DiffuBox: Refining 3D Object Detection with Point Diffusion
Authors: Xiangyu Chen
Zhenzhen Liu
Katie Z Luo
Siddhartha Datta
Adhitya Polavaram
Yan Wang
Yurong You
Boyi Li
Marco Pavone
Wei-Lun Chao
Mark Campbell
Bharath Hariharan
Kilian Q Weinberger
Venue: NeurIPS 2024 poster (2024)
Keywords: Domain Adaptation
Denoising Diffusion Models
3D Object Detection
Primary area: diffusion_based_models

Abstract: Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining high performance when applied to domains with differing sensor setups or geographic locations, often resulting in poor localization accuracy due to domain shift. To overcome this challenge, we introduce a novel diffusion-based box refinement approach. This method employs a domain-agnostic diffusion model, conditioned on the LiDAR points surrounding a coarse bounding box, to simultaneously refine the box's location, size, and orientation. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets, object classes and detectors. Our PyTorch implementation is available at https://github.com/cxy1997/DiffuBox.

DiffuBox: Refining 3D Object Detection with Point Diffusion
Xiangyu Chen
Zhenzhen Liu"
Siddhartha Datta² Boyi Li Marco Pavone 3 Bharath Hariharan'
Katie Z Luo"
Adhitya Polavaram"
Yan Wang"
Yurong You"
Wei-Lum Chao Mark Campbell'
Kilian Q. Weinberger' "The Ohio State University
CComell University University of Oxford
NVIDIA Research
Abstract
Ensuring robust 3D object detection and localization is crucial for many applica- tions in robotics and autonomous driving. Recent models, however, face difficulties in maintaining high performance when applied to domains with differing sensor setups or geographic locations, often resulting in poor localization accuracy due to domain shift. To overcome this challenge, we introduce novel diffusion-based box refinement approach. This method employs domain-agnostic diffusion model, conditioned on the LiDAR points surrounding coarse bounding box, simulta- neously refine the box's 's location, size, and orientation. We evaluate this approach under various domain adaptation settings, and results reveal significant im- provements across different datasets, object classes and detectors. Our PyTorch implementation is available https///ithub..co/eeyy wtt.o//////x//9//f////// 1997/DiffuBox.
Introduction
3D object detection is fundamental task for embodied agents to safely navigate in complex en- vironments. For autonomous vehicles to navigate complicated traffic conditions, this amounts to identifying and localizing other road agents. Detection models under this setting need to make sense of LiDAR point clouds to identify accurate bounding boxes for pre-specified objects. Given the diverse driving environments that occur in practice it is common, however, for the train- and test-time distributions to differ significantly. Domain distributional differences mainly arise from differences in object size, point cloud density, and LiDAR beam angles. Consequently, models trained in one region or particular dataset (e.g. Germany) may not perform well in another region or dataset (e.g. USA) [48]. As result, the domain adaptation problem raises concerns over the reliability and safety of 3D object detection in self-driving, that are often trained in a particular setting, then deployed into a diverse of regions and locations.
Wang et al. [48] have obtained reductions in the domain adaptation gap by resizing boxes with a simple scaling heuristic after the fact. Consequently, share the belief that the performance gap associated with domain adaptation is dominated by incorrect box sizes, shapes, and orientations, rather than false positives and negatives in detections - egg. model trained in Germany can detect US cars, but struggles to capture their larger dimensions.
In this paper we observe that, although the relationship of bounding boxes to the surrounding environment varies across domains, the relative position of LiDAR points with respect to their bounding boxes is surprisingly consistent [23]. Bounding boxes of these detections are, by definition. supposed to tightly fit the corresponding objects. Furthermore, the objects within the same object class (e.g., cars) have similar shape with minor variance across different domains. What mostly
DDenotes equal contribution. "Correspondences could be directed to xc4298cornoll.ede
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Noisy Prediction
Denoising Steps
Refined Output
T-1
T-5
T-10 T-14
Figure I: Box refinement through denoising steps. We visualize the correction of noisy prediction, shown in yellow, using DiffuBox. The ground truth box visualized in green for reference. Boxes being refined are colored blue based on timestep. The output is refined iteratively though the denoising steps, resulting in the final, corrected output of our method.
varies, then, is the dimensions of the object, as opposed to this "surface" shape when normalized to be the same size. Specifically, the distribution of points the LiDAR detector receives, is therefore consistent when normalized across object sizes, regardless of domains; points will always land near the edge of the bounding box no matter where the object is located. Thus, if we can somehow capture the distribution of points relative to box's coordinate system, we would be able to use same process to fix incorrectly positioned bounding boxes to fit the correct point- coint-distribution, even across domains.
Recognizing this observation, we propose DiffuBox, novel point diffusion model that learns the distribution of points relative to the object's s bounding box in order to refine noisy bounding box proposals from the detection models for off-the-shelf domain adaptation. Given bounding box proposals, DiffuBox denoises them into accurate detection boxes conditioned on the points near proposed bounding boxes. Our method naturally avoids the domain gap caused by the scale difference [48], since DiffuBox is designed to operate on object scale-invariant data, where we transform the LiDAR points around bounding box proposals into a normalized box view that is relative to the box instead of in absolute measure. This eliminates the size priors presented in the source domain and forces the diffusion model to recover the accurate bounding box solely based on the relative position of points to the bounding box proposal, allowing for improved robustness in self-driving systems.
of noisy
To summarize, our contributions include: We empirically validate our method, DiffuBox, by adapting models trained on a dataset from Germany (KITTI [7]) into two large, real-world datasets from the USA (Lyft L5 [15] and Ithaca365 [5]). Under both settings, we observe that DiffuBox is able to refine the output bounding boxes drastically from the noisy initial predictions (Figure 1). Quantitatively, we observe strong improvements in mAP performance (up to 24 mAP), particularly in near-range boxes, where more points are present for DiffuBox to refine the box predictions. When paired with a representative set of domain adaptation methods, including Output Transform, Statistical Normalization [48], and Rote-Domain Adaptation [59], DiffuBox is able to further improve the results, and closing the gap between all method's ' final performance.
2 Related Work
3D Object Detection. In general, most 3D object detection methods require supervision from aanan-annotated data. They take 3D sensory data (e.g. LiDAR point clouds) and infer bounding boxes around 3D objects. 3D detection methods can be grouped into two categories based on the input representations: Point-based methods [32, 33, 31, 39, 40, 55, 29] that directly operate on point clouds, and grid-based methods [51, 65, 20, 38, 49, 26] that first voxelize point clouds into 3D grids and then leverage convolutional architectures. Like other supervised models, 3D detection models suffer from decreased performance when the data distribution during inference differs from that during training. Our method DiffuBox is designed to reduce the domain gap for general 3D object detection, agnostic underlining model design.
Domain Adaptation in 3D. Domain adaptation aims to alleviate the performance drop of 3D perception models under domain shift. [48] by Wang et al. is one of the first works studying the domain gap in 3D object detection and proposes Statistical Normalization (SN) that reduces the shape bias across domains. ST3D [53], Rote-DA [59], and ST3D++ [54] propose self-training pipeline that iteratively improve the target domain 3D detection performance with pscudo-label training

