ComPC: Completing a 3D Point Cloud with 2D Diffusion Priors
Authors: Tianxin Huang
Zhiwen Yan
Yuyang Zhao
Gim Hee Lee
Venue: ICLR 2025 Poster (2025)
Keywords: Gaussian Splatting
Diffusion Model
Point Cloud Completion
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Introducing priors from a pre-trained 2D diffusion model for 3D point cloud completion through Gaussian Splatting

Abstract: 3D point clouds directly collected from objects through sensors are often incomplete due to self-occlusion. Conventional methods for completing these partial point clouds rely on manually organized training sets and are usually limited to object categories seen during training. In this work, we propose a test-time framework for completing partial point clouds across unseen categories without any requirement for training. Leveraging point rendering via Gaussian Splatting, we develop techniques of Partial Gaussian Initialization, Zero-shot Fractal Completion, and Point Cloud Extraction that utilize priors from pre-trained 2D diffusion models to infer missing regions and extract uniform completed point clouds. Experimental results on both synthetic and real-world scanned point clouds demonstrate that our approach outperforms existing methods in completing a variety of objects. Our project page is at \url{https://tianxinhuang.github.io/projects/ComPC/}.

Published as a conference paper at ICLR 2025
COMPC: COMPLETING A 3D POINT CLOUD WITH 2D DIFFUSION PRIORS
Tianxin Huang, Zhiwen Yan, Yuyang Zhao, Gim Hee Lee School of Computing, National University of Singapore University (huangtx, gimhee. lee) (nuuseduu.s edu.sg sg
ABSTRACT
3D point clouds directly collected from objects through sensors are often incom- plete due to self-occlusion. Conventional methods for completing these partial point clouds rely on manually organized training sets and are usually limited to object categories seen during training. In this work, we propose test-time framework for completing partial point clouds across unseen categories without any requirement for training. Leveraging point rendering via Gaussian Splatting, we develop tech- niques of Partial Gaussian Initialization, Zero-shot Fractal Completion, and Point Cloud Extraction that utilize priors from pre-trained 2D diffusion models to infer missing regions and extract uniform completed point clouds. Experimental results on both synthetic and real-world scanned point clouds demonstrate that our ap- proach outperforms existing methods in completing a variety of objects. Our project www.ong....hu....... tot wowpooooeeooooooettttooooooo
page is at htt.a:/://Laaaaaaaaaoooooooooooo
INTRODUCTION
3D point clouds have always been an important perceptual approach for the physical world, finding extensive use in various applications such as SLAM (Cadena et al. (016) or 3D detection (Geiger (et al. 2013 Reddy et 2018). However, point clouds are often captured from specific camera viewpoints Yuan et al., 2018 Kasten et al.) (024) in real applications, which may lead to the incompleteness of collected points due to the self-occlusion. Robust completion for partial point clouds can greatly reduce the cost for data collection, and are useful for subsequent 3D perception.
As illustrated in Fig. (a). most existing completion methods (Yuan et al., 2018 Zhao al. 202T Zhou et al. 2022, Yu et al.
2023) adopt well-designed deep neural networks to directly generate complete point clouds from partial ones. These methods are usually trained on specific point cloud datasets Yuan et al. 2018 Yu et al. 2023) and demonstrate outstanding performances on their respective test sets. However, they face challenges in handling data that differs from what they were trained on, such as unseen object categories or real-world scans. This limitation significantly hinders the practical deployment of these point cloud completion methods.
Leveraging the impressive capabilities of 2D diffusion models (Rombach al. 2022 Saharia et al. 2022] Ho et al., 2020). SDS-complete (Kasten et al.) 2024) firstly propose a test-time point cloud completion methods utilizing text-to-3D generative models (Poole et al.] 2022 Wang et al. 2023). As shown in Fig. (b). this method optimizes a Neural surface ( (Yariv et al. 1021) guided by Score Distillation Sampling (SDS) (Poole et al. 2022) of the text-conditioned Stable Diffusion (Rombach
et al. 2022 The Neural surface, modeled as a Signed Distance Field (SDF) following VoISDF Yariv al. 202T , incorporates the geometric details from the partial points by setting their SDF values to zero. The completed points are then generated from the optimized surface for assessment. By tapping into the extensive 2D knowledge provided by diffusion models, SDDS-complete (Kasten et al.) 2024) manages to achieve significantly robust point cloud completion without any training specific training sets. However, a notable limitation of the method proposed by SDS-complete (Kasten et al. 2024) is its dependency manually created text prompts for each point cloud to guide the completion. This requirement can encounter a challenge in real-world applications, where providing detailed and accurate text descriptions for incomplete point clouds is not always feasible.
In view of the above-mooeeeee mentioned issues, we propose a novel test-time point cloud completion frame- work that eliminates the need for any extra manually provided information such as text descriptions.

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Published as a conference paper at ICLR 2025
Partial points
Partial points
Text prompt
Partial points
Reference image
GS rendertng
SDF-0
Constrwint
SDS
SDS
Neural surface
GS
Stable Diffusion
Zero 1-to-3
Completed points (a)
Completed points
Completed points
(c)
Figure 1: Different point cloud completion methods. (a) Existing network-based completion methods; (b) Test-time SDS-complete (Kasten et al. (024) with text prompts to guide Neural surface for completion; (c) Our method based on 3D Gaussian Splatting (GS) guided by the diffusion model from Zero 1-to-3 (Liu et al. 2023) conditioned on the reference image rendered from partial points.
As discussed in PCN (uuan et al. 2018) and SDS-complete (Kasten et al. 2024), existing completion methods concentrate mainly on point clouds incomplete due to self-occlusion, which means that these point clouds often appear nearly complete from at least one viewpoint. Inspired by the amodal perception (Lehar 1999, Breckon & Fisher 2005), we aim complete point cloud by utilizing the observation from a reference viewpoint that provides the most complete view of the point cloud.
As illustrated in Fig. (c), estimate such viewpoint and acquire reference image of the partial point cloud. Inspired by the capability of novel view synthetic diffusion model, e.g., Zero 1-to-3 (Liu al. 2023), we propose to the reference image condition for guidance from the diffusion model to infer the missing regions. Utilizing 3D Gaussian Splatring (GS) (Kerbl et al., (023). which can render 2D images from discrete 3D Gaussians initialized from point clouds, we can effectively render the reference image. This approach also allows us to incorporate 2D diffusion priors into the process of modifying 3D geometry. Consequently,
can complete the missing regions by optimizing the 3D Gaussians with guidance from the 2D diffusion model. Moreover, we propose Preservation Constraint to maintain the geometric integrity of partial point clouds. The completed point clouds would be finally acquired from the 3D Gaussian centers.
Our main contributions can be summarized as below:
We propose the Partial Gaussian Initialization generate reference image for partial points, which is observed from an estimated reference viewpoint;
Based on the reference image, develop the Zero-shot Fractal Completion complete the missing regions by introducing 2D diffusion priors;
We propose Point Cloud Extraction extract uniform point clouds from 3D Gaussians;
Through comprehensive evaluation across various data, we demonstrate that our approach surpasses conventional completion methods in handling both synthetic and world scanned point clouds.
2 RELATED WORKS
2.1 3D GENERATION VIA PRIORS
Since the notable success of 2D diffusion models in text-to-image generation (Rombach et al. 2022 Saharia et al. 2022 Ho et al., 2020), text-to-3D and image-to-3D generation have attracted the attention of an increasing number of researchers. To achieve robust and generalizable 3D generation, researchers propose to lift 2D priors for 3D generation (Poole et al. 2022 Wang et al. 2023 Mohammad Khalid et al. 2022: Michel et al. 2022). These works usually optimize specific 3D representations by guidance from 2D diffusion models ss under different viewpoints, where the guidance calculated with Score Distillation Sampling (SDS) (Poole et al. (022) through rendered images. Score Distillation Sampling (SDS) guides target model (e.g., NeRF) by using gradients from pre-trained diffusion model. This aligns the target model's 's output with the diffusion model's learned distribution, enabling high-quality generation in specialized domains.
