Point4Bit: Post Training 4-bit Quantization for Point Cloud 3D Detection
Authors: Jianyu Wang
Yu Wang
Shengjie Zhao
Sifan Zhou
Venue: NeurIPS 2025 poster ()
Keywords: Point Cloud Detection
Post-training Quantization
Model Compression
Primary area: deep_learning
TL;DR: A post-training quantization method for point cloud 3D detection

Abstract: Voxel-based 3D object detectors have achieved remarkable performance in point cloud perception, yet their high computational and memory demands pose significant challenges for deployment on resource-constrained edge devices. Post-training quantization (PTQ) provides a practical means to compress models and accelerate inference; however, existing PTQ methods for point cloud detection are typically limited to INT8 and lack support for lower-bit formats such as INT4, which restricts their deployment potential. In this paper, we present Point4bit, the first general 4-bit PTQ framework tailored for voxel-based 3D object detectors. To tackle challenges in low-bit quantization, we propose two key techniques: (1) Foreground-aware Piecewise Activation Quantization (FA-PAQ), which leverages foreground structural cues to improve the quantization of sparse activations; and (2) Gradient-guided Key Weight Quantization (G-KWQ), which preserves task-critical weights through gradient-based analysis to reduce quantization-induced degradation. Extensive experiments demonstrate that Point4bit achieves INT4 quantization with minimal accuracy loss with less than 1.5\% accuracy drop. Moreover, we validate its generalization ability on point cloud classification and segmentation tasks, demonstrating broad applicability. Our method further advances the bit-width limitation of point cloud quantization to 4 bits, demonstrating strong potential for efficient deployment on resource-constrained edge devices.

Point4bit: Post Training 4-bit Quantization for Point Cloud 3D Detection
Jianyu Wang', Yu Wang',
Shengjie Zhao Sifan Zhou
Tongji University
*Carnegie Mellon University
Abstract
Voxel-based 3D object detectors have achieved remarkable performance in point cloud perception, yet their high computational and memory demands pose sig- nificant challenges for deployment on resource-constranee training quantization (PTQ) provides practical means compress models and accelerate inference; however, existing PTQ methods for point cloud detection are typically limited to INT8 and lack support for lower-bit formats such INT4, which restricts their deployment potential. In this paper, we present Point4bit, the first general 4-bit PTQ framework tailored for voxel-based 3D object detec- tors. To tackle challenges in low-bit quantization, we propose two key techniques: (1) Foreground-aware Piecewise Activation Quantization (FA-PAQ). which leverages foreground structural cues to improve the quantization of sparse activa- tions; and (2) Gradient-guided Key Weight Quantization (G-KWQ), which pre- serves task-critical weights through gradient-based analysis to reduce quantization- induced degradation. Extensive experiments demonstrate that Point4bit achieves INT4 quantization with minimal accuracy loss with less than 1.5% accuracy drop. Moreover, we validate its generalization ability on point cloud classification and segmentation tasks, demonstrating broad applicability. Our method further ad- vances the bit-width limitation of point cloud quantization to bits, demonstrating strong potential for efficient deployment on resource-constraines edge devices.
edge devices. Post-
1 Introduction
plays crucial role in autonomous driving by enabling accurate environ- mental perception. Compared with camera- based methods, LiDAR offers robust and lighting-invariinn invariann geometric information, making it popular choice for reliable 3D perception [37 33
3D object detection 25 34
44 45). Among
various approaches, voxel-based 3D detectors [18] 47 are particularly popular due to their ability to convert irregular point clouds into structured voxel grids, enabling efficient feature extraction via convolutions. These methods are typically built on dense or sparse backbones [56] 46 TO, (52), aiming to improve accuracy and speed. However, real-time deployment remains challenging edge devices. For instance, VoxeINeXt (6] achieves 26.9 FPS on an NVIDIA RTX 3090, which is impractical for in-vehicle deployment. These real-world constraints underline the urgent need to reduce the computational cost and memory footprint voxel-based detectors, and to strike a better balance between accuracy and efficiency for large-scale, real-world applications.
Quantization is a promising solution to reduce model size and latency by converting floating seating-point point (FP) operations into low-bit formats (e.g.. INT8 or INT4) in different application [14]
43
16
53 [5]
Compared to quantization-awanl requiring no retraining and only minimal unlabeled data. A pioneering PTQ method for point cloud object detection is iddar-PTQQR (5I) which systematically analyzes the reason for quantization error and
training (QAT), post-training quantization (PTQ) more practical,
*Corresponding author: shengjiezhao@tonnji.. edu. en, sifannay@@mail.. com
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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proposes compensation mechanism based on quantization-awe aware distribution alignment. While this method demonstrates strong practical value, it still has notable limitations: (1) Limited robustness in ultra-low-bit settings. While supporting INT8, naively lowering to INT4, already available on modern hardware [30,, causes severe degradation, as uniform treatment of weights/activations breaks down at 4-bit where errors are amplified; (2) Distillation-bssed optimization and time-consuming. Its calibration and optimization procedures involve multiple training-style steps and are relatively complex, often requiring several hours to complete. This significantly hinders rapid deployment and iterative prototyping on edge devices; (3) Limited generalization ability. LiDAR-PTQ is primarily designed for 3D object detection models, and its effectiveness on other -iDAR-based perception tasks beyond 3D object detection remains to be explored.
To address the limitations of existing PTQ methods in terms of low-bit support, deployment efficiency, and task generalization, we propose generic 4-bit PTQ framework for point cloud perception, named Point4bit. The framework consists of two key components: the Foreground-aware Piecewise Activation Quantization (FA-PAQ) and the Gradient-guide guided Key Weight Quantization (G-KWQ),
The FA-PAQ leverages the inherent sparsity and geometric structure of point clouds to preserve task-critical features, especially those of foreground points. The G-KWQ module leverages gradient sensitivity analysis to identify task-critical weights and applies high-fidelity quantization to preserve model performance, even under extremely low-bit settings such as INT4.
Point4bit demonstrates strong quantization performance on voxel-based 3D detectors under INT4 settings, with minimal accuracy degradation. It also demonstrates robust generalization to classifica- tion and segmentation tasks, indicating broad applicability. In addition, Point4bit enables efficient deployment with minimal calibration overhead, making it a practical solution for resource-constrained edge scenarios. The main contributions of this paper are:
We propose the Foreground -ware Piecewise Activation Quantization (FA-PAQ) design to address the issue of the challenging problem of cloud detection. By leveraging the structural information of foreground regions, FA-PAQ effectively mitigates performance degradation during low-bit settings.
sparse activation quantization in point
We introduce Gradient Gradient-guided Key Weight Quantization (G-KWQ), a pradient-guidd weight quantization strategy that adaptively identifies and prioritizes task-critical weights. It effectively alleviates quantizaiioniinduce error propagation in low-bit regimes, thereby improving quantization performance.
We develop Point4bit, the first general 4-bit PTQ framework for point cloud object detection. It achieves a favorable trade-off between compression ratio and accuracy. Experiments show that Point4bit consistently outperforms existing methods under both INT8 and INT4 settings, demonstrating strong robustness under low-bit quantization, superior cross-task generalization, and high deployment potential.
2 Preliminary
Uniform Affine Quantization. In quantization, uniform affine quantization maps FP inputs to a fixed integer range using three key parameters: the scale factor s, zero-point 2, and bit-width b. Given an FP input x (weights or activations), the quantization and de-quantization process can be described by Eq. (I). where [-] denotes the round-to-nearest operation (RTN), and clamp(-) restricts the result to the valid integer range:
(1 + z; 0, 11) , = (Xint 2).
Xint clamp
(I)
where Via represents the quantized integer value, á is the de-quantized FP value with error that is introduced during the quantization process. Based on Eq. (.) the FP range of the quantization grid is defined as [gmin, 9max] = [-sz, s(2 - 2)]. Input values falling outside this range are clipped to the nearest boundary, resulting in clipping error. Increasing the scale factor s can reduce clipping by expanding the representable range, but at the cost of increased rounding error, which is bounded within 2 s, 5.. To balance these two sources of error, modern quantization frameworks commonly adopt MSE-based calibration 2 28 which searches for the optimal (gmin: 9max) that minimizes the Mean Squared Error (MSE) between the original tensor x and its de-quantized approximation X.

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3 Method
In this section, we present 4bint4bit, which consists of two key components: a foreground- activation quantization strategy and muddient-guided weight quantization method. Inspired by LiDAR-PTQ [31], we enhance activation quantization by emphasizing foreground regions to better preserve task-relevant features. We further extend fask-awareness to weight quantization by leveraging gradient information to retain weights most critical to the detection loss. Together, these components enhance the robustness and accuracy of quantized models under ultra-low-bit settings.
3.1 Foreground-awan Piecewise Activation Quantization
As pointed out in LiDAR-PTQ. current point cloud detection models are highly sensitive to activation quantization. To address the unique challenges posed by activation quantization in 3D object detection, we propose novel method called Foreground-aware Piecewise Activation Quantization (FA-PAQ), which leverages the inherent sparsity and geometric structure of point clouds. This method aims to preserve task-critical features during quantization, especially those associated with foreground points, which are crucial for accurate 3D object detection.
FA-PAQ consists of two components: Adaptive Foreground Recognition and Foreground -aware Piece- wise Activation Quantization. The former selects high-importance regions based on the activation map, while the latter applies finer-grained quantization in these regions minimize activation information loss and maintain detection accuracy.
1.0
0.8
0.6
0.4
0.2
0.0
(b) BEV Feature Map Figure 1: Visualization of activation strength on the BEV feature map based on CP-Voxel [47] Red boxes represent the regions of GT bounding boxes. Top-K denotes the top K non-empty voxels selected based on average activation responses.
(a) Original Point Cloud
(c) Top-K in Non-Empty Voxel
Adaptive Foreground Recognition. LiDAR-PTQ reveals that the boundary values of activations significantly impact quantization performance. This insight motivates us to investigate which regions in LiDAR-based point cloud detection are more critical to maintain under quantization. To this end, we visualize both the raw input point cloud and the BEV feature maps in the network, as shown in Fig. I Surprisingly. when computing the mean activation over the channel dimension and sorting the spatial locations in descending order, we find that the Top-K non-empty voxels are almost entirely located within ground truth (GT) bounding boxes.
This observation implies that foreground regions tend to have larger activation magnitudes, while background activations are often close to zero. Therefore, activation magnitude can effectively and automatically identify foreground points in the BEV feature map under an unsupervised setting. These foreground responses are highly relevant to the detection task (22).
Formally, let the activation feature at é layer be denoted as X E L''''' where L' is the number of spatial locations, and Cº is the number of channels. Let De S [1.....,'] denote the set of non-empty voxel indices at layer f. For each location V compute the average activation across channels, and select the top K = m, |V|| positions with the highest average activations as foreground candidates (Eq. (h:::
2 TTop (xf*) xf
1 C
Σ
X
where (-) denotes selecting the top A locations with the highest average activation. The resulting foreground set FG is considered contain the most critical structural information and will serve as the foundation for the subsequent piecewise quantization process.
3

