LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency

Weilong Yan; Haipeng Li; Hao Xu; Nianjin Ye; Yihao Ai; Shuaicheng Liu; Jingyu Hu

Figure 1: Our new framework supports category-agnostic shape completion across diverse partial patterns, including (a) random crops, (b) single-view scans, and (c) missing semantic parts. It further supports both unconditional and text-guided completion, offering flexible control for real-world applications, see (d).

Figure 2: Overview of the LaS-Comp framework. Starting from Gaussian noise, the process iteratively refines a latent feature

\boldsymbol{x}_{t}

under the guidance of the partial input

\boldsymbol{S}_{p}

. At each iteration

t

, this refinement is performed in two stages: the Explicit Replacement Stage (ERS) and the Implicit Alignment Stage (IAS) . The ERS explicitly injects the known geometry of

\boldsymbol{S}_{p}

into

\boldsymbol{x}_{t}

to produce an updated latent

\boldsymbol{x}^{*}_{t}

. The IAS then refines

\boldsymbol{x}^{*}_{t}

using a gradient-based optimization, yielding a spatially aligned latent

\boldsymbol{x}_{t-dt}

for the next step. After the final iteration, the completed shape

\boldsymbol{S_{\text{c}}}

is obtained by decoding the refined latent.

Figure 3: Overview of the Explicit Replacement Stage (ERS). At each timestep

t

, ERS decomposes the latent generation into two parallel branches. The clean branch (top) enforces spatial consistency, yielding

\boldsymbol{x}^{*}_{0|t}

. Concurrently, the noisy branch (bottom) enhances fidelity, producing

\boldsymbol{x}^{*}_{1|t}

. These two branch outputs are then interpolated to compute the final aligned latent

\boldsymbol{x}^{*}_{t}

.

CD $\downarrow$ / EMD $\downarrow$	Table	Exe-Chair	Out-Chair	Old-Chair	Vase	Off-Can	Vespa	Tricycle	Trash	Couch	Average
SVDFormer [ 89 ]	5.48/6.68	3.20/5.85	0.79 /1.45	3.79/5.95	5.70/6.98	5.13/6.71	3.10/4.91	2.73/4.96	3.67/5.05	1.86/2.92	3.54/5.15
AdaPoinTr [ 80 ]	5.02/6.25	2.58/4.80	0.82 / 1.37	3.62/5.64	5.14/6.50	4.47/6.35	1.96/3.54	1.83 /3.66	1.21 /3.08	1.01 / 2.09	2.77/4.33
Shape-Inv [ 82 ]	1.58/2.84	3.59/5.75	1.36/2.12	4.55/7.39	3.91/7.40	3.10/4.77	4.36/7.24	5.05/7.40	2.48/3.83	1.95/2.76	3.19/5.15
P2C [ 14 ]	1.57/2.64	3.87/6.42	1.28/2.10	3.72/5.82	4.54/7.04	3.36/4.82	6.75/10.7	7.78/11.2	3.28/4.21	2.55/3.16	3.87/5.81
SDS-Comp [ 37 ]	1.35/2.30	1.96/2.65	2.51/3.92	2.77/3.77	3.00/5.25	3.79/4.28	3.36/5.73	3.18/3.49	2.69/3.21	2.95/4.56	2.74/3.93
PCDreamer [ 65 ]	0.82 /2.36	1.43/3.56	1.19/2.07	2.61/5.34	2.41 / 3.64	2.62 /3.89	2.20/4.30	2.53/3.82	1.55/2.47	1.47/2.61	1.88/3.41

Table 1: Quantitative comparisons on Redwood [ 10 ] . We highlight the best and second-best results.

Figure 4: Qualitative comparison on Redwood dataset [ 10 ] . We compare with various supervised and unsupervised methods [ 89 , 80 , 37 , 65 , 31 ] , and visualize the output as meshes utilizing the commonly-used mesh reconstruction method [ 54 ] .