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and auxiliary priors. Other methods can be grouped into feature- based [50, 21, 28, 19, 19,37, 17] and architecture-based [12, 35, 46, 22, 36] methods. Some of them also apply data augmentation to construct and train domain-invariant representations to reduce the domain gap [19, 37, 12]. Our proposed method orthogonal these methods and can be applied together with these models.
Diffusion Models. Recently, diffusion models [41, 9, 42, 43] have shown high-quality generative ability for image [4, 34], video [10, 8] and 3D shape [25, 61, 27] modalities. Zhou et al. [62] uses diffusion models with a point-voxel representation for shape generation and point-cloud completion. LION [61] uses hierarchical VAE mapped to latent space and trains diffusion models latent encodings to generate point clouds. In perception tasks, Chen et al. [3] and Zhou el al. [64] propose diffusion-basees object detection frameworks. Kim el al. [18] proposes a diffusion-based module to enhance the proposal refinement stage of two-stage object detectors. Unlike these approaches. our work focuses on leveraging diffusion for post-processing superior performance over previous methods.
detector-agnostic manner that shows
Method
3.1 Problem Setup
Despite great in-domain performance, 3D object detection models often struggle to maintain their accuracy when generalized to new domains (datasets). It has been concluded that such poor perfor- mance is mainly caused by mislocalization rather than misdetection [48]. That is, although objects can be correctly recognized by the object detector, the detected boxes lack sufficient overlap with the ground truth box and do not count as true positive (iee., detections with IoU < 0.7 with ground-truth)
In this work we introduce DiffuBox, which focuses on correcting the localization of bounding box proposals, as illustrated in Figure 3, to improve domain adaptation for 3D object detection. Unlike existing domain adaptation algorithms that require careful re-training on the target [59, 53] or source [48] domain data, DiffuBox can be deployed off- off-the-shellf any novel domain.
post-processing procedure in
Let P RN33 denote N --point 3D point cloud from the target domain. Let B {b...... b. be a set of M imperfect bounding boxes proposed by an underadapted object detector given P. where each bounding box b; is 7-DoF (degrees of freedom) upright box, parameterized with center (zr,, y/i 2;), size [w;, L2, h.] and yaw angle O:. We aim obtain better localized object proposals B by refining the boxes in B without any re-training.
B= -b....... bus 6 = refine(b,, P)}.
(1)
3.2 Learning Shapes in the Normalized Box View
While domain differences between 3D object detection datasets exist in many aspects, the analysis from [48] shows that the most significant hurdle for adaptation comes from the difference in object size. For instance, the American cars in the Lyft dataset [15] are about 20% larger than German cars in the KITTI dataset [7] average, and object detector trained on KITTI will tend still predict small boxes when tested on Lyft. Unfortunately, as long as 3D object detectors are trained explicitly predict object sizes, such size priors will be inevitably memorized during training and carried on to other domains as learned bias.
We aim to achieve ccale-invarian object detection, which would be naturally immune to size priors. Motivated by Luo et al. 's [23] finding that the relative distribution of points to ground-truth bounding boxes is consistent across domains, f.e. points tend to concentrate near the surface of boxes, propose to disentangle object size from shape by transforming pointclouds into a normalized box view (NBV), where point coordinates are box-relative rather than absolute.
Using homogeneous transformation, we define NBV E R RN3, the normalized box view of point cloud P relative bounding box b [z. y, 2, w. L. h. 1), be
cos 0
sin 0
0
07
1
0
0
PN 1
0
sin 0
cos 0
0
0
0
1
0
y
P
(2)
0
0
0
1
0
0
1
-2
1
0
0
0
1
0
0
0
1
3

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Good Localization
Bad Localization
Good Localization
Bad Localization
Global Scene View
Normalized Box View
Figure 2: Example Car objects converted into normalized box view (NBV). Foreground/bacground points marked in black/gray, respectively for better visualization. Foreground LiDAR points distributing tightly within a [-1, 1 NBV cube is a domain-consistent sign for good localization.
As shown in Figure 2,
Equation 2 transforms the bounding box b into a [-1, 1] cube, eliminating the size prior. The same transform also transforms P into box-relative, scale-invariant PNBY
In practice, we only consider points within a certain depth range of the bounding box for efficiency. We refer to this range as context limit. In the sections below, we overload PNBY the point cloud within the context limit for ease of reference.
3.3 Bounding Box Refinement via Diffusion
Inspired by recent works on diffusion based shape generation [52, 63, 25, 61] and knowledge distillation from pretrained diffusion models [30], we show that size-agnostic shape knowledge learned by a point cloud diffusion model can help to improve object localization across domains. The underlining assumption that despite size difference, objects of the same category (e.g. car, cyclist, pedestrian) share similar shapes.
Figure 2 illustrates our hypothesis that the good localization of a bounding box is closely correlated to its corresponding PNV forming "standard" point distribution, a.k.a. shape. Thus, improving the shape of PR will also lead better localization of b. Since ultimate goal is to optimize the bounding box + b, we propose to use a point diffusion model learn to "denoise" NBV + NBV
Specifically, we begin by discussing the training of the diffusion model to learn the probabilistic flow for each point to good box in Section 1. Then, we go into how our method refines the bounding box by computing the improvement step relative to the learned probabilistic flow in Section 3.3.2. Finally, we go into how we leverage the shape guidance to embed heuristics into training procedure in Section 3.3.3. To enhance clarity, we additionally provide an algorithmic description of DiffuBox's training and inference workflow in Appendix S1.
3.3.1 Diffusion Training
The learning objective of a diffusion model can be viewed as a variant of the score function [43] V. log (æ; where indicates the noise level and p (æ:; 0) Pdata- the true data distribution that is hard to directly sample from. As the score function points data to a higher likelihood, samples can instead be drawn from p (z; mmxx) which is usually modeled an i...d. Gaussian distribution
and denoised into Paata by solving a probabilistic flow ODE [1 J/SE [45].
Let Fo denote a diffusion model with parameter 0. Considering the full design space [13], in general its training loss can be written as:
Eay.g. Mar) (o)² Fo (Gin (a) (y + n): Cnoise (o)) Cou (a)
(y Caki (a) (y + n)) (3)
where o ~ Prain y ~ Pdata- and n ~ N (0, X(o) denotes the effective training weight, Cnoise (a) denotes noise level preconditioning. Gn (o), Caut (o), and Cakip (0) are input/output scaling factors.
Modelling p (zx; maax) as N (0, I) allows for easy sampling. However, an i.i.d. Gaussian noise doesn't 't suit the shift of point cloud in NBV caused by object mislocalization. As shown in Figure S8,

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noisy NBV point cloud is formed from a 3D distortion on the standard shape, rather than adding Gaussian noise. The distortion includes rotation (caused by incorrect raw angle), rescaling (caused by incorrect size), and translation (caused by incorrect box center).
Because of this, made few adaptations to the diffusion process. We set the effective training weight A(o) = 1, all input/ouiput scaling factors Cin (σ) Cour (Ø) = Cakip (α) 1, as the noise level is unknown during inference. We apply Gaussian noise on the bounding box, rather than on the point cloud, to simulate mislocalized bounding boxes. With these adapatations, our new training loss becomes:
)P,((,,"),,n Fo Fo (P +ni +na Caoise (o)) (P NBV b" b'+ [2]
(4)
where a ~ Prrain P.b ba ~ Derain and n ~v N (0, d (E)) diag Σ) is the variance of box noise, which is roughly estimated from direct domain adaptation performance.
3.3.2 Bounding Box Updates
Since Fg is trained to approximate the score function v log ()) the regular denoising process can be implemented by solving probablistic flow ODE:
dæ à (1)o æ logp (æ; (1) dt,
(5)
where (t) denotes a noise schedule in which (T) = max. (0) 0, and the dot stands for a time
derivative. Thus, wo Pdata can be generated by evolving Ør p (æ;; mmax) N (0; o2) from time 1 = T to = 0.
The NBV point cloud diffusion model F& (P (aoose(()) Vlog p( phin :o) V
to denoise the bound-
ing box, rather than the point cloud, we take further step following the chain rule (PN is differentiable according to Equation 2 of the main text):
V llggp p (b; o, P)
V log (PARY, : V
V
Vb Fe BP Cnoise (a))
(6)
vb
PPNB
Vb
Let by p (b; max) be the imperfect bounding box predicted by an adapted object detector, and let bn ~ p (b; 0) denote the corresponding box after refinement. Similarly, bounding box refinement can be achieved by evloving bt to bo following:
db -o(()(()) V log (b; a, P) P)
dl.
Vb
3.3.3 Shape Guidance
The probabilistic flow ODE allows adding objectives other than the score function to bounding box refinement without any retraining. For instance, [48] assumes the average object size (17, h in the target domain is available. Such information can be used to further improve domain adaptation performance by simply rewriting Equation 7 as:
logo (b: P) Vb
Visize (b, w. I,))
db (t)
dt.
(8)
Vb
where a denotes shape weight and
Eiie (b, W. h,, w|w r0|² + ||/- Tull 117 77p
4 Experiments
4.1 Experimental Setup
Datasets. We primarily consider three datasets: The KITTI dataset [6]. the Lyft Level 5 Perception dataset [16], and the Ithaca365 dataset [5]. For KITTI, we follow the official splits. For Lyft, we follow various existing works [58, 59, 24] and use the splits separated by geographical locations, consisting of 11,873 point clouds for training and 4,901 for testing. For Ithaca365. we utilize the annotated point clouds with 4,445 for training and 1,644 for testing. Additionally, we include experiments with the nuScenes dataset in the supplementary to evaluate DiffuBox's performance on larger-scale and more diverse data.
5

