MuViSeg: Multi-View Segment Correspondences from Dense Geometry Priors

D2M-Net: decoupled dual matching for few-shot 3D point-cloud semantic segmentation

SAM3R: Object-Centric 3D Mapping via Foundation-Model-Guided Data Association in Changing Scenes

For embodied agents to navigate and reason indoor spaces, they need object-level 3D representations that stay consistent over time as new frames arrive from a monocular camera. Current online 3D instance segmentation methods either depend on posed RGB-D input with ground-truth depth or couple tightly to the internal representations of specific foundation models, sacrificing modularity. We observe that appearance-based and geometry-based object matching exhibit complementary failure modes: appearance is ambiguous among spatially separated duplicates, while geometry is unreliable for visually distinct objects at similar locations. This motivates SAM3R, a training-free pipeline that fuses spatial overlap, 3D centroid displacement, and visual-semantic similarity into a single assignment cost solved via bipartite matching. The cost is constructed entirely from the outputs of frozen foundation models without accessing internal representations. Object tracks are classified through a cascaded decision tree that detects scene changes via field-of-view gated temporal voting. On ScanNet200 and Replica, SAM3R performs competitively with methods that require architecture-specific features or additional training, despite operating in a fully online, monocular setting. Qualitative evaluation on the Aria Digital Twin dataset further demonstrates that the pipeline maintains correct object identities through physical object manipulation, including hand occlusion and large spatial displacement.

Back to the Feature: Zero-Shot 6DoF Pose Estimation via Dense Local Features

We present B2TFPose, a training-free zero-shot method for 6DoF pose estimation of unseen objects from RGB images. Using a single frozen DINOv3 vision transformer as its only pretrained component within the pose estimation pipeline, B2TFPose extracts dense patch-level features that generalize across the synthetic-to-real domain gap without any task-specific fine-tuning, revisiting the classical local feature matching paradigm through the lens of large-scale self-supervised foundation models. Three contributions advance the training-free state of the art. A geodesic non-maximum suppression strategy retrieves a viewpoint-diverse template set for coarse-to-fine correspondence matching. Render-guided Re-Correspondence (RRC) synthesizes object-specific views at the estimated pose and re-establishes dense 2D-3D correspondences to sharpen the initial estimate without additional learned parameters. A multi-mask hypothesis selection strategy jointly scores competing segmentation candidates to resolve segmentation ambiguity. On the seven core datasets of the BOP Benchmark, B2TFPose achieves 40.7 mean AR without refinement and 56.4 with refinement, establishing state-of-the-art performance among training-free RGB methods and outperforming trained counterparts including GigaPose and GenFlow, at competitive inference speed.

Using textureless, low-detailed 3D city models for visual localization

This work enhances the existing iterative object-basesd visual localization approach with an additional semantic feature derived from a pretrained semantic segmentation model and conducts a systematic baseline study of contemporary feature matching techniques on such cross-domain query-reference image pairs.

VSLG-net: visual-spatial latent graph network for two-view correspondence learning

Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation

Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applications in scene reconstruction, computational photography, and robotics, among others. Despite the field's maturity, recent models still struggle to generalize to out-of-distribution inputs and to produce sharp and detailed depth maps. In this paper, we revisit Marigold, a set of techniques for repurposing modern image generation and editing models, powered by the diffusion transformer (DiT) architecture, into state-of-the-art monocular depth estimators. Our recipes target single-step inference from pretrained multi-step flow-matching models, with quantization where needed, preserving model capacity while remaining cheap to run. We analyze the artifacts of naive training and identify two effective remedies: aligning the model's internal representations with semantic features extracted from ground-truth, and adopting a 2-stage fine-tuning protocol built around a novel Sinkhorn-based loss. The results are crisper, cleaner depth maps that generalize well out-of-distribution, with 16-26% improvement in AbsRel over the previous best on KITTI and ETH3D. Qualitatively, our model resolves fur, foliage, and hair-thin edges that have eluded prior models. Furthermore, Marigold V2 achieves state-of-the-art results when applied to other dense regression tasks, such as surface normals estimation and intrinsic image decomposition. Project website: https://hf.co/spaces/huawei-bayerlab/marigold-v2-web