GeoWeaver: Accurate Long-Sequence 3D Reconstruction via Hierarchical Geometric Assembly

Tinghao Jiang; Sheng Tang; Shengzhe Wei; Juntong Fang; Weiqi Zhang; Junsheng Zhou; Zesong Li

cs.CVAug 18, 2026

GeoWeaver: Accurate Long-Sequence 3D Reconstruction via Hierarchical Geometric Assembly

Authors: Tinghao Jiang, Sheng Tang, Shengzhe Wei, Juntong Fang, Weiqi Zhang, Junsheng Zhou, Zesong Li

Organizations: Kosmo Research · Shanghai Jiao Tong University · Tsinghua University

Abstract

Long-sequence 3D reconstruction from RGB videos requires both accurate local geometry and globally consistent camera motion. Feed-forward models provide strong depth and pose predictions, but their memory cost prevents joint inference over long sequences. Chunk-wise processing improves scalability, yet independently predicted chunks often exhibit scale drift, pose errors, and point-cloud misalignment. We present GeoWeaver, a unified framework comprising a Geometric Prior Model (GPM) and Test-Time Adaptation (TTA). The GPM predicts chunk-wise depth, confidence, and camera parameters as adjustable geometric priors. TTA then performs sequential initialization, global chunk-level Sim(3) alignment, and coarse-to-fine refinement of camera poses, affine depth corrections, and intrinsics. Dense correspondences provide adjacent, cross-chunk, and long-range constraints, while a robust CDF-style objective jointly optimizes weighted 2D reprojection and 3D consistency residuals. This design preserves local geometric accuracy while correcting accumulated pose, scale, depth, and calibration errors. Experiments across diverse long-sequence benchmarks demonstrate improved camera accuracy, global consistency, and point-cloud quality. Ablations verify the contribution of each adaptation stage, and applying the same TTA procedure to different geometric prior models consistently improves their trajectory estimates, demonstrating that GeoWeaver is not tied to a specific GPM.

Figures & tables

Figure 1: Qualitative results across four benchmarks. Reconstructed point clouds and camera trajectories are shown for each scene.

Figure 2: Overview of GeoWeaver. The Geometric Prior Model predicts chunk-wise depth, confidence, and cameras, while Test-Time Adaptation performs sequential initialization, chunk alignment, and frame-level refinement using local and long-range correspondences.

Figure 3: Qualitative comparison on representative indoor and outdoor scenes. “Overlap” denotes the number of shared frames between adjacent 60-frame chunks. Compared with baselines using 30-frame overlap, GeoWeaver uses only one shared frame while producing more coherent geometry, better aligned trajectories, and fewer inter-chunk discontinuities.

Method	Depth		Camera
Method	AbsRel $\downarrow$	SqRel $\downarrow$	AUC ${}_{5}\uparrow$	AUC ${}_{30}\uparrow$
DA3	0.086	0.088	53.2	77.6
$\pi^{3}$	0.082	0.229	43.3	74.9
VGGT	0.125	0.602	43.2	68.7
Scal3R	0.147	0.170	34.9	67.0
LingBot-Map	0.114	0.177	29.0	64.3

Table 1: GPM evaluation across benchmarks. Camera AUC is reported at

5^{\circ}

and

30^{\circ}

. Best and second-best results are shown in bold and underlined, respectively.

Type	Method	T&T		Mip-NeRF 360		VKITTI 2		Oxford Spires
Type	Method	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	RRE $\downarrow$	ATE $\downarrow$
Opt.	MBA [ 46 ]	67.7	0.053	43.5	0.470	30.9	46.836	3.290	29.323
FF	DA3 [ 21 ]	63.9	0.358	66.4	0.037	16.9	38.867	9.961	16.640
Chunk-FF	VGGT-Long [ 7 ]	52.3	11.472	55.4	0.211	66.1	2.631	18.983	12.380
Chunk-FF	Scal3R [ 42 ]	58.1	0.338	37.8	0.194	67.4	0.608	6.840	5.565
Chunk-FF	LoGeR [ 44 ]	22.8	0.757	21.1	0.455	0.0	36.183	8.100	3.985

