SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Network settings. Our network architecture is designed with four stages by default and each stage is characterized by a specific channel configuration and a corresponding number of blocks. Specifically, the channel numbers for these stages are set to  $\{C, 2C, 4C, 4C\}$  and the corresponding block numbers  $\{N_1, N_2, N_3, N_4\}$  are  $\{2, 2, 6, 2\}$ . And  $C$  is set to 96 and the head numbers  $H$  are set to  $1/16$  of the channel numbers in our experiments. For the task of semantic segmentation, following the methodology of Stratified Transformer [34], we employ a U-Net structure to gradually upsample the features from the four stages back to the original resolution. Additionally, we employ an MLP to perform point-wise prediction, enabling accurate semantic segmentation. In the case of object detection, we adopt FCAF3D [63] and CAGroup3D [71] as the baseline and replace the network backbone with our proposed architecture, leveraging its enhanced capabilities for improved object detection performance.
To validate the effectiveness of our ConDaFormer, we conduct experiments on 3D semantic segmentation and 3D object detection tasks. We also perform extensive ablation studies to analyze each component in our ConDaFormer. Additional ablation results of window size and position encoding and more experiment results on outdoor perception tasks and object-level tasks are available in the appendix.
4.1 Semantic Segmentation
Datasets and metrics. For 3D semantic segmentation, we conduct comprehensive experiments on three benchmark datasets: ScanNet v2 [15], S3DIS [1], and the recently-introduced ScanNet200 [62].
The ScanNet v2 [15] dataset comprises a collection of 1513 3D scans reconstructed from RGB-D frames. The dataset is split into 1201 scans for training and 312 scans for validation. The input point cloud is obtained by sampling vertices from the reconstructed mesh and annotated with 20 semantic categories. In addition, we utilize the ScanNet200 [62] dataset, which shares the same input point cloud as ScanNet v2 but provides annotations for 200 fine-grained semantic categories.
Table 1. Semantic segmentation on ScanNet v2.
Method	Input	Val	Test
PointNet++ [58]	point	53.5	55.7
3DMV [16]	point	-	48.4
PanopticFusion [53]	point	-	52.9
PointCNN [39]	point	-	45.8
PointConv [79]	point	61.0	66.6
JointPointBased [11]	point	69.2	63.4
PointASNL [90]	point	63.5	66.6
SegGCN [38]	point	-	58.9
RandLA-Net [28]	point	-	64.5
KPCov [69]	point	69.2	68.6
JSENet [29]	point	-	69.9
SparseConvNet [21]	voxel	69.3	72.5
MinkUNet [12]	voxel	72.2	73.6
PTv1 [97]	point	70.6	-
PointNeXt [59]	point	71.5	71.2
FPT [54]	voxel	72.1	-
LargeKernel [8]	voxel	73.2	73.9
Stratified [34]	point	74.3	73.7
PTv2* [80]	point	75.5	75.2
ConDaFormer	point	75.1	74.7
ConDaFormer*	point	76.0	75.5
Table 2. Semantic segmentation on S3DIS Area 5.
Method	Input	OA	mAcc	mIoU
PointNet [56]	point	-	49.0	41.1
SegCloud [68]	point	-	57.4	48.9
TanConv [67]	point	-	62.2	52.6
PointCNN [39]	point	85.9	63.9	57.3
PointWeb [96]	point	87.0	66.6	60.3
HPEIN [31]	point	87.2	68.3	61.9
GACNet [73]	point	87.8	-	62.9
PAT [91]	point	-	70.8	60.1
ParamConv [75]	point	-	67.0	58.3
SPGraph [35]	point	86.4	66.5	58.0
SegGCN [38]	point	88.2	70.4	63.6
MinkUNet [12]	voxel	-	71.7	65.4
PACConv [86]	point	-	-	66.6
KPConv [69]	point	-	72.8	67.1
PTv1 [97]	point	90.8	76.5	70.4
PointNeXt [59]	point	90.6	-	70.5
FPT [54]	voxel	-	77.3	70.1
Stratified [34]	point	91.5	78.1	72.0
PTv2* [80]	point	91.6	78.0	72.6
ConDaFormer	point	91.6	78.4	72.6
ConDaFormer*	point	92.4	78.9	73.5
The S3DIS [1] dataset consists of 271 room scans from six areas. Following the conventions of previous methods, we reserve Area 5 for validation while utilizing the remaining areas for training. The input point cloud of the S3DIS dataset is sampled from the surface of the reconstructed mesh and annotated with 13 semantic categories.
