Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention
Authors: Haomeng Zhang
Chiao An Yang
Raymond A. Yeh
Venue: NeurIPS 2024 poster (2024)
Keywords: 3D grounding
vision and language
Primary area: machine_vision

Abstract: Multi-object 3D Grounding involves locating 3D boxes based on a given query phrase from a point cloud. It is a challenging and significant task that has numerous applications in visual understanding, human-computer interaction, and robotics. To tackle this challenge, we introduce D-LISA, a two-stage approach that incorporates three innovations. First, a dynamic vision module that enables a variable and learnable number of box proposals. Second, a dynamic camera positioning that extracts features for each proposal. Third, a language-informed spatial attention module that better reasons over the proposals to output the final prediction. Empirically, experiments show that our method outperforms the state-of-the-art methods on multi-object 3D grounding by 12.8% (absolute) and is competitive in single-object 3D grounding.

Multi-Object 3D Grounding with Dynamic Modules and Language-Informed Spatial Attention
Haomeng Zhang Chiao-An Yang Raymond A. Yeh Department of Computer Science, Purdue University (zhan5050, yang2300, rayyeh) edu
Abstract
Multi-object 3D Grounding involves locating 3D boxes based on given query phrase from point cloud. It is a challenging and significant task with numerous applications in visual understanding, human-computer interaction, and robotics. To tackle this challenge, we introduce D-LISA, a two-stage approach incorporating three innovations. First, dynamic vision module that enables a variable and learn- able number of box proposals. Second, dynamic camera positioning that extracts features for each proposal. Third, a language-informe that better reasons over the proposals to output the final prediction. Empirically, experiments show that our method outperforms the state-of state-of-the-ar the-art methods on multi-object 3D grounding by 12.8% (absolute) and is competitive in single-object 3D grounding.
spatial attention module
Introduction
Building agents that can operate in real-world environments with humans has been fundamental goal artificial intelligence. Importantly, the agent would need to understand the 3D scene and natural language to take instructions from humans. To benchmark these capabilities, there is an increasing amount of interest in the task of object grounding in 3D [2, 4, 8, 17, 21, 23, 39, 40, 46, 50]. Recently, the task of multi-object 3D grounding [52] has been proposed, i.e., given text description and 3D scene localize all objects referred by the description.
Along with the benchmark, Zhang et al. [52] proposes, M3DRef-CL.IP, two-stage approach that first detects all the potential objects (capped reasons about which of the objects are relevant to the text description by extracting features for each of the objects. Specifically, they leverage both 3D features from the point cloud, and 2D features extracted from renderings of the detected objects at fixed camera poses. These object features along with the text embedding are passed into a Transformer to make the final prediction. Model training is formulated multi-output classification, where each potential object classified based whether referred to by the text.
maximum number) from the 3D scene, and then
In this work, we identify several directions in which M3DRef-CLIP could be improved. First, the generation of object proposals is based on fixed maximum. Prior work [30] points out the dilemma of deciding the number of boxes in the 3D grounding task under the two-stage diagram. Excessive proposals may increase complexity and lead redundant computations while sparse proposals may miss critical information in the scene. Second, the camera poses of the renderer are fixed to hand-selected viewpoints, which seems unlikely to be ideal given the variability object sizes. Third, the fusion module does not effectively reason over the spatial relationship of the objects based on the text description.
Project page: https://haomengg: tto....ooo.b..........e Code: https://githee httpthttwc.caaataoooeeee/////// wwm./haomnngz/D-II....
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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To address these shortcomings, we propose D-LISA, a two-stage approach that incorporates three innovative modules. First, instead of using all detected objects, we use a dynamic proposal module to select the key box proposals. Second, we incorporate a dynamic multi-view renderer module that optimizes the viewing angles tailored to a specific scene. Third, we introduce a language-inforee informed spatial fusion module that uses textual description to guide reasoning based on spatial relations.
To evaluate our proposed method, we conduct experiments on the Multi3DRefer benchmark for multi-object 3D grounding and achieve a substantial 12.8% absolute increase over the existing baseline M3DRef-CLIP. We also validate the effectiveness of our method by achieving the state-of- the-art performance on ScanRefer benchmark [8] and competitive results on Nr3D benchmark [2] for single-object 3D grounding. Our contributions are summarized as follows:
We introduce a dynamic hox proposal module that automatically determines the key box proposals for the later reasoning stage, which could potentially replace the fixed object proposals prevalent in existing two-stage grounding pipelines. Also, we learn the camera pose for 2D rendering dynamically based on the scene, enhancing the quality of auxiliary object features in uncertain environments.
We propose a language-inforned spatial fusion module that dynamically captures the spatial relations among objects, significantly improving the model's contextual understanding and performance in the multi-object 3D grounding task.
We conduct thorough experiments to validate the proposed framework. The proposed approach not only significantly outperforms the state-of-the-art model in multi-object 3D grounding, but also maintains robust performance in the single-object 3D grounding task.
2 Related Work
2D grounding aims to identify the target object in 2D image based on a natural language description. The conventional detection-anddselection two-stage pipeline first extracts the visual features for the proposals and language features for the description then employs the attention mechanism to effectively align the visual features and language features [15, 27, 42, 48, 55J. Alternatively, one-stage methods directly regress the target boxes by integrating object detection and language understanding [28, 33, 44, 45]. While relational graphs have been used to explicitly model the object relations in 2D images [29, 37, 43], extending the modeling to 3D is challenging due to larger number of objects and more complex spatial relations.
3D grounding. Similar to 2D grounding. 3D grounding aims to target the language-referred object in 3D scene. There have been variety of datasets [1, 8] and approaches [5, 17, 39, 40, 50) to tackle this challenging problem. M3DRef-CLID [52] is the pioneered work to explore targeting multiple objects that match the language description. Other than the one-stage methods that directly identify the target box [30, 38), two-stage methods like M3DRef-CLIP following the detection-andd-slection diagram facing the issue of determining the number of boxes from the detection stage. We propose a module that dynamically selects the key box proposals from object candidates.
2D features have been widely used assist with 3D grounding [4, 17, 21, 23, 46] well as other 3D tasks [3, 22, 31, 36, 47, 51]. However, most studies rely on fixed camera poses to generate these 2D image features, which is sub-optimal given the varying object sizes across different 3D scenes. In contrast, we propose to learn scene-conditioned camera poses for object rendering.
Many works have studied how to model the object relations in complex 3D scenes [7, 16, 18- 20, 34, 49, 53]. For example, 3DVG-Trans [53] and M3DRef-CLIP [52] model the spatial relations based on distances. ViL3DRef [9] and CORE-3DVG [41] incorporate language and hand-selected features to guide the spatial relations. Differently, we propose a simple yet effective language- informed balancing strategy to explicitly reason over the spatial relation that solely depends on distances.
Approach
Given 3D point cloud of a scene S, and text description T, the task of multi-object 3D grounding aims to predict the set of bounding boxes P for objects that are referred to in the text description. Our proposed Multi-Object 3D Grounding with Dynamic Modules and Language Informed Spatial

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Sec. 3.1 Dynamic Vision Module
Dynamic Box
Dynamic Multi-view Readerer
FF2
Object Detector
Proposal
S
biack anecharr conter unoogg other chairs near white bourd.
Obgect Candidatos C
Box Proposals
B
F3D
Sec. 3.2 Language-informed Spatial Fusion Module
Spartal Attention
Language- Languge-Inforicc Informed
Grcunding Hoad
Visual
F
Text
Encoder
Prodiered Boxes
Word Features
W
P
x L
Figure 1: Illustration of the overall pipeline. Our D-LISA processes the 3D point cloud through the dynamic visual module (Sec. 3.1) and encodes the text description through encoder. The visual and word features are fused through a language informed spatial fusion module (Sec. 3.2).
Attention (D-LISA) follows the detecttion-and-seleciioo paradigm for multi-object 3D grounding task [52]. This paradigm involves three components: (i) a text encoder to extract text features; (ii) vision module to detect object proposals and extract corresponding features given a point cloud; (iii) fusion module that combines the text and object features to select the final referred bounding- boxess. Specifically, our D-LISA is designed with novel vision module that allows for dynamic number of proposal boxes and extracts features from dynamic viewpoints (Sec. 3.1) per scene. Furthermore, we propose fusion model that is spatially aware with explicit language conditioning (Sec. 3.2). An overview our approach is illustrated in Fig.
3.1 Dynamic Vision Module
Our dynamic vision module takes 3D scene point cloud S as the input and generates of box proposals B with corresponding visual features F. As in prior work [52]. we adopt the backbone detector of PointGroup [25] obtain fixed number of M box candidates C, i.e., C M. To eliminate irrelevant detected objects, we employ a dynamic box proposal module with non-maximum suppression (NMS). This module dynamically selects subset with variable sizes, from the M candidates, to form the set of box proposals B, which are then used by the fusion model.
Dynamic box proposal. To achieve box proposals with flexible number, we learn dynamic proposal probability Om for each of the M box candidates.
We model the proposal probability am as normalized linear function of the detector score 8m. i.e.,
Om Sigmoid((iinear(ss)))
At prediction time, object candidate would be selected if the dynamic proposal probability exceeds filtering threshold Ty:
B = (bm C Om > T
where bm denotes the 3D box of the mh object.
We then use non-maximum suppression (NMS) [14] to remove overlapping boxes from the box proposal candidates E' and finalize the box proposals B. First, the proposal probabilities are sorted descending order. Then we sequentially select the candidate with the highest probability as a box proposal and remove other box proposal candidates that have an Intersection over Union (IoU) greater than threshold Nass. The NMS module ensures the box proposals B do not include duplicated boxes for the same object.
3

