GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision
Authors: Rui Wang, Haoyu Zhang, Ran Cheng
Organizations: School of Information Science and Technology, Hangzhou Normal University, Hangzhou, P.R. China · Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University, Hong Kong SAR, China
Depth estimation and Gaussian reconstruction share a need for multi-scale feature processing, yet use different prediction interfaces and training objectives. Whether a decoder's internal computation can be optimized once and reused across these systems remains a structural design question. Search over primitive configurations alone leaves the organization of transformations fixed. To address this gap, we propose GeoBridge, an evolutionary framework for transferable decoder topologies. First, GeoBridge makes stage-internal topology searchable through residual, sequential, and parallel composition of configurable primitives. Shape-preserving composition retains the stage interfaces of a fixed multi-scale scaffold. Second, a functional-tree encoding supports stage-aware crossover and hierarchical mutation, while a gradient sensitivity-alignment proxy evaluates candidates without training. Multi-objective evolution balances this proxy with computation and parameter cost. Third, selected topologies are instantiated and trained in target systems with task-specific prediction interfaces. A single source search takes approximately 12 GPU hours. GeoBridgeNet-A3 achieves the best depth accuracy among four adapted decoder baselines under a common encoder and training protocol on NYU Depth V2 and KITTI. The same topology reaches 27.11 dB PSNR in Gaussian novel-view synthesis on RealEstate10K, and its refined representations support open-vocabulary segmentation on LERF. These results support decoder topology as a reusable design object across the evaluated geometric vision systems. Code and pretrained weights are available at https://github.com/EMI-Group/GeoBridge-NAS.
Figures & tables
Fig. 1: GeoBridge searches once and instantiates the selected decoder topology in target systems. (a) TartanAir images and cached teacher-derived targets define the source evaluation environment. (f) NSGA-II varies stage compositions and primitive configurations through stage-aware crossover and hierarchical mutation; GSAP, MACs, and parameter count define candidate fitness. (c) The Pareto set supplies (b) the selected GeoBridgeNet-A3 structure. Its topology is retained for (d) depth estimation on real images and (e) the monocular branch of a Gaussian reconstruction system. Each target system trains its own weights and prediction interface.
Fig. 2: Fixed scaffold and searchable stage computation in GeoBridge. DINOv2 ViT-S blocks B2, B5, B8, and B11 supply features resized to 1/4–1/32 of the input resolution. Lateral fusion and upsampling define the connections between four Tree-Mamba stages. Inside each stage, a functional tree determines the composition of Mamba primitives. Search varies this internal topology and its primitive configurations while retaining the scaffold. Selected structures are trained with the prediction interface required by each target system.
Fig. 3: Compositional decoder topology and its tree encoding. Left: RES, SEQ, and PAR define residual paths, serial processing, and parallel aggregation. Top right: the fields of an OP leaf specify its Mamba primitive. Bottom right: an example genotype is decoded into stage computation through recursive composition. Every subtree preserves its stage interface. Table I gives the primitive domains and scale-dependent bounds; Fig. 6 shows the evaluated structures.
Fig. 4: Structure and computation of GSAP. Top: a fixed batch of source images passes through the frozen DINOv2 encoder and a randomly initialized candidate decoder, while the frozen Depth Anything V2 teacher provides a spatially uniform auxiliary target that is computed once and cached. One backward pass yields one raw sensitivity–alignment response without updating any parameter. Bottom: the illustrated sensitivity and alignment constitute one raw response; during search, this computation is repeated for three independent decoder initializations, the three raw products are averaged, and the logarithm is then applied to obtain JGSAP , which forms the fitness together with MACs and parameters.
Fig. 5: Search dynamics of the 100-generation main run using three-repeat GSAP aggregation. Left: distribution of searched architectures in the three-objective space of JGSAP , MACs, and parameters. (a)–(c) Pairwise projections of the same objective space, with the non-dominated boundary highlighted. (d) Normalized hypervolume over generations; generations 29 and 82 are marked to emphasize that improvement continues beyond the early search before the late-stage plateau. This run is separate from the encoding comparison in Table VIII .
Fig. 6: Stage-wise architectures of GeoBridgeNet-A1, A2, and A3, decoded from their genotypes. Each row shows the searched Mamba cells of one variant in decoding order from s32 to s4 ; Up denotes the fixed lateral fusion and upsampling of the scaffold in Fig. 2 . Each card is one OP leaf, whose scan paths, depth-wise kernel, path fusion, projection sharing, and state fusion follow the legend, and a stacked card denotes a two-block primitive. RES, SEQ, and PAR compositions are drawn as in Fig. 3 , and the genotype string of each cell is given beneath it.
Fig. 7: NYU Depth V2 qualitative comparison. White boxes mark boundary differences within 0–3 m.
Fig. 8: KITTI qualitative comparison. White boxes mark boundary differences within 0–60 m.
Fig. 9: RealEstate10K novel-view comparison for GeoBridgeNet-A3. Red boxes mark geometric and detail differences.
Fig. 10: Open-vocabulary 3-D segmentation and object isolation with the GeoBridgeNet-A3-initialized Gaussian model. Red boxes mark queried objects, and labels report IoU (%).