3 citations · 3 across the 1 of their papers we have counts for
4 papers
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation
Zhaoshuo Li, Nathan Drenkow, Hao Ding +5
Scene depth estimation from stereo and monocular imagery is critical for extracting 3D information for downstream tasks such as scene understanding. Recently, learning-based method…
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?
Max Lennon, Nathan Drenkow, Philippe Burlina
Perturbation-based attacks, while not physically realizable, have been the main emphasis of adversarial machine learning (ML) research. Patch-based attacks by contrast are physical…
Attack Agnostic Detection of Adversarial Examples via Random Subspace Analysis
Nathan Drenkow, Neil Fendley, Philippe Burlina
Whilst adversarial attack detection has received considerable attention, it remains a fundamentally challenging problem from two perspectives. First, while threat models can be wel…
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers
Zhaoshuo Li, Xingtong Liu, Nathan Drenkow +4
Stereo depth estimation relies on optimal correspondence matching between pixels on epipolar lines in the left and right images to infer depth. In this work, we revisit the problem…