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achieve remarkable 3D generation quality by using SDS guidance from their pre-trained novel view synthesis diffusion model explicitly conditioned on the reference image and camera transformation. Conditioned on single image, Zero 1-to-3 predicts an image consistent with plausible 31) shapes for any given camera pose. However, its reliance on NeRF representation leads to prolonged optimization times. 3D Gaussian Splatting (GS) (Kerbi et al.) 2023) is an efficient 3D representation that encodes both geometrical and appearance information using a set of 3D Gaussians. Each Gaussian is defined by attributes such 3D coordinates, scaling, opacity, rotation, and spherical harmonics parameters. By optimizing these attributes, information from 2D images can be incorporated into the Gaussians, enabling efficient novel-view rendering. Dreamgaussian (Tang et al., 2023) offers solution by optimizing 3D Gaussians through SDS from Zero 1-to-3, achieving a balance between high-quality outputs and acceptable optimization durations.
Zero 1-to-3 (Liu al., 2023)
Motivated by Dreamgaussian, we recognize the potential of GS to refine 3D coordinates of Gaussian centers using guidance from 2D diffusion models. This insight presents an opportunity to apply 21) diffusion priors to tasks related to 3D point clouds, such as point cloud completion.
2.2 POINT CLOUD COMPLETION
Point cloud completion aims to recover completed point clouds from partial input point clouds. Ever
since PCN (uaan al., 2018) firstly applied deep neural networks to predict complete point clouds from partial inputs, numerous ad advancements (Zhang et al. [2200] Xie et al. 2020 Huang ct al. 2020 Yu et al, 2021; Wang et al., 2020: Xiang et | |202]] Wen et al. (021) have been made enhance the accuracy of point cloud completion by altering network architectures. For example, GRNet (Xie et al. 2020) converts point clouds into grid formats and employs 3D CNNs for predicting the completed structures, while PFNet (Huang al. 2020) adopts fractal approach to better preserve existing shape details. The Fractal approach focuses on predicting only the missing regions of point clouds, preserving existing details by retaining the shapes from the partial input. RFNet (Huang al. 1 utilizes a differentiable layer to merge existing geometrical details from partial point clouds into
completed results. More recent approaches (Wang et al.. 2024 Zhu et al. 2023 Li et al. 2023
Yu et al.] [02T] Xiang et al., 2022 Zhou et 2022 Yu et al. 2023 Yan et al. 2022) integrate dessineed transformers to improve completion accuracy by considering broader geometric relationships. DiffComplete Chu et al. (2023) is a diffusion-based model for 3D shape completion, leveraging probabilistic modeling to predict missing parts of 3D shapes while preserving structural
coherence and diversity.
However, the effectiveness of these point cloud completion methods diminishes when applied to data that differ from their training sets, such as point clouds from unseen categories or other datasets. SDDS-coppltte (Kasten et al.] 2024) proposed a test-time completion framework that employs VoISDF (Yariv et al. 2021) for rendering, drawing on priors from pre-trained text-to- image 2D diffusion models (Rombach et al. 2022). This approach maintains the original shapes by constraining the Signed Distance Field (SDF) values of the partial inputs. Yet, this strategy's 's reliance on eext-to-image diffusion models for guidance necessitates well-defined text prompts for each partial point cloud, which may not be practical in real-world applications. Moreover, the optimization of SDS-Complete is quite time-consuning, which may take more than 1000 minutes for one point cloud.
In this study, we propose leverage 3D Gaussian Splatting (GS) (Kerbl et al. 2023) to bridge point clouds with priors from 2D diffusion models. By generating a reference image of the partial point cloud serve as a condition for guidance from Zero 1-to-3 (Liu et al. 2023), our method can extract uniform and completed point clouds from the 3D Gaussian centers. Since our method exclusively utilizes information gathered from the incomplete point cloud for completion, it eliminates the need for any additional manually specified prompts for each point cloud. Due to the efficient rendering from 3D GS, and stronger priors from Zero 1-to-3, our method can achieve much higher optimization efficiency than SDS IDS-Complete (Kasten et al. 2024).
3 METHODOLOOGY
As shown in Fig, 2 the whole completion process is composed of Partial Gaussian Initialization (PGI), Zero-shot Fractal Completion (ZFC), and Point Cloud Extraction (PCE). For the given partial point cloud P,n, we firstly transform it into colorized reference image Ti, and 3D Gaussians Ga,
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Partial Gauzelan Initialization
Set attributes
Render under %
Rirleeenh Vinepoiitt Eerouation
Noise
Sas
Pin
dependent Guidance
Go
Gps
%
Preservtionn Comsraint
Centers
Ppra
Pm
Froze parameters
Virtual Camera
Zero-shot
Gaundion surfac edtraction
Oril Pulling
30 Gaussians
Completion
Po
Point Cloud Extraction
Pour
Par
Our Completion Pipeline
Figure Illustration of our framework. OIn Partial Gaussian Initialization (PGI) Reference Viewpoint Estimation estimates camera pose Vp where Pm can be most completely observed. We initialize 3D Gaussians G.n from P.. and render the reference image 4in under Vz. IIn Zero-shot Fractal Completion (ZFC), 3D Gaussians G, begins with initialization using noisy Pv and undergoes optimization guided by view hiew-dependent guidance from the diffusion model fz in Zero 1-to-3 (Liu al. 2023) based on randomly chosen camera pose V. Additionally, incorporates Preservation Constraint computed with respect to V,, Ga, is mixed with Gm to form Gaut, introducing the partial geometry from P.n. AAter ZFC, we use Point Cloud Extraction (PCE) to extract surface points P Surf from centers of Gall, and convert P Paurf into uniform Pout with Grid Pulling.
with Partial Gaussian Initialization. Subsequently, Lin and Gin are introduced to Zero-shot Fractal Completion to acquire 3D Gaussians Gall with the completed shape. Specifically, we use Inn to guide the optimization of 3D Gaussians Gm by borrowing priors from the 2D diffusion model in Zero 1-to-3 (Liu et al.) 2023). Finally, we extract uniform completed point clouds Paur from the centers of Gau with Point Cloud Extraction. Please note that the completion is mainly achieved by optimizing 3D Gaussian parameters in Gm,, without networks as Yuan et al. 2018).
3.1 PARTIAL GAUSSIAN INITIALIZATION
Following the definition of point cloud completion task by PCN Yuan et al., 2018), only 3D coordinates are provided input to infer the complete geometry. To introduce priors from pre- trained 2D diffusion models, we use 3D Gaussian Splatting (GS) to achieve differentiable rendering from point clouds to images. Partial Gaussian Initialization, we first estimate a reference camera pose Vp with the Reference Viewpoint Estimation. We then initialize 3D Gaussians Gin from the incomplete point cloud P P.n- A reference image Ln for subsequent completion would be rendered from Gan under pose Vp. G.n is frozen to preserve the geometrical characteristics of P...
Reference Viewpoint Estimation. For any point cloud to be completed, we first determine an reference camera pose Vp,, that captures its most completed observation. The completion process then builds upon this observation. Since the incomplete point cloud P.n. typically spans across a surface, its most complete view is characterized by minimal self-occlusion and closeness to the camera.
Considering the potential occlusion of rear Gaussians by those in the foreground during rendering. implement filter h(G V,,) to identify the indices of the frontmost 3D Gaussians in Gū from the camera pose V,, Given that the centers of Gin are anchored to P... we can estimate V,, by minimizing:
V, arg mig CD(P..(h(Gi.. Vo)], Pm.) + wo Depth( Vn), Vn
(I)
where CD(..) is the Chamfer Distance (Fnn et al. 2017 to measure shape differences between two point clouds. Depth(P... Vn) calculates the mean depths of P.,. observed from the camera pose V,, for regularization, and Wo is weighting factor to ensure balance. For this study, we estimate Vp by examining 5,000 camera positions uniformly distributed around the partial point cloud.
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opacity
0 < opacity <1
opacity
Ours
Original
Figure 3: Differences between our binarized opacity and original continuous opacity. smaller but not approaching.
denotes
Gaussian Attributes Setting. Upon estimating the reference camera pose V,, we render a reference image In from 3D Gaussians Gan initialized from partial point cloud P.n. To render a characteristic reference image, we make few modifications the original 3D Gaussians:
1) The opacity G for all 3D Gaussians within Gin is set to a constant value of 1. This step ensures that Gaussians representing all partial points are nearly opaque and clearly visible during rendering.
2) The color G are set as scaled normal map as: G (1 + ((P.))) where the normal vectors (P)) are estimated with Open3d (Zhou et al. 2018
We scale them from 1 to
3.2 ZERO-SHOT FRACTAL COMPLETION
sero-shot Fractal Completion (ZFC) aims to introduce priors to transform Gin with the partial shape into Gaul with the completed shape. As illustrated in Fig. 2 ZFC optimizes 3D Gaussians Gm for completion and is guided by the View-dependent Guidance and the Preservation Constraint.
