SlowGuess/NeurIPS_2023_OCR huggingface dataset:
where  $\mathcal{L}_{\mathrm{recon}}$  denotes the reconstruction loss, i.e., the L1 distance between  $\mathcal{I}_{\mathrm{o}}^{\mathrm{S}}$  and  $\hat{\mathcal{I}}_{\mathrm{o}}^{\mathrm{S}}$ .  $\mathcal{L}_{\mathrm{gan}}$  is the generative adversarial network (GAN) loss, where we additionally train a discriminator to classify  $\mathcal{I}_{\mathrm{o}}^{\mathrm{S}}$  and  $\hat{\mathcal{I}}_{\mathrm{o}}^{\mathrm{S}}$ .  $\mathcal{L}_{\mathrm{percep}}$  means the perceptual loss, which is the L1 distance between the feature maps of  $\mathcal{I}_{\mathrm{o}}^{\mathrm{S}}$  and  $\hat{\mathcal{I}}_{\mathrm{o}}^{\mathrm{S}}$  in each level of VGGNet [41]. Even though the reconstruction is only supervised on training instances, we observe that it can roughly reconstruct novel views for unseen instances. We thus reason that the pre-trained voxel mapping can roughly encode the geometry of an instance.
Detection base training : In order to empower  $\mathcal{M}(\cdot)$  with geometry encoding capability, we initialize it with the reconstruction pre-trained weights and conduct the instance detection training stage. In addition to the open-world detection loss [14], we introduce the instance classification loss  $\mathcal{L}_{\mathrm{cls}}^{\mathrm{Ins}}$  and rotation estimation loss  $\mathcal{L}_{\mathrm{rot}}^{\mathrm{Ins}}$  to supervise our VoxDet.
We define  $\mathcal{L}_{\mathrm{cls}}^{\mathrm{Ins}}$  as the binary cross entropy loss between the true labels  $\mathbf{s} \in \{0,1\}^{N}$  and the predicted scores  $\hat{\mathbf{s}} \in \mathbb{R}^{N \times 2}$  from the QVM module. The rotation estimation loss is defined as:
$$
\mathcal {L} _ {\mathrm {r o t}} ^ {\mathrm {I n s}} = \left\| \hat {\mathbf {R}} ^ {Q} \mathbf {R} ^ {Q \top} - \mathbf {I} \right\|, \tag {6}
$$
Table 1: Overall performance comparison on synthetic-real datasets LM-O [17] and YCB-V [18]. Compared with various 2D methods, including correlation [5], attention [6], and feature matching [9, 19], our VoxDet holds superiority in both accuracy and efficiency. OLN* means the open-world object detector (OW Det.) [14] is jointly trained with the matching head while OLN denotes using fixed modules.  $\dagger$  the model is trained on both synthetic dataset OWID and real images.
Test/Metric
Method	OW Det.	Train	LM-O	YCB-V	Avg.
mAR	AR50	AR75	AR95	mAR	AR50	AR75	AR95	mAR	AR50	AR75	Speed
VoxDet	OLN*	OWID	29.2	43.1	33.3	0.8	31.5	51.3	33.4	1.7	30.4	47.2	33.4	6.5
OLNCorr. [14, 5]	OLN*	OWID	22.3	34.4	24.7	0.5	24.8	41.1	26.1	0.7	23.6	37.8	25.4	5.5
DTOID [6]	N/A	OWID	9.8	28.9	3.7	<0.1	16.3	48.8	4.2	<0.1	13.1	38.9	4.0	2.8
OS2D [7]	N/A	OWID	0.2	0.7	0.1	<0.1	5.2	18.3	1.9	<0.1	2.7	9.5	1.0	5.3
OLNCLIP [14, 19]	OLN	OWID†	16.2	32.1	15.3	0.5	10.7	25.4	7.3	0.2	13.5	28.8	11.3	2.8
OLNDINO [14, 9]	OLN	OWID†	23.6	41.6	24.8	0.6	25.6	53.0	21.1	0.8	24.6	47.3	23.0	2.8
Gen6D [5]	N/A	OWID†	12.0	29.8	6.6	<0.1	12.1	37.1	5.2	<0.1	12.1	33.5	5.9	1.3
BHRL [42]	N/A	COCO	14.1	21.0	15.7	0.5	31.8	47.0	34.8	1.4	23.0	34.0	25.3	N/A
Table 2: Overall performance comparison on the newly built real image dataset, RoboTools. For fairness, we only compare with the models fully trained on synthetic dataset here, more comparison see Appendix D. VoxDet shows superiority even under sim-to-real domain gap compared with other 2D representation-based methods [14, 5-7].