Table 2: Average trajectory results across four long-sequence benchmarks. T&T, Mip-NeRF 360, VKITTI 2, and Oxford Spires contain 6, 9, 5, and 4 evaluated scenes or sequences, respectively, spanning object-, room-, road-, and urban-scale reconstruction. AUC denotes AUC@3 ∘ and is reported in percentage; RRE is in degrees and ATE is in metres. Best and second-best results are shown in bold and underlined, respectively. Type abbreviations are Opt. (optimization-based), FF (feed-forward), Chunk-FF (chunk-wise feed-forward), Stream (streaming), and Hybrid.

Type	Method	Barn		Caterpillar		Courthouse		Ignatius		Meetingroom		Truck		Average
		AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$
Opt.	MBA [ 46 ]	39.3	0.144	56.5	0.026	58.9	0.069	78.6	0.038	85.4	0.025	87.7	0.017	67.7	0.053
FF	DA3 [ 21 ]	69.5	0.039	63.7	0.023	32.4	2.000	67.4	0.047	76.9	0.021	73.3	0.021	63.9	0.358
Chunk-FF	VGGT-Long [ 7 ]	65.5	0.116	46.1	0.269	51.5	2.601	76.7	0.263	62.1	0.129	77.5	0.067	52.3	11.472
Chunk-FF	Scal3R [ 42 ]	59.9	0.053	44.6	0.158	43.8	1.656	59.9	0.076	71.3	0.039	69.4	0.046	58.1	0.338
Chunk-FF	LoGeR [ 44 ]	17.8	0.814	33.7	0.229	9.9	2.669	17.7	0.374	19.2	0.320	37.4	0.133	22.8	0.757

Table 3: Per-scene results on Tanks and Temples. AUC@3 ∘ is reported in percentage and ATE in metres. Best and second-best results are shown in bold and underlined, respectively.

Type	Method	Bicycle		Bonsai		Counter		Flowers		Garden
		AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$
Opt.	MBA [ 46 ]	79.1	0.031	78.2	0.020	84.4	0.016	8.7	0.019	5.6	3.430
FF	DA3 [ 21 ]	63.1	0.054	76.4	0.027	77.1	0.016	72.9	0.028	64.4	0.039
Chunk-FF	VGGT-Long [ 7 ]	62.9	0.117	33.1	0.249	62.8	0.086	41.9	0.525	78.2	0.062
Chunk-FF	Scal3R [ 42 ]	42.9	0.120	27.5	0.170	63.7	0.033	5.1	0.570	65.2	0.059
Chunk-FF	LoGeR [ 44 ]	34.7	0.143	9.4	0.217	25.7	0.105	6.7	2.051	28.7	0.285

Table 4: Per-scene results on Mip-NeRF 360, split into two panels for readability. AUC@3 ∘ is reported in percentage and ATE in metres. Best and second-best results are shown in bold and underlined, respectively.

Type	Method	Scene01		Scene02		Scene06		Scene18		Scene20		Average
		AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$	AUC $\uparrow$	ATE $\downarrow$
Opt.	MBA [ 46 ]	36.5	39.340	23.4	1.240	36.3	0.260	52.1	18.300	6.4	175.040	30.9	46.836
FF	DA3 [ 21 ]	3.9	22.726	45.3	0.151	26.1	0.075	4.6	12.061	4.4	159.321	16.9	38.867
Chunk-FF	VGGT-Long [ 7 ]	56.3	0.762	72.0	0.723	58.0	0.365	85.2	1.651	59.3	9.655	66.1	2.631
Chunk-FF	Scal3R [ 42 ]	81.3	0.376	74.5	0.038	42.4	0.031	83.3	0.165	55.6	2.430	67.4	0.608
Chunk-FF	LoGeR [ 44 ]	0.0	73.801	0.0	0.661	0.1	2.068	0.0	1.898	0.0	102.489	0.0	36.183

Table 5: Per-scene results on Virtual KITTI 2. AUC@3 ∘ is reported in percentage and ATE in metres. Best and second-best results are shown in bold and underlined, respectively.