For evaluating the performance of our ConDaFormer on the ScanNet v2 and ScanNet200 dataset, we employ the widely adopted mean intersection over union (mIoU) metric. In the case of the S3DIS dataset, we utilize three evaluation metrics: mIoU, mean of class-wise accuracy (mAcc), and overall point-wise accuracy (OA).
Experimental Setup. For ScanNet v2 and ScanNet200, we train for 900 epochs with voxel size and batch size set to  $0.02\mathrm{m}$  and 12 respectively. We utilize an AdamW optimizer [47] with an initial learning rate of 0.006 and a weight decay of 0.02. The learning rate decreases by a factor of 10 after 540 and 720 epochs respectively. The initial window size  $S$  is set to  $0.16\mathrm{m}$  and increases by a factor of 2 after each downsampling. Following Point Transformer v2 [80] and Stratified Transformer [34], we use some data augmentation strategies, such as rotation, scale, jitter, and dropping color.
For S3DIS, we train for 3000 epochs with voxel size and batch size set to  $0.04\mathrm{m}$  and 8 respectively. We utilize an AdamW optimizer with an initial learning rate of 0.006 and a weight decay of 0.05. The learning rate decreases by a factor of 10 after 1800 and 2400 epochs respectively. The initial window size  $S$  is set to  $0.32\mathrm{m}$  and increases by a factor of 2 after each downsampling. The data augmentations are identical to those used in Stratified Transformer [34].
Quantitative results. In our experiments, we compare the performance of our ConDaFormer against state-of-the-art methods in 3D semantic segmentation. The results on ScanNet v2 and S3DIS datasets are shown in Table 1 and Table 2 respectively. Since Point Transformer v2 [80] employs the test-time-augmentation (TTA) strategy to improve the performance, we also adopt such strategy for fair comparison and the results with TTA strategy are marked with  $\star$ . Clearly, on the validation set of ScanNet v2, our ConDaFormer achieves the best performance and surpasses Point Transformer v2 [80] by  $0.5\%$  mIoU (TTA also exploited). Similarly, on S3DIS, our ConDaFormer outperforms prior methods and achieves a new state-of-the-art performance of  $73.5\%$  mIoU. Additionally, on the challenging ScanNet200 dataset, as shown in Table 3, our ConDaFormer still performs better than Point Transformer v2 [80] and substantially outperforms the rest of competitors that are pre-trained with additional data or a large vision-language model. The state-of-the-art performance on these datasets demonstrates the effectiveness of our ConDaFormer.
Figure 4. Visualization of semantic segmentation results on ScanNet v2.
Figure 5. Visualization of semantic segmentation results on S3DIS.
Qualitative results. Figure 4 and Figure 5 show the visualization comparison results of semantic segmentation on ScanNet v2 and S3DIS datasets, respectively. Compared to Stratified Transformer [34] and the baseline with cubic window attention, our ConDaFormer is able to produce more accurate segmentation results for some categories, such as Wall, Window, and Sofa.
4.2 Object Detection
Data and metric. We evaluate our ConDaFormer on the SUN RGB-D [64] dataset annotated with oriented 3D bounding boxes of 37 categories. The dataset consists of about 10k indoor RGB-D scans, which are split into 5285 scans for training and 5050 scans for validation. Following prior works, we use the average precision (mAP) under IoU thresholds of 0.25 (mAP@0.25) and 0.5 (mAP@0.50) for evaluating the performance.
Experimental Setup. We implement the object detection network based on FCAF3D [63] and CAGroup3D [71], respectively. We replace the backbone with our ConDaFormer while keeping the detection head unchanged. The detection network built upon our ConDaFormer is trained with AdamW optimizer for 24 epochs with batch size and weight decay set to 32 and 0.01 respectively. The initial learning rate is set to 0.001 and decreases by a factor of 10 after 12 and 20 epochs, respectively. The data augmentation techniques are the same as those in FCAF3D and CAGroup3D.
Quantitative results. Following FCAF3D [63], we run ConDaFormer 5 times and record the best and average (in bracket) performance to reduce the impact caused by random sampling. As shown in Table 5, in comparison with FCAF3D and CAGroup3D, our method achieves comparable performance and the gap between the best and average performance of our method is smaller. In addition, ours has fewer parameters than FCAF3D (23 million v.s. 70 million).
