3DET-Mamba: Causal Sequence Modelling for End-to-End 3D Object Detection
Authors: Mingsheng Li
Jiakang Yuan
Sijin Chen
Lin Zhang
Anyu Zhu
Xin Chen
Tao Chen
Venue: NeurIPS 2024 poster (2024)
Keywords: 3D Object Detection
Mamba
Primary area: machine_vision

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
Mingsheng Li Jiakang Yuan 1., Sijin Chen 1.2 Lin Zhang',
Anyu Zhu', Xin Chen?, Tao Chen
Fudan University Tecent PCG
Equal contribution f *Corresponding author
Abstract
Transformer-basee architectures have been proven successful in detecting 3D objects from point clouds. However, the quadratic complexity of the attention mechanism struggles encode rich information 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- which novel SSM-based model designed for indoor 3D object detection. Specifically, we divide the point cloud into different patches and use lightweight yet effective Inner Mamba to capture local geometric information. To observe the scene from global perspective, we introduce a novel Dual Mamba module that models the point cloud in terms of spatial distribution and continuity. Additionally, we design Query-aware Mamba module that decodes context features into object sets under the guidance learnable queries. Extensive experiments demonstrate that previous 3DETR on indoor 3D detection benchmarks such as ScanNet, improving AP@0.25/AP@0 from 55.0% to 70.4% respectively.
surpasses
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-bane based 3D detectors. 3DETR [26] proposes an end- to-end transformer-base
architecture to generate bounding boxes from 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 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 3D object detection tasks due to the following challenges. Firstly, SSMs like Mamba causal models designed for handling 1-D sequence data, making it difficult to model unordered and
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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non-causal 3-D point clouds. Secondly, the original Mamba block focuses on modeling rangee 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, 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-gel -oo-olballl scanning technique that can capture local geometry as well as global representation. Specifically, the local-to-globa) 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-Mambe 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 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,
51, 52] from point clouds has attracted
increasing attention. Existing works can be divided into grid-based and point-based methods. Grid- based (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 fully convolution anchor-free framework. CAGroup3D [42] designs two-stage pipeline by introducing class-aware proposal generation and Rol-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], Point- based methods [37, 26, 24, 31, 57, 48, 5] directly generate 3D proposals from raw points. As pioneer, VoteNet [3] ]] proposes a voting mechanism to generate accurate proposals by generating new points close to the objects' center. RBGNet [43] design ray-based representation which largely improves the performance. Motivated by the success of Transformer on image detection, 3DETR [26] and Groupfree [24] introduce 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) [13,33 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

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0000 P00000
Inner Mamba Block
Dual Marbaa Block
Query-award Mamba
Patch Partition
Encoded
features
Block
MLP
queries
Scone Feature Agprogator
Decoder x.N
Figure I:
The overview of the proposed 3DET-Mamba. The point clouds are first patched and fed into the Inner Mamba block learn fine-grained local features, which are then Mamba block to extract global representations. These encoded scene information andd box queries go through the decoder, which includes Query-aware Mamba blocks and MLPs to generate the final bounding boxes. We employ the bipartite graph to match the predicted boxes with the ground truth and use set loss for end-to-end optimization. Color is utilized only for visualization purposes.
the Dual
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 Mamha with MoE and Graph data, respectively.
Mamba in Vision. Thanks the breakthrough in natural language processing, Mamba has been rapidly transferred to various vision tasks [60, 23]. Vim[60] proposes bi-directional SSM that can efficiently compress the vision representation and achieve satisfactory results on multiple vision tasks low cost. VMamba [23] employs cross-scan module enable ID selective scanning in 2D image space. Mamba-Unet [47] and MedMamba [53] introduce Mamba to medical image segmentation and classification, respectively. Video Mamba Suite [i] and VideoMamba [18] verify the effectiveness and efficiency of Mamba in various video tasks. Query Mamry [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 model the global information of 3D point clouds through 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, propose 3DET-Mamba which fills the gap of Mamba in 3D scene perception with designs like local-to-global scanning, dual Mamba, and query-awar 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 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 hidden state h()), SSMs can efficiently map ID sequences input (t) to the output y(t) using the following equations:
(((t) Ah(t) + Bx((), v(t) Ch(t).
where A E R denotes the evolution matrix, and B R Nx1_ C = R are the projection matrices. To
make SSMs can handle discrete signals such as language and point clouds, $4 [13] and Mamba [10] timescale parameter A to transform the continuous parameters A and B into discrete ones A
3

