SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Implementation. We use PointRCNN [40] as our default architecture and we use the implementation provided by OpenPCDet [42]. We train DRIFT with 120 epochs in Lyft and 30 epochs in Ithaca365 as the default setting, and observe that the performance generally improves with more training epochs (Fig. 1). We use  $\lambda_{shape} = 1$ ,  $\lambda_{align} = 1$ ,  $\lambda_{dyn} = 0.001$  and  $\lambda_{bg} = 0.001$ . We use  $\mu_{scale} = 0.8$  and  $\sigma_{scale} = 0.2$  for the alignment reward. We define the shape priors based on four typical types of traffic participants: Car, Pedestrian, Cyclist, and Truck. Specifically, we use the mean and standard deviation of box sizes of each class in the Lyft dataset, but we show that they generalize well to other domains like Ithaca365 and are not sensitive (Tab. 2) The exact prototype sizes  $S$  and other implementation details can be found in the supplementary materials.
Baselines. To the best of our knowledge, MODEST [55] is the only prior work on this problem and we compare our method DRIFT against it with various variants of MODEST: (1) No Finetuning: the model trained with seed labels from PP-score without repeated self-training (MODEST (R0)) in [55]; (2) Self-Training ( $i$  ep): the model initialized with (1) and self-trained with  $i$  epochs without PP-score filtering; (3) MODEST ( $i$  ep): the model initialized with (1) and self-trained with  $i$  epochs with PP-score filtering (full MODEST model). For self-training in (2) and (3), we adopt 60 epochs for each self-training round in the Lyft dataset (same as that in [55]) and 30 epochs for the Ithaca365 dataset. To ensure a fair comparison, DRIFT is also initialized from (1) and use the same detector configurations as the baselines. Following [55], we also compare with the supervised counterparts trained with human-annotated labels from the same dataset (Lyft or Ithaca365) and from another out-of-domain dataset (KITTI).
Table 1: Detection performance on Lyft. DRIFT outperforms both baselines with  $10\%$  training time, and approaches the performance of the out-of-domain supervised detector trained on KITTI. Please refer to the setup of Sec. 4 for the metrics.
Method	mAP IoU @ 0.5 (↑)	mAP IoU @ 0.7 (↑)
0-30	30-50	50-80	0-80	0-30	30-50	50-80	0-80
No Finetuning	44.1	21.1	1.2	23.9	24.4	6.0	0.1	10.5
Self-Train. (60 ep)	50.0	29.0	3.4	28.6	32.5	10.0	0.3	14.0
Self-Train. (600 ep)	56.7	41.1	9.1	37.2	35.1	20.7	1.6	19.9
MODEST (60 ep)	49.6	29.7	3.4	28.8	31.3	10.2	0.3	14.4
MODEST (600 ep)	56.4	45.4	11.3	39.6	33.6	18.6	1.4	18.8
DRIFT (30 ep)	60.1	40.2	9.1	38.3	39.0	24.2	3.6	23.1
DRIFT (60 ep)	60.3	43.8	14.6	41.8	42.0	29.2	5.8	26.7
DRIFT (120 ep)	61.4	45.1	21.7	45.3	42.7	31.7	9.9	29.6
Sup. on KITTI	71.9	49.8	22.2	49.9	47.0	26.2	6.4	27.9
Sup. on Lyft	76.9	60.2	37.5	60.4	62.7	50.9	28.2	48.5
Table 2: Detection performance on Ithaca365. We observe DRIFT outperforms both baselines with significantly less training time. Please refer to the setup of Sec. 4 for the metrics.