We perform ablation experiments of semantic segmentation on the S3DIS dataset to demonstrate the effectiveness of each component of our proposed method.
Table 3. Semantic segmentation on ScanNet200.
Method	Input	mIoU (%)
CSC [26]	voxel	26.4
LGround [62]	voxel	28.9
PTv2* [80]	point	31.9
ConDaFormer*	point	32.3
Table 4. Ablation of window attention type.
Type	mIoU (%)	Time (hours)
Cubic	71.2	40.8
DaFormer w.o Split	71.1	39.9
DaFormer w.o Share	69.9	24.1
DaFormer	70.7	24.1
Table 5. Detection results on SUN RGB-D.
Method	mAP@0.25	mAP@0.5
VoteNet [55]	57.7	-
MLCVNet [84]	59.8	-
3DETR [52]	59.1	32.7
H3DNet [95]	60.1	39.0
BRNet [9]	61.1	43.7
HGNet [6]	61.6	-
VENet [83]	62.5	39.2
GroupFree [46]	63.0 (62.6)	45.2 (44.4)
FCAF3D [63]	64.2 (63.8)	48.9 (48.2)
ConDaFormer	64.9 (64.7)	48.8 (48.5)
CAGroup3D [71]	66.8 (66.4)	50.2 (49.5)
ConDaFormer	67.1 (66.8)	49.9 (49.5)
Table 6. Ablation of LSE.
Before	After	mIoU
70.7
✓	72.0
✓	71.5
✓	✓	72.6
Table 7. Ablation of window size.
Window Size Method	0.16m	0.32m
Cubic	DaFormer	Ours	Cubic	DaFormer	Ours
Params (M)	7.4	7.0	8.2	8.0	7.2	8.4
Time (hours)	26.5	21.2	26.7	40.8	24.1	29.8
mIoU (%)	69.9	69.7	71.7	71.2	70.7	72.6
Window disassembly. We begin by investigating the impact of disassembling the 3D cubic window into three orthogonal 2D planes. We first compare our disassembled attention with the vanilla 3D window attention, which we refer to as "Cubic". We also further analyze our DaFormer by examining some involved settings. Specifically, 1) "DaFormer w.o Split": the number of channels equals that of the input for all three planes and the position embedding is also shared across the three planes, and the Concatenation in Eq. 4 is replaced by Add. 2) "DaFormer w.o Share": the number of channels equals  $\frac{1}{3}$  of that of the input for all three planes and the position embedding is not shared. Table 4 shows the results with the window size set to  $0.32\mathrm{m}$ . The computational cost is evaluated with the GPU hours for training the network with a single NVIDIA Tesla V100 GPU. Compared to "Cubic" and "DaFormer w.o Split", although ours ("DaFormer") has a slightly lower score, the computational effort is significantly reduced. Notably, the comparison between "DaFormer w.o Share" and ours suggests that sharing the position embedding across different planes yields substantial performance gains. The potential reason may be the fact that when not shared across planes, the position embedding is not sufficiently trained due to the small number of neighborhood points within each plane.
Local structure enhancement. Next, we explore the influence of the proposed local structure enhancement module, which aims to capture local geometric information and address the challenge of context ignorance introduced by window disassembly. The results presented in Table 6 indicate that incorporating local sparse convolution before or after self-attention leads to improved performance. And the best result is achieved when both of them are adopted. These comparisons highlight the significance of leveraging local prior information in the context of 3D semantic segmentation, emphasizing the importance of capturing detailed local geometric cues alongside global context.
Window size. Finally, we evaluate the effect of window size. Specifically, we evaluate the segmentation performance and computational costs of the vanilla cubic window attention, disassembled window attention, and our ConDaFormer (i.e., disassembled window attention with local structure enhancement) by setting the initial window size to  $0.16\mathrm{m}$  and  $0.32\mathrm{m}$ . We have also experimented with a window size of  $0.08\mathrm{m}$  on the cubic window and got  $67.7\%$  mIoU, significantly worse than the result of  $69.9\%$  mIoU obtained with a window size of  $0.16\mathrm{m}$ . As shown in Table 7, compared to the cubic window attention, our proposed disassembled window attention ("DaFormer") significantly reduces the training time, especially when the window size is set to  $0.32\mathrm{m}$ , a relatively large window. Concomitantly, under both window sizes, the "DaFormer" brings a slight degradation in segmentation performance. However, compared to the "Cubic" under the window size of  $0.16\mathrm{m}$ , the "DaFormer" under the window size of  $0.32\mathrm{m}$  not only improves the mIoU by  $0.8\%$  but also has less computational cost. Moreover, compared to the "DaFormer", our ConDaFormer significantly improves the segmentation performance with a small increase in the number of parameters and training time, which verifies the effectiveness and efficiency of our model.