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In this section, we introduce novel Mamba-based scene feature aggregator designed for local- to-global scanning, which is aimed at learning detailed representations of complex 3D scenes, as illustrated Fig. 2. Specifically, our scene feature aggregator includes the Inner Mamba block and and B. Specifically, Mamba uses zero-order hold (ZOH) method as follows:
A exp AA): B (AA) (exp) - I) AB.
(2)
Then, the Eq. (1) can be reformulated to the discrete version as follows:
hr Ah,- B y/ Ch.-
Finally, global convolution can be used to perform the model's calculation:
K = (CB, CAB .... CA 1ar'BB BB) y x K,
(4)
where I. denotes the length of input sequence and K R presents a convolution kernel.
3.2 Overview
As shown Fig. 1, our 3DET-Mambs takes a set of N points E Rxxx as input and generates 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 -oglbbal scanning, extracting both the fine-gra grained local geometries and global contexts within the scene. On the decoding side, the decoder employs query- 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 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, propose 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.
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 point cloud sequence {I1, "2. where x; E .dd and 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 No nearest neighbors for each key point, subsequently forming K patches P R Xd
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, treat the aggregation of local features eequence-to-sequemee generation process and use a causal Mamba model to extract local features within patch. Specifically, points within each patch are first normalized and then sorted based their distance from the key point. These sequential points are fed into a lightweight Mamba block, reduced compared to the original Mamba block, generate
of which the dimensions feature sequence. Finally, max- maxx-pooling is used to obtain the embedding for each patch, denoted as 1s E Kx where K denotes the number of patches and the embedding dimension.
3.3.2 Dual Mamba
To encode point cloud data, previous works such as PointMamba [21] propose learning 3D represen- tations by utilizing the original Mamba, which 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

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Sumple and Group
Pooling
HPS
Linear
Distunce from center
SSM
SSM
SSM
Coavid
SILU
SILU
Convid
Convid
SiLU
Lincar
Lincar
Linear
Linear
Linear
Lincar
Pooling
NPS
Sample and Group
Serialized point
soquence
NPS token soquaeeee
Inner Mamba Block
Dual Mamba Block x3
Figure 2: Architecture of our scene feature aggregator that employs novel local-to-global scanning mechanism. The raw point clouds are first sampled and patched using FPS and KNN. Within each patch, points ranked by their distance from the patch center. The Inner Mamba block then scans these ranked points to extract local geometric features. Subsequently, patches are treated tokens and serialized in two manners before being fed into the Dual Mamba block. This step scans all tokens, extracting comprehensive scene contexts.
Algorithm Dual Mamba Block
Input: token sequence T C,
Output: token sequence T₃ (B, K, C)
1: /* process with different point orders */
2: T (B, C) <- Ti-1
3: T (B, + NPS(T..1)
4: o [ N] do
5:
Ty <- Norm(TT
6:
2. (B, + Linear,, (T '
7:
x, (B,K,C") <- Linear x (T
8:
x (B, <- SiLU (x.))
9:
B. (B,K,D) <- Linear (x()
10: C. [B,K, < Lincar (xx)
14 softplus ensures positive A.
A, (B, c') + log(1 exinea (x%) + Parameter
13: /* shape of Parameter is (C',
14: A:: (B. D). B, (B, K. C¹ D) + Disc (A,, Parameter, B,
15: :o: (B, c²) SSM B. Cx)(
16: 1* get gated yo */
17: Y' (B, C') + O (z.
18: end for
19: y'y (B, CB) + Fy)(y'))
20: T:: (B, C) t Linear' (y's + +
Return: T,
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 problem, we introduce novel Dual Mamba block (as shown in Fig. 2) model long-range dependencies from the global view. Specifically, we treat Fs E Kx 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 the sequence, whereas latter ensures that adjacent points remain spatial neighbors, thus maintaining local consistency. The Dual Mamba block incorporates two branches handle FPS and NPS token sequences, which first undergo normalization and independent linear projection. For each sequence, an initial ID convolution transforms it into x which is then projected

