Title: 3DET-Mamba: Causal Sequence Modelling for End-to-End 3D Object Detection
Venue: NeurIPS 2024
Accepted: yes
Keywords: 3D Object Detection

Mamba
Abstract: Transformer-based architectures have been proven successful in detecting 3D objects from point clouds. However, the quadratic complexity of the attention mechanism struggles to encode rich information as point cloud resolution increases. Recently, state space models (SSM) such as Mamba have gained great attention due to their linear complexity and long sequence modeling ability for language understanding. To exploit the potential of Mamba on 3D scene-level perception, for the first time, we propose 3DET-Mamba, which is a novel SSM-based model designed for indoor 3d object detection. Specifically, we divide the point cloud into different patches and use a lightweight yet effective Inner Mamba to capture local geometric information. To observe the scene from a global perspective, we introduce a novel Dual Mamba module that models the point cloud in terms of spatial distribution and continuity. Additionally, we design a Query-aware Mamba module that decodes context features into object sets under the guidance of learnable queries. Extensive experiments demonstrate that 3DET-Mamba surpasses previous 3DETR on indoor 3D detection benchmarks such as ScanNet, improving AP25/AP50 from 65.0\%/47.0\% to 70.4\%/54.4\%, respectively.

3DET-Mamba: State Space Model for End-to-End 3D Object Detection

Abstract

1 Introduction

The aim of 3D object detection [24, 43, 26, 35] from point clouds is to locate and recognize objects present in 3D scenes. It is a challenging task since point clouds are often irregular, sparse, and unordered. To directly work with point clouds, VoteNet [31] utilizes PointNet++ [33] to extract features from irregular point clouds, which are then fed into a decoder to generate the 3D bounding boxes. Motivated by the success of Transformer [40] in computer vision [8, 34, 20, 3, 19], some works [24, 14, 26, 4] try to design Transformer-based 3D detectors. 3DETR [26] proposes an endto-end transformer-based architecture to generate bounding boxes from raw point clouds. However, with limited computational resources, the quadratic complexity of the attention mechanism struggles to encode detailed representations, as it relies on increasing the point cloud resolution ( i.e. , longer point cloud sequences).

Recently, state space models (SSMs) [10, 45, 16] have received significant attention due to their linear complexity and long-sequence modeling ability. As Mamba [10] demonstrates a strong ability to handle long sequences in natural language processing, it has rapidly been employed on different tasks ( e.g. , image and 3D object classification [23, 60, 21, 22], video understanding [18, 1] and motion generation [55, 46]) and achieves great success. This motivates us to take advantage of Mamba in capturing long-range dependencies to extract more detailed representations in complex 3D scenes.

However, directly integrating Mamba [10] into the off-the-shelf detectors cannot achieve satisfactory results on 3D object detection tasks due to the following challenges. Firstly, SSMs like Mamba are causal models designed for handling 1-D sequence data, making it difficult to model unordered and

non-causal 3-D point clouds. Secondly, the original Mamba block focuses on modeling long-range global information but lacks the ability to extract local features, which are essential for point cloud learning [25, 29]. Besides, previous works primarily use Mamba as an encoder for single object analysis, it remains unexplored for more complex scene-level point clouds and detection tasks.

To tackle the above challenges and exploit the potential of Mamba [10] in 3D scene understanding, in this paper, we propose 3DET-Mamba, an end-to-end 3D detector that fully takes advantage of Mamba. To effectively extract scene representations from unordered point cloud sequences, we introduce a local-to-global scanning technique that can capture local geometry as well as global representation. Specifically, the local-to-global scanning technique utilizes the Inner Mamba block to capture finer details in each local patch and then uses Dual Mamba blocks to further extract scene features in a global view. Additionally, given that the naive Mamba block struggles to effectively model the relationship between object queries and scene features, we propose a query-aware Mamba block that decodes scene context information into object sets more effectively, guided by box queries. Extensive experiments on standard benchmarks show that our method can outperform 3DETR [26] on ScanNet and SUN RGB-D. Moreover, the performance can be further improved by increasing the input point cloud and learnable query sequences.