Modification for 3D Gaussians. 1) Considering point clouds are observed as multiple equal size spheres, we set the scaling of all 3D Gaussians to single shared scalar value keep the shape of Gaussians consistent as points. To better cover the space around the partial point cloud Pox. we create noised PN P. + (O,, of) for the initialization of Gm- The scaling attribute of Gm is initialized as G = Px Σ Neighbor(n)) (Px) from the noisy Pv as shown in Fig. 2 where Neighbor denotes the nearest neighbor distance of each point in PN.
2) Furthermore, as demonstrated in Fig. the original approach opacity can lead dispersion of Gaussian centers around the actual surface due to the range of opacities 0 < opacity < used in rendering. To address this problem, we apply a differentiable quantization (Huang et al. 2022) for Gaussian opacity to binarize the values. For 3D Gaussians Gm with original opacity 6 m. the binarization is implemented follows:
Go fetop (round(dGG) G G
(2)
where
round [1 if Go > 0.5,
otherwise.
with fstop(-) designed to halt gradient propagation. Here, the forward propagation result of Eq, 2 is round(G, ). while the gradient during backpropagation is calculated based on G is predetined small constant set to 0.01 in this work because lower opacity may make the Gaussians hard to optimize. Consequently, 3D Gaussians with Go cluster near the surface as shown in Fig.
while those with G + O will be considered noise and excluded in subsequent processing.
View-dependent Guidance. To complete the missing regions, we leverage 2D diffusion priors from Zero 1-to-3 (L.iu et al., 2023) due to its capability to deduce the unseen regions based on available imagery. As illustrated in Fig. we utilize the reference image J,n from Partial Gaussian Initialization to derive the SDS guidance (Poole al. 2022) based image I; rendered with Gaussian Splatting in randomly selected viewpoint V,, referred to as View-dependent guidance. Defining E as the noise anticipated by the 2D diffusion model fz with t and indicating the time step and standard noise, respectively, the SDS guidance calculated as:
01:
V Gua LSDS Er,a (Ir; Iin, V..t) c) 8Gall
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Algorithm 1 Gaussian Surface Extraction
Input: 3D Gaussians Gaul and corresponding centers Pa. h(-) following Sec. 3.1
2: Filtering with opacity:
3: Let opacity of Gall be Gall.
4: Effective 3D Gaussian indexes id, Gaul 0.5,
5: Extracting the surface points:
6: Set index list idr
[]. generate N uniform camera poses V,
7: for i = 1 to do
8: Adding the first observed Gaussian indexes: idr. append [ido], V(i))
9: end for
Remove the repeated indexes: idr Unique(idz)
11: Acquire the surface points: Paurf Pa [id [idz]
g ((p)) 5r
P in
Sampled points Ps
g
Merge
P Pout
g
Figure 4: Illustration of Grid Pulling module. g(-) a MLsed SDF learned from the completed point cloud Psurr P. Merge denotes merge layer from (Huang et al. 2021). Given the 3D grids G, is the diagonal length of unit grid. Sampled points would Ps
P (g) 0.5r, p E 9).
For the task of point cloud completion, we adopt fractal approach discussed in PFNet (Huang) ctt al.) 2020) that focuses on optimizing only Gm within Gau to reconstruct the missing regions. Gin remains unchanged to preserve the original geometric characteristic of the partial point clouds P.n- Additionally, to manage the scaling G of 3D Gaussians Gn during optimization, we implement a regularization with weighting factor of W1:
Lmrey W1 |G
Preservation Constraint. To maintain the geometric shapes of the initial partial point clouds, we introduce Preservation Constraint aimed at reducing the shape differences between the partial point cloud Pi, and Gaussian center coordinates Pore acquired from the partial observation of 3D Gaussians Gaul under Vp. Utilizing the surface filter h(-,)) presented in Sec. 3.1 and considering Gaul the combined set of Gm and G with Po representing the centers of Galt. the observed Gaussians centers would Pore = Po ( Vp)]. The Preservation Constraint is formulated as:
Lp = W2 CD(Ppre, P.n),
(5)
where .D(..) is the Chamfer Distance (Fan et al. 2017). w2 the weighting factor. This constraint ensures the alignment of Gaul with P., when observed from the reference camera pose Vpp.
3.3 POINT CLOUD EXTRACTION
After the optimization of ZFC, we extract point cloud Pout from centers of 3D Gaussians Gall with Point Cloud Extraction. Specifically, we firstly select surface points Psur from Gaussian centers Pe with Gaussian surface extraction. We then resample uniform Pout from P by Grid Pulling.
Gaussian Surface Extraction. The centers the 3D Gaussians can lie both on and below the surface of the shape after optimization. As result, it is unsatisfactory to directly use these centers as the complete point cloud. To address this issue, we introduce Gaussian Surface Extraction process to select surface points P.w. Peury from the centers of 3D Gaussian Gau- This procedure is detailed in Alg. [ By adjusting the opacity of all 3D Gaussians to either 8 or 1, we note that Gaussians with minimal opacity 5 hardly contributes to the rendering process. Consequently, our initial step involves

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Published as a conference paper at ICLR 2025
Input
PoinTr
SVDFormer
AdaPoinTr
PointAttN
Ours
GT
Figure 5: Qualitative comparison on synthetic data.
Table 1: Quantitative comparison synthetic data. Bold marks the best results.
Object
Horse
MaxPlanck Armadillo
Cow
Homer
Teapot
Bunny
Nefertiti
Bimba
Ogre
Aver
Metrics
CD/EMD
CD/EMD
CD/EMD CD/EMD CDVEMD CD/EMD CD/EMD CD/EMD CD/EMD CD/EMD CD/EMD
PoinTr
2.75/4.47
6.34/6.84
3.51/6.07
3.13/4.25 190/4.19 3.81/5.12 6.39/8.03 4.29/5 50550 5.53/6.73 3.41/5.06 4.10/5.63
SeedFormer
3,24/5.30
6.91/7.62
3.28/6.21
3.11/4.00 2.04/3.52 3.41/4.94 6,92/9.10 10 4,25/5.78 78 5.63/7.09 3.31/5.73 4.21/5.93
PointAuN
5,25/6.76
8.10/8.54
5.09/6.65
3.73/4.56
2.39/3.54 5,25/6.36 9.35/9.52
5,16/5.87
8.09/7.52 52 4.80/6.
5.72/6.54
ShapeFFrmer
4.17/5.38
3,48/4,49
3.76/4.88
4.53/5.29
2.27/2.84
2.55/2.86
4.52/4.44
3.09/3.87 87 5.00V5. 3.39/4.69 3.68/4.44
SVDFormer 2.70/3.89
8.37/6.45
4.12/6.53
3.55/4.39
2.42/3.35
5.87/6.08
6,59/6.90
4,27/5.02 02.0 5.47/4.91
4,59/5.36 36 4.79/5.29
AdaPoinTr 4.88/5.45
8.60/8.51
5.14/5.95
3.48/4.53 2.28/3.34 3.92/4.56
9.33/8.87
5,54/6,14
8.16/7.64
4.5M/.41 5,59/6.04
Ours 0.96/1.32
1.23/1.53
2.49/4.05 1.45/1.64 1.34/1.76 0.99/1.22 1.43/1.78 1.81/2.20 1.39/1664 1.22/1.67 1,43/188
To this end, Gall is examined from N uniformly distributed camera positions and ((.,-) is employed to extract the centers of the frontmost visible Gaussians the surface points P. Psurf- We set N 500 in this work.
filtering Gaul based opacity as outlined in Alg. 1
Grid Pulling. is evident Fig. 2 that the density of points in the completed regions of Psurf can significantly differ from that in the original partial point clouds. Ideally, we aim for consistently dense and uniform distribution of points across the entire shape. Direct attempts to enhance point density within the Zero-shot Fractal Completion (ZFC) would lead a substantial increase in computational cost. Inspired by NeuralPull (Ma et al.) (020).. we introduce a Grid Pulling (GP) module designed resample points uniformly from initially non- non-uniform point clouds.
NeuralPull (a et al. 2020) employs a Signed Distance Field (SDF) 9(-) to pull randomly sampled points Paam that are often generated by adding noise to Pgt as Psam Pgt + [O, og) towards the surface defined by the original point cloud Pgt- do is the standard deviation for normal distribution NO0, NO),a3). The pulling operation is defined as: Ppull = Peam g(Peam) (P(Peam)) (lggm)/ll//Ppa.. (Peam) 1l2 The optimization of g(-) is guided by the Chamfer Distance (CD) a measure of the distance between Pgt and the adjusted points:
Lpuu(PPam (Peam Pgt) = CD(Ppall, Pat).