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into Ao, B.. and A.. Subsequently, A, and B, are discretized using A.. Finally, the corresponding tokens from the two branches are added and passed through linear layer generate the scene representations. The specific details are shown in Algorithm 1.
Algorithm 2 Query-aware Mamba Block
Input: 3D scene features Fē-1 1: (B, K, C), Box query sequence F ft-1' (B, M, C)
Linear
Output: Query sequence F.; (B, M,
1: for o in q) do
2:
F?-1
Norm
3:
Xo: (B. -,)
Linear (F
4:
xg: (B, (B,-,C' + SiLu (iLu(Convo(Xo))
1 D*Dsc and SSM $/
SSM
SSM
D), , B: (B, -B. C,, 6: A: (B,,,,',,),
CB
< Disc(x')
SiLU
Convld ld
Convid
7:
Yo: (B)) < SSM(A, B (x,))
8:
ZB - -CC) + Linear, (F; (F;-1)
Linear
Linear
Lincar
yo: (B--C') ty @ SiLu(z)
10: end for
11: Yy (B, M, K) +- Yq 3 V
Scene features
Query sequence
12: Ft. (B,M, C) + Linearr oy
13: F.. (B,M,C) < F. Norm FF
Figure 3: Query-aware Mamba block.
14: return Ft;
3.4 Decoder
DETR-based models leverage set of object queries to extract features for object classification and localization. However, directly using scene context prefix and concatenating with queries in the Mamba model leads to suboptimal performance, as struggles to capture discriminative features for independent queries. To address this, introduce the Query-aware Mamba block for the decoder, as shown 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 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 is fed into a standard Mamba block to model the dependencies between queries. This process can be formulated as follows:
F Linear (Norm
F SiLU((F) SSM(Conv(F!))
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 the Algorithm 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 {b} with the ground truth boxes (b). denoted

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as Leiou(b b). Then we calculate the discrepancies between {ô} and {b} using & loss for centers and dimensions, and Huber loss for angular residuals:
Lgo = Aclle - cllı + AalId d|II + Xallā, - ar anllhaberr
(6)
here, Ae. Ad-Aa are set 5, 1, and 0.5. We also employ cross-entropy losses to assess angular and semantic classifications:
Twgem
-acca, log a. s, log 8,
here, Aac and A are set as 0.1 and 1. Finally, the total loss is formulated as:
Mamba LADET Mamba
Engiou + I Lugeur
Toge--
(8)
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 3D indoor object detection, evaluate models 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
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.
Metrics. Following 26]. we use standard evaluation protocols [31, 35] and report the detection performance on the validation set using mean Average Precision (mAP) at two different IoU thresholds (i.em 0.25 and 0.5), denoted mAP@0.22 and mAP@0.5, respectively.
Implementation Details. The input to our model is a point cloud P E Nx3 representing a 3D scene, with NN set 20,000 for SUN RGB-D [39] and 40,000 for ScanNet [7]. We employ 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 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 W optimizer with base learning rate of x 10-4 decayed to 10 using a cosine schedule, and weight decay of 0. Gradient clipping with &2 norm of 0. is applied 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 3D Object Detection
In this section, we compare our 3DET-Mamba with previous 3D detectors. As shown Tab. 1, our 3DET-Mamba outperform previous 3DETR [26] (Tramsformer-bee based detectors) both SUN RGB-D and ScanNet datasets. For example, with 256 queries and 2048 points, our method achieves 66.9% mAP 0.25 and 48.7% m.P@0.5 ScanNet, surpassing 3DETR-M, which obtains 65.0% mAP@0.25 25 and 47.0% mAP@0. 0.5 respectively. Besides, since Mamba can effectively handle long sequences, we further conduct experiments using point clouds with higher resolution and more box queries (i.e., 4096 point clouds and 512 box queries), we can observe that the performance can be further improved with longer point and query sequences (i.e., +5.7 mAP@0.5).
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
7