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Dynamic Proposal loss. To train this proposal probability, we incorporated regularization term penalizing the expected value of the number boxes
M
Layn
Om
(3)
m==
This loss encourages the model to use as few box proposals possible while maintaining the grounding performance.
Object proposal featuree extraction. Given the N box proposals B, i.e., JB = N, we extract visual features that is a concatenation of both the 3D features F=3 from the detector and 2D features F from our dynamic multi-view renderer.
3D feature from detector backbone. Each box b,, in the box proposals B has a corresponding 3D feature f:D that can be extracted from the detector backbone. Next, to ensure that the proposal probability Omm reflect the quality of the box bm., we weight the 3D features with the probability, ie.,
F=D = {ar fi. 02 f2
ON JN
2D feature from Dynamic multi-view renderer The dynamic multi-view renderer takes as input the box proposals B and generates the corresponding 2D features FrD, Instead of using fixed camera poses for rendering all objects across different scenes, we learn scene-condiee crene-conditioned camera poses for rendering. We predefined V base camera poses dijm for 1, .... V. Next, we calculate the average size all boxes denoted as E I² with the average length, width, and height respectively, We use a Multi-Layer Perceptron (MLP) to learn the camera pose offset for each view j based on the average box size q:
Appi 5am (q)
(5)
The final camera pose for each view j is
P cam dm App cam
(6)
For each view j. the renderer generates the 2D image for each box proposal b; with camera pose pyam, The pre-trained CLIP image encoder extracts the 2D features for each view. Finally,
compute the average over all the extracted features from each view to obtain the 2D features
CLIP(Render((n. ));")) bn B
3.2 Lanfuage Spatial Fusion Module
Given the visual features FF from the dynamic vision module and the word features W from CLIP's text encoder, the language-informen object in box b, is targeted in the text description. The module consists of stack of transformer layers followed by an MLP grounding head.
spatial fusion module predicts probability Pr, on whether the
To better capture the spatial relationship among objects, we introduce the language-informed spatial attention (LISA) block that balances the visual attention weights and the spatial relations using the sentence feature language-inforra informeedd spatial attention block and cross-attention block, as illustrated in Fig. 1. Finally, we only predicted a box if the associated probability Pn. exceeds threshold Tpred i.e., the predicted box set
weighted sum over all word features. Each transformer layer comprises a
(bn Pa Tpred)
(8)
We now discuss the details LISA. The details of the cross-attention block are provided Ap- pendix Sec. A4,
Language informed spatial attention (LISA). Given the visual feature matrix [fi, f2..... fx]" E Nxd. where fn E F and the sentence feature g. language-informed spa- tial attention block (Fig. 2) updates the visual features with spatial information by balaneing the visual attention weights and spatial relations guided by language.

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Concatenate
Add
Elementwise-proouet
Visual Features
K
Visual Attention
Sentence Feature
Spatial Scores
g
B
Language-
Informed
Spatial Attention
Proposals
B
Centers
Spatial Distance
D
Figure 2: Illustration of language informed spatial attention (LISA). We model the object relations through spatial distance D. For each box proposal, a spatial score is predicted to balance the visual attention weights and spatial relations.
LISA follows the standard self-attention mechanism proposed by Vaswani al. [35] consisting of queries, keys, and values. Given F, the queries Q, keys and values V are computed as follows:
Q = FWQ. = F V = FWW
with linear projections WQ/v/K Rd
To explicitly build in spatial reasoning. we introduce spatial scores B, conditioned on the sentence feature and visual features, to weight between the standard attention terms and spatial distance matrix D. The overall maaguage-inffomen spatial attention as follows:
LISA(F, g, D) = softmax (LN- QK
+ BBODVV
(10)
Vd
where 1 is an all-ones matrix and softmax normalizes along each row. We now describe B and D.
Spatial scores B. Given variety of objects in complex scene, want the model dynamically learn whether an object should pay more attention to the spatial relationship based on text description. For the th object in the box proposal, feature f; and the sentence feature g. followed by a linear projection. To align with the attention weights, we construct the spatial scores
predict the normalized score by concatenating the visual
IR N as
8
8
81
32
82
82
B
where B, = Siigmid((inear( @ fc)
(II)
AN
[N
and @ denotes concatenation.
Spatial distance matrix D. We model the spatial relationship among objects through relative distances. We construct this matrix D e NxN by computing the pairwise læ-distance between the box centers e, and Cj. i.e., dij ||c - ey cyl12- To ensure that closer objects should receive greater attention, we define D de
3.3 Training details
In addition to the dynamic proposal loss for our dynamic box proposal module, we follow the loss functions the prior work [52] for end-to-end training. These include detection loss, reference loss, and contrastive loss. We briefly discuss these losses for completeness.

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Table Quantitative comparison FI @0.5 5 on the Multi3DRefer [52] val set.
FI @0.5 on Val (f)
Method
ZT w/o D
ZT w/D
ST w/o
ST w/D
MT
All
3DVG-Trans [53]
87.1
45.8
27.5
16.7
26.5
25.5
D3Net [9]
81.6
32.5
38.6
23.3
35.0
32.2
3DJCG [6]
94.1
66.9
26.0
16.7
26.2
26.6
M3DRef-CLIP [52]
81.8
39.4
47.8
30.6
37.9
38.4
M3DRef-CLIP w/NSS
79.0
40.5
67.6
40.0
49.1
49.3
D-LISA
82,4
43.7
67.1
42.5
51.0
51.2
Detection loss. We use Pointgroup [25] as our detector backbone and adopt their training losses. The detection loss Lae consists of four components: a) semantic segmentation loss, b) an offset regression loss, c) an offset direction loss, and d) a proposal score loss.
Reference loss. For multi-object 3D grounding, we adopt the binary cross-entropy loss over the detected objects as the reference loss Liet- We apply the Hungarian algorithm [26] to find optimal match based on the pairwise loU between the detected objects and ground truth. A detected box is successfully grounded if matches one ground truth box the Hungarian solution and the pairwise IoU is greater than a threshold Tuain For single-object 3D grounding, we use the cross-entropp loss. We identify the highest IoU between the detected boxes and the ground truth box and consider it success if this maximal IoU is greater than the threshold Timin
Contrastive loss. We apply symmetric contrastive loss Lar between the object features and the word features. A positive pair is formed if the object features and the word features come from the same scene-instruction pair, while negative pair formed if they come from different scene-instruction pairs. For computing efficiency, we only identify the positive and negative pairs within single batch. This loss has been proven effective for learning better multi-modal embeddings [52].
The total loss function is a weighted sum over all loss terms
C Asahae + Avehhret + Aacheu + Adya
(12)
where 0 is the individual loss weight for each loss term L,
4 Experiments
We conduct experiments on the Multi3DRefer [52] dataset. We also compare our model with other two-stage methods single-object grounding using the ScanRefer [8] and the Nr3D [2] datasets. Finally, we ablate the effectiveness each proposed module.
4.1 Multi-object 3D grounding
Dataset and evaluation metric. Multi3DRefer is a dataset based on ScanRefer [8]. contains 61,926 descriptions of 11,609 objects, with each text description potentially referencing zero, single, or multiple target objects.
Using the standard evaluation protocol [52], we report the Fl score at the intersection over union (IoU) threshold of 0.5 over five different categories: a) target without distractors of the same semantic class (ZT w/o D); b) zero target with distractors (ZT w/D); c) single target without distractors (ST w/o D); single target with distractors (ST w/D); and e) multiple targets (MT). The average over these categories is reported as an overall score.
Baselines. Following prior work [52], consider two-stage methods that perform well on the ScanRefer dataset as baselines; including, 3DVG-Trans [53], D3Net [9], 3DJCG [6] and M3DRef- CLIP [52]. We also report the performance of M3DRef-CLID with NMS after the first-stage detector for a fair comparison.
Implementation details. We train our model single NVIDIA A 1000 GPU. We set the batch size with the AdamW optimizer using learning rate of 5e -4, We follow the same train/val set split the baselines [52]. For the PointGroup detector, we use the same pre-trained PointGroup