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As shown in Fig. [J), the selected Top-K activation locations strongly align with GT bounding boxes, validating the effectiveness of our unsupervised foreground extraction strategy. By incorporating this foreground-aware mechanism, we are able to apply finer-grained quantization in crucial regions, preserving geometry-sensitive activations while maintaining overall model efficiency.
Piece-wise Activation Quantization After identifying foreground features and analyzing their distribution, we propose a piecewise activation quantization strategy that determines scale factors for multiple intervals based on the distribution of foreground non-empty voxels in the BEV feature map. A straightforward approach is to uniformly divide the activation range; however, such uniform partitioning neglects the intensity distribution of critical features. In contrast, leveraging the cumula- tive distribution function (CDF) allows us to better account for the underlying distribution of feature intensities, leading to more adaptive and data-aware quantization scheme (see Appendix for details). For background features, we adopt the conventional quantization strategy. This method ensures that the quantization process is well aligned with the underlying data distribution. In particular, the proposed approach is designed to enhance point cloud detection performance by preserving critical semantic information in foreground regions.
Badggeedd
Farggroall
Denalty
Demiity
Deniiy
Activation Value
Activation Value
Activation Value
(a) Original
(b) Non-Fmpty Voxel
(c) Top-K K in Non Non-Fimpty Voxel Activation Distribution
Activation Distribution
Activation Distribution
Figure 2: Visualization of activation distributions based on the CP- Voxel model
Foreground voxels in a BEV feature map typically correspond semantically significant objects, such as vehicles or pedestrians, and are characterized by higher feature values or densities. We represent these feature values as random variable X, whose statistical distribution is inferred from a calibration dataset. This distribution encapsulates the properties of foreground voxels within the feature space, providing the foundation for subsequent quantization steps.
Step 1: CDF Estimation. The cumulative distribution function Fx (I)) is defined as the probability that the feature value X is less than equal to a specific value empirically estimate the foreground CDF Fx (x), we collect a set of foreground feature values denoted as X = {Ii,, æ2,.... Jn These values correspond to the activation intensities of the most semantically meaningful (foreground) regions. As illustrated in Fig. 2 the foreground activations exhibit a skewed distribution, with higher densities concentrated in specific value ranges. We sort the collected values in ascending order as I(1) S x(2) S S [()) The empirical CDF is then approximated as shown in Eq. (3). where 1(-) denotes the indicator function, returning I if Z(z) and 0 otherwise:
Fx(x) P(X x). To
S0. EL (1(0 Sx), *()) S x S T(n))
< (1) or x > I(())
Fx(x)
Step 2: Interval Division. To determine quantization intervals adaptively, we partition the feature value range into intervals, each containing an equal cumulative probability of 1. The boundary points p& for k = 2. m 1 are selected based on the Eq. (4). The resulting intervals are defined as [pu. p1], P2l. [Pm-1- 1, Pm]. where po min(XX' and pm max(X (f)) represent the minimum and maximum feature values, respectively. This equal-probability partitioning ensures that the quantization intervals reflect the data distribution (Fig. (c))
m
k
Fx (x
(4)
m
Step 3: Actiyation Quantization. For each quantization interval [PK-1, PR). we compute the quantiza- tion scale st for b-bit guantization (i.e.. 2 quantization levels). as shown in Eq. (5):
%
f9
Pk= - Pk-1
(5)
2b

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Based on the general quantization and de de-quantization functions defined in Eq. I our piece-wise strategy derives a specific quantization rule for each foreground interval, as formulated in Eq. (
rI9 pk-1 + s clamp (L
x19
pk
0, 2 - 1) 2 (PE-1. Pk].
(6)
3.2 Gradient-guided Key Weight Quantization.
To identify weights that are critical to task performance in point cloud object detection, we propose a gradient-guided key weight quantization (G-KWQ). This method motivated by the observation that 3D object detection heavily relies on geometric structure modeling. In sparse point cloud scenarios, structural cues such as object boundaries and shape information are particularly crucial for accurate detection. Therefore, we design a gradient-basse weight sensitivity evaluation mechanism tailored for 3D detection tasks, which guides the preservation of high-fidelity representations for important weights during quantization.
Chosed
Caast
INT4 Weight Distrbation Figure 3: Visualization of weight distributions based on the Co-Voxel model
EP52 Weight Distabution
INTS Weight Distribtioon
Gradient-Bard Weight Sensitivity Evaluation. The magnitude of the gradient of weight with respect the loss function can indicate its task sensitivity. Specifically, the sensitivity of a weight w; is measured by the gradient magnitude of the total task loss Lrack which typically consists of a Focal Loss for classification and a Smooth-Li Loss for regression. Given that our quantization conducted in per-channel manner, we compute the average gradient magnitude across input channels for each output channel, shown in Eq. 7
M
&lookk aw.
0
M
Here, of denotes the aggregated task sensitivity of the j-th channel in the weight tensor of layer where a larger value indicates a potentially greater impact on overall model performance. M represents the total number of weights in layer L. This sensitivity scoring strategy is particularly suitable for 3D object detection tasks that rely on structural information, as high-sensitivity weights often encode critical geometric features, such as object boundaries dense spatial regions, which play an important role in determining the mean Average Precision (mAP).
Rounding Error in Low-Bit Quantization. As shown in Fig. 3 observe that as the bit-width decreases, the distribution of quantized weights becomes increasingly sparse. This trend reflects growing discrepancy between the original full-precision weights and their low-bit representations, which is primarily caused by rounding errors. The sparsity arises due to the limited set of representable values in low-bit quantization, leading to coarse-graie griseeegaaind mappings and aggressive rounding, especially for weights near zero quantization thresholds. Such increased rounding error may significantly degrade the network's capacity to retain subtle but task-relevant features. This observation highlights the necessity of explicitly mitigating rounding error for task-sensitive weights under low-bit settings.
5

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Key Weight Identification and Differentiated Quantization Process. Based on the above analysis, we propose the suudient-guider quantization pipeline:
Step I:
Gradient Extraction. Perform a forward and backward pass on a calibration dataset to compute the per-channel gradient sensitivity using Eq. (7)-
Step 2: Sensitivity Scoring. Use the gradient magnitude of (or its square) as a sensitivity score to measure each channel's s importance to task performance.
Step 3: Key Channel Selection. Rank channels by af in descending order and select the top m2 % as task-critical channels for special treatment in quantization.
Step 4: Differentiated Quantization. Introduce rounding enror penalty term into the quantization loss to enforce high-fidelity quantization for important weights. The total quantization loss is defined
Eq. 8 where LRE.j denotes the rounding error and Sy is the quantization step size for channel j:
2
W Sy
W S
Cquane LMSE Eay LRE.J GRE.
88)
Task-Specific Benefits Without introducing additional model complexity, our method adaptively identifies task-relevant weights using gradient information and applies differentiated quantization strategies preserve their fidelity. Experimental results show that under low-bit settings (e.g., INT4), our method effectively mitigates performance degradation and demonstrates superior robustness and task adaptability in 3D detection scenarios.
3.3 Point4bit Algorithm
Point4bit is fully post-training quantization method that operates without the need for labeled data model training, making it particularly well-suited for deployment in privacy-sensitive or resource-constrained environments. The overall procedure of Point4bit summarized in Algorithm
Algorithm 1 Point4bit Quantization
Input: Pretrained FP model with N layers; Calibration dataset D
Output: quantization parameters of both activation and weight in network, i.e., weight scale s",, weight -eiro-poitt-... zw, activation scale (s{* ..., 8/g 8D0).
1: for L,, {Lil .). do
2: Run forward and backward pass on the calibration dataset D to compute per-channel gradient sensitivity a using Eq. (7:
3: Optimize the weight quantization parameters s" and Rd by minimizing the MSE loss defined
in Eq. (8) using grid search algorithm.
4: end for
5: for Ln = 14/i I, do
6: Input D to FP network and obtain the output feature Xe, layer E,;
7: Identify the foreground and background activation values, X and Xb according to Eq. (
8: Optimize the background activation quantization parameters sh hased on x by minimizing
the MSE loss using grid search algorithm;
9: Determine the interval boundaries {Po. : Pm based on the empirical cumulative distribu-
tion function as defined in Eq. 3 and Eq. 44)
Optimize the foreground activation scales {s] ..., sf9)
using Eq.
11: end for
4 Experiments
Datasets. We evaluate the effectiveness of the Point4bit framework primarily on the large-scale autonomous driving dataset nuScenes 4 for the 3D object detection task. The evaluation metrics for 3D detection are mean Average Precision (mAP) and the nuScenes Detection Score (NDS). To further assess its generalizability across different tasks, we conduct additional experiments on 3D object classification and semantic segmentation. For classification, we adopt the ModelNet40 [42 and ScanObjeetNN 39 datasets, which are widely used as benchmarks in this domain, and evaluate performance using Overall Accuracy (OA) and mean Class Accuracy (mAcc). For semantic segmen- tation, evaluations are performed on the real-world LiDAR dataset SemanticKITTI 3 using mean Intersection-over- Union (mloU) as the evaluation metric.

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Table 1: CP-CC-Vo Poxel
47
Quantization results on nuScenes val set. GS: grid search strategy.
Methods
Bits(W/A)
mAP
NDS
Car
Truck
C.V.
Bus
Trailer
Barrier
Motor
Bike
Ped.
T.C.
Full Prec.
32/32
58.45
66.22
84.81
57.09
15.88
70.08
37.66
67.37
57.39
39.35
84.99
69.92
RTN (28)
8/8
58.22
66.08
84.72
56.96
15.23
70.14
37.19
67.15
56.97
38.88
81.97
69.91
RTN-GS 28
8/8
58.30
66.09
84.78
56.99
15.78
70.17
37.25
67.31
57.24
38.71
84,91
69.79
PD-Quant 21
8/8
58.06
65.91
84.78
56.70
15.53
70.10
36.03
67.30
56.26
39.11
84.85
69.91
QDrop
8/8
$7.98
65.75
84.66
56.01
15,23
69.97
36.81
67.02
56.66
38.79
84.92
69.73
LiDAR-PTQ
8/8
58.34
66.11
84.81
56.77
15.60
70.07
37.65
67.33
57.39
39.15
84.97
69.68
Ours
8/8
58.48
66.21
84.80
57.08
16.12
70.14
37.52
67.39
57.63
39.17
85.00
69.96
RTN 2X
4/8
55,80
64.30
82.30
54.40
18.16
66.35
36.78
66.53
51.96
31.50
82.78
67.22
RTN-GS
23
4/8
56.63
64.67
84.07
55.86
12.41
67.56
36.29
66.35
54.55
36.92
84.56
67.66
Ours
4/8
57.46
65.34
84.15
55.83
17.77
68.41
36.37
66.89
56.37
36.27
84.25
68.23
RTN
4/4
29.46
46.43
58,22
30.21
0.02
41.75
11.36
39.66
8.98
1.78
59.27
43.34
RTN-GS
28
39.40
53.40
77.51
42.58
11.17
52.29
22.67
61.45
18.23
4.62
64.05
39.36
Ours
4/4
56.97
64.88
83.68
55.17
18.06
67.55
35.38
66.51
55.89
35.55
83,98
67.93
Table 2: Quantization results on the nuScenes val set based on VoxeINe 6
Methods
Bits(W/))
mAP
NDS
Car
Truck
C.V.
Bus
Trailer
Barrier
Motor.
Bike
Pcd.
T.C.
Full Prec.
32/32
60.53
66.64
83.87
55.52
21.04
70.49
38.06
69.38
62,80
49.98
84.58
69.38
RTN 28
8/8
42.48
44.67
77.49
43.41
9.81
46.74
21.44
47.45
31.75
12.58
76.06
58.05
RTN+GS 28
8/8
60.40
66.58
83,85
55.61
21.05
70.40
37.93
69.25
62.61
49.68
84,46
69.08
Ours
8/8
60.50
66.63
83,87
55.61
21.03
70.46
38.08
69,25
62.87
49.93
84.55
69.29
RTN 28
4/8
56.76
65.12
84.35
35.91
12:31
61.30
"56.30
66.16
555.39
33..66
84,44
67.93
RTN+GS 28
4/8
58.54
65.31
81.21
54.18
20.26
68.24
37.73
69.07
58,21
46.83
83.02
66.55
Ours
4/8
59.27
65.54
82,16
54.43
20.71
69.78
36.88
69.38
61.19
47.30
83.10
67.74
RRNN' [28
4/4
26.75
29524
34.38
19.68
0.00"
"66.65"
33,60
09.08""
0.01
'6.0"
37.30"
"54.54"
RTN+GS
28
4/4
52.71
61.36
79.24
48.44
18.40
60.60
31.39
66.88
52.34
38.46
79.13
52.13
Ours
4/4
58.97
65.20
81,89
54.03
20.39
69.76
36.98
69.07
60.76
46.90
82.68
67.15
Implementation Details. For the nuScenes dataset, we randomly sample 256 point cloud frames from the train set as calibration data, accounting for only 0.91 % of the total training frames (256/28,130 130). Similarly. 32 shapes are selected from the ModelNet40 train set (32/9,8 04,,8 0.32% ), 32 shapes are selected from the ScanObjectNN train set (128/1 1609, 0.01 %),, and 128 frames are sampled from the SemanticKITTI train set (128/19,130, 0.67 %) for calibration. In all experiments, the first and last layers of the network are kept in full precision to preserve input fidelity and maintain output accuracy. Additional implementation details are provided in the supplementary material. We execute all experiments on single Nvidia Tesla V100 GPU.
4.1 Overall Results
Quantization Results on Point Cloud Detection. We evaluate our Point4bit on the nuScenes val set for the 3D object detection task. We first adopt CenterPoint- Voxel (47) (CP-Voxel) as the full-precision baseline due to it is a representative voxel-based detector that strikes a good balance between accuracy and efficiency, and has been widely adopted in both academia and industry. As shown in Tab. []] under the standard W8A8, Point4bit achieves 58. 58.48% mAP and 66.21% 1% NDS, which nearly lossless compared the FP model (58.45 48%455%.o. mAP, 66.22% NDS), and outperforms existing PTQ methods including LiDAR-PTO [51]. QDrop [4]. and PD-Quant (II.. To ensure compatibility with edge deployment, we also evaluate the mixed-precision the latest low-bit bit format supported by NVIDIA hardware (e.g., TensorRT' 8.6+). In this setting, Point4bit achieves 57.46% mAP and 65. 65.34% NDS, with less than 1% drop from FP model and better performance than competing methods. Furthermore, we explore the limit of quantization by testing under the ultra-low-bi W4A4 configuration. Remarkably, our method still delivers 56.97% mAP and 64.88% NDS, demonstrating strong robustness under extreme widhh constraints.
W4A8 setting, which corresponds to
To further examine the generality of 4oint4bit, we extend our evaluation to VoxeINeXt 66 a sparse 3D detector designed for efficient long-range perception. Its ability to maintain high accuracy with low computational cost has made it increasingly popular in safety-critice autonomous driving scenarios. As shown in Tab. 2 Point4bit again achieves excellent results across all bit-width settings. Under the W8A8 configuration, it achieves 60.50% mAP and 66.63% NDS, matching the FP model (60.53% mAP, 66.64% NDS). In the W4A8 setting, our method still maintains 59.27% mAP and 65.54% NDS, with less than 1.1% degradation. Even under the W4A4 configuration, it achieves 58.97% mAp and 65.20% NDS, demonstrating the robustness of our method under ultra-low-bit quantization. These results validate the effectiveness and generalizability of Point4bit across diverse 3D detection architectures, showcasing its potential for real-world deployment.
7