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Table 1: 3D detection results on ScanNet V2 [7] and SUN RGB-D [39]. We compare 3DET-Mamba against open-source methods that directly process point clouds, using [3intNet++(33] (PN) and Transformer(40) (Tran.) as backbones. 3DET-Mamba employs the same number of key points and queries 3DETR [26]. 3DET-Mambaj doubles the number of key points and queries to model longer sequences of point clouds. Compared to 3DETR [26]. 3DET-Mamen achieves superior performance.
ScanNet
SUN RGB-D
Methods
Backbone
mAP@0.25
mAP@0.5
m.P@0.25
mAP@0.5
VoteNet [31]
58.6
33.5
57.7
MI.CVNet [49]
64.5
41.4
59.8
H3DNet [56]
67.2
48.1
60.1
39.0
PN
BRNet [6]
66.1
50.9
61.1
43.7
GroupFree3D [24]
67.3
48.9
63.0
45.2
GroupFree3D [24]
69.1
52.8
3DETR [27]
62.7
37.5
58.0
30.3
Tran.
3DETR-m [27]
65.0
47.0
59.1
32.7
3DET-Mamba
66.9
48.7
59.3
33.4
Mamba
3DET-Mambat
70.4
54.4
61.3
42.2
Table Effect of our designed point cloud encoder which contains Inner and Dual Mamba blocks. We compare the performance of encoder combinations using Pointnet++ [33] and Transformer [26] with Inner and Dual Mamba blocks, with results showing that the proposed Inner Mamba & Dual Mamba achieves the best performance.
Encoder
mAP@0.25
mAP@0.5
Pointnet++ & Transformer Inner Mamba & Transformer
63.1
44.1
64.9
45.4
+ointnet++ & Dual Mamba
65.6
48.4
Inner Mamba & Dual Mamba
66.9
48.7
Mamba to +ointNet++ results in performance drop of 1.8% mAP@0.25 and 1.3% mAP@0.5 when using Transformer and Dual Mamba, respectively. This because our Inner Mamba can effectively aggregate and propagate local features. Besides, our Dual Mamba block can bring +4.3% mAP@0.5 and +3.3% mAP@0.5 improvement compared using Transformer as the spatial encoder since our Mamba layers can learn both spatial representation and local consistency at the same time. By combining the Inner and Dual Mamba, the best results can be achieved which further verifies our designs.
Efffet of Dual Mamba block. To further show the advantage of our designed Dual Mamba block, we replace the Dual Mamba block with the original Mamba [10] block and Bi-Mamba block [60, 22, 54], respectively. The original Mamba block only contains forward SSM and the Bi-Mamba block composed of forward and backward SSM. As shown in Tab. 3, proposed dual Mamba block can outperform both the original Mamba block and Bi-Mamba block by +1.++0000 +1.0% and +1.2% mAP@0.5. This is because our dual Mamba block can provide point cloud sequence with short-range and long ranger dependencies and further make better use of the powerful causal modeling ability of Mamba.
Efffet 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) transformer-batt that directly concatenates 3D scene information with queries, feeding them into the Mamba block and leveraging Mamba's 's state transition matrix for further modeling; and (c) our proposed Query- aware Mamba block. Additionally, strategy to ensure fair comparison. As demonstrated in Tab. 4, the decoder based on the Query- aware Mamba block achieves superior results compared to both baselines. This improvement can be attributed to main factors: (1) Directly concatenating queries and 3D scene representations do
decoder proposed in 3DETR [26]; (b) a naive approach
experiments are conducted with the same encoder and training