Our contribution can be summarized as follows:

We introduce Mamba into 3D scene perception for the first time and construct an end-to-end detector named 3DET-Mamba which fully takes advantage of Mamba.

We design a local-to-global scanning mechanism and develop the Inner Mamba and Dual Mamba, which account for both local detailed features and global spatial representations, respectively. Further, we propose a Query-aware Mamba to decode scene context features through learnable queries and generate bounding boxes for objects of interest.

Extensive experiments demonstrate that 3DET-Mamba outperforms previous 3DETR on both the ScanNet and SUN RGB-D datasets, proving that Mamba can serve as a promising foundational component for 3D scene understanding in the future.

2 Related Work

In this section, we will briefly introduce existing works on 1) 3D object detection, and 2) state space models and Mamba, especially Mamba in vision tasks.

2.1 3D Object Detection

With the development of robotics, 3D object detection [31, 9, 2, 51, 52] from point clouds has attracted increasing attention. Existing works can be divided into grid-based and point-based methods. Gridbased (voxel-based) methods convert the irregular point clouds into 2D grids [17] or 3D voxels [42, 35, 36, 59, 50] to utilize the strong feature extraction ability of CNN. Among them, FCAF3D [35] proposes a fully convolution anchor-free framework. CAGroup3D [42] designs a two-stage pipeline by introducing class-aware proposal generation and RoI-conv pooling. However, these grid-based methods struggle to utilize the inherent sparsity of the data and incur significant computational expenses due to the 3D convolution operations [31]. Inspired by PointNet family [32, 33], Pointbased methods [37, 26, 24, 31, 57, 48, 5] directly generate 3D proposals from raw points. As a pioneer, VoteNet [31] proposes a voting mechanism to generate accurate proposals by generating new points close to the objects' center. RBGNet [43] design a ray-based representation which largely improves the performance. Motivated by the success of Transformer on image detection, 3DETR [26] and Groupfree [24] introduce a transformer-based architecture and boost the detection accuracy. However, the quadratic complexity of the attention mechanism struggles to model long-range dependencies with limited computational resources. In this paper, we present a novel detection framework that can serve as a promising foundational module in 3D detection.

2.2 State Space Models and Mamba

State Space Models and Mamba. Inspired by the success in control theory, state space model (SSM) [13, 38, 28, 12] has been utilized to model long-range dependence. Structured State-Space Sequence (S4) [13] replaces CNN and Transformer with SSM in vision and language tasks by

Scene Feature Aggregator

···

Patch Partition

Dual Mamba Block

···

···

Query-aware Mamba

Block

×

N

MLP

Encoded

features

Box

queries

···

Decoder

combining linear SSM and HiPPO [11] framework. Smith et al. [38] propose S5 layer which designs a multi-input, multi-output SSM which outperforms S4 layer in both performance and efficiency. More recently, Mamba [10] with selective SSM is introduced which achieves higher performance than Transformer and leads to lots of further research on SSM [30, 44, 41]. For example, MoE-Mamba [30] and GraphMamba [41] combine Mamba with MoE and Graph data, respectively.

Mamba in Vision. Thanks to the breakthrough in natural language processing, Mamba has been rapidly transferred to various vision tasks [60, 23]. Vim[60] proposes a bi-directional SSM that can efficiently compress the vision representation and achieve satisfactory results on multiple vision tasks at low cost. VMamba [23] employs a cross-scan module to enable 1D selective scanning in 2D image space. Mamba-Unet [47] and MedMamba [53] introduce Mamba to medical image segmentation and classification, respectively. Video Mamba Suite [1] and VideoMamba [18] verify the effectiveness and efficiency of Mamba in various video tasks. QueryMamba [58] combines a query-based transformer decoder and the Mamba encoder to handle video action forecasting tasks. TM-Mamba [46] modifies the Mamba parameters as the function of the input and text query to ground the human motion. More recently, several works [54, 22, 21, 15] have explored the feasibility of Mamba on 3D tasks by introducing different point cloud ordering strategies. For example, PointMamba [21] utilizes Mamba to model the global information of 3D point clouds through a reordering mechanism and largely reduces the computation cost. Despite the great success achieved in object-level classification and part segmentation, Mamba in 3D scenes is under-explored. In this paper, we propose 3DET-Mamba which fills the gap of Mamba in 3D scene perception with designs like local-to-global scanning, dual Mamba, and query-aware Mamba.

