing 3D object detection approaches operate globally and implicitly rely on the a priori existence of metric camera poses, our method, Rooms from Motion (RfM) operates on a collection of un-posed images. By replacing the standard 2D keypoint-based matcher of structure-from-motion with an object-centric matcher based on image-derived 3D boxes, we estimate metric camera poses, object tracks, and finally produce a global, semantic 3D object map. When a priori pose is available, we can significantly improve map quality through optimization of global 3D boxes against individual observations. RfM shows strong localization performance and subsequently produces maps of higher quality than leading point-based and multi-view 3D object detection methods on CA-1M and ScanNet++, despite these global methods relying on overparameterization through point clouds or dense volumes. Rooms from Motion achieves a general, object-centric representation which not only extends the work of Cubify Anything to full scenes but also allows for inherently sparse localization and parametric mapping proportional to the number of objects in a scene.

A Professional Overview of "Rooms from Motion: Un-posed Indoor 3D Object Detection as Localization and Mapping"

The paper "Rooms from Motion: Un-posed Indoor 3D Object Detection as Localization and Mapping" presents a novel framework for indoor scene mapping and camera localization using unhashed images without relying on traditional methods such as point clouds or explicit 2D keypoints. This approach, termed Rooms from Motion (RfM), introduces a paradigm shift from point-based representations to object-centric modeling using oriented 3D boxes as its primary geometric primitive. The method challenges existing practices in 3D object detection, which typically require predefined camera poses and rely heavily on dense reconstruction techniques.

Methodology

RfM operates on unordered collections of RGB images, performing a sequence of operations aimed at detecting and mapping objects in 3D space:

1. Object Detection: Utilizing Cubify Transformer, each image undergoes an independent object detection phase, producing oriented 3D bounding boxes imbued with metric accuracy.
2. Object Matching: A learned network matches detected objects across frames, establishing correspondences and subsequently facilitating pose estimation using derived 3D boxes.
3. Pose Estimation: The relative pose between frames is estimated from matched 3D boxes, leading to the computation of global camera poses through averaging of relative estimations.
4. Map Generation: Establishing 3D object tracks from matched pairs across the breadth of observed frames results in the formation of a semantic global 3D map.
5. Optimization: Further refinement through optimization enhances the representation quality by overcoming occlusions and partial object views.

Experimental Results

The framework exhibits compelling results across large-scale datasets, notably CA-1M and ScanNet++. RfM demonstrates superior localization and mapping capabilities in varied experimental settings:

• Using RGB-D images with ground-truth depth and pose information, RfM outperforms leading point-based techniques, achieving higher precision in 3D object detection.
• In scenarios leveraging monocular RGB input, RfM not only maintains competitive performance in object detection but also excels in localization tasks, showing robustness even in the absence of explicit depth data.

Implications

The Rooms from Motion framework signifies a significant advancement in indoor scene understanding. By fundamentally shifting from points to objects as the basis for both localization and mapping, RfM promises more semantically rich and computationally efficient methods for indoor scene reconstruction. Its reliance on object-centric data and avoidance of dense point clouds highlights a scalable solution fit for environments where traditional SLAM techniques may falter. The potential adaptability of RfM to integrate seamlessly with device-centric captures, such as those on mobile platforms, offers practical benefits for real-world applications in augmented reality and robotics.

Future Developments

The implications for future research in AI are substantial. Extending the framework to handle more diverse and complex scenes, including outdoor environments, represents a logical next step. Additionally, developing standardized datasets with comprehensive labels for 3D objects in varied conditions would enhance the training efficacy of similar models, providing a reference point for extended comparative studies. The concept of object-centric modeling could pave the way for innovations in how machines perceive and interact with their spatial surroundings, enhancing both theoretical capabilities and practical deployments in AI-driven technologies.