Metric	OW Det.	mAR	\( AR_{50} \)	\( AR_{75} \)	\( AR_{95} \)
VoxDet	OLN*	18.7	23.6	20.5	5.1
\( OLN_{Corr.} \)[14, 5]	OLN*	14.4	18.1	15.7	3.8
DTOID [6]	N/A	3.6	9.0	2.0	<0.1
OS2D [7]	N/A	2.9	6.5	2.0	<0.1
Table 3: Per module efficiency comparison. All the four methods share the same open-world detector [14]. Compared with 2D baselines that adopt cosine similarity [9, 19] or learnable correlation [5], our Voxel matching is more efficient, which shows  $\sim 2\times$  faster speed. The numbers presented below are measured in seconds.
Method/Module	Open-World Det.	Matching	ToTal
VoxDet	0.032	0.154
OLNCLIP [14, 19]	0.248	0.370
OLNDINO [14, 9]	0.122	0.235	0.357
OLNCorr. [14, 5]	0.060	0.182
where  $\mathbf{R}^Q$  is the ground-truth rotation matrix of the query voxel. Note that here we only supervise the positive samples. Together, our instance detection loss is defined as:
$$
\mathcal {L} _ {\mathrm {d}} = w _ {1} \mathcal {L} _ {\mathrm {c l s}} ^ {\mathrm {R P N}} + w _ {2} \mathcal {L} _ {\mathrm {r e g}} ^ {\mathrm {R P N}} + w _ {3} \mathcal {L} _ {\mathrm {c l s}} ^ {\text {H e a d}} + w _ {4} \mathcal {L} _ {\mathrm {r e g}} ^ {\text {H e a d}} + w _ {5} \mathcal {L} _ {\mathrm {c l s}} ^ {\text {I n s}} + w _ {6} \mathcal {L} _ {\text {r o t}} ^ {\text {I n s}}, \tag {7}
$$
Remark 1: In both training stages, we only use the training objects,  $\mathcal{O}_{\mathrm{base}}$ . During inference, VoxDet doesn't need any further fine-tuning or optimization for  $\mathcal{O}_{\mathrm{novel}}$ .
Our research employs datasets composed of distinct training and test sets, adhering to  $\mathcal{O}$  base  $\cap$ $\mathcal{O}$  novel  $= \phi$  to ensure no overlap between semantic classes of  $\mathcal{O}$  base and  $\mathcal{O}$  novel.
Synthetic Training set: In response to the scarcity of instance detection training sets, we've compiled a comprehensive synthetic dataset using 9,901 objects from ShapeNet [15] and ABO [16]. Each instance is rendered into a 40-frame, object-centric  $360^o$  video via Blenderproc [43]. We then generate a query scene using 8 to 15 randomly selected objects from the entire instance pool, each initialized with a random orientation. This process yielded 55,000 scenes with 180,000 boxes for training and an additional 500 images for evaluation, amounting to 9,800 and 101 instances respectively. We've termed this expansive training set "open-world instance detection" (OWID-10k), signifying our model's capacity to handle unseen instances. To our knowledge, this is the first of its kind.
Synthetic-Real Test set: We utilize two authoritative benchmarks for testing. LineMod-Occlusion [17] (LM-O) features 8 texture-less instances and 1,514 box annotations, with the primary difficulty being heavy object occlusion. The YCB-Video [18] (YCB-V) contains 21 instances and 4,125 target boxes, where the main challenge lies in the variance in instance pose. These datasets provide real test images while lacks the reference videos, we thus render synthetic videos using the CAD models in Blender.
Fully-Real Test set: To test the sim-to-real transfer capability of VoxDet, we introduced a more complex fully real-world benchmark, RoboTools, consisting of 20 instances, 9,109 annotations,
Figure 4: Number of templates analysis of VoxDet and 2D baseline,  $\mathrm{OLN_{DINO}}$  [14, 9] on YCB-V benchmark. Thanks to the learned geometry-aware 2D-3D mapping, VoxDet can work well with very few reference images, while 2D method suffers from such setting, dropping up to  $87\%$ .
Figure 5: Top-K analysis of VoxDet and One-shot object detector [7]. By virtue of the instance-level matching method, QVM, VoxDet can better classify the proposals, so that  $90\%$  of the true positives lie in Top-10, while for OS2D, this ratio is only  $60\%$ .
and 24 challenging scenarios. The instances and scenes are presented in Fig. 3. Compared with existing benchmarks [17, 18], RoboTools is much more challenging with more cluttered backgrounds and more severe pose variation. Besides, the reference videos of RoboTools are also real-images, including real lighting conditions like shadows. We also provide the ground-truth camera extrinsic.