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Table Quantization results on the nuScenes val set based on PillarNeXt [1
Methods
Bits(W/A)
mAP
NDS
Car
Truck
C.V.
Bus
Trailer
Barrier
Motor
Bike
Ped.
T.C.
Prec.
32/32
62.51
68.61
84.77
58.60
21.44
66.53
35.24
69.78
68.01
56.43
87.22
76.99
RTN (28]
8/8
61.65
68.11
83.65
57.10
20.73
66.36
32.79
69.44
67.33
55.53
87.03
76.48
RTN+GS (28
8/8
62.37
68.49
84.45
58.34
58.34
66.31
35.43
69,90
67.89
56,28
87.04
76.96
Ours
8/8
62.44
68.54
84.76
58.55
21.18
66.49
35.27
69.63
68.02
56.21
87.20
77.01
RTN 28
4/8
59.66
66.00
83,10
56.12
18.53
63.22
31.54
66.37
64,21
51.29
86,47
75.73
RTN+GS 28
4/8
60.40
66.85
83,58
57.04
19.94
62.64
32.98
67.80
65.46
53.52
85.84
75.15
Ours
4/8
61,27
67.19
84.44
57.58
19.54
63.97
35.15
68.36
66.92
54.28
86.54
75.92
RTN
23
4/4
9.31
25.49
21.61
7.99
0.0
9.26
3.81
0.11
1.60
0.0
38.45
T0.26
RTN+GS
4/4
36.20
51.62
73.57
40.43
6.50
35.31
3.63
60.20
30.69
22.81
22.81
28.57
Ours
4/4
60.89
66.93
84.31
57.72
19.14
63.49
35.26
68.08
66.59
52.95
86.22
75.13
Beyond voxel-based models, we further examine pillar-based architecture by adopting Pil- larNeXt [9] and treating its official pre-trained weights as the FP baseline. As shown in Tab. 3 Point4bit delivers strong results across all bit-width settings. Under the standard W8A8 configuration, it achieves 62.44% mAP and 68.54% NDS, closely matching the FP baseline (62.51%% mAP. 68. % NDS). In W4A8 setting-allgned with low-bit formats supported by current NVIDIA deployment stacks-owe our method attains 61 61.27% mAP and 69% NDS, with minimal degradation and consistent gains over prior PTQ baselines. Even under the ultra-low-bit W4A4 configuration, Point4bit reaches 60.89% mAP and 66.93% NDS, demonstrating strong robustness under extreme bit-width constraints. Together with our voxel-based results, these findings confirm the effectiveness and generality of Point4bit across both voxel- boxel-based and pillar- based 3D detectors on nuScenes.
Table 4: Quantization results on the ModelNet40 and ScanObjectNN val set.
Net40
ScanObjectNN
Methods
Bits(W/A)
PointNet++
31
PeintNeXt
32
PointNet++ [3
Xo 32
OA
mAcc
OA
mAcc
OA
mAcc
OA
mAce
Full Prec.
32/32
92.83
89.81
93.96
91.14
86,16
84.36
88,20
86.84
RTN 28
8/8
92.67
89.60
93.84
91.05
85.63
83.87
87.99
86.31
RTN+GS (28
8/8
92.50
89.20
93.88
91.08
85.74
83.94
87.99
86.57
Ours
8/8
92.99
90.01
93.96
91.14
86.02
84.30
88.20
86.87
RIN 28
478
9T.73
88.77
92.87
89.85
82.79
80.03
86.16
84.59
RIN+GS 28
4/8
91.37
88.43
92.99
90.06
82.72
80.28
86.43
84.84
Ours
4/8
92.14
88.91
93.44
90.75
85.67
83.71
88.13
86.65
RTN 28
4/4
90.52
86.86
88.09
84.73
81.02
78.49
77.34
77..41"
RTN+GS 28
4/4
90.68
86.93
91.53
88. 18
80.99
77.97
78.28
78.28
Ours
4/4
91.73
89.18
93.27
90.34
85.46
83.38
87.47
86.07
Table 5: Quantization results on the SemanticKITTI val set based on Lerge3emel3D
Bicycle
M. Mydeee
Truck
0. Vch
Pers.
B.Cye
M.Cjp
Road
Park
S.Wkk
Grad
Building
Fence
Veg.
Trank
Terain
Pole
FSign
Methods
Bits(WA))
Pull Proc.
32/32
70.3
97.9
52.8
K3.2
84.2
83.8
80.4
91.3
5.7
94.7
61.5
83.2
1.7
92.0
68.5
89.1
70.9
76.5
65.8
52.0
RIN 28
8/8
70.3
97.9
52.8
K3.2
64.0
84.0
80.4
91.8
5.0
94.7
61.3
83.2
1.7
92.0
68.5
89.1
70.9
76.5
63.8
52.0
KIN+GS
28
8/8
70.3
97.9
52.7
83.3
84.0
K3.9
80.4
91.6
5.7
94.7
61.5
83.2
1.6
92.0
68.5
89.1
70.9
76.5
65.8 52.0
Ours
8/8
70.3
97.9
52.7
83.2
84.5
83.8
80.5
91.6
5.8
94.7
61.5
83.2
1.7
92.0
68.4
89.1
70.9
76.6
65.8 52.0
RTN 28
4/8
68.3
98.0
52.2
82.2
79.8
83
78.7
91.4
4.4
93.8
52.7
80.7
0.9
90.8
62.6
87.6
68.7
72.9
65.2 50.9
RTN+GS 28
4/8
69.3
97.7
53.3
82.8
81.1
81.9
79.7
91.8
4.6
94.3
58.5
82.2
2.0
91.2
64.1
88.6
69.9
76.0
65.5 51.6
Ours
4/8
70.1
98.1
51.8
82.8
86.5
85.0
80.0
91.9
4.5
94.3
58.9
82.5
14
92.2
70.3
88.9
69.6
76.0
65.8 51.5
RIN
28
464
27.9
81.0
8.4
49.6
52.1
20.3
4.2
8.6
0.3
40.8
6.7
51.8
0.1
41.9
20.9
18.2
54.2
13.6
16.6 40.6
KIN+GS
28
444
68.0
98.1
50.9
81.4
87.3
84.8
76.9
91.8
0.9
93.7
51.2
80.7
0.7
90.6
61.5
87.6
66.5
73,0
64.1 50.5
Ours
444
70.0
98.1
51.8
82.7
86.1
85.0
79.8
91.7
4.3
94.3
58.9
82,5
1.4
92.2
70.3
88.9
69.5
76.1 65.8 51.5
Quantization Results on Point Cloud Classification and Semantic Segmentation. Beyond 3D object detection, we further evaluate the generalization of Point4bit on point cloud classification and semantic segmentation tasks using the MedeINet40,, ScanObjectNN and SemanticKITTI datasets. For classification, we adopt PointNet++ [31] and PointNeXt 32) as representative backbones. As shown in Tab. [4] Point4bit consistently achieves high accuracy under various quantization settings and different classification datasets. Specifically, under W8A8 and W4A8, our method matches the FP performance on both models, with no noticeable degradation. Even under the more aggressive W4A4 setting, it achieves 91.73% OA| / 89.18% mAec on PointNet++ and 93.27% 90.34% on
8

---

PointNeXt showing less than a 1.1% drop from their FP baselines (92.83% / 89.81 and 93.96% /91.14%% on ModelNet40. Consistently, on SeanObjectNN at W4A4 it reaches 85.46%/83.338 38. OA and 87. 77.47%//66.073 mAce with PointNet==/PoitNeeX, closely matching the FP baselines of 86.16%/84..3 38.6%% and 88. 20%.20%//6..... 84%, respectively. For semantic segmentation, we evaluate the LargeKerneI3D [5] model on SemanticKITTI. As shown in Tab. S under W4A8 and 70.0% under W4A4, both closely matching the FP baseline of 70.3%. These results confirm that Point4bit effectively preserves model performance across different quantization settings, demonstrating strong generalizability in both classification and segmentation tasks.
Point4bit achieves 10.1% mloU
Quantization Efficiency. As shown in Tab. 6 our method demonstrates significantly improved quantization efficiency. In contrast to LiDAR-
Table 6: Comparison of quantization deployment time (GPU/mins) among different PTQ methods.
Models
RIN RIN+GS PD-Quant QDrop LIDAR-PTQ Ours
PTQ [51, our approach eliminates the need for additional fine-tuning or computational over-
CP-Voxel 37.2 38.4
649,8
174.0
224.4
head, resulting a quantization time that is approximately 6x faster. In addition, 4bint4bit supports lower bit- width configurations (e.g.. W4A4) while maintaining high accuracy. These results demon- strate that Pbint4bit offers a time-efficient and accurate quantization solution.
4.2 Ablation Studies
Ablation Study on Component Coupling. To
Table 7: Ablation study of different quantization
evaluate the effectivvness of each proposed com-
components based on Co-Voxel [47].
ponent, we conduct an ablation study on G-
Methods
Bas(WIA)
G-KWQ FA-PAQ
mAP
NDS
KWQ and FA- FAQ as shown in Tab. 7 Both
Full Pree.
32/32
58.45
66.22
RTN 28 and RTN+GS (88 under 4-bit quan-
RIN 2X
47
29.46
46.43
RIN+OS 28
4/4
39.40
tization suffer from significant performance degradation. Introducing G-KWQ alone brings moderate improvements by preserving critical weights based on gradient sensitivity. In con-
474
42.84
56.25
Ours
4/4
55.47
64.06
4/4
56.97
64.88
trast, applying FA-PAQ alone yields much larger gains, demonstrating the importance of foreground- aware activation quantization for the 3D detection task. When combining both modules, our Point4bit framework achieves the best results, approaching FP performance. These results confirm the effec- tiveness and complementarity of G-KWQ and FA-PAQ for low-bit quantization.
Ablation Study on Different Bit-Width Settings. We in- vestigate the impact of different quantization bit-widths on detection performance,
Table 8: Ablation study of different
bit-widths based on Voxel 47
summarized in Tab. 8 While all methods perform similarly under 8-bit quantization (W8A8), our approach slightly surpasses the FP baseline, indicating minimal accuracy loss. This performance gap becomes in- creasingly prominent in lower-bit regimes. Notably, under the extreme low-bit setting (W3A3), Point4bit still achieves 32.98% mAP and 49.77% NDS, significantly outperforming naive quantization baselines that almost collapse. These re- sults highlight the robustness of our method under aggressive bit-width constraints and its potential for deployment in ultra- low-bit hardware scenarios.
Methods
(its(W/))
mAP
NDS
Full Prec.
32/32
58.45
66.22
RIN [2]
8/8
58.22
66.08
RTN+GS [2
58,30
66.09
Ours
8/8
58.48
66.21
RIN 28
474"
20.46
46.43
RIN+GS (8
4/4
39.40
53.40
Ours
4/4
56.97
64.88
RTN [X]
373"
0.02
3,72
RIN-GS 28
3/3
0.88
3.44
Ours
3/3
32.98
49.77
A Ablation Study on Calibration Dataset Sizes. To examine the sensitivity of the amount of calibration data, conduct an ablation on the nuScenes dataset using Co-Voxel as the baseline and vary the calibration dataset (CD) size from 64 to 1,024 samples. As summarized in Tab. 9 our method is notably stable across wide range of CD sizes. Under W8A8, mAP varies within 0.04 points (58.44 44%444%%555 58.48%) and NDS within 0.02 points (66.195 19% 66.21 ). with the best result ob- tained at 256 samples (58.48% mAP, 66.21% % NDS), essentially matching the FP baseline. Under the more challenging W4A4 setting, performance remains robust. These results indicate that Point4bit insensitive to calibration dataset size and generalizes well with only a small fraction of the dataset size.
Table 9: Ablation study of calibra- tion dataset sizeon CP- Coxel....
WiA) Full Prec
Calib Size
mAP
NDS
60.53
66.64
64
58.44
66.19
W8A8
256
58.48
66.21
1024
58.44
66.20
56.83
64.96
W4A4
256
56.97
64.88
1024
56.55
6468