(6)
Leveraging P Paury obtained from Gaussian Surface Extraction, GP module learns SDF g(-) to align uniformly sampled points around Paurs with its surface. Unlike NeuralPull, which optimizes using only noised point clouds, our approach trains g(-) with both noised point clouds Pnear = Peurs N(0, (8), and Pfor being randomly sampled within the 3D bounding box encompassing Paury- The loss functions are defined as Lfar Lm fars Pours) and Lnear Lpult (Pout(Preenn P Pourf).
Additionally, we utilize merge layer as suggested by Huang al. (2021) to incorporate ge- ometric details from P.n. into Ppuli- Given the distances from pull points to their nearest neighbors in P.. as dist MiizeEPaa.VyEPP. || vll2, and corresponding neighbor indexes idr arg minzer Ppaare VyE ||x vl12, the merge layer Im outputs set of merged points:
Im (apuPl
Pin [idz] + (1
)
Ppull:
where a is small optimizable variable to decide how much to merge. The corresponding loss would be Lmee Lpmit (gm (Pputt, P, Psury) + W3 ||0|||2 where W3 is the weighting factor for the regularization of a. The overall training loss for s(-) is then:
Lg = Lfar + Lnear + Lmeer
(8)
As depicted in Fig. 4 we initialize a 1283 3D grid 9 according to the bounding box of P Uniform points P. would be selected by P. {p (()) < 0.5r, G). P. is then pulled to
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Input
PoinTr
SVDFormer
AdaPoinTr
PointAttN
SDS-Complete
Ours
GT
Figure 6: Qualitative comparison on Redwood dataset (Choi et al. 2016
Kasten al.
2024).
Table 2: Quantitative comparison on Redwood dataset (Choi et al.
2016 Kasten et al.
2024). For the
convenience, we re-optimize and normalize the results of SDS- DDS-Comleete consistently to 0.5 ~ 0.5.
In Domain
Out Domain
Object
Average CD/EMD CD/EMD CD/EMD CD/EMD CD/EMD CD/EMD CD/EMD CD/EMD CD/EMD CD/EMD
Table
Exe-Chair Out-Chair Old-Chair Average
Vase
Off Can
Vespa
Tricycle
Metries
PoinTr
3.56/7.42
1,91/4,50 0.67/1.41 2.48/6.28 2.16/4 3.84/6.55 3.76/5.83 1.84/3 94 5.91/11.63 3.84/6.99
SeedFormer
3.38/7.01
1,90/4.55 0.76/1.44 2.72/5.16 19/4/4 3.99/6.36 3.88/6.11 2.38/4.38
2.10/3 3.09/5.06
PointAttN
5.71/7.11
2.88/5.65
0.73/1.46 3.73/6.02
3.26/5.06 5.35/6.97 4.93/6.44
2.69/4.70
1.72/3.59 3.67/5.43
ShapeFormer
3.48/5.67
3,41/5.32 3.87/6.93 3.00/4.07
3.44/5.50
4,79/6.50 2.96/3.89 3.21/4.20
3,21/4.20
4.01/5.43
SVDFormer
2.13/3.29
3.606.02 1.15/2.15 3.69/5.83
2.64/4.32
5,20/7.28 5.42/7.05 3,30/5.25
3,78/4.55
4.42/6.03
AdaPoin'rr
5.02/6.23
2.58/4.79
0.82/1.38
3.62/5.61
3.01/4.50
5.14/6.48 4.47/6.32 1.94/3.52
1.83/367
3,34/4.98
SDS-Complete
1.35/2.30 1.96/2.65 2.51/3.92 2.77/3.77
2.15/3.16 3.00/5.25 3.79/4.28 3.36/5.73
3.18/3.49 3.33/4.69
Ours 1.67/..11 1.04/1.39 1.28/1.73 144/1.87 1.35/2.03 2.94/4.63 3.51/3.86 1.39/2.27 2.42/1.94 2.57/3.17
the surface of Paurf and combined with P.., through merge layer. The output point clouds would be Pout Im (P. - (P.)) Vg (P(P)))llo(P.))l1. Pin). As Pout is quite dense, we sample it to the specified resolution during comparisons.
EXPERIMENTS
Considering the impracticality of applying test-time completion methods (Kasten et al., 2024 to benchmarks such as Completion3D (Tchapmi et al. 2019) or ShapeNet (Chang et al. 2015 containing thousands of point clouds, we sample an appropriate amount of test data following SDS- Complete (Kasten et al., 2024). For synthetic data, we sample partial point clouds by sampling from various viewpoints around completely modeled objects from established sources (Krishnamurthy & Levoy, 1996; DeCarlo et al. 2003; |raun et al., (000) Lipman et al., 2008). For real scans, we use Redwood (Choi et al. 2016) following SDS CDS-complete (Kasten et al. 2024). Single scans are used as partial input, while the ground truths are adopted by composing multiple scans. Comparisons on ShapeNet (Chang et al. 2015) and Kitti (Geiger et al. 2013) are presented in the appendix A
We compare approach with state-of-the-arr supervised methods including PointAttN Wang ctt al. 2024), PoinTr (Yu et al. 202I), SVDFormer (Zhu et al. 2023), AdaPoinTr (Yu et al. 2023). SeedFormer (Zhou al. 2022). ShapeFormer (Yan et al. 2022). As SDS-complete (Kasten et al.) 2024) only provides codes for the processing of the Redwood dataset (Choi et al. 2016). we implement corresponding comparisons on Redwood. The evaluation metrics include the LI Chamfer Distance (CD) and Earth Mover's ' Distance (EMD) (Fan et al. (017)), which measure the similarity between the reconstructed point clouds and the ground truths. All metrics are multiplied by 102 in subsequent comparisons. We standardize point clouds and perform comparisons at resolution of 16,384 points following PCN (Yuan et al., 2018). Our results presented for comparisons on both synthetic data and real scans are averaged over three repeated experiments
4.1 COMPARISON ON SYNTHETIC POINT CLOUDS
In this section, we conduct an evaluation on synthetic point clouds. The quantitative and qualitative results are presented in Table I and Fig. [5] respectively. Existing network-based methods create noisy and incorrect shapes due to the discrepancies between their training data and the test data. As shown in Fig. 5 our method creates correct and reasonable completed results, which may benefit from abundant
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Partial
Depth map
Coordinate
Normal
GT
Pout
Put
Pour
Figure 7: Qualitative comparison between different colorization strategies. in and Pout denote the colorized reference image and completed point clouds, respectively.
Table 4: Ablation for ZFC and PCE.
Table 3: Ablation for colorization.
Redwood Synthetic CD EMD CD EMD
Guidance Pres Surf GP
Depth Coordinates Noumsl(Ours)
1.98 3,20 3.35 6.01
CD
2.25
2.01
1.96
1.97 3.07
2.55 4,41
EMD
2.88
2.64
2.60
1.50 3.38
1.17 3.74
1.96 2.60 1.43 1.88
priors from the pre- pre-trained diffusion model. An interesting case is that our method completes an appropriate handle for the teapot the first row of Fig. without any prompts and related geometries. It confirms that the pipeline can actually percept the actual categories of completed objects instead of simply inferring a shape to fill in the missing regions.
4.2 COMPARISON ON REAL SCANS
We follow SDS compceee (Kasten et al. 2024) for the comparison on real scans from Redwood (Choi itt al., 22166). Scans are divided into the "in domain" categories similar as training datasets of existing completion networks YYu al. 2021 Zhou et al. 2022, Yu et al. 2023 Wang et al.) 2024), and "out domain" categories unseen during their training. The qualitative and quantitative comparison results are illustrated Fig. and Table 2 methods on both "in domain" and "out domain" models, which further confirms the effectiveness and generalizability of our method. Existing fully-supervised methods may perform inferior even on the in-domain objects as illustrated in Table 2 which reveals their limitation datasets differing from the training one. By introducing abundant priors from 2D diffusion model (Liu et al. 2023) our method can achieve robust completion for objects across different datasets.
respectively. As shown Table 2 our method outperforms other
4.3 ABLATION STUDY FOR COLORIZATION STRATEGIES PGI
To confirm the necessity of using normal map for colorization in Partial Gaussian Initialization, compare their performances against other strategies including using depth values and normalized coordinates. As shown in Fig. these alternative strategies clearly outperformed by the normal map composed of normal vectors, particularly in the circled areas. This superiority likely stems from the ability of normal vectors more distinctly reflect surface changes in colors, thus better capturing the geometric characteristics in the reference image. We also provide quantitative comparisons of different colorization strategies in Table using average metrics from in-domain and out-of- Redwood dataset. The results show that the normal map consistently outperforms other methods.