3 Method

The overview framework of 3DET-Mamba is shown in Fig. 1. To better illustrate 3DET-Mamba, we first briefly review SSMs in Sec. 3.1. Then, we provide an overview of our 3DET-Mamba in Sec. 3.2. In Sec. 3.3 and Sec. 3.4, we detail the design of the encoder and decoder, respectively.

3.1 Preliminaries: State Space Models

State Space Models (SSMs) are derived from continuous systems and have been widely used in sequence modeling recently. Through a hidden state h ( t ) , SSMs can efficiently map 1D sequences input x ( t ) to the output y ( t ) using the following equations:

where A ∈ R denotes the evolution matrix, and B ∈ R N × 1 , C ∈ R 1 × N are the projection matrices. To make SSMs can handle discrete signals such as language and point clouds, S4 [13] and Mamba [10] use a timescale parameter ∆ to transform the continuous parameters A and B into discrete ones A

and B . Specifically, Mamba uses zero-order hold (ZOH) method as follows:

Then, the Eq. (1) can be reformulated to the discrete version as follows:

Finally, a global convolution can be used to perform the model's calculation:

where L denotes the length of input sequence and K ∈ R L presents a convolution kernel.

3.2 Overview

As shown in Fig. 1, our 3DET-Mamba takes a set of N points S ∈ R N × d as input and generates a set of 3D (oriented) bounding boxes with semantic labels for all objects of interest. Specifically, 3DET-Mamba mainly consists of the Mamba-based scene feature aggregator and decoder designed for 3D object detection. The 3D feature aggregator combines Inner Mamba and Dual Mamba blocks to perform local-to-global scanning, extracting both the fine-grained local geometries and global contexts within the scene. On the decoding side, the decoder employs query-aware Mamba and Multi-Layer Perceptrons (MLPs) to decode the aggregated 3D features into discrete sets of objects. In the following sections, we will detail the designs of each component.

3.3 Scene Feature Aggregator

Recent advancements in point cloud processing have demonstrated the importance of capturing both local geometric features and global scene information [25]. However, previous methods primarily focus on global modeling using state-space models [21, 22, 54], resulting in a lack of fine-grained details. To tackle this challenge, we propose a novel local-to-global scanning technique that emphasizes scanning locally to uncover these finer details in each patch, followed by global scanning to capture the dependencies among local features.

In this section, we introduce a novel Mamba-based scene feature aggregator designed for localto-global scanning, which is aimed at learning detailed representations of complex 3D scenes, as illustrated in Fig. 2. Specifically, our scene feature aggregator includes the Inner Mamba block and Dual Mamba block, which will introduced in Sec. 3.3.1 and Sec. 3.3.2 respectively.

3.3.1 Inner Mamba

Given an unordered N point cloud sequence { x 1 , x 2 , . . . , x n } , where x i ∈ R d and d is the dimension of features, farthest point sampling (FPS) is first used to select K key points from the input point cloud. Then k-nearest neighbors (KNN) is utilized to identify N 0 nearest neighbors for each key point, subsequently forming K patches P ∈ R K × N 0 × d .

Previous works [33, 21, 22] employ MLPs to learn features from each patch, they struggle with the effective aggregation of local features. To address this, we treat the aggregation of local features as a sequence-to-sequence generation process and use a causal Mamba model to extract local features within a patch. Specifically, points within each patch are first normalized and then sorted based on their distance from the key point. These sequential points are fed into a lightweight Mamba block, of which the dimensions are reduced compared to the original Mamba block, to generate a new feature sequence. Finally, max-pooling is used to obtain the embedding for each patch, denoted as F s ∈ R K × C , where K denotes the number of patches and C is the embedding dimension.