Method	mAP (↑)	Errors 0-80m (↓)
0-30	30-50	50-80	0-80	ATE	ASE	AOE
No Finetuning	18.7	4.8	0.0	7.7	1.17	0.60	1.64
Self-Train. (30 ep)	25.9	9.2	1.2	12.4	1.08	0.62	1.57
Self-Train. (300 ep)	16.3	3.6	1.8	6.8	1.19	0.74	1.57
MODEST (30 ep)	14.6	0.7	0.0	3.7	0.83	0.52	1.53
MODEST (300 ep)	27.5	26.3	21.0	27.1	1.06	0.67	1.09
DRIFT (15 ep)	39.1	24.3	17.7	28.0	0.73	0.33	1.23
DRIFT (30 ep)	47.1	31.2	22.9	35.1	0.49	0.35	1.20
Sup. on KITTI	59.8	28.3	4.0	32.0	0.26	0.22	0.46
Sup. on Ithaca365	75.7	48.3	22.6	51.5	0.18	0.13	0.33
Dynamic Object Detection Results. We report the performance of DRIFT and baseline detectors on Lyft in Tab. 1, and show the performance over the training epochs in Fig. 1. We report the performance on Ithaca365 in Tab. 2. Notably, DRIFT demonstrates significantly faster learning and strong performance. It provides more than  $10 \times$  speedup as compared to the baselines. On Lyft, DRIFT's performance at 60 epochs already surpasses the performance of both baselines at 600 epochs (10 self-training rounds) and approaches the performance of the out-of-domain supervised detector trained on KITTI [19]. On Ithaca365, its performance at 30 epochs significantly surpasses both baselines trained at 300 epochs. It even outperforms the out-of-domain supervised detector trained on KITTI in mAP.
Table 3: Analysis on rewards of boxes produced by different detectors. We report mean and std of box reward on the Lyft dataset.
Models	Mean	StD.
Rand. Boxes	0.02	0.15
Self-Train.	0.44	0.57
MODEST	0.57	0.58
DRIFT	0.61	0.64
Ground Truth	1.00	0.42
Observe that the self-training performance starts collapsing with more rounds of self-training, and does not continue to improve.
Fig. 4 visualizes the detection on two scenes. Ground truth boxes are colored in green, predictions from the detector without fine-tuning are in yellow, and predictions from DRIFT are in red. We observe that the detector without fine-tuning occasionally produces false positive predictions, produces boxes with incorrect sizes, or misses moving objects, while DRIFT produces accurate detection.
Rewards ablations. We report the average reward per box for ground truth boxes, random boxes, and predicted boxes from different detectors in Tab. 3. The ground truth boxes have the highest rewards on average, while the random boxes have the lowest. This indicates that the reward reasonably reflects
Lyft Dataset
Ithaca365 Dataset
Figure 4: Visualization of detections. Qualitative results on two scenes from Lyft [24] and Ithaca365 [12] datasets. Ground truth boxes are labeled with green in the LiDAR figures and predictions without fine-tuning and DRIFT are in yellow and red, respectively. We observe DRIFT learns to produce accurate detection with correct shape and localization.
Table 4: Ablation on the reward components. We report the mAP (0-80m) on Lyft. Removing components significantly degrades performance.
Filter	Shape	Align.	mAP (0 - 80m)
IoU 0.5	IoU 0.7
✓	0.6	0.0
✓	✓	0.0	0.0
✓	✓	0.0	0.0
✓	✓	22.4	3.2
✓	✓	✓	38.3	23.1
Table 5: Ablation on the alignment reward's  $\mu_{scale}$ . We report the mAP (0-80m) on the Lyft dataset. We show the detection performance is not sensitive to the choice of  $\mu_{scale}$ .
μscale	mAP (0 - 80m)
IoU 0.5	IoU 0.7
0.8	38.3	23.1
0.9	38.1	23.4
Table 6: Ablation on the alignment reward's  ${\sigma }_{\text{scale }}$  . We report the mAP (0-80m) on the Lyft dataset. The detection performance is not sensitive to the variance.
σscale	mAP (0 - 80m)
IoU 0.5	IoU 0.7
0.1	34.2	20.9
0.2	38.3	23.1
0.3	38.1	20.0
the quality of the bounding box. And we observe the boxes predicted by DRIFT have higher rewards than those predicted by the baseline detectors.
Ablation study on the components of our reward is presented in Tab. 4, and visualization is shown in Fig. 5. Detection performance significantly drops when we remove one or more of the components. For example, when only common sense filtering is used, the detector just predicts boxes around foreground points. Without the shape prior reward, the detector predicts boxes with incorrect sizes.
