3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection

• URL: http://arxiv.org/abs/2507.23567v1
• Date: Thu, 31 Jul 2025 13:56:41 GMT
• Title: 3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection
• Authors: Yung-Hsu Yang, Luigi Piccinelli, Mattia Segu, Siyuan Li, Rui Huang, Yuqian Fu, Marc Pollefeys, Hermann Blum, Zuria Bauer,
• Abstract summary: We introduce the first end-to-end 3D Monocular Open-set Object Detector (3D-MOOD)<n>We lift the open-set 2D detection into 3D space through our designed 3D bounding box head.<n>We condition the object queries with geometry prior and overcome the generalization for 3D estimation across diverse scenes.
• Score: 58.78881632019072
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monocular 3D object detection is valuable for various applications such as robotics and AR/VR. Existing methods are confined to closed-set settings, where the training and testing sets consist of the same scenes and/or object categories. However, real-world applications often introduce new environments and novel object categories, posing a challenge to these methods. In this paper, we address monocular 3D object detection in an open-set setting and introduce the first end-to-end 3D Monocular Open-set Object Detector (3D-MOOD). We propose to lift the open-set 2D detection into 3D space through our designed 3D bounding box head, enabling end-to-end joint training for both 2D and 3D tasks to yield better overall performance. We condition the object queries with geometry prior and overcome the generalization for 3D estimation across diverse scenes. To further improve performance, we design the canonical image space for more efficient cross-dataset training. We evaluate 3D-MOOD on both closed-set settings (Omni3D) and open-set settings (Omni3D to Argoverse 2, ScanNet), and achieve new state-of-the-art results. Code and models are available at royyang0714.github.io/3D-MOOD.

Related papers

• Detect Anything 3D in the Wild [34.293450721860616]
  We introduce DetAny3D, a promptable 3D detection foundation model capable of detecting any novel object under arbitrary camera configurations.<n>To effectively transfer 2D knowledge to 3D, DetAny3D incorporates two core modules: the 2D Aggregator and the 3D Interpreter with Zero-Embedding Mapping.<n> Experimental results validate the strong generalization of our DetAny3D, which achieves state-of-the-art performance on unseen categories and novel camera configurations.
  arXiv  Detail & Related papers  (2025-04-10T17:59:22Z)
• Unifying 2D and 3D Vision-Language Understanding [85.84054120018625]
  We introduce UniVLG, a unified architecture for 2D and 3D vision-language learning.<n>UniVLG bridges the gap between existing 2D-centric models and the rich 3D sensory data available in embodied systems.
  arXiv  Detail & Related papers  (2025-03-13T17:56:22Z)
• V-MIND: Building Versatile Monocular Indoor 3D Detector with Diverse 2D Annotations [17.49394091283978]
  V-MIND (Versatile Monocular INdoor Detector) enhances the performance of indoor 3D detectors across a diverse set of object classes.<n>We generate 3D training data by converting large-scale 2D images into 3D point clouds and subsequently deriving pseudo 3D bounding boxes.<n>V-MIND achieves state-of-the-art object detection performance across a wide range of classes on the Omni3D indoor dataset.
  arXiv  Detail & Related papers  (2024-12-16T03:28:00Z)
• General Geometry-aware Weakly Supervised 3D Object Detection [62.26729317523975]
  A unified framework is developed for learning 3D object detectors from RGB images and associated 2D boxes. Experiments on KITTI and SUN-RGBD datasets demonstrate that our method yields surprisingly high-quality 3D bounding boxes with only 2D annotation.
  arXiv  Detail & Related papers  (2024-07-18T17:52:08Z)
• Learning 3D Scene Priors with 2D Supervision [37.79852635415233]
  We propose a new method to learn 3D scene priors of layout and shape without requiring any 3D ground truth. Our method represents a 3D scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories. Experiments on 3D-FRONT and ScanNet show that our method outperforms state of the art in single-view reconstruction.
  arXiv  Detail & Related papers  (2022-11-25T15:03:32Z)
• CMR3D: Contextualized Multi-Stage Refinement for 3D Object Detection [57.44434974289945]
  We propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework. Our framework takes a 3D scene as input and strives to explicitly integrate useful contextual information of the scene. In addition to 3D object detection, we investigate the effectiveness of our framework for the problem of 3D object counting.
  arXiv  Detail & Related papers  (2022-09-13T05:26:09Z)
• FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [78.00922683083776]
  It is non-trivial to make a general adapted 2D detector work in this 3D task. In this technical report, we study this problem with a practice built on fully convolutional single-stage detector. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020.
  arXiv  Detail & Related papers  (2021-04-22T09:35:35Z)
• 3D-to-2D Distillation for Indoor Scene Parsing [78.36781565047656]
  We present a new approach that enables us to leverage 3D features extracted from large-scale 3D data repository to enhance 2D features extracted from RGB images. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data.
  arXiv  Detail & Related papers  (2021-04-06T02:22:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.