3.3.2 Dual Mamba

To encode point cloud data, previous works such as PointMamba [21] propose learning 3D representations by utilizing the original Mamba, which is initially designed for 1-D ordered sequences. Since understanding 3D data requires capturing global information, Point Cloud Mamba [54] suggests reversing the order of tokens and employing both forward and backward State Space Models (SSMs) to better capture the global context. However, due to the unordered and irregular nature of point

Distance from

center

···

···

Linear

SSM

Conv1d

Linear

Linear

SiLU

Serialized point cloud

···

···

···

···

Pooling

Pooling

FPS

NPS

SSM

Conv1d

Linear

Linear

SiLU

···

···

Linear

SSM

Conv1d

Linear

Linear

SiLU

···

FPS token sequence

NPS token sequence

Dual Mamba Block

×3

Sample and

Group

Sample and

Group

Algorithm 1 Dual Mamba Block

clouds, simply reversing the order of tokens cannot ensure the causal dependency of the point cloud sequence and may lead to unreliable results.

To mitigate such a problem, we introduce a novel Dual Mamba block (as shown in Fig. 2) to model long-range dependencies from the global view. Specifically, we treat F s ∈ R K × C as tokens and sort them based on their coordinates into two categories: farthest and nearest neighbor orders. The former strategy enhances the model's perception of spatial distribution by maximizing the distances between adjacent points in the sequence, whereas the latter ensures that adjacent points remain spatial neighbors, thus maintaining local consistency. The Dual Mamba block incorporates two branches to handle FPS and NPS token sequences, which first undergo normalization and independent linear projection. For each sequence, an initial 1D convolution transforms it into x ′ o , which is then projected

into A o , B o , and ∆ o . Subsequently, A o and B o are discretized using ∆ o . Finally, the corresponding tokens from the two branches are added and passed through a linear layer to generate the scene representations. The specific details are shown in Algorithm 1.

SSM

Conv1d

Linear

···

Linear

SSM

Conv1d

Linear

Linear

SiLU

···

Query sequence

Scene features

···

3.4 Decoder

DETR-based models leverage a set of object queries to extract features for object classification and localization. However, directly using scene context as a prefix and concatenating it with queries in the Mamba model leads to suboptimal performance, as it struggles to capture discriminative features for independent queries. To address this, we introduce the Query-aware Mamba block for the decoder, as shown in Fig. 3, which effectively models the relationship between learnable queries and scene features to generate bounding boxes.

We first generate object queries by selecting a defined number of M points from the set of K key points using FPS, ensuring these query points cover the entire scene. For each of these points, we follow 3DETR [26] to transform their spatial coordinates into positional embeddings using the Fourier Transform. These embeddings are subsequently processed through an MLP to produce the initialized query embeddings.

In detail, the decoder is composed of D same blocks and each block consists of a query-aware Mamba block and multiple MLP layers. The Query-aware Mamba block takes box queries and scene context as input, extracting tasked-related features from the scene context guided by the learnable queries. Specifically, each query sequence F q is fed into a standard Mamba block to model the dependencies between queries. This process can be formulated as follows:

Meanwhile, scene features undergo the same process as the query sequence. Then, by multiplying the scene features with query embeddings, scene contexts are integrated into the query embeddings, and the updated queries are then passed through multiple MLP layers. After D blocks decoding process, the bounding boxes and semantic categories can be generated using MLP-based heads. Please refer to the Algorithm 2 for more details.

