Toward Zero-Shot Point Cloud Registration Across Diverse Scales, Scenes, and Sensor Setups

Hyungtae Lim; Minkyun Seo; Luca Carlone; Jaesik Park

Fig. 2: (a) Variation in the number of points after voxelization with different voxel sizes

v

across datasets. Even in indoor scenes, point counts vary significantly depending on the sensor type ( i.e., TIERS [ 6 ] vs. 3DMatch [ 2 ] ). Notably, TIERS and KITTI [ 4 ] , both using omnidirectional LiDARs, yield different point densities due to indoor vs. outdoor environments. (b) Empirical distribution of the datasets’ maximum range.

Fig. 3: Overview of our BUFFER-X , which mainly consists of three steps. (a) Geometric bootstrapping ( Sec. IV-A ) to determine the appropriate voxel size and radii for the given source

\mathcal{P}

and target

\mathcal{Q}

clouds. (b) Multi-scale patch embedder ( Sec. IV-B ) to generate patch-wise descriptor

\mathcal{S}_{\xi}

for multiple scales

\xi\in\{l,m,g\}

, where

l

,

m

, and

g

represent local, middle, and global scales, respectively. Specifically, Mini-SpinNet [ 14 ] outputs cylindrical feature maps

\mathcal{C}_{\xi}

and vector feature sets

\mathcal{F}_{\xi}

. (c) Hierarchical inlier search ( Sec. IV-C ), which first performs nearest neighbor-based intra-scale matching using

\mathcal{F}^{\mathcal{P}}_{\xi}

and

\mathcal{F}^{\mathcal{Q}}_{\xi}

at each scale, followed by pairwise transformation estimation. Finally, it identifies globally consistent inliers

\mathcal{I}

across all scales to refine correspondences based on consensus maximization [ 87 , 88 ] .

Fig. 4: (a) Visual description of local (

r_{l}

), middle (

r_{m}

), and global (

r_{g}

) radii for the same point to illustrate scale differences and (b) normalized patches ranging from

[-1,1]

. Note that their reference frames follow the eigenvectors obtained from principal component analysis (PCA) [ 91 , 90 ] . The

z

-axis is assigned to the eigenvector

{\bm{v}}_{3}

, which corresponds to the smallest eigenvalue.

Fig. 5: Dataset statistics and characteristics analysis, which demonstrates the diversity of point cloud characteristics across different environments, sensor types, and acquisition scenarios in our generalizability benchmark. (a)-(b) Box plots of mean and 99th percentile range per frame across different datasets, respectively. (c) Distribution of the number of point clouds and the number of selected test pairs across indoor and outdoor environments. (d)-(e) Gaussian distribution of ranges for indoor and outdoor scenes, respectively, and (f)-(g) those for different sensor types in the TIERS and KAIST sequences.

Fig. 6: (a) Different scales of sequences from the TIERS dataset [ 6 ] in our experiments. While each scan is utilized for our evaluation, we build and then visualize map clouds using LiDAR point cloud scans and corresponding poses to illustrate the different scales of the surroundings. (b) Scale comparison of three sequences: Oxford from the NewerCollege dataset [ 7 ] , MIT from Kimera-Multi [ 9 ] , and KAIST from the HeLiPR dataset [ 11 ] at the same scale ( i.e., 500 m). Note that although these sequences fall under the same campus category, their scales differ. For clarity, the map clouds are visualized with respect to their

z

values. (c) (T-B) Examples of visualized LiDAR scans from different LiDAR sensors in the Indoor10 of TIERS dataset [ 6 ] and KAIST05 sequence of the HeLiPR dataset [ 11 ] . Note that even in the same environment, differences in the number of LiDAR rays and the field of view result in point clouds with different patterns.

Param.	Description	Value
$\kappa_{\text{spheric}}$	Coefficient for voxel size when sphericity is high	0.15
$\kappa_{\text{disc}}$	Coefficient for voxel size when sphericity is low	0.10
$\tau_{v}$	Threshold in Eq. 3	0.05
$\tau_{l}$	Threshold for the local ( $l$ ) search radius	0.005
$\tau_{m}$	Threshold for the middle ( $m$ ) search radius	0.02
$\tau_{g}$	Threshold for the global ( $g$ ) search radius	0.05

TABLE I: Global configuration used by BUFFER-X. The same values are fixed for all 12 datasets without any per-dataset tuning.

TABLE II: Quantitative performance comparison of registration success rates to evaluate generalization capability. Deep learning-based models were trained only on 3DMatch [ 2 ] and KITTI [ 4 ] , respectively, and to ensure a fair comparison, RANSAC was employed for pose estimation across all learning-based approaches, with a maximum of 50K iterations. For conventional methods, FPFH [ 102 ] was used for feature extraction.