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Table I: mAP@IoU 0.7 for KITTI -> Lyft (cars). Higher is Better. DiffuBox leads improvement in almost all cases, with especially significant gain for the Direct and OT detections.
BEVT
3D1
Method
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
Direct
68.03
38.62
9.99
39.06
25.76
7.84
1.04
12.07
Direct+DiftuBxx
88.95
73.27
23.84
59.70
62.94
35.44
6.67
35.56
OT
75.07
61.84
20.44
51.95
18.67
10.57
1.82
11.89
OT+DiffuBox
92.67
74.46
23.95
60.98
50.99
33.06
6,87
31.21
SN
92.88
69.97
25.68
61.67
70.40
32.96
6.18
36.64
SN+DiffuBox
94.77
72.09
25.47
62.70
69.62
40.39
7.17
38.72
Rote-DA
89.64
70.10
27.96
60.63
50.65
24.92
7.43
28.63
Role-DA+DiffuBox
95.10
75.10
23.35
62.14
71.00
48.89
6.48
41.06
ST3D
72,86
64.23
34.96
55.58
35.22
26.33
6,06
22.47
ST3D+DiffuBox
92.08
75.03
35.35
66.08
61.58
45.44
10.16
39.81
Baselines. We consider five domain adaptation baselines: (1) directly applying an out-of-domain de- tector without adaptation (Direct); (2) Output Transformation (OT) [48]; (3) Statistical Normalization (SN) [48]; (4) Rote-DA [59]; (5) ST3D [53]. OT and SN perform resizing based on the average sizes from the target domain. OT directly resizes the predicted bounding boxes the target domain, while SN trains the detector with resized objects and boxes from the source domain. Rote-DA and ST3D perform self-training. Rote-Da leverages additional context in the form of persistency- pernnc [i] and enforces consistency across domains. ST3D leverages better data augmentation and a memory bank for high-quality detections. As DiffuBox is complementary to these methods, compare the detection performance these methods before and after refining with DiffuBox.
Evaluation Metrics. We evaluate the detection performance Bird's Eye View (BEV) and 3D. At depth ranges of 0-30m, 30-50m and 50-80m, we report the mean Average Precision (mAP) with Intersection over Union (IoU) thresholds set at 0.7 for cars, and 0.5 for pedestrians and cyclists. We also consider the nuScenes true positive metries [2]: translation error, scale error and rotation error. These measure the error in center offset, size difference, and orientation offset, respectively, of all true positive detections.
Implementation Details. We use the implementation and configurations from OpenPCDet [44] for detectors, and [14]'s implementation for diffusion models. We set the context limit to 4x the bounding box size. We use shape weight 0.1 for cars and pedestrians, and 0.01 for eyclists as cyclists have more shape variation. More details can be found in the supplementary.
4.2 Experimental Results
We present the results for KITTI Lyft cars in Table (mAP@IoU 0.7) and Table S8 (nuScenes TP metrics), and the results for KITTI - Ithaca365 cars in Table 2 (mAP@ IoU 0.7) and Table $9 (nuScenes TP metrics). We additionally include KITTI - nuScenes results in Table $10 in the supplementary. We use PointRCNN [39] detectors; evaluations with other detectors can be found in the section below. DiffuBox consistently attains significant performance gain across different domain adaptation methods and datasets. The improvement of DiffuBox is especially significant for near-range and middle-range detections. Notably, for KITTI + Lyft cars, DiffuBox applied upon the Direct outputs is able to attain comparable performance with domain adaptation methods that require training such as ST3D. We hypothesize that this because there are more LiDAR points for near-range and middle-range objects, which allows DiffuBox to better correct the detections.
DiffuBox with Other Detectors. To further demonstrate the robustness and versatility of DiffuBox, we present DiffuBox's performance for refining predictions from other detectors. We consider PointPillar [20], SECOND [51], PV-RCNN [38], CenterPoint [56] and DSVT [47] trained on KITTI. We report the mAP@ loU 0.7 for KITTI -> Lyft cars in Table Results show that DiffuBox consistently improves the predictions from different detectors.

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Table 2: mAP@ IoU 0.7 for KITTI upon different adaptation methods.
Ithaca365 (cars). DiffuBox leads significant improvement
BEVT
3D
Method
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
Direct 52.59
21.19
3.20
25.08
25.09
6.25
0.17
10.53
Direct-DiffuBox
61.89
32.09
6.05
32.27
42.23
17.79
1.47
20.51
OT 59.34
29.18
5.26
30.11
32.05
12.00
1.16
14.71
OT+DiffuBox
60.76
32.56
6.07
31.89
40,43
18.33
1.61
19.80
SN 60.48
31.04
4.04
29.80
32.17
13.03
0.85
15.02
SN+DiffuBox
60.79
34,49
3.79
30.81
37.21
18,90
1.33
18.31
Rote-DA
71.14
44.76
14.00
42.38
43.07
22.42
2.46
22.38
Rote-DADDiffuBon
71.52
45.44
14.56
42.28
46.77
25.72
4.17
25.02
Table 3: Io 0.7 for KITTI - Lyft (cars) with other detectors. DiffuBox consistently
improves the detections from different detectors.
BEV
3D1
Method
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
PointPillar (Direct)
65.77
39.02
11.28
36.80
16.58
5.06
0.56
6.87
PointPillar (Direet)DDiffuox
84.76
65.57
17.46
53.67
65.41
32.77
4.55
33.82
SECOND (Direct)
65.61
38.83
13.92
38.06
24.39
8.32
0.86
SECOND (Direct)-Diffuox
89.70
66.98
19.11
57.04
61.90
32.24
4.39
33.19
PV-RCNN (Direct)
73.56
46.39
13.63
43.72
34.20
14.03
1.53
16.17
PV-RCNN (Direct)-DiffuBox
92.40
68.35
20.68
59.30
63.82
35.78
5.03
PV.RCNN (OT) PV-RCNN (OT)+DifiuBox
80.48
54,40
17.54
51.03
19.87
8.15
0.75
10.74
93.65
69.06
21.35
60.16
53.68
31.31
4.00
30.63
PV-RCNN (SN)
94.16
68.58
22.22
62.16
72.72
27.86
3.43
33.47
PVARCNN (SN)+DifiuBox
93.99
69.24
21.13
61.23
67.83
35.08
4.50
34.93
CenterPoint (Direct)
74.91
36.64
2.47
36.23
28.63
4.05
0.15
10.29
CenterPoint (Direc)+DifftBox
90.25
58.65
6.95
51.38
71.02
34.91
1.48
34.40
CenterPoint (OT)
82.81
$1.02
6,26
46.10
25.34
12.35
0.52
13.34
CenterPPintt DOf)xDifiuBox
91.79
59.24
7.45
51.88
63.01
32.53
1.24
30.95
DSVT (Direct)
68.93
47.49
11 11.32
41.77
33.72
11.74
1.42
15.67
DSVT (Direct)
89.01
63.41
17.50
55.27
65.22
36.31
5.02
35.61
DSVT (OT) DSVT (OT)-DifuBBx
71.85
42.93
13.18
43.05
15.66
4.47
0.31
8.06
90.21
63.50
17.91
55.61
56.84
31.19
4.63
31.12
DiffuBox on Other Object Classes. present DiffuBoo''s performance on other object classes, specifically pedestrians and eyclists. We use the same configurations as cars, except cyclist shape weight 0.01 as mentioned in the implementation details. We report the KITTI + Lyft performance in Table 4, and the KITTI - Ithaca365 performance in Table S11. For Ithaca365. for pedestrians as 1thaca365 has very few cyclists. Results show that DiffuBox consistently attains significant improvement across object classes and domains, and can improve upon other domain adaptation methods. This shows the robustness and versatility of DiffuBox.
only evaluate
4.3 Qualitative Results
Figure 3 visualizes four scenes from the Lyft and Ithaca365 datasets. We compare the ground truth bounding boxes (green), the detections directly obtained from a PointRCNN trained on KITTI (yellow), and the refined detections using DiffuBox (blue). The out-of-domain PointRCNN produces reasonable results, but occasionally it produces false positives or boxes with incorrect shapes or alignment. DiffuBox effectively moves the incorrect boxes towards having better location, shape and alignment. Also observe that for the already accurate boxes, DiffuBox makes little change them.
DoffuBox Extension: Detector Retraining