Ablations Tab. 5 and Tab. 6 present the sensitivity analysis of the choices of  $\mu_{\mathrm{scale}}$  and  $\sigma_{\mathrm{scale}}$ . DRIFT achieves stable performance across reasonable choices of  $\mu_{\mathrm{scale}}$  and  $\sigma_{\mathrm{scale}}$ , showing the robustness of our method.
Exploration. We study the necessity of the exploration component and the effect of incorporating other sources for box sampling. In Tab. 7, we compare no exploration to: (1) sampling 200 boxes from box predictions, (2) sampling 100 from proposals near dynamic points and 100 from predictions, and (3) sampling 100 from seed labels and 100 from predictions. Observe that the exploration component is crucial for our method; by performing local exploration instead of simply updating from its own predictions, DRIFT avoids confirmation bias and ensures that labels improve over what it predicts. Furthermore, results show that sampling from the box predictions is sufficient for obtaining good performance; other sources do not provide obvious benefits.
We also explore the effects of modifying the exploration strategy. Tab. 8 compares the detector performance of using sample size of 50, 100 and 200, and noise scale  $\sigma$  (i.e. variation) of 0.3 vs. 0.6. Each detector is trained for 30 epochs. At noise scale 0.3, increasing the sample size from 50 to 200 significantly improves the detection performance. Using noise scale 0.6 significantly reduces the detection performance, indicating that smaller noise may be preferable.
Filtering Budget of the Ranked Boxes. We study the effect of the choice of top  $k\%$  for filtering boxes by reward ranking. Tab. 9 presents the detection performance with top  $55\%$ ,  $65\%$ ,  $75\%$  and  $85\%$ . DRIFT is robust to the choice of  $k$ , with slightly decreased performance when  $k$  is too high.
Table 7: Detection performance with additional sampling sources in exploration. Sampling near the predictions is sufficient; additional sources do not provide obvious benefits. Trained for 60 ep.
mAP (0 - 80m)
IoU 0.5	IoU 0.7
No Exploration	0.0	0.0
Sample near pred.	41.8	26.7
+ near dynamic	39.3	22.6
+ init. seed labels	39.5	22.8
Table 8: Ablation on exploration parameters. Large sampling size and moderate noise scale are preferable.
Noise	Samples	mAP (0 - 80m)
IoU 0.5	IoU 0.7
0.3	50	32.9	14.3
100	36.4	18.9
200	38.3	23.1
0.6	50	5.6	0.0
100	17.2	1.3
200	0.6	0.0
Table 9: Ablation on the choice of top  $k\%$  for box filtering by reward ranking. DRIFT is robust to different values of  $k$ .
Top K	mAP (0 - 80m)
IoU 0.5	IoU 0.7
55%	37.0	21.0
65%	38.9	23.0
75%	38.3	23.1
85%	35.8	20.2
(-) Size Reward
Figure 5: Visualization of reward ablation. Removing components leads to predictions with incorrect shape or position.
(-) Alignment Reward
Figure 6: Per-class BEV mAP at IoU 0.7. We assign each predicted box to the class with the most similar prototype size. DRIFT outperforms the baselines for all classes.
Extension to Detection with Classes. Our prototype sizes are defined by different classes of traffic participants. Thus, given a predicted box from a class agnostic detector, we can compute the likelihood of its size under the Gaussian prior of each prototype size, and assign it to the class with the highest likelihood. Fig. 6 presents the per-class performance of DRIFT and baselines under such assignment. All detectors are trained for 60 epochs. DRIFT outperforms both baselines, with especially significant improvement for the Car and Cyclist classes. More details can be found in the supplementary materials.
In this work, we propose a framework, DRIFT, to tackle object discovery without labels. Instead of requiring expensive 3D bounding box labels, our method utilizes succinctly described heuristics as a reward signal to initialize and subsequently fine-tune an object detector. To optimize this nondifferentiable reward signal, we propose a simple but very effective reward finetuning framework, inspired by recent successes of reinforcement learning in the NLP community. Compared to prior self-training based methods [55], such a framework is an order of magnitude faster to train, while achieving higher accuracy. Traditional self-training iteratively generates pseudo-labels and retransits the model, requiring convergence before generating the next set of pseudo-labels. In general, training a detector to mimic pseudo-labels can lead to undesirable artifacts, further amplified by repeated training (confirmation bias). DRIFT addresses this issue by leveraging reinforcement learning principles, where the exploration component is crucial. Our method avoids confirmation bias by performing local exploration and ensures that labels improve over what it predicts. Thus, DRIFT is able to perform updates per-training iteration as opposed to per self-training round, which allows it to converge significantly faster and achieve higher performance.