Fig. 7: Qualitative comparison of registration results across diverse scenes (source: orange and target: cyan). All methods were trained on 3DMatch . Top three rows: scan pairs from ScanNet++F , Oxford , and MIT . Bottom four rows: scan pairs with heterogeneous LiDAR sensor setups from TIERS (Vel16

\rightarrow

OS128, OS64

\rightarrow

Vel16) and KAIST (Aeva

\rightarrow

Avia, Ouster

\rightarrow

Aeva), respectively, where A

\rightarrow

B means that the source acquired by sensor type A is registered to the target point cloud from sensor type B.

Fig. 8: Relative translation error (RTE) and relative rotation error (RRE) of our approach to state-of-the-art methods, all trained on 3DMatch and tested on KITTI , with oracle tuning and scale alignment, corresponding to those in Table II under the + + setting. The **** annotations indicate measurements with a

p

-value

<10^{-4}

after a paired

t

-test. Note that even if other state-of-the-art approaches showed promising high success rates in Table II , but in terms of RTE and RRE, our BUFFER-X showed substantially lower values.

TABLE III: Registration success rates under heterogeneous sensor configurations. Deep learning-based methods are trained only on 3DMatch [ 2 ] . Conventional methods use FPFH [ 102 ] . To ensure a fair comparison, RANSAC was employed for pose estimation across all learning-based approaches, with a maximum of 50K iterations.

Fig. 9: Visualization of patch-level similarity across heterogeneous LiDAR sensors. Despite large differences in overall point density distributions and fields of view captured by Livox Avia and Ouster OS2-128, local patches from different point clouds (A and B) exhibit consistent feature distributions, showing high cosine similarity close to 1.

Fig. 10: PaCMAP visualization of descriptor distributions across datasets. Unlike state-of-the-art approaches [ 73 , 3 , 79 ] , our BUFFER-X shows persistent descriptors that are robust to domain shift, as evidenced by the overlap between 3DMatch (training domain) and other datasets. That is, the closer and more overlapped with sky blue, the better.

Fig. 11: Distribution of correspondences at different scales (global, middle, local) across datasets, showing the contribution of multi-scale matching.

Local	Middle	Global	RTE [cm] $\downarrow$	RRE [°] $\downarrow$	Succ. rate [%] $\uparrow$	Hz $\uparrow$
✓			6.57	2.15	84.06	5.61
	✓		5.87	1.85	93.38	5.47
		✓	6.06	1.91	93.57	5.49
✓	✓		5.73	1.81	94.31	2.35
✓		✓	5.77	1.81	94.02	2.36
	✓	✓	5.78	1.81	94.62	2.33

TABLE IV: Ablation study on multi-scale combinations and early exit strategies evaluated on 3DMatch [ 2 ] . Top: different scale combinations. Bottom: comparison with RANSAC and KISS-Matcher solver [ 45 ] (referred to as our solver ) for early exit.

Fig. 12: Benchmark-average recall obtained by varying one parameter group at a time while fixing all other parameters to the default values in Table I . Red stars indicate the default parameter values used throughout our experiments. OOM ( i.e., out of memory) denotes configurations that exceeded the available GPU memory. Full per-dataset recalls are reported in Tables S2 and S3 of the supplementary material.

Fig. 13: Comparison of success rate and total computation time between BUFFER-X-Lite at different early-exit thresholds and the original BUFFER-X.

Fig. 14: Pose estimation time comparison between RANSAC and KISS-Matcher solver given the same correspondences. RANSAC, which is still widely used for pose estimation in learning-based approaches, exhibits substantially longer computation time for LiDAR-acquired point clouds ( i.e., in TIERS , KAIST , and MIT ), whereas KISS-Matcher solver achieves up to 100

\times

faster performance.

Fig. 15: Peak GPU memory (left) and total runtime (right) versus raw input point count for the default three-scale pipeline, the single-scale variant, and pair-adaptive early exit. Dots denote individual registration pairs, and solid lines denote binned medians. Note that pooling multiple datasets yields uneven point-count coverage and varying dataset composition across bins, thereby producing local non-monotonicity in the median curves.

Fig. 16: Comparison of mutual correspondences versus inliers for RANSAC and KISS-Matcher solver across 3DMatch , ScanNet++i , TIERS , and KAIST . The number of estimated inliers in failure cases for RANSAC is relatively widespread across lower values, whereas that of KISS-Matcher solver is more likely to be near-zero.

Fig. 17: Application of our approach to robotic systems: online submap-to-map localization between two sessions on the Cantwell scene from the Gibson dataset [ 106 ] .

Fig. 18: Overlap-conditioned analysis on outdoor benchmarks. Registration recall and pair count of BUFFER-X over all outdoor benchmark pairs grouped by overlap ratio (left) and controlled KITTI evaluation using a protocol adopted in prior work [ 107 ] , where the odometry-based pair-selection distance is increased from 10 m to 20 m, 30 m, and 40 m without cropping or otherwise modifying the scans (right).