This paper is focused on the transformer for point cloud understanding. Aiming at reducing the computational costs in the previous 3D non-overlapping window partition, we disassemble the 3D window into three mutually perpendicular 2D planes. In this way, the number of points decreases within a similar attention range. However, the contexts also accordingly reduce for each query point. To alleviate this issue and also model the local geometry prior, we introduce the local structure enhancement strategy which inserts the sparse convolution operation before and after the self-attention. We evaluate our developed transformer block, i.e., ConDaFormer, for 3D point cloud semantic segmentation and 3D object detection. The experimental results can demonstrate the effectiveness.
**Limitation:** In our experiments, we find that a larger attention window might cause drops in performance. For example, if we further enlarge the window size from  $0.32\mathrm{m}$  to  $0.48\mathrm{m}$ , the training loss drops from around 0.048 to around 0.045 while the mIoU does not increase on the S3DIS dataset. As pointed out by LargeKernel [8], a large convolutional kernel might cause difficulties in optimizing proliferated parameters and leads to over-fitting. We guess that in our transformer block larger attention range requires more positional embeddings, which might cause similar issues. However, this phenomenon has not been fully explored in the 3D point cloud community. We think it would be worth studying in the future with efficient learning strategies, such as exploring self-supervised learning on large-scale datasets.
This research is supported by the NSFC Grants under the contracts No. 62325111 and No.U22B2011.
In the appendix, we provide additional ablation results of window size and position encoding in Sec. A, more experiment results on outdoor perception tasks and object-level tasks in Sec. B.
A Additional Ablation Results
Ablation of window size. As stated in the main paper, our proposed disassembled window attention offers a notable advantage over the vanilla 3D cubic window attention by significantly reducing computational effort, enabling the potential enlargement of the receptive field through an increase in the window size. However, we also acknowledge a limitation of our ConDaFormer, which is the degradation in performance when utilizing a larger attention window. In this section, we present detailed ablation results to further investigate this issue. To assess the impact of window size on the performance of ConDaFormer, we conduct experiments using varying window sizes:  $\{0.32m, 0.48m, 0.64m\}$ , and present the corresponding results in Table 8. It is worth noting that as the window size expands, the training loss  $(\mathrm{Loss}_{\mathrm{t}})$  gradually decreases, and the performance on the training set  $(\mathrm{mIoU}_{\mathrm{t}})$  steadily improves. However, contrary to expectations, the performance on the validation set experiences a decline, indicating the occurrence of over-fitting. This phenomenon aligns with the observation made in LargeKernel [8] when increasing the convolutional kernel size. The introduction of a larger attention window incorporates additional positional embeddings, potentially resulting in optimization difficulties and leading to over-fitting. To address this issue, future research can explore techniques such as self-supervised or supervised pre-training on large-scale datasets. These approaches have shown promise in mitigating over-fitting and improving generalization performance. By leveraging such techniques, it is possible to enhance the robustness of ConDaFormer and enable the utilization of larger attention windows without suffering from performance degradation.
Ablation of position encoding. To enhance the modeling of crucial position information necessary for self-attention learning, we employ the contextual relative position encoding (cRPE) scheme introduced by Stratified Transformer [34]. In this context, we compare the performance of cRPE with two alternative position encoding schemes: Swin [44], wherein the learned relative position bias is directly added to the similarity between query and key, and PTv2 [80], which generates the position bias through an MLP that takes the relative position as input and subsequently adds it to the similarity between query and key. As shown in Table 9, cRPE outperforms the other schemes in two out of three metrics, indicating the significance of contextual features in effectively capturing fine-grained position information.
Table 8. Ablation of window size.
Window	mIoU	mAcc	OA	Losst	mIoUt
0.32m	72.6	78.4	91.6	0.048	95.9
0.48m	72.1	78.3	91.5	0.045	96.0
0.64m	71.6	78.4	91.4	0.044	96.1
Table 9. Ablation of position encoding.