CD

\downarrow

/ EMD

\downarrow

Horse

Max-Planck

Armadillo

Cow

Homer

Teapot

Bunny

Nefertiti

Bimba

Ogre

Lucy

Dragon

Average

SVDFormer [ 89 ]

4.33/5.17

8.84/7.95

4.97/6.13

3.50/4.39

2.30/3.20

5.00/5.91

9.61/9.19

5.46/5.94

7.54/7.18

4.90/5.55

2.08/2.93

2.88/4.35

5.12/5.66

AdaPoinTr [ 80 ]

4.88/5.45

8.60/8.52

5.14/5.97

3.48/4.51

2.28/3.30

3.92/4.53

9.33/8.86

5.54/6.16

8.16/7.62

4.53/5.41

1.85/2.79

3.07/4.54

5.07/5.64

Shape-Inv [ 82 ]

6.55/10.4

4.94/5.72

4.79/7.22

4.74/7.83

2.36/3.75

3.29/4.53

5.79/6.58

4.39/5.70

5.29/6.85

5.76/9.51

2.99/4.07

4.48/7.91

4.62/6.68

PCDreamer [ 65 ]

2.52/4.34

5.20/3.85

2.78/4.53

1.79/3.06

1.54/2.97

1.95/3.57

5.48/5.66

2.72/3.43

3.83/5.39

2.57/3.97

1.50/2.61

2.41/4.97

2.86/4.11

ComPC [ 31 ]

1.29/1.77

1.22/ 1.51

2.18/3.23

1.78/1.90

1.32/1.65

1.09/1.34

1.46 / 1.76

1.84/2.13

1.65/1.90

1.56/2.11

1.97/2.70

1.99/3.10

1.61 /2.09

Ours (Direct3D-S2)

0.96 / 1.42

1.11 /1.63

1.15 / 1.67

1.21 / 1.63

0.87 / 1.26

0.88 / 1.24

1.55 /1.97

0.96 / 1.40

1.11 / 1.58

1.03 / 1.51

0.96 / 1.43

1.51 / 2.12

1.11 / 1.57

Table 2: Quantitative comparisons on the synthetic data [ 31 , 44 ] . We highlight the best and second-best results.

UCD $\downarrow$ / UHD $\downarrow$	ScanNet-Chair	ScanNet-Table	KITTI-Car
SVDFormer [ 89 ]	1.4 / 2.4	1.4 / 2.4	1.8 / 5.0
AdaPoinTr [ 80 ]	1.4 / 2.4	1.3 / 2.4	1.6 / 4.9
Shape-Inv [ 82 ]	4.0 / 9.3	6.6 / 11.0	5.3 / 12.5
P2C [ 14 ]	3.4 / 5.0	2.4 / 4.9	3.9 / 5.8
PCDreamer [ 65 ]	1.5 / 3.5	1.3 / 3.7	2.7 / 6.6
ComPC [ 31 ]	2.0 / 5.3	3.0 / 7.0	1.1 / 5.7

Table 3: Completion fidelity on ScanNet [ 15 ] and KITTI [ 21 ] .

CD $\downarrow$ / EMD $\downarrow$	Single Scan	Random Crop	Semantic Part
SVDFormer [ 89 ]	5.32 / 6.84	5.35 / 6.73	5.66 / 7.19
AdaPoinTr [ 80 ]	5.27 / 6.87	5.33 / 6.68	5.59 / 7.23
Shape-Inv [ 82 ]	4.98 / 7.31	5.39 / 6.79	5.37 / 6.91
SDS-Comp [ 37 ]	5.42 / 6.27	5.12 / 6.35	5.60 / 6.75
PCDreamer [ 65 ]	3.32 / 4.74	3.78 / 4.55	3.88 / 4.98
ComPC [ 31 ]	4.24 / 4.61	5.48 / 5.95	6.37 / 6.18

Table 4: Quantitative comparisons on our proposed Omni-Comp.

Figure 5: Visual examples on ScanNet [ 15 ] and KITTI [ 21 ] real-world datasets, which only contain real scans of table, chair, and car, with very sparse points.

Figure 6: Qualitative comparisons on cases with different partial patterns from our Omni-Comp benchmark. Our approach produces more reasonable results than the latest methods [ 31 , 65 ] .