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Iagut Text
lapust Scate
MSDRAFFCLLPP
MEDR CLIP w/NMS
D-LISA (OurS)
Grund Trali
the round ratle ote. chai color Mlse
Teraffar dutt a outiseed aud and ralls
Figure 3: Qualitative examples of Multi3DRefer val For each scene-text pair, we visualize the predictions of M3DRef-CLIP, M3DRef-CLIP wNMS, D-LISA and ground truth labels in magenta/blue/greennrred separately.
module following Zhang et al. [52] with the same loss coefficients. We set the dynamic proposal loss coefficient Oeyyn to 5. We set the Tirann to 0.25 and search for the optimal value of Tpred over (0.05, 0.1, 0.15, 0.2, 0.25) during evaluation for M3DRef- CLIP w/NMS and our model.
Results. We compare the @0.5 metric of our model and state-of-the-an baselines on Multi3DRefer val set in Tab. I. Our D-LISA A achieves 12.8% absolute increase in the overall F1 @0.5 score over M3DRef-CL.IP. Comparing M3DRef-CL.P and M3DRef-CL.PP w/NMS, we observe that NMS key factor in the final Fl score, successfully removing duplicate predictions leading to improved recall.
Next, D-LISA achieves better overall Fl score, especially for multiple targets and sub-categories where the distractors of the same semantic class exist. We further provide qualitative results over our method and the baselines in Fig. 3. The top two rows are examples from multiple target categories. Our D-LISA successfully identifies more objects that match the text description. The last row shows an example of a single target with distractors. Our D-LISA accurately identifies the object while the baselines are affected by the distractors and predict additional incorrect targets.
4.2 Single-object 3D grounding.
Dataset and evaluation metric. We evaluate the single-object 3D grounding performance the ScanRefer and the Nr3D datasets. The ScanRefer dataset contains 51,583 human- wrintnwwitten sentences for 800 scenes in ScanNet [13]. ScanRefer divides scenes into "Unique" and "Multiple" subsets based on whether the semantic class of the target object is unique the scene.
The Nr3D dataset consists of 41 11,503 human-annotated text descriptions across 707 indoor scenes from ScanNet. Nr3D divides scenes into "Easy" and "Hard" subsets based on whether there exist the distractors of the same semantic class, and into "View-dependent" and "iew-indepeendent" subsets based on whether a specific viewpoint is required to identify the target. Both ScanRefer and Nr3D annotated for single-object grounding. Different from ScanRefer, Nr3D assumes perfect object proposals are provided.
Following prior work [52], for the ScanRefer dataset we report Acc@0.5 on both val and test sets over different subsets. The number represents the proportion of predicted target boxes that have an IoU value greater than 0.5 compared to the ground truth box. For the Nr3D dataset, we report the accuracy selecting the target bounding box among candidate proposals on the test set over different subsets.

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Table 2: A.c of different methods on the ScanRefer dataset [8]. For joint models indicated by the best grounding performance with extra captioning training data is reported.
A.c@0.5 on Val (1)
A.c Test (1)
Method
Unique
Multiple
All
Unique
Multiple
All
TGNN [20]
56.8
23.2
29.7
58.9
25.3
32.8
FFL-3DOG [16]
67.9
25.7
34.0
InstanceRefer [49]
66.8
24.8
32.9
66.7
26.9
35.8
3DVG-Trans [53]
62.0
30.3
36.4
57.9
31.0
37.0
3DJCG [6]
64.3
30.8
37.3
60.6
31.2
37.8
D3Net [9]
72.0
30.1
37.9
68.4
30.7
39.2
UniT3D* [12]
73.1
31.1
39.1
HAM [10]
67.9
34.0
40.6
63.7
33.2
40.1
CORE-3DVG [41]
67.1
39.8
43.8
M3DRef-CLIP [52]
77.2
36.8
44.7
70.9
38.1
45.5
M3DRef-CLIP w/NMS
75.6
38.5
45.7
D-LISA
75.5
40.0
46.9
69.0
39.7
46.3
Table 3: Grounding accuracy of different methods on Nr3D dataset [2].
Accuracy on Test (f)
Method
Easy
Hard
View-Dep
View-Indep
All
TGNN [20]
44.2
30.6
35.8
38.0
37.3
InstanceRefer [49]
46.0
31.8
34.5
41.9
38.8
3DVG-Trans [53]
48.5
34.8
34.8
43.7
40.8
FFL-3DOG [16]
48.2
35.0
37.1
44.7
41.7
HAM [10]
54.3
41.9
41.5
51.4
48.2
M3DRef-CLIP [52]
55.6
43.4
42.3
52.9
49.4
D-LISA
60.2
46.2
44.3
57.4
53.1
Baselines. We focus comparing the two-stage methods designed for the situation where the ground truth box proposals are not provided. For the ScanRefer dataset, we compare with the baselines: TGNN [20], FFL-3DOG [16], InstanceRefer [49], 3DVG-Tran Trans [53], 3DJCG [6]. D3Net [9], UniT3D [12], HAM [10], CORE-3DVG [41] and M3DRef-CLIP [52]. For joint captioning and grounding models 3DJCG, D3Net, and UniT3D, we compare their best grounding performance with extra captioning training data. For the Nr3D dataset, we compare with the above baselines which reported the performance in their paper.
Implementation details. We follow the multi-object setting to adapt the single-object setting. Differently, we let the fusion module return the most likely box among all the proposal boxes instead of using threshold. For the Nr3D dataset, we follow the prior work [52] to directly crop the box features from the detector backbone based on the ground truth bounding boxes. We follow the same train/val/test set split for both datasets as the baselines.
Results. We report the A.c@0.5 of different methods on the ScanRefer val set and test set in Tab. 2. Comparing M3DRef-CI.IP and M3DRef-CLII w/NMS, could see that non-maximum suppression slightly improves the performance. Our D-LISA outperforms all existing baselines on both the ScanRefer val set and test set, especially for the subsets where there are multiple objects with the semantic class of the target object in the scene.
Next, report the grounding accuracy of different methods the Nr3D test set in Tab. 3. Our D-LISA outperforms all baselines on the Nr3D test set over all subsets. For more comparison with other methods on the ScanRefer and the Nr3D datasets, see Sec. A2 in the Appendix.
Limitations: As with other two-stage methods, the grounding performance of our designed two-stage model is upper bounded by the detector quality. From Tab. and Tab. 2, we can see that our model achieves better performance for complex scenarios but sacrifice some performance for the simpler single-object settings.

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Table Ablation study proposed modules on Multi3DRefer dataset. 'LIS." 'DBP' and 'DMR.' stands for 'Language informed spatial fusion', 'Dynamic box proposal", and 'Dynamic multi-view renderer' respectively.
FI 1.55 on Val (7)
Row #
LIS.
DBP.
DMR.
ZT w/o D
ZT w/D
ST w/o D
ST
w/D
MT
All
K
K
79.0
40.5
67.6
40.0
49.1
49.3
X
V
79.5
42.3
41.2
X
X
80.3
41.4
50.6
4
/
X
X
80.1
42.6
66.6
41.8
49.9
50.4
5
/
J
V
82.4
43.7
67.1
42,5
51.0
51.2
oynamic pese distribution baseline
Fixed Pose
Dynamic Pose
Comparison
0.5
0.5
0.5
-1.0
1.0
(a) Dynamic distribution and fixed base- line pose Multi3DRefer val
(b) Examples of rendered 2D images through dynamic camera pose fixed camera pose.
Figure 4: Qualitative results of dynamic multi-view renderer. On the left, we show the learned pose distribution over the Multi3DRefer val and visualize one camera ray example. On the right, we present examples of comparison between rendering with fixed pose and dynamic learned pose.
4.3 Ablation studies
conduct ablation studies on the proposed modules to validate their effectiveness under the multi- object grounding setting on the M3DRef dataset. The ablations follow the same experiment settings for the multi-object grounding Sec. 1. The baseline Row #1 shows the result of M3DRef-CLIP w/NMS.
Dynamic box proposal. In Tab. 4, comparing Row #3 with baseline Row #1, we validate the effectiveness of the dynamic box proposal module. We also validate the number of box candidates in the reasoning stage after using the dynamic box proposal module. For our complete model Row #5, an average of 30.5 boxes are selected for the fusion stage on the M3DRefer val set. This is much smaller number of boxes compared to the 62.4 boxes used in baseline Row #1.
Dynamic multi-view renderer. In Tab. 4, comparing Row #2 with baseline Row #1, we validate the effectiveness of the dynamic multi-view renderer module. We provide the qualitative results for the dynamic multi-view renderer in Fig. 4. Instead of using fixed camera poses, the dynamic renderer adapts different camera poses from scene scene, enhancing the quality of 2D object features.
Language informed spatial fusion. In Tab, 4, comparing Row #4 with baseline Row #1, we validate spatial fusion module, especially for the sub-categories
the effectiveness of the language-infonte where distractors exist (ZT w/D and ST w/D). For more ablation results on the language-informe spatial fusion module, please refer to Appendix Sec. A3.
Computational cost. We report the FLOPs and inference time of each proposed module comparison with the baseline model M3DRef-CLIIP in Tab. 5. All experiments are conducted on Defer validation set on single NVIDIA A100 GPU. The reported FLOPs and inference time the average over the validation set. We observe that the dynamic box proposal module