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Table 4: mAP@ IoU 0.5 for KITTI - Lyft (pedestriansscyccistt)) DiffuBox attains consistent
improvement across object classes and is able to improve upon other domain adaptation methods.
BEVT
3D7
Class
Method
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
Direct
31.89
25.75
0.51
20.74
21.29
16.59
0.18
14.17
Dixful-D
43.64
26.97
25.10
34.30
22.54
35.63
25.47
21.69
27.76
19.02
16.77
Pedestrian
OT+DifiuBox
42.63
25.28
0.55
24.02
37.34
22.16
0.42
20.94
SN
43.75
36.87
0.67
34.
26.08
0.45
SN+DifiuBox
50.98
35.88
1.08
29.45
38.55
30.24
0.60
23.50
Rote-DA
49.14
46.86
1.23
33.60
37.68
39.25
26.75
DoeeDAADDfffBox
54.14
50.30
1.22
36.04
42.76
43.25
1.04
29.93
Direct
48.50
8.91
0.13
26.96
38.13
5.19
0.02
21.11
Direct-DifluBx
61.28
10.60
0.06
34.90
49.76
6.87
0.02
27.35
OT
55.37
9.99
0.14
30.90
20.09
4.11
0.02
10.96
Cyclist
OT+DifuBox
65.93
10.26
0.06
36.86
32.93
6.11
0.02
18.81
SN
46.75
11,42
0.05
26.30
36.87
6.23
0.03
20.18
SN+DiffuBox
59.48
15.82
0.10
34.62
48.92
10,31
0.02
27.38
Rote-DA
77.19
34.61
0.09
48.66
70.35
30.77
0.05
44.29
Cote-DA+DiffuBae
83.80
31,36
0.05
$1.49
75.79
27.98
0.04
45.77
No Adaptation
DiffuBox
No Adaptation
w/ DiffuBBo
KITTI~ Lyft
KITTI Ithaca365
Figure Illustration of 3D object detection on Lyft/Ithaca365 before and after DifTuBox's refinement. We visualize detections from an out-of-domain PointRCNN on four scenes from each dataset. We color the ground truth boxes in green, the detector outputs yellow, and DiffuBox's refinements in blue. The out-of-domain detector sometimes produces false positives or boxes with incorrect shape alignment. DiffuBox effectively improves the wrong inaccurate boxes, while making little change to the accurate boxes.
Before Refinement
One extension of DiffuBox for domain adap- tation is retrain detectors [57, 58] with Dif- fuBox's 's refined boxes. We can take a detector trained on the source domain, obtain its predic- tions on the target domain, refine the predictions with DiffuBox, and then retrain a detector using the refined boxes labels.
After Refinement
Percencage
We provide the KITTI + Lyft results in Table We compare the performance of (1) directly ap- plying the KITTI detector, (2) retraining for one round with the KITTI detector's predictions as labels, and (3) retraining for one round with the KITTI detector's 's predictions after DiffuBox's refinement as labels. Retraining performed based on the KITTI detector's predictions the Lyft training split, and the evaluation is con- ducted over Lyft's testing split. Retraining using
8.0
Intersection over Union (loU)
Figure Comparison bounding box qual- ity before and after refinement with DiffuBox. We report the distribution of Intersection over Union (IoU) with ground truth labels from the Lyft dataset. The unrefined predictions are from an un- adapted Point-RCNN model trained on KITTI. We show that DiffuBox leads to significant improve- ment in bounding box localization.

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the KITTI detector's predictions directly only provides limited improvement over directly applying the KITTI detector, while retraining with DiffuBox's refinement leads significant improvement.
Table 5: Retraining Performance (mAP@@ IoU 0.7) for KITTI + Lyft. Retraining with DiffuBox's
refined detections attains significant performance improvement.
BEV?
Class
Method
0.30m
30-50m
50-80m
0-80m
0.30m
30-50m
50-80m
0-80m
Direct
68.03
38.62
9.99
39.06
25.76
7.84
1.04
12.07
Retraining Direct
1.69
14.55
Retruining w/ Direct-DiffuBox
91.79
77.52
35,33
64.71
67.80
36.17
10.56
37.55
Direct
31.89
25.75
0.51
20.74
21.29
16.59
0.18
14.17
Pedestrian
Retraining Direct
27.58
20.22
Retraining w/ Direct-DiffuBox
44.17
39.39
0.84
29.22
35.60
34,67
0.36
24.71
Direct
48.50
8.91
0.13
26.96
38.13
5.19
0.02
21.11
Cyclist
Retraining Direct
44.06
6.05
0.21
24.29
33.55
5.73
0.14
18.73
Retraining w/ Direct-DiffuRox
57.57
6.54
0.23
31,36
45,79
5.19
0.11
24.39
4.5 Ablation Studies and Analysis
Context Limit. We conduct ablation study on the context limit, and consider ranges of 2x, 4x and 6x the box size. We evaluate under the setting of no adaptation (Direct) for KITTI + Lyft cars, and compare the mAP@ loU 0.7 in Table All three context limits lead significant performance improvement. Larger limit attains more gain, and the gain saturates at around the 4x limit.
Denoising Steps. We perform ablation on the number of denoising steps used, and present the results in Figure 5. In general, a majority of the performance is already reached using 8 diffusion steps, and it saturates around using 14 steps.
BEV
3D
100
80
BBV mAP @100
50
50-
40
30-
0
10
11
13
3
5
10
11
12
13
diifustoo stogs
datfatton stops
6-30ms
50-80m
Figure 5: mAP vs. Number of Diffusion Steps. We report the BEV (left) and 3D (right) mAP @ IoU 0.7 for the setting of KITTI -> Lyft Cars and PointRCNN detector.
Shape Weight. We perform ablation shape guidance, and consider shape weight 0, 0.01, 0.1 and 0.5. We evaluate under the setting of no adaptation (Direct) for KITTI Lyft cars and report the mAP@ loU 0.7 in Table 7. DiffuBox improves the detector's output even without shape weight. Using shape weights leads to additional gain, which saturates at around shape weight 0.1.
IoU Performance Analysis. We visualize the comparison of IoU of the bounding boxes with ground truth bounding boxes both before and after using DiffuBox in Figure The IoU of predictions after DiffuBox refinement (in blue) improves significantly over those before refinement (in yellow). This suggests that majority of our refinement in improving the bounding boxes' shape and alignment to fit into the new domain, thus resulting in higher IoU values.

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Table 6: Ablation Context Limit. DiffuBox is robust against the choice of context limit. Larger limit could lead to better performance, with the gain saturated at around 4x limit.
BEVT
3D1
Method
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
Direct
68.03
38.62
9.99
39.06
25.76
7.84
1.04
12.07
Direct+DiftuBox 2x
83.
68.91
17.65
55.53
54.63
27.63
3,62
29.51
Direct+DiffuBox 4x
88.95
73.27
23.84
59.70
62.94
35,44
6.67
35.56
Direct+ Diffuuox
91.68
72.56
24.60
60.59
61.92
30.91
5.39
33.61
Table 7: Ablation on Shape Weight. DiffuBox improves the detector's output significantly even without using shape weight, and using shape weight attains additional gain.
BEVT
3D1
Method
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
Direct
68.03
38.62
9.99
39.06
25.76
7.84
1.04
12.07
No SW
72.62
50.70
14.32
45.27
34.63
13.47
2.17
17.54
SW 0.01
74.81
57.48
17.18
48.89
47.11
17.74
2.99
23.10
SW 0.1
88.95
73.27
23.84
59.70
62.94
35.44
6.67
35.56
SW 0.5
91.36
73.09
24.48
60.23
50.70
32.73
7.21
30.54
We perform analysis on DiffuBox's effect on detection recall in Figure 6. As DiffuBox improves IoU for mislocalized detections, it reduces false negatives that arise from match IoU being lower than the threshold. The improvement is observed for objects across different sizes.
Recall Analysis.
80
Before Refinement
60
Before Refinement
After Refinement
After Refinement
60
Recall %
40
Recallugra
40
30
20
10
8.0
0.2
0.4
0.6
0.8
1.0
10.0
17.5 Valume / m²
30.0
Intersection over Union (loU)
(a) Recall vs. IoU
(b) Recall vs. Object volume
Figure 6: Recall improvement with DiffuBox. We report recall on the class (KITTI - Lyft, PointRCNN) before and after refinement with DiffuBox.
5 Discussion and Future Work
In this work, we propose DiffuBox, a diffusion-basel approach that refines bounding boxes for better domain adaptation. While DiffuBox effectively improves the existing bounding boxes, one limitation is that DiffuBox currently does not consider false negatives that are due to completely missed detections. This could potentially be addressed through further distilling the detectors by DiffuBox's s refined boxes, or by incorporating exploration strategies to capture possibly missed objects. Alternatively, view that we did not discuss in this work, but is of potential interest the field, is the use of DiffuBox for automatic label refinement. This can be useful for correcting slightly mis-aligned boxes, or labels across sensors that may have slightly unsynchronized sensors. We leave this discussion for further work, and will provide code and model checkpoints for this use case. With our work, we do not foresee any negative societal impacts and hope the field continues to develop such label refinement methods for 3D object detection.
10

