CVPR 2024 Papers

This paper is not motivated to seek innovation within the attention mechanism. Instead it focuses on overcoming the existing trade-offs between accuracy and efficiency within the context of point cloud processing leveraging the power of scale. Drawing inspiration from recent advances in 3D large-scale representation learning we recognize that model performance is more influenced by scale than by intricate design. Therefore we present Point Transformer V3 (PTv3) which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the overall performance after scaling such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns. This principle enables significant scaling expanding the receptive field from 16 to 1024 points while remaining efficient (a 3x increase in processing speed and a 10x improvement in memory efficiency compared with its predecessor PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that span both indoor and outdoor scenarios. Further enhanced with multi-dataset joint training PTv3 pushes these results to a higher level.

Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However due to the unordered and non-uniform density characteristics of point clouds it is non-trivial to explore the prior knowledge of point clouds and pre-train a point cloud backbone. In this paper we propose a novel pre-training method called Point cloud Diffusion pre-training PointDif. We consider the point cloud pre-training task as a conditional point-to-point generation problem and introduce a conditional point generator. This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object. We also present a recurrent uniform sampling optimization strategy which enables the model to uniformly recover from various noise levels and learn from balanced supervision. Our PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification segmentation and detection. Specifically PointDif attains 70.0% mIoU on S3DIS Area 5 for the segmentation task and achieves an average improvement of 2.4% on ScanObjectNN for the classification task compared to TAP. Furthermore our pre-training framework can be flexibly applied to diverse point cloud backbones and bring considerable gains. Code is available at https://github.com/zhengxiaozx/PointDif

3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF) while popular for view synthesis and 3D reconstruction are typically reliant on multi-view images. Existing methods for single-view 3D reconstruction with NeRF rely on either data priors to hallucinate views of occluded regions which may not be physically accurate or shadows observed by RGB cameras which are difficult to detect in ambient light and low albedo backgrounds. We propose using time-of-flight data captured by a single-photon avalanche diode to overcome these limitations. Our method models two-bounce optical paths with NeRF using lidar transient data for supervision. By leveraging the advantages of both NeRF and two-bounce light measured by lidar we demonstrate that we can reconstruct visible and occluded geometry without data priors or reliance on controlled ambient lighting or scene albedo. In addition we demonstrate improved generalization under practical constraints on sensor spatial- and temporal-resolution. We believe our method is a promising direction as single-photon lidars become ubiquitous on consumer devices such as phones tablets and headsets.

Conventional image sensors digitize high-resolution images at fast frame rates producing a large amount of data that needs to be transmitted off the sensor for further processing. This is challenging for perception systems operating on edge devices because communication is power inefficient and induces latency. Fueled by innovations in stacked image sensor fabrication emerging sensor--processors offer programmability and processing capabilities directly on the sensor. We exploit these capabilities by developing an efficient recurrent neural network architecture PixelRNN that encodes spatio-temporal features on the sensor using purely binary operations. PixelRNN reduces the amount of data to be transmitted off the sensor by factors up to 256 compared to the raw sensor data while offering competitive accuracy for hand gesture recognition and lip reading tasks. We experimentally validate PixelRNN using a prototype implementation on the SCAMP-5 sensor--processor platform.

The integration of visual inputs with large language models (LLMs) has led to remarkable advancements in multi-modal capabilities giving rise to vision large language models (VLLMs). However effectively harnessing LLMs for intricate visual perception tasks such as detection and segmentation remains a challenge. Conventional approaches achieve this by transforming perception signals (e.g. bounding boxes segmentation masks) into sequences of discrete tokens which struggle with the precision errors and introduces further complexities for training. In this paper we present a novel end-to-end framework named PerceptionGPT which represent the perception signals using LLM's dynamic token embedding. Specifically we leverage lightweight encoders and decoders to handle the perception signals in LLM's embedding space which takes advantage of the representation power of the high-dimensional token embeddings. Our approach significantly eases the training difficulties associated with the discrete representations in prior methods. Furthermore owing to our compact representation the inference speed is also greatly boosted. Consequently PerceptionGPT enables accurate flexible and efficient handling of complex perception signals. We validate the effectiveness of our approach through extensive experiments. The results demonstrate significant improvements over previous methods with only 4% trainable parameters and less than 25% training time.