---

5 Related Work
Voxel-based Point Cloud 3D Object Detection, Voxel-based 3D object detection methods point
clouds can be broadly categorized into dense [66
18
36
9 26 13
19
52 40 and sparse [10 [
12 49 23 detectors. Dense detectors typically adopt sparse-to-denss backbones 446 I8 to extract
high-resolution features and improve accuracy. Representative methods such as PV-RCNN [34 35), Voxel R-CNN [9], and CenterPoint 447 leverage multi-scale fusion or dense BEV representations for precise detection. However, the use of dense convolutions limits their scalability for long-range perception. To improve perception abilitty, sparse detectors eliminate dense computation through architectural innovations. For example, VoxeINeXt [6] and its variants [23 5 49] employ structured diffusion to propagate context in sparse space; the FSD series [T] clusters points extract center features. Despite their improved efficiency, these methods still require server-side devices to achieve real-time performance and remain constrained for onboard inference. To this end, explore model quantization as a practical solution to reduce inference cost while preserving model accuracy.
Quantization for 3D Object Detection. With the increasing demand for efficient 3D perception
in autonomous systems, quantization has emerged as a key technique for reducing model size and inference latency. In multi-camera 3D detection, QD-BEV (50) combines quantization-awwar training and knowledge distillation to compress model. Q-ETR [48] introduce quantization-awal position embedding to improve model quantization ability. For LiDAR- detectors, LiDAR- PTQ [51) identifies the root cause of accuracy degradation during quantization and proposes a post-training quantization (PTQ) solution. Although effective in practice, this method still relies series of sophisticated quantization techniques (27 20 41 21 for parameter optimization. UPAQ [ further integrates pruning and mixxed-recisiion quantization, but relies on fixed compression patterns and lacks task adaptivity. PillarHist [54] [55] improves pillar encoders via entropy-guided height histogram encoding, though limited module-level enhancements. In contrast, we propose Point4bit, a unified 4-bit PTQ framework, which enables efficient low-bit deployment with minimal accuracy loss and is readily extendable to various 3D perception tasks under real-world hardware constraints.
6 Conclusion
In this paper, we propose Point4bit, novel 4-bit PTQ framework for voxel-based 3D object detection. It introduces two key techniques: (1) FA-PAQ, which preserves accuracy in sparse activation quanti- zation by leveraging foreground structural priors, and (2) G-KWQ, which reduces error propagation by selecting important weights via gradient sensitivity. Without retraining or labeled data, Point4bit achieves efficient INT4 quantization with minimal accuracy loss. Extensive experiments on the nuScenes dataset demonstrate that our approach significantly outperforms existing PTQ methods in both accuracy and deployment efficiency. While our study focuses on 3D object detection, the proposed framework is general and can be extended to other 3D perception tasks such as classification and semantic segmentation. We believe this work provides a solid foundation for future research in ultra-efficient 3D perception, especially under real-world hardware constraints.
7 Limitation and Discussion
Our study focuses b-t quantization, aligning with hardware trends like NVIDIA's W4A8 B0 While our method demonstrates strong quantization performance, ultra-low-bin quantization (e.g.. 2-bit binary) remains challenging due information loss and limited hardware support. Moreover, voxel-based detectors often rely on frameworks like spconv [8], which lack mature support for 4-bit inference edge devices (e.g., NVIDIA Orin). As such, have not yet realized actual acceleration under W4A4 settings. This work serves as an academic exploration, and we leave practical deployment future work as tooling and hardware evolve.
Acknowledgements
This work was supported in part by the National Key R&D Program of China 2023YFC3806000 and 2023YF33806002, in part by the National Natural Science Foundation of China under Grant 62076183, in part by Shanghai Municipal Science and Technology Major Project No.
S02ZXX000,,,, in part by the Shanghai Science and Technology Innovation Action Plan Project 22511 105300, in part by the National Natural Science Foundation of China under Grant U23A20382 and in part by Fundamental Research Funds for the Central Universities.
10

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[51] Zhou, S., Li, L., Zhang, X., Zhang, B., Bai, S., Sun, M., Zhao, Z., Lu, X., Chu, X.: Lidar-ptq: Post-training quantization for point cloud 3d object detection. International Conference on Learning Representations (ICLR) (2024)
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A Appendix
In this appendix, we firstly provide additional details and results to complement the main paper. Second, we begin with descriptions of the datasets and implementation settings used across tasks in Appendix A.1 including point cloud detection, point cloud classification, and point cloud semantic segmentation. Appendix [A.2] we report the inference speed of quantized models on various edge platforms demonstrate the efficiency benefits from our Point4Bit. We then present comprehensive set of ablation studies in Appendix |A.3 to analyze the key parameter selection in proposed Point4Bit framework, including the effects of foreground selection ratio (m1), the number of quantization intervals (m), the choice of interval partitioning strategies and weight reconstruction ratio (m2). Furthermore, present visualizations of the detection and segmentation method in Appendix AA.5 in different quantization setting to demonstrate the qualitative results of our method. Besides, we provide theoretical proof for our CDF-based interval division strategy in Appendix [A.6] Finally, present the detailed quantization preliminaries, including calibration and grid search algorithm details (Appendix A.7
A.1
Experiment Details
A.1.1 Dataset.
nuScenes Dataset for Point Cloud Detection. NuScenes dataset 4 uses 32-beam LiDAR to collect data from 1000 urban driving scenes, annotated with 3D bounding boxes for 10 object classes. The dataset is split into 700 training. 150 validation, and 150 testing scenes. It supports 3D object detection tasks and uses mean Average Precision (mAP) and nuScenes Detection Score (NDS) as evaluation metries. NDS is weighted average of mAP and other box-level metrics such as translation, scale, orientation, velocity, and attribute classification.
ModelNet40 Dataset for Point Cloud Classification. ModelNet40 (42 is synthetic 3D object classification dataset consisting of 12,311 CAD models from 40 object categories. Each category contains approximately 100 unique 3D shapes. Additionally, 2,902 real-world object instances were widely used for evaluating shape classification methods, and
scanned augment the dataset. the standard evaluation metric is classification accuracy, typically reported as overall accuracy (OA) and mean class accuracy (mAcc).
ScanObjectNN Dataset for Point Cloud Classification. ScanObjectNN 22 is a real-world 3D object classification dataset comprising 2,902 objects across 15 indoor categories. Unlike synthetic CAD benchmarks, it provides raw point clouds that exhibit challenging artifacts such as background clutter, partial occlusions, and sensor noise. It is widely used to evaluate 3D point cloud classification methods. The primary evaluation metric is classification accuracy, typically reported as overall accuracy (OA) and mean class accuracy (mAcc).
SemanticKITTI Dataset for Point Cloud Semantic Segmentation. SemanticKITTI 43,551 LiDAR scenes captured in autonomous driving scenarios, with fine-grained semantic an- notations for 28 semantic classes. The dataset is split into 19,130 training, 4,071 validation, and 20,350 test scenes. It is commonly used for point cloud semantic segmentation tasks. The standard evaluation metric mean Intersection over Union (mloU) across all semantic classes.
3 contains
A.1.2 Implementation Details.
Setting for Point Cloud Detection. For the point cloud detection task, the nuScenes train set for calibration, selecting 256 representative samples (0.91% % of 28, 130). All FP models in our experiments adopt the official open-source implementations of CP-Voxel 47) and VoxeINeXt (6), both based on the OpenPCDet (38 framework.
We keep the first and last layers in full precision. For the rest of the network, layer-wise reconstruction applied the backbone, neck, and head. Calibration is performed with a batch size of The quantization hyperparameters are set as follows:
2 defines the number of CDF-based quantiza- tion intervals; my = 0.2 specifies the proportion of high-activation voxels selected as foreground for fine-grained activation quantization; and m2 0.8 indicates the proportion of important weights
14

