10 citations · 19 across the 9 of their papers we have counts for
5 papers · 1 filter
Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations
Tristan Aumentado-Armstrong, Ashkan Mirzaei, Marcus A. Brubaker +4
Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations, capable of high quality novel view synthesis of complex scenes. While NeRFs have been applied to graph…
Watch Your Steps: Local Image and Scene Editing by Text Instructions
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4
Denoising diffusion models have enabled high-quality image generation and editing. We present a method to localize the desired edit region implicit in a text instruction. We levera…
Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation
Andrej Janda, Pierre Merriaux, Pierre Olivier +1
Advances in lidar technology have made the collection of 3D point clouds fast and easy. While most lidar sensors return per-point intensity (or reflectance) values along with range…
Reference-guided Controllable Inpainting of Neural Radiance Fields
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4
The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools. Here, we focus on inpainting regions in a view-consistent and contro…
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data
Andrej Janda, Brandon Wagstaff, Edwin G. Ng +1
Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is particularly important for semantic segmentation…