Generative 6D Pose Estimation via Conditional Flow Matching

Amir Hamza; Davide Boscaini; Weihang Li; Benjamin Busam; Fabio Poiesi

cs.CVFeb 23, 2026

Generative 6D Pose Estimation via Conditional Flow Matching

Authors: Amir Hamza, Davide Boscaini, Weihang Li, Benjamin Busam, Fabio Poiesi

Organizations: Fondazione Bruno Kessler · University of Trento · Technical University of Munich · Munich Center for Machine Learning

Abstract

Existing methods for instance-level 6D pose estimation typically rely on neural networks that either directly regress the pose in $\mathrm{SE}(3)$ 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 $\mathbb{R}^3$ . 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 $\mathcal{Q}$ and an RGBD image I as input (left), Flose estimates the 6D pose $(\hat{\textbf{R}},\hat{\textbf{t}})$ (bottom right) through three stages: feature encoding (red), generative denoising (blue) and pose estimation (green). Feature encoding: an overlap-aware encoder $\Phi_{\Theta}$ and appearance-aware encoder $\Gamma$ produce per-point descriptors, that are fused via a feature fusion to produce $\textbf{F}^{\mathcal{Q}},\textbf{F}^{\mathcal{T}}$ . Colors encode feature similarity: corresponding regions share similar colors (overlap-awareness), while semantically distinct parts differ (appearance-awareness). Generative denoising: a generative network $\Psi_{\Omega}$ , conditioned on $\textbf{F}^{\mathcal{Q}},\textbf{F}^{\mathcal{T}}$ , learns a displacement field that iteratively denoise the Gaussian noised X (1) into an aligned position X (0). Pose estimation: the 6D pose $\hat{\textbf{R}},\hat{\textbf{t}}$ is recovered via RANSAC-based Kabsch solver followed by ICP refinement.

Panel of Figure 2 — Figure 2: Sensitivity of the Inlier Ratio (IR) to the spatial threshold $\tau$ . IR is the percentage of points in $\hat{\mathcal{T}}$ whose distance to the corresponding points in $\mathcal{T}^{r}$ is smaller than $\tau$ . 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.

Panel of Figure 3 — 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).

Panel of Figure 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.

Object Pose Estimation

Object pose estimation recovers an object's 3D position and orientation from images, depth, point clouds, or contact sensing.

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Foundational feature fusion for conditional flow matching in 6D pose estimation

Amir Hamza, Davide Boscaini, Fabio Poiesi

Fondazione Bruno Kessler Trento, Italy · University of Trento Trento, Italy

Object Pose Estimation Flow Matching

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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/.

Abstract

Object Pose Estimation

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