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Acknowledgments and Disclosure of Funding
This research supported by grants from the National Science Foundation NSF (IS--207761,, and IIS-1724282, HDR-21 18310). The Cornell Center for Materials Research with funding from the NSF MRSEC program (DMR-1719875), DARPA, arXiv, LinkedIn, Nvidia, and the New York Presbyterian Hospital.
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Supplementary Material: DiffuBox: Refining 3D Object Detection with Point Diffusion
S1
Algorithmic Description of DiffuBox
Below we provide an algorithmic description of the training and inference workflow of DiffuBox.
Algorithm 1 DiffuBox Training
Algorithm 2 DiffuBox Inference
Require: Train set Duain noise distribu-
Require: Set of initial bounding boxes {b) Jeeene point
tion Prain
cloud P. F& approximating V. log (()
Ensure: Diffusion model Fø approxi- mate Væ logplo,
Ensure: Set of refined bounding boxes {b)
p(æ, σ)) of bounding box
for each bounding box in {b) do
points noise 1: repeat
2: for denoising steps = 1, ..... ... T do
3: Obtain (() from denoising schedule
2: Sample point cloud P, true bound-
Compute PNBY from Eq.
ing box b* where P, b* ~ Drain
5:
Compute db with (t) and Fe from Eq. 8 b b db end for b end for
3: Sample noise level
Prain
6:
4: Compute Ph from Eq. 2
7:
5: Update Fg with diffusion objec-
8:
tive Eq. 4
9:
6: until convergence
10: {8) scene <- NMS((b) (cbme))
$2 Implementation Details
Experimental Setup. For detectors, we use the implementation and configurations from Open- PCDet [44]. For diffusion models, we use [14]'s implementation and follow their noise schedule Oman = 80. During inference, we denoise each box for 14 steps, starting from a noise level between [10,80] linear in the detector confidence; higher confidence boxes begin from smaller noise. For each bounding box, we consider the points that fall within the range of four times its size. Additionally, inspired by OT and SN. we slightly regularize the bounding box shape with the average size of the target domain, with shape weight 0. for cars and pedestrians, and 0.01 for cyclists. We use NVIDIA A6000 for all of our experiments.
Training Details. In the diffusion process, we follow [13] and use noise level distribution In a N' (-1.2, 1.2²), ODE schedule (t) = f. and 2nd order Heun solver. The denoiser transformer model contains 12 self-attention layers with hidden size 1024; each layer has 2048 intermediate dimensions and 8 heads. The diffusion model is trained with batch size 128 and learning rate 0.0001 for 100k steps. A visualization of our Diffusion model architecture can be found in Figure S7.
S3
Additional Experiment Results
S3.1
nuScenes True Positive Metrics
We report the nuScenes TP metrics for KITTI - Lyft cars in Table S8, and those for KITTI Ithaca365 in Table $9. All results are based on PointRCNN detectors. DiffuBox effectively reduces all three types of errors, with especially significant improvement for translation and scale errors.
S3.2 Additional Experimental Results
We report the performance on KITTI - nuScenes cars Table S10, and KITTI - Ithaca365 pedestrians in Table S11. Both sets of experiments are based on PointRCNN detectors. For nuScenes,
15

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Transformer Encoder x
K
V
add
Attention
MLP
MLP
Points (x, y, 2)
Outputs
MLP
embedding
Noise level
Figure S7: Architecture overview of DiffuBox's denoiser model. The model is composed of 2 MLP layers and L transformer encoder layers, which maps 3D points to a higher dimensional space for effective self-attention.
Table S8: nuScenes TP metries for KITTI + Lyft (cars). Lower is better. DiffuBox reduces all three types of errors, with especially significant improvement for translation and scale errors.
Method
Translation Erroe)
Scale Emro]]
Orientation Faror]]
0:30m
30-50m
50-80m
0-80m
0-30m
30-50m
50 40-380m
0-90m
0-30m
30-50m
50-80m
0-80m
Direct
0.347
0.510
DiffaBox
0.221
0.289
0.424
0.293
0.162
0.171
0.198
0.173
0.329
0.473
0.607
0.447
0,273
0.376
0.466
0.356
0.217
0.207
0.219
0.314
0.338
0.496
0.623
0.463
OT-DiffaDox
SN
0.332
0.436
01.40)
0. 193
0.160
0.178
0.204
0.178
0.320
0.540
0.678
0.493
SiffiffBBox
0.201
0.251
0.410
0.274
166
0.171
0.207
0.178
0.309
0.505
0.631
0.463
Rate-DA
Rote- DA DifffuBoo
0.206
0.260
0.423
0.275
0.161
0.170
0.201
0.173
0.244
0.437
0.609
0.398
ST3D
0.386
0.489
0.451
0.4-0
0.240
0.225
0.239
0.234
0.352
0,562
0.683
0.517
ST3D=DimuBox
0.350
0.267
0.171
0.326
Table 89: nuSeenes TP metries for KITTI Ithaca365 (cars). Lower is better.
Translation Error]
Scale Erroc]
Orientation Error]
Method
0-30m
30 50-500m
50-80m
0-80m
0-30m
30-50m
50-80m
0-50m
0-30m
30-50m
50-80m
0-80m
Direct
0.483
0.523
0.722
0.510
0.200
0.200
0.201
0.204
01.492
0.827
1.001
0.705
Dincet-DiflBox
0.381
0.506
0.720
0.494
0.150
0.150
0.139
0.152
0.492
0.827
1.004
0.707
OoDDiffaoxx
0.320
0.458
0.753
0.456
0.124
0.133
0.173
0.138
0.494
0.829
0.997
0.705
0.590
SN-DiffuBox
0.351
0.492
0.862
0.520
0.128
0.137
0.179
0.144
0.574
0.862
1,072
0.783
Rote-DA
0.492
0.484
0.461
0.482
0.140
0.144
0. 0.167
0.147
0.476
0.731
1.023
0.683
Rele-DA+DiflBBox
we evaluate for up to 50m based on the point cloud range. DiffuBox attains significant improvement upon the detections in both settings.
Table S10: mAP@ IoU 0.7 for KITTI nuScenes (cars). DiffuBox consistently attains significant improvement.
BEV
3D
Method
0-30m
30-50m
0-50m
0-30m
30-50m
0-50m
Direct
44.78
0.70
15.86
14.82
0.00
4.66
DiffuuoDiffluox
58.07
1.06
20.70
22.77
0.00
7.40
+ Lyft performance on all three traffic participant classes with the CenterPoint [56] detector Table $12 and DSVT [47] in Table S13. DiffuBox achieves consistent improvement across object detectors, classes, and domain adaptation methods.
In addition, we report the full KITTI
16