3.5 Training Objectives

We adopt the same training objectives as [26] to train 3DET-Mamba. Specifically, we use the bipartite graph to match the set of predicted 3D bounding boxes { ˆ b } with the ground truth boxes { b } , denoted

Algorithm 2 Query-aware Mamba Block Input: 3D scene features F s t - 1 : ( B , K , C ) , Box query sequence F q t - 1 : ( B , M , C ) Output: Query sequence F q t : ( B , M , C ) 1: for o in { s , q } do 2: F o t - 1 ′ ← Norm ( F o t - 1 ) 3: x o : ( B , - , C ′ ) ← Linear x o ( F o t - 1 ′ ) 4: x ′ o : ( B , - , C ′ ) ← SiLu ( Conv o ( x o )) 5: /* Disc and SSM */ 6: A : ( B , - , C ′ , D ) , B : ( B , - , C ′ , D ) , C : ( B , - , C ′ , D ) ← Disc ( x ′ o ) 7: y o : ( B , - , C ′ ) ← SSM ( A , B , C )( x ′ o ) 8: z : ( B , - , C ′ ) ← Linear z ( F s t - 1 ) 9: y ′ o : ( B , - , C ′ ) ← y o ⊙ SiLu ( z ) 10: end for 11: y ′ q : ( B , M , K ) ← y q ⊙ y ′ s 12: F q t : ( B , M , C ) ← Linear F ( y ′ s ) ⊙ y ′ q 13: F q t : ( B , M , C ) ← F q t + Norm ( F q t - 1 ) 14: return F q t ;

as L giou ( ˆ b, b ) . Then we calculate the discrepancies between { ˆ b } and { b } using ℓ 1 loss for centers and dimensions, and Huber loss for angular residuals:

here, λ c , λ d , λ a are set as 5, 1, and 0.5. We also employ cross-entropy losses to assess angular and semantic classifications:

here, λ ac and λ s are set as 0.1 and 1. Finally, the total loss is formulated as:

4 Experiments

In this section, we first describe our experiment setups, including our benchmark datasets, metrics, and implementation details in Sec. 4.1. Then we present our main results in Sec. 4.2 and take out ablation studies to analyze the effectiveness of the proposed component in Sec. 4.3. Finally, we showcase some visualization results in Section Sec. 4.4.

4.1 Datasets, Metrics, and Implementation Details

Datasets. Following previous works on 3D indoor object detection, we evaluate our models on two challenging benchmarks: SUN RGB-D [39] and ScanNet [7]. The SUN RGB-D [39] dataset consists of 10,335 single-view RGB-D scans, with 5,285 used for training and 5,050 for validation. Each sample is annotated with rotated 3D bounding boxes. Following [31, 26], we convert the RGB-D images into point clouds using camera parameters and evaluate models on the 10 most common object categories. ScanNet [7] comprises 1,201 training samples and 312 validation samples, with each sample annotated with axis-aligned bounding box labels for 18 object categories.

Implementation Details. The input to our model is a point cloud P ∈ R N × 3 representing a 3D scene, with N set as 20,000 for SUN RGB-D [39] and 40,000 for ScanNet [7]. We employ a single-layer inner mamba block that generates 2048 patches, each with 256-dimensional features. The dual mamba encoder has 3 layers and outputs scene features with a hidden dimension of 256. The decoder has 8 layers and is closely followed by MLPs as the bounding box prediction head. During training, we employ standard data augmentation methods, including random cropping, sampling, and flipping. We use the AdamW optimizer with a base learning rate of 7 × 10 -4 , decayed to 10 -6 using a cosine schedule, and a weight decay of 0.1. Gradient clipping with an ℓ 2 norm of 0.1 is applied to stabilize training. The whole model is implemented in PyTorch, and all experiments are conducted on 8 NVIDIA 3090 GPUs (24 GB) with a total batch size of 64.

4.2 Comparisons on 3D Object Detection

4.3 Ablation Studies

Analysis of the encoder. To verify the effectiveness of the designed Mamba-based encoder, we first conduct ablation studies on Inner Mamba and Dual Mamba. Specifically, we replace Inner Mamba and Dual Mamba with Pointnet++ and Transformer. As reported in Tab. 2, transforming Inner

Effect of Query-aware Mamba. We conduct ablation studies on the decoder to demonstrate the effectiveness of the designed Query-aware Mamba block. We compare our module against the following baselines: (a) a transformer-based decoder as proposed in 3DETR [26]; (b) a naive approach that directly concatenates 3D scene information with queries, feeding them into the Mamba block and leveraging Mamba's state transition matrix for further modeling; and (c) our proposed Query-aware Mamba block. Additionally, all experiments are conducted with the same encoder and training strategy to ensure a fair comparison. As demonstrated in Tab. 4, the decoder based on the Queryaware Mamba block achieves superior results compared to both baselines. This improvement can be attributed to two main factors: (1) Directly concatenating queries and 3D scene representations do

