NEWS · RESEARCH · #233
MELON: reconstructing 3D objects from unposed images using a lightweight CNN and modulo loss
Google Research presents MELON (Modulo Equivalent Latent Optimization of NeRF), a method spotlighted at 3DV 2024 that jointly infers object-centric camera poses from scratch and reconstructs a NeRF from as few as 4–6 unposed images. MELON uses a tiny CNN (initialized from noise, no pre-training) to regress poses and a modulo loss that accounts for object pseudo-symmetries, integrating both into standard NeRF training to achieve state-of-the-art accuracy without initial pose estimates or complex GAN/pretraining schemes.
KEY POINTS
- Google Research presents MELON (Modulo Equivalent Latent Optimization of NeRF), a method spotlighted at 3DV 2024 that jointly infers object-centric camera poses from scratch and reconstructs a NeRF from as few as 4–6 unposed images.
- MELON uses a tiny CNN (initialized from noise, no pre-training) to regress poses and a modulo loss that accounts for object pseudo-symmetries, integrating both into standard NeRF training to achieve state-of-the-art accuracy without initial pose estimates or complex GAN/pretraining schemes.
- This matters because MELON reduces data and supervision needs for 3D reconstruction from sparse, unposed views, simplifying pipelines for applications like e-commerce model creation and robotics perception.
WHY IT MATTERS
This matters because MELON reduces data and supervision needs for 3D reconstruction from sparse, unposed views, simplifying pipelines for applications like e-commerce model creation and robotics perception.