Baselines: Our baselines comprise template-driven instance detection methods, such as correlation [5] and attention-based approaches [6]. However, these methods falter in cluttered scenes, like those in LM-O, YCB-V, and RoboTools. Therefore, we've self-constructed several 2D baselines, namely,  $\mathrm{OLN_{DINO}}$ ,  $\mathrm{OLN_{CLIP}}$ , and  $\mathrm{OLN_{Corr}}$ . In these models, we initially obtain open-world 2D proposals via our open-world detection module [14]. We then employ different 2D matching methods to identify the proposal with the highest score. In  $\mathrm{OLN_{DINO}}$  and  $\mathrm{OLN_{CLIP}}$ , we leverage robust features from pre-trained backbones [9, 19] and use cosine similarity for matching. For  $\mathrm{OLN_{Corr}}$ , we designed a 2D matching head using correlation as suggested in [5]. These open-world detection based 2D baselines significantly outperform previous methods [5, 6]. In addition to these instance-specific methods, we also include a class-level one-shot detector, OS2D [7] and BHRL [42] for comparison.
Hardware and configurations: The reconstruction stage of VoxDet was trained on a single Nvidia V100 GPU over a period of 6 hours, while the detection training phase utilized four Nvidia V100 GPUs for a span of  $\sim 40$  hours. For the sake of fairness, we trained the methods referenced [5-7, 14, 19, 9] mainly on the OWID dataset, adhering to their official configuration. Inferences were conducted on a single V100 GPU to ensure fair efficiency comparison. During testing, we supplied each model with the same set of  $M = 10$  template images per instance, and all methods employed the top  $N = 500$  ranking proposals for matching. In the initial reconstruction training stage, VoxDet used  $98\%$  of all 9,901 instances in the OWID dataset. For each instance, a random set of  $K = 4$  images were designated as output  $\mathcal{I}_{\mathrm{o}}^{\mathrm{S}}$ , while the remaining  $M - K = 6$  images constituted the inputs  $\mathcal{I}_{\mathrm{i}}^{\mathrm{S}}$ . For additional configurations of VoxDet, please refer to Appendix A and our code.
Metrics: Given our assumption that only one desired instance is present in the query image, we default to selecting the Top-1 proposal as the predicted result. We report the average recall (AR) rate [44] across different IoU, such as mAR  $(\mathrm{IoU} \in 0.5 \sim 0.95)$ , AR<sub>50</sub> (IoU 0.5), AR<sub>75</sub> (IoU 0.75), and AR<sub>95</sub> (IoU 0.95). Note that the AR is equivalent to the average precision (AP) in our case.
Overall Performance Comparison: On the synthetic real datasets, we comprehensively compare with all the potential baselines, the results are detailed in Table 1, demonstrating that VoxDet consistently delivers superior performance across most settings. Notably, VoxDet surpasses the
we default to the ViT-B model. DINO [9] and CLIP [19] might already be familiar with the test instances.
Support Images and AR
Query Images
Figure 6: Detection qualitative results comparison between VoxDet and 2D baselines on the three benchmarks. VoxDet shows better robustness under pose variance (e.g. Obj. 5@LM-O first and second columns) and occlusion (e.g. Obj. 13@YCB-V second column and Obj. 9@RoboTools).
next best baseline,  $\mathrm{OLN_{DINO}}$ , by an impressive margin of up to  $20\%$  in terms of average mAR. Furthermore, due to its compact voxel representation, VoxDet is observed to be markedly more efficient. On the newly built fully real dataset, RoboTools, we only compare methods trained on the same synthetic dataset for fairness. As shown in Table 2, VoxDet demonstrates better sim2real transferring capability compared with the 2D methods due to its 3D voxel representation. We present the results comparison with the real-image trained models in Appendix D.
Efficiency Comparison: As QVM has a lower model complexity than  $\mathrm{OLN}_{\mathrm{CLIP}}$  and  $\mathrm{OLN}_{\mathrm{DINO}}$ , it achieves faster inference speeds, as detailed in Table 3. Compared to correlation-based matching [5], VoxDet leverages the aggregation of multi-view templates into a single compact voxel, thereby eliminating the need for exhaustive 2D correlation and achieving  $2\times$  faster speed.