Ori. Mamba

Bi. Mamba

Dual Mamba

65.4

66.4

66.9

47.7

47.5

48.7

not effectively model the relationship between them, making it difficult to capture the most relevant 3D features for each query. (2) Our Query-aware Mamba decoder more effectively aggregates dependencies between queries and scene features, enhancing the extraction of task-relevant features from the context.

4.4 Qualitative Results.

To demonstrate the effectiveness of 3DET-Mamba more intuitively, we show some visualization results. As shown in Fig. 4, we can observe that our 3DET-Mamba can accurately detect objects.

Input

Our

GT

5 Conclusion

In this paper, for the first time, we exploit the potential of Mamba in 3D object detection tasks. Specifically, we introduce 3DET-Mamba, an end-to-end detector based on encoder-decoder architecture. We first propose an SSM-based encoder that uses an Inner Mamba block to capture local geometric information and uses Dual Mamba blocks to further aggregate features in a global view. Besides, a Query-aware Mamba module is designed to effectively decode scene representations into object sets with the guide of learnable box queries. Extensive experiments on standard benchmarks like ScanNet and SUN RGB-D demonstrate the effectiveness of 3DET-Mamba, proving Mamba as a promising building block for future 3D scene understanding. In addition, with the increased length of input point sequence and query sequence, the performance will be further boosted since Mamba is an expert in long sequence modeling.

6 Limitations and Broader Impacts

While 3DET-Mamba has demonstrated effectiveness in modeling point cloud sequences, we have yet to explore its potential for handling other 3D data types, such as meshes. Additionally, given Mamba's

strength in modeling long-sequence data, a promising future direction is to develop Mamba-based 3D foundation models capable of addressing a broader range of scene-level tasks, including 3D dense caption, visual grounding, and 3D QA. We leave these explorations for future work.