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Table S11: mAP@IoU IoU 0.5 for KITTI significant improvement.
- Ithaca366 (pedestrians). DiffuBox consistently attains
BEV 1
3D 1
Method
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
Direct
41.70
16.87
1.63
23.07
31.38
10.06
0.60
16.09
Dirfct-DiffuBox
50.53
20.26
0.49
26.62
43.36
14.74
0.10
21.74
OT 42.96
18.66
1.75
23.69
33,00
11.64
0,63
17.15
OT+DiffuBox
50.52
21.27
0.38
26.95
44.67
15.45
0.05
22.48
SN
48.95
16.97
2.46
26.36
38.47
9.17
0.97
17.89
SN+DiffuBox
57.32
17.27
0.51
28.20
47.63
10.44
0.22
21.62
Rote-DA
43.36
1.68
0.00
14.18
30.21
0.13
0.00
8.40
Bote-DA+DiffuBon
56.91
21.31
1.18
29.75
50.94
14.63
0.31
24.18
Table S12: mAP for KITTI- Lyft with CenterPoint [56]. DiffuBox attains consistent improve- ment across object classes and domain adaptation methods.
BEV
3D f
Class
Method
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
Direct
74.91
36.64
2.47
36.23
28.63
4.05
0.15
10.29
Direct-DiffuBxx
90.25
58.65
6.95
51.38
71.02
34.91
1.48
34.40
Car@loU 0.7
OT
82.81
$1.02
6.26
46.10
25.34
12.35
0.52
13.34
OT+DiffuBox
91.79
59,24
7.45
51.88
63.01
32.53
1.24
30.95
Direct
0.85
1.22
0.02
0.49
0.47
0.01
0.19
Direct-DiffuBox
5.58
2.06
0.03
1.78
3.99
1.75
0.02
1.20
Pedestrian @ IoU 0.5
OT DifDifuBox
1.88
2.02
0.05
0.87
0.61
1.22
0.01
0.37
5.78
2.32
0.03
1.78
4.04
1.90
0.02
1.16
Direct
23.34
0.84
0.01
8.19
16.53
0.34
0.00
5.42
Cyclist@loUU 0.5
Dircct=DiffuBox
42.22
1.49
0.02
15.22
31.74
0.58
0.01
11.28
OT OT-DiffuBox
27.40
0.95
0.01
9.73
7.32
0.05
0.01
1.98
42.22
1.54
0.02
15.23
31.35
0.67
0.01
11.20
Table S13: mAP for KITTI Lyft with DSVT [47]. DiffuBox attains consistent improvement across object classes and domain adaptation methods.
Class
Method
BEV
3D
0-30m
30-50m
50-80m
0-80m
0-30m
30-50m
50-80m
0-80m
Direct
68.93
47.49
11.32
41.77
33.72
11.74
1.42
15.67
Direct+Diffuox
89.01
63.41
17.50
55.27
65.22
36.31
5.02
35.61
Car@loU 0.7
OT
71.85
42.93
13.18
43.05
15.66
4.47
0.31
8.06
OT+DiffuBox
90.21
63,50
17.91
55.61
56.84
31.19
4.63
31.12
Direct
16.42
6.48
0.47
8.09
11.49
4.41
0.10
5.32
Direct+DiffuBor
27.89
8.13
1.05
12.42
23.55
6.49
0.53
10.28
Pedestrian @ IoU 0.5
OT OifDiffuBox
20.89
7.47
1.06
10.65
14.83
5.46
0.33
6.84
27.68
7.84
1.25
12.18
6.66
10.64
38.46
2.04
0.00
19.82
30.16
1.56
0.00
15.65
Cyclist@loUI 0.5
DirfetDDiffuBox
54.41
3.19
0.00
28.01
47.57
1.94
0.00
24,06
OT
43.43
2.18
0.00
22.30
14.65
1.23
0.00
8.01
DifDiBuB
55.26
3,04
0.00
28.75
38.12
1.89
0.00
19.52
$3.3 Additional Qualitative Results
We present additional visualizations of box refinement over denoising steps. in both original and normalized views, in Figure S8. We show the box refinement process of the initial noisy prediction across different denoising steps, up to 14 steps. In addition, we show the points from the normalized box view (NBV) from the perspective of the refined boxes. Observe how the points of the car (colored in black) gradually get corrected to be inside the bounding box.
17

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Noisy Prediction
Denoising Steps
T-5
T-8
T-11
T-14
Global Scene View
Normal Box View
Figure $8: Box refinement through denoising steps. We visualize the correction of noisy prediction, shown yellow, using DiffuBox, as well as the normalized box view. The detection output is refined iteratively though the denoising steps, resulting in the final, corrected output of our method.
No Adaptation
w/ DiffuBox
SN
SN w/ DiffuBBx
KITTI > Lyft LS
KITTI > Ithuca365
Figure S9: Additional Qualitative Results of DiffuBox. We include additional visualizations of 3D object detection on Lyft/Ithaca365 before and after DiffuBox's 's refinement. We color the ground truth boxes in green, the detector outputs in yellow, and the boxes refined with DiffuBox in blue.
We present additional visualizations of detections before and after using DiffuBox on another set of scene in Figure $9, and on different classes of traffic participants in Figure $10. Observe that DiffuBox's refinement is able to better align the boxes, and the corrected boxes can reduce false positives as they get corrected into the same set of points.
S3.4 Improvement Expectations and Upper Bounds
We validate our claims that improvements in localization is the major source of performance gain Table $14. In particular, we show that large gains in performance can be obtained across multiple domain adaptation algorithms - if localization is corrected to the ground truth position. This suggests that there is significant room for improvement object detection performance from better localization, which DoffuBox aims to tackle.
18

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Cars
Cyclists
Pedestrians
0:77
Toertico
Intro.70
Adupcation
DiffuBox
No Adaptation
DiffuBox
No Adaptation
w DiffuBox
Figure S10: Additional Qualitative Results of DiffuBox for other classes. We include additional visualizations of 3D object detection for different classes. We color the ground truth boxes in green. the detector outputs in yellow, and the boxes refined with DiffuBox in blue.
Table S14: Domain adaptation performance and potential improvement from correcting local- ization. We report the performance of multiple domain adaptation algorithms (including Directly applying the model) on Point-RCNN [39] detector adapted from KITTI [7] to Lyft [15] dataset. We evaluate mean Average Precision (mAP) on the Car category IoUnd 0.7. Oracle Loc means assigning ground-truth bounding boxes to any intersecting detections (Le., infinitesimal IoUnp)
Method
0-30m
30-50m
50-80m
0-80m
Direct
25.76
7.84
1.04
12.07
Orrect+Oracle Loc.
95.50
80.63
38.24
68.24
Rote-DA [60]
50.63
24.80
7.07
29.19
Rote-DA+ Oreclle Loc.
98.54
80.15
38.55
70.80
SN [48] SN+Oracle Loc.
70.40
32.96
6.18
36.64
96.90
78.19
38.75
70.36
S3.5 Further Discussion
Interpretation of the effectiveness the Statistical Normalization (SN) method. Recent domain adaptation algorithms typically rely on Statistical Normalization (SN) [48] tackle the domain bias in object size. SN's solution is to adapt the source dataset first, rescaling its objects (i.e. bounding box dimensions & points in bounding boxes) to match size statistics in the target domain. Afterwards, they finetune the object detector on the adapted dataset. In short, SN attempts to explicitly construct new size prior replace the old one. Despite great effectiveness. SN very sensitive the accuracy of size statistics in the target domain, and requires careful retraining for each new target domain. In contrast, our work is more general interpretation of size normalization, and instead rely on shape invariance to adapt it across all models and domains.
19