4.4 ABLATION STUDY FOR ZFC AND PCE
In this work, we propose ZFC to introduce diffusion priors to infer the missing regions, and PCE to extract uniform point clouds from the 3D Gaussian centers. ZFC is composed of view dependent guidance and Preservation Constraint, while PCE consists of Gaussian surface extraction and Grid Pulling. From Fig. g
that our method with all components have uniform and reasonable

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Zero-shot Fractal Completion
Point Cloud Extraction
Partial
Only Guldance
Pres
Surf
Surr
Figure 8: Qualitative ablation study for ZFC and PCE. Surf, Pres, and GP denote Gaussian Surface Extraction, Preservation Constraints, and Grid Pulling, respectively.
completed results. In PCE, GP obviously generates quite uniform point clouds from the non-uniform ones directly acquired from 3D Gaussians. Gaussian Surface Extraction operation extracts the surface from relatively disorganized Gaussian centers. In ZFC, view dependent guidance creates coarse results with relatively correct overall shapes. Preservation Constraint avoids redundant shapes by introducing strict constraints between partially observed points and existing partial point clouds.
We also provide quantitative ablation study for our proposed components Table We evaluated our method on both Redwood and synthetic datasets. The results demonstrate that the Preservation Constraint improves performance compared standard dieeddepedeent diffusion guidance. Although Gaussian surface extraction significantly enhances the CD metric by selecting surface points, it negatively affects the EMD metric due to the high non-uniformity, as shown in the fourth column of In contrast, the final Grid Pulling (GP) module acquire more uniform surface points, leading to better EMD performance, although the CD metric experiences a slight decline due to precision loss caused by potential deformations in GP. More detailed ablation study can be found in the appendix A
Fig. 8
5 CONCLUSION
In this work, we introduce a test-time point cloud completion framework that leverages the rich priors from 2D diffusion models (Liu al. 2023 Zhang et al. 2023) through 3D Gaussian splatting, which can robustly complete partial 3D point clouds without any training requirements. Our framework consists of three main components: Partial Gaussian Initialization (PGI) render the reference image, Zero-shot Fractal Completion (ZFC) to complete the shape, and Point Cloud Extraction (PCE) to extract point clouds. Our method outperforms both existing network- baaed and test-time approaches in achieving robust completion across multiple categories of both synthetic and real scanned data.
LIMITATION
Our method shares similar limitations as claimed by SDS-complete (Kasten et al. 2024). As a test-time completion method, although method does not require any training, the optimization on the test data would take relatively long time cost. For instance, completing a point cloud from the Redwood dataset takes approximately 15 minutes with our method on RTX A6000 GPU. However, our framework is much more efficient than the existing test-time method SDS-complete (Kasten et al.) 2024), which takes up to 1950 minutes for optimization as reported in their supplementary material.
ACKNOWLEDGEMENT
This research / project is supported by the National Research Foundation (NRF) Singapore, under its NRF- Innetsestiigtt Programme (Award ID. RRF--RF900-00088 and the Agency for Science, Technology and Research (A*STAR) under its MTC Programmatic Funds (Grant No. M23L766021).
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REFERENCES
Toby P Breckon and Robert B Fisher. Amodal volume completion: 3d visual completion. Computer Vision and Image Understanding. 99(3):99--526, 2005.
Cesar Cadena, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, José Neira, lan Reid, and John J Leonard. Past, present, and future of simultaneous localization and mapping: Toward the robust- perusstprr age. IEEE Transactions on robotics, 32(6): 1309:13393133, 2016.
Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, al. Shapenet: An information-rie richh 3d model repository. arXiv preprint arXiv: 1512.33012, 2015.
Sungjoon Choi, Qian- Yi Zhou, Stephen Miller, and Vladlen Koltun. A large dataset of object scans. arXiv preprint arXiv: 1602.0248), 2016.
Gene Chou, Yuval Bahat, and Felix Heide. Diffusion Conditional generative modeling of signed distance functions. In Proceedings of the IEEE/CVE international conference on computer vision, pp. 2262-2272, 2023.
Ruihang Chu, Enze Xie, Shentong Mo, Zhenguo Li, Matthias Nießner, Chi-Wing Fu, and Jiaya Jia. Diffcomplete: Diffusion-basee generative 3d shape completion. Advances in Neural Information Processing Systems, 2023.
Douglas DeCarlo, Adam Finkelstein, Szymon Rusinkiewicz, and Anthony Santella. Suggestive contours for conveying shape. ACM Trans. Graph., 22(3):848 855, 2003. doi: 10.1 10.1242/8. 882354. UtLhttps::/
doi..oio... /tw.g//...... 1145/ 822622.22222.2 862354
Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.
2017.
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets roboties: The kitti dataset. The International Journal of Robotics Research, 32(11)11231-1237, 2013.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-683 2020.
Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang. Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, and Yong Liu. Rfnet: Recurrent forward network for dense point cloud completion. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 2021.
Tianxin Huang, Jiangning Zhang, Jun Chen, Zhonggan Ding, Ying Tai, Zhenyu Zhang, Chengjie Wang, and Yong Liu. 3qnet: 3d point cloud geometry quantization compression network. ACM Transactions on Graphies (TOG), 41(6):1-13, 2022.
Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le. Pf-net: Point fractal network for 3d point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 7662-7670, 2020.
Yoni Kasten, Ohad Rahamim, and Gal Chechik. Point cloud completion with pretrained text-to-image diffusion models. Advances in Neural Information Processing Systems, 36, 2024.
Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics, 42(4), 2023.
Venkat Krishnamurthy and Mare Levoy. Fitting smooth surfaces to dense polygon meshes. In John Fujii (ed.), Proceedings of the 23rd Annual Conference on Computer Graphics and Inteructive Techniques, SIGGRAPH 1996, New Orleans, LA, USA, August 4-9, 1996, pp. 313-324, ACM, 1996. doi: 10.1145//31170.237770 URL htthstts::/ /oi. 0tt.///.0//....1.3...3. 237270
Steven Lehar. Gestalt isomorphism and the quantification spatial perception. Gestalt theory, 21:
122-139, 1999.
11

---

Published as a conference paper at ICLR 2025
Shanshan Li, Pan Gao, Xiaoyang Tan, and Mingqiang Wei. Proxyformer: Proxy alignment assisted point cloud completion with missing part sensitive transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
2023.
Yaron Lipman, David Levin, and Daniel Cohen-Or. Green coordinates, ACM Trans. Graph, 27(3): 78, 2008. doi: 141111/66112..36666667 URL htL.tppss//oo..o0//11115//////11 1360677
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. Zero-1-to-3 Zero-shot one image 3d object. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9298-9309 2023.
Baorui Ma, Zhizhong Han, Yu-Shen Liu, and Matthias Zwicker. Neural-pull: Learning signed distance functions from point clouds by learning pull space onto surfaces. arXiv preprint WrXiv:2011.13495, 2020.
Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka. Text2mesh: Text-driven neural stylization for meshes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13492-13502, 2022.
Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa. Clip-mesh: Generating textured meshes from text using pretrained image-text models. In SIGGRAPH Asia 2022 conference papers, pp. 1-8, 2022.
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion, arXiv preprint arXiv:2209. 24989 2022.
Emil Praun, Adam Finkelstein, and Hugues Hoppe. Lapped textures. Judith R. Brown and Kurt
Akeley (eds.), Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 2000, New Orleans, LA, USA, July 23-28, 2000, pp. 465-470, ACM, 2000. doi: 10.1145/344779.344933 344987.. htthttss:: otg////o..o.rgg////
N Dinesh Reddy, Minh Vo, and Srinivasa G Narasimhan. Carfusion: Combining point tracking and part detection for dynamic 3d reconstruction of vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1906-1915, 2018.
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF confer- ence on computer vision and pattern recognition, pp. 10684-10695, 2022.
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang. Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Bureu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479-3666 -36494, 2022.
Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309. 2023.
Lyne P Tchapmi, Vineet Kosaraju, Hamid Rezatofighi, Ian Reid, and Silvio Savarese. Topnet: Structural point cloud decoder. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 383-392, 2019.
Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Ych, and Greg Shakhmarovich. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12619-12629, 2023.