MMD $\downarrow$ / TMD $\uparrow$	Redwood	Synthetic
PCDreamer [ 65 ]	1.88 / 0.13	2.86 / 0.11
ComPC [ 31 ]	2.01 / 0.72	1.65 / 0.62
Ours (Direct3D-S2)	1.81 / 1.35	1.23 / 0.89
Ours (TRELLIS)	1.62 / 0.99	1.20 / 0.94

Table 5: Completion diversity evaluation on Redwood [ 10 ] and synthetic data [ 31 , 44 ] .

CD $\downarrow$ / EMD $\downarrow$	Redwood	Synthetic
(a) Baseline (latent replacement)	2.15 / 2.93	2.33 / 2.78
(b) Full w/o ERS	3.42 / 4.94	3.53 / 4.85
(c) Full w/o PNS	1.94 / 2.56	2.27 / 2.67
(d) Full w/o IAS	1.88 / 2.14	1.17 / 1.56
(e) IAS w/ 10 optimization steps	1.42 / 1.87	1.20 / 1.48
(f) Full pipeline (Ours)	1.42 / 1.84	1.11 / 1.41

Table 6: Ablation studies. The baseline uses only latent replacement for completion.

Figure 7: Visual examples of the completion diversity from our method on the real-world data.

Figure 8: Visual comparison of the ablation studies. The red boxes highlight the artifacts and holes.

Figure 9: Illustration of the latent gap between the partial input

\boldsymbol{S}_{\text{p}}

and the ground truth

\boldsymbol{S}_{\text{gt}}

. Although

\boldsymbol{S}_{\text{p}}

and

\boldsymbol{S}_{\text{gt}}

share the same surface geometry in the observed regions, their latents obtained by the VAE encoder in the corresponding regions show an obvious difference.

Redwood		YCB		Synthetic
ID	Category	ID	Category	ID	Category
1	Bicycle	11	Cracker Box	21	Dinosaur
2	Plant	12	Mustard Bottle	22	Glass
3	Trash Bin	13	Banana	23	Guitar
4	Car	14	Bleach Cleanser	24	Headphone
5	Motorcycle	15	Skillet	25	Laptop

Table 7: Category list in the Omni-Comp benchmark. Representative object categories from Redwood [ 10 ] , YCB [ 4 ] , and Synthetic [ 63 ] .

Figure 10: Visual examples of the completion results under extremely noisy partial inputs. Despite the severe noise that heavily corrupts the observed points, our method can still recover a reasonable global structure and overall object silhouette, but many fine details and thin structures are degraded or missing, revealing the limitation of our model when strong noise overwhelms the underlying geometry.

Figure 11: Visual examples of the proposed Omni-Comp benchmark. We show the ground truth mesh, with its corresponding partial samples of random crop, single scan, and semantic part.

Figure 12: Visual examples of the proposed Omni-Comp benchmark. We show the ground truth mesh, with its corresponding partial samples of random crop, single scan, and semantic part.

Figure 13: Visual Comparison on the Redwood dataset [ 10 ] , with point cloud representation. Obviously, our method significantly outperforms prior approaches in most of the examples, with very high-quality geometry and good completion correctness.

Figure 14: Visual Comparison on the synthetic dataset [ 31 ] , with point cloud representation. Obviously, our method significantly outperforms prior approaches in most of the examples, with very high-quality geometry and good completion correctness.

Figure 15: More visual examples on the proposed Omni-Comp benchmark, under the random crop partial pattern. Our method consistently provides better completion results compared with the latest methods [ 65 , 31 ] , regardless of the partial pattern and object category.

Figure 16: More visual examples on the proposed Omni-Comp benchmark, under the single scan partial pattern. Our method consistently provides better completion results compared with the latest methods [ 65 , 31 ] , regardless of the partial pattern and object category.

Figure 17: More visual examples on the proposed Omni-Comp benchmark, under the semantic part partial pattern. Our method consistently provides better completion results compared with the latest methods [ 65 , 31 ] , regardless of the partial pattern and object category.

Figure 18: More visual examples to show the completion diversity, under unconditional and text-guided completion. In both completion settings, our method can provide reasonable results with good geometry.

LaS-Comp: Zero-shot 3D Completion with Latent-Spatial Consistency

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