Method	mIoU	mAcc	OA
cRPE	70.7	76.9	90.6
Swin	69.6	76.1	90.6
PTv2	70.1	76.4	91.2
B Additional Quantitative Results
In this section, we present additional quantitative results on four benchmark datasets: Model-Net40 [81] for shape classification, ShapeNet-Part [77, 36] for part segmentation, SemanticKITTI [3] for 3D semantic segmentation, and nuScenes [4] for both 3D semantic segmentation and 3D object detection.
Results on ModelNet40 and ShapeNet-Part. As shown in Table 10 and 11, we can find that for small-scale point cloud data, our method still achieves comparable or even better performance in comparison with Point Transformer v1, Point Transformer v2, and Stratified Transformer.
Results on SemanticKITTI and nuScenes. In summary, our ConDaFormer achieves comparable or slightly better performance on outdoor perception tasks compared to current methods, demonstrating ConDaFormer's potential usage as a generalized 3D backbone. Specifically, for 3D semantic segmentation, as shown in Table 12 and 13, ConDaFormer achieves  $72.0\%$  mIoU and  $79.9\%$  mIoU on the
test set of the SemanticKITTI and the validation set of nuScenes datasets, respectively. Obviously, ConDaFormer outperforms most prior methods designed specifically for outdoor LiDAR data, such as Cylinder3D [99] and RPVNet [85], and performs only slightly worse than 2DPASS [89], which utilizes additional image information. For 3D object detection, we choose TransFusion-L [2] as the baseline model and replace the backbone with our ConDaFormer. ConDaFormer achieves  $68.5\%$  NDS and  $63.0\%$  mAP on the validation set of the nuScenes dataset. The comparable performance on different datasets and tasks demonstrates the effectiveness of our ConDaFormer.
Table 10. Shape classification on ModelNet40.
Method	mAcc (%)	OA (%)
PointNet [56]	86.0	89.2
PointNet++ [58]	-	91.9
PointCNN [39]	88.1	92.5
PointConv [79]	-	92.5
KPConv [69]	-	92.9
DGCNN [76]	90.2	92.9
RS-CNN [42]	-	92.9
PointASNL [90]	-	92.9
DensePoint [41]	-	93.2
PosPool [43]	-	93.2
PCT [23]	-	93.2
PA-DGC [86]	-	93.9
CurveNet [82]	-	94.2
PTv1 [97]	90.6	93.7
PTv2 [80]	91.6	94.2
ConDaFormer	90.8	94.0
Table 11. Part segmentation on ShapeNet-Part.
Methods	cls. mIoU	ins. mIoU
PointNet [56]	80.4	83.7
PointNet++ [58]	81.9	85.1
PointCNN [39]	84.6	86.1
DGCNN [76]	82.3	85.1
RSCNN [42]	84.0	86.2
KPConv [69]	85.1	86.4
PointConv [79]	82.8	85.7
PointASNL [90]	-	86.1
PCT [23]	-	86.4
PAConv [86]	84.6	86.1
AdaptConv [98]	83.4	86.4
PTv1 [97]	83.7	86.6
CurveNet [82]	-	86.8
PointMLP [49]	84.6	86.1
PointNeXt [59]	85.2	87.0
ConDaFormer	84.6	86.8
Table 12. Semantic segmentation results on SemanticKITTI test set.