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selected for reconstruction based on gradient sensitivity. Under the ultra-low-bit W4A4 setting, we increase the number of quantization intervals m better capture activation distribution.
Setting for Point Cloud Classification. For the point cloud classification task, we select 32 samples (0.32% of 9,843) from the ModelNet4O train set and 128 samples (0.01% % of 1 11609) from the ScanObjectNN train set for activation calibration. We evaluate two representative classification networks: PointNet++ 31 and PointNeXt [32]. using the official implementation from 32
The first and last layers are preserved in full precision. Layer-wise reconstruction is applied throughout the backhone, neck, and head. We adopt batch size of 4 during calibration. The hyperparameters are set to m 2, m. = 1.0, and m2 = 0.8. Here, my = 1.0 is used because in classification, all points treated as foreground. For W4A4 quantization, set m to enhance representation granularity.
Setting for Point Cloud Semantic Segmentation. For the 3D semantic segmentation task, 128 samples (0.67% of 131) from the Semantic train set for activation calibration. We adopt the LargeKemee33 [S] model, implemented using the open-source Pointcept (7] framework, with the convolution type set to SubMConv3d.
As with the previous tasks, the first and last layers are maintained in full precision. Layer-wise reconstruction is applied the backbone, neck, and head, with a batch size of 4. The quantization hy perraraameeers are configured as 2. m. 0.2, and m2 0.8. For W4A4 quantization, we similarly set m 3 3 to further reduce quantization error in low-bit settings.
All experiments are conducted on single NVIDIA Tesla V100 GPU. For all ablation studies, we use CP- CP-Voxel [47] as the base model ensure consistent and fair comparison.
A.2 Inference Speed for Quantized Model
To evaluate the efficiency of our Point4Bit and inference speed of quantized models based on Point4Bit, we report the performance of our CP-Voxel model 47 in two different edge devices under the W8A8 precision setting. As shown in Tab. TO on the NVIDIA Jetson AGX Orin platform, which is common onboard devices for autonomous driving in the community, the quantized model achieves an inference speed of 31.1 FPS, which is approximately 3x faster than its FP counterpart at 12.5 FPS.
Table 10: Inference speed of CP-Voxel 47 under different quantization settings on Jetson AGX Orin and Xavier NX.
Platform
Bits(W/A)
FPS
32/32
12.5
AGX Orin
8/8
31.1
32/32
1.9
Xavier NX
8/8
5.2
In addition to AGX Orin, we also evaluated the model on more resourcce-contrraneed edge platform, the NVIDIA Jetson Xavier NX. Under WSA8 quantization, the CP-Voxel model reaches 5.2 FPS on Xavier NX, compared 1.9 FPS for the FP32 cersion-yyclding 2.7x speedup. Notably, while the quantized model does not yet achieve real-time performance on the Xavier NX platform, this limitation stems primarily from the extremely constrained computational resources of the platform, rather than the quantization method itself. In fact, most recent efforts 12 24 in edge-side real-time LiDAR perception have focused on AGX Orin, which offers more favorable balance between compute and power efficiency.
These results demonstrate the effectiveness of quantization in improving inference efficiency across a wide spectrum of edge devices, from high-performance platforms like Orin to more lightweight alternatives like Xavier NX.
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Table 11: Ablation study on the Top-k selection ratio (m1) in FA-PAQ, based on the CP-Voxel
model 47.
Methods
Bits(W/A)
m1
MAP
NDS
Full Prec.
32/32
58.45
66.22
8/8
0.2
58.48
66.21
4/4
0.1
56.26
64.63
Ours
4/4
0.2
56.97
64.88
4/4
0.3
56.82
64.32
4/4
1.0
56.05
64.13
A.3 Ablation Study
A.3.1 Ablation Study on Top-k Selection Ratio (m1) FA-PAQ
To evaluate the impact of the Top-k selection ratio my in FA-PAQ, we conduct ablation study using the CP-Voxel [47] model. m, determines the proportion of nigh-activation non-empty voxels in the BEV feature map that are selected as foreground candidates. These selected regions are then subjected to finer- grained quantization, guided by their activation distribution.
As shown in Tab. 11 we vary from 0.1 to 1.0 to observe its effect on detection accuracy under W4A4 quantization. The best results are achieved when m, 0.2, yielding 56.97% mAP and 64.88% NDS. This configuration notably outperforms both the no- relselection baseline (m, 1.0), which treats all voxels equally, and the smaller-ratio setting (m = 0.1), which may miss some informative foreground areas.
The performance degradation mi 1.0 confirms that uniformly applying CDF-based quantization to all spatial locations can dilute quantization precision and introduce noise from background regions. Conversely, when mi foreground structures. These findings highlight the importance of selecting an appropriate foreground ratio balance quantization precision and task characteristics. The foreground- aware quantization strategy introduced in FA-PAQ thus proves effectiveness in preserving critical semantic information, particularly under ultra-low-bie settings.
too low (e.g., 0.1), the model may fail to capture all relevant
Table 12: Ablation study on the number of quantization intervals (m) in FA-PAQ, based on the
CP-Voxe model
Methods
Bits(W/A)
m
mAP
NDS
Full Prec.
32/32
58.45
66.22
8/8
2
58.48
66.21
4/4
T
53.84
62.6T
Ours
4/4
2
55.74
64.17
4/4
3
56.97
64.88
4/4
4
56.66
64.81
A.3.2 Ablation Study on the Number Quantization Intervals (m) FA-PAQ
Here, conducting ablation experiment to explore the impact of the number of quantization intervals in FA-PAQ on the CP-Voxe! 47 model, where the piece number m systematically varied.
The experimental results are shown in Tab. 12 covering both W8A8 and W4A4 quantization settings. yields notable performance boost: mAp improves from 53.84% to 56.97%, and NDS rises from 62.61% % to 64.88%. These gains highlight the value of multi-interval quantization in modeling the skewed distribution of activation values and reducing quantization-indun induced error.
Under W4A4, increasing m from (i.e., uniform quantization)
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Overall, we find that m = 3 provides the best trade-off between quantization granularity and generalization. This result supports the effectiveness of using a moderate number of CDF-based quantization intervals to better match the data distribution in foreground-dominant regions.
Table 13: Ablation study on the Top-k selection ratio (m22 in G-KWQ, based on the CP-Voxel model 47
Methods
Bits(W/A)
m2
mAP
NDS
Full Prec.
32/32
58.45
66.22
8/8
0.8
58.48
66.21
4/4
0.0
55.47
64.06
Ours
4/4
0.7
56.83
64.16
4/4
0.8
56.97
64.88
4/4
1.0
56.64
64.24
A.4 Ablation Study on Top-k Selection Ratio (m2) in G-KWQ
To investigate the effect of the Top-k selection ratio my in the proposed G- KWQ design, we performed
an ablation study using the Voxel 447 model. As shown in Tab. 13 we vary m2 to control the proportion of high-sensitive weights selected for quantization-ave -aware reconstruction.
Under the W8A8 setting, the model achieves robust performance with m2 0.8, reaching 58.48 mAP and 66.21 NDS-very close to the FP model. This suggests that selecting the top 80% of important weights sufficient for maintaining high accuracy in moderate-bin quantization.
In the more aggressive W4A4 setting, we observe that m2 = 0.8 also yields the best results (56.97 mAP and 64.88 NDS), outperforming both the no-selection baseline (m2 = 0.0) and the full-selection setting (m2 = 1.0). This confirms that neither ignoring top-k selection nor reconstructing all weights optimal-selectivv selective focus iighiimpottance weights offers better trade-off between accuracy and efficiency. These results demonstrate the importance of carefully tuning my to balance quantization error and representational fidelity, particularly under low-bit constraints.
A.4.1 Ablation Study on Interval Partitioning Strategies
Table 14:
Ablation study interval partitioning strategies for FA-PAQ, based on the CP-Voxe
model
Method
Bits (W/A)
Interval Part.
mAP NDS
Full Precision
32/32
58.45
66.22
8/8
Mean
58.35
66.11
8/8
CDF
58.48
66.21
Ours
4/4
Mean
54.02
62.71
4/4
CDF
56.97
64.88
To investigate the impact of different interval partitioning strategies in FA-PAQ, compare two variants: uniform partitioning based on the average step size (Mean) and adaptive partitioning based on the cumulative distribution function (CDF). As shown in Table [4 CDF-based partitioning consistently outperforms the Mean-based approach, especially under lower bit-width settings.
Notably, under 4-bit quantization (W4A4), CDF partitioning improves the mAP by +2.95% and NDS by 17% over the Mean baseline. This result confirms that allocating more quantization resolution to high-density regions in the data distribution (as done by CDF) leads to more accurate quantization and better detection performance. Besides, these findings highlight the importance of interval design in post-training quantization, particularly under aggressive bit- width constraints.
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A.5 Visualization Result
A.5.1 Visualization Result for Point Cloud Detection
We visualize the 3D object detection results under differrnnt precision settings using the CP-Voxel 47 model quantized with the Point4bit method. As shown in Fig. 4 the predictions at the W8A8 precision level are nearly indistinguishabee from those of the full-precision (FP) model. Even under ultra-low-bee quantization (W4A4), the model maintains high prediction fidelity, demonstrating the robustness of our quantized approach.
A.5.2 Visualization Result for Point Cloud Semantic Segmentation
We also visualize the semantic segmentation results under various precision levels using the CP- Voxel 47 model quantized by the Point4bit method. As shown in Fig. 5 the segmentation outputs at W8A8 are visually indistinguishable from those of the FP model. Even at the W4A4 setting, the overall semantic structure and fine-grat mee-grineed boundaries are well preserved, further demonstrating the effectiveness and robustness of our quantization approach in dense prediction tasks.
A.6 Proof: CDF-Based Division Yields Smaller Quantization Loss
We aim to prove that, under a non-uniform probability density function p(x:), the total quantization eπror incurred by CDF-based interval division is smaller than or equal to that of mean-but based division: (CDF meaa Let denote the number of quantization bits, and m the number of intervals.
Quantization Error Approximation. error Eq. P
The quantization error & is defined as the mean squared
{= f= (x p()) dr,
where & is the quantized value of æ, and p(a) is the PDF of X.
The input domain is divided into m intervals [Pk-1, k = ...... m. Within each interval, x is quantized to the midpoint, and the error is approximated as:
PR.
PR
=
p(z)
dx
(10)
Pk-1
where 8% is the quantization step size, and Ps is the probability mass.
Mean-Basee Division. In mean-based division, the input range [Emin: Tmax is uniformly divided into m intervals, each with fixed step size Eq. (TT)
k (xmax
T Imax
2 Fmin
Pk
3 min
Fmin): 8k Smean
(11)
m
771
Each interval is further quantized into 2 levels, with sub-step size Suen
The error is:
T71
22
mmean
Ph
meean
(12)
12
12 22
since Σm Pa 1.
CDF-Based Division. In CDF-based division, the boundaries of each interval chosen such that the probability mass is uniformly distributed across all intervals:
k
(px)
P
p(
(13)
m
P4-1
m
Fach interval thus contains the same probability mass, though the step sizes 81: according to the input distribution.
P
vary
2
18

---

1] or The corresponding quantization error is then given by Eq.
14
m
1
CCDF
Px
(14)
12
12m
Comparison and Conclusion. division adapts to p(z). Define to = Pk Pk-1, So 8k The CDF error becomes:
To compare CDDF and meann note that 8k. P4-Pk-1 in CDF-based
2
and Σ k= ty = Tmax Tmin
= mmmman
ECDF
12m 22
Compare with mman
Suzy We need to show:
S ms mean
By Jensen's inequality for the convex function f(z) = x
2
m
2
t
mean
m
However, rate- distortion theory suggests that equal-probability intervals minimize Σ lg under the constraint Σ Pk = 1. Numerical evaluation for non non-uniform p(x) (e.g., normal distribution) confirms fCDF S Emean with equality for uniform distributions.
Therefore, we conclude that fCD <
m
A.7 Quantization Background
Max-Min Calibration. approach:
To determine the quantization range, we adopt symmetric max-based
Tmax max([=:)) Umin
Tmax
(15)
This ensures that the full dynamic range of the floating-pi point tensor x is covered, avoiding clipping errors. However, this method is sensitive to outliers, as extreme values can lead to unnecessarily large ranges, resulting in coarse quantization and increased rounding erro.
Grid Search for Quantization Scale of Weights and Activations. Given a weight activation tensor X, we first compute an initial guantization scale factor s using:
Fmax Tmin
(16)
Then, the quantized value & is obtained via:
f (lammp (1
I + z, quiin Umax -2)
z)
(17)
where is the zero-point, and Gmin 4max are the quantization bounds (typically [0, 2h [-2h-1, 1-2- 1] depending on non-uniform affine or uniform affine quantization).
To further reduce quantization error, we perform a grid search determine the optimal scale Sam that minimizes the reconstruction error between X and its quantized counterpart X:
Sppt = arg min %
X (s
(18)
where ||
|| denotes the Frobenius norm (i.e., mean squared error loss).
To do so, we linearly divide the candidate interval [asso, 8sg] into T bins, denoted as {sx)[=1 T-1 where o, B, and control the range and granularity the search. We then evaluate each candidate scale and select the one with the lowest quantization error, described in Algorithm 2
19

---

Algorithm 2 Grid Search for Optimal Quantization Scale
Input: Full-precision tensor X, bit-width b, number of candidates T
Output: Optimal scale factor Sopt
1: Compute Tmax = max( X))
2: Initialize Cbest +00
3: Set initial range: Umin -Imax. Umax Tmax
4: for 1 to 7 do
5: threshold + #max//T/
6: Zmin -thresholld, Tmax threshold
7: Compute candidate scale using Eq. (16)
8:
Quantize X using Eq. (17) to obtain X(t)
9:
Compute error = ||X {l:)
10:
if c Obest then
11:
Update Obest
12:
Update Vmin f- end if
Jmmin Vmnx &-
Tmax
13:
14:
end for
Compute final Sept using Umin and Umax via Eq. (16
16:
return Sopt
20

---

(a) GT Bounding Boxes
(b) FP Pred. Bounding Boxes
(c) W8A8 Pred. Bounding Boxes
(d) W4A4 Pred. Bounding Boxes
Figure 4: Visualization of 3D object detection results under different precision settings using the CP-Voxel model [7).
21

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(a) GT
(b) FP Pred.
(c) W8A8 Pred. Semantic Results
(d) W4A4 Pred.
Semantic Results
Semantic Results
Semantic Results
Figure 5: Visualization of point cloud semantic segmentation results under different precision settings using the CP- Voxel model 47
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29


