Existing methods for instance-level 6D pose estimation typically rely on neural networks that either directly regress the pose in SE(3) or estimate it indirectly via local feature matching. The former struggle with object symmetries, while the latter fail in the absence of distinctive local features. To overcome these limitations, we propose a novel formulation of 6D pose estimation as a conditional flow matching problem in R3. We introduce Flose, a generative method that infers object poses via a denoising process conditioned on local features. While prior approaches based on conditional flow matching perform denoising solely based on geometric guidance, Flose integrates appearance-based semantic features to mitigate ambiguities caused by object symmetries. We further incorporate RANSAC-based registration to handle outliers. We validate Flose on five datasets from the established BOP benchmark. Flose outperforms prior methods with an average improvement of +4.5 Average Recall. Project Website : https://tev-fbk.github.io/Flose/
Figures & tables
Figure 1: Overview of Flose. Given the query object point cloud Q and an RGBD image I as input (left), Flose estimates the 6D pose (R^,t^) (bottom right) through three stages: feature encoding (red), generative denoising (blue) and pose estimation (green). Feature encoding: an overlap-aware encoder ΦΘ and appearance-aware encoder Γ produce per-point descriptors, that are fused via a feature fusion to produce FQ,FT . Colors encode feature similarity: corresponding regions share similar colors (overlap-awareness), while semantically distinct parts differ (appearance-awareness). Generative denoising: a generative network ΨΩ , conditioned on FQ,FT , learns a displacement field that iteratively denoise the Gaussian noised X (1) into an aligned position X (0). Pose estimation: the 6D pose R^,t^ is recovered via RANSAC-based Kabsch solver followed by ICP refinement.
Figure 2: Sensitivity of the Inlier Ratio (IR) to the spatial threshold τ . IR is the percentage of points in T^ whose distance to the corresponding points in Tr is smaller than τ . At strict thresholds, the majority ( >80% ) of correspondences are outliers.
Method
S.M.
LM-O
T-LESS
TUD-L
IC-BIN
YCB-V
Avg
1
Pix2Pose [ 22 ]
58.8
51.2
82.0
39.0
78.8
62.0
2
ZebraPose [ 23 ]
75.2
72.7
94.8
65.2
86.6
78.9
3
GDRNPP (BOP22) [ 17 ]
77.5
87.4
96.6
72.2
92.1
85.2
4
HccePose(BF) [ 28 ]
80.5
87.9
94.4
72.4
91.1
85.3
5
GDRNPP (BOP23) [ 17 ]
79.4
91.4
96.4
73.7
92.8
86.7
6
Koenig-Hybrid [ 32 ]
✓
63.1
65.5
92.0
43.0
70.1
66.7
Table 1: Comparison with RGBD methods in terms of AR. The top block (rows 1-5) trains a separate model for each object, while the bottom block (rows 6-11) trains a Single Model (S.M.) per dataset. Row 12 quantifies the absolute improvement of Flose over the strongest S.M. competitor.
Figure 3: Qualitative comparison of Flose (center) vs. an RPF-based [ 24 ] baseline adapted for pose estimation (right). By integrating semantic features and outlier-robust registration, Flose predicts more accurate poses under severe occlusions (rows 1-2) and resolves symmetry ambiguities where pure geometric methods fail (rows 3-4).
Figure 4: Ablation study on LM-O: (a) Impact of the conditioning features on the flow matching process, measured in terms of AR (top) and IR gain relative to a baseline using only overlap-aware features (bottom); (b) Comparison of pose solvers (SVD vs. RANSAC) and the effect of ICP-based refinement. Hyperparameter study on LM-O: (c) Pose accuracy and inference time as a function of Euler integration steps.
Conditional flow matching has enabled a step forward in object 6D pose estimation, achieving state-of-the-art performance by progressively denoising and registering object representations to observed scenes. Existing methods require training task-specific encoders supervised on object-scene overlap and rely on trivial feature fusion strategies to resolve pose ambiguities. We present FunFlow6D, a novel flow matching-based formulation that leverages features from geometric and appearance foundation models for pose estimation, eliminating the need for task-specific encoder training. We also introduce a cross attention-based fusion mechanism that dynamically combines geometric and appearance features to provide richer conditioning for the flow matching module. Experiments on four datasets from the BOP benchmark show that FunFlow6D outperforms the previous state of the art while reducing supervision requirements and memory overhead. Extensive ablations validate the contribution of each proposed component. Project website: https://tev-fbk.github.io/FunFlow6D/.
Amir Hamza, Davide Boscaini, Fabio Poiesi
Fondazione Bruno Kessler Trento, Italy · University of Trento Trento, Italy
6D pose estimation is a key task in computer vision and embodied AI, widely used in robotic manipulation, augmented reality, etc. Existing methods directly regress in a high-dimensional continuous space, facing two key challenges in category-level pose estimation: limited accuracy due to noise and local optima, and inefficient search over an infinite space that hinders real-time performance. This paper proposes Flow6D, a hierarchical flow matching framework with a two-stage discrete latent space localization-continuous pose regression strategy. Rotation and translation parameters are first discretized into bins, with a discrete flow matching model locking the latent space around the true pose to reduce search complexity. Then, by sampling in the latent space, a continuous flow matching model predicts local pose residuals to optimize the estimate and regress to an accurate pose. The framework also naturally extends to articulated objects, outperforming state-of-the-art methods on synthetic and real datasets with real-time inference at 70 FPS. Project website: https://flow6d.github.io/.
Mingyu Mei, Li Zhang, Zibo Dai +4
Zhejiang University · University of Science and Technology of China · Shanghai Jiao Tong University
Object 6D pose estimation formulations have progressively reduced reliance on object-specific priors, evolving from explicit 3D models to multi-view object captures to single reference images. We take this progression to its extreme by introducing prior-free relative 6D pose estimation, which lifts the assumption of knowing which object is to be posed within the scene. This novel setting aims to estimate the relative poses of multiple instances of an unknown object within the same image, without requiring CAD models, templates, or reference images. We solve this by formulating a novel method (PROSE) that finds coarse correspondences between object instances using multimodal foundation features, thus requiring no training. We refine these correspondences by imposing cycle consistency across tuples of instances, and leverage the resulting globally consistent correspondences to estimate the relative 6D pose between any pair of instances. To enable systematic evaluation, we design a novel benchmark (PRENCH) built from three multi-instance BOP datasets and enriched with task-specific metadata. PROSE consistently outperforms baselines obtained by adapting state-of-the-art single-image methods to the proposed setting, while requiring neither task-specific supervision nor additional learned components. Project website: https://tev-fbk.github.io/PROSE/
Behdad Khodabandehloo, Andrea Caraffa, Davide Boscaini +1
Fondazione Bruno Kessler, Trento, Italy · University of Trento, Trento, Italy