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Table Computational cost for proposed modules during inference.
Module
FLOPs
Inference time
Baseline detector
943.1 M
0.235 s
Detector w/ dynamic box proposal
943.1 M
0.241 s
Baseline multi-view renderer
638.9
0.271 s
Dynamic multi-view renderer
638.9 G
0.276
Baseline fusion
155.3 M
0.004 S
Language-informed spatial fusion
247.4 M
0.007
and the dynamic multi-vies view renderer in the dynamic vision module contribute marginally to the computation. The additional computations the language-infore informeed spatial fusion module are also minimal. In other words, our model achieves better grounding performance without significantly increasing computations.
Conclusion
In this paper, present D-LISA, two-stage pipeline for multi-object 3D grounding, featuring three novel components. Our dynamic box proposal module dynamically selects the key box proposals from detected objects. We enhance the 2D features through optimized prene-conditione rendering poses using a dynamic multi-view renderer. Furthermore, our language-inforee module facilitates explicit reasoning over the object spatial relations. Our proposed approach not only outperforms the state-of-the-art model multi-object 3D grounding but also is competitive in single-object 3D grounding.
spatial fusion
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References
[1] A. Abdelreheem, K. Olszewski, H.-Y. Lee, Wonka, and P. Achlioptas. ScanEnts3ID: Exploiting phrase-to-3d-objee correspondences for improved visio-linguistic models in 3d scenes. In WACV, 2024.
[2] Achlioptas, A. Abdelreheem, Xia, M. Elhoseiny, and L. Guibas. ReferIt3D: Neural listeners for fine-grained 3D object identification in real-world scenes. In ECCV, 2020. 1, 2, 6,
8, 16
[3] X. Bai, Z. Hu, X. Zhu, Q. Huang, Chen, H. Fu, and C.-L. Tai. Transfusion: Robust lidar-camera fusion for 3d object detection with transformers. In CVPR, 2022. 2
[4] E. Bakr, Y. Alsaedy, and M. Elhoseiny. Look around and refer: 2d synthetic semantics knowledge distillation for 3d visual grounding. NeurlPS, 2022. 1,2, 16
[5] E. M. Bakr, M. Ayman, M. Ahmed, H. Slim, and M. Elhoseiny. Cot3dref: Chain-of-thoughee data-efficient 3d visual grounding. arXiv preprint arXiv:2310.06214 2023. 2, 16
[6] D. Cai, L. Zhao, J. Zhang, L. Sheng, and D. Xu. 3djeg: A unified framework for joint dense captioning and visual grounding 3d point clouds. In CVPR, 2022. 6,
[7] C.-P. Chang, S. Wang, A. Pagani, and D. Stricker. MiKASA: Multi-key-anchon & scene-aware
transformer for 3d visual grounding. arXiv preprint arXiv:2403.03077 2024. 2
[8] D. Z. Chen, A. X. Chang, and M. Nießner. ScanRefer: 3D object localization RGB-D scans
using natural language. In ECCV, 2020. 1, 6, 8, 15
[9] D. Z. Chen, Q. Wu, M. Nießner, and A. X. Chang. D3Net: A unified speaker-listener architecture for 3d dense captioning and visual grounding. In ECCV, 2022. 2, 6,
[10] J. Chen, W. Luo, X. Wei, L. Ma, and W. Zhang. Ham: Hierarchical attention model with high performance for 3d visual grounding. arXiv preprint arXiv:2210. 12513, 2022. 8
[11] S. Chen, P.-L. Guhur, M. Tapaswi, C. Schmid, and I. Laptev. Language conditioned spatial relation reasoning for 3d object grounding. In NeurIPS, 2022. 15, 16, 17
[12] Z. Chen, R. Hu, X. Chen, M. Nießner, and X. Chang. Unit3d: A unified transformer for 3d dense captioning and visual grounding. In ICCV, 2023. 8
[13] A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner. ScanNet: Richly- annotated 3d reconstructions of indoor scenes. In CVPR, 2017.
[14] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In CVPR, 2005.
3
[15] J. Deng, Z. Yang, D. Liu, T. Chen, W. Zhou, Y. Zhang, H. Li, and W. Ouyang. Trans VG++: End-to-end visual grounding with language conditioned vision transformer. IEEE TPAMI, 2023.
2
[16] M. Feng, Z. Li, Q. Li, L. Zhang, X. Zhang, G. Zhu, H. Zhang, Y. Wang, and A. Mian. Free-form description guided 3d visual graph network for object grounding in point cloud. In ICCV, 2021.
2, 8
[17] Z. Guo, Y. Tang, R. Zhang, D. Wang. Z. Wang, B. Zhao, and X. Li. ViewRefer: Grasp the multi-view knowledge for 3d visual grounding. In CVPR, 2023. 1,2
[18] D. He, Y. Zhao, J. Luo, T. Hui, S. Huang, A. Zhang, and S. Liu. TransRefer3D: Entity- and-relation aware transformer for fine- fine-graine grained 3d visual grounding. In ACM MM, 2021. 2, 16
[19] J. Hsu, J. Mao, and J. Wu. Ns3d: Neuro-symbolic grounding of 3d objects and relations. In CVPR, 2023.
[20] P.-H. Huang, H.-H. Lee, H.-T. Chen, and T.-L. Liu. Text- graph neural networks for referring 3d instance segmentation. In AAAI, 2021. 2, 8

---

[21] S. Huang, Y. Chen, J. Jia, and L. Wang, Miew transformer for 3d visual grounding. In CVPR, 2022. 1, 2, 15, 16
[22] T. Huang, B. Dong, Y. Yang. Huang, R. W. Lau, W. Ouyang, and W. Zuo. CLIP2Point: Transfer clip to point cloud classification with image-depth pre- training... In ICCV, 2023. 2
[23] A. Jain, N. Gkanatsios, I. Mediratta, and K. Fragkiadaki. Looking outside the box to ground language in 3d scenes. arXiv preprint arXiv:2112.0883 2021. 1,2
[24] A. Jain, N. Gkanatsios, I. Mediratta, and K. Fragkiadaki. Bottom up top down detection transformers for language grounding in images and point clouds. In ECCV, 2022. 15, 16
[25] L. Jiang, H. Zhao, S. Shi, S. Liu, C.-W. Fu, and J. Jia. PointGroup: Dual-set point grouping for
3d instance segmentation. In CVPR, 2020. 3,
[26] H. W. Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 1955. 6
[27] M. Li and L. Sigal. Referring transformer: A one-step approach to multi-task visual grounding. NeurIPS, 2021. 2
[28] Y. Liao, Liu, G. Li, F. Wang, Y. Chen, C. Qian, and B. Li. A real-time cross-modality correlation filtering method for referring expression comprehension. In CVPR, 2020. 2
[29] D. Liu, H. Zhang, F. Wu, and Z.-J. Zha. Learning to assemble neural module tree networks for visual grounding. In ICCV, 2019.
[30] J. Luo, J. Fu, X. Kong, C. Gao, H. Ren, H. Shen, H. Xia, and S. Liu. 3D-SPS: Single-stage 3d visual grounding via referred point progressive selection. In CVPR, 2022. 1, 15, 16
[31] C. R. Qi, X. Chen, O. Litany, and L. Guibas. Imvotenet: Boosting 3d object detection in point clouds with image votes. In CVPR, 2020. 2
[32] J. Roh, K. Desingh, A. Farhadi, and D. Fox. Languagerefer: Spatial-language model for 3d visual grounding. In CORL, 2022. 16
[33] A. Sadhu, K. Chen, and R. Nevatia. Zero-shot grounding of objects from natural language queries. In ICCV, 2019. 2
[34] O. Unal, C. Sakaridis, S. Saha, F. Yu, and L. Van Gool. Three ways to improve verbo-viste fusion for dense 3d visual grounding. arXiv preprint arXiv:2309.0456/ 2023. 2, 15
[35] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and
I. Polosukhin. Attention is all you need. In NeurlPS, 2017. 5, 16
[36] C. Wang, M. Chai, M. He, D. Chen, and J. Liao. CLIP-NeRF: loxt-and-imagee driven manipula- tion of neural radiance fields. In CVPR, 2022.
[37] Wang, Q Wu, J. Cao, C. Shen, L. Gao, and A. v. d. Hengel. Neighbourhood watch: Referring expression comprehension via language- guided graph attention networks. In CVPR, 2019. 2
[38] Z. Wang, H. Huang, Y. Zhao, L. Li, X. Cheng, Y. Zhu, A. Yin, and Z. Zhao. 3DRP-Net: 3d relative position-aware network for 3d visual grounding, EMNLP, 2023. 2, 15, 16
[39] T.-Y. Wu, S.-Y. Huang, and Y.-C. Wang. Dora: 3d visual grounding with order-aware referring.
arXiv preprint 1:Xx::2403.16539 2024. 2, 15, 16
[40] J. Yang, X. Chen, S. Qian, N. Madaan, M. Iyengar, D. F. Fouhey, and J. Chai. Lim-grounder: Open-vocabulary 3d visual grounding with large language model as an agent, arXiv preprint 23Xi:2300.123114 2023. 1,2
[41] L. Yang, Z. Zhang, Z. Qi, Y. Xu, W. Liu, Y. Shan, B. Li, W. Yang, P. Li, Y. Wang, et al. Exploiting contextual objects and relations for 3d visual grounding. In NewrIPS, 2023. 2, 8
[42] Yang, G. Li, and Y. Yu. Dynamic graph attention for referring expression comprehension. In ICCV, 2019.
12