Jun Wang, Ying Cui, Dongyan Guo, Junxia Li, Qingshan Liu, and Chunhua Shen. Pointattn: You only need attention for point cloud completion. In Proceedings of the AAAI Conference on artificial intelligence, pp. 5472-5480, 2024.
Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 790-799, 2020.
12

---

Published as a conference paper at ICLR 2025
Xin Wen, Peng Xiang. Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Yu-Shen Liu. Pmp-net: Point cloud completion by learning multi-step point moving paths. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pp. 7443-7452, 2021.
Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, and Zhizhong Han. Snowflake point deconvolution for point cloud completion and generation with skip-transformer IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(5):6320-66 00066000-338,, 2022.
Haozhe Xie, Hongxun Yao, Shangchen Zhou, Jiageng Mao, Shengping Zhang, and Wenxiu Sun. Gr- net: Gridding residual network for dense point cloud completion. arXiv preprint arXiv:2006.0361, 2020.
Xingguang Yan, Liqiang Lin, Niloy Mitra, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. Shapeformer: Transformer-based shape completion via sparse representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6239-6249, 2022.
Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. Advances in Neural Information Processing Systems, 34:4805 48140054415,, 2021.
Xumin Yu, Yongming Rao, Ziyi Wang. Zuyan Liu, Jiwen Lu, and Jie Zhou. Pointr: Diverse point cloud completion with geometry-aware transformers. In Proceedings of the IEEE/CVF
international conference on computer vision, pp. 12498-12507, 2021.
Xumin Yu, Yongming Rao, Ziyi Wang, Jiwen Lu, and Jie Zhou. Adapointr: Diverse point cloud completion with adaptive geometry-aware transformers. arXiv preprint arXi::2301.04545, 2023.
Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. Pen: Point completion network. In 2018 International Conference on 3D Vision (3DV), pp.
IEEE, 2018.
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3836-38477 3847, 2023.
Wenxiao Zhang, Qingan Yan, and Chunxia Xiao. Detail preserved point cloud completion via separated feature aggregation. arXiv preprint arXiv:2007.02374. 2020.
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun. Point transformer. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 16269, 2021.
Haoran Zhou, Yun Cao. Wenqing Chu, Junwei Zhu, Tong Lu, Ying Tai, and Chengjie Wang. Scedformer: Patch seeds based point cloud completion with upsample transformer. European conference on computer vision, pp. 416-432. Springer, 2022.
Qian-Yi Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3d: A modern library for 3d data processing. arXiv preprint wrXiv:1801.088// 2018
Zhe Zhu, Honghua Chen, Xing He, Weiming Wang, Jing Qin, and Mingqiang Wei. Svdformer: Complementing point cloud via self-view augmentation and self-structure dual-generator, In Proceedings of the IEEF/CVF International Conference on Computer Vision (ICCV) pp. 14508- 14518, October 2023.
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A APPENDIX
A.1 DETAILS OF PARAMETER SETTINGS
In Table 5 provide detailed information on the hyper- syper-parameters discussed Sec. Our experiments are conducted on RTX A6000/A5000 GPU, with Py 1.12 and CUDA 11.6.
Table 5: The setting of mentioned hyper-parameters in Sec.
Hyper- paraae-asrmeters
Wi
le-3, 1e3, 1e2, 0.1
6, Ca. Iterations 1000 (ZFC), 5000 (PCE)
0.01, 0.005, 0.05
Ablation for Grid Pulling. Grid Pulling (GP) module is proposed to resample uniform and regular point clouds from non-uniform Paury in the Point Cloud Extraction. As claimed in Sec. [3.3] far and Lnear are used to optimize an continuous surface presented by MLP while Merge layer is introduced merge output point clouds with partial input. From results in Fig. g can observe that far and Lnear contribute to overall contours and local shapes, respectively. Nonetheless, they are still limited the over-smoothed results. The merge layer helps preserve local geometrical details in the circled regions from the partial input point cloud. We also provide a quantitative comparison on GP module in Table We can see that each component in GP contributes to the final performance.
Partial
Far
Far Near
Far Near Merge
Figure 9: Ablation study for Grid Pulling module. Far, Near, and Merge denote the Lfrr Linarr and merge layer gm()) (+), respectively.
Partial
Vanilla Gaussian
Opaciity
Opacity + Scaling
Figure 10: Ablation Study for the 3D Gaussian modifications. Scaling and Opacity denotes the parameter-shared scalar scaling and binary opacity operations mentioned in Sec. (.2 respectively.
A.2 ABLATION STUDY FOR MODIFICATIONS OF 3D GAUSSIANS
As presented in Sec. 3.2 we make a few modifications to the original 3D Gaussian Splatting (Kerbl
al. 2023 including the parameter-shared scalar scaling definition and binary opacity estimation. In
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Table 6: Quantitative comparison for Grid Pulling module evaluated on Redwood dataset.
No Merge & Near
No Merge
Ours
CD
3.11
2.04
1.96
EMD
3.11
2.64
2.60
Table 7: Quantitative comparison for 3D Gaussian modifications evaluated on Redwood dataset.
No Opacity & Scaling
No Scaling
Ours
CD
6.35
2.50
1.96
EMD
10.48
3.58
2.60
this section, we conduct few experiments to validate the effectiveness of these proposed operations. To better illustrate their performances, we conduct comparisons based on Pours directly acquired from the Gaussian centers in ZFC. The results are presented in Fig. 10
Adopting shared scalar scaling helps in revealing more defined geometric details in the point cloud completion task. The original settings of separate scaling across different 3D Gaussians tend to produce blurring edges and lose finer details. In addition, the binary opacity operation obviously reduce the noises in P- With the original opacity settings, a considerable number of 3D Gaussians with moderate opacity values would scatter around the actual surfaces, blurring the distinction between the object and its surroundings. The binary opacity method effectively eliminates this issue, ensuring cleaner bounding and more accurate surface representation. As shown in Table 7 the modifications on 3D Gaussians have significant influence on the completion performances.
A.3 EFFECT OF THE FRACTAL COMPLETION STRATEGY
As illustrated in Fig. we introduce the fractal completion strategy in ZFC by optimizing 3D Gaussians Gm together with frozen 3D Gaussians Gan initialized from partial point clouds. this section, we conduct experiments to verify the effect of this strategy. A few visualized examples are presented Fig. II When not using fractal completion strategy, we directly optimize 3D Gaussians Gm for all structures without concatenation with Gin- We observe that completions without the fractal completion strategy tend to overlook some shape details present in the input partial point clouds. The quantitative results in Table further validate the advantages of the fractal strategy.
A.4 FAILURE CASES
Fig. 12 presents some failure cases. Our method encounters similar problems SDS SDS-complete (Kas ten et al. 2024) when generating thin surfaces in occluded areas. In these cases, 2D diffusion priors tend to imagine the thin occluded regions as reasonable but thicker structures shown in Fig. 12] This problem could be potentially addressed by fine-tuning the 2D diffusion priors, or introducing some regularization during the optimization process. We will explore it in future work.
A.5 DISCUSSION ABOUT NOISY REFERENCE OBSERVATION
Partial point clouds may also be affected by noise due poor natural illumination reflections from object surfaces. To evaluate the robustness of our method to such noise, we introduce varying levels of noise the synthetic partial point clouds described in Sec The quantitative results are summarized in Table 99| and qualitative comparisons are shown in Fig. 13 While the performance of our method decreases as the noise level increases, consistently outperforms existing approaches. As illustrated in Fig. [3] noise with standard deviation of 0.01 introduces noticeable blurring to the input partial points, yet our method still able to recover the overall contour effectively. This demonstrates that our approach exhibits a degree of robustness to noise. The primary contribution of this work lies the development of a practical framework that leverages 2D diffusion priors for 3D point cloud completion. Comparisons validate the effectiveness of our method in handling real-world data. Enhancing its robustness may further remain promising direction for future research.
real scans from the Redwood dataset Choi et al. (2016)
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Partial
w/0 Fractal
w/
Figure 11:
Ablation Study for the Fractal completion strategy. W/ Fractal and W/O Fractal denote using and not using Fractal completion strategy, respectively.
