Uneven rotation and dropped frames make equal-angle assumptions unreliable for turntable reconstruction. We present OC-GS, an object-centric Gaussian splatting that refines each image's angle while maintaining a shared camera, rotation axis, and pivot. This orbit-consistent refinement jointly optimizes image-derived geometry and angles to reconstruct objects from sparse, irregular captures. On rendered objects with 12, 8, and 6 irregularly spaced views, OC-GS achieves mean foreground PSNR scores of 21.26, 19.36, and 15.83dB, respectively, exceeding all four evaluated pose-free Gaussian splatting baselines in each condition. Under a shared trainer, refining image-estimated angles improves mean foreground PSNR by 7.88dB over keeping those estimates fixed. An ablation study shows that both image-derived angle initialization and the shared motion model contribute to the improvement. On real captures, OC-GS's refinement increases mean foreground PSNR by 0.70dB. Results show that refining uncertain angles within a shared motion model improves reconstruction from sparse, irregular turntable captures.
Figures & tables
Figure 1: OC-GS overview. (a) A fixed camera captures an object rotating about one axis with unequal angular gaps. (b) VGGT predicts initial geometry and cameras. A circle fit to the camera centers gives starting angles. OC-GS jointly refines the Gaussian scene, shared camera, axis, pivot, and per-view angles. (c) The shared turntable rig (blue) constrains the effective cameras, unlike independent cameras (orange). (d) Reconstruction is evaluated by comparing rendered and target images.
Figure 2: Reconstruction quality under irregular capture. Mean target FG-PSNR with 12, 8, and 6 views (higher is better). Objects must have valid camera alignment in all five component configurations and all three seeds. Each baseline’s alignment failures are also excluded. Methods retain their own training settings.
Figure 3: Selected reconstructions under irregular capture. Hereford Bull with six views and Bathroom Set with twelve, shown at the first target view with the same crop across methods. SPFSplat has no valid placement for Hereford Bull.
FG-PSNR (dB) ↑
Configuration
Regular-12
Irregular-12
Irregular-8
Irregular-6
No shared rig
13.07
12.48
12.47
11.61
Rig + uniform angles
26.29
6.54
6.65
6.28
Rig + frozen estimated angles
12.04
12.19
12.13
11.61
OC-GS
22.75
21.26
19.36
15.83
Rig + true angles (reference)
26.16
25.22
22.24
18.19
Table 1: Effect of the shared rig and angle refinement. FG-PSNR with the same trainer and VGGT predictions. Uniform angles assume equal spacing. Frozen angles retain image-derived estimates, while OC-GS refines them. The reference receives true angles. Means use the same valid objects per condition across all five variants and three seeds. Bold and underline indicate the best and second-best pose-free variants.
Figure 4: Selected examples of improvement with OC-GS. Black Flag (12 views), Therizinosaurus (8), and Hereford Bull (6) were selected to illustrate reconstruction gains. Each row uses the same target view and crop across methods. Scores report FG-PSNR (dB) / object-crop LPIPS. Bold indicates the best valid pose-free result.
Configuration
Regular-12 ↑
Irregular-12 ↑
Irregular-8 ↑
Irregular-6 ↑
Uniform angles + refinement
23.39
12.78
10.65
7.57
Independent cameras (higher rate)
13.30
12.75
12.70
11.83
OC-GS
22.75
21.26
19.36
15.83
Table 2: Refinement without image-derived angles or without the rig. Uniform angles + refinement removes image-derived angular initialization. Bold marks the best mean per column using FG-PSNR (dB).
OC-GS initializer
Regular-12
Irregular-12
Irregular-8
Irregular-6
VGGT
22.75
21.26
19.36
15.83
AnySplat
19.88
20.28
18.30
15.10
π3
19.64
20.45
18.61
15.19
VGGT- Ω
21.19
20.13
18.87
15.57
Depth Anything 3
20.24
19.77
18.19
15.28
CUT3R
17.26
18.45
15.71
14.70
Table 3: OC-GS with different initializers. We show the performance of different initializers and report them with the mean FG-PSNR (dB ↑ ). The best is bolded, which is from VGGT.
Configuration / training views
24
17
13
7
OC-GS shared rig, initialized azimuths frozen
AnySplat azimuths
22.62
21.53
19.50
15.73
π3 azimuths
21.82
20.91
19.50
15.72
Index angles (no network)
21.20
17.12
16.40
13.56
Per-view cameras, optimized from
π3
20.45
18.70
16.75
14.03
Table 4: Pose initialization and camera models on real captures. Mean FG-PSNR (dB), evaluated using each reconstruction’s cameras. The named methods provide initial poses. The OC-GS rig keeps its azimuths fixed, while independent cameras are optimized from the predictions. Bold and underline indicate the best and second-best predictors within each block at the displayed precision.
RMS spacing error
Frozen ↑
Refined ↑
Δ (dB)
0∘ (exact angles)
26.04
22.88
−3.16
0.5∘
18.44
23.19
+4.75
1∘
15.89
22.92
+7.03
2∘
13.50
22.60
+9.10
3∘
12.36
22.71
+10.35
10∘
9.08
20.33
+11.25
Table 5: Angle-error experiment. RMS spacing error is the root-mean-square angular noise in degrees after removing a common rotation. Frozen keeps the supplied angles fixed, while Refined optimizes them. Δ reports the refinement gain.
Method
Type
Time (min) ↓
Peak VRAM (GB) ↓
OC-GS
Per-scene
16.2
1.51
RotGS (index schedule)
Per-scene
20.8
2.20
Nerfacto + VGGT poses
Per-scene
23.7
3.05
InstantSplat
Per-scene
2.5
6.69
AnySplat
Feed-forward
0.5
8.31
SPFSplat
Feed-forward
0.5
6.06
Table 6: Reconstruction quality, runtime, and peak GPU memory. Quality uses valid benchmark runs. Cost uses separate profiling runs. VGGT inference is excluded from runtime and memory for OC-GS and Nerfacto. Bold and underline indicate the best and second-best values at the displayed precision.
School of Electronics and Information, Northwestern Polytechnical University, Xi’an, China · College of Engineering, Xi’an International University, Xi’an, China