---

[43] S. Yang. G. Li, and Y. Yu. Graph-structured referring expression reasoning in the wild. In CVPR, 2020. 2
[44] Z. Yang. B. Gong, L. Wang, Huang, D. Yu, and Luo. A fast and accurate one-stage approach to visual grounding. In CVPR, 2019. 2
[45] Z. Yang, T. Chen, L. Wang, and Luo. Improving one-stage visual grounding by recursive sub-query construction. In ECCV, 2020.
[46] Z. Yang, S. 7.aagg L.. Wang, and Luo. SAT: 2d semantics assisted training for 3d visual grounding. In ICCV, 2021. 2, 15, 16
[47] Yin, Zhou, and P. Krähenbühl. Multimodal virtual point 3d detection. NeurIPS, 2021.
[48] L. Yu, Lin, X. Shen, J. Yang, X. Lu, M. Bansal, and L. Berg. Mattnet: Modular attention network for referring expression comprehension. In CVPR, 2018. 2
[49] Z. Yuan, X. Yan, Y. Liao, R. Zhang, S. Wang, Z. Li, and S. Cui. InstanceRefer: Coopera- tive holistic understanding for visual grounding on point clouds through instance multi-level contextual referring. In ICCV, 2021. 2,
[50] 7. Yuan, Ren, C.-M. Feng, H. Zhao, S. Cui, and 7. Li. Visual programming for zero-shot open-vocabulary 3d visual grounding. arXiv preprint arXiv:2311.1538, 2023, 1,2
[51] R. Zhang, Z. Guo, W. Zhang, K. Li, X. Miao. B. Cui, Y. Qiao, P. Gao, and H. Li. PointCLIP: Point cloud understanding by clip. In CVPR, 2022. 2
[52] Y. Zhang, Z. Gong, and A. X. Chang. Multi3drefer: Grounding text description to multiple 3d objects. In ICCV, 2023. 2, 5, 6, 7, 8, 16
[53] L. Zhao, D. Cai, L. Sheng, and D. Xu. 3DVG-Tranffrmner: Relation modeling for visual grounding on point clouds. /CCV, 2021. 2, 6, 8
[54] Z. Zhu, X. Ma, Y. Chen, 7. Deng, S. Huang, and Q. Li. 3D-VisTA: Pre-trained transformer for 3d vision and text alignment. In CVPR, 2023. 15, 16
[55] B. Zhuang, Q. Wu, C. Shen, I. Reid, and A. Van Den Hengel. Parallel attention: A unified framework for visual object discovery through dialogs and queries. In CVPR, 2018. 2
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Appendix
The appendix is organized follows:
In Sec. AI, we provide additional results on the Multi3DRefer dataset for multi-object grounding.
In Sec. A2, we provide additional comparisons with state-of-the-art methods on ScanRefer and Nr3D datasets for single-object grounding.
In Sec. A3, provide additional comparisons and ablation results for our proposed LISA block.
In Sec, A4, we provide additional details for D-LISA.
In Sec. A5, we provide additional qualitative results.
A1 Additional multi-object grounding results
F1 @ 0.25 evaluation Multi3DRefer validation set. We provide additional comparisons with M3DRef-CLIP over F1 @0.25 in Tab. A1. We observe that our D-LISA achieves a better overall FI @0.25 score, especially for multiple targets and sub-categories where the distractors of the same semantic class exist. This aligns with our observation for Fl @0.5 results in Tab.
Table A1: F1 @0.25 results on the Multi3DRefer validation set.
FI @1205 on Val (f)
Method
ZT w/o D
ZT w/D
w/o
ST w/D
MT
All
M3DRef-CLIP
81.8
39.4
53.5
34.6
43.6
42.8
M3DRef- CLIP w/NMS
79.0
40.5
76.9
46.8
57.0
56.3
D-LISA
82.4
43.7
75.5
49.3
58.4
57.8
Additional ablation results on question types. Additional ablations for different query types, including queries with spatial, color, texture, and shape information reported in Tab, A2. We observe that each proposed module effectively improves the performance for the queries that contain spatial, color, and shape information, and competitive with the baseline for queries with texture information. The overall model achieves better grounding performance across all query types than the baseline.
Table A2: Ablation studies on question types on Multi3DRefer dataset. 'LIS., 'DBP' and 'MR stands for 'Language informed spatial fusion', *Dynamic box proposal', and Dynamicc multi-view renderer' respectively. FI @0.5 results are reported.
Module
Question Type
LIS.
DBP.
DMR.
Spatial
Color
Texture
Shape
48.9
50.8
52.1
51.7
X
X
/
49.4
51.1
51.7
51.8
X
49.9
51.4
51.8
53,0
X
X
50.0
51.7
53.4
52,4
V
50.9
52.1
52.9
53.3
Additional ablation results on the filtering threshold Ty- To determine the optimal filtering threshold Ty Eq. (2), we conduct experiments with different filtering threshold on Mulei dataset. The result is shown in Tab. A3. We observe that using 0.5 results in the best performance.
Table A3: Ablation studies on the filtering threshold Ty. F1 @0.5 results are reported.
Ty
0.4
0.5
0.6
F1 ())
50.0
51.2
49.0
Additional comparison on the NMS module. We show the additional comparison between our proposed D-LISA and D-LISA without NMS on Multi3DRefer in Tab, A4. We observe that our
14

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Table A4: Ablation studies on the NMS module. F1 results are reported.
NMS M3DRef-CLIP
D-LISA
38.4
39.8
49.3
51.2
designed D-LISA outperforms the baseline M3DRef-CLIP both with and without the NMS module. Using the NMS module would lead to a higher F1 score compared to not using it.
A2 Additional single-object grounding comparisons
We provide additional comparisons with state-of-the-art methods ScanRefer and Nr3D datasets for single-object grounding. These methods do not follow the detection-and seseseoo-nnd-seleection two-stage diagram. Different from ScanRefer, Nr3D assumes perfect object proposals provided. We focus the grounding performance on the ScanRefer dataset as the task setting is more realistic. We report the grounding performance both ScanRefer and Nr3D for completeness.
Table A5: Grounding 0.55 additional methods on the ScanRefer dataset [8].
Acc@0.5 on Val (t)
Acc@0.5 on Test (1)
Method
Unique
Multiple
All
Unique Multiple
All
SAT [46]
50.8
25.2
30.1
MVT [21]
66.5
25.3
33.3
3D-SPS [30]
66.7
29.8
37.0
ViL3DRef [11]
68.6
30.7
37.7
3DRP-Net [38]
67.7
32.0
38.9
BUTD-DETR [24]
66.3
35.1
39.8
ED-VisTAAsserathh [54]
70.9
34.8
41.5
ConcreteNet [34]
75.6
36.6
43.8
69.3
37.6
44.7
DOrA [39]
44.8
D-LISA
75.5
40.0
46.9
69.0
39.7
46.3
ScanRefer dataset. We provide additional comparisons with other state-of-the-art methods on the ScanRefer dataset in Tab. A5. For the methods using object proposals input instead of the 3D scene, typically a separate pre-trained detector is used to pre-process the scene [11, 21, 39, 46, 54]. Our D-LISA outperforms all existing methods and achieves the best grounding accuracy both the validation set and test set, which further validates the effectiveness of our proposed modules.
Nr3D dataset. We provide additional comparisons with other state-of of-e-offtheear methods on the Nr3D dataset Tab. A6. Our D-LISA still achieves comparable results.
A3 Additional results for LISA
We provide experimental results our designed language informed spatial attention (LISA) module. We show the ablation results on the design choice and compare our module with other language-guided attention modules.
Design choice. We analyze the factors that affect the spatial score and report the @0.5 metric on Multi3DRefer dataset in Tab. A7. The result shows using both the sentence feature and object feature to predict the spatial score 3 yields the best grounding performance.
Additional comparison. We compare our designed LISA with the spatial self-attention in ViL3DRef [11], which also models the object relations guided by language. ViL3DRef pre-defines object relations through hand-crafted features. These hand-selected features work with ground truth object proposals but lead to worse performance when the object proposals are predicted, i.e. noisy. As is shown in Tab. A5 and Tab. A6, though ViL.DReel works well on the Nr3D benchmark which provides ground truth box proposals, the performance much worse when validating on the ScanRefer benchmark where no ground truth proposals provided.
15