In addition to inference speed, VoxDet also demonstrates greater efficiency regarding the number of templates. We tested the methods on the YCB-V dataset [18] using fewer templates than the default. As illustrated in Fig. 4, we found that the 2D baseline is highly sensitive to the number of provided references, which may plummet by  $87\%$  when the number of templates is reduced from 10 to 2. However, such a degradation rate for VoxDet is  $2 \times$  less. We attribute this capability to the learned 2D-3D mapping, which can effectively incorporate 3D geometry with very few views.
Top-K Analysis: Compared to the category-level method [7], VoxDet produces considerably fewer false positives among its Top-10 candidates. As depicted in Fig. 5, we considered Top- $K = 1, 5, 10, 20, 30, 50, 100$  proposals and compared the corresponding AR between VoxDet and OS2D [7]. VoxDet's AR only declines by  $5 \sim 10\%$  when  $K$  decreases from 100 to 10, whereas OS2D's AR suffers a drop of up to  $38\%$ . This suggests that over  $90\%$  of VoxDet's true positives are found among its Top-10 candidates, whereas this ratio is only around  $60\%$  for OS2D.
Ablation Studies:
The results of our ablation studies are presented in Table 4. Initially, we attempted to utilize the 3D depth-wise convolution for matching (see the fourth row). However, this proved to be inferior to our proposed instance-level voxel relation. Reconstruction pre-training is crucial for VoxDet's ability to learn to encode the geometry of an instance (see the last row). Additionally, we conducted an ablation on the rotation measurement module (R) in the QVM, and also tried not supervising the predicted rotation. Both are inferior to our default settings.
Table 4: Ablation study for VoxDet in RoboTools benchmark. All the three critical modules are helpful in our design. Supervising the estimated rotation achieves slightly better results. Comparison with more matching module see Appendix B.
Recon.	R	R w/ sup.	Voxel Rel.	mAR	AR50	AR75
✓	✓	✓	✓	18.7	23.6	20.5
✓	✓	X	✓	18.2	23.2	20.0
✓	X	X	✓	15.6	21.9	17.0
✓	✓	✓	X	15.1	19.4	16.2
X	✓	✓	✓	14.2	18.3	15.7
Figure 7: Visualization of the high activation grids during matching. As query instance rotates along a certain axis, the location of the high-activated grids roughly rotates in the corresponding direction.
Detection Visualization The qualitative comparison is depicted in Fig. 6, where we compare VoxDet with the two most robust baselines, OLNDINO and OLNCorr.. We notice that 2D methods can easily falter if the pose of an instance is not seen in the reference, e.g., 2-nd query image in the 1-st row, while VoxDet still accurately identifies it. Furthermore, 2D matching exhibits less robustness under occlusion, where the instance's appearance could significantly differ. VoxDet can effectively overcome these challenges thanks to its learned 3D geometry. More visualizations and qualitative comparisons see Appendix C.
Deep Voxels Visualization To better validate the geometry-awareness of our learned voxel representation, we present the deep visualization in Fig. 7. The gradient of the matching score is backpropagated to the template voxel and we visualize the activation value of each grid. Surprisingly, we discover that as the orientation of the query instance changes, the activated regions within our voxel representations accurately mirror the true rotation.
Figure 8: Reconstruct results of VoxDet on unseen instances. The voxel representation in VoxDet can be decoded with a relative rotation and synthesize novel views, which demonstrate the geometry embedded in our learned voxels.
This demonstrates that the voxel representation in VoxDet is aware of the orientation of the instance.
Reconstruction Visualization The voxel representation in VoxDet can be decoded to synthesize novel views, even for unseen instances, which is demonstrated in Fig. 8. The voxel, pre-trained on 9500 instances, is capable of approximately reconstructing the geometry of unseen instances.
Conclusion: This work introduces VoxDet, a novel approach to detect novel instances using multiview reference images. VoxDet is a pioneering 3D-aware framework that exhibits robustness to occlusions and pose variations. VoxDet's crucial contribution and insight stem from its geometry-aware Template Voxel Aggregation (TVA) module and an exhaustive Query Voxel Matching (QVM) specifically tailored for instances. Owing to the learned instance geometry in TVA and the meticulously designed matching in QVM, VoxDet significantly outperforms various 2D baselines and offers faster inference speed. Beyond methodological contributions, we also introduce the first instance detection training set, OWID, and a challenging RoboTools benchmark for future research.
