42 research outputs found
Robustness and Accuracy of Feature-Based Single Image 2-D–3-D Registration Without Correspondences for Image-Guided Intervention
published_or_final_versio
Globally optimal 2D-3D registration from points or lines without correspondences
We present a novel approach to 2D-3D registration from points or lines without correspondences. While there exist established solutions in the case where correspondences are known, there are many situations where it is not possible to reliably extract such correspondences across modalities, thus requiring the use of a correspondence-free registration algorithm. Existing correspondence-free methods rely on local search strategies and consequently have no guarantee of finding the optimal solution. In contrast, we present the first globally optimal approach to 2D-3D registration without correspondences, achieved by a Branch-and-Bound algorithm. Furthermore, a deterministic annealing procedure is proposed to speed up the nested branch-and-bound algorithm used. The theoretical and practical advantages this brings are demonstrated on a range of synthetic and real data where it is observed that the proposed approach is significantly more robust to high proportions of outliers compared to existing approaches
Globally optimal 2D-3D registration from points or lines without correspondences
We present a novel approach to 2D-3D registration from points or lines without correspondences. While there exist established solutions in the case where correspondences are known, there are many situations where it is not possible to reliably extract such correspondences across modalities, thus requiring the use of a correspondence-free registration algorithm. Existing correspondence-free methods rely on local search strategies and consequently have no guarantee of finding the optimal solution. In contrast, we present the first globally optimal approach to 2D-3D registration without correspondences, achieved by a Branch-and-Bound algorithm. Furthermore, a deterministic annealing procedure is proposed to speed up the nested branch-and-bound algorithm used. The theoretical and practical advantages this brings are demonstrated on a range of synthetic and real data where it is observed that the proposed approach is significantly more robust to high proportions of outliers compared to existing approaches
Alakzatok lineáris deformációinak becslése és orvosi alkalmazásai = Estimation of Linear Shape Deformations and its Medical Applications
A projekt fĹ‘ eredmĂ©nye egy általánosan használhatĂł, teljesen automatikus alakzat regisztráciĂłs mĂłdszer, amely az alábbi tulajdonságokkal rendelkezik: • nincs szĂĽksĂ©g pontmegfeleltetĂ©sekre illetve iteratĂv optimalizálĂł algoritmusokra; • kĂ©pes 2D lineáris Ă©s (invertálhatĂł) projektĂv deformáciĂłk, valamint 3D affin deformáciĂłk meghatározására; • robusztus a geometriai Ă©s szegmentálási hibákra; • lineáris idĹ‘komplexitásĂş, ami lehetĹ‘vĂ© teszi nagy felbontásĂş kĂ©pek közel valĂłs idejű illesztĂ©sĂ©t. Publikusan elĂ©rhetĹ‘vĂ© tettĂĽnk 3 demĂł programot, amelyek a 2D Ă©s 3D affin, valamint sĂkhomográfia regisztráciĂłs algoritmusainkat implementálják. Továbbá kifejlesztettĂĽnk egy prototĂpus szoftvert csĂpĹ‘protĂ©zis röntgenkĂ©pek illesztĂ©sĂ©re, amit átadtunk a projektben közreműködĹ‘ radiolĂłgusoknak további felhasználásra. Az eredmĂ©nyeinket a terĂĽlet vezetĹ‘ konferenciáin ( pl. ICCV, ECCV) illetve vezetĹ‘ folyĂłiratokban (pl. IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition). A projekten dolgozĂł egyik MSc hallgatĂł második helyezĂ©st Ă©rt el az OTDK-n. Domokos Csaba PhD fokozatot szerzett, továbbá munkáját Kuba Attila dĂjjal ismerte el a KĂ©pfeldolgozĂłk Ă©s AlakfelismerĹ‘k Társasága. A projekt eredmĂ©nyeirĹ‘l rĂ©szletesebb informáciĂł a projekt honlapokon találhatĂł: • http://www.inf.u-szeged.hu/ipcg/projects/AFFSHAPE.html • http://www.inf.u-szeged.hu/ipcg/projects/AffinePuzzle.html • http://www.inf.u-szeged.hu/ipcg/projects/diffeoshape.html | The main achievement of the project is a fully functional automatic shape registration method with the following properties: • it doesn’t need established point correspondences nor the use of iterative optimization algorithms; • capable of recovering 2D linear and (invertible) projective shape deformations as well as affine distortions of 3D shapes; • robust in the presence of geometric noise and segmentation errors; • has a linear time complexity allowing near real-time registration of high resolution images. 3 demo programs are publicly available implementing our affine 2D, 3D and planar homography registration algorithms. Furthermore, we have developed a prototype software for aligning hip prosthesis X-ray images, which has been transfered to collaborating radiologists for further exploitation. Our results have been presented at top conferences (e.g. ICCV, ECCV) and in leading journals (e.g. IEEE Trans. on Patt. Anal. & Mach. Intell., Patt. Rec.). An MSc student working on the project received the second price of the National Scientific Student Conference. Csaba Domokos obtained his PhD degree and his work has been awarded the Attila Kuba Prize of the Hungarian Association for Image Processing and Pattern Recognition. More details about our results can be found at: • http://www.inf.u-szeged.hu/ipcg/projects/AFFSHAPE.html • http://www.inf.u-szeged.hu/ipcg/projects/AffinePuzzle.html • http://www.inf.u-szeged.hu/ipcg/projects/diffeoshape.htm
SDFReg: Learning Signed Distance Functions for Point Cloud Registration
Learning-based point cloud registration methods can handle clean point clouds
well, while it is still challenging to generalize to noisy, partial, and
density-varying point clouds. To this end, we propose a novel point cloud
registration framework for these imperfect point clouds. By introducing a
neural implicit representation, we replace the problem of rigid registration
between point clouds with a registration problem between the point cloud and
the neural implicit function. We then propose to alternately optimize the
implicit function and the registration between the implicit function and point
cloud. In this way, point cloud registration can be performed in a
coarse-to-fine manner. By fully capitalizing on the capabilities of the neural
implicit function without computing point correspondences, our method showcases
remarkable robustness in the face of challenges such as noise, incompleteness,
and density changes of point clouds
SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
In this paper, we introduce an SE(3) diffusion model-based point cloud
registration framework for 6D object pose estimation in real-world scenarios.
Our approach formulates the 3D registration task as a denoising diffusion
process, which progressively refines the pose of the source point cloud to
obtain a precise alignment with the model point cloud. Training our framework
involves two operations: An SE(3) diffusion process and an SE(3) reverse
process. The SE(3) diffusion process gradually perturbs the optimal rigid
transformation of a pair of point clouds by continuously injecting noise
(perturbation transformation). By contrast, the SE(3) reverse process focuses
on learning a denoising network that refines the noisy transformation
step-by-step, bringing it closer to the optimal transformation for accurate
pose estimation. Unlike standard diffusion models used in linear Euclidean
spaces, our diffusion model operates on the SE(3) manifold. This requires
exploiting the linear Lie algebra associated with SE(3) to
constrain the transformation transitions during the diffusion and reverse
processes. Additionally, to effectively train our denoising network, we derive
a registration-specific variational lower bound as the optimization objective
for model learning. Furthermore, we show that our denoising network can be
constructed with a surrogate registration model, making our approach applicable
to different deep registration networks. Extensive experiments demonstrate that
our diffusion registration framework presents outstanding pose estimation
performance on the real-world TUD-L, LINEMOD, and Occluded-LINEMOD datasets.Comment: Accepted by NeurIPS-202