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Table A6: Grounding accuracy of additional methods on the Nr3D dataset [2].
Accuracy on Val (1)
Method
Easy
Hard
View-Dep
View-Indep
All
TransRefer3D [18]
48.5
36.0
36.5
44.9
42.
LanguageRefer [32]
51.0
36.6
41.7
45.0
43.9
LAR [4]
56.1
41.8
46.7
50.2
48.9
SAT [46]
56.3
42.4
46.9
50.4
49.2
3D-SPS [30]
58.1
45.1
48.0
53.2
51.5
BUTD-DETR [24]
60.7
48.4
46.0
58.0
54.6
MVT [21]
61.3
49.1
54.3
55.4
55,4
3D-VisTAAseratch) [54]
65.9
49.4
53.7
59.4
57.5
DOrA [39]
59.7
66.6
53.1
59.2
59.9
CoT3DRef [5]
70.4
57.3
61.5
64.8
64.0
ViL3DRef [1]]
70.2
57.4
62.0
64.5
64.4
3DRP-Net [38]
71.4
59.7
64.2
65.2
65.9
D-LISA
60.2
46.2
44.3
57.4
53.1
Table A7: Ablation study of different design choices for LISA on Multi3DRefer dataset.
Fl @0.5 Val (1)
Sentence
Object
ZT w/o D
ZT w/D
ST w/o
ST w/D
MT
All
X
79.0
40.5
67.6
40.0
49.1
49.3
X
77.8
39.4
67.9
41.4
49.2
50.0
V
V
80.1
42.6
66.6
41.8
49.9
50.4
We substitute LISA with the spatial self-attention in ViL3DRef and report the FI @1055 metric on Multi3DRefer dataset in Tab. A8. Our proposed LISA achieves better grounding performance with simpler relation representation.
A4 Additional details for D-LISA
We provide additional architecture details for our D-LISA and additional implementation details for the experiment setup.
Cross-attention. In the language informed fusion module, a language informed spatial attention block followed by pross-attention block 3.2). The cross-attention block takes the spatially enhanced visual features F from LISA and word features after a self-attention block as input and generates language-infomeed
informed visual features F We follow the standard tross-attention mechanism described in Vaswani et al. [35]. We formulate the word feature matrix input as Fr = [t,, t2,..., .e....,te t E Rddd
where t, e Rd the corresponding feature for wy E W after self-attention. Given F and Fr. queries Q.. keys Ke and values Ve correspond to:
Q F"WS, K. Fr W
===FFW
(A13)
with linear projections W / Rdxd The overall cross-attention is formulated
F Cross- Attention(F's Fr) = softiaxx( Q.K!
V
(A14)
Vd
where softmax is the softmax normalization along rows.
Additional implementation details. Following the prior work [52], we take point coordinates, point normals, and per-pon point multi-view features S E IR H (3+3++ 128) as scene input, where H denotes the total number of points the scene. For NMS process, we the threshold TNMS be 0.4. For CLIP, we use frozen pre-trained CLIP with ViT-B/32. For loss coefficient terms in Eq. (12), we set Ate- We initialize
Arer and Aet to and Adyn poses in prior work [52], where for each view the rendering camera is set to be meter away from the object, with elevation angle of 45" For the fusion module, we follow the same settings in terms of dimension size, layer number, and head size used for the baseline [52].
camera baseline poses following the fixed camera
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Table A8: Comparison of language guided spatial attention methods on Multi3DRefer dataset.
F1 @0.5 on Val (t)
Attention module
ZT w/o D
ZT w/D
ST w/o
ST w/D
MT
All
Spatial Self-Attention [11]
79.0
31.2
66.6
39.8
49.0
48.7
LISA
80.1
42.6
66.6
41.8
49.9
50.4
Japur
Jspar
MSDRaF-CLIP
WINMS
(ours)
Groud Trath
Toe trown cilic to partloedduch foring
A Arrenti with far anyfor for supeorting wwnddggpiigt
as maste! wAd de pire Marcher
Figure A1: Additional qualitative examples of Multi3DRefer val set in MT category. For each scene-text pair, visualize the predictions of M3DRef-CLIP, M3DRef-CLIP w/NMS, D-LISA and ground truth labels in magents/blue/greenfred separately.
A5 Additional qualitative results
We provide additional qualitative comparisons for MT category (Fig. Al) and ST w/D category (Fig, A2). For MT category examples, D-LISA successfully identifies all objects that match the text description. For ST w/D category examples, our D-LISA accurately identifies the object without being distracted by the distractors.
17

