The ability to sense and reconstruct 3D appearance and geometry is critical to applications in vision, graphics, and beyond.

The large-scale propagation of human-human handover research has been hindered by two challenges.

Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors.

Moreover, the proposed method delivers impressive robustness at an extremely low scanning grid (i.e., 8

Behavior is shaped by various factors operating across different timescales.

We provide statistics of the splits for the D-NVS task of our benchmark in Table 1.

When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or P A TE, computes output labels by aggregating the predictions of a (possibly distributed) collection of teacher models via a voting mechanism. The mechanism adds noise to attain a differential privacy guarantee with respect to the teachers' training data. In this work, we observe that this use of noise, which makes P A TE predictions stochastic, enables new forms of leakage of sensitive information. For a given input, our adversary exploits this stochasticity to extract high-fidelity histograms of the votes submitted by the underlying teachers. From these histograms, the adversary can learn sensitive attributes of the input such as race, gender, or age. Although this attack does not directly violate the differential privacy guarantee, it clearly violates privacy norms and expectations, and would not be possible at all without the noise inserted to obtain differential privacy. In fact, counter-intuitively, the attack becomes easier as we add more noise to provide stronger differential privacy. We hope this encourages future work to consider privacy holistically rather than treat differential privacy as a panacea.