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Reviews:
[{"review_id": "zcCC4qgYGI", "reviewer": "Reviewer_spFc", "summary": "This paper proposes a diffusion model-based box refinement module to enhance detection. The experiments are conducted on several setting.", "questions": "Please refer to the above weaknesses.", "limitations": "Please refer to the above weaknesses.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- Using diffusion model to refine box sounds interesting. \n- The proposed module has been proven effectiveness by a series of experiments.", "weaknesses": "- The idea is simple. Refining box is not innovate although the authors use recently popular diffusion model to implement this goal. \n- The proposed diffusion-guided box refine module can be seem as a new stage for detection. However, only old PointPillar. SECOND and PV-RCNN are used as baselines, without new works.\n- I’m worried about the motivation of the article, refining the box doesn't seem to be necessarily related to domain adaptation detection. The authors may have written it in this way to avoid comparisons with SOTA detection models.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you so much for the insightful comments. Your valuable suggestions are very helpful for further strengthening our paper.\n\n**Q1: The idea is simple. Refining box is not innovate although the authors use recently popular diffusion model to implement this goal.**\n\nThank you for your comments regarding the novelty of our work. Domain adaptation for 3D object detection is indeed a challenging task. Existing methods [1, 2] typically require finetuning the model based on target dataset statistics or pseudo labels. In contrast, our model achieves **zero-shot adaptation without any finetuning**, outperforming these baselines. We deliberately kept our algorithm simple to highlight our key insight: that the diffusion model is domain-agnostic and can effectively refine box size and location.\n\n**Q2: The proposed diffusion-guided box refine module can be seem as a new stage for detection. However, only old PointPillar. SECOND and PV-RCNN are used as baselines, without new works.**\n\nThank you for the valuable suggestions. We believe adding more advanced baselines will strengthen our paper. We are currently working on incorporating more recent baselines with our proposed diffusion model. Unfortunately, we could not obtain the results before the rebuttal deadline, but we will provide updates during the reviewer-author discussion period.\n\n**Q3: The authors may have written it in this way to avoid comparisons with SOTA detection models.**\n\nThank you for the suggestion to apply our model directly to the detection task. Existing works [1] demonstrate significant challenges in domain adaptation for 3D object detection, particularly in terms of shape normalization and localization accuracy. We focus on domain adaptation because the diffusion model naturally addresses these issues by learning normalized box shapes that are disentangled from size, and by correcting mislocalized boxes. Nevertheless, we tried our method in the in-domain evaluation and only observed a minor improvement (mAP from 77.77 to 79.53 for KITTI in-domain). Such a result is expected: a model trained on an in-domain dataset inherits biases from that dataset, giving it an advantage when evaluated in-domain. Conversely, our denoising model aims to remove these biases, enabling better performance in out-domain evaluations. Without the learned bias, in-domain performance does not necessarily improve.\n\n[1] Wang, Yan, et al. \"Train in germany, test in the usa: Making 3d object detectors generalize.\" *CVPR* 2020.\n\n[2] Yang, Jihan, et al. \"St3d: Self-training for unsupervised domain adaptation on 3d object detection.\" *CVPR* 2021.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zBfDqES2xi", "reviewer": "Reviewer_uVLB", "summary": "A novel diffusion-based method for refining 3D object detection bounding boxes to address domain adaptation issues.\nDomain-agnostic approach, this paper leverages the consistency of point distributions relative to bounding boxes across different domains, improving robustness", "questions": "Would be great if the authors include an image of overall architecture of the model, for now it's unclear to me how all components connect as a whole?\nHave the authors try the approach on bigger datasets like Nuscenes or Waymo?\nSince the paper is for 3D object detection for autonomous driving, the authors should include ablation on the model's latency, and contributions of each module to the final performance?", "limitations": "None", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "Usage of diffusion model to fix incorrectly positioned bounding boxes to fit the correct point-distribution, even across domains.\nCan be used as a post-processing step with various existing 3D object detection methods to enhance their performance under domain shifts\nProvides strong quantitative improvements in mAP across multiple datasets and detectors, showcasing its effectiveness\nCan be integrated with various detection models without requiring retraining, making it versatile and easy to adopt", "weaknesses": "I didn't find any major issue in this paper, I think the approach of using Diffusion model to refine box locations is interesting.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for finding our method interesting, robust, and pointing our the benefits of our domain agnostic method. We further address their concerns below:\n\n**Q1: Cumulative image description of overall method**\n\nWe thank the reviewer for pointing out this possible point of confusion, and included an algorithmic description of the overall training and inference workflow in Alg. 1 of the ***Rebuttal PDF***. We hope this clarifies the overall DiffuBox method, and will include it in the final write up.\n\n**Q2: Evaluation on bigger dataset**\n\nThank you for the suggestion. We are happy to report DiffuBox’s strong performance on a larger dataset. We conducted additional experimental results on NuScenes and reported the performance in the **common questions section**.\n\n**Q3: Latency vs. mAP**\n\nWe perform ablation on the number of denoising steps and measure their corresponding performance and latency. The results are reported in Fig. 1 in the ***Rebuttal PDF*** and further discussed in the **common questions section**.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "BV3AgRx5L0", "reviewer": "Reviewer_DshE", "summary": "The article presents DiffuBox, a novel method to refine 3D object detection using point diffusion model. This approach addresses the challenges posed by domain shift, where 3D object detectors trained in one geographic region or sensor setup may not perform well in different settings. DiffuBox uses a domain-agnostic diffusion model conditioned on LiDAR points around a coarse bounding box to refine the box’s location, size, and orientation. This model operates on object scale-invariant data, transforming LiDAR points into a normalized box view relative to the bounding box, thus eliminating the shape priors from the source domain. The paper demonstrates significant improvements in mean Average Precision (mAP) across various datasets and object classes, especially in near-range detections where more LiDAR points are present.", "questions": "Please see weaknesses.", "limitations": "Please see weaknesses.", "rating": 5, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The use of a diffusion-based model for refining 3D object detection is novel and addresses significant limitations of current domain adaptation methods.\n- The method shows substantial improvements in mAP across different datasets and object classes, highlighting its effectiveness.\n- The paper provides a thorough explanation of the problem of 3D object detectioin, the proposed solution, and the underlying theory, which enhances understanding and reproducibility.\n- DiffuBox is shown to improve the performance of various detectors and across different domain adaptation methods, demonstrating its broad applicability.\n- The approach has significant implications for improving the reliability of 3D object detection in autonomous driving and robotics, which is a critical application area.", "weaknesses": "- Limited Discussion on False Negatives. The method focuses on refining existing bounding boxes but does not address the issue of false negatives, which could be an important aspect of overall detection performance. \n- The reason why the diffusion model can refine the output bounding boxes should be further pointed out.\n- More recent SOTA methods should be used for comparison.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for finding our method novel and effective, and address their concerns below:\n\n**Q1: Additional discussion on false negatives**\n\nThank you for this suggestion! We include an analysis plot of Recall vs IoU threshold in Fig. 2(a) of the uploaded ***Rebuttal PDF***. DiffuBox is able to improve IoU for mislocalized detections and reduce false negatives that arise from match IoU being lower than the threshold, which forms a major cause of reduced cross-domain performance [1]. However, one limitation of our method is that we are inherently a refinement method, and we cannot recover from false negatives due to completely missed detections. We will include these results and discussion in the final version as well.\n\n**Q2: Intuition behind the quality of our refinement results**\n\nOur method can be thought of as learning a relevant shape as a prior, as well as correcting for the localization. Diffusion models excel at object-level shape refinement [2, 3] due to their iterative nature and ability to approximate the scoring function. Viewing the points as part of a distribution within the bounding box, the diffusion model is able to guide the box towards better localization and shape. We will further point out reason why the diffusion model can refine the output bounding boxes in the final version.\n\n**Q3: More recent methods as baselines**\n\nThank you for your valuable suggestion, and we believe an additional baseline will strengthen our results. We are actively performing experiments with more recent detectors. Unfortunately, we could not obtain the results before the rebuttal deadline, and will provide updates during the reviewer-author discussion period.\n\n[1] Wang, Yan, et al. \"Train in germany, test in the usa: Making 3d object detectors generalize.\" *CVPR* 2020.\n\n[2] Zhou, Linqi, Yilun Du, and Jiajun Wu. \"3d shape generation and completion through point-voxel diffusion.\" *Proceedings of the IEEE/CVF international conference on computer vision*. 2021.\n\n[3] Vahdat, Arash, et al. \"Lion: Latent point diffusion models for 3d shape generation.\" *Advances in Neural Information Processing Systems* 35 (2022): 10021-10039.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "tes4F9r6UH", "reviewer": "Reviewer_YzBS", "summary": "The paper introduces a diffusion-based box refinement method aimed at enhancing the robustness of 3D object detection and localization across diverse  sensor setups or geographic locations. DiffuBox utilizes a domain-agnostic diffusion model, conditioned on LiDAR points around a coarse bounding box, to refine the box’s location, size, and orientation. Evaluated under various domain adaptation settings, the method has shown significant improvements in localization accuracy across different datasets, object classes, and detectors.", "questions": "Minor weaknesses which are not the final reason to the final rating:\n \"groun truth\" in fig 1 is wrongly spelled, etc.", "limitations": "Yes.", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper is well-organized, providing a clear structure and offering a comprehensive analysis of existing experimental results.\n2. The paper provides a detailed analysis of the domain gap across different datasets.\n3. Using diffusion models to address domain adaptation in point cloud 3D object detection is a good idea.", "weaknesses": "1. The paper lacks experimental results on larger and more diverse datasets such as nuScenes and Waymo, as seen in similar studies ST3D[52], SN[47], etc.\n2. Given the iterative nature and complexity of diffusion processes, utilizing diffusion models solely for bounding box refinement may introduce disproportionately high computational costs and increase training difficulty. Discussing how to assess the trade-off between performance gains and increased computational demands in the paper is essential. \n3. The discussion and experimental validation of domain differences caused by varying sensor setups across different datasets are very limited in the paper. However, this remains a significant issue between datasets.\n\nAs highlighted in the above weaknesses, the paper still lacks key experiments and discussions necessary to fully validate the method's effectiveness. Addressing these limitations would result in a more comprehensive and robust paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for their valuable feedback! Below, we address additional concerns: \n\n**Q1: Request for results on additional dataset**\n\nWe are happy to provide additional experimental results on the nuScenes dataset, and report the results in the ***common questions section***. Our results confirms DiffuBox’s effectiveness, even on this new dataset.\n\n**Q2: Computational expense vs performance trade-off analysis**\n\nWe investigate the tradeoff between performance and computational cost by varying the number of diffusion steps, and plot the mAP against per box refinement time (corresponding to different number of diffusion steps). The figure is included in Fig. 1 of the uploaded ***Rebuttal PDF***, and additional discussion is provided in the ***common questions section***.\n\n**Q3: Additional discussion about varying sensor set-ups**\n\nThe three datasets we experimented with have significant differences in the sensor setup, and we further report results on nuScenes in the **common questions section**. We detail the sensor set-up for different datasets in the table below:\n\n| Dataset | LiDAR Type | Beam Angles |\n| --- | --- | --- |\n| KITTI | 1 x 64-beam | [-24°, 4°] |\n| Lyft | 1 x 40 or 64 + 2 x 40-beam | [-29°, 5°] |\n| Ithaca365 | 1 x 128-beam | [-11.25, 11.25°] |\n| nuScenes | 1x32-beam | [-30°, 10°] |\n\nWe believe that the diversity of sensor set-ups within these datasets help justify our method’s robustness to sensor distributions. We admit that we do not evaluate on other modalities of LiDAR such as solid state LiDAR sensors, and will leave that to future work. We will include this discussion in the final version.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pU9RgCrVaz", "reviewer": "Reviewer_7LbH", "summary": "In this paper, the authors propose a method that make use of a point diffusion model for 3D bounding box refinement. The points around proposals are transformed into a normalized box view, and the model denoises them into accurate boxes conditioned on the points near the proposals. The model learn the distribution of points relative to the object’s bounding box to refine noisy proposals from the detection models for off-the-shelf domain adaptation. The paper conducts many domain adaptation experiments on KITTI, Lyft L5, and Ithaca365 datasets, and demonstrates ablation studies on Context Limit and Shape Weight.", "questions": "Have you evaluated the robustness of DiffuBox when the sensor data quality varies? For instance, how does DiffuBox perform if the point cloud contains noise?\n\nHow does DiffuBox perform when handling objects of different sizes? For example, is there a difference in accuracy between small and large objects?", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Originality: It is a novel approach to 3D bounding box refinement using point diffusion model in 3D domain adaptation.\nQuality: The code is provided in supplementary material. The paper is evaluated on multiple datasets, KITTI, Lyft Level 5, and Ithaca365, and get good performance. The ablation studies are also good.\nClarity: The derivation of formulas and visualizations are clear and easy to understand.\nSignificance: Good application of point diffusion model for 3D bounding box refinement in 3D domain adaptation area.", "weaknesses": "From the experiment results, when the domain adaptation baselines are higher (by adding OT and SN), the performance gain of DiffuBox is smaller, which weakens the significance of the paper. It is recommended to try recent 3D detector (like CenterPoint, and other SOTA detectors) and SOTA domain adaptation baselines to see if DiffuBox can still improve the performance, which can help to prove the contribution.\n\nLack of discussion on the model size/runtime. Because the diffusion model brings computational overhead (larger model size and longer runtime), it's better to take them into consideration when comparing the performance with other methods, make apple to apple comparisons, to prove the performance gain of DiffuBox is not only because of larger model size/capacity, which further enhances the significance of the paper.\n\nThe diversity of the qualitative visualization results is limited, and it is suggested to include more diverse categories (like pedestrian, cyclist), size ranges (like rare vehicles), depth ranges, and environments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for finding our method novel and of high quality, pointing out that we have extensive experimentation across datasets. We address detailed concerns below.\n\n**Q1: Comparison to more recent 3D detectors**\n\nThank you for this suggestion, and we are actively working towards including more recent baselines to strengthen our work. While we are currently in the middle of performing these experiments and cannot provide the results before the rebuttal deadline, we will include the updated results during the discussion period.\n\n**Q2: Computation and runtime vs performance trade-off analysis**\n\nWe perform an analysis on the latency and runtime of our method in the **common questions section**. Additionally, we plot an ablation figure showing the number of diffusion steps to performance trade-off in Fig. 1 of the uploaded **Rebuttal PDF**, and further discuss their implications in the common questions section. We will include these in the final version.\n\n**Q3: More diverse visualizations**\n\nWe have included additional, more diverse visualizations in Fig. 3 of the uploaded **Rebuttal PDF**. To summarize, we provide additional qualitative results that showcases DiffuBox on car, cyclist, and pedestrian classes. IoU values of the boxes before and after DiffuBox are included for visualization purposes, showing gains in alignment and better shape estimation after applying our method. Our method is effective across different actor class types. We thank you for this valuable suggestion, and will include these in the final version.\n\n**Q4: Robustness of method across object sizes and sensor quality**\n\nWe have performed additional analysis on objects of different sizes in Fig. 2(b) of the uploaded ***Rebuttal PDF***. Observe that performance gain is clear across all object sizes. This result arises naturally, since we conduct our diffusion modeling in the normalized box view (NBV), which effectively corrects for boxes of all sizes. We believe that this is a benefit of our work, and we will include these results in the final version as well. Regarding sensor set-up robustness, we experimented with have significant differences in the sensor setup, and we further report results on nuScenes in the **common questions**. Regarding different sensor modalities, please refer to our response to reviewer YzBS Q3.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We thank all reviewers for their time and effort and are encouraged by the positive feedback. We are very excited that all reviewers are generally happy with our work, and find our method “novel” and “interesting” (spFc, DshE) and all reviewers found our work to be an “effective solution” towards addressing the challenge presented by domain adaptation. We are happy that reviewers found our work to be “well-organized” and provides a “detailed analysis” of the problem and our proposed solution (YzBS, 7LbH).  Below, we address the common questions raised. We will respond to individual concerns and questions under each reviewer comment.