Table 8: Quantitative comparison for the Fractal strategy evaluated on Redwood dataset.
w/o Frac
w/ Frac
CD
2.00
1.96
EMD
2.69
2.60
A.6 DISCUSSION ABOUT DIFFERENT INCOMPLETENES, LEVELS
In this section, we evaluate the performances of our method on partial input with different incom- pleteness levels. For convenience, with varying levels of incompleteness. Specifically, we initialize the first virtual camera at pose of elevation 0, azimuuh -140, and foo 80". Additional virtual cameras are placed along the azimuth at 15 intervals. By merging 1, 3, and 7 consecutive depth maps, we generate partial point clouds with different levels of incompleteness. These data are used for comparison experiments, The qualitative and quantitative comparisons are presented in Fig, [4 and Table I0, respectively. We can see that more completed partial input constructed from more depth maps will bring finer details to the completed results. Our method consistently outperforms other methods in this setting.
synthetic objects from Sec./4tt construct evaluation sets
A.7 EVALUATION ON MULTI-MODAL METRICS
Since method relies SDS guidance from Zero 1-to-3 Liu et al. (023), it may produce different completion results with each optimization. To evaluate its performance under these variations, we assess our method using multi-modal metrics, including TMD, UHD, and MMD, following the approach in (Chou et al. 2023). We perform four repeated optimizations for both our method and SDS SDS-complete on the in in-domain categories of Redwood, as detailed in Sec. 4.2 to compute these metrics. The results are summarized in Table TT Our method achieves superior performance on UHD and MMD metrics, further validating its effectiveness for 3D point cloud completion. Although it shows lower TMD, which evaluates completion diversity, this actually reflects its steady convergence toward the ground truths-a a positive attribute for the task of 3D point cloud completion.
A.8 COMPARISONS BASED ON MESHES
Our method potentially support the generation of 3D meshes due the introducing of Grid Pulling module. As mentioned in Sec the Grip Pulling is proposed to re-sample uniform points from the non-uniformed point cloud P Ps where a SDF fuction g(-) is introduced fit the overall shape of P P to do the resampling. Therefore, we can use Marching Cubes following NeuralPull Ma et al. (2020) to extract meshes from g-)). The results are presented in Fig. 15 We can see that our method can also create more accurate mesh shapes than SDS- SDS-Complete Kasten et al. (2024).
A.9 EVALUATION ON SHAPENET
In this section, further compare methods with network-based methods on 16 common models from different categories of ShapeNet dataset. The results are presented in Table [12 and Fig. [6 Although our method performs slightly inferior network-based methods the known category
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Partial
Reference Image
Output
Ground Truth
Figure 12: Some failure cases.
Std
Input
SVDFormer
AdaPoinTr
ShapeFormer
Ours
GT
0.0
0.001
0.005
0.01
Qualitative comparisons under different noise perturbations. Std denotes the Standard deviation of added noises. The green box marks a local area of a noised point cloud.
Figure 13:
objects, it surpasses other methods on the unknown category objects. Please note that network-based methods use 3D ground truths from known categories for supervision during training, while our method does not introduce any training with such ground truths. As test-time point cloud completion method, the core contribution of our method is its generalizablity for point cloud objects from any category. This has been confirmed by experiments
multiple types of data including synthetic objects in Sec. 4.1 Redwood dataset in Sec. 4.2 and ShapeNet in Sec. A.9
A. EVALUATION ON LIDAR POINTS
As discussed in Sec. 3] we render the reference image J,, from the incomplete point cloud Pin. under the estimated camera pose V,, This operation means that actually observe the point cloud from pinhole camera model, which may be closer to point clouds from depth scanners, such as Redwood dataset (Choi et al., 2016; Kasten et al. 2024). To validate the effectiveness of our method across different sensor types, we conduct a comparison using point clouds from the Kitti dataset (Geiger al.) 2013), which are acquired with LiDAR sensors. Point clouds from Pedestrian, Cyclist, Car, and Truck are adopted for evaluation. Since ground truth data are unavailable for these point clouds, mainly present qualitative comparison in Fig. 17 Notably, our method demonstrates the ability for reasonable completion even with LiDAR-derived point clouds.
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Table 9: Quantitative comparisons on noised input point clouds. Std denotes the Standard deviation of added noises.
PoinTr
Seedformer PointAuN SVDFormer Shapeformer AdaPoinTr
Ours
Std
CD/EMD CD/EMD
CD/EMD
CD/EMD
CD/EMD
CD/EMD CD/EMD
4. 44.10/5.6
4.21/5.93
5.72/6.54
4.79/5.29
3.68/4.44
5.59/6.04
1.43/1.88
0.001
4.15/5.59
4.17/5.91
5.76/6.55
4.73/5.22
3.65/4.52
5.59/6.04
1.53/1.87
0.005 4.24/5.83
4.31/6.11
5.75/6.59
4.92/5.52
4.03/4.89
5.65/6.19
2.02/2.25
0.01 4.16/5.85
4.34/6.22
5.62/6.73
4.86/5.75
4.06/5.00
5.57/6.44
3.18/4.07
Table 10: Quantitative comparisons under different incompleteness levels. The levels denote how many depth maps are used construct the partial input.
PoinTr Seedformer PointAuN SVDFormer ShapeFormer AdaPoinTr
Ours
Level
CD/EMD CD/EMD CD/EMI
CD/END
CD/EMI)
CD/EMD CD/EMD
3.77/5.13
4.16/6.02
5.52/6.29
4.63/5.08
3,30/4.07
5.33/5.82
1.86/2.01
3 3.61/5.10
3.92/5.93
5.45/6.28
4.36/5.02
3.42/4.26
5.28/5.82
1.76/2.04
3.06/4.95
3.48/5.72
5.18/6.13
4.16/4.95
3.00/3.77
5.26/5.85
1.48/1.87
Level
Input
SVDFormer
AdaPoinTr
ShapeFormer
Ours
GT
Figure 14: Qualitative comparisons under different incompleteness levels. The levels denote how many depth maps are used to construct the partial input.
Table 11: Quantitative comparisons on multi- modal metrics.
Methods
Metrics Table Exe- Cheir Out-Chair Old-Chair Aver
TMD
1.26
1.70
1.18
1.25
1.35
SDS-Complete
UHI 1
9.31
10.39
10.63
16.67
11.75
MMD
1.27
1.66
1.86
2.11
1.73
TMD
0.57
0.53
0.42
0.65
0.54
Ours
UHD
8.47
4.73
8.64
12.02
8.47
MMD
1.47
1.04
1.28
1.42
1.30
18

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Published as a conference paper at ICLR 2025
Table 12: Quantitative comparison on ShapeNet dataset. "Known category" and "Unknown category"
denote categories included and not included in the training set of network- methods, respectively.
Known category
Unknown category
Categories Metrics
Chair
Table
Pistol
Tower
CD/EMD
CD/EMD
CD/EMD
CD/EMD
PoinTr
1.31/2.64
0.74/2.86
1.84/3.84
2.38/3.05
SeedFormer
1.39/2.77
0.80/2.17 1.79/3.91 1.95/3.24
AdaPoin'Tr
1.45/2.54
0.74/1.58
2.28/4.15
1.99/3.30
PointAuN
1.26/2.56
0.92/1.93
2,48/4.83
1.72/3.03
SVDFormer
1.21/2.49
1.68/3.15
2.02/4.25
3,47/4.22
Ours
1.38/1.94
1.08/1.56
1.09/1.63
1.41/1822
Input
SDS-Complete
GT
Figure 15: Comparisons based on Meshes.
Input
PoinTr
SVDFormer
AdaPoinT:
PointAuN
Ours
GT
Figure 16: Qualitative comparison on objects from ShapeNet (Chang et al. 2015) dataset.
19

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Published as a conference paper at ICLR 2025
Input
PoinTr
AdaPoinTr
SVDFormer
PointAttN
Ours
Figure 17: Comparison on Kitti (Geiger et al.) 2013) dataset.
