Research map / 3D vision / Depth and stereo
A Low Power, High Throughput, Fully Event-Based Stereo System
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add erosion and dilation post-processing (ablation) | Proposed TrueNorth stereo method · RDS synthetic dataset | average absolute disparity error: 0.19 -> 0.04 | Helped |
| add erosion and dilation post-processing (ablation) | Proposed TrueNorth stereo method · RDS synthetic dataset | recall: 0.66 -> 0.63 | Hurt |
| use temporal scales rather than relying on a single temporal scale (ablation) | Proposed event-based stereo method · Fan and Butterfly sequences | median relative error | Mixed |
| vary spatial scale (ablation) | Proposed event-based stereo method · Fan and Butterfly sequences | median relative error | No clear effect |
| add left-right consistency constraints (ablation) | Proposed event-based stereo method · Fan sequences | median relative error | Helped |
| add left-right consistency constraints (ablation) | Proposed event-based stereo method · Butterfly sequences | median relative error | No clear effect |
| multiplex each feature-extraction/WTA circuit across 5 pixels (ablation) | Proposed TrueNorth stereo method · Live DAVIS input | disparity maps per second: 2,000Hz -> 400Hz | Hurt |
| use a 0.5ms tick period (ablation) | Proposed TrueNorth stereo method · Live event-based stereo input | disparity maps per second: 400Hz -> 2,000Hz | Helped |
Closest papers by meaning
- Active Event-based Stereo Vision (CVPR 2025 · similarity 0.91)
- Zero-Shot Event-Intensity Asymmetric Stereo via Visual Prompting from Image Domain (NeurIPS 2024 · similarity 0.89)
- DERD-Net: Learning Depth from Event-based Ray Densities (NeurIPS 2025 · similarity 0.87)
- Event-Driven Dynamic Scene Depth Completion (NeurIPS 2025 · similarity 0.86)
- EventPS: Real-Time Photometric Stereo Using an Event Camera (CVPR 2024 · similarity 0.86)
- PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching (NeurIPS 2025 · similarity 0.85)
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