**Computational Cost Benchmarking**

A few reviewers mentioned wishing to see the computation cost of our method. We conducted multiple DiffuBox experiments with varying numbers of denoising steps, ranging from 0 to 14. On average, one denoising iteration (which can be parallelized across bounding boxes) takes 0.09 seconds on an Nvidia A6000 GPU. In general, we see a majority of the performance is already reached with 8 steps of diffusion, and it saturates at around 14 steps. We have included a detailed breakdown in Fig. 1 of the rebuttal PDF, plotting number of diffusion steps to performance. We thank the reviewers for proposing this analysis and will include it in the final version. ***Please see Rebuttal PDF for details.***

**Experiment Results on NuScenes Dataset**

We have included additional results on a large, real world dataset, nuScenes, at the request of a few reviewers. We report the performance of direct adaptation vs. direct adaption + DiffuBox on NuScenes below:

***BEV Performance:***

|  | 0-30m | 30-50m | All Range |
| --- | --- | --- | --- |
| Direct | 44.78 | 0.70 | 15.86 |
| Direct+DiffuBox | 58.07 | 1.06 | 20.70 |

***3D Performance:***

|  | 0-30m | 30-50m | All Range |
| --- | --- | --- | --- |
| Direct | 14.82 | 0.00 | 4.66 |
| Direct+DiffuBox | 22.77 | 0.00 | 7.40 |

DiffuBox consistently performs strongly, even on the nuScenes dataset, further suggesting the effectiveness of our method. We thank the reviewers for this suggestion and will include these strong results in the final version of our work.

Meta-review: This paper introduces DiffuBox, a novel method for refining 3D object detection bounding boxes using a diffusion model, demonstrating improvements in localisation accuracy across multiple datasets and detectors. Reviews were mixed, with five reviewers providing a range of ratings from borderline accept to clear accept. Positive comments highlighted the originality of the approach, its effectiveness in domain adaptation, and the strong performance across different datasets. However, some reviewers raised concerns about the method's novelty, the adequacy of baseline comparisons, and the computational cost. The authors addressed these concerns effectively in their responses, providing additional experiments, comparisons with recent detectors, and analyses of computational trade-offs. Given the positive feedback and the authors' satisfactory responses to critiques, the AC recommends accepting the paper condition on the inclusion of the promised additional experiments, comparisons, and clarifications. This decision has been approved by Senior AC. Congratulation!!
Decision: Accept (poster)