CALIPER: Metric-Grounded Model-Free Recognition of Visually Similar Industrial Parts

Alankrit Gupta; Chenxi Tao; Seung-Kyum Choi

cs.CVSep 15, 2026

CALIPER: Metric-Grounded Model-Free Recognition of Visually Similar Industrial Parts

Authors: Alankrit Gupta, Chenxi Tao, Seung-Kyum Choi

Organizations: George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta, GA, U.S.A.

Abstract

Fine-grained recognition of visually similar industrial parts is challenging when classes differ primarily in physical dimensions. Normalizing detected object crops to a fixed input size suppresses absolute scale, while CAD models and large class-specific datasets may be unavailable in evolving industrial inventories. We present CALIPER, a model-free RGB-D framework that couples support-based appearance matching with metric size evidence. Each training class is onboarded from a single turntable RGB-D video and one to two labeled real images; 3D reconstruction provides novel-view appearance support, while aligned depth yields a class-specific metric size profile. At inference, a coarse YOLOv8n-seg model localizes parts, and a frozen DINOv2 backbone with an episodically trained embedding head performs fine-grained support matching. Margin-conditioned metric fusion activates probabilistic size evidence only for appearance-ambiguous decisions. New classes are enrolled from a small RGB-D support set without updating network parameters. We evaluate CALIPER on 18 visually similar industrial parts: 16 classes are used for training, while two screws are reserved for training-free enrollment. CALIPER achieves 88.2% closed-set accuracy with 99.8% localization recall and 85.7% overall accuracy after 10-shot enrollment of the two unseen screws. Metric fusion improves unseen-class accuracy by up to 37.4 percentage points without statistically significant degradation of the original inventory. Robot-arm deployment identifies 17/18 parts without deployment-specific retraining.

Figures & tables

Figure 1: Overview of CALIPER. (A) Training classes are onboarded to build appearance support banks, a coarse detector, and class-specific metric size profiles; limited real data provide sim-to-real supervision and calibrate the fusion parameters. (B) An unseen class is enrolled from SI10 RGB-D images by forming a new support bank and live size profile, without retraining. (C) At inference, the coarse detector provides object crops and masks for appearance matching and metric measurement, respectively. Existing and newly enrolled banks/profiles are combined, and margin-conditioned fusion produces the final fine-grained identity. Here,

\mathcal{E}

and

\mathcal{N}

denote existing and newly enrolled class sets; TV1 , RLI12 , BV2 , SI10 are different data tiers explained in Sec. 3 .

Figure 2: The benchmark includes 18 classes across three coarse categories – two gears, seven nuts, and nine screws. screw_h (M14) and screw_i (M20) are the two unseen enrollment probes; they appear nearly identical in the visual feature space but carry distinct sizes.

Figure 3: Directional separability among fine-grained classes within three coarse categories – gears, nuts, and screws. Each cell $(A,B)$ reports $S(A{\rightarrow}B)$ , measuring how well class $A$ is separated from class $B$ in the head-embedding space using onboarding ( $16$ training classes) and enrollment ( $2$ unseen screws) data. Lower values indicate greater visual similarity and higher confusion potential; diagonal cells are empty because self-separability is undefined.

Method	$K=1$	$K=5$	$K=10$
One-stage YOLO *	63.4	63.4	63.4
Raw DINOv2	39.7	71.9	75.8
Linear classifier *	85.3	85.3	85.3
ProtoNet	87.3	88.3	87.6
CALIPER, rendered support	87.9	87.9	88.2
CALIPER, real support	88.2	87.4	88.2

Table 1: Closed-set accuracy (%) on the 16 training classes. Support-based results are averaged over

100

random draws. The default CALIPER configuration is marked in bold font.

	Overall			Training16			Unseen2
Method	$K=1$	$K=5$	$K=10$	$K=1$	$K=5$	$K=10$	$K=1$	$K=5$	$K=10$
One-stage YOLO	61.4	53.8	55.1	63.6	53.4	53.4	42.9	56.9	68.4
Raw DINOv2	37.2	64.1	67.5	36.3	66.2	69.7	44.2	47.4	49.4
Linear classifier	71.5	78.6	78.8	77.6	83.0	81.1	20.3	41.7	60.0
ProtoNet	79.1	80.8	81.1	85.3	87.0	86.3	27.7	29.8	38.0
CALIPER	83.3	85.1	85.7	87.3	86.6	86.9	50.0	72.5	75.3

Table 2: Accuracy (%) after enrolling two unseen classes. Support-based results are averaged over

100

random support draws. For methods sharing CALIPER’s coarse detector, Overall , Training16 , and Unseen2 contain

557

497

, and

60

matched detections, respectively. One-stage YOLO is evaluated on detections from its retrained fine-grained detector, trained once per

K

CALIPER vs Linear classifier
	$K=1$			$K=5$			$K=10$
Comparison	$b$	$c$	$p$	$b$	$c$	$p$	$b$	$c$	$p$
Overall	$94$	$24$	$<0.001$	$64$	$23$	$<0.001$	$62$	$22$	$<0.001$
Training16	$73$	$19$	$<0.001$	$40$	$16$	$0.002$	$45$	$14$	$<0.001$
Unseen2	$21$	$5$	$0.002$	$24$	$7$	$0.003$	$17$	$8$	$0.108$
CALIPER vs ProtoNet

Table 3: McNemar’s exact test (

b

c

p

) for CALIPER against ProtoNet and the Linear classifier in the 18-class setting of Table 2 .

	16-class Training16			18-class Overall			18-class Training16			18-class Unseen2
Method	$K=1$	$K=5$	$K=10$	$K=1$	$K=5$	$K=10$	$K=1$	$K=5$	$K=10$	$K=1$	$K=5$	$K=10$
No metric fusion	87.1	87.3	88.3	78.5	80.4	81.2	84.8	85.5	86.5	26.8	38.3	37.9
With metric fusion	88.1	87.8	88.2	83.3	85.1	85.7	87.3	86.6	86.9	50.0	72.5	75.3
$\Delta$	+1.0	+0.5	$-0.1$	+4.8	+4.7	+4.5	+2.5	+1.1	+0.4	+23.2	+34.2	+37.4

Table 4: Ablation of margin-conditioned metric fusion. Accuracy (%) is reported with and without metric fusion for the 16-class closed-set and 18-class enrollment settings, averaged over

100

random support draws;

\Delta

denotes the change due to fusion.

Figure 4: Effect of training-free enrollment on existing and unseen classes. Accuracies are averaged over $100$ random support draws for each $K$ . Training16 (blue) is evaluated before enrollment (16-way, dashed) and after adding the two probes (18-way, solid). Unseen2 (red) reports enrolled-class accuracy in the 18-way setting.

Figure 5: Robot-arm deployment. (a,b) A seven-part scene and classification. (c,d) Motion above nut_b and to the predefined drop pose; no gripper is used. (e,f) Reclassification after one object is removed. (g,h) Additional scenes; (h) shows the only error (orange bounding box), screw_h $\rightarrow$ screw_c . All panels use frozen $K=10$ with no deployment-specific retraining or recalibration.

True class	screw_h	screw_i	screw_c	Other
screw_h , no fusion	42.5	31.3	22.9	3.3
screw_i , no fusion	39.9	33.3	10.9	15.9
screw_h , fused	82.0	0.0	14.7	3.3
screw_i , fused	16.7	68.7	0.0	14.6

Table 5: Row-normalized confusion (%) for the two enrolled classes at

K=10

in the 18-class setting, with and without fusion.

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