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SLTN: Shadow and Lighting Transformation Network for Efficient 3d Shape Recognition
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- Authors
- Yongsik Lee, Dongjun Kim, Seungjae Lee, Seungjoon Yang
- Issue Date
- 2026-05
- Citation
- International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2026, pp.5726-5730
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- We propose SLTN, a Shadow and Lighting Transformation Network for efficient 3D shape recognition. Unlike prior methods that only optimize viewpoints, SLTN jointly learns camera poses and illumination, leveraging a differentiable shadow renderer to integrate cast shadows as discriminative cues. This joint optimization reduces viewpoint ambiguity and enriches geometric perception within a single rendered view. Experiments on ModelNet10/40 show that SLTN achieves single-view accuracy comparable to multi-view baselines such as MVTN with 12 views, while using fewer parameters and significantly shorter inference time. These results demonstrate the effectiveness of shadow- and lighting-aware rendering for resource-constrained 3D recognition and highlight its potential in robotics, AR/VR, and other real-time systems.
- Keyword
- 3D shape recognition, optimization, differentiable rendering, shadow-aware learning
- KSP Keywords
- 3D recognition, 3D shape, Cast Shadows, Joint optimization, Multi-view, Real-Time Systems, Resource-constrained, Shape Recognition, single view