Limitations: Despite its strengths, VoxDet has two potential limitations. Firstly, the model trained on the synthetic OWID dataset may exhibit a domain gap when applied to real-world scenarios, we present details in Appendix D. Secondly, we assume that the relative rotation matrixes and instance masks (box) for the reference images are known, which may not be straightforward to calculate. However, the TVA module in VoxDet doesn't require an extremely accurate rotation and 2D appearance. We present further experiments addressing these issues in Appendix E.
This work was sponsored by SONY Corporation of America #1012409. This work used Bridges-2 at PSC through allocation cis220039p from the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program which is supported by NSF grants #2138259, #2138286, #2138307, #2137603, and #213296. The authors would also like to express the sincere gratitude on the developers of BlenderProc2 [43].
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To make our model fully reproducible, we present complete implementation details in Appendix A. Besides, our code library will be released upon acceptance. We report more comparisons between our QVM module and the 2D matching/relation techniques [1, 5, 45] in Appendix B to demonstrate the superiority of QVM in instance-level 3D matching. In Appendix C, we present more detection qualitative results. We further present some discussions about the sim2real domain gap of VoxDet in Appendix D. To test the robustness of VoxDet under interference on the voxel representation, we display results obtained from the flawed voxel in Appendix E. Finally, we provide extended related works discussions in Appendix F, where we exhaustively compare VoxDet with the existing instance-level tasks, including visual tracking, instance pose estimation, and instance retrieval.
Model Structure: We adopt ResNet50 [46] with feature pyramid network [26] as our feature extractor  $\psi(\cdot)$ . The default multi-scale ROIAngle in [26] is leveraged to obtain the 2D proposal features, where the dimensions are  $N = 500$ ,  $C = 256$ ,  $w = 7$ . In our 2D-3D mapping, we set  $C / d = 32$ ,  $d = 8$ , which results in the voxel feature dimension  $C_v = 256$ ,  $D = 16$ ,  $L = 14$ . All the 3D convolutions in TVA and QVM take kernel size as 3 and the padding equals to 1, so that the dimension of the voxels remains the same throughout the two modules. For the  $\mathrm{Rot}(\cdot, \cdot)$  function, we have followed [10] to use torch.nn.Functional.affine_grid() and torch.nnfunctional.grid_sample() functionalities. Though the 2D-3D mapping can learn the rotations in the physical world, it sacrifices some semantics information in the feature channels when reshaping. Therefore, in QVM, we have a global matching branch to retrieve the lost semantic information. To be more specific, we apply global average pooling on the support features to get a support vector  $\mathbf{k} \in \mathbb{R}^{1 \times C \times 1 \times 1}$ . Then we adopt depth-wise convolution between  $\mathbf{k}$  and  $\mathbf{F}^{\mathrm{Q}}$  to get a correlation map. Note that this correlation map preserved all the semantic channels from the backbone  $\psi(\cot)$ , so that the lost information in the 2D-3D mapping. The map is added to the voxel relation output  $\mathcal{R}_{\mathrm{v}}(\mathbf{V}^{\mathrm{S}}, \mathrm{Rot}(\mathbf{V}^{\mathrm{Q}}, \hat{\mathbf{R}}^{\mathrm{Q}}))$  for the final score.
Training Details: In the first reconstruction stage, we set the loss weights as  $w_{\mathrm{recon}} = 10.0$ ,  $w_{\mathrm{gan}} = 0.01$ ,  $w_{\mathrm{percep}} = 1.0$ . The model is trained for 16 epoch on the 9600 instances from OWID datasets. We leveraged Adam optimizer [47] with a base learning rate of  $5 \times 10^{-5}$  during training. In the second detection stage, we initialize the 2D-3D mapping modules in TVA and QVM with the reconstruction pre-trained weights. VoxDet first only learns the detection task, without learning the rotation estimation, i.e., the loss weights are set as  $w_{1} = w_{2} = w_{3} = w_{4} = w_{5} = 1.0$ ,  $w_{6} = 0$  in the first 10 epochs, where SGD is leveraged as an optimizer with 0.02 base learning rate. Note that in this stage, the 2D-3D mapping part only takes  $\frac{1}{10}$  of the base learning rate. Then in the final epoch, VoxDet learns the rotation estimation with the detection part fixed, i.e.,  $w_{1} = w_{2} = w_{3} = w_{4} = w_{5} = 0.0$ ,  $w_{6} = 1.0$ . However, supervising rotation is not the key requirements and is optional for VoxDet. It improves the performance slightly by  $1 \sim 2\%$ .
B More Matching Module Comparison