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Axput Test
Ixpar Scove
MIDRiFCLIP
MEDRof
wWMS
D-LISA (nun)
Grond Truch
Peooongaer bocutiety mosile alepsslea Aneeriy Mack colsect
The
postioon oighte the vel.
potte not sas somted Ansken tn senddass meah acmppinn.
Figure A2: Additional qualitative examples of Multi3DRefer val set ST w/D category. For each scene-text pair, visualize the predictions of M3DRef-CLIP, M3DRef-CLIP w/NMS, D-LISA and ground truth labels in magenta/hlue/green/ee separately.
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Reviews:
[{"review_id": "GknrG5Tnd2", "reviewer": "Reviewer_su8L", "summary": "The paper introduces D-LISA, a two-stage approach for multi-object 3D grounding that incorporates three innovative modules. First, a dynamic vision module generates variable and learnable box proposals. Second, a dynamic multi-view renderer extracts features from optimized viewing angles. Third, a language-informed spatial attention module reasons over the proposals to output final predictions. Empirically, D-LISA outperforms state-of-the-art methods by 12.8% in multi-object 3D grounding and is competitive in single-object 3D grounding.", "questions": "See weakness.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. This paper is generally well-written and clearly stated. \n2. The key idea lies in enhancing visual understanding and human-computer interaction by improving the ability to locate objects in 3D scenes based on natural language descriptions.\n3. Experiments demonstrate that D-LISA outperforms the existing state-of-the-art, indicating the effectiveness of the proposed innovations.", "weaknesses": "1. It is not clear what core issue this paper is targeting in the task of dynamic multi-object 3D grounding. I suggest the authors include this in the abstract and introduction section. To me, the three improvements over M3DRef-CLIP are very engineering. \n2. The paper could benefit from a more detailed ablation study that isolates the impact of each dynamic component (the dynamic proposal module, the dynamic multi-view renderer, and the LISA module) on different types of scenes and queries to better understand their individual contributions.\n3. The paper does not provide details on the computational cost of the dynamic components, such as the multi-view rendering and the language-informed spatial attention module.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## To Reviewer su8L\n```\nQ1. Core issues targeted for multi-object 3D grounding\n```\n\nIn L26-33, we summarized the targeted issues and the proposed solutions. Concretely, we identified that: \n- object proposals are selected based on a **fixed** maximum number,\n- the feature extractions from the proposals are based on a set of **fixed** camera poses for all scenes,\n- the fusion module, of the existing method, lacks effective reasoning over spatial relations among objects. \n \nWe believe these aspects are crucial to building an effective multi-object 3D grounding system. We then propose a dynamic vision module and a language-informed spatial fusion module to address these issues. We will clarify the introduction.\n\n```\nQ2. Ablation studies on different types of scenes and queries\n```\n\nWe evaluate the contribution of each proposed component in Tab. 4. The performance of individual modules is shown in rows 2, 3, and 4 respectively. The different categories of scenes and queries are introduced in L197-201, including \n- zero target without distractors of the same semantic class (ZT w/o D); \n- zero target with distractors (ZT w/D); \n- single target without distractors (ST w/o D); \n- Single target with distractors (ST w/D); \n- multiple targets (MT). \n\nThese scene and query categories follow the prior work M3DRef-CLIP. \n\nAdditional ablations for different query types, including queries with spatial, color, texture, and shape information are reported in the table below. In Tab. R1, We report the F1@0.5 (&uparrow;) metric on the Multi3DRefer validation set. We observe that each proposed module effectively improves the performance for the queries that contain spatial, color, and shape information, and is competitive with the baseline for queries with texture information. The overall model achieves better grounding performance across all query types than the baseline.\n\n**Table R1. Additional ablation studies on question types**\n| LIS       | DBP      | DMR       | Spatial  | Color  | Texture | Shape |\n|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n| &cross;    | &cross;    | &cross;    |  48.9    | 50.8   | 52.1    | 51.7  |\n| &cross;    | &cross;    | &check;    |  49.4    | 51.1   | 51.7    | 51.8  |\n| &cross;    | &check;    | &cross;    |  49.9    | 51.4   | 51.8    | 53.0  |\n| &check;    | &cross;    | &cross;    |  50.0    | 51.7   |**53.4** | 52.4  |\n| &check;    | &check;    | &check;    | **50.9** |**52.1**| 52.9    | **53.3** |\n\n```\nQ3. The computational cost for each proposed module\n```\n\nThanks for the suggestion. We show the FLOPs and inference time of each proposed module and a comparison with the baseline model M3DRef-CLIP in Tab. R2. All experiments are conducted on Multi3DRefer validation set on a single NVIDIA A100 GPU. The reported FLOPs and inference time are averaged over the validation set. \n\nWe observe that the dynamic box proposal module and the dynamic multi-view renderer in the dynamic vision module contribute marginally to the computation. The additional computation in the language-informed spatial fusion module is also minimal. In other words, our model achieves better grounding performance without significantly increasing computations.\n\n**Table R2. Computational cost for proposed modules**\n| Module                    | FLOPs    | Inference time |\n|:-|:-:|:-:|\n| Baseline detector            | 943.1 M  | 0.235 s        |\n| Detector w/ dynamic box proposal | 943.1 M  | 0.241 s        |\n| Baseline renderer            | 638.9 G  | 0.271 s        |\n| Dynamic multi-view renderer  | 638.9 G  | 0.276 s        |\n| Baseline fusion module       | 155.3 M  | 0.004 s        |\n| Language-informed spatial fusion | 247.4 M  | 0.007 s        |", "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": "GxwxydqfuF", "reviewer": "Reviewer_W21s", "summary": "This paper proposes D-LISA, a two-stage framework for multi-object 3D grounding. D-LISA consists of three novel components that make the method effective, namely a dynamic box proposal module, a dynamic multi-view renderer and a language informed spatial fusion module. Comprehensive Experiments are done on Multi3DRefer, ScanRefer and Nr3D datasets to prove the superiority of D-LISA over previous methods. Experimental results show that D-LISA not only outperforms previous methods on multi-object grounding, but also achieves comparable results on single-object grounding.", "questions": "Please see the weakness part.", "limitations": "The authors have adequately addressed the limitations and potential negative societal impact of their work.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1. The paper is well-written and easy to understand and the figures help with the illustration of the overall idea.\n2. Comprehensive experiments and ablation studies are conducted.\n3. Implementation and evaluation details are clearly provided, making this work easy to follow.", "weaknesses": "1. In Table A2, the proposed method falls behind state-of-the-art method by a large margin, which somehow is not competitive.\n2. In the ablation study (Table 4), I'm curious what would happen if putting two modules together, e.g. abandon LIS and use DBP and DMR, since the improvements of the full model is not significant compared to the model with one component.\n3. The analysis of dynamic pose distribution is lacking. Readers can't tell from Figure 4 (b) if the dynamic pose rendered results are better. Fixed pose results seem to be looking at the object from a more informative view.\n4. How much effect NMS has on the proposed method seems to be unclear, the paper only conducted experiments by adding NMS to a baseline method, resulting in a much better performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## To Reviewer W21s\n\n```\nQ4. Comparisons with SOTA methods on Nr3D benchmark in Tab. A2.\n```\n\nAs is mentioned in L415-416, the Nr3D benchmark **assumes perfect object proposals**, which is not the most realistic setting. Hence, we follow M3DRef-CLIP to consider the setting where object proposals need to be detected. \n\nWe note that M3DRef-CLIP has made a similar observation: models designed for non-perfect objective proposals generally perform less effectively in the perfect object proposal setting. In Tab. A2, we report the comparisons for completeness of the evaluation. However, we do not believe this is a fair comparison as the models are designed with different intentions.\n\n```\nQ5. Additional ablation study on the proposed modules.\n```\n\nHere we provide additional ablation by gradually adding proposed modules in Tab. R3. The experiments follow the setting in Tab. 4. We observe that each proposed module improves the model. The overall model with all proposed modules achieves the best result.\n\n**Table R3. Additional ablation studies on combined modules.**\n| LIS      | DBP       | DMR       | F1@0.5(&uparrow;) |\n|:-:|:-:|:-:|:-:|\n| &cross;   | &cross;    | &cross;    | 49.3   |\n| &check;   | &cross;    | &cross;    | 50.4   |\n| &check;   | &check;    | &cross;    | 50.9   |\n| &check;   | &check;    |  &check;   | **51.2**   |\n\n```\nQ6. Why is the rendering with dynamic pose better?\n```\n\nSorry for the confusion. It is difficult to judge what is *\"more informative\"* from a model perspective. The provided illustrations in Fig. 4 (a) and (b) meant to show the differences in the learned poses versus the fixed pose. Empirically, we observe that the dynamic camera pose leads to better grounding performance, see row 1 and row 2 in Tab. 4.\n\n```\nQ7. What is the effect of NMS on the proposed method?\n```\n\nAs is mentioned in L214-216, adding an NMS module removes the duplicate predictions and leads to a higher recall, thus improving the F1 score. The state-of-the-art method M3DRef-CLIP did not include the NMS module in their pipeline. We show the effect of the NMS module by adding a baseline of M3DRef-CLIP with NMS in Tab. 1. \n\nTo provide a more comprehensive analysis of the NMS module, we show the additional comparison between our proposed D-LISA and D-LISA **without** NMS on Multi3DRefer. See Tab. R4 below.  We observe that our designed D-LISA outperforms the baseline M3DRef-CLIP both with and without the NMS module. Using the NMS module would lead to a higher F1 score compared to not using it.\n\n**Table R4. The F1@0.5 (&uparrow;) w/ and w/o the NMS module.**\n|NMS| M3DRef-CLIP | D-LISA |\n|:-:|:-:|:-:|\n| &cross; | 38.4   | 39.8   |\n| &check; | 49.3   | **51.2**   |", "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": "3GA3lj7RS2", "reviewer": "Reviewer_WVQA", "summary": "This paper introduces a novel two-stage approach for multi-object 3D grounding from a point cloud based on a given query phrase. The first stage of D-LISA uses a dynamic proposal module that selects a variable number of box proposals instead of a fixed maximum, addressing the issue of determining the optimal number of proposals in the scene. D-LISA incorporates a dynamic multi-view renderer module that optimizes viewing angles for each proposal based on the specific scene, moving away from the fixed camera poses used in prior work. The second stage introduces a module that reasons over the spatial relationships among objects, guided by the textual description, improving the contextual understanding of the model. Experiments conducted on the Multi3DRefer benchmark demonstrate that D-LISA outperforms the state-of-the-art methods by a significant 12.8% absolute increase in multi-object 3D grounding performance. It also shows competitive results in single-object 3D grounding tasks.", "questions": "Could the authors re-organize the novelty according to the weakness to highlight the key points more clearly?", "limitations": "Limitations are adequately addressed.", "rating": 3, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The method shows a substantial improvement over existing baselines, indicating effective handling of complex 3D scenes with multiple objects.\n2. The dynamic module can reduce the proposals effectively.", "weaknesses": "1. The novelty of the dynamic vision module is limited. In fact, I think the statement of dynamic vision module is kind of exaggeration. From my perspective, the authors just calculate the probability of each box candidate to remove the low-probability boxes and use NMS to filter overlapping boxes. Both the two operations has nothing to do with a novel dynamic vision module.\n2. Also, the novelty of the LISA module is also doubtful. The authors use spatial scores to modulate the self-attention and spatial distance matrix. Although this operation is innovative to some extent, it is not enough for acceptance. It is more like a trick rather than a novelty.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## To Reviewer WVQA\n\n```\nQ8. The dynamic vision module only removes the low-probability boxes and uses NMS to filter overlapping boxes, which is not novel.\n```\n\nWe believe the reviewer is referring to the dynamic box proposal module. For the dynamic box proposal module, we do not claim NMS to be the novel contribution. We clearly state *\"we employ a dynamic box proposal module **with** non-maximum suppression (NMS)\"* in L98-99. \n\nOur contribution to this module is the learning of dynamic proposal probability, supervised by the new dynamic proposal loss introduced in L113-116. This enables the end-to-end training by weighting the detector features with the probabilities, as is shown in Eq. 4. Additionally, we also propose the dynamic multi-view renderer to have dynamic camera viewpoints based on different scenes. \n\nWe respectfully disagree with the reviewer that the proposed method is not novel. To the best of our knowledge, prior works in 3D grounding have not studied these aspects. We would be grateful if the reviewer could provide references to back up this novelty claim.\n\n```\nQ9. Novelty of the LISA module\n```\n\nOur baseline model M3DRef-CLIP follows 3DVG-Transformer, which directly uses spatial distances as additional attention weights without further reasoning. Prior works, like ViL3DRef [A] and CORE-3DVG [B] explored the spatial relations with hand-selected features. These hand-selected features work with ground truth object proposals but lead to worse performance when the object proposals are predicted, i.e. noisy. As is shown in Tab. A1 and Tab. A2, though ViL3DRel works well on the Nr3D benchmark which provides ground truth box proposals, the performance is much worse when validating on the ScanRefer benchmark where no ground truth proposals are provided.\n\nTo address these shortcomings, we propose to use language-guided spatial scores to balance the visual attention weights and spatial attention weights. We believe our LISA design greatly differs from the existing works.\n\n* [A] S. Chen, P.-L. Guhur, M. Tapaswi, C. Schmid, and I. Laptev. \"Language conditioned spatial relation reasoning for 3d object grounding\". In NeurIPS, 2022\n* [B] L. Yang, Z. Zhang, Z. Qi, Y. Xu, W. Liu, Y. Shan, B. Li, W. Yang, P. Li, Y. Wang, et al. \"Exploiting contextual objects and relations for 3d visual grounding\". In NeurIPS, 2023.", "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": "NE3bsVca6I", "reviewer": "Reviewer_rjTU", "summary": "This paper improves upon the previous work, M3DRef-CLIP, through three key modifications: First, the authors incorporate an additional proposal probability prediction branch followed by a NMS operator to filter out low-confidence and redundant object proposals. Second, they learn camera pose residuals to dynamically render the object proposals from multiple views and extract multi-view CLIP features as the 2D feature. Third, they incorporate spatial relations weighted by learnable weights from visual and text features. The proposed method outperforms M3DRef-CLIP in several settings and datasets.", "questions": "Please refer to the questions in the weaknesses section. I would consider increasing the rating if the authors can address my questions and concerns properly.", "limitations": "The authors have discussed some limitations and indicated that there are no potential societal impacts of the proposed method.", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "•  The proposed method demonstrates state-of-the-art performance in both multi-object and single-object 3D grounding tasks.\n\n•  The ablation study clearly shows the performance improvement brought by each modification.", "weaknesses": "1. Despite the performance improvement, the time and memory complexity of the proposed methods seem substantial, particularly the multi-view rendering step. It would be beneficial to include a complexity comparison with baseline methods.\n\n2. In M3DRef-CLIP, both F1@0.25 and F1@0.5 are reported. Including the same evaluation metrics would be advantageous, as this paper is an improvement over M3DRef-CLIP.\n\n3. The paper would benefit from more detailed motivations and explanations. For example, what motivates the design of camera pose offset prediction (Eq. 5) in this manner? Why is the designed LISA module better than the language-conditioned spatial self-attention in ViL3DRel [9]?\n\n4. What would the performance be if the camera pose offset were removed while keeping the average of multi-view CLIP features?\n\n5.  Is there any supervision on the predicted proposal probability (Eq. 1)? How does the threshold in Eq. 2 affect the performance?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "## To Reviewer rjTU\n\n```\nQ10. Time and memory complexity comparisons\n```\n\nThanks for the suggestion. We show the FLOPs and inference time of each proposed module and a comparison with the baseline model M3DRef-CLIP in Tab. R5. All experiments are conducted on Multi3DRefer validation set on a single NVIDIA A100 GPU. The reported FLOPs and inference time are the averaged over the validation set.\n\n\nWe observe that the dynamic box proposal module and the dynamic multi-view renderer in the dynamic vision module contribute marginally to the computation. The additional computation in the language-informed spatial fusion module is also minimal. In other words, our model achieves better grounding performance without significantly increasing computations.\n\n**Table R5. Computational cost for proposed modules**\n| Module                       | FLOPs    | Inference time |\n|:-|:-:|:-:|\n| Baseline detector            | 943.1 M  | 0.235 s        |\n| Detector w/ dynamic box proposal | 943.1 M  | 0.241 s        |\n| Baseline renderer            | 638.9 G  | 0.271 s        |\n| Dynamic multi-view renderer  | 638.9 G  | 0.276 s        |\n| Baseline fusion module       | 155.3 M  | 0.004 s        |\n| Language-informed spatial fusion | 247.4 M  | 0.007 s        |\n\n```\nQ11. Comparison of the additional metric F1@0.25 with M3DRef-CLIP\n```\n\nPlease note, M3DRef-CLIP did not provide quantitative results over F1@0.25 in their tables. Here, we use their model to provide additional comparisons with M3DRef-CLIP over F1@0.25 in Tab. R6. We observe that our D-LISA achieves a better overall F1@0.25 score, especially for multiple targets and sub-categories where the distractors of the same semantic class exist. This aligns with our observation for F1@0.5 results in Tab 4.\n\n**Table R6. F1@0.25 (&uparrow;) result on Multi3DRefer validation set**\n| Model           | ZT w/o D | ZT w/D | ST w/o D | ST w/D | MT   | All  |\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n| M3DRef-CLIP     | 81.8     | 39.4   | 53.5     | 34.6   | 43.6 | 42.8 |\n| M3DRef-CLIP w/NMS | 79.0   | 40.5   | **76.9**     | 46.8   | 57.0 | 56.3 |\n| D-LISA          | **82.4**     | **43.7**   | 75.5     | **49.3**   | **58.4** | **57.8** |\n\n```\nQ12. What is the motivation for camera pose offset prediction?\n```\n\nIn L30-32, we motivated that the viewpoints of the renderer may need to be different across different scenes and object sizes. To avoid poor viewpoint initializations, we start from the fixed viewpoints and predict an offset for each viewpoint. In Eq. 5, we use an MLP to learn the camera pose offset for each view based on the average box size, as the object size is a strong indicator when choosing the rendering camera pose.\n\n```\nQ13. Why is the proposed LISA better than the language-conditioned spatial self-attention in ViL3DRel?\n```\n\nViL3DRef [C] incorporates hand-selected features to guide spatial relations. While these hand-selected features perform well when provided with perfect (ground-truth) object proposals, they are not robust to errors, a typical issue with hand-designed features. In particular, when these features are used with detected (noisy) object proposals, their performance decreases.\n\nAs is shown in Tab. A1 and Tab. A2, though ViL3DRel works well on the Nr3D benchmark which provides ground truth box proposals, the performance is much worse when validating on the ScanRefer benchmark where no ground truth proposals are provided. We compare our LISA and the language-conditioned spatial self-attention in ViL3DRel on the Multi3DRefer benchmark in Tab. A4. The results validate the effectiveness of our module.\n\n* [C] S. Chen, P.-L. Guhur, M. Tapaswi, C. Schmid, and I. Laptev. \"Language conditioned spatial relation reasoning for 3d object grounding\". In NeurIPS, 2022\n\n```\nQ14. What would the performance be if camera pose offset were removed?\n```\n\nPlease see the ablation study in rows 1 and 2 of Tab. 4 where using the camera pose offset (DMR) improves the model performance.\n\n```\nQ15. How is the predicted proposal probability supervised? \n```\n\nThe predicted probability is end-to-end supervised by weighting the detector features with the probabilities, as is shown in Eq. 4. To encourage fewer numbers of boxes, we also propose to regularize with the loss proposed in Eq. 3.\n\n```\nQ16. How does the threshold 0.5 in Eq.2 affect the performance?\n```\n\nThanks for the suggestion. As we used a Sigmoid nonlinearity in Eq. 1, the maximum-likelihood estimate results in a threshold of 0.5. Here in Tab. R7, we further conduct experiments, following the setting in Sec. 4.3, but using different thresholds. We observe that using 0.5 results in the best performance. \n\n**Table R7. Additional ablation studies on the filtering threshold.**\n| Threshold | 0.4  | 0.5  | 0.6  |\n|:-|:-:|:-:|:-:|\n| F1@0.5 (&uparrow;)   | 49.2 | **50.3** | 47.2 |", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We thank all the reviewers and the AC for the thorough reviews. We are happy to see the reviewers' supportive comments and feedback. Reviewers **#su8L** and **#W21s** commend the paper for its clear and well-structured writing. Reviewers **#W21s** and **#rjTU** appreciate the comprehensive experiments and ablation studies, with detailed implementation and evaluation. Reviewers **#su8L**, **#WVQA**, and **#rjTU** all acknowledge the substantial improvement in both multi-object and single-object 3D grounding, indicating the effectiveness of the proposed model. We begin the response by restating our contribution, individual questions are addressed in the responses below.