Type	Method	keble-04		observatory-01		blenheim-05		christ-church-02		Average
		RRE $\downarrow$	ATE $\downarrow$	RRE $\downarrow$	ATE $\downarrow$	RRE $\downarrow$	ATE $\downarrow$	RRE $\downarrow$	ATE $\downarrow$	RRE $\downarrow$	ATE $\downarrow$
Opt.	MBA [ 46 ]	4.250	35.240	1.270	23.710	1.810	38.260	5.830	20.080	3.290	29.323
FF	DA3 [ 21 ]	14.442	22.664	7.087	8.365	2.123	1.538	16.193	33.992	9.961	16.640
Chunk-FF	VGGT-Long [ 7 ]	16.850	13.350	14.990	4.250	18.480	11.240	25.610	20.680	18.983	12.380
Chunk-FF	Scal3R [ 42 ]	7.830	2.130	5.270	1.570	7.430	2.560	6.830	16.000	6.840	5.565
Chunk-FF	LoGeR [ 44 ]	11.000	4.134	8.000	5.632	9.800	2.621	3.700	3.553	8.100	3.985

Table 6: Per-scene results on Oxford Spires. RRE is reported in degrees and ATE in metres. Best and second-best results are shown in bold and underlined, respectively.

Figure 4: Convergence of GeoWeaver TTA. The objective decreases rapidly during Stage 2 chunk-level alignment and saturates during Stage 3 refinement.

Seq. Init.	Chunk Align.	Frame Refine	ATE $\downarrow$	AUC $\uparrow$
$\checkmark$	–	–	0.674	24.4
$\checkmark$	$\checkmark$	–	0.075	43.8
$\checkmark$	$\checkmark$	$\checkmark$	0.023	72.9

Table 7: Ablation of the three-stage Test-Time Adaptation procedure on Tanks and Temples. AUC denotes AUC@3 ∘ . The full configuration progressively introduces sequential initialization, chunk-level adaptation, and frame-level adaptation. Highlighted ablation results are shown in bold.

Prior Model	ATE $\downarrow$	AUC $\uparrow$
Scal3R [ 42 ]	0.338	58.1
Scal3R + TTA	0.033	63.6
DA3 [ 21 ]	0.358	63.9
DA3 + TTA	0.033	68.5
GeoWeaver-GPM + TTA	0.025	71.3

Table 8: Compatibility of Test-Time Adaptation with different Geometric Prior Models on Tanks and Temples. AUC denotes AUC@3 ∘ . The same TTA configuration improves multiple prior models without source-specific tuning. Highlighted ablation results are shown in bold.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Data domain	Datasets
Real indoor	ScanNet, ScanNet++, HM3D
Synthetic indoor	Hypersim, ASE
Procedural	MegaSynth
Multi-view / stereo	MVSSynth, Unreal4K, BlendedMVS
Dynamic / motion	DynamicStereo, TartanAir

Appendix

Table 9: Training datasets grouped by data domain.

Stage	Parameter	Value
Stage 1: sequential initialization	Confidence retention	$50\%$
Stage 2: global chunk alignment	Learning rate	$10^{-2}$
	Maximum iterations	$5{,}000$
	3D range / weight	$0.8$ / $2.0$
Stage 3: coarse refinement	CDF range / bins	$15$ / $250$
	Smoothing bandwidth	$2$

Appendix

Table 10: Default hyperparameters of GeoWeaver TTA.

Figure 5: Residual distributions before and after optimization. The optimized 2D reprojection and 3D distance residuals concentrate closer to zero, producing steeper CDF curves and sharper PDF peaks.

Figure 6: Convergence curve of GeoWeaver TTA. The objective drops rapidly during Stage 2 chunk-level alignment and then saturates during Stage 3 refinement.

Figure 7: Additional qualitative results across diverse scenes. For each scene, we visualize the reconstructed point cloud together with the estimated camera poses.

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