3D Shape Modeling
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Benchmarks
Most implemented
Mesh R-CNN
Temporal 3D Shape Modeling for Video-Based Cloth-Changing Person Re-Identification
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders
3D VR Sketch Guided 3D Shape Prototyping and Exploration
Papers
LoG3D: Ultra-High-Resolution 3D Shape Modeling via Local-to-Global Partitioning
Generating high-fidelity 3D contents remains a fundamental challenge due to the complexity of representing arbitrary topologies-such as open surfaces and intricate internal structures-while preserving geometric details. …
3D Shape ModelingBeyond Heuristics: Globally Optimal Configuration of Implicit Neural Representations
Implicit Neural Representations (INRs) have emerged as a transformative paradigm in signal processing and computer vision, excelling in tasks from image reconstruction to 3D shape modeling. Yet their effectiveness is fun…
Image Reconstruction3D Shape ModelingDirect3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
Generating high-resolution 3D shapes using volumetric representations such as Signed Distance Functions (SDFs) presents substantial computational and memory challenges. We introduce Direct3D-S2, a scalable 3D generation …
3D Generation3D geometry3D Object Reconstruction3D Reconstruction+3SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modeling
Creating high-fidelity 3D meshes with arbitrary topology, including open surfaces and complex interiors, remains a significant challenge. Existing implicit field methods often require costly and detail-degrading watertig…
3D Shape Generation3D Shape Modeling3D Shape RepresentationDora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders
Recent 3D content generation pipelines commonly employ Variational Autoencoders (VAEs) to encode shapes into compact latent representations for diffusion-based generation. However, the widely adopted uniform point sampli…
3D Shape ModelingBenchmarking3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes
Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more …
3D Shape ModelingImage Generation