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Table 3: Effect of Dual Mamba block. We com- pare it with the original Mamba block [10] and the Bi-directional Mamba block [60, 22].
Table 4: Effect of Query-aware Mamba. We com- pare it with the 3DETR [26] decoder and an im- plementation based on the original Mamba.
Mamba Block
mAP@0.25
mAP@0.5
Decnder
mAP@0.25
m.P@0.5
Ori. Mamba
65.4
47.7
Transformer
64.3
42.6
Bi. Mamba
66.4
47.5
Ori. Mamba
56.6
28.0
Dual Mamba
66.9
48.7
Quety-awar Mamba
66.9
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. shown Fig. we can observe that our 3DET-Mamba can accurately detect objects.
Input
Our
GT
Figure 4: Visualization of detection results. 3DET-Mamba is able to generate tight bounding boxes for objects of interest in these complex and diverse scenes.
Conclusion
In this paper, for the first time, exploit the potential of Mamba in 3D object detection tasks. Specif- ically, we introduce 3DET-Mamba, an end-to-end detector based on encoder-decoder architecture. We first propose SSM-based encoder that uses Inner Mamba block to capture local geometric information and uses Dual Mamba blocks to further aggregate features in a global view. Besides, Query-aware Mamba module is designed 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 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 an expert in long sequence modeling.
Limitations and Broader Impacts
While 3DFT-Mamba has demonstrated effectiveness in modeling point cloud sequences, we have yet to explore its potential for handling other 3D data types, such meshes. Additionally, given Mamba's 'ss