Previous methods for Video Frame Interpolation (VFI) have encountered challenges notably the manifestation of blur and ghosting effects. These issues can be traced back to two pivotal factors: unavoidable motion errors and misalignment in supervision. In practice motion estimates often prove to be error-prone resulting in misaligned features. Furthermore the reconstruction loss tends to bring blurry results particularly in misaligned regions. To mitigate these challenges we propose a new paradigm called PerVFI (Perception-oriented Video Frame Interpolation). Our approach incorporates an Asymmetric Synergistic Blending module (ASB) that utilizes features from both sides to synergistically blend intermediate features. One reference frame emphasizes primary content while the other contributes complementary information. To impose a stringent constraint on the blending process we introduce a self-learned sparse quasi-binary mask which effectively mitigates ghosting and blur artifacts in the output. Additionally we employ a normalizing flow-based generator and utilize the negative log-likelihood loss to learn the conditional distribution of the output which further facilitates the generation of clear and fine details. Experimental results validate the superiority of PerVFI demonstrating significant improvements in perceptual quality compared to existing methods. Codes are available at https://github.com/mulns/PerVFI

Recent temporal LiDAR-based 3D object detectors achieve promising performance based on the two-stage proposal-based approach. They generate 3D box candidates from the first-stage dense detector followed by different temporal aggregation methods. However these approaches require per-frame objects or whole point clouds posing challenges related to memory bank utilization. Moreover point clouds and trajectory features are combined solely based on concatenation which may neglect effective interactions between them. In this paper we propose a point-trajectory transformer with long short-term memory for efficient temporal 3D object detection. To this end we only utilize point clouds of current-frame objects and their historical trajectories as input to minimize the memory bank storage requirement. Furthermore we introduce modules to encode trajectory features focusing on long short-term and future-aware perspectives and then effectively aggregate them with point cloud features. We conduct extensive experiments on the large-scale Waymo dataset to demonstrate that our approach performs well against state-of-the-art methods. The source codes and trained models will be made publicly available. Code and models will be made publicly available at https://github.com/kuanchihhuang/PTT.

In this paper we present an end-to-end 3D-building-wireframe reconstruction method to regress edges directly from aerial light-detection-and-ranging (LiDAR) point clouds. Our method named parametric-building-wireframe reconstruction (PBWR) takes aerial LiDAR point clouds and initial edge entities as input and fully uses the self-attention mechanism of transformers to regress edge parameters without any intermediate steps such as corner prediction. We propose an edge non-maximum suppression (E-NMS) module based on edge similarity to remove redundant edges. Additionally a dedicated edge loss function is utilized to guide the PBWR in regressing edges parameters when the simple use of the edge distance loss is not suitable. In our experiments our proposed method demonstrated state-of-the-art results on the Building3D dataset achieving an improvement of approximately 36% in Entry-level dataset edge accuracy and around a 42% improvement in the Tallinn dataset.

We propose the problem of point-level 3D scene interpolation which aims to simultaneously reconstruct a 3D scene in two states from multiple views synthesize smooth point-level interpolations between them and render the scene from novel viewpoints all without any supervision between the states. The primary challenge is on achieving a smooth transition between states that may involve significant and non-rigid changes. To address these challenges we introduce "PAPR in Motion" a novel approach that builds upon the recent Proximity Attention Point Rendering (PAPR) technique which can deform a point cloud to match a significantly different shape and render a visually coherent scene even after non-rigid deformations. Our approach is specifically designed to maintain the temporal consistency of the geometric structure by introducing various regularization techniques for PAPR. The result is a method that can effectively bridge large scene changes and produce visually coherent and temporally smooth interpolations in both geometry and appearance. Evaluation across diverse motion types demonstrates that "PAPR in Motion" outperforms the leading neural renderer for dynamic scenes. For more results and code please visit our project website at https://niopeng.github.io/PAPR-in-Motion/.