This paper tackles the problem of multi-object 3D grounding where we identify shortcomings in the prior work, e.g., using a fixed number of boxes and features used for reasoning. To address these shortcomings, we propose a dynamic vision module and a language-informed spatial fusion module. For the dynamic vision module, we introduce a dynamic box proposal module that automatically learns the relevant box proposals instead of using a fixed maximum number of proposals. We propose to learn the camera pose for rendering dynamically based on the scene instead of using fixed camera viewpoints. For the language-informed spatial fusion module, we enable efficient reasoning over spatial relations by learning to balance the visual attention weights and spatial relations guided by language. Extensive experiments on both multi-object and single-object 3D grounding benchmarks validate the effectiveness of the proposed model.

We look forward to a constructive discussion period and hope to address any concerns that the reviewers may have!

Meta-review: The paper proposes to improve multi-object 3D grounding by introducing a dynamic vision module that selects a variable number of detection based on a proposal probability, camera poses that are predicted by a MLP based on the bounding box size, and a language informed spatial attention.  Experiments on Multi3DRefer and ScanRefer show that the proposed method can performs well.

The submission received divergent ratings with two reviewers (su8L, W21s) giving it a borderline accept and two reviewers (WVQA, rjTU) favoring reject.  

Overall, reviewers find the paper to be well-written (su8L,W21s) with experiments that shows the proposed method is effective (su8L,WVQA,rjTU).  However, reviewers had questions about the complexity of the proposed method (especially as it required rendering), and requested additional ablations and clarifications.  

While the rebuttal adequately addressed reviewer concerns, reviewers WVQA and rjTU remain negative, stating mainly that the novelty of the proposed modules is limited.  The AC note that WVQA did not follow up during the author response period.    

Based on the above, the AC believe that the techniques proposed by the paper are effective (providing a boost in performance over prior work), but there is lack of excitement from reviewers as the techniques are perceived to be more "engineering" and not sufficient "novel".  As novelty is subjective, and the AC believe the authors had done a good job in responding to reviewer questions, the AC feels the paper can be accepted at NeurIPS and to leave it to the community to determine the potential impact of the proposed techniques.

The AC recommend the authors include the additional ablations done for the rebuttal and to update the title of the paper to be clearer.  The paper itself may have also overused the word "dynamic".
Decision: Accept (poster)