Prior-free relative 6D pose estimation of multiple object instances

¹Fondazione Bruno Kessler, Trento, Italy
²University of Trento, Trento, Italy

Abstract

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.

Cycle-consistent Correspondence Refinement

Before-and-after comparison showing conflicting object matches before refinement and consistent matches afterward

This figure shows how PROSE makes matches between multiple instances of the same object more reliable. On the left, each pair of objects is matched separately, which can produce conflicting results. On the right, PROSE considers all the objects together and adjusts the matches until they agree.

Quantitative Results

Method	IC-BIN				IC-MI				XYZ-IBD
Method	AR ↑	ADD(S) ↑	RE (°) ↓	TE (mm) ↓	AR ↑	ADD(S) ↑	RE (°) ↓	TE (mm) ↓	AR ↑	ADD(S) ↑	RE (°) ↓	TE (mm) ↓
One2Any	17.7	61.0	70.5	26.9	47.7	98.0	47.2	12.3	9.2	49.2	85.0	33.2
ConceptPose	18.8	58.8	76.2	36.6	50.8	80.2	51.1	29.1	36.1	70.1	64.6	24.4
PROSE (ours)	38.0	83.3	61.3	16.8	70.3	100	30.4	4.6	55.8	92.1	28.6	9.1
Improvement	+19.2	+22.3	−9.2	−10.1	+19.5	+2.0	−16.8	−7.7	+19.7	+22.0	−36.0	−15.3

Results on PRENCH. Arrows indicate whether higher or lower values are better; PROSE is highlighted, and Improvement shows its margin over the strongest baseline.

PROSE performs best across every metric on all three datasets. It improves average recall by about 19 points over the strongest competing method on each dataset, while also reducing both rotation and translation errors. The largest reductions in pose error occur on XYZ-IBD, showing that PROSE is particularly effective on challenging industrial objects with limited visual texture. Overall, the results show that jointly reasoning across multiple object instances improves the reliability and accuracy of the estimated poses.

Qualitative Results

Input image

One2Any

ConceptPose

PROSE (ours)

Qualitative results on PRENCH. Green borders denote the ground-truth 6D poses, while red borders show each method's predictions. Accurate poses show good alignment between the two colored borders. The rows show IC-BIN coffee cups and juice boxes, XYZ-IBD metallic gears and brackets, and IC-MI milk bottles.

Citation

If you find this work useful, please cite:

@misc{khodabandehloo2026prior, title = {Prior-free Relative 6D Pose Estimation of Multiple Object Instances}, author = {Khodabandehloo, Behdad and Caraffa, Andrea and Boscaini, Davide and Poiesi, Fabio}, year = {2026}, eprint = {2609.08949}, archivePrefix = {arXiv}, primaryClass = {cs.CV}, url = {https://arxiv.org/abs/2609.08949} }