20


Reviews:
[{"review_id": "zkvHboIxkk", "reviewer": "Reviewer_tTgt", "summary": "This paper addresses the problem of 3D point cloud completion from partial and noisy data, specifically in an open-set setting, where point clouds from unseen categories are completed. The proposed approach introduces a novel, training-free framework that leverages 3D Gaussian Splatting (3DGS) representations and pre-trained 2D diffusion models for guiding the completion process.\n\nFirst, a Partial Gaussian Initialization (PGI) module estimates a reference viewpoint, $V_p$, that maximizes coverage of the input partial point cloud, $P_{in}$. This viewpoint is used to initialize partial 3D Gaussians, $G_{in}$, and render an image $I_{in}$. The Zero-shot Fractal Completion (ZFC) module then combines $G_{in}$ with another set of 3D Gaussians, $G_m$, which is generated from a noisy variant of the input point cloud, $P_m$. Using the Zero-1-to-3 diffusion model, $G_m$ is refined via Score Distillation Guidance based on the reference viewpoint image $I_{in}$, which optimizes the Gaussians based on a randomly rendered image $I_i$.  A geometric preservation constraint is also introduced by measuring the Chamfer Distance between the input point cloud and the predicted point cloud, $P_{pre}$the predicted one $P_{pre}$ under the reference viewpoint $V_p$. \n\nFinally, the Point Cloud Extraction (PCE) module extracts surface points, $P_{surf}$, from the 3D Gaussian centers, $G_{all} = {G_{in}, G_m}$, excluding those with low opacity and selecting the ones that are visible from a set of uniform camera poses. To address the non-uniform density of $P_{surf}$, a Grid Pulling step is applied that promotes uniform alignment of $P_{surf}$ along the underlying surface. The framework is evaluated on synthetic and real-world point clouds for the point cloud completion task, achieving superior or comparable performance w.r.t. existing methods.", "questions": "- As noted in the **Weaknesses** section, this method incorporates the input point cloud across all stages of the pipeline. It would be beneficial to examine how the method performs under varying occlusion levels and perturbation intensities.\n- Following the above, could the method leverage multiple reference viewpoints during the ZFC step? Would incorporating additional viewpoints lead to more accurate completion of point clouds, particularly in cases with high noise levels and significant occlusions?\n- Could this method benefit from adopting 3D scene representations such as VolSDF [1], as used in SDS-complete, which implicitly encodes the SDF of the underlying surface?\n- Clarity: \n  - It’s important to clarify that the SDS-complete method leverages VolSDF [1], rather than NeuS, to represent the 3D scene.\n  - Adding a horizontal separator line after the Metrics row in Tables 1, 2, and 9 would improve readability. \n  - Please also add the SeedFormer method in line 418.\n\n[1] L. Yariv et al., “Volume Rendering of Neural Implicit Surfaces”, In Proc. NeurIPS, 2021.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed framework generates complete point clouds that accurately capture the underlying surface, entirely bypassing the need for training on extensive 3D shape categories. By leveraging a 2D diffusion model to synthesize novel views from a specific input viewpoint, it also removes the need for manual text prompts. Furthermore, this approach achieves superior or comparable performance to other test-time frameworks (e.g., SDS-complete) while significantly reducing computational time. Finally, the adaptation of the NeuraPull approach, using near and far points from the 3D bounding box of the $P_{surf}$ point cloud, while also incorporating geometric details from the input partial point cloud, is able to produce uniformly-sampled point clouds.", "weaknesses": "Although this method claims to rely solely on 3D coordinates, as required by the point cloud completion task, it implicitly introduces additional information by incorporating the 3D normal of each point to encode the color map $G_{in}^c$. This additional input signal, which is not utilized by other methods such as PCN or SDS-complete, may introduce an advantage, as evidenced by its significant impact on performance shown in Table 3 and Figure 7. Furthermore, the method heavily depends on the input partial point cloud throughout all stages: in the ZFC module, the SDS guidance is conditioned on the reference viewpoint image derived from the input partial point cloud, and a geometric preservation constraint is based on the original point cloud. In the PCE module, the initial point cloud is merged with $P_{pull}$ in the Grid Pulling step, further reinforcing reliance on the input partial point cloud.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "gS3kF05Czr", "reviewer": "Reviewer_Xxcz", "summary": "The paper introduces a test-time point cloud completion framework that leverages 2D diffusion priors. Unlike previous methods, which require manually provided information such as text prompts, the proposed method conditions the diffusion model on a reference image from the partial observation. After initializing the partial Gaussians and rendering the reference image, the framework optimizes the parameters of the Gaussians representing the 'missing' parts and then extracts the completed point cloud. The approach is validated on synthetic and real-world scanned point clouds.", "questions": "All of my questions are listed in the weaknesses section, and I may adjust the rating if they are well addressed.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "a) The problem of completing partial point clouds in a generalizable way is important, as existing methods mostly yield good results only on in-domain samples. b) 2D/3D priors from diffusion models are well-suited for achieving generalization. c) The paper is well-written, the problem is clearly specified and the solution is well presented.", "weaknesses": "a) As the incompleteness of the partial point cloud increases, the diffusion model may produce different completion results at each optimization. However, the multi-modal nature of the approach is not analyzed. For a small set of shapes, multiple runs can be performed on the same partial input, and the resulting completions can be evaluated using multi-modal metrics such as TMD, UHD, MMD. b) The experiment in A.5 could be performed on observations with varying levels of incompleteness, rather than artificially eliminating points, to better demonstrate the method’s actual performance. For instance, depth maps could be used to obtain incomplete observations by merging points from a different number of views. c) The paper does not include a comparison with recent related work such as [1, 2]. The authors are suggested to compare ComPC with also these recent baselines to better evaluate its completion and generalization performance.  \n\n[1] Chu, R., Xie, E., Mo, S., Li, Z., Nießner, M., Fu, C. W., & Jia, J. (2024). Diffcomplete: Diffusion-based generative 3d shape completion. Advances in Neural Information Processing Systems, 36.\n[2] Yan, X., Lin, L., Mitra, N. J., Lischinski, D., Cohen-Or, D., & Huang, H. (2022). Shapeformer: Transformer-based shape completion via sparse representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 6239-6249).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "triO6CgbSk", "reviewer": "Reviewer_y9Sz", "summary": "This paper proposes a new pipeline for point cloud completion. The proposed pipeline consists of three steps. The pipeline initializes a single optimal viewpoint for initial render of point cloud. This render is made so the point cloud is most completely observed. The Gaussian Splatting is used to initialize a frozen set of 3D gaussians by utilizing an optimal viewpoint. In the next step the set 3D gaussians are prepared in the optimal way to cover missing point cloud regions. Further Zero 1-to-3 approach is used to complete missing parts of the point cloud. On the final step the point cloud is extracted from the 3D gaussians distributed uniformly (unlike in the start of pipeline) on the shape surface.\n\nDespite the weaknesses, I recommend accepting this work because the authors definitely introduced a new strong pipeline that incorporates many existing methods. Authors proved that the proposed sequence of methods is successfully used for the important complicated task.\n\nThe main reason to accept this work is good results that superior existing point cloud completion methods based on single-modality input data. The method shows good performance in the training domain and out of the training domain. In some cases (depicted in the paper) the density of points seems to be more uniform than in GT point clouds. This feature can be useful in tracking and segmentation tasks.", "questions": "It might be interesting to compare the density of completed point clouds and compare it with GT density. What is the reason for the density difference? In the section 4.4. of ablation study we can see that point cloud extraction causes a kind of separate clusters. What is the reason?\n\nIt is recommended to provide a possibility to test the pipeline on a custom dataset. Moreover, it is recommended to split Figure 2 into two-three separate pictures with clarified steps description. The united scheme makes understanding the pipeline hard.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "Learning-based methods perform not good when tested out of the domain they were trained on. The strong point of the proposed method is that authors propose a framework that performs out of the training domain as good as in it. Moreover, the model does not require any additional modalities, so the possibility of application is maximal. Lastly, there are empirical experiments done that prove that proposed method outperforms overviewed analogical approaches. The ablation study is well done: this part of the paper is split into categories and each one is devoted to the specific part of the pipeline.", "weaknesses": "Despite the strengths of the work, there are a number of questions about it. There is a method of missing parts completion in section 3.2. Many side methods like Zero 1-to-3, SDS guidance, PFNet are referenced in this section while there are very few particular discussions that are taken from these works. For example, what is the fractal approach discussion taken from PFNet? The second question is: what is the point of applying differentiable quantization to set the gaussians' opacity? It seems that there is a quite simple solution. Another weakness of the proposed work is the absence of the possibility to test the pipeline. The source code that aggregates the mentioned methods can clarify the approach that authors suggest.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "6GhM7ldPrJ", "reviewer": "Reviewer_p7jk", "summary": "The work tries to complete a general partial point cloud using  zero123, which gets an object's image and outputs images of the exact object from any unseen direction.\nThey first model the partial point cloud as gaussians, and render them from the view direction, where the object seems full.\nthan through SDS iterations using zero123 they complete the object from any noval direction.", "questions": "1. I can't see how your work can generate a surface like SDS-complete?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "They continue works that complete point clouds in the wild and challenge the current weakness of long-running time and the need for textual description.\nThe work exploits advances in 3D generation for further improvement in point cloud completion.", "weaknesses": "good work", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Poster)