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strength in modeling long-sequentt sequence data, promising future direction is to develop Mamba- based 3D foundation models capable of addressing broader range of scene-level tasks, including 3D dense caption, visual grounding, and 3D QA. We leave these explorations for future work.
Acknowledgement
This work is supported by National Natural Science Foundation of China (No. 6207 127, and 62101137), National Key Research and Development Program of China (No. 20222D0160101) Shanghai Natural Science Foundation (No. 23ZR 1402900), Shanghai Municipal Science and Tech- nology Major Project NN.22011HH2DZ200000 The computations in this research were performed using the CFFF platform Fudan University.
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Reviews:
[{"review_id": "sEF9Zirmwx", "reviewer": "Reviewer_m4H4", "summary": "This 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.", "questions": "Besides 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.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. 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.", "weaknesses": "1. 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your valuable comments.\n\n**Q1**: Comparison of time and space complexity between 3DET-Mamba and other methods and the effects of changing resolution.\n\n**A1**: To demonstrate the superior performance of 3DET-Mamba, we compare the FLOPs and latency of our model with the pervious transformer-based architecture, 3DETR [A], as presented in the Table r1 below. It can be seen that 3DET-Mamba achieves **higher accuracy** (as detailed in Table 1 of our manuscript), **reduced FLOPs** and **lower latency**, as shown by Table r1. In Table 1 of our manuscript, we show that 3DET-Mamba effectively models high-resolution point clouds (i.e., 4096 point clouds), achieving a significant +5.7 mAP@0.5 increase. Furthermore, to explore the impact of changing point resolution, we conduct experiments on changing the density of point clouds. As shown in Figure 1 in the attached pdf file, by **increasing the density of point clouds**, the performance can be continuously **improved** which shows the effectiveness of 3DET-Mamba again.\n\n**Table r1: Performance Comparison of Computational Efficiency**\n\n| Method| FLOPs (↓) | Latency (↓) |\n|-|-|-|\n| 3DETR|14.3|0.22|\n| 3DET-Mamba (our)|9.8|0.13|\n\n \n**Q2**: Additional ablation studies.\n\n**A2**: Thanks for the valuable comment. \n- We first want to clarify that the intention of our work is to explore the feasibility of Mamba in 3D object detection which has not been studied. Previous works mainly explored the usage of Mamba in classification and segmentation tasks. However, this task is challenging due to 1) the non-casual and irregular 3D point clouds hinder the modeling of causal sequences. 2) The original Mamba block is good at extracting global information from long sequences but ignores detailed information to a certain extent which is important in 3D detection tasks.\n- We address the first challenge using ranked queries. To further verify the effectiveness of this approach, we conduct ablation studies shown in Table r2 below, which indicate that discarding ranked queries leads to a decrease in performance.\n\n  **Table r2: Ablation Study on Ranked Queries**\n  | Rank Query | mAP25 | mAP50 |\n  |-|-|-|\n  | ✗ | 65.6|48.1|\n  |✓|66.9|48.7|\n\n- To clarify the relevance of NPS  and FPS in modeling patch sequences, our results in Table r3 demonstrate that combining FPS and NPS can further improve performance by enabling Mamba to models the point cloud in terms of spatial distribution and continuity.\n\n  **Table r3: Impact of NPS and FPS**\n  | NPS | FPS | mAP25 | mAP50 |\n  |-|-|-|-|\n  |✓|✗|65.4|47.7|\n  |✗|✓|65.0|46.3|\n  |✓| ✓|66.9|48.7|\n\n- Existing Mamba-based works mainly use Mamba as the encoder. However, there is still no exploration of using Mamba as a decoder. To explore the potential of Mamba as a 3D decoder, we propose query-based Mamba which can better model the relationship between each query by taking advantage of Mamba. As shown in Table 5 in our article, our query-aware Mamba block can outperform the transformer-based decoder.\n\n- As shown in Table r1, compared to the transformer-based models, our method (which includes the query-aware Mamba block) exhibits lower FLOPs and latency. This demonstrates that the improvements are indeed due to the design of the novel Mamba block, rather than increased computational power.\n\n**Q3**: Comparisons of FCAF3D and speed.\n\n**A3**: Thank you for your valuable comments. In this paper, we focus on indoor 3D object detection using point clouds, a crucial 3D data representation form. Compared to voxels (used in FACF3D), point clouds offer benefits such as high-efficiency storage. We benchmark our 3DET-Mamba against other open-source point-based methods, with results in Table 1 of our manuscript demonstrating our approach's effectiveness. However, it is not trivial to directly apply Mamba to point cloud scenes due to their unordered and non-causal nature. 3DET-Mamba addresses these challenges by introducing a novel local-to-global scanning mechanism and developing the Inner Mamba and Dual Mamba to capture fine-grained point cloud features. Additionally, we designed a Query-aware Mamba Block to decode point cloud information. We hope our work will inspire further use of Mamba as a foundational component for point cloud understanding.\n\n**Q4**: Clarification of FPS and NPS.\n\n**A4**: The FPS and NPS in our article represent the furthest point sampling and the nearest point sampling. The furthest and nearest point order can be obtained after sampling. We will modify the words in dual Mamba to avoid confusion.\n\n**Q5**: Existing methods of query-based mamba works.\n\n**A5**: Thanks for your valuable comments. We carefully investigate query-based mamba works. Currently, there is still no work to explore query-based mamba for object detection. However, some concurrent works introduce query-based mamba for other tasks [B][C]. TM-Mamba[B] modifies the Mamba parameterizes $A$, $B$, $C$, $∆$ as the function of the input and text query to ground the human motion. However, it is not applicable when the number of queries is greater than 1. Besides, QueryMamba[C] combines a query-based transformer decoder and the Mamba encoder. However, it still does not explore query-based Mamba decoder. We will add these related works in the revised version.\n\n[A] Misra I, Girdhar R, Joulin A. An end-to-end transformer model for 3d object detection[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021.