Semantic instance and panoptic segmentation of 3D point clouds have been addressed using task-specific models of distinct design. Thereby the similarity of all segmentation tasks and the implicit relationship between them have not been utilized effectively. This paper presents a unified simple and effective model addressing all these tasks jointly. The model named OneFormer3D performs instance and semantic segmentation consistently using a group of learnable kernels where each kernel is responsible for generating a mask for either an instance or a semantic category. These kernels are trained with a transformer-based decoder with unified instance and semantic queries passed as an input. Such a design enables training a model end-to-end in a single run so that it achieves top performance on all three segmentation tasks simultaneously. Specifically our OneFormer3D ranks 1st and sets a new state-of-the-art (+2.1 mAP50) in the ScanNet test leaderboard. We also demonstrate the state-of-the-art results in semantic instance and panoptic segmentation of ScanNet (+21 PQ) ScanNet200 (+3.8 mAP50) and S3DIS (+0.8 mIoU) datasets.

Despite the commercial abundance of UAVs aerial data acquisition remains challenging and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally the color content of the scenes solar zenith angle and population density of different geographies influence the data diversity. These factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground view data including the open-world foundational models. To pave the way for a transformative era of aerial detection we present Multiview Aerial Visual RECognition (MAVREC) a video dataset where we record synchronized scenes from different perspectives --- ground camera and drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences more than 0.5 million frames and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial view dataset and the fourth largest among all drone-based datasets across all modalities and tasks. Through our extensive benchmarking on MAVREC we recognize that augmenting object detectors with ground view images from the corresponding geographical location is a superior pre-training strategy for aerial detection. Building on this strategy we benchmark MAVREC with a curriculum-based semi-supervised object detection approach that leverages labeled (ground and aerial) and unlabeled (only aerial) images to enhance aerial detection.

A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net a one-of-a-kind framework for fulfilling multi-task multi-dataset multi-modality LiDAR segmentation in a universal manner using just a single set of parameters. To better exploit data volume and diversity we first combine large-scale driving datasets acquired by different types of sensors from diverse scenes and then conduct alignments in three spaces namely data feature and label spaces during the training. As a result M3Net is capable of taming heterogeneous data for training state-of-the-art LiDAR segmentation models. Extensive experiments on twelve LiDAR segmentation datasets verify our effectiveness. Notably using a shared set of parameters M3Net achieves 75.1% 83.1% and 72.4% mIoU scores respectively on the official benchmarks of SemanticKITTI nuScenes and Waymo Open.

In this paper we propose a new framework for online 3D scene perception. Conventional 3D scene perception methods are offline i.e. take an already reconstructed 3D scene geometry as input which is not applicable in robotic applications where the input data is streaming RGB-D videos rather than a complete 3D scene reconstructed from pre- collected RGB-D videos. To deal with online 3D scene per- ception tasks where data collection and perception should be performed simultaneously the model should be able to process 3D scenes frame by frame and make use of the temporal information. To this end we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability. Specifically we propose a queued memory mechanism to cache the supporting point cloud and image features. Then we devise aggregation modules which directly perform on the memory and pass temporal information to current frame. We further propose 3D-to-2D adapter to enhance image features with strong global context. Our adapters can be easily inserted into mainstream offline architectures of different tasks and significantly boost their performance on online tasks. Extensive experiments on ScanNet and SceneNN datasets demonstrate our approach achieves leading performance on three 3D scene perception tasks compared with state-of-the-art online methods by simply finetuning existing offline models without any model and task-specific designs.

Perception plays a crucial role in various robot applications. However existing well-annotated datasets are biased towards autonomous driving scenarios while unlabelled SLAM datasets are quickly over-fitted and often lack environment and domain variations. To expand the frontier of these fields we introduce a comprehensive dataset named MCD (Multi-Campus Dataset) featuring a wide range of sensing modalities high-accuracy ground truth and diverse challenging environments across three Eurasian university campuses. MCD comprises both CCS (Classical Cylindrical Spinning) and NRE (Non-Repetitive Epicyclic) lidars high-quality IMUs (Inertial Measurement Units) cameras and UWB (Ultra-WideBand) sensors. Furthermore in a pioneering effort we introduce semantic annotations of 29 classes over 59k sparse NRE lidar scans across three domains thus providing a novel challenge to existing semantic segmentation research upon this largely unexplored lidar modality. Finally we propose for the first time to the best of our knowledge continuous-time ground truth based on optimization-based registration of lidar-inertial data on large survey-grade prior maps which are also publicly released each several times the size of existing ones. We conduct a rigorous evaluation of numerous state-of-the-art algorithms on MCD report their performance and highlight the challenges awaiting solutions from the research community.