Limitations and Future Works. One limitation is that the current framework is geared explicitly towards dynamic objects. Static objects would require different heuristics, not based on PP-scores. Similarly, currently we restricted our framework entirely to LiDAR signals. However, the reward
based framework is extremely flexible, and could easily be extended to other data modalities. For example, one could use image features to help identify objects inside of box proposals. Although in supervised settings image features have typically not added much in to the higher resolution LiDAR point clouds, in our unsupervised setting it is certainly possible that pixel information can help disambiguate objects from background. Further, we plan to explore the use of reward fine-tuning for other vision applications beyond object discovery.
This research is supported in part by grants from the National Science Foundation (III-2107161, IIS 2144117, and IIS-1724282), Nvidia Research, and DARPA Geometries of Learning (HR00112290078). We also thank Wei-Lun Chao, Yihong Sun, Travis Zhang, and Junan Chen for their valuable discussion.
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Supplementary Material: Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
S1 Additional Implementation Details
We train DRIFT on four NVIDIA RTX A6000 GPUs, with batch size 10 per GPU. The values we use for the shape priors are reported in Tab. S10. In practice, we scale the mixtures such that the probabilities at the Gaussian means are equal for stability reasons. The values in Tab. S10 are computed from the box shapes in the Lyft dataset, but the method generalize well to other domains like Ithaca365. Bear in mind, all models trained on Ithaca365 directly uses the hyperparameters found in Lyft, suggesting the generalizability of our method. DRIFT achieves stable performance when alternative values are used (Tab. S16).
Table S10: Shape prior values used in our implementation.
Mixture Component	Width w	Length l	Height h
Mean	StD.	Mean	StD.	Mean	StD.
1 (Car)	1.911	0.162	4.745	0.559	1.711	0.248
2 (Pedestrian)	0.780	0.153	0.797	0.182	1.745	0.177
3 (Truck)	2.832	0.278	9.403	3.145	3.299	0.430
4 (Cyclist)	0.613	0.256	1.752	0.326	1.364	0.343
S2 Extended Quantitative Results
S2.1 Full Tables from Main Paper
We provide results on all ranges for all ablation tables shown in the main paper. In Tab. S11, we report the results of the alignment reward ablation on  $\mu_{scale}$ . In Tab. S12, we report ablation results on reward components. In Tab. S13 and Tab. S14 we report ablation on exploration method and sample counts, respectively. In Tab. S15, we report ablation results for varying the filter budget.
Table S11: Ablation on the alignment reward's  $\mu_{scale}$ . We report the mAP (0-80m) on the Lyft dataset. This corresponds with Table 5 of the main paper.
Alignment Reward	mAP IoU 0.5	mAP IoU 0.7
0-30	30-50	50-80	0-80	0-30	30-50	50-80	0-80
μ = 0.8	60.1	40.2	9.1	38.3	39.0	24.2	3.6	23.1
μ = 0.9	58.3	39.2	10.1	38.1	37.1	26.0	4.4	23.4
S2.2 Ablation on Shape Prior Reward
We additionally include an ablation study on the shape-prior reward in Tab. S16. We show that it is not sensitive to the choice in variance used for each class. We assume that the means of the shape priors are user defined.
S2.3 Lower IoU and Recall Evaluation
We report mAP results at IoU 0.25 match in Tab. S17. In particular, IoU 0.25 metric evaluates "localization", i.e. if there is a bounding box with a very small overlap with the ground truth. However,
Table S12: Ablation on the reward components. We report the mAP (0-80m) on the Lyft dataset. This table corresponds to Table 4 in the main paper.