Method	mIoU	road	sidewalk	parking	other gro.	building	car	truck	bicycle	motorcycle	other-veh.	vegetation	trunk	terrain	person	bicyclist	motorcyclist	fence	pole	traffic sign
RandLA-Net [28]	55.9	90.5	74.0	61.8	24.5	89.7	94.2	43.9	29.8	32.2	39.1	83.8	63.6	68.6	48.4	47.4	9.4	60.4	51.0	50.7
KPConv [69]	58.8	90.3	72.7	61.3	31.5	90.5	95.0	33.4	30.2	42.5	44.3	84.8	69.2	69.1	61.5	61.6	11.8	64.2	56.4	47.4
PolarNet [94]	54.3	90.8	74.4	61.7	21.7	90.0	93.8	22.9	40.3	30.1	28.5	84.0	65.5	67.8	43.2	40.2	5.6	61.3	51.8	57.5
JS3C-Net [88]	66.0	88.9	72.1	61.9	31.9	92.5	95.8	54.3	59.3	52.9	46.0	84.5	69.8	67.9	69.5	65.4	39.9	70.8	60.7	68.7
SPVNAS [66]	67.0	90.2	75.4	67.6	21.8	91.6	97.2	56.6	50.6	50.4	58.0	86.1	73.4	71.0	67.4	67.1	50.3	66.9	64.3	67.3
Cylinder3D [99]	68.9	92.2	77.0	65.0	32.3	90.7	97.1	50.8	67.6	63.8	58.5	85.6	72.5	69.8	73.7	69.2	48.0	66.5	62.4	66.2
RPVNet [85]	70.3	93.4	80.7	70.3	33.3	93.5	97.6	44.2	68.4	68.7	61.1	86.5	75.1	71.7	75.9	74.4	43.4	72.1	64.8	61.4
(AF)2-S3Net [10]	70.8	92.0	76.2	66.8	45.8	92.5	94.3	40.2	63.0	81.4	40.0	78.6	68.0	63.1	76.4	81.7	77.7	69.6	64.0	73.3
PVKD [27]	71.2	91.8	70.9	77.5	41.0	92.4	97.0	67.9	69.3	53.5	60.2	86.5	73.8	71.9	75.1	73.5	50.5	69.4	64.9	65.8
2DPASS [89]	72.9	89.7	74.7	67.4	40.0	93.5	97.0	61.1	63.6	63.4	61.5	86.2	73.9	71.0	77.9	81.3	74.1	72.9	65.0	70.4
Ours	72.0	88.3	72.8	70.5	35.7	92.7	97.3	62.6	66.5	69.3	64.8	84.9	73.9	69.7	73.6	73.1	65.1	70.7	65.2	71.3
Table 13. Semantic segmentation results on nuScenes val set.  $\ddagger$  denotes using rotation and translation testing-time augmentations.
Method	mIoU	barrier	bicycle	bus	cur	construction	motorcycle	pedestrian	traffic cone	trailer	truck	drivable	other flat	sidewalk	terrain	mammade	vegetation
RangeNet53++ [51]	65.5	66.0	21.3	77.2	80.9	30.2	66.8	69.6	52.1	54.2	72.3	94.1	66.6	63.5	70.1	83.1	79.8
PolarNet [94]	71.0	74.7	28.2	85.3	90.9	35.1	77.5	71.3	58.8	57.4	76.1	96.5	71.1	74.7	74.0	87.3	85.7
Salsanext [14]	72.2	74.8	34.1	85.9	88.4	42.2	72.4	72.2	63.1	61.3	76.5	96.0	70.8	71.2	71.5	86.7	84.4
AMVNet [40]	76.1	79.8	32.4	82.2	86.4	62.5	81.9	75.3	72.3	83.5	65.1	97.4	67.0	78.8	74.6	90.8	87.9
Cylinder3D [99]	76.1	76.4	40.3	91.2	93.8	51.3	78.0	78.9	64.9	62.1	84.4	96.8	71.6	76.4	75.4	90.5	87.4
PVKD [27]	76.0	76.2	40.0	90.2	94.0	50.9	77.4	78.8	64.7	62.0	84.1	96.6	71.4	76.4	76.3	90.3	86.9
RPVNet [85]	77.6	78.2	43.4	92.7	93.2	49.0	85.7	80.5	66.0	66.9	84.0	96.9	73.5	75.9	76.0	90.6	88.9
2DPASS [89]‡	79.4	78.8	49.6	95.6	93.6	60.0	84.1	82.2	67.5	72.6	88.1	96.8	72.8	76.2	76.5	89.4	87.2
SphereFormer [33]‡	79.5	78.7	46.7	95.2	93.7	54.0	88.9	81.1	68.0	74.2	86.2	97.2	74.3	76.3	75.8	91.4	89.7
Our	78.2	78.1	44.6	94.9	92.2	54.3	84.9	80.7	66.0	70.1	85.8	96.9	73.4	75.7	74.8	90.7	89.0
Our‡	79.9	79.2	47.8	95.3	94.1	57.2	86.6	82.9	69.0	73.6	87.4	97.1	74.8	76.6	76.0	91.2	89.6
Table 14. Object detection results on nuScenes val set.
Method	NDS	mAP
Sparse Conv [21]	68.48	63.07
Ours	68.54	62.95