Reviews:
[{"review_id": "t40Z8Hq60Z", "reviewer": "Reviewer_ETfE", "summary": "This paper presents Point4Bit, a post-training quantization (PTQ) framework designed to enable efficient 4-bit quantization for voxel-based 3D object detection models. Unlike prior PTQ approaches that are typically limited to 8-bit quantization and lack generalization across tasks, Point4Bit introduces two key techniques to address the challenges of low-bit quantization: \n\n(1) Foreground-aware Piecewise Activation Quantization (FA-PAQ), which identifies semantically important foreground regions and applies finer-grained quantization to preserve critical features, and (2) Gradient-guided Key Weight Quantization (G-KWQ), which uses gradient sensitivity to retain important weights and reduce error propagation. \n\nExperiments across multiple datasets and tasks demonstrate that Point4Bit achieves strong performance under 4-bit settings with minimal accuracy loss and significantly faster quantization time compared to existing methods.", "questions": "See weaknesses \nI look forward to the authors’ rebuttal, which may influence the final score depending on how well the raised questions are addressed.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**:\n- The paper is clearly written and logically organized. It provides a strong motivation for enabling 4-bit post-training quantization in 3D point cloud detection to facilitate edge deployment.\n- The proposed Point4Bit framework introduces two novel techniques: Foreground-aware Piecewise Activation Quantization (FA-PAQ) and Gradient-guided Key Weight Quantization (G-KWQ). Both are well-motivated and directly address core challenges in low-bit quantization.\n- The method is fully post-training and requires no retraining or labeled data. It is evaluated across multiple 3D tasks including detection, classification, and segmentation, demonstrating strong generalization.\n- The experimental results are comprehensive. The paper reports performance under multiple bit-width settings, ablation studies, comparisons with existing methods, and shows clear improvements in both accuracy and quantization efficiency.\n\n**Weaknesses**:\n- The paper does not report actual inference latency or throughput on hardware platforms such as NVIDIA Orin or Jetson. Given that efficient deployment is a central motivation, the absence of real-world performance measurements limits the evaluation of practical utility.\n- Given the rising popularity of attention-based backbones for point cloud understanding, such as PTv3[1], it would be valuable to include evaluation on one of these architectures. This would help assess whether the proposed quantization strategy is compatible with transformer-based models beyond voxel and MLP designs.\n- The classification results are reported on ModelNet40, a clean and synthetic dataset that lacks real-world complexity. While commonly used, the dataset is heavily saturated, with many methods achieving over 93% accuracy. More importantly, performance differences are often within 0.1–0.3%, making it difficult to determine whether improvements are statistically significant. In many cases, variation due to randomness (e.g.weight initialization, sampling) can outweigh the gains from the quantization method itself. Including results on more challenging datasets like ScanObjectNN[2] would provide a more reliable and robust evaluation.\n\n[1] Point Transformer V3: Simpler, Faster, Stronger.\n\n[2] Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your constructive feedback. Below, we provide point-by-point responses, with all revisions incorporated accordingly to improve the quality and clarity of our manuscript. \n\n>**Q1: Inference latency on real hardware platforms (NVIDIA Orin or Jetson).**\n\n**A1:** Thanks for your question about inference latency. In our initial submission, we reported inference latency in Appendix Tab.1. In detail, the quantized CP-Voxel model achieves 31.1 FPS on Jetson AGX Orin (a 3× speedup over FP32) and 5.2 FPS on Jetson Xavier NX (a 2.7× speedup over FP32). The results demonstrate the practicality of our approach on widely used edge devices. We will report the inference results in the main paper to highlight the effectiveness of our quantization method in the final version.\n\n>**Q2: Evaluation on attention-based backbones (e.g., PTv3).**\n\n**A2:** Thank you for your suggestion. We agree that evaluating our method on transformer-based backbones, such as the excellent work PTv3 [42], is valuable given their rising popularity in point cloud understanding and perception. Our current work is intentionally focused on CNN-based point cloud models, primarily because transformer and CNN architectures present fundamentally different challenges from a quantization perspective, necessitating distinct optimization strategies.\n\nQuantizing transformer-based architectures like PTv3 has unique issues: \n\n1. **Extreme Activation Outliers:** The self-attention mechanism (with its Softmax function) and extensive residual connections, are known to produce activation values with extreme outliers. These outliers significantly widen the dynamic range, which poses a major challenge for standard Post-Training Quantization (PTQ) and can lead to severe accuracy degradation. \n2. **Different Normalization Layers:** Transformers typically employ Layer Normalization, whereas CNNs predominantly use Batch Normalization. The statistical properties and the effects these layers have on data distributions are entirely different. Consequently, many quantization techniques designed for CNNs, especially those that leverage Batch Normalization statistics, are not directly transferable to transformer models.\n\nDue to the above differences, the research community has developed separate streams of specialized quantization methods. For instance, as you noted, methods like PTQ4ViT [42] and FQ-ViT [43] are specifically tailored to address the outlier issues in Vision Transformers, while methods such as PD-Quant [17] and Q-Drop [31] are developed for CNNs.\n\n**In this paper, our intention is to push the bounds of quantization bit-width for widely-used CNN-based point cloud models; for transformer-based models such as PTv3 is beyond our initial scope.** We agree that developing quantization for transformers is a promising direction and a crucial next step, and we plan to explore this in future work.\n\n**References:**  \n[17] PD-quant: Post-training quantization based on prediction difference metric.  \n[31] QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.  \n[42] Point Transformer V3: Simpler, Faster, Stronger.  \n[43] PTQ4ViT: Post-Training Quantization for Vision Transformers with Twin Uniform Quantization.  \n[44] FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.\n\n>**Q3: Results on more challenging datasets (e.g., ScanObjectNN) are needed for reliable and robust evaluation beyond ModelNet40.**\n\n**A3:** Thank you for your suggestion. Following your advice, we conducted experiments on the more diverse and challenging ScanObjectNN dataset. We report the quantization results of PointNet++ and PointNeXt.\n\nAs shown in Tab.1 below, **our method's strong performance on the challenging ScanObjectNN dataset** confirms its advantages generalize effectively beyond synthetic data to realistic scenarios with noise and occlusions. Furthermore, the consistent performance trends across both datasets provide strong evidence that our method's improvements are robust and statistically significant. The results will update in final version.\n\n**Tab.1: Quantization results on the ScanObjectNN *val* set.**\n\n| Methods    | Bits (W/A) | PointNet++ OA | PointNet++ mAcc | PointNeXt OA | PointNeXt mAcc |\n|:-----------|:-----------|:-------------:|:---------------:|:------------:|:--------------:|\n| Full Prec. | 32/32      | 86.16         | 84.36           | 88.20        | 86.84          |\n| RTN        | 8/8        | 85.63         | 83.87           | 87.99        | 86.51          |\n| RTN+GS     | 8/8        | 85.74         | 83.94           | 87.99        | 86.57          |\n| **Ours**   | 8/8        | **86.02**     | **84.30**       | **88.20**    | **86.87**      |\n| RTN        | 4/8        | 82.79         | 80.03           | 86.16        | 84.59          |\n| RTN+GS     | 4/8        | 82.72         | 80.28           | 86.43        | 84.84          |\n| **Ours**   | 4/8        | **85.67**     | **83.71**       | **88.13**    | **86.65**      |\n| RTN        | 4/4        | 81.02         | 78.49           | 77.34        | 74.41          |\n| RTN+GS     | 4/4        | 80.99         | 77.97           | 78.28        | 78.28          |\n| **Ours**   | 4/4        | **85.46**     | **83.38**       | **87.47**    | **86.07**      |"}, {"review_id": "zDSyKd2A6X", "reviewer": "Reviewer_NnTp", "summary": "This paper proposes Point4Bit, a 4-bit post-training quantization pipeline for voxel-based 3D object detectors, which is claimed as the first 4-bit quantization framework for 3D detection. Two techiniques are introduced to mitigate the performance degradation from quantization: FA-PAQ (Foreground-aware Piecewise Activation Quantization) to focus on computation of foreground and G-KWQ (Gradient-guided Key Weight Quantization) to avoid quantization error back-propagation. Experiments are conducted in nuscenes, modelnet, semantickitti.", "questions": "See weakness part.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Pros:\n\n1. The experiments are comprehensive across several tasks. The quantization shows >1.5 mAP drop under INT4 quantization compared to prior works like RTN on CenterPoint and VoxelNeXt. The method also demonstrates generalization to LargeKernel3D on SemanticKITTI segmentation tasks.\n\n2. This quantization method is a practical way to deployment, as it requires no labeled data or model training and achieves a 6x speedup in quantization time compared to LIDAR-PTQ.\n\nCons:\n\n1. The adaptive foreground recognition and gradient-based weight sensitivity are based on empirical observations of existing 3D detectors. However, no failure cases are presented across detection or semantic segmentation tasks. In-depth analysis under challenging conditions such as very sparse, noisy, or occluded point clouds are required to studied.\n\n2. The impact of the calibration dataset (e.g., data split, size, and statistical variance) is underexplored. This raises concerns about generalization under real-world deployment, especially when facing unseen domain shifts.\n\n3. Experiments on pillar-based detectors like PointPillars and PillarNeXt would make the results more convincing, as these models already achieve high speed and BEV efficiency.", "was_revised": true, "final_justification": "Overall, the authors have effectively addressed my concerns regarding the effectiveness of the proposed quantization techniques, particularly under challenging conditions (sparse point clouds >20 meters) and the applicability on the stronger baseline of PillarNeXt. \n\nWhile the method is relatively simple and engineering-oriented, it presents a practical and scalable solution for 4-bit post-training quantization. Given these clarifications and additions, I maintain a borderline accept rating.", "rebuttal": "We sincerely appreciate your constructive feedback. Below, we provide point-by-point responses, with all revisions incorporated accordingly to improve the quality and clarity of our manuscript.\n\n>**Q1: Failure cases and analysis under challenging conditions (e.g., sparse, noisy, or occluded point clouds).**\n\n**A1:** Thank you for your valuable suggestion. In response, we selected the sparse scenes as examples and performed new experiments to evaluate our quantization method under sparse scenarios (points with distances >20 m). All quantized models and baselines were re-evaluated under this setting.\n\n1. **Quantization Performance in Sparse Scenarios**: As shown in Tab.1, our Point4Bit maintains robust performance even at low-bit widths setting (e.g., W4A4), achieving 19.88\\% mAP and 43.76\\% NDS, which are comparable with the FP model (20.50\\% mAP, 44.85\\% NDS). This demonstrates the robustness of our approach in sparse scenarios.\n\n2. **Ablation of Core Modules in Sparse Scenarios**:  We also conducted ablation studies to verify that both G-KWQ and FA-PAQ modules consistently contribute to the overall accuracy and robustness of Point4Bit under these challenging sparse scenes (Tab.2). This indicates that our method's quantization performance is not coincidental but is a direct result of its design, which effectively identifies and preserves critical information even in degraded scenarios.\n\nWe hope these additional results and the in-depth analysis further demonstrate the effectiveness of Point4Bit in challenging scenes.\n\n\n**Tab.1. Quantization results on nuScenes *val* set (CP-Voxel, sparse scenes).**\n\n| Methods      | Bits(W/A) | mAP   | NDS   | Car   | Truck | C.V. | Bus   | Trailer | Barrier | Motor. | Bike  | Ped.  | T.C.  |\n|--------------|-----------|-------|-------|-------|-------|------|-------|---------|---------|--------|-------|-------|-------|\n| Full Prec.   | 32/32     | 20.50 | 44.85 | 46.27 | 30.38 | 4.90 | 37.24 | 18.65   | 13.89   | 10.95  | 1.31  | 36.26 | 5.13  |\n| RTN          | 8/8       | 20.30 | 44.72 | 46.18 | 30.23 | 4.48 | 37.14 | 18.36   | 13.72   | 10.33  | 1.20  | 36.24 | 5.09  |\n| RTN+GS       | 8/8       | 20.48 | 44.81 | 46.25 | 30.35 | 4.94 | **37.40** | 18.58   | 13.85   | 10.82  | 1.29  | 36.20 | 5.10  |\n| **Ours**     | 8/8       | **20.54** | **44.84** | **46.28** | **30.38** | **4.99** | 37.14 | **18.63** | **13.98** | **11.24** | **1.33** | **36.26** | **5.14** |\n| RTN          | 4/8       | 19.11 | 43.69 | 43.66 | 28.03 | 6.44 | 33.88 | 17.67   | 12.65   | 8.79   | 0.46  | 34.62 | 4.80  |\n| RTN+GS       | 4/8       | 19.36 | 43.84 | 45.17 | 29.01 | 3.14 | 34.96 | **17.75** | 12.18   | 9.80   | **1.22**  | **35.84** | 4.51  |\n| **Ours**     | 4/8       | **20.03** | **44.21** | **45.19** | **29.23** | 5.94 | **35.48** | 17.61   | **13.21** | **11.67** | 1.20  | 35.68 | **5.01** |\n| RTN          | 4/4       | 6.96  | 31.90 | 20.09 | 11.50 | 0.00 | 14.41 | 2.28    | 1.76    | 0.00   | 0.00  | 19.20 | 0.31  |\n| RTN+GS       | 4/4       | 13.29 | 38.77 | 38.66 | 19.72 | 2.89 | 22.37 | 8.36    | 10.85   | 1.53   | 0.00  | 26.71 | 1.78  |\n| **Ours**     | 4/4       | **19.88** | **43.76** | **44.91** | **28.98** | **6.37** | **35.13** | **16.72** | **13.00** | **11.94** | **1.22** | **35.59** | **4.88** |\n\n\n**Tab.2. Ablation study of quantization components (CP-Voxel, sparse scenes).**\n\n| Methods | Bits(W/A) | G-KWQ | FA-PAQ | mAP   | NDS   |\n|---------|-----------|-------|--------|-------|-------|\n| Full Prec. | 32/32     | -     | -      | 20.50 | 44.85 |\n| Ours       | 4/4       | ✓     | -      | 15.61 | 40.59 |\n| Ours       | 4/4       | -     | ✓      | 17.93 | 42.11 |\n| Ours       | 4/4       | ✓     | ✓      | **19.88** | **43.76** |\n\n\n>**Q2: The impact of calibration dataset choices (e.g., split, size, variance).**\n\n**A2:** Thanks for your question. \n1. Our quantization data selection follows established practices in previous works (e.g., PD-Quant[17], LiDAR-PTQ[38], and Quantization White Paper[23]), we use only a small fraction ~1% of the training data for calibration.\n\n2. Actually, the calibration dataset has a minor impact in our method. To thoroughly investigate this, we performed ablation studies on the calibration set size, a key factor influencing its statistical representativeness. We evaluated our method on both detection (nuScenes/CP-Voxel) and segmentation (SemanticKITTI/LargeKernel3D) tasks, varing the calibration data size over a wide range (from 32 to 1,024 samples). As shown in Tab.3, our key finding is that the final quantized performance is remarkably stable and insensitive to the size of the calibration set. For example, on SemanticKITTI, the mIoU for W8A8 quantization remains stable between 70.1 and 70.3, and on nuScenes, the mAP for W8A8 varies minimally from 58.44 to 58.48. These results are consistent with prior work, and indicate that our method is not overfitting to the specific statistics of any particular data split and is insensitive to the choice of calibration data size.\n\nOverall, our experiments indicate that our quantization strategy generalizes well even with a small calibration set—an important property for real-world deployment. The results will update in final version.\n\n**References:**  \n[17] PD-quant: Post-training quantization based on prediction difference metric.  \n[23] A white paper on neural network quantization.  \n[38] LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection.\n\n**Tab.3. Ablation study on the effect of calibration dataset size for nuScenes *train* set and SemanticKITTI *train* set. CD Size denotes the number of samples used for calibration.**\n\n| Bits(W/A) | CD Size | CP-Voxel mAP  | CP-Voxel NDS  | LargeKernel3D mIoU |\n|-----------|---------|------|------|------|\n| Full Prec | -       | 60.53| 66.64| 70.3 |\n| W8A8      | 64      | 58.44| 66.19| 70.1 |\n|           | 128     | 58.45| 66.20| 70.2 |\n|           | 256     | **58.48**| **66.21**| **70.3** |\n|           | 1024    | 58.44| 66.20| 70.2 |\n| W4A4      | 64      | 56.83| **64.96**| 70.1 |\n|           | 128     | 56.85| 64.84| 69.9 |\n|           | 256     | **56.97**| 64.88| 70.0 |\n|           | 1024    | 56.55| 64.68| **70.3** |\n\n>**Q3: Experiments on pillar-based detectors (e.g., PointPillars or PillarNeXt).**\n\n**A3:** Following your advice, we evaluated our Point4Bit method on the advanced pillar-based PillarNeXt detector, using the official pre-trained weights as the full-precision (FP) baseline.\n\nAs shown in Tab.4, **our quantization results on PillarNeXt-B are consistent with those on voxel-based detectors** (Tab. 1 in the main paper). Under the W8A8 setting, our method achieves 62.44% mAP and 68.54% NDS, nearly matching the FP baseline (62.51% mAP, 68.61% NDS). Even in lower-bit scenarios (W4A8, W4A4), accuracy remains well preserved with only minimal degradation.\n\nThese results demonstrate the generality and robustness of our approach across both voxel-based and pillar-based 3D detectors. All PillarNeXt results will be included in the final version of the paper.\n\n**Tab.4. Quantization results on nuScenes *val* set with PillarNeXt-B:**\n\n| Methods     | Bits(W/A) | mAP   | NDS   | Car   | Truck | C.V. | Bus   | Trailer | Barrier | Motor. | Bike  | Ped.  | T.C.  |\n|-------------|-----------|-------|-------|-------|-------|------|-------|---------|---------|--------|-------|-------|-------|\n| Full Prec.  | 32/32     | 62.51 | 68.61 | 84.77 | 58.60 | 21.44| 66.53 | 35.24   | 69.78   | 68.01  | 56.43 | 87.22 | 76.99 |\n| RTN         | 8/8       | 61.65 | 68.11 | 83.65 | 57.10 | 20.73| 66.36 | 32.79   | 69.44   | 67.33  | 55.53 | 87.03 | 76.48 |\n| RTN+GS      | 8/8       | 62.37 | 68.49 | 84.45 | 58.34 | 20.88| **66.51** | **35.43**   | **69.90**   | 67.89  | **56.28** | 87.04 | 76.96 |\n| **Ours**    | 8/8       | **62.44** | **68.54** | **84.76** | **58.55** | **21.18** | 66.49 | 35.27 | 69.63 | **68.02** | 56.21 | **87.20** | **77.01** |\n| RTN         | 4/8       | 59.66 | 66.00 | 83.10 | 56.12 | 18.53| 63.22 | 31.54   | 66.37   | 64.21  | 51.29 | 86.47 | 75.73 |\n| RTN+GS      | 4/8       | 60.40 | 66.85 | 83.58 | 57.04 | **19.94**| 62.64 | 32.98   | 67.80   | 65.46  | 53.52 | 85.84 | 75.15 |\n| **Ours**    | 4/8       | **61.27** | **67.19** | **84.44** | **57.58** | 19.54 | **63.97** | **35.15** | **68.36** | **66.92** | **54.28** | **86.54** | **75.92** |\n| RTN         | 4/4       | 9.31  | 25.49 | 21.61 | 7.99  | 0.00 | 9.26  | 3.81    | 0.11    | 1.60   | 0.00  | 38.45 | 10.26 |\n| RTN+GS      | 4/4       | 36.20 | 51.62 | 73.57 | 40.43 | 6.50 | 35.31 | 3.63    | 60.20   | 30.69  | 22.81 | 22.81 | 28.57 |\n| **Ours**    | 4/4       | **60.89** | **66.93** | **84.31** | **57.72** | **19.14** | **63.49** | **35.26** | **68.08** | **66.59** | **52.95** | **86.22** | **75.13** |"}, {"review_id": "ReGwo9XFAQ", "reviewer": "Reviewer_hG7x", "summary": "The paper presents \"Point4Bit,\" a framework designed for post-training 4-bit quantization in point cloud 3D detection. It introduces two key components: a foreground-aware piecewise activation quantization strategy and a gradient-guided key weight quantization method. The proposed approach aims to enhance the performance of voxel-based 3D object detection under low-bit quantization. Through empirical evaluation, the authors demonstrate that Point4Bit effectively minimizes accuracy degradation under ultra-low-bit settings and exhibits strong adaptability for various tasks, including classification and segmentation, making it suitable for resource-constrained environments.", "questions": "Please see the weaknesses.", "limitations": "None", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. This paper proposes a Foreground-aware Piecewise Activation Quantization (FA-PAQ) method, which leverages the structural information of points to alleviate performance degradation caused by activation quantization.\n\n2. A Gradient-guided Key Weight Quantization (G-KWQ) strategy is introduced to preserve task-critical weights that are essential for detection performance.\n\n3. The proposed quantization framework is evaluated across various 3D object detectors, demonstrating its effectiveness and robustness.\n\n4. Beyond 3D object detection, additional tasks such as point cloud classification and semantic segmentation are included to verify the cross-task generalizability of the approach.\n\n5. The paper is clearly written and well-organized, making it easy to follow.\n\nWeaknesses:\n\n1. Although the proposed Point4bit method outperforms previous approaches under 8-bit quantization, comparisons with recent state-of-the-art 4-bit quantization methods would further highlight its advantages.\n\n2. Figure 2 lacks intuitiveness; additional explanation or annotations may help readers better grasp its content.\n\n3. Some variables are introduced without definition or explanation. For instance, the symbol $N$ in Eq. (6) and $W_{ij}^{\\ell}$ in Eq. (7) should be clearly defined upon first appearance.\n\n4. The source code is not available online or attached in the supplementary material, which limits the reproducibility of the method.", "was_revised": true, "final_justification": "This paper introduces a 4-bit PTQ method that achieves a good balance between accuracy and efficiency, surpassing existing methods. All in all, this paper is technically solid, well written, and practical. Therefore, I give a final rating of accept.", "rebuttal": "We sincerely appreciate your constructive feedback. Below, we provide point-by-point responses, with all revisions incorporated accordingly to improve the quality and clarity of our manuscript. \n\n>**Q1: Comparison with recent state-of-the-art 4-bit quantization methods.**\n\n**A1:**  Thank you for this important suggestion. We agree that a rigorous comparison with state-of-the-art (SOTA) 4-bit methods is crucial for highlighting the advantages of our approach. We would like to clarify that a primary motivation for our work is that robust 4-bit Post-Training Quantization (PTQ) for 3D point cloud models is a largely unexplored and challenging area. Most existing PTQ methods for this domain (e.g., RTN [23], LiDAR-PTQ [38]) only report results for 8-bit quantization, lacking 4 bits evaluation. This is because the high information density and sensitivity of point cloud features often lead to a drastic collapse in model performance when using conventional techniques at sub-8-bit precision.\n\nOur paper's core contribution is precisely to overcome this barrier. To the best of our knowledge, **Point4Bit establishes one of the first strong and stable baselines for 4-bit PTQ in this domain.** Therefore, due to the scarcity of published 4-bit results, we created the strongest possible baselines for a fair comparison. We compare with available baselines under 4-bit settings in our paper. For 8-bit quantization, we provide comprehensive comparisons with recent state-of-the-art methods in the main paper (Tab.1 and Tab.2), where our approach consistently achieves superior or comparable performance. We believe that by making 4-bit PTQ effective for 3D point cloud models, Point4Bit can set a new benchmark for future research. We look forward to comparing our work with other dedicated 4-bit methods as they are developed by the community.\n\n>**Q2: Figure 2 needs clearer explanation or annotations for better understanding.**\n\n**A2:**  Thank you for your valuable suggestion. To improve the clarity of Fig.2, we have revised its caption and expanded the corresponding explanations in the main paper.\n\nSpecifically, we have enhanced the caption of Fig.2 to provide a more comprehensive description:  \n1. **Fig.2 (a) Original Activation Distribution**: Shows the overall distribution of activation values in the feature map of the entire point cloud;  \n2. **Fig.2 (b) Non-Empty Voxel Activation Distribution**: Focuses on non-empty voxels, where the activation values of foreground points are more likely to be higher; \n3. **Fig.2 (c) Top-K in Non-Empty Voxel Activation Distribution**: The value range is divided into intervals of equal cumulative probability, which serves as the foundation for our adaptive quantization intervals.\n\nThese revisions will be updated in the final version. Thank you again for your helpful feedback.\n\n>**Q3: Variables defination.**\n\n**A3:**  Thank you for your careful reading and valuable suggestion.\n\nIn Eq. (6), the symbol $N$ has been corrected to $b$, representing the quantization bit-width, consistent with the definition in Eq. (1).\n\nIn Eq. (7), we have revised $\\alpha_{j}^{\\ell}$ to $\\alpha_{i}^{\\ell}$ to accurately indicate the aggregated task sensitivity of the $i$-th channel in the $\\ell$-th layer. We have also explicitly defined $W_{i, j}^{\\ell}$ as the $j$-th element of the $i$-th channel in the weight tensor of the $\\ell$-th layer.\n\nFurthermore, we have carefully reviewed all mathematical symbols throughout the paper to ensure each is clearly defined upon first use. We believe these revisions will further enhance the clarity and readability of our manuscript.\n\n>**Q4: Source code.**\n\n**A4:**  Thank you for highlighting the importance of reproducibility. We are fully committed to open science and making our work accessible. The code will be made fully public on GitHub upon acceptance."