Filtering	Size Prior	Align.	mAP IoU 0.5	mAP IoU 0.7
0-30	30-50	50-80	0-80	0-30	30-50	50-80	0-80
✓	1.3	0.8	0.0	0.6	0.0	0.0	0.0	0.0
✓	✓	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
✓	✓	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
✓	✓	41.2	18.2	1.7	22.4	5.3	5.0	0.3	3.2
✓	✓	✓	60.1	40.2	9.1	38.3	39.0	24.2	3.6	23.1
Table S13: Detection performance with additional sampling sources in exploration. Sampling near the predictions is sufficient; additional sources do not provide obvious benefits.
mAP IoU 0.5	mAP IoU 0.7
0-30	30-50	50-80	0-80	0-30	30-50	50-80	0-80
Sample near pred.	60.3	43.8	14.6	41.8	42.0	29.2	5.8	26.7
+ near dynamic	59.8	40.9	10.6	39.3	39.1	22.8	3.2	22.6
+ init. seed labels	60.3	40.7	10.4	39.5	38.4	23.9	3.3	22.8
our method excels at predicting the size and proper orientation, which is better captured at higher IoUs metrics. To summarize, as compared to the prior work, MODEST can localize boxes well, but it's not able to figure out the size; in contrast, our method does both well. As stated in the main paper, we use 0.5 and 0.7 to (1) following the KITTI and Lyft standards of reporting, and (2) emphasize the strength of our method.
In addition, we report recall metrics in Tab. S18. We see that the recall improves over additional training, which suggests that part of the improvement comes from being able to better detect bounding boxes by locating them in the scene.
S2.4 Ablation on class mixtures
We include additional experiments on the class type mixtures that we use in the reward function of DRIFT to evaluate the robustness of our method. We report the results of using 4 (the ground truth number), 5, and 6 factors in the Gaussian mixture in Tab. S19.
S3 Extended Qualitative Visualizations
We showcase additional qualitative results in Fig. S8. Observe that DRIFT improves significantly over the model without fine-tuning, and close to supervised performance, without any labels.
Table S18: Recall by epoch. We report class recall numbers at different training epochs. Observe that DRIFT training improves the recall.
Epoch	Recall (0 - 80m)
IoU 0.5	IoU 0.7
30	0.47	0.30
60	0.51	0.34
90	0.53	0.36
120	0.56	0.38
Table S14: Ablation on sampling hyperparameters for exploration. Large sampling size and moderate noise scale are preferable.
Noise	Samples	mAP IoU 0.5	mAP IoU 0.7
0-30	30-50	50-80	0-80	0-30	30-50	50-80	0-80
0.3	50	57.3	31.3	4.8	32.9	26.0	14.4	1.4	14.3
100	59.8	36.2	7.0	36.4	33.0	18.9	1.8	18.9
200	60.1	40.2	9.1	38.3	39.0	24.2	3.6	23.1
0.6	50	17.8	1.2	0.0	5.6	0.1	0.0	0.0	0.0
100	39.5	10.4	0.2	17.2	4.0	0.3	0.0	1.3
200	0.1	0.9	1.1	0.6	0.0	0.0	0.0	0.0
Table S15: Ablation on the choice of top  $k\%$  for box filtering by reward ranking. DRIFT is robust to different values of  $k$ .
Top k%	mAP IoU 0.5	mAP IoU 0.7
0-30	30-50	50-80	0-80	0-30	30-50	50-80	0-80
55%	60.0	36.5	7.5	37.0	36.5	20.1	2.3	21.0
65%	60.1	39.9	9.7	38.9	39.1	23.4	3.5	23.0
75%	60.1	40.2	9.1	38.3	39.0	24.2	3.6	23.1
85%	58.1	36.6	6.6	35.8	35.8	20.6	2.3	20.2
Figure S7: Per-class BEV mAP at IoU 0.5. We assign each predicted box to the class with the most similar prototype size. This corresponds to Figure 6 of the main paper.
Table S16: Ablation on variance of the class shape priors.
Class Shape Var.	mAP IoU 0.5	mAP IoU 0.7
0-30	30-50	50-80	0-80	0-30	30-50	50-80	0-80
σi=0.5 StD.	57.6	34.5	5.1	34.5	36.8	22.5	1.7	21.3
σi=0.2 StD.	55.3	40.2	11.1	37.1	39.8	27.9	5.2	25.5
True Variance	60.1	40.2	9.1	38.3	39.0	24.2	3.6	23.1