\n[B] Wang X, Kang Z, Mu Y. Text-controlled Motion Mamba: Text-Instructed Temporal Grounding of Human Motion[J].\n[C] Zhong Z, Martin M, Diederichs F, et al. QueryMamba: A Mamba-Based Encoder-Decoder Architecture with a Statistical Verb-Noun Interaction Module for Video Action Forecasting@ Ego4D Long-Term Action Anticipation Challenge 2024[J].", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "x9kJTRzT0w", "reviewer": "Reviewer_2ZE6", "summary": "The 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.", "questions": "See the Weaknesses.", "limitations": "N/A", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- 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.", "weaknesses": "- 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: Difference between Algorithm 2 and Figure 3.\n\n**A1**: Thanks for reviewing our article carefully. The algorithm 2 shows the detailed operation steps of our model. The figure 3 is a schematic figure and may omit some details. According to the suggestion of the reviewer, we will modify Figure 3 in the revised version.\n\n**Q2**: Question about Table 4.\n\n**A2**:\n- Thanks for pointing out our mistakes. We will modify the headers of Table 4 in the revised version.\n- We want to clarify that in Table 1, we report the results of 3DETR in the original paper which is trained for 1080 epochs. However, in Table 4, for a fair comparison, we only change the decoder to 3DETR decoder and use our mamba-based encoder. We train for 540 epochs which is consistent with 3DET-Mamaba. We will clarify it in the next version.\n\n**Q3**: Some typos.\n\n**A3**: Thanks for your comments. We will revise all the typos in the next version.\n\n**Q4**: Question about randomly choosing the initial point.\n\n**A4**: The aim of this paper is to explore the potential of Mamba in 3D object detection. We follow 3DETR[A] to random sample an initial point and then sample a set of points using FPS which are evenly distributed in 3D space. Besides, based on our experiments, the experimental results are stable and we repeat the experiments. The results are shown in the following table. Since the difference between the random seed and the GPU device, there is a slight deviation from the submitted article. However, it can be observed that the experimental results are still stable.\n\n**Repeated experimental results of 3DET-Mamba**\n|Method|mAP25|mAP50|\n|-|-|-|\n|Result in the main paper|66.9|48.7|\n|Replicate|66.3|48.2|\n\n[A] Misra I, Girdhar R, Joulin A. An end-to-end transformer model for 3d object detection[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 2906-2917.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1Y88xMnwxF", "reviewer": "Reviewer_eAYh", "summary": "This 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.", "questions": "Sec. 4.2 title: Obejct -> Object\nline 241: Transforme -> Transformer", "limitations": "None.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- 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.", "weaknesses": "- 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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**Q1**: Computational costs and scaling up performance.\n\n**A1**: \n- To further verify the effectiveness of 3DET-Mamba, we report latency and memory costs in the following table. It can be seen that our model achieves better results with less computational cost and lower latency compared to 3DETR. The results further verify the effectiveness of 3DET-Mamba.\n- Besides, we conduct more experiments in which we scale up the model size (more mamba block). As shown in Figure 2 and Figure 3 in the PDF file, both scaling the encoder and decoder can further improve the performance on ScanNet which further shows the scaling ability of 3DET-Mamba.\n\n**Performance Comparison of Computational Efficiency**\n| Method          | FLOPs (↓) | Latency (↓) |\n|-----------------|-----------|-------------|\n| 3DETR           | 14.3      |        0.22     |\n| 3DET-Mamba (our)| 9.8       |     0.13        |\n\n**Q2**: Discussion on the proposed method.\n\n**A2**: \n- In this paper, we first explore the potential of Mamba in 3D object detection tasks which have not been studied. However, directly using mamba to deal with 3D object detection tasks results in poor results. This is mainly caused by 1) It is difficult for Mamba to model unordered and non-causal 3-D point clouds, 2) the original Mamba block lacks the ability to extract local features, and 3) previous works only explore Mamba as an encoder for classification tasks.\n- To handle the above challenges, we propose a local-to-global scanning technique which is composed of Inner Mamba blocks and dual Mamba blocks and can aggregate local and global information. Besides, in dual Mamba blocks, we use furthest point sampling and nearest point sampling to construct the ordered point cloud sequence to handle the non-casual problem. Finally, the query-aware Mamba block is designed to decode scene context information into object sets with the guidance of box queries.\n- We hope our work can Inspire further exploration of mamba in 3D detection tasks and construct Mamba-base 3D foundation models in the future.\n\n**Q3**: Some typos.\n\n**A3**: Thanks for carefully reviewing our article. We will modify all these typos in the next version.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Dear AC and reviewers,

We thank all reviewers (Reviewer m4H4-R1, Reviewer 2ZE6-R2, Reviewer eAYh-R3) for approving our contributions, including **our exploration of mamba for end-to-end indoor 3D object detection for the first time** (R3). The experimental results are **convincing** (R3), demonstrating that the method is **effective** (R1, R2, R3). We also appreciate the acknowledgment of our **clear writing** (R2, R3).


We also thank all reviewers for their insightful feedback to help us improve our paper. We will address all reviewers' concerns and carefully revise the manuscript. **Additional results are provided in the attached PDF file**. We are happy to have further discussion on anything unclear about our paper.

Best regards,
Authors of Paper112

Meta-review: All three reviewers tendentially like the paper and mildly recommend acceptance. The rebuttal helped resolve some of the open issues, and caused one reviewer to raise their rating. Given the technically relevant contribution, the AC recommends acceptance. The authors are encouraged to update their paper in the camera-ready version to include all improvements from the rebuttal.
Decision: Accept (poster)