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NeurIPS Paper Checklist

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes 3DET-Mamba, the first attempt to exploit the State Space Model for end-to-end 3D object detection. It introduces a local-to-global scanning technique, including an Inner Mamba block to capture local geometry and Dual Mamba blocks to extract scene features in a global view. Furthermore, it proposes a query-aware Mamba block to effectively decode scene context information into object sets with the guidance of box queries. Experiments show promising results compared to 3DETR and validate the effectiveness of the proposed modules with detailed ablation studies.\n\nSTRENGTHS:\n- The basic idea is easy to follow, and the key challenges are clearly presented.\n- The methodology and implementation details are also clear, with good illustrations and mathematical / algorithm presentations.\n- The proposed framework is the first to successfully adopt Mamba in end-to-end 3D object detection.\n- The experimental results and ablation studies are solid and can support the method convincingly.\n\nWEAKNESSES:\n- One of the motivations is the computational costs of transformers compared to Mamba, but there are no analyses regarding memory costs, latency, etc. I am curious about more advantages of the Mamba framework in this task, such as computational efficiency and scaling up performance, except for the current traditional benchmarks.\n- Although the paper proposes several methods to deal with the unordered point clouds in the ordered manner that is inherently designed in the State Space Model, even though the performance also seems good, the mechanism is still not most suitable for this task or handling point clouds. I would expect a more matching mechanism to deal with point clouds with the fundamental idea of the State Space Model instead of converting the problem with a non-perfect workaround.\n- There are several typos and I just provide a few examples in Questions. The paper needs more check on such details.\n\nQUESTIONS:\nSec. 4.2 title: Obejct -> Object\nline 241: Transforme -> Transformer\n\nLIMITATIONS:\nNone.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes an end-to-end 3D detector named 3DET-Mamba that fully takes advantage of Mamba. 3DET-Mamba can model long-range global information (\bDual Mamba) while exploiting local information (Inner Mamba). Experiments conducted on the ScanNet and SUN RGB-D datasets validate the effectiveness of the proposed method.\n\nSTRENGTHS:\n- The proposed method is simple and effective: 1) The use of distances from the center to rank points and query features is intriguing and straightforward to implement; 2) Employing FPS and NPS simultaneously to generate token sequences appears to be a novel approach, effectively modeling long-range dependencies while maintaining local consistency.\n- The writing is clear and concise, making it easy to understand.\n- The experimental results are impressive.\n\nWEAKNESSES:\n- The structure depicted in Figure 3 is inconsistent with the description provided in Algorithm 2. Specifically, the branch for the Query sequence in Figure 3 lacks the inclusion of Linear-SiLU, which is mentioned in Algorithm 2.\n- In Table 4, 1) the first column should correctly be labeled as \"mAP@0.25,\" and the second column should be labeled as \"mAP@0.5.\" 2) when using the Transformer as a decoder, the value of mAP@0.5, which is 42.6%, appears abnormal compared to other results. Specifically, in Table 1, the model DETR-m achieved 65.0% mAP@0.25 and 47.0% mAP@0.5. However, in Table 4, the first model listed achieves 64.3% mAP@0.25 but only 42.6% mAP@0.5. Are there any mistakes?\n- Line 241 `PointNet++ and Transforme` -> 'PointNet++ and Transformer'.\n- Line 180-181, `We randomly choose an initial point and sort the remaining points based on their distance to this point`, are the experimental results stable? It seems unbelievable.\n\nQUESTIONS:\nSee the Weaknesses.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes leveraging Mamba blocks for 3D point cloud modeling in the form of 3DET-Mamba, an application of SSM for 3D object detection. Similar to the prior 3DETR model, this approach partitions the point cloud into \"patch\" using Mamba blocks to capture local information, complemented by global modeling through dual Mamba blocks (organized by farthest and nearest point order). Additionally, query-aware Mamba blocks, akin to transformer blocks, are designed to decode objects in a DETR-like manner. The novelty of the approach lies in its ability to enhance 3DETR by integrating SSMs innovatively for 3D indoor scene understanding, utilizing ScanNet and SUN RGB-D datasets.\n\nSTRENGTHS:\n1. The proposed architecture adopts a Mamba-style approach and introduces 3D-specific customizations, such as partitioning the point clouds using an inner Mamba network, employing FPS/NPS ordering of points, and implementing a query-aware Mamba block.\n2. The experiments detailed in Tables 2, 3, and 4 conduct ablation studies that demonstrate the effectiveness of the Mamba block compared to baseline designs using transformers, although these comparisons do not include assessments of speed.\n\nWEAKNESSES:\n1. Mamba is praised for handling linear complexity and long-range sequences better than transformers, which have higher computational demands. However, the paper lacks theoretical analysis or experimental evidence to compare time and space complexities between Mamba and traditional architectures. The effects of changing point resolutions or patch sizes on performance are also not explored.\n\n2. The clarity of some ablation studies is lacking. For example, the role of ranked queries, a key contribution, is not evaluated. The relevance of NPS and FPS ordering in modeling patch sequences remains ambiguous. Additionally, the rationale for query-aware Mamba blocks is unconvincing; they seem to be simply added to cross-attention blocks with extra links. Table 4 does not clearly show that improvements are due to the Mamba block rather than just increased computational power.\n\n3. The paper does not provide solid proof of Mamba’s effectiveness in terms of performance or speed. It omits a comparison with the once leading FCAF3D detector, which scored 71.5 on ScanNet, without explanation. Comparisons of inference speeds across architectures are also absent.\n\nQUESTIONS:\nBesides weakness part, there are some other questions:\n1. Some terms could potentially cause confusion. For instance, FPS is an acronym for farthest point sampling, but in the context of the dual Mamba, it refers to farthest point order, while NPS refers to nearest point order.\n2. Are there any existing studies that use Mamba in a query-based method? It would be beneficial to reference these works to better understand the uniqueness of this module.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-13"}