Title: Point4Bit: Post Training 4-bit Quantization for Point Cloud 3D Detection
Venue: NeurIPS 2025
Accepted: yes
Keywords: Point Cloud Detection

Post-training Quantization

Model Compression
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.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \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?\n\nQUESTIONS:\nPlease see the weaknesses above and consider responding in your rebuttal.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nPlease see the weaknesses.\n\nLIMITATIONS:\nNone", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\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.\n\nQUESTIONS:\nSee weakness part.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nSee weaknesses \nI look forward to the authors’ rebuttal, which may influence the final score depending on how well the raised questions are addressed.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}