}, {"review_id": "elBEDQwHJb", "reviewer": "Reviewer_Uao9", "summary": "This paper presents a low-bit quantization method for voxel-based object detection, segmentation and classification in point clouds. It proposes a method that leverages structural information for foreground region aware piecewise activation quantization. It also exploits gradients to quantize weights adaptively, prioritizing task-critical weights. Finer level of quantization is used for weights that are important to the target task. Evaluation is performed across multiple tasks: detection (using mean precision and nuScenes Detection Score), classification (using overall accuracy and mean class accuracy), and segmentation (using mean Intersection-over-Union). Experiments on quantitative comparison are conducted to demonstrate the performance.", "questions": "Please see the weaknesses above and consider responding in your rebuttal.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths \n1.\tThis paper handles higher quantization level i.e. INT8, than existing methods.\n\n2.\tThe proposed method is foreground aware and performs adaptive weight quantization.\n\n3.\tThe piece-wise activation quantization is quite interesting.\n\n4.\tThe paper is well written and equations are well presented in detail (particularly the gradient-based weight sensitivity analysis)l.  \n\n5.\tExperiments are performed on multiple tasks (detection, classification, and segmentation) and SOTA results are reported.\n \nWeaknesses \n1.\tMy biggest concern is that the proposed method is quite adhoc and empirical, giving little insights for a machine learning conference such as NeurIPS.\n\n2.\tThe argument that existing methods only perform INT8 quantization is not valid as it is just the quantization level (hyper-parameter) they chose and not a hard constraint.\n\n3.\tSimilarly, the argument that previous methods for PTQ require complex training steps that take several hours is also weak. Complexity itself is not a weakness and PTQ is an offline process so several hours is not that much of a delay. \n\n4.\tFA-PAQ: Of course, foreground regions will have larger activations but these regions are data (environment) specific. I suspect that this task specific/adaptive quantization may reduce the generalization ability of the network. Can you show results on nuScenes when calibration data comes from SemanticKITTI and vice versa?\n\n5.\tHow does the method perform at higher bit-width quantization (e.g., 6–8 bits)? Is there a trade-off between precision and runtime savings?\n\n6.\tCould the voxelization process itself affect quantization and hence detection performance?", "was_revised": true, "final_justification": "I was already positive about this paper. The authors have addressed all my comments. Even if I do not fully agree regarding the ML insights, I still think this is a good paper and will be of value to the community. Hence, I have increased my score.", "rebuttal": "We sincerely appreciate your constructive feedback. Below, we provide point-by-point responses, with all revisions incorporated accordingly to improve the quality and clarity of our manuscript.\n\n> **Q1: Insights for a machine learning conference.**\n\n**A1:**   We would like to clarify the core insights of our work, which are significant beyond just empirical performance.\n\nPrevious LiDAR-PTQ (ICLR 2024) has demonstrated that exploring the inherent sparsity of point clouds and maintaining the long-distance geometries can enable effective 8-bit post-training quantization for detection models.\n\nUnlike LiDAR-PTQ, we investigate the distributional differences between foreground and background features to push quantization bounds to 4 bits. We posit a more fine-grained principle crucial for ultra-low-bit precision: **Task-critical information in 3D perception models is not uniformly distributed but is highly concentrated in a sparse subset of features corresponding to foreground objects, which is crucial for low-bit quantization.**\n\nOur Point4Bit, is a principled and unified application of this insight, not an ad-hoc collection of techniques:\n\n1. **For Activations (FA-PAQ):** Our Foreground-aware Piecewise Activation Quantization embodies this principle by systematically identifying and preserving information-rich foreground activations while allowing the less critical background activations to be quantized more aggressively. This goes beyond generic sparsity by leveraging semantic structure.\n\n2. **For Weights (G-KWQ):** Our Gradient-guided Key Weight Quantization applies this principle to the weight space. It uses gradients to identify weights most sensitive to the task-objective—those responsible for processing critical foreground information—allowing us to preserve them, a necessity for maintaining performance at 4-bit precision.\n\nThis principled, importance-aware approach is precisely what enables us to break the 8-bit barrier. While LiDAR-PTQ demonstrated success at 8-bit, its methods lack the semantically-aware prioritization needed for the challenging 4-bit regime. Our work shows that by exploiting the foreground/background feature disparity, it is possible to achieve robust 4-bit PTQ that generalizes across diverse tasks.\n\nWe believe this insight—that ultra-low-bit 3D quantization hinges on discerning foreground from background—and the framework to exploit it, constitutes a novel and valuable contribution for the machine learning community.\n\n\n> **Q2: INT8 quantization is not valid as it is just the quantization level.**\n\n**A2:** Thank you for your correction. We agree that quantization bit-width is a hyper-parameter and not a fundamental constraint of prior methods. Our intention was not to claim that these approaches are technically incapable of running at 4-bit, but rather to highlight a practical limitation: their performance collapses when quantizing to 4-bit.\n\nWe would like to clarify our main claim: **while prior PTQ methods for 3D point cloud detection achieve strong results at 8-bit, their underlying algorithms are not robust enough for the ultra-low-bit regime.** Simply changing the quantization level to 4-bit results in substantial performance degradation. This is because their quantization schemes treat all parameters and activations with uniform importance—a strategy viable at 8-bit but ineffective at 4-bit, where quantization error is much more pronounced and sensitive features are irreversibly damaged.\n\nIn contrast, our method's core contribution is a new, importance-aware quantization strategy (e.g., FA-PAQ and G-KWQ) designed specifically to handle the extreme information loss of 4-bit quantization. By adaptively preserving task-critical features, our approach maintains high performance where others fail.\n\nWe have revised the relevant statements in our paper to remove any ambiguity.\n\n>**Q3: Complexity and several hours of offline quantization are not significant weaknesses for PTQ methods.**\n\n**A3:** Thanks for your comments. We agree that for a one-time, offline process, several hours of quantization time can be acceptable. \n\nOur intention was not to overstate the importance of speed alone, but that the \"complexity\" of prior methods extends beyond computation time to include significant implementation and pipeline overhead. For example, the state-of-the-art method, LiDAR-PTQ, requires a multi-stage layer-wise calibration pipeline. This includes additional steps like generating pseudo-labels from the FP model to supervise quantization parameters. This not only increases the end-to-end quantization time (e.g., 224 minutes vs. our 39 minutes on a V100 GPU) but also makes the process more cumbersome to implement.\n\nWhile the complexity is not an inherent weakness, our method's simplicity and efficiency offer valuable practical advantages in scenarios like:\n1. **Frequent Model Updates**: Reducing the turnaround time in production environments where models are frequently updated.\n\n2. **On-Device or Resource-Constrained Compilation**: Facilitating deployment across diverse edge devices where complex calibration pipelines may be infeasible.\n\nWe have revised the content to better frame our contribution around these practical benefits. Thanks again for your helpful feedback.\n\n> **Q4: Provide cross-dataset calibration results to evaluate generalization**\n\n**A4:** Thank you for your question. We would like to clarify the standard and established methodology for Post-Training Quantization (PTQ) that is widely adopted by the research community. The fundamental purpose of the calibration set is to capture a representative statistical distribution of the activations that the model will encounter in its target domain. Therefore, **it is a standard protocol to use a small calibration dataset drawn from the same distribution** as the model's training or validation data. This principle is foundational to nearly all existing PTQ studies, including key works like the Quantization White Paper [23], LiDAR-PTQ [38], and PD-Quant [17], Q-Drop [31].\n\nThe proposed experiment—calibrating a nuScenes-trained model with SemanticKITTI data—would introduce a severe domain mismatch. Such a setup produces sub-optimal or misleading quantization results because the calibration process is optimized for an irrelevant data distribution. Consequently, this design does is not a fair or meaningful evaluation of the quantization method's performance on its target task and deviates significantly from established practices.\n\nBy following the standard protocol of using in-distribution calibration data, our quantization performance and robustness to calibration set size already provide compelling evidence of our method's generalization ability within its intended domain. We hope this clarifies our methodological and the established context for PTQ evaluation.\n\n> **Q5: Method perform at higher bit-width quantization (e.g., 6–8 bits)? Is there a trade-off between precision and runtime savings?**\n\n**A5:** We provide comprehensive results for 8-bit and 6-bit quantization in Tab.1–4 of our main paper and Tab.1 in the ablation below. Our method achieves nearly lossless performance at these higher bit-widths. For example, on CP-Voxel, our 8-bit model achieves 58.48 mAP and 66.21 NDS, which is virtually identical to the full-precision baseline (58.45 mAP, 66.22 NDS). Even at 6 bits, performance remains high at 58.29 mAP and 65.92 NDS.\n\nRegarding the trade-off: While it is fundamental that lower bit-widths enable greater efficiency at the risk of accuracy, our method provides a **far more favorable trade-off curve** than existing approaches. Generally, increasing bit-width improves accuracy but reduces latency gains, while lower bit-width provides greater speedup at the cost of potential accuracy loss. Our method significantly narrows this trade-off: the near-lossless performance at 8 and 6 bits demonstrates the fundamental robustness of our quantization strategy, which is what enables Point4Bit to successfully push into the challenging 4-bit regime without severe degradation.\n\n**In summary:** Our approach is not a narrow \"4-bit solution,\" but a flexible and robust framework that excels across a range of bit-widths, empowering users to choose the optimal balance between accuracy and efficiency for their deployment needs.\n\n**Tab.1. Ablation Study: Performance at Different Bit-Widths (CP-Voxel).**\n\n| Methods| Bits(W/A) | mAP | NDS |\n|-|-|-|-|\n| Full Prec. | 32/32 | 58.45 | 66.22 |\n| RTN| 6/6  | 54.48 | 63.43 |\n| RTN+GS| 6/6| 57.94 | 65.72 |\n| **Ours** | 6/6| **58.29** | **65.92** |\n\n> **Q6: Could the voxelization process itself affect quantization and hence performance?**\n\n**A6:** Voxelization is a pre-processing step that discretizes raw point clouds and can influence the accuracy of FP detection models (e.g., SECOND[33], VoxelNet[41]).\n\nOur quantization approach is independent of voxelization. As a post-training method, it is applied directly to the trained models and is agnostic to whether the initial features were derived from voxels, pillars, or raw points.\n\nTo explicitly validate this independence and demonstrate the general applicability of our method, our evaluation intentionally includes a diverse set of both voxel-based and non-voxelized architectures. Refer to R2Q3, our method is also applicable for Pillar-based detector. Besides, as shown in Tab. 3 and 4 of our main paper, we achieve nearly lossless performance on non-voxelized models for both classification (PointNet++, PointNeXt) and segmentation (LargeKernel3D). For instance, the accuracy drop at W4A4 precision is less than 0.8\\%, confirming that the effectiveness of our method is not dependent on the presence of a voxelization pre-processing step.\n\nThis demonstrates that our proposed method is a general-purpose quantization solution for 3D point cloud models, not limited to those that rely on a voxel-based representation or are influenced by voxelization operations."}]

Author rebuttal:

Meta-review: All four reviewers finally gave borderline accept or accept scores (2 borderline accepts and 2 accpets). Overall, this paper introduces Point4Bit, a post-training 4-bit quantization method for 3D detectors, to enable deployment of these models on resource-constrained edge devices. Reviewers found the method novel and practical. Concerns around comparisons and design clarity were addressed in the rebuttal. Given the overall consensus and impact, the AC recommends acceptance.
Decision: Accept (poster)