FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation

• URL: http://arxiv.org/abs/2605.27178v1
• Date: Tue, 26 May 2026 15:32:32 GMT
• Title: FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation
• Abstract summary: We present Found, a novel framework featuring a superpoint-based object discovery agent that incrementally merges suitable neighboring geometric superpoints.<n>Our method exhibits strong generalization in zero-shot and long-tail scenarios, underscoring its potential for scalable, label-free 3D object segmentation.
• Score: 16.814652408859025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the challenging task of 3D object segmentation in complex scene point clouds without relying on any scene-level human annotations during training. Existing methods are typically constrained to identifying simple objects, primarily due to insufficient object priors in the learning process. In this paper, we present FoundObj, a novel framework featuring a superpoint-based object discovery agent that incrementally merges suitable neighboring superpoints, guided by our innovative semantic and geometric reward modules. These modules synergistically leverage semantic and geometric priors from self-supervised 2D/3D foundation models, providing complementary feedback to the object discovery agent and enabling robust identification of multi-class objects through reinforcement learning. Extensive experiments on diverse benchmarks demonstrate that our approach consistently outperforms existing baselines. Notably, our method exhibits strong generalization in zero-shot and long-tail scenarios, underscoring its potential for scalable, label-free 3D object segmentation.

Related papers

• EvObj: Learning Evolving Object-centric Representations for 3D Instance Segmentation without Scene Supervision [16.459953869241595]
  Ev is an unsupervised 3D instance segmentation system that bridges the geometric domain gap between synthetic pretraining data and real-world point clouds.<n>To address this, Ev integrates two innovative modules: (1) An object discerning module that dynamically refines object candidates, enabling continuous adaptation of object priors to target domains; and (2) An object completion module that reconstructs partial geometries after discovering objects.<n>We conduct extensive experiments on both real-world and synthetic datasets, demonstrating superior 3D object segmentation performance over all baselines while achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2026-05-13T08:17:45Z)
• Object-X: Learning to Reconstruct Multi-Modal 3D Object Representations [114.57192386025373]
  Object-X is a versatile multi-modal 3D representation framework.<n>It can encoding rich object embeddings and decoding them back into geometric and visual reconstructions.<n>It supports a range of downstream tasks, including scene alignment, single-image 3D object reconstruction, and localization.
  arXiv  Detail & Related papers  (2025-06-05T09:14:42Z)
• GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision [7.511342491529451]
  We study the hard problem of 3D object segmentation in complex point clouds without requiring human labels of 3D scenes for supervision.<n>By relying on the similarity of pretrained 2D features or external signals such as motion to group 3D points as objects, existing unsupervised methods are usually limited to identifying simple objects like cars or their segmented objects are often inferior due to the lack of objectness in pretrained features.
  arXiv  Detail & Related papers  (2025-04-16T04:13:53Z)
• Are We Done with Object-Centric Learning? [65.67948794110212]
  Object-centric learning (OCL) seeks to learn representations that only encode an object, isolated from other objects or background cues in a scene.<n>With recent sample-efficient segmentation models, we can separate objects in the pixel space and encode them independently.<n>We address the OOD generalization challenge caused by spurious background cues through the lens of OCL.
  arXiv  Detail & Related papers  (2025-04-09T17:59:05Z)
• IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments [56.85804719947]
  We present IAAO, a framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction.<n>We first build hierarchical features and label fields for each object state using 3D Gaussian Splatting (3DGS) by distilling mask features and view-consistent labels from multi-view images.<n>We then perform object- and part-level queries on the 3D Gaussian primitives to identify static and articulated elements, estimating global transformations and local articulation parameters along with affordances.
  arXiv  Detail & Related papers  (2025-04-09T12:36:48Z)
• SAI3D: Segment Any Instance in 3D Scenes [68.57002591841034]
  We introduce SAI3D, a novel zero-shot 3D instance segmentation approach. Our method partitions a 3D scene into geometric primitives, which are then progressively merged into 3D instance segmentations. Empirical evaluations on ScanNet, Matterport3D and the more challenging ScanNet++ datasets demonstrate the superiority of our approach.
  arXiv  Detail & Related papers  (2023-12-17T09:05:47Z)
• Weakly-supervised Contrastive Learning for Unsupervised Object Discovery [52.696041556640516]
  Unsupervised object discovery is promising due to its ability to discover objects in a generic manner. We design a semantic-guided self-supervised learning model to extract high-level semantic features from images. We introduce Principal Component Analysis (PCA) to localize object regions.
  arXiv  Detail & Related papers  (2023-07-07T04:03:48Z)
• OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds [4.709764624933227]
  We propose the first unsupervised method, called OGC, to simultaneously identify multiple 3D objects in a single forward pass. We extensively evaluate our method on five datasets, demonstrating the superior performance for object part instance segmentation.
  arXiv  Detail & Related papers  (2022-10-10T07:01:08Z)
• 3D Spatial Recognition without Spatially Labeled 3D [127.6254240158249]
  We introduce WyPR, a Weakly-supervised framework for Point cloud Recognition. We show that WyPR can detect and segment objects in point cloud data without access to any spatial labels at training time.
  arXiv  Detail & Related papers  (2021-05-13T17:58:07Z)
• Unsupervised Learning of 3D Object Categories from Videos in the Wild [75.09720013151247]
  We focus on learning a model from multiple views of a large collection of object instances. We propose a new neural network design, called warp-conditioned ray embedding (WCR), which significantly improves reconstruction. Our evaluation demonstrates performance improvements over several deep monocular reconstruction baselines on existing benchmarks.
  arXiv  Detail & Related papers  (2021-03-30T17:57:01Z)
• A Deep Learning Approach to Object Affordance Segmentation [31.221897360610114]
  We design an autoencoder that infers pixel-wise affordance labels in both videos and static images. Our model surpasses the need for object labels and bounding boxes by using a soft-attention mechanism. We show that our model achieves competitive results compared to strongly supervised methods on SOR3D-AFF.
  arXiv  Detail & Related papers  (2020-04-18T15:34:41Z)

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.