Figure 22

Figure 23

Figure 24

Figure 25

Aspect	Prior work	This extended manuscript
Core BUFFER-X components	Geometric bootstrapping, distribution-aware FPS, patch-level coordinate normalization, hierarchical multi-scale matching, and cross-scale consensus maximization.	Inherited without being presented as a new contribution of this extension; retained to provide a self-contained description.
Primary research focus	Establishes the feasibility of zero-shot transfer from a single training dataset to unseen indoor and outdoor scenes.	Shifts the focus from whether transfer is possible to why it works and under which conditions it remains reliable.
Efficiency extension	Not included.	Presents BUFFER-X-Lite through a system-level integration of the BUFFER-X front-end, pair-adaptive early exit, and the previously developed KISS-Matcher correspondence-pruning and pose-estimation modules; Algorithm 2 provides the complete procedure.
Evaluation scope	Focuses primarily on homogeneous indoor and outdoor registration.	Expands to a 12-dataset benchmark spanning object-scale, indoor, and outdoor scenes, including ModelNet40 and six heterogeneous LiDAR source–target directions: OS128 $\rightarrow$ OS64, OS64 $\rightarrow$ Vel16, Vel16 $\rightarrow$ OS128, Aeva $\rightarrow$ Avia, Avia $\rightarrow$ Ouster, and Ouster $\rightarrow$ Aeva.
New quantitative analyses	Component-level ablations supporting the initial zero-shot feasibility study.	Adds five analysis categories: descriptor-distribution consistency, correspondence complementarity across scales, benchmark-wide parameter sensitivity, pipeline-wide efficiency, and overlap-conditioned failure behavior.
New practical material	Not included.	Adds one submap-to-map localization application, complete BUFFER-X-Lite pseudocode, and an analysis of global-optimum ambiguity under severe partial overlap.

TABLE S1: Explicit comparison between our prior work and this extended manuscript. This extension retains the BUFFER-X backbone for a self-contained presentation while adding a broader evaluation scope, quantitative analyses, and an efficiency-oriented system-level integration of the front-end and solver.

Fig. S1: Registration recall versus measured pair overlap for pooled indoor benchmarks ( 3DMatch , 3DLoMatch , ScanNet++i , and ScanNet++F ). Bars show the number of evaluated pairs in each overlap bin.

Fig. S2: Illustration of the global optimum ambiguity problem in point cloud registration. (a) Two misaligned L-shaped point clouds and (b)–(d) possible alignments: overlap along the longer segment ( L ), overlap along the shorter segment ( S ), and full overlap ( F ), respectively. (e) German–McClure (GM) surrogate costs at different control parameters

\mu

and (f) the corresponding loss values. (g) Truncated least-squares (TLS) surrogate costs and (h) their corresponding loss values. The fully overlapping case produces the lowest loss for all tested kernel shapes.

Fig. S3: Qualitative low-overlap registration examples on 3DLoMatch . The top four rows show the behavior of BUFFER-X in failed cases, where the prediction can favor a geometrically plausible but incorrect high-overlap alignment. The bottom two rows show successful low-overlap cases, where distinctive anchor objects or structures provide sufficient geometric evidence for correct registration.

$N_{\mathrm{FPS}}$
	Object	Indoor					Outdoor						Avg.
Setting	ModelNet40	3DMatch	3DLoMatch	ScanNet++i	ScanNet++F	TIERS	KITTI	WOD	KAIST	MIT	ETH	Oxford	Avg.
500	99.13	93.67	69.54	94.07	99.36	92.30	99.10	100.00	93.02	93.48	89.76	97.67	93.43 (-2.71 %p)
1,000	99.45	94.36	72.33	95.03	99.80	92.76	99.82	100.00	98.79	98.70	99.02	99.67	95.81 (-0.33 %p)
1,500	99.84	95.58	74.18	94.99	99.90	93.45	99.82	100.00	99.15	97.39	99.72	99.67	96.14
2,000	99.45	94.79	72.68	95.56	99.95	91.95	99.82	100.00	99.20	97.83	100.00	99.34	95.88 (-0.26 %p)

TABLE S2: Full per-dataset recalls (%) for the one-factor parameter sweeps in Fig. 12. Shaded rows denote default settings. Red numbers indicate a decrease from the default parameter setting, while green numbers indicate an increase.

	Outdoor						Avg.
$r_{\mathrm{max}}$	KITTI	WOD	KAIST	MIT	ETH	Oxford	Avg.
2.5 m	99.82	100.00	97.94	96.52	99.86	99.00	98.86 (-0.43 %p)
5.0 m	99.82	100.00	99.15	97.39	99.72	99.67	99.29
7.5 m	99.82	100.00	99.35	99.13	99.86	99.67	99.64 (+0.35 %p)
10.0 m	99.82	100.00	99.35	97.83	100.00	99.67	99.44 (+0.15 %p)

TABLE S3: Outdoor-specific sensitivity to the radius cap

r_{\mathrm{max}}

. This parameter applies to large-scale LiDAR scans; therefore, only outdoor datasets are reported. Shaded rows indicate default settings. Values are registration recall (%). Red numbers indicate a decrease from the default parameter setting, while green numbers indicate an increase.

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