Rotation invariance is an important requirement for point shape analysis. To achieve this current state-of-the-art methods attempt to construct the local rotation-invariant representation through learning or defining the local reference frame (LRF). Although efficient these LRF-based methods suffer from perturbation of local geometric relations resulting in suboptimal local rotation invariance. To alleviate this issue we propose a Local-consistent Transformation (LocoTrans) learning strategy. Specifically we first construct the local-consistent reference frame (LCRF) by considering the symmetry of the two axes in LRF. In comparison with previous LRFs our LCRF is able to preserve local geometric relationships better through performing local-consistent transformation. However as the consistency only exists in local regions the relative pose information is still lost in the intermediate layers of the network. We mitigate such a relative pose issue by developing a relative pose recovery (RPR) module. RPR aims to restore the relative pose between adjacent transformed patches. Equipped with LCRF and RPR our LocoTrans is capable of learning local-consistent transformation and preserving local geometry which benefits rotation invariance learning. Competitive performance under arbitrary rotations on both shape classification and part segmentation tasks and ablations can demonstrate the effectiveness of our method. Code will be available publicly at https://github.com/wdttt/LocoTrans.

Photorealistic simulation plays a crucial role in applications such as autonomous driving where advances in neural radiance fields (NeRFs) may allow better scalability through the automatic creation of digital 3D assets. However reconstruction quality suffers on street scenes due to largely collinear camera motions and sparser samplings at higher speeds. On the other hand the application often demands rendering from camera views that deviate from the inputs to accurately simulate behaviors like lane changes. In this paper we propose several insights that allow a better utilization of Lidar data to improve NeRF quality on street scenes. First our framework learns a geometric scene representation from Lidar which are fused with the implicit grid-based representation for radiance decoding thereby supplying stronger geometric information offered by explicit point cloud. Second we put forth a robust occlusion-aware depth supervision scheme which allows utilizing densified Lidar points by accumulation. Third we generate augmented training views from Lidar points for further improvement. Our insights translate to largely improved novel view synthesis under real driving scenes.

LiDAR localization is a fundamental task in robotics and computer vision which estimates the pose of a LiDAR point cloud within a global map. Scene Coordinate Regression (SCR) has demonstrated state-of-the-art performance in this task. In SCR a scene is represented as a neural network which outputs the world coordinates for each point in the input point cloud. However SCR treats all points equally during localization ignoring the fact that not all objects are beneficial for localization. For example dynamic objects and repeating structures often negatively impact SCR. To address this problem we introduce LiSA the first method that incorporates semantic awareness into SCR to boost the localization robustness and accuracy. To avoid extra computation or network parameters during inference we distill the knowledge from a segmentation model to the original SCR network. Experiments show the superior performance of LiSA on standard LiDAR localization benchmarks compared to state-of-the-art methods. Applying knowledge distillation not only preserves high efficiency but also achieves higher localization accuracy than introducing extra semantic segmentation modules. We also analyze the benefit of semantic information for LiDAR localization. Our code is released at https://github.com/Ybchun/LiSA.

Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS) LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple shift from image NVS methods while ignoring the dynamic nature and the large-scale reconstruction problem of LiDAR point clouds. In light of this we propose LiDAR4D a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis. In consideration of the sparsity and large-scale characteristics we design a 4D hybrid representation combined with multi-planar and grid features to achieve effective reconstruction in a coarse-to-fine manner. Furthermore we introduce geometric constraints derived from point clouds to improve temporal consistency. For the realistic synthesis of LiDAR point clouds we incorporate the global optimization of ray-drop probability to preserve cross-region patterns. Extensive experiments on KITTI-360 and NuScenes datasets demonstrate the superiority of our method in accomplishing geometry-aware and time-consistent dynamic reconstruction. Codes